{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "568b3cae-6a1b-41ab-b3d9-8d8190de802e",
   "metadata": {},
   "source": [
    "# Grounded co-planar waveguide with via fence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1187e70-6aa6-417a-93fc-082528ee17e2",
   "metadata": {},
   "source": [
    "Co-planar waveguide (CPWs) transmission lines offer many advantages, including better EM shielding, ease of fabrication, and lower radiation loss when compared to microstrips. However, conventional CPWs can be susceptible to coupling to higher-order modes in nearby structures, thus introducing undesirable resonances that restrict the operating bandwidth of the CPW. In particular, at high frequencies, the side ground planes in a conventional CPW can support higher-order resonances that radiate similarly to a patch antenna.\n",
    "\n",
    "One solution is to introduce via fences on either side of the signal line. This reduces the transverse electrical size of the CPW and \"pushes\" the resonances out beyond the design bandwidth of the CPW. It also provides the added benefit of better shielding from EM interference.\n",
    "\n",
    "In this notebook, we will simulate some of the designs investigated in this paper:\n",
    "\n",
    "* Sain, Arghya, and Kathleen L. Melde, \"Impact of ground via placement in grounded coplanar waveguide interconnects.\" IEEE Transactions on Components, Packaging and Manufacturing Technology 6.1 (2015): 136-144.\n",
    "\n",
    "We begin by simulating a conventional CPW with a bottom ground plane to demonstrate the undesirable resonant effect. Then, we will compare it to a design with via fences. Finally, we will test a setup fabricated by Sain et al. in order to validate the simulation results. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e6a5e5-9114-40f2-9dc6-4171d2027b25",
   "metadata": {},
   "source": [
    "<center><img src=\"./img/gcpw_via_fence_render.png\" width=540 /></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1071b980-19c0-4b93-8425-c0a7757be4f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.rf as rf\n",
    "from tidy3d import web\n",
    "from tidy3d.plugins.dispersion import FastDispersionFitter\n",
    "\n",
    "td.config.logging.level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e63afeb-9c29-4337-ba62-eb5709852a03",
   "metadata": {},
   "source": [
    "## Building the simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ac7055-75f1-4fd9-9917-da308862ce19",
   "metadata": {},
   "source": [
    "### Key parameters and materials"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "120b0333-feb4-43a8-9456-292bf3f20a61",
   "metadata": {},
   "source": [
    "The key parameters for this model are the operating frequency range, the geometry, and the material properties. These are defined below. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00beab83-4b93-4121-9fae-332f44f3509a",
   "metadata": {},
   "source": [
    "<center><img src=\"./img/gcpw_via_fence_schematic.png\" width=800 /></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffe92a69-3784-47cc-b961-4a53edd27d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequency\n",
    "f_min, f_max = (1e9, 100e9)  # Frequency range (Hz)\n",
    "f0 = (f_max + f_min) / 2  # Center frequency\n",
    "freqs = np.linspace(f_min, f_max, 301)  # Sample frequency points\n",
    "field_mon_freqs = np.linspace(f_min, f_max, 21)  # Frequency points for field monitor data\n",
    "\n",
    "# CPW Geometry\n",
    "len_inf = 1e5  # Effective infinity\n",
    "ws = 177.8  # Signal strip width\n",
    "wg = 1600  # Ground strip width\n",
    "t = 17.78  # Conductor thickness\n",
    "h = 101.6  # Substrate thickness\n",
    "gg = 101.6  # Gap between signal and side ground\n",
    "L = 6000  # CPW line length\n",
    "\n",
    "# Via geometry\n",
    "vr = 76.2  # Via radius\n",
    "vp = 287.3  # Via pitch (longitudinal spacing)\n",
    "vl = 400  # Via transverse spacing (from center of signal strip)\n",
    "es = 127  # Distance from first/last via to CPW start/end\n",
    "\n",
    "# Overall simulation size and center\n",
    "sim_size = (5600, 1000, 7500)\n",
    "sim_center = (0, 500 - h - t, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9b3d62-6036-4428-9de1-01e5a372bbcc",
   "metadata": {},
   "source": [
    "Both the substrate and metal are assumed to have constant loss over the frequency range. The materials used for the simulation are defined below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08ffd728-1169-4e0d-94e9-88d2a63564dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Material properties\n",
    "cond = 58  # Metal conductivity in S/um\n",
    "eps = 3.5  # Relative permittivity, substrate\n",
    "losstan = 0.002  # Loss tangent, substrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cc5db8b-c640-4776-88bd-f68dab4047dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "62742acea96e4175a42ac1c16bb30ea9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "med_sub = FastDispersionFitter.constant_loss_tangent_model(\n",
    "    eps, losstan, (f_min, f_max), tolerance_rms=3e-4\n",
    ")\n",
    "med_metal = rf.LossyMetalMedium(conductivity=cond, frequency_range=(f_min, f_max), name=\"Metal\")\n",
    "med_air = td.Medium(permittivity=1.0, name=\"Air\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b2d755-27a0-4e6b-b619-506e015e18c7",
   "metadata": {},
   "source": [
    "### Geometry construction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e98052-887c-4b12-a58c-9298de5dad8b",
   "metadata": {},
   "source": [
    "We start by creating all the planar structures (substrate and metal strips). The ground plane and substrate are assumed to be infinite in width and length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ef3bcac-72fd-4dce-bff3-197684df7f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substrate\n",
    "str_sub = td.Structure(\n",
    "    geometry=td.Box(center=(0, -h / 2, 0), size=(len_inf, h, len_inf)), medium=med_sub\n",
    ")\n",
    "\n",
    "# Signal strip\n",
    "str_sig = td.Structure(geometry=td.Box(center=(0, t / 2, 0), size=(ws, t, L)), medium=med_metal)\n",
    "\n",
    "# Side and bottom grounds\n",
    "str_gnd1 = td.Structure(\n",
    "    geometry=td.Box(center=((ws + wg) / 2 + gg, t / 2, 0), size=(wg, t, L)), medium=med_metal\n",
    ")\n",
    "str_gnd2 = td.Structure(\n",
    "    geometry=td.Box(center=(-(ws + wg) / 2 - gg, t / 2, 0), size=(wg, t, L)), medium=med_metal\n",
    ")\n",
    "str_gnd3 = td.Structure(\n",
    "    geometry=td.Box(center=(0, -h - t / 2, 0), size=(len_inf, t, len_inf)), medium=med_metal\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b6f4610-48d8-42d5-8278-b6bc76825ab7",
   "metadata": {},
   "source": [
    "We define a reusable function to create the via structure at a given (x, z) position. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3656c0c6-5a02-49ce-93d1-ee98274c4ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_via(xpos, zpos):\n",
    "    \"\"\"Create one via structure at given x and z positions\"\"\"\n",
    "    via_structure = td.Structure(\n",
    "        geometry=td.Cylinder(center=(xpos, -h / 2, zpos), radius=vr, length=h, axis=1),\n",
    "        medium=med_metal,\n",
    "    )\n",
    "\n",
    "    return via_structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98cc2bfc-98a9-4839-aee1-c22405e28da9",
   "metadata": {},
   "source": [
    "Then, we loop over all x- and z-positions to create the via fence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7f20430-c780-40fa-8749-07308f8c8a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create vias\n",
    "str_via_fence = []\n",
    "for zpos in np.arange(-L / 2 + es, L / 2 - es + 10, vp):\n",
    "    for xpos in [-vl, vl]:\n",
    "        str_via_fence += [create_via(xpos, zpos)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e96df18-38e0-4b46-bf85-e41ae75de676",
   "metadata": {},
   "source": [
    "We consolidate the structures into two lists, with and without the via fence. We will use these in two separate simulations later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "617896a3-7ed4-4b69-8ac8-cf9fc1d6fc20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of all structures (with and without via fence)\n",
    "str_list_1 = [str_sub, str_gnd1, str_gnd2, str_gnd3, str_sig]\n",
    "str_list_2 = [str_sub, str_gnd1, str_gnd2, str_gnd3, str_sig] + str_via_fence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9533498e-1efe-4a62-b8d2-80574393855a",
   "metadata": {},
   "source": [
    "### Grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8221085b-f308-4881-86ba-fbecdd626f21",
   "metadata": {},
   "source": [
    "We use the `LayerRefinementSpec` feature to automatically apply refinement to metal edges and corners. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a5b023c-195a-4fef-bef1-2e5f29341ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_layer_refinement_spec(structure_list):\n",
    "    \"\"\"Creates a LayerRefinementSpec using the bounding box of the given list of structures\"\"\"\n",
    "    lr_spec = rf.LayerRefinementSpec.from_structures(\n",
    "        structures=structure_list,\n",
    "        axis=1,\n",
    "        min_steps_along_axis=1,\n",
    "        bounds_snapping=\"bounds\",  # snap grid to metal boundaries\n",
    "        corner_refinement=td.GridRefinement(\n",
    "            dl=t, num_cells=2\n",
    "        ),  # snap to corners and apply added refinement\n",
    "    )\n",
    "\n",
    "    return lr_spec\n",
    "\n",
    "\n",
    "# Apply layer refinement to top and bottom metal planes\n",
    "lr_spec_1 = create_layer_refinement_spec([str_sig, str_gnd1, str_gnd2])\n",
    "lr_spec_2 = create_layer_refinement_spec([str_gnd3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034cbf1f-0d39-454e-b07f-2d47f0b9c7c2",
   "metadata": {},
   "source": [
    "For the rest of the model, we define the grid size using the `min_steps_per_sim_size` parameter since the simulation is largely sub-wavelength. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46e59d13-3d19-4086-942f-2f85e049e00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define overall grid specification\n",
    "grid_spec = td.GridSpec.auto(\n",
    "    min_steps_per_sim_size=100,\n",
    "    wavelength=td.C_0 / f_max,\n",
    "    layer_refinement_specs=[lr_spec_1, lr_spec_2],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba3aefb-d902-4fcf-8fa8-8f58e715577a",
   "metadata": {},
   "source": [
    "### Boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bf1b2b-7672-41c2-a6d3-106df5972e3e",
   "metadata": {},
   "source": [
    "We use the Perfectly Matched Layer (PML) boundary condition on all sides except the bottom. For the bottom boundary, we use the PEC condition for simplicity, since it is adjacent to the infinite bottom ground plane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00c58bbf-e535-4ba7-858f-6bd288fca584",
   "metadata": {},
   "outputs": [],
   "source": [
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.pml(),\n",
    "    y=td.Boundary(plus=td.PML(), minus=td.PECBoundary()),\n",
    "    z=td.Boundary.pml(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d047a619-9f41-4834-9791-5221cf5199bd",
   "metadata": {},
   "source": [
    "### Monitors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca6608e-9105-45eb-ab95-70d6266c2db9",
   "metadata": {},
   "source": [
    "We define two field monitors for visualization purposes. One is in the longitudinal substrate plane, while the other is along the transverse plane halfway down the CPW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19e05161-e03c-4c0b-8191-9dd841d4e4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "field_mon_1 = td.FieldMonitor(\n",
    "    center=(0, -h / 2, 0),\n",
    "    size=(td.inf, 0, td.inf),\n",
    "    freqs=field_mon_freqs,\n",
    "    name=\"field (substrate plane)\",\n",
    ")\n",
    "\n",
    "field_mon_2 = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=field_mon_freqs, name=\"transverse field\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490311c3-1eb8-4b7f-ac96-eebe206653ff",
   "metadata": {},
   "source": [
    "### Ports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56869188-c594-4a4a-90ae-2a1fdc73d906",
   "metadata": {},
   "source": [
    "In general, we have a choice of using `WavePort` or `LumpedPort` to excite the structure. Because we are probing the resonances of this finite length CPW, it makes more sense to use the `LumpedPort`. \n",
    "\n",
    "The lumped port is positioned in the gap between the signal strip and side ground planes, with the port voltage axis aligned along the x-direction. The design impedance of the CPW is around 50 ohms, so each lumped port should be 100 ohms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81346fc9-10fd-43bf-97f8-445eb72e6780",
   "metadata": {},
   "outputs": [],
   "source": [
    "LP_len = 50  # Lumped port length\n",
    "\n",
    "LP1 = rf.LumpedPort(\n",
    "    center=((ws + gg) / 2, t / 2, -L / 2 + LP_len / 2),\n",
    "    size=(gg, 0, LP_len),\n",
    "    name=\"LP1\",\n",
    "    impedance=100,  # in Ohms\n",
    "    voltage_axis=0,  # 0 = x-axis\n",
    ")\n",
    "\n",
    "LP2 = LP1.updated_copy(name=\"LP2\", center=((ws + gg) / 2, t / 2, L / 2 - LP_len / 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12730e2c-1a56-4fcb-9f06-86246b0b229f",
   "metadata": {},
   "source": [
    "### Simulation and TerminalComponentModeler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ded9990-6387-41f9-b3ea-3521734d09b2",
   "metadata": {},
   "source": [
    "The `Simulation` object contains all of the previously defined specifications. The first simulation implements the conventional CPW layout without via fences. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66772086-16cf-4dd2-8730-be2fd29b6dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_without_via_fence = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=sim_center,\n",
    "    medium=med_air,  # background medium is air/vacuum\n",
    "    grid_spec=grid_spec,\n",
    "    boundary_spec=boundary_spec,\n",
    "    structures=str_list_1,\n",
    "    monitors=[field_mon_1, field_mon_2],\n",
    "    run_time=3.5e-9,  # simulation run time\n",
    "    symmetry=(1, 0, 0),  # odd symmetry in x-direction\n",
    "    plot_length_units=\"mm\",  # set plot units to mm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e2e2e3-ff79-44b1-9b42-e974db9f4d5e",
   "metadata": {},
   "source": [
