diff --git a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/ssd-cube.ipynb b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/ssd-cube.ipynb
index b77bc7d1d80..624ed8ad900 100644
--- a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/ssd-cube.ipynb
+++ b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/ssd-cube.ipynb
@@ -5,12 +5,13 @@
    "id": "34c87f77-b604-4200-b102-da8290ef81b3",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"SteadyStateDiffusion Cube Test\"\n",
     "date = \"2021-11-09\"\n",
     "author = \"Lars Bilke\"\n",
     "web_subsection = \"elliptic\"\n",
     "draft = true\n",
-    "<!--eofm-->\n"
+    "+++\n"
    ]
   },
   {
diff --git a/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb b/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb
index a7d6399627e..ded6fcfdbc6 100644
--- a/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb
+++ b/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb
@@ -5,11 +5,12 @@
    "id": "09d7a481-d07d-465f-8f3b-a098b650295d",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Seabed response to water waves\"\n",
     "date = \"2023-01-11\"\n",
     "author = \"Linda Günther\"\n",
     "web_subsection = \"hydro-mechanics\"\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -139,7 +140,7 @@
     "\\end{align}\n",
     "$$\n",
     "\n",
-    "where $p$ is the pore pressure, $\\tilde{p}$ is the amplitude of the applied load, $\\sigma'_{yy}$ is the effective vertical stress and $\\sigma'_{xy}$ is the effective shear stress. The boundary condition of  the pore pressure describes the space- and time-dependent water wave. Compared to Arnold Verruijt's solution, in this example there is a phase shift of $-\\frac{\\pi}{2}$ in the time-dependent part. The phase shift is necessary to obtain a water wave that starts oszillating from the equilibrium state (sine instead of cosine) and thus to be able to set an initial condition of $p=0$ Pa on the whole domain in the numerical solution. \n",
+    "where $p$ is the pore pressure, $\\tilde{p}$ is the amplitude of the applied load, $\\sigma'_{yy}$ is the effective vertical stress and $\\sigma'_{xy}$ is the effective shear stress. The boundary condition of  the pore pressure describes the space- and time-dependent water wave. Compared to Arnold Verruijt's solution, in this example there is a phase shift of $-\\frac{\\pi}{2}$ in the time-dependent part. The phase shift is necessary to obtain a water wave that starts oszillating from the equilibrium state (sine instead of cosine) and thus to be able to set an initial condition of $p=0$ Pa on the whole domain in the numerical solution.\n",
     "\n",
     "With these boundary conditions, the following four constants are determined:\n",
     "\n",
@@ -180,13 +181,15 @@
     "import numpy as np\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.rc ('font', size = 8)\n",
-    "plt.rc ('axes', titlesize = 10)\n",
-    "plt.rc ('axes', labelsize = 10)\n",
+    "\n",
+    "plt.rc(\"font\", size=8)\n",
+    "plt.rc(\"axes\", titlesize=10)\n",
+    "plt.rc(\"axes\", labelsize=10)\n",
     "\n",
     "import gmsh\n",
     "\n",
     "import pyvista as pv\n",
+    "\n",
     "pv.set_plot_theme(\"document\")\n",
     "pv.set_jupyter_backend(\"static\")"
    ]
@@ -200,37 +203,90 @@
    },
    "outputs": [],
    "source": [
-    "def compute_pressure_and_stresses(t,x,z):\n",
-    "    \n",
-    "    n=0.4\n",
-    "    G=100e3             # [Pa]\n",
-    "    K=2/3*G             # [Pa]    (with ny=0)\n",
-    "    ny=0                # E = 3K(1-2ny) = 2G(1+ny)\n",
-    "    Cf=0                # in the book: Cf = 0.001/K\n",
-    "    Cs=0\n",
-    "    Cm=1/K\n",
-    "    my=1.3e-3           # [Pa*s]\n",
-    "    kappa=1e-11         # [m²]    (medium sand, kf=10e-4 m/s)   \n",
-    "    gamma_w = 9.81e3    # [Pa/m]\n",
-    "    lam=2*np.pi*0.1*10/100\n",
-    "    omega=2*np.pi*0.1\n",
-    "   \n",
-    "    k = kappa*gamma_w/my                                       # Gl. (1.33)\n",
-    "    alpha = 1-Cs/Cm                                            # Gl. (4.15)\n",
-    "    S = n*Cf + (alpha-n)*Cs                                    # Gl. (1.28)\n",
-    "    theta = S*G/alpha**2                                       # Gl. (4.13)\n",
-    "    m = 1/(1-2*ny) # = K+1/3*G/G                               # Gl. (4.5)\n",
-    "    cv = k*G*(1+m) / (alpha**2*(1+theta+m*theta)*gamma_w)      # Gl. (4.12)\n",
-    "    xi_2 = complex(lam**2, (omega/cv))                         # Gl. (4.19)\n",
-    "   \n",
-    "    B1 = (1+m)*(xi_2-lam**2)-2*lam*(np.sqrt(xi_2)-lam)\n",
-    "    B2 = 2*m*theta*lam*np.sqrt(xi_2)+theta*((1+m)*(xi_2-lam**2)-2*lam*(np.sqrt(xi_2)-lam))\n",
-    "    B3 = 2*m*theta*lam\n",
-    "    D = 2*lam*(2*lam*(np.sqrt(xi_2)-lam)-(1+m)*(1+m*theta)*(xi_2-lam**2))\n",
-    "    p_rel = np.real((-2*lam*B1*np.exp(-lam*z) - (1+m)*(xi_2-lam**2)*B3*np.exp(-np.sqrt(xi_2)*z))/D * np.exp((omega*t-np.pi*0.5)*1j)*np.cos(lam*x))\n",
-    "    sig_xx_rel = np.real(((-2*(m-1)*lam*theta + 2*lam*(1+m*theta)*lam*z)*B1*np.exp(-lam*z) - 2*lam*B2*np.exp(-lam*z) + ((m-1)*(xi_2-lam**2) - 2*lam**2)*B3*np.exp(-np.sqrt(xi_2)*z))/D * np.exp((omega*t-np.pi*0.5)*1j)*np.cos(lam*x))\n",
-    "    sig_zz_rel = np.real(((-2*(m+1)*lam*theta - 2*lam*(1+m*theta)*lam*z)*B1*np.exp(-lam*z) + 2*lam*B2*np.exp(-lam*z) + ((m-1)*(xi_2-lam**2) + 2*xi_2)*B3*np.exp(-np.sqrt(xi_2)*z))/D * np.exp((omega*t-np.pi*0.5)*1j)*np.cos(lam*x))\n",
-    "    sig_xz_rel = np.real(((-2*lam*(1+m*theta)*lam*z-2*lam*theta)*B1*np.exp(-lam*z) + 2*lam*B2*np.exp(-lam*z) + 2*np.sqrt(xi_2)*lam*B3*np.exp(-np.sqrt(xi_2)*z))/D * np.exp((omega*t-np.pi*0.5)*1j)*np.sin(lam*x))\n",
+    "def compute_pressure_and_stresses(t, x, z):\n",
+    "    n = 0.4\n",
+    "    G = 100e3  # [Pa]\n",
+    "    K = 2 / 3 * G  # [Pa]    (with ny=0)\n",
+    "    ny = 0  # E = 3K(1-2ny) = 2G(1+ny)\n",
+    "    Cf = 0  # in the book: Cf = 0.001/K\n",
+    "    Cs = 0\n",
+    "    Cm = 1 / K\n",
+    "    my = 1.3e-3  # [Pa*s]\n",
+    "    kappa = 1e-11  # [m²]    (medium sand, kf=10e-4 m/s)\n",
+    "    gamma_w = 9.81e3  # [Pa/m]\n",
+    "    lam = 2 * np.pi * 0.1 * 10 / 100\n",
+    "    omega = 2 * np.pi * 0.1\n",
+    "\n",
+    "    k = kappa * gamma_w / my  # Gl. (1.33)\n",
+    "    alpha = 1 - Cs / Cm  # Gl. (4.15)\n",
+    "    S = n * Cf + (alpha - n) * Cs  # Gl. (1.28)\n",
+    "    theta = S * G / alpha**2  # Gl. (4.13)\n",
+    "    m = 1 / (1 - 2 * ny)  # = K+1/3*G/G                               # Gl. (4.5)\n",
+    "    cv = (\n",
+    "        k * G * (1 + m) / (alpha**2 * (1 + theta + m * theta) * gamma_w)\n",
+    "    )  # Gl. (4.12)\n",
+    "    xi_2 = complex(lam**2, (omega / cv))  # Gl. (4.19)\n",
+    "\n",
+    "    B1 = (1 + m) * (xi_2 - lam**2) - 2 * lam * (np.sqrt(xi_2) - lam)\n",
+    "    B2 = 2 * m * theta * lam * np.sqrt(xi_2) + theta * (\n",
+    "        (1 + m) * (xi_2 - lam**2) - 2 * lam * (np.sqrt(xi_2) - lam)\n",
+    "    )\n",
+    "    B3 = 2 * m * theta * lam\n",
+    "    D = (\n",
+    "        2\n",
+    "        * lam\n",
+    "        * (\n",
+    "            2 * lam * (np.sqrt(xi_2) - lam)\n",
+    "            - (1 + m) * (1 + m * theta) * (xi_2 - lam**2)\n",
+    "        )\n",
+    "    )\n",
+    "    p_rel = np.real(\n",
+    "        (\n",
+    "            -2 * lam * B1 * np.exp(-lam * z)\n",
+    "            - (1 + m) * (xi_2 - lam**2) * B3 * np.exp(-np.sqrt(xi_2) * z)\n",
+    "        )\n",
+    "        / D\n",
+    "        * np.exp((omega * t - np.pi * 0.5) * 1j)\n",
+    "        * np.cos(lam * x)\n",
+    "    )\n",
+    "    sig_xx_rel = np.real(\n",
+    "        (\n",
+    "            (-2 * (m - 1) * lam * theta + 2 * lam * (1 + m * theta) * lam * z)\n",
+    "            * B1\n",
+    "            * np.exp(-lam * z)\n",
+    "            - 2 * lam * B2 * np.exp(-lam * z)\n",
+    "            + ((m - 1) * (xi_2 - lam**2) - 2 * lam**2)\n",
+    "            * B3\n",
+    "            * np.exp(-np.sqrt(xi_2) * z)\n",
+    "        )\n",
+    "        / D\n",
+    "        * np.exp((omega * t - np.pi * 0.5) * 1j)\n",
+    "        * np.cos(lam * x)\n",
+    "    )\n",
+    "    sig_zz_rel = np.real(\n",
+    "        (\n",
+    "            (-2 * (m + 1) * lam * theta - 2 * lam * (1 + m * theta) * lam * z)\n",
+    "            * B1\n",
+    "            * np.exp(-lam * z)\n",
+    "            + 2 * lam * B2 * np.exp(-lam * z)\n",
+    "            + ((m - 1) * (xi_2 - lam**2) + 2 * xi_2) * B3 * np.exp(-np.sqrt(xi_2) * z)\n",
+    "        )\n",
+    "        / D\n",
+    "        * np.exp((omega * t - np.pi * 0.5) * 1j)\n",
+    "        * np.cos(lam * x)\n",
+    "    )\n",
+    "    sig_xz_rel = np.real(\n",
+    "        (\n",
+    "            (-2 * lam * (1 + m * theta) * lam * z - 2 * lam * theta)\n",
+    "            * B1\n",
+    "            * np.exp(-lam * z)\n",
+    "            + 2 * lam * B2 * np.exp(-lam * z)\n",
+    "            + 2 * np.sqrt(xi_2) * lam * B3 * np.exp(-np.sqrt(xi_2) * z)\n",
+    "        )\n",
+    "        / D\n",
+    "        * np.exp((omega * t - np.pi * 0.5) * 1j)\n",
+    "        * np.sin(lam * x)\n",
+    "    )\n",
     "    return p_rel, sig_xx_rel, sig_zz_rel, sig_xz_rel"
    ]
   },
@@ -241,7 +297,7 @@
    "source": [
     "By evaluating these equations at different times $t$ and depths $y$, we gain a better understanding of the pressure and stress distribution in the seabed. The below plot illustrates the pore pressure and the amplitude of the effective stresses as a function of depth directly underneath an anti-node of the standing water wave (the place where the amplitude is at maximum, i.e. for $x = k \\cdot \\frac{L}{2}$, where $k=0, 1, 2,$ ...).\n",
     "\n",
-    "Along the top edge of the seabed, the pore pressure is always as large as the applied load and the effective stresses are zero. This means, that all the change in pressure is absorbed by the fluid while the soil particles remain in their initial stress state (in this case zero, since body forces are being disregarded). The increased pore pressure at the top edge cannot propagate freely downwards into the seabed because seepage is limited by the hydraulic conductivity of the soil. Consequently, the pore pressure decreases with depth as the soil matrix gradually takes up the remaining share of the total stress in the seabed. "
+    "Along the top edge of the seabed, the pore pressure is always as large as the applied load and the effective stresses are zero. This means, that all the change in pressure is absorbed by the fluid while the soil particles remain in their initial stress state (in this case zero, since body forces are being disregarded). The increased pore pressure at the top edge cannot propagate freely downwards into the seabed because seepage is limited by the hydraulic conductivity of the soil. Consequently, the pore pressure decreases with depth as the soil matrix gradually takes up the remaining share of the total stress in the seabed."
    ]
   },
   {
@@ -249,6 +305,7 @@
    "execution_count": 3,
    "id": "4f48e224-330e-4f3f-89aa-734981c8fdd4",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -264,29 +321,56 @@
     }
    ],
    "source": [
-    "y = np.linspace(0,100,1000)\n",
-    "y_rel = y/100\n",
-    "colors = {0:\"orangered\", 2:\"gold\", 4:\"blueviolet\", 6:\"forestgreen\", 8:\"darkorange\", 10:\"royalblue\"}\n",
-    "\n",
-    "fig, ax = plt.subplots(ncols=2, figsize=(15,7))\n",
-    "for idx in (0,1):\n",
+    "y = np.linspace(0, 100, 1000)\n",
+    "y_rel = y / 100\n",
+    "colors = {\n",
+    "    0: \"orangered\",\n",
+    "    2: \"gold\",\n",
+    "    4: \"blueviolet\",\n",
+    "    6: \"forestgreen\",\n",
+    "    8: \"darkorange\",\n",
+    "    10: \"royalblue\",\n",
+    "}\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols=2, figsize=(15, 7))\n",
+    "for idx in (0, 1):\n",
     "    ax[idx].grid(True)\n",
     "    ax[idx].set_ylabel(\"$y$ / $L$\")\n",
-    "    ax[idx].set_xlim(-1.1,1.1)\n",
+    "    ax[idx].set_xlim(-1.1, 1.1)\n",
     "\n",
-    "for t in [0,2,4,6,8,10]:\n",
-    "    ax[0].plot(compute_pressure_and_stresses(t,0,y)[0], -y_rel, color=colors[t], label= \"t = %.1f s\" %t)\n",
+    "for t in [0, 2, 4, 6, 8, 10]:\n",
+    "    ax[0].plot(\n",
+    "        compute_pressure_and_stresses(t, 0, y)[0],\n",
+    "        -y_rel,\n",
+    "        color=colors[t],\n",
+    "        label=\"t = %.1f s\" % t,\n",
+    "    )\n",
     "\n",
-    "t=2.5\n",
+    "t = 2.5\n",
     "ax[0].set_xlabel(\"$p$ / $\\\\tilde{p}$\")\n",
     "ax[0].legend()\n",
-    "ax[1].plot(compute_pressure_and_stresses(t,0,y)[1], -y_rel, color = colors[6], label = r\"$\\sigma'_{xx}/(\\alpha\\tilde{p})$\")\n",
-    "#ax[1].plot(compute_pressure_and_stresses(t,0,y)[1]+compute_pressure_and_stresses(t,0,y)[0], -y_rel, linestyle = \"--\", color = colors[3], label = \"$\\\\sigma_{xx}$/$\\\\alpha\\\\tilde{p}$\")  # Total horizontal stress\n",
-    "ax[1].plot(compute_pressure_and_stresses(t,0,y)[2], -y_rel, color = colors[2], label = r\"$\\sigma'_{yy}/(\\alpha\\tilde{p})$\")\n",
-    "#ax[1].plot(compute_pressure_and_stresses(t,0,y)[2]+compute_pressure_and_stresses(t,0,y)[0], -y_rel, linestyle = \"--\", color = colors[1], label = \"$\\\\sigma_{yy}$/$\\\\alpha\\\\tilde{p}$\")  # Total vertical stress\n",
-    "ax[1].plot(compute_pressure_and_stresses(t,0,y)[3], -y_rel, color = colors[4], label = r\"$\\sigma'_{xy}/(\\alpha\\tilde{p})$\")\n",
+    "ax[1].plot(\n",
+    "    compute_pressure_and_stresses(t, 0, y)[1],\n",
+    "    -y_rel,\n",
+    "    color=colors[6],\n",
+    "    label=r\"$\\sigma'_{xx}/(\\alpha\\tilde{p})$\",\n",
+    ")\n",
+    "# ax[1].plot(compute_pressure_and_stresses(t,0,y)[1]+compute_pressure_and_stresses(t,0,y)[0], -y_rel, linestyle = \"--\", color = colors[3], label = \"$\\\\sigma_{xx}$/$\\\\alpha\\\\tilde{p}$\")  # Total horizontal stress\n",
+    "ax[1].plot(\n",
+    "    compute_pressure_and_stresses(t, 0, y)[2],\n",
+    "    -y_rel,\n",
+    "    color=colors[2],\n",
+    "    label=r\"$\\sigma'_{yy}/(\\alpha\\tilde{p})$\",\n",
+    ")\n",
+    "# ax[1].plot(compute_pressure_and_stresses(t,0,y)[2]+compute_pressure_and_stresses(t,0,y)[0], -y_rel, linestyle = \"--\", color = colors[1], label = \"$\\\\sigma_{yy}$/$\\\\alpha\\\\tilde{p}$\")  # Total vertical stress\n",
+    "ax[1].plot(\n",
+    "    compute_pressure_and_stresses(t, 0, y)[3],\n",
+    "    -y_rel,\n",
+    "    color=colors[4],\n",
+    "    label=r\"$\\sigma'_{xy}/(\\alpha\\tilde{p})$\",\n",
+    ")\n",
     "ax[1].set_xlabel(r\"$\\sigma'/(\\alpha\\tilde{p})$\")\n",
-    "ax[1].legend();"
+    "ax[1].legend()"
    ]
   },
   {
@@ -302,6 +386,7 @@
    "execution_count": 4,
    "id": "9cc304d2-bd0a-443f-98dc-9c8488989d61",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -317,28 +402,43 @@
     }
    ],
    "source": [
-    "t=np.linspace(0,20,200)\n",
-    "colors = {1:\"gold\", 2:\"blueviolet\", 3:\"forestgreen\", 4:\"royalblue\"}\n",
+    "t = np.linspace(0, 20, 200)\n",
+    "colors = {1: \"gold\", 2: \"blueviolet\", 3: \"forestgreen\", 4: \"royalblue\"}\n",
     "\n",
-    "fig, ax = plt.subplots(ncols=2, figsize=(15,7))\n",
-    "for idx in (0,1):\n",
+    "fig, ax = plt.subplots(ncols=2, figsize=(15, 7))\n",
+    "for idx in (0, 1):\n",
     "    ax[idx].grid(True)\n",
     "    ax[idx].set_xlabel(\"$t$ / s\")\n",
     "\n",
-    "for y in (np.linspace(0,100,6)):\n",
-    "    ax[0].plot( t, compute_pressure_and_stresses(t,0,y)[0], color = colors[4])\n",
+    "for y in np.linspace(0, 100, 6):\n",
+    "    ax[0].plot(t, compute_pressure_and_stresses(t, 0, y)[0], color=colors[4])\n",
     "    ax[0].set_ylabel(\"$p/\\\\tilde{p}$\")\n",
-    "    ax[1].plot(t, compute_pressure_and_stresses(t,0,y)[1], color = colors[3], label = r\"$\\sigma'_{xx}/(\\alpha\\tilde{p})$\")\n",
-    "    ax[1].plot(t, compute_pressure_and_stresses(t,0,y)[2], color = colors[1], label = r\"$\\sigma'_{yy}/(\\alpha\\tilde{p})$\")\n",
-    "    ax[1].plot(t, compute_pressure_and_stresses(t,0,y)[3], color = colors[2], label = r\"$\\sigma'_{xy}/(\\alpha\\tilde{p})$\")\n",
+    "    ax[1].plot(\n",
+    "        t,\n",
+    "        compute_pressure_and_stresses(t, 0, y)[1],\n",
+    "        color=colors[3],\n",
+    "        label=r\"$\\sigma'_{xx}/(\\alpha\\tilde{p})$\",\n",
+    "    )\n",
+    "    ax[1].plot(\n",
+    "        t,\n",
+    "        compute_pressure_and_stresses(t, 0, y)[2],\n",
+    "        color=colors[1],\n",
+    "        label=r\"$\\sigma'_{yy}/(\\alpha\\tilde{p})$\",\n",
+    "    )\n",
+    "    ax[1].plot(\n",
+    "        t,\n",
+    "        compute_pressure_and_stresses(t, 0, y)[3],\n",
+    "        color=colors[2],\n",
+    "        label=r\"$\\sigma'_{xy}/(\\alpha\\tilde{p})$\",\n",
+    "    )\n",
     "    if y == 0:\n",
     "        ax[1].legend(loc=\"upper right\")\n",
     "\n",
     "ax[1].set_ylabel(\"$\\sigma$'/$\\\\alpha\\\\tilde{p}$\")\n",
     "\n",
-    "    \n",
+    "\n",
     "ax[0].set_title(\"Pore pressure over time\")\n",
-    "ax[1].set_title(\"Effective stresses over time\");"
+    "ax[1].set_title(\"Effective stresses over time\")"
    ]
   },
   {
@@ -354,6 +454,7 @@
    "execution_count": 5,
    "id": "739678d6-b1a4-4d0d-94e8-1774a2eb5b17",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -369,28 +470,28 @@
     }
    ],
    "source": [
-    "x, y = np.meshgrid(np.linspace(0,200,1000),np.linspace(0,100,1000))\n",
+    "x, y = np.meshgrid(np.linspace(0, 200, 1000), np.linspace(0, 100, 1000))\n",
     "t = 2.5\n",
     "\n",
-    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(15,7))\n",
-    "l1=ax[0][0].contourf(x,-y, compute_pressure_and_stresses(t,x,y)[0], 15)\n",
-    "l2=ax[0][1].contourf(x,-y, compute_pressure_and_stresses(t,x,y)[1], 15)\n",
-    "l3=ax[1][1].contourf(x,-y, compute_pressure_and_stresses(t,x,y)[2], 15)\n",
-    "l4=ax[1][0].contourf(x,-y, compute_pressure_and_stresses(t,x,y)[3], 15)\n",
-    "fig.colorbar(l1,ax=ax[0][0])\n",
-    "fig.colorbar(l2,ax=ax[0][1])\n",
-    "fig.colorbar(l3,ax=ax[1][1])\n",
-    "fig.colorbar(l4,ax=ax[1][0])\n",
-    "for i in (0,1):\n",
-    "    for j in (0,1):\n",
-    "        ax[i][j].set_aspect('equal')\n",
-    "        ax[i][j].set_xlabel('$x$ / m')\n",
-    "        ax[i][j].set_ylabel('$y$ / m')\n",
+    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(15, 7))\n",
+    "l1 = ax[0][0].contourf(x, -y, compute_pressure_and_stresses(t, x, y)[0], 15)\n",
+    "l2 = ax[0][1].contourf(x, -y, compute_pressure_and_stresses(t, x, y)[1], 15)\n",
+    "l3 = ax[1][1].contourf(x, -y, compute_pressure_and_stresses(t, x, y)[2], 15)\n",
+    "l4 = ax[1][0].contourf(x, -y, compute_pressure_and_stresses(t, x, y)[3], 15)\n",
+    "fig.colorbar(l1, ax=ax[0][0])\n",
+    "fig.colorbar(l2, ax=ax[0][1])\n",
+    "fig.colorbar(l3, ax=ax[1][1])\n",
+    "fig.colorbar(l4, ax=ax[1][0])\n",
+    "for i in (0, 1):\n",
+    "    for j in (0, 1):\n",
+    "        ax[i][j].set_aspect(\"equal\")\n",
+    "        ax[i][j].set_xlabel(\"$x$ / m\")\n",
+    "        ax[i][j].set_ylabel(\"$y$ / m\")\n",
     "ax[0][0].set_title(\"$p/\\\\tilde{p}$\")\n",
     "ax[0][1].set_title(\"$\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\")\n",
     "ax[1][1].set_title(\"$\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\")\n",
     "ax[1][0].set_title(\"$\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\")\n",
-    "fig.tight_layout();"
+    "fig.tight_layout()"
    ]
   },
   {
@@ -436,7 +537,7 @@
     "import os\n",
     "\n",
     "# out_dir will contain all data we produce\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "os.makedirs(out_dir, exist_ok=True)"
    ]
   },
@@ -449,10 +550,10 @@
    },
    "outputs": [],
    "source": [
-    "def generate_mesh_axb(a,b,Nx,Ny,P):\n",
+    "def generate_mesh_axb(a, b, Nx, Ny, P):\n",
     "    output_file = f\"{out_dir}/square_{a}x{b}.msh\"\n",
-    "    \n",
-    "    lc=0.5\n",
+    "\n",
+    "    lc = 0.5\n",
     "\n",
     "    # Before using any functions in the Python API, Gmsh must be initialized:\n",
     "    gmsh.initialize()\n",
@@ -464,13 +565,12 @@
     "    dim2 = 2\n",
     "\n",
     "    # Outer points (ccw)\n",
-    "    gmsh.model.geo.addPoint(0,   -b,    0, lc,  1)\n",
-    "    gmsh.model.geo.addPoint(a,   -b,    0, lc,  2)\n",
-    "    gmsh.model.geo.addPoint(a,   -b/2,  0, lc,  3)\n",
-    "    gmsh.model.geo.addPoint(a,    0,    0, lc,  4)\n",
-    "    gmsh.model.geo.addPoint(0,    0,    0, lc,  5)\n",
-    "    gmsh.model.geo.addPoint(0,   -b/2,  0, lc,  6)\n",
-    "\n",
+    "    gmsh.model.geo.addPoint(0, -b, 0, lc, 1)\n",
+    "    gmsh.model.geo.addPoint(a, -b, 0, lc, 2)\n",
+    "    gmsh.model.geo.addPoint(a, -b / 2, 0, lc, 3)\n",
+    "    gmsh.model.geo.addPoint(a, 0, 0, lc, 4)\n",
+    "    gmsh.model.geo.addPoint(0, 0, 0, lc, 5)\n",
+    "    gmsh.model.geo.addPoint(0, -b / 2, 0, lc, 6)\n",
     "\n",
     "    # Outer lines (ccw)\n",
     "    gmsh.model.geo.addLine(1, 2, 1)\n",
@@ -481,11 +581,10 @@
     "    gmsh.model.geo.addLine(6, 1, 6)\n",
     "    gmsh.model.geo.addLine(6, 3, 7)\n",
     "\n",
-    "\n",
-    "    # The third elementary entity is the surface. In order to define a surface \n",
+    "    # The third elementary entity is the surface. In order to define a surface\n",
     "    # from the curves defined above, a curve loop has first to be defined (ccw).\n",
-    "    gmsh.model.geo.addCurveLoop([ 1,  2,  -7,  6], 1)\n",
-    "    gmsh.model.geo.addCurveLoop([ 7,  3,   4,  5], 2)\n",
+    "    gmsh.model.geo.addCurveLoop([1, 2, -7, 6], 1)\n",
+    "    gmsh.model.geo.addCurveLoop([7, 3, 4, 5], 2)\n",
     "\n",
     "    # Add plane surfaces defined by one or more curve loops.\n",
     "    gmsh.model.geo.addPlaneSurface([1], 1)\n",
@@ -494,13 +593,13 @@
     "    gmsh.model.geo.synchronize()\n",
     "\n",
     "    #  Prepare structured grid\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 1, Nx)\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 2, int(Ny*0.3))\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 3, Ny, \"Progression\", -P)\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 4, Nx)\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 5, Ny, \"Progression\", P)\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 6, int(Ny*0.3))\n",
-    "    gmsh.model.geo.mesh.setTransfiniteCurve( 7, Nx)\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(1, Nx)\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(2, int(Ny * 0.3))\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(3, Ny, \"Progression\", -P)\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(4, Nx)\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(5, Ny, \"Progression\", P)\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(6, int(Ny * 0.3))\n",
+    "    gmsh.model.geo.mesh.setTransfiniteCurve(7, Nx)\n",
     "\n",
     "    gmsh.model.geo.mesh.setTransfiniteSurface(1, \"Alternate\")\n",
     "    gmsh.model.geo.mesh.setTransfiniteSurface(2, \"Alternate\")\n",
@@ -530,10 +629,10 @@
     "\n",
     "    gmsh.model.mesh.generate(dim2)\n",
     "    # gmsh.option.setNumber('Mesh.SecondOrderIncomplete', 1)   # serendipity elements\n",
-    "    gmsh.model.mesh.setOrder(2)   # higher order elements (quadratic)\n",
+    "    gmsh.model.mesh.setOrder(2)  # higher order elements (quadratic)\n",
     "    gmsh.write(output_file)\n",
     "\n",
-    "    gmsh.finalize()\n"
+    "    gmsh.finalize()"
    ]
   },
   {
@@ -541,6 +640,7 @@
    "execution_count": 8,
    "id": "137f610d-adbc-4e3f-9ea0-8ee958bbb817",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -579,7 +679,7 @@
     }
    ],
    "source": [
-    "generate_mesh_axb(200,100,25,45,1.07)"
+    "generate_mesh_axb(200, 100, 25, 45, 1.07)"
    ]
   },
   {
@@ -587,6 +687,7 @@
    "execution_count": 9,
    "id": "f0aaf7e1-9c00-4c16-a0ef-605cbf877377",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -599,6 +700,7 @@
    "execution_count": 10,
    "id": "a362da7c-dcc4-47f9-9e43-c5871f1006ed",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -662,6 +764,7 @@
    "execution_count": 11,
    "id": "9c745eea-75c9-49db-8b40-833aca73b436",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -681,7 +784,7 @@
     "pv.set_jupyter_backend(\"static\")\n",
     "\n",
     "mesh = pv.read(f\"{out_dir}/square_200x100_domain.vtu\")\n",
-    "plotter = pv.Plotter(window_size = [1000, 800])\n",
+    "plotter = pv.Plotter(window_size=[1000, 800])\n",
     "plotter.add_mesh(mesh, show_edges=True, show_scalar_bar=False, color=None, scalars=None)\n",
     "\n",
     "plotter.show_bounds(ticks=\"outside\", xlabel=\"x / m\", ylabel=\"y / m\")\n",
@@ -702,7 +805,7 @@
     "\n",
     "In this example, the boundary conditions are defined as follows:\n",
     "\n",
-    "**Top:** \n",
+    "**Top:**\n",
     "$$\n",
     "\\begin{align}\n",
     "p(y=0)&=\\tilde{p}\\cdot \\sin(\\omega \\cdot t) \\cdot \\cos(\\frac{2 \\pi}{L} \\cdot x) \\\\\n",
@@ -768,74 +871,87 @@
    "outputs": [],
    "source": [
     "## Helper Functions\n",
-    "def read_timestep_mesh(a,time):\n",
+    "def read_timestep_mesh(a, time):\n",
     "    reader = pv.PVDReader(f\"{out_dir}/square_{a}x100.pvd\")\n",
-    "    reader.set_active_time_point(int(time*4)) # time [s], delta t = 0.25 s\n",
+    "    reader.set_active_time_point(int(time * 4))  # time [s], delta t = 0.25 s\n",
     "    mesh = reader.read()[0]\n",
     "    return mesh\n",
     "\n",
+    "\n",
     "def slice_along_line(mesh, start_point, end_point):\n",
-    "    line = pv.Line(start_point, end_point, resolution = 2)\n",
+    "    line = pv.Line(start_point, end_point, resolution=2)\n",
     "    return mesh.slice_along_line(line)\n",
     "\n",
+    "\n",
     "def get_pressure_sorted(mesh):\n",
     "    pressure = mesh.point_data[\"pressure_interpolated\"]\n",
-    "    depth = mesh.points[:,1]\n",
+    "    depth = mesh.points[:, 1]\n",
     "    indices_sorted = np.argsort(depth)\n",
     "    pressure_sorted = pressure[indices_sorted]\n",
     "    return pressure_sorted\n",
     "\n",
+    "\n",
     "def get_stresses_sorted(mesh):\n",
     "    sigma = mesh.point_data[\"sigma\"]\n",
-    "    depth = mesh.points[:,1]\n",
+    "    depth = mesh.points[:, 1]\n",
     "    indices_sorted = np.argsort(depth)\n",
-    "    sigma_xx = - sigma[indices_sorted, 0] # switching sign convention\n",
-    "    sigma_yy = - sigma[indices_sorted, 1]\n",
-    "    #sigma_zz = - sigma[indices_sorted, 2]\n",
-    "    sigma_xy = + sigma[indices_sorted, 3]\n",
-    "    return sigma_xx, sigma_yy, sigma_xy  #,sigma_zz\n",
+    "    sigma_xx = -sigma[indices_sorted, 0]  # switching sign convention\n",
+    "    sigma_yy = -sigma[indices_sorted, 1]\n",
+    "    # sigma_zz = - sigma[indices_sorted, 2]\n",
+    "    sigma_xy = +sigma[indices_sorted, 3]\n",
+    "    return sigma_xx, sigma_yy, sigma_xy  # ,sigma_zz\n",
+    "\n",
     "\n",
     "def get_depth_sorted(mesh):\n",
-    "    depth = mesh.points[:,1]\n",
+    "    depth = mesh.points[:, 1]\n",
     "    indices_sorted = np.argsort(depth)\n",
     "    return depth[indices_sorted]\n",
     "\n",
+    "\n",
     "def compute_abs_and_rel_pressure_error(pressures, depth, t, x):\n",
     "    num_points = pressures.shape[0]\n",
     "    f_abs = np.zeros(num_points)\n",
     "    f_rel = np.zeros(num_points)\n",
-    "    \n",
-    "    for pt_idx in range(num_points): \n",
-    "        y =  -depth[pt_idx]\n",
-    "        pressure_ana = compute_pressure_and_stresses(t,x,y)[0]  # returns pressure normalised to the pressure amplitude\n",
-    "        pressure_num = pressures[pt_idx]/0.1e5                  # absolute pressure divided by pressure amplitude\n",
+    "\n",
+    "    for pt_idx in range(num_points):\n",
+    "        y = -depth[pt_idx]\n",
+    "        pressure_ana = compute_pressure_and_stresses(t, x, y)[\n",
+    "            0\n",
+    "        ]  # returns pressure normalised to the pressure amplitude\n",
+    "        pressure_num = (\n",
+    "            pressures[pt_idx] / 0.1e5\n",
+    "        )  # absolute pressure divided by pressure amplitude\n",
     "        f_abs[pt_idx] = pressure_num - pressure_ana\n",
-    "        \n",
+    "\n",
     "        if pressure_ana == 0:\n",
     "            f_rel[pt_idx] = f_abs[pt_idx] / 1e-2\n",
     "        else:\n",
     "            f_rel[pt_idx] = f_abs[pt_idx] / pressure_ana\n",
-    "            \n",
+    "\n",
     "    return f_abs, f_rel\n",
     "\n",
+    "\n",
     "def compute_abs_and_rel_stress_error(sigmas, depth, t, x):\n",
     "    num_points = depth.shape[0]\n",
     "    f_abs = np.zeros((3, num_points))\n",
     "    f_rel = np.zeros((3, num_points))\n",
-    "    \n",
-    "    for stress_idx in (0,1,2):\n",
-    "        \n",
+    "\n",
+    "    for stress_idx in (0, 1, 2):\n",
     "        for pt_idx in range(num_points):\n",
     "            y = -depth[pt_idx]\n",
-    "            sigma_ana = compute_pressure_and_stresses(t,x,y)[stress_idx+1]   # returns stresses normalised to the pressure amplitude\n",
-    "            sigma_num = sigma[stress_idx][pt_idx]/0.1e5                      # absolute stresses divided by pressure amplitude\n",
+    "            sigma_ana = compute_pressure_and_stresses(t, x, y)[\n",
+    "                stress_idx + 1\n",
+    "            ]  # returns stresses normalised to the pressure amplitude\n",
+    "            sigma_num = (\n",
+    "                sigma[stress_idx][pt_idx] / 0.1e5\n",
+    "            )  # absolute stresses divided by pressure amplitude\n",
     "            f_abs[stress_idx][pt_idx] = sigma_num - sigma_ana\n",
     "\n",
     "            if sigma_ana == 0:\n",
-    "                f_rel[stress_idx][pt_idx] = f_abs[stress_idx][pt_idx]/ 1e-2\n",
+    "                f_rel[stress_idx][pt_idx] = f_abs[stress_idx][pt_idx] / 1e-2\n",
     "            else:\n",
     "                f_rel[stress_idx][pt_idx] = f_abs[stress_idx][pt_idx] / sigma_ana\n",
-    "            \n",
+    "\n",
     "    return f_abs, f_rel"
    ]
   },
@@ -844,6 +960,7 @@
    "execution_count": 14,
    "id": "848ce73f-688f-4b19-94ef-a4718ad61570",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -857,9 +974,10 @@
     }
    ],
    "source": [
-    "model = ogs.OGS(INPUT_FILE=\"seabed_response_200x100.prj\", PROJECT_FILE=\"seabed_response_200x100.prj\")\n",
-    "model.run_model(logfile=f\"{out_dir}/out.txt\",\n",
-    "                args=f\"-o {out_dir} -m {out_dir}\")"
+    "model = ogs.OGS(\n",
+    "    INPUT_FILE=\"seabed_response_200x100.prj\", PROJECT_FILE=\"seabed_response_200x100.prj\"\n",
+    ")\n",
+    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m {out_dir}\")"
    ]
   },
   {
@@ -875,6 +993,7 @@
    "execution_count": 15,
    "id": "ed7c61b9-18e9-48d5-87d7-2fb3f932adf9",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -892,14 +1011,21 @@
    "source": [
     "time = 2.5  # [s]\n",
     "reader = pv.get_reader(f\"{out_dir}/square_200x100.pvd\")\n",
-    "reader.set_active_time_point(int(time*4))\n",
+    "reader.set_active_time_point(int(time * 4))\n",
     "mesh = reader.read()[0]\n",
     "\n",
     "plotter = pv.Plotter()\n",
     "\n",
-    "sargs = dict(title=\"p / Pa\" , height=0.25, position_x=0.2, position_y=0.02)\n",
-    "plotter.add_mesh(mesh, scalars = \"pressure_interpolated\", show_edges=False, show_scalar_bar=True, label=\"p\", scalar_bar_args=sargs)\n",
-    "plotter.show_bounds(ticks=\"outside\", xlabel = \"x / m\", ylabel = \"y / m\")\n",
+    "sargs = dict(title=\"p / Pa\", height=0.25, position_x=0.2, position_y=0.02)\n",
+    "plotter.add_mesh(\n",
+    "    mesh,\n",
+    "    scalars=\"pressure_interpolated\",\n",
+    "    show_edges=False,\n",
+    "    show_scalar_bar=True,\n",
+    "    label=\"p\",\n",
+    "    scalar_bar_args=sargs,\n",
+    ")\n",
+    "plotter.show_bounds(ticks=\"outside\", xlabel=\"x / m\", ylabel=\"y / m\")\n",
     "plotter.add_axes()\n",
     "plotter.view_xy()\n",
     "plotter.show()"
@@ -910,9 +1036,9 @@
    "id": "d4631296-5f3a-42fa-96d1-5ce0556bf206",
    "metadata": {},
    "source": [
-    "For a more detailed comparison between the analytical and the numerical solution, both solutions are evaluated along the vertical line directly underneath an anti-node of the standing wave. As before, the pore pressure and the amplitude of the effective stresses are illustrated as a function of depth. The results of the numerical solution are marked as dots in the same color as the analytical solution. Additionally, the absolute errors $\\Delta p = p_{numerical}-p_{analtical}$ and $\\Delta \\sigma_{i}' = \\sigma_{i, numerical}'-\\sigma_{i, analytical}'$ are illustrated on the right. \n",
+    "For a more detailed comparison between the analytical and the numerical solution, both solutions are evaluated along the vertical line directly underneath an anti-node of the standing wave. As before, the pore pressure and the amplitude of the effective stresses are illustrated as a function of depth. The results of the numerical solution are marked as dots in the same color as the analytical solution. Additionally, the absolute errors $\\Delta p = p_{numerical}-p_{analtical}$ and $\\Delta \\sigma_{i}' = \\sigma_{i, numerical}'-\\sigma_{i, analytical}'$ are illustrated on the right.\n",
     "\n",
-    "The plot shows that the absolute errors are very small at about $2 \\%$ of the wave's amplitude. They can mostly be ascribed to the space- and time-discretization. Close to the top boundary of the domain, larger errors occur. These errors could originate in the definition of both a pressure and displacement (Neumann-) boundary condition along the top edge. "
+    "The plot shows that the absolute errors are very small at about $2 \\%$ of the wave's amplitude. They can mostly be ascribed to the space- and time-discretization. Close to the top boundary of the domain, larger errors occur. These errors could originate in the definition of both a pressure and displacement (Neumann-) boundary condition along the top edge."
    ]
   },
   {
@@ -920,6 +1046,7 @@
    "execution_count": 16,
    "id": "2e06842d-1f73-4182-9ffa-02498a9f9341",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -936,28 +1063,55 @@
    ],
    "source": [
     "x = 0\n",
-    "y = np.linspace(0,100,1000)\n",
-    "y_rel = y/100\n",
-    "colors = {0:\"orangered\", 2:\"gold\", 4:\"blueviolet\", 6:\"forestgreen\", 8:\"darkorange\", 10:\"royalblue\"}\n",
-    "\n",
-    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(15,15))\n",
+    "y = np.linspace(0, 100, 1000)\n",
+    "y_rel = y / 100\n",
+    "colors = {\n",
+    "    0: \"orangered\",\n",
+    "    2: \"gold\",\n",
+    "    4: \"blueviolet\",\n",
+    "    6: \"forestgreen\",\n",
+    "    8: \"darkorange\",\n",
+    "    10: \"royalblue\",\n",
+    "}\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(15, 15))\n",
     "\n",
     "## Plotting analytical solution\n",
-    "for t in [2,4,6,8,10]:\n",
-    "    ax[0][0].plot(compute_pressure_and_stresses(t,x,y)[0], -y_rel, color=colors[t], label= \"analytical, t = %.1f s\" %t)\n",
-    "\n",
-    "ax[1][0].plot(compute_pressure_and_stresses(2.5,x,y)[1], -y_rel, color = colors[6], label = \"analytical, $\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\")\n",
-    "ax[1][0].plot(compute_pressure_and_stresses(2.5,x,y)[2], -y_rel, color = colors[2], label = \"analytical, $\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\")\n",
-    "ax[1][0].plot(compute_pressure_and_stresses(2.5,x,y)[3], -y_rel, color = colors[4], label = \"analytical, $\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\")\n",
+    "for t in [2, 4, 6, 8, 10]:\n",
+    "    ax[0][0].plot(\n",
+    "        compute_pressure_and_stresses(t, x, y)[0],\n",
+    "        -y_rel,\n",
+    "        color=colors[t],\n",
+    "        label=\"analytical, t = %.1f s\" % t,\n",
+    "    )\n",
+    "\n",
+    "ax[1][0].plot(\n",
+    "    compute_pressure_and_stresses(2.5, x, y)[1],\n",
+    "    -y_rel,\n",
+    "    color=colors[6],\n",
+    "    label=\"analytical, $\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\",\n",
+    ")\n",
+    "ax[1][0].plot(\n",
+    "    compute_pressure_and_stresses(2.5, x, y)[2],\n",
+    "    -y_rel,\n",
+    "    color=colors[2],\n",
+    "    label=\"analytical, $\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\",\n",
+    ")\n",
+    "ax[1][0].plot(\n",
+    "    compute_pressure_and_stresses(2.5, x, y)[3],\n",
+    "    -y_rel,\n",
+    "    color=colors[4],\n",
+    "    label=\"analytical, $\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\",\n",
+    ")\n",
     "\n",
     "## Plotting numerical solution\n",
-    "p1 = (x+1e-6, 0, 0)\n",
-    "p2 = (x+1e-6, -100, 0)\n",
+    "p1 = (x + 1e-6, 0, 0)\n",
+    "p2 = (x + 1e-6, -100, 0)\n",
     "\n",
     "for t_num in (2, 2.5, 4, 6, 8, 10):\n",
     "    mesh = read_timestep_mesh(200, t_num)\n",
-    "   \n",
-    "    line_mesh = slice_along_line(mesh, p1, p2) \n",
+    "\n",
+    "    line_mesh = slice_along_line(mesh, p1, p2)\n",
     "    pressure = get_pressure_sorted(line_mesh)\n",
     "    sigma = get_stresses_sorted(line_mesh)\n",
     "    depth = get_depth_sorted(line_mesh)\n",
@@ -965,31 +1119,76 @@
     "    f_abs_sigma = compute_abs_and_rel_stress_error(sigma, depth, t_num, x)[0]\n",
     "\n",
     "    if t_num != 2.5:\n",
-    "        ax[0][0].plot(pressure/0.1e5, depth/100, \"o\", markevery=10, color=colors[t_num], label= \"numerical, t = %.1f s\" %t_num) \n",
+    "        ax[0][0].plot(\n",
+    "            pressure / 0.1e5,\n",
+    "            depth / 100,\n",
+    "            \"o\",\n",
+    "            markevery=10,\n",
+    "            color=colors[t_num],\n",
+    "            label=\"numerical, t = %.1f s\" % t_num,\n",
+    "        )\n",
     "        ax[0][0].set_xlabel(\"$p$ / $\\\\tilde{p}$\")\n",
     "\n",
-    "        ax[0][1].plot(f_abs_pressure, depth/100, color=colors[t_num], label = \"t = %.1f s\" %t_num)\n",
+    "        ax[0][1].plot(\n",
+    "            f_abs_pressure, depth / 100, color=colors[t_num], label=\"t = %.1f s\" % t_num\n",
+    "        )\n",
     "        ax[0][1].set_xlabel(\"$\\\\Delta p /\\\\tilde{p}$\")\n",
-    "   \n",
+    "\n",
     "    if t_num == 2.5:\n",
-    "        ax[1][0].plot(sigma[0]/0.1e5, depth/100, \"o\", markevery=10, color = colors[6], label = \"numerical, $\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\")\n",
-    "        ax[1][0].plot(sigma[1]/0.1e5, depth/100, \"o\", markevery=10, color = colors[2], label = \"numerical, $\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\")\n",
-    "        ax[1][0].plot(sigma[2]/0.1e5, depth/100, \"o\", markevery=10, color = colors[4], label = \"numerical, $\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\")\n",
+    "        ax[1][0].plot(\n",
+    "            sigma[0] / 0.1e5,\n",
+    "            depth / 100,\n",
+    "            \"o\",\n",
+    "            markevery=10,\n",
+    "            color=colors[6],\n",
+    "            label=\"numerical, $\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\",\n",
+    "        )\n",
+    "        ax[1][0].plot(\n",
+    "            sigma[1] / 0.1e5,\n",
+    "            depth / 100,\n",
+    "            \"o\",\n",
+    "            markevery=10,\n",
+    "            color=colors[2],\n",
+    "            label=\"numerical, $\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\",\n",
+    "        )\n",
+    "        ax[1][0].plot(\n",
+    "            sigma[2] / 0.1e5,\n",
+    "            depth / 100,\n",
+    "            \"o\",\n",
+    "            markevery=10,\n",
+    "            color=colors[4],\n",
+    "            label=\"numerical, $\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\",\n",
+    "        )\n",
     "        ax[1][0].set_xlabel(\"$\\\\sigma$'/$\\\\alpha\\\\tilde{p}$\")\n",
-    "        \n",
-    "        ax[1][1].plot(f_abs_sigma[0], depth/100, color = colors[6], label = \"$\\\\Delta\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\")\n",
-    "        ax[1][1].plot(f_abs_sigma[1], depth/100, color = colors[2], label = \"$\\\\Delta\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\")\n",
-    "        ax[1][1].plot(f_abs_sigma[2], depth/100, color = colors[4], label = \"$\\\\Delta\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\")\n",
+    "\n",
+    "        ax[1][1].plot(\n",
+    "            f_abs_sigma[0],\n",
+    "            depth / 100,\n",
+    "            color=colors[6],\n",
+    "            label=\"$\\\\Delta\\\\sigma'_{xx}/\\\\alpha\\\\tilde{p}$\",\n",
+    "        )\n",
+    "        ax[1][1].plot(\n",
+    "            f_abs_sigma[1],\n",
+    "            depth / 100,\n",
+    "            color=colors[2],\n",
+    "            label=\"$\\\\Delta\\\\sigma'_{yy}/\\\\alpha\\\\tilde{p}$\",\n",
+    "        )\n",
+    "        ax[1][1].plot(\n",
+    "            f_abs_sigma[2],\n",
+    "            depth / 100,\n",
+    "            color=colors[4],\n",
+    "            label=\"$\\\\Delta\\\\sigma'_{xy}/\\\\alpha\\\\tilde{p}$\",\n",
+    "        )\n",
     "        ax[1][1].set_xlabel(\"$\\\\Delta\\\\sigma$'/$\\\\alpha\\\\tilde{p}$\")\n",
-    "        \n",
-    "        #ax[1][0].plot(sigma[3]/0.1e5, depth/100, \"o\", markevery=10, color = colors[4], label = \"numerical, $\\\\sigma'_{zz}/\\\\alpha\\\\tilde{p}$\")\n",
+    "\n",
+    "        # ax[1][0].plot(sigma[3]/0.1e5, depth/100, \"o\", markevery=10, color = colors[4], label = \"numerical, $\\\\sigma'_{zz}/\\\\alpha\\\\tilde{p}$\")\n",
     "\n",
     "## layout settings\n",
-    "ax[0][0].set_title('Comparison numerical and analytical solution')\n",
-    "ax[0][1].set_title('Absolute error')\n",
+    "ax[0][0].set_title(\"Comparison numerical and analytical solution\")\n",
+    "ax[0][1].set_title(\"Absolute error\")\n",
     "\n",
-    "for idx_1 in (0,1):\n",
-    "    for idx_2 in (0,1):\n",
+    "for idx_1 in (0, 1):\n",
+    "    for idx_2 in (0, 1):\n",
     "        ax[idx_1][idx_2].grid(True)\n",
     "        ax[idx_1][idx_2].set_ylabel(\"$y$ / $L$\")\n",
     "        ax[idx_1][0].set_xlim(-1.1, 1.1)\n",
diff --git a/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb b/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb
index d5ff704aebc..1a801d39c0e 100644
--- a/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb
+++ b/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb
@@ -1,15 +1,16 @@
 {
  "cells": [
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "id": "bb0907b4-4e26-4c4e-ab1f-22b5330cb1d2",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Linear elasticity: disc with hole convergence study\"\n",
     "date = \"2022-09-15\"\n",
     "author = \"Linda Günther, Sophia Einspänner, Robert Habel, Christoph Lehmann and Thomas Nagel\"\n",
     "web_subsection = \"small-deformations\"\n",
-    "<!--eofm-->"
+    "+++"
    ]
   },
   {
@@ -76,6 +77,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -107,7 +109,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -307,6 +310,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -322,6 +326,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -562,6 +567,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -597,6 +603,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -615,6 +622,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -666,6 +674,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -920,6 +929,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -947,6 +957,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -975,6 +986,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [],
@@ -1042,6 +1054,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -1642,6 +1655,7 @@
     "jupyter": {
      "source_hidden": true
     },
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
diff --git a/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb b/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb
index bce8e212961..c69a1a6da29 100644
--- a/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb
+++ b/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb
@@ -5,11 +5,12 @@
    "id": "96f29a77",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"SimpleMechanics\"\n",
     "date = \"2021-09-10\"\n",
     "author = \"Lars Bilke, Jörg Buchwald\"\n",
     "web_subsection = \"small-deformations\"\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -24,7 +25,9 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "420713a5-74d6-47ad-815c-4ca9e7e914bf",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -38,7 +41,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "8da3a8e8-be97-4092-88a9-1fb7792fa644",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -179,7 +184,9 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "1d730e79",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
diff --git a/Tests/Data/Mechanics/PLLC/PLLC.ipynb b/Tests/Data/Mechanics/PLLC/PLLC.ipynb
index ee751646246..5c5de5df16c 100644
--- a/Tests/Data/Mechanics/PLLC/PLLC.ipynb
+++ b/Tests/Data/Mechanics/PLLC/PLLC.ipynb
@@ -5,11 +5,12 @@
    "id": "bb0907b4-4e26-4c4e-ab1f-22b5330cb1d2",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Power Law Linear Creep\"\n",
     "date = \"2023-01-02\"\n",
     "author = \"Florian Zill\"\n",
     "web_subsection = \"small-deformations\"\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -27,7 +28,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "7962f42f-fd53-4fc1-b966-a8ba924aca6c",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "import contextlib\n",
@@ -39,16 +42,18 @@
     "from ogs6py import ogs\n",
     "\n",
     "prj_name = \"uniax_compression\"\n",
-    "data_dir = os.environ.get('OGS_DATA_DIR', str(os.getcwd()).split(\"/Data/\")[0] + \"/Data/\")\n",
+    "data_dir = os.environ.get(\n",
+    "    \"OGS_DATA_DIR\", str(os.getcwd()).split(\"/Data/\")[0] + \"/Data/\"\n",
+    ")\n",
     "input_file = f\"{data_dir}/Mechanics/PLLC/{prj_name}.prj\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', f'{data_dir}/Mechanics/PLLC/_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", f\"{data_dir}/Mechanics/PLLC/_out\")\n",
     "\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
     "os.chdir(out_dir)\n",
     "\n",
     "prj_file = f\"{out_dir}/{prj_name}_out.prj\"\n",
-    "ogs_model = ogs.OGS(INPUT_FILE=input_file, PROJECT_FILE=prj_file)\n"
+    "ogs_model = ogs.OGS(INPUT_FILE=input_file, PROJECT_FILE=prj_file)"
    ]
   },
   {
@@ -66,18 +71,123 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "5a20a14e",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Unfortunately the source for the WIPP data has gone missing - will be added if it's found again\n",
     "ExData = {\n",
-    "    \"WIPP CS 25\": (25, \"^\", [[9.87970002, 2.013560846E-05], [11.84642707, 3.178356756E-05], [7.87388785, 1.66059726E-06]]),\n",
-    "    \"WIPP CS 60\": (60, \"^\", [[3.98589289, 5.7824853E-06], [5.94266985, 2.075776623E-05], [7.87388785, 1.953209818E-05], [9.96978837, 5.841438703E-05], [11.84642707, 0.00011762092257], [13.94911482, 0.00026749321794], [17.9857158, 0.00111804208073], [1.9814251, 8.7645834E-07], [3.91418422, 4.01350889E-06], [5.88897108, 3.34371363E-06], [7.87388785, 1.129440706E-05], [9.87970002, 2.99068674E-05], [11.84642707, 7.681792203E-05], [13.82306874, 0.00011067584933], [15.83934389, 0.00052247037957]]),\n",
-    "    \"DeVries 1988 25\": (25, \"s\", [[4.99, 2.10816E-06], [4.99, 2.4192E-06], [5, 1.8144E-06], [9.99, 2.2032E-05], [14.96, 9.2448E-05], [14.98, 0.000216]]),\n",
-    "    \"DeVries 1988 100\": (100, \"s\", [[4.95, 9.6768E-05], [6.77, 0.000292896], [7.46, 0.000324], [8.55, 0.000664416], [8.92, 0.00091584], [8.98, 0.0009936], [9.91, 0.00124416], [10.1, 0.00139968], [10.22, 0.00093312], [10.27, 0.00132192], [12.1, 0.00216], [12.3, 0.00409536], [12.35, 0.00320544], [12.37, 0.00292032], [12.39, 0.00253152], [12.4, 0.0026784], [12.46, 0.0025056], [12.49, 0.00347328], [13.57, 0.00273024], [13.78, 0.00242784], [14.7, 0.00482112], [16.87, 0.0095904], [17.2, 0.0123552], [19.96, 0.030672]]),\n",
-    "    \"DeVries 1988 200\": (200, \"s\", [[3.47, 0.00117504], [4.71, 0.0032832], [6.67, 0.0104544], [6.78, 0.0132192], [9.86, 0.214272]]),\n",
-    "    \"Berest 2015 14.3\": (14.3, \"P\", [[0.09909639, 8.944207E-08], [0.19575886, 1.4118213E-07], [0.29452325, 1.4118213E-07], [0.49411031, 9.799173E-08]]),\n",
-    "    \"Berest 2017 7.8\": (7.8, \"P\", [[0.19575886,2.2285256E-07], [0.19575886,9.505469E-08], [0.19754389,2.5947583E-07], [0.19754389,2.647936E-08], [0.39379426,4.9162047E-07], [0.39738509,6.801413E-08], [0.59247161,4.0957628E-07], [0.59247161,5.7241269E-07], [0.59787408,1.0735864E-07], [1.0591736,1.11804208E-06]])}"
+    "    \"WIPP CS 25\": (\n",
+    "        25,\n",
+    "        \"^\",\n",
+    "        [\n",
+    "            [9.87970002, 2.013560846e-05],\n",
+    "            [11.84642707, 3.178356756e-05],\n",
+    "            [7.87388785, 1.66059726e-06],\n",
+    "        ],\n",
+    "    ),\n",
+    "    \"WIPP CS 60\": (\n",
+    "        60,\n",
+    "        \"^\",\n",
+    "        [\n",
+    "            [3.98589289, 5.7824853e-06],\n",
+    "            [5.94266985, 2.075776623e-05],\n",
+    "            [7.87388785, 1.953209818e-05],\n",
+    "            [9.96978837, 5.841438703e-05],\n",
+    "            [11.84642707, 0.00011762092257],\n",
+    "            [13.94911482, 0.00026749321794],\n",
+    "            [17.9857158, 0.00111804208073],\n",
+    "            [1.9814251, 8.7645834e-07],\n",
+    "            [3.91418422, 4.01350889e-06],\n",
+    "            [5.88897108, 3.34371363e-06],\n",
+    "            [7.87388785, 1.129440706e-05],\n",
+    "            [9.87970002, 2.99068674e-05],\n",
+    "            [11.84642707, 7.681792203e-05],\n",
+    "            [13.82306874, 0.00011067584933],\n",
+    "            [15.83934389, 0.00052247037957],\n",
+    "        ],\n",
+    "    ),\n",
+    "    \"DeVries 1988 25\": (\n",
+    "        25,\n",
+    "        \"s\",\n",
+    "        [\n",
+    "            [4.99, 2.10816e-06],\n",
+    "            [4.99, 2.4192e-06],\n",
+    "            [5, 1.8144e-06],\n",
+    "            [9.99, 2.2032e-05],\n",
+    "            [14.96, 9.2448e-05],\n",
+    "            [14.98, 0.000216],\n",
+    "        ],\n",
+    "    ),\n",
+    "    \"DeVries 1988 100\": (\n",
+    "        100,\n",
+    "        \"s\",\n",
+    "        [\n",
+    "            [4.95, 9.6768e-05],\n",
+    "            [6.77, 0.000292896],\n",
+    "            [7.46, 0.000324],\n",
+    "            [8.55, 0.000664416],\n",
+    "            [8.92, 0.00091584],\n",
+    "            [8.98, 0.0009936],\n",
+    "            [9.91, 0.00124416],\n",
+    "            [10.1, 0.00139968],\n",
+    "            [10.22, 0.00093312],\n",
+    "            [10.27, 0.00132192],\n",
+    "            [12.1, 0.00216],\n",
+    "            [12.3, 0.00409536],\n",
+    "            [12.35, 0.00320544],\n",
+    "            [12.37, 0.00292032],\n",
+    "            [12.39, 0.00253152],\n",
+    "            [12.4, 0.0026784],\n",
+    "            [12.46, 0.0025056],\n",
+    "            [12.49, 0.00347328],\n",
+    "            [13.57, 0.00273024],\n",
+    "            [13.78, 0.00242784],\n",
+    "            [14.7, 0.00482112],\n",
+    "            [16.87, 0.0095904],\n",
+    "            [17.2, 0.0123552],\n",
+    "            [19.96, 0.030672],\n",
+    "        ],\n",
+    "    ),\n",
+    "    \"DeVries 1988 200\": (\n",
+    "        200,\n",
+    "        \"s\",\n",
+    "        [\n",
+    "            [3.47, 0.00117504],\n",
+    "            [4.71, 0.0032832],\n",
+    "            [6.67, 0.0104544],\n",
+    "            [6.78, 0.0132192],\n",
+    "            [9.86, 0.214272],\n",
+    "        ],\n",
+    "    ),\n",
+    "    \"Berest 2015 14.3\": (\n",
+    "        14.3,\n",
+    "        \"P\",\n",
+    "        [\n",
+    "            [0.09909639, 8.944207e-08],\n",
+    "            [0.19575886, 1.4118213e-07],\n",
+    "            [0.29452325, 1.4118213e-07],\n",
+    "            [0.49411031, 9.799173e-08],\n",
+    "        ],\n",
+    "    ),\n",
+    "    \"Berest 2017 7.8\": (\n",
+    "        7.8,\n",
+    "        \"P\",\n",
+    "        [\n",
+    "            [0.19575886, 2.2285256e-07],\n",
+    "            [0.19575886, 9.505469e-08],\n",
+    "            [0.19754389, 2.5947583e-07],\n",
+    "            [0.19754389, 2.647936e-08],\n",
+    "            [0.39379426, 4.9162047e-07],\n",
+    "            [0.39738509, 6.801413e-08],\n",
+    "            [0.59247161, 4.0957628e-07],\n",
+    "            [0.59247161, 5.7241269e-07],\n",
+    "            [0.59787408, 1.0735864e-07],\n",
+    "            [1.0591736, 1.11804208e-06],\n",
+    "        ],\n",
+    "    ),\n",
+    "}"
    ]
   },
   {
@@ -95,18 +205,27 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8066a6d3",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "A1 = 0.18 # d^-1\n",
-    "Q1 = 54e3 # kJ / mol\n",
-    "A2 = 6.5e-5 # m^3 K d^−1\n",
-    "Q2 = 24.5e3 # kJ / mol\n",
-    "dGrain = 5e-2 # m\n",
-    "sref = 1. # MPa\n",
-    "BGRa = lambda sig, T: A1 * np.exp(-Q1/(8.3145*(273.15+T))) * np.power(sig/sref,5.)\n",
-    "PLLC = lambda sig, T: A1 * np.exp(-Q1/(8.3145*(273.15+T))) * np.power(sig/sref,5.) + \\\n",
-    "                      A2 * np.exp(-Q2/(8.3145*(273.15+T))) * sig/sref / np.power(dGrain, 3) / (273.15+T)"
+    "A1 = 0.18  # d^-1\n",
+    "Q1 = 54e3  # kJ / mol\n",
+    "A2 = 6.5e-5  # m^3 K d^−1\n",
+    "Q2 = 24.5e3  # kJ / mol\n",
+    "dGrain = 5e-2  # m\n",
+    "sref = 1.0  # MPa\n",
+    "BGRa = (\n",
+    "    lambda sig, T: A1\n",
+    "    * np.exp(-Q1 / (8.3145 * (273.15 + T)))\n",
+    "    * np.power(sig / sref, 5.0)\n",
+    ")\n",
+    "PLLC = lambda sig, T: A1 * np.exp(-Q1 / (8.3145 * (273.15 + T))) * np.power(\n",
+    "    sig / sref, 5.0\n",
+    ") + A2 * np.exp(-Q2 / (8.3145 * (273.15 + T))) * sig / sref / np.power(dGrain, 3) / (\n",
+    "    273.15 + T\n",
+    ")"
    ]
   },
   {
@@ -124,29 +243,36 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "7e2e294c-e803-4f02-b5ab-9bdfef94b00f",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "lo_stresses = np.array([0.2e6, 0.6e6])\n",
     "hi_stresses = np.array([2e6, 10e6])\n",
-    "Exps = {7.8: ('blue', lo_stresses), 14.3: ('orange', lo_stresses),\n",
-    "        25: ('lime', hi_stresses), 60: ('red', hi_stresses),\n",
-    "        100: ('gray', hi_stresses), 200: ('mediumpurple', hi_stresses)}\n",
+    "Exps = {\n",
+    "    7.8: (\"blue\", lo_stresses),\n",
+    "    14.3: (\"orange\", lo_stresses),\n",
+    "    25: (\"lime\", hi_stresses),\n",
+    "    60: (\"red\", hi_stresses),\n",
+    "    100: (\"gray\", hi_stresses),\n",
+    "    200: (\"mediumpurple\", hi_stresses),\n",
+    "}\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
-    "ax.set_xlabel('$\\\\sigma_\\\\mathrm{ax}$ / MPa')\n",
-    "ax.set_ylabel('$\\\\dot{\\\\epsilon}_{zz}$ / d$^{-1}$')\n",
+    "ax.set_xlabel(\"$\\\\sigma_\\\\mathrm{ax}$ / MPa\")\n",
+    "ax.set_ylabel(\"$\\\\dot{\\\\epsilon}_{zz}$ / d$^{-1}$\")\n",
     "ax.set_xlim(0.15, 30)\n",
     "ax.set_ylim(1e-15, 1e1)\n",
-    "ax.grid(visible=True, which='both')\n",
-    "points = {'pt0': (1., 1., 1.)}\n",
+    "ax.grid(visible=True, which=\"both\")\n",
+    "points = {\"pt0\": (1.0, 1.0, 1.0)}\n",
     "\n",
     "sigs = np.logspace(-1, 2, 100)\n",
-    "for temp, (col, stresses) in Exps.items():   \n",
+    "for temp, (col, stresses) in Exps.items():\n",
     "    # plot analytical curves\n",
     "    if temp >= 25:\n",
-    "        ax.plot(sigs, BGRa(sigs, temp), color=col, ls='--')\n",
-    "        ax.plot(sigs, PLLC(sigs, temp), color=col, ls='-')\n",
+    "        ax.plot(sigs, BGRa(sigs, temp), color=col, ls=\"--\")\n",
+    "        ax.plot(sigs, PLLC(sigs, temp), color=col, ls=\"-\")\n",
     "\n",
     "    # simulation in ogs and plot results\n",
     "    eps_dot = []\n",
@@ -156,14 +282,15 @@
     "        ogs_model.write_input()\n",
     "        # hide output\n",
     "        with contextlib.redirect_stdout(None):\n",
-    "            ogs_model.run_model(logfile=f\"{out_dir}/out.txt\", \n",
-    "                                args=\"-m \" + f\"{data_dir}/Mechanics/PLLC/\")\n",
+    "            ogs_model.run_model(\n",
+    "                logfile=f\"{out_dir}/out.txt\", args=\"-m \" + f\"{data_dir}/Mechanics/PLLC/\"\n",
+    "            )\n",
     "            pvdfile = vtuIO.PVDIO(f\"{prj_name}.pvd\", dim=3)\n",
     "        eps_zz = pvdfile.read_time_series(\"epsilon\", points)[\"pt0\"][:, 2]\n",
     "        eps_zz_dot = np.abs(np.diff(eps_zz)) / np.diff(pvdfile.timesteps)\n",
     "        # omit the first timestep\n",
     "        eps_dot += [np.mean(eps_zz_dot[1:])]\n",
-    "    ax.loglog(1e-6*stresses, eps_dot, 'o', c=col, markeredgecolor=\"k\")\n",
+    "    ax.loglog(1e-6 * stresses, eps_dot, \"o\", c=col, markeredgecolor=\"k\")\n",
     "\n",
     "# plot experimental data points\n",
     "for Ex, (temp, m, Data) in ExData.items():\n",
@@ -171,20 +298,23 @@
     "    ax.loglog(stresses, eps_dot, m, c=Exps[temp][0])\n",
     "\n",
     "# create legend\n",
-    "patches = [mpl.patches.Patch(color=col, label=str(temp) + '°C')\n",
-    "           for temp, (col, _) in Exps.items() if temp >= 25][::-1]\n",
-    "addLeg = lambda **args : patches.append(mpl.lines.Line2D([], [], **args))\n",
-    "addLeg(c='k', label='PLLC')\n",
-    "addLeg(c='k', ls='--', label='BGRa')\n",
-    "addLeg(c='w', ls='None', marker='o', mec=\"k\", label='OGS')\n",
-    "addLeg(c='k', ls='None', marker='s', label='DeVries (1988)')\n",
-    "addLeg(c='k', ls='None', marker='^', label='WIPP CS')\n",
-    "addLeg(c='b', ls='None', marker='P', label='Bérest (2017) 7.8°C')\n",
-    "addLeg(c='orange', ls='None', marker='P', label='Bérest (2015) 14.3°C')\n",
-    "ax.legend(handles=patches, loc='best')\n",
+    "patches = [\n",
+    "    mpl.patches.Patch(color=col, label=str(temp) + \"°C\")\n",
+    "    for temp, (col, _) in Exps.items()\n",
+    "    if temp >= 25\n",
+    "][::-1]\n",
+    "addLeg = lambda **args: patches.append(mpl.lines.Line2D([], [], **args))\n",
+    "addLeg(c=\"k\", label=\"PLLC\")\n",
+    "addLeg(c=\"k\", ls=\"--\", label=\"BGRa\")\n",
+    "addLeg(c=\"w\", ls=\"None\", marker=\"o\", mec=\"k\", label=\"OGS\")\n",
+    "addLeg(c=\"k\", ls=\"None\", marker=\"s\", label=\"DeVries (1988)\")\n",
+    "addLeg(c=\"k\", ls=\"None\", marker=\"^\", label=\"WIPP CS\")\n",
+    "addLeg(c=\"b\", ls=\"None\", marker=\"P\", label=\"Bérest (2017) 7.8°C\")\n",
+    "addLeg(c=\"orange\", ls=\"None\", marker=\"P\", label=\"Bérest (2015) 14.3°C\")\n",
+    "ax.legend(handles=patches, loc=\"best\")\n",
     "\n",
     "fig.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -197,7 +327,7 @@
     "\n",
     "Zill, Florian, Wenqing Wang, and Thomas Nagel. Influence of THM Process Coupling and Constitutive Models on the Simulated Evolution of Deep Salt Formations during Glaciation. The Mechanical Behavior of Salt X. CRC Press, 2022. https://doi.org/10.1201/9781003295808-33.\n",
     "\n",
-    "Li, Shiyuan, and Janos Urai. Numerical Studies of the Deformation of Salt Bodies with Embedded Carbonate Stringers. Online, print. Publikationsserver der RWTH Aachen University, 2012. http://publications.rwth-aachen.de/record/211523/files/4415.pdf "
+    "Li, Shiyuan, and Janos Urai. Numerical Studies of the Deformation of Salt Bodies with Embedded Carbonate Stringers. Online, print. Publikationsserver der RWTH Aachen University, 2012. http://publications.rwth-aachen.de/record/211523/files/4415.pdf"
    ]
   }
  ],
diff --git a/Tests/Data/Notebooks/SimplePETSc.ipynb b/Tests/Data/Notebooks/SimplePETSc.ipynb
index f3990b10e54..34c1fc3519a 100644
--- a/Tests/Data/Notebooks/SimplePETSc.ipynb
+++ b/Tests/Data/Notebooks/SimplePETSc.ipynb
@@ -5,11 +5,12 @@
    "id": "bb0907b4-4e26-4c4e-ab1f-22b5330cb1d2",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"SimplePETSc\"\n",
     "date = \"2021-11-09\"\n",
     "author = \"Lars Bilke\"\n",
     "web_subsection = \"elliptic\"\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -24,15 +25,17 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "7962f42f-fd53-4fc1-b966-a8ba924aca6c",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "import os\n",
     "\n",
     "prj_name = \"square_1e1_neumann\"\n",
-    "data_dir = os.environ.get('OGS_DATA_DIR', '../../../Data')\n",
+    "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../../Data\")\n",
     "prj_file = f\"{data_dir}/EllipticPETSc/{prj_name}.prj\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
@@ -42,6 +45,7 @@
     "! mpirun -np 2 ogs {prj_file} > out.txt\n",
     "\n",
     "from datetime import datetime\n",
+    "\n",
     "print(datetime.now())"
    ]
   },
@@ -79,10 +83,11 @@
     "\n",
     "pvdfile = vtuIO.PVDIO(f\"{prj_name}.pvd\", dim=2)\n",
     "time = pvdfile.timesteps\n",
-    "points={'pt0': (0.3,0.5,0.0), 'pt1': (0.24,0.21,0.0)}\n",
+    "points = {\"pt0\": (0.3, 0.5, 0.0), \"pt1\": (0.24, 0.21, 0.0)}\n",
     "pressure_linear = pvdfile.read_time_series(\"pressure\", points)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "plt.plot(time, pressure_linear[\"pt0\"], \"b-\", label=\"pt0 linear interpolated\")\n",
     "plt.plot(time, pressure_linear[\"pt1\"], \"r-\", label=\"pt1 linear interpolated\")\n",
     "plt.legend()\n",
diff --git a/Tests/Data/Notebooks/testrunner.py b/Tests/Data/Notebooks/testrunner.py
index 27710943c0b..3cc73eb667f 100644
--- a/Tests/Data/Notebooks/testrunner.py
+++ b/Tests/Data/Notebooks/testrunner.py
@@ -11,61 +11,49 @@
 import toml
 from pathlib import Path
 import jupytext
+import subprocess
 
 
 def save_to_website(exec_notebook_file, web_path):
-    from nb2hugo.writer import HugoWriter
-
-    output_path = "docs/benchmarks"
+    output_path_arg = ""
     notebook = nbformat.read(exec_notebook_file, as_version=4)
     first_cell = notebook.cells[0]
-    if is_jupytext:
-        if "Tests/Data" not in exec_notebook_file:
-            output_path = str(Path(exec_notebook_file).parent.parent)
-        else:
-            lines = first_cell.source.splitlines()
-            toml_begin = lines.index("+++")
-            toml_end = max(loc for loc, val in enumerate(lines) if val == "+++")
-            toml_lines = lines[toml_begin + 1 : toml_end]
-            parsed_frontmatter = toml.loads("\n".join(toml_lines))
-            output_path = (
-                Path(build_dir)
-                / Path("web/content")
-                / Path(output_path)
-                / Path(parsed_frontmatter["web_subsection"])
-            )
-    elif first_cell.cell_type == "raw":
+    if "Tests/Data" in exec_notebook_file:
         lines = first_cell.source.splitlines()
-        last_line = lines[-1]
-        if "<!--eofm-->" not in last_line:
-            print(
-                f"Warning: {exec_notebook_file} does not contain '<!--eofm-->' as the "
-                "last line in the RAW cell!"
-            )
-        parsed_frontmatter = toml.loads("\n".join(lines[:-1]))
-        if "web_subsection" not in parsed_frontmatter:
-            print(
-                f"Error: {exec_notebook_file} frontmatter does not contain "
-                "'web_subsection'!"
-            )
-        output_path = os.path.join(output_path, parsed_frontmatter["web_subsection"])
-        output_path = Path(build_dir) / (Path("web/content") / Path(output_path))
-    else:
-        print(
-            f"Warning: {exec_notebook_file} does not contain a RAW cell as its first "
-            "cell!"
+        toml_begin = lines.index("+++")
+        toml_end = max(loc for loc, val in enumerate(lines) if val == "+++")
+        toml_lines = lines[toml_begin + 1 : toml_end]
+        parsed_frontmatter = toml.loads("\n".join(toml_lines))
+        output_path = (
+            Path(build_dir)
+            / Path("web/content/docs/benchmarks")
+            / Path(parsed_frontmatter["web_subsection"])
         )
-        output_path = os.path.join(output_path, "notebooks")
-    writer = HugoWriter()
-    writer.convert(
-        exec_notebook_file,
-        web_path,
-        output_path,
-        os.path.join(
-            os.path.dirname(os.path.abspath(__file__)),
-            "nbconvert_templates/collapsed.md.j2",
-        ),
+        output_path_arg = (
+            f"--output-dir={Path(output_path) / Path(exec_notebook_file).stem}"
+        )
+
+    template = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "nbconvert_templates/collapsed.md.j2",
     )
+    subprocess.run(
+        [
+            "jupyter",
+            "nbconvert",
+            "--to",
+            "markdown",
+            f"--template-file={template}",
+            "--output=index",
+            output_path_arg,
+            exec_notebook_file,
+        ],
+        check=True,
+    )
+
+    if not "Tests/Data" in exec_notebook_file:
+        return
+
     for subfolder in ["figures", "images"]:
         figures_path = os.path.abspath(
             os.path.join(os.path.dirname(notebook_file_path), subfolder)
@@ -140,7 +128,7 @@ def save_to_website(exec_notebook_file, web_path):
                 nb = nbformat.read(f, as_version=4)
         ep = ExecutePreprocessor(kernel_name="python3")
 
-        # 1. Run the notebook
+        # Run the notebook
         print(f"[Start]  {notebook_filename}")
         start = timer()
         try:
@@ -162,15 +150,7 @@ def save_to_website(exec_notebook_file, web_path):
             pass
         end = timer()
 
-        # 2. Instantiate the exporter. We use the `classic` template for now; we'll get
-        # into more details later about how to customize the exporter further.
-        html_exporter = HTMLExporter()
-        html_exporter.template_name = "classic"
-
-        # 3. Process the notebook we loaded earlier
-        (body, resources) = html_exporter.from_notebook_node(nb)
-
-        # 4. Write new notebook
+        # Write new notebook
         with open(convert_notebook_file, "w", encoding="utf-8") as f:
             repo = "https://gitlab.opengeosys.org/ogs/ogs"
             branch = "master"
@@ -178,19 +158,36 @@ def save_to_website(exec_notebook_file, web_path):
                 repo = os.environ["CI_MERGE_REQUEST_SOURCE_PROJECT_URL"]
                 branch = os.environ["CI_MERGE_REQUEST_SOURCE_BRANCH_NAME"]
 
+            # Check frontmatter has its own cell
+            first_cell = nb["cells"][0]
+            if (
+                first_cell.cell_type == "markdown"
+                and first_cell.source.startswith("+++")
+                and not first_cell.source.endswith("+++")
+            ):
+                print(
+                    f"Error: {notebook_filename} notebook metadata is not a separate cell (in markdown: separate by two newlines)!"
+                )
+                success = False
+
+            # Check second cell is markdown
+            second_cell = nb["cells"][1]
+            if second_cell.cell_type != "markdown":
+                print(
+                    f"Error: {notebook_filename} first cell after the frontmatter needs to be a markdown cell! Move the first Python cell below."
+                )
+                success = False
+
             # Modify metadata
-            meta_cell = nb["cells"][0]
-            if is_jupytext:
-                if meta_cell.source.startswith("---"):
-                    print(
-                        f"Error: {notebook_filename} frontmatter is not in TOML format! Use +++ delimitiers!"
-                    )
-                    success = False
-                meta_cell.source = meta_cell.source.replace(
-                    "+++\n", "+++\nnotebook = true\n", 1
+            first_cell = nb["cells"][0]
+            if first_cell.source.startswith("---"):
+                print(
+                    f"Error: {notebook_filename} frontmatter is not in TOML format! Use +++ delimitiers!"
                 )
-            else:
-                meta_cell.source = f"notebook = true\n{meta_cell.source}"
+                success = False
+            first_cell.source = first_cell.source.replace(
+                "+++\n", "+++\nnotebook = true\n", 1
+            )
 
             # Insert Jupyter header with notebook source and binderhub link
             binder_link = f"https://mybinder.org/v2/gh/bilke/binder-ogs-requirements/master?urlpath=git-pull%3Frepo={repo}%26urlpath=lab/tree/ogs/{notebook_file_path_relative}%26branch={branch}"
@@ -216,41 +213,10 @@ def save_to_website(exec_notebook_file, web_path):
         src="https://img.shields.io/static/v1?label=&message=Launch notebook&color=5c5c5c&logo=data:image/png;base64,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" />
 </a>"""
             text += f"""</p></div>\n\n"""
+            second_cell.source = text + second_cell.source
 
-            for cell in nb["cells"]:
-                # Check frontmatter has its own cell
-                if (
-                    cell.cell_type == "markdown"
-                    and cell.source.startswith("+++")
-                    and not cell.source.endswith("+++")
-                ):
-                    print(
-                        f"Error: {notebook_filename} notebook metadata is not a separate cell (in markdown: separate by two newlines)!"
-                    )
-                    success = False
-                # Get first regular markdown cell
-                if cell.cell_type == "markdown" and not cell.source.startswith("+++"):
-                    first_markdown_cell = cell
-                    break
-
-            first_markdown_cell.source = text + first_markdown_cell.source
             nbformat.write(nb, f)
 
-        # 5. Symlink images or figures subfolder
-        for subfolder in ["figures", "images"]:
-            figures_path = os.path.abspath(
-                os.path.join(os.path.dirname(notebook_file_path), subfolder)
-            )
-            symlink_figures_path = os.path.join(notebook_output_path, subfolder)
-            if os.path.exists(figures_path) and not os.path.exists(
-                symlink_figures_path
-            ):
-                print(
-                    f"{subfolder} folder detected, symlink {figures_path} to "
-                    f"{symlink_figures_path}"
-                )
-                os.symlink(figures_path, symlink_figures_path)
-
     status_string = ""
     if notebook_success:
         status_string += "[Passed] "
diff --git a/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb b/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb
index 43ce56d5cfa..2f57bd7e3ae 100644
--- a/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb
+++ b/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb
@@ -1,284 +1,317 @@
 {
-    "cells": [
-        {
-            "cell_type": "raw",
-            "metadata": {},
-            "source": [
-                "author = \"Jörg Buchwald\"\n",
-                "date = \"2022-05-27T12:39:58+01:00\"\n",
-                "title = \"Thermo-Osmosis in a one-dimensional column\"\n",
-                "weight = 70\n",
-                "web_subsection = \"thermo-hydro-mechanics\"\n",
-                "<!--eofm-->"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "## Problem description\n",
-                "\n",
-                "The problem describes a one-dimensional column at $T$=300 K in sudden contact with a temperature reservoir at one side at $T_1$ = 350 K.\n",
-                "\n",
-                "Thermo-osmotic and filtration effects are described by contributions to the hydraulic flux $J^w$\n",
-                "\\begin{equation}\n",
-                "J^w=-\\rho_w \\frac{\\mathbf{k}}{\\mu}\\left(\\nabla p-\\rho_w \\mathbf{g} \\right)-\\rho_w \\mathbf{k}_{pT} \\nabla T,\n",
-                "\\end{equation}\n",
-                "and the conductive heat flux $I$\n",
-                "\\begin{equation}\n",
-                "I=- \\mathbf{\\lambda}_s (1-\\phi)+\\mathbf{\\lambda}_w \\phi)- \\mathbf{k}_{Tp} \\nabla p,\n",
-                "\\end{equation}\n",
-                "\n",
-                "where $\\mathbf{k}_{pT}$ is the phenomenological coefficient of thermo-osmosis and $\\mathbf{k}_{Tp}$ the phenomenological coefficient of thermo-filtration. \n",
-                "It can be shown that $\\mathbf{k}_{Tp}=T*\\mathbf{k}_{pT}$ (Zhou et al. 1998)."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Get benchmark results\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/home/buchwalj/.local/lib/python3.10/site-packages/vtuIO.py:147: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  df[\"r_\"+str(i)] = (df[x]-val[x]) * (df[x]-val[x]) + (df[y]-val[y]) * (df[y]-val[y])\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import os\n",
-                "import vtuIO\n",
-                "import numpy as np\n",
-                "\n",
-                "filename = \"expected_Column_ts_68_t_7200000.000000.vtu\"\n",
-                "data_dir = os.environ.get('OGS_DATA_DIR', '../../Data')\n",
-                "file = {}\n",
-                "file[\"THM\"] = f\"{data_dir}/ThermoHydroMechanics/Linear/ThermoOsmosis/{filename}\"\n",
-                "file[\"TR\"] = f\"{data_dir}/ThermoRichardsFlow/ThermoOsmosis/{filename}\"\n",
-                "file[\"TRM\"] = f\"{data_dir}/ThermoRichardsMechanics/ThermoOsmosis/{filename}\"\n",
-                "x=np.array([i*0.1 for i in range(200)])\n",
-                "r = np.array([[i,0.5,0.0] for i in x])\n",
-                "resp = {}\n",
-                "respvars = [\"temperature\", \"pressure\"]\n",
-                "for model in file:\n",
-                "    resp[model] = {}\n",
-                "    f = vtuIO.VTUIO(file[model], dim=2)\n",
-                "    for var in respvars:\n",
-                "        if \"M\" in model:\n",
-                "            resp[model][var] = f.get_set_data(f\"{var}_interpolated\",pointsetarray=r)\n",
-                "        else:\n",
-                "            resp[model][var] = f.get_set_data(f\"{var}\",pointsetarray=r)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Read-in the analytical solution\n",
-                "\n",
-                "An analytical solution was provided by Zhou et al. 1998 and can be obtained via [github](https://github.com/joergbuchwald/thermo-osmosis_analytical_solution).\n",
-                "For this example we used $\\mathbf{k}_{pT}=2.7e-10\\, m^2/(s K)$ and a fully saturated material. More details on model parameters can be found in the corresponding project files.\n",
-                "The Thermo-Richards (TR) model uses a correction to account for mechanical effects in the mass-balance equation. See Buchwald et al. 2021 for further details."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import zhou_solution_thermo_osmosis\n",
-                "aTO = zhou_solution_thermo_osmosis.ANASOL(0,50,100)\n",
-                "aNoTO = zhou_solution_thermo_osmosis.ANASOL(0,50,100)\n",
-                "aNoTO.Sw = 0\n",
-                "t=7.2e6"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Plot temperature and pressure along the column"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 864x720 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "import matplotlib.pyplot as plt\n",
-                "plt.rcParams['figure.figsize'] = (12, 10)\n",
-                "plt.rcParams['font.size'] =  22\n",
-                "marker = ['|', '+', 'x']\n",
-                "\n",
-                "for i, model in enumerate(resp):\n",
-                "    plt.plot(x,resp[model][\"temperature\"], marker[i], label=model)\n",
-                "plt.plot(x,(aTO.T(x,t,10)+300), label=\"analytical solution\")\n",
-                "plt.plot(x,(aNoTO.T(x,t,10)+300), label=\"analytical solution no thermo-osmosis\")\n",
-                "plt.xlabel(\"$x$ / m\")\n",
-                "plt.xlim([0,20])\n",
-                "plt.ylabel(\"$T$ / K\")\n",
-                "plt.legend()\n",
-                "plt.title(\"temperature\");"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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GmIXW2kM1HB9U6ftM97Y78B9gPLCshjYVPTLFuGYhA8Bau8sY8yWutVPOABZUazcG13CtjdbaVU61k6MrKysnI20H3361kewNewlpG0j3nu3p3rM9gxJ60qdvJ6dDFBGp0W9+8xtuv/127rvvPtq1a8cTTzzhdEgiIl7FK3ta3PxwTVdckzvc26+ttVnVjl1R/WT3LGO3uB8+a62tPnPXHPf2RmOMf7VjN7m3NfXxN3c7qcWab7N57N5PeOvl78nesBcMFB0sYVPmHr75YiMLnljO5/9bT3m5el9EfMllZ7eOdUn8/f3585//THZ2Nn/4wx/w96/+X4OISOvmrT0tpe7tw8aYSOBtXDUp0cBs4CwgB7iqUpsy9/Z3xhgDPA9sASJxJSyjcPXAHFHZaK39zF3cfxew0BgzFzgM3AhcACyw1i5wup3U7IeVm1n6ZgoAXbqHMWJMP+KH9ab4UAk7tuWzYf1uvv1qI59/mEH2hj1c9NuTCGsX7HDUIlIfl58z0OkQms3VV19NdHQ0p556qtOhiIh4Ha/sabHW/gvXMK8XgLNxzRT2M/AmrnVO7gDirLU/V2qzBYgB7sG1AOV/cBWyv4trpq7pwJnW2iJqYK29G5gEdAa+AL7DtVDl76y1V9cRa7O2k6rWrt7K0rdcCcvZFw7mhtnjGDGmL8FtAmgfHsKAQd05d1Icv71uNG3Dgtj4cy7zkz9n3566lskREXEZNWqUZyX78847j/vu+2Wpr+eee84zY1jF7GF79uzxHL/ooou48847Abj22mu5/vrrj2iXmJjIiy++CEBwcDBnnnkmwcGuD1VefPHFI66/bdu2JnuuIiLezKhY2bcNHz7crlrVOktf1q/bycIXv6O83DL+1zGceuaAOs8v2H+It15exZZN++jRsz1X3/grAgM1BEOkMaSnpxMbG+t0GCIichzq8bfc1HWwKXllT4vI0ezZXchbL62ivNxy8rgofnXGiQDkLFpMXoprRuw7Fi8AIC8llZxFi2nXoQ2XXTuKjp3bsmNbPu+9laIZxkRERER8gJIW8Un/e3ctpaXl9O9SzvCIUowx3LF4AWHRUfx0/4NkPvk0Yas/ZeviJWQkJfPmTtckc4d+/omxfQoICPTjx1U5fP/1ZoefiYiIiIgcjZIW8TmZP+3i5/RdBAUHcPrpkax/6BHyUlLJKP4GAAvkfrWcznmlbFrwMr0vvoivO2W5zklKJiIhiom/SQDgg8VpbM3e5+CzEREREZGj8dbZw0RqVFZWzv+WrAXgtLNOpOeoaNqGzCIjKZnREaWkLL6fxSeH0HvnYUalFZPeN5iDr77M6BNDyFiaTMzsWYTHDyEc2Jqdx7dfbeS/b6Vw7Z9Ow8/PsWGaIiIiIlIH9bSIT1m1YhO5Owvp0C6Akaf244a37mV6+jxSOxUzKu0gq/q7CuuHbyjjm7i2RG4/zOYTghiVdpDUTsVMT5/HDW/dC8CwyDJCgyw7tubz43dbnHxaIiIiIlIHJS3iMw4eOMznH64HoP+WLylct449ZVt5JPjXxG4+THrfYE7eAFO+Pkz83/7GyvgwBl1+JbGbD7O5eyCxmw/zSPCv2VO2lbyUVDY88ii/GtUVgE/f/4niQ6V13V5EREREHKKkRXzGis+yOFRUQr8Tu/CrG/+PjKRkzl6xn00LXqbvtCv53ykd6PqrU6iYD+zkvVHkvP0OfaddyZYTgug77Uo2LXiZs1fsJyPJNVRs5IWj6RXZkQMFxXz16c913l9EREREnKGkRXxCaUkZq79xzfRVPiCXGT89xfKIUmI3FZMeGcRNxe/T2b8X0b+/gdi//YXCzCx+0z2amNmz6DXpAjYNGcBNxe+THhlE7KZilkeUMj19Hm+u/S/nXDgYgJWfb9CikyIiIiJeSIX44hPWpWyn6GAJPXq158rxp3Fhal8yliazPK4tY7IDuCR2JuHxQwBchfbu7ys8dcntrtnDliazPM6fk38u49IRv6ZX3ATuWLyAk06KJ/WHrSx95iOuuPVCJ56iiIiIiNRCPS3i9XIWLeabj9a5vg/Zxv7UNH66/0E6jx7FyvgwYma7Zg+rWFSyJhXTHcfMnsXK+DAiLv8/Ni142bWOS/E3DO9v8LNlbNzjR+6uwuZ6aiIiIiJSD0paxOsd6tSH7buLCQo0rO+wnNwvv8ICXU4dQ0zwKMLjhxAzexaFmVm1XqMwM8sz3XFM8Ch6TbqAvtOuJPvV1xmdUsi2eY8RO6ADACs/r/06IiIiItL8NDxMvF5GbiAA3fdnMnLtfvZkf0vs3/5CePwQ7qljSFhlvSdP8nx/z6RpLExbylvF7zP6RH9GpR1keVxbfmjzXwYwlh9X5TDu3IGEtQtu0uclIiIiIvWjpEW8WvGhUlZ/5yrAzw3/mVPcCcbD6fO4xO98psRNaNB1p8RN4OzyyCp1Mb+79Co+LC8iY+1Ovv1qI6f/emBjPhUR8RF9+/albdu2BAUFAbBjxw527txJbGxslX0BAQF06dKFH3/8kY4dOxIREQFAQUEBAQEBTJs2jdmzZxMQoP9qRUSOl4aHiVdL/SGH8lLo2S2Y03IO8o07wZgfO7PBCQscWeNSURczJMIAsGr5Jg4Xa90WkdbqvffeY82aNaxZs4brr7++xn3XXnsta9asAeCCCy7wHMvKyuKRRx7h9ttvZ+7cuQ4+CxGRlkNJi3i1H7529bJ0+fnLIxKMugrvj6Zyjcu56zsDuK7/9bv0juzIoaISlj3730Z5DiLiW8aOHUtISEid50RHRxMdHV3r8fPOO4+4uDhefvnlxg5PRKRVUtIiXmtv7gF2bMsn0N/yq9//xlNEX5/C+6PpPXmSpwZm8oWXkpGUDMAHA/aQ0M81lGPd7iDKy8qP/4mISIMtTFva7Pd86aWX6N69e53nTJ06lalTp9Z5TmlpKXv37m3M0EREWi0lLeK1fkrdAUBMfG+6DI0HXEX04Cq8r1xcfzwqkqCMpGRGpxRSsvAZwtsHkF9Yyvp1OxvlHiLSMG+t9b0eT2stL730EuvWrePUU091OhwRkRZB1YHilXIWLWbt+rYArCn6icmcRF5KKoWZWY2WrFRYmLaUt9L/y+iIUkalHWRFXFt+Dk/hhPxBrP4mm4FDTmjU+4lIy7NkyRISExMpLi5m48aNWGs5//zzeeaZZ5wOTUSkRVBPi3gl0yuS7bsO4e8H60NXeArnw6KjGv1eU+ImMD92JmOyAzyF/neefA5+/obMn3aRv7+o0e8pIrVbmLaUKW/cwJQ3bgDwfO/EULH6qijET09P580332TgwIHce++99OrVy+nQRERaBCUt4pV2lHcEY+hUtJ2Ra/d7Zvqqay2WhqppJrHsx/9J/94hWAs/fpfT6PcUkdpNiZvAwkufYuGlTwF4vj+eGQOb08SJE4mPj+eiiy7i8OHDTocjItIiKGkRr7Tsa9fMYPmhOa7FHyNKmZ4+r0k+aa08k1jlQv/+oQUArPk2G1tuG/2+ItJy/eUvf2HTpk288sorTociItIiKGkRr3OoqISiHQZj4JTtOxttbZbaVJ5JrHKh/8irL6R9eBv27TnIpg17Gv2+InJ0lww+3+kQGmTIkCGMHz+eBx98kPJyzUIoInK8lLSI1/k5fSflZZbw4t0M+fP/a7S1WeojZ9Fizz3uWvISiSNcK1yveGt5k95XRGrmK0PCavLHP/6R9evXs2jRIqdDERHxeUpaxOtUTHUcN2ZAo67NUh9h0VGe5Cij+Bv6tz8A1rJpjx9FBzU2XaS1GTVqFE8//TTgWjDyvvvu8xx77rnnSExMBH6ZPWzFihWe4xMnTqR///7MmDGDxMRE8vLymjN0EZEWxVirsfq+bPjw4XbVqlVOh9FoSkrKeOjODyk5XMaNt51BeKe2zR5DRWH+8ohSxmQHsC7hcrZsP8SvL4pjxK/6NXs8Ir4gPT2d2NhYp8MQEZHjUI+/5aa5YqlOPS3iVTZn7aHkcBk9erZ3JGFZmLaU6enzWO5es2V5RCkr2n4NQOoPW5s9HhERERFR0iJeZlNmLgD9Y7o5cv+a1mz5+6gLCQzyJ2fzPvL2HnQkLhEREZHWTEmLeI2cRYvJTNkCwBe7fwBcQ7VyFi1uthhqWrNlw6OP0rdnMADrftzWbLGIiIiIiIuSFvEagRF92ZVbjJ+BjOCvPQlEWHRUs8VQ25otfdq41mxZu0ZJi4iIiEhzU9IiXiOvTQ8whvbFuYxau9/T41GxhkpzqG3NltHTJhIUHMD2nP3s2V3YbPGIiIiIiJIW8SL/++Y7AA6E7PQUwU9Pn8fCtKUORwYBgf4MjOsBqLdFREREpLkpaRGvEbSvPQCJuXs8RfDzY2c6urhc5cUmfzi0DoDUlRuatc5GREREpLVT0iJe4UBhMTu35+Nnyxjxh6meIviKhR6dUmWxyZDlBAf5sSevhOLOfRyLSURERKS1UdIiXmFz1h4Aep4QSpehCVWK4AszsxyLqyKGjKRkRqUV0GX/BgCyi0Idi0lERESktVHSIl6hYn2W6MRIoGoRfO/JkxyK6sjFJve03wTAym9+xlrrWFwiIiIirYmSFvEKmzJdPS39ors4HElV1RebPHXrPtoE+1GSD7k7NYuYiIiISHNQ0iKOK9h/iNxdhQQG+dMzItzpcKqovthk7Oyb6JS3EYCf0nY4HJ2IiIhI66CkRRy3Kcs1NCyifyf8/b3rR7KmxSaHnjccgIy1SlpEWqK+ffsyaNAgEhMTSUxMpEePHhhjjtjXu3dvEhMTMcbQqVMnz7GoqChiYmK4//77KS0t9Vz33//+N4mJiQQFBWGM4Ztvvqk1hnXr1uHn50dYWBiJiYl89tlnzfHURUS8lne9Q5RWqaIIv2+Udw0Ng5oXmxxy1nACAvzYlp1Hwf5DDkYnIk3lvffeY82aNaxZs4brr7++xn3XXnsta9asAeCCCy7wHMvKyuKRRx7h9ttvZ+7cuZ5r/va3v2XNmjX07NkTYwz3339/rfd/4IEHABg+fDhr1qxh/PjxTfdkRUR8gJIWcUzFGihbN+cB0KdvR/JSUr1+DZSg4AD6x3QF1Nsi0hKNHTuWkJCQOs+Jjo4mOjq61uPnnXcecXFxvPzyyzUev+CCC1iyZAlr16494tjmzZtJT08nIiLi2AIXEWnBlLSIY8Kio1j70D/ZtSMfSzkheTlkJCUTFh3ldGg1qrzQ5LrDrmmY167M9PokS8TXVP5dq9CcH2i89NJLdO/evc5zpk6dytSpU+s8p7S0lL1799Z47OabbyYgIMDTo1LZQw89xM0331z/gEVEWgElLeKY8PghdJg6A2vB3+xnwyOPeupHvFHlhSZ/DluJMZCdU0hgRD+nQxNpUSr/rsEvE2J46wca1Vlreemll1i3bh2nnnpqjef06dOHyy+/nNdee41NmzZ59u/evZvPP/+cSy65pJmiFRHxDUpaxDEL05Yyb90yAE7I28nyiFKmp89jYdpSZwOrReWFJkek76VDcS7W+JMb0NXp0ERalMq/a5v//ZpnBj9v/UADYMmSJSQmJhIbG0tISAjTp0/n/PPPZ/78+bW2+etf/0pZWRkPPfSQZ9+jjz7K73//e/z9/ZsjbBERn6GkRRwzJW4CY8xJAOR2zmdMdgDzY2cyJW6Cw5HVrPpCkwfbbgXgo+XfOxyZSMsTHj+EHueeTc7Ct+hx7tlenbDAL4X46enpvPnmmwwcOJB7772XXr161domNjaWCy64gOeff55du3aRn5/PokWLmDZtWvMFLiLiI5S0iGPyUlLJ2eiaOSwttsjzyWr1sezeovpCkyN3uorwi7cFUFZW7nB0Ii1LXkoqOz74H72nXMKOD/7ntX8XajJx4kTi4+O56KKLOHz4cJ3n/u1vf+PQoUM8+uijPPXUU0ybNo3g4OBmilRExHcoaRHH7FybRbF/CMFtAujbIc4zJKQwM8vp0GpUfaHJk2bNILQ0n+JDpZ5pm0Xk+FX+XYv87WVe/4FGTf7yl7+wadMmXnnllTrPGzVqFGPHjmXevHm88MIL3HDDDc0UoYiIb1HSIo4pHzwKgF4R4dxz0TTANSSk9+RJzgVVh5oWmhyY2BuAzJ92ORydSMtR+XcN8PoPNGoyZMgQxo8fz4MPPkh5ed09sbfccgv79+9n8uTJtG/fvpkiFBHxLUpaxDFbs/cB0Cuio8OR1E9NC00OPm0woKRFpDFV/l2r4M0faNTmj3/8I+vXr2fRokV1nnfuuefyySefcMsttzRTZCIivkdJizhma3YeAL0ifSNpqUmfvp0IbhNA7s5C8vYedDocEWlko0aN4umnnwZcC0bed999nmPPPfcciYmJwC+zh61YscJzfOLEifTv358ZM2aQmJjIq6++SmJiItu2bTviWqeffjodOnQA4MMPP/Sct2rVKhITE1m1alUzPFsREe9lrLVOxyDHYfjw4dYX/zMrLyvngds/oORwGTfPOZvQMN8tPH3zpVWkp2znvIuHMPyUvk6HI+KI9PR0YmNjnQ5DRESOQz3+lpvmiqU69bSII3btLKDkcBnhndr6dMICED2wGwA/p2uImIiIiEhTUNIijti6OQ9wFeH7opxFiz0zGS3a9BkAG9fvYtNbix2MSkRERKRlUtIijvAU4ftoPUtYdJRnCtYMVtKlUxClpZb9oT2dDk1ERESkxVHSIo7wFOH7aE9LxRSsGUnJjE4ppN0WV6/LjsNtHY5MREREpOXx2qTFGNPXGHOjMeYDY8x2Y0yJMabAGLPGGHOPMabWj+iNMRONMZ8ZY/LcbVYaY66qxz19op0vy1m0mNwffiR3ZwEWS49eHchLSSVn0WKnQzsmC9OWMj19HssjShmVdpAdHbcCsHrNRocjExEREWl5vDZpAVYCjwJrgQuAaOBsIBW4HfjeGNOleiNjzB3AEmAvMA4YCawBFhhjnq3tZr7SzteFRUfxw+MvYS0UhxRyIH0dGUnJhEVHOR3aMZkSN4H5sTMZkx3AN3Ft+VXOfoKD/DicD3tzDzgdnoiIiEiL4s1JC8A8a+3N1trvrLWbrbVfW2uvAD4H+gHXVz7ZGDMWmAusBqZYa9dYa9OttdcD/wGuNcZcWf0mvtKuJQiPH0Lb8y8BIKx4LxlJyVVWvvYVeSmpnthXxocRO/smOuZnA1poUkRERKSxeXPSMgN4oJZj37u33artv8u9fcxaW1btWLJ7e2cN1/OVdj5vYdpS3sxy/fP13bWL5RGlTE+fx8K0pQ5HdmwKM7M8yVZM8CjC44cQd0YCAFlKWkREREQaldcmLdbad621W6rvN8YYXEOpAD6ttL8bMNb98JMaLrkcKAaijDHDfK1dSzElbgJDiAEg54SDjMkOYH7sTKbETXA4smPTe/IkT+/QPZOmARB3xlAANmXtoay03KnQRERERFocr01aqjPGBBtjEoBXgJOB+6y1iyudMgzX8zlQU7JjrS0BNrgfjvDBdi3Cvh9T2LmjEIDVcSWeGbgq1jzxZe07hNC1exglh8vI2bzP6XBEREREWgyfSFqMMV8Dh3AVqg8Exlhrb692WkUl9846LrXdve3vg+1ahO1rN1DmF0hYu2Ciw4Z6pg4uzMxyOrRG0X9AVwA2rN/tcCQiIiIiLYdPJC3AFGAwMBkoBFa4pz2uHH979/ZgHdcpcm87+GA7D2PMdGPMKmPMqt27fevNsd+QUQB079neM6wqPH4IvSdPci6oRtRPSYuINMC2bdtITEwkLCyMcePGOR1OvVx77bVERERgjGHTpk1Nfr+nn36aQYMGYYxhwYIFTX4/EfEuPpG0WGu3WGvXWWvfAU4HvsY17fGDx3gpU3FJX25nrZ1vrR1urR3etWvXY7y0s3ZszQdcSUtL1DeqM37+hm1b8ig6eNjpcETER/Ts2ZM1a9YwfPjwBrVfvHgxjz766BH7V69eTadOnfj222+PM8IjPffcc8ydO7fRr1ub66+/nvfee++4ruHE6yQijcMnkpbKrLXlwBz3wz8YY8Ld3+e7t3UtSd6m2rm+1K5F2LltPwA9etbYieSzchYtJi8llaDgAErDDmMtrP3kB59bNFNEfFNtb8ZDQ0OJjIwkNDS0+YPyQnqdRHyXzyUtbhVV20HAUPf3FUUR3etod4J7u6HSPl9p1yLs2NYye1rCoqM8Ewrsbuf6Z0t5/xufWzRTRFqWAQMGsHr1agYPHux0KF5Nr5OI9/PKpMUYE3OUxRUr14MEubffA+VAqDGmTw3XDMS1ICXAqkqHfKWdzztUVML+fUX4B/jRuWvL+jSrYkKBjKRk+u5yTQqX33WAzy2aKSIun332GRMnTuSkk04iISGBUaNGHTE06bzzzqNHjx4YY/juu+8455xz6Nu3L6NHj2bt2rVVzn377bc544wzGD58OPHx8YwbN46VK1fWGcO///1vevXqhTGGuLg43nnnHQCefPJJ+vfvT6dOnZg7dy7nnHMOS5Ys8dTFJCYm8o9//IMPP/yQxMREjDHcfffdVa69bt06Jk6cSGRkJAkJCYwcOZJ//OMfFBa6ZnfcsGEDv/vd70hMTGTo0KEkJiby8MMPU1ZWfWmx+tm6dSuXXnop8fHxDB06lNGjR/PAA1WXYtuyZQuXXXYZkZGRREVFccopp/DJJzWtDPCLL7/88ojnmJeXR2JiIkFBQUybNs1zbkNep7S0NCZMmEDfvn3p168fZ599Nj/88IPneOU6myeffJLp06eTkJBA3759eeKJJxr0WolILay1XvcFTAPKgDa1HB+Nq97DAlGV9i9z75tWQ5tx7mMbajjmE+1q+ho2bJj1FZsyc+2cWUvs/OTPnQ6l0b2R+h/7m9evtw/fOtV+ecHFds6sN+2cWUvsK8uXOB2aSLNYt26d0yE0qhkzZthbb73VlpeXW2utXb58uQ0JCbHfffddlfPuuusuC9gbb7zRlpWV2ZKSEnvaaafZk08+ucp555xzjn3mmWc8j9966y0bGhpqs7Ozq5w3duxYO3bsWM/jL7/80gL2nXfeqXLerbfeap944gnP46uuuspGRkbW+FwAe9ddd3keZ2Zm2vDwcDtr1izP81u0aJE1xtjVq1dba6197bXX7Pjx421RUZG11trt27fbE0880T788MNVrv3iiy9awG7cuLHGe1c444wz7HXXXee539KlS63rLYhLbm6u7dOnj7300kttSUmJtdbaF154wfr7+9sPP/zQc97GjRstYF988cU6n6O11kZGRtqrrrqqyr5jeZ1+/vln2759e3vzzTd74r7rrrtsaGioTUlJOSKmIUOG2A0bNlhrrX3mmWesMcamp6fX+bqIeJt6/C13LD8IaKTcpyn4AdcCNX1UcYd7+7W1tvJcuXNwLTh5ozHmFVt1tfmb3NuaqgZ9pZ1P29FC61nAtWjm2eWRZCxNZnlcCN3zctnZphcDUU+LtF5zb/6P0yEAcOfDE4+5za233krXrl1xrWcMp5xyCvHx8Tz//PM1FstfffXV+Pn54efnx8SJE5k9ezbFxcUEBwcD8Pjjj9O//y+z2F988cX8/ve/59VXX+Wvf/1rrXGMGTOG/v3788orrzBp0iTA9WHjwoULj9pTU5u7776bsrIy7rnnHs/zu+iii/jVr36Fn59rAMY555zD6aefTps2rhLLHj16MHnyZJ599llmzZp1zPdcuXIlp59+uud+559/Prfeeqvn+COPPEJOTg5JSUkEBLjemlx99dU8/vjj/PnPfyYlJaVBz/V4VPS6VH6dbrvtNp566iluu+02lixZUuX8008/nX79XAMlJk+ezIwZM/jiiy8YOHBgs8Yt0lJ55fAwoNS9fdgYk2SMGW2M6WeMOcsY8z/gPCAHuKpyI2vtZ7gSgqHAQmNMgjEm1hjzFHABsMBau6D6zXylna/b2ULrWQDyUlLJSEomZvYsVsaHMXh8PAAZ37WM9WdEWpvQ0FBuv/12hg0bRnx8PImJiaSlpbFhQ82lhgMGDPB836lTJwB27drl2demTRtuuOEGhg4dSkJCAomJiezdu7fW61UwxjB16lSWLl3Kvn2uRWuXLVtGXFwcnTt3btBz++ijjxg8eDBt21adD+aLL74gPt71t6tdu3a88cYbjBkzhri4OBITE1mwYMFR463Naaedxpw5c7jhhhtYsWIF5eXl3HfffZ7jH3/8MT169KBPn6qjpkeOHElqaio7d9a1tFnT+Pjjjxk8eDAhISGefYGBgQwdOpSPP/64YnSER00/A07ELdJSeWVPi7X2X8aYHOBS4GxgBq7ZtgqBn3D1tDxurd1fQ9u7jTGrcfVYfAH4A2uB31lrX6zjnj7Rzpe15KSlMDOLmNmzXLUtG0Yx+NREPv36E7ZsPUB5ucXPzxz9IiItTEN6OLxBeXk5EydOZP/+/Xz44Yf07t0bgHHjxlFcXFxjm8oJQEVvRUX9x4EDBxg/fjy9e/fm008/pWPHjgD07du31utVdsUVVzB37lzeeOMNrr/+el566SWuvLKuss+65ebmMmzYsDrPuf322/nnP//JJ598wimnnAK4eh7mzJlTZ7vavPXWWzz00EM899xzPP3000RERHDHHXdw7bXXemKqeF0qq3jzn5ubS/fudc1f0/hqe506depEUVERBw8erDLbWF0/AyJy/Ly1pwVr7TJr7Q3W2gRrbXtrbYC1NtxaO9pae29NCUultu9aa8dZaztYa8OstaPqkwj4SjtfVF5Wzq4dBUDLTFp6T57kKbq/Z9I0OnZuS8fObTlcZtixtdYfVRHxQpmZmXz99ddcc801noTleCxfvpysrCxuvPHGGt+YH010dDSjR4/mlVde4eDBgyxbtozzzz+/wfF06dLF02tTm5dffpmzzjrLk7Acr7Zt23LnnXeyefNmPvnkEyIjI7nuuuv4+OOPPTHt3bv3iHYV+7p06VLn9f38/I7o+Thw4MBxxVxXTCEhIUf0VIlI0/LapEValtzdBygrLSe8UwhtQgKdDqdZ9I12/Se78edchyMRkWNR0ftRUcdQYceOHY12vfLycnbv3l3va1x55ZWsWLGCBx98kPPPP5+goKAqxwMDAz1v2g8cOHBEvUVlZ511FmvXrqWoqKjK/ilTprBs2TJPzI31/AEuu+wywPUanH766SxevBjAU6ty5plnsnPnTrKzs6u0++677xgyZMhRe1m6detWJRHbs2cPe/bsOeK8Y3mdzjzzTNauXcvBg79MWFpaWsqaNWs488wzj3h9RKRpKWmRJpezaDGbvkkDYC+uXoe8lNQWv/BiP3fSsilTSYuILxk4cCD9+/fnxRdf9LwRfvPNN8nIyGjQ9U455RTCw8OZN28ehw4dAuDhhx+u8mb4aC699FKCgoK49957axwa1q9fP3JzcykuLmbFihX86U9/qvVad999N35+ftx9992eN/D/+te/WL16NaNGjQJchfIfffQRqamuZdHWr1/PG2+8Ue94q3v99ddZtGiR5/FXX32Fv78/p512GgA33XQTvXv3Zvbs2ZSWuspaX375ZX788Uceeuiho15/7NixfPTRR54pmx955BHCwsKOOO9YXqe77roLYwx33HGH53X6+9//TkFBQZV6HBFpJk5OXaav4//yhSmP9/2YYv814wE7Z9YS+/+Sku2+H1PsyqnT7L4fU47e2IcV7C+yc2YtsX+/5b+2tKTM6XBEmlRLm/I4LS3Njh8/3nbv3t2OHTvW/ulPf7LDhg2zoaGhNiEhwRYXF9vLL7/cdu/e3QI2ISHBpqSk2Pvvv9/26dPHAjY2Nta++eab1lrX1MUjRoywPXv2tOPGjbNz5syxvXr1sh07drRnnHGG3bp1q01ISLChoaGee+zcubNKTJMmTbIDBgyoMd6dO3facePG2RNPPNEOHjzYvvvuu/aDDz6wCQkJFrDdu3e3EydO9Jy/du1ae/7559uIiAibkJBgL7zwQpuVleU5vnfvXnvllVfa7t2729GjR9spU6bYK6+80vNcly9fbq+55poqz/XZZ5+t9fV84IEH7IgRI2x8fLyNj4+3I0eOtIsXL65yzubNm+2ll15q+/TpY/v3729PPvlk+9FHH3mOP/XUUzY2NtYCtk+fPnbGjBmeY9nZ2faMM86wvXr1suPGjbPvv/++jYyMtB07drSV/5881tcpNTXVnnfeeTYiIsJGRkbaM888065atcpz/PXXX68S07333mvXrl1b5XpXXHFFra+LiLfx5imPjbW21oRGvN/w4cPtqlXevwblq499QubmgwQHfsO4rVs9Rest3VMPfsbunYVM+/0pRPRv2Ew/Ir4gPT2d2NhYp8No0ebOnYu/vz+33Xab06GISAtVj7/ljo2L1PAwaXIL05aStss17WP8hl0sjyhlevo8FqYtdTiyppGzaDF5Ka4hFTv8XGPW05eva/HD4USkab3zzjtMnTrV6TBERByhpEWa3MWx59G2uB0A66IPMyY7gPmxM5kSN8HhyJpGWHQUGUnJrrqd0HUArP/2Z8KioxyOTER8zdixYykuLubLL7/khBNOIDIy0umQREQc4ZXrtEjLsnnFasrLoV2oP18PDuGa/5vpWYixJQ4RC48fQszsWWQkJTOojx8H7Ensb9OV0IEaOiMix8YYw8CBA+natSuvvPKK0+GIiDhGPS3S5Lb9tAWA7n06ExM8yvOmvjCzZa4WvzBtKdPT57E8opTRa/Pw89tHeTn8a9n7TocmIj5m2bJlbNy4kW+//ZaYmBinwxERcYySFmlytt8gALp0D+OeSdMAV29E78mTnAuqCU2Jm8D82JmMyQ7gm7i2RBS6pjyOKI12ODIRERER36SkRZrc7h2uefO7dm/ncCTNIy8l1TP8bWV8GPEX/gqAzB+zj9JSRERERGqipEWaXO6uAsDV09IaFGZmeep1YoJHMej0YfgZ2JVbzKGiEqfDExEREfE5SlqkSZWXW3J3tq6elt6TJ3kmGLhn0jSCggPoFdkRi2Hzhj0ORyciIiLie5S0SJPav+8gpaXlhLUPpk1IoNPhOCYyugsAm7OUtIiIiIgcKyUt0qR2t7JeltpE9u8MQLZ6WkRERESOmZIWaVK5O131LK09aenTtyN+fobtOfspPlTqdDgiIiIiPkVJizSpinqW1lKEX5ug4ABO6BOOtbBl016nwxERL7Nt2zYSExMJCwtj3LhxTodTL9deey0REREYY9i0aVOT3+/pp59m0KBBGGNYsGBBk9+vNVq1ahWJiYkEBQUxbdo0p8MRqUJJizSp3Ttb18xh1eUsWkxeSioA2WXbAMj4+idyFi12MCoR8TY9e/ZkzZo1DB8+vEHtFy9ezKOPPnrE/tWrV9OpUye+/fbb44zwSM899xxz585t9OvW5vrrr+e99947rms48Tp5q5pei+HDh7NmzRp69uzpTFDSIK3l51dJizQZa22rr2kJi44iIymZvJRUtrRNAyBr9QbCoqMcjkxEWpLa3oyHhoYSGRlJaGho8wflhfQ6/aK210J8T2v5+Q1wOgBpuQryD3G4uJSQtoGEhgU7HY4jwuOHEDN7FhlJyQzuAwfscPKDOtM2Jtbp0ESkFRgwYACrV692Ogyvp9dJfFlr+flVT4s0md073L0sPVpnLwvAwrSlTE+fx/KIUkavzcfPbx/lFv79+ftOhyYidfjss8+YOHEiJ510EgkJCYwaNeqIoUnnnXcePXr0wBjDd999xznnnEPfvn0ZPXo0a9eurXLu22+/zRlnnMHw4cOJj49n3LhxrFy5ss4Y/v3vf9OrVy+MMcTFxfHOO+8A8OSTT9K/f386derE3LlzOeecc1iyZImnLiYxMZF//OMffPjhhyQmJmKM4e67765y7XXr1jFx4kQiIyNJSEhg5MiR/OMf/6Cw0PV3e8OGDfzud78jMTGRoUOHkpiYyMMPP0xZWVmDXs+tW7dy6aWXEh8fz9ChQxk9ejQPPPBAlXO2bNnCZZddRmRkJFFRUZxyyil88skndV73yy+/POI55uXl1ViX0ZDXKS0tjQkTJtC3b1/69evH2WefzQ8//OA5XrnO5sknn2T69OkkJCTQt29fnnjiiQa9VsfycwXw/PPPExcXR0xMDH379uVPf/oTBw4cqPMetb0W1SUlJXHyySfTs2dPZsyYweHDh6sc37t3L9dddx2RkZEMGDCAkSNH8v77v/z/tmTJEs9re8cdd/C3v/2NUaNG0aZNGyZNmsSdd95JdHQ0xhjefPNNLr30UmJiYhg0aBDLli2jsLCQq6++moEDBxIXF8dXX311RIxLlixhxIgRnHjiiURGRnL11Veza9euer3WX331FWPHjqV///5ERkZy8cUXk5WVVeWc//73v4waNYqTTjqJ+Ph4Jk+ezLJly2p8fn/5y18YOnQovXr18vz733fffQwfPpzIyEiefPLJI2I42s8YwNdff83YsWMZOnQoCQkJ/PrXv/b8Pajt5zc/P5/p06czZMgQhg4dyrBhw7jzzjs5ePBgvV4br2St1ZcPfw0bNsx6q5WfZ9k5s5bYpW/+6HQojtr3Y4pdOXWaffjWqXbB9Q/bObOW2E/fS3c6LJFGtW7dOqdDaFQzZsywt956qy0vL7fWWrt8+XIbEhJiv/vuuyrn3XXXXRawN954oy0rK7MlJSX2tNNOsyeffHKV88455xz7zDPPeB6/9dZbNjQ01GZnZ1c5b+zYsXbs2LGex19++aUF7DvvvFPlvFtvvdU+8cQTnsdXXXWVjYyMrPG5APauu+7yPM7MzLTh4eF21qxZnue3aNEia4yxq1evttZa+9prr9nx48fboqIia62127dvtyeeeKJ9+OGHq1z7xRdftIDduHFjjfeucMYZZ9jrrrvOc7+lS5da11sQl9zcXNunTx976aWX2pKSEmuttS+88IL19/e3H374oee8jRs3WsC++OKLdT5Ha62NjIy0V111VZV9x/I6/fzzz7Z9+/b25ptv9sR911132dDQUJuSknJETEOGDLEbNmyw1lr7zDPPWGOMTU9v2N/6+v5cPfDAAzY0NNSuWLHCWmvtnj177NChQ+24ceNsWVlZnfeo67WIjIy0PXr0sEuWLLHWWrt27Vrr7+9f5Wf40KFDdujQoXbUqFF2//791lpr33zzTevv728//fTTKtcDbK9evexnn31mrbX27bffthdeeKG11trPPvvMAnb8+PF237591lprL7vsMtulSxc7Z84cz75LL73URkZG2tLSUs91X3/9devv728XL15srbW2qKjInnPOOTY2NtYWFhbW+fy//PJLGxQUZB977DFrrbVlZWX26quvtt27d7fbtm2z1rp+V4KCguxXX31lrbX28OHD9rLLLjvi5wqwffv2tWvWrLHWuv79AfvnP//Zs2/evHnWGGMzMjI87erzM5afn2/Dw8Ptv/71L2utteXl5Xb27NlV/k5UxFD55/eaa66x5557ruf3adWqVbZNmzZH/V2tx99yx97zaniYNLqcRYsJi44id5cFoEu3MPJSUinMzKL35EnOBtfM8lJSyUhKJmb2LB5On8ctoWPY/NkuslKyGf/rgU6HJ9Kkvv/fbKdDAGDY2UnH3ObWW2+la9euGGMAOOWUU4iPj+f555+vsVj+6quvxs/PDz8/PyZOnMjs2bMpLi4mONg1NPbxxx+nf//+nvMvvvhifv/73/Pqq6/y17/+tdY4xowZQ//+/XnllVeYNGkS4PqwceHChUftqanN3XffTVlZGffcc4/n+V100UX86le/ws/PNQDjnHPO4fTTT6dNmzYA9OjRg8mTJ/Pss88ya9asY77nypUrOf300z33O//887n11ls9xx955BFycnJISkoiIMD11uTqq6/m8ccf589//jMpKSkNeq7Ho+JT68qv02233cZTTz3FbbfdxpIlS6qcf/rpp9OvXz8AJk+ezIwZM/jiiy8YOLDhf+vr+rnav38/c+bM4fLLL+fkk08GoFOnTtx9991ceOGFLF68mMmTJzf43t27d2fixIkADBo0iIEDB7Js2TKmT58OwCuvvMLq1av56KOPaN++PQCXXHIJw4cPZ86cOYwfP77K9RISEjwz402YMOGI36NJkyYRHh4OuH4/XnvtNcLDwz37LrnkEt544w02bNjAiSeeiLWW2bNnM378eC688EIA2rRpw4MPPkhCQgLPPPNMnT+rt9xyCxEREfzhD38AwM/PjwcffJB///vf3H///Tz22GOsXr2aw4cPe/5dAwMDue222/jxxx+PuF5FL0hF/DNmzCArK8uz7ze/+Q0zZ87k888/Z8CAAUD9fsYyMjLIy8vzxGCM4aabbuLtt9+u9bmB63fulFNO8fw+DRs2jHvvvdfzb+WLNDxMGl1F8fnOjTsBCD6wm4yk5FZZfF6YmUXM7Fmu2pbgUcSePhSAHbsPUVLSsGEWItL0QkNDuf322xk2bBjx8fEkJiaSlpbGhg0bajy/4k0IuN44AlWGqLRp04YbbrjB88YmMTGRvXv31nq9CsYYpk6dytKlS9m3bx8Ay5YtIy4ujs6dOzfouX300UcMHjyYtm3bVtn/xRdfEB8fD0C7du144403GDNmDHFxcSQmJrJgwYKjxlub0047jTlz5nDDDTewYsUKysvLue+++zzHP/74Y3r06EGfPn2qtBs5ciSpqans3LmzQfc9Hh9//DGDBw8mJCTEsy8wMJChQ4fy8ccf4/pw+xc1/Qwcb9x1/Vx9/fXXHDx4kBEjRlRpM3LkSMD179xY9664f+Xn8/HHH2OM4ZRTTqlyXlxcHCtWrKCkpKTK/tjYX2o5g4KCiIiIqHI8Ojq6yr2q76v4ed+xYwcAGRkZbNmy5YjnHx8fT5s2bep8/gcPHuTrr78+om2XLl3o16+fp+2IESMICQlhzJgxJCcns2XLFgYPHszll19+xDWPNX6o389YTEwM3bt3Z9KkSdxzzz2sX7+eE044gf/3//5frc8PXL9zzz33HJdffjkfffQRpaWl3HzzzZ7YfJF6WqTRVRSff/7COvBvy743XmGo+417a1O5Z+meSdMA6N6zPTu35bN18z76RndxJjCRZtCQHg5vUF5ezsSJE9m/fz8ffvghvXv3BmDcuHEUFxfX2KZyAlDRW1FR/3HgwAHGjx9P7969+fTTT+nYsSMAffv2rfV6lV1xxRXMnTuXN954g+uvv56XXnqJK6+8ssHPLzc3l2HDhtV5zu23384///lPPvnkE8+b0rvvvps5c+Y06J5vvfUWDz30EM899xxPP/00ERER3HHHHVx77bWemCpel8oq3mDl5ubSvXv3Bt27oWp7nTp16kRRUREHDx6sMltTXT8DDVXXNXNzcwGOeN0qv2aNde+K+1d+Prm5uTUmLQUFBXTq1Il9+/bRrVs3z/6wsLqXPqh8v4peh5r2He35V+yrOH7nnXdW6RWbO3cuJ510EuXl5bX+zP38888AREZG8s0333D//fdz2223cfPNN3P66afz+OOPM2jQoOOKv+I5HO1nrF27dnzzzTf8/e9/56GHHuLOO+9kxIgRPPLII4wZM+aIthUee+wxYmJiePrpp3nttdfo2rUrs2bN4i9/+YvnZ8nX+GbU4tUWpi1lRtrTHPJvi7Fl/NAzn+np81iYttTp0LxCZJTr05bNWXscjkREapKZmcnXX3/NNddc40lYjsfy5cvJysrixhtvrPFN0tFER0czevRoXnnlFQ4ePMiyZcs4//zzGxxPly5dPL02tXn55Zc566yzjnhD2lBt27blzjvvZPPmzXzyySdERkZy3XXX8fHHH3ti2rv3yIV3K/Z16VL3Bzx+fn5H9HwcrRj9aOqKKSQk5Ig39c2t4jWpHmN9X7PGuL+fnx/ff/89a9as8XxlZWWxY8eOKglLU90fjnz+APv27fMcnzt3bpX4LrjgAjp16oSfn1+t/76VX7shQ4bw6quvsmPHDp588knWrFnDueeeS3l5eaM8h/r8jEVGRvLMM8+wY8cOXnnlFXbu3Mmvf/3rGttWCAgI4I9//CPp6el88803nHLKKfztb3/jhRdeOO64naKkRRrdlLgJ3Nfzd64Hfgf41RZ/5sfOZErcBGcD8xKR/V2fgm3eUPsfGxFxTkXvR8UnoxUqD+s43uuVl5eze/fuel/jyiuvZMWKFTz44IOcf/75BAUFVTkeGBjoedN+4MCBI+otKjvrrLNYu3YtRUVFVfZPmTLFMytScXFxoz1/gMsuuwxwvQann346ixcvBvDUqpx55pns3LmT7OzsKu2+++47hgwZctRelm7dulVJxPbs2cOePUd+MHQsr9OZZ57J2rVrq8y2VFpaypo1azjzzDOPeH2a28knn0zbtm357rvvquyveHzWWWfV2f5YXouanHXWWZSWlrJu3boq+1evXs2MGTOO6VoNERMTQ58+fY54/qmpqRw6dKjO59+2bVtOPvnkI9ru2bOHjRs3etp+8sknPPfccwB06NCBmTNnctttt7Flyxby8vKO+znU52csNTWVv//97wCEhIQwdepUHnnkEQoKCti0aVOt177mmms81x05ciRvv/024eHhjtSHNRYlLdLo8lJSWfuvRQDs71DkWaekYmX41i6iv6unJWfzXspKj/+TGhFpXAMHDqR///68+OKLnjfCb775JhkZGQ263imnnEJ4eDjz5s3j0KFDADz88MPHNPXopZdeSlBQEPfee2+NQ8P69etHbm4uxcXFrFixgj/96U+1Xuvuu+/Gz8+Pu+++2/Om9V//+herV69m1KhRgKtQ/qOPPiI11fV3e/369bzxxhv1jre6119/nUWLFnkef/XVV/j7+3PaaacBcNNNN9G7d29mz55NaWkp4Ort+fHHH3nooYeOev2xY8fy0UcfeaZsfuSRR2ocjnQsr9Ndd93lmcq24nX6+9//TkFBQZV6nGMxZswYT9H48erQoQN33XUXr7/+Ol9//TXg6mG4++67GTdunGfihtocy2tRkyuuuIJhw4Zx8803U1BQALh6CP7whz8QExPTkKd0TIwxJCUl8dlnn3mS4EOHDvHXv/6VgQMHHjVx+sc//kF2djaPPfYY4Pog4ZZbbqFjx4787W9/A1zTcD/wwAOeWp7S0lK++eYb4uPjG6U2pD4/Y3v27OHhhx/2DFmz1rJixQp69OhRpU6ouk8++YRnnnnG8zglJYWCgoIjJkjwKU5OXaav4//yximPt7z9jv3oX1/YObOW2DsffsNa65r2d8vb7zgbmBeZ98Cnds6sJXbzhj1OhyLSKFralMdpaWl2/Pjxtnv37nbs2LH2T3/6kx02bJgNDQ21CQkJtri42F5++eW2e/fuFrAJCQk2JSXF3n///bZPnz4WsLGxsfbNN9+01rqmVx0xYoTt2bOnHTdunJ0zZ47t1auX7dixoz3jjDPs1q1bbUJCgg0NDfXcY+fOnVVimjRpkh0wYECN8e7cudOOGzfOnnjiiXbw4MH23XfftR988IFNSEiwgO3evbudOHGi5/y1a9fa888/30ZERNiEhAR74YUX2qysLM/xvXv32iuvvNJ2797djh492k6ZMsVeeeWVnue6fPlye80111R5rs8++2ytr+cDDzxgR4wYYePj4218fLwdOXKkZ5raCps3b7aXXnqp7dOnj+3fv789+eST7UcffeQ5/tRTT9nY2FgL2D59+tgZM2Z4jmVnZ9szzjjD9urVy44bN86+//77NjIy0nbs2NFW/n/yWF+n1NRUe95559mIiAgbGRlpzzzzTLtq1SrP8ddff71KTPfee69du3ZtletdccUVnvN79uxpb7vttlpfJ2vtMf1cWWvts88+awcPHmwHDBhgIyMj7Y033mgLCgrqvEdtr0VmZqZNSEiwgYGBtmPHjnbSpEnWWmtPPvnkKj+bxcXF1lpr9+3bZ2fMmGH79Olj4+Pj7bBhw6pMxf3FF19UeS0SEhKqTPOdnJxso6KiLGCjoqLsCy+8YF944YWj7ktOTvZcY/HixXbYsGE2Ojra9unTx1511VVH/O7U5osvvrCnnXaa7devn42IiLAXXXSRzczM9BzfsGGDnTFjhh00aJBNSEiwsbGx9v/+7//s5s2ba3x+M2fOtCkpKUfdd/nll3vucbSfsd27d9tZs2bZuLg4m5CQYAcNGmTPP/98z5TItf38Pv/88/bUU0/1tEtISLDPPffcUV8Tb57y2FhrEd81fPhwu2rVKqfDOMKSN9aw5tstnHfxEIaf0tfpcLzOe2+nsGrFZsb/eiCnnnmi0+GIHLf09PQ6P/WT4zd37lz8/f257bbbnA5FGuC///0vl156KWlpafTt29fpcERqVI+/5Y6Ni9TwMGkSe3a7CiA7dQk9ypmtU2R/FeOLyLF55513mDp1qtNhSAMUFxcza9YsXn31VSUsIg2kpEWaxN5cV9LSuauSlspyFi0mLyXVM4PYlk172bMmhZxFi50NTES80tixYykuLubLL7/khBNOIDIy0umQpAGCg4P58ccfueCCC5wORcRnKWmRRld8qIQDBcUEBPjRvkPI0Ru0IhULb5Zu+pniNoWUHC5j1T8XtMqFN0Xk6IwxDBw4kJtvvplHHnnE6XDkOLRp08bpEER8mhaXlEZX0cvSsUsoxs/ZKSG9TcXCmxlJyYSekEApYQScNblVLrwpIkdXMQWxiEhrp54WaXR7Vc9Sq4VpS5mePo/lEaUM2LoVgA8yM7TwpoiIiEgdlLRIo9ujepZaTYmbwPzYmYzJDmBT5H4AOh/sziWDGr66tYiIiEhLp6RFGp16WmqXl5JKRlIyMbNn8W2CP+3DAjhcYsn8/HunQxM5bppCX0TEd3n733AlLdLoKnpaOqmn5QiFmVnEzJ7lqm0JHkW/2BMA2JC21eHIRI5PYGAgRUVFTochIiINVFRURGBgoNNh1EpJizS6vbsLAejcJczhSLxP78mTPEX390ya5lmvZX/oCU6GJXLcunXrxtatWzl48KDXf1onIiK/sNZy8OBBtm7dSrdu3ZwOp1aaPUwaVdHBwxQdLCEwyJ+w9sFOh+P1KtZr2bxhL7bcarY18Vnt27cHYNu2bZSUlDgcjYiIHIvAwEC6d+/u+VvujZS0SKPa465n6dwlFGP0BvxowjuF0L5DG/L3H2L3zgK6neC9fyxEjqZ9+/Ze/R+eiIj4Lg0Pk0a1V/Usx8QYQ4R7iFj2xr0ORyMiIiLinZS0SKPSzGHHrk+/TgBkb1DSIiIiIlITJS3SqDw9LSrCr7eI/u6kZeMehyMRERER8U5KWqRR7c11zRym4WFHl7NoMXkpqXTr3g4bUE5+3iG2rFhNzqLFTocmIiIi4lWUtEijyFm0mH0/plQpxM9LSdUb8DqERUeRkZTM/rQ0CkJzAVj1wjuERUc5HJmIiIiId1HSIo0iLDqKtIefpPhQKWV+JRzekEFGUrLegNchPH4IMbNnkZGUTJcDO1w7R5zuWcdFRERERFyUtEijCI8fQrdp1wEQaAtZ/9AjnpXfpWYL05YyPX0eyyNKGbIpB4DvtmxjYdpShyMTERER8S5KWqRRLExbStJP/wWgc0E+yyNKmZ4+T2/A6zAlbgLzY2cyJjuAn6IO4mfLaFPUjon9z3Y6NBERERGvoqRFGsWUuAn8X4fTAcjrcIgx2QHMj53JlLgJDkfmvfJSUslISiZm9iy+SWhLj+5tAUj/bLXDkYmIiIh4FyUt0ijyUlLJXu56s725d6mnViMvJdXhyLxXYWaWZwhdTPAo+g3pA8DmjB0ORyYiIiLiXZS0SKMozMzC9I8FoFtYH0+ReWFmlsORea/ekyd5an7umTSNCPcik/sCOjsZloiIiIjXUdIijaL35EkUHnb9ON044ULAVZzfe/IkB6PyLX36dgID23LyKCkpczocEREREa+hpEUaRXlZOXn7igAI7xjicDS+qU1IIN1PaE95mWVr9j6nwxERERHxGkpapFHszzuELbe069CGgEB/p8PxWRVDxLI37HU4EhERERHv4bVJizEm2hhzjzHmG2PMfmPMYWPMVmPM28aY02tpM80YY4/yFVbHPScaYz4zxuQZYwqMMSuNMVfVI9ZmbeeN8vYeBKBj57YOR+LbKpKWLRuVtIiIiIhU8MqkxRgzEcgA/gS8A4wD4oC/AaOBT4wx99bSvMjdtrav8lrueQewBNjrvt9IYA2wwBjzbB2xNms7b7VvzwEAOnZS0nI8+vR3Jy2b9lFebh2ORkRERMQ7BDgdQC0640qopltrX6u0f70xZjXwA3CbMeYja+3n1dp+a60ddyw3M8aMBeYCq4Ep1tqKKujrjTE9gWuNMV9aa192sp032+fuaQlX0nJc2ncIIbxTW/L2HmTntnxO6N3B6ZBEREREHOeVPS1uBcDC6juttanAN+6HlzTSve5ybx+rlEBUSHZv7/SCdl4rb4+Ghx2PnEWLPWva7A1wFeFnLE8jZ9FiB6MSERER8Q7emrS8CvSq4Q19hRz3ttPx3sgY0w0Y6374SQ2nLAeKgShjzDCn2nk7T09L51CHI/FNYdFRnsU4t7ddD0DGFymERUc5HJmIiIiI87wyabHWHrbWFtRxygnubVoNx9oZY+4yxnxvjNlljMkxxnxgjJlqjKnp+Q7D9TocsNZuqSGWEmCD++EIB9t5NfW0HJ+KxTgzkpI5cZsrJy/oGEmHIXEORyYiIiLiPK9MWupijOkIjAIOAS/UcMpJuIrab8NV4H4t4A+8AvzHGBNU7fyKj7J31nHb7e5tfwfbea3iQyUcPHCYgAA/wtoFOx2OT1qYtpTp6fNYHlHK6HU7gWIOFpXx76+XOh2aiIiIiON8LmkBbgKCgVuttdXf+K8DbrbWnm+t/cBau85a+wFwLrAKOA+4r1qb9u7twTruWeTeVq6Kbu52XuuXoWFtMcY4HI1vmhI3gfmxMxmTHcC3cW3pVrwHgLiARGcDExEREfECPpW0GGNG4Zr2+C3g0erHrbXfWmuTa9hfBvzd/fD3xpg2x3rrikt5QztjzHRjzCpjzKrdu3cf46Ubn2domGYOa7C8lFQykpKJmT2LlfFhDBgzGIDMHzIdjkxERETEeT6TtBhjBgJLgY+B31prjzUR+MG9DQGGVtqf797W9Y67IsnJr7Svudt5WGvnW2uHW2uHd+3atY7LNI99noUlVYTfUIWZWcTMnuWqbQkexYBTXElLzpa6SrtEREREWgdvXaelCmNMDK5k5WvgN9baww24TOWhZB0rfZ/l3navo21F4f+GSvuau53XquhpCe8U4nAkvqv35Eme7++ZNI2ysnICg/zJP1TGgYJiQlUrJCIiIq2Y1/e0GGMGA58DK4GLrbXFtZwXYoyZYIyp7eP+yklCXqXvvwfKgVBjTJ8arhsI9HM/XOVgO6+1b496Whqbv78fvSJcuXX2xr0ORyMiIiLiLK9OWowxicAyXOuZXOqeDrji2FnGmJcqnd4d+A+1TxNcMSSsGFhTsdNauwv40v3wjBrajcE1XGujtXaVU+282b49BwBXIb40noh+rmWIlLSIiIhIa+e1SYsxZiTwKa5E5IoaFprsxS+LNFZ2RQ3X8gNucT981lpbfeauOe7tjcYY/2rHbnJv59Zwr+Zu53VsuSVvn2uyMxXiN66I/q6kZcvGPQ5HIiIiIuIsr0xa3AnLR0A4kAB8WzFbVsUXcFe1ZhVJze+MMS8YY8YYYyKMMafiSnxG4eq1+Uv1+1lrP8OVSAwFFhpjEowxscaYp4ALgAXW2gVOt/NGBQWHKCstp21YEEHBPlEi5TN6R3bE+Bm2b83ncHGp0+GIiIiIOMZb32Wexy/rmZxUx3mbK76x1m5xF+xPBc7Elai0xzUD14/AdOCFGnpsKtrfbYxZjaun4wtcC1KuBX5nrX2xtgCau503yVm0mP3tegFQaFxDxPJSUinMzKpSWC4NExQcwAm92rNty35yNu+j/wDnZ4oTERERcYJXJi3W2ruBuxvQbj1wp/urIfd9F3jX29t5i7DoKL59/C1oP5z8wN1V1hqRxtGnXye2bdlP9sa9SlpERESk1fLK4WHiG8LjhxB22pkAdN2/35OwhMcPcTgy35ezaDF5KalE9OsMQPaGveSlpJKzaLGzgYmIiIg4QEmLNNjCtKW8tzUVgP7b81geUcr09HksTFvqcGS+Lyw6ioykZDoUuZYXytm0h/SkRwiLjnI4MhEREZHmp6RFGmxK3AQSAk4EYHOvEsZkBzA/diZT4iY4HJnvC48fQszsWWx54p8Yk09pqaXjtJnqxRIREZFWSUmLNFheSip7tuwGIDUGYmbPIiMpmbyUVIcj830L05YyPX0eyyNK6bF/BwD/TP1UvVgiIiLSKilpkQYr+DmL4qB2APRtP9jTO1CYmeVwZL5vStwE5sfOZEx2AHs75QEwhmHqxRIREZFWSUmLNFjHs86lrBxC2gZyz8XTANewJk13fPwqz8SWOsi1FuqWzfvY92OKw5GJiIiIND8lLdJgefuKAOjQMcThSFqewswsz0xs/doPIaxdMCV+wWxN3eB0aCIiIiLNTkmLNNh+T9LS1uFIWp7ekyd5iu7vuWgaffp1AuBw9FAnwxIRERFxhJIWabD9+1zDlsLV09LkIvq7kpbsjXsdjkRERESk+SlpkQbbr+FhzSbC3dOyRUmLiIiItEJKWqTBNDys+XTv2YGg4AD27TlI/v4ip8MRERERaVZKWqTB1NPSfPz8DL0jOwKwZYN6W0RERKR1UdIiDZanmpZmpboWERERaa2UtEiDHCoqofhQKYFB/oSEBjkdTouWs2gxeSmpVZKWvJRUchYtdjYwERERkWaipEUapPLQMGOMw9G0bGHRUWQkJRO2fyvlppyd2/JJe+hxwqKjnA5NREREpFkoaZEGqRgapnqWphceP4SY2bPIeuRRAnANDQv7v2s867iIiIiItHRKWqRBKnpawjVzWJNbmLaU6enzWB5RSp+92wF4ft0KFqYtdTgyERERkeahpEUaRDOHNZ8pcROYHzuTMdkB7OyWB0BC2WCmxE1wNjARERGRZqKkRRpkv4aHNZu8lFQykpKJmT2L1UMOAbB950FyV//ocGQiIiIizUNJizRInhaWbDaFmVnEzJ5FePwQokNPoluPdljjz6Y1G50OTURERKRZKGmRBvmlpkU9LU2t9+RJnqL7eyZN80x9fChisJNhiYiIiDQbJS1yzEpLyjhQUIyfnyGsfRunw2l1Ivp1BiB7gxaZFBERkdZBSYscs/15rl6W9uEh+PlpjZbm1qefq6dly6a9lJdbh6MRERERaXpKWuSY5e3VzGFO6tAxhA4dQyg+VMqu7flOhyMiIiLS5JS0yDGrmDlM9SzOqahryd6oIWIiIiLS8ilpkXrLWbSYvJTUSmu0tCUvJZWcRYudDawVinAPEVNdi4iIiLQGSlqk3sKio8hISmb3hm0ABB7YQ0ZSMmHRUQ5H1vp4ivE37sFa1bWIiIhIy6akReotPH4IMbNnsfOnTQDs/+9iz/oh0jwqeru6dA/DBpRTmF9M9orV6u0SERGRFk1Ji9TbwrSlTE+fR16wa5rjdd0LmZ4+j4VpSx2OrPWo6O3an5pGQdguAL5f8K56u0RERKRFU9Ii9TYlbgLPDLwBU94WgJNzDjM/diZT4iY4HFnrUdHblZGUTJfCnQCUDxun3i4RERFp0ZS0SL3lpaSS+vA8yo0/pf6HGTz7j2QkJZOXkup0aK1GRW/X8ohS4jduAWD1lh3q7RIREZEWTUmL1FthZhY9rr4WAP82fp5P/QszsxyOrPWYEjeB+bEzGZMdQHp0Ef62lOBDYZwfeZbToYmIiIg0GSUtUm+9J0/Cdu0NwICInoBruFLvyZMcjKp1yUtJJSMpmZjZs/gmIZQTTggDIH3ZaocjExEREWk6SlrkmOzPc63R0j5cC0s6oTAzyzNjW0zwKPoPiQBgc8ZOhyMTERERaToBTgcgviXfk7S0cTiS1qlyr9Y9k6axYf1uAPYFdHQoIhEREZGmp54WOSb5eYcA9bR4i96RHTF+hu1b8zlcXOp0OCIiIiJNQkmLHJP8/Roe5k2CggM4oVcHbLlly6Z9TocjIiIi0iSUtMgx8QwP66DhYd6iT79OAGRv3ONwJCIiIiJNQ0mL1Jstt+Tv1/AwbxPZ35W0bNm41+FIRERERJqGkhapt8LCYsrLLG1DgwgM9Hc6HHGr6GnJ2byPstJyh6MRERERaXxKWqTefinC19AwbxIaFkznrqGUlpSzfet+p8MRERERaXRKWqTefqln0dAwb5GzaDF5KalE9O8MQPaGPeSlpJKzaLGzgYmIiIg0IiUtUm/5WljS64RFR5GRlEyXwIMAZP24iYykZMKioxyOTERERKTxKGmRetuvhSW9Tnj8EGJmz+LQ4n8BkLM5jwF/vonw+CEORyYiIiLSeJS0SL1V1LR0UE+L11iYtpTp6fP4oVc+waUHKPEL5sYfXmFh2lKnQxMRERFpNEpapN4qFpZsp54WrzElbgLzY2fyqy0BlAW51mmZHnYRU+ImOByZiIiISONR0iL1VlHTop4W75GXkkpGUjIxs2eR3acAgJ+WrSYvJdXhyEREREQaj5IWqZfycktBfjEA7Tqop8VbFGZmETN7FuHxQ+jauZdrX+d+FGZmORyZiIiISONR0iL1Uph/CFtuCW0XTECAFpb0Fr0nT/IU3c+5bCrBbQIoOFBGu/FnOxyZiIiISONR0iL1kr+/oghfvSzeys/P0KdfJwCyN+51OBoRERGRxqOkReqlop6lnRaW9GoRFUnLBiUtIiIi0nIoaZF62a8ifJ8Q0b8zoJ4WERERaVmUtEi95GthSZ/Qs08H/AP82L2jgIMHDjsdjoiIiEijUNIi9VKxsGR79bR4tYAAf3pFhAOwRb0tIiIi0kIoaZF68fS0aLpjr5WzaDF5KalERrmGiG3K2kNeSio5ixY7G5iIiIjIcfLapMUYE22MuccY840xZr8x5rAxZqsx5m1jzOlHaTvRGPOZMSbPGFNgjFlpjLmqHvf0iXbNqeKNcOWeFr0R9k5h0VFkJCXTxeQDsCEth4ykZMKioxyOTEREROT4eGXSYoyZCGQAfwLeAcYBccDfgNHAJ8aYe2tpewewBNjrbjcSWAMsMMY8W8c9faJdcwuLjiI96REK8g9hsZRlZ+qNsJcKjx9CzOxZ5P9rPlDO7j3F9P3jnzzruIiIiIj4KmOtdTqGIxhjpgEvApdba1+rdmwI8AMQAIyz1n5e6dhYYBmwGhhhrS2rdGwJMBG4ylr7crVr+kS7mgwfPtyuWrXqaKcdly1fr+bFt3IwFHH2rvc9K7CLd1mYtpS31v6X0SmF+O8bx/6Qbmwa8B3njhnJlLgJTocnIiIivs84dWOv7GlxKwAWVt9prU0FvnE/vKTa4bvc28cqJwJuye7tnTXcy1faNbuFaUu5fZ3rnyHs0AGWR5QyPX0eC9OWOhyZVDclbgLzY2cyJjuAg2F7ALg46AwlLCIiIuLzvDVpeRXoVcMb+go57m2nih3GmG7AWPfDT2posxwoBqKMMcN8rZ1TpsRN4A/dLgTgYNtixmQHMD92pt4Ie6G8lFQykpKJmT2Ln6MKAMhcvYm8lFSHIxMRERE5Pl6ZtFhrD1trC+o45QT3Nq3SvmG4ns8Ba+2WGq5ZAmxwPxzhg+0ckZeSyob/fAzAri6lxMyeRUZSst4Ie6HCzCzP0L1enaMwfob8oE7sy8hyOjQRERGR4+KVSUtdjDEdgVHAIeCFSocqKsN31tF8u3vb3wfbOaIwM4vQk08FIDykq6fYuzBTb4S9Te/Jkzy1RvdcMo0TenXAWiiPP8XhyERERESOj88lLcBNQDBwq7W28hv/9u7twTraFrm3HXywnYcxZroxZpUxZtXu3bvruMzx6z15EoeD2gFw0WjXm9/w+CH0njypSe8rx69ivZbNWXscjkRERETk+PhU0mKMGYVr2uO3gEcbcgn39linTPOqdtba+dba4dba4V27dj3GSx+7/P2uNVraaWFJn6KkRURERFoKn0lajDEDgaXAx8Bv7ZFzNee7t23ruEzFu+78Svt8pZ1jCtxJS3slLT4lol8nMLB1Sx4lJbXNaSEiIiLi/XwiaTHGxOBKVr4GJllrD9dwWkWRRfc6LlVRwL+h0j5faecIay0F+epp8UVtQgLpcUJ7ysssOZv3OR2OiIiISIN5fdJijBkMfA6sBC621hbXcur3QDkQaozpU8N1AoF+7oerfLCdI4oPlVJyuIzAIH+C2wQ4HY4cIw0RExERkZbAq5MWY0wirpXjPwEudU8HXHHsLGPMSxWPrbW7gC/dD8+o4XJjcA272mitXeVr7ZySX2lomDGOLYIqDaSkRURERFoCr01ajDEjgU+B/wBX1LDQZC9+WaSxwhz39kZjjH+1Yze5t3NruJ2vtGt2BftdE5lpaJhvyVm0mLyUVFddC7B18z5yV6eQs2ixs4GJiIiINIBXJi3uhOUjIBxIAL6tmOK34gu4q3o7a+1nuBKCocBCY0yCMSbWGPMUcAGwwFq7wFfbOaFAM4f5pLDoKDKSkjm8YT2HQgooLS1n1eMvExYddfTGIiIiIl6myYoUjDGhwM3W2ob0GJzHL+uZnFTHeZur77DW3m2MWY2rx+ILwB9YC/zOWvtibRfylXbNzTPdcfsQhyORY1GxCGhGUjLtThhCCe3wG3+hZ/FJEREREV9ijpw5uJEubEx3YJu1tvrwJ2lEw4cPt6tWNV3py3/fSuH7rzdz7qQ4Rp7a7+gNxCssTFvKW2v/y+iUQvpu7EraCeMpbL+b4Rd3Y0rcBKfDExEREd/kWIFzg3tajDEX4+oR6QUE1XBKTfvEx2h4mG+aEjeBs8sjyViazJd9C6AYOh7sxsWx5zkdmoiIiMgxO+akxRgTALwLnIsr27LUnnU1TTeONBut0eKb8lJSyUhKJmb2LB5On8epPwWwL7+U9cu+J/aMEU6HJyIiInJMGlKI/0fgdOB24CygGJgKjHd//RZY7N7/f40SpTim8pTH4jsKM7OImT3LVdsSPIp+g3oCsHHtNocjExERETl2DRke9lvgTmttEoAxpgxYaa2tvHr7a8aYf+KakvjN4w9TnFBWVs6BwmKMgbB2wU6HI8eg9+RJnu/vmTSN1B9y+GFlNnkhPZwLSkRERKSBGtLTMgB4v9q+moaHPQuc34Dri5cozD8EFkLbBePn75WzY0s9VSwymb1xL+Vl5Q5HIyIiInJsGvJOtAwoqPS4CFcxfnX+QM+GBCXeoSC/GNDQsJagfYcQOnZuy+HiUnZsy3c6HBEREZFj0pCkZTMQX+lxNq4hY9VNBwobEpR4h4L9RQC0a6+kpSWo6G3ZvGGPw5GIiIiIHJuGJC1fAg8ZY4a6H38IXGuM+Y8x5k/urw+A64FljRSnOMCzsGQHLSzZEniSliwlLSIiIuJbGlKI/wZwDfAqEAs8hKun5Xxc67aAq8alANcMY+KjtEZLyxLZ313XsmEv5eUWPz/H1ocSEREROSbH3NNirf3CWtvGWhvrfrwPGA08DawFfgJeBkZYa9MbM1hpXgWa7rjFyFm0GHKy6Ni5LYeKStixdT95Kamu/SIiIiJerlGmhLLW7rDWzrTWxltrB1trp1lr1zfGtcU5+eppaTHCoqPISEqmZyfXr3z6l2lkJCUTFh3lcGQiIiIiR3dMSYsxJsAY090Yo0U7WgEND2s5wuOHEDN7Fn6rPgUgY0W6Z/FJEREREW9Xr6TFGNPFGPMKsB/YBhQYY5YaY6KbNDpxjLWWgnwND2spFqYtZXr6PLK67QAgN7gz09c+xcK0pQ5HJiIiInJ0R01ajDGhwOfA5UAIriL7AFxF918ZY2pao0V8XPGhUkoOlxEU7E9wm0Cnw5HjNCVuAvNjZ3LqllKM2Q8EMqfr1UyJm+B0aCIiIiJHVZ+elptwzRK2BbgfmAncA6QB3YB7myw6cUTOosVs/TYFgGK/wwAq2vZxeSmpZCQlEzN7Frld9wGw+s1PyEtJdTgyERERkaOrT9JyMfAtMNBae5u19mlr7V1AIvA2cFETxicOCIuO4qeXFwJwIHCf5w2virZ9V2FmlqeGpUNn19THh6KHUZiZ5XBkIiIiIkdXn6QlGrjbWnuo8k5rbTlwG9DOGNOjKYITZ4THD6Hj+Re6vi8s9HxCr6Jt39V78iTPv98tl10CwPbcw/SYONHJsERERETqpT5JSyiwrpZjPwOluGpdpIVYmLaUf29eAUCvPQUsjyhlevo8FW23EG1Dg+jRsz1lpeVs2bzP6XBEREREjqq+Ux4fqmmntdYCh3EV51fhnhq57DhiE4dMiZvAWaHDAdjetZQx2QHMj52pou0WpO+JXQDY9HOuw5GIiIiIHF2jLC5ZhyOSGfF+eSmp7PjxJwAyIy0xs2eRkZSsou0WpG+0K2nZmKmkRURERLxfQD3Pu9kYc6CWY4HAjcaYvdX2hwG2wZGJYwozs/Dr0x92F9MzNMqzMGFhZpbqWlqIyP6dMH6Gbdl5FB8qJbhNff8UiIiIiDS/+r5T+XMdxwzwh1r2K2nxQb0nT6Lo3o8B+NP5rsnhwuOHKGFpQYLbBNKzdwe2ZueRvXEPJ8Z2dzokERERkVrVN2l5Eyg6xmu3BS45xjbiBay1FOYXA9CufbDD0Uhjy1m0mLDoKPqe2IWt2XlsytxD15JdFGZm0XvyJKfDExERETlCfZOWG621u47lwu5pkJW0+KCigyWUlZUT3CaAwCANG2ppwqKjyEhKputvbwAgK3UL7Re+TszsWQ5HJiIiIlKz+hTif45rhrBjVQx80YB24rCCfNdkce06tHE4EmkKnhqlf88HytiVW0zkjX/U8D8RERHxWkdNWqy14621ecd6YWvtPmvt+AZFJY4qdCctYe2UtLREC9OWMj19Hisjigkv2g3GcNv3i7UOj4iIiHitpp7yWHxQwX53PUsH1bO0RFPiJjA/diZjsgM42M415fEFAeO0Do+IiIh4LSUtcoTCAvW0tGR5KalkJCUTM3sWP0UXAJCVmqN1eERERMRrKWmRIxTsV01LS1aYmUXM7FmExw+hT+cBBAT6URjQgd3rspwOTURERKRGSlrkCIUF7uFh7TQ8rCXqPXmSp+j+nsnTiOjXCYDDMSOcDEtERESkVkpa5AgVPS1h6mlpFfpGdwFgY2auw5GIiIiI1ExJixzBM+VxeyUtrUFF0rIpc4/DkYiIiIjU7KhJizHmNGOMkptWwlpLYb5reFiYhoe1Cj17dyAoOIC9uQfYv6/I6XBEREREjlCfZGQZsNMY86Ix5iJjTNsmjkkcVHSwhLKycoLbBBAUHOB0ONIM/Pz9iIzqDMAmDRETERERL1SfpOVWYD1wBfAWkGuM+Y8x5jpjTI8mjU6aXaGGhrU6OYsW06PtYQDe+fQbwDUtcs6ixQ5GJSIiIvKLoyYt1tp/WGvHAD2BGcDHwOnAM0COMWalMeYWY8zgpg1VmkNFPUtYew0Nay3CoqMo+/BNAMr2Wfb9mEJGUjJh0VEORyYiIiLiUu9aFWvtLmvtc9baC4AuwGTgZaAf8HcgxRiTaYx5yBgzVnUwvqminkU9La1HePwQht10DcFlRQSWtOH7R573rOMiIiIi4g0alFhYa4ustYuttb8DegCnAslAKTAL+BTYZYxZYIyZbIwJbbSIpUn90tOipKW1WJi2lBk/PUVZ4A4AVnXvwvT0eSxMW+pwZCIiIiIux90bYl2WW2tnW2sHArG46mAygKn8UgdzzfHeS5reLz0tGh7WWkyJm8D82JnE7nMV4bc7cALzY2cyJW6Cw5GJiIiIuDT6EC5rbYa19gF3HcwJwHXAh0DHxr6XND71tLQ+eSmpZCQlM+q6ya7HbXuwLulR8lJSHY5MRERExKVJ606stbuttc9baydZax9qyntJ49DCkq1PYWYWMbNn0XNUIja0lLIyS7upMyjMzHI6NBERERGgiZMW8T2Fmj2s1ek9eZKn6P6UETEA7CwJo/fkSQ5GJSIiIvILJS3iYa2lQLOHtWpRMV0B2JCx2+FIRERERH6hpEU8DhWVUFZaTlBwAEHBAU6HIw6I6NeJgAA/dmzL50BBsdPhiIiIiABKWqSSAs0c1uoFBPoT0b8zABt+Vm+LiIiIeAclLeJRsF8zh4mGiImIiIj3UdIiHoUFmjmstctZtJiu5AGwJiUbay15KankLFrsaFwiIiLSuilpEY+KnpZ2HZS0tFZh0VHkPv8Ebdv4YQ77seGLH8hISiYsOsrp0ERERKQVa1DSYoxJMMa8ZozZaIzZbYxZa4z5lzFmUiPHJ80gZ9Fi8lJSKXQXXoe1D9an661UePwQBs6eRYe8TQCs+vf7xMye5ZkSWURERMQJx5y0GGPGACuBKUAk0BmIBS4H3jbG/GiMGdGoUUqTCouOIiMpmb3ZOwHwz9ulT9dbqYVpS5mePo+97bYDkNmhG9PT57EwbanDkYmIiEhr1pCelvuAYOBL4EwgAhgEXOPeNwT4whhzcWMFKU0rPH4IMbNnkftzNgB73l2kT9dbqSlxE5gfO5OTt+8BwJZ1Zd6AG5gSN8HhyERERKQ1a0jSMgLYCvzaWvuptTbHWvuTtfZFa+044AKgGHjFGDOgEWOVJlLx6fr+YNdUx2u7H9Cn661UXkoqGUnJxN88k0Mh+ZSbAL59/BXyUlKdDk1ERERasYYkLeXASmttUU0HrbVLgYuBNsAtxxGbNJMpcRN4ZuAN+JWHADB6awnzY2fq0/VWqDAzy9PLFtgpEAB76kQKM7McjkxERERas4YkLVlAx7pOsNZ+AnwPnN2QoKozxpxrjNlqjLGNcT2pKi8llXUPP06ZCaDcr4zBN/+BjKRkfbreCvWePMkzLPCK88YBsC3PtV9ERETEKQ1JWhYDpxljjlalnYmrSL/BjDGhxpingPeAnvU4f5oxxh7lK6yO9hONMZ8ZY/KMMQXGmJXGmKvqcd9mbdfYCjOz6DX9BldMQdAxIZ6Y2bP06XorF9m/E/4Bfmzfup8DhcVOhyMiIiKtWEOSlvlABvCWMaZLHecNALIbFBVgjIkG1gDjgUuPoWmRO77avsprud8dwBJgLzAOGOm+/wJjzLN1xNms7ZpC78mTMCdEAtCnh+ufNDx+iD5db+UCgwKI6NcJLGz8OdfpcERERKQVC2hAm2xgD9AFWGeMuR/4t7V2F4Axxg/4KzDUvW2oQcD77mt0P4Z237onBKg3Y8xYYC6wGphirS1zH7reGNMTuNYY86W19mUn2zUlzxot7YKb65biA/oP6MrGn3PZsH43cUN7OR2OiIiItFIN6WnZD3QFDK7E5SFgu3uhyR9xJTT3Ag9ba5OOI7al1tobayv4b2R3ubePVUogKiS7t3d6QbsmU1hwCICwdm2a87bi5aJiugKwIWM31qqkTERERJxxzEmLtbYzEIVryFYS8BmuRCYS1xotHXAlNH8yxqQYY14wxtxgjBlujAk8hvvUOIyrsRljugFj3Q8/qeGU5bimcI4yxgxzql1TK8x397S0V0+LuOQsWkxw7mZCw4LI33+I3F2F5KWkkrNosdOhiYiISCvTkJ4WrLUbrbVvWmv/aq0901rbCVcNy+XAw8DnwAEgDpgGPAF8A+Q3StR1a2eMucsY870xZpcxJscY84ExZqp76Fp1w3C9DgestVuqH7TWlgAb3A9HONiuSWl4mFQXFh3F+oceoXc312cNqZ/+SEZSMmHRR5uDQ0RERKRxNShpqYm1NtNa+7q1dra1dry1NhyIBaYC/wRWACWNdb86nISrqP02XAXu1wL+wCvAf4wxQdXOr3gHtrOOa253b/s72K5JFea7h4e11/AwcQmPH0LM7FkErf4MgPQVP3nWcBERERFpTg0pxK83a23FjF2vAhhjTFPeD1gH3GytTa68zxjzEbASOA+4D5hd6Xh79/ZgHdetqKvp4GC7JqWeFqluYdpS3kr/L6O6HYZDlj1BXbg+7Rkm+52rhUdFRESkWTVaT0t92Cau5LXWflstYanYXwb83f3w98aYY+1OqEi2jjX+JmlnjJlujFlljFm1e/fuY7x0zTxJi2paxG1K3ATmx87kV1vK8fPbC/jzlw5XKGERERGRZtesSYvDfnBvQ3BNx1yhos6mbR1tK5KcyjU5zd3Ow1o731o73Fo7vGvXrnVcpn7Ky8pdiwcaCA1T0iIueSmpZCQlEzN7Ftt77gHgx6UryUtJdTgyERERaW1aU9JSuYakY6XvK5Z9r2stmBPc2w2V9jV3uyZz4MBhsNA2NAh//9b0IyF1KczM8tSwdO7q+nHd320ABT9nHaWliIiISONqMe9QjTEhxpgJxpjQWk6pnCTkVfr+e6AcCDXG9KnhuoFAP/fDVQ62azKe6Y5VzyKV9J48yVN0P+e3v6VtaBD5haWE/OoMhyMTERGR1qbFJC24kpL/UPs0wRVDwoqBNRU7rbW7gC/dD2t6NzYG13CtjdbaVU61a0paWFKOxvgZz0KTmT/tcjgaERERaW1aUtJS4YrqO9zrs9zifvistbb6zF1z3NsbjTH+1Y7d5N7OreFezd2uSWhhSamP6NhugJIWERERaX5enbQYY7oaY3oAXSvt6+H+ql6BXube/s4Y84IxZowxJsIYcyquHphRwDLgL9XvY639DFciMRRYaIxJMMbEGmOeAi4AFlhrFzjdrqmop0XqI2pAVzCwOWsvh4tLnQ5HREREWhGvTlqA73AttPhtpX3b3V/fVT7Rvbp8DHAPMBBXorIBeBfXTF3TgTOttUXUwFp7NzAJ6Ax84b7+ScDvrLVX1xZgc7drCuppkfrY+7/36d45iLKycu791xuAa4axnEWLnQ1MREREWrwmXVzyeFlr+x7j+euBO91fDbnfu7iSHK9u19i0sKTUR1h0FGEfvsfOsEHsz91XZUpkERERkabk7T0t0gwK893Dw9TTInUIjx/C8Etdc0d02dWFn9wJS8UMYyIiIiJNRUmLVOppUU2L1G5h2lLu2PU8hiIsoXzZO5Tp6fNYmLbU6dBERESkhfPq4WHSPDQ8TOpjStwEzi6P5O0vlrE1pC+99/bmutjzCY9TT4uIiIg0LfW0tHLFh0opOVxGQKAfwW2Uw0rtKmpYEn/tWgqpsN9JZCQlk5eS6nBkIiIi0tIpaWnlKk93bIxxOBrxZoWZWcTMnkXcWcOxxrI9t5iIP/yRwswsp0MTERGRFk4frbdymu5Y6qv35Eme76NO7MaG9bvZ7deZ+MmJjsUkIiIirYN6Wlo51bNIQwwY1B2A9et2ORyJiIiItAZKWlq5ysPDROrrxEHdAMjK2EVZWbnD0YiIiEhLp6SlldPwMGmIjp1D6dI9jOJDpWRv3Ot0OCIiItLCKWlp5TQ8TBoiZ9FiIrq4/nwsWPIp4JpdLGfRYgejEhERkZZKSUsr5xke1l7Dw6T+wqKj8P/qPwCU5pZ7pkMOi45yODIRERFpiZS0tHKe4WHqaZFjEB4/hFE3XklA+WGCi0P5Pnk+MbNnER6vhSZFRESk8SlpaaVyFi0mLyX1l+Fh7YM1vEfqbWHaUq7PeAoCtgHwXffuTE+fx8K0pQ5HJiIiIi2RkpZWKiw6ivSkRzhQUIzFUrLxZw3vkXqbEjeB+bEzGbxvBwBtD/ZmfuxMpsRNcDgyERERaYmUtLRS4fFDiPj9HwAwFJP58CMa3iP1VlHDcvINUyg3ZeQFdSbloSfJS0l1OjQRERFpgZS0tFIL05byt3WvARBaXMTyiFIN75F6K8zMImb2LLoNS4DwMgD8L7ySwswshyMTERGRlkhJSys1JW4CN59wCQCHQg4zJjtAw3uk3npPnuTplZtw+jAAtuwPoPfkSQ5GJSIiIi2VkpZWKi8llaxF/wVgT8dSYmbPIiMpWcN75JjFDO4BwIb1uyk5XOpwNCIiItISKWlppQozs+gw/iwA2rXpRHj8EGJmz9LwHjlm7Tq0oWefcEpLytmwPtfpcERERKQFUtLSSvWePImysI4AnJ14EuAqztfwHmmIAYO7A5CRtsPhSERERKQlUtLSih1wr9ESGqaFJaXhchYtpmdQAQCr12ymvNxqzR8RERFpVEpaWrHKC0uKNFRYdBS5zz1B+7AATIkfP32ySmv+iIiISKNS0tKKHShUT4scv/D4IQycPYuOu9IB+G7Rl1rzR0RERBqVkpZWrGJ4WFg7JS3ScAvTljI9fR67wrMByA49genrtOaPiIiINJ4ApwMQZ5SXWw4eOAxA27Agh6MRXzYlbgJnl0fy09JkPupWhLVh3Nv9GgbEDXc6NBEREWkh1NPSSh08cBhrIaRtIP7++jGQhstLSSUjKZmBs2eR220XAN++8ZnW/BEREZFGo3errZSGhkljKczM8tSwdOjmmkY7r+cQrfkjIiIijUZJSytVMXNYqJIWOU69J0/yFN3fdeVltAkJZO/+EtqMOcPhyERERKSlUNLSSh0oOARAWLs2DkciLYm/v59nocmftNCkiIiINBIlLa1UYYGrCD+0nYrwpXENjOsBwE+p2x2ORERERFoKJS2tlNZokabS5qdvCAgwbNuynztefxlwFevnLFrsbGAiIiLis5S0tFIqxJemEh4TRefCHAByd273zC4WFh3lcGQiIiLiq5S0tFIqxJemEh4/hMSzEgDotaUrGUnJntnFRERERBpCSUsrpeFh0lQWpi3lkYP/BsooL+/Cl30CmZ4+j4VpS50OTURERHxUgNMBiDM0PEyaypS4CZxdHsnrK1awK7gnEbkRXBt7IeFx6mkRERGRhlFPSytUXm7V0yJNpqKGZeiZ8QDkR40kIymZvJRUhyMTERERX6WkpRUqOnAYayGkbSD+AfoRkMZVmJlFzOxZJJ47Amss23YV02vmjRRmZjkdmoiIiPgovWNthQoLVYQvTaf35EmExw8huE0AsXEnALC1tAO9J09yNjARERHxWUpaWqGKehYNDZOmNii+JwDr1mxzOBIRERHxZUpaWiEV4UtzOXFQd/wD/NiyaR/5+4ucDkdERER8lJKWVkjDw6S57H5vKX17tgHg4X+9C7gK9XMWLXYwKhEREfE1SlpaIQ0Pk+YSFh1FaOoyAA7tKvbMLBYWHeVoXCIiIuJblLS0QoUaHibNJDx+CKfccAl+tozQwk6sefgpYmbPIjxea7aIiIhI/SlpaYU8PS1KWqSJLUxbyv/LfBo/f1ch/soePZmePo+FaUsdjkxERER8SYDTAUjzUyG+NJcpcRM4uzySL/73GmvC+xBcFMn82AsJj1NPi4iIiNSfelpaIU8hvmpapIlV1LCc8v/+jzL/EgoCw/k++VnyUlKdDk1ERER8iJKWVsaWWw4UHgYgtF2Qw9FIS1eYmUXM7Fl0GRqP6VIGQMn4SyjMzHI4MhEREfElSlpamaKDh7HlljYhgQQE+DsdjrRwvSdP8hTdXzlpHACZ20rpddGFDkYlIiIivkZJSyuSs2gx279PA6CIQ4DWzJDm0ze6C6Htgtmbe4DtOfudDkdERER8iJKWViQsOoqMl94A4GBAntbMkGa1bfG7RPdyDUl85s3/AUqaRUREpH6UtLQi4fFD6HzBRQB0OHCAjKRkrZkhzSYsOoo2K5YAUL7LsHdNipJmERERqRclLa3IwrSlLNj8BQA99hayPKJUa2ZIswmPH8LIP00jpOwAgSVtWPnYK0qaRUREpF6UtLQiU+ImMKH9aAB2dSljTHYA82NnMiVugsORSWuwMG0pM356itKgzQCs6dJHSbOIiIjUi5KWViQvJZXt37nWx9jY2xIzexYZSclaM0OaxZS4CcyPncmoXTkA2JLePHni9UqaRURE5Kh8ImkxxpxrjNlqjLFOx+LLCjOzCIweCED3tn0Jjx9CzOxZWjNDmkXFxA8nzZrOwdA8yvwCWT7vLSXNIiIiclRenbQYY0KNMU8B7wE9j6HdRGPMZ8aYPGNMgTFmpTHmqpbSrqF6T57EYeOavWnGWecBrjqD3pMnNdUtRTwqFpoMjx9CcI9AAPYPGqukWURERI7Ka5MWY0w0sAYYD1x6DO3uAJYAe4FxwEj3dRYYY5719XbHq7CgGIDQsOCmuoVIjSovNPnXKy/Cz8+Qvf0Q4Wee63BkIiIi4u0CnA6gDoOA94G/At3r08AYMxaYC6wGplhry9yHrjfG9ASuNcZ8aa192RfbHS9bbjngTlrC2ilpEee0DQsmOrYb69fuJG31VkaP1bTHIiIiUjuv7WkBllprb7TWFh1Dm7vc28cqJQIVkt3bO3243XEpKiqhvNwS3CaAgED/xr68yDHpVboNgJTvc7hj8QJAi02KiIhIzbw2abHWlh/L+caYbsBY98NPajhlOVAMRBljhvlau8agXhbxJrEj+hFQfpgdW/PZtG+tp1Bfi02KiIhIdV6btDTAMFzP54C1dkv1g9baEmCD++EIH2x33AoL3fUsSlrEC3QZmsDAgZ0AGJTejYykZC02KSIiIjVqSUlLxcezO+s4Z7t7298H2x23AyrCFy+yMG0p7/r/DwBTHMFXfcq02KSIiIjUyJsL8Y9Ve/f2YB3nVNTHdPDBdh7GmOnAdICIiIg6LlOVhoeJN5kSN4GzyiJ4ITWNAwHtGLyzF9fEXk54nHpaREREpKqW1NNSH8a9PdZFKr2qnbV2vrV2uLV2eNeuXet9UQ0PE2+Sl5LK+oceIXGUK/HOjz+TjKRkLTYpIiIiR2hJSUu+e9u2jnPaVDvXl9odtwP56mkR71Gx2OTICcOxWDbmFNHn//1Ri02KiIjIEVrS8LCKdzp1relygnu7odI+X2l33Dw9LappES/Qe/Ikz/cxg3qwft1ONh9qx8mV9ouIiIhAy+pp+R4oB0KNMX2qHzTGBAL93A9X+WC74+YpxFdPi3iZ3uWuNVvWfLuFO95ZAGjNFhEREflFi0larLW7gC/dD8+o4ZQxuIZdbbTWrvK1do1BhfjirWKH9yeo/BC7dxSQvfcnrdkiIiIiVbSYpMVtjnt7ozGm+pLvN7m3c324XYNZazlQeBhQT4t4n85D4xkc1w2AmIzuWrNFREREqvDqpMUY09UY0wPoWmlfD/fXEdNmWWs/w5UQDAUWGmMSjDGxxpingAuABdbaBb7a7ngcKiqhrKycoOAAAgOr50kizlqYtpSFfq71WezhCL7qY7Vmi4iIiHh4eyH+d0BktX0VCy9uBvpWb2CtvdsYsxpXj8UXgD+wFvidtfbF2m7kK+0aSkPDxJtNiZvA2eWRvLT6e/YHdqbf7r5cE3ux1mwRERERwMuTFmtt3wa2exd4t6W2awit0SLerKKGZcRF1/Hx8j3s7n+yhoiJiIiIh1cPD5PGozVaxJtVrNkyYsJIrH85O3MP0+ma/6c1W0RERARQ0tJqaI0W8Wa9J08iPH4IgUEBjBzdH4Cf9wRVWctFREREWi8lLa2E1mgRX9HrgKt3JfWHHO54ewGgNVtERERaOyUtrcSBAtd0x2HtghyORKRuEQlRhJfsofhQKbu2b9WaLSIi/7+9O4+vs6zz//+6zjnZkybN2qxN1zRdQqE7i4CAsrW0oBU3QMdxVxYHf+N3Bme+OjN8RxS3ERzGcSoqo6hYkFHUUVm70I3uLSRd0qRtmjTNepKzXr8/zkkJJW2T9CT3ycn7+XiEc5L7vq/zyc11rp5Prk1ElLSMFxoeJmNFTs08Fl1VBUD5oVJNyBcRERElLeOFhofJWPHkrmf5lu8ngJ+wzePFskzt2SIiIjLOxfWSxxI73V1aPUzGhr49W9a++L/Up08nt30Gj1VfrT1bRERExjH1tIwD1lq6OjQ8TMaGvjksV37gSgCaMqey8+vfpW3HTocjExEREacoaRkHfL1BQqEwySluklPUuSbxrW/PlilXXEI4x08wZAnccIf2bBERERnHlLSMA12d6mWRsaNvzxaA9996GQC7D/ooWXmLk2GJiIiIg5S0jAOahC9j1YzqIjJTLKdOeqndd4IH1q4BtG+LiIjIeKOkZRzQJHwZq1wuw0VzcwFY/9xO9vs2at8WERGRcUhJyzig4WEyli27ZTFut+FwYw+LX0P7toiIiIxDSlrGAQ0Pk7Hs2YN/oGViPQB5Jyp5pSKofVtERETGGSUtCazhqbW07dh5uqclMytFcwFkzFk992Y+tXQxAEdyZrDkSCqPVX+a1XNvdjgyERERGS1KWhJY5vRp7H/oYdoamwEwJ49rLoCMOW07dnLqh9+jdFIqkIT/hjsj9Vr7toiIiIwbxlrrdAxyARYuXGg3b9581uNtO3byox9spT0pl8WnnmfpPXcl1FyAcMhPV9shOk8doKfzOAFfBwF/J0F/Ny6XB5c7GZc7meTUbNIyi0nLmkT6hDLSMosxxjgdvgxCw1NryZw+jSZXAT//r01kT0zjwzcX4j1wgLJbVzodnoiIyHji2Icn7TSYwJ7c9Sy/3Ps/zEt+B1jYVeTlh3sf4T2um8b00JpwKMCpEztpaXiV7rZDWBsa8LxQOEAo2AOAz9tMZ2vt6WNJKRPILqgmu2A2E/Jm4nLprRCv+hKT7LAlNz+D1pZujpt8Zt9a42xgIiIiMmr0SS2BrZ57M+8KT+Z7G2oJuWBZY5CPvf/z5Mwdmz0tAV8HTYdepOXoJkIBb/SnhvSsUjJzp5GRXUFyajZJyRPwJGdgbYhwyE8o6MPnPUlP1zF6uo7RdeogAV8HLQ0baWnYiCc5k/yyJRSULSU5NcfJX1HOwbgMVRM6Wd/iYsMLB/jvg3/gqyvviszbqq1Tr4uIiEgCU9KSwNp27GT3Q98mVHgLYRNi9hc+NyaXiw2HAjQdfpHjB/9MOOQHIC2rhILyZUwsqsGTlH72i6PH0jKLyCmcDYC1lp7ORtqa93Dq+A56u5s4fuBPHD/wZ3KK5lEy7TrSMieN+O8lQzd/aSWbamtpOHyK+oz9p/dsqbr/PqdDExERkRGkOS1j3LnmtDQ8tZbwpArW/KoRmxLiH/5l5Zj7q3Tbid0c2fc0/t5TAGQXzKZ46jWkTyiPyZwUay1dbQdpPrKetqad0aFmhtxJ8ymedh2pGQUX/BoSW//zXy+yZVc7Hlc91xzfPOaScBERkTFMc1ok9spuXUlj/SmgkZKCyK7iOTXzxsQHvHAoQMPr/0PzkVcASMucRFnVCibkzYjp6xhjyJo4layJU/H3tnP84J9padhI6/FttDZtp6jiCoqnXYfboz1u4sGTu55lred/mcXVBENlvFS2k28kwDwtEREROTclLQmuuysynCojc+x86O7tbubAjp/S09mIMW5KZ9xAYcXlGJd7RF83OTWbiupVFFVexbEDf+Rk42aaDr9A6/HXKK9aTk5RjVYcc1jfPK1frn+Bo2mTyWubxWPV14zZeVoiIiIyONqnJcF1RzeWzMhMdjiSwek4+Tp7N3ybns5GktNyqVr8GYoqrxzxhKW/lLSJVM5ZzawlnyN9QjkBXzsHdvyEutfWEPB1jloc8nZ9c1iuft8yAI5mTWfH17+nPVtEREQSnJKWBNfdFUla0sdAT8up49up3fpDwiEfOUU1zF56DxnZ5Y7Fk5Fdzqwln6Wi+lbcnlTam/ewZ903ONW0w7GYxruu2jqq7r+PaVcuINVzilDI0v3OD/DU0z8HIklNw1NrnQ1SREREYk5JS4IbK8PDmo+s48COn2JtiMKKK5ha80HcSWlOh4UxLgrKlzH70i+QlTuDYKCbA9t/zMGdPyMU9Dkd3rhTduvK03Oyll9XDcCO/Z38YVr76V6YzOnTnAxRRERERoCSlgTnjfa0ZGTF7/CwpkMvUL/314ClZPr1lFUtx5j4qprJqTnMWPAxymetxLiSaD22hX0bv0NPV5PToY1b1dcuoqQoBZ8/zPztRWNyOW8REREZnPj6ZCgx1zc8LF57WloaN9Hw+rMAVFTfSvHUa+J2srsxLgorLqN66d2kZhTS232CfRu+zcmjW5wObVx6ctezrMt+OfJN7wxeLg/z8b2P8OSuZ50NTERERGJOSUuCe3N4WPz1tLSd2M3hPb8EoLzqFgrKlzkc0eCkZRYxa8nnyS2+hHA4wKFdP6N+31psOOR0aOPK6rk3842l7yEz2I7fk87k5qk8Vv1pLX0sIiKSgJS0JLh47WnpPHWAAzt+AjbMpCnXUDj5cqdDGhK3J4XKubdTMfs2jHHTXP8Kb2z9AUF/t9OhjRttO3by+te/yeXvjMxhaShZzN6HvqmVxERERBKQkpYEZsMWb7SnJT2Oelp83lbqtq3BhoPkly2hZPq7nQ5pWIwxFJQtZebCT+BJzqSztZa9mucyavpWElu4fCk2LUhHVxC78mN01dY5HZqIiIjEmJKWBNbbGyActqSkevB4Rm+fk3MJh/zUbf8RoWAP2fnVVFTfGrdzWAYrc+IUqpfeTXpWKf6eVva/+j06Tx1wOqyE17eSmMtluHXVIgA27Wyn5JYVPLB2DaAlkEVERBKFkpYE9ubGkvExNMxay+E9v6Kn8ygp6flUznt/3K0SNlzJqTlULf40OYVzCQV7eGPLf3DquPZzGS1z5peQM8FDe2eQjc9sZL9vo5ZAFhERSSCJ8YlRBhRvk/Cbj6yj9dhWXK4kpl10B5442IclllzuZKZe9GEKypdhw0EO7PgJJw6/7HRY44LL7eLKG+cC8MrzB1myvVtLIIuIiCQQJS0J7PQk/Czne1q62+s5sv8ZACbPWU1aVrHDEY0MY1yUz1pFyfQbAMuR/U/T8PqzWBt2OrSEty/lNfwp3Xg9WVQeLuKViqCWQBYREUkQSloS2Js9Lc4mLaGgn4M7/xtsmMKKy8ktnu9oPCPNGEPx1HdSOfd9YFw0HXqBQzt/RjgcdDq0hPa+muXcsKACgD3F81lWn6QlkEVERBKEkpYE1tfT4vTKYY1vPIvP20Jq5iRKZ9zoaCyjKa9kITMu/igudwqtx7dRu/U/CQV7nQ4rYbXt2Ilr7Q/IzvJg7QTCKz/G/oce1hLIIiIiCUBJSwLzxsEeLe0t+2k+sh5j3EyZezsud5JjsThhQn4VVYs+eXpJ5Nc3P0Yw4HU6rITUVVtH9f338s7lkTksr25vY9p99/LU0z8HtJKYiIjIWKakJYE5PTwsGPByePeTAJRMfxfpE0odicNp6RPKmLX4sySnTsTbcYTXN32fgK/L6bASTt8SyHMuLiU3J4nO7hC7X+/kuZkntZKYiIjIGKekJYGdnojv0PCwI/ueJuDrICOnkqLKqxyJIV6kpOdRtfjTpKQX0NN1jNc3P4q/t93psBKSy2W4duVFAKxff5Ql23u0kpiIiMgYp6QlgTm5T0vHyVpaj23FuDxUzn1fwuzHciGSU3OoWvQpUjMn0dt9gv2bHsHnbXU6rIS0nc14M9rwu1OZ1DhZK4mJiIiMcfokmcCc2qclHA5Sv/cpAIqnXktqev6ovn48S0rJomrhJ0mfUIa/p5X9mx6ht7vZ6bASzvvmLef9y6oAqMufx+IjaVpJTEREZAxT0pKgQsEwvT0BjMuQlj66Scvxg3/B520mNaOQosorR/W1xwJPcgYzF3yczJwpBHzt7N/0CD2dx5wOK6G07dhJzxOPUjopFUih7aoPaiUxERGRMUxJS4Lydkd7WTKSMS4zaq/b293M8YN/BqCi+lZcLs+ovfZY4k5KY/olHyMrdwZBfxf7N38fb0ej02EljK7aOmbdfx833L4IgO17Oyj+1Oe1kpiIiMgYpaQlQXU7sNyxtZb6fb/GhoPklSwgK1crNZ2L25PM9Is/QnZ+NaGAl9c3/zvd7UecDish9K0kVlKew8wpGYTCsH7bKa0kJiIiMkYpaUlQTmws2d68l86Tb+D2pFE6U3MHBsPlTmLq/DvIKZxDKNjDG1seo7ut3umwEsr1H1iKywX7D3Sz6LUkrSQmIiIyBilpSVCjvUeLDYdoeD2yMlPxtOtISs4clddNBC6Xh6k1HyancB6hYC+vb3mMrrZDToeVMP5w9M+cKDwAQGrbXF4u10piIiIiY42SlgR1enhY1uj0tDQ3bMDnbSYlPZ+C8mWj8pqJxLjcTK35IBMnXUQ45OONLT+g89QBp8NKCKvn3sz/uepKPGE/p9KLmdlUppXERERExhglLQmqu3P0elqCAS9H6/4AQNnMmzT5fpiMy82Uue8nt/hiwiEftVt+QGdrndNhjXltO3Zy+DvfZtmSIgAOVryDvQ99UyuJiYiIjCFKWhKUdxQn4h8/8CdCAS+ZE6eSXTBnxF8vkRmXm8q5t5NXsoBwOMAbW/+TjpNvOB3WmNZVW0fV/ffxjvdeik0L0tYRpG3JSrpq63hg7RpAq4mJiIjEOyUtCWq0JuL7vCc5Uf8KYCivWo4xo7e8cqIyxsXkOavJK12MDQeo3fZDOlr2Ox3WmNW3kpjb7eKDH7oMgK11QUzJZPb7Nmo1MRERkTFASUuCGq2J+Efr/oi1IXKLLyZ9QtmIvtZ4YoyLybNvI79sKTYcpPa1NbQ373U6rDFv+qxCZs4uIuRK4nePv8LSHV1aTUxERGQMUNKSYBqeWkvbjp1v2adlpIa+9HQ10XpsKxgXJdPeHfPyxztjXFRU30pB+aXYcJC6135E24k9Toc15gWrmwibEEfTJjPrjXReqdBqYiIiIvFOSUuCyZw+jX0PPUx3Ry8AwUNvjNjQl2N1fwQs+aWLSUnPjXn5AsYYymetpLDicqwNcWD747Sd2OV0WGPahy5dzqI5kfq6teJSLq33aDUxERGROJdwSYsx5i5jjD3P14CbiBhjlhtj/mKMaTPGdBpjNhhj7hzEa47qdeeSUzOPqffcQzBkgSAHvvWtERn64u08yqmm7RiXh+Kp18S0bHkrYwxlVSsomvwOrA1Rt/3HnDq+w+mwxqy2HTvJ+cvjZKa7CdtcOpetYv9DD/PNR79++rgm5YuIiMSXhEtaonqA/ef4Cp95gTHmAeAZoBW4ClgMvAasMcb8x9leaLSvO58ndz3Lvbt+BEBqoGfEhr4crf09AAVly0hOzYlp2fJ2xhhKZ95MUeXVYMMc2PlTWo+/5nRYY1JXbR1z/uZublx9MQCb3giSu3wV/uM7NClfREQkThlrrdMxxJQx5i7gLmvtVUO45krgeWAbsMhaG+p37BlgOXCntfZxJ68byMKFC+3mzZvf8rO9f9rEL357HJc5yXVNL8S8p6W7vZ59G7+Ly5XE3Cu+RFJKVszKlnOz1nK07vccP/AnwFA573byii9xOqwxyVrLz364iTf2NDGp9whdOeu5rN6jSfkiIiJn59gysYna0zJU/xB9/E7/BCLq4ejjl+PguvNq27GT2id+HXmeHaTq/vvY/9DDMd1I72jdHwEonHy5EpZRZoyhdPr1FE+7DrAc2vkzTjZuPu918nbGGEKzmwi7QhxPLWd6XY4m5YuIiMSpcZ+0GGMKgSuj3/5pgFNeAXzANGPMAqeuG6yu2jom3hCZUJyZkk1OzTyq7r+PrtrY7Kzu7Wiko2UfLlcSRZOvPP8FMiJKpr2LkunXA5ZDu5+kpfFVp0Makz60bDlXXJwPwPayZSytT9akfBERkTiUqElLljHmH4wxW4wxJ4wxDcaY54wxHzLGnPk7LyByH7qttUfOLMhaGwAORL9d5OB1g1J260psTgEAV8yZC0Qm55fdunKoRQ3o2ME/A5BfvgxPckZMypThKZ56DaUzbgQsh3f/guaGDU6HNOa07dhJ+u9+SF5OEtZm0XTJLZqULyIiEocSNWm5hMjE9r8jMsn9Y4Ab+DHwG2NM/23i+2bcNp2jvGPRx6kOXjdo3tN7tCSf58yh6e0+QVvTToxxUzT5HTEtW4Zn0pSrKZsZ6RWo3/MrTtSvcziisaWrto7q++9l5Z1LAMvOI5DyLk3KFxERiTeJmLTsAb5grb3JWvuctXaPtfY54HpgM3Aj8M/9zp8QffSeo8ye6GO2g9cNWneXH4hsLBlLxw/+GbDklS4iOXVYockIKKq8kvKqWwA4su/XnDj8ssMRjR1lt64kp2YepRUTWXbVdDCGP2/uwBMw7H/oYU3KFxERiRMJl7RYa1+11j48wM9DwL9Ev/2MMSZ1CMX2rZQw1KXWRuQ6Y8zHjTGbjTGbm5ub33a8O9rTkh7DpMXX08rJY9vAuJhUeVXMypXYKJx8OeWzVgJwZP/TNB16wdmAxqCT5XX4Urrp8mRTcGy6JuWLiIjEkYRLWs5ja/QxDbg4+rwj+ph+juv6EpyOfj8b7etOs9Y+Zq1daK1dWFBQ8LbjfUlLRlbshoc1HXoebJjcSfNJSc+LWbkSO4UVl1Ex+zYAGl5/luMH/+JwRGPL7fOX84GrI0PBDuXWMO9onibli4iIxInxlrT0n0cyMfrYt6xW0TmuK44+Huj3s9G+btC6O/vmtMSmpyXg66KlcRMQmUMh8augbCmT57wXMDS+8VuOHRhogToZSNuOnXQ9/gg1s7IAF3snXcGuB79O246dPLB2zelzNDFfRERk9CVU0mKMSTPG3GyMOduyVv0Thbbo4xYgDGQYY8oHKDMJmBL9tv+GGKN93aCEwxZvd2ROS3pGbHpamo+sw4aDZBfMJi1zUkzKlJGTX7qYyjmrAcPR2uc4WvcHp0MaE7pq66i6/z5uvONybFqINn8ydRPm0fLSK+z3bdTEfBEREQclVNJCJCn5DWdfKrhvSJgPeA3AWnsCeCn682sGuOYyIsO1DlprTycRo33dYPV4/VgLaelJuN0X/r83HArQfCSyIpVWDBs78koXUjnvdsBwrO6PNNY+h7VDnVo1vvRNyk9O8fBXf30lxmU4nFnF61vqWLqjSxPzRUREHJRoSUufD5/5g+j+LH8b/fY/rLX9V+/6v9HHzxtj3Gdcem/08SsDvM5oX3desV457OSxLQQD3aRPKCNz4rBWYBaH5BVfwpSaD4BxcfzAnzha+zslLoNUNnkiuTWRe7Vl4gIW7AloYr6IiIiDEi1pCUUfP2qM+aEx5jJjTIUx5goiPTBLgOeBL/a/yFr7FyKJxMXAk8aYi4wx1caYR4EVwBpr7ZozX2y0rxuMN1cOu/ChYdaGaTr0IhDpZTHGnOcKiTe5k+YzteaDkcTl4F9ofP1ZJS6D9MkP3ERRXjKWDF6ZfjmX1ns0MV9ERMQhCZW0RHeYrwK+CswikqgcAJ4mslrXx4FrrbU9A1z7j8BKIA94EdhEZJPKj1prP3KO1xzV687HG8NJ+O0t+/B5m0lKzWFiUc0FlyfOmFhUw7SLPowxbpoOv8iR/U9jbdjpsOJe5+7dzHj9WTweQzA8mZ7LV7H/oYf55qNfBzQpX0REZDR5nA4g1qy1rwNfjn4N9dqniSQ4cX3ducRyeNjpXpaKyzGuM0exyViSUziXqfPv4MBrj9Nc/wohv5fJc1fjciVcExAzXbV1XHLfx8ntzeGZn7/Guv0hVt6wCv/+X56elF91/31OhykiIjIuJFRPi/Tbo+UCh4d5OxroOlWHy51CfuniWIQmDsspmM30S/4KlzuF1uPbqNu2hlDQ73RYcatvYv5Fi8qYt6CUkPHw+40deAIuTcoXEREZZUpaEsybG0teWE/LifpXAMgvW4w7Ke2C45L4MCFvBjMXfRJPUgYdJ/fzxpZ/J+jvdjqsuGaMobe6EV9qF12ebLJPzNWkfBERkVGmpCXBvDk8bPg9LQF/F63HXwMMheWXxiYwiRsZE8qoWvwZklMn0t1ez/5Nj+DvbXM6rLj2/ouXc9d1M3DZEEezq5jcWM43U25g9dybtfGkiIjIKFDSkmDeXD1s+D0tLQ2vRjaTzJ9FSnp+rEKTOJKaUUDV4s+QmjmJ3u4T7Hv1e/R2n3A6rLjVtmMnrf/5Pa66tBCAfQVL2fnEb2hc+4w2nhQRERkFSloSjPcCJ+LbcIjmhvUAFFRcFrO4JP4kp2ZTtehTZORUEuhtY9+r36Or7bDTYcWlrto6qu6/j8tuW0a4qJdQ2LB3xnLqfvaUNp4UEREZBUpaEsyFTsRva95DoLeNlPR8JuTNiGVoEoc8SenMXPDXZOdXEwp4eX3z9znVtMPpsOJO36R8Ywx/f++tpOZBZ6/hpfJLWbyrR3NcRERERpiSlgQSDITw9QZxuQypaUnDKqNvAn5h+WUYo+oxHrjcyUybfyf5pUuw4SAHtv+EpkMvaBPKs/Akufn4J68hNcVFKFzMtvIFLDsAj1V/mp21LYDmt4iIiMSaPpUmkP57tAxn9/qezmOnlznOK10Y6/AkjhmXm4rZt1E640bA0vD6sxzZ+2tsOOR0aPGpoY65x1/AGDiVMpdjaZPZ++DX6K5/SfNbRERERoCSlgRyoUPDThxZB0BeySW4Pakxi0vGBmMMk6ZczZSaD2FcHpob1lP72hpCwV6nQ4s7XbV1XHrPh7l+5VwA9uQtoS05j0W7ujW/RUREZAQoaUkgF7JyWCjYS+uxbQAUlGsC/niWO+kiZi78BO6kdDpa9kWXRG53Oqy40jfHZdHlU8iZBeEwbM6/jIKTKezM9fHxvY/wqV/+E6ChYiIiIrGgpCWBnF45LGvoPS2tx7YRDvnInDiVtMyiWIcmY0xmTiWzlnyOlPQCejqPsW/jd+luP+J0WHHpsx+9iYqSVCypvDr5OqYfgW+m3MDJUKOGiomIiMSIkpYE8ubwsKH1tFhr31zmuGxpzOOSsSk1PZ9Ziz9DZs4UAr529m96hJNHtzgdVtzp2L2b6dt/QW52EkGTzf6LbqfuRz/hXevaNVRMREQkRpS0JJDuYe7R0t1eT0/nMTxJGeQU6cOVvMmTnMGMhR8nvyyystihXT/jyP5nNEG/n67aOub+zef48OeuJJTsp6nDsKHyKmYd8mspZBERkRhR0pJAhjsRv6VhAwB5pYtwuTwxj0vGNpfLw+TZ76Gi+laMcXPi8Eu8sfUHBP3dTocWF/rmt2RPTOMzn7+O5CRDr6ucl6ouZekbIb6ZcgOr597MA2vXAJrjIiIiMhxKWhLIcCbiBwNeWo+/BkB+2ZKRCEsSREH5MmYu/ASe5Ew6W2vZu+HbeDuPOh1WXEluPsxFJ17A5YJAaAYnL7udQ2sep3HtM+z3bdQcFxERkWFS0pJAvMMYHnby6BZsOEhW3gxS0/NHKjRJEJkTp1C99G7SJ5Th7z3F/o3/Ruvx7U6HFTe6autYds8d3PbhhVgs2+oNHdfcQf0TP2Ppji7NcRERERkmJS0JpLtzaMPDrLWnh4YVlC0bsbgksSSn5lC16NPkFl9COBzg4I6fUL/314RDAadDc1zfULHqmmJWfeASADYdMrwwpYolu7xaDllERGSYlLQkCGttv4n4g0tautsO0dt9Ak9yFjkFs0cyPEkwLncSlXNvp3zWSoxx03xkHfte/R693hanQ4sbNQvKuOk9kR4Vf/ASNk+ppvqwX8shi4iIDIOSlgTh6w0SCoVJTnGTlDy4yfQtRzcBkF+6EONyj2R4koCMMRRWXEbVks+SnJZLT2cje9d/S8PF+pmW0UlV5w4A2t1L6Hn3HRxa87iWQxYRERkiJS0JYqh7tISCvZyKfrjMK1k0YnFJ4suYUMbspfeQU1RDOOSLDBfb85SGixGZ4/Luz67guhWRnsx1tYbNlXOoPuTTUDEREZEhUNKSABqeWkvT9r0AnPS3A+f/AHSqaQfhkJ/MnCmkZhSMRpiSwNxJaUyt+dCbw8Ua1rPv1X+jt7vZ6dAc1TfHZdmV03j3yjkAtLsXsmlKjYaKiYiIDIGSlgSQOX0atf+9FoAud+ugPgC1NL4KRPZmEYmFvuFis5Z8lpS0PHo6j7Jn/Tc5Uf8K1oadDs9xVdndVHduA6DDfQktV93BQQ0VExERGRQlLQkgp2YeE6+/CYDczu7zfgDq7T5Bd9thXO4UJhZdNJqhyjiQPqGM6qV3k1t8CTYc4Mi+tbyx9Qf4e9ucDs1RXbV1XPfZVdxy+3wslu1HDBumXMqsQ34OpPXwlee/xZO7ntUmlCIiIgNQ0pIAntz1LD87sh6AsuZOXqkI8vG9j/DkrmcHPL+vlyV30kW4PYNbaUxkKNxJaUyZ936mXvRh3EnpdJ58gz3rvsHJY1ux1jodniP6hopdtKic2z+yGLfb4HXN4IWZ76S8M4XV6/28KzxZm1CKiIgMQElLAlg992auy1oAwNHCMJfVe3is+tOsnnvz28614RAnj24BNDRMRt7EohrmXPoFsvOrCQV7ObTzvzmw4ycE/d1Oh+aoonAzC1qeJznJEAqXs3f+B/G5Utj34Ne0CaWIiMgAlLQkgLYdOznxWmQift1kqLr/PvY/9DBtO3a+7dz2lr0E/V2kZhSSkT15tEOVcSgpZQLTLv4Ik2e/F5c7hbamHexe93VOHd8+bntdumrrWHrPnXzk7ndgk0McO+Hjz2XXsj87TZtQioiIDEBJSwLoqq3DXTkdgOK0qeTUzKPq/vvoqq1727ktjZG9WfJKF2GMGdU4ZfwyxpBftpjZy+4jM2cKQX8XB3b8hLpt/4Wv55TT4Y26vqFiRcUTuO9vr6e4LBt3MIPGtHfz2pQKrSwmIiJyBiUtCaDs1pX4bWRDyc9cvwKITM4vu3XlW84L+Dpob9kHxkVe8YLRDlOElPRcZi76JBXVt+LypNLespc9675O06EXsOGQ0+E5Iis7lZXvyKbQd5SgK5kW99V0XhtZWez2351k74Nfo+r++/jGgciwTvW6iIjIeKSkJUF0d/mBc28uefLoFrBhcgqqSUrJGq3QRN7CGBcF5cuYe9n9TCyqIRzy0/D6s+zb+F26OxqcDs8RvsMHWf2xZVx2zXQMhlcPGtZPuZL8NvD5vHzl+W9pgr6IiIxrHqcDkAsXDoXxev1gID09acBzrLX9hoYtHs3wRAaUlDKBqRd9mLbmPRzZ+2u8nY3s2/AdCisuo3jadXiS0p0OcdT09YpeMx+KS7N5+omt9AQreXF6NkuPr2f1+i7WTfWz/1lN0BcRkfFJPS0JwOsNgIX09GRc7oH/l3a3HcLnbSYpZQLZeVWjHKHI2eUUzGb2pX9D4eR3AHCi/mV2vfyvnKhfNy6HjJWYkyxq+iPZWR7CdiKvTLqOek8RS3Z5tZ+LiIiMW0paEkB3lw+AjMyz77lyupelZAHG5R6VuEQGy+1JobxqOdVL7yZz4lRCAS9H9v2aPeu/SUfLfqfDG1VdtXUsvO9jfPJvryWc78PYJPYVXsmmissp70rVfi4iIjIuKWlJAN2dkaQl/SzzWULBXk41bQcgr0R7s0j8Sp9QysyFn2TqRXeQnJZLb3cTb2z9AbVbf0hv9wmnwxsVfSuLpaQmcfeNM5ndsQ23CzqSp7O16n20JeWy78GvsepPpzRJX0RExg0lLQnAe55J+KeadhAO+cnMmUJqRsFohiYyZMYYJhbNY85l91M640Zc7hTaW/aye903qN/7a/y97U6HOGq66w5w7edW8Vf3vgObHqStI8jGgneyOW8WZU1BTdIXEZFxQ0lLAjjf8LD+e7OIjBUul4dJU65m7uX/H/mlS8Bamo+sY9fL/4+G/b8h4O9yOsQR19frMqkkm7/78gqWXjkVg4vWlBpemLECX0YJq9f71esiIiIJT0lLAjidtGS9vafF522hu+0QLlcSE4tqRjs0kQuWlJLF5DnvYfal95FTOA8bDtJ0+EV2vfQgjW88RzDgdTrEUeFJcrO4MszCUy+SleEmbCeyofBa9qbPovREWL0uIiKS0JS0JIA392h5e0/LyWNbAcgpmofbc/Y9XETiXVrmJKbNv4PqpXeTnV9NOOTn+ME/seulBzlW90eCgR6nQxxxXbV1XHrPHXzm795FqKQHg4sjOTW8MuUWOidMYfV6P0t3dLH3nx6k7LZV5NTM0ypjIiKSEJS0JIA3h4e9NSmx1kY2lCSyaphIIkifUMb0Sz5K1eLPkJU7nVCwl6N1f2Dni/9Mw/5nE3rOS99wseQUD/deV82S1ufJzU4i4JrAtrwr2Zq9mIv2GXYXw54nHmftM/+hnhcREUkISloSQF9Py5mrh3W3HcLf00pSSjZZudOdCE1kxGTmVDJz4SeYufATZOVOJxzy0XT4BXa99CCHdj9JT1eT0yGOqK7aOpbcexef+j/vIjSlmySPoSWjknWTV5Hmm8+M991B8S/Wab6LiIgkBI/TAciF855lIv7JY5EPKLnFF2OM8lNJTFm508nKnU53+xGaDj3PqaadnGzcxMnGTWQXzGFS5ZVk5FRijHE61Jgqu3Xl6ef3vmMO27/xKEcX3sYbh7zUZVRT/5KX9LxSFhzch88d4CvPf4uikwEayaXhV79m3aVzuJdIAtNVW/eW8kREROKNPskmgIGGh4VDAU4d3wFAXrGGhkniy8guZ+pFH2bO5V8kv2wpxuWhvXk3+zc9wt4N36K5YSOhoN/pMEdEV20dF33hU7z/c9cQrGmnpCKHgDuddvcSnp+xkrbsGbx3vZ+8tiCH1jxO2W2rWJ9bp2FjIiIyZhhrrdMxyAVYsGCBveWqf8TtdvF//vXG039NPnV8Bwd2/Jj0rFKql93jbJAiDgj4OjlR/wotDRsIBroBcHlSyS9ZSEH5MlIzCh2OcOSc2r6DF777Cw6WLKOzOwRApq+FaSdf40RhM5XH/OyckcayN0JUfOB2Sleu4IG1a/jqyrvU8yIiIufi2LAFDQ8b48KhSNKZkZn8luEvp4eGaQK+jFNJKVmUzrie4mnXcqppB81H1tHddpgT9S9zov5lsnKnk1+6hJzCObjcSU6HG1PddQe46vOrWT57Dl957OdMOJpNF/lsL7mWdH8re0v2s2jX6+yuTKbricfZ5Goic9tLGjomIiJxS0nLGBcOR5OWfnu0BPxdtLfsA+Mid9J8hyITiQ8ul4e84kvIK74Eb0cjzQ3raT26lc7WWjpba3F7UplYdBF5JQsSZu7LW+a7XD6XPQ99i94bP8Jfth/DSy5elvHitBqmd9Uy4301NP3i17TkRoaOVd51B+t9v+Mj0aFjVfff59wvIiIiEqWkZYwLhcMApPebhH/q+Gtgw2TnV5OUkuVQZCLxJ31CKZNnv4eyGTdx8thWTh7djLejgZbGjbQ0biQ5LZe8kgXkFS8kJT3X6XBjoqu2jtn330NOzTxeO/V1Krd1caToEtp7MtifdRG1L/lIy5/J/MO72Ts5Ge8Tj7Mqx8PetV+j+ktf5BsHtvDVmnnqdREREUdpTssYN7u6xt5+4z9Ts7CMle+/GIC9G76Nt6OBKTUfVE+LyHn0dDVx8ugWWo9tJeB7c4+X9AnlTCyqYWLRPFLS8xyMMHYanlpL5vRpZM+dyz/81xNUeifTcOhU9GiYXO8RSDnI/IOHCbotz1yVQ0NRMv/SUkPzy+veTGI090VEZLzSnBYZntPDw6Irh/V0NeHtaMDlSSWnYI6ToYmMCWmZRZTNvJHSGdfT2VrLyaNbaGvaibfjCN6OIzS+8T+kZZUysWgeE4tqSM0ocDrkYeufYHzlrz5I246dbNj2a1pqbqTuSBet6ZOBybwwrZvKzoOs2HiQrZVdNB9Yd/pfqcxtf9bcFxERGXVKWsa4cCgyPKwvaWntm4BfVJNwk4tFRpIxLibkzWRC3kzCs2+jvWU/bU07aWveQ09nIz2djRytfY7UjCKyC6rJzp9FZk4lxuV2OvRh66qtY+k9d5FTM49vfPeblOzycTx/Np29GdRNmEtd1mzyTzby4uQGmia1sPLBB8kr5PTcF/+2X9K49hklMCIiMuI0PGyMmz51tr1j1b9yy/vnU7OglJ0v/gsBXzszF32KrIlTnQ5PZMwLhwJ0nHyDU007aG/eQyjYc/qYy5PKhLyZZOfPIju/iqSUCQ5GemHeMnTsR08wq3MSdYc6sSaSlFkTxGOOUd1YS0tBM1OO9XC4OJnqw34q77qDe32/0zAyEZHEp+FhMjxvLnmcQmfrAQK+dpJTJ5KZU+lsYCIJwuVOIqdwNjmFswmHg3SdOkRHy17aW/bR232CtqYdtDVFNnJNyywmK3c6WbnTyJw4FU9SmsPRD17/pOK+BTXsf+hhrvrs3Xx1w++5uOdijjdDyJazq7gcVzhAe+lRpjfVsXvySbxPPM7SGWkaRiYiIiNGPS1j3OTyKvtXq7/OX997Bb625zh5dAvFU6+lZPq7nQ5NJOH5vK20t+yjo2UfHa212HCg31FD+oRSsiZOIyt3Ghk5lWMmienrdcmpmccDa9fwhakL2Pa1f6Or+gpe8VnSu3NOn2tsmCTbTEVrPY0lJzk2qZeVG3p4vRD1woiIJB71tMjw9PW0pKUZju/fBUBu8SVOhiQybqSk51JYcSmFFZcSDgXobq+P7v9SR3d7Pd6OBrwdDTQdfgGA1IxCMnImk5k9mYycyaRmFGKMy+Hf4u36JxFfXXkXDU+t5eIvfpacmnnUPvp15m/YQM/Cd7Ol/iSE8/CbImrzi8APExq9vFx+jFkN9eyafOq8vTAHJuVww/RpADz19M/56AP/pGRGRETeRj0tY1zppBn2Ex98mE/dW0z9np+TkV3BrCWfczoskXEvFPTT3X6IztY6Olvr8HY0YG3oLee4PamkZ5eTMaGM9KxS0ieUkpyWF9cbXPbvhfnmo19n4frX4YqbeaH2MCmeqfT0ht9yvse2U9RxnJb8U5wobOOWTafe0gvzjfqnWb3ejwV+sSyZT7sXnu6Reerpn3PrLe8DlNCIiMQJ9bTI8KWkemhr2gZAXskCh6MREQC3J/n0amQA4XAQb0cj3e2H6W47TFfbYQK+djpPvkHnyTf6XZdKWlYJ6RNKSc8sITVrEmkZhbjcyWd7qVHVP1l4b9F0Mv/m3eTUzOMPa9fwmSnlvPqNH9AzcwnrQn6yu/IIhrNpzM6GAGQ3wgulXRR2nGBTZRvHf/V7SsvDWCL/CpY1+d/SI7Mr+xizH/waFvCXhLVSmYjIOKaeljGudNIM++W/+TYLa57HGBc1V30ZT1K602GJyCD4e9vobj+Ct7MRb0cjPZ1HCfg6BjjTkJw2kbTMSaRlTiI1s4jU9AJS0vPjap7Mmb0wS9ftIfW6lfx2707KC5bQcKSdoOuM5MtaXK4OXKaVqU1N1Jf00FTUwy2vtrN5qptlByIJza7C0FnnyPTvkXn++49w1Sc/Dah3RkRkBDjW06KkxWHGmOXAfcDFgBvYDTxqrf3RYK4vnTTDfv/rf0dJ4W5yCucybf6dIxitiIy0gK8Db0cj3s5GejqP09N1nF5vM9jwgOe7k9JJTc8nJT2flPQ8UtLzSU3LJyUj39E/YAw0mX/vgw/hWfQOfpDUxDt753H0cCudyRNhgHk9hh5yvKfoyurCZdqZe6iZhkm9TDnWya4ZaaeTmVlf+iJfef5bp4eYPV9luHq/1XAzEZGRoaRlPDLGPAB8BXgK+CrgA+4GPgH8wFr71+cro3TSdPuL/7yLlKR2ps2/k5zCuSMas4iMvnA4iM/bEklgupro6TqOz3sSn7eF8FtWLHsrtyeN5LSJJKfmkJyaTVJK5DHyfQ5Jqdm4XKMzSvjMXphL1+2m7LZVPPPiL5jakkt7Ui7by3KZnFZFc1MXIdfAm+Ma6yfT10FPupeurB7mH2rlcFEPC470kBrqYcPU8OmEZt1UzprcbC8Js/yK950ebvbeoukYlwsbDvOH3ZvVcyMiMjAlLeONMeZK4HlgG7DI9puha4x5BlgO3Gmtffxc5ZSXTLVr19yGOymdmisfGLUPICLiPGstAV9HJIHpacHnPUmvtwWfN/I8HPKdtwxPcmYkgUnOwpOSRVJyZvR59DE5k6SULNyetJgtENA/gfnhV//+Lb0ft97yPvY++DXSlryDJ3oPcnFdBp2ebBpyJpAeysEfOlcMlpSgl0BSLy7jpeRkO8cLApya6OeKPW3sK/Oz7KAPN+G3DTf7ZsoNHFrz+NsWBxhsz82Zyc275iwcMAlS4iMiY5ySlvHGGPNn4GrgI9baNWccuwr4C1BnrZ1+rnKmTS63T/7H7RSUX0pF9aoRilZExhprLUF/F/7ettNfgd42/L3t+H2Rx4Cv46zDzs5kjBtPciae5HQ8nnTcSel4kvoe0/o9T8edlIbHk4bLk4LbnYJxuQcd99kSmt/9+IdMOd5G/vJV/GLHX7jqktXUPvciPZ5MTqSnkxRMx+dKHXCo2dv5Sff3EEzykdnTQ0dWgMkdAQprZuHdupH9+b1c1Ogn2frZWhFg2cEALs7dc3NmcvOFilsGTIIG28NzrsRnsAnSuc77d1eb9soRkeHQ6mHjiTGmELgy+u2fBjjlFSJDxaYZYxZYa7ecrayk5CAAyd3ZNDy1Vv/wiAgAxhiSUrJISskiI7t8wHNsOETA3xlJaHydBP1dBPydBH2Rx4C/K/IzXyfhkI+Ar52Ar33osbiScHtScHtScblTIs/d0e89b37v8qSQsqiIkNvHqePbufWTH8DlSsLlTmLpZQvIqKgkp3oOweQTVFWmYLp2kX/ZZTwSepnV6/2EMLwwI42LD6fT405j05QJLClaytHt+/C50+lKSgWbgjXJeJMjCwK0pgMhqMuAujogeykEYGNhNHgf/G9JGAiQ2unnT6UBfClBtn7/RfIzL2LvhCAeG8T0BDmcEcRtgxQdDbJ1+3qKV97B7qefp2RSCj0uNx4bxB30c3DN40y56w7W+37HatfM08lN/9XSdi1L5t0u14DH9lYZJl3geefaK2ckk6WRPC8eY4r38+Ixpng/Lx5jGunz4qV3WD0tDjDG3AD8Fui21mae5Zw9QDXwKWvt989W1uyZk+zj3/kUoZ8eour++8ipmTcyQYvIuBYOBQj4uwgFugkGvAQDPYQCXoIBb/Sxp99zL6FgL+GQj1DQB4zcvzPGuAkGQiQnp2GMm562DjIm5mFw0XqqlbzcQryH6knOmcjBcBulLWHCYUN9rocpyaV0HTlGR0oSqf4kXDn5eFu76PJ48ATcBG0SAePBWjfWmsgXBms5/T02+j3R4/2P8eZze8Z5hMNYwiSFLe6UZKzXiy8pTLovjMuG6UoNk91jSSsqwH/8GCfTQhR2hjA2TFOWpbjd4iJMY7alvN2QPXsWHbv2cDgnSOVJi8taDuSFmdZsMTZMbaGlqtlQsGwpJ9et43CWn8nH/RTf8G5+duJFrtkZACx/mufhQ8VXceyZ31B6y3J+dOzPXL89gMWyaQosOWABy+8uTuJjpdfR+MtfUf7e2/j3xt+zYosfi2XdNLi8NnLe2sVJfK7sJup/+gSVH/oA3274De/ZEDnvhZmGq1+P9E79cmkS91XcwqEfPU7lnXfwcP3TrN4wtF6scx0bbBnj7bx4jCnez4vHmEb6vC9fdQ/7H3q473OmhoeNJ8aYzwLfBQ5Ya6ed5Zw/Ae8EHrLWfvFsZc2eOck++vEPc9G77lDCIiJxx1pLOBQgHOolFPQRCvkIB6PP+yU2oaCPcMhHOByInB8OYkMBwiF/5GfhAOFQkHA4EPl59LyRTIjGg76PAJHHaKI14IkD/XBwn12G9jHj7WUOPp7BsRhObw7U/3FIZbwZqY3+xxgD1r7t2Nuex+N58RhTvJ8XjzGN0Hmduz0suXF13+dMDQ8bZyZEH73nOKcn+ph95gFjzMeBjwNUzyhiZ3cP39v7CO9x3cTquTfHNlIRkQtgjMHtScbtSSYpJbZlW2uxNkQ4FMD2T3ZsCBsOnfUxbEO0btpESmE+qSWT+O2O9SyzqUQ+y4Y42HKMGdOrsIQ4unsXxbNnA5bD9QeoKK+kY/duUgrzqbOnKDsVBgMnswx53YCBEzkuylPz8J04QUphAcf8p8jttmCgPcOQnzyBQHs7/mRDctiFOz2dUI8XnxuSQwYMBNyQFDa4kpMJB/wEXeCOJhbhvucGwgZcFow7MsTDRi6PfA43NvqhJPr/Ivqf/usp9D2PPA41E1DCKDIeHM7y8PE4+JyppCV+veWPOP1Zax8DHgOYXFZoF9R6+dCqe8mZq54WERk/jDEY44mumji0TTbzVlxy+vldFZe95disfs9nXP7m85lEFgoouuxycmrmsfmrf8/V0THgjd9/hBnRMeA7n/45F82Zh8l2YXvCbN69mSX9FhTIOl5P2W2r+Mm2X/Khi9/DoX+PDs84/ObwjF/1Dd14LHrs4HmGePxgaENB7vX9jodTrufwmh+ztzKZOSfcGAPrp8ClRzyUrrqFxl8/zbryIEsPRf5BWl/J6ecbpsCyIx5KV66gce0zrC8PsuTgm8fefG5Y1uCm5JYVHH36GdaXhVjSV0Ylb3m+rMFDyS03c/TpZ9lQHmTxQcDAxsmw+HDkvI2TYWmjh+LlN3P0N//DxtIgiw5Hkq5Nk2Fxg4fim2/i2LO/5dWyIAui122eDAsOm9PPFzV6mHTj9Rz77XNsLg1yyZHIsS3lcMmRyDVbK2DBUQ+Trn83x577PVtLQsyvjxzbVkH0uWFbBVx81MOkd1/L8d//L9tKgsxviNSZ18p4y/N4PO/CyzBnnOem6F3X0vSH/2VbSegc5Y3N87b13Yt3XcPxP/yJbSVBLu5/LAHPW1hveezeux3/nDmYZVYk9vq2vD7Xzm+pZ5w7oM4UqL7/XvY/9DBtO3bGJDgRERlY2a0rTw/F/egD/0ROzTxyauax8pFHTz//6AP/RNmtKylduYKyW1e+5bwrll1J1f33UbpyBV0XvxMbDlN51x3YcJi57cXM+tIXqf7SF5nbXnzWY9UnMi74vGWt02j81Voq77qDoMtgbGQhuSOFyZStWsXhNT+hbNUqjhQmY0JgQ9DrMaef1xckU7ZyFYfX/JSylauoL3jzvB53//OSKLtlFfVrfhp5LEjCBC02aOlxc/p55LyV1K95grJbVnI4P3Iegch5rkDkeX1BEuUrVtLwoyeoWHEL9QUe3IEwxh/mcL6HihW30Pj4E1SsWMHhfDcefwiXP4TXFcbjD+LyBzmc72Ly8uUc+/F/U7l8OYfzXST1BnD3BvC6gqefH8ozVN50M8d//N9MuelmDuVBcq8fT6+fbhOIPvdxKM8y9aYbOfGTnzH1phs5lGdJ9vpI8vroNv7Tz+PxvNi8lo9kby9J3l4O5YWZetONNP+077zw6WOJct7hvDDTbrqB5p/+jGk33cDhfse8/cpIpPPm3Ht3XHzOVE+LM+qij0XnOKc4+njgXAWlmgxyauZRdf99dNXWaV6LiEgc67/yzldX3vWWYx/td+yjZ7Tl/Y+tfOTRCz7vvU+tJfP+d5NTM4/kna8y665IT9Dcp38+QBIUOdb0/UeY9aVPx+158RhTvJ8XjzHF+3nxGNNInxcvnzM1Ed8B0SWPjxHp6aqw1h4543gSkR6WVCIbT24+W1kLFy60mzef9bCIiIiISKw4NhFfw8McYK09AbwU/faaAU65jEjCcvBcCYuIiIiIyHigpMU5/zf6+HljzJnbRd8bffzKKMYjIiIiIhKXlLQ4xFr7FyKJy8XAk8aYi4wx1caYR4EVwBpr7RonYxQRERERiQdKWhxkrf1HYCWQB7wIbAIuAT5qrf2Ic5GJiIiIiMQPrR7mMGvt08DTTschIiIiIhKv1NMiIiIiIiJxTUmLiIiIiIjENSUtIiIiIiIS15S0iIiIiIhIXFPSIiIiIiIicU1Ji4iIiIiIxDUlLSIiIiIiEteUtIiIiIiISFxT0iIiIiIiInFNSYuIiIiIiMQ1JS0iIiIiIhLXlLSIiIiIiEhcU9IiIiIiIiJxTUmLiIiIiIjENSUtIiIiIiIS14y11ukY5AIYYzqB/U7HkUDygRang0ggup+xo3sZW7qfsaX7GTu6l7Gl+xlbqdbauU68sMeJF5WY2m+tXeh0EInCGLNZ9zN2dD9jR/cytnQ/Y0v3M3Z0L2NL9zO2jDGbnXptDQ8TEREREZG4pqRFRERERETimpKWse8xpwNIMLqfsaX7GTu6l7Gl+xlbup+xo3sZW7qfseXY/dREfBERERERiWvqaRERERERkbimpEVERMYEY8z1xphGY4yGCMSA7mds6X7Gju6lDERJSxwxxiw3xvzFGNNmjOk0xmwwxtx5AeVdbox51hjTYozxGmO2G2PuNca4Yxl3vDHGTDfGfNUYs9EY026M8Ucbv18ZY945jPKuMsbY83w5smb5aDDG3DWI3z9zGOWOu/ppjKkcxL3s+7pnkGUmfP00xmQYYx4FfguUDOG6mLap0TLHfL0d6v2MdZsaLTNh6u0w7ueItKnRssd0/RzKvRyJ9jRabkLUzQt538Zr26l9WuKEMeYB4CvAU8BVgA+4G1hjjLncWvvXQyzvTuCHwMvAcqAZuAP4BvAuY8xya20wdr9BfDDGLAfWAl7gn4HfA93AUuBB4FZjzD9ba/9+iEUHgbpzHPcNPdoxpQeoP8fx8FAKG6/1s58DQOAsx/KIbIa2bwjlJWz9NMZMB34HhID3AU8O8rqYtqnRMsd8vR3q/RzBNhUSoN4Ot34S4zY1GsuYrp8XcC9j3Z7CGK+bF/K+jeu201qrL4e/gCsBC2wF3GcceyZ67I4hlDcD8AONQOYZx74TLe/LTv/eI3Qv74r+fu8f4Ng8Ig2bBa4cQplXAYec/t0cvqfPx7C88Vw/K6O/X+U5zvkj8DrRhVIGUWZC109gRbRepPW7f/Y818S0TY1elxD1dqj3cyTa1Oi1CVFvh1k/Y9qmRssc8/VzGHUz5u1p9JoxXzeH+76N97ZTw8Piwz9EH79jrQ2dcezh6OOXh1Del4Ak4D+stV1nHPtm9PF+Y0z60MIcMzoZ4C801tqdwMbot+8Z1Yikv/FcP33AFs7yVzpjzCzgWuARG23RhWettZ+31vYM4ZpYt6mQOPV2OPdTberZDed+joREqJ9DvZdqT89tOO/buG47lbQ4zBhTSCSzBfjTAKe8QuQNOc0Ys2AQ5bmBVWcrz1p7EDgIZAI3DCfmOPcEUDrAm61PQ/Qxd5TikX7Ge/201h6z1i601h47yymfJdKF/1+jGFZcs9YOdehhTNvUaJkJU2+Hej9Rm3pOw7ifMZco9XOo91Lt6TkN+X07FtpOJS3OW0Dk/0O3tfbImQettQEi4zUBFg2ivJlATvT52cZw9v18MOWNKdZav7W28xynFEcfdw2x6CRjzD3RyWjHjTFHjTHPG2M+Y4xJGWa4Y0mWMeYfjDFbjDEnjDENxpjnjDEfMsYMpR0Z1/XzXIwxWUTG+P7EWts+xMvHe/3sL9ZtKozjejuCbSqM73obqzYVxnH9PJsLbE9hjNfNYb5v477tVNLivGnRx6ZznNP3V4SpQygvZK1tjkF5CcMYMxFYAvQSmRA2FCXAauBfgWuADwLHgX8DXo6WncguARYDf0dkvO/HADfwY+A3xpjkQZaj+nl2dwJZROrUUI33+tlfrNvU/mWq3vZzgW0qjO96G6s2FVQ/B3Ih7SkkcN08x/s27ttOrR7mvAnRR+85zukb35k9hPLONSZ0KOUlknuBFOA+a+253pRnagD+L/DP0b80AOwG/hL9h2UV8Bjw3lgGG0f2AF+w1j7c/2fGmD8CG4AbiaxOcv8gylL9PLvPEJmcO9S/WI/3+nmmWLep/ctUvX2r4bapML7rbSzbVFD9HMhw21NI/Lp5tvdt3Led6mkZG0z0MVYTyWJdXtwzxiwhMhnsl8C3hnKttbbWWvuP/Rqv/r4afXyPMabygoKMU9baV8/4x7Xv5yHgX6LffsYYkxqjlxyP9fM6YBbD+KvgeK+fwzQSdWxc1dsLaVNhfNdbB9pUGEf180LaU0jsunmh71scbjuVtDivI/p4rlUT+hqujnOcc2Z5aTEqb8yLriDyLPC/wAdjvIrIDiLruQMsi2G5Y8XW6GMacPEgzlf9HNhnifx1b22Myx2P9TPWbWr/81RvGfE2FcZnve0z1DYVVD/PNFLtKYzhujmI923ct51KWpzXt3lR0TnO6ZswdeAc55xZntsYUxCD8sY0Y0wVkTfoemCltdYfy/Kjfxk7Gf12zI5xvQD9u5YH8/urfp7BGDMZuBn4/jlWehmWcVo/Y92m9i9z3NfbkW5TYdzW2z5DbVNB9fO0kWxPYezWzUG+b+O+7VTS4rwtRHa+zTDGlJ950BiTBEyJfrt5EOW9DvStlDHrLOf0/Xww5Y1Zxpg5wAtExgjfZq0d1g62xpibjTH5ZznmJrLjLkDbcMqPZ8aYtOjvn3GWU/o3bm2DKFL18+0+TWSjr/8YzsXjuX6eRazbVFC9BWLXpkbLGpf1dgTaVFD97O+C2lNIvLo5hPdt3LedSlocZq09AbwU/faaAU65jEjX2UFr7Xn/h0b/CrD2bOUZY6YQqXTdwO+GEfKYYIyZDzxPZF3w9/Ufm2qMuc4Y86MhFPcbIn+5Gcg83lzQYsPQI417RUR+/7MtRdg3fMEHvHa+wlQ/3yo6Zv2vgCejbcFwjOf6+TaxblOjZY77ehvjNhXGb72NaZsKqp99YtSeQgLVzaG8b8dE22mt1ZfDX8DVRCYgbQXcZxx7OnrsrjN+/hEiG/J8aYDyZgJ+oBHIPOPYt6Pl/aPTv/cI3s/FQCuRpfxcAxy/Czg0hPtpgf89y2v9Inr8N07/3iN0Lyujv99/DnDMRaTRtsB3h3A/x3X9POP3/Wj09118nvNUP+1b6qM9z3lDblMHcZ8Trt4O4X4OuU0dxP1MuHo7mPs53DZ1EPczoernYOvmGdcMqj0dxL1MiLo5nPdtvLedjt9UfZ3+H/eP0f9xvwIuAqqBR6M/+68Bzt8VPdZ5lvI+CoSIdAkuBaYTWcIvDPwBSHL6dx6h+7iYSFdkmEhX5+YBvg4O8EY96/0kMunORt+wV0cb08XAj6I/3wkUOP27j9D9LO/7hyPa8F0GVABXAP8T/flfgDTVz2Hd3y3Aq4M4b1zXT6AAmETkr9N99XFS9GvA322ober57nP0eELU26Hcz+G2qeOp3g7xfg6rTR0v9XM47/V+1w6qPR0PdfMC37f/SJy2nY7fWH295X/qLUS68dqBLmAj8JGznPsFoBN4+Bzl9TWCrUTWwd4Rvc7j9O86gvew7812vq9Dg72fQBnwt9F/SE5EG7Q2IhPavjDQPy6J9EXkryRfAdZF61Iw+vgX4K85468xqp+Dvq+XRuviHYM4d1zXT+DQYN/LZ1w36Db1fPe53zljvt4O5X4Ot00dT/V2qPVzOG3qeKmfF/BeH3R7Oh7q5oW8b6PXx2XbaaIFiYiIiIiIxCVNxBcRERERkbimpEVEREREROKakhYREREREYlrSlpERERERCSuKWkREREREZG4pqRFRERERETimpIWERERERGJa0paREREREQkrilpERERERGRuKakRURExgVjzN8aY6wx5iqnYxERkaEx1lqnYxARERlxxph1QBVQZK0NOh2PiIgMnnpaREQk4RljioAlwP8oYRERGXuUtIiIyHiwnMi/ec84HYiIiAydkhYREYkbxph/ic47+eMAx4wx5qfR4781xiQNoegVgA947jyvf3m0/L6vNcaYSmPMU8aYNmNMszHmx9GeG4wxVcaYZ40xHcaY1uj52UP6pUVE5LyUtIiISDx5EDgBXGuMufaMY98FPgC8BNxmrQ0MpkBjTDpwLfBna23XeU7fCBQD90S/LwB+AHwHWAR8G/gQ8JwxphT4Z+ArRIae/Qy4E3h8MHGJiMjgaSK+iIjEFWPMp4HvAZuttYuiP/sK8ACwBXintbZjCOWtBH4NfMpa+/1BXnMX8F/Rby+21r7W79iLwBXAVuAGa+2J6M9dwCGgHKiw1h4ZbIwiInJu6mkREZF48xiwD1hojHmPMeZuIgnLXuD6oSQsUSsAy/Dms+zun7BEbYk+HutLWACstWEiiQzA/GG8loiInIXH6QBERET6s9YGjTH/H/A08CiQR6QH4zprbctQyor2ftxMpNfm6DDCOTjAzzrOcaw9+pgzjNcSEZGzUE+LiIjEHWvtM8BuIB9oBq611jYOo6hLicxLeXqYobQOFN4gjrmH+XoiIjIAJS0iIhJ3jDGfB+ZEv03lzd6Nobol+jjcpY7PNfFTk0JFREaJkhYREYkrxpg7gW8BjcBvgAnAPwyzuBXAQWvtzthEJyIiTlDSIiIiccMYswr4TyJDr64DPgP0Ap8wxswcYlmzgJkMf2iYiIjECSUtIiISF6L7svw34CWyStje6LLB/0Zk4Zj/N8QiL3RomIiIxAklLSIi4jhjzFJgbfTbW6y1m/sdfpDIqlyrjDGXD6HYFcApIptRDjYOtzFmEtC3q32aMWaSMSat37HM6LHM6LHk6PFJQFr0WHb0mCbki4jEgDaXFBERRxlj5gEvAFnAe6y1bxvOZYz5EvAvwEZr7dJBlFkIHAOesNZ+eAixVDLwUsYfAZ4/y7GrgUre3IyyvynW2kODfX0RERmYkhYREUk4xpi/An4ArLbW/sLpeERE5MJoeJiIiCSiFYAfeM7pQERE5MJ5nA5ARERkBLwCPGut7XQ6EBERuXAaHiYiIiIiInFNw8NERERERCSuKWkREREREZG4pqRFRERERETimpIWERERERGJa0paREREREQkrilpERERERGRuKakRURERERE4pqSFhERERERiWv/P3/Qkpczdz7PAAAAAElFTkSuQmCC",
-                        "text/plain": [
-                            "<Figure size 864x720 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "for i, model in enumerate(resp):\n",
-                "    plt.plot(x,resp[model][\"pressure\"], marker[i], label=model)\n",
-                "plt.plot(x,(aTO.p(x,t,10)), label=\"analytical solution\")\n",
-                "plt.plot(x,(aNoTO.p(x,t,10)), label=\"analytical solution, no thermo-osmosis\")\n",
-                "plt.xlabel(\"$x$ / m\")\n",
-                "plt.ylabel(\"$p$ / Pa\")\n",
-                "plt.xlim([0,20])\n",
-                "plt.legend()\n",
-                "plt.title(\"pressure\");"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Diffference between analytical and the numerical solution:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 864x720 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "for i, model in enumerate(resp):\n",
-                "    plt.plot(x,(resp[model][\"temperature\"]-(aTO.T(x,t,10)+300)), marker[i], label=model)\n",
-                "plt.xlabel(\"$x$ / m\")\n",
-                "plt.xlim([0,20])\n",
-                "plt.ylabel(\"$\\Delta T$ / K\")\n",
-                "plt.legend()\n",
-                "plt.title(\"temperature\");"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 864x720 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "for i, model in enumerate(resp):\n",
-                "    plt.plot(x,resp[model][\"pressure\"]-aTO.p(x,t,200), marker[i], label=model)\n",
-                "plt.xlabel(\"$x$ / m\")\n",
-                "plt.ylabel(\"$\\Delta p$ / Pa\")\n",
-                "plt.xlim([0,20])\n",
-                "plt.legend()\n",
-                "plt.title(\"pressure\");"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The differences between the analytical solution and OGS is assumed to come from the neglectance of the advective heat-flux in the analytical solution.\n",
-                "\n",
-                "## References\n",
-                "\n",
-                "[1] Zhou, Y., Rajapakse, R. K. N. D., & Graham, J. (1998). A coupled thermoporoelastic model with thermo-osmosis and thermal-filtration, International Journal of Solids and Structures, 35(34-35), 4659-4683.\n",
-                "\n",
-                "[2] Buchwald, J., Kaiser, S., Kolditz, O., & Nagel, T. (2021). Improved predictions of thermal fluid pressurization in hydro-thermal models based on consistent incorporation of thermo-mechanical effects in anisotropic porous media. International Journal of Heat and Mass Transfer, 172, 121127."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.9.13 64-bit",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.2"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "eb550d4c",
+   "metadata": {},
+   "source": [
+    "+++\n",
+    "author = \"Jörg Buchwald\"\n",
+    "date = \"2022-05-27T12:39:58+01:00\"\n",
+    "title = \"Thermo-Osmosis in a one-dimensional column\"\n",
+    "weight = 70\n",
+    "web_subsection = \"thermo-hydro-mechanics\"\n",
+    "+++\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "157965aa",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Problem description\n",
+    "\n",
+    "The problem describes a one-dimensional column at $T$=300 K in sudden contact with a temperature reservoir at one side at $T_1$ = 350 K.\n",
+    "\n",
+    "Thermo-osmotic and filtration effects are described by contributions to the hydraulic flux $J^w$\n",
+    "\\begin{equation}\n",
+    "J^w=-\\rho_w \\frac{\\mathbf{k}}{\\mu}\\left(\\nabla p-\\rho_w \\mathbf{g} \\right)-\\rho_w \\mathbf{k}_{pT} \\nabla T,\n",
+    "\\end{equation}\n",
+    "and the conductive heat flux $I$\n",
+    "\\begin{equation}\n",
+    "I=- \\mathbf{\\lambda}_s (1-\\phi)+\\mathbf{\\lambda}_w \\phi)- \\mathbf{k}_{Tp} \\nabla p,\n",
+    "\\end{equation}\n",
+    "\n",
+    "where $\\mathbf{k}_{pT}$ is the phenomenological coefficient of thermo-osmosis and $\\mathbf{k}_{Tp}$ the phenomenological coefficient of thermo-filtration.\n",
+    "It can be shown that $\\mathbf{k}_{Tp}=T*\\mathbf{k}_{pT}$ (Zhou et al. 1998)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc9428e6",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Get benchmark results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d752c49e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/buchwalj/.local/lib/python3.10/site-packages/vtuIO.py:147: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
+      "  df[\"r_\"+str(i)] = (df[x]-val[x]) * (df[x]-val[x]) + (df[y]-val[y]) * (df[y]-val[y])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import vtuIO\n",
+    "import numpy as np\n",
+    "\n",
+    "filename = \"expected_Column_ts_68_t_7200000.000000.vtu\"\n",
+    "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../Data\")\n",
+    "file = {}\n",
+    "file[\"THM\"] = f\"{data_dir}/ThermoHydroMechanics/Linear/ThermoOsmosis/{filename}\"\n",
+    "file[\"TR\"] = f\"{data_dir}/ThermoRichardsFlow/ThermoOsmosis/{filename}\"\n",
+    "file[\"TRM\"] = f\"{data_dir}/ThermoRichardsMechanics/ThermoOsmosis/{filename}\"\n",
+    "x = np.array([i * 0.1 for i in range(200)])\n",
+    "r = np.array([[i, 0.5, 0.0] for i in x])\n",
+    "resp = {}\n",
+    "respvars = [\"temperature\", \"pressure\"]\n",
+    "for model in file:\n",
+    "    resp[model] = {}\n",
+    "    f = vtuIO.VTUIO(file[model], dim=2)\n",
+    "    for var in respvars:\n",
+    "        if \"M\" in model:\n",
+    "            resp[model][var] = f.get_set_data(f\"{var}_interpolated\", pointsetarray=r)\n",
+    "        else:\n",
+    "            resp[model][var] = f.get_set_data(f\"{var}\", pointsetarray=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd0745b1",
+   "metadata": {},
+   "source": [
+    "## Read-in the analytical solution\n",
+    "\n",
+    "An analytical solution was provided by Zhou et al. 1998 and can be obtained via [github](https://github.com/joergbuchwald/thermo-osmosis_analytical_solution).\n",
+    "For this example we used $\\mathbf{k}_{pT}=2.7e-10\\, m^2/(s K)$ and a fully saturated material. More details on model parameters can be found in the corresponding project files.\n",
+    "The Thermo-Richards (TR) model uses a correction to account for mechanical effects in the mass-balance equation. See Buchwald et al. 2021 for further details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b6c14590",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import zhou_solution_thermo_osmosis\n",
+    "\n",
+    "aTO = zhou_solution_thermo_osmosis.ANASOL(0, 50, 100)\n",
+    "aNoTO = zhou_solution_thermo_osmosis.ANASOL(0, 50, 100)\n",
+    "aNoTO.Sw = 0\n",
+    "t = 7.2e6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d326273",
+   "metadata": {},
+   "source": [
+    "## Plot temperature and pressure along the column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7b617eeb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 10)\n",
+    "plt.rcParams[\"font.size\"] = 22\n",
+    "marker = [\"|\", \"+\", \"x\"]\n",
+    "\n",
+    "for i, model in enumerate(resp):\n",
+    "    plt.plot(x, resp[model][\"temperature\"], marker[i], label=model)\n",
+    "plt.plot(x, (aTO.T(x, t, 10) + 300), label=\"analytical solution\")\n",
+    "plt.plot(x, (aNoTO.T(x, t, 10) + 300), label=\"analytical solution no thermo-osmosis\")\n",
+    "plt.xlabel(\"$x$ / m\")\n",
+    "plt.xlim([0, 20])\n",
+    "plt.ylabel(\"$T$ / K\")\n",
+    "plt.legend()\n",
+    "plt.title(\"temperature\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "42ee5994",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i, model in enumerate(resp):\n",
+    "    plt.plot(x, resp[model][\"pressure\"], marker[i], label=model)\n",
+    "plt.plot(x, (aTO.p(x, t, 10)), label=\"analytical solution\")\n",
+    "plt.plot(x, (aNoTO.p(x, t, 10)), label=\"analytical solution, no thermo-osmosis\")\n",
+    "plt.xlabel(\"$x$ / m\")\n",
+    "plt.ylabel(\"$p$ / Pa\")\n",
+    "plt.xlim([0, 20])\n",
+    "plt.legend()\n",
+    "plt.title(\"pressure\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db25861f",
+   "metadata": {},
+   "source": [
+    "## Diffference between analytical and the numerical solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "539dcf5f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i, model in enumerate(resp):\n",
+    "    plt.plot(\n",
+    "        x,\n",
+    "        (resp[model][\"temperature\"] - (aTO.T(x, t, 10) + 300)),\n",
+    "        marker[i],\n",
+    "        label=model,\n",
+    "    )\n",
+    "plt.xlabel(\"$x$ / m\")\n",
+    "plt.xlim([0, 20])\n",
+    "plt.ylabel(\"$\\Delta T$ / K\")\n",
+    "plt.legend()\n",
+    "plt.title(\"temperature\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "228b014e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i, model in enumerate(resp):\n",
+    "    plt.plot(x, resp[model][\"pressure\"] - aTO.p(x, t, 200), marker[i], label=model)\n",
+    "plt.xlabel(\"$x$ / m\")\n",
+    "plt.ylabel(\"$\\Delta p$ / Pa\")\n",
+    "plt.xlim([0, 20])\n",
+    "plt.legend()\n",
+    "plt.title(\"pressure\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4213a93",
+   "metadata": {},
+   "source": [
+    "The differences between the analytical solution and OGS is assumed to come from the neglectance of the advective heat-flux in the analytical solution.\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "[1] Zhou, Y., Rajapakse, R. K. N. D., & Graham, J. (1998). A coupled thermoporoelastic model with thermo-osmosis and thermal-filtration, International Journal of Solids and Structures, 35(34-35), 4659-4683.\n",
+    "\n",
+    "[2] Buchwald, J., Kaiser, S., Kolditz, O., & Nagel, T. (2021). Improved predictions of thermal fluid pressurization in hydro-thermal models based on consistent incorporation of thermo-mechanical effects in anisotropic porous media. International Journal of Heat and Mass Transfer, 172, 121127."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb b/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb
index 9b42ac7ae0a..ad096f9e061 100644
--- a/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb
+++ b/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb
@@ -5,27 +5,12 @@
    "id": "f4d3389f-6a41-4c88-aa25-971c9a277d60",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"(Advection-)diffusion-sorption-decay problem\"\n",
     "date = \"2022-03-09\"\n",
     "author = \"Renchao Lu, Jaime Garibay-Rodriguez, Lars Bilke, Christoph Lehmann, Haibing Shao\"\n",
     "web_subsection = \"hydro-component\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "78389cc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import ogs6py\n",
-    "import vtuIO\n",
-    "import numpy as np\n",
-    "from scipy import special\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.pyplot import cm"
+    "+++\n"
    ]
   },
   {
@@ -134,57 +119,87 @@
     "Here the concentration profiles are illustrated at $t$ = 10$^3$, 10$^4$, 10$^5$, and 10$^6$ years."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "78389cc7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import ogs6py\n",
+    "import vtuIO\n",
+    "import numpy as np\n",
+    "from scipy import special\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import cm"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "fc7b6202",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "###Model parameters###\n",
-    "#Effective diffusion coefficient [m2/s]\n",
+    "# Effective diffusion coefficient [m2/s]\n",
     "De = 1e-11\n",
-    "#Porosity [-]\n",
+    "# Porosity [-]\n",
     "phi = 0.12\n",
-    "#Pore diffusion coefficient [m2/s]\n",
+    "# Pore diffusion coefficient [m2/s]\n",
     "Dp = De / phi\n",
-    "#Porous medium bulk density [kg/m3]\n",
+    "# Porous medium bulk density [kg/m3]\n",
     "rho = 2.394e3\n",
-    "#Distribution coefficient [m3/kg]\n",
+    "# Distribution coefficient [m3/kg]\n",
     "Kd = 0.5\n",
-    "#135-Cs Half-life [year]\n",
+    "# 135-Cs Half-life [year]\n",
     "half_life = 2.3e6\n",
-    "#Decay constant [1/s]\n",
-    "alpha = np.log(2)/half_life/3.1536e7 # unit conversion from year to second\n",
+    "# Decay constant [1/s]\n",
+    "alpha = np.log(2) / half_life / 3.1536e7  # unit conversion from year to second\n",
     "\n",
     "###Spatial and temporal discretization###\n",
-    "#Distance [m]\n",
+    "# Distance [m]\n",
     "x = np.linspace(0, 2, num=201)\n",
-    "#Time [year]\n",
+    "# Time [year]\n",
     "time = np.array([1e3, 1e4, 1e5, 1e6])\n",
     "\n",
     "###Initial condition and boundary conditions###\n",
-    "#Initial condition [mol/L]\n",
+    "# Initial condition [mol/L]\n",
     "c_ini = 0\n",
-    "#Inlet concentration [mol/L]\n",
+    "# Inlet concentration [mol/L]\n",
     "c_inlet = 1\n",
     "\n",
     "###Intermediate parameters###\n",
-    "#Retardation factor [-]\n",
-    "R = 1 + rho*Kd/phi\n",
+    "# Retardation factor [-]\n",
+    "R = 1 + rho * Kd / phi\n",
     "\n",
     "###Analytical solution###\n",
     "c = np.empty((0, x.size))\n",
-    "for t in time*3.1536e7: #unit conversion from year to second\n",
-    "    c_t = c_inlet/2*(np.exp(-x*(alpha*R/Dp)**0.5)*special.erfc(x/2*(R/Dp/t)**0.5-(alpha*t)**0.5) \\\n",
-    "               + np.exp(x*(alpha*R/Dp)**0.5)*special.erfc(x/2*(R/Dp/t)**0.5+(alpha*t)**0.5))\n",
+    "for t in time * 3.1536e7:  # unit conversion from year to second\n",
+    "    c_t = (\n",
+    "        c_inlet\n",
+    "        / 2\n",
+    "        * (\n",
+    "            np.exp(-x * (alpha * R / Dp) ** 0.5)\n",
+    "            * special.erfc(x / 2 * (R / Dp / t) ** 0.5 - (alpha * t) ** 0.5)\n",
+    "            + np.exp(x * (alpha * R / Dp) ** 0.5)\n",
+    "            * special.erfc(x / 2 * (R / Dp / t) ** 0.5 + (alpha * t) ** 0.5)\n",
+    "        )\n",
+    "    )\n",
     "    c = np.vstack([c, c_t])"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "c1b55aee",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "The analytically computed $^{135}$Cs concentration profiles at $t$ = 10$^3$, 10$^4$, 10$^5$, and 10$^6$ years are plotted as shown in the figure below."
    ]
@@ -193,7 +208,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "780c4779",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -207,30 +224,37 @@
     }
    ],
    "source": [
-    "#plot analytical solution\n",
+    "# plot analytical solution\n",
     "def plot_analytical_solutions():\n",
     "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    ax.set_xlim((0,2))\n",
-    "    ax.set_ylim((0,1))\n",
-    "    \n",
-    "    plt.xlabel('Distance [m]')\n",
-    "    plt.ylabel('$^{135}$ Cs concentration [mol/L]')\n",
-    "\n",
-    "    color_map=iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
-    "    \n",
+    "\n",
+    "    ax.set_xlim((0, 2))\n",
+    "    ax.set_ylim((0, 1))\n",
+    "\n",
+    "    plt.xlabel(\"Distance [m]\")\n",
+    "    plt.ylabel(\"$^{135}$ Cs concentration [mol/L]\")\n",
+    "\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
+    "\n",
     "    for c_t, t, color in zip(c, time, color_map):\n",
-    "        ax.plot(x, c_t, linestyle='-', lw=1.5,\n",
-    "                label=str(np.format_float_scientific(t))+' years',\n",
-    "                c=color, zorder=10, clip_on=False)\n",
-    "    \n",
-    "    ax.legend(frameon=False, loc='upper right', numpoints=1, \n",
-    "              fontsize=12, ncol=1)\n",
-    "    \n",
-    "    ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
-    "    \n",
-    "plot_analytical_solutions()    "
+    "        ax.plot(\n",
+    "            x,\n",
+    "            c_t,\n",
+    "            linestyle=\"-\",\n",
+    "            lw=1.5,\n",
+    "            label=str(np.format_float_scientific(t)) + \" years\",\n",
+    "            c=color,\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "    ax.legend(frameon=False, loc=\"upper right\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "    ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "    ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "\n",
+    "\n",
+    "plot_analytical_solutions()"
    ]
   },
   {
@@ -255,7 +279,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "52b3251d",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -276,10 +302,10 @@
     }
    ],
    "source": [
-    "#Run OGS simulation\n",
+    "# Run OGS simulation\n",
     "prj_name = \"1D_DiffusionSorptionDecay\"\n",
     "prj_file = f\"{prj_name}.prj\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
@@ -287,38 +313,49 @@
     "print(f\"ogs {prj_file} > out.txt\")\n",
     "! ogs {prj_file} -o {out_dir} > {out_dir}/out.txt\n",
     "\n",
-    "#Read simulation results\n",
+    "# Read simulation results\n",
     "pvdfile = vtuIO.PVDIO(f\"{out_dir}/{prj_name}.pvd\", dim=1)\n",
     "\n",
+    "\n",
     "def plot_simulation_results():\n",
     "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    ax.set_xlim((0,2))\n",
-    "    ax.set_ylim((0,1))\n",
-    "        \n",
-    "    plt.xlabel('Distance [m]')\n",
-    "    plt.ylabel('$^{135}$ Cs concentration [mol/L]')\n",
-    "    \n",
-    "    color_map = iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
-    "    #Plot analytical solutions \n",
+    "\n",
+    "    ax.set_xlim((0, 2))\n",
+    "    ax.set_ylim((0, 1))\n",
+    "\n",
+    "    plt.xlabel(\"Distance [m]\")\n",
+    "    plt.ylabel(\"$^{135}$ Cs concentration [mol/L]\")\n",
+    "\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
+    "    # Plot analytical solutions\n",
     "    for c_t, color in zip(c, color_map):\n",
-    "        ax.plot(x, c_t, linestyle='-', lw=1.5,\n",
-    "                c=color, zorder=10, clip_on=False)\n",
-    "        \n",
-    "    #Add simulation results\n",
-    "    color_map=iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
+    "        ax.plot(x, c_t, linestyle=\"-\", lw=1.5, c=color, zorder=10, clip_on=False)\n",
+    "\n",
+    "    # Add simulation results\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
     "    for t, color in zip(time, color_map):\n",
-    "        c_t = pvdfile.read_set_data(t*3.1536e7, 'Cs', data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "        plt.plot(x, c_t, label=\"Sim. \"+str(np.format_float_scientific(t))+' years', \n",
-    "                 color=color, marker='o', markevery=5, linestyle=\"\", zorder=10, clip_on=False)\n",
-    "    \n",
-    "    ax.legend(frameon=False, loc='upper right', numpoints=1, \n",
-    "              fontsize=12, ncol=1)\n",
-    "    \n",
-    "    ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
-    "    \n",
-    "plot_simulation_results()    "
+    "        c_t = pvdfile.read_set_data(\n",
+    "            t * 3.1536e7, \"Cs\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    "        )\n",
+    "        plt.plot(\n",
+    "            x,\n",
+    "            c_t,\n",
+    "            label=\"Sim. \" + str(np.format_float_scientific(t)) + \" years\",\n",
+    "            color=color,\n",
+    "            marker=\"o\",\n",
+    "            markevery=5,\n",
+    "            linestyle=\"\",\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "    ax.legend(frameon=False, loc=\"upper right\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "    ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "    ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "\n",
+    "\n",
+    "plot_simulation_results()"
    ]
   },
   {
@@ -349,9 +386,11 @@
     "l2_norm_error = np.empty((0, 1))\n",
     "\n",
     "for c_ext, t in zip(c, time):\n",
-    "    c_sim = pvdfile.read_set_data(t*3.1536e7, 'Cs', data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "    \n",
-    "    l2_norm_error_t = np.log10(np.sum((c_sim - c_ext)**2)**0.5)\n",
+    "    c_sim = pvdfile.read_set_data(\n",
+    "        t * 3.1536e7, \"Cs\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    "    )\n",
+    "\n",
+    "    l2_norm_error_t = np.log10(np.sum((c_sim - c_ext) ** 2) ** 0.5)\n",
     "    l2_norm_error = np.vstack([l2_norm_error, l2_norm_error_t])"
    ]
   },
@@ -364,25 +403,28 @@
    "source": [
     "def plot_l2_norm_error():\n",
     "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    ax.set_xlim((0,1e6))\n",
-    "    ax.set_ylim((-4,0))\n",
-    "    \n",
-    "    plt.xlabel('Time [year]')\n",
-    "    plt.ylabel('Log $||\\mathbf{c}-\\mathbf{c^{exact}}||_{2}$')\n",
-    "    \n",
-    "    ax.plot(time, l2_norm_error, linestyle='-', lw=1.5, \n",
-    "            marker='o', zorder=10, clip_on=False)\n",
-    "    \n",
-    "    ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "    ax.yaxis.grid(color='gray', linestyle='dashed')"
+    "\n",
+    "    ax.set_xlim((0, 1e6))\n",
+    "    ax.set_ylim((-4, 0))\n",
+    "\n",
+    "    plt.xlabel(\"Time [year]\")\n",
+    "    plt.ylabel(\"Log $||\\mathbf{c}-\\mathbf{c^{exact}}||_{2}$\")\n",
+    "\n",
+    "    ax.plot(\n",
+    "        time, l2_norm_error, linestyle=\"-\", lw=1.5, marker=\"o\", zorder=10, clip_on=False\n",
+    "    )\n",
+    "\n",
+    "    ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "    ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "id": "3f8b3261",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -459,7 +501,7 @@
     "| OPA bulk density $\\rho$ | 2394 | kg/m$^3$ |\n",
     "| Distribution coefficient $k_{\\mathrm{d}}$ | 0.5 | m$^3$/kg |\n",
     "| $^{135}$Cs half life $t_{1/2}$ | 2.3e6 | year |\n",
-    "| Darcy velocity $q$ | 2e-11 | m/s | \n",
+    "| Darcy velocity $q$ | 2e-11 | m/s |\n",
     "| Time step size $\\Delta t$ | 1e3 | year |\n",
     "| Grid size $\\Delta x$ | 0.01 | m|"
    ]
@@ -507,7 +549,9 @@
   {
    "cell_type": "markdown",
    "id": "75dd1682",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "With the python script provided below, the $^{135}$Cs concentration profiles at $t$ = 10$^3$, 10$^4$, 10$^5$, and 10$^6$ years are analytically computed."