    "The `TerminalComponentModeler` is a wrapper class that conducts a port sweep on the simulation. The lumped ports are set here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92575cf6-3db7-4a24-aea9-6201bab5d8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tcm_without_via_fence = rf.TerminalComponentModeler(\n",
    "    simulation=sim_without_via_fence,  # simulation, previously defined\n",
    "    ports=[LP1, LP2],  # lumped ports, previously defined\n",
    "    freqs=freqs,  # S-parameter frequency points\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b018532-43a9-4127-adde-ae5aaceba44e",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac13b33-f072-42e1-b089-88da50af4b83",
   "metadata": {},
   "source": [
    "Before executing the simulation, we plot the structure and grid along various planes to check that they are adequately set up and resolved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cef367a5-26ab-4596-aff7-01e55fbf9707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLUPv3r1x8sknR8TxzTffwO/3h+dddNFFqKysxEsvvRSeV11djZUrV+L8888P/0Zt+PDhGDJkCJ544omIxxg8/vjjkCQp/Iy2zowbNw4WiyXq/pEpNm/eHPF5ERGlE4dBEhGl0P33348333wTkyZNCt/W/sCBA1i5ciU++OCDiFvLt9erVy/ccccduOeee3D22Wdj2rRp2L59Ox577DGccMIJuPzyywEAb7/9NubMmYOLL74Yxx57LFRVxT/+8Q+YTCZceOGFAIAFCxbg/fffx9SpU9G/f39UVVXhscceQ58+fWLewj2R911xxRV44YUXcN111+Gdd97BKaecgkAggG+++QYvvPAC3njjDRx//PEYNGgQ/vjHP+Lee+/FqaeeigsuuABWqxWffPIJSkpKsHDhQgDBE/jHH38c9913HwYNGoSCgoKoDyQ2m8144IEHMGvWLEyaNAmXXXZZ+Nb9AwYMwG9/+9sjrbYIJ598Mnr06IGrrroKN910EyRJwj/+8Y+one1x48ZhxYoVmDt3Lk444QQ4nU6cf/75Udd700034eDBg7j11lvx/PPPR/xt1KhRGDVqFIAj+81anz59cPPNN2PRokXw+/044YQTsGrVKqxbtw7Lly+PGAZ4xx134JlnnkFZWVn4Zh4XXXQRTjzxRMyaNQvbtm1Dfn4+HnvsMQQCAdxzzz0R21q0aBGmTZuGs846C5deeim2bt2KJUuW4Fe/+lXcK1I2mw1nnXUW3nrrLSxYsCDpcna1TZs2oba2FtOnT093KEREQWm6CyURkWHt3r1bXHnllaJXr17CarWKo446Stxwww3C6/UKIQ7fuj/a7f2FCN6qf8iQIcJsNovCwkJx/fXXi0OHDoX//v3334tf/vKX4uijjxY2m03k5eWJ008/Xbz11lvhZdauXSumT58uSkpKhMViESUlJeKyyy4T3377bczYE32fz+cTDzzwgBg+fLiwWq2iR48eYty4ceKee+4R9fX1Ecs++eSTYuzYseHlJk2aJNasWRP+e0VFhZg6darIzs4WAMK38W9/6/6QFStWhNeXl5cnfv7zn4t9+/ZFLHPVVVeJrKysDuW76667RCKHvg8//FCceOKJwm63i5KSEnHrrbeGb/feNp7Gxkbxs5/9TOTm5goAMW/jH7q1fLTXXXfdFTemeAKBgLj//vtF//79hcViEcOHDxf//Oc/OywXeqxB29veCyFEbW2tuPrqq0XPnj2Fw+EQkyZN6jRHX375ZTFmzBhhtVpFnz59xLx584TP50sozpdeeklIktTh9v8AxA033BAxr6ysTAAQixYtipgfyo22j6+YNGmSGD58eIft9e/fX0ydOrXD/Gjbu+2220S/fv0iHi1BRJROkhBxxuUQERERpUggEMCwYcMwc+ZM3HvvvekOJ8zr9WLAgAG4/fbb8Zvf/Cbd4RARAeBv1oiIiEhHJpMJCxYswNKlS9HY2JjucMKeeuopmM3mDs/mIyJKJ15ZIyIiIiIiykC8skZERERERJSB2FkjIiIiIiLKQOysERERERERZSB21oiIiIiIiDIQH4qdIE3TUF5ejuzsbEiSlO5wiIiIiIgoTYQQaGhoQElJCWS5C69/pfUpb93I3r17xfyHnhL5hSWdPtSUr8x75SBHLMIikYOctMeS6pcDDnEf7hMOONIeC1+RL+YdX3zxxRdffP04Xnv37u3SPghv3Z+g+vp6PPy/L+OqC85Efp4r3eHEtXPnTgDA0UcfneZIOtIzNr0/h2+3fYuyf5bhzPlnQrF17YVrvcqmx3aMVBY9twPom3OAsfffZGRybD+EUcvVVfh5JY+fWffAeorP7Xajb9++qKurg8vVdX0DDoNMUGjoo9PpRE5OTpqjiS87OxsAMjJWPWPLsmah4v0KOIY7oFi6Pt0tfgt2L90N8+1mZBdkd+m27JIdn9/zOYb/YzgsTkuXbUeP+tIrJ/Tajp55p2fOAfrlHcB2LB2MWq6uws8refzMugfWU+K6+udRvMEIGZqnxoON129ES1VLukNJOdWjovLdSvib/ekOhdph3hEREVEqsLOWBJPJlO4QKEmOYgdm7JiB7D5df8VBb7Z8G8795FxkFWSlOxRqh3lHREREqcDOWhKc2bnpDoGSpHpU7FqxC6pHTXcoKedr8OHj6z6Gx+1JdyjUDvOOiIiIUoGdtSQ0NtSlOwRKkr/ej8/mfwZPrfFOLIVfoG5rHTSflu5QqB3mHREREaUCO2tJCAQC6Q6BkmQvtGPG9hlwljjTHUrKWfOsOPuDs+HId6Q7FGqHeUdERESpwM4aGZrqUbH98e2GHY627ufrOBwtAzHviIiIKBXYWSNDUxtUbF+yHZ46A55YBgDvQS+EykclZhrmHREREaUCO2tkaLZeNkz7ahqcRcYbjmbJtaD0zVLY8+zpDoXaYd4RERFRKmRkZ23p0qUYMGAAbDYbJkyYgI0bN8ZcfuXKlRgyZAhsNhtGjhyJ119/vdNlr7vuOkiShMWLF6c4aspEarOKL+//Er5mX7pDSTmf24d3ZrxjzKs33RzzjoiIiFIh4zprK1aswNy5c3HXXXdh8+bNGD16NKZMmYKqqqqoy3/00Ue47LLLcPXVV2PLli2YMWMGZsyYga1bt3ZY9uWXX8bHH3+MkpKSri4GZQi1OXgLdX+jPg/wlRUZskWGpHTt0+wps+mZd8w5IiIi48q4ztrDDz+Ma665BrNmzcKwYcOwbNkyOBwOPPnkk1GX/8tf/oKzzz4bt9xyC4YOHYp7770Xxx13HJYsWRKx3P79+3HjjTdi+fLlMJvNRxRbdk7uEb2P0seWb8P5n5+v2wN89Rz+Zsmx4PRVp8OWa+vybVFy9Mw7vYdcMu+IiIj0k1GdNZ/Ph02bNqG0tDQ8T5ZllJaWYv369VHfs379+ojlAWDKlCkRy2uahiuuuAK33HILhg8fnlAsXq8Xbrc74uX3GW9Ik9GpjSo23bIJvkbj1Z2vzoe3znoLLbUt6Q6F2mHeERERUSpkVGeturoagUAAhYWFEfMLCwtRUVER9T0VFRVxl3/ggQegKApuuummhGNZuHAhXC5X+NW3b194PM1JlIYygepRUfluJfzN+gyD1JUJsPaycvhbBmLeERERUSpkVGetK2zatAl/+ctf8PTTT0OSEj+5uOOOO1BfXx9+7d27twujpK5iy7fh3E/O1W0YpJ4s2RacuvxU2HI4HC3TMO+IiIgoFTKqs5afnw+TyYTKysqI+ZWVlSgqKor6nqKiopjLr1u3DlVVVejXrx8URYGiKNi9ezd+97vfYcCAAZ3GYrVakZOTE/Gi7sfX4MPH131syAf4emu9WD1xNZqrecU30zDviIiIKBUyqrNmsVgwbtw4rF27NjxP0zSsXbsWJ510UtT3nHTSSRHLA8CaNWvCy19xxRX44osv8Nlnn4VfJSUluOWWW/DGG290XWEoIwi/QN3WOmg+Ld2hpJxklpA7IheyJaN2YwLzjoiIiFJDSXcA7c2dOxdXXXUVjj/+eIwfPx6LFy9GU1MTZs2aBQC48sor0bt3byxcuBAA8Jvf/AaTJk3CQw89hKlTp+L555/Hp59+iieeeAIA0LNnT/Ts2TNiG2azGUVFRRg8eLC+hSPdWfOsOPuDs+HId6Q7lJSzZFtw4rITORwtAzHviIiIKBUy7qvRSy65BH/+858xf/58jBkzBp999hlWr14dvonInj17cODAgfDyJ598Mp577jk88cQTGD16NF588UWsWrUKI0aMSFcRKIP4GnxY9/N1hhyO5qn24PUTXkdTVVO6Q6F2mHdERESUChl3ZQ0A5syZgzlz5kT927vvvtth3sUXX4yLL7444fXv2rXriOKy2RzYvXs33HU1R/R+Pe3evTvdIXRKz9j27tqLxvJGfP/t97DkWrp8e3qWbf/B/cg6IQt7y/dCcXfdrqxHmfT63PTajp55p/e+rlfeAWzH0sGo5eoq/LySx8+se2A9xdfQ0KDLdjLuylomM1u6/mSfUkvJUTDs6WG6dNT0pjgUDLhtABRnRn7n8qPGvCMiIqJU4NE2CQ3uOvTv3x8F+T3SHUrCBg0alO4QOqVHbD63Dx9e+SFO+eAU2HK7/jc2mqbBU+nBUUcdBVnu2u9CPNUerDlzDU4sO1GXW8TrUV965WtXb0fPvNMz5wD98w5gO5YORi1XV+HnlTx+Zt0D66lzbrdbl+3wyhpRCnkqPXjztDfRVN71v+dRHAoGXDIAZqe5y7dFmUvPnAOYd0RERHpiZ40MzZJjwemrTtflqpreFIeCkX8YCYvDeEPtujvmHREREaUCO2tkaL46H9466y201LakO5SU8xz04NXhr6KxojHdoVA7zDsiIiJKBXbWyNhMgLWXFZIipTuSlFOyFQyeM9iQV2+6PeYdERERpQA7a2RolmwLTl1+qiEf4KvYFAy+fjAUG+8TlGmYd0RERJQK7KwlwWQypTsESpK31ovVE1ejubo53aGkXEtlC1YNXoXGcg5HyzTMOyIiIkoFdtaS4MzOTXcIlCTJLCF3RC5ki/FS3ewyY8yCMbDlGe/qTXfHvCMiIqJUMN6ZRBdqbKhLdwiUJEu2BScuO9Gww9EGXDKAw9EyEPOOiIiIUoGdtSQEAoF0h0BJ8lR78PoJr6OpSp9nUOmp+UAzVh2zCg37GtIdCrXDvCMiIqJUYGeNDE2xKSicXAizw3gP8LX1tGH84+NhL7CnOxRqh3lHREREqcDOGhma4lQwbtE4WJzGe4CvbJFRUloCxcLhaJmGeUdERESpwM4aGZqn2oP/G/1/ug1HsxXacNb7ZyGrJKvLt8XhaJlLz7zTM+cA5h0REZGe2FkjQ1McwZshmJ36DEeTZRmOYgdkuet3LVsvGya9OAlZRfqcpFPi9Mw7PXMOYN4RERHpiZ01MjTFoWDkH0bC4jDecDTIgLXAyr04AzHviIiIKBV4uE1Cdk5uukOgJHkOevDq8FfRWGG8B/h6Kj1487Q30VRuvDsOdnfMOyIiIkoFdtaS4Pf50h0CJUnJVjB4zmDYco33vCu9f6tEiWPeERERUSqws5YEj6c53SFQkhSbgsHXDzbmA3w1wFvlBbR0B0LtMe+IiIgoFdhZI0NrqWzBqsGr0FhuwOFoBz1476L30FTB4WiZhnlHREREqZCRnbWlS5diwIABsNlsmDBhAjZu3Bhz+ZUrV2LIkCGw2WwYOXIkXn/99fDf/H4/brvtNowcORJZWVkoKSnBlVdeifLy8q4uBmUAs8uMMQvGwJZnvOFojmIHZuyYgew+2ekOhdph3hEREVEqZFxnbcWKFZg7dy7uuusubN68GaNHj8aUKVNQVVUVdfmPPvoIl112Ga6++mps2bIFM2bMwIwZM7B161YAQHNzMzZv3ow777wTmzdvxksvvYTt27dj2rRpehaL0kSxBW+hbsThaJpPQ/lb5VB9arpDoXaYd0RERJQKGddZe/jhh3HNNddg1qxZGDZsGJYtWwaHw4Enn3wy6vJ/+ctfcPbZZ+OWW27B0KFDce+99+K4447DkiVLAAAulwtr1qzBzJkzMXjwYJx44olYsmQJNm3ahD179uhZNEoDIz/A11PjwcbrN6KlqiXdoVA7zDsiIiJKhYzqrPl8PmzatAmlpaXhebIso7S0FOvXr4/6nvXr10csDwBTpkzpdHkAqK+vhyRJyM3N7XQZr9cLt9sd8aLux9bThvGPj4e9wJ7uUFKOw9EyF/OOiIiIUiGjOmvV1dUIBAIoLCyMmF9YWIiKioqo76moqEhqeY/Hg9tuuw2XXXYZcnJyOo1l4cKFcLlc4Vffvn1hszmSLBGlm2yRUVJaAsWiz3A0PZ+vpXpU7FqxC6qHw9EyjZ55p/cz3Zh3RERE+smozlpX8/v9mDlzJoQQePzxx2Mue8cdd6C+vj782rt3L8wWi06RUqroPRxNUzVoPg1CFV2+LX+9H5/N/wyeWk+Xb4uSo2fe6ZlzAPOOiIhITxn16/f8/HyYTCZUVlZGzK+srERRUVHU9xQVFSW0fKijtnv3brz99tsxr6oBgNVqhdVqjZjX4K5LsCSUKWy9bJj04iRkFRnvAb72QjtmbJ8BZ4kz3aFQO8w7IiIiSoWM6qxZLBaMGzcOa9euxYwZMwAAmqZh7dq1mDNnTtT3nHTSSVi7di1uvvnm8Lw1a9bgpJNOCv8/1FHbsWMH3nnnHfTs2fPIAvQL7Pz6e1SZg504k9WEgCcAyIDJYkKgJQCYgtOaX4PZaYZskaE2q5DNMmSzDLVJhWyVISsy/I1+mGwmAMGhTIpdgWSS4G/0Q3EogASoTSqULAUQgNqswpJjgclmgtqswpxthqZqCHgCMDuD05pXg5KlYNfOXQj4AvA3+KH5NWiqBsWuIOALAAHAZDcFpzXAZDMh4A1AcSpQbEqwTFKwfGqLCkmWgtPNKmRFhmyR4an2QJKlYJmaVciWYJnal0+xK1CcCgItgWA5JKDs6zLIDhlCE1CbguUQARH+nNQWNVyO0FUDxaFElEPza4AEWHtYEfAGIDQRLJ83AIjDZdq1axc0v4avP/saIiA61JPa0lqm1u3KihysE4EO9SQrMvwNwbqRTBL8DcH5aosarqfd23cDAHZs3QFbhQ1qixpRN/Yie0Q9aX4Nmr+1fL7W8jmC5fDX+6PWUyj3yraVofq1arTc2ALFrkA2yx3qKVruaT4Nfre/Qz1Fyz1rnhV7y/dCawk+ATlUT9Fyz9/gh6zIHeopWu4JEayLUJn27NkD2SJHzT3JJMFb6+1QT9Fyz2Q3weKydKinaLnnb/JDghQsU7t6ipZ7slWGOcvcoZ6i5d53W7+DWqPi68+/hvCJqG1EqBzmnOA6Y7URodyTZCkYe5t6KvuiDADw7RffwlxmhtkZ3J+0gAZbT1unbUTb3FMbVfib/XHbCADYV74PB1cdhPc3Xlhd1k7bCLU5OF/za3HbiFDu2QvtwdxrVKE4FezetbvT3PNWe+O2EaFpSZEAIGobEdGWS63baVRjthGyWcaub3fB5Ay237HaiFDueWu9kM1yuC0P1VO03IMESJIUt40QmoBsluGr90VtI9ofnySzBGsPa8w2Yl/lPkgmCb4GH4Qq4rYRoeMTZMCSbYnZRoTK56vzIeALxGwjQrln6RFcZ/t6inZ8Ci0Xq40I5V7AF4A119qhnkK5B4Hw8SnQEoC3zhu1jdjz/R6Y7CZ4a73Q1NbjviLHbSMC3gBEQEAERKfnEe1zz9rDGreNCOVe84HmTs8j2ueebJHD5yzxjk+hzzdWGxGqJ5PdBLPT3KGe9pbvPVw3rbnnPeQFgLhthOJQIFtkCFV0qKdouWeymYLnQXHaiIAvAElIsOZb47YRJqsJ/mY/Ai2BmG1E29wTCOZArDYilHtqowp/oz9mGxFq96w9rYBAzDYilHuBlgAkkxSzjQifw6oaymvKO9RT1HNYLXjjqYTOYV2WcB7EaiM0f3A+gITPYc3Z5rhtRKjda6lqidtGhHLPnG2G2qxGbSPqKuuOrD+RJEkIoc/YmQStWLECV111Ff76179i/PjxWLx4MV544QV88803KCwsxJVXXonevXtj4cKFAIK37p80aRL+9Kc/YerUqXj++edx//33Y/PmzRgxYgT8fj8uuugibN68Gf/5z38ift+Wl5cHS4JDG91uN5b85BmYdvthqtTiLq/YA+g50oPcQV7sfiMHeUNbkN3Pj7LXclAwtgVZJX7sfDkXJRMbofkl7Hs3sR/rW1wqSk5pwp43c3DMzENoOaig/AMnjv5pHZrKzajaYsfAqW7s3u6F77se0OoTf86T6xgPik5oRsUGBxS7QP6oFuxf54Q9X0XeUA/2vp2N7H4+5A7y4tsVPSACUkLr7TO5AfvezcZRMw5BNgt8tzIPzrN3osCWh+9X9cDgn9XCW2fCnjdzkN3fi/qdicVsdgZw1LR61H5tQ0u1gt6nNqL6CzvUFglFE5pRtckBd4sHgT25sPTww3fInNB6HUV+qC1Sh3qy91Kx44Ue6HeWG9bcALY/l4c+p7ux753YV2nDJGDGtzNQua4SW+/fijP/eyb2/3c/vnvyO0xaOQm7XtiF8v+W4+SnTsZ3T36Hrx78KnzyEU+vib1QeGohBv1yED6a9RFKzinBgJkD8N7F72HQLweh9zm9sfactRjxhxHY+eROVH0Q/VEY7Y28cyS0YRq+uuwrzNgxA+4dbrx/8fs477PzULulFhtv3IizPzgblesqsen3m+Cr9SW0XtcwF3KH52Ls/WPx5f1fwu12o8/1fVD711rYimwYetNQbLxxI/LG5iH7mGys/2XnNwxqy9HbgbPePQv/GfMfnLbyNOQck4NVx6zClPenQHEqeO241zDqlVEo7lGMN057I6F1huSOzMXklyZ3qKfaLbUY///G4+tHv4anwoOx94/FK2NegWhKrO5OevIkfPPoNx3qqfDUQqyeuBrj/9945I3Nw3/G/Ae9TumFA28eSGi9kixw7KWdtxENe8yo/dqO/lPcKP8oCw27rPFXCkDq2QxRY0ePwR4UjOu8jdj9Rg6y+3lxcEviVxfbthGDLq7FgUP1aHprYEQbEWr39qzJBpBYG9RjSAv8TaaobQQAFIw73O61VCtorkisrTAPrMNRJ2kx24i2ZUqUo9gHtVnuUE9131nRsMeCvmc0hNu93KO9CR8/TLYABl1Q36Ge2h6fTEOqoBQ0o/mtoxBoSe6XEtHqKVru7VzlgtpsSmidfSY3oOlAsD7a1lO045MIAC0HE6s7SRbof3b0ehp0cS00vxQ+PlV/YUPN1sR+s+4o8iO7n69DPUXLvYBXgjvB/Q4QKJnY2Ol5RLTcS1TRSY2o/swRs40I5R6AhPePrBIf+kxu7FBPgYJDsPR3w//RgHDufffvXAS8ieVbzkAP3GW2DvUULfeKJjQlvH9IioZjZ9bFbSPyR7Vg3/tONO1L/KcxJruG3gm0EYMursXu1TnwNyR2LaXP5AZoKmK2EaHcc+82J7xeszMA+aiaDvUULfe89SZUbkysnXcU+VFwXHPcNqJhjxk1W+3w1iV4TUkS6HGsN24bEWr3tj/XA4kePwacW49dr7s6bSNuwS2or6+PO2Lvh8i4zhoALFmyBIsWLUJFRQXGjBmDRx99FBMmTAAATJ48GQMGDMDTTz8dXn7lypWYN28edu3ahWOOOQYPPvggzj33XADArl27MHDgwKjbeeeddzB58uSEYnK73Xjo8X9j8u56ZDW3fiuhSAioApIEyKbIaS0goFik4Hy/gCQH56t+AdkEyLIE1Scgt+ahr1nApABS63xTa1sY8CNiWrFKMJmAgAooFgmaJqC1nQ4AilnC7kMHoQWAvjn50AICQgNM5mBcQgRjbzsdUIPbVMxyZJnaxN522tusBafl6GUKTZsUwGSRoKmHy7GrphqyAvRz5QfLZAleLQiogCQDATV6mTRNQAQOl0OSALNN7rR8AVVgd101hB/ok5sHCVKHeupQPhNgMknBL4pjlClUT7IpGHeonsoqarD79TzM2jYLjiIH1Kbg1VBN1aC2qMjunR2c9qiwOC0I+AMI+AKwZFkQ8AUQ8AenVa8KzyEPzA5zxLdjoZs6KDYFO77aAcjAMUOPCX6LZDFBsSrwNflgMptgspiC0xYTTGYTfI0+KDYFqleFt84b/pbP5/ZByVIgm4LTZqcZkAB/gx/2XnaU7S6D2qRi6Nih0AIa/E1+WHOs4TJZsoPl89R7YDKbEPAHoPk0mLPMCPgCwW9ss4Ll0AIazA4zNE2DYlVgtpuhelTs3LkTJqsJ/Xv3hyRLUKwK/M1+SCYJsklG88Hm4JVDiwn+Jj9kixxRJlmR4WvwwZxlhi3XBq/bC3OWGbJJhtfthcVpASTgm8++geJUMOjoQfA2eCFJErSA1qGeQmVqW0+KVQlOt6snERAwO8zBaU3AbDfjy4+/hLfKi+GnDYfm1cLf7Pmb/ZBNwSug/iY/ZLMMW64NqlftUE+hMoXqyev2Bn9lrCGinr546wu8f9H7OHEmYLZK4f1JCMBilzttI9ruN6pPa83h2G0EABxoqQEA9MvNj9lGBPwCgIAQ8duI0P5kzZIi2r099dXQVGBgfq9wGxEqh69ZxG0jQuWTJAFAitpGtG/LhQao/ththCxL2F1bDZMCDMjvFbNMoXbP7wmuJ9yWt2n32rflAiJ4Za1dOaJNSyZA9cYuU7jdkwGLTe60zlS/wP7GakgyUGzrCSFil6nt8QkAzFa50zK1rSefV4NQOylTu9wz2ySI1u9HO5SpXTk0Lfi5ta2nznJPCwiYrVL8Y65FQsCvwe+NXqa9dTWQFaC3sydEIDhfktF5nbWpJ6EF87Kz84j25TPbpE7PI9rXk6dRi1mmtvUkyQJCk2K2EaFpIHiMjtVGhM8pFECxdMy3fQ3VkExAb0d+eL7PE1x5vDbCZA6eJ0BIMcsUqidZAVRf/DZCCwgIAFa7HLeNkE3BNlMLxG4j2taTEAIms5TQ/qT6BQK+2G1EqJ4sdgkQMfKtzbTqF5DlOG1E67TQBMqbazrUU9RzWAH4WhI7hzVbJcgJnMNqAQFNFYAUv40IlUOxSHHbiPA5bJMWt40I1ZNikRBQEbVM9U1eTP7HH36cnbVM5Ha78dDfX8KZu+vh8se/spZue+oPAgD6uXqlOZKO9Ixt16GD8B6SccyAHpDlrr+fzs4DB/H9qjxcu/faLr+1+TdffIOvF3+NqUumwuLoupvffPfddwCAQYMGdett6Lmdb7/5FvVf1WPc9HGQla7Nuy/WfYE3T3sTp14hwebs+hzfVXMQ1V/YMWaiA4q5a7fHdkx/Ri1XV+HnlTx+Zt0D6ym+Rp8HE/+36ztrP6q7QdKPT8AD7HkjF77mdEeSempz8Bbq/kZ/ukOhdjwHPXjvovfQVNGU7lBSTlOBuu9s0Jh2REREXY6dtSSYTImNsafMYXYEf0ehxxUHvdnybTj/8/ORVWC8Ow52d0Z+cLRiA46deQgWh/H2KSIiokzDo20SnNm56Q6BkqSpQMNec/guVkaiNqrYdMsm+BoTu6kH6UfzaSh/qxyqz3gPjtZ8QPlHWVB9xtuniIiIMg07a0lobKhLdwiUpIAPKF/nhM+Az+9VPSoq362Ev5nj0TKNp8aDjddvREtVS7pDSTlNA5rKzdCM1w8lIiLKOOysJSEQCKQ7BEpScBjkId2GQZoswNC5Q2HPt3f5tmz5Npz7ybkcBpmB9BwGaelhQf6oZiiJP6XjB1FswDEX1XEYJBERkQ54tCVD01SgbocVqk7DIGUFGHz9YCi2rn/evK/Bh4+v+xgetwEvG3Zzqid485fQoxa6kmJT0HOEB0oX33UyRPUB+95zchgkERGRDthZI0PTfEDlJw6oBuzPCL9A3dY6aDxpzjj+ej8+m/8ZPLUGTDwN8NQq0DjQgIiIqMuxs0aGpug8DFJP1jwrzv7gbDjyHekOhdqxF9oxY/sMOEuc6Q4l5RQbMOindbDYjbdPERERZRoebcnQNBWo2WrTbRiknnwNPqz7+ToOg8xAqkfF9se36zIMUm+qD9izJpvDIImIiHTAzhoZmuYHarbaoRrx7vYBwHvQC6GKdEdC7agNKrYv2Q5PnQE70hqgeiRo7KsRERF1OXbWkpCdk5vuEChJih049tJDsBnwznWWXAtK3yyFPa/r7zxJybH1smHaV9PgLDLmMMijznfDYjPePkVERJRpeLRNgt9nxMszxqapQNVmO1S/8S4D+Nw+vDPjHWNevenm1GYVX97/JXzNxmszVB+w67858HmMt08RERFlGnbWkuDxNKc7BEqSpgJ139mg8bnRpCO1OXjrfn8jE4+IiIiOHDtrZGiKDTh25iFDPsDXkmPB6atOhy1Xp6chU8Js+Tac//n5hnxguWIBBpzDYZBERER64NGWDE3zAeUfZRnyznW+Oh/eOusttNS2pDsUakdtVLHplk3wNRpwGKQH+P7/OAySiIhID+yskaFpGtBUboam0x3UVQ/060CZAGsvKyRF6vptUVJUj4rKdyvhb+76YZC+Op++nScZUGwCMo8eREREXY6HWzI0xQYcc1GdbsMghQY0ljVCbe763qEl24JTl58KWw6HQWYaW74N535yri7DINUWFf4GRbcvJBQL0O8nDVAsPHwQERF1NR5tydBUH7DvPachh0F6a71YPXE1mqt545tM42vw4ePrPjbkA8tVD/Ddy7nwtRhvnyIiIso07KwlwWZzpDsESpYGeGoVaIF0B5J6kllC7ohcyLzCkXGEX6Buax00A35JABmw5amQTekOhIiIyPh4lpcEs8WS7hAoSYoNGPTTOljsxkt1S7YFJy47kcMgM5A1z4qzPzgbjnzjfcGjWIA+kxo5DJKIiEgHGXm0Xbp0KQYMGACbzYYJEyZg48aNMZdfuXIlhgwZApvNhpEjR+L111+P+LsQAvPnz0dxcTHsdjtKS0uxY8eOpONqcNcl/R5KL9UH7FmTbchhkJ5qD14/4XU0VTWlOxRqx9fgw7qfrzPsMMgdL+bC12y8fYqIiCjTZFxnbcWKFZg7dy7uuusubN68GaNHj8aUKVNQVVUVdfmPPvoIl112Ga6++mps2bIFM2bMwIwZM7B169bwMg8++CAeffRRLFu2DBs2bEBWVhamTJkCj8d4J1LUjgaoHgmaAc8rFZuCwsmFMDvM6Q6F2gsA3oNeCFWkO5KUk2Ugq8QPWUl3JERERMaXcZ21hx9+GNdccw1mzZqFYcOGYdmyZXA4HHjyySejLv+Xv/wFZ599Nm655RYMHToU9957L4477jgsWbIEQPCq2uLFizFv3jxMnz4do0aNwrPPPovy8nKsWrVKx5JROig24KjzjfkAX8WpYNyicbA4OTw301hyLSh9sxT2PHu6Q0k52QKUnNzEYZBEREQ6yKjvRn0+HzZt2oQ77rgjPE+WZZSWlmL9+vVR37N+/XrMnTs3Yt6UKVPCHbGysjJUVFSgtLQ0/HeXy4UJEyZg/fr1uPTSS6Ou1+v1wuv1hv/vdrsBAHsra1ET8EOSAM0PSK2foFAjp00WQJIBLQCYzMFbuosAIEeZ1lQAUut8DZCV4N+FaDMNQDYdfp9sOvy+aNMHmmoBCfC3BOOQ5OD88LQfkExtppVgnJqK8Dfm7adD5dACgITgM8wiyhSKvd20JAU/Dy0AQAAVLbUQgcPrD938QzYFh1gBwdg0Nfje8HS7csgKAK1dOVqnA/7g+vYfqkPLRyXwnXkwGE+7epLNwc85PK0F16nYo9RZlLoJ+A+XQwSAAzVuAHn4ZvM3sO+zw2Q1IeAJADJgspggKRIkWYLJaoLarEJWZMgWOThtliGbZahNKmSrjEBLIDwtKzL8jX4odgWSSYK/0Y+9O/fim+u/wWkrToPrWBckWYLapMKcbYYICKjNwWlN1RDwBGB2Bqd9dT6YrCZofg2aqkGxKxHTAV8ACAAmuwmQgH3l+4LxeIMVFS6TFJxWW1RIcvBZb756X7BMZhlqi3p4ulmFbAmWQ21SoTgVmJ1m+Bv8UBwK9uzbg0BTAEITgBR8qLTiVAAB+Nw+yCY5WKYWNVwOzatByVKC0z4NikOBCAjIFhmKQ4Hmay2TQ0HAG1z3/qr90HzBz0O2yvDWeCPL1FpPgZYAYApOh8ph6WFBoCXQoZ5CdWOymYLTDX58v+17lM0vw4n/70Q4Ch3BMjWpULKCZVKbg+UQAQEBAWuutUM9hcvn16D5g+XwN/ihtqgR9bRnxx4AwO6DNbB4Du9PJnNwf+usjWi7b0EEF43XRkgSsL/SjZYP+8E/LbhPddZGhNqTgC