    ]
@@ -516,7 +560,9 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "c777721c",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -530,59 +576,67 @@
     }
    ],
    "source": [
-    "#Auxilary functions\n",
+    "# Auxilary functions\n",
     "def H(x, t):\n",
-    "    return 0.5*np.exp((v-u)*x/2/D)*special.erfc((R*x-u*t)/2/(D*R*t)**0.5) \\\n",
-    "    + 0.5*np.exp((v+u)*x/2/D)*special.erfc((R*x+u*t)/2/(D*R*t)**0.5)\n",
+    "    return 0.5 * np.exp((v - u) * x / 2 / D) * special.erfc(\n",
+    "        (R * x - u * t) / 2 / (D * R * t) ** 0.5\n",
+    "    ) + 0.5 * np.exp((v + u) * x / 2 / D) * special.erfc(\n",
+    "        (R * x + u * t) / 2 / (D * R * t) ** 0.5\n",
+    "    )\n",
+    "\n",
     "\n",
     "def M(x, t):\n",
-    "    return - c_ini*np.exp(-mu*t/R)*(0.5*special.erfc((R*x-v*t)/2/(D*R*t)**0.5)\\\n",
-    "    + 0.5*np.exp(v*x/D)*special.erfc((R*x+v*t)/2/(D*R*t)**0.5)) \\\n",
-    "    + c_ini*np.exp(-mu*t/R)\n",
+    "    return -c_ini * np.exp(-mu * t / R) * (\n",
+    "        0.5 * special.erfc((R * x - v * t) / 2 / (D * R * t) ** 0.5)\n",
+    "        + 0.5\n",
+    "        * np.exp(v * x / D)\n",
+    "        * special.erfc((R * x + v * t) / 2 / (D * R * t) ** 0.5)\n",
+    "    ) + c_ini * np.exp(-mu * t / R)\n",
+    "\n",
     "\n",
     "###Input parameters###\n",
-    "#Effective diffusion coefficient [m2/s]\n",
+    "# Effective diffusion coefficient [m2/s]\n",
     "De = 1e-11\n",
-    "#Porosity [-]\n",
+    "# Porosity [-]\n",
     "phi = 0.12\n",
-    "#Pore diffusion coefficient [m2/s]\n",
+    "# Pore diffusion coefficient [m2/s]\n",
     "D = De / phi\n",
-    "#Porous medium bulk density [kg/m3]\n",
+    "# Porous medium bulk density [kg/m3]\n",
     "rho = 2.394e3\n",
-    "#Distribution coefficient [m3/kg]\n",
+    "# Distribution coefficient [m3/kg]\n",
     "Kd = 0.5\n",
-    "#Retardation factor [-]\n",
-    "R = 1 + rho*Kd/phi\n",
-    "#135-Cs Half-life [year]\n",
+    "# Retardation factor [-]\n",
+    "R = 1 + rho * Kd / phi\n",
+    "# 135-Cs Half-life [year]\n",
     "half_life = 2.3e6\n",
-    "#Decay constant [1/s]\n",
-    "k = np.log(2)/half_life/3.1536e7 # unit conversion from year to second\n",
-    "#Include advective mechansim\n",
-    "#Darcy velocity [m/s]\n",
+    "# Decay constant [1/s]\n",
+    "k = np.log(2) / half_life / 3.1536e7  # unit conversion from year to second\n",
+    "# Include advective mechansim\n",
+    "# Darcy velocity [m/s]\n",
     "q = 2e-11\n",
-    "#Pore water velocity [m/s]\n",
+    "# Pore water velocity [m/s]\n",
     "v = q / phi\n",
     "\n",
     "###Spatial and temporal discretization###\n",
-    "#Distance [m]\n",
+    "# Distance [m]\n",
     "x = np.linspace(0, 2, num=201)\n",
-    "#Time [year]\n",
+    "# Time [year]\n",
     "time = np.array([1e3, 1e4, 1e5, 1e6])\n",
     "\n",
     "###Initial condition and boundary conditions###\n",
-    "#Initial condition [mol/L]\n",
+    "# Initial condition [mol/L]\n",
     "c_ini = 0\n",
-    "#Inflow concentration [mol/L]\n",
+    "# Inflow concentration [mol/L]\n",
     "c0 = 1\n",
     "\n",
     "###Intermediate parameters###\n",
     "mu = k * R\n",
-    "u = v*(1+4*mu*D/v**2)**0.5\n",
+    "u = v * (1 + 4 * mu * D / v**2) ** 0.5\n",
     "\n",
     "###Analytical solution###\n",
     "c = np.empty((0, x.size))\n",
-    "for t in time*3.1536e7: #unit conversion from year to second\n",
-    "    c_t = c0*H(x, t) + M(x, t)\n",
+    "for t in time * 3.1536e7:  # unit conversion from year to second\n",
+    "    c_t = c0 * H(x, t) + M(x, t)\n",
     "    c = np.vstack([c, c_t])\n",
     "\n",
     "plot_analytical_solutions()"
@@ -610,7 +664,9 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "7c1b574a",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -631,10 +687,10 @@
     }
    ],
    "source": [
-    "#Run OGS simulation\n",
+    "# Run OGS simulation\n",
     "prj_name = \"1D_AdvectionDiffusionSorptionDecay\"\n",
     "prj_file = f\"../AdvectionDiffusionSorptionDecay/{prj_name}.prj\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
@@ -642,11 +698,11 @@
     "print(f\"ogs {prj_file} > {prj_name}.txt\")\n",
     "! ogs {prj_file} -o {out_dir} > {prj_name}.txt\n",
     "\n",
-    "#Read simulation results\n",
+    "# Read simulation results\n",
     "pvdfile = vtuIO.PVDIO(f\"{out_dir}/{prj_name}.pvd\", dim=1)\n",
     "\n",
-    "#Plot simulation results\n",
-    "plot_simulation_results()    "
+    "# Plot simulation results\n",
+    "plot_simulation_results()"
    ]
   },
   {
diff --git a/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb b/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb
index 19f81af003a..ca2ddb03e1d 100644
--- a/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb
+++ b/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb
@@ -5,29 +5,12 @@
    "id": "e758cbb0",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Two-layer diffusion problem\"\n",
     "date = \"2022-03-09\"\n",
     "author = \"Renchao Lu, Dmitri Naumov, Lars Bilke, Christoph Lehmann, Haibing Shao\"\n",
     "web_subsection = \"hydro-component\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6a13a295",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import ogs6py\n",
-    "import vtuIO\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy import special\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.pyplot import cm\n",
-    "from IPython.display import Image"
+    "+++\n"
    ]
   },
   {
@@ -51,7 +34,7 @@
    "id": "882866e7",
    "metadata": {},
    "source": [
-    "In waste repositories, radionuclide release can be expected after rupture of waste canisters to occur in the engineered barrier system, which contains multiple layers of materials and host rocks. In this benchamrk, a tracer (HTO) diffusion process through a two-layer barrier is simulated. The barrier is comprised of a bentonite buffer layer and an opalinus clay (OPA) layer. \n",
+    "In waste repositories, radionuclide release can be expected after rupture of waste canisters to occur in the engineered barrier system, which contains multiple layers of materials and host rocks. In this benchamrk, a tracer (HTO) diffusion process through a two-layer barrier is simulated. The barrier is comprised of a bentonite buffer layer and an opalinus clay (OPA) layer.\n",
     "\n",
     "Over the one-dimensional model domain, the diffusion process of HTO can be described with the following governing equation:\n",
     "\n",
@@ -112,11 +95,33 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6a13a295",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import ogs6py\n",
+    "import vtuIO\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import special\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import cm\n",
+    "from IPython.display import Image"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "8829d272",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -130,41 +135,55 @@
     }
    ],
    "source": [
-    "#plot semi-analytical solution\n",
-    "#Time [year]\n",
+    "# plot semi-analytical solution\n",
+    "# Time [year]\n",
     "time = np.array([1e3, 1e4, 1e5, 1e6])\n",
     "\n",
     "result_file = \"./SemiAnalyticalSolutionResults.csv\"\n",
-    "soln = pd.read_csv(result_file, sep=',', header=None, skiprows=0, \n",
-    "                       names=['x','1e3','1e4','1e5','1e6'], index_col=False)\n",
-    "    \n",
+    "soln = pd.read_csv(\n",
+    "    result_file,\n",
+    "    sep=\",\",\n",
+    "    header=None,\n",
+    "    skiprows=0,\n",
+    "    names=[\"x\", \"1e3\", \"1e4\", \"1e5\", \"1e6\"],\n",
+    "    index_col=False,\n",
+    ")\n",
+    "\n",
+    "\n",
     "def plot_analytical_solutions():\n",
     "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    ax.set_xlim((0,20))\n",
-    "    ax.set_ylim((0,1))\n",
-    "    \n",
-    "    plt.xlabel('Distance [m]')\n",
-    "    plt.ylabel('HTO concentration [mol/L]')\n",
-    "    \n",
-    "    color_map=iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
-    "\n",
-    "    #represent the bentonite layer\n",
-    "    plt.axvspan(0, 0.625, facecolor='royalblue', alpha=0.2)\n",
-    "    #represent the OPA host rock\n",
-    "    plt.axvspan(0.625, 20, facecolor='orange', alpha=0.05)\n",
-    "    \n",
-    "    for col_name, t, color in zip(soln[['1e3','1e4','1e5','1e6']], time, color_map):\n",
-    "        ax.plot(soln['x'], soln[col_name], linestyle='-', lw=1.5,\n",
-    "                label=str(np.format_float_scientific(t))+' years',\n",
-    "                c=color, zorder=10, clip_on=False)\n",
-    "    \n",
-    "    ax.legend(frameon=False, loc='center right', numpoints=1, \n",
-    "              fontsize=12, ncol=1)\n",
-    "    \n",
-    "    ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
-    "    \n",
+    "\n",
+    "    ax.set_xlim((0, 20))\n",
+    "    ax.set_ylim((0, 1))\n",
+    "\n",
+    "    plt.xlabel(\"Distance [m]\")\n",
+    "    plt.ylabel(\"HTO concentration [mol/L]\")\n",
+    "\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
+    "\n",
+    "    # represent the bentonite layer\n",
+    "    plt.axvspan(0, 0.625, facecolor=\"royalblue\", alpha=0.2)\n",
+    "    # represent the OPA host rock\n",
+    "    plt.axvspan(0.625, 20, facecolor=\"orange\", alpha=0.05)\n",
+    "\n",
+    "    for col_name, t, color in zip(soln[[\"1e3\", \"1e4\", \"1e5\", \"1e6\"]], time, color_map):\n",
+    "        ax.plot(\n",
+    "            soln[\"x\"],\n",
+    "            soln[col_name],\n",
+    "            linestyle=\"-\",\n",
+    "            lw=1.5,\n",
+    "            label=str(np.format_float_scientific(t)) + \" years\",\n",
+    "            c=color,\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "    ax.legend(frameon=False, loc=\"center right\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "    ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "    ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "\n",
+    "\n",
     "plot_analytical_solutions()"
    ]
   },
@@ -190,7 +209,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "da8c6104",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -211,10 +232,10 @@
     }
    ],
    "source": [
-    "#Run OGS simulation\n",
+    "# Run OGS simulation\n",
     "prj_name = \"1D_MultiLayerDiffusion\"\n",
     "prj_file = f\"{prj_name}.prj\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
@@ -222,45 +243,64 @@
     "print(f\"ogs {prj_file} > out.txt\")\n",
     "! ogs {prj_file} -o {out_dir} > {out_dir}/out.txt\n",
     "\n",
-    "#Read simulation results\n",
+    "# Read simulation results\n",
     "pvdfile = vtuIO.PVDIO(f\"{out_dir}/{prj_name}.pvd\", dim=1)\n",
     "\n",
+    "\n",
     "def plot_simulation_results():\n",
     "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    ax.set_xlim((0,20))\n",
-    "    ax.set_ylim((0,1))\n",
-    "        \n",
-    "    plt.xlabel('Distance [m]')\n",
-    "    plt.ylabel('HTO concentration [mol/L]')\n",
-    "\n",
-    "    #represent the bentonite layer\n",
-    "    plt.axvspan(0, 0.625, facecolor='royalblue', alpha=0.2)\n",
-    "    #represent the OPA host rock\n",
-    "    plt.axvspan(0.625, 20, facecolor='orange', alpha=0.05)\n",
-    "    \n",
-    "    color_map=iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
-    "    \n",
-    "    #Plot semi-analytical solutions \n",
-    "    for col_name, t, color in zip(soln[['1e3','1e4','1e5','1e6']], time, color_map):\n",
-    "        ax.plot(soln['x'], soln[col_name], linestyle='-', lw=1.5,\n",
-    "                c=color, zorder=10, clip_on=False)\n",
-    "    \n",
-    "    #Add simulation results\n",
+    "\n",
+    "    ax.set_xlim((0, 20))\n",
+    "    ax.set_ylim((0, 1))\n",
+    "\n",
+    "    plt.xlabel(\"Distance [m]\")\n",
+    "    plt.ylabel(\"HTO concentration [mol/L]\")\n",
+    "\n",
+    "    # represent the bentonite layer\n",
+    "    plt.axvspan(0, 0.625, facecolor=\"royalblue\", alpha=0.2)\n",
+    "    # represent the OPA host rock\n",
+    "    plt.axvspan(0.625, 20, facecolor=\"orange\", alpha=0.05)\n",
+    "\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
+    "\n",
+    "    # Plot semi-analytical solutions\n",
+    "    for col_name, t, color in zip(soln[[\"1e3\", \"1e4\", \"1e5\", \"1e6\"]], time, color_map):\n",
+    "        ax.plot(\n",
+    "            soln[\"x\"],\n",
+    "            soln[col_name],\n",
+    "            linestyle=\"-\",\n",
+    "            lw=1.5,\n",
+    "            c=color,\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "    # Add simulation results\n",
     "    x = np.linspace(0, 20, num=201)\n",
-    "    color_map=iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
     "    for t, color in zip(time, color_map):\n",
-    "        c_t = pvdfile.read_set_data(t*3.1536e7, 'HTO', data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "        plt.plot(x, c_t, label=\"Sim. \"+str(np.format_float_scientific(t))+' years', \n",
-    "                 color=color, marker='o', markevery=5, linestyle=\"\", zorder=10, clip_on=False)\n",
-    "    \n",
-    "    ax.legend(frameon=False, loc='center right', numpoints=1, \n",
-    "             fontsize=12, ncol=1)\n",
-    "    \n",
-    "    ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
-    "    \n",
-    "plot_simulation_results()    "
+    "        c_t = pvdfile.read_set_data(\n",
+    "            t * 3.1536e7, \"HTO\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    "        )\n",
+    "        plt.plot(\n",
+    "            x,\n",
+    "            c_t,\n",
+    "            label=\"Sim. \" + str(np.format_float_scientific(t)) + \" years\",\n",
+    "            color=color,\n",
+    "            marker=\"o\",\n",
+    "            markevery=5,\n",
+    "            linestyle=\"\",\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "    ax.legend(frameon=False, loc=\"center right\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "    ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "    ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "\n",
+    "\n",
+    "plot_simulation_results()"
    ]
   },
   {
@@ -291,7 +331,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "c46600e3",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -310,13 +352,16 @@
    ],
    "source": [
     "from IPython.display import display, Image\n",
+    "\n",
     "display(Image(filename=f\"./sketch_molar_flux_calculation.jpg\", width=400))"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "5291ccaa",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "Additionally, we compute the molar flux profiles at $t$ = 10$^3$, 10$^4$, 10$^5$, and 10$^6$ years. The implementation of molar flux output can be viewed <a href=\"https://gitlab.opengeosys.org/ogs/ogs/-/merge_requests/4006\">at this link</a>."
    ]
@@ -325,7 +370,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "affb96c7",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -341,32 +388,45 @@
    "source": [
     "def plot_molar_flux():\n",
     "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    ax.set_xlim((0,20))\n",
-    "        \n",
-    "    plt.xlabel('Distance [m]')\n",
-    "    plt.ylabel('Mass flux [mol/m$^2$/s]')\n",
-    "\n",
-    "    #represent the bentonite layer\n",
-    "    plt.axvspan(0, 0.625, facecolor='royalblue', alpha=0.2)\n",
-    "    #represent the OPA host rock\n",
-    "    plt.axvspan(0.625, 20, facecolor='orange', alpha=0.05)\n",
-    "    \n",
-    "    #plot total mass flux\n",
+    "\n",
+    "    ax.set_xlim((0, 20))\n",
+    "\n",
+    "    plt.xlabel(\"Distance [m]\")\n",
+    "    plt.ylabel(\"Mass flux [mol/m$^2$/s]\")\n",
+    "\n",
+    "    # represent the bentonite layer\n",
+    "    plt.axvspan(0, 0.625, facecolor=\"royalblue\", alpha=0.2)\n",
+    "    # represent the OPA host rock\n",
+    "    plt.axvspan(0.625, 20, facecolor=\"orange\", alpha=0.05)\n",
+    "\n",
+    "    # plot total mass flux\n",
     "    x = np.linspace(0, 20, num=201)\n",
-    "    color_map=iter(cm.rainbow(np.linspace(0,1,len(time))))\n",
+    "    color_map = iter(cm.rainbow(np.linspace(0, 1, len(time))))\n",
     "    for t, color in zip(time, color_map):\n",
-    "        c_t = pvdfile.read_set_data(t*3.1536e7, 'molar_flux', data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "        plt.plot(x, c_t, label=\"Sim. \"+str(np.format_float_scientific(t))+' years', \n",
-    "                 color=color, linestyle='-', lw=1.5, zorder=10, clip_on=False)\n",
-    "    \n",
-    "    ax.legend(frameon=False, loc='center right', numpoints=1, \n",
-    "             fontsize=12, ncol=1)\n",
-    "    \n",
-    "    ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "    ax.yaxis.grid(color='gray', linestyle='dashed')\n",
-    "    \n",
-    "plot_molar_flux()    "
+    "        c_t = pvdfile.read_set_data(\n",
+    "            t * 3.1536e7,\n",
+    "            \"molar_flux\",\n",
+    "            data_type=\"point\",\n",
+    "            pointsetarray=[(i, 0, 0) for i in x],\n",
+    "        )\n",
+    "        plt.plot(\n",
+    "            x,\n",
+    "            c_t,\n",
+    "            label=\"Sim. \" + str(np.format_float_scientific(t)) + \" years\",\n",
+    "            color=color,\n",
+    "            linestyle=\"-\",\n",
+    "            lw=1.5,\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "    ax.legend(frameon=False, loc=\"center right\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "    ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "    ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "\n",
+    "\n",
+    "plot_molar_flux()"
    ]
   },
   {
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb
index 5b1a621f315..d0eedda04c9 100644
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb
@@ -5,34 +5,12 @@
    "id": "f4d3389f-6a41-4c88-aa25-971c9a277d60",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Decay-chain problem\"\n",
     "date = \"2022-08-05\"\n",
     "author = \"Renchao Lu, Christoph Behrens, Dmitri Naumov, Haibing Shao\"\n",
     "web_subsection = \"reactive-transport\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "78389cc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import time\n",
-    "\n",
-    "import ogs6py\n",
-    "import vtuIO\n",
-    "\n",
-    "import numpy as np\n",
-    "from scipy import special\n",
-    "\n",
-    "import h5py\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.pyplot import cm\n",
-    "from IPython.display import display, Image"
+    "+++\n"
    ]
   },
   {
@@ -59,11 +37,38 @@
     "This benchmark is meant to model the migration of radionuclides in the Curium-247 decay chain through a semi-infinite porous column. The diagram below maps the Curium-247 decay chain which contains 6 radionuclides before ending with Actinium-227."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "78389cc7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import ogs6py\n",
+    "import vtuIO\n",
+    "\n",
+    "import numpy as np\n",
+    "from scipy import special\n",
+    "\n",
+    "import h5py\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import cm\n",
+    "from IPython.display import display, Image"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "63c3a7e3",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -153,7 +158,9 @@
   {
    "cell_type": "markdown",
    "id": "9aa217c2",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "**Analytical solution**\n",
     "\n",
@@ -170,7 +177,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "fcb499da",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -188,118 +197,216 @@
     "    value = 1\n",
     "    for l in range(j, i):\n",
     "        value *= k[l] / (k[l] - k[i]) * c_inlet[j]\n",
-    "    \n",
+    "\n",
     "    return value\n",
     "\n",
+    "\n",
     "def computeInitialAuxiliaryVariable(c_inlet, k):\n",
     "    a_inlet = np.empty((0))\n",
-    "    \n",
+    "\n",
     "    for i in range(len(c_inlet)):\n",
     "        value = c_inlet[i]\n",
-    "        if (i > 0):\n",
+    "        if i > 0:\n",
     "            for j in range(0, i):\n",
     "                value += computeProduct(j, i, k, c_inlet)\n",
     "        a_inlet = np.append(a_inlet, value)\n",
-    "    \n",
+    "\n",
     "    return a_inlet\n",
     "\n",
+    "\n",
     "def computeAnalyticalSolution(x, t, c_0, k, v, D):\n",
-    "    t *= 3.1536e7 #unit conversion from year to second\n",
-    "    \n",
-    "    beta = (v**2/4/D**2 + k/D)**0.5\n",
-    "    c_t = c_0/2 * np.exp(v*x/2/D) * (np.exp(-beta*x) \\\n",
-    "        * special.erfc((x-(v**2+4*k*D)**0.5*t)/2/(D*t)**0.5) \\\n",
-    "        + np.exp(beta*x) * special.erfc((x+(v**2+4*k*D)**0.5*t)/2/(D*t)**0.5))\n",
-    "    \n",
+    "    t *= 3.1536e7  # unit conversion from year to second\n",
+    "\n",
+    "    beta = (v**2 / 4 / D**2 + k / D) ** 0.5\n",
+    "    c_t = (\n",
+    "        c_0\n",
+    "        / 2\n",
+    "        * np.exp(v * x / 2 / D)\n",
+    "        * (\n",
+    "            np.exp(-beta * x)\n",
+    "            * special.erfc((x - (v**2 + 4 * k * D) ** 0.5 * t) / 2 / (D * t) ** 0.5)\n",
+    "            + np.exp(beta * x)\n",
+    "            * special.erfc((x + (v**2 + 4 * k * D) ** 0.5 * t) / 2 / (D * t) ** 0.5)\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "    return c_t\n",
     "\n",
+    "\n",
     "###Model parameters###\n",
-    "#Diffusion coefficient [m2/s]\n",
+    "# Diffusion coefficient [m2/s]\n",
     "D = 1e-11\n",
-    "#Pore water velocity [m/s]\n",
+    "# Pore water velocity [m/s]\n",
     "v = 0\n",
-    "#Half life [year]\n",
-    "radionuclides = np.array([\"[Cm-247]\", \"[Am-243]\", \"[Pu-239]\", \"[U-235]\", \"[Pa-231]\", \"[Ac-227]\"])\n",
+    "# Half life [year]\n",
+    "radionuclides = np.array(\n",
+    "    [\"[Cm-247]\", \"[Am-243]\", \"[Pu-239]\", \"[U-235]\", \"[Pa-231]\", \"[Ac-227]\"]\n",
+    ")\n",
     "half_lifes = np.array([1.56e7, 7.37e3, 2.41e4, 7.04e8, 3.28e4, 21.773])\n",
-    "#First-order decay constant [1/s]\n",
-    "k = dict([(radionuclide, np.log(2)/half_life/3.1536e7) for radionuclide, half_life in zip(radionuclides, half_lifes)])\n",
+    "# First-order decay constant [1/s]\n",
+    "k = dict(\n",
+    "    [\n",
+    "        (radionuclide, np.log(2) / half_life / 3.1536e7)\n",
+    "        for radionuclide, half_life in zip(radionuclides, half_lifes)\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "###Initial and boundary conditions###\n",
     "c_inlet = np.ones(6)\n",
-    "a_inlet = dict([(radionuclide, a) for radionuclide, a in zip(radionuclides, computeInitialAuxiliaryVariable(c_inlet, list(k.values())))])\n",
+    "a_inlet = dict(\n",
+    "    [\n",
+    "        (radionuclide, a)\n",
+    "        for radionuclide, a in zip(\n",
+    "            radionuclides, computeInitialAuxiliaryVariable(c_inlet, list(k.values()))\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "###Spatial and temporal discretization###\n",
-    "#Distance [m]\n",
+    "# Distance [m]\n",
     "x = np.linspace(0, 20, num=2001)\n",
-    "#Time [year]\n",
+    "# Time [year]\n",
     "t = 1e5\n",
     "\n",
     "###Analytical solution###\n",
     "c = {}\n",
     "a = {}\n",
     "\n",
-    "c[\"[Cm-247]\"] = computeAnalyticalSolution(x, t, a_inlet[\"[Cm-247]\"], k[\"[Cm-247]\"], v, D)\n",
-    "\n",
-    "a[\"[Am-243]\"] = computeAnalyticalSolution(x, t, a_inlet[\"[Am-243]\"], k[\"[Am-243]\"], v, D)\n",
-    "c[\"[Am-243]\"] = a[\"[Am-243]\"] - k[\"[Cm-247]\"] / (k[\"[Cm-247]\"] - k[\"[Am-243]\"]) * c[\"[Cm-247]\"]\n",
-    "\n",
-    "a[\"[Pu-239]\"] = computeAnalyticalSolution(x, t, a_inlet[\"[Pu-239]\"], k[\"[Pu-239]\"], v, D)\n",
-    "c[\"[Pu-239]\"] = a[\"[Pu-239]\"] - k[\"[Cm-247]\"] / (k[\"[Cm-247]\"] - k[\"[Pu-239]\"]) * \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Pu-239]\"]) * c[\"[Cm-247]\"] - \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Pu-239]\"]) * c[\"[Am-243]\"]\n",
+    "c[\"[Cm-247]\"] = computeAnalyticalSolution(\n",
+    "    x, t, a_inlet[\"[Cm-247]\"], k[\"[Cm-247]\"], v, D\n",
+    ")\n",
+    "\n",
+    "a[\"[Am-243]\"] = computeAnalyticalSolution(\n",
+    "    x, t, a_inlet[\"[Am-243]\"], k[\"[Am-243]\"], v, D\n",
+    ")\n",
+    "c[\"[Am-243]\"] = (\n",
+    "    a[\"[Am-243]\"] - k[\"[Cm-247]\"] / (k[\"[Cm-247]\"] - k[\"[Am-243]\"]) * c[\"[Cm-247]\"]\n",
+    ")\n",
+    "\n",
+    "a[\"[Pu-239]\"] = computeAnalyticalSolution(\n",
+    "    x, t, a_inlet[\"[Pu-239]\"], k[\"[Pu-239]\"], v, D\n",
+    ")\n",
+    "c[\"[Pu-239]\"] = (\n",
+    "    a[\"[Pu-239]\"]\n",
+    "    - k[\"[Cm-247]\"]\n",
+    "    / (k[\"[Cm-247]\"] - k[\"[Pu-239]\"])\n",
+    "    * k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[Pu-239]\"])\n",
+    "    * c[\"[Cm-247]\"]\n",
+    "    - k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Pu-239]\"]) * c[\"[Am-243]\"]\n",
+    ")\n",
     "\n",
     "a[\"[U-235]\"] = computeAnalyticalSolution(x, t, a_inlet[\"[U-235]\"], k[\"[U-235]\"], v, D)\n",
-    "c[\"[U-235]\"] = a[\"[U-235]\"] - k[\"[Cm-247]\"] / (k[\"[Cm-247]\"] - k[\"[U-235]\"]) * \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[U-235]\"]) * \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[U-235]\"]) * c[\"[Cm-247]\"] - \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[U-235]\"]) * \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[U-235]\"]) * c[\"[Am-243]\"] - \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"]- k[\"[U-235]\"]) * c[\"[Pu-239]\"]\n",
-    "\n",
-    "a[\"[Pa-231]\"] = computeAnalyticalSolution(x, t, a_inlet[\"[Pa-231]\"], k[\"[Pa-231]\"], v, D)\n",
-    "c[\"[Pa-231]\"] = a[\"[Pa-231]\"] - k[\"[Cm-247]\"] / (k[\"[Cm-247]\"] - k[\"[Pa-231]\"]) * \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Pa-231]\"]) * \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[Pa-231]\"]) * \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Pa-231]\"]) * c[\"[Cm-247]\"] - \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Pa-231]\"]) * \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[Pa-231]\"]) * \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Pa-231]\"]) * c[\"[Am-243]\"] - \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[Pa-231]\"]) * \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Pa-231]\"]) * c[\"[Pu-239]\"] - \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Pa-231]\"]) * c[\"[U-235]\"]\n",
-    "\n",
-    "a[\"[Ac-227]\"] = computeAnalyticalSolution(x, t, a_inlet[\"[Ac-227]\"], k[\"[Ac-227]\"], v, D)\n",
-    "c[\"[Ac-227]\"] = a[\"[Ac-227]\"] - k[\"[Cm-247]\"] / (k[\"[Cm-247]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Pa-231]\"] / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"]) * c[\"[Cm-247]\"] - \\\n",
-    "        k[\"[Am-243]\"] / (k[\"[Am-243]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Pa-231]\"] / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"]) * c[\"[Am-243]\"] - \\\n",
-    "        k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Pa-231]\"] / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"]) * c[\"[Pu-239]\"] - \\\n",
-    "        k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Ac-227]\"]) * \\\n",
-    "        k[\"[Pa-231]\"] / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"]) * c[\"[U-235]\"] - \\\n",
-    "        k[\"[Pa-231]\"] / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"]) * c[\"[Pa-231]\"]    \n",
+    "c[\"[U-235]\"] = (\n",
+    "    a[\"[U-235]\"]\n",
+    "    - k[\"[Cm-247]\"]\n",
+    "    / (k[\"[Cm-247]\"] - k[\"[U-235]\"])\n",
+    "    * k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[U-235]\"])\n",
+    "    * k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[U-235]\"])\n",
+    "    * c[\"[Cm-247]\"]\n",
+    "    - k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[U-235]\"])\n",
+    "    * k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[U-235]\"])\n",
+    "    * c[\"[Am-243]\"]\n",
+    "    - k[\"[Pu-239]\"] / (k[\"[Pu-239]\"] - k[\"[U-235]\"]) * c[\"[Pu-239]\"]\n",
+    ")\n",
+    "\n",
+    "a[\"[Pa-231]\"] = computeAnalyticalSolution(\n",
+    "    x, t, a_inlet[\"[Pa-231]\"], k[\"[Pa-231]\"], v, D\n",
+    ")\n",
+    "c[\"[Pa-231]\"] = (\n",
+    "    a[\"[Pa-231]\"]\n",
+    "    - k[\"[Cm-247]\"]\n",
+    "    / (k[\"[Cm-247]\"] - k[\"[Pa-231]\"])\n",
+    "    * k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[Pa-231]\"])\n",
+    "    * k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[Pa-231]\"])\n",
+    "    * k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Pa-231]\"])\n",
+    "    * c[\"[Cm-247]\"]\n",
+    "    - k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[Pa-231]\"])\n",
+    "    * k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[Pa-231]\"])\n",
+    "    * k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Pa-231]\"])\n",
+    "    * c[\"[Am-243]\"]\n",
+    "    - k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[Pa-231]\"])\n",
+    "    * k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Pa-231]\"])\n",
+    "    * c[\"[Pu-239]\"]\n",
+    "    - k[\"[U-235]\"] / (k[\"[U-235]\"] - k[\"[Pa-231]\"]) * c[\"[U-235]\"]\n",
+    ")\n",
+    "\n",
+    "a[\"[Ac-227]\"] = computeAnalyticalSolution(\n",
+    "    x, t, a_inlet[\"[Ac-227]\"], k[\"[Ac-227]\"], v, D\n",
+    ")\n",
+    "c[\"[Ac-227]\"] = (\n",
+    "    a[\"[Ac-227]\"]\n",
+    "    - k[\"[Cm-247]\"]\n",
+    "    / (k[\"[Cm-247]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Pa-231]\"]\n",
+    "    / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"])\n",
+    "    * c[\"[Cm-247]\"]\n",
+    "    - k[\"[Am-243]\"]\n",
+    "    / (k[\"[Am-243]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Pa-231]\"]\n",
+    "    / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"])\n",
+    "    * c[\"[Am-243]\"]\n",
+    "    - k[\"[Pu-239]\"]\n",
+    "    / (k[\"[Pu-239]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Pa-231]\"]\n",
+    "    / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"])\n",
+    "    * c[\"[Pu-239]\"]\n",
+    "    - k[\"[U-235]\"]\n",
+    "    / (k[\"[U-235]\"] - k[\"[Ac-227]\"])\n",
+    "    * k[\"[Pa-231]\"]\n",
+    "    / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"])\n",
+    "    * c[\"[U-235]\"]\n",
+    "    - k[\"[Pa-231]\"] / (k[\"[Pa-231]\"] - k[\"[Ac-227]\"]) * c[\"[Pa-231]\"]\n",
+    ")\n",
     "\n",
     "###Plot figure###\n",
     "fig, ax = plt.subplots()\n",
-    "    \n",
-    "ax.set(xlim=(0,20), ylim=(0,1.4))\n",
+    "\n",
+    "ax.set(xlim=(0, 20), ylim=(0, 1.4))\n",
     "ax.set(xlabel=\"Distance [m]\", ylabel=\"Concentration [mol/L]\")\n",
-    "    \n",
-    "color_map=cm.rainbow(np.linspace(0,1,radionuclides.size))\n",
     "\n",
-    "for radionuclide,color in zip(radionuclides, color_map):\n",
-    "    ax.plot(x, c[radionuclide], linestyle='-', lw=1.5, label=radionuclide, c=color, zorder=10, clip_on=False)\n",
+    "color_map = cm.rainbow(np.linspace(0, 1, radionuclides.size))\n",
+    "\n",
+    "for radionuclide, color in zip(radionuclides, color_map):\n",
+    "    ax.plot(\n",
+    "        x,\n",
+    "        c[radionuclide],\n",
+    "        linestyle=\"-\",\n",
+    "        lw=1.5,\n",
+    "        label=radionuclide,\n",
+    "        c=color,\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "ax.legend(frameon=False, loc=\"upper right\", numpoints=1, fontsize=12, ncol=1)\n",
     "\n",
-    "ax.legend(frameon=False, loc='upper right', numpoints=1, fontsize=12, ncol=1)\n",
-    "    \n",
-    "ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax.yaxis.grid(color='gray', linestyle='dashed')"
+    "ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")"
    ]
   },
   {
@@ -322,7 +429,9 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "52b3251d",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -350,7 +459,7 @@
     "prj_name = \"1d_decay_chain\"\n",
     "prj_file_GIA = f\"./GlobalImplicitApproach/{prj_name}_GIA.prj\"\n",
     "prj_file_OS = f\"./{prj_name}_OS.prj\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
@@ -358,89 +467,167 @@
     "print(f\"ogs {prj_file_GIA} > out.txt\")\n",
     "start_time = time.time()\n",
     "! ogs {prj_file_GIA} > {out_dir}/out.txt\n",
-    "end_time =time.time()\n",
+    "end_time = time.time()\n",
     "runtime_GIA = round(end_time - start_time, 2)\n",
     "print(\"Execution time for the GIA model is \", runtime_GIA, \"s\")\n",
     "\n",
     "###Read simulation results###\n",
     "pvdfile_GIA = vtuIO.PVDIO(f\"./GlobalImplicitApproach/{prj_name}_GIA.pvd\", dim=1)\n",
-    "#Given the fact that the runtime of the OS model (about 1800s) is \n",
-    "#far longer than the time constraint specified (600s), we decide not\n",
-    "#to use the OS simulation results obtained from automated testing.\n",
-    "#Instead, the pre-prepared reference simulation results are used.\n",
+    "# Given the fact that the runtime of the OS model (about 1800s) is\n",
+    "# far longer than the time constraint specified (600s), we decide not\n",
+    "# to use the OS simulation results obtained from automated testing.\n",
+    "# Instead, the pre-prepared reference simulation results are used.\n",
     "pvdfile_OS = vtuIO.PVDIO(f\"./{prj_name}_OS.pvd\", dim=1)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(2,1, figsize=(6, 9))\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 9))\n",
     "\n",
-    "color_map=cm.rainbow(np.linspace(0,1,radionuclides.size))\n",
+    "color_map = cm.rainbow(np.linspace(0, 1, radionuclides.size))\n",
     "\n",
     "###Plot subfigure 1###\n",
-    "ax1.set(xlim=(0,20), ylim=(0,1.4))\n",
+    "ax1.set(xlim=(0, 20), ylim=(0, 1.4))\n",
     "ax1.set(xlabel=\"Distance [m]\", ylabel=\"Concentration [mol/L]\")\n",
     "\n",
-    "#Analytical solution     \n",
-    "for radionuclide,color in zip(radionuclides, color_map):\n",
-    "    ax1.plot(x, c[radionuclide], linestyle='-', lw=1.5, label=\"Exact - \"+radionuclide, \\\n",
-    "             c=color, zorder=10, clip_on=False)      \n",
-    "    \n",
-    "#GIA numerical solution    \n",
-    "for radionuclide,color in zip(radionuclides, color_map):\n",
-    "    c_gia = pvdfile_GIA.read_set_data(t*3.1536e7, radionuclide, data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "    ax1.plot(x, c_gia, label=\"GIA - \" + radionuclide, color=color, marker='o', \\\n",
-    "             markevery=50, linestyle=\"\", zorder=10, clip_on=False)\n",
-    "    \n",
-    "#OS numerical solution    \n",
-    "for radionuclide,color in zip(radionuclides, color_map):\n",
-    "    c_os = pvdfile_OS.read_set_data(t*3.1536e7, radionuclide, data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "    ax1.plot(x, c_os, label=\"OS - \" + radionuclide, color=color, marker='^', \\\n",
-    "             markevery=50, linestyle=\"\", zorder=10, clip_on=False)\n",
-    "\n",
-    "#numerical solution by reference code\n",
-    "#added once the bc value is double-checked\n",
+    "# Analytical solution\n",
+    "for radionuclide, color in zip(radionuclides, color_map):\n",
+    "    ax1.plot(\n",
+    "        x,\n",
+    "        c[radionuclide],\n",
+    "        linestyle=\"-\",\n",
+    "        lw=1.5,\n",
+    "        label=\"Exact - \" + radionuclide,\n",
+    "        c=color,\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "# GIA numerical solution\n",
+    "for radionuclide, color in zip(radionuclides, color_map):\n",
+    "    c_gia = pvdfile_GIA.read_set_data(\n",
+    "        t * 3.1536e7,\n",
+    "        radionuclide,\n",
+    "        data_type=\"point\",\n",
+    "        pointsetarray=[(i, 0, 0) for i in x],\n",
+    "    )\n",
+    "    ax1.plot(\n",
+    "        x,\n",
+    "        c_gia,\n",
+    "        label=\"GIA - \" + radionuclide,\n",
+    "        color=color,\n",
+    "        marker=\"o\",\n",
+    "        markevery=50,\n",
+    "        linestyle=\"\",\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "# OS numerical solution\n",
+    "for radionuclide, color in zip(radionuclides, color_map):\n",
+    "    c_os = pvdfile_OS.read_set_data(\n",
+    "        t * 3.1536e7,\n",
+    "        radionuclide,\n",
+    "        data_type=\"point\",\n",
+    "        pointsetarray=[(i, 0, 0) for i in x],\n",
+    "    )\n",
+    "    ax1.plot(\n",
+    "        x,\n",
+    "        c_os,\n",
+    "        label=\"OS - \" + radionuclide,\n",
+    "        color=color,\n",
+    "        marker=\"^\",\n",
+    "        markevery=50,\n",
+    "        linestyle=\"\",\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "# numerical solution by reference code\n",
+    "# added once the bc value is double-checked\n",
     "porosity = 0.12\n",
-    "with h5py.File(f\"./solution_reference_code.hdf5\",\"r\") as f:\n",
+    "with h5py.File(f\"./solution_reference_code.hdf5\", \"r\") as f:\n",
     "    species_ = f[\"species\"][:]\n",
     "    x_ = f[\"x\"][:]\n",
     "    for s_, radionuclide, color in zip(species_, radionuclides, color_map):\n",
     "        data_ = f[s_][:]\n",
-    "        ax1.plot(x_, data_[:,1]/porosity, label=\"Reference code - \" + radionuclide, \\\n",
-    "                 color=color, marker='D', markevery=5, linestyle=\"\", zorder=10, clip_on=False)\n",
-    "        \n",
-    "ax1.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=4)\n",
-    "    \n",
-    "ax1.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax1.yaxis.grid(color='gray', linestyle='dashed')\n",
+    "        ax1.plot(\n",
+    "            x_,\n",
+    "            data_[:, 1] / porosity,\n",
+    "            label=\"Reference code - \" + radionuclide,\n",
+    "            color=color,\n",
+    "            marker=\"D\",\n",
+    "            markevery=5,\n",
+    "            linestyle=\"\",\n",
+    "            zorder=10,\n",
+    "            clip_on=False,\n",
+    "        )\n",
+    "\n",
+    "ax1.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=4)\n",
+    "\n",
+    "ax1.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax1.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
     "\n",
     "###Plot subfigure 2###\n",
-    "ax2.set(xlim=(0,0.6), ylim=(0,1.2))\n",
+    "ax2.set(xlim=(0, 0.6), ylim=(0, 1.2))\n",
     "ax2.set(xlabel=\"Distance [m]\", ylabel=\"[Ac-227] Concentration [mol/L]\")\n",
     "\n",
-    "#Analytical solution\n",
-    "ax2.plot(x[np.where(x<0.6)], c[\"[Ac-227]\"][np.where(x<0.6)], linestyle='-', lw=1.5, label=\"Exact solution\", \\\n",
-    "         c=color_map[-1], zorder=10, clip_on=False)      \n",
-    "\n",
-    "#GIA numerical solution\n",
-    "Ac_227_gia = pvdfile_GIA.read_set_data(t*3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "ax2.plot(x[np.where(x<0.6)], Ac_227_gia[np.where(x<0.6)], label=\"GIA solution\", color=color_map[2], \\\n",
-    "         linestyle=\"--\", zorder=10, clip_on=False)\n",
-    "\n",
-    "#OS numerical solution\n",
-    "Ac_227_os = pvdfile_OS.read_set_data(t*3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "ax2.plot(x[np.where(x<0.6)], Ac_227_os[np.where(x<0.6)], label=\"OS solution\", color=color_map[-2], \\\n",
-    "         linestyle=\"-.\", zorder=10, clip_on=False)\n",
-    "\n",
-    "#numerical solution by reference code\n",
-    "with h5py.File(f\"./solution_reference_code.hdf5\",\"r\") as f:\n",
+    "# Analytical solution\n",
+    "ax2.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    c[\"[Ac-227]\"][np.where(x < 0.6)],\n",
+    "    linestyle=\"-\",\n",
+    "    lw=1.5,\n",
+    "    label=\"Exact solution\",\n",
+    "    c=color_map[-1],\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "# GIA numerical solution\n",
+    "Ac_227_gia = pvdfile_GIA.read_set_data(\n",
+    "    t * 3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    ")\n",
+    "ax2.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    Ac_227_gia[np.where(x < 0.6)],\n",
+    "    label=\"GIA solution\",\n",
+    "    color=color_map[2],\n",
+    "    linestyle=\"--\",\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "# OS numerical solution\n",
+    "Ac_227_os = pvdfile_OS.read_set_data(\n",
+    "    t * 3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    ")\n",
+    "ax2.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    Ac_227_os[np.where(x < 0.6)],\n",
+    "    label=\"OS solution\",\n",
+    "    color=color_map[-2],\n",
+    "    linestyle=\"-.\",\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "# numerical solution by reference code\n",
+    "with h5py.File(f\"./solution_reference_code.hdf5\", \"r\") as f:\n",
     "    Ac_227_ = f[\"species\"][-1]\n",
     "    x_ = f[\"x\"][:]\n",
     "    Ac_227_ = f[Ac_227_][:]\n",
-    "    ax2.plot(f[\"x\"][np.where(x_<0.7)], Ac_227_[np.where(x_<0.7),1][0]/porosity, label=\"Reference code\", \\\n",
-    "                 color=color_map[0], linestyle=\"--\", zorder=10, clip_on=False)\n",
-    "        \n",
-    "ax2.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
-    "    \n",
-    "ax2.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax2.yaxis.grid(color='gray', linestyle='dashed')"
+    "    ax2.plot(\n",
+    "        f[\"x\"][np.where(x_ < 0.7)],\n",
+    "        Ac_227_[np.where(x_ < 0.7), 1][0] / porosity,\n",
+    "        label=\"Reference code\",\n",
+    "        color=color_map[0],\n",
+    "        linestyle=\"--\",\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "ax2.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "ax2.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax2.yaxis.grid(color=\"gray\", linestyle=\"dashed\")"
    ]
   },
   {
@@ -465,7 +652,9 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "559103cd",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -479,58 +668,104 @@
     }
    ],
    "source": [
-    "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(6, 13.5))\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(6, 13.5))\n",
     "\n",
-    "color_map=cm.rainbow(np.linspace(0,1,radionuclides.size))\n",
+    "color_map = cm.rainbow(np.linspace(0, 1, radionuclides.size))\n",
     "\n",
     "###Plot subfigure 1###\n",
-    "ax1.set(xlim=(0,20), ylim=(0, 0.0005))\n",
+    "ax1.set(xlim=(0, 20), ylim=(0, 0.0005))\n",
     "ax1.set(xlabel=\"Distance [m]\", ylabel=\"Concentration [mol/L]\")\n",
-    "    \n",
-    "#GIA numerical solution    \n",
-    "for radionuclide,color in zip(radionuclides[:-1], color_map):\n",
-    "    c_gia = pvdfile_GIA.read_set_data(t*3.1536e7, radionuclide, data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "    ax1.plot(x, abs(c_gia - c[radionuclide]), label=\"GIA - \" + radionuclide, color=color, linestyle='-', \\\n",
-    "             lw=1.5, zorder=10, clip_on=False)\n",
-    "    \n",
-    "ax1.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
-    "    \n",
-    "ax1.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax1.yaxis.grid(color='gray', linestyle='dashed')\n",
+    "\n",
+    "# GIA numerical solution\n",
+    "for radionuclide, color in zip(radionuclides[:-1], color_map):\n",
+    "    c_gia = pvdfile_GIA.read_set_data(\n",
+    "        t * 3.1536e7,\n",
+    "        radionuclide,\n",
+    "        data_type=\"point\",\n",
+    "        pointsetarray=[(i, 0, 0) for i in x],\n",
+    "    )\n",
+    "    ax1.plot(\n",
+    "        x,\n",
+    "        abs(c_gia - c[radionuclide]),\n",
+    "        label=\"GIA - \" + radionuclide,\n",
+    "        color=color,\n",
+    "        linestyle=\"-\",\n",
+    "        lw=1.5,\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "ax1.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "ax1.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax1.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
     "\n",
     "###Plot subfigure 2###\n",
-    "ax2.set(xlim=(0,20), ylim=(0, 0.01))\n",
+    "ax2.set(xlim=(0, 20), ylim=(0, 0.01))\n",
     "ax2.set(xlabel=\"Distance [m]\", ylabel=\"Concentration [mol/L]\")\n",
     "\n",
-    "#OS numerical solution    \n",
-    "for radionuclide,color in zip(radionuclides[:-1], color_map):\n",
-    "    c_os = pvdfile_OS.read_set_data(t*3.1536e7, radionuclide, data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "    ax2.plot(x, abs(c_os - c[radionuclide]), label=\"OS - \" + radionuclide, color=color, linestyle='-.', \\\n",
-    "             lw=1.5, zorder=10, clip_on=False)\n",
-    "    \n",
-    "ax2.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
-    "    \n",
-    "ax2.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax2.yaxis.grid(color='gray', linestyle='dashed')\n",
+    "# OS numerical solution\n",
+    "for radionuclide, color in zip(radionuclides[:-1], color_map):\n",
+    "    c_os = pvdfile_OS.read_set_data(\n",
+    "        t * 3.1536e7,\n",
+    "        radionuclide,\n",
+    "        data_type=\"point\",\n",
+    "        pointsetarray=[(i, 0, 0) for i in x],\n",
+    "    )\n",
+    "    ax2.plot(\n",
+    "        x,\n",
+    "        abs(c_os - c[radionuclide]),\n",
+    "        label=\"OS - \" + radionuclide,\n",
+    "        color=color,\n",
+    "        linestyle=\"-.\",\n",
+    "        lw=1.5,\n",
+    "        zorder=10,\n",
+    "        clip_on=False,\n",
+    "    )\n",
+    "\n",
+    "ax2.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "ax2.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax2.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
     "\n",
     "###Plot subfigure 3###\n",
-    "ax3.set(xlim=(0,0.6), ylim=(0,1.0))\n",
+    "ax3.set(xlim=(0, 0.6), ylim=(0, 1.0))\n",
     "ax3.set(xlabel=\"Distance [m]\", ylabel=\"[Ac-227] Concentration [mol/L]\")\n",
     "\n",
-    "#GIA numerical solution\n",
-    "Ac_227_gia = pvdfile_GIA.read_set_data(t*3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "ax3.plot(x[np.where(x<0.6)], abs(Ac_227_gia[np.where(x<0.6)] - c[\"[Ac-227]\"][np.where(x<0.6)]), \\\n",
-    "         label=\"GIA - [Ac-227]\", color=color_map[-1], linestyle='-', lw=1.5, zorder=10, clip_on=False)\n",
-    "\n",
-    "#OS numerical solution\n",
-    "Ac_227_os = pvdfile_OS.read_set_data(t*3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "ax3.plot(x[np.where(x<0.6)], abs(Ac_227_os[np.where(x<0.6)] - c[\"[Ac-227]\"][np.where(x<0.6)]), \\\n",
-    "         label=\"OS - [Ac-227]\", color=color_map[-1], linestyle='-.', lw=1.5, zorder=10, clip_on=False)\n",
-    "\n",
-    "ax3.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
-    "    \n",
-    "ax3.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax3.yaxis.grid(color='gray', linestyle='dashed')\n"
+    "# GIA numerical solution\n",
+    "Ac_227_gia = pvdfile_GIA.read_set_data(\n",
+    "    t * 3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    ")\n",
+    "ax3.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    abs(Ac_227_gia[np.where(x < 0.6)] - c[\"[Ac-227]\"][np.where(x < 0.6)]),\n",
+    "    label=\"GIA - [Ac-227]\",\n",
+    "    color=color_map[-1],\n",
+    "    linestyle=\"-\",\n",
+    "    lw=1.5,\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "# OS numerical solution\n",
+    "Ac_227_os = pvdfile_OS.read_set_data(\n",
+    "    t * 3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    ")\n",
+    "ax3.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    abs(Ac_227_os[np.where(x < 0.6)] - c[\"[Ac-227]\"][np.where(x < 0.6)]),\n",
+    "    label=\"OS - [Ac-227]\",\n",
+    "    color=color_map[-1],\n",
+    "    linestyle=\"-.\",\n",
+    "    lw=1.5,\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "ax3.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "ax3.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax3.yaxis.grid(color=\"gray\", linestyle=\"dashed\")"
    ]
   },
   {
@@ -547,7 +782,9 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "26ec818f",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -568,29 +805,54 @@
     "fig, ax = plt.subplots()\n",
     "\n",
     "ax.set(xlim=(0, 0.6), ylim=(0, 1.2))\n",
-    "ax.set(xlabel='Distance [m]', ylabel='[Ac-227] Concentration [mol/L]')\n",
-    "\n",
-    "color_map=cm.rainbow(np.linspace(0,1,radionuclides.size))\n",
-    "\n",
-    "#Analytical solution\n",
-    "ax.plot(x[np.where(x<0.6)], c[\"[Ac-227]\"][np.where(x<0.6)], linestyle='-', lw=1.5, \\\n",
-    "        label=\"Exact solution\", color=color_map[-1], zorder=10, clip_on=False)      \n",
-    "\n",
-    "#OS solution with a time step size of 100 years\n",
-    "Ac_227_os = pvdfile_OS.read_set_data(t*3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i,0,0) for i in x])\n",
-    "ax.plot(x[np.where(x<0.6)], Ac_227_os[np.where(x<0.6)], label=\"Time step size $\\Delta$t = 100 years\", \\\n",
-    "        color=color_map[1], linestyle=\"--\", zorder=10, clip_on=False)\n",
-    "\n",
-    "#OS solution with a time step size of 5 years\n",
-    "Ac_227_os_small_ts = pvdfile_OS_small_ts.read_set_data(t*3.1536e7, \"[Ac-227]\", data_type=\"point\", \\\n",
-    "                                                       pointsetarray=[(i,0,0) for i in x])\n",
-    "ax.plot(x[np.where(x<0.6)], Ac_227_os_small_ts[np.where(x<0.6)], label=\"Time step size $\\Delta$t = 5 years\", \\\n",
-    "        color=color_map[-2], linestyle=\"-.\", zorder=10, clip_on=False)\n",
-    "\n",
-    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
-    "    \n",
-    "ax.xaxis.grid(color='gray', linestyle='dashed')\n",
-    "ax.yaxis.grid(color='gray', linestyle='dashed')"
+    "ax.set(xlabel=\"Distance [m]\", ylabel=\"[Ac-227] Concentration [mol/L]\")\n",
+    "\n",
+    "color_map = cm.rainbow(np.linspace(0, 1, radionuclides.size))\n",
+    "\n",
+    "# Analytical solution\n",
+    "ax.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    c[\"[Ac-227]\"][np.where(x < 0.6)],\n",
+    "    linestyle=\"-\",\n",
+    "    lw=1.5,\n",
+    "    label=\"Exact solution\",\n",
+    "    color=color_map[-1],\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "# OS solution with a time step size of 100 years\n",
+    "Ac_227_os = pvdfile_OS.read_set_data(\n",
+    "    t * 3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    ")\n",
+    "ax.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    Ac_227_os[np.where(x < 0.6)],\n",
+    "    label=\"Time step size $\\Delta$t = 100 years\",\n",
+    "    color=color_map[1],\n",
+    "    linestyle=\"--\",\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "# OS solution with a time step size of 5 years\n",
+    "Ac_227_os_small_ts = pvdfile_OS_small_ts.read_set_data(\n",
+    "    t * 3.1536e7, \"[Ac-227]\", data_type=\"point\", pointsetarray=[(i, 0, 0) for i in x]\n",
+    ")\n",
+    "ax.plot(\n",
+    "    x[np.where(x < 0.6)],\n",
+    "    Ac_227_os_small_ts[np.where(x < 0.6)],\n",
+    "    label=\"Time step size $\\Delta$t = 5 years\",\n",
+    "    color=color_map[-2],\n",
+    "    linestyle=\"-.\",\n",
+    "    zorder=10,\n",
+    "    clip_on=False,\n",
+    ")\n",
+    "\n",
+    "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\", numpoints=1, fontsize=12, ncol=1)\n",
+    "\n",
+    "ax.xaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
+    "ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")"
    ]
   },
   {
@@ -613,7 +875,9 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "72a27e52",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -645,38 +909,49 @@
     "print(f\"mpirun -np 4 ogs {prj_file_GIA_4} -o {out_dir} > out.txt\")\n",
     "start_time = time.time()\n",
     "! mpirun -np 4 ogs {prj_file_GIA_4} -o {out_dir} > {out_dir}/out.txt\n",
-    "end_time =time.time()\n",
+    "end_time = time.time()\n",
     "runtime_GIA_4 = round(end_time - start_time, 2)\n",
-    "print(\"Execution time for the parallelized GIA model with 4 processors is \", runtime_GIA_4, \"s\")\n",
+    "print(\n",
+    "    \"Execution time for the parallelized GIA model with 4 processors is \",\n",
+    "    runtime_GIA_4,\n",
+    "    \"s\",\n",
+    ")\n",
     "\n",
     "print(f\"mpirun -np 8 ogs {prj_file_GIA_8} -o {out_dir} > out.txt\")\n",
     "start_time = time.time()\n",
     "! mpirun -np 8 ogs {prj_file_GIA_8} -o {out_dir} > {out_dir}/out.txt\n",
-    "end_time =time.time()\n",
+    "end_time = time.time()\n",
     "runtime_GIA_8 = round(end_time - start_time, 2)\n",
-    "print(\"Execution time for the parallelized GIA model with 8 processors is \", runtime_GIA_8, \"s\")\n",
+    "print(\n",
+    "    \"Execution time for the parallelized GIA model with 8 processors is \",\n",
+    "    runtime_GIA_8,\n",
+    "    \"s\",\n",
+    ")\n",
     "\n",
-    "#runtime [second]\n",
+    "# runtime [second]\n",
     "runtime = {\n",
     "    \"OS - 1 Processor\": 1980,\n",
     "    \"GIA - 1 Processor\": runtime_GIA,\n",
     "    \"GIA - 4 Processor\": runtime_GIA_4,\n",
-    "    \"GIA - 8 Processor\": runtime_GIA_8}\n",
+    "    \"GIA - 8 Processor\": runtime_GIA_8,\n",
+    "}\n",
     "\n",
     "###Plot figure###\n",
     "fig, ax = plt.subplots()\n",
     "\n",
     "ax.set(xlim=(-1, 2), ylim=(0, 2500))\n",
     "\n",
-    "ax.set_ylabel('Runtime [second]')\n",
+    "ax.set_ylabel(\"Runtime [second]\")\n",
     "ax.set_yticks(np.arange(0, 3000, 500))\n",
     "\n",
-    "ax.yaxis.grid(color='gray', linestyle='dashed', zorder=0)\n",
+    "ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\", zorder=0)\n",
     "\n",
     "ax.bar(list(runtime.keys())[:2], list(runtime.values())[:2], width=0.5, zorder=3)\n",
     "\n",
     "for i in range(0, 2):\n",
-    "    ax.annotate(list(runtime.values())[i],(i,list(runtime.values())[i]+50), ha=\"center\")"
+    "    ax.annotate(\n",
+    "        list(runtime.values())[i], (i, list(runtime.values())[i] + 50), ha=\"center\"\n",
+    "    )"
    ]
   },
   {
@@ -707,7 +982,9 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "45e5fd3f",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -726,13 +1003,15 @@
     "\n",
     "ax.set(ylim=(0, 30))\n",
     "\n",
-    "ax.set_ylabel('Runtime [second]')\n",
-    "ax.yaxis.grid(color='gray', linestyle='dashed', zorder=0)\n",
+    "ax.set_ylabel(\"Runtime [second]\")\n",
+    "ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\", zorder=0)\n",
     "\n",
     "ax.bar(list(runtime.keys())[1:], list(runtime.values())[1:], width=0.5, zorder=3)\n",
     "\n",
     "for i in range(1, 4):\n",
-    "    ax.annotate(list(runtime.values())[i],(i-1,list(runtime.values())[i]+2), ha=\"center\")"
+    "    ax.annotate(\n",
+    "        list(runtime.values())[i], (i - 1, list(runtime.values())[i] + 2), ha=\"center\"\n",
+    "    )"
    ]
   },
   {
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb
index 086214a7281..21f979062c4 100644
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb
@@ -1,10 +1,32 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "791cdfb3",
+   "metadata": {},
+   "source": [
+    "+++\n",
+    "title = \"Performance measurements for RTP\"\n",
+    "date = \"2023-08-18\"\n",
+    "author = \"Christoph Lehmann\"\n",
+    "+++\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97389d70",
+   "metadata": {},
+   "source": [
+    "Not shown on the website. Added via [!4730](https://gitlab.opengeosys.org/ogs/ogs/-/merge_requests/4730)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "dd267901-843a-4f53-b161-421cce229047",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "from ogs6py.log_parser.log_parser import parse_file\n",
@@ -29,7 +51,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -101,7 +124,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -136,7 +160,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "e94565dc-eab8-4b04-93db-38872403c8f9",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -184,7 +210,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -350,7 +377,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -383,7 +411,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -431,7 +460,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -445,7 +475,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -487,7 +518,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -519,7 +551,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -569,7 +602,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -643,7 +677,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -662,7 +697,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -715,7 +751,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -729,7 +766,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -759,7 +797,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -807,7 +846,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -830,7 +870,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -881,7 +922,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "13bf177b-1f11-45f7-bfa8-ca91beeee4b6",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "computes = []\n",
@@ -918,7 +961,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -938,7 +982,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -976,7 +1021,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -999,7 +1045,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -1022,7 +1069,8 @@
    "metadata": {
     "jupyter": {
      "source_hidden": true
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb
index 157063e0ca1..d62c0fe8c23 100644
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb
@@ -5,27 +5,12 @@
    "id": "f4d3389f-6a41-4c88-aa25-971c9a277d60",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Radionuclides migration in Opalinus clay\"\n",
     "date = \"2022-05-13\"\n",
     "author = \"Jaime Garibay-Rodriguez, Chaofan Chen, Haibing Shao, Lars Bilke, Olaf Kolditz, Vanessa Montoya, Renchao Lu\"\n",
     "web_subsection = \"reactive-transport\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "78389cc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import ogs6py\n",
-    "import vtuIO\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.pyplot import cm\n",
-    "import time"
+    "+++\n"
    ]
   },
   {
@@ -80,7 +65,7 @@
    "source": [
     "## Cs-135 migration\n",
     "\n",
-    "A Cs concentration at the left boundary of 1.019e-7 mol/kgw is set constant throughout the simulation. The sorption model is taken from Águila et al., 2021. Cs-135 migration is simulated by using OGS#iPHREEQC via the `<surface>` keyword with the following parameters: \n",
+    "A Cs concentration at the left boundary of 1.019e-7 mol/kgw is set constant throughout the simulation. The sorption model is taken from Águila et al., 2021. Cs-135 migration is simulated by using OGS#iPHREEQC via the `<surface>` keyword with the following parameters:\n",
     "\n",
     "| Site name | Description | Capacity (mol/kgw) |\n",
     "| :-: | :-: | :-: |\n",
@@ -88,7 +73,7 @@
     "| Su\\_ii | Type II sites of intermediate strength | 1.38e-1 |\n",
     "| Su\\_fes | Frayed edge sites | 1.8e-3 |\n",
     "\n",
-    "The corresponding reactions of the sorption model are added to the latest version (2020) of the PSI/Nagra thermodynamic database (`PSINagra2020v1-1_davies.dat`). \n",
+    "The corresponding reactions of the sorption model are added to the latest version (2020) of the PSI/Nagra thermodynamic database (`PSINagra2020v1-1_davies.dat`).\n",
     "\n",
     "In the `Cs.prj` file, the surface input under the `<chemical_system>` keyword is:\n",
     "```XML\n",
@@ -147,7 +132,7 @@
     "                </phase>\n",
     "```\n",
     "\n",
-    "Where the site names should be an exact match in both `<media>` and `<chemical_system>` keywords. This means that it is possible to setup complex simulations comprised of multiple physical domains with distinct chemical systems (e.g., different sorption sites, diffusion, porosity) such as the multiple barriers in a nuclear waste repository. "
+    "Where the site names should be an exact match in both `<media>` and `<chemical_system>` keywords. This means that it is possible to setup complex simulations comprised of multiple physical domains with distinct chemical systems (e.g., different sorption sites, diffusion, porosity) such as the multiple barriers in a nuclear waste repository."
    ]
   },
   {
@@ -160,11 +145,31 @@
     "The simulation takes approximately 23 hrs. to complete when t = 1e6 years. Therefore, only the first 50 time-steps are simulated in this notebook. To run the full simulation, the time loop parameters can be easily adapted."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "78389cc7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import ogs6py\n",
+    "import vtuIO\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import cm\n",
+    "import time"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "6cece0c2-41b5-468b-b4e3-7a982d5c6ef9",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -176,7 +181,7 @@
     }
    ],
    "source": [
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)\n",
     "\n",
@@ -198,14 +203,16 @@
    "source": [
     "### 2) Plot the results\n",
     "\n",
-    "Results can be conveniently visualized with <a href=\"https://github.com/joergbuchwald/VTUinterface\">VTUinterface</a>. Note that the results of the full simulation are plotted (provided in the folder `./Cs_full_simulation`). The results of the first 50 time steps can be plotted by replacing `pvdfile` with the commented line below. "
+    "Results can be conveniently visualized with <a href=\"https://github.com/joergbuchwald/VTUinterface\">VTUinterface</a>. Note that the results of the full simulation are plotted (provided in the folder `./Cs_full_simulation`). The results of the first 50 time steps can be plotted by replacing `pvdfile` with the commented line below."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "id": "71077607-dfbe-436a-83cf-830f57147546",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -224,24 +231,30 @@
     "x = np.array(xaxis)[:, 0]\n",
     "fieldname = \"Cs\"\n",
     "\n",
-    "#Read simulation results\n",
+    "# Read simulation results\n",
     "pvdfile = vtuIO.PVDIO(\"./Cs_full_simulation/out.pvd\", dim=1)\n",
     "# pvdfile = vtuIO.PVDIO(f\"{out_dir}/Cs.pvd\", dim=1)\n",
     "\n",
-    "color_map=iter(cm.rainbow(np.linspace(0,1,len(pvdfile.timesteps))))\n",
+    "color_map = iter(cm.rainbow(np.linspace(0, 1, len(pvdfile.timesteps))))\n",
     "\n",
-    "#Plot the results\n",
+    "# Plot the results\n",
     "fig, ax = plt.subplots()\n",
     "\n",
     "for t, c in zip(pvdfile.timesteps, color_map):\n",
     "    y = pvdfile.read_set_data(t, fieldname, pointsetarray=xaxis)\n",
-    "    ax.plot(x, y, color=c, marker='.', label=\"{:.0e} yr\".format(round(t/3600/24/365, 0)))\n",
-    "\n",
-    "ax.set_xlim(0,5)\n",
+    "    ax.plot(\n",
+    "        x,\n",
+    "        y,\n",
+    "        color=c,\n",
+    "        marker=\".\",\n",
+    "        label=\"{:.0e} yr\".format(round(t / 3600 / 24 / 365, 0)),\n",
+    "    )\n",
+    "\n",
+    "ax.set_xlim(0, 5)\n",
     "ax.set_title(\"Cs migration in Opalinus clay\")\n",
     "ax.set_ylabel(\"Cs concentration [mol/kgw]\")\n",
     "ax.set_xlabel(\"x [m]\")\n",
-    "ax.set_yscale('log')\n",
+    "ax.set_yscale(\"log\")\n",
     "ax.legend()\n",
     "plt.tight_layout()"
    ]
@@ -261,7 +274,7 @@
    "source": [
     "## U-238 migration\n",
     "\n",
-    "Similarly, a U concentration at the left boundary of 1.060e-6 mol/kgw is applied to the domain. In this case, the sorption model is taken from Hennig et al., 2020. U-238 migration is simulated by using OGS#iPHREEQC via the `<surface>` keyword definition with the following parameters: \n",
+    "Similarly, a U concentration at the left boundary of 1.060e-6 mol/kgw is applied to the domain. In this case, the sorption model is taken from Hennig et al., 2020. U-238 migration is simulated by using OGS#iPHREEQC via the `<surface>` keyword definition with the following parameters:\n",
     "\n",
     "| Site name | Description | Capacity (mol/kgw) |\n",
     "| :-: | :-: | :-: |\n",
@@ -273,7 +286,7 @@
     "| Mll\\_sOH | Montmorillonite strong site | 2.516e-3 |\n",
     "| ChlOH | Chlorite | 9.414e-3 |\n",
     "\n",
-    "The Opalinus clay parameters remain the same as in the Cs case, as well as the space-time discretization. In the same way, the reactions of the sorption model are added to the themodynamic database `PSINagra2020v1-1_davies.dat`. "
+    "The Opalinus clay parameters remain the same as in the Cs case, as well as the space-time discretization. In the same way, the reactions of the sorption model are added to the themodynamic database `PSINagra2020v1-1_davies.dat`."