9+GxGaluRgLKF24UDDIUgmEdFGtG0v47URoWmTuXWdUdqI8HRrOVRPcJlYbYQIAFWeQxCSgOaP3UaE2j20HjNCbXlEmVrb1LblUKwxyte2HN7W1berp2jHJwnBONrXU9u2bn99LaBJECJYd1HrLMrxSbG2tp+Inm9tp0UAQJR6ilY+oQVjbF9P0Y5PAV/r/GjH3HblMFmCn0f7eoo2LQKt9de+niSgsvkQhCpB9bbGG2hz/G+/D7XLvXC+dXIe0T732p4DxDs+CdH5eUT73JNNh/fXztqIUN2oLcG6i9VGhKZl+fD229bTfnctoAaPGaFyhPIhXhshWmNXbFHqLEruhXI4XhshyQg/OzJeG6Gph9cXq41oWzeKrbWdjdFGhKZDn2vMNqK1biBaY4zRRoSmhRb8PGK1EaFpk7X1PLJdPUU9h/UH6y+hc9jWv8drIyQp+P/QfpXIOaxiid9GhNq9qGVK8By2bZ15xOF+QlfKqM5adXU1AoEACgsLI+YXFhbim2++ifqeioqKqMtXVFSE/x6a19ky0SxcuBD33HNPxLz5Dz2F8heyISUwsqn6eBuqTnFg8LJD+PyuXsja48fA5+qx9fZ8ZH/rRZ/XG/H1zT2R+6UHxe81wX4wsdsVqnYJNcfZsH9qNvq95IY/R8aBUicGPlePpn5mVE10YNBTdTgwoAcODgYmLkn8gcmfzc/HmAXV+PK2nghYJYxZUI3P5ufD5BUY+UANNt9fAFulisHLDsHnkmGvSizmphIF2+fkoeCDZmTt8eOLKT3Q/yNgV5Udey7IQe/XGgAA+6dmY9jDNbBVJ7beA2c4kLvVi33nOtFwrBUj/lSNsp+50NTPjNH3HMT263qgpmcNTn1Dwjp3D/R5O7EHVWsK8NmCgg71VLC+Gd9en4een7Sgx1YvvpuVi5L/NqBoXdv15gEA3pv+Xof1BqwS3MdYIurp0Agrak6w49jHa1E10YG6kTYMXVyDfec6cdS/3DB54yfbe9PfwzfX9YDfJXeop2i5d9Rzbph8iQ3PqxtiQYvixbdTgCEfB/fBznLP5NFQ/HZijxGoG2LB91fmhuvpoFKDSQ9JWH7b/qi5lyhvjgy/S+5QT4dzz4P+HwFO/3bsO9eJMQuqE173Z/PzMeipug71FC33vBYNjgYJ7059N+56A2bg83sKYrYRBR8Ec++Yv9Yie3f0B6rtWZ0X8f8DZzhgdmudthFtc6+lWEGvjYkOC8/D/pECnw0ehDF/qu20jfj8rl7o8VkLBq5qSGitAauEA2dmIWuPH2U/c6H4rUagqkdr7gVv1NI29wo+bElo/wCA3TOcqBnjiNpGeAoVHPeHqoh2L9HPYe9Yger9Ssw2IpR7ngJTwvtHYx8F3/46L3obMSIy90bedxDm5sQ+h5aeMuw1Wsw2QtlUiaPfBT64oVfS+0fx2uDvZ+Mdn2wVflgaEos5YAa2/zovaj21z72By+vQ4ytfQus9cIYDPpepQz2Fcs/s1sLHJ3uFipydnT3gPrjf7Wz9n2qTsOen2R3qKVruAcCwv9Qm9jlYJXw3K7fzNqJd7mXt8Se8fxycYMPe6Tkx24hQ7o2++2DCx4895znR7z+NHeopsL0SY/8FvH93YTj3ao63Jbx/eHvI+OqW/A71BHTMvVhtZnstBSaYPCKhNuKL2/Iw6oHE6g4I7h85O3wx24hQ7vX6qAWKJ7HPuKlEQfUEe8w2IpR7PT9tQf6nibXzB85wYF/fHh3qKVru9X67EdZDif3WxJsro264NW4bcWiEFU39zInvH63H0XhtRKjdA5Dw/tHYT8G31+VFbSN2nqvPz1AkIUTG/KiivLwcvXv3xkcffYSTTjopPP/WW2/Fe++9hw0bNnR4j8ViwTPPPIPLLrssPO+xxx7DPffcg8rKSnz00Uc45ZRTUF5ejuLi4vAyM2fOhCRJWLFiRdRYol1Z+9sLb+GQ2gOq3QbIEqSWAIRVBiRA8mgQNhkQgOTVIJwKhCIFp+0mICAg+dpM+zUImwlQBSRfINh1VzVIAQTX6dcgCUBYIqclNfg+YZaD65MAmOXgduTQdAD11QcgTEAPWyGEIgEmCZI3AKHIwWlPAMLcOt1aDuEwBcvRWZnsJkATkLxa8CsetU05AiIYmzU0LYLrUTXAJEE4lGA5NKCu7gAkVcDVqyRYJn9wJxdmGXKjGry6Eq18JgBK67QiQVhlSAERWQ5LZJnqD5ZD9mjILi6BpIqYZQrXTUCD5rJ0rKdAa/lULbhdqwnwBiB7tHA9tezch+FL6rB7/rHQelg61BNkKaKehElqLVMAwiQDyuG6kXxa1HoK5V69O/hlgyunCJor+FVZp2Vqk3tyox/C0rGeouWegIC7pgLCLCE3rzhcT1FzD4DcFOhQT1Fzz2aCsJs61lPfPh1zrzkAmKSOZYqWe5oAFKljPUXLPbMEuUGNXqYouSecSnB+u3qKlnvuyv2AJMGVXRh3f4IIrjtmG9Gae1KzCsknIuqpac9+jFh8CLvnH4tAgS28P2l2U7AeO2kjInIPgKQhbhsBWULjvv1QnTJcRb3j709+LbhPx2kjQrknFAmSdjgP3ZXlneaepIq4bUQo97QsJbhMlDaifVsu1/uDy8RqI/wa6t2VEBLgKiiJ3Ua05p6QAaklcLgtb9Pudcg9RYLIUuK2ETBJkJrU4NfWMcoUricAkKSYbUR9+T7IqkB23z6QmgJx24jQtJajQGr9jWa84xP8GmCK00a0lg8BAeEwxT/m2k2QPAFIzYHYbURr7mlZwZEDHeqpXfkkvxY8jkGK2ka4D1VA9gk4i4qD9eTXoDmUuG2EsAbrQ67zJ3TMFXZTMJ44bUQo96Ch8/OIdrmn2WTAaordRrTmnlzvC07HaCPC5xQWCZKKDmWqP7APsk8gu3/fw7nXer4Ur42AX4MkS9CcSswyheupORCsvzhthFCC5UPo84q3P1klSC1azDaibe5pLjMQQOw2orV8ckvrOUCsNiJ0TiFEMN5YbURr7kkBAalRjd1GtNaTlq2gvrq8Yz1Fyz2vGrwsmcg5bGv547URwiQBsgTZrSZ+DpulxG0jwuewQNw2InwOq0gQdiVqG+ENtODJOdNRX1+PnJycqP2JVMioK2v5+fkwmUyorKyMmF9ZWYmioqKo7ykqKoq5fOjfysrKiM5aZWUlxowZ02ksVqsVVqs1Yp7JZILmtEAowY9N2A8/aEi0uUO3yGozHVrGJEVOm1qnFSm8PsCEcM/ZHn1aoM022/xmJNTwB6dNweQGoOUe7vVHxJvMtNRmWpYilzlc1E6mDy8Ls3y4HIoUjl+YD8euOQ+nZEIxtvlSI9oycrOGIcsO4cCdxdCcSuf11HY6HHr7egpNyxChMK0maNbW+XYT/HkKGgYoUHtZAUfHPGlLWE3Rp22m8PtifQZyLTDiwRrsfrAIMMmRy8hSp7mnuQ7/xq1tPUXNPQDC3DoMok09dZZ7Wm6b+Unmm2YLHkAi5kutHZnQ8tHqqd10WNt66iT3tJwk948o9RR1eSFhyOPBvBNZrXnQSe6FxWwjWnPC0SaHW+tJzTEdzrn2OdNm9e3biPbTIsHPABIw5q6D2P1gAbRcS3he1Nwzy9B6RMu3TnKv7XyzHDP3hPkHtGkxpsNlQuw2QvO1xmHrWE+d5l7b6WTzrbPcy0qgzYz2BIlYbYRJCnbqnEr8NqLt8cl8eCRHrOMTQjmXSL61Ee/4JGymyM+m7Xs7m45ST+3rDOZ27Wubaa1FhmYDRLa54zKx2ohWWl6bfEvk+BSnjYjIvc7qqdN8i91GAIg8fiRzjG7XRmg26XA5QvXXtg6QwPGpfT2FptvmnsOE0HlISvKtzbTIkuOfR7SdVhC3jYBZhhYj32LGG+f4JEwSRF4n9Rct9zqpp465F6rk1J3DhmTqOWx4OrEBWz+YHH8R/VgsFowbNw5r164Nz9M0DWvXro240tbWSSedFLE8AKxZsya8/MCBA1FUVBSxjNvtxoYNGzpdZ2ec2blJLU8ZQAb8TgmaTpmu2WTsmJ0X7qh1JdUq4cAZjuC3uJRZdMw7PXMOYN4RERHpKeOOtnPnzsVVV12F448/HuPHj8fixYvR1NSEWbNmAQCuvPJK9O7dGwsXLgQA/OY3v8GkSZPw0EMPYerUqXj++efx6aef4oknngAASJKEm2++Gffddx+OOeYYDBw4EHfeeSdKSkowY8aMpGJrbKgD7PmpLC51sdCJrMuIJ5YWGZWnO+HiXfkyDvOOiIiIUiHjjraXXHIJ/vznP2P+/PkYM2YMPvvsM6xevTp8g5A9e/bgwIED4eVPPvlkPPfcc3jiiScwevRovPjii1i1ahVGjBgRXubWW2/FjTfeiNmzZ+OEE05AY2MjVq9eDZvNllRsgUBiN76gzCE3ahjxp+rwb5OMRHGrGPvHKsiHEvtBPemHeUdERESpkJFf+86ZMwdz5syJ+rd33323w7yLL74YF198cafrkyQJCxYswIIFC1IVInUXCtBcokCLPsS7W1NtMvZMzw7ePIEyC/OOiIiIUiDjrqwRpZJmk/H9lbm6/Z5HVxYZNePtAIejZRzmHREREaUCj7ZkaEqjhlH3HYTs7uwZOd2XUq9i7B+qINdyOFqmYd4RERFRKhjwa1+iwzQFqB9sgWZJ/OHg3YWaJeP7y3OAHO7GmYZ5R0RERKnAK2tkaJpNxu6LXYDNgCeWioz6YTZA4W6caZh3RERElAo82iYhOyc33SFQkpRGDaPvrjLscLTjOBwtIzHviIiIKBXYWUuC38eTk+5GtQDVJ9ihWY13Wz41S8bX1+dC43C0jMO8IyIiolRgZy0JHk9zukOgZFlk7J+aDVj1SXXZo6H/ynrAo8PztWTAnyNzL85EOuadrjkHMO+IiIh0xMMtGZrSEMCYO6sg1+lzVVT2aui5xQu5WevybSkNGkY9UAu5zngPXu7u9Mw7PXMOYN4RERHpiZ01MjTVKuHAGQ5oBnzelZot44vb8qDlGq9s3R3zjoiIiFKBnTUyNouMytOdxnyArwaY3RqgzwUVSgbzjoiIiFLAgGcSRIcpbhVj/1gF+ZDxbg6jNGkY+ngdZDeHo2Ua5h0RERGlAjtrZGiqTcae6dnQsow3ZEt1Kdh8fwG0PEu6Q6F2mHdERESUCuysJcFmc6Q7BEqWRUbNeLsxh6OpGlzbPIDK8WgZh3lHREREKWDAM4muY7bwm+TuRqlXMdagD/BVmjQc9U83h6NlIOYdERERpQI7a0locNelOwRKkpol4/vLcwz5AF/VpWALh6NlJOYdERERpQI7a2Rsioz6YTZAMWCq+zT03NgC+DgcLeMw74iIiCgFDHgmQXSYUq/iOKMOR/No6PdKA+QmDkfLNMw7IiIiSgV21sjQ1CwZX1+fq99wNJMM1SEBitTlm1JzFGz5nwJoPTgcLdPomnc65hzAvCMiItITO2tkbDLgz5F1y3TVKeOLeb2g5Zi7fmM+DYXvNHI4WibSMe90zTmAeUdERKQj4/36vYs11FTBD32+wf4hGmoq0x1Cp/SMzbvnII57QsIXtwVvjNDV9Cxby4GDKH4b2Hr8PqjZpi7bjh5l0utz02s7euad3vu6XnkHsB1LB6OWq6vw80oeP7PugfUUn9/Tost2MurKmhAC8+fPR3FxMex2O0pLS7Fjx46471u6dCkGDBgAm82GCRMmYOPGjeG/1dbW4sYbb8TgwYNht9vRr18/3HTTTaivr086PpOpa09MKPV8WcD62QJqdkalekqoTmDdzejyE2ZKHvOOiIiIUiGjrqw9+OCDePTRR/HMM89g4MCBuPPOOzFlyhRs27YNNpst6ntWrFiBuXPnYtmyZZgwYQIWL16MKVOmYPv27SgoKEB5eTnKy8vx5z//GcOGDcPu3btx3XXXoby8HC+++GJS8Tmzc5FtVxBQus9vNVyFvdMdQqd0iU3VYPeosPTqreud+XQpm09D7zVNaPx5MWDt+rLpUSa98rXLt5OGvNNtX9c57wC2Y+lg1HJ1FX5eyeNn1j2wnjrna2nSZTsZ87WvEAKLFy/GvHnzMH36dIwaNQrPPvssysvLsWrVqk7f9/DDD+Oaa67BrFmzMGzYMCxbtgwOhwNPPvkkAGDEiBH497//jfPPPx9HH300zjjjDPzP//wP/u///g+qmtzdzBob6n5ACSkdlCYNQx+vM+QDfBUfkP9JC2RvIN2hUDvMOyIiIkqFjOmslZWVoaKiAqWlpeF5LpcLEyZMwPr166O+x+fzYdOmTRHvkWUZpaWlnb4HAOrr65GTkwNF6fzCotfrhdvtjngFAjw56W5Ul4LNBn2Ar+qU8fndBfrdWIISxrwjIiKiVMiYzlpFRQUAoLCwMGJ+YWFh+G/tVVdXIxAIJP2ee++9F7Nnz44Zz8KFC+FyucKvvn37JloUyiSqBtc2D6Aa7851skdD/5X1gMd4V2+6PeYdERERpUDaOmvLly+H0+kMv/x+f5dv0+12Y+rUqRg2bBjuvvvumMvecccdqK+vD7/27t3b5fFR6ilNGo76p9uQw9FkFXBt90H2iXSHQu0w74iIiCgV0naDkWnTpmHChAnh/3u9XgBAZWUliouLw/MrKysxZsyYqOvIz8+HyWRCZWXk7UUrKytRVFQUMa+hoQFnn302srOz8fLLL8Nsjj2Ex2q1wmq1JlMkykCqS8GW+wvgMuhwtC/m9YKLw9EyDvOOiIiIUiFtV9ays7MxaNCg8GvYsGEoKirC2rVrw8u43W5s2LABJ510UtR1WCwWjBs3LuI9mqZh7dq1Ee9xu90466yzYLFY8Oqrr3Z6Z0kyIJ+GnhtbDPkAX9mj4ahn64Bm41296faYd0RERJQCGfObNUmScPPNN+O+++7Dq6++ii+//BJXXnklSkpKMGPGjPByZ555JpYsWRL+/9y5c/G3v/0NzzzzDL7++mtcf/31aGpqwqxZswAc7qg1NTXhf//3f+F2u1FRUYGKigreMORHQPFo6PdKA+QmnU4s9fytkgo4ylXITOOMo2ve6f37OOYdERGRbjLqOWu33normpqaMHv2bNTV1WHixIlYvXp1xJWwnTt3orq6Ovz/Sy65BAcPHsT8+fNRUVGBMWPGYPXq1eGbjmzevBkbNmwAAAwaNChie2VlZRgwYEDC8WXn5KLGeF+UG5qao2DL/xTA1UOf4WhKk4aj/+lG2Wi1y+8EqDllbL09H67sjNqNCfrmnZ45BzDviIiI9JRRR1tJkrBgwQIsWLCg02V27drVYd6cOXMwZ86cqMtPnjwZQqTmh/B+nw9Q+Du2bsWnofDDZngu0ABLxlxITgnZo+HoZ+tQ9btCwJFRuzIx74iIiCgFjHUW0cU8nuZ0h0BJUrwCxW83Qzbi72s0wNwoIPNqb8Zh3hEREVEqHNHXonv27MHu3bvR3NyMXr16Yfjw4bxzImUkNduEz+4tgCvXeHfl0xwyts3tCZeTVzcyDfOOiIiIUiHhK2u7du3Cbbfdhv79+2PgwIGYNGkSzjnnHBx//PFwuVz4yU9+gpUrV0LT+HUrZRCfht6vNQBe4+Wl3Kxh8JIaY1696e6Yd0RERJQCCXXWbrrpJowePRplZWW47777sG3bNtTX18Pn86GiogKvv/46Jk6ciPnz52PUqFH45JNPujpuooQoPiD/kxbIXt66jvTDvCMiIqJUSGgcS1ZWFr7//nv07Nmzw98KCgpwxhln4IwzzsBdd92F1atXY+/evTjhhBNSHixRslSnjM/vLjDkA3w1h4ztc3rCxZs8ZBzmHREREaVCQlfWFi5cGLWjFs3ZZ5+NCy644AcFRZQqskdD/5X1gMd4Q7bkZg3DHq6B3Gi8snV3zDsiIiJKBd4NMgk2myPdIVCSZBVwbfdB9qXm8Q0ZRQb8Tgka9+KMw7wjIiKiVEj6cFtTU4MbbrgBw4YNQ35+PvLy8iJeRma2GO/ObkanOmV8Ma8XNCMOR7PJ2DE7j8+6ykDMOyIiIkqFpI+2V1xxBb777jtcffXVKCwshCRJXRFXRmpw1wHOwnSHQUmQPRoGvOBGzQ36PcBXr2spcqOGYUtqsX9BIbRsnjhnEr3zTs/rd8w7IiIi/SR9pF23bh0++OADjB49uiviIUotFXCUqzgUAPS4ibrqUrDl/gK48nS4CqsAzSUKNFPXb4qSpGPe6ZpzAPOOiIhIR0kPgxwyZAhaWlq6IhailNOcMrbenm/IKwCaTcb3V+ZyOFoGYt4RERFRKiTdWXvsscfwxz/+Ee+99x5qamrgdrsjXkSZRPZoOOaJWsCAD/BVGjWMuu8gZLc/3aFQO8w7IiIiSoWkvxrNzc2F2+3GGWecETFfCAFJkhAI8CGwlEE0wNwoIGv6DIPUk6YA9YMt0Cw/nt+NdhvMOyIiIkqBpDtrP//5z2E2m/Hcc8/96G4wQt2P5pCxbW5PuJzGG7Kl2WTsvtgFl814ZevumHdERESUCkkfbbdu3YotW7Zg8ODBXRFPRjOZ+Iv67kZu1nDMk4dQdUchNIP9xkZp1DD8z9XY80CBIW8R350x74iIiCgVkv7N2vHHH4+9e/d2RSwZz5mdm+4QiMJUC1B9gh2alV8ikH6Yd0RERPpJ+ivfG2+8Eb/5zW9wyy23YOTIkTCbI79ZHTVqVMqCyzSNDXWAPT/dYVASNIeM7XN6wmWwqxsAAIuM/VOz4bIm/Z0LdTHmHREREaVC0mcSl1xyCQDgl7/8ZXieJEk/ihuMGLlsRiU3axiy7BAO3FkIzWC/H1IaAhjxYA12P9gLWq5Oz9iihDDviIiIKBWSPosoKyvrijiIuoYM+J0SNANeBFCtEg6c4TDcb6IMgXlHREREKZD00bZ///5dEQdRl9BsMnbMzjPscLTK051wWQzYI+jmmHdERESUCkd0tC0vL8cLL7yAJUuW4NFHH414/RBCCMyfPx/FxcWw2+0oLS3Fjh074r5v6dKlGDBgAGw2GyZMmICNGzd2uv5zzjkHkiRh1apVPyhW6h7kRg0j/lQNuUGfhxMr9SrG/qEKcq2v67flVjH2j1WQD3X9tig5euadnjkHMO+IiIj0lPTXvk8//TSuvfZaWCwW9OzZM+I5a5Ik4aabbjriYB588EE8+uijeOaZZzBw4EDceeedmDJlCrZt2wabzRb1PStWrMDcuXOxbNkyTJgwAYsXL8aUKVOwfft2FBQURCy7ePFiPhfux0YBmksUaDreuE6vDFNtMvZMz4aWZcCrN92dznmnZ6vGvCMiItJP0lfW7rzzTsyfPx/19fXYtWsXysrKwq/vv//+iAMRQmDx4sWYN28epk+fjlGjRuHZZ59FeXl5zKtgDz/8MK655hrMmjULw4YNw7Jly+BwOPDkk09GLPfZZ5/hoYce6jCfjE2zyfj+ylzAoMPRasbbAQ5HyzjMOyIiIkqFpI+2zc3NuPTSSyHLqT1Ql5WVoaKiAqWlpeF5LpcLEyZMwPr166O+x+fzYdOmTRHvkWUZpaWlEe9pbm7Gz372MyxduhRFRUUJxeP1euF2uyNe2Tm5R1Y4ShulUcOo+w5CdvvTHUrK6T38jRLHvCMiIqJUSLrHdfXVV2PlypUpD6SiogIAUFhYGDG/sLAw/Lf2qqurEQgE4r7nt7/9LU4++WRMnz494XgWLlwIl8sVfvXt2xd+H09OuhtNAeoHW6BZjDf8Vc2S8f3lOdByDHj1pptj3hEREVEqJH20XbhwIc477zysXr066kOxH3744YTWs3z5clx77bXh/7/22mvJhpKQV199FW+//Ta2bNmS1PvuuOMOzJ07N/x/t9uNv73wFuDMTnWI1IU0m4zdF7vgshnwxFKRUT/MBpfC4WiZhnlHREREqXBEnbU33ngDgwcPBoAONxhJ1LRp0zBhwoTw/71eLwCgsrISxcXF4fmVlZUYM2ZM1HXk5+fDZDKhsrIyYn5lZWV4uOPbb7+NnTt3Ijc3N2KZCy+8EKeeeirefffdqOu2Wq2wWq0d5jfUVMGv68/5j0xDTWX8hdJEz9ha9lZiwt+BbbdoUJ1df3LpPVQDQEJD9QGo/q49UffuqsRJT0j44jYVqqvrtqVHfemVE3ptR8+80zPnAP3yDmA7lg5GLVdX4eeVPH5m3QPrKT6/p0WX7SR9pA3dpOMXv/jFD9pwdnY2srMPX6USQqCoqAhr164Nd87cbjc2bNiA66+/Puo6LBYLxo0bh7Vr12LGjBkAAE3TsHbtWsyZMwcAcPvtt+NXv/pVxPtGjhyJRx55BOeff/4PKgNlPk0BDowEVEu6I0k9Xxaw6WcCUhavcGQa5h0RERGlQtKdNavVilNOOSXlgUiShJtvvhn33XcfjjnmmPCt+0tKSsIdMQA488wz8dOf/jTcGZs7dy6uuuoqHH/88Rg/fjwWL16MpqYmzJo1CwBQVFQU9aYi/fr1w8CBA5OOM7tnAQJK9zkDcxX2TncIndIrtoMX6betpnoVQC2y84uh5XVxnmgaFIuGrOLeQIpv+BONHp+hXvWkx3b0yjtdcw7QPe8AtmPpYNRydRV+XsnjZ9Y9sJ4652tp0mU7SR9pf/Ob3+D//b//1xWx4NZbb8WNN96I2bNn44QTTkBjYyNWr14d8Yy1nTt3orq6Ovz/Sy65BH/+858xf/58jBkzBp999hlWr17d4aYj9OOkNAQw5s4qyHXGuzmM0qBh1AO1kOv0eeA3JY55R0RERKmQ9JW1jRs34u2338Z//vMfDB8+vMMNRl566aUjDkaSJCxYsAALFizodJldu3Z1mDdnzpzwlbZECCGOJDzqhlSrhANnOKAZ8HlXaraML27LQ1au8crW3THviIiIKBWSPtrm5ubiggsu6IpYMp7N5kh3CJQsi4zK051w6fQAXzVLxs7LcwA9bmuuAWa3Bmg4gmvk1KV0zDtdcw5g3hEREeko6aP7U0891RVxdAtmiyV4gkLdhuJWMfKBWuxa1AtaDx1+z6Pjbc2VJg1DH69D2aLe+vxWiRKma97pfCt95h0REZF++L1oEhrcdekOgZKk2mTsmZ4NLct4Q7ZUl4LN9xfwhDkDMe+IiIgoFRLqrJ199tn4+OOP4y7X0NCABx54AEuXLv3BgRGlhEVGzXg7oNMwSF2pGlzbPIDKy70Zh3lHREREKZDQ174XX3wxLrzwQrhcLpx//vk4/vjjUVJSApvNhkOHDmHbtm344IMP8Prrr2Pq1KlYtGhRV8dNlBClvs1wNINdCVCaNBz1Tzd2jVYNV7bujnlHREREqZBQZ+3qq6/G5ZdfjpUrV2LFihV44oknUF9fDyB4B8dhw4ZhypQp+OSTTzB06NAuDZgoGWqWjO/1vPmCjlSXgi33F8DFE+aMw7wjIiKiVEj4TMJqteLyyy/H5ZdfDgCor69HS0sLevbs2eH2/UQZQ+ebL+jKp6HnZ16oUzRjDrfrzph3RERElAJH/LWvy+WCy+VKZSzdgg1NEPCmO4y4vAg+Vd2BhjRH0pGesQXqG3DsAy2oXOSAltf1Vzl0LZunCf1eaUHVxHpolq4rmx5l0utz02s7euad3vu6XnkHsB1LB6OWq6vw80oeP7PugfUUnwnNumzHeGN0upDJZMJQ0yewmXzpDiWuClMdAKDIlJvWOKLRM7YDOXXADTaM6rEfsqnLN6dr2Sp61AF/Asbk7+/a7ehQJr0+N722o2fe6b2v65V3ANuxdDBquboKP6/k8TPrHlhP8TWb9Ll4wzEsSXBm56Y7BEqWDCBHNWSmCx8g1vaElvnfHfz4MO+IiIgoBQx4KtF1Ghvq0h0CJcutAAsHQ6szYKp7TMDaAmjNBixbd8e8IyIiohTg0TYJgUAg3SFQsnJU4I7tkHP1eSaUaJQh7hmMgLvrdy0pJwDp/q+h6FQ2SoKOeadnzgHMOyIiIj0l/Zu1q666CldffTVOO+20rogn430dOAFCyvyf+h0KHAAAlAeK0xxJR3rGdsi/H7Y6gQP+EkCHO/M1+Pbj2OYWfOk9CVqga/PkUMt+FKzxo/zn/QBr15VNj/rSKyd0246OeadnzgH65R3AdiwdjFqursLPK3n8zLoH1lN83kAzgMe7fDtJH9nr6+tRWlqK/v37Y9asWbjqqqvQu3fvrogtI3mQhQAy//lCHrgBAM3ITnMkHekZm9pkx1GP16JskV2XB/h6YAfQgmZkQeviPFF9WejxSTX2XOSAZu26x2foUV965YRe29Ez7/TMOUC/vAPYjqWDUcvVVfh5JY+fWffAeorPp9MAxaS3smrVKuzfvx/XX389VqxYgQEDBuCcc87Biy++CL/f3xUxEh0x1aVg8/0FunTU9KY6ZXx+dwG0HD7nMNMw74iIiCgVjqhL2KtXL8ydOxeff/45NmzYgEGDBuGKK65ASUkJfvvb32LHjh2pjpPoyKgaXNs8gGq839fIHg39V9YDHjXdoVB7zDsiIiJKgR90/e7AgQNYs2YN1qxZA5PJhHPPPRdffvklhg0bhkceeSRVMRIdMaVJw1H/dEN2G+/EUlYB13YfZJ9IdyjUDvOOiIiIUiHpzprf78e///1vnHfeeejfvz9WrlyJm2++GeXl5XjmmWfw1ltv4YUXXsCCBQu6It60ys7JTXcIlCTVpWCLgYejfTGvF4ejZSDmHREREaVC0jcYKS4uhqZpuOyyy7Bx40aMGTOmwzKnn346cnNzUxBeZvH7fIBiTXcYlAyfhp6feaFO0QCLsZ5UIXs0DHjBjZobCgFH5t+h9EeFeUdEREQpkPSR9pFHHsHFF18Mm83W6TK5ubkoKyv7QYFlIo+nGXDyrjjdieLR0O+VBuyaqEKzGOwqhwo4ylUcCgDG+2VU98a8IyIiolRI+ivfK664ImZH7YcQQmD+/PkoLi6G3W5HaWlpQjcrWbp0KQYMGACbzYYJEyZg48aNHZZZv349zjjjDGRlZSEnJwennXYaWlpauqIYlEHUHAVb/qcAWg+DnTAD0Jwytt6eDy2bVzcyDfOOiIiIUiGjxuc8+OCDePTRR7Fs2TJs2LABWVlZmDJlCjweT6fvWbFiBebOnYu77roLmzdvxujRozFlyhRUVVWFl1m/fj3OPvtsnHXWWdi4cSM++eQTzJkzB7KcUcWnruDTUPhOI+Az3jUA2aPhmCdqgWbj3cSi22PeERERUQpkTG9FCIHFixdj3rx5mD59OkaNGoVnn30W5eXlWLVqVafve/jhh3HNNddg1qxZGDZsGJYtWwaHw4Enn3wyvMxvf/tb3HTTTbj99tsxfPhwDB48GDNnzoTVyt+fGZ3iFSh+uxmyTieWmlVGzVgrNIcOu5YGmBsFZOP1B7o9PfNO15wDmHdEREQ6ypjOWllZGSoqKlBaWhqe53K5MGHCBKxfvz7qe3w+HzZt2hTxHlmWUVpaGn5PVVUVNmzYgIKCApx88skoLCzEpEmT8MEHH8SMx+v1wu12R7yo+1GzTfjs3gJoufoMR9NsMnZf7AJsXT9ETHPI2Da3JzQnh6NlGj3zTs+cA5h3REREesqYzlpFRQUAoLCwMGJ+YWFh+G/tVVdXIxAIxHzP999/DwC4++67cc0112D16tU47rjjcOaZZ8b8PdzChQvhcrnCr759+x5x2SiNfBp6v9YAeI13GUBu1jB4SY1uVw0pCcw7IiIiSoG0ddaWL18Op9MZfvn9/i7ZjqYFT5auvfZazJo1C2PHjsUjjzyCwYMHRwyVbO+OO+5AfX19+LV3717YbI4uiZG6juID8j9pgewNpDsU+hFh3hEREVEqpG0cy7Rp0zBhwoTw/71eLwCgsrISxcXF4fmVlZVRn+UGAPn5+TCZTKisrIyYX1lZiaKiIgAIr2vYsGERywwdOhR79uzpND6r1drhN21mi4X3qu5mVKeMz+8ugMuAD/DVHDK2z+kJF591lXGYd0RERJQKabuylp2djUGDBoVfw4YNQ1FREdauXRtexu12Y8OGDTjppJOirsNisWDcuHER79E0DWvXrg2/Z8CAASgpKcH27dsj3vvtt9+if//+ScXc4K5LanlKP9mjof/KesBjvCFbcrOGYQ/XQG40Xtm6O+YdERERpULGfDUqSRJuvvlm3HfffTjmmGMwcOBA3HnnnSgpKcGMGTPCy5155pn46U9/ijlz5gAA5s6di6uuugrHH388xo8fj8WLF6OpqQmzZs0Kr/eWW27BXXfdhdGjR2PMmDF45pln8M033+DFF19MR1FJR7IKuLb74PYJaF3zeMD0kQG/U4KWMb88pRDmHREREaVCxnTWAODWW29FU1MTZs+ejbq6OkycOBGrV6+OeAj3zp07UV1dHf7/JZdcgoMHD2L+/PmoqKjAmDFjsHr16oibjtx8883weDz47W9/i9raWowePRpr1qzB0UcfrWv5SH+qU8YX83oZcziaTcaO2XkcjpaBmHdERESUChl1tJUkCQsWLMCCBQs6XWbXrl0d5s2ZMyd8pa0zt99+O26//fYfGiJ1M7JHw4AX3Ki5oRAw2Mml3Khh2JJa7F9QCC3bWGXr7ph3RERElAocyELGpgKOchWyEW/KpwDNJQo0U7oDoQ6Yd0RERJQC7KyRoWlOGVtvzzfkFQDNJuP7K3MNd+XGCJh3RERElAo82ibBZDKhoaYKfkjpDiWuhprK+AuliZ6xNe2vxMhXgF2/1KDZuv67CT2317K3EuOfAr6eq0F1dt229KgvvXJCr+3omQd657heeQewHUsHo5arq/DzSh4/s+6B9RSf39Oiy3Z4ZS0JzuzcdIdASZIFYGmCbs/HU/xA7j4JsrfrN6gpQM3A4L+UWfTMOz1zDmDeERER6YmH2yQ0NtQhu2cBAool3aEkzFXYO90hdEqv2L65Rb9tNdWrAGqRnV8MLa/r86T8Cv3Kpsd2jFQWvfJO75wD9M07gO1YOhi1XF2Fn1fy+Jl1D6ynzvlamnTZDq+sJSEQMOLdAoxNbtYweEkN5GbjPcBXadQw+u4qyG5/ukOhdph3RERElArsrBF1U6oFqD7BDs3K2/KRfph3RERE+mFnjQxNc8jYPqcnNCPeuc4iY//UbMDK3TjTMO+IiIgoFXi0JUOTmzUMe7gGcqMBh6M1BDDmzirIdb50h0LtMO+IiIgoFdhZI2OTAb9TgmbATFetEg6c4TDm1ZvujnlHREREKWDAUwmiwzSbjB2z84z5AF+LjMrTnYCFu3GmYd4RERFRKvBom4TsnNx0h0BJkhs1jPhTNeQGAw5Hc6sY+8cqyIc4HC3TMO+IiIgoFdhZS4Lfx5OTbkcBmksUaAa8cZ1qk7Fneja0LANevenumHdERESUAuysJcHjaU53CJQkzSbj+ytzdRuOppllePJN+gwRs8ioGW/ncLQMpGfe6ZpzAPOOiIhIRzzakqEpjRpG3XdQtwf4ag4Z2+b2hObs+pN0pV7F2D9UQa7lFd9Mo2fe6ZlzAPOOiIhITxzHQoamKUD9YAs0i5TuUFJOzZLx/eU5QA5340zDvCMiIqJU4JU1MjTNJmP3xS7AZsATS0VG/TAboHA3zjTMOyIiIkoFHm3J0JRGDaPvrtJtGKSelHoVx3E4WkZi3hEREVEqsLNGhqZagOoT7NCsxrstn5ol4+vrc6FxOFrGYd4RERFRKrCzlgSbzZHuEChZFhn7p2YDVgOmugz4c2TuxZmIeUdEREQpkFGHWyEE5s+fj+LiYtjtdpSWlmLHjh1x37d06VIMGDAANpsNEyZMwMaNGyP+XlFRgSuuuAJFRUXIysrCcccdh3//+99Jx2e2WJJ+D6WX0hDAmDurINcZb8iW0qBh1AO1kOuM9+Dl7o55R0RERKmQUZ21Bx98EI8++iiWLVuGDRs2ICsrC1OmTIHH4+n0PStWrMDcuXNx1113YfPmzRg9ejSmTJmCqqqq8DJXXnkltm/fjldffRVffvklLrjgAsycORNbtmxJKr4Gd92RFo3SRLVKOHCGA5pOz1nTk5ot44vb8qDlGq9s3R3zjoiIiFIhYzprQggsXrwY8+bNw/Tp0zFq1Cg8++yzKC8vx6pVqzp938MPP4xrrrkGs2bNwrBhw7Bs2TI4HA48+eST4WU++ugj3HjjjRg/fjyOOuoozJs3D7m5udi0aZMOJaO0ssioPN1pzAf4aoDZrQFaugOhDph3RERElAIZcyZRVlaGiooKlJaWhue5XC5MmDAB69evj/oen8+HTZs2RbxHlmWUlpZGvOfkk0/GihUrUFtbC03T8Pzzz8Pj8WDy5MmdxuP1euF2uyNe1P0obhVj/1gF+ZABh6M1aRj6eB1kN4ejZRrmHREREaVCxnTWKioqAACFhYUR8wsLC8N/a6+6uhqBQCDue1544QX4/X707NkTVqsV1157LV5++WUMGjSo03gWLlwIl8sVfvXt2/dIi0ZppNpk7JmeDS1LpyFbPg2F7zQCvq6/7KC6FGy+vwBaHn9LmWl0zTsdcw5g3hEREekpbZ215cuXw+l0hl9+f9c9j+jOO+9EXV0d3nrrLXz66aeYO3cuZs6ciS+//LLT99xxxx2or68Pv/bu3dtl8VEXssioGW/XbTia0qKh95pmyI06XHVQNbi2eQCV49Eyjo55p2vOAcw7IiIiHaXtF+LTpk3DhAkTwv/3er0AgMrKShQXF4fnV1ZWYsyYMVHXkZ+fD5PJhMrKyoj5lZWVKCoqAgDs3LkTS5YswdatWzF8+HAAwOjRo7Fu3TosXboUy5Yti7puq9UKq9V6xOWjzKDUqxj5QC12LepluCsBSpOGo/7pxq7RquHK1t0x74iIiCgV0nZlLTs7G4MGDQq/hg0bhqKiIqxduza8jNvtxoYNG3DSSSdFXYfFYsG4ceMi3qNpGtauXRt+T3NzM4Dgb9naMplM0LTkvhk2mYz3gFujU7NkfH95jiEf4Ku6FGzhcLSMxLwjIiKiVMiY36xJkoSbb74Z9913X/gW+1deeSVKSkowY8aM8HJnnnkmlixZEv7/3Llz8be//Q3PPPMMvv76a1x//fVoamrCrFmzAABDhgzBoEGDcO2112Ljxo3YuXMnHnroIaxZsyZivYlwZuemoKSkK0VG/TAboGRMqqeOT0PPjS26/VaJksC8IyIiohTIqK99b731VjQ1NWH27Nmoq6vDxIkTsXr1athstvAyO3fuRHV1dfj/l1xyCQ4ePIj58+ejoqICY8aMwerVq8M3HTGbzXj99ddx++234/zzz0djYyMGDRqEZ555Bueee25S8TU21AH2/JSUlfSh1KsY9UAtyow4HM2jod8rDdg1UYXGB7ZnFOYdERERpYIkhBDpDqI7cLvdeOjvL2GXV4IfUrrDiauhJvg7vuyehXGW1J+esTUcrISzEpCG9NLlKod3VyVOekLCF7flQXV17Xchen2OemzHSGUB9M07PXMO0Hn/ZTumO6OWq6vw80oeP7PugfUUn9/TghcXXIf6+nrk5OR02XYMOEaHKJLPCWNmugr0/RgcjpahmHdERET0Q2XUMMjuILtnAQJK9xn64yrsne4QOqVHbEq9ilGP1KJsUaEuw9Ga6lUAtcjOL+7y7SkNAQz4uAa7LyiEltv1ZdOjvvTK167ejp55p2fOAfrnHcB2LB2MWq6uws8refzMugfWU+d8LU26bMeI3/sShanZMr64LQ9arvG+l1CzTfjs3gLdTpgpccw7IiIiSgV21sjYNMDs1gAjjtjyaej9WgPgNWLhujnmHREREaUAO2tkaEqThqGP10F2q/psUJagmaDLnqX4gPxPWiB7A12/MUqKrnmnY84BzDsiIiI9sbOWhOyc3HSHQElSXQo26/gAXz2HiKlOGZ/fXQAtx9zl26Lk6Jl3eg9LZN4RERHph521JPh9vnSHQMlSNbi2eQDVeEO2ZI+G/ivrAY9OVw0pccw7IiIiSgF21pLg8TSnOwRKktKk4ah/uvUbBqkjWQVc232QfXxUYqZh3hEREVEqsLNGhqa6FGzRcRiknlSnjC/m9eJwtAzEvCMiIqJUYGeNjM2noefGFkM+wFf2aDjq2Tqg2XhXb7o95h0RERGlADtrZGiKR0O/VxogNxnwxFIFHOUqZN6UL+Mw74iIiCgV2FkjQ1NzFGz5nwJoPYw3HE1zyth6ez60bOM9eLm7Y94RERFRKrCzRsbm01D4TqNhh6Md80Qth6NlIuYdERERpQA7a0mw2RzpDoGSpHgFit9uhmzEE0sNMDcKyMbrD3R7zDsiIiJKBXbWkmC2GG9Ik9Hp/cBgPWkOGdvm9oTm5HC0TMO8IyIiolRgZy0JDe66dIdAyfJp6P1aA+DV6TKApkGpVwGt67cnN2sYvKTGmFdvujs9807HnAOYd0RERHpiZ40MTfEB+Z+0QPbqc+s6pUHDqAdqIdfxRPbHTM+8Y84REREZFztrZGiqU8bndxcY8gG+mkPG9jk9oTk4HC3TMO+IiIgoFdhZI0OTPRr6r6wHPMa76iA3axj2cA3kRuOVrbtj3hEREVEqsLNGhiargGu7D7JPpDuU1JMBv1OCxr044zDviIiIKBUy6nArhMD8+fNRXFwMu92O0tJS7NixI+Z73n//fZx//vkoKSmBJElYtWpVStZLxqA6ZXwxr5cxh6PZZOyYnQdwOFrGYd4RERFRKmRUZ+3BBx/Eo48+imXLlmHDhg3IysrClClT4PF4On1PU1MTRo8ejaVLl6Z0vdGYTKaklqf0kz0ajnq2zpAP8JUbNYz4UzXkBuOVrbtj3hEREVEqZMxXo0IILF68GPPmzcP06dMBAM8++ywKCwuxatUqXHrppVHfd8455+Ccc85J+XqjcWbnoornJ92LCjjKVRwKAIZ7hq8CNJco0PgdQuZh3hEREVEKZMyVtbKyMlRUVKC0tDQ8z+VyYcKECVi/fr3u6/V6vXC73RGvxoa6I46D0kNzyth6ez607Iz5XiJlNJuM76/M5XC0DMS8IyIiolTImM5aRUUFAKCwsDBifmFhYfhveq534cKFcLlc4Vffvn0RCOjzrC5KHdmj4Zgnag05HE1p1DDqvoOQ3f50h0LtMO+IiIgoFdLWWVu+fDmcTmf45fdn1oH/jjvuQH19ffi1d+/edIdER0IDzI0CsuHGogGaAtQPtkCzSOkOhdpj3hEREVEKpG0cy7Rp0zBhwoTw/71eLwCgsrISxcXF4fmVlZUYM2bMEW+nqKjoiNZrtVphtVojZ/oFmg5UQPUF/ysUCZJfAFKU6QCgWSUIRYLsE8Hfd5gkyF4NmiJFTgNQGgPQzBIgt85vPRGSfSJy2iZDM0vhaQQEZFVAs0ZON1ZVQgoEv+GXAiIYj0WCpApAAMLccVqzSBAWObIcPgHICJdDtE4rDYHgrbtNrbEoOBx723KYJWhWGbK/tRwS0HygCgELAE0cLkfrtDBJkPxacHlr63w1GDsCAnKoHAEBSBICDjlYDg0Q7cvnF2hoOIgNVwGu6gOQKkWHegpuExCmNvVklgEhOtaTSYLsaa0buXVaAWS/CNeTr6IGgISm/eVQ6+XIcvgFVJfSsc7a1I2kHZ6WW7So9RTKveaqg/jmRCCr8gAEAChSh3qKlntyAJBbAh3qKVruBRwyGuoPwtSa8xF11q4cJk/ws2xfT9FyDyIYX6ieGmurIBREzz0JUJq0jvUULfcsEjS7qWM9Rcs9rwZIUrhu2tZTtNyDEszl9vUULfeaaw5i00VAVs0BSBXR24hQ7gVscvDzitVGtOaekABJIKKe1L2tObdvP7RqORy7pAEBp6nTNqJtPckeLZzDsdoIAGhorMa2UwFnTUXMNkL2CWhScBvx2ojQtJptAiRA9gpoVgkNNVWd5p7SGIjbRoRyT8gAIEVtI4A20xIgaYDk1WK3ESYJnoqDUG3B0GK2Ea25Z2rN4XBbHi3fWqcFWlMzThsBDRAmwNSiRW0jOhyfZCCQZYrZRjS4DwIyIHtaf3AZp41o22nX7KaYbURoWm4OhOss3vEp4JAhtX7p0baeouUeAsG/xWwjWsshqcH2rX09hXIP4vAxV/JpMLVoUevMU1UNzQLITYFgPdlkCBlx2wjJL4L7qUCn5xHtc0/NkuO2EaHcU+rVzs8j2pVDMwGyhphtRCj3AEDyaTHbiFA5hFlq/fwi66mxrgrCFFlPpiYNQhJx2wjNIgXPEzR0qKeouacE3xevjQjFHnCa4rYRQpEgeYPnKbHaiLa5BwhoZjlmGxHKPdmrtU533kaE2j01K3jNJWYbEZrfWg8x24jQtAY0Nh7sUE/Rck8SgKkpwXNYuyl43hSnjUAgWFYAiZ/DWuW4bUT4HNYdiNtGhM9hbfLhcrRrI7SGFughbZ217OxsZGdnh/8vhEBRURHWrl0b7kS53W5s2LAB119//RFvZ+DAgSlbr/WlRgzZ7YepMv7X5Yo9gJ4jPcgd5MXuN3KQN7QF2f38KHstBwVjW5BV4sfOl3NRMrERml/Cvnez464TACwuFSWnNGHPmzk4ZuYhtBxUUP6BE0f/tA5N5WZUbbFj4FQ3dm/3wvddD2j11QmXz3WMB0UnNKNigwOKXSB/VAv2r3PCnq8ib6gHe9/ORnY/H3IHefHtih4QgcS+We8zuQH73s3GUTMOQTYLfLcyD86zd6LAVofvV/XA4J/Vwltnwp43c5Dd34v6nbaE1mt2BnDUtHrUfm1DS7WC3qc2ovoLO9QWCUUTmlG1yYEGjwdanQ0W5QA8NYndRt1R5IfaInWoJ3svFTte6IF+Z7lhzQ1g+3N56HO6G/veyWnz7uBnMmLxoShrFhj8s0MR9dSwx4zar+3oP8WNuu+saNhjQd8zGlD7tQ0HP7MHeyqdCv2tGo4iP7KK/R3qKVru1X5jQ3NFYp9Fr3FNaO5Rhaa3BmLwz74J11O03DuwPgsBb2IX6609VNjy1HA9Nfo9sA2vhrzN2yH3rK5AwvuHkqXi6OnuDvXUWe4lw5qnYsDZHespWu5tfyEHUBUA8fe/PpMbUP2lPWYbEco9R7EPjXvbfYkUyrm/1EXOlQWOvbTzNqJt7pV/lIWGXe3XG53UswWi1o4eg/eg4LjO24jdb+Qgu58XB7dkJbReABH1NOjiWhw4VN9p7u1Zk43D+0BsPYa0wN9kitpGAEDBuMPtXku1kuD+IcE8sA5HnXQoZhvRtkyJchT7oDbLcduIlmoFuUd7E94/TLYABl1QH7ONMA2pglLQjOa3BAItyQ2+aduWxzo+7Vzlgtqc2B1q+kxuQNOBYH20radoxycRAFoOJta2SbJA/7Oj19Ogi2uh+aXw8an6Cxtqtjo6W1PrvzUAgseP7H6+uG1E1SYHAl4J7gT3O0CgZGJj3Daibe4lquikRlR/5ojZRoRyD0DCx4+sEh/6TG7sUE+BgkOw9HfD/1EgnHvf/Ts34eNHzkAP3GW2DvUULfeKJjQlvH9IioZjZ9bFbSPyR7Vg3/tONO2zJLReADDZNfROoI0YdHEtdq/Ogb8hsdPzPpMboKmI2UaEcs+925zwes3OAOSjajrUU7Tc89abULkxsXbeUeRHwXHNcduIhj1m1Gy1w1uXYDdFEuhxrDduGxFq97Y/1wOJHj8GnFuPXa+7Om0jViQW4Q8iCSEy5qmtDzzwAP70pz/hmWeewcCBA3HnnXfiiy++wLZt22CzBU/gzzzzTPz0pz/FnDlzAACNjY347rvvAABjx47Fww8/jNNPPx15eXno169fwuuNx+1246HH/43Ju+uR1Rz87ZpJkRBQBSQJkE2R01pAQLFIwfl+AUkOzlf9ArIJkGUJqk9Abs1DX7OASQGk1vmm1rYw4EfEtGKVYDIBARVQLBI0TUBrOx0AFLOE3YcOQgsAfXPyoQUEhAaYzMG4hAjG3nY6oAa3qZjlyDK1ib3ttLdZC07L0csUmjYpgMkiQVMPl2NXTTVkBejnyg+WySJBaAIBFZBkIKBGL5OmCYjA4XJIEmC2yZ2WL6AKlFVXo/zdHIw7R4E5VB+xymcCTCYJAohZplA9yaZg3KF62uuugVCB/j3zw2VqW082pxxZpvaxt5n2e0XUegrlXtnBahz6yo5RE+1QTBJkpWO+Rcs9EQD83s7L1DbfzDZgX2MNNBUYmN8raplC06pPQJY71lO0uhFCQDZJ4TLtdVdDNgG9s/I71A0kwN/SsZ6i5p4ZsFjlTsvUNvdUn4AkSTHL1Db3ZBlQLFHyLUr5vt17EOXrcjDhfAWySYraRoTKp1gliABithGhaQSvuUSUaU9tDSQF6JvTM2J/EgKw2OXOy9QmdtWnteZw7DYCAPY31KBmqx2jT7HDYjN1mm8BfzBeIeK3EaF6smZJEe3envrqTnPP1yzithGh8klS8HOL1ka0b8uFBqj+2G2ELEvYXVsNkwIMyO8Vs0yhevJ7WvM51JZbOs89gWBuxmsjhAZIJkD1xi5TOPdkwGKTY7YR+xurIclAsa0nhIjfRpjanLebrXJC+5PPq0Gosfeh0LTZJkG0fj/aoUztyqFpwc8tZhvRWk9aQMBsleIfcy0SAn4Nfm/0Mu2tq4GsAL2dPSECwfmSjLhtREAVEFowLzs7j2hfPrNNittGhOrJ06jFLFPbepJkAaFJMduI0DQQPEbHaiPC5xRKsM1sX459DdWQTEBvR354vs8TXHm8NsJkDraVEFLMMoXqSVYA1Re/jdACAgKA1S7HbSNkU7DN1AKx24i29SSEgMksJbQ/qX6BgC92GxGqJ4u9daRKjDYiNK36g8fomG1E67TQBMqbazrUU9RzWAH4WhI7hzVbJcgJnMNqAQFNDY6gSvQcVrFIcduI8Dlskxa3jQjVk2KREFARtUz1TV5M/scfUF9fj5yctl/cp1ZG3c7r1ltvRVNTE2bPno26ujpMnDgRq1evjuhQ7dy5E9XVh7+t/vTTT3H66aeH/z937lwAwFVXXYWnn3464fUmxBw82Fsdh7/9UdoMAWk73bbHbjK3WcYcfXmbM/p8c5sv3dpOK61f6MiyBLntdGtokgyY5NC6osfVdrrtNk1K9NjbTifyGbSdltt8ARVKckmSwuWQ5MPTbbefSOyySQJMbabblMNiBwac40aWq1dS8bYVe/lgwxOqG8UCwHL4b4fPY9rE26aeOsTeZtrqkDq8t+32rU6gaEILbA5nRLyd1Vk49+R28zspX6hMktSmztrUU/vcs9g6xpvItEkJfoah6Wixh07iY8WbyHTb3DNbD8cbrZ4SzrcouWfLAY6a6oY9uxfa6jTf2nyh3Fkb0XZfjqin1i/8zVa5TRvRLt+itBFtY1cscrhe47URih8oPL4FFlsw7zrLt+B0x3gTm27deozcO9xmHkF7EYpRiTJtSnD/sCPuMm2nLfZo7Xr83IuZb6aO605sP+i8jQh1KM3W+G28OcpFoVhtRCj3LFYZiJKrnU632T9iHZ9MSeRbZ+2q0uZYFT4mmeU2ndLIdYTywGJrd1UoThsRLMcR7h8J5KfNeTie6OcRR358avu5d/45dl5PUpT1tP38kj9Gt0bSSe4dPjZFxnvEbQSCbWZy8Sbe7pktEsxt2uP4xyfEbSNkkwSLKbn9Q/K2rjoN57CySWpTD/HrLOlz2Kzk6i9au9B+uitlVGdNkiQsWLAACxYs6HSZXbt2Rfx/8uTJiHdxMJH1JiI7JxdA/Q9aB+lL9QB71uSg6CKt48G0m1NbgO9f6YGCKzTYHMYqW3fHvCMiIqJU4JE2CX6fL/5ClFlkQLGJ8Lc1RiKbgZ4jWnT7ZoeSwLwjIiKiFDDgqUTX8Xia0x0CJUmxAP1+0hAxZMEoZAXoOcIDRTFe2bo75h0RERGlAo+2ZGiqB/ju5Vz4Woz3wCu1Gdj+XA94Go1Xtu6OeUdERESpwM4aGZsM2PLU8A0sjES2AIUnNENJ8j45pAPmHREREaUAO2tkaIoF6DOp0bDD0XKP8XI4WgZi3hEREVEq8GhLhqZ6gB0v5sLXbLwhW34OR8tYzDsiIiJKBXbWyNBkGcgq8YcfPm4kJgtQcmojLByOlnGYd0RERJQK7KwlwWZzpDsESpJsAUpObtJtOJraAnz7fA94dLiiIitAdl8/ZA5Hyzh65p2eOQcw74iIiPTEo20SzBY+WKi7UT3Aty/00G04mhCA0CRAh80Fh6PlcThaBtIz7/TMOYB5R0REpCd21pLQ4K5LdwiUJFkBcgd5IJvTHUnqmWxAvyl1sPCCb8Zh3hEREVEqsLNGhiYrQMFxLVDMxkx1xc6rG5mIeUdERESpYMwzCaJWev+eR08BD/D9qjz4mtMdCbXHvCMiIqJUYGeNDE02Az1HtEAx4M8NTTbgqBm1HI6WgZh3RERElArsrJGhyQrQc4THsA/wVVuMWa7ujnlHREREqcAjLhmaauAH+AY8wJ43cjkcLQMx74iIiCgV2FlLgslkSncIlCTZAhSe0AzFgA/wNTuAwT+rhc3J3TjTMO+IiIgoFXi0TYIzOzfdIVCSZAXIPcZryOFomgo07DVDU4139aa7Y94RERFRKhjvTKILNTbUpTsESpLfyMPRfED5Oid8nnRHQu0x74iIiCgV2FlLQiAQSHcIlCSTBSg5tREWnYajmSxA/ih9hr8Fh6Md4nC0DKRn3umZcwDzjoiISE882pKhyQqQ3dcPWafhaHreBVBTgbodVqgcjpZx9Mw7ve88ybwjIiLST0Z11oQQmD9/PoqLi2G321FaWoodO3bEfM/777+P888/HyUlJZAkCatWrYr4u9/vx2233YaRI0ciKysLJSUluPLKK1FeXt6FJaFMERyOlmfI4WiaD6j8xAGVw9EyDvOOiIiIUiGjOmsPPvggHn30USxbtgwbNmxAVlYWpkyZAo+n87OCpqYmjB49GkuXLo369+bmZmzevBl33nknNm/ejJdeegnbt2/HtGnTuqoYlEFMNqDflDpDPsBX4XC0jMW8IyIiolSQhBAi3UEAwatqJSUl+N3vfoff//73AID6+noUFhbi6aefxqWXXhp3HZIk4eWXX8aMGTNiLvfJJ59g/Pjx2L17N/r165dQfG63G7+/+2EM2FoBi+aDJAFClQFT6zfngchp2axBkoPTkqJBaAC0ttMSJEVAaIBQJQg5OA0hQTIJCE0CBDpMIyBBVlrnByRAAiS53bQqobqlHkIWyFNyAUlAkoPbgRyalgFZOzxt0mBSOpaj7bSkaBAiOC2gQYtRpsPlAGRIkC2Hy1HtqQM0CfkO1+FyIFimgE+CQJQyBaTD5Widlk2AJGKXqbqxHmixokeuDZBilylUDknIMFmjlCnKtKZK0AKH66m20Q2YBPJsrnCZ2pZDliLr6XDsHac1LXqZQrlX21wHW3kJHAOaIdvb5Vu7MrWtJ80vAaaO9RQt9yQhoabFDZg05Ntyo5YpNC1E8PNoX0/Rck8yaTCZ29RTcz2gmpCf4+yQe8IvQ8ixyxSqD2iALEers465BzmYb7HK1Db3TGYAgc7qLLKeDjbUAX4z8pxZgDn2/iQLGbIldhsRmg74AaFF1lOtpx7QJOQ5ciL2J1kRkNB5G9G2HEIICCl+GyFJQE1dE2xVBcg6pgmyFHt/0lRAU+O3EaFpWY5s96obO889TYvfRoTKJysCshJ7fwqVQ/MF8y1WGwFNRq23DpAFetlzY7YRoXoSENACbcoX6Dz3JBkwmeO3EZIcjFdD7DKFck8CIEux24iDjfWAJiM/OxsBf/w2IjRtsghAS2B/UiVomgDitBGhepI0GZK58zK1raeA2tq+xDnmQgRzQpI61lOH8gUkiACCx+goZaptqQcCMvKczmAbrLWuO4FjLgQQ8CV2zJUUDbIcv40ITWuIXaa29SSbgu1FrDYiNB3wShBS7DYiNC1LgISOZTrYWA8ETOjlcobrSUgatED8NkJoEiQE8y1WmULl0PwSNBG/jYAsIMut8cZpIxAIthFaILFjLjQJJqsAROw2IlQ+zY/gMTpGGxGallrXEauNCNWTEIDmj91GhKZls4aalo719IPPYbVgvsVrIyAHuyYBf/w2IlQOxRy7TG3rSUP8NqKzc9i2ZfIKL+54aznq6+uRk5ODLiMyxM6dOwUAsWXLloj5p512mrjpppsSWgcA8fLLL8ddbs2aNUKSJFFfX9/pMh6PR9TX14dfe/fuFQDEvbhXFKJQABCLsEi44BJWWMUiLBJWWIULLrEIiwQAUYhCcS/uFQBEf/QXf8QfBQBxLI4Vv8PvBAAxCqPEHMwRAMR4jBe/wq8EAHEaThOX43IBQPwEPxEX4SIBQJyP88X5OF8AEBfhIvET/EQAEJfjcnEaThMAxK/wKzEe4wUAMQdzxCiMEgDE7/A7cSyOFQDEH/FH0R/9DV+mHOSIRVgkeqO3YcoUqqdiFIv7cb+hymSU3DsaR4tFWGSoMrWtp/txv8hDnqHKZMR6YplYJpaJZWKZurZMAGL2J1IhY66sffTRRzjllFNQXl6O4uLi8PyZM2dCkiSsWLEi7joSubLm8XhwyimnYMiQIVi+fHmny91999245557IuZtehFQm60wWX2QTCI4bfMBkkCgxQqT3QsICQGPBYrDCxGQEPAGp7WADM1nhmIPTStQ7D5oqgyhmmCy+aH5TRAB+fC0JsNkDU1LMFlVBHwKAMBkURHwKsGrNeYAAl4zJFkLTnvMkExtppUAZEWD2mKBbFEhmzSoLVbIFn9w2sBlEpJAc3k+7EU1kE3GKJMR68loZZIsPvjrsyFbvTA7jFEmI9YTy8QysUwsE8vEMv2QMo27CF1+ZS1tnbXly5fj2muvDf//tddew+TJk7u0s+b3+3HhhRdi3759ePfdd2N+sF6vF16vN/x/t9uNPo19EygZZRJNlVH5dQkKh5ZDVox1swdfs4IPHz0bp9y0GhaHmu5wqA3mHRERkfFJQ7u+s5a2X4hPmzYNn332WfiVn58PAKisrIxYrrKyEkVFRT94e36/HzNnzsTu3buxZs2auB+q1WpFTk5OxIu6n6ZqJ5bP/A2aqp3pDiXl/C0WfLHyRPhbLOkOhdph3hEREVEqZMwwSNF6g5Hf//73+N3vfgcgeDWroKDgB99gJNRR27FjB9555x306tUr6fjcbjdyyl1Jv4+IiIiIiIzH0FfW2pMkCTfffDPuu+8+vPrqq/jyyy9x5ZVXoqSkJKLzdeaZZ2LJkiXh/zc2NoavzgFAWVkZPvvsM+zZswdAsKN20UUX4dNPP8Xy5csRCARQUVGBiooK+Hw+PYtIaaD6ZOxYOwyqL2NSPWV8TRa8fvsl8DXxCkemYd4RERFRKmTUmcStt96KG2+8EbNnz8YJJ5yAxsZGrF69GjabLbzMzp07UV1dHf7/p59+irFjx2Ls2LEAgLlz52Ls2LGYP38+AGD//v149dVXsW/fPowZMwbFxcXh10cffaRvAUl3LbVOvDLnF2ipNeBwNI+C798bCr9HSXco1A7zjoiIiFIhY4ZBZjoOgyQiIiIiopAf1TBIoq6gehR8vmICVANeBfA0WvDyr38BTyOHo2Ua5h0RERGlAjtrZGgetx1vLbgAHrc93aGknOZTULmtNzSf8ToE3R3zjoiIiFKBwyATxGGQREREREQUwmGQRD+Q6lGwftkZhh2O9vyV13E4WgZi3hEREVEqsLNGhuZpsOHjx0rhabDFX7ibEaqMpoPZECp340zDvCMiIqJU4DDIBHEYJBERERERhXAYJNEP5GtW8M6fzoOv2YDD0dw2PHvhb+BxG+/qTXfHvCMiIqJUYGeNDM3fYsEXK0+Ev4W/ryH9MO+IiIgoFTgMMkH19fVwHchNdxhERERERJQBpKFAXV0dXK6u+6mU8cbodJGamhrkDk13FERERERElClqamrYWcsEeXl5AIA9e/Z0aYUQud1u9O3bF3v37u3SH6wSMddIL8w10gtzjfRSX1+Pfv36hfsIXYWdtQTJcvDnfS6Xizs/6SInJ4e5RrpgrpFemGukF+Ya6SXUR+iy9Xfp2omIiIiIiOiIsLNGRERERESUgdhZS5DVasVdd90Fq9Wa7lDI4JhrpBfmGumFuUZ6Ya6RXvTKNd66n4iIiIiIKAPxyhoREREREVEGYmeNiIiIiIgoA7GzRkRERERElIHYWSMiIiIiIspAP9rO2tKlSzFgwADYbDZMmDABGzdujLn8ypUrMWTIENhsNowcORKvv/56xN+FEJg/fz6Ki4tht9tRWlqKHTt2dGURqJtIJte++uorXHjhhRgwYAAkScLixYt/8DrpxyOZvPjb3/6GU089FT169ECPHj1QWlraYXm2a9SZZHLtpZdewvHHH4/c3FxkZWVhzJgx+Mc//hGxDHONOnOkx7vnn38ekiRhxowZEfOZa9SZZHLt6aefhiRJES+bzRaxTMpyTfwIPf/888JisYgnn3xSfPXVV+Kaa64Rubm5orKyMuryH374oTCZTOLBBx8U27ZtE/PmzRNms1l8+eWX4WX+9Kc/CZfLJVatWiU+//xzMW3aNDFw4EDR0tKiV7EoAyWbaxs3bhS///3vxb/+9S9RVFQkHnnkkR+8TvpxSDYvfvazn4mlS5eKLVu2iK+//lr84he/EC6XS+zbty+8DNs1iibZXHvnnXfESy+9JLZt2ya+++47sXjxYmEymcTq1avDyzDXKJojPd6VlZWJ3r17i1NPPVVMnz494m/MNYom2Vx76qmnRE5Ojjhw4ED4VVFREbFMqnLtR9lZGz9+vLjhhhvC/w8EAqKkpEQsXLgw6vIzZ84UU6dOjZg3YcIEce211wohhNA0TRQVFYlFixaF/15XVyesVqv417/+1QUloO4i2Vxrq3///lE7az9knWRcPzQvVFUV2dnZ4plnnhFCsF2jzqWiDRo7dqyYN2+eEIK5Rp07klxTVVWcfPLJ4u9//7u46qqrIjprzDXqTLK59tRTTwmXy9Xp+lKZaz+6YZA+nw+bNm1CaWlpeJ4syygtLcX69eujvmf9+vURywPAlClTwsuXlZXh/7d3/zFR13Ecx1+HxwEbCgh62FInc/7CHCbSzqZoLp0V2T9NcjFdaLpZm9NSGxYlG96Utco/WsNfm/1xA//xD39kXmOALkYIE0UpSP5gDoSl/MjU4j794bp1clhHF3ccz8fG2H2+n1/cXvseb753Xzo6Onz6JCQk6LnnnhtyTkS+4WQtFHNi9AtGLu7du6fff/9dEydOlMR5Df7916wZY+R2u9Xc3Kxly5ZJImvwb7hZ27dvnyZPnqz8/PxBx8ga/Blu1vr7+zV9+nRNnTpVa9eu1bVr17zHgpm1MVesdXd3a2BgQHa73afdbrero6PD75iOjo4n9v/reyBzIvINJ2uhmBOjXzBysXv3bj311FPeFxbOa/BnuFnr6elRfHy8bDabXn75ZR06dEgvvviiJLIG/4aTterqah05ckSlpaV+j5M1+DOcrM2ePVtHjx7VqVOn9PXXX8vj8WjJkiVqb2+XFNysWQPqDQCIOE6nUy6XSxUVFYM+IA0Ew/jx49XQ0KD+/n653W7t2LFDaWlpWr58eai3hgjR19envLw8lZaWKiUlJdTbQYRzOBxyOBzex0uWLNHcuXP11VdfqaioKKhrjbliLSUlRePGjVNnZ6dPe2dnp1JTU/2OSU1NfWL/v753dnZqypQpPn0yMjKCuHuMJsPJWijmxOj3X3JRUlIip9OpCxcuaMGCBd52zmvwZ7hZi4qK0syZMyVJGRkZun79uvbv36/ly5eTNfgVaNZaW1vV1tamnJwcb5vH45EkWa1WNTc3kzX4FYzfraKjo7Vw4UK1tLRICu5r6Jh7G6TNZtOiRYvkdru9bR6PR26326dC/juHw+HTX5K+/fZbb/8ZM2YoNTXVp09vb69qamqGnBORbzhZC8WcGP2Gm4sDBw6oqKhI586dU2Zmps8xzmvwJ1jnII/HowcPHkgia/Av0KzNmTNHjY2Namho8H69+uqrWrFihRoaGjR16lSyBr+CcV4bGBhQY2OjtzALatYCuh1JhHC5XCYmJsYcP37cNDU1mbffftskJiZ6b7mZl5dn9uzZ4+1/8eJFY7VaTUlJibl+/bopLCz0e+v+xMREc+rUKXPlyhWzdu1abgWLgLP24MEDU19fb+rr682UKVPMe++9Z+rr681PP/30r+fE2BRo1pxOp7HZbObkyZM+tx7u6+vz6cN5DY8LNGvFxcXm/PnzprW11TQ1NZmSkhJjtVpNaWmptw9Zgz+BZu1xj98N0hiyBv8Czdonn3xivvnmG9Pa2mrq6upMbm6uiY2NNdeuXfP2CVbWxmSxZowxhw4dMtOmTTM2m81kZWWZ77//3nssOzvbbNiwwad/WVmZmTVrlrHZbCY9Pd2cPn3a57jH4zEffvihsdvtJiYmxqxcudI0NzePxI+CMBdI1m7evGkkDfrKzs7+13Ni7Aoka9OnT/ebtcLCQm8fzmsYSiBZKygoMDNnzjSxsbEmKSnJOBwO43K5fOYjaxhKoL+v/Z2/Yo2sYSiBZG379u3evna73bz00kvm8uXLPvMFK2sWY4wJ7FocAAAAAOD/NuY+swYAAAAAowHFGgAAAACEIYo1AAAAAAhDFGsAAAAAEIYo1gAAAAAgDFGsAQAAAEAYolgDAAAAgDBEsQYAAAAAYYhiDQAASUeOHNGqVatCsvaePXv07rvvhmRtAED4shhjTKg3AQBAKN2/f19paWkqLy/X888/P+Lrd3d3Ky0tTQ0NDUpLSxvx9QEA4YkrawCAMe/kyZOaMGFCSAo1SUpJSdHq1av15ZdfhmR9AEB4olgDAESMrq4upaamqri42Nt26dIl2Ww2ud3uIce5XC7l5OT4tG3cuFGvvfaaiouLZbfblZiYqH379umPP/7Q+++/r4kTJ+rpp5/WsWPHvGPa2tpksVhUVlampUuXKi4uTosXL9aPP/6o2tpaZWZmKj4+XmvWrFFXV5fPejk5OXK5XEF6JgAAkYBiDQAQMSZNmqSjR4/q448/1g8//KC+vj7l5eXpnXfe0cqVK4ccV11drczMzEHt3333nW7duqXKykp9+umnKiws1CuvvKKkpCTV1NRo69at2rJli9rb233GFRYWau/evbp8+bKsVqvWr1+vXbt26fPPP1dVVZVaWlr00Ucf+YzJyspSe3u72tragvJcAABGPz6zBgCIONu2bdOFCxeUmZmpxsZG1dbWKiYmxm/fu3fvKikpSZWVlVq6dKm3fePGjaqoqNDPP/+sqKhHf9ucM2eOJk+erMrKSknSwMCAEhISdPjwYeXm5qqtrU0zZszQ4cOHlZ+fL+nRVbs33nhDbrdbL7zwgiTJ6XTq+PHjunHjhne93t5eJSQkqKKiQtnZ2f/L8wIAGF2sod4AAADBVlJSovnz56u8vFx1dXVDFmqS9Ntvv0mSYmNjBx1LT0/3FmqSZLfbNX/+fO/jcePGKTk5Wbdv3/YZt2DBAp8xkvTMM8/4tD0+Ji4uTpJ07969f/z5AABjA2+DBABEnNbWVt26dUsej+cf31aYnJwsi8WiO3fuDDoWHR3t89hisfht83g8Q46zWCx+2x4f88svv0h69FZOAAAkijUAQIR5+PCh3nzzTa1bt05FRUXatGnToKtYf2ez2TRv3jw1NTWN4C4Hu3r1qqKjo5Wenh7SfQAAwgfFGgAgohQUFKinp0dffPGFdu/erVmzZumtt9564pjVq1erurp6hHboX1VVlfcOkgAASBRrAIAIUlFRoc8++0wnTpzQhAkTFBUVpRMnTqiqquqJ/8MsPz9fZ86cUU9Pzwju1pfL5dLmzZtDtj4AIPxwN0gAACS9/vrrevbZZ/XBBx+M+Npnz57Vzp07deXKFVmt3PsLAPAIV9YAAJB08OBBxcfHh2TtX3/9VceOHaNQAwD44MoaAAAAAIQhrqwBAAAAQBiiWAMAAACAMESxBgAAAABhiGINAAAAAMIQxRoAAAAAhCGKNQAAAAAIQxRrAAAAABCGKNYAAAAAIAxRrAEAAABAGPoTySqob2pfbPIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect transverse grid near CPW gap\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "sim_without_via_fence.plot(z=1, ax=ax)\n",
    "sim_without_via_fence.plot_grid(z=1, ax=ax, hlim=(0, 500), vlim=(-130, 50))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5130f629-548c-4d34-af9a-38ee10179cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect longitudinal grid\n",
    "fig, ax = plt.subplots(figsize=(6, 8))\n",
    "sim_without_via_fence.plot(y=1, ax=ax)\n",
    "sim_without_via_fence.plot_grid(y=1, ax=ax, hlim=(0, 3000), vlim=(-L / 2 - 200, L / 2 + 200))\n",
    "ax.set_aspect(0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045a3410-15bd-49e5-a009-fab066a86be6",
   "metadata": {},
   "source": [
    "We can also use the built-in 3D viewer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39116e3d-725d-4ebf-969e-0a4d4036f678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_without_via_fence.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfff5bc-bd03-4dcf-a3fb-52770b736859",
   "metadata": {},
   "source": [
    "The lumped ports can be viewed using the `plot_sim()` method of the TCM. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58735651-7f8e-4c20-aa32-55ca4035c21b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot lumped ports\n",
    "fig, ax = plt.subplots(1, 2, figsize=(8, 6), tight_layout=True)\n",
    "tcm_without_via_fence.plot_sim(y=t / 2, ax=ax[0])\n",
    "ax[0].set_xlim(-1000, 1000)\n",
    "ax[0].set_ylim(L / 2 - 1000, L / 2 + 1000)\n",
    "tcm_without_via_fence.plot_sim(y=t / 2, ax=ax[1])\n",
    "ax[1].set_xlim(-1000, 1000)\n",
    "ax[1].set_ylim(-L / 2 - 1000, -L / 2 + 1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89ffbae-5a4e-4307-b6e6-40a30d6e41f2",
   "metadata": {},
   "source": [
    "## Conventional CPW with Lower Ground Plane"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be74688d-5249-431c-be5a-c3c5deb7d7f5",
   "metadata": {},
   "source": [
    "We execute the TCM to obtain the S-parameter matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b8bf78d2-11c0-4393-88b5-8a82421cbdbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:55:13 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'GCPW no via fence'</span> with resource_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-fa4cd34a-c5a7-4b52-b41d-0d69869b442b'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:55:13 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'GCPW no via fence'\u001b[0m with resource_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-fa4cd34a-c5a7-4b52-b41d-0d69869b442b'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f5-d8111a44e5e4'</span></a>.                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=789947;https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=459818;https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=789947;https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=377988;https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=789947;https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\u001b\\\u001b[32m-68ef49c8-3189-477b-9a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=789947;https://tidy3d.simulation.cloud/rf?taskId=pa-68ef49c8-3189-477b-9af5-d8111a44e5e4\u001b\\\u001b[32mf5-d8111a44e5e4'\u001b[0m\u001b]8;;\u001b\\.                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=107066;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54c64894863041ed929dd215f84fbbe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:55:23 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.114</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:55:23 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.114\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m after run.         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:55:24 EST </span>Subtasks status - GCPW no via fence                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-68ef49c8-3189-477b-9af5-d8111a44e5e4'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:55:24 EST\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - GCPW no via fence                                \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-68ef49c8-3189-477b-9af5-d8111a44e5e4'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b7f064326a54d70b4b12849c9d0b807",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = preprocess                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:55:30 EST </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:55:30 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:20 EST </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:20 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:35 EST </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:35 EST\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.059</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.059\u001b[0m.                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8598e7de2bb41b48376f7df9270fd4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:37 EST </span>Loading component modeler data from                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/tcm_data_without_via_fence.hdf5                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:37 EST\u001b[0m\u001b[2;36m \u001b[0mLoading component modeler data from                                \n",
       "\u001b[2;36m             \u001b[0mdata/tcm_data_without_via_fence.hdf5                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tcm_data_without_via_fence = web.run(\n",
    "    tcm_without_via_fence,\n",
    "    task_name=\"GCPW no via fence\",\n",
    "    path=\"data/tcm_data_without_via_fence.hdf5\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8bc3dcda-66e0-49dd-9fdf-2ec559ff8730",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate s-matrix\n",
    "s_matrix = tcm_data_without_via_fence.smatrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b15aa3-7226-4907-800d-338f1d6d5133",
   "metadata": {},
   "source": [
    "The S-parameters are extracted and S21 is plotted below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b839dc1-c1a2-499b-a464-a09b7f340d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "S11 = np.conjugate(s_matrix.data.sel(port_in=\"LP1\", port_out=\"LP1\"))\n",
    "S21 = np.conjugate(s_matrix.data.sel(port_in=\"LP1\", port_out=\"LP2\"))\n",
    "S11dB = 20 * np.log10(np.abs(S11))\n",
    "S21dB = 20 * np.log10(np.abs(S21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3a2faa1-596d-48fd-8e41-74b4b1a4e4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting S-parameters\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.plot(freqs / 1e9, S21dB)\n",
    "ax.set_xlim(0, 100)\n",
    "ax.set_ylim(-30, 1)\n",
    "ax.set_title(\"Insertion loss\")\n",
    "ax.set_ylabel(\"|S21| (dB)\")\n",
    "ax.set_xlabel(\"f (GHz)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837b56a4-186c-407c-bdaf-2c5f4f858dd5",
   "metadata": {},
   "source": [
    "At frequencies above 40 GHz, higher-order resonances in the CPW limit its usable bandwidth. The resonant frequencies are partly determined by the width and length of the side ground planes. We visualize one such resonance near f=71 GHz below. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44718866-ab05-4add-a9b7-5d29dccf4e4c",
   "metadata": {},
   "source": [
    "In order to view the field monitor data, we load it from the dictionary object associated with the TCM data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "92643e6c-67bd-4f84-8f4e-0604b173419b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load monitor data\n",
    "sim_data_without_via_fence = tcm_data_without_via_fence.data[\"LP1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad953377-2f21-42b7-9df9-823d59e20bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Field plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sim_without_via_fence.plot_structures(y=0, ax=ax, fill=False)\n",
    "sim_data_without_via_fence.plot_field(\n",
    "    \"field (substrate plane)\", field_name=\"E\", val=\"abs\", f=71e9, ax=ax, eps_alpha=0.2\n",
    ")\n",
    "ax.set_title(\"Field magnitude in substrate plane (f=71 GHz)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a87a3592-6697-49a7-ac0e-47056796bd7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Field plot\n",
    "fig, ax = plt.subplots(figsize=(12, 3))\n",
    "sim_without_via_fence.plot_structures(z=0, ax=ax, fill=False)\n",
    "sim_data_without_via_fence.plot_field(\"transverse field\", field_name=\"E\", val=\"abs\", f=71e9, ax=ax)\n",
    "ax.set_title(\"Field magnitude at z=0 (f=71 GHz)\")\n",
    "ax.set_ylim(-100, 500)\n",
    "ax.set_xlim(-2000, 2000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5f4a3b-f2e5-46ba-8c97-63b8186702f7",
   "metadata": {},
   "source": [
    "The higher-order mode in the side ground planes is clearly visible from the field plots above. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7805dbdc-14eb-4dcc-b5db-a7eddf4f3bc8",
   "metadata": {},
   "source": [
    "## Adding Via Fences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be69a6b-caaa-49a1-bac8-a8d9fce96326",
   "metadata": {},
   "source": [
    "In order to restrict the higher-order modes, we will add via fences on both sides of the signal strip. \n",
    "\n",
    "We make use of the `updated_copy()` method to easily modify the structure list, without having to redefine from scratch the `Simulation` or `TerminalComponentModeler` objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f940c2f9-85dd-438b-9e33-1380c14b5880",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_via_fence = sim_without_via_fence.updated_copy(structures=str_list_2)\n",
    "tcm_via_fence = tcm_without_via_fence.updated_copy(simulation=sim_via_fence)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dfa8e0f-e63d-4ed7-a476-68caf49d52d1",
   "metadata": {},
   "source": [
    "Executing the simulation below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ac23319-d05f-4978-9bf4-594c3c7319ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:39 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'GCPW via fence'</span> with resource_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-bb0dde4d-4cab-41d1-b83b-9f8df3247562'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:39 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'GCPW via fence'\u001b[0m with resource_id                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-bb0dde4d-4cab-41d1-b83b-9f8df3247562'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">83-af58d3322f61'</span></a>.                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=368047;https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=986314;https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=368047;https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=216752;https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=368047;https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\u001b\\\u001b[32m-883eaf17-23ac-49c8-bd\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=368047;https://tidy3d.simulation.cloud/rf?taskId=pa-883eaf17-23ac-49c8-bd83-af58d3322f61\u001b\\\u001b[32m83-af58d3322f61'\u001b[0m\u001b]8;;\u001b\\.                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=825540;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "378808409b53430ba97e0f4049dfa9eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:46 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.143</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:46 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.143\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m after run.         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:47 EST </span>Subtasks status - GCPW via fence                                   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-883eaf17-23ac-49c8-bd83-af58d3322f61'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:47 EST\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - GCPW via fence                                   \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-883eaf17-23ac-49c8-bd83-af58d3322f61'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7e99958d530499b81d4495a1abe01ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = preprocess                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:56:54 EST </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:56:54 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:57:41 EST </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:57:41 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:57:56 EST </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:57:56 EST\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.063</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.063\u001b[0m.                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbac341f97084e7196ed1c629f84f848",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:57:59 EST </span>Loading component modeler data from data/tcm_data_via_fence.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:57:59 EST\u001b[0m\u001b[2;36m \u001b[0mLoading component modeler data from data/tcm_data_via_fence.hdf5   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tcm_data_via_fence = web.run(\n",