    ]
   },
   {
@@ -283,14 +296,16 @@
    "source": [
     "### 1) Solve the numerical model\n",
     "\n",
-    "The model is solved for the first 50 time steps only to minimize the CPU time of this notebook. The time loop parameters in `U.prj` can be adapted accordingly to cover the full simulation time of 1 million years. "
+    "The model is solved for the first 50 time steps only to minimize the CPU time of this notebook. The time loop parameters in `U.prj` can be adapted accordingly to cover the full simulation time of 1 million years."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "id": "5337b8ee-0432-4a34-aaee-e97af466bab6",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -327,7 +342,9 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "8472539b-4514-48c4-8c0a-09a35f2c38ef",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -346,24 +363,31 @@
     "x = np.array(xaxis)[:, 0]\n",
     "fieldname = \"U\"\n",
     "\n",
-    "#Read simulation results\n",
+    "# Read simulation results\n",
     "pvdfile = vtuIO.PVDIO(\"./U_full_simulation/out.pvd\", dim=1)\n",
     "# pvdfile = vtuIO.PVDIO(f\"{out_dir}/U.pvd\", dim=1)\n",
     "\n",
-    "color_map=iter(cm.rainbow(np.linspace(0,1,len(pvdfile.timesteps))))\n",
+    "color_map = iter(cm.rainbow(np.linspace(0, 1, len(pvdfile.timesteps))))\n",
     "\n",
-    "#Plot the results\n",
+    "# Plot the results\n",
     "fig, ax = plt.subplots()\n",
     "\n",
     "for t, c in zip(pvdfile.timesteps, color_map):\n",
     "    y = pvdfile.read_set_data(t, fieldname, pointsetarray=xaxis)\n",
-    "    ax.plot(x, y, color=c, marker=\".\", markevery=5, label=\"{:.0e} yr\".format(round(t/3600/24/365, 0)))\n",
-    "\n",
-    "ax.set_xlim(0,40)\n",
+    "    ax.plot(\n",
+    "        x,\n",
+    "        y,\n",
+    "        color=c,\n",
+    "        marker=\".\",\n",
+    "        markevery=5,\n",
+    "        label=\"{:.0e} yr\".format(round(t / 3600 / 24 / 365, 0)),\n",
+    "    )\n",
+    "\n",
+    "ax.set_xlim(0, 40)\n",
     "ax.set_title(\"U migration in Opalinus clay\")\n",
     "ax.set_ylabel(\"U concentration [mol/kgw]\")\n",
     "ax.set_xlabel(\"x [m]\")\n",
-    "ax.set_yscale('log')\n",
+    "ax.set_yscale(\"log\")\n",
     "ax.legend()\n",
     "plt.tight_layout()"
    ]
@@ -373,7 +397,7 @@
    "id": "7926b866-5b51-433c-bffc-aa7f5b8977fb",
    "metadata": {},
    "source": [
-    "In this case, it is clear that the migration of U-238 is much more pronounced than Cs-135. After 1 million years, U-238 shows a breakthrough to around 20 m. "
+    "In this case, it is clear that the migration of U-238 is much more pronounced than Cs-135. After 1 million years, U-238 shows a breakthrough to around 20 m."
    ]
   },
   {
diff --git a/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb b/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb
index 299d6d6fed5..0c05c074c9e 100644
--- a/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb
+++ b/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb
@@ -5,21 +5,34 @@
    "id": "ac287b2f",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"H process: Theis solution (Pumping well)\"\n",
     "date = \"2022-08-24\"\n",
     "author = \"Wenqing Wang, Olaf Kolditz\"\n",
     "web_subsection = \"liquid-flow\"\n",
-    "<!--eofm-->"
+    "+++\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d9d7741",
+   "metadata": {},
+   "source": [
+    "<img class=\"special-img-class\" style=\"width: 25%; float: left\" src=\"./figures/ogs-jupyter-lab.png\" />\n",
+    "<img class=\"special-img-class\" style=\"width: 10%; float: left\" src=\"./figures/h-tet-1.png\" />\n",
+    "<div style=\"clear: both;\"></div>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "c3848074",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "#modules\n",
+    "# modules\n",
     "from IPython.display import Image\n",
     "import os\n",
     "import pyvista as pv\n",
@@ -36,80 +49,24 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "ffd2a84d",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
-    "#settings\n",
-    "path='./'\n",
+    "# settings\n",
+    "path = \"./\"\n",
     "fig_dir = \"./figures/\"\n",
     "prj_name = \"axisym_theis\"\n",
     "prj_file = f\"{prj_name}.prj\"\n",
     "pvd_name = \"liquid_pcs\"\n",
     "vtu_name = \"axisym_theis.vtu\"\n",
     "title = \"H process: Theis solution (Pumping well)\"\n",
-    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
     "if not os.path.exists(out_dir):\n",
     "    os.makedirs(out_dir)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "688250b6",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Zl6926el5hNmbsYOonQHVMNbzU/sR6Zr20fX9Z+nhYbNMNH9g8GQ2wAjHzmYWSbOE9bHXQOrlfVYHlxNvKtEEEIvZPXdFXJe3Z4uelx7wNAFIvm0b9id1j9ZfNKTqTH1cDWoP+WbMiv18Zd4GpAkrczUNWvZLSl8kITRkzZfIjR5mD43yg2tPsw3PE59TFfl499Y8ju0Ex7Lu8tlR7x3S7lV3U0hQ+O7S/NizVwfy/JwGhO19Pz8Pqb/kCLpWiwYcufcxLLYerPWCuL9YHlZgIPWyO/fEZpy0XwhcFH1d3lpmWOjLfd2cxF6vqcaQZmbh5LfCaLZxSHdPnSR+Pjba2ZH3BY8xj80xCg8RGrLCy+RR5hfHUe/d1MNsw/uBBco/1jiGswJDq9MR3rt7EWfFZRtP6sY3E/pC5maNY/hZ2DWrl2gXNbsu60vwi6WjPoOMJizc62lpe1B4MPbOtO11qYZ1Xd6cHVVcJ3HjZKKwKc+S753ia5EycPt29ZRwzDnsrZna+uOLppoIDVnqpTLn7JOnUl/wI9azj9mGW4nOZavPmZFtiPVQcGB1M4b7ebboNn05sLDwdQOO09KWvyZ6fu5X+PkHBS0tf+t3wbyccKHZayjvB/UBN0HgRdhc1tKz1Web1Mt1R3YvNstnh31d3ltWuFtMjXMGIxNZVp9ti4zj65+Tr0Pk2daDH3OsekBoyCcvlbmXJk+iQ2QbYAx+X8M+Q8NCZxe+F1gNtlHKbNGluNRgaZ3lr8V8Y9pXaNh+cXE+kGt2WsS1yr2MaGL7dI3y9/xi+VkZexaabbK4Js1+cM2ehU8jCwxfOi8h5M28L+QkZhtm2yvy+Xkvug6LLuaDCQ2b3wVjGHMK+lICoSHlBQb7Y5gRV2Bdd8ewr2EfoWE76630/fQGH4i3XxiUsEdkihlsRYS4fYSG7d6FdwO7Zr0Gh4suqnlnDPn8MeDf74slhMNe8vrOkr260/OAA5evxTY4iX+t7vqefZf9GZjIEsps4VPB9Uz+HEdantx8STGuGc3nfscjNOS9F8yce13dTaiuXzLP3rpPdB5ZQ8Pnf74OfNbbfCD3Zx0s3Y8wMCzqWuQODdvlyE+u16ovvxlmNVgGNI7f7YuA+X7UgdQAG080TRPGPbvwvZC3t70Oo3e2XeZ8J7BMOWMIVWRAlGm2ZSUwvBrcDFSEhvT7UpszODibWG0vMr90bww5NBx44PFS0d+Gj6jOxY85OUPDdpn50K9XL2Fa1tlJ9TJQ3QqH+Tv9uDqYTDB1fD2Iz2vtrM+LSYWF/1Gd9vM8pO7y+6bH2feqqCZaCcaZw2whbImduRfbPhT9zIwyMPzN5xOEhmR6of3I7sTqOx96fXOFhj3UKvUeh0V+qK1DmYmEhZ37rZ75O9bQcDas/QuX8TXrNXp+ATWLi2Je5EvbO6/gF8g2MLyddmD4J+T92cMzcdLT+d6PecZh01wp3++j4rYjyLLsPSB4HnVgGFgbhIYItWIFKZP69qKHUHY+kHM4f/UzfowwqLqfldfJd4qB4W+XYwwN28BwyF2v35J1v7+sS5PrmR06JgsMh7Vc7bLIayIw7H25aQ97wRazNDt5bfOFUkU1mMkSmH67Ctoqaza//jHuLyA0Q0FoyH9faHPuZ/hrojXOufz7Z4LjTxoajmyG1GtVKUH5LH/DoxId9VD3ZKHhSAPD7NdqlnUz/+tBNUuiCQz3zWIrK5wRGJZzrZrtFvo954sxfhHTbIUwwS+y6vu35KXJvQblQkOEhvQYJOTsUHs40RrnDMVuExx/ktBwhEsqi9xTr72Gm7NhN5eJ5Sn3svFUoeHIA8PaQ33fZgoNHzO+nG367DGg39+LWS+Pkw+iFg0oiliy1i4NvBEQlnGtmiXs/Z/zw9hmHTb3ea6xp6DGXFlmWH6vtgLu9/F/WSE0RGjIGy+0OYOEYr7Jylzjo5yhyEBCj18jDzxeB1WbPd+DlcDwj5sRhIYPE3l+ks/Kyzxr4NbnjoH9/s46C7X4MOqmiGvST/ONoV2ru1x7URa2dP92TIFHxnu9KuJ86xnEqRtNBYxjTYf2acxWFhoiNKTzMrudc9bIhOu8l/lFeyvy8W8JmsoPPz64fnP1/4/TgYeGU7IznhdeS5OH9cI+kRfETMv6Il2TI9egrPGmXSpe2vlfzL5XXwc/Bj2fQ7aaFTALPsuS7OPqZM37fDN5oCk0RGhIoWHWfsYXvyI30h5hOFvbjXz8Qo+BzjZs772nvmf2tTMdX7ov4Hp8zXQNPD8FL+/POmvpuNr32WMwL+vbvb8gLpbgVq/cF7D09WtP1+TrZF7a4405e1muTbnLxauhByDNrNE898q8gHP9VerS5OfPCmc93cOPb/we+FeK+0NoiNCQVy+zJ1OYaVVIrXO+ZB8IPYp0nvme+zLLu2fpy6XnB7MlGsC0oea8p+XTd7MMTWo8P8GSzlLPuj+RrsnD+H3d15559QtzPdNmiWWlTai5mAlZ9XCc2Za+vromd4UGUyUvU37IMYNsAM2C6mXLB4Mcj46rk1xfUvQ+7qbewzFkaXLOL2x+/y5YMuBsandc7Tb3yiJ4Dauj0BChIa9eZs8zvvj1/g1Wz7W+y1jro8jHLvQYyFLLV9ftNON5Pc4We3duBBzvRv3lQubrsS80HIQkH2Dbl5RsL60+dwzk9/W8Os06i6RechsQKC+Wh17/yDyDbX/E12T5QK4746fvLsLvLtXNc40G0SDivgnbB/QFTtuMKU99emx2VM+KLXZpcr5l4uex7s2mnvVKinXGpREs7UdoSNyX2Zyze/YmXuucTQtOhR7Fush0v33NeU4xl17XwWrGGZJVhmvh+Qn3M8m1qWdrZXtZtZ/hIH5X59xDrN53LeIssKapT74ZklXGa7JTwFLxuya4XGIGzotZP4ezUhrpPNcwQ+hT+mzDV0s+r3+EdNHNOi7l6Cjc6G/P0lmOLtylLk2ux7eEX8Q0z+YqS7+tikBoyKuX2Zz7ig3iF3PCWv8c6jLYgYQeT20Ift7OsNtrnba1r//dQyHHup3hfrvMVPP9hOeQayb0TuJrMZTQ8H72370nH0tZopxiKXmWmQ09zcyi4Jfz1C+IOV6+MwVR7fn0M4OtmaFTnYbOvqqDinam5H1/IVmmpijDmG3436Wghc+sytIgpA3Hexx7U8/UrQLu6/vEnw/y7D1az1pdBKCPJd4DCA0pN8jK1SDhSa2zdrC9jHzspYYeT22w9HXJ8/jSLqG97/m4TxLfaztjmT2cKTj8kfgcSg4N79qxafuD46+XjR9mni2d5X7L2onVN/fl/56uZ7TlCZDTj51ZgsP0QVSPs9fOUzyzvTVTqIPqDPtQDmy24X/2uyt138Ms+/31uDS1mZmbftw9KvL3wvH1z+z1bra0aL7IeCzheBAaUnaItZHxZW/yeznN8naqvol87CWGHj9DlsS2IUhfMw+rxPdajn0BDzM+O6nDqqdZwoYohT4/N+uEcLNF45qbno45ehfljC/w9jMcwu/pHPsCHlf5xs7UsyYzBFHZZ67Vs50Sh7rtDOfH7KFYpu642Wa6pnNf4szwZn+6LPfJ9VkP55b+d/GaW0EkX5GQoVHRh2F0vcfn7yZTdXDuC06Ehrx6+dsZS0gykHrv5lxmOOLQ4y7WctL2vPqadbiZ6D7byDCD+Czzs7OZ4ZyOEh5/Sc/PY4xzrWfLZpyp/lv0/UBX2uNnIPu/dc6vXha56LxZDcKiY+RJM+skd3feeuZF6n3zMr+MN0vRkp9TlW7szD1rrX5xzvTC3Da3yL2UN8uXFyPqdH1bUhfZbPutfrt6yP+FTfKl+1XAOHSYMqCWSSA0pPQQay/jy94v9RYaxggNZgHdeQsLDg8T3Wepl8Hfp5yV90lINcjZ0AU9PzezuA1rDnJ/YZAgNMzVNOI8+zOzCCUeB/vCvpjxle2FvZltkXr2UuYgtD2vk6EGURmfz8X1ydwUo31G77M+V5meqabJVN/Na+KNRZe59u9c4pm4G9N90t4rOxnOZ+0vNxJ3br8o4b4CoSEfvfAdZnzZO1fvrN1sxxganiYOdXIvVb5IdC6pA9C9np6fL+0s05TntpXw/ur7+fmR6NxOMp9H1NAlw8br2fZ+e+PcLkfxwp5vSeV94vPoZ+zMMesrQdi2aB6S7T577CsUamaP5Q3XsoUUiWdo9REe/uq723KGLwGyf9GVOJQLXgKcdLn98/WUSSA0RGgoNOwrOHgY8LH3sn/eLO+ek0n20ctwDhc9P0OpZ1HujzQ0PEx8XXLucRh1g/Z8L5zVadZnpdnkfDQv6k+p93zKsAy237Ez9SzKBPu/ZQkSeg50ewnXMjxP3XPLtA9fVtc/+tqHrp2dmidIzzQzOvky/W9XQc0hk34BJzREaMgAQqycM0TO1DtvcDCS0OMp58y2dvlzzvPbi3z8l0MM1VY4v9R7G/4Yw7PfQ+C+k/F8ou6fljGUOMr6rAy5g2kPMzWTz8rsualC+r0N41+fjMt2i1geWB9HxvHoMO94NMLgsH6eMs2Czj5eZRy3sswoDrzfZ4s9dwc/oxOEhqz7onc6hqWlA6r3ptCw7JCqvUaPGc/v54Cu0UMhz1HKYLRKdMxbYw0Mewjco4YTGV/SD/IGEBm6AGd+QR/sS2sPTQV6CBqijp1NI5xcwU8pe9Xl2Nftz3h0/TP7+eWcOZp3bLrMPeuw/n2Sazl2hnOZJ3/GA5sbJQ0Nv13dyCQQGiI0FBq+rrnQsMDAo8fn4ibicadeulvEbOFZ2uYb0ZeMT+X5meVr9HQZ7Zhz7pmWuQNn0j2Y+vK92h7kS2vmjsm9BA31i3nEpYz5Aqb8e41+8tzmmm1428/5jTY4fMi5xL3ZpzRHk6vIz3X2QC7C0uT2C5dfY56JDkJDSgpHDtVcaDiEkDnzjNDHiMedemnyTiHP0PbQznMqz88sTzOhaDOasoaGiQKv3l7E+pDopTz5Ur9yZrJtD+U8s9y/mff2W/Ia5Ztt2EMn7za8PhpNV+UeQ+hsS74TLmXPsj9jhOPPUOv7vp5HEBqyzAveudBQaFhg6NH7/kKZ9zbcjHC8XxLv9fdY2HOU8lznAw8Nf/Z4XX4OanZuztAw9/K1cYaG0T9HtJ2FUwYYZY2dKc810v5uGa5JtqWXRYbYPc1+fiPAvh3prMNfOQKgput2puXXCQPkw8THHrw0efH7NMMM2fq5FxwiNERoKDQUGi7lNsXy0DXOdy/jtdobwPEWsVH8i/O9TXiu5wmON9fzU/X5/GR6bu4jvrDsZnuR/F5tCQ0LDA2Pq73Ex13W2Jk2qIkydmabbZd5n9EV7sl5pvOf93qezRLbke29mjkAev45d0OekZshIL8Y1DNZj88RQk4QGjLk0FBLeaHhp7PZ6p9T0LW6z3StjiIc64/Exzgv7Dn6NYTlr5mfn/p+3ej5umwJDa+KWKJab64uNFwmREscWvQczLxxX6Tcm6uK9FzOswQhhc7qSb6MvLCZlm1wfz/C8LBKHQA91+4k09h7FP3Yc8wojvTFQIYvl14+lw+5u5uD0JCSQsNTNc8aGj5GPu6tqd0jswF1g82wn+FuYdfmdAih1BSfn8RLx2vRuuhmDQ3zN0Ix03C5EO1yTNf98/si6TK7+0j37sVUlya/qEGOEO2+mPOtA6Q6LK4Dk3GNW7cpw+ksewIunpfoHX6Td4CO+MVAti0TXs9WzbwXMggN6ePFW2jYT3CQJPiYaGg4z3StLiMc62PiY9ya0Nj1IDQMOte7ocyizjajp4elkELDpev0OKZl6b2Ght+uooydWYKjQpcmv7gvz7M8U4UthayPp7lHx9Uo5TxpzXLtgRl5LEvekTjyFwPZ6vzGEuvSvnxCaIjQsOjZVEJDoWHm67WT6Vo9BB5njm7PRS3jSj12CQ2DzvViKPdj1kYomZcbJX8hG0FomGWWTmFLYFNv6D+YZ7LwGTzJG0T8+0ztF3n+zbM5ov0OEyzvfXGvHGQ6h2jbTC32s0z8hU3kLwYy7mv4/qzVuvO4PQ8RGjLy0PBcvWfbQsPhhIbteT9lul4bAceYuvlEUd0/M41dG56ftc81x7YXW4MLDedV1uszO77+KTT89CUw9T5V5Y2dqbuABr7QZgnLvl09lXZd3hibdob4TCWow1aW5eoZ7rm623GSGuXrNn43mLE3wZ6leT8vfHJu9Uxksw8RGiI0HG29dzPW+ybysU81NLzJdL22Ao7xJPVMyPbeLcn5EEIpoaHQ8N+X8+uzvKFhdSQ0/LRGJ4lf7h6afTNLknrZa+ASxixhd4L92aI/v80y3SzP1HwQn58XIWo18DHsPtUssWzL2SPN0E3+nCfas7TAAPu++SKosG0wEBoyvhDrMGOIVfSm0yMMDS+EHlHO+9es/NDwR8b7aiq+en6Kvh+jfUAey75W77xkCw0/vPYjWv5YisDZVFleyo+vfw7iM2Pq5Zs9zICOMK59HfbWC9dJtmrK1tQr0pdfyfctTbTsvvDfq1UzU7vQrvAIDREaLqtS7+TLSF/6GfnYpxoanmW6XrsDCDanZNfzs/a55pjBPrzQ8NvVZfZr8e3qTmj4YX3Gt+9j/9doN/B5rDIc43wgoeHtUEOsDOHhVjNbbWgNU+rj/evvzUT3y32OmW1Rgt+0x/iYtGN16cvlfy9f/l7tyDoQGhLr5W5faJi13kcZ630S+dinGhoelR5S1c+WkC+6Q8/PRELDHJ1a21kAPbxYfx1VF9LoMw1H2GF64NcoS9B9XO0N4jNjnlB70FsHLRqmNN2WH4bznCSabZh6v9JYXwwcX58N+Z4e2Cz+C+EhQkNivNzlXC77oN5Zl5EKPeKcd67ZoQcBx3gv5PP8CA3XfjG/yfThvZemGM2ytUX4UCWTq4bxQ8P7Ab38TSM0zHFNBtJAIEO40oQKo/h8XTcDWezjWv4znWi2YbY9egOX9ye/Rhm+FBjg1haVxikIDSn9RfalSe+zUO8zaHnl4ELDndJDKgGf0FBoGPQCk2+p0Ui/8c/3sho9NBTylRca5ngOtwbxXOWZOTa6VUD1fnazLEu7Q6SabZhh9nQ9q3PN5b91I5UhL03OXus04eG+DAShIau+3H3J/CK+PfF63w211hMODXMF60dCQ6Gh0LCP0DDjjIGB7KUmNKSPa9TMFhMa5g4Nb0f7mXvRHKTMYGcx23AjwTkfZnrO9wq9p7Mtt2+Wxg9qWXz3uQ9tWoXQkOkFWU8ZX8Qn/e1G5lpvRD52oWGB515fZwGf0FBoGPSSNc+5v5DQsIzQsH5hF/IVFhrmuo8ShDWJQq8cY9P9GMekV/fVTt2IqsBnJfo7URO859nHdq1wLvlWFpn3K223/xjuvsH1Fgi6LSM0ZMkXvJz7oc0nXOecS8EfBnr8QsOy76sp2fP8TCY03M84u2SUe/sOMjTMdczTCw33Aq5JlgYDg3mu8swaexzjmPROPfcK6yifZFZc/fdmmSm5YtiUfMzt6fdr2w36cbBjdv1MaJaC0JAlXvCqjC/iPyZc572Mdb4UGkY9d6Hh9FjeP5XQcPGBP98H9IEsjRQasub9vfbY2S4nFRrmDQ0HU4+IdZ0XEvLcJzq/3Uzj8cGKx3WUeNbczx5/B9Z7Nd4PeOx+HEpXeYSG9PeCdzHkMGtAdT7KWOczoaHQkCBfPD8TCQ3zLeeKHnwJDYWGxQlY6laHEEKy7KHhZGYa/uf5z9X1/eOQfSfJ+eUJsC5WPKYq8e+HXfdUgPpz0IpBMEJDphVmnWV8Eb+bcJ3PM9b5IMHxCw3TOlrz2LYFfNHde36mExq2LzO3Q1+SJjRc+Zi3B/tyV66gsdPMul7qMfo9DT/8wihnI6y3x7STNKFhhiY6KzRzSb6HbEFbf9TXdNj7HI7vi02EhsR5wTvI+DL+FHsGz4DqnHPvyB2hx6DOu3ZY+PFNydzzM7HQ8Pj659CXpAkNCz3mKQnsDt7sOSc0zBsafru6mfx7UK6Ow2/X/3LgY/JRETXucWnyB19KVQMey3dlJAgNef2Ct5P5hXxfjZP7kuAchIYFPhdCw+gePD9TDA0zvzSO7AO50JB6pk9oF85se7H99ffmIJ6rHLPF5leVd6EM++31ENpmCq2qJY/lYoq/U9sxbYjh4eMY919GaEjYC96XzC/lk2uGkukl+rfbAYdnJYaGuQLf3YKvy1Q8pVjaLzQcQGiYqWvrbM29oISGQsPCA8Moe2FlWzI+kJfhTKHh6LZLKLze2WaeZ/sy7JMQPvm+wQUtTR5ZeHgb+kUQQkPGF2rdDT3UKry+1dBD2QmHhruZrtvXgYT+Y3Uzi9wxWWg4nNBw8cKYuZtmog3whYZLHnOzn5nAL8ZMqZCOyb3cR0MJDY+vz9I/U2Ut6+w/OEw8I+6NGV3JziVXk69P9mVM3uDo+TkZzP01uPDwenITfRAa8vFL3kXmF/RBfGCLVNuNzLXdE3pEPe+D0p+JTEt2T0fqYN1Znp6fkYWG364ufRifTmjYBgTpl+w2s5dGqA4CIi8JzBbkDiSwr2cBpq9HVdznrp7Hstyzzv8Z/D30yRLr5Mfwvfo6uPusDg+zf+ZY69o+DWU7B4SG5HnJy9lBuXY4odqOotHMhEPDXEvLSw4NJ9td0fMzodAw976GI/owLjS8yr70cLRjZZ776GAgtbjIUIu5++513TN3VE448zXbPqHvzDZuvwh4NMZ+9LuzWRZ/b7YhQkOG8EK7mzk0nMweKvW5ZqxrlfA8phoa/spx7QKP8T71TEPjpOdn9KHhX39vZv8wPqBlVSMNDdO+qA1gr63i7qVmdmby++hkILW4G9ozNYp7MPfvgsQzX/MEUm/PWE0eWo7kd2hbq73nZ/6X2YYIDSn5hfZLO0stV7j1OJG6bmau60nCc5lqaHg7gNDwpuTjQ2g4lC0usu81FKHjrNAwKJS5GfLSw1GOlXlm3BT/xXW2pdrH1Z77rrf78Pc12E17LlkavNy/HYRd/7Q0eeXQeqPp5p3jSwPbGCA0ZI0XvZzNOmr7E6jpz8w13RF6RD/vx9KX/z7/cyk0FBoKDSMc+3F1kn+24fCXBw44NLwUGhZ2L2UIcj/bg62QZyrP3nojasg0tLEh20zDfA2GvmYOX0e//UNd02ZPyBwNbT42uSamCA3p90XvpVEPQHWAl7med0KPwV7Di8DjPB9qSCM0FBoWFRrmerl6vfQnUvdZoeGKx52jScBAOvUWcy/lmO07gGXjyTvOLkxi1c9692HGfQ0zjBF5ZtF3975rAy+z32Jcv3r24WLG6GNvwaHfZUJDaF/0cu9rOOrZhrP8HalPEp/PFEPDk0zX7izwOHOENLvGSc/P2EPD5vhzzHT6b4hxN+RlysMNDTMs20u89HCEoeG5F+BmWedZhjpcuOfeDW2PRhUa5mj09SqMT34PD/zLtqDwsI+Zh8/PhLFBaAh97Gs42tmGPcwyTD4TbKKhYa4l+/uBx5mjQ/fcOOn5mURomLuL8juzNISGGULDHLO5dKctM6wpvAFIni8v7FP2wX2YbauKLOdT75GZI2h68SVJ0j35nv/uSd+f36vtrEvoFzW/NDYIDeH3y96vHsKu/RHWMfcswyrDOU0qNMwcom8HHmuOkPqXMdLzM4nQcPFy9dBLcHhcDfL34WBDwxz7xn27MnaWdk0Kvy4Zn6dd99w71yDf8uTHfOeUZRbv+Z9QS+CdI9w+yvgZZfR7SCI0ZPmXvf0eQsNRzTZ8/mevhxoeZjivHKHHfSl752WavVd7GkjA6cOC0HASoeHig3iWpYFvvkAOcd+gwYaGeWbfGDtXvS55lt4Vu59fPTs1y/P0198b7rd3A7aLTGP+bcb7ajfL77B6XE09U3OCS5M/uK4HUx8zERqS/2WvDh8exxh6Zarfdg/1q8OiLxnObSvT+dzmOJ8lzvcy0/neRDre2wzH6gVDaDiN0PCvvzd722y83rB+YPsbDjU0bMOBW+FMcYFNlWkvuZ0Jn79uqB8H13dZ7sHMM15nabsZ/5kxn3R5/cSXJr8bHI5kKT1CQ4bzcnveQ2hYB19fB163jXamXO7a/ch0flsZz+my52uZc0/Kn5GOOcfWAnNjpNBwCqFh+3J10WtwOKCgadCh4fNLu30NSwvNci0NLW8f0XxfWAx3D9UM49l2trH+uDrJ+2xlaP6UPJi0NLm3Lxt0UBYawosXvt0egq9/2hl6gxyM2hmaVU9h6+YIQ8NoYdoAgvOjSMd8mOFYfbsrNJxOaJhrb7UPZlMM5QP6oEPDPF1FjZ2rXZP9TM/YUx3STTIwjfgsLZa9VqfP/3dvyF3gX5zPScbQ8GCUY7Xgqo/fw1+nOjsboSH9vfQ99BQc3g1xCWQdcPVUrx8Zz3Grh/M76eFabs7ydhH/OrDrs2eMFBpOITRsziXHLLTP9ocaQLOCQYeG+Y7d2FlisJF5pteH513PMsyzN1m04OU/HVzrJlKFd6b+PLjNsGVBjwFYtuX/aYL+G2Pkh8/jU+LxsvjPIwgNyfvSN+8pBPunnbE3mG8qM70g9zrLsMfQMPt+lxn3Mvw9u/ZLxGPPsTz+whgpNJxMaJjzJf6j2VCFv4QPOTRsX6LvMxy/sXO1l9+HTM/XQymz42YDa77xyYzQ2yEGDJm70d72dI6Hgw0NCwr5Jxl4m+UpNIRXL31fepxt+E+7P9tG4TWq9zC86LFGPzKf71aP53qU6RwPM5/XeeTjz7WsenAvAkJDoeH6L1i9dVJ+9bL0fByFLv0bQWh4nun4jZ3lBWhFBBFZt0OItMfmUs9NPVu7sCXg757PYj/Jx3zjej/7SmbqGi+06mfcTDuLdATbDyA0JP6L30mPIdE/7aypIpuj1MfVc6iadZZhAaFh8kCkPb+nzOe0G/kccoWeD0PcRkBoKDQMeMF6KOKFabH076i4+3vooWGumTf19dNJedlrsp/12eox0K3viaxLYiOFeEt3GG4CqusfJd/77Th/mfme2+8xXDofXGBoafIy1zXlOPKoxkJDeOvFb7OHEOUtZyUtV+5xOfJL85GGHlln5r04t3rW6G3mc3lIcB5fMj6zv4yTQsMphIZZQ6Xl3Ze0ZHnwoWHOmTffroydy4dSOcP6x172l6vvvZx7zH27uox4fZ5WrnHdZKSw8LCXwLDnZfGL5jUDCw1LmRFccDOQpDNlNfQSGsIHL38/CgiKfjdI+dpzLeqX/psCanHb4/mXcC9cRt4HsI/AsAnDE12n84znMDdOCg2nEBq2L8h9N0V580N8n7NV2pfPvWwv3AmD0qwzbyItDx39uJmzi+3vffgyBznZx5VI40XQFwXNPq3XP0tYatrM8swdGPa4NPnVmHc/qNCwx2XubbB8U+oXd5mC4MrvJaEhvPfyt9nzMtzX6i7F25lr8LXdY7GUGuxMPDSMFiK353Q3putYL3ke896aEa73fLZotvTYOh/R8yM0TP1yWepLVhMeXp/l6tDbvEDVsy+XXZ44hNAw+8yb/kODlQKi42rezoh7bJ1neObyNyKqg4Hv1XaWZyj/EtHbqPdEpJmPz/fWQR+z7tox7GFqAdi/oWF1OqDQsNfA6t0vMAr44u7fY7z+mfganA/ldxZCQ/p5ATwsKCz6rZ7xd5BqX786mGzDhRJmFhYR0BQWGr5cur6xxrl8aa9vX4H4XeJrdZ/5fC5K2kLgjS8+jj55lk9G8vwIDdO/OJS/pGsRslw0XUAjzuRpApzFMu2LHpenHSa9vvlD4Ytim9ssrvfRq9k12ZcL9nS/PSYOqPd6+QIiYrgRvXFLPW7Vsy4zBIjNz8i5JPyN576YLwOGszS51718PxwHf3/Z0Oe+qPXzmPwLlqqIz5gIDSk7OKwKDIxezjr72QYDu6u+TLahwm773//ocebZMuf5pcd7oMTQ8J92D78fy8xALSAszBLutOfYx/25X8h49TsoXHbcuh3J8yM0zHF+w5qdsVhG1Sy/q4+7VYefrTeCot//7qD5s/V/m3tGYV+h4WI23eSWmP8nKFw+UEk/dvYb1J9HDt43Zv01oLgdzHVZhB9VO1btx5iV93wdvzazsftualWfW4aZrCuE8tUgfo/1PDNztvxegVWuGf+dcSXH7+iC7luEhpQbGm7PymiKstLef21o8J7HAZ3LY58vyYWHhi89tEvJT184a693SWHwVuJr9aXHYPS2j/Dw+Z+99lqvuz/llxE8P0LDHOfXx6b55AkN++2UfdtHeNjMeqvDlHW7bmaYKTnL2Vn4/SDgaJ3gonmh73uG7uLZ2Yt83+QNc+vn8r9ffsxffgHywt7i3zf3dWGhWFmztQps8lXe0uT6y5R1vqzL0PSnmS2aIzDUuVoWpAgU9iLI2/YLuP5DCA2H4Eem6zXv+Tzv2jFjL3Yg184k3H8RCA8hyBUajiQ0/BMu9R9kCA3TvETPez3H5gWwCUT2Ygdy7UzC/ahhSoZmFr1fk/8EiHX9mmt09EZgtf9vsNVzUPhH/P0zB9l9t2/1s13YdgRZO8cPdWlyaNOfBMvu61l/zR6Gua5dAZ2rERoyrODwQuiTXSlBgNAwzkzIzUzXq8/Zhm+5aZeR14FSvR/pbmvnxTF/ffG//3bS/jeXs7TbJAgNhYarBjDlNkYRGoa+RD8UFDTc1KFPG1Id/Amnvlc7L14gv74RXp38WV6ecrZVjtBwcU3KWCIvqBIarj9+7Rb5btffkvnlQre+lybX42usc1kEiPN174XmS4k+VjoU0OkcoSHDCg03ClvmOXZFbJYsNIxmnvmaHaj50nZH8PwIDfuYgVBSwCQ0jBWIHKh3WUFIM7Om9BlRJQYuifYha66HGq/wnFz/LPbdruQA+NvV5ajrs2iwUnWW3f/H1fmnjVgSz66WgaAIrPvy+yhkSO5yVlBHWqFhsNuertsvtRcaCg2Thhl1F9FHL8bjCQ2b61rPCFHzomZPFbZMeQjXZp5w3BtO593+g6+H1HvbBV/PUmfNZxzzBxmqTuQaIDRkuMHhrpBhOoGh0LD8UOqD67ZZ2DJloaHQcFSh4YsX6DJfury8rHdN6/3/zCItKjRsw40LdV8qqLpM/HxsqPPSz8h+8e91ixlt5c2ULSBsnXhoaJYhQkOCXwzrBgdD66gsMBQa9uFXz9du3zUQGgoNk4dMGz0vIRIaxn9Z3Ff3wkLDxXMmzP18ZlvyfeBmZliPZpZWkTNHC1ia3NZmZ6LjyJO9DBEaIjgUGAoN86ifj80Crt/ctRAaCg0Tn/uiq7KZUOlewPeyX1NLYosKDdtrognHRy/6ufaZ9CXJKALDFyFwpX4DCVTz1F/HZISGRH1BrDuePgodgv0o/DoLDddzUNA1/Ol6vOur0FBoGC/UuD7z0pzAi47Bma/nT/V/95p87eWalLicsozAcM84J/BaM4w/LOpeLmQfyEmGht+ubmQcCA1J8ZK4M7Nv2rrqwHV/ANc4R+hxP7Jre1bgdbz0zL1pcwTPj9CwpGfte/XVMsqoLzEPvV7Pb1eXrsMbMiyDFVqVFxj+GePUfhSBYXM965ny5XQovyiqNtPq3P6Yqus6QkOoXxQ3BBIru3s2iIE5V+jx7HAkS96L+sDz6lqeefY6nsby/AgNC3vWFs0Czr1Ix3gRv/7Z+/UUUv0nqOr/mlQHE3uhLyIwfBGmWKI8gsDwz/Us5ffV83NdWF1uJxQY7sg1EBqS44VxPpLQJ7V6qejGgK5rttBjtujOPeQl78XuTfnieu4PvMYx3QoNhYaJg43DmaYBQbMMS9mQvW2O4lou3BZxTRYz3qZ5Tb5d3fX5kl/PSPI8tGFLYUHXmuNb//uF1iH4//1d1GfoSWxRsfg9KzBEaEjWl8addhadQOKNgCL1/mljCD3qGZgDXa5cfGD4qsae0wxhm9Bw2qFhU5d61mE9U23qs6LWm0W1W9S1rIOSOqyZ/PWpihhz2muyNaHZQH+WcJaw75vnobkOvTe8i3Y951f3PY/5v4qryfiX4t/rlIzQkD5fHutZh/Y6/HfvwsF2ouoj9Kj3mWtD1sEsSR5KYPiixhvtcU/5udwc4/MjNCw2PNwsZglY+YHhZal7K7VLzy8mPbOqsKCkuSZT2HtyEaTPC/xSZFoNg+qZWT0tC08bGvbcZKjQGZuz0rpLx52tLDBEaEgRocTZhJcs1+d9niOYGGPoUYdwAwi16uDpcODX93SCz2Z93fbG/PwIDQt+5r5XO5prvD/TpLTZhcW+YPe3FLPYsKS9JmNdMlv0rLZ2RtYUZnyel9LdN8E13Opx7C9uafKrL/zGc283Xz5cn5Vab4SGTDc83Gz38ZtKePjYhqWjWLLQd+jx/M9BobNWL0Z2jacy6zBrkC80FBp+/IJ2/WPEIceybmfH1dEQl/m1L9lTmXV4PoRrtJgJOqLwsJ4NNKBZbe3en+ObmbW4DoP4QiMseO9pNnyBS5M7dak7TI+jIValQzJCQ0oPJuqZh0cj3kvtoV2WvTGy69Z76NHeO+cFXefRLUtp67zzrBrhs3n/7EdfgVOG0LuI+/H5n6+pn71RPnfNy0h1OKm9werOq8fVfCxLo5rZo+NcwnbfBNsDvE4jCA9vh9xgo515eD7ovVwXx34+hbDwxXOz2cs1G0gw3jYAGt4XRYsl9YfyCISGDC2c2Hv2azb8Lq4P7SzK0X6gKGmmVBtK9Bk6n48tFH6nztvtfT3k5/O+lGcz8Qy8+jyLWWIyS7sX6enon70mfGqCjrEt87ttwqfj6mBMjQPefKFc7O825Flu9805jCQoGWB4WI1pv7zFnofV0aC+FFnMKjwa6zLkT69ZPU7n3vZgYEtlm1nm9ThZeii+uJfnU72XERoyroBit50FNJQZiKMPCksNDdvjqfc6PJnl67D82F7vyU3nb2t90Ab8pTc2emyXWB+VuIS1rWPMe7bIPVPbWcE/Im9HUddtPrnnr57xUb+4DnP5a9UENcfV3hT3TWpnjx40ezTWMzzKvlaPzT1W32sj3hT/RXhV3n6iixf7k7E3JViE6tW80C9FHpsQ6Hv11btZO1M039h1PpKxvowAsbluzQxxy5ARGjLqgOqwfeksZZnkXftyfjjR4KjYPdnamYfnCWbEPbUB1GFJs7gKuRcO2hD1tufn8qY9jkE9l+0szsN29uFFO84t66wNzAfxhUX7hdBJe9yrnOdFW59JjrkfvsQtQsTzYl66Fy+VVTsz7WRKy/hWvHZbzYvlYhbibc/X7Ka9XodTfalsA8TDJiztI9RdhAtVGxRO9RpsNtegr2diMfPxvA3Ld4xTb1yjXDMOxzSztv6ibLH3Ye57+ra9n0e5fRJCQ1j2JfugfYn81b5UppiV+PuF9bS1N4WlqEMODV8c4+8ZcRdrhlkP7bX/Mdb9ChPdGxvtc3L64vmJGeA+tn/nZfsz9ut9F9Ue/sxw2F0Eic3yy/MmiIi9DPB3wFHPpFjMHtwXDkYJrfba61a1Yi6ffWyv2eWfayYY+SzAWlyPxUzEKnI4VQmolhzPmvu1CRKrxZ6aEcew37OfLdVc7prkmeU+uKXJK4aIu+3s2vP2nr6NGBDO/S5GaAjLh0W7rxy+CP5ee/1nLUP4vMbFh4YfHPvXN+6JE9e/t+fzvWfz8NWfG+2eZ5A5mNpd2QSXExcboPzrcBEwvtLM1Hrx50a8X2TPL/0fX4ffYZTnKM01qPd67V6Hozfqf6D+UX9/5GmIcnz90zjf6t7P8//8bhZ208d9qgjAUoPFgENDAABghc/+9dL5PEuTd9UbCh4LFAFYarAQGgIAwDQ++8daGv7JnrhqDYWPBYoALDVYCA0BAGD8n/ub/T1zzDKc7tJkGMx4oAjAUoOF0BAAAMb/uT9PAxRLk2EI44EiAEsNFkJDAAAY92f+XA1Q5lf36g0DGBMUAVhqsBAaAgDAuD/zZ2uAcn2m3jCAMUERgKUGC6EhAACM+zN/jgYote/VV/WGAYwJigAsNVgIDQEAYLyf93M1QLE0GYYzLigCsNRgITQEAIDxft7P1gDF0mQYzLigCMBSg4XQEAAAxvlZP18DlHpp8raaw0DGBkUAlhoshIYAADDOz/q5GqB8u7pTbxjQ2KAIwFKDhdAQAADG+Vk/VwOUeeXzPgxpbFAEYKnBQmgIsM7Yuflsrx7fXqhe+NH+b/NnO2rW6zU4fGbJHDC9cfK42s3UAMXSZBja+KAIwJIvXUJDgOXHzDqoulhjHKxDrA017PUaXLoGwKTGy29XvzKFhrfqDQMbHxQBBvCg1hsTH1d79XT+N9X/7vnPJH75EhoCLD9mXgSMhftq6BoAZHvPyNUA5bg6UXMY2BihCFDwA/q92n7+BXu+wsbCv1KFh0JDgKXHy43AsXBPHXu/BrvqCExivDyu5hmXJm+pOQxsjFAEKPUX+PVZSR3JhIYAS4+Xu4Fj4YY6ugYAWcbL53eHTF2Tb9QbBjhGKAIU+GAeV/uBU/93E7yACQ0Blhsv5wHj4L0a9n4N7tQQmMg7R74GKJYmwzDHCUWAwh7K79VW8L4ix9c/E7yACQ0BlhsvfwWMgxdq2Ps1+KWGwCTGynwNUP5Jvf86kGicUAQo7KFsmpsE/2I+T/ACJjQEWG68vAsYB83E6P8azNUQGP04mbMByvyqUnMY6FihCFDYQxllX5HqNMELmNAQ4POxUhOU4V+DXXUERj9W5myAclwdqTkMdKxQBCjogayXJhf6i1loCLDUWKkBh2sAUP5YmasBiqXJMOyxQhGgoAcy1jd+x1X0mSpCQ4ClxkpNUIZ9DTRBAabwzpGvAYqlyTDs8UIRoKAHMsZ+hvXeJP/395cEL2FCQ4DPx0pNUIZ9DTRBAcY/TuZsgHJcHao5DHi8UAQo6IGcX51HCA2TvPAIDZnMc/h/f39pZv0uPlDffvLM3TbPrQ/E/DtWaoIy7GswV0Ng1GNkzgYo9c/56+8NdYcBjxmKAAU9kPOri1K/zRMaMqEP0ndrPnsCn6nfP5qgjOEa7KojMOpxMmcDlG9Xl2oOAx8zFAEKeiDDlwrcp1ia3L6ICQ0Z/zM4v/4R8Pydq+HE7x8NOFwDgPLfN+4sTQaWHjMUAQp6II+rvaDp/9+rnYQvYkJDxv8Mfr4c2UxDPhonNUEZ9jXQBAUY9xj5vdrOOMvQ0mQYw7ihCFBcaHG/1i/l4+og8YvYZobQUOhCf89evZdhYV3LGdg9pAnK0K+BJijAuMfI4+owX9fk6x9qDiMYNxQBCgwujquT51+2j0uEhQ/Nn/3r781ML2M3iUPDbfcAvT1736uvQR+OfZvuHtIEZejXYK6GwKjHyOPqIFNoeOtzEYxk3FAEKPoX++7iG8HqtOO42k+5FPmDl7F6r6inRIGh/eDo+3kL2Rjc0tKp3z+aoIzhGuyqIzDqcfKvvzeCtmJZco9ngSGMaNxQBGDFl7J6mfJZvf9WhKCwDiAvnu2rLb3f2/WH3PU/IFtaamzUgMM1ACh/rGyCw+sfzfZGcULCenVU1Xz5+r3aUmMY2ZihCEDAC1rdHGWvbmDSqsPE6h3nL/7coRkdFHc/a4JC2HioCcqwr4EmKMD0xs3v1c7zZ5ijP6uZvl1dNgHgW46vz16sejpqVkRl2iIJ6HGcUAQAJv/LUBMUQu8hTVCGfg00QQEAeP35ShEAmPwvQ01QCL2HNEEZ+jWYqyEAwKvPV4oAwOR/GWqCQsj9M5t90QRl8NdgVx0BAF59xlIEACb/y1ATFELun9nsqwYcrgEAwOg+YykCQOaB93u1NTuuDhcbSdebSv/ZZPr8xQbTu/U+e+qV6ZpMoAlKc0/Vft9jjRcbnH+7+tX5dyO+BxfL0Zvn7+d/nr/n81757xtgE5R2Zt5u25jqd5Oqy1eNq07aP7NV/DUNuwa3K/6szWcHbY1+vGj4dfmilnXdjOEAwLA/NysCFPRA/vX3Zt1Q4d2X+vn1j/al9vD5pXc74gv0VidM6AZZ3UDhuDqI+bPffaH/HaotOrn9ereD26Jz20ak+m+8qsPPF+d/+SJM2Vv1Zy5q/Pz3fbu6WzqM+nb11Jx7XfPI4U3bLe/jGr+85xJf8/9cg2a58HvH1R7T4lrsL3MtXvy9bwtrgnLy4d/92vO9kKWWi+fopK3bPwFum5pnbvbyn/Gwey/8DvhO6vNc+u9b/D0PS5zzY30PrhAinQ+hCUobdh3VP3ON46wDsYMVf95uGzzWIdrZi3CtehGu1eHbZoRzC7kGP5f4+zfaYHLVfRPrc/2a89kBAIj2+VERoJCwcH51scbLfBUamDVhQB1OFTC7qm1GsfqMrzoECAy1mvBpuTDh98+8q6/bUuHcIuz4J0j985YMRz4Nz9a51+p7ZI0ZWCteg5MmrFnn+n9wLZprsM7fm9JxdZgoKNxa+5le9j48ro5Sz0BcBNorXLPj63dDn6Yzdv0lw6o1qf/8kuNrPVOt5CYoz//stKHfPxE8fHbMz/9srxiuPdbHGHiOIdfg8IO/d7MNPB8D63YxhBmbAABCQyjtQWxmsa0dPhys/XProKyg5g/PdbjpI4RpQoVVAsN/f+buJwHoRfyw6fosJLBpZ68Wt3dfHfoGhnDz9885YOlxKt+ufsUPC4P2ZVzVY7Lgsw62Vw8979+py85Ks3tfW2JWaMlNUNrA61eksPC1mzocfOfn3uQMTyNcg613gtbzBHU787kHABAaAsu/IIeFJbvrh4bN8tt1Q4+bqHWog7uwOqz94t0GfOsECtvvhDdV4sBm7fAuKMj8dvWQ7DkICzPr63/09j0euPQ4nSri+HHeY/h5ucyM29XGpXpJ8urH8c61D5thukRAX2oDjnbp71OiwPC3+u8/evVzN9b8u/YDzjXkGty/8fcdJa7dD59/AAChIZDmBfmlgOXJYTOwrqO+9Kwd3MWoQ7N/3urLdd/4ew6yLYP9YDlmstAwsM4fHlfIbLAPwvMoS8MThW1Rxo51ZsimmHX4vdqJOCaerDMD943aPAVeo6VC8tKaoLSh3UXisPC10xc/fzfWbL8VznkeI8BrZyz+ylSzuc9AAIDQEIj/gvxif7G1f+5iSe5TQEhzGLkO84AX/KAX77Vmar2aaRk0azPy7LqP6xx4nBH2VXwjMN4KDHee3psRVtxehoGhb3NOfc8ufH9f0a0o98M6wfZxtf9iLDnMORu0pCYoa+wlGNNhQID3GHjeIddgr8fa7fscBAAIDYG4L8gR9kVrm0OEhEc7kesQEoJcBP7sNWZc/jvTsrcAZ9EAZCNjOBu0h+YHx3QSWIdf79zj20UGhp/swbhEwHpf6Hndx2iQstb5tYFlxMBw6dnUpTRBaffge+wpMPy9VHlv3c7Mged+G3DMX9rjfuypZps+CwEAQkMg3gtyYPDQvlwfhczsSlCH24A6rL+B/rp7Kbaz/HqZYRiwTDx4Ofy8Oo1+7UMa4LyaZfbqXA+LDQ3XCN3bELTUwDDKtgVr7vH6+Kc+MbtGLzGTt5QmKPXS3p4Dw5ddkB9yNgcJvAYXBYStJz4LAQBCQyDWC/Kn+7gtFx5pgtIGDes2QdkJnrUXZ7bh0yqNKOo/m2JWX0D9Q5cmP7wfRAc2V0m4B+Aaddopdql1xFnI6zZBaceQ28jn8elS/BKaoLQdku8LCAx7WaYbeA3mBYStdcj6xWciAEBoCIS/IEdqShE2u0sTlOaYY85qyri3YWD4dBv5GTgKO/frsyTBeFrnKz4f24MJDEO3TVhrqfr1j/o+iB3sLtk5udcmKO0su9uBB4Z9NUGplwbfFHL+hz4TAQBCQyDCC/KfF/O7oJ9dVhOUkOAofxOUekZT6JLauKoVr33IsT9GvfbPxx44G2z7g/Mss3PyCjPxmtnIoZ2lX4bdTU2q03qW8guH7azMi2g/Z829Ddc6hhTh8JLBZ99NUJ7/+TmCwLCvJihPBdXgxmciAEBoCIS/IMeYzVNaE5Swl/4emqAUOOtrhdmWwSHLCsuhPw3EwoKdm6TnWcIswzhB3mMTzC8zc64eG+rlvuGzXw/WfB7v1wop48/eXeqLkT6boDz/szuCwLDPJiilsUQZABAaAoEvyP++1M7XD+kCGkRoghI6w2u/DlSaJZUxZyu+0wzknesftiR4ib3ekt+HSwQ7zSy6usN0OYHhxWrhboQ9M+t77IPZmOnCytUb5gSHyPGe14cllyb31gSl/dmx9zGsnp22y3332///Z4ZQrq8mKOvW56heTtz+/zGvwVefiwAAoSEQ5wU5pAlKHVglmt21VnAXtlQ6fxOUFZZON8HPB0FRtGYqK4Snwee94h6KHwRTVcB9uPYS2D8/P2z58kXS8WEx4+8pd3DXGZ/CAtfzlX9mcGfvpd229171Zo0/2CfzVWDVWxOUyMuSfz3b/uTn7bR/bkxNUJZtUlKHg5sfHMNppJ8199kIABAaAnFekAMCk6AZbsfXP6OHI5mW5SYL7N7bZ23JaxQ8427F5erhgfX1WYTrvhV4zufBxxCyV2DADNfkz2hgmP7nGNbb73OtfTbb5+Ak2fNYB6D18/7GM9mOxefN/VAHycs+tz01QWkDvCh7Ca4a2LUhXexuw1sBtZgnDAx/LRvsBu5t+efn+WwEAAgNgRgvyEEdbANn9h1FrsPQmqAss3/c/hrHchv4c29XvAdCZpGFN3AI7XYbMNM2TnAaHsp98Ezs9x3qBj+ba+y5OovViCUgwF8xsOqlCUr930YIqO7XDevq/+7Z3cCboHwWph6ueBxfItTkro/PIgAAQkMo8eELe0Fee5ZVcU1QQpZK99ME5ePAcM36RJhltVKAGtjs4jb4uoeFlvfBP7/e77CnGa4fHtdiuf5DQFh3EysgCwoN1wgug/Z4fW8Je+RO76+CouxNUCLNMnwImd3XHsdGpP38SmuCUgeGO2sey1lokOuzEQAgNATCX5B7bIISe8ZO2FLpopqg3NdLbtc+nr/+3gwNSFa7/4L2tQxqhhM+ky58aXDg0vRkL/dBx1U/n2s0PXn/WAK6T684Izl6E5RFYJhuNmh4A479NX9ujFmG+5FqUC9VfhpRE5S1A8MX9QgKc302AgCEhkD4C3JA99rAmX230WsxjiYojyGBYZRaPFsxnArtoLx+QBoyy7au0V9/b0aodXFNUIJnGUbcOiB3g6LoTVASBoaRAqLNNX7mZoSQ7jJyHQ5H0gTlKSQwfHFMQfs9+nwEAAgNAU1Q/g3uxtEEZY09DN8J0+6zhYbhHZTXCmWaGZVB4ej1jyi1LrAJSmCQ+xhzFnDQfp9rzEiO/DweJR/DwxpwPKz5M08iBGPbkeuwOZImKIeR6nEvNAQAhIZAaDigCcr/grsGF9IEpTqNGNRU2ULD8EYg8zWveVg4FGFPzVKboITtsRkpTG2W7QfNRl7ry4XAmZ/JZ4G+EQ5lb4ISodHGWYI67I+gCcp5xHpUQkMAQGgIhIYDmqD8bxRNUG4j3xfZQsM2qHnIHVIFzfCLFAiV2AQlePZxjDC1rkvY9Vn7WKL83Pp+TtSg5o1wKKQBx+kaP283QvOTFB2kTwfeBOU+Zl2EhgCA0BCIEQ5pgvK/ETRBiR6iZg4Nw/YWXPmFP8KS6N1IAV1xTVACZ9qtHV7Xe1O248JtlGfi+Zle+RhiNUGJtE3AEsFQ9iYoEWbVnSWqxeXAm6DsR66H0BAAEBoCAQ+dJigvQ6vHwTZB+Xb1K0E97rOGhsfXZwHnf7fG+V2UcP+V1gQlQtORo6XGnXom4XF1UC+pb5fn30cJ6wIbAgXP/FwzxA4IhvpognIf+DO3E9XisY/ALlITlMsE9bgTGgIAQkNg/YeuzyYoITPZ4jdB2R5sE5RIHXzfuD6PAcd1v0YdDnLdi8FL4yPOIiutCUrwdSjFmtcoShOUyLN+PwmGsjZBqZuFBIZjN4nqEHpcWz1dg2jdkt84rofcDXIAAISGMK7QsL8mKGEz+2I3QTnIGZC9qkNoE5TzJPdG2DFVK/+88D0ut1c4t5Cl0A+xlsaX2AQlYhOQHq3fiCXC+V9kHcMzN0GpO/sGBmRHieow5CYoF4lqUly4CwAgNIQhPXT9NUHZLmkmz6CboCSY1VTPXMwdZAbv7bjkzLIIswzn0epcWBOU4KXJfauPPfD6BDdByTjLsA2GcjdBOQ+cUbeRqA5nA26CspugHhuBx/Qr530MACA0hDJDw76aoBwEBQOxm6CE1aG/JihrNHrIEmbNq9O1fm7YUt35cud2/TNon7yI915pTVCi7K/Zn/uQPVab8w9vgnKbdfzupwnKfV/h3CfHFdL0o88mKLeJ6rFTYrMaAAChIQzlgdME5XdQstXXEtEIHXwPEoWG8z6OK2jm6xL7XDYzKINm0q2/7PXN4ymtCUqM/fx62b/w+meMWZfBYXnkbROWCIayNkGJsG9gqq7JG+0sxiE2QZknqkmRy8gBAISGMJQHThOUOEFJn01QIi9PjRRm/bNO19pFPQI6KC+xj2JQWJ2g4UxxTVCGtJ/hIvw9X/deG9Lz+EEwlLsJymHumY1LHtd+4HFt9XQNgn72J8cVus/ijs9JAIDQEKYdGmqCsghKLgOCi7vAOpz3dQ2ShVkBy2YDl61/GIIUN8uwxCYoIcv083lswt8UHcPDQtPb7GN4/iYo5zlnNq5wXD8H2gTlPuG9cdtXTQAAhIYwhgdOE5Twpcnfrn4FXoPbUkKsaGFW2L0R1qTkg9mvxc0yLK0JSnjzm5Rum1mogXsWLhEa3pX2PCYMhk7X+Hl3hQZkDwNtgvIzUT1Cm6Bc5L6XAQCEhlDaA/ft6mHqTVAC91YMq0OmbsHZw6zj6rC3mrwTKJc2y7Ctc2lNULYLCQjvm+0L6pCwHisyLfmNMPNzP+v43U8TlOKCqAhLk8+GdA2WPK69EvdZBAAQGsJQHrbQWUUjaILSBFQhwekiKNgNCGnCmqCk2s8wNEgN3GMuaPblOw1YgmbVJphl2BxTeU1QAjtmN/uUrqC+z6rTRr2/auJZhBmex82sxzu8Jiiniepw2Vdwl/sarHBcZ/YzBACEhkBIQLA/9SYoUTrF9tUEJXAvxXePKTxIDZ4BFxSmvdEcJLjhT6pl4KU1QUm4n+RAxsT5kM6/hyYou4FB1EmCGmwHHlOfTVBKXa5tP0MAoNzP7IoAmR62ZoZPP7P9SmiCEmWWYZ9NUAL3UvwgONkPDFLPIxzDSay6tNf5LuDvSzPLsMQmKM/PVkCdboY/JgY1Jcq+B1wPTVCOAsO5w8Jq0HcTlFTLtUOXJtvPEAAo9zO7IkC2F+RhNkGJFJYEhqb9N0EJ2Evxw2MK6SQduJ/hi/BqP1Z4FT6bNNEsw8KaoAQ/E4lC7MxjYkBTouo0+/Hmb4JyWtL+ffUS2gizDPtsgnKS6L74ZT9DAGC0OYYiQKaHLawJytqz/YKXikaY9RXeFCM8uIvQ8CP6/m9RuucG7mfYHEdYsPwY8To/ptqnrrQmKItjuv7Z5wzTXsfDQpsSfRAM9dEEJTQ03I1cg9sIoWGfTVB2E9wXddfkp8Dj2vYZCQAo9nO7IkCGBy28CcpOT2HJP1HOP2yWZf9NUCJ3kP63LoGzLyMuUQ0K+9pZeIGNRpLN5oxwbBflHVP+mXZRz314TVBCZ9ltrvEzf5QSktVLnSMEhqFNUEKvwUaC+2IeeEy3Q36OAQChIRDjQQtb/hkUWAXOZrqPcO7zKIFhn01QInaQ/nM8MWZfRlia/Od4QpaK1gFQ6PLfeh/EBMHsi4DurrSALqhBUaJl3BnHxKE1QQnZX/BhzZ95XkJo2DY/eYoUGm71dA1uE9wTm4ENUGpHQ36OAQChIRAlHOixCcq3q5u+QsMmTIqxLLnvJigRO0i/OJ6LwHpEnf0Y2EH5KGx/urTLTSMshT1Iclxhz+agmycMsAnKz9yNLiKEhgcRzrteEnwXKTB87PEanCe4J0Kvz1OK2Y8AAFE/8ygCZHlBvugrsKqDvz5Cw3qvvcCfHbcJStBMs+fziNgII0LH5OhBZlAH5ZDu3AtV0ufve7XT17L4T+7Jy75C9ALGRE1QPv+ZZ313T47Q5KOUJig3dQAa8X7Yi1CPQe9LCgAIDYF44UAvTVDal/OgQGfNkCZuYBjaBOWvvzciHEOU2U3trLfw2gTsc/nmccUIMgs5lzfO7SDw+JI0Kgiabfft6mmw4+Ewm6CELM/dX/PnnvYZSkWYSRe7CcpTCSFdeyz3EeqxM9RnGAAQGgKxHrIem6C0wUTW7rxJAsPQJiih++1FbNQRuCwz2fLM9rrlDwwTLP3+b80DG84k6Jy9OK7rHyUeV/LroQlKrtDwcd3ZdQkCw76boJQ2+3LQ2wsAAEJDINZD1mcTlNDAchGUnawUzoXMqiyzCcra9UgSGNb3xIpB7grH95g1MKzvlQwBUHDdU+1pGBpmJl7WnXBM1ARluZ97ECGc2lvxZ9bNPS4TBIZ9NkF5vY/gYcBxxApTd3w+AgCEhkC/TVBizB5bBDsfBnaL5YbBAUi6JighTT7edr5KmNt2Sr6M87PTdc0NbMzRa/fnT87rssRwLnjZdM4a1mNJE/Y9P+eBy7U1QVn658aYXXe/bFjX7tP3kCgw7LMJylt+rPjzY4apZhkCAEJD4M8Lcm9NUNqfHyW0e2t222ImY3WabHZhGU1Q3m+OUocoHwSqzV6Ki87CsWbwPaacmVffbxlDwyrjM1iFH+/HzTfaJbfnzc9actlwlL0262cv1czTRVB49J/6hT6PmqAs+3O/RAqpPgwO27Aw1ezCEpqgvOeuncH45YOfu/Fs3i71jjXTcWuon2cAAKEhEPsh67EJSvuCHnPJ6W0TjNQzt9IEcaU2Qfmsc3A1O74+W8y2/O3qollKPKCZeVGXcX++xHo74zN4GS0obvYh7Fzn8zee8fMVns/bOMf2fFyBHb7bkPCgCY8/fr7XDnw1QVn5599FDMl+tfsk/vYrUkOPITRB+Wzvx6rtVv2yPpcJfu6Rz0UAgNAQ+B1Y9doEJW4oESksWifELKEJSv+1u0x+vx5Xe5man5xlfQ7jNJ9JMotyETbHDLCvfyyCv+f7/oNZqc3MyPrPHFcnzfL9Vb7cCLgXNUFZ+eefZQr1VpkpN9QmKH0b5B6kAIDQEEgXwvTWBKXHwOTDpYVrhYalNEHpz2OWhiExGucsM1svwn292jMQ2KU44b579RcDA7wfzwOexyNNUFb6+buFBV/rNgLZ6ukalBS2bvlcBAAIDYEXYUV/TVCKC83qJhvrBCTlNUHJL1H33ndC5sfE57Kb/TnM/QysuBdp0L6n/dyP8/WvRdC+mZNpgvLqGB5KmSn37GQETVD6cOAzEQAgNATihQERmqA0x/C92i4gaLhv90vbXyM0LLEJyiACmjXrdVNKmBbtnHLP5ltx78nBzTYM2S5AE5R1juFHAaHXfdsU5GIkTVByso8hACA0BN4MYHptgpIlCFpmaW3b9GKtmZdlN0FJ7PpH9ns2XQfl7MuSX4VVj9mu2xrdjAcz2zBg24SmCUpIc6CJNUF5dRx9zjasG4Vst8eyTuOUPpqg3BcSGJ76LAQACA2BtwKr3pug/BsEVYc9hQy3LwOUtYKR6TZBOe/lvk21lLeHZcndUC7bvob3a44XG02wWn5ouPbM3+AZlRNrgvLqWOY9hV63v/fia2caDqUJyk4BMxTPfRYCAISGwHvhS+9NUP4cSz3DJ3cX5XovwVfnsFYo0lcTlMXszNt+wpn8yzBfBDtf4weGebslvxPKbQbNcsswO7RZwl96cBgw208TlKBj+dJDCFYvRd54cQx7A2mC8tj+t1s9zjic+xwEAAgNgfcfrgKaoLwKJLbzhCZvLylea6lwr01Qrn+0x5wzOHzMvQQzynX65Br2uSy5G1pdn2VYmvw18DndCdrWIO0XAZeB9dcEJex4NjOGYGdv/PyhNEG5fPHf77TLq3Mu5d71GQgAEBoCn4SG/TdB+e9Le3WYNDisw453Qq+1ZrD12QSl7VjchGh59oS8/b33Y+/3bszQKjBES3Bulwnv/5sox7gIbs8LCw1vQ5cHa4IS5ZhSh2AP73X6HVATlLNXf8fXZ3eZuktv+fwDAAgNgbTBS8QmKG8Eh7sJmkLcN0sPP5hRtta+igF1CJ4x92pPyedjOUkUuN73PbswXbDW3zLrd89tsVT/PEFg+BRzH9L2ntsrYNbhp8/2CqHhfaznMVNoGDKrbyfhcW0/u0nQHfmwXgb9wc+tci/TXfMa7Lzx99TLu88S7vtY1BgOACA0hJIfrIKaoLx5fIu9084jBCV3dRi45M/cXikAqZcyBgQVQU1Qvl09fXBdLyKGMfMi798YTUPq2ZmFLEv+IJC7i3QtL9bpmLzCsR4knSH5xjLkZj/Q2CHoOls2LGYvH/RyjyyajzzFmqmX4PhOArs7/9POwDtc8uedrvh3//gohFzhHJf9eXUtTj75+76uGX4G1Q4AYLDZhiJAkpf8YpqgfHicdXi42OdtlWWDj01IssbsuGaWVx3m1bP2mgDhDfW/j9AlNbQJyod/dx0eLv7+VZdb3jbnWMgy5HfPL2gvyD+z7oo+xxfPwNcmQF8lQKyDrEWwdpSzo++f+25xfW4jhIP1zNmqCejrZ7J+9hKPPUuNAYtxYL+Ee6jtGLzbBmYf2a9nAPZ0fIcrBmF1sPlrndlx7fLow09qsfuygUqEc9z85GeetD/zywp/51YbCt+tMavwpI9rDQDQy+dhRYAED1ZYw4Wql2NeBBLvv8zXAUkPSwTXPp/AJigrhE5bbd0O3qnbXvPvE85Ei3vvrrGMPOPy+sTPwEZ7LfdeXcOT9n/fDenmneSY69BzcWzzD0O4l/digedBhHthEa7ttgHmW+HaPOWy6QHXbfuTuu21/34QYzgAQNTPSooACR6soKWE5e0DN9BrENIE5XCSNYvRYTuwwy4AAACFvCMqAkR+qOrldyHBy3G1p46B1yByE5QJ3bd3gYHhQ87lugAAACR8T1QEiPxQNctUA4IXywZjXIPdIewpWVTNQvcxFHgDAACM6z1RESDyQxXWoOBWDSNcg5AmKBO8BoH1+tPt2r0HAAAwondFRYCID1Rw+GI/wyjXIWTW3MTCr6aRRvg+hndTnJ0JAAAw6vdFRYBID1Pdfbje0y1sL70ttYxwLcKaoAyy8++a92y99+N9YGD4VAeP7jsAAICRvTMqAkR4kOo99EIDw29XN2oZ4VpogrJ8reZXF8HLks2OBQAAGOc7oyLAEg9K3Vn2uDp6dtgEhL/Nq9N6OWt48NLMcDtR6wjXKqQJSm0iy2yj7GM4v6rccwAAACN9b1QEWOJBiTIj65Mlnn/9vanWEa6VJiif1yjGPobzq0fL6QEAAEb87qgI8MlDUgcsKQPDxvUPtY50vUKaoMyvzkdfnxj7GC5mxh663wAAAEb8/qgI8MlDEmv58UcztswyjHe9wpqgzEdfnxizZr9d/XKvAQAAjPz9URHgk4cktMGJoCrftQpvgjLqLsBR9jGsn4fnOrvfAAAARv6OrQjwwQPyvdpKvJfhzVQab2S5XqFNUEYchgXX5t+Qe9e9BgAAMIF3bEWADx6QultyymXJGknEvl7zgAD3brR1+evvzSgzZo+vz9xnAAAAE3nHVgT44AE5vj5LuCx5X40jX6+QJigj3qfv+fyqCPfsrVmxAAAAE3rHVgT44AGJ0TTiv+HU0+y4OlDfBNdLE5Q37uHqNMo9+73ado8BAABM6B1bEeCDByTODK1uE4mRN9vo7VqFNkEZ4V59EfcxnLvHAAAAJvaerQjwwQNSNyqJFxpe1HvLqWuia/W9+qoJyqua1EuKw4PuS/cXAADABN+zFQE+eECOr39GCAur2fdqRz1TX6uAWXUj3M8weObl76DbPoYAAADTfM9WBPjgAWmCl+sfs/nV/aqzs2bH1YmwMPP1Oq6OVtzX8HbMS2/bejys3PCkDsvtYQgAADDtd2xFgCUfljpArGezHVeHi+YSLxxXe+2/21WrIq7VZns99l9dp5Pf12lKM+hm36utd+px+Kce9toEAADg5bukIgAAAAAALykCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANChCAAAAABAhyIAAAAAAB2KAAAAAAB0KAIAAAAA0KEIAAAAAECHIgAAAAAAHYoAAAAAAHQoAgAAAADQoQgAAAAAQIciAAAAAAAdigAAAAAAdCgCAAAAANDx/6Dfdq4bkpSWAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {
-      "image/png": {
-       "height": 100,
-       "width": 150
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename = fig_dir + \"ogs-jupyter-lab.png\", width=150, height=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d2ff19ce",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "image/png": {
-       "height": 100,
-       "width": 150
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename = fig_dir + \"h-tet-1.png\", width=150, height=100)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "4fae4b91",
@@ -127,10 +84,10 @@
    "source": [
     "**Problem description**\n",
     "\n",
-    "Theis’ problem examines the transient lowering of the water table induced by a pumping well. \n",
+    "Theis’ problem examines the transient lowering of the water table induced by a pumping well.\n",
     "The assumptions required by the Theis solution are:\n",
     "\n",
-    "The aquifer \n",
+    "The aquifer\n",
     "- is homogeneous, isotropic, confined, infinite in radial extent,\n",
     "- has uniform thickness, horizontal piezometric surface.\n",
     "\n",
@@ -192,13 +149,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#source: https://scipython.com/blog/linear-and-non-linear-fitting-of-the-theis-equation/\n",
+    "# source: https://scipython.com/blog/linear-and-non-linear-fitting-of-the-theis-equation/\n",
+    "\n",
     "\n",
     "def calc_u(r, S, T, t):\n",
     "    \"\"\"Calculate and return the dimensionless time parameter, u.\"\"\"\n",
     "\n",
     "    return r**2 * S / 4 / T / t\n",
     "\n",
+    "\n",
     "def theis_drawdown(t, S, T, Q, r):\n",
     "    \"\"\"Calculate and return the drawdown s(r,t) for parameters S, T.\n",
     "\n",
@@ -212,16 +171,17 @@
     "    \"\"\"\n",
     "\n",
     "    u = calc_u(r, S, T, t)\n",
-    "    s_theis = Q/4/np.pi/T * exp1(u)\n",
-    "    return s_theis\n",
-    " "
+    "    s_theis = Q / 4 / np.pi / T * exp1(u)\n",
+    "    return s_theis"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "id": "a24764b3",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -235,8 +195,8 @@
     }
    ],
    "source": [
-    "Q = 2000        # Pumping rate from well (m3/day)\n",
-    "r = 10          # Distance from well (m)\n",
+    "Q = 2000  # Pumping rate from well (m3/day)\n",
+    "r = 10  # Distance from well (m)\n",
     "\n",
     "# Time grid, days.\n",
     "t = np.array([1, 2, 4, 8, 12, 16, 20, 30, 40, 50, 60, 70, 80, 90, 100])\n",
@@ -248,9 +208,9 @@
     "# Plot the data\n",
     "titlestring = \"Theis: Analytical solution\"\n",
     "plt.title(titlestring)\n",
-    "plt.plot(t, s, label='r = '+str(r)+' m')\n",
-    "plt.xlabel(r'$t\\;/\\;\\mathrm{days}$')\n",
-    "plt.ylabel(r'$s\\;/\\;\\mathrm{m}$')\n",
+    "plt.plot(t, s, label=\"r = \" + str(r) + \" m\")\n",
+    "plt.xlabel(r\"$t\\;/\\;\\mathrm{days}$\")\n",
+    "plt.ylabel(r\"$s\\;/\\;\\mathrm{m}$\")\n",
     "plt.legend()\n",
     "plt.grid()\n",
     "plt.show()"
@@ -260,7 +220,9 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "f748dd1e",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -275,12 +237,12 @@
    ],
    "source": [
     "# Recalculation from days in sec\n",
-    "Q = 0.016      # Pumping rate from well (m3/s)\n",
-    "t = 864000     # Time in s.\n",
+    "Q = 0.016  # Pumping rate from well (m3/s)\n",
+    "t = 864000  # Time in s.\n",
     "\n",
     "# Distance from well (m)\n",
     "##r = np.array([0.5, 1, 2, 4, 8, 12, 16, 20, 25, 30, 35, 40])\n",
-    "r = np.arange(1,41,1)\n",
+    "r = np.arange(1, 41, 1)\n",
     "##print(r)\n",
     "\n",
     "# Calculate some synthetic data to fit.\n",
@@ -288,14 +250,14 @@
     "T = 9.2903e-4\n",
     "u = calc_u(r, S, T, t)\n",
     "s = theis_drawdown(t, S, T, Q, r)\n",
-    "s = s-5 #reference head\n",
+    "s = s - 5  # reference head\n",
     "\n",
     "# Plot the data\n",
     "titlestring = \"Theis: Analytical solution\"\n",
     "plt.title(titlestring)\n",
-    "plt.plot(r, s, label='t = '+str(t)+' days')\n",
-    "plt.xlabel(r'$r\\;/\\mathrm{m}$')\n",
-    "plt.ylabel(r'$hydraulic head\\;/\\;\\mathrm{m}$')\n",
+    "plt.plot(r, s, label=\"t = \" + str(t) + \" days\")\n",
+    "plt.xlabel(r\"$r\\;/\\mathrm{m}$\")\n",
+    "plt.ylabel(r\"$hydraulic head\\;/\\;\\mathrm{m}$\")\n",
     "plt.legend()\n",
     "plt.grid()\n",
     "plt.show()"
@@ -314,6 +276,7 @@
    "execution_count": 8,
    "id": "f66a6aec",
    "metadata": {
+    "lines_to_next_cell": 2,
     "scrolled": true
    },
    "outputs": [
@@ -371,7 +334,9 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "9e5ae5ee",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -403,19 +368,19 @@
    ],
    "source": [
     "print(\"inspecting mesh and initial conditions\")\n",
-    "#file\n",
+    "# file\n",
     "reader = vtk.vtkXMLUnstructuredGridReader()\n",
     "reader.SetFileName(vtu_name)\n",
     "reader.Update()  # Needed because of GetScalarRange\n",
     "data = reader.GetOutput()\n",
     "pressure = data.GetPointData().GetArray(\"OGS5_pressure\")\n",
-    "#points\n",
+    "# points\n",
     "points = data.GetPoints()\n",
     "npts = points.GetNumberOfPoints()\n",
     "x = vtk_to_numpy(points.GetData())\n",
-    "triang = tri.Triangulation(x[:,0], x[:,1])\n",
-    "#plt.triplot(triang, 'go-', lw=1.0)\n",
-    "plt.triplot(triang,lw=0.2)\n",
+    "triang = tri.Triangulation(x[:, 0], x[:, 1])\n",
+    "# plt.triplot(triang, 'go-', lw=1.0)\n",
+    "plt.triplot(triang, lw=0.2)\n",
     "plt.tricontour(triang, pressure, 16)"
    ]
   },
@@ -431,7 +396,9 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "66ef056a",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -444,7 +411,7 @@
     }
    ],
    "source": [
-    "#run ogs\n",
+    "# run ogs\n",
     "t0 = time.time()\n",
     "print(\"run ogs\")\n",
     "print(f\"ogs {prj_file} > log.txt\")\n",
@@ -466,6 +433,7 @@
    "execution_count": 14,
    "id": "c2f0bf2c",
    "metadata": {
+    "lines_to_next_cell": 2,
     "scrolled": true
    },
    "outputs": [
@@ -494,27 +462,32 @@
     "import vtuIO\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "#Read simulation results\n",
+    "\n",
+    "# Read simulation results\n",
     "pvdfile = vtuIO.PVDIO(f\"{out_dir}/{pvd_name}.pvd\", dim=2)\n",
-    "xaxis =  [(i,0,0) for i in np.linspace(start=1.0, stop=40, num=40)]\n",
+    "xaxis = [(i, 0, 0) for i in np.linspace(start=1.0, stop=40, num=40)]\n",
     "\n",
-    "r_x = np.array(xaxis)[:,0]\n",
-    "time = [8.64,86.4,1728,24192,172800,604800,864000]\n",
+    "r_x = np.array(xaxis)[:, 0]\n",
+    "time = [8.64, 86.4, 1728, 24192, 172800, 604800, 864000]\n",
     "\n",
-    "pressure_xaxis_t = pvdfile.read_set_data(t, 'OGS5_pressure', data_type=\"point\", pointsetarray=xaxis)\n",
+    "pressure_xaxis_t = pvdfile.read_set_data(\n",
+    "    t, \"OGS5_pressure\", data_type=\"point\", pointsetarray=xaxis\n",
+    ")\n",
     "\n",
-    "plt.plot(r_x, pressure_xaxis_t, 'x', label='OGS5, t = 1728 sec')\n",
+    "plt.plot(r_x, pressure_xaxis_t, \"x\", label=\"OGS5, t = 1728 sec\")\n",
     "\n",
     "for t in time:\n",
-    "    pressure_xaxis_t = pvdfile.read_set_data(t, 'pressure', data_type=\"point\", pointsetarray=xaxis)\n",
-    "    plt.plot(r_x, pressure_xaxis_t, label='t = '+str(t)+' sec')\n",
+    "    pressure_xaxis_t = pvdfile.read_set_data(\n",
+    "        t, \"pressure\", data_type=\"point\", pointsetarray=xaxis\n",
+    "    )\n",
+    "    plt.plot(r_x, pressure_xaxis_t, label=\"t = \" + str(t) + \" sec\")\n",
     "titlestring = \"Theis: Numerical solution\"\n",
     "plt.title(titlestring)\n",
-    "plt.xlabel(r'$r\\;/\\mathrm{m}$')\n",
-    "plt.ylabel(r'$hydraulic head\\;/\\;\\mathrm{m}$')\n",
+    "plt.xlabel(r\"$r\\;/\\mathrm{m}$\")\n",
+    "plt.ylabel(r\"$hydraulic head\\;/\\;\\mathrm{m}$\")\n",
     "plt.legend()\n",
     "plt.grid()\n",
-    "#plt.savefig(\"theis.png\")\n",
+    "# plt.savefig(\"theis.png\")\n",
     "plt.show()"
    ]
   },
@@ -522,7 +495,9 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "5366c257",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -539,20 +514,22 @@
    ],
    "source": [
     "time = [864000]\n",
-    "pressure_xaxis_t = pvdfile.read_set_data(t, 'pressure', data_type=\"point\", pointsetarray=xaxis)\n",
-    "#plot configuration\n",
+    "pressure_xaxis_t = pvdfile.read_set_data(\n",
+    "    t, \"pressure\", data_type=\"point\", pointsetarray=xaxis\n",
+    ")\n",
+    "# plot configuration\n",
     "##plt.rcParams['figure.figsize'] = (16, 6)\n",
     "##plt.rcParams['font.size'] = 12\n",
     "##fig1, (ax1, ax2) = plt.subplots(1, 2)\n",
     "\n",
-    "fig, ax=plt.subplots(ncols=2, figsize=(12,4))\n",
+    "fig, ax = plt.subplots(ncols=2, figsize=(12, 4))\n",
     "titlestring = \"Theis: Comparison analytical and numerical solution\"\n",
     "ax[0].set_title(titlestring)\n",
-    "ax[0].set_xlim(0,40)\n",
-    "ax[0].plot(r_x, pressure_xaxis_t, 'x', label='numerical solution (ogs6)')\n",
-    "ax[0].plot(r, s, label='analytical solution')\n",
-    "ax[0].set_xlabel(r'$radius\\;/\\;\\mathrm{m}$')\n",
-    "ax[0].set_ylabel(r'$hydraulic head\\;/\\;\\mathrm{m}$')\n",
+    "ax[0].set_xlim(0, 40)\n",
+    "ax[0].plot(r_x, pressure_xaxis_t, \"x\", label=\"numerical solution (ogs6)\")\n",
+    "ax[0].plot(r, s, label=\"analytical solution\")\n",
+    "ax[0].set_xlabel(r\"$radius\\;/\\;\\mathrm{m}$\")\n",
+    "ax[0].set_ylabel(r\"$hydraulic head\\;/\\;\\mathrm{m}$\")\n",
     "ax[0].grid()\n",
     "ax[0].legend()\n",
     "\n",
@@ -561,12 +538,12 @@
     "titlestring = \"Difference between analytical and numerical solutions\"\n",
     "caption = \"Differences are due to different boundary conditions\"\n",
     "ax[1].set_title(titlestring)\n",
-    "ax[1].set_xlim(0,40)\n",
-    "ax[1].plot(r, s-pressure_xaxis_t, label='')\n",
-    "ax[1].set_xlabel(r'$radius\\;/\\;\\mathrm{m}$')\n",
-    "ax[1].set_ylabel(r'$diff\\;/\\;\\mathrm{m}$')\n",
+    "ax[1].set_xlim(0, 40)\n",
+    "ax[1].plot(r, s - pressure_xaxis_t, label=\"\")\n",
+    "ax[1].set_xlabel(r\"$radius\\;/\\;\\mathrm{m}$\")\n",
+    "ax[1].set_ylabel(r\"$diff\\;/\\;\\mathrm{m}$\")\n",
     "ax[1].grid()\n",
-    "ax[1].text(5,0.7,caption,ha='left')\n",
+    "ax[1].text(5, 0.7, caption, ha=\"left\")\n",
     "\n",
     "##plt.savefig(\"theis-ana+num.png\")\n",
     "plt.show()"
@@ -576,7 +553,9 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "78afcf25",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -588,6 +567,7 @@
    ],
    "source": [
     "import time\n",
+    "\n",
     "print(time.ctime())"
    ]
   },
diff --git a/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb b/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb
index 0892905fe13..b8e2a325342 100644
--- a/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb
+++ b/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb
@@ -5,11 +5,12 @@
    "id": "c65efb9d",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Liquid flow in blocking and conducting fractures\"\n",
     "date = \"2023-08-05\"\n",
     "author = \"Mehran Ghasbeh\"\n",
     "web_subsection = \"liquid-flow\"\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -61,7 +62,9 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "5734603b",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# HIDDEN\n",
@@ -78,7 +81,9 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "44af4b59",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Setup model\n",
@@ -97,7 +102,9 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "4311e1db",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -119,7 +126,9 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "90f87e2b",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -148,7 +157,9 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "92a1e953",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Get the nodal coordinates from vtufilhe porous media include e\n",
@@ -160,7 +171,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "ac9f7f8a",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Triangulation# Post-Processing (Pressure Field in Conducting and Blocking Fracture)\n",
@@ -171,7 +184,9 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "8d364a9a",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Get the pressure field from vtufile\n",
@@ -184,7 +199,9 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "1b559c58-8ec6-4ea4-8ced-1a0249b0b678",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Get the velocty fields along x and y directions\n",
@@ -198,7 +215,9 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "04194f56",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -242,7 +261,9 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "9ba0c137",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -282,7 +303,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "a2fa5765",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# Calculate the magnitude of the velocity vector fieldlevels = np.linspace(np.min(field), np.max(field), 58)\n",
@@ -293,7 +316,9 @@
    "cell_type": "code",
    "execution_count": 28,
    "id": "4bdece05",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
@@ -346,7 +371,9 @@
    "cell_type": "code",
    "execution_count": 31,
    "id": "fa9a267d",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -368,6 +395,7 @@
    "execution_count": 32,
    "id": "98351c6e",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
diff --git a/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb b/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb
index 340ae3aad10..124c46ffb24 100644
--- a/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb
+++ b/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb
@@ -1,430 +1,507 @@
 {
-    "cells": [
-        {
-            "cell_type": "raw",
-            "metadata": {},
-            "source": [
-                "author = \"Boyan Meng and Yonghui Huang\"\n",
-                "date = \"2022-07-01\"\n",
-                "title = \"Heat pipe problem\"\n",
-                "web_subsection = \"thermal-two-phase-flow\"\n",
-                "<!--eofm-->"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import os\n",
-                "import vtuIO\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "import matplotlib.pyplot as plt\n",
-                "from mpl_toolkits.axes_grid1.inset_locator import (inset_axes, InsetPosition, mark_inset)\n",
-                "from IPython.display import display, Image\n",
-                "\n",
-                "plt.rcParams['legend.fontsize']=20\n",
-                "plt.rcParams['font.size'] = 20"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "## Introduction\n",
-                "\n",
-                "When an unsaturated porous medium is subject to a constant heat flux and the temperature is sufficiently high, water is heated and vaporizes. Vapor flows under its pressure gradient towards the cooler end where it condenses. Vaporization and condensation produce a liquid saturation gradient, creating a capillary pressure gradient inside the porous medium. Condensate flows towards the hot end under the influence of a capillary pressure gradient. This is a heat pipe in an unsaturated porous medium.\n",
-                "\n",
-                "A benchmark regarding the heat pipe problem was proposed by Udell and Fitch (1985). A semi-analytical solution was provided for a non-isothermal water-gas system in a porous medium, in which heat convection, conduction, and latent heat transport as well as capillary effects play a key role."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Physical Scenario\n",
-                "\n",
-                "As shown in the below figure, the heat pipe was represented by a 2D horizontal column (2.4 m in length and 0.2 m in width) of porous media, which was partially saturated with a liquid phase ($S_w$ = 0.41) at the beginning. A heater is installed on the right-hand-side of the horizontal column generating a constant heat flux of 100 $\\mathrm{W/m^2}$ and raises the temperature gradually above the boiling point. At the left-hand boundary, we impose the constant gas phase pressure ($P_g$ = 101934 Pa), constant liquid saturation ($S_w$ = 0.97) and constant temperature ($T$ = 343 K) as Dirichlet boundary conditions."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
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-                        "text/plain": [
-                            "<IPython.core.display.Image object>"
-                        ]
-                    },
-                    "metadata": {
-                        "image/jpeg": {
-                            "width": 1000
-                        }
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "display(Image(filename=f\"./model_domain.jpg\", width=1000))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Model parameters and numerical settings\n",
-                "\n",
-                "In this benchmark, the thermal conductivity for an unsaturated medium is given as:\n",
-                "\\begin{equation}\n",
-                "    \\lambda(S_w)=\\lambda_{S_w=0}+\\sqrt {S_w}\\left(\\lambda_{S_w = 1}-\\lambda_{S_w = 0})\\right.\n",
-                "\\end{equation}\n",
-                "The capillary pressure is dependent on the liquid saturation via the Leverett (Leverett et al. (1941)) function:\n",
-                "\\begin{equation}\n",
-                "        P_c(S_w)=\\sqrt{\\frac{\\phi}{K}}\\gamma\\left(1.417(1-S_w)-2.12(1-S_w)^2+1.263(1-Sw)^3)\\right.\n",
-                "\\end{equation}\n",
-                "where $\\gamma$ = 0.05878 N/m stands for the surface tension of water. The relative permeabilities are calculated using the Udell (Udell and Fitch (1985)) model:\n",
-                "\\begin{equation}\n",
-                "    k_{rL}=S_w^3, \n",
-                "\\end{equation}\n",
-                "\\begin{equation}\n",
-                "    k_{rG}=(1-S_w)^3. \n",
-                "\\end{equation}\n",
-                "The rest of the parameters used in this benchmark are listed in the following table.\n",
-                "\n",