    "    tcm_via_fence, task_name=\"GCPW via fence\", path=\"data/tcm_data_via_fence.hdf5\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f63cf0c7-07ef-4341-b45c-63506a1eccef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate s-matrix\n",
    "s_matrix_vf = tcm_data_via_fence.smatrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f3d5f3-6669-45ae-ae91-c09c51b1fc26",
   "metadata": {},
   "source": [
    "Like before, we extract and plot the insertion loss. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cdb5d265-a922-4af7-afb9-80d59eed8e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting S-parameters\n",
    "S11_vf = np.conjugate(s_matrix_vf.data.sel(port_in=\"LP1\", port_out=\"LP1\"))\n",
    "S21_vf = np.conjugate(s_matrix_vf.data.sel(port_in=\"LP1\", port_out=\"LP2\"))\n",
    "S11dB_vf = 20 * np.log10(np.abs(S11_vf))\n",
    "S21dB_vf = 20 * np.log10(np.abs(S21_vf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ff21ad10-af3d-4a7d-b960-69cc9b1bcb69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting S-parameters\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.plot(freqs / 1e9, S21dB_vf, label=\"with via fence\")\n",
    "ax.plot(freqs / 1e9, S21dB, label=\"without via fence\")\n",
    "ax.set_title(\"Insertion loss\")\n",
    "ax.set_xlabel(\"f (GHz)\")\n",
    "ax.set_ylabel(\"|S21| (dB)\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c112b03-a417-4962-b50a-87a38efc20ec",
   "metadata": {},
   "source": [
    "The addition of the via fences has suppressed the higher-order resonances in the side ground planes. This allows the CPW to have a much wider operational bandwidth. Let's compare the fields at the same resonance point below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa86dfca-1099-48a0-aa42-d80147f805cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load monitor data\n",
    "sim_data_via_fence = tcm_data_via_fence.data[\"LP1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "88102649-2095-44cb-b298-e4472372aa76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Field plot\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5), tight_layout=True)\n",
    "sim_data_without_via_fence.plot_field(\n",
    "    \"field (substrate plane)\", field_name=\"E\", val=\"abs\", f=71e9, ax=ax[0], eps_alpha=0.2\n",
    ")\n",
    "sim_data_via_fence.plot_field(\n",
    "    \"field (substrate plane)\", field_name=\"E\", val=\"abs\", f=71e9, ax=ax[1], eps_alpha=0.2\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d1062a-c532-42b8-a863-7db88d61cb64",
   "metadata": {},
   "source": [
    "With the via fence, the field is very well confined to the region close to the signal line. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7aa981c-18b2-4969-a4c9-33a9c892e8b9",
   "metadata": {},
   "source": [
    "## Experimental Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79f234e-f0ca-4299-bfee-4d20b056191b",
   "metadata": {},
   "source": [
    "For validation, Sain et al. fabricated a design shown in Fig. 9 in the reference paper. They used HFSS, a commercial FEM tool, to compare with the measured data. In this section, we will do the same with our RF solver. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a297eb9c-f310-40f6-ad44-bac657bc74a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geometry parameters for experimental setup\n",
    "wg2 = 1200\n",
    "vl2 = 1212.7\n",
    "vp2 = 508\n",
    "es2 = 460\n",
    "\n",
    "# Create new side ground planes\n",
    "str_gnd4 = td.Structure(\n",
    "    geometry=td.Box(center=((ws + wg2) / 2 + gg, t / 2, 0), size=(wg2, t, L)), medium=med_metal\n",
    ")\n",
    "str_gnd5 = td.Structure(\n",
    "    geometry=td.Box(center=(-(ws + wg2) / 2 - gg, t / 2, 0), size=(wg2, t, L)), medium=med_metal\n",
    ")\n",
    "\n",
    "# Create via fence\n",
    "str_via_fence_2 = []\n",
    "for zpos in np.arange(-L / 2 + es2, L / 2 - es2 + 10, vp2):\n",
    "    for xpos in [-vl2, vl2]:\n",
    "        str_via_fence_2 += [create_via(xpos, zpos)]\n",
    "\n",
    "# Structure list\n",
    "str_list_3 = [str_sub, str_gnd4, str_gnd5, str_gnd3, str_sig] + str_via_fence_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b56a30ec-6d8d-4e9d-893d-799d471ea9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update simulation and TCM\n",
    "freqs_exp = np.linspace(1e9, 40e9, 301)\n",
    "sim_exp = sim_without_via_fence.updated_copy(\n",
    "    structures=str_list_3,\n",
    "    monitors=[],\n",
    ")\n",
    "tcm_exp = tcm_without_via_fence.updated_copy(simulation=sim_exp, freqs=freqs_exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c68a8b4-e9ee-4960-a4fd-1bb54278af88",
   "metadata": {},
   "source": [
    "Let's example the setup in 3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "10e9c895-0a78-4667-a480-f71294264b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_exp.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af715ed-bd29-4080-9c91-a51cc97b8890",
   "metadata": {},
   "source": [
    "We run the simulation below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de46e76a-9399-431f-ae36-06d1c59d1d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:58:01 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'GCPW experiment'</span> with resource_id                    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-211f5cd1-5dd0-4c27-9e49-dd131062ec63'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:58:01 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'GCPW experiment'\u001b[0m with resource_id                    \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-211f5cd1-5dd0-4c27-9e49-dd131062ec63'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-95</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">69-0c90b150c2d1'</span></a>.                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=315501;https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=394825;https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=315501;https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=290683;https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=315501;https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\u001b\\\u001b[32m-6258e65e-90e5-4e34-95\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=315501;https://tidy3d.simulation.cloud/rf?taskId=pa-6258e65e-90e5-4e34-9569-0c90b150c2d1\u001b\\\u001b[32m69-0c90b150c2d1'\u001b[0m\u001b]8;;\u001b\\.                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=771149;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d17c3a57b3d4cfea93562dcb5e615b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:58:08 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.129</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:58:08 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.129\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m after run.         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:58:09 EST </span>Subtasks status - GCPW experiment                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-6258e65e-90e5-4e34-9569-0c90b150c2d1'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:58:09 EST\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - GCPW experiment                                  \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-6258e65e-90e5-4e34-9569-0c90b150c2d1'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f258919ae1ef48618de3dbc1c2014061",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = preprocess                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:58:16 EST </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:58:16 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:58:54 EST </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:58:54 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:59:05 EST </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:59:05 EST\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.050</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.050\u001b[0m.                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "888b238bcea94c7380c66a3f512eb391",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:59:06 EST </span>Loading component modeler data from data/tcm_data_exp.hdf5         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:59:06 EST\u001b[0m\u001b[2;36m \u001b[0mLoading component modeler data from data/tcm_data_exp.hdf5         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tcm_data_exp = web.run(tcm_exp, task_name=\"GCPW experiment\", path=\"data/tcm_data_exp.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "40bfb555-055b-4f7d-92aa-4bf8ff317fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate s-matrix\n",
    "s_matrix_exp = tcm_data_exp.smatrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1558b082-1c74-493f-b868-2c13108759f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract S-parameters\n",
    "S11_exp = np.conjugate(s_matrix_exp.data.sel(port_in=\"LP1\", port_out=\"LP1\"))\n",
    "S21_exp = np.conjugate(s_matrix_exp.data.sel(port_in=\"LP1\", port_out=\"LP2\"))\n",
    "S11dB_exp = 20 * np.log10(np.abs(S11_exp))\n",
    "S21dB_exp = 20 * np.log10(np.abs(S21_exp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "897cc1b2-476f-4a9d-b2cb-be75234725a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import reference data\n",
    "freqs_sain_measured, S21dB_sain_measured = np.loadtxt(\n",
    "    \"./misc/gcpw_sain_experimental.csv\", delimiter=\",\", unpack=True, skiprows=1\n",
    ")\n",
    "freqs_sain_hfss, S21dB_sain_hfss = np.loadtxt(\n",
    "    \"./misc/gcpw_sain_simulated.csv\", delimiter=\",\", unpack=True, skiprows=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f27cde4d-b4d2-4e6d-a1a5-51a48c49afd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XLrwf/HUo5KnVuFWVA2o5A3ThRCp+R7X14MKjtS1SbbhRSfcUM0QXSbwuACN6DCi4UfEcSaoU0NqudecxXBdNL10IugY+bBR7bKjAUncWw3RR9NOH1dsOoFp1ogI/uQ6SogtngD6iwbZu1Zt4fe/KoKcumCH6qAbb+pLHBc4M4nUBpOqjG2zrUN2YFD0/Og8SofNjpD6mwbY21YWfYuBnVyb+GBlniDvuawtQjCx3ZWFEzwRDfINtq1QHgYqJ31yHcaMyyfD7h29V9f4uVLxJpA0nwYqZ1a5cqnAwXh+PgoK/YsCjqpRTg1tVceHBhJ5wnYX1rjxy1Wr66ELRo5CsD8ehutnoLsB95H3RTxdGvC6QTe4C9nvKMKPHHwNnG3vgVD0scmVoMQ3RR5KkC2Gru5DdnlLM6DGjZ4oxCVVVayX5A3UR9NaHst1dzA5PCRZFj1HRM1XvTQR+JBMXKigKKWoovQkmQ6lin6HySOKncKbD+ztZZSnGo/MmbYlOC90dfuQbHWT613jbKjqGlfqhQ2FnaA1ug7dthE1HbJWecotKQSjoFB2KotAj14NehZxYAx6Td1tgpYeQEid2fz2VsX5HzqsQerASnUulqnsQ+BlRFB3G8hpMuZV4Ak24k8K1trrdBVDjQukTjT7ID0VRUMusuA4UoAu04Nc/QYu3ettB3NYagvp3xxwehKIoOEsrKdt+CGOgHzEjU7z/HyoK+Zt2U1NeRcyQvgRFh3t7i0srydqwHVOAH/0mjtSS8Iy1W6ksKiUpdQBR3eMBBWtZBdtXrMXkZ2H0+WceSZZhx6qNFOXkkTxiKN379vTGVVHBygVLMJpMnDVzmvb73LpqHTkHs0gZPoSe/fsCYK+ysuybRej0OqZecYm3oaKwbc1GsvYdoN/QgfQZPAAAp72GxZ9/C8D5V8/U4t2+fjMZu/bSe2AK/YcPQUHB7XKx8KMvADjvikswHvmiY+fmbezdtoOeKX0ZPHqEFtu3730MwJTLpmPx9yZ++7fvZteWdLr37smQMb+3/fnrBbjdbiaedw6BQYHoFIVD+zLYtTWd+MQEho8d7f1bqSgsWfADDoeDyeecRUhoKDpFITPjEFs2byYuPp5xE8Z7f++KwuIff8JabeWMM84gIjICnaIjOzuLDes3EB0dzemnn67FsHTpUiorKxk/fjxRUd6/Z/n5+WzYsIGwsDDGjx+vtV2zZg1VVVUMHTpUa1tRUcGePXsICAigf//+Wtvc3FwtWfYlpS6XC6vVisFg0BJo398XSRyFEJ1Rp02CjtXYF9qeuD3ej7a+RMmjeo5KmrzPf//Zm0h5au2vb5vnqPPV3uaus+94x/6+z6PFCfB7exUV1ePd71FVVI8Hj+pB0etB8f7j6nI48bjc2rAsLRk8cm6Dvxl0Ch5VxW1z4LI7tGugXRM8qoo+wAx6nbdtjQPV7vReFw+q6u11UH2xmQygU1B1oDrdqDUucHtQPR5UBVS9DlTvcR4FVB14DDoUpxtdtdPbG+XyJl2qAopHBY9Kjb8el0HBZdZhqnYRVOJE8XiT0GM/RpQEgd2kUGNU8bNDbFnD74V8PxfVRg82vYqfE3pVNdyDl22yU2xwUqO48XPrGWwParDtPl0lh/VWnKqHQNXAWHfDieMeytijluNW3QRg5GxdYsNt3aXs8ZTixIMJHRcYezXYNsNdzj61DLvqxoPKdGPvetu5VA+5nioy1SqqVAcVRxI8Fx78MKCiUqjacOLBqXowKAoWDJSodnLUavww4MTNUH0UOhTS3IU48f4OE5RAeupCKFCt7PaUatc8TR+PAR3r3HlY8fY0xisB9NOFUaja2O4p1tqO08dhRs8Gdz5VOAGIUfzprwunRLWz1VOktR2tj8UfA5vdBZTjACBK8WOgLoIytYY0T+FRbWMIxESap5AS1Q5ApGIhVR9NhepgrTtPaztGH0uoYmazu5AC1dtDEq5YGKuPpUp18ov7sNZ2rD6WKMWfTe4CDqtVAIRiZpIhAStOFrsya7WNUwJJcxeQoVYAEIyJcw09cODm26OS2jH6WHrogklzF7DHUwaAPwYuNPbCpXr40rVPaztCF01vfSjb3EXs9JQAYELHDGMfAD517tHa+r4w2ekuYduRe6lH4VKjN9H60rnXmywDg3UR9NdHsMddWuteXm7sB8A3zv3U4Aagvy6cwfpIDnjK2eDO19peYuiDQdHxvfOA9rvvqwslVR/NIU9Frft+kaEXFsXAD86DVBz5ffZSQhhpiOGwp4qV7hyt7QWGngQoRpa4MrXfZ3cliLGGOPI9Vn7z5MCRhOksJYFgxcQq8ihVHCiKQgx+jPBEUqY42GAsQQH0Oj0jHGEEeQzsDbBjsygY9XqCXQa6lYDTqFCa6I9Rb8Co0+OfVYnO6oQeEZgigzDqDChWJ1XbD6G3mOg2brC3rV7P4U27qCwsoefwgcT37I5RZ8BeVU3aitX4+ftx9kUXYDYYMRtNbFqznpzDhxk7ZozW+1pZWcnChQsxm821RnJs3ryZ7OxsBgwYQO/e3v/nXS4XaWlp+Pv7k5KSUmvYrSRiQojm1iGSoJMZDnesjpgECeFz9IcAj8eD0+n0JpdHHr7kzuPx4Ofnpw3HqqmpoaSkRGvj8XhwuVw4nU6cTicxMTHat8ZlZWWsX79e23fsY/jw4aSmpgLe4WPz5s1rMN5Ro0Zx2sTTtfPOe/udBtsOHDyICZMmoqoqVdVVfPTu/2q97qP1TunHyNPGoaoe7HY7337wWYPnje2RwJDxowEVl9vN0k+/1fbpDQb0RsOR/+oJiYqgW//e2n08vPsAOr3Om7DrdHCktw1U9CYTfuHBWqJekVt4ZD/eJF49MvRUp0NnMqAP9NO+RHB7jiTONPxlhS/JP/bLB1Wlni8xvFEdfQ7tnMf5AqXeL1M4dvtR5zzy2o79kqU+9f9T0VDb+n93aj3tG/onqN6tHlX70kFVfm9oOLLV6Rt/CxhUBYOq4FZUHMrvw3gD3d7hv5U4vT3fqorZo+CHAbvqppKaI/cCIlUzigqFqhWH6v1SKAAjYYoZGy6KjyQ7AN2UQAwo5KrVOI4k4YEYiVb8seEiV63W2nZXgjArerI9VdiOJGKBGOmmC8SmushUK7W2vZQQ/BUDBz0VWhIeipkUfTg21cmWo5LwUfoYwhVLrWQ5UvFjkr4bVTj50XVIaztR341YXQBrXLna9cIUM+cYelCtOllwVA/weH0cCbogNrkLyDXYsZjMhOv9SLUG41ZUchPN+BnN+JnM+OdZodSKf48YInp2w2I0oXer7Fq6FkVROPeKi/E3W/Azmti8Zj37du1h/PjxjB07FvAmTL/88gv+/v6MHj1aS5hsNhuqqmI2m9Hrjz+EWwjRtXWIJAi8hRFGjx7NSy+9BHg/CHbv3p3bb7+93sIIx5IkSIiOye1243A4cDgc6PV6bf6J0+lk+/bt2r6amppaP/fo0UObY2S323nxxRcb/CCdkpLC9OnTAe/flmeffbbBeHr37s3MmTO158899xxut7vetomJiVx11VXa81deeQWXy4XFYsFsNtf6b0REBKNHj9baZmZmotPptP1+fn7a3CjR/h2dgAK1ktE6ySa1E09vwttwEnpsouz2uHG53TjdLlweN84jP3sfbly+nz2/b3f5tnuOaetyHtnm3e52eR8O1YPD4zqyzY3RqeL0uCn22KhxObA7HYRixk8xUK7WaL1nRnTEKwF4gKyjkrZYxZ9gxUShaqNUPVKIBQND9VEowGp3rtZ2rD6W7rpgtniKyDPVEGj2I8zgz4ByCypgTQ4n0OJPoNkPd3YJ1pxiYvsm0XfoAALNfpgVA7/++DN+ZgtXXnmlljBlZGRQUFBAt27dtPl9qqpSWFiI2WwmODhYeqCE6MQamxu0+b+8d911F9dffz0jR45k9OjR/Oc//6G6upobbrihrUMTQrQgvV6Pn59frUp1AEajkWHDhjXqHBaLhXvvvReXy1UrYfL99+g5EB6Ph9TUVG2/y+Wqda6YmNpzwpKSkuokQaqqanMujmaz2XC73djtdo6VmJhYKwn69ttvqa6urtXGYDBgsViIj4/n4osv1ravXbsWVVWxWCxa9T7fXBqLxULPnj21tpmZmbhcLq0i4NFVEo1GY63X5+tFPLqNwWDQih74PkyKuhRFQa8o6Nt+hYlWoaoqDreLGqc3IbIfSYysDjtWRw0233+dNdgcNUd+9m7z7bc5a6h2OrA5HXRzRGF3ettvdBSS7i7GgRuHzUO5rZoi9Oh0Yd6hrem/D5scpY+hpy6EH3at48UdPwG/D8d0qx4m/OsWgvwCCLb408cRSJhNwRHhR1BSLGH+QQSb/Njzs3eI/RU3XkdkcCj+Jgvr1q1j+/btDBkyhJEjR2qv+bfffsNsNpOamqoV4aiurtb+rvgKiXRkTqeTnJwcXC6XNnQRYPv27SQkJBASEtKG0QnR8to8CfrDH/5AYWEhjzzyCHl5eQwbNowffvihzgcSIYSoj+9DvtForFWh7FgGg4Fzzz230ec9ulfoeFRVZc6cOdjtdmpqaur899iYQkNDMRqNWhtVVXG5XFRVVWmV1nzWr19fJ2HyiYmJqZUELVy4kPLy8nrbhoeHM3v2bO35V199RVFRUb1tAwICuP3227Xn33//PSUlJVr1NqPRqCVkZrOZM888U2u7ZcsWKioq0Ol02sOXlBkMhlrJ7aFDh6iurq6ViB2dvPXq1Uv7tr6oqAibzVZvO51OR2RkpNbWarXidDrrXN/3s8FgaFe9AG63u9bw1KOHtTqdThITE2tVJSwtLdXe78c+WiJ5VRTFOzfIYCTYLwCHw0F1dTVOxYxT58RlcOE0HonX5I3XV4GwsLCQ9PR0XC6XlqDHx8eTmJhIaGgoAFaHnaoam/dhP/LfGitVdhsTfdtrbFTZreTZrQTWRNPPEUSV3YbVbuNXx2F0KDhUF8VV5RRXleNRgojW+XM4/zA5eVsAsKDnXEMP9Chc8sbDgLf4xShjHD3UQD5Y9SNv7lxGkMWfIKMZy27vvMC97jKC/QMIsviTv/sgB3fuYdCQwZw3dSo6RYeqqrzzzjuYTCYuuugi7T1WWFhITk4OcXFxJCUlaffT1xt19JIPrcVut3P48GGysrLIysoiLy8Pj8dDVFSUlgSVlZWxcOFCAIYOHcq4ceMICmp4/qkQHVmbJ0EAt99+e61/dIUQoqPwlZI+upz08VxzzTXaz76eJZvNht1ur/PhfNCgQVRXV2O323G73b8XAFHVWqWuASIjI7FYLLXmkvkexw4H8A3D880n8yViqqrWKj0N3g9tDS1e7e/vXysJSk9PJzs7u962x/bwrV27loyMjHrbAtx///3az7/++it79uxpsO1dd92lxb1s2TLS09MbbHvHHXdoPYRLlixhy5Yt9SZLOp2Oq6++Wrt3vh6DY9v6nk+dOlX75nznzp3s2bNHu6/HJjkzZ84kIsJbEXP16tWsXLmywXivvfZa4uO9FSb37NnD0qVLG2x7+eWXa4nxnj17WLNmTa0kyfd+cLvdnHbaacTGxgKwd+9eVq5cicfjwe12a3MSfQnatGnT6NOnj9b2+++/bzCGCy+8UHtvlpWVsW7dulr7t2zxJiWBgYGcffbZJCcnE2D2I8zhwBXgwmAwYDQaG52oelQP1TV2Ku1WKuzVVNisVNqrqbBbKbdVUVxWRmV5OdU2KwU2G/YaOwOJRPGoGNFzyFHGQaWM6gonoZVmovWRuFFwoEMF0pavwn2kcqsTD92UQD7ZvJw3Nv/IMEM0RvSYjkxQe/3117W4AiJCqS4uI7FvLyrwLqkQGxXDx+9/gKqq3HrrrVpysXXrVtLS0khOTtaG+gJs2rQJg8FAcnKy1vPkmzdqMpmalMx/8cUX7Nu3r872wMBAoqKi8Hg82vqAiYmJHDp0iM2bN7N161aGDRvG2LFjG/03ToiOol0kQUII0RUpioLZbG5waM3kyZMbfa7G9lwBXH311XW2+T4cHztM8Nxzz8Vms+FwOLQP8r4PysdOUE9OTiY6OrpWcQ/f49i2MTExdYp/+H4+VlBQEOHh4XXa+R5Hfxj0fRPvu+6xjv723ddD0RiVlZUNJoO+c/kUFhaya9euBts6HA7tZ1/ydnSPptFo1JKBo++bxWIhKiqqVlJ19LmOnltWUVFBbu7v82+O5SuGAt4egvz8/AbbHhuvr0fQF+PRMR/d8xkWFsbo0aO1dZ0cDgdZWVnk5uZSVVVVayjs1q1b+fnnn7Xner1eO7/BYOCCCy7Q5vccOHCAtLQ0dDqdNrzV93A4HFx66aV0798d8CYSi7ctxnslo/ehAEdu66xzpxMcG0ml3UbWgQwObd5Z67Wbld9j3OtnY6krjzJrFd2UQALUuh+h3KqKDlhXsA8/xcjqnSv5dOdvjDuyVIKqeot3PPba8zhNCpgNhKhGAis9lKh28swOooLCCPULZMmSJaiqSlxiNyKPJD2bNm1i+fLlDBo0iAsuuEC77sKFC3G73cTHx1NQUEBBQQEXX3wxNTU1BAYGar2JYWFhJCYmkpCQQGJiIiEhIbX+/4mMjOSKK64gMzOTX3/9lezsbDZu3MiWLVtITU1l/Pjx2rmE6OjavDDCqZLCCEIIIRriS5x8j6O/QfcNWTy6zdE/R0dHa0lIcXEx5eXltRKxo4/p06ePlswePnyY3NxcLbE5NlmIiorSFob1zTvz9SidzOvz9TYdXTmtvLycwsLCWsnr0T1YPXv21P7NrKiooLCwUBvmqNPp0Ov1WvISEBCgxdscfHNRunXrpiVua9asYcWKFQ0ec+WVV9K9uzex2bhxI0uWLGmw7SWXXELfvt4S67t372bFihXalw0mk6nWz/3799cqaVqtVkpLS1EURbunvkTZ5XLRo0cPQkNDcbpd5BYXcOhwNtWuGiodNsprbJQ7rVT7hvU5bFQ57FTZrfjbPHR3+hOIEb1SdwjcBlcedtxYVRfBionB+kgcuLEc+Z46T63GgRsXKv46Iz0J5pBSxSGDlRjVQpjHRJTHfGR1v99F90qk4EAWvfv24cwzzsBkNBEYGMjnn3+OTqfjnHPO0XqjSkpKKCkpITQ0lIiICFweN3pFpyVDOTk5mEwm5syZU2cepxDtTYepDneqJAkSQgghOj5fqX/f4+gkJDIyUksyCwsLOXz4MG63u1ZS43sEBgbWGdbZHtQ4HeQVFpBbkE9hUSGlJaVUllcQOSCJcred/IpSrIeLCC2rvyolwApXNkWqDQVIVIIYZYjV9qmqSrFqp0i1UajaCFFM9NOFcchTwXZKCQ8Ixt9oYVRlIAoKmwIqqPI4cLpddHNaSFFDOeSpZO2RCn56RccFhp7oFYXqMDNhAUF079uLQKeCvagci97I2WedrfUM5eTkoOh0qCY9BdVl5JQVk1teRH5FCTUuJ26PhwCzhW6hUSRFxpGa2Jcgi399L1OIUyJJkBBCCCFEB2K1WikvL8dut2Oz2bDZbVRVV1NZXYXdZmdA6hACg4Nwe9zs2bGL7IxDhEVFYg4NxGnSUVpTTXF1OYWVZRRUlpJfXkJhVZm2/peCd00rs6LngKdcW4+rpxJML10oOWqVtsAxwExDX3SKwrfO/diPLEScrAtjqN7be+bEQ7bBRp6xhhG2UEzoai0uHK8EkKILJ1etrnVeM3pcisqgbr2Y1G8YZ6aMIDE8uuVvsOgSJAkSQgghhOjiXB43RZXllForqK6xY3c5MOoMmAwGjHoDJoMRo95w5KHHqDdg0OmxOWrIKy4gv7SYMk8NOeVFHC4rpLSgGL9KJ2GqmQDF2+NWo7q1xZB/9mQTGhxMXEgkiU4LxkIrfjFhhKf0oNxWTXZpAQH7StF5YLErU0uYBkd2Z1RsH8YPTGVonxR09QwdFKIxJAkSQgghhBDNTlVVSq2VbNm2lR2bt1JV4V0w12yxMHToUE4/7TQMBgPl5eXk5eURGBhIt27dAO+csP/85z94PB6SJg9nxb6tbDi4i35KKIP1kRR6bKzW5dM7qhsRAcEEmP3wN1kIMFsI9QtkbK+B9ItJbFel7kX7IkmQEEIIIYRoUR6Ph507d7Jy5UpKS0sJDw/nj3/843GTFI/HQ2VlpVZWvtRayTc//0DWvgw21eSS6aoAQAckKEFkq5UcXeuxW2gUN512AdOHnd6Cr0x0VJIECSGEEEKIVuHxeNi3bx+KomjV+ZxOJ59++ikDBw5kwIABjaoy6PZ4yCot4EDhYTL3ZZC/fT/6QAtK3xgOlxWy5sAOalzeIXRzJs3gj6ddKL1CopbG5gayTpAQQgghhDglOp2Ofv361dq2Y8cOsrOzyc7OZvny5QwePJjhw4fXWez5aHqdjqSIWJIiYkl3mVlxKJ+RI0dqC8naHDXMW7WQt3/7ntdXfE2lvZq/nv0HSYREk0lPkBBCCCGEaHZ2u51t27axefNmSktLte29evUiKSmJ1NRUba0ou92OTqfDaDTWSmhcLheqqmplz4uKiti4cSPFITr+veRjAF684k4m9Bnciq9MtGcyHE4IIYQQQrQ5VVU5cOAAmzZt4sCBA9r2e+65R1vg97vvvmPHjh3o9Xr8/Pzw9/fHz89P+3nSpEkoisJ///tfqqqqGDp0KNvdxXy8eRl9YxN5/6a/S0U5AchwOCGEEEII0Q4oikLv3r3p3bs3paWlbNu2jcrKSi0BAqipqQHA7XZTVVVFVVVVrXOceeaZ6PV6zjnnHH744Qe2bNkCwExjX8qLavho4ddcetb52uKtQpyI9AQJIYQQQog2paoqTqfTu0iszYbVatV+rqmpYcKECVrbRYsWsX//fmw2Gx7P73XjDAYDycnJnHfeebUSLNG1SE+QEEIIIYToEBRFwWQyYTKZtNLZDTnvvPMAb+KUlr6VNxd9TrDHQJjLQmlpaa0EyOVyafOOhDiaDJ4UQgghhBAdTlZWFkt++IkeSjBrXHkUxpo444wztP02m41XXnmFpUuX1uoxEgIkCRJCCCGEEB1QfHw8CQkJJCZ1pxoHK3N2ER4dqe3fu3cvdrud9evX89VXX+F0OtswWtHeSBIkhBBCCCE6HIPBwMyZM7ni0suIC43E4Xax5sAObf/gwYOZPn06BoOBffv28emnn2K329swYtGeSBIkhBBCCCE6JKPRiE6nY1K/VAB+2b1Z26coCikpKVx++eWYzWays7P54IMPqKysbKtwRTvSZknQwYMHuemmm+jZsyd+fn707t2bRx99FIfD0VYhCSGEEEKIDmhUfF9O08fj2pWLy+2utS8xMZGrrrqKwMBAioqKeP/992st3iq6pjZLgnbt2oXH4+GNN95g+/btzJ07l9dff52HHnqorUISQgghhBAd0PBeKcToAgjCyJqdW+rsj46O5pprriEsLAyj0SjrCYm2K5E9depUpk6dqj3v1asXu3fv5rXXXuO5555rq7CEEEIIIUQHE+gfgCMukOWZ2zHn7eO0QcPrtAkJCeGaa67B5XLh5+fXBlGK9qRdzQkqLy8nPDz8uG1qamqoqKio9RBCCCGEEF3bhJFjqMTByn3bGmzj7+9fawHNzZs3s2PHjgbbi86r3SRB+/bt46WXXuJPf/rTcds9/fTThISEaI/ExMRWilAIIYQQQrRXw7v3AyCjKJfqGtsJ22dnZ/PTTz/x3XffsWHDhpYOT7QzzZ4EPfDAAyiKctzHrl27ah1z+PBhpk6dymWXXcbs2bOPe/4HH3yQ8vJy7ZGVldXcL0EIIYQQQnQwEYEhJAVEMkQXwU/Ll56wfbdu3RgxYgQAP//8syRCXUyzzwm6++67mTVr1nHb9OrVS/s5JyeHM844g/Hjx/Pf//73hOc3m82YzeZTDVMIIYQQQnQy/UJiCXU42L9rD+q556MoSoNtFUXhrLPOwmKxsHLlSpYuXUpUVBQ9evRoxYhFW2n2JCgqKoqoqKhGtT18+DBnnHEGI0aMYN68eeh07WZ0nhBCCCGE6GB69urF5vx1xITHN6q9oihMmDCBsrIytm/fzjfffMOsWbNqzRsSnVObZR2HDx9m8uTJdO/eneeee47CwkLy8vLIy8trq5CEEEIIIUQHNjChF+vceWytyjluL9DRFEVhypQpxMTEYLPZ+PLLL3G5XC0cqWhrbVYie/Hixezbt499+/aRkJBQa5+qqm0UlRBCCCGE6Kj6x3mHsmUW51NVYyPQ3LhS2EajkYsvvpj33nuP3r17o9frWzJM0Q60WU/QrFmzUFW13ocQQgghhBBNFR4QTExQOP4Y+GX96iZ9rgwJCWH27Nmcfvrpje5FEh2XTMIRQgghhBCdRv/Y7pxnSGLbyvVUVlY26ViLxaL97HQ6KSwsbO7wRDshSZAQQgghhOg0+scnUaLacZv12GwnXi+oPlVVVXz44Yd8/PHHVFRUNHOEoj2QJEgIIYQQQnQaA+KTWOrOYoOphJiYmJM6h9lsxuPxYLVa+frrr/F4PM0cpWhrkgQJIYQQQohOo39sEgCHSrzFEU6Gr1CC2WwmNzeXXbt2NWOEoj2QJEgIIYQQQnQaYQFBxASFAbAvP/uki26FhoYyZswYAH777TfcbnezxSjaniRBQgghhBCiU+keEcM4fRxLvviOkpKSkz7PiBEj8Pf3p7S0lG3btjVjhKKtSRIkhBBCCCE6le7hMfhhwO1wkpube9LnMZlMjB07FoBVq1bJIqqdiCRBQgghhBCiU0kIi2aLp5CqhED69et3SudKTU0lKCiIsLAwrFZrM0Uo2pqhrQMQQgghhBCiOXUPj6FYtZNhL8ZkMp3SuQwGA9dffz0BAQHNFF3nU2m3EmTxb+swmkR6goQQQgghRKeSGB4NQFZJwUkXRjiaJEANK6ws5ZLX/sYry77E6e44wwUlCRJCCCGEEJ1Kt9AoAAIdCst//eWkF009ltVqZcWKFdjt9mY5X0dnt9t5/n9vUlZdycr925ol4WwtkgQJIYQQQohOxWI0ERMcTqo+mnWr15Cfn98s5/3yyy9Zs2YNv/zyS7OcryMrKyvjlTffILRSZaQxlqcu/hMmg7Gtw2o0SYKEEEIIIUSnkxgWTY6nipC4qFOeF+QzYcIEADZv3syePXua5ZwdUWZmJu+8Ow+X1Y5NdTFx9HiSImLbOqwmkSRICCGEEEJ0Ot3DY9jiKULtHk58fHyznLNnz56MHj0agEWLFlFRUdEs5+1I0tLS+OSTT3DWOCjx2LH3DOWKyee1dVhNJkmQEEIIIYTodLTiCKUFzXreiRMnEhcXh91u59tvv8Xj8TTr+duz4uJilixZgsfjIdNTwa/kcvf5V6MoSluH1mSSBAkhhBBCiE4nIcybBGWXFuB0Optt0r5er+eiiy7CZDJx+PBhfvvtt2Y5b0cQERHB9ddfT1mYnjXuPC4YMp6ooNC2DuukSBIkhBBCCCE6ne5HeoKSCuH5559v1qFroaGhnHeedwjY9u3bcTgczXbu9q7MY+engp3oFIXrxk1p63BOmiyWKoQQQgghOp1uYd4y2S7VAwqUlpYSEhLSbOdPSUnBbreTnJzcbIUX2qs9e/YQFBREXFwc81YtAuDs/iNJDI9p48hOniRBQgghhBCi0/EzmokJCmNVZS7PXXEHPXr0aPZrDBs2TPvZ5XKhqioGg6FDzpFpSEVFBQsWLMDpdDLtkhks2bEegOvHd7xiCEdrF0lQTU0NY8aMYcuWLWzevLnWG0oIIYQQQoiTkRAezcbK3RTYyls0MVFVlW3btvHTTz8BYDAYtIfRaMTPz49rr71Wa79mzRqKi4sxm8306dOHHj16tMvESVVVFi5ciMPhID4+nn3VhbhVD72jupES2/xJZWtqF0nQfffdR3x8PFu2bGnrUIQQQgghRCfRPTyGjYd2k1nSPIulNiQ3N5fly5drz10uFy6XS3tut9trtc/IyCAzMxOAjRs3Eh0dzahRo+jfvz96vb5FY22KtLQ0Dh06hMFg4IILLuDppR8BMDk5tY0jO3VtngQtWrSIn376iS+++IJFixa1dThCCCGEEKKTiAuJwISOooxsflF+YeLEiS1ynfj4eP7yl79oyY/T6cTpdGrPjy2jnZqaSq9evSgtLWXHjh0UFBSwYMECVqxYwZgxYxg5cmSLxNkU5eXlLFu2DIBJkyYREBzE6v3bAZjcT5KgU5Kfn8/s2bP5+uuv8ff3b9QxNTU11NTUaM+74iJVQgghhBDixKKCQtGhYMyvZk3BGsaPH4/B0DIff3U6HSaTqVFFElJSUrSfJ02aRFpaGps2baKqqorS0tIWia+p1q5di9PpJCEhgREjRvDbvq3YnDVEB4XRP65jD4WDNkyCVFVl1qxZzJkzh5EjR3Lw4MFGHff000/z+OOPt2xwQgghhBCiw4sJCseOm2KjkwtHTsTtdrdYEnSy/Pz8GDduHKNGjWLnzp0kJia2dUhYrVa2bdsGwOmnn46iKKzYkwbAxH5D2+X8paZq9nWCHnjgARRFOe5j165dvPTSS1RWVvLggw826fwPPvgg5eXl2iMrK6u5X4IQQgghhOgEooNDAVjrzmPixImYzea2Deg4DAYDgwcPJjQ0FAC3282aNWtwOp2tHovRaOTMM88kJSWFxMREPKqHX/Z65+53hqFw0AI9QXfffTezZs06bptevXqxdOlSVq9eXefNOHLkSK6++mrmz59f77Fms7ldv4GFEEIIIUT7EBUUBkBVjQ2rw46/ydLGETXewoUL2bFjB7m5uUyfPh2drtn7LhpkNBpJTU0lNdWb8KQfzqC4qpwAsx8jk1JOcHTH0OxJUFRUFFFRUSds9+KLL/LEE09oz3NycpgyZQqffPIJY8aMae6whBBCCCFEFxNo9iPAZKHaYSe3rJi4wLBGz0Nva8OGDWP37t3s2bOH5cuXc+aZZ7ZZLJsz9wAwOqk/Rn37Gk54slovpTxG9+7dGTRokPbo168fAL179yYhIaGtwhJCCCGEEJ1IVFAYiUoQn897n++++66tw2m0xMREzj//fADWr1/Pxo0bW/yabrebTz75hK1bt+J2u7Xt6YcPADAkoXeLx9BaOkcqJ4QQQgghRD2ig0LJKCkDoKqqqsWv51E9lFurKaoup8pupcblxOasobS6kjJrFaXWSkqtlZgMRgbF9yS1e196RsbXe64BAwZQXl7OL7/8ws8//0xISAh9+vRpsdh37drFwYMHKSwsZMCAAdr29JwMAAbG92yxa7e2dpMEJSUloapqW4chhBBCCCE6kejgMDaou4gYncKNky9qseuUWav435of+WzDUqod9hMfAHyT9isAM4adzn1Tr8ZsMNZpM3bsWMrKyti6dSvffvstV111FbGxsc0aO3grN69duxaAESNGaFX0CivLyK8oQaconaI0tk+7SYKEEEIIIYRobtFBYXhQKa6parHSzgu3reHpRe9hdfy+lmWofyDBlgDMBiMWo4lQ/yDC/AMJ8w8i1D+ICls12w4fYOOh3Xyd9it78rN4ZuatxIVE1Dq3oiice+65VFRUkJOTg81ma5HXcODAAQoLCzGZTFpBBIAduQcB6BkZrxWWqK6uJicnh4SEBPz8/FoknpYmSZAQQgghhOi0ooJCASiobJlFSPcWZPPP7+fhcLtIju3OnyZOZ3zvQY0uILB6fzp/+/q/7Mg9yO0fzuXD2Y/W6RHS6/XMmDGDioqKRhUgOxlr1qwBYOjQoVgsv1fR880HGnTUULiMjAwWLFhAXFwc1113XYvE09LarDCCEEIIIYQQLS0mOBwAW2EZixcvJiMjo9nObXc6+NtXb+Bwu5jQZzDv3/R3JvUbVicBcjgcvPvuuyxatAiXy1Vr37jeg3j/pkeICAzhYHEu81YuqPdaZrO5VgLk8Xia7XVkZ2eTnZ2NXq9n1KhRtfZtPzIfaFC3XrW2h4eH071792aLobVJEiSEEEIIITqt6CM9QfpqJ5s2bSIrK6vZzv3iz5+xvzCHiIBgHpt2IzrF+9H68OHDbN++XWtXUFBAfn4+Bw4c0ObaANp8+PjQSO4990oA5q1cSEZRznGvu3//fv773/9SWFjYLK/D1ws0aNAggoKCtO0e1aMlQUcXRRg0aBCzZ89m4sSJzXL9tiBJkBBCCCGE6LR8C6YedJQxavRoevRonsn9uWVFfLphGQCPXXQT4QHB3u25uXz44YcsWrSIgoICACIjI5kxYwaTJk3SjrfZbHzwwQfs2LEDgLP7j+T0vkNxedz88/v5eNSGe3rS0tIoLy9nwYIFtUpZn6xRo0aRlJRUZ63OzJICqmpsmA0mekXVrWDXmgu4NjeZEySEEEIIITqt8IAg9Do9OZ4qBgwfSmxIeLOcd9H2taiojEpKYXzvQdr22NhYevbsidFoJCQkBACLxUJycnKt47ds2cLhw4cpLS2lT58+mEwm7p96NRsO7mJL9j7WHtjBuKPOe7QpU6aQnZ1Nfn4+a9asYcKECaf0Wnr06FFvcuibD5QS210b4ud2u9Hr9ad0vfag46ZvQgghhBBCnIBO0REV6E1GCpupOIKqqiza5h1Cdt6gsbX2KYrC9OnTueiiizCbzQ2eY8yYMYwYMYIrrrgCk8kEQFxIBBcN9SY0X6X90uCxgYGBnHPOOQCsWrWK/Pz8U3o9Dfl9PtDvQ+EWL17M66+/rvVgdVSSBAkhhBBCiE4tOtg7JC6vrIjy8vJTLiqwJz+LA0U5mPQGzkwZgaqq7N+/X5vjYzQaT1iOW1EUzj777FrFDlRV5eJU7zyb5bvTKK4qb/D4/v37069fPzwez0kPi1u2bBnLly+nurq63v178r3zp1Jik7Rt2dnZlJeXa4lbRyVJkBBCCCGE6NSij8wL2rRwBa+//jpVVVWndL5F6d5eoNP7DSXI4s/WrVv5/PPP+frrr7VEqKmKi4v56KOP6B4azaBuvXB73Hy/dVWD7X3rB/n5+VFYWMjKlSubdL3q6mo2btzI2rVrKSkpqbfNoeI8AHpG/r4467XXXstll11GYmJik67X3kgSJIQQQgghOjVfEoRBh16vx263n/S53B4PP2xfC/w+FM7hcKDX6+nWrdtJLciqqioLFiwgKyuLtWvXcvEwb2/QV5t/OW5SFRAQwJQpUwCorKxsUgK2adMm3G43cXFxJCQk1NlfYaum1FoJQPfwGG272WymV69exx3q1xFIYQQhhBBCCNGp+RZMregRxBOX3HxSiYrPpsw9FFaWEWTxZ0LvwYC3ulqfPn20QghNpSgKY8eOZdeuXQwZMgSTv4V/L/6IrNICNhzazaiklAaPTU5O5tprryU+vm71toY4nU42b96sxV7f/ThU4p1nFBUUSoDZr4mvqP2TniAhhBBCCNGpxRyZE1RQXXZKCRDA+gxvQYCJfYdhMhi17WFhYadUMrpfv35cdNFFhIaG4m+yMGWgt1z1T9vXnfDYpiRAANu3b8dmsxEcHFynap2Pbyhcj/Dfh8ItXryYDRs2nFJPWnshSZAQQgghhOjUfMPhCirLTvlc6Ucqpg3u1otFixZRVnbq56zPpH7DAFh9IL3Rw9wqKytZsmQJDoejwTYHDhxgxYoVAIwcObLBxE1LgiK8SVB1dTWbNm3i559/Pul5T+2JJEFCCCGEEKJTizhSIlupsrN48WK2bt16UufxqB6tbHSCJZStW7fy/vvvN2tSUFNTw4oVK8hN24NJbyC3vJiDRxKS41FVlU8//ZSNGzeyfv36BtulpaVht9uJi4tj6NChDbbzJUFJR5IgRVGYOHEiw4YNw8+v4w+PkzlBQgghhBCiUwvzDwLA4taxadMmkpOTGTJkSJPPk1lSQFWNDbPBRGJ4NP369cNkMp3yELuj2Ww21q1bh8fjYWxMX37J2cnq/en0jIw77nGKojBhwgS++eYbVq1axeHDh+nZsydJSUlERkZqMZ5//vmsW7eO8ePHYzA0nAocPKYnyN/fn3HjxjXTq2x7kgQJIYQQQohOLdDsh1FvoMhjY3DqUHr36Hnig+qRfvgAACmx3UmI70bCxRc3Z5gAhIaGMnnyZMLCwlhduI9fcnayan86V40554THJicn069fP/bs2UNGRgYZGd5eq8TERK688koURcFisTBx4sTjnsft8ZB1pDCCLwnqbCQJEkIIIYQQnZqiKIT7B5FfWUrSoBSS45NO6jy+JGhQt17NGF1do0aNAkAJ8eM/P3/Gpszd2J0OLMbjL1CqKAozZsygqKiIjIwMDh48SFZWFllZWWzfvp1BgwY16vp55cU43C5MegNxIRHU1NRQUFBAXFzccXuPOpLO8SqEEEIIIYQ4jtAAbxJUYq046XP45gMlR3SjpqamxdfK6RUZT0xQGPmVpWzK3M34IyW5j0dRFKKiooiKimL06NE4nU5KS0sJDw9v9HUPlXiHwiWGR6PX6dh38CBff/01sbGxXH/99Sf9etoTKYwghBBCCCE6vXD/YACKKsooLy/H4/E06fgal5M9+VkAqAUVvPDCC6xatarZ4/RRVZU1a9Zwui4eEzpW799+UucxGo1ER0c3qQfn2PlANTU1+Pv7N7kUd3vW5knQggULGDNmDH5+foSFhTFjxoy2DkkIIYQQQnQy4QHe4gi7l6zh9ddfp7KysknH78nLxOVxEx4QjMNqR1XVk14ctTEURWHHjh3orE5ilADWHDi5JOhkHLtG0JAhQ7j99tuZPHlyq8XQ0tp0ONwXX3zB7NmzeeqppzjzzDNxuVykp6e3ZUhCCCGEEKITCj1SIQ6jDr3bW4WtKUnMthzvfKCB8T255JJLKC8vx2KxtESomtGjR1NhreLbxfOwF1VSabcSZPFv0WtC3TWCwJuUGY3Ghg7pcNosCXK5XPzlL3/h2Wef5aabbtK2DxgwoK1CEkIIIYQQnZSvJ6g6KZTHZ9zU5LLW2w975wMNivdWlmvJXiCfwYO9c4Ai1n/D4bJCtudkMLbXwBa/7qFib2W4pE5aGQ7acDjcpk2bOHz4MDqdjtTUVOLi4jjvvPNO2BNUU1NDRUVFrYcQQgghhBDH45sTVGavOql1ffYVHgYgJa5Hs8bVGL5qdL7qdC3J5qihoLIUgO4RMaSlpfHee++xcePGFr92a2qzJOjAAe8v8bHHHuPhhx/m+++/JywsjMmTJ1NSUtLgcU8//TQhISHaIzExsbVCFkIIIYQQHVTYkZ6gk6kO51E9ZJUUAHB4y25++OEHysrKmjO8BjkcDnqbw+imBJJ+pDpdS8qr8H4ODzBZCPELpKCggNzcXKqrq1v82q2p2ZOgBx54AEVRjvvYtWuXVpHjb3/7G5deeikjRoxg3rx5KIrCZ5991uD5H3zwQcrLy7VHVlZWc78EIYQQQgjRyYQdmRPkrrCxePFi0tLSGn1sYWUZNS4HRp2BzAMH2bJly0n1Jp2Mffv2UbTtAAP1EaQfPoCqqi16vfwjSVBMsLek9qhRo5gxYwYpKSktet3W1uxzgu6++25mzZp13Da9evUiNzcXqD0HyGw206tXLzIzMxs81mw2t3hNdiGEEEII0bmEB3iHwyl2F5s2baJfv34MGzasUcf65sjEh0Rw0dkXUVhYSHBwcEuFWkvPnj0JCQ1hf0kmZdZKcsqK6BYW1WLX8yVBsSHeJCgsLIywsLAWu15bafYkyLc404mMGDECs9nM7t27Oe200wBwOp0cPHiQHj1af6ylEEIIIYTovHw9QQXuakaOPY+E+G6NPjarxJsEJUREk5ycTHJycovEWB8/Pz/m/GkO173zBGoOpOccaNEkKK+8dk9QZ9Vmc4KCg4OZM2cOjz76KD/99BO7d+/mlltuAeCyyy5rq7CEEEIIIUQn5Gcy42c0U0oN/YYOalIik1nqTYK6h8e0VHgn5KtKl364ZecFHT0czmazsWPHDm0EV2fSpoulPvvss1xxxRVce+21jBo1ikOHDrF06dJO2eUmhBBCCCHalq83qLSJxRF8RRHCnUYKCwtbfF5OfQZ160UgRtKz97fodfK0JCiMgoICvvvuO77//vsWvWZbaNMkyGg08txzz5Gfn09FRQWLFy9m4MCWr30uhBBCCCG6Hl+FuKKKMsrKynC73Y06LrMkHxM6CtP3884772C321syzHoVbNnL+caelOYX4nS7Wuw6+RXe8tixwREoikJCQgLx8fEtdr220maLpQohhBBCCNGafMURNixcxnr3z8yePZvw8OPPfXF7PGSXFmJGT3RcLIpHxc/PrzXCrSUuKobcwzkEqkb2FWTTPy6p2a+hqip5FcWAtzBC9/AYrr766ma/TnsgSZAQQgghhOgSwvwDAVCMBnS4G9Wjk19R4u150Ru47ppr0bVSaexjTZw4ka/ytrAzq4R9BYdbJAmqsFdjdzoAiA7q3NNTJAkSQgghhBBdQtiRniD6RXP31KsatdZP5pHKcN1Co9Dr2m4miZ+fH71iE1iftZv9hYdb5Bq+oXCh/oFYjKYWuUZ7IUmQEEIIIYToEsKPFEYos1c3erFTXxLUIzy6xeJqrD5R3rLeLZUE+YoixAZHAPDmm29isViYMWMGQUFBLXLNttKmhRGEEEIIIYRoLb45QaXWykYfk1VSgD8GYjLtfPrpp21SGc4nsEZhvD6O6pziFjl/Xrn3vDHBYdhsNkpKSsjJycFisbTI9dqS9AQJIYQQQoguIfRIT1BNWRU//fQTUVFRpKamHveYzJJ8whULeFRsNluje5BagsWtkKALIstRSaXdSpDFv1nP/3tluHBMJhOzZs2ioqICo9HYrNdpD6QnSAghhBBCdAnhR0pku+0ONm/ezP79J15zJ7Mkn8NqFaPOncRZZ53V0iEeV//kFPYbqtntKW2RIXFHL5Sq1+uJiYmhb9++zX6d9kCSICGEEEII0SWE+3uHwx2uKWfcuHEMGjTouO1dHjeHy4pQgQG9+pGQkNAKUTYsKioKS3w4Jaqd/QUtlwTFhhy/bHhnIEmQEEIIIYToEnyLpZZ4bAwdNZyUlJTjts8vL8HtcWPSG4gODm2FCE+sV5R34dKW6AnKK/+9J2j37t3s3LmTqqqqZr9OeyBJkBBCCCGE6BKMegOBZu9CpyXVJy6OkF9Rgh8GRpjj2b/vxEPnWkPPsFiiFD+ys7Ob9bwe1UNBpXdOUExwOKtWreLbb78lLy+vWa/TXkgSJIQQQgghugxfhbjiilLKyspwu90Nts2rKCFcsZDotLB69erWCvG4LNUuzjAk4ldc06yV6oqrKnB53OgUhaigUBISEujWrRsRERHNdo32RJIgIYQQQgjRZYQdqRC3/OtFvPHGG5SWljbYNq+iBDsuXCFm+vTp01ohHtfAvilYVScVHjsl1RXNdl7ffKCowFAMOj3nnHMO11xzDWFhYc12jfZEkiAhhBBCCNFl+OYF6UwGDAYDdru9wbb5FSUUq3bCUnowfvz41grxuLrFxrElqJp17nz2F+Y023l9C6XGdIGiCCBJkBBCCCGE6EJ8FeICBnbnrrvuOm7FN1+hgNjg9pMYKIpC7+huQPMWRyisLAMgKigUj8fTbOdtryQJEkIIIYQQXYavJ6isxnrChU/zy0swoSOmHSVBAL0jvRXiMoqaryfIN7QuIiCEJUuW8NJLL5GWltZs529vJAkSQgghhBBdhq8wQmPm01grKplh7MOWn1e2dFhNEuLQc66hB9YDzVe5rfjI/QgPCKa4uBir1YrBYGi287c3nfeVCSGEEEIIcYww/0AAbKUV/PTTT4SHhzNy5Mg67awOOzjcYIBA/4DWDvO4wgNCCFXMVNiczXbO33uCgrngknMpLS0lODi42c7f3khPkBBCCCGE6DLCjswJqrHa2Lx5M/v317/+T35FCYfVKn5Qspl+0UWtGeIJDeqXwi+uw/zmyMLpdjXLOUuO6gkym83Exsbi7+/fLOdujyQJEkIIIYQQXUb4kTlBuc5Kxo8fz5AhQ+pt5yuKEBUSRkhISKvF1xgJUTGUG1xUqU5yyoqa5ZzFVeWAtyeoK2jTJGjPnj1Mnz6dyMhIgoODOe2001i2bFlbhiSEEEIIITox35ygHFs54yaMp3///vW285WMbk+V4XwURSEhLAqArNKCUz6fqqpaT5Cr0srq1avJyso65fO2Z22aBF144YW4XC6WLl3Kxo0bGTp0KBdeeCF5ec03yUsIIYQQQgifEL9AFBRUVMqtVQ22yystYow+ltgaU7ssGZ0UEEFPJZh9GRmnfK6qGhuOI8PqSvIK+eWXX9i5c+cpn7c9a7MkqKioiL179/LAAw8wZMgQ+vbty//93/9htVpJT09v8LiamhoqKipqPYQQQgghhGgMvU5HyJFCB8VV5RQXF1NTU1OnXWFRIT10wRhKbOh07W8GSaTDyChDLPlZp75WkK8XyN9kJiG+GwMHDiQxMfGUz9uetdlvNCIiguTkZN577z2qq6txuVy88cYbREdHM2LEiAaPe/rppwkJCdEenf0XJIQQQgghmpdvwdSfv/uBt956i4MHD9ZpU2CtYKu7kKie3Vo5usaJjI4mz1NNqdt2yuf6vShCCMnJyVx44YUNDhPsLNosCVIUhSVLlrB582aCgoKwWCw8//zz/PDDD4SFhTV43IMPPkh5ebn26OzjFYUQQgghRPPyLZiq9zNiNBqx2+112uRYS9nlKWVQ6rBWjq5xBgwYwC/uw+xxFp/yuYqPKo/dVTR7EvTAAw+gKMpxH7t27UJVVW677Taio6P59ddfWbduHTNmzGDatGnk5uY2eH6z2UxwcHCthxBCCCGEEI0V5u9NggJ6x/PXv/6VoUOH1tqvqip5FaVA+yyMAJAYHg3A4bIi3Kc4Z0nrCfIPwulsvrWH2rNmXyz17rvvZtasWcdt06tXL5YuXcr3339fayGmV199lcWLFzN//nweeOCB5g5NCCGEEEIIrUJceY0VRVHq7C+3VePnUnCjEBPc8AilthQdFIZRb8DldpFTUkBiZOxJn6u46kgSpPjx/PPPExsby/XXX99cobZLzZ4ERUVFERUVdcJ2VqsVoM5EM51O1y4rcAghhBBCiM7B1xNUWl1Z7/5DBTmca+yBiorq9rTAJ+ZTp9fpGGdJILLGwMYtaSSeNfWkz+XrCQrSmbACRqOxmaJsv9rsVzpu3DjCwsK4/vrreeSRR/Dz8+PNN98kIyODCy64oK3CEkIIIYQQnZxvTlBJdTlLliyhuLiYadOm4e/vD0B2fi5W1YnOoMdsNrdlqMcVaPHH4HCd8vIyJVZvEhSblMC0Cy6rt1peZ9NmhREiIyP54YcfqKqq4swzz2TkyJH89ttvfPPNN3XGZQohhBBCCNFcwn09QdYq9u7dy8GDByku/r3AQLXezfeuDKoSgtoqxEYJToxmkfMgtmi/UzpPyVGFESwWCyEhIc0RXrvWpp17I0eO5Mcff2zLEIQQQgghRBfjmxNUaq1k/ITzURSlVnXivHJvQhQd1j6LIvh0j4mnEgfZZYWndB7fnKAw/65TcKwdjnAUQgghhBCi5YQdSYJKqiuOWxkupp1WhvPxVYjLLj21JMjXE5S1Yy81OcUMHToUP79T611q79rf8rdCCCGEEEK0oDD/QACqamw4XLVLQu/YsQPLgRL66kKJDYloi/AarVtoFOGKheBSJ+np6Sd1DpujBpuzBgXYtTWdFStW4HK5mjfQdkiSICGEEEII0aUEWfzR6/QAlFRVUFpayqFDhwA4ePAgZpeCBQOx7bQ8tk90cBgRioU+hJC+fftJncO3UKqfwcS4ceMYNGgQgYGBzRlmuyTD4YQQQgghRJeiU3SE+QdSVFVObnEBP33+LYqicNtttzFx0iTe2rKYMk9Nux8O52c0YzPDvpoyxiQlnNQ5SqrLAQgJCOL0009vzvDaNekJEkIIIYQQXY6vOIINN/Hx8cTHx2O1WrF5nGR6KqlWXEQGhrZtkI0QEBLMJk8B+vCT673x9QT57kdXIT1BQgghhBCiy/l9wdQKLr/8cm09oK3Z+wCICgpFr2v//QWxweHszsskr6LkpI4vOVIZLtIShNPp7BILpYL0BAkhhBBCiC5IS4KslVoCtGnTJranb8eErt0PhfOJPRJnbmEB+fn5TT7e1xMUWa7y/PPPs2nTpmaNr72SJEgIIYQQQnQ5vuFfJdZKwFsae/Xq1WRu2UWwYm73leF8YoLDSVQCsW05yNKlS5t8vK88tulIWhAQENCs8bVXMhxOCCGEEEJ0OZGBIQAUHFkTyO12YzKZwKCjxGlr95XhfGKCw7HiQj3J431JULcRA5gxeAIGQ9dID6QnSAghhBBCdDndwqIAyC4tAMBgMHD55ZdTHG/BQ/tfKNUnNiScEtXOKv8SrrzyyiYf70uCIgKC8fPzkzlBQgghhBBCdFYJWhJUqG0LCQkh11oG0KGGw6lAfmUpHtXT5OPLbFUAhB6ZI9VVSBIkhBBCCCG6nISwaMBbGKG6xqZtzz9SZS2mgwyHiwoKRacouDxuSqorm3x8ua2acMXCwbQdpKent0CE7ZMkQUIIIYQQossJNPtpFeJ8vUEOl1OrltZRhsMZdHqiAkPpoQTz/bffsnPnzkYf61E9lFurCFPMZOzZx549e1ow0vZFkiAhhBBCCNEl+YbEZR2ZF5R/pEiC2WAk1O/kFh9tCzEh4YQqJvKzcsjJyWn0cVV2G27VQ6law5ixY0hOTm7BKNsXSYKEEEIIIUSX5BsSl60lQd6hcLHB4SiK0mZxNVVMcDjZahWhfRIYOHBgo48rPzIfyG5UmTxpcpOO7egkCRJCCCGEEF3SscUR8rT5QB1jKJxPTHAYxaqd6mA9sbGxjT6uzOpNgkI6UK9Xc5EkSAghhBBCdEkN9QR1tCQoNthbyc6XxDVW6ZGeoChzIE6ns9njas8kCRJCCCGEEF1S4pEkKKvkmOFwIR0rCfIlbcWlpWRmZuJwOBp1XJnVW02uX5mZ559/nrKyspYKsd1psSToySefZPz48fj7+xMaGlpvm8zMTC644AL8/f2Jjo7m3nvvxeVytVRIQgghhBBCaHzD4fIrSnG4nOSVd9SeIG+8icUKH330EQUFBY06rtxWjREdvtlPgYFdZ1icoaVO7HA4uOyyyxg3bhxvv/12nf1ut5sLLriA2NhYVq1aRW5uLtdddx1Go5Gnnnqq2eNxu91drptPCNH2TCYTOp10ugshRHsUHhCMn9GMzVnDoZJ8tudmAL8Pk+sofD1X5R47seHdGv2Zt8xahRMPuiEJ/HnypRgMLZYatDst9koff/xxAN5999169//000/s2LGDJUuWEBMTw7Bhw/jnP//J/fffz2OPPYbJZGqWOFRVJS8vr0t17wkh2g+dTkfPnj2b7W+aEEKI5qMoCglhUewtyOb9NT9SZq0iKiiU1O592zq0JgnzD8KkN/CL+zD3/uHPxIdGNuo433C4sIBgLBZLS4bY7rRZurd69WoGDx5MTEyMtm3KlCnccsstbN++ndTU1HqPq6mpoaamRnteUVFx3Ov4EqDo6Gj8/f07VLlDIUTH5vF4yMnJITc3l+7du8vfHyGEaIcSw6PZW5DNgq2rAZg+9HQMOn0bR9U0iqIQHRxOdmkBeeXFjU+CjhRGCPXvOsPgfNosCcrLy6uVAAHa87y8vAaPe/rpp7VephNxu91aAhQREXHywQohxEmKiooiJycHl8uF0Whs63CEEEIcwzf0TUVFQWHGsNPaOKKTEx0USnZpAUVV5Y0+ptxaRXcliIq9h9kfup/evXu3YITtS5MGqj/wwAMoinLcx65du1oqVgAefPBBysvLtUdWVlaDbX3jIf39/Vs0JiGEaIhvGJzb7W7jSIQQQtQnITRK+3lc74HENbIXpb2JCAzBHwO71mzi008/bdQxZbYqYnT+FB063OhiCp1Fk3qC7r77bmbNmnXcNr169WrUuWJjY1m3bl2tbfn5+dq+hpjNZsxmc6Ou4SNDUIQQbUX+/gghRPt2dBGEi1MntWEkpyYyMAQ3Kraicg4WV+B2u9Hrjz+sr8xahc3j4bzUCSQmJrZSpO1Dk5KgqKgooqKiTtywEcaNG8eTTz5JQUEB0dHeN9/ixYsJDg5mwIABzXINIYQQQgghjqdfTCJ+RjPhAcGc3ndIW4dz0iICQqjBTU1MAFedNvWE7T2qhwpbNW7Vw/gJ44kKCmuFKNuPFqvbmpmZSVpaGpmZmbjdbtLS0khLS6OqyjsB69xzz2XAgAFce+21bNmyhR9//JGHH36Y2267rck9PaL5JCUl8Z///Ketw2hQe41v+fLlKIrSplUIJ06cyIcfftjo9oqi8PXXX7dcQF3AAw88wB133NHWYQghhDgFYQFBfHLz48yb9SBGfcctER0ZGAJAvslBnz59TtgLVGW34VY9AIT4db3CCC2WBD3yyCOkpqby6KOPUlVVRWpqKqmpqWzYsAEAvV7P999/j16vZ9y4cVxzzTVcd911/OMf/2ipkDqMWbNmaXOsjEYjPXv25L777sNut9dqV9+crNNOa/vJfLNmzWLGjBktcu7169dz8803t8i5O7Jvv/2W/Px8rrjiCm1bUlJSnfdHQkJCG0bZfp3se/aee+5h/vz5HDhwoPmDEkII0Wq6hUURcSSJ6Kh8SVBjCyOU2arQAVHGABS1BQNrp1os3X333XcbXCPIp0ePHixcuLClQujQpk6dyrx583A6nWzcuJHrr78eRVH417/+VavdvHnzmDr19y7Pzr4WSXMNx+yIHA5Hg7/fF198kRtuuKHOopz/+Mc/mD17tvb8RN8KiaaJjIxkypQpvPbaazz77LNtHY4QQoguzJcElVdWkJWVhdls1qac1KfMWkkgJs6gG6+++ip/+ctfWivUdqFLLWOuqio2R02bPFS1aSm22WwmNjaWxMREZsyYwdlnn83ixYvrtAsNDSU2NlZ7hIeHN3jOyZMnc/vtt3P77bcTEhJCZGQkf//73+vEZrVaufHGGwkKCqJ79+7897//rbV/27ZtnHnmmfj5+REREcHNN9+sDXN87LHHmD9/Pt98843W+7B8+fITHge/fxv/3HPPERcXR0REBLfddlutVY+PHQ5XVlbGn/70J2JiYrBYLAwaNIjvv/8egEOHDjFt2jTCwsIICAhg4MCBx026X331Vfr27YvFYiEmJoaZM2dq+2pqavjzn/9MdHQ0FouF0047jfXr19d7noqKCvz8/Fi0aFGt7V999RVBQUFYrVYAsrKyuPzyywkNDSU8PJzp06dz8ODBOvfjySefJD4+nuTk5HqvV1hYyNKlS5k2bVqdfUFBQbXeH8dLIo8Xz65du/D396813O7TTz/Fz8+PHTt2aPfo/vvvJzExEbPZTJ8+fXj77be19itWrGD06NGYzWbi4uJ44IEHcLlc2v7Jkydzxx13cOeddxIWFkZMTAxvvvkm1dXV3HDDDQQFBdGnT59a99U3DHHBggUMGTIEi8XC2LFjSU9P19o89thjDBs2rNZr/c9//kNSUpK2v6H37Il+RwDTpk3j448/bvC+CiGEEK0hMjAUgIgaPR9++GGDn1N8ymzVmBQdblSCgoJaIcL2peMOfDwJdqeD0565tU2u/dt9r+JnOrm5Tunp6axatYoePXqcchzz58/npptuYt26dWzYsIGbb76Z7t271+ot+Pe//80///lPHnroIT7//HNuueUWJk2aRHJyMtXV1UyZMoVx48axfv16CgoK+OMf/8jtt9/Ou+++yz333MPOnTupqKhg3rx5AISHh5/wOJ9ly5YRFxfHsmXL2LdvH3/4wx8YNmxYrfh8PB4P5513HpWVlbz//vv07t2bHTt2aL0dt912Gw6Hg19++YWAgAB27NhBYGD9Y143bNjAn//8Z/73v/8xfvx4SkpK+PXXX7X99913H1988QXz58+nR48ePPPMM0yZMoV9+/bVSTyDg4O58MIL+fDDDznvvPO07R988AEzZszA398fp9Op3Y9ff/0Vg8HAE088wdSpU9m6davW4/Pzzz8THBxcbwLs89tvv+Hv70///v0bbHMiJ4onJSWF5557jltvvZXTTjsNnU7HnDlz+Ne//qUVMrnuuutYvXo1L774IkOHDiUjI4OioiIADh8+zPnnn8+sWbN477332LVrF7Nnz8ZisfDYY49pccyfP5/77ruPdevW8cknn3DLLbfw1VdfcfHFF/PQQw8xd+5crr32WjIzM2uVvr/33nt54YUXiI2N5aGHHmLatGns2bOnUevyNPSebezvaPTo0WRnZ3Pw4EEtsRJCCCFaW4h/AHqdnirVSVBwMBaL5bjty6yVFKl2chJM3Hv5ta0UZfvRpZKgjuT7778nMDAQl8tFTU0NOp2Ol19+uU67K6+8stYQp/fff/+4cxsSExOZO3cuiqKQnJzMtm3bmDt3bq0k4/zzz+fWW73J4v3338/cuXNZtmwZycnJfPjhh9jtdt577z0CAgIAePnll5k2bRr/+te/iImJwc/Pj5qamlqlzufPn3/C4wDCwsJ4+eWX0ev1pKSkcMEFF/Dzzz/XmwQtWbKEdevWsXPnTvr16wfULtGemZnJpZdeyuDBg+vsO1ZmZiYBAQFceOGFBAUF0aNHD1JTUwGorq7mtdde491339WSmjfffJPFixfz9ttvc++999Y539VXX821116L1WrF39+fiooKFixYwFdffQXAJ598gsfj4a233tJKKM+bN4/Q0FCWL1/OueeeC0BAQABvvfXWcYc5Hjp0iJiYmDpD4cD7+3v44Ye150899RR//vOf67RrTDy33norCxcu5JprrsFkMjFq1CitKMCePXv49NNPWbx4MWeffXad+/3qq6+SmJjIyy+/jKIopKSkkJOTw/33388jjzyixT506FAt3gcffJD/+7//IzIyUvv9P/LII7z22mts3bqVsWPHaud/9NFHOeeccwDvey0hIYGvvvqKyy+/vMH75hMYGFjve/b9999v1O8oPj5e+z1IEiSEEKKt6BQdEQHBZFeWMmn6VAbG9zxu+zKrdzROaEBQl1zMu0slQRajid/ue7XNrt0UZ5xxBq+99hrV1dXMnTsXg8HApZdeWqfd3LlztQ+dAHFxccc979ixY2utWzJu3Dj+/e9/16olP2TI7+UhFUUhNjZWW0Br586dDB06VEtkACZMmIDH42H37t1aMnOsxh43cODAWkldXFwc27Ztq/ecaWlpJCQkaAnQsf785z9zyy238NNPP3H22Wdz6aWX1nptRzvnnHPo0aMHvXr1YurUqUydOpWLL74Yf39/9u/fj9PpZMKECVp7o9HI6NGj2blzZ73nO//88zEajXz77bdcccUVfPHFFwQHB2u/qy1btrBv37463c92u539+/drzwcPHnzCeV42m63Bb3vuvffeWmt7RUbWvwBcY+N555136NevHzqdju3bt2vvpbS0NPR6PZMm1b++ws6dOxk3blyt996ECROoqqoiOzub7t27A7Xfe3q9noiICC2JBbT3ybELuo0bN077OTw8nOTk5AZ/N43V2Hvi5+cHoA1zFEIIIdpKZGAIBZWlFDeiOEKZ7UgS5N/1KsNBF0uCFEU56SFprS0gIIA+ffoA3g+eQ4cO5e233+amm26q1S42NlZr11yO/TZAURQ8Hk+zXqM5ru378NmQP/7xj0yZMoUFCxbw008/8fTTT/Pvf/+73pLGQUFBbNq0ieXLl/PTTz/xyCOP8Nhjj51wPG1DTCYTM2fO5MMPP+SKK67gww8/5A9/+AMGg/d/uaqqKkaMGMEHH3xQ59ij5+0cnTQ2JDIyktLS0gb3Neb90dh4tmzZQnV1NTqdjtzcXC3pPtHvorHq+/0fvc2XRDXl/ajT6erMezt6nllDGntPSkpK6mwTQggh2kJTKsSVW6sYpIvAkFNJYWFhl/t3rEsVRuiodDodDz30EA8//DA2m+2UzrV27dpaz9esWUPfvn0bXTWsf//+2gdhn5UrV6LT6bSJ+yaTCbfb3eTjmmrIkCFkZ2ezZ8+eBtskJiYyZ84cvvzyS+6++27efPPNBtsaDAbOPvtsnnnmGbZu3crBgwdZunQpvXv3xmQysXLlSq2t0+lk/fr1x13Y9+qrr+aHH35g+/btLF26lKuvvlrbN3z4cPbu3Ut0dDR9+vSp9QgJaVqJztTUVPLy8hpMhBqjMfGUlJQwa9Ys/va3vzFr1iyuvvpq7f04ePBgPB4PK1asqPf8/fv3Z/Xq1bWSkZUrVxIUFNQsZbvXrFmj/VxaWsqePXu0OVJRUVHk5eXVunZaWlqt4+t7zzb2d5Seno7RaGTgwIGn/DqEEEKIU+Er852Zvpt3332XrKysBtuW2apI0AVRk1/aJUczSBLUQVx22WXo9XpeeeWVUzpPZmYmd911F7t37+ajjz7ipZdealJJxKuvvhqLxcL1119Peno6y5Yt44477uDaa6/VhiolJSWxdetWdu/eTVFREU6ns1HHNdWkSZOYOHEil156KYsXLyYjI4NFixbxww8/AHDnnXfy448/kpGRwaZNm1i2bFmDxQO+//57XnzxRdLS0jh06BDvvfceHo+H5ORkAgICuOWWW7j33nv54Ycf2LFjB7Nnz8ZqtdbpmTvaxIkTiY2N5eqrr6Znz56MGTOm1n2MjIxk+vTp/Prrr2RkZLB8+XL+/Oc/k52d3aT7kJqaSmRkZK0krakaE8+cOXNITEzk4Ycf5vnnn8ftdnPPPfcA3t/59ddfz4033sjXX3+tHf/pp58CcOutt5KVlcUdd9zBrl27+Oabb3j00Ue566676p3L1FT/+Mc/+Pnnn0lPT2fWrFlERkZqc+MmT55MYWEhzzzzDPv37+eVV16pU7mvofdsY35Hv/76K6effnqz9YYJIYQQJ8vXE2SvspKfn09xcXGDbcusVex0FxPbN4mIiIjWCrHdkCSogzAYDNx+++0888wztXpTmuq6667DZrMxevRobrvtNv7yl780afFRf39/fvzxR0pKShg1ahQzZ87krLPOqlW0Yfbs2SQnJzNy5EiioqJYuXJlo447GV988QWjRo3iyiuvZMCAAdx3333aN/put5vbbruN/v37M3XqVPr168err9Y/Jyw0NJQvv/ySM888k/79+/P666/z0Ucfad/u/9///R+XXnop1157LcOHD2ffvn38+OOPhIWFNRiboihceeWVbNmypVYvkO8+/vLLL3Tv3p1LLrmE/v37c9NNN2G32wkODm7SPdDr9dxwww31DttqrBPF895777Fw4UL+97//YTAYCAgI4P333+fNN9/UEorXXnuNmTNncuutt5KSksLs2bO192q3bt1YuHAh69atY+jQocyZM4ebbrqpVtGGU/F///d//OUvf2HEiBHk5eXx3XffaXOp+vfvz6uvvsorr7zC0KFDWbdunZa8+TT0nm3M7+jjjz+ut3CHEEII0dp8ZbJLA+DSSy897pD4clsVh9RKBgwf2mD13M5MUZu6gE07U1FRQUhICOXl5XU+PNrtdjIyMujZs+cJywR2BZMnT2bYsGG11tkRnUNeXh4DBw5k06ZNzVJKvaNYvnw5Z5xxBqWlpYSGhrb69RctWsTdd9/N1q1btflex5K/Q0IIIVrLst2buOezVxgY35P3bjz+F41nP38npdZKPr75cfpGn/rQ9PbieLnB0aQnSIhOIDY2lrfffpvMzMy2DqVLqa6uZt68eQ0mQEIIIURr8vUEnag6nEf1YLNZCcRIoLFjFA1rbvIvtxCdxPHWhxItY+bMmW0dghBCCKHxzQkqrq4gKyuLsrIyBg0aVGuJCoBKu5VuSiAjDDGsXLqCmZd2vX/PJAnqQpYvX97WIQjRrCZPnlyn/LUQQgjRVUUEeId/udwuPv74YzweDz169KgzLKzcVo0eBRceQkNC2yDStidJkBBCCCGEEJ2AyWAkxC+Acls1EdFR+JstuFyuOu3KrJXs8ZRRFWjggTPOaINI254kQUIIIYQQQnQSkYEhlNuqGXj6aMb0qn89wzJrFQChAYGNXiuys5HCCEIIIYQQQnQSEQHeeUFFVWUNttGSIP+uVxrbR5IgIYQQQgghOomjiyM0pNRayQR9PDHlCg6Ho7VCa1dkOJwQQgghhBCdRGRQKACFhQW89957qKrK9ddfX6tNWUU53XSBUF7TZZd56JqvWgghhBBCiE7IVyGuxF6NK7cCRVFwuVy1kp3yGitbXXlM6j0Ena5rDgzrmq9atGuTJ0/mzjvvPGG7iRMn8uGHHzbrtWfNmtWh1ts5Ubw//PADw4YNw+PxtF5QQgghhGgzvgVTC+0VzJgxg5tuuqlO8YNyh5UMtYK4vj3bIML2ocWSoCeffJLx48fj7+9PaGhonf1btmzhyiuvJDExET8/P/r3788LL7zQUuF0KLNmzUJRFObMmVNn32233YaiKMyaNav1A2tHvv32W/Lz87niiiu0bVu2bOGiiy4iOjoai8VCUlISf/jDHygoKGj0eV944QXefffdFoj4dwcPHkRRFNLS0lr0OgBTp07FaDTywQcftPi1hBBCCNH2fHOCiqorSE5OJiIios5iqVIYoQWTIIfDwWWXXcYtt9xS7/6NGzcSHR3N+++/z/bt2/nb3/7Ggw8+yMsvv9xSIXUoiYmJfPzxx9hsNm2b3W7nww8/pHv37m0YWeO09CS7F198kRtuuEHrwi0sLOSss84iPDycH3/8kZ07dzJv3jzi4+Oprq5u9HlDQkLqTdo7slmzZvHiiy+2dRhCCCGEaAURviSoqrzBNrpyO/4YCPGTJKjZPf744/z1r39l8ODB9e6/8cYbeeGFF5g0aRK9evXimmuu4YYbbuDLL79sqZA0DoejwcexC0odr63T6WxU25MxfPhwEhMTa92PL7/8ku7du5Oamlqrrcfj4emnn6Znz574+fkxdOhQPv/8c22/2+3mpptu0vYnJyfX6XVbvnw5o0ePJiAggNDQUCZMmMChQ4eA+odc3XnnnUyePFl7PnnyZG6//XbuvPNOIiMjmTJlCgDp6emcd955BAYGEhMTw7XXXktRUZF2XHV1Nddddx2BgYHExcXx73//+4T3prCwkKVLlzJt2jRt28qVKykvL+ett94iNTWVnj17csYZZzB37lx69uzZ6Ptw7GudPHkyf/7zn7nvvvsIDw8nNjaWxx577IQxvvXWW/Tv3x+LxUJKSgqvvvqqts8XT2pqKoqi1LqPR2tMvI0xbdo0NmzYwP79+5t8rBBCCCE6Fl9PUHWNjeKyEtLT00lPT9f2V1ZW0sfmx/mGnvjrTW0VZptrV4URysvLCQ8PP26bmpoaampqtOcVFQ2X/2vI3LlzG9zXq1cvLrvsMu35yy+/XCfZ8UlMTOSqq67Snr/++uu1em587r///ibHCN5Ecd68eVx99dUAvPPOO9xwww0sX768Vrunn36a999/n9dff52+ffvyyy+/cM011xAVFcWkSZPweDwkJCTw2WefERERwapVq7j55puJi4vj8ssvx+VyMWPGDGbPns1HH32Ew+Fg3bp1dbpOT2T+/PnccsstrFy5EoCysjLOPPNM/vjHPzJ37lxsNhv3338/l19+OUuXLgXg3nvvZcWKFXzzzTdER0fz0EMPsWnTJoYNG9bgdX777Tf8/f3p37+/ti02NhaXy8VXX33FzJkz6439RPfheK/rrrvuYu3ataxevZpZs2YxYcIEzjnnnHrbf/DBBzzyyCO8/PLLpKamsnnzZmbPnk1AQADXX38969atY/To0SxZsoSBAwdiMtX/B+hk4z1W9+7diYmJ4ddff6V3796NPk4IIYQQHU+g2Q+zwUSNy0FGdhY/L/iBsLAwBg0aBEC1tZoi1QYqRIce/3N3Z9ZukqBVq1bxySefsGDBguO2e/rpp3n88cdbKaq2dc011/Dggw9qPTIrV67k448/rpUE1dTU8NRTT7FkyRLGjRsHeBO53377jTfeeINJkyZhNBpr3bOePXuyevVqPv30Uy6//HIqKiooLy/nwgsv1D4kH51gNFbfvn155plntOdPPPEEqampPPXUU9q2d955h8TERPbs2UN8fDxvv/0277//PmeddRbgTTgSEhKOe51Dhw4RExNTq5rJ2LFjeeihh7jqqquYM2cOo0eP5swzz+S6664jJiYG4IT3oSFDhgzh0Ucf1V7jyy+/zM8//9xgEvToo4/y73//m0suuUS7zo4dO3jjjTe4/vrriYqKAiAiIoLY2NgGr3uy8dYnPj5eex8JIYQQovNSFIXIwGAOlxXhNulITEwkKioKVVVRFAW/4ECWurJQgKe68JygJiVBDzzwAP/617+O22bnzp2kpKQ0KYj09HSmT5/Oo48+yrnnnnvctg8++CB33XWX9ryiooLExMQmXe+vf/1rg/uOLRN4++23N9j22N6G+goZnIqoqCguuOAC3n33XVRV5YILLiAyMrJWm3379mG1Wut8IHc4HLWGzb3yyiu88847ZGZmYrPZcDgcWm9LeHg4s2bNYsqUKZxzzjmcffbZXH755cTFxTUp3hEjRtR6vmXLFpYtW0ZgYN3/wfbv36/FMWbMGG17eHg4ycnJx72OzWbDYrHU2f7kk09y1113sXTpUtauXcvrr7/OU089xS+//KINyzzefWjIkCFDaj2Pi4trsNhCdXU1+/fv56abbmL27NnadpfLRUhIyHGvU5+Tibc+fn5+WK3WJh8nhBBCiI4nMjCUw2VFVLlrao1agt+LIvibLBj17aY/pNU16ZXffffdJ6xK1qtXryYFsGPHDs466yxuvvlmHn744RO2N5vNmM3mJl3jWA0NP2rNto114403aonYK6+8Umd/VZX3jbxgwQK6detWa5/vPn388cfcc889/Pvf/2bcuHEEBQXx7LPPsnbtWq3tvHnz+POf/8wPP/zAJ598wsMPP8zixYsZO3YsOp0OVVVrnbu+IYIBAQF1Yps2bVq9iXNcXBz79u1rzC2oIzIyktLS0nr3RUREcNlll3HZZZfx1FNPkZqaynPPPcf8+fMbdR/qYzQaaz1XFKXBktO+38ebb75ZK7kD6pSnPJGTjbc+JSUlWg+UEEIIITq3yAaKIzidTkqrKoGuXRkOmpgERUVFNesHqe3bt3PmmWdy/fXX8+STTzbbeTuTqVOn4nA4UBRFKzZwtAEDBmA2m8nMzGTSpEn1nmPlypWMHz+eW2+9VdtW3yT51NRUUlNTefDBBxk3bhwffvghY8eOJSoqqtaEOoC0tLQ6ycGxhg8fzhdffEFSUlK9qxH37t0bo9HI2rVrtYp3paWl7Nmzp8HX4oszLy+