-                "| Parameter | Value | Unit |\n",
-                "| :-: | :-: | :-: |\n",
-                "| Intrinsic permeability $K$ | 1e-12 | m$^2$ |\n",
-                "| Porosity                                        $\\phi$          | 0.4                 |   -                     |\n",
-                "| Thermal conductivity of dry porous medium       $\\lambda_{S_w=0}$ | 0.582               | W/m/K   |\n",
-                "| Thermal conductivity of saturated porous medium $\\lambda_{S_w=1}$ | 1.14                | W/m/K  |\n",
-                "| Specific heat capacity of soil grain            $c_{p,s}$       | 700                 | J/kg/K   |\n",
-                "| Specific heat capacity of air                   $c_{v,a}$       | 733                 | J/kg/K   |\n",
-                "| Specific heat capacity of water                 $c_{p,w}$       | 4187                | J/kg/K   |\n",
-                "| Density of water                                $\\rho_w$        |1000                | kg/m$^3$          |\n",
-                "| Density of soil grain                           $\\rho_s$        | 2650                | kg/m$^3$          |\n",
-                "| Dynamic viscosity of the liquid phase           $\\mu_{L}$       | 2.938e-4  | Pa s                |\n",
-                "| Dynamic viscosity of the gas phase              $\\mu_{G}$       | 1.8e-5   | Pa s                |\n",
-                "| Diffusion coefficient in free gas               $D_{0a}$        | 2.6e-6  | m$^2$/s         |\n",
-                "| Diffusion coefficient in free water             $D_{0w}$        | 3.0e-9   | m$^2$/s           |\n",
-                "| Latent heat of water vaporization               $h_{\\Delta e}$  | 2.258e6   | J/kg          |\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Results and analysis\n",
-                "\n",
-                "In the CTEST-small, the comparison is made for the time of 10000 seconds. The profiles of saturation and temperature are plotted below."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 2000x800 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "filename = \"ref_t_10000.000000.vtu\"\n",
-                "x=np.array([i*0.012 for i in range(201)])\n",
-                "r = np.array([[i,0.1,0.0] for i in x])\n",
-                "\n",
-                "f = vtuIO.VTUIO(filename, nneighbors=100, dim=2)\n",
-                "resp = {}\n",
-                "resp[0] = f.get_set_data(\"saturation\",pointsetarray=r)\n",
-                "resp[1] = f.get_set_data(\"temperature\",pointsetarray=r)\n",
-                "\n",
-                "fig, ax = plt.subplots(ncols=2,figsize=(20,8))\n",
-                "for i in range(2):\n",
-                "    ax[i].plot(x, resp[i], lw=2, label= \"OGS, $t$ = 10000s\")\n",
-                "    ax[i].set_xlim([0,2.4])\n",
-                "    ax[i].set_xlabel('$x$ / m')\n",
-                "    ax[i].legend()   \n",
-                "ax[0].set_ylabel('$S_w$ / -')  \n",
-                "ax[1].set_ylabel('$T$ / K') \n",
-                "ax[0].set_title('saturation')  \n",
-                "ax[1].set_title('temperature')\n",
-                "fig.tight_layout()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "In the CTEST-large, the comparison is made for the time of 1.4e6 seconds. Around this time, the water is fully evaporated from the heating boundary (right hand side), and single phase zone of gas phase is formulated, while the temperature at this part begins to increase significantly, as shown below."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 2000x800 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "filename = \"ref_t_1400000.000000.vtu\"\n",
-                "\n",
-                "f = vtuIO.VTUIO(filename, nneighbors=100, dim=2)\n",
-                "resp = {}\n",
-                "resp[0] = f.get_set_data(\"saturation\",pointsetarray=r)\n",
-                "resp[1] = f.get_set_data(\"temperature\",pointsetarray=r)\n",
-                "\n",
-                "fig, ax = plt.subplots(ncols=2,figsize=(20,8))\n",
-                "for i in range(2):\n",
-                "    ax[i].plot(x, resp[i], lw=2, label= \"OGS, $t$ = 1.4e6s\")\n",
-                "    ax[i].set_xlim([0,2.4])\n",
-                "    ax[i].set_xlabel('$x$ / m')\n",
-                "    ax[i].legend(fontsize=20)   \n",
-                "ax[0].set_ylabel('$S_w$ / -')  \n",
-                "ax[1].set_ylabel('$T$ / K') \n",
-                "ax[0].set_ylim([0,1])\n",
-                "ax[0].set_title('saturation')  \n",
-                "ax[1].set_title('temperature')\n",
-                "fig.tight_layout()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "After the gas phase appearance, it is recommended to change to an adaptive time stepping scheme (e.g. Evolutionary PID Controller or Iteration Number Based) to assure the numerical stability. In the case of Iteration Number Based Time Stepping, the time step size is kept around 175 s with 4.5 iterations on average.\n",
-                "\n",
-                "For the steady-state solution of this problem, a semi-analytical solution was derived by Udell and Fitch (1985) and extended by Huang et al. (2015). Here we provide the semi-analytical solution as a <a href=\"https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/SemianalyticalSolutionHeatPipe.zip\">MATLAB script</a> which enables us to compute the steady-state gas pressure, saturation and temperature profiles along the $x$-direction (see calculated values in <a href=\"https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/SemianalyticalSolutionResults.csv\">SemianalyticalSolutionResults.csv</a>). In the following, the numerical solution by OpenGeoSys at quasi-steady state ($t$ = 2e7 s) is plotted against the semi-analytical solution for comparison. In addition, the absolute and relative errors are also illustrated."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "result_file = f\"SemianalyticalSolutionResults.csv\"\n",
-                "soln = pd.read_csv(result_file, sep=',', header=None, skiprows=0, \n",
-                "                       names=['x','saturation','temperature','pressure'], index_col=False)\n",
-                "\n",
-                "filename = \"ref_steady_status.vtu\"\n",
-                "\n",
-                "f = vtuIO.VTUIO(filename, nneighbors=100, dim=2)\n",
-                "resp = {}\n",
-                "resp[0] = f.get_set_data(\"saturation\",pointsetarray=r)\n",
-                "resp[1] = f.get_set_data(\"temperature\",pointsetarray=r)\n",
-                "resp[2] = f.get_set_data(\"gas_pressure\",pointsetarray=r)  "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[<matplotlib.axis.YTick at 0x174795300>,\n",
-                            " <matplotlib.axis.YTick at 0x1747ea680>,\n",
-                            " <matplotlib.axis.YTick at 0x174794340>]"
-                        ]
-                    },
-                    "execution_count": 7,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 1800x600 with 4 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "plt.rcParams['legend.fontsize']=14\n",
-                "plt.rcParams['font.size'] = 14\n",
-                "fig, ax = plt.subplots(ncols=3,figsize=(18,6))\n",
-                "\n",
-                "ax[0].plot(x, soln['saturation'], lw=1.5, label=\"semianalytical\")\n",
-                "ax[0].plot(x, resp[0], lw=1.5, marker='o', linestyle=\"\", markevery=5, color='r', label=\"OGS steady state\")\n",
-                "ax[1].plot(x, soln['saturation'] - resp[0], lw=1.5)            \n",
-                "ax[2].plot(x, (soln['saturation'] - resp[0])/soln['saturation'], lw=1.5)    \n",
-                "\n",
-                "for i in range(3):\n",
-                "    ax[i].set_xlim([0,2.4]) \n",
-                "    ax[i].set_xlabel('$x$ / m') \n",
-                "ax[0].set_ylabel('$S_w$ / -')\n",
-                "ax[1].set_ylabel('$\\Delta S_w$ / -')\n",
-                "ax[2].set_ylabel('$\\Delta S_w/S_{w, analytical}$')\n",
-                "ax[0].set_ylim([0,1])\n",
-                "ax[0].set_title('Saturation')  \n",
-                "ax[1].set_title('Absolute error')\n",
-                "ax[2].set_title('Relative error')\n",
-                "ax[0].legend()\n",
-                "fig.tight_layout()\n",
-                "\n",
-                "ax2 = plt.axes([0,0,0,0])\n",
-                "ip = InsetPosition(ax[0], [0.45,0.4,0.5,0.4])\n",
-                "ax2.set_axes_locator(ip)\n",
-                "patch, pp1,pp2 = mark_inset(ax[0], ax2, loc1=3, loc2=4, fc=\"none\", ec='0.5')\n",
-                "#pp1.loc1 = 3 \n",
-                "pp1.loc2 = 2 \n",
-                "#pp2.loc1 = 4 \n",
-                "pp2.loc2 = 1\n",
-                "ax2.plot(x, resp[0], lw=1.5, marker='o', linestyle=\"\", markevery=1, color='r', label=\"OGS steady state\")\n",
-                "ax2.plot(x, soln['saturation'], lw=1.5, label=\"semianalytical\")\n",
-                "ax2.set_xlim(1.57,1.63)\n",
-                "ax2.set_ylim(0,0.1)\n",
-                "ax2.set_yticks(np.arange(0,0.15,0.05))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 1800x600 with 3 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "fig, ax = plt.subplots(ncols=3,figsize=(18,6))\n",
-                "\n",
-                "ax[0].plot(x, soln['temperature'], lw=1.5, label=\"semianalytical\")\n",
-                "ax[0].plot(x, resp[1], lw=1.5, marker='o', linestyle=\"\", markevery=5, color='r', label=\"OGS steady state\")\n",
-                "ax[1].plot(x, soln['temperature'] - resp[1], lw=1.5)            \n",
-                "ax[2].plot(x, (soln['temperature'] - resp[1])/soln['temperature'], lw=1.5)    \n",
-                "\n",
-                "for i in range(3):\n",
-                "    ax[i].set_xlim([0,2.4]) \n",
-                "    ax[i].set_xlabel('$x$ / m') \n",
-                "ax[0].set_ylabel('$T$ / K')\n",
-                "ax[1].set_ylabel('$\\Delta T$ / K')\n",
-                "ax[2].set_ylabel('$\\Delta T/T_{analytical}$')\n",
-                "ax[0].set_title('Temperature')  \n",
-                "ax[1].set_title('Absolute error')\n",
-                "ax[2].set_title('Relative error')\n",
-                "ax[0].legend()\n",
-                "fig.tight_layout()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1800x600 with 3 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "fig, ax = plt.subplots(ncols=3,figsize=(18,6))\n",
-                "\n",
-                "ax[0].plot(x, soln['pressure'], lw=1.5, label=\"semianalytical\")\n",
-                "ax[0].plot(x, resp[2], lw=1.5, marker='o', linestyle=\"\", markevery=5, color='r', label=\"OGS steady state\")\n",
-                "ax[1].plot(x, soln['pressure'] - resp[2], lw=1.5)            \n",
-                "ax[2].plot(x, (soln['pressure'] - resp[2])/soln['pressure'], lw=1.5)    \n",
-                "\n",
-                "for i in range(3):\n",
-                "    ax[i].set_xlim([0,2.4]) \n",
-                "    ax[i].set_xlabel('$x$ / m') \n",
-                "ax[0].set_ylabel('$P_g$ / Pa')\n",
-                "ax[1].set_ylabel('$\\Delta P_g$ / Pa')\n",
-                "ax[2].set_ylabel('$\\Delta P_g/P_{g, analytical}$')\n",
-                "ax[0].set_title('Gas pressure')  \n",
-                "ax[1].set_title('Absolute error')\n",
-                "ax[2].set_title('Relative error')\n",
-                "ax[0].legend()\n",
-                "fig.tight_layout()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "From the above results, it can be seen that a very good agreement is obtained with respect to the variables saturation, temperature and gas pressure, especially for the latter two. For the saturation, there is only one data point that the divergence between numerical and semi-analytical solutions is obvious, which situates at the end of the two-phase zone (see the embedded subplot above). This might be due to the sharp saturation change around this point which necessitates further mesh refinement locally. Nevertheless, the extent of the heat pipe region at steady state was modeled accurately. The disappearance of the water phase associated with a change of the phase state was carried out well. Note that the OGS solution allows a region near the heated boundary to completely dry out, thus creating increased temperatures (superheated steam) in comparison to the semi-analytical results which assumes coexistence of the liquid and gas phases.\n",
-                "\n",
-                "## References\n",
-                "\n",
-                "[1] K. Udell and J. Fitch. Heat and mass transfer in capillary porous media considering evaporation, condensation, and non-condensible\n",
-                "gas effects. 23rd ASME/AIChE national heat transfer conference, Denver, CO. 1985, pp. 103-110.\n",
-                "\n",
-                "[2] Leverett M et al. (1941) Capillary behavior in porous solids. Trans AIME 142(01):152-169\n",
-                "\n",
-                "[3] Y. Huang, O. Kolditz, and H. Shao. Extending the persistent primary variable algorithm to simulate non-isothermal two-phase two-component flow with phase change phenomena. Geothermal Energy 3 (1) (2015). http://dx.doi.org/10.1186/s40517-015-0030-8."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.2"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "3ad1abe0",
+   "metadata": {},
+   "source": [
+    "+++\n",
+    "author = \"Boyan Meng and Yonghui Huang\"\n",
+    "date = \"2022-07-01\"\n",
+    "title = \"Heat pipe problem\"\n",
+    "web_subsection = \"thermal-two-phase-flow\"\n",
+    "+++\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65613075",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "When an unsaturated porous medium is subject to a constant heat flux and the temperature is sufficiently high, water is heated and vaporizes. Vapor flows under its pressure gradient towards the cooler end where it condenses. Vaporization and condensation produce a liquid saturation gradient, creating a capillary pressure gradient inside the porous medium. Condensate flows towards the hot end under the influence of a capillary pressure gradient. This is a heat pipe in an unsaturated porous medium.\n",
+    "\n",
+    "A benchmark regarding the heat pipe problem was proposed by Udell and Fitch (1985). A semi-analytical solution was provided for a non-isothermal water-gas system in a porous medium, in which heat convection, conduction, and latent heat transport as well as capillary effects play a key role."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46cdedcd",
+   "metadata": {},
+   "source": [
+    "## Physical Scenario\n",
+    "\n",
+    "As shown in the below figure, the heat pipe was represented by a 2D horizontal column (2.4 m in length and 0.2 m in width) of porous media, which was partially saturated with a liquid phase ($S_w$ = 0.41) at the beginning. A heater is installed on the right-hand-side of the horizontal column generating a constant heat flux of 100 $\\mathrm{W/m^2}$ and raises the temperature gradually above the boiling point. At the left-hand boundary, we impose the constant gas phase pressure ($P_g$ = 101934 Pa), constant liquid saturation ($S_w$ = 0.97) and constant temperature ($T$ = 343 K) as Dirichlet boundary conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "22134488",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {
+      "image/jpeg": {
+       "width": 1000
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, Image\n",
+    "\n",
+    "display(Image(filename=f\"./model_domain.jpg\", width=1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fd19cc3",
+   "metadata": {},
+   "source": [
+    "## Model parameters and numerical settings\n",
+    "\n",
+    "In this benchmark, the thermal conductivity for an unsaturated medium is given as:\n",
+    "\\begin{equation}\n",
+    "    \\lambda(S_w)=\\lambda_{S_w=0}+\\sqrt {S_w}\\left(\\lambda_{S_w = 1}-\\lambda_{S_w = 0})\\right.\n",
+    "\\end{equation}\n",
+    "The capillary pressure is dependent on the liquid saturation via the Leverett (Leverett et al. (1941)) function:\n",
+    "\\begin{equation}\n",
+    "        P_c(S_w)=\\sqrt{\\frac{\\phi}{K}}\\gamma\\left(1.417(1-S_w)-2.12(1-S_w)^2+1.263(1-Sw)^3)\\right.\n",
+    "\\end{equation}\n",
+    "where $\\gamma$ = 0.05878 N/m stands for the surface tension of water. The relative permeabilities are calculated using the Udell (Udell and Fitch (1985)) model:\n",
+    "\\begin{equation}\n",
+    "    k_{rL}=S_w^3,\n",
+    "\\end{equation}\n",
+    "\\begin{equation}\n",
+    "    k_{rG}=(1-S_w)^3.\n",
+    "\\end{equation}\n",
+    "The rest of the parameters used in this benchmark are listed in the following table.\n",
+    "\n",
+    "| Parameter | Value | Unit |\n",
+    "| :-: | :-: | :-: |\n",
+    "| Intrinsic permeability $K$ | 1e-12 | m$^2$ |\n",
+    "| Porosity                                        $\\phi$          | 0.4                 |   -                     |\n",
+    "| Thermal conductivity of dry porous medium       $\\lambda_{S_w=0}$ | 0.582               | W/m/K   |\n",
+    "| Thermal conductivity of saturated porous medium $\\lambda_{S_w=1}$ | 1.14                | W/m/K  |\n",
+    "| Specific heat capacity of soil grain            $c_{p,s}$       | 700                 | J/kg/K   |\n",
+    "| Specific heat capacity of air                   $c_{v,a}$       | 733                 | J/kg/K   |\n",
+    "| Specific heat capacity of water                 $c_{p,w}$       | 4187                | J/kg/K   |\n",
+    "| Density of water                                $\\rho_w$        |1000                | kg/m$^3$          |\n",
+    "| Density of soil grain                           $\\rho_s$        | 2650                | kg/m$^3$          |\n",
+    "| Dynamic viscosity of the liquid phase           $\\mu_{L}$       | 2.938e-4  | Pa s                |\n",
+    "| Dynamic viscosity of the gas phase              $\\mu_{G}$       | 1.8e-5   | Pa s                |\n",
+    "| Diffusion coefficient in free gas               $D_{0a}$        | 2.6e-6  | m$^2$/s         |\n",
+    "| Diffusion coefficient in free water             $D_{0w}$        | 3.0e-9   | m$^2$/s           |\n",
+    "| Latent heat of water vaporization               $h_{\\Delta e}$  | 2.258e6   | J/kg          |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c8fb7d3",
+   "metadata": {},
+   "source": [
+    "## Results and analysis\n",
+    "\n",
+    "In the CTEST-small, the comparison is made for the time of 10000 seconds. The profiles of saturation and temperature are plotted below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e483f1b7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import vtuIO\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes, InsetPosition, mark_inset\n",
+    "\n",
+    "plt.rcParams[\"legend.fontsize\"] = 20\n",
+    "plt.rcParams[\"font.size\"] = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2baf7738",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"ref_t_10000.000000.vtu\"\n",
+    "x = np.array([i * 0.012 for i in range(201)])\n",
+    "r = np.array([[i, 0.1, 0.0] for i in x])\n",
+    "\n",
+    "f = vtuIO.VTUIO(filename, nneighbors=100, dim=2)\n",
+    "resp = {}\n",
+    "resp[0] = f.get_set_data(\"saturation\", pointsetarray=r)\n",
+    "resp[1] = f.get_set_data(\"temperature\", pointsetarray=r)\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols=2, figsize=(20, 8))\n",
+    "for i in range(2):\n",
+    "    ax[i].plot(x, resp[i], lw=2, label=\"OGS, $t$ = 10000s\")\n",
+    "    ax[i].set_xlim([0, 2.4])\n",
+    "    ax[i].set_xlabel(\"$x$ / m\")\n",
+    "    ax[i].legend()\n",
+    "ax[0].set_ylabel(\"$S_w$ / -\")\n",
+    "ax[1].set_ylabel(\"$T$ / K\")\n",
+    "ax[0].set_title(\"saturation\")\n",
+    "ax[1].set_title(\"temperature\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5324979b",
+   "metadata": {},
+   "source": [
+    "In the CTEST-large, the comparison is made for the time of 1.4e6 seconds. Around this time, the water is fully evaporated from the heating boundary (right hand side), and single phase zone of gas phase is formulated, while the temperature at this part begins to increase significantly, as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "38331c68",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"ref_t_1400000.000000.vtu\"\n",
+    "\n",
+    "f = vtuIO.VTUIO(filename, nneighbors=100, dim=2)\n",
+    "resp = {}\n",
+    "resp[0] = f.get_set_data(\"saturation\", pointsetarray=r)\n",
+    "resp[1] = f.get_set_data(\"temperature\", pointsetarray=r)\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols=2, figsize=(20, 8))\n",
+    "for i in range(2):\n",
+    "    ax[i].plot(x, resp[i], lw=2, label=\"OGS, $t$ = 1.4e6s\")\n",
+    "    ax[i].set_xlim([0, 2.4])\n",
+    "    ax[i].set_xlabel(\"$x$ / m\")\n",
+    "    ax[i].legend(fontsize=20)\n",
+    "ax[0].set_ylabel(\"$S_w$ / -\")\n",
+    "ax[1].set_ylabel(\"$T$ / K\")\n",
+    "ax[0].set_ylim([0, 1])\n",
+    "ax[0].set_title(\"saturation\")\n",
+    "ax[1].set_title(\"temperature\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19cc9194",
+   "metadata": {},
+   "source": [
+    "After the gas phase appearance, it is recommended to change to an adaptive time stepping scheme (e.g. Evolutionary PID Controller or Iteration Number Based) to assure the numerical stability. In the case of Iteration Number Based Time Stepping, the time step size is kept around 175 s with 4.5 iterations on average.\n",
+    "\n",
+    "For the steady-state solution of this problem, a semi-analytical solution was derived by Udell and Fitch (1985) and extended by Huang et al. (2015). Here we provide the semi-analytical solution as a <a href=\"https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/SemianalyticalSolutionHeatPipe.zip\">MATLAB script</a> which enables us to compute the steady-state gas pressure, saturation and temperature profiles along the $x$-direction (see calculated values in <a href=\"https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/SemianalyticalSolutionResults.csv\">SemianalyticalSolutionResults.csv</a>). In the following, the numerical solution by OpenGeoSys at quasi-steady state ($t$ = 2e7 s) is plotted against the semi-analytical solution for comparison. In addition, the absolute and relative errors are also illustrated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "173bbe9f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "result_file = f\"SemianalyticalSolutionResults.csv\"\n",
+    "soln = pd.read_csv(\n",
+    "    result_file,\n",
+    "    sep=\",\",\n",
+    "    header=None,\n",
+    "    skiprows=0,\n",
+    "    names=[\"x\", \"saturation\", \"temperature\", \"pressure\"],\n",
+    "    index_col=False,\n",
+    ")\n",
+    "\n",
+    "filename = \"ref_steady_status.vtu\"\n",
+    "\n",
+    "f = vtuIO.VTUIO(filename, nneighbors=100, dim=2)\n",
+    "resp = {}\n",
+    "resp[0] = f.get_set_data(\"saturation\", pointsetarray=r)\n",
+    "resp[1] = f.get_set_data(\"temperature\", pointsetarray=r)\n",
+    "resp[2] = f.get_set_data(\"gas_pressure\", pointsetarray=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d61a530b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.axis.YTick at 0x174795300>,\n",
+       " <matplotlib.axis.YTick at 0x1747ea680>,\n",
+       " <matplotlib.axis.YTick at 0x174794340>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     },
-    "nbformat": 4,
-    "nbformat_minor": 5
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"legend.fontsize\"] = 14\n",
+    "plt.rcParams[\"font.size\"] = 14\n",
+    "fig, ax = plt.subplots(ncols=3, figsize=(18, 6))\n",
+    "\n",
+    "ax[0].plot(x, soln[\"saturation\"], lw=1.5, label=\"semianalytical\")\n",
+    "ax[0].plot(\n",
+    "    x,\n",
+    "    resp[0],\n",
+    "    lw=1.5,\n",
+    "    marker=\"o\",\n",
+    "    linestyle=\"\",\n",
+    "    markevery=5,\n",
+    "    color=\"r\",\n",
+    "    label=\"OGS steady state\",\n",
+    ")\n",
+    "ax[1].plot(x, soln[\"saturation\"] - resp[0], lw=1.5)\n",
+    "ax[2].plot(x, (soln[\"saturation\"] - resp[0]) / soln[\"saturation\"], lw=1.5)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    ax[i].set_xlim([0, 2.4])\n",
+    "    ax[i].set_xlabel(\"$x$ / m\")\n",
+    "ax[0].set_ylabel(\"$S_w$ / -\")\n",
+    "ax[1].set_ylabel(\"$\\Delta S_w$ / -\")\n",
+    "ax[2].set_ylabel(\"$\\Delta S_w/S_{w, analytical}$\")\n",
+    "ax[0].set_ylim([0, 1])\n",
+    "ax[0].set_title(\"Saturation\")\n",
+    "ax[1].set_title(\"Absolute error\")\n",
+    "ax[2].set_title(\"Relative error\")\n",
+    "ax[0].legend()\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "ax2 = plt.axes([0, 0, 0, 0])\n",
+    "ip = InsetPosition(ax[0], [0.45, 0.4, 0.5, 0.4])\n",
+    "ax2.set_axes_locator(ip)\n",
+    "patch, pp1, pp2 = mark_inset(ax[0], ax2, loc1=3, loc2=4, fc=\"none\", ec=\"0.5\")\n",
+    "# pp1.loc1 = 3\n",
+    "pp1.loc2 = 2\n",
+    "# pp2.loc1 = 4\n",
+    "pp2.loc2 = 1\n",
+    "ax2.plot(\n",
+    "    x,\n",
+    "    resp[0],\n",
+    "    lw=1.5,\n",
+    "    marker=\"o\",\n",
+    "    linestyle=\"\",\n",
+    "    markevery=1,\n",
+    "    color=\"r\",\n",
+    "    label=\"OGS steady state\",\n",
+    ")\n",
+    "ax2.plot(x, soln[\"saturation\"], lw=1.5, label=\"semianalytical\")\n",
+    "ax2.set_xlim(1.57, 1.63)\n",
+    "ax2.set_ylim(0, 0.1)\n",
+    "ax2.set_yticks(np.arange(0, 0.15, 0.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f5745c10",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(ncols=3, figsize=(18, 6))\n",
+    "\n",
+    "ax[0].plot(x, soln[\"temperature\"], lw=1.5, label=\"semianalytical\")\n",
+    "ax[0].plot(\n",
+    "    x,\n",
+    "    resp[1],\n",
+    "    lw=1.5,\n",
+    "    marker=\"o\",\n",
+    "    linestyle=\"\",\n",
+    "    markevery=5,\n",
+    "    color=\"r\",\n",
+    "    label=\"OGS steady state\",\n",
+    ")\n",
+    "ax[1].plot(x, soln[\"temperature\"] - resp[1], lw=1.5)\n",
+    "ax[2].plot(x, (soln[\"temperature\"] - resp[1]) / soln[\"temperature\"], lw=1.5)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    ax[i].set_xlim([0, 2.4])\n",
+    "    ax[i].set_xlabel(\"$x$ / m\")\n",
+    "ax[0].set_ylabel(\"$T$ / K\")\n",
+    "ax[1].set_ylabel(\"$\\Delta T$ / K\")\n",
+    "ax[2].set_ylabel(\"$\\Delta T/T_{analytical}$\")\n",
+    "ax[0].set_title(\"Temperature\")\n",
+    "ax[1].set_title(\"Absolute error\")\n",
+    "ax[2].set_title(\"Relative error\")\n",
+    "ax[0].legend()\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e17e7db8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(ncols=3, figsize=(18, 6))\n",
+    "\n",
+    "ax[0].plot(x, soln[\"pressure\"], lw=1.5, label=\"semianalytical\")\n",
+    "ax[0].plot(\n",
+    "    x,\n",
+    "    resp[2],\n",
+    "    lw=1.5,\n",
+    "    marker=\"o\",\n",
+    "    linestyle=\"\",\n",
+    "    markevery=5,\n",
+    "    color=\"r\",\n",
+    "    label=\"OGS steady state\",\n",
+    ")\n",
+    "ax[1].plot(x, soln[\"pressure\"] - resp[2], lw=1.5)\n",
+    "ax[2].plot(x, (soln[\"pressure\"] - resp[2]) / soln[\"pressure\"], lw=1.5)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    ax[i].set_xlim([0, 2.4])\n",
+    "    ax[i].set_xlabel(\"$x$ / m\")\n",
+    "ax[0].set_ylabel(\"$P_g$ / Pa\")\n",
+    "ax[1].set_ylabel(\"$\\Delta P_g$ / Pa\")\n",
+    "ax[2].set_ylabel(\"$\\Delta P_g/P_{g, analytical}$\")\n",
+    "ax[0].set_title(\"Gas pressure\")\n",
+    "ax[1].set_title(\"Absolute error\")\n",
+    "ax[2].set_title(\"Relative error\")\n",
+    "ax[0].legend()\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "761e2f64",
+   "metadata": {},
+   "source": [
+    "From the above results, it can be seen that a very good agreement is obtained with respect to the variables saturation, temperature and gas pressure, especially for the latter two. For the saturation, there is only one data point that the divergence between numerical and semi-analytical solutions is obvious, which situates at the end of the two-phase zone (see the embedded subplot above). This might be due to the sharp saturation change around this point which necessitates further mesh refinement locally. Nevertheless, the extent of the heat pipe region at steady state was modeled accurately. The disappearance of the water phase associated with a change of the phase state was carried out well. Note that the OGS solution allows a region near the heated boundary to completely dry out, thus creating increased temperatures (superheated steam) in comparison to the semi-analytical results which assumes coexistence of the liquid and gas phases.\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "[1] K. Udell and J. Fitch. Heat and mass transfer in capillary porous media considering evaporation, condensation, and non-condensible\n",
+    "gas effects. 23rd ASME/AIChE national heat transfer conference, Denver, CO. 1985, pp. 103-110.\n",
+    "\n",
+    "[2] Leverett M et al. (1941) Capillary behavior in porous solids. Trans AIME 142(01):152-169\n",
+    "\n",
+    "[3] Y. Huang, O. Kolditz, and H. Shao. Extending the persistent primary variable algorithm to simulate non-isothermal two-phase two-component flow with phase change phenomena. Geothermal Energy 3 (1) (2015). http://dx.doi.org/10.1186/s40517-015-0030-8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23a7d661",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/Tests/Data/Parabolic/TwoPhaseFlowPrho/MoMaS/MoMaS.ipynb b/Tests/Data/Parabolic/TwoPhaseFlowPrho/MoMaS/MoMaS.ipynb
index 199a836e17c..96e4ed95e6c 100644
--- a/Tests/Data/Parabolic/TwoPhaseFlowPrho/MoMaS/MoMaS.ipynb
+++ b/Tests/Data/Parabolic/TwoPhaseFlowPrho/MoMaS/MoMaS.ipynb
@@ -1,135 +1,142 @@
 {
-    "cells": [
-        {
-            "cell_type": "raw",
-            "metadata": {},
-            "source": [
-                "title = \"MoMaS Benchmark\"\n",
-                "date = \"2022-10-24\"\n",
-                "author = \"Yonghui Huang, Falko Vehling\"\n",
-                "web_subsection = \"two-phase-flow\"\n",
-                "<!--eofm-->"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "## Introduction\n",
-                "\n",
-                "The background of this benchmark is the production of hydrogen gas due to the corrosion of the metallic container in the nuclear waste repository. Numerical model is built to illustrate such gas appearance phenomenon. The model domain is a two dimensional horizontal column representing the bentonite backfill in the repository tunnel, with hydrogen gas injected on the left boundary. This benchmark was proposed in the GNR MoMaS project by French National Radioactive Waste Management Agency. Several research groups has made contributions to test the benchmark and provided their reference solutions Neumann et al. (2013); Bourgeat et al. (2009); Marchand and Knabner (2014); Ben Gharbia and Jaffré (2014). Here we adopted the results proposed in Marchand’s paper Marchand and Knabner (2014) for comparison."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Physical Scenario\n",
-                "\n",
-                "Here a 2D rectangular domain $Ω$ = [0, 200] × [−10, 10] m (see Figure 1) was considered with an impervious boundary at $Γ_\\mathrm{imp}$ = [0, 200] × [−10, 10] m, an inflow boundary at $Γ_\\mathrm{in}$ = 0 × [−10, 10] m and an outflow boundary at $Γ_\\mathrm{out}$ = 200 × [−10, 10] m. The domain was initially saturated with water, hydrogen gas was injected on the left-hand-side boundary within a certain time span ([0, 5 × 10<sup>4</sup> centuries]). After that the hydrogen injection stopped and no flux came into the system. The right-hand-side boundary is kept open throughout the simulation. The initial condition and boundary conditions were summarized as\n",
-                "\n",
-                "-    $X$($t$ = 0) = 10$^5$     and     $p_\\mathrm{L}$($t$ = 0) = $p_\\mathrm{L}^\\mathrm{out}$ = 10$^6$ Pa   on $Ω$\n",
-                "-    $q^\\mathrm{w}$$\\cdot$$v$ = $q^\\mathrm{h}$$\\cdot$$v$ = 0   on $\\Gamma_\\mathrm{imp}$\n",
-                "-    $q^\\mathrm{w}$$\\cdot$$v$ = 0, $q^\\mathrm{h}$$\\cdot$$v$ = $Q_\\mathrm{d}^\\mathrm{h}$ = 0.2785 [mol century$^{-1}$ m$^{-2}$]  on $\\Gamma_\\mathrm{in}$\n",
-                "-    $X$ = 0 and $p_\\mathrm{L}$ = $p_\\mathrm{L}^\\mathrm{out}$ = 10$^6$ Pa on $\\Gamma_\\mathrm{out}$\n",
-                "\n",
-                "![](figures/Geo_BC_H2_inj_bench.png \"Geometry and boundary condition for the $H_2$ injection benchmark.\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Model parameters and numerical settings\n",
-                "\n",
-                "The capillary pressure $p_\\mathrm{c}$ and relative permeability functions are given by the van-Genuchten model (Van Genuchten 1980).\n",
-                "\\begin{equation}\n",
-                "    p_\\mathrm{c}=p_\\mathrm{d}\\left((S_\\mathrm{L}^\\mathrm{eff})^{\\frac{-1}{m}}-1\\right)^{\\frac{1}{n}}\n",
-                "\\end{equation}\n",
-                "\n",
-                "\\begin{equation}\n",
-                "    k_\\mathrm{L}^\\mathrm{rel}=\\sqrt{S_\\mathrm{L}^\\mathrm{eff}}\\left(1-\\left(1-(S_\\mathrm{L}^\\mathrm{eff})^{\\frac{1}{m}}\\right)^{m}\\right)^{2}\n",
-                "\\end{equation}\n",
-                "\n",
-                "\\begin{equation}\n",
-                "    k_\\mathrm{G}^\\mathrm{rel}=\\sqrt{1-S_\\mathrm{L}^\\mathrm{eff}}\\left(1-(S_\\mathrm{L}^\\mathrm{eff})^{\\frac{1}{m}}\\right)^{2m}\n",
-                "\\end{equation}\n",
-                "where $m$ = 1 - 1/$n$ , $p_r$ and $n$ are the van-Genuchten model parameters and the effective saturation $S_\\mathrm{L}^\\mathrm{eff}$ is given by\n",
-                "\\begin{equation}\n",
-                "        S_\\mathrm{L}^\\mathrm{eff}=\\frac{1-S_\\mathrm{G}-S_\\mathrm{L}^\\mathrm{rel}}{1-S_\\mathrm{L}^\\mathrm{rel}-S_\\mathrm{G}^\\mathrm{rel}}\n",
-                "\\end{equation}\n",
-                "here $S_\\mathrm{L}^\\mathrm{rel}$ and $S_\\mathrm{G}^\\mathrm{rel}$ indicate the residual saturation in liquid and gas phases, respectively. Values of parameters applied in this model are summarized in Table 1.\n",
-                "\n",
-                "Table 1: Fluid and porous medium properties applied in the H$_2$ migration benchmark.\n",
-                "\n",
-                "| Parameter | Symbol | Value | Unit |\n",
-                "| :-: | :-: | :-: | :-: |\n",
-                "| Intrinsic permeability                        |  $k$                         | 5 $\\cdot$ 10<sup>-20</sup>| m$^2$ |\n",
-                "| Porosity                                      |  $\\phi$                      | 0.15            |   -   |\n",
-                "| Residual Saturation of liquid phase           |  $S_\\mathrm{L}^\\mathrm{rel}$ | 0.4             |   -   |\n",
-                "| Residual Saturation of gas phase              |  $S_\\mathrm{G}^\\mathrm{rel}$ | 0               |   -   |\n",
-                "| Viscosity of liquid                           |  $\\mu_\\mathrm{L}$            | 1 $\\cdot$ 10<sup>-3</sup>| Pa $\\cdot$ s  |\n",
-                "| Viscosity of gas                              |  $\\mu_\\mathrm{G}$            | 9 $\\cdot$ 10<sup>-6</sup>| Pa $\\cdot$ s  |\n",
-                "| van Genuchten paramteter                      |  $p_d$                       | 2 $\\cdot$ 10<sup>6</sup> | Pa |\n",
-                "| van Genuchten paramteter                      |  $n$                         | 1.49            |   -   |\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Results and analysis \n",
-                "\n",
-                "The results of this benchmark are depicted in Figure 2. The evolution of gas phase saturation and the gas/liquid phase pressure over the entire time span are shown. In additional, we compare results from our model against those given in Marchand’s\n",
-                "paper (Marchand and Knabner, 2014). In Figure 2, solid lines are our simulation results while the symbols are the results fromMarchand et al. It can be seen that a good agreement has been achieved.\n",
-                "\n",
-                "![](figures/Res_H2_inj_bench.png \"Evolution of pressure and saturation over time.\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## References\n",
-                "\n",
-                "Ben Gharbia, I., Jaffré, J., 2014. Gas phase appearance and disappearance as a problem with complementarity constraints. Mathematics and Computers in Simulation 99, 28–36.\n",
-                "\n",
-                "Bourgeat, A., Jurak, M., Smaï, F., 2009. Two-phase, partially miscible flowand transport modeling in porous media; application to gas migration in a nuclear waste repository. Computational Geosciences 13 (1), 29–42.\n",
-                "\n",
-                "Marchand, E., Knabner, P., 2014. Results of the momas benchmark for gas phase appearance and disappearance using generalized mhfe. Advances in Water Resources 73, 74–96.\n",
-                "\n",
-                "Neumann, R., Bastian, P., Ippisch, O., 2013. Modeling and simulation of two-phase two-component flow with disappearing nonwetting phase.ComputationalGeosciences 17 (1), 139–149.\n",
-                "\n",
-                "Van Genuchten, M. T., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil science society of America journal 44 (5), 892–898."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.2"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "421204c6",
+   "metadata": {},
+   "source": [
+    "+++\n",
+    "title = \"MoMaS Benchmark\"\n",
+    "date = \"2022-10-24\"\n",
+    "author = \"Yonghui Huang, Falko Vehling\"\n",
+    "web_subsection = \"two-phase-flow\"\n",
+    "+++\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f72edf37",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "The background of this benchmark is the production of hydrogen gas due to the corrosion of the metallic container in the nuclear waste repository. Numerical model is built to illustrate such gas appearance phenomenon. The model domain is a two dimensional horizontal column representing the bentonite backfill in the repository tunnel, with hydrogen gas injected on the left boundary. This benchmark was proposed in the GNR MoMaS project by French National Radioactive Waste Management Agency. Several research groups has made contributions to test the benchmark and provided their reference solutions Neumann et al. (2013); Bourgeat et al. (2009); Marchand and Knabner (2014); Ben Gharbia and Jaffré (2014). Here we adopted the results proposed in Marchand’s paper Marchand and Knabner (2014) for comparison."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae77638b",
+   "metadata": {},
+   "source": [
+    "## Physical Scenario\n",
+    "\n",
+    "Here a 2D rectangular domain $Ω$ = [0, 200] × [−10, 10] m (see Figure 1) was considered with an impervious boundary at $Γ_\\mathrm{imp}$ = [0, 200] × [−10, 10] m, an inflow boundary at $Γ_\\mathrm{in}$ = 0 × [−10, 10] m and an outflow boundary at $Γ_\\mathrm{out}$ = 200 × [−10, 10] m. The domain was initially saturated with water, hydrogen gas was injected on the left-hand-side boundary within a certain time span ([0, 5 × 10<sup>4</sup> centuries]). After that the hydrogen injection stopped and no flux came into the system. The right-hand-side boundary is kept open throughout the simulation. The initial condition and boundary conditions were summarized as\n",
+    "\n",
+    "-    $X$($t$ = 0) = 10$^5$     and     $p_\\mathrm{L}$($t$ = 0) = $p_\\mathrm{L}^\\mathrm{out}$ = 10$^6$ Pa   on $Ω$\n",
+    "-    $q^\\mathrm{w}$$\\cdot$$v$ = $q^\\mathrm{h}$$\\cdot$$v$ = 0   on $\\Gamma_\\mathrm{imp}$\n",
+    "-    $q^\\mathrm{w}$$\\cdot$$v$ = 0, $q^\\mathrm{h}$$\\cdot$$v$ = $Q_\\mathrm{d}^\\mathrm{h}$ = 0.2785 [mol century$^{-1}$ m$^{-2}$]  on $\\Gamma_\\mathrm{in}$\n",
+    "-    $X$ = 0 and $p_\\mathrm{L}$ = $p_\\mathrm{L}^\\mathrm{out}$ = 10$^6$ Pa on $\\Gamma_\\mathrm{out}$\n",
+    "\n",
+    "![](figures/Geo_BC_H2_inj_bench.png \"Geometry and boundary condition for the $H_2$ injection benchmark.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caa70a84",
+   "metadata": {},
+   "source": [
+    "## Model parameters and numerical settings\n",
+    "\n",
+    "The capillary pressure $p_\\mathrm{c}$ and relative permeability functions are given by the van-Genuchten model (Van Genuchten 1980).\n",
+    "\\begin{equation}\n",
+    "    p_\\mathrm{c}=p_\\mathrm{d}\\left((S_\\mathrm{L}^\\mathrm{eff})^{\\frac{-1}{m}}-1\\right)^{\\frac{1}{n}}\n",
+    "\\end{equation}\n",
+    "\n",
+    "\\begin{equation}\n",
+    "    k_\\mathrm{L}^\\mathrm{rel}=\\sqrt{S_\\mathrm{L}^\\mathrm{eff}}\\left(1-\\left(1-(S_\\mathrm{L}^\\mathrm{eff})^{\\frac{1}{m}}\\right)^{m}\\right)^{2}\n",
+    "\\end{equation}\n",
+    "\n",
+    "\\begin{equation}\n",
+    "    k_\\mathrm{G}^\\mathrm{rel}=\\sqrt{1-S_\\mathrm{L}^\\mathrm{eff}}\\left(1-(S_\\mathrm{L}^\\mathrm{eff})^{\\frac{1}{m}}\\right)^{2m}\n",
+    "\\end{equation}\n",
+    "where $m$ = 1 - 1/$n$ , $p_r$ and $n$ are the van-Genuchten model parameters and the effective saturation $S_\\mathrm{L}^\\mathrm{eff}$ is given by\n",
+    "\\begin{equation}\n",
+    "        S_\\mathrm{L}^\\mathrm{eff}=\\frac{1-S_\\mathrm{G}-S_\\mathrm{L}^\\mathrm{rel}}{1-S_\\mathrm{L}^\\mathrm{rel}-S_\\mathrm{G}^\\mathrm{rel}}\n",
+    "\\end{equation}\n",
+    "here $S_\\mathrm{L}^\\mathrm{rel}$ and $S_\\mathrm{G}^\\mathrm{rel}$ indicate the residual saturation in liquid and gas phases, respectively. Values of parameters applied in this model are summarized in Table 1.\n",
+    "\n",
+    "Table 1: Fluid and porous medium properties applied in the H$_2$ migration benchmark.\n",
+    "\n",
+    "| Parameter | Symbol | Value | Unit |\n",
+    "| :-: | :-: | :-: | :-: |\n",
+    "| Intrinsic permeability                        |  $k$                         | 5 $\\cdot$ 10<sup>-20</sup>| m$^2$ |\n",
+    "| Porosity                                      |  $\\phi$                      | 0.15            |   -   |\n",
+    "| Residual Saturation of liquid phase           |  $S_\\mathrm{L}^\\mathrm{rel}$ | 0.4             |   -   |\n",
+    "| Residual Saturation of gas phase              |  $S_\\mathrm{G}^\\mathrm{rel}$ | 0               |   -   |\n",
+    "| Viscosity of liquid                           |  $\\mu_\\mathrm{L}$            | 1 $\\cdot$ 10<sup>-3</sup>| Pa $\\cdot$ s  |\n",
+    "| Viscosity of gas                              |  $\\mu_\\mathrm{G}$            | 9 $\\cdot$ 10<sup>-6</sup>| Pa $\\cdot$ s  |\n",
+    "| van Genuchten paramteter                      |  $p_d$                       | 2 $\\cdot$ 10<sup>6</sup> | Pa |\n",
+    "| van Genuchten paramteter                      |  $n$                         | 1.49            |   -   |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c85955b",
+   "metadata": {},
+   "source": [
+    "# Results and analysis\n",
+    "\n",
+    "The results of this benchmark are depicted in Figure 2. The evolution of gas phase saturation and the gas/liquid phase pressure over the entire time span are shown. In additional, we compare results from our model against those given in Marchand’s\n",
+    "paper (Marchand and Knabner, 2014). In Figure 2, solid lines are our simulation results while the symbols are the results fromMarchand et al. It can be seen that a good agreement has been achieved.\n",
+    "\n",
+    "![](figures/Res_H2_inj_bench.png \"Evolution of pressure and saturation over time.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03833eb5",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "Ben Gharbia, I., Jaffré, J., 2014. Gas phase appearance and disappearance as a problem with complementarity constraints. Mathematics and Computers in Simulation 99, 28–36.\n",
+    "\n",
+    "Bourgeat, A., Jurak, M., Smaï, F., 2009. Two-phase, partially miscible flowand transport modeling in porous media; application to gas migration in a nuclear waste repository. Computational Geosciences 13 (1), 29–42.\n",
+    "\n",
+    "Marchand, E., Knabner, P., 2014. Results of the momas benchmark for gas phase appearance and disappearance using generalized mhfe. Advances in Water Resources 73, 74–96.\n",
+    "\n",
+    "Neumann, R., Bastian, P., Ippisch, O., 2013. Modeling and simulation of two-phase two-component flow with disappearing nonwetting phase.ComputationalGeosciences 17 (1), 139–149.\n",
+    "\n",
+    "Van Genuchten, M. T., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil science society of America journal 44 (5), 892–898."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb b/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb
index 909ce675ccd..362dd43b28d 100644
--- a/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb
+++ b/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb
@@ -4,33 +4,12 @@
    "cell_type": "raw",
    "metadata": {},
    "source": [
+    "+++\n",
     "author = \"Mostafa Mollaali, Keita Yoshioka\"\n",
     "date = \"2023-03-03\"\n",
     "title = \"Hydraulic Fracturing in the Toughness-Dominated Regime\"\n",
     "web_subsection = \"phase-field\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ogs6py import ogs\n",
-    "import numpy as np\n",
-    "import ogs6py\n",
-    "import matplotlib.pyplot as plt\n",
-    "import time\n",
-    "import math\n",
-    "import gmsh\n",
-    "import os\n",
-    "from ogstools.msh2vtu import run  \n",
-    "import argparse\n",
-    "import re\n",
-    "\n",
-    "pi = math.pi\n",
-    "plt.rcParams[\"text.usetex\"] = True"
+    "+++\n"
    ]
   },
   {
@@ -113,8 +92,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* In order to  have the hydraulic fracturing in the toughness dominated-regime, add, $\\texttt{<pressurized_crack_scheme>propagating</pressurized_crack_scheme>}$ in the project file. \n",
-    "* **Yoshioka _et al._, 2019**  provides additional information on the implementation, use of real material properties, and rescaling of the phase-field energy functional.  \n"
+    "* In order to  have the hydraulic fracturing in the toughness dominated-regime, add, $\\texttt{<pressurized_crack_scheme>propagating</pressurized_crack_scheme>}$ in the project file.\n",
+    "* **Yoshioka _et al._, 2019**  provides additional information on the implementation, use of real material properties, and rescaling of the phase-field energy functional.\n"
    ]
   },
   {
@@ -146,10 +125,36 @@
     "| _Initial crack length_         | 0.1               | $2a_0$     |"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from ogs6py import ogs\n",
+    "import numpy as np\n",
+    "import ogs6py\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import math\n",
+    "import gmsh\n",
+    "import os\n",
+    "from ogstools.msh2vtu import run\n",
+    "import argparse\n",
+    "import re\n",
+    "\n",
+    "pi = math.pi\n",
+    "plt.rcParams[\"text.usetex\"] = True"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "E = 1.0\n",
@@ -171,7 +176,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "# file's name\n",
@@ -184,7 +191,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "# Mesh Generation\n"
    ]
@@ -192,7 +201,9 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "def mesh_generation(lc, lc_fine):\n",
@@ -289,7 +300,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "# Pre-Processing"
    ]
@@ -322,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Run the Simulation \n"
+    "# Run the Simulation\n"
    ]
   },
   {
@@ -332,22 +345,24 @@
    "outputs": [],
    "source": [
     "import pyvista as pv\n",
+    "\n",
     "pv.set_plot_theme(\"document\")\n",
     "if \"PYVISTA_HEADLESS\" in os.environ:\n",
     "    pv.start_xvfb()\n",
     "pv.set_jupyter_backend(\"static\")\n",
     "\n",
-    "def Hydraulic_Fracturing_Toughness_Dominated_numerical(h,phasefield_model):\n",
-    "    #mesh properties\n",
-    "    ls = 2*h\n",
-    "    #generate prefix from properties\n",
-    "    filename =  'results_h_%0.4f_%s'%(h,phasefield_model)\n",
+    "\n",
+    "def Hydraulic_Fracturing_Toughness_Dominated_numerical(h, phasefield_model):\n",
+    "    # mesh properties\n",
+    "    ls = 2 * h\n",
+    "    # generate prefix from properties\n",
+    "    filename = \"results_h_%0.4f_%s\" % (h, phasefield_model)\n",
     "    mesh_generation(0.1, h)\n",
     "    # Convert GMSH (.msh) meshes to VTU meshes appropriate for OGS simulation.\n",
-    "    input_file = f\"{out_dir}/\"+meshname+\".msh\"\n",
+    "    input_file = f\"{out_dir}/\" + meshname + \".msh\"\n",
     "    args = argparse.Namespace(\n",
     "        filename=input_file,\n",
-    "        output=f\"{out_dir}/\"+meshname,\n",
+    "        output=f\"{out_dir}/\" + meshname,\n",
     "        dim=0,\n",
     "        delz=False,\n",
     "        swapxy=False,\n",
@@ -358,14 +373,19 @@
     "    run(args)\n",
     "\n",
     "    # As a preprocessing step, define the initial phase-field (crack).\n",
-    "    pre_processing(h,a0)\n",
-    "    #change properties in prj file #For more information visit: https://github.com/joergbuchwald/ogs6py\n",
-    "    model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True, args=f\"-o {out_dir}\")\n",
+    "    pre_processing(h, a0)\n",
+    "    # change properties in prj file #For more information visit: https://github.com/joergbuchwald/ogs6py\n",
+    "    model = ogs.OGS(\n",
+    "        INPUT_FILE=prj_name,\n",
+    "        PROJECT_FILE=f\"{out_dir}/{prj_name}\",\n",
+    "        MKL=True,\n",
+    "        args=f\"-o {out_dir}\",\n",
+    "    )\n",
     "    model.replace_parameter_value(name=\"ls\", value=ls)\n",
     "    model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n",
     "    model.replace_text(filename, xpath=\"./time_loop/output/prefix\")\n",
-    "    model.write_input()       \n",
-    "    #run simulation with ogs\n",
+    "    model.write_input()\n",
+    "    # run simulation with ogs\n",
     "    t0 = time.time()\n",
     "    print(\">>> OGS started execution ... <<<\")\n",
     "    ! ogs {out_dir}/{prj_name} -o {out_dir} > {out_dir}/log.txt\n",
@@ -377,6 +397,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
+    "lines_to_next_cell": 2,
     "scrolled": true
    },
    "outputs": [
@@ -450,12 +471,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Post-Processing "
+    "# Post-Processing"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "source": [
     "## Analytical Solution for the Evolution of Fracture Length and Pressure"
    ]
@@ -463,7 +486,9 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "def Analytical_solution(phasefield_model, h):\n",
@@ -528,7 +553,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -612,7 +639,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "plt.subplots(figsize=(12, 4))\n",
@@ -689,13 +718,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Hydraulic Fracturing Animation  (Using Phase Field Approach) "
+    "## Hydraulic Fracturing Animation  (Using Phase Field Approach)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "from IPython.display import Image\n",
@@ -708,7 +739,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "filename = \"results_h_%0.4f_%s\" % (h, phasefield_model)\n",
@@ -757,13 +790,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Phase Field Profile at Last Time Step "
+    "## Phase Field Profile at Last Time Step"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "data": {
diff --git a/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb b/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb
index 27365c10e36..81ea7ba8d32 100644
--- a/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb
+++ b/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb
@@ -5,30 +5,12 @@
    "id": "90c995c2",
    "metadata": {},
    "source": [
+    "+++\n",
     "author = \"Vahid Ziaei-Rad, Mostafa Mollaali\"\n",
     "date = \"2022-12-15\"\n",
     "title = \"Pre-notched shear test\"\n",
     "web_subsection = \"phase-field\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41d0a5bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ogs6py import ogs\n",
-    "import os\n",
-    "import shutil\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pyvista as pv\n",
-    "import time\n",
-    "import pandas as pd\n",
-    "from xml.dom import minidom\n",
-    "from types import MethodType"
+    "+++\n"
    ]
   },
   {
@@ -59,6 +41,25 @@
     "## Two helper functions"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41d0a5bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ogs6py import ogs\n",
+    "import os\n",
+    "import shutil\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pyvista as pv\n",
+    "import time\n",
+    "import pandas as pd\n",
+    "from xml.dom import minidom\n",
+    "from types import MethodType\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -85,7 +86,7 @@
     "def set_timestepping(model,repeat_list, delta_t_list):\n",
     "    model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n",
     "    for i in range(len(repeat_list)):\n",
-    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])"
+    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])\n"
    ]
   },
   {
@@ -165,7 +166,7 @@
     "        print(\"  > OGS started execution - \")\n",
     "        ! ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n",
     "    tf = time.time()\n",
-    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")"
+    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")\n"
    ]
   },
   {
@@ -223,7 +224,7 @@
     "# With the AT2 model, we are verifying two different anisotropic models, namely, orthotropic volumetric-deviatoric and orthotropic no-tension:\n",
     "# For more details of each model, please see the reference of Ziaei Rad et al., 2022.\n",
     "for b in [\"OrthoMasonry\", \"OrthoVolDev\"]:\n",
-    "    ogs_ortho(\"AT2\", b, length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1.e-2'], ncores = mpi_cores)"
+    "    ogs_ortho(\"AT2\", b, length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1.e-2'], ncores = mpi_cores)\n"
    ]
   },
   {
@@ -281,7 +282,7 @@
     "    plotter.view_xy()\n",
     "    plotter.write_frame()\n",
     "\n",
-    "plotter.close()"
+    "plotter.close()\n"
    ]
   },
   {
@@ -319,7 +320,7 @@
     "p.view_xy()\n",
     "p.camera.zoom(1.)\n",
     "p.window_size = [800,400]\n",
-    "p.show()"
+    "p.show()\n"
    ]
   },
   {
@@ -390,7 +391,7 @@
     "ax.set_ylabel('$F_y [N]$',fontsize =18)\n",
     "plt.legend(fontsize =18, ncol = 2)\n",
     "ax.axhline(y = 0, color = 'black',linewidth=1)  \n",
-    "ax.axvline(x = 0, color = 'black',linewidth=1)"
+    "ax.axvline(x = 0, color = 'black',linewidth=1)\n"
    ]
   },
   {
diff --git a/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb b/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb
index 5635174b7bb..a6eaa167e28 100644
--- a/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb
+++ b/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb
@@ -5,29 +5,12 @@
    "id": "90c995c2",
    "metadata": {},
    "source": [
+    "+++\n",
     "author = \"Matthes Kantzenbach, Keita Yoshioka, Mostafa Mollaali\"\n",
     "date = \"2022-11-28\"\n",
     "title = \"Beam\"\n",
     "web_subsection = \"phase-field\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41d0a5bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ogs6py import ogs\n",
-    "import os\n",
-    "import ogs6py\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pyvista as pv\n",
-    "import time\n",
-    "from xml.dom import minidom\n",
-    "from types import MethodType"
+    "+++\n"
    ]
   },
   {
@@ -62,6 +45,24 @@
     "## Define some helper functions"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41d0a5bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ogs6py import ogs\n",
+    "import os\n",
+    "import ogs6py\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pyvista as pv\n",
+    "import time\n",
+    "from xml.dom import minidom\n",
+    "from types import MethodType\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -88,7 +89,7 @@
     "def set_timestepping(model,repeat_list, delta_t_list):\n",
     "    model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n",
     "    for i in range(len(repeat_list)):\n",
-    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])"
+    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])\n"
    ]
   },
   {
@@ -164,7 +165,7 @@
     "    print(\"  > OGS started execution ...\")\n",
     "    ! mpirun -n 3 ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n",
     "    tf = time.time()\n",
-    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")"
+    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")\n"
    ]
   },
   {
diff --git a/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb b/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb
index 08302a697c9..0b364fb9812 100644
--- a/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb
+++ b/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb
@@ -4,30 +4,12 @@
    "cell_type": "raw",
    "metadata": {},
    "source": [
+    "+++\n",
     "author = \"Mostafa Mollaali, Keita Yoshioka\"\n",
     "date = \"2022-12-06\"\n",
     "title = \"Static fracture opening under a constant pressure – Sneddon solution\"\n",
     "web_subsection = \"phase-field\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ogs6py import ogs\n",
-    "import numpy as np\n",
-    "import ogs6py\n",
-    "import matplotlib.pyplot as plt\n",
-    "import time\n",
-    "import math\n",
-    "import gmsh\n",
-    "import os\n",
-    "\n",
-    "pi = math.pi\n",
-    "plt.rcParams[\"text.usetex\"] = True"
+    "+++\n"
    ]
   },
   {
@@ -92,6 +74,25 @@
     "| _Initial crack length_         | 0.2               | $2a_0$     |"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ogs6py import ogs\n",
+    "import numpy as np\n",
+    "import ogs6py\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import math\n",
+    "import gmsh\n",
+    "import os\n",
+    "\n",
+    "pi = math.pi\n",
+    "plt.rcParams[\"text.usetex\"] = True\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -111,7 +112,7 @@
     "    2 * (round(3.0 * a0 / h)) + 1\n",
     ")  # number of slices for calcute width of fracture\n",
     "\n",
-    "phasefield_model = \"AT1\""
+    "phasefield_model = \"AT1\"\n"
    ]
   },
   {
@@ -121,7 +122,7 @@
    "outputs": [],
    "source": [
     "h_list = [0.01]  # list of mesh sizes (h)\n",
-    "# h_list =[0.01, 0.005, 0.0025]  # list of mesh sizes (h), for mesh sensitivity"
+    "# h_list =[0.01, 0.005, 0.0025]  # list of mesh sizes (h), for mesh sensitivity\n"
    ]
   },
   {
@@ -142,7 +143,7 @@
     "meshname = \"mesh_full_pf\"\n",
     "\n",
     "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
-    "os.makedirs(out_dir, exist_ok=True)"
+    "os.makedirs(out_dir, exist_ok=True)\n"
    ]
   },
   {
@@ -240,7 +241,7 @@
     "    output_file = f\"{out_dir}/\" + meshname + \".msh\"\n",
     "    gmsh.model.mesh.generate(dim2)\n",
     "    gmsh.write(output_file)\n",
-    "    gmsh.finalize()"
+    "    gmsh.finalize()\n"
    ]
   },
   {
@@ -271,7 +272,7 @@
     "            phase_field[node_id] = 0.0\n",
     "\n",
     "    mesh.point_data[\"phase-field\"] = phase_field\n",
-    "    mesh.save(f\"{out_dir}/mesh_full_pf_OGS_pf_ic.vtu\")"
+    "    mesh.save(f\"{out_dir}/mesh_full_pf_OGS_pf_ic.vtu\")\n"
    ]
   },
   {
@@ -315,7 +316,7 @@
     "    print(\">>> OGS started execution ... <<<\")\n",
     "    !ogs {out_dir}/{prj_name} -o {out_dir} > {out_dir}/log.txt\n",
     "    tf = time.time()\n",
-    "    print(\">>> OGS terminated execution  <<< Elapsed time: \", round(tf - t0, 2), \" s.\")"
+    "    print(\">>> OGS terminated execution  <<< Elapsed time: \", round(tf - t0, 2), \" s.\")\n"
    ]
   },
   {
@@ -341,7 +342,7 @@
    ],
    "source": [
     "for h_j in h_list:\n",
-    "    sneddon_numerical(h=h_j)"
+    "    sneddon_numerical(h=h_j)\n"
    ]
   },
   {
@@ -426,7 +427,7 @@
     "            (point[0] - point_a[0]) ** 2 + (point[1] - point_a[1]) ** 2\n",
     "        ) ** 0.5 - dist_a_b / 2\n",
     "\n",
-    "    return r_i, width_line"
+    "    return r_i, width_line\n"
    ]
   },
   {
@@ -456,7 +457,7 @@
     "            2 * a_eff * (1 - nu**2) * P / E * math.sqrt(1.0 - ((x[i]) / (a_eff)) ** 2)\n",
     "        )\n",
     "\n",
-    "    return x, uy, a_eff"
+    "    return x, uy, a_eff\n"
    ]
   },
   {
@@ -534,7 +535,7 @@
     "plt.title(\"%s\" % phasefield_model)\n",
     "\n",
     "legend = plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
diff --git a/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb b/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb
index c07dad1c05e..e1da2db3749 100644
--- a/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb
+++ b/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb
@@ -4,11 +4,12 @@
             "cell_type": "raw",
             "metadata": {},
             "source": [
+                "+++\n",
                 "author = \"Mostafa Mollaali, Keita Yoshioka\"\n",
                 "date = \"2022-06-28\"\n",
                 "title = \"Surfing boundary\"\n",
                 "web_subsection = \"phase-field\"\n",
-                "<!--eofm-->"
+                "+++\n"
             ]
         },
         {
@@ -161,7 +162,7 @@
                 "G_i = 2.7\n",
                 "ls = 2*h\n",
                 "# We set ls=2h in our simulation\n",
-                "phasefield_model='AT1'# AT1 and AT2 "
+                "phasefield_model='AT1'# AT1 and AT2 \n"
             ]
         },
         {
@@ -184,7 +185,7 @@
                 "\n",
                 "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
                 "if not os.path.exists(out_dir):\n",
-                "    os.makedirs(out_dir)"
+                "    os.makedirs(out_dir)\n"
             ]
         },
         {
@@ -213,7 +214,7 @@
             "source": [
                 "# https://www.opengeosys.org/docs/tools/meshing/structured-mesh-generation/\n",
                 "! generateStructuredMesh -o {out_dir}/surfing_quad_1x2.vtu -e quad --lx 2 --nx {round(2/h)+1} --ly 1 --ny {round(1/h)+1}\n",
-                "! NodeReordering -i {out_dir}/surfing_quad_1x2.vtu -o {out_dir}/surfing_quad_1x2_NR.vtu"
+                "! NodeReordering -i {out_dir}/surfing_quad_1x2.vtu -o {out_dir}/surfing_quad_1x2_NR.vtu\n"
             ]
         },
         {
@@ -272,7 +273,7 @@
                 "p.view_xy()\n",
                 "p.camera.zoom(1.5)\n",
                 "p.window_size = [800,400]\n",
-                "p.show()"
+                "p.show()\n"
             ]
         },
         {
@@ -313,7 +314,7 @@
                 "! ogs {out_dir}/{prj_name} -o {out_dir} > {out_dir}/log.txt\n",
                 "\n",
                 "tf = time.time()\n",
-                "print(\">>> OGS terminated execution  <<< Elapsed time: \", round(tf - t0, 2), \" s.\")"
+                "print(\">>> OGS terminated execution  <<< Elapsed time: \", round(tf - t0, 2), \" s.\")\n"
             ]
         },
         {
@@ -355,7 +356,7 @@
                 "if phasefield_model=='AT1':\n",
                 "    G_eff=G_i*(1+3*h/(8*ls))\n",
                 "elif phasefield_model=='AT2':\n",
-                "    G_eff= G_i*(1+h/(2*ls))"
+                "    G_eff= G_i*(1+h/(2*ls))\n"
             ]
         },
         {
@@ -529,7 +530,7 @@
                 "        G_theta += mean_value*area\n",
                 "    G_theta_time[t][1]= G_theta\n",
                 "    G_theta_time[t][0]= time_value\n",
-                "mesh.save(f\"{out_dir}/surfing_Post_Processing.vtu\")"
+                "mesh.save(f\"{out_dir}/surfing_Post_Processing.vtu\")\n"
             ]
         },
         {
@@ -584,7 +585,7 @@
                 "plt.xlim(-0.05,0.8)\n",
                 "# plt.ylim(0,4)\n",
                 "legend = plt.legend(loc='upper right')\n",
-                "plt.show()"
+                "plt.show()\n"
             ]
         },
         {
@@ -656,7 +657,7 @@
                 "    plotter.view_xy()\n",
                 "    plotter.write_frame()\n",
                 "\n",
-                "plotter.close()"
+                "plotter.close()\n"
             ]
         },
         {
@@ -707,7 +708,7 @@
                 "p.view_xy()\n",
                 "p.camera.zoom(1.5)\n",
                 "p.window_size = [800,400]\n",
-                "p.show()"
+                "p.show()\n"
             ]
         },
         {
diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb
index 5f0f880eb8f..d68470ca65a 100644
--- a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb
+++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb
@@ -5,30 +5,12 @@
    "id": "90c995c2",
    "metadata": {},
    "source": [
+    "+++\n",
     "author = \"Tao You, Keita Yoshioka, Mostafa Mollaali\"\n",
     "date = \"2022-11-28\"\n",
     "title = \"Three point bending test\"\n",
     "web_subsection = \"phase-field\"\n",
-    "<!--eofm-->"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "41d0a5bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ogs6py import ogs\n",
-    "import os\n",
-    "import shutil\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pyvista as pv\n",
-    "import time\n",
-    "import pandas as pd\n",
-    "from xml.dom import minidom\n",
-    "from types import MethodType"
+    "+++\n"
    ]
   },
   {
@@ -58,6 +40,25 @@
     "## Define some helper functions"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "41d0a5bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ogs6py import ogs\n",
+    "import os\n",
+    "import shutil\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pyvista as pv\n",
+    "import time\n",
+    "import pandas as pd\n",
+    "from xml.dom import minidom\n",
+    "from types import MethodType\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -83,7 +84,7 @@
     "def set_timestepping(model,repeat_list, delta_t_list):\n",
     "    model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n",
     "    for i in range(len(repeat_list)):\n",
-    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])"
+    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])\n"
    ]
   },
   {
@@ -156,7 +157,7 @@
     "        print(\"  > OGS started execution ...\")\n",
     "        ! ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n",
     "    tf = time.time()\n",
-    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")"
+    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")\n"
    ]
   },
   {
@@ -302,7 +303,7 @@
    "source": [
     "# Load experimental data\n",
     "data_lower = pd.read_csv(f\"figures/experiment_data_lower_limit.csv\")  \n",
-    "data_upper = pd.read_csv(f\"figures/experiment_data_upper_limit.csv\")  "
+    "data_upper = pd.read_csv(f\"figures/experiment_data_upper_limit.csv\")  \n"
    ]
   },
   {
@@ -356,7 +357,7 @@
     "ax.axhline(y = 0, color = 'black',linewidth=1)\n",
     "ax.axvline(x = 0, color = 'black',linewidth=1)\n",
     "plt.fill_between(data_upper.iloc[:,0],0,data_upper.iloc[:,1], facecolor='green', alpha=0.3)\n",
-    "plt.fill_between(data_lower.iloc[:,0],0,data_lower.iloc[:,1], facecolor='white', alpha=1)"
+    "plt.fill_between(data_lower.iloc[:,0],0,data_lower.iloc[:,1], facecolor='white', alpha=1)\n"
    ]
   },
   {
diff --git a/Tests/Data/TH2M/H/diffusion/diffusion.ipynb b/Tests/Data/TH2M/H/diffusion/diffusion.ipynb
index 36c88960456..53c065378e2 100644
--- a/Tests/Data/TH2M/H/diffusion/diffusion.ipynb
+++ b/Tests/Data/TH2M/H/diffusion/diffusion.ipynb
@@ -5,6 +5,7 @@
    "id": "94e3d277",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Gas Diffusion\"\n",
     "date = \"2022-10-19\"\n",
     "author = \"Norbert Grunwald\"\n",
@@ -12,7 +13,7 @@
     "web_subsection = \"th2m\"\n",
     "coupling = \"h\"\n",
     "weight = 1\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -97,7 +98,7 @@
     "\n",
     "# Boundary and initial concentration\n",
     "c_b = concentration(1. - (beta_c*H*pGR_b))\n",
-    "c_i = concentration(1. - (beta_c*H*pGR_i))"
+    "c_i = concentration(1. - (beta_c*H*pGR_i))\n"
    ]
   },
   {
@@ -119,7 +120,7 @@
     "\n",
     "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
     "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir)"
+    "    os.makedirs(out_dir)\n"
    ]
   },
   {
@@ -148,7 +149,7 @@
     "model.write_input()\n",
     "\n",
     "# Run OGS\n",
-    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")"
+    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")\n"
    ]
   },
   {
@@ -160,7 +161,7 @@
    "source": [
     " #Colors\n",
     "cls1 = ['#4a001e', '#731331', '#9f2945', '#cc415a', '#e06e85', '#ed9ab0']\n",
-    "cls2 = ['#0b194c', '#163670', '#265191', '#2f74b3', '#5d94cb', '#92b2de']"
+    "cls2 = ['#0b194c', '#163670', '#265191', '#2f74b3', '#5d94cb', '#92b2de']\n"
    ]
   },
   {
@@ -187,7 +188,7 @@
     "x_axis=[(i,0,0) for i in length]\n",
     "\n",
     "# Discrete locations for c vs. t plots\n",
-    "location = [0.01,0.05,0.1,0.2,0.5,1.0]"
+    "location = [0.01,0.05,0.1,0.2,0.5,1.0]\n"
    ]
   },
   {
diff --git a/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb b/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb
index fa00151d8ff..cc0dc6c45ac 100644
--- a/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb
+++ b/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb
@@ -5,6 +5,7 @@
    "id": "4d4c5b87",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Phase Appearance/Disappearance\"\n",
     "date = \"2022-10-19\"\n",
     "author = \"Norbert Grunwald\"\n",
@@ -12,7 +13,7 @@
     "web_subsection = \"th2m\"\n",
     "coupling = \"h2\"\n",
     "weight = 3\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -82,7 +83,7 @@
     "\n",
     "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
     "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir)"
+    "    os.makedirs(out_dir)\n"
    ]
   },
   {
@@ -129,7 +130,7 @@
     "model.write_input()\n",
     "\n",
     "# Run OGS\n",
-    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")"
+    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")\n"
    ]
   },
   {
@@ -140,7 +141,7 @@
    "outputs": [],
    "source": [
     " #Colors\n",
-    "cls=['#e6191d','#337fb8','#4eae4c','#984ea3','#984ea3','#feff32']"
+    "cls=['#e6191d','#337fb8','#4eae4c','#984ea3','#984ea3','#feff32']\n"
    ]
   },
   {
@@ -162,7 +163,7 @@
     "\n",
     "num_results= [1.-saturation['A'], gas_pressure['A'], liquid_pressure['A']]\n",
     "\n",
-    "time_years = time / 365.2425 / 86400"
+    "time_years = time / 365.2425 / 86400\n"
    ]
   },
   {
@@ -179,7 +180,7 @@
     "        pd.read_csv(f\"references/bourgeat_pGR.csv\"),\n",
     "        pd.read_csv(f\"references/bourgeat_pLR.csv\")]\n",
     "\n",
-    "header = list(refs[0].keys())"
+    "header = list(refs[0].keys())\n"
    ]
   },
   {
@@ -190,7 +191,7 @@
    "outputs": [],
    "source": [
     "indices = {\"Gas saturation\" : 0,\"Gas pressure\" : 1,\"Liquid pressure\" : 2}\n",
-    "labels = [\"$s_{G}$\", '$p_{GR}$', '$p_{LR}$']"
+    "labels = [\"$s_{G}$\", '$p_{GR}$', '$p_{LR}$']\n"
    ]
   },
   {
@@ -267,7 +268,7 @@
     "    ax1.legend()\n",
     "\n",
     "\n",
-    "fig1.savefig('results_sG_pGR_pLR.pdf')"
+    "fig1.savefig('results_sG_pGR_pLR.pdf')\n"
    ]
   },
   {
diff --git a/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb b/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb
index 8a76622f4eb..f6082c759a6 100644
--- a/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb
+++ b/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb
@@ -5,6 +5,7 @@
    "id": "4001a58a",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"McWhorter & Sunada Problem\"\n",
     "date = \"2022-10-19\"\n",
     "author = \"Norbert Grunwald\"\n",
@@ -12,7 +13,7 @@
     "web_subsection = \"th2m\"\n",
     "coupling = \"h2\"\n",
     "weight = 4\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -84,7 +85,7 @@
     "import numpy as np\n",
     "# Import analytical solution from a CSV file\n",
     "exact = np.loadtxt('data/ref_solution_saturation.csv', delimiter=\",\")\n",
-    "# Zeroth column is location, first column is saturation"
+    "# Zeroth column is location, first column is saturation\n"
    ]
   },
   {
@@ -106,7 +107,7 @@
     "\n",
     "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
     "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir)"
+    "    os.makedirs(out_dir)\n"
    ]
   },
   {
@@ -129,7 +130,7 @@
     "\n",
     "# run OGS\n",
     "model=OGS(PROJECT_FILE=\"mcWhorter_h2.prj\")\n",
-    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")"
+    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")\n"
    ]
   },
   {
@@ -141,7 +142,7 @@
    "source": [
     "import vtuIO\n",
     "# read OGS results from PVD file\n",
-    "pvdfile = vtuIO.PVDIO(f\"{out_dir}/result_McWhorter_H2.pvd\", dim=2, interpolation_backend=\"vtk\")"
+    "pvdfile = vtuIO.PVDIO(f\"{out_dir}/result_McWhorter_H2.pvd\", dim=2, interpolation_backend=\"vtk\")\n"
    ]
   },
   {
@@ -165,7 +166,7 @@
     "\n",
     "# Absolute and relative errors\n",
     "err_abs = exact[:,1] - sL_num\n",
-    "err_rel = err_abs / exact[:,1]"
+    "err_rel = err_abs / exact[:,1]\n"
    ]
   },
   {
@@ -219,7 +220,7 @@
     "ax2.legend(lns, labs, loc=0)\n",
     "\n",
     "\n",
-    "fig1.savefig(f\"{out_dir}/mcWhorter.pdf\")"
+    "fig1.savefig(f\"{out_dir}/mcWhorter.pdf\")\n"
    ]
   },
   {
diff --git a/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb b/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb
index c727e9e61c8..7805e383240 100644
--- a/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb
+++ b/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb
@@ -5,6 +5,7 @@
    "id": "ac287b2f",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"H2M Liakopoulos benchmark\"\n",
     "date = \"2022-08-16\"\n",
     "author = \"Norbert Grunwald, Olaf Kolditz\"\n",
@@ -12,7 +13,15 @@
     "web_subsection = \"th2m\"\n",
     "coupling = \"h2m\"\n",
     "weight = 7\n",
-    "<!--eofm-->"
+    "+++\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaca06f7",
+   "metadata": {},
+   "source": [
+    "## Notebook setup"
    ]
   },
   {
@@ -33,7 +42,7 @@
     "import matplotlib.pyplot as plt\n",
     "#import vtk\n",
     "import matplotlib.tri as tri\n",
-    "import vtuIO"
+    "import vtuIO\n"
    ]
   },
   {
@@ -52,7 +61,7 @@
     "\n",
     "prj_file_test = \"liakopoulos_TH2M.prj\"\n",
     "pvd_file_test = f\"{out_dir}/result_liakopoulos.pvd\"\n",
-    "vtu_mesh_file = \"domain.vtu\""
+    "vtu_mesh_file = \"domain.vtu\"\n"
    ]
   },
   {
@@ -81,7 +90,7 @@
     }
    ],
    "source": [
-    "Image(filename = fig_dir + \"ogs-jupyter-lab.png\", width=150, height=100)"
+    "Image(filename = fig_dir + \"ogs-jupyter-lab.png\", width=150, height=100)\n"
    ]
   },
   {
@@ -108,17 +117,7 @@
     }
    ],
    "source": [
-    "Image(filename = fig_dir + \"h2m-tet.png\", width=150, height=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e520ca69",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#3-description (markdown)"
+    "Image(filename = fig_dir + \"h2m-tet.png\", width=150, height=100)\n"
    ]
   },
   {
@@ -222,7 +221,7 @@
    "source": [
     "mesh = pv.read(vtu_mesh_file)\n",
     "print(\"inspecting vtu_mesh_file\")\n",
-    "mesh"
+    "mesh\n"
    ]
   },
   {
@@ -260,7 +259,7 @@
     "plotter.view_xy()\n",
     "plotter.add_axes()\n",
     "plotter.show_bounds(mesh, xlabel=\"x\", ylabel=\"y\")\n",
-    "plotter.show()"
+    "plotter.show()\n"
    ]
   },
   {
@@ -293,7 +292,7 @@
     "print(f\"ogs -o {out_dir} {prj_file_test} > {out_dir}/log.txt\")\n",
     "! ogs -o {out_dir} {prj_file_test} > {out_dir}/log.txt\n",
     "tf = time.time()\n",
-    "print(\"computation time: \", round(tf - t0, 2), \" s.\")"
+    "print(\"computation time: \", round(tf - t0, 2), \" s.\")\n"
    ]
   },
   {
@@ -320,7 +319,7 @@
     "#print(yaxis)\n",
     "line_mesh = mesh.slice_along_line(yaxis)\n",
     "y_num = line_mesh.points[:,1]\n",
-    "reader = pv.get_reader(pvd_file_test)"
+    "reader = pv.get_reader(pvd_file_test)\n"
    ]
   },
   {
@@ -434,7 +433,7 @@
     "ax2[1].plot(y_num, u_y4800, label=r\"$u_y$ t=4800\")\n",
     "ax2[1].plot(y_num, u_y7200, label=r\"$u_y$ t=7200\")\n",
     "ax2[1].legend()\n",
-    "ax2[1].grid()"
+    "ax2[1].grid()\n"
    ]
   },
   {
@@ -490,7 +489,7 @@
     "fig.colorbar(contour_left,ax=ax[0],label='$p$ / [MPa]')\n",
     "fig.colorbar(contour_mid,ax=ax[1],label='$S$ / [-]')\n",
     "fig.colorbar(contour_right,ax=ax[2],label='$u$ / [m]')\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
diff --git a/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb b/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb
index dd0e45d8c0b..36c2fe0415b 100644
--- a/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb
+++ b/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb
@@ -5,6 +5,7 @@
    "id": "10a2c579",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Ogata-Banks Problem\"\n",
     "date = \"2022-10-19\"\n",
     "author = \"Norbert Grunwald\"\n",
@@ -12,7 +13,7 @@
     "web_subsection = \"th2m\"\n",
     "coupling = \"th\"\n",
     "weight = 5\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -138,7 +139,7 @@
     "domain_size = 50 # metre\n",
     "\n",
     "# Groundwater velocity\n",
-    "v_x = 1.5e-6"
+    "v_x = 1.5e-6\n"
    ]
   },
   {
@@ -172,7 +173,7 @@
     "    a2 = np.divide((x+v_x*t),d,where=t!=0)\n",
     "    \n",
     "    result = (T_0-T_i) / 2. * (erfc(a1)+np.exp(v_x*x/alpha)*erfc(a2)) + T_i\n",
-    "    return result"
+    "    return result\n"
    ]
   },
   {
@@ -194,7 +195,7 @@
     "\n",
     "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
     "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir)"
+    "    os.makedirs(out_dir)\n"
    ]
   },
   {
@@ -223,7 +224,7 @@
     "model.replace_text(delta_time, xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair/delta_t\")\n",
     "# Output every timestep \n",
     "model.replace_text(1, xpath=\"./time_loop/output/timesteps/pair/each_steps\")\n",
-    "model.write_input()"
+    "model.write_input()\n"
    ]
   },
   {
@@ -243,7 +244,7 @@
    ],
    "source": [
     "# Run OGS\n",
-    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m . -s .\")"
+    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m . -s .\")\n"
    ]
   },
   {
@@ -282,7 +283,7 @@
     "x_axis=[(i,0,0) for i in length]\n",
     "\n",
     "# Discrete locations for T vs. t plots\n",
-    "location = [1.,5.,10.,20.,50.]"
+    "location = [1.,5.,10.,20.,50.]\n"
    ]
   },
   {
@@ -295,7 +296,7 @@
     "# The sample locations have to be converted into a 'dict' for vtuIO\n",
     "observation_points = dict(('x='+str(x),(x,0.0,0.0)) for x in location)\n",
     "# Samples temperature field at the observation points for all timesteps\n",
-    "T_over_t_at_x = pvdfile.read_time_series('temperature_interpolated', observation_points)"
+    "T_over_t_at_x = pvdfile.read_time_series('temperature_interpolated', observation_points)\n"
    ]
   },
   {
@@ -360,7 +361,7 @@
     "\n",
     "ax1.legend()\n",
     "ax2.legend()\n",
-    "fig1.savefig(f\"{out_dir}/ogata_banks.pdf\")"
+    "fig1.savefig(f\"{out_dir}/ogata_banks.pdf\")\n"
    ]
   },
   {
@@ -395,7 +396,7 @@
     "\n",
     "# von-Neumann-Stability-Criterion\n",
     "Ne = alpha * delta_time / (dx*dx)\n",
-    "print (Ne)"
+    "print (Ne)\n"
    ]
   },
   {
@@ -422,7 +423,7 @@
    ],
    "source": [
     "dt = 0.5*(dx*dx)/alpha\n",
-    "print(\"Smallest timestep should not exceed\",dt, \"seconds.\")"
+    "print(\"Smallest timestep should not exceed\",dt, \"seconds.\")\n"
    ]
   },
   {
@@ -452,7 +453,7 @@
    ],
    "source": [
     "dx = np.sqrt(2*alpha*delta_time)\n",
-    "print(\"Minimum element size should be\",dx,\" metre.\")"
+    "print(\"Minimum element size should be\",dx,\" metre.\")\n"
    ]
   },
   {
diff --git a/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb b/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb
index 62469b77aa3..31199770888 100644
--- a/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb
+++ b/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb
@@ -5,6 +5,7 @@
    "id": "75afeb19",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Confined Gas Compression\"\n",
     "date = \"2022-10-19\"\n",
     "author = \"Norbert Grunwald\"\n",
@@ -12,7 +13,7 @@
     "web_subsection = \"th2m\"\n",
     "coupling = \"h2\"\n",
     "weight = 2\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -74,7 +75,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n"
    ]
   },
   {
@@ -102,7 +103,7 @@
     "kappa=c_p/c_v\n",
     "\n",
     "# density\n",
-    "rho_GR = rho_0*np.exp(-e)"
+    "rho_GR = rho_0*np.exp(-e)\n"
    ]
   },
   {
@@ -160,7 +161,7 @@
    "outputs": [],
    "source": [
     "# gas pressure\n",
-    "p_GR=p_0*np.exp(-kappa*e)"
+    "p_GR=p_0*np.exp(-kappa*e)\n"
    ]
   },
   {
@@ -184,7 +185,7 @@
    "outputs": [],
    "source": [
     "# temperature\n",
-    "T = p_GR*M/R/rho_GR"
+    "T = p_GR*M/R/rho_GR\n"
    ]
   },
   {
@@ -203,7 +204,7 @@
    "outputs": [],
    "source": [
     "from ogs6py.ogs import OGS\n",
-    "import vtuIO"
+    "import vtuIO\n"
    ]
   },
   {
@@ -217,7 +218,7 @@
     "\n",
     "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
     "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir)"
+    "    os.makedirs(out_dir)\n"
    ]
   },
   {
@@ -238,7 +239,7 @@
    "source": [
     "# run OGS\n",
     "cube_compression=OGS(PROJECT_FILE=\"compression_gas.prj\")\n",
-    "cube_compression.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")"
+    "cube_compression.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")\n"
    ]
   },
   {
@@ -251,7 +252,7 @@
     "# read PVD file\n",
     "pvdfile = vtuIO.PVDIO(f\"{out_dir}/result_compression_gas.pvd\", dim=2)\n",
     "# get all timesteps\n",
-    "time = pvdfile.timesteps"
+    "time = pvdfile.timesteps\n"
    ]
   },
   {
@@ -455,7 +456,7 @@
     "ax3.set_ylim(-0.0035,0.0002)\n",
     "\n",
     "fig1.tight_layout()\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
@@ -515,7 +516,7 @@
     "ax3.set_ylim(-0.002,0.000125)\n",
     "\n",
     "fig1.tight_layout()\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   }
  ],
diff --git a/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb b/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb
index 06c306e7041..d0b163f3547 100644
--- a/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb
+++ b/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb
@@ -5,6 +5,7 @@
    "id": "7613d48f",
    "metadata": {},
    "source": [
+    "+++\n",
     "title = \"Heat pipe verification problem\"\n",
     "date = \"2022-09-14\"\n",
     "author = \"Kata Kurgyis\"\n",
@@ -12,7 +13,7 @@
     "image = \"figures/placeholder_heatpipe.png\"\n",
     "coupling = \"h2t\"\n",
     "weight = 6\n",
-    "<!--eofm-->"
+    "+++\n"
    ]
   },
   {
@@ -120,7 +121,7 @@
     "k_rG_min = 1e-5 # used for normalization of BC model\n",
     "k_rL_min = 1e-5 # used for normalization of BC model\n",
     "p_thr_BC = 5.0e3 # entry pressure for Brooks-Corey model [Pa]\n",
-    "exp_BC = 3.0 # Corey exponent for Brooks-Corey model [-]"
+    "exp_BC = 3.0 # Corey exponent for Brooks-Corey model [-]\n"
    ]
   },
   {
@@ -157,7 +158,7 @@
     "    return max(k_rG_min, ((1.-sL_eff) ** 2) * (1-(sL_eff ** ((2.+exp_BC)/exp_BC))) )\n",
     "\n",
     "def relative_permeability_liquid(sL_eff):\n",
-    "    return max(k_rL_min, sL_eff ** ((2.+3*exp_BC)/exp_BC))"
+    "    return max(k_rL_min, sL_eff ** ((2.+3*exp_BC)/exp_BC))\n"
    ]
   },
   {
@@ -185,7 +186,7 @@
     "\n",
     "def partial_pressure_vapour(p_G, p_c, xA_G, T):\n",
     "    p_sat = saturation_vapour_pressure(T)\n",
-    "    return vapour_pressure(p_sat, p_G, p_c, xA_G, T)"
+    "    return vapour_pressure(p_sat, p_G, p_c, xA_G, T)\n"
    ]
   },
   {
@@ -218,7 +219,7 @@
     "def kinematic_viscosity_gas_phase(p_G, xA_G, T):\n",
     "    mu_G = viscosity_gas_phase(xA_G)\n",
     "    rho_G = density_gas_phase(p_G, xA_G, T)\n",
-    "    return mu_G / rho_G"
+    "    return mu_G / rho_G\n"
    ]
   },
   {
@@ -237,7 +238,7 @@
    "outputs": [],
    "source": [
     "def diffusivity(sL_eff):\n",
-    "    return phi * (1. - sL_eff) * D_pm"
+    "    return phi * (1. - sL_eff) * D_pm\n"
    ]
   },
   {
@@ -260,7 +261,7 @@
     "    phi_G = (1. - sL) * phi\n",
     "    phi_L = sL * phi\n",
     "    phi_S = 1. - phi\n",
-    "    return lambda_G * phi_G + lambda_L * phi_L + lambda_S * phi_S"
+    "    return lambda_G * phi_G + lambda_L * phi_L + lambda_S * phi_S\n"
    ]
   },
   {
@@ -451,7 +452,7 @@
     "    gamma = gamma_(sL_eff, p_G, xA_G, T)\n",
     "    nu_G = kinematic_viscosity_gas_phase(p_G, xA_G, T)\n",
     "    th_cond = thermal_conductivity(sL_eff)\n",
-    "    return dpC_dsL_eff * (1. - eta) / eta * dh_evap / (nu_G * th_cond) * K / gamma"
+    "    return dpC_dsL_eff * (1. - eta) / eta * dh_evap / (nu_G * th_cond) * K / gamma\n"
    ]
   },
   {
@@ -526,7 +527,7 @@
     "dsL_eff = (sL_eff_high - sL_eff_low) / n_dsL_eff\n",
     "\n",
     "# execute analytical solution\n",
-    "M, sL_eff_list = full_Euler(dsL_eff, y0, sL_eff_low, sL_eff_high)"
+    "M, sL_eff_list = full_Euler(dsL_eff, y0, sL_eff_low, sL_eff_high)\n"
    ]
   },
   {
@@ -570,7 +571,7 @@
     "\n",
     "prj_file = \"heat_pipe_rough.prj\"\n",
     "model=ogs.OGS(INPUT_FILE=prj_file, PROJECT_FILE=prj_file)\n",
-    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")"
+    "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")\n"
    ]
   },
   {
@@ -616,7 +617,7 @@
     "S_num_interp = np.interp(M[0,:], x_num, S_num)\n",
     "xA_G_num_interp = np.interp(M[0,:], x_num, xA_G_num)\n",
     "p_G_num_interp = np.interp(M[0,:], x_num, p_G_num)\n",
-    "T_num_interp = np.interp(M[0,:], x_num, T_num)"
+    "T_num_interp = np.interp(M[0,:], x_num, T_num)\n"
    ]
   },
   {
@@ -777,7 +778,7 @@
     "ax3.legend()\n",
     "ax3.grid(True)\n",
     "fig3.tight_layout()\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
diff --git a/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb b/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb
index 4448cf6a282..80751ba8b26 100644
--- a/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb
+++ b/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb
@@ -4,6 +4,7 @@
             "cell_type": "raw",
             "metadata": {},
             "source": [
+                "+++\n",
                 "author = \"Jörg Buchwald and Kata Kurgyis\"\n",
                 "date = \"2022-11-02\"\n",
                 "title = \"Point-Heatsource Problem\"\n",
@@ -11,7 +12,7 @@
                 "image = \"figures/placeholder_pointheatsource.png\"\n",
                 "web_subsection = \"th2m\"\n",
                 "coupling = \"thm\"\n",
-                "<!--eofm-->"
+                "+++\n"
             ]
         },
         {
@@ -304,7 +305,7 @@
                 "        self.Y = 1/(self.lambd+2*self.G) * (self.X/((1-self.c/self.kappa)*self.a_u)+self.bprime/self.a_u)\n",
                 "        self.Z = 1/(self.lambd+2*self.G) * (self.X/((1-self.c/self.kappa)*self.a_u))\n",
                 "\n",
-                "ana_model = ANASOL()"
+                "ana_model = ANASOL()\n"
             ]
         },
         {
@@ -353,7 +354,7 @@
                 "prj_file_trm = \"point_heat_source_2D.prj\"\n",
                 "path_trm = f\"{data_dir}/ThermoRichardsMechanics/PointHeatSource\"\n",
                 "prj_filepath_trm = f\"{path_trm}/{prj_file_trm}\"\n",
-                "ogs_model_trm = ogs.OGS(INPUT_FILE=prj_filepath_trm, PROJECT_FILE=f\"{out_dir}/pointheatsource_trm.prj\")"
+                "ogs_model_trm = ogs.OGS(INPUT_FILE=prj_filepath_trm, PROJECT_FILE=f\"{out_dir}/pointheatsource_trm.prj\")\n"
             ]
         },
         {
@@ -367,7 +368,7 @@
                 "ogs_model_lin.set(t_end=t_end)\n",
                 "ogs_model_quad.set(t_end=t_end)\n",
                 "ogs_model_th2m.set(t_end=t_end)\n",
-                "ogs_model_trm.set(t_end=t_end)"
+                "ogs_model_trm.set(t_end=t_end)\n"
             ]
         },
         {
@@ -380,7 +381,7 @@
                 "ogs_model_quad.set(output_prefix=\"pointheatsource_quad\")\n",
                 "ogs_model_th2m.set(output_prefix=\"pointheatsource_th2m\")\n",
                 "ogs_model_th2m.replace_text(\"150\", xpath=\"./parameters/parameter[name='temperature_source_term']/value\")\n",
-                "ogs_model_trm.set(output_prefix=\"pointheatsource_trm\")"
+                "ogs_model_trm.set(output_prefix=\"pointheatsource_trm\")\n"
             ]
         },
         {
@@ -392,7 +393,7 @@
                 "ogs_model_lin.write_input()\n",
                 "ogs_model_quad.write_input()\n",
                 "ogs_model_th2m.write_input()\n",
-                "ogs_model_trm.write_input()"
+                "ogs_model_trm.write_input()\n"
             ]
         },
         {
@@ -433,7 +434,7 @@
                 "    for result in results:\n",
                 "        print(result[0])\n",
                 "        runtimes.append(result[1])\n",
-                "print(f\"Elapsed time for all simulations: {timer() - start} s\")"
+                "print(f\"Elapsed time for all simulations: {timer() - start} s\")\n"
             ]
         },
         {
@@ -463,7 +464,7 @@
                 "\n",
                 "pvds = []\n",
                 "for i, prj in enumerate(projects):\n",
-                "    pvds.append(vtuIO.PVDIO(f\"{out_dir}/{prj}.pvd\", dim=2))"
+                "    pvds.append(vtuIO.PVDIO(f\"{out_dir}/{prj}.pvd\", dim=2))\n"
             ]
         },
         {
@@ -525,7 +526,7 @@
                 "    \n",
                 "ax2.legend(loc='upper right')   \n",
                 "\n",
-                "fig1.tight_layout()"
+                "fig1.tight_layout()\n"
             ]
         },
         {
@@ -561,7 +562,7 @@
                 "\n",
                 "ax2.legend(loc='upper right')\n",
                 "\n",
-                "fig1.tight_layout()"
+                "fig1.tight_layout()\n"
             ]
         },
         {
@@ -597,7 +598,7 @@
                 "\n",
                 "ax2.legend(loc='lower right')   \n",
                 "\n",
-                "fig1.tight_layout()"
+                "fig1.tight_layout()\n"
             ]
         },
         {
@@ -620,7 +621,7 @@
                 "\n",
                 "# Radial coordinates for plotting\n",
                 "x = np.linspace(start=0.0001, stop=10.0, num=100)\n",
-                "r =  [(i,0,0) for i in x]"
+                "r =  [(i,0,0) for i in x]\n"
             ]
         },
         {
@@ -656,7 +657,7 @@
                 "    \n",
                 "ax2.legend()\n",
                 "\n",
-                "fig1.tight_layout()"
+                "fig1.tight_layout()\n"
             ]
         },
         {
@@ -691,7 +692,7 @@
                 "\n",
                 "ax2.legend()\n",
                 "\n",
-                "fig1.tight_layout()"
+                "fig1.tight_layout()\n"
             ]
         },
         {
@@ -726,7 +727,7 @@
                 "\n",
                 "ax2.legend()\n",
                 "\n",
-                "fig1.tight_layout()"
+                "fig1.tight_layout()\n"
             ]
         },
         {
@@ -749,7 +750,7 @@
                 "mesh = ['thm linear', 'thm quadratic', 'th2m', 'trm']\n",
                 "ax.bar(mesh,runtimes)\n",
                 "plt.ylabel(\"exec. time / s\")\n",
-                "plt.show()"
+                "plt.show()\n"
             ]
         },
         {
diff --git a/Tests/Data/requirements-dev.txt b/Tests/Data/requirements-dev.txt
index e04f5bd31f8..3d158732b79 100644
--- a/Tests/Data/requirements-dev.txt
+++ b/Tests/Data/requirements-dev.txt
@@ -1,4 +1,3 @@
-git+https://github.com/bilke/nb2hugo@f9744903ed17d46afb3877ffe244420a50aaecc6#egg=nb2hugo
 nbconvert==7.2.9
 nbformat==5.7.3
 toml==0.10.2
diff --git a/scripts/ci/extends/template-build-linux.yml b/scripts/ci/extends/template-build-linux.yml
index 1066bb556fe..ee5ab676da3 100644
--- a/scripts/ci/extends/template-build-linux.yml
+++ b/scripts/ci/extends/template-build-linux.yml
@@ -76,6 +76,10 @@
             ctest_arguments="${ctest_arguments} -LE large"
           fi
 
+          if [[ "$CI_MERGE_REQUEST_LABELS" =~ [.*web\ only.*] ]]; then
+            ctest_arguments="${ctest_arguments} -R nb-"
+          fi
+
           if [[ ! -z "$CTEST_TIMEOUT" ]]; then
             ctest_timeout="$CTEST_TIMEOUT"
           fi
diff --git a/scripts/docker/Dockerfile.web b/scripts/docker/Dockerfile.web
index 67c8511717b..df4ced3d635 100644
--- a/scripts/docker/Dockerfile.web
+++ b/scripts/docker/Dockerfile.web
@@ -18,4 +18,4 @@ ENV HUGO_VERSION=0.117.0
 RUN curl -fSL -O "https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_linux-amd64.deb" \
     && DEBIAN_FRONTEND=noninteractive apt-get install -y /hugo_extended_${HUGO_VERSION}_linux-amd64.deb \
     && rm /hugo_extended_${HUGO_VERSION}_linux-amd64.deb
-RUN pip install git+https://github.com/bilke/nb2hugo@f9744903ed17d46afb3877ffe244420a50aaecc6#egg=nb2hugo
+RUN pip install nbconvert
diff --git a/web/.gitignore b/web/.gitignore
index d0b7de38029..96a23300b6c 100644
--- a/web/.gitignore
+++ b/web/.gitignore
@@ -17,6 +17,6 @@ data/bibliography.json
 # generated css
 static/css/all.css
 
-# Generated by nb2hugo
+# Generated by nbconvert
 content/docs/benchmarks/notebooks
 static/docs/benchmarks/notebooks
diff --git a/web/content/docs/devguide/documentation/jupyter-docs/index.md b/web/content/docs/devguide/documentation/jupyter-docs/index.md
index 15c3c23bf4a..d88b4e67717 100644
--- a/web/content/docs/devguide/documentation/jupyter-docs/index.md
+++ b/web/content/docs/devguide/documentation/jupyter-docs/index.md
@@ -35,14 +35,15 @@ If you use additional images put them into the `my-page`-folder.
 If the notebook result should appear as a page on the web documentation a frontmatter with some meta information (similar to [regular web pages]({{< ref "web-docs.md" >}})) is required as the first cell in the notebook:
 
 - Add a new cell and move it to the first position in the notebook
-- Change the cell type to `raw`!
-- Add meta information, conclude with a end-of-file marker (`<!--eofm-->`) e.g.:
+- Cell type needs to be `markdown` or `raw`
+- Add meta information e.g.:
 
   ```md
+  +++
   title = "BHE Meshing"
   date = "2023-08-18"
   author = "Joy Brato Shil, Haibing Shao"
-  <!--eofm-->
+  +++
   ```
 
 ---
@@ -54,10 +55,10 @@ Make sure that you execute the cells in the notebook and save the notebook (with
 To get a preview of the web page run the `convert_notebooks`-script:
 
 ```bash
-# You need the converter-tool nb2hugo installed. Recommended way is to
+# You need the converter-tool nbconvert installed. Recommended way is to
 # create and activate a virtual environment and install it there:
 python -m venv .venv  # or `python3 -m ...` on some systems
-pip install git+https://github.com/bilke/nb2hugo@ogs
+pip install nbconvert
 
 python web/scripts/convert_notebooks.py
 
@@ -101,10 +102,24 @@ web_subsection = "small-deformations" # required for notebooks in Tests/Data onl
 - Frontmatter needs to be in [TOML](https://toml.io)-format.
 - For notebooks describing benchmarks `web_subsection` needs to be set to a sub-folder in [web/content/docs/benchmarks](https://gitlab.opengeosys.org/ogs/ogs/-/tree/master/web/content/docs/benchmarks) (if not set the notebook page will not be linked from navigation bar / benchmark gallery on the web page).
 - If you edit a Markdown-based notebook with Jupyter and the Jupytext extension please don't add the two newlines but make sure that the frontmatter has its own cell (not mixed with markdown content).
-- For (deprecated) `.ipynb`-based notebooks the frontmatter has to given as a `raw`-cell containing a special `<!--eofm-->`-marker. See existing notebooks (e.g. [SimpleMechanics.ipynb](https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb)) for reference.
+- For (deprecated) `.ipynb`-based notebooks the frontmatter needs to be given in the first cell. See existing notebooks (e.g. [SimpleMechanics.ipynb](https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb)) for reference.
 
 ### Notebook setup
 
+The first cell after the frontmatter needs to be a `markdown`-cell!
+
+#### Markdown cells
+
+- HTML inside Markdown cells may be used for specific reasons (e.g. better image formatting).
+- For notebooks in `Tests/Data` only: Static images e.g. for the gallery image or to be used in Markdown cells have to be located in either `images`- or `figures`-subdirectories beneath the Notebook file! Otherwise they will not show up on the web site.
+  - For image captions add a title in quotation marks after the image path, e.g.
+
+    ```md
+    ![Alt text](figures/my_image.png "This will be the image caption.")
+    ```
+
+  - Please note that in merge request web previews static images are not shown at all.
+
 #### Python cells
 
 - Do not use machine-specific or absolute paths! See the following example to set up notebook output paths:
@@ -136,18 +151,6 @@ web_subsection = "small-deformations" # required for notebooks in Tests/Data onl
 - Do not write anything into the source directories. Use an `out_dir` as above.
 - Assume that `ogs` and other tools are in the `PATH`.
 
-#### Markdown cells
-
-- Do not use HTML inside Markdown blocks.
-- Static images e.g. for the gallery image or to be used in Markdown cells have to be located in either `images`- or `figures`-subdirectories beneath the Notebook file! Otherwise they will not show up in the web site.
-  - For image captions add a title in quotation marks after the image path, e.g.
-
-    ```md
-    ![Alt text](figures/my_image.png "This will be the image caption.")
-    ```
-
-  - Please note that in merge request web previews static images are not shown at all.
-
 ### Execution environment
 
 In CI the notebooks are executed with all dependencies installed into a virtual environment in the build directory. The installed packages are defined in `Test/Data/requirements.txt`. The same setup can be enabled locally by setting the CMake option `OGS_USE_PIP=ON`. E.g.
diff --git a/web/content/docs/userguide/basics/jupyter-notebooks/index.md b/web/content/docs/userguide/basics/jupyter-notebooks/index.md
index 283fdffeddb..cf93d3847d7 100644
--- a/web/content/docs/userguide/basics/jupyter-notebooks/index.md
+++ b/web/content/docs/userguide/basics/jupyter-notebooks/index.md
@@ -38,7 +38,6 @@ Image `registry.opengeosys.org/ogs/ogs/ogs-serial-jupyter` contains:
 - Jupyter-related tools:
   - [nbconvert](https://nbconvert.readthedocs.io) — Format conversion
   - [nbdime](https://nbdime.readthedocs.io) — Diffs for notebooks
-  - [nb2hugo](https://github.com/bilke/nb2hugo/tree/ogs) — Notebook to website markdown conversion
 - [Gmsh](https://gmsh.info) — Mesh generator (incl. Python bindings)
 
 Image `registry.opengeosys.org/ogs/ogs/ogs-petsc-jupyter` additionally contains:
diff --git a/web/data/versions.json b/web/data/versions.json
index 6eda2385be2..43efacea260 100644
--- a/web/data/versions.json
+++ b/web/data/versions.json
@@ -59,7 +59,6 @@
         "git+https://github.com/joergbuchwald/ogs6py@71f49a896381152e648801740833450022115981#egg=ogs6py",
         "git+https://github.com/joergbuchwald/VTUinterface@05793c7be84fbcb7d9f8f740c3dc667089a61505#egg=VTUinterface",
         "git+https://github.com/joergbuchwald/heatsource_thm@main#egg=heatsource-py",
-        "git+https://github.com/bilke/nb2hugo@53d62ae5aef1be271eb91491d3b37da487bd1498#egg=nb2hugo",
         "ogstools==0.0.3",
         "ipykernel==6.9.1",
         "jinja2==3.0.3",
diff --git a/web/package.json b/web/package.json
index 38588e883e9..b15691e5de5 100644
--- a/web/package.json
+++ b/web/package.json
@@ -3,7 +3,6 @@
   "scripts": {
     "build": "npx tailwindcss -o static/css/all.css -i assets/css/main.css -m && hugo",
     "watch": "npx tailwindcss -o static/css/all.css -i assets/css/main.css -m --watch",
-    "build-with-nb": "find ../Tests/Notebooks -type f -iname '*.ipynb' -not -path \"*.ipynb_checkpoints*\" | xargs -n1 nb2hugo --site-dir . --section docs/benchmarks/notebooks --template ../Tests/Notebooks/nbconvert_templates/collapsed.md.j2 && npx tailwindcss -o static/css/all.css -i assets/css/main.css -m && hugo",
     "server": "hugo server",
     "index": "hugo-algolia -toml",
     "upload-index": "hugo-algolia --toml -s"
diff --git a/web/scripts/convert_notebooks.py b/web/scripts/convert_notebooks.py
index 19237c167b7..2663be46ec3 100644
--- a/web/scripts/convert_notebooks.py
+++ b/web/scripts/convert_notebooks.py
@@ -16,10 +16,20 @@
         )
         exit_code = 1
         continue
-    print(f"Converting {notebook} ...")
+    template = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "../../Tests/Data/Notebooks/nbconvert_templates/collapsed.md.j2",
+    )
     subprocess.run(
-        f"nb2hugo --site-dir .. --section {nb.parent.parent} {notebook}",
-        shell=True,
+        [
+            "jupyter",
+            "nbconvert",
+            "--to",
+            "markdown",
+            f"--template-file={template}",
+            "--output=index",
+            notebook,
+        ],
         check=True,
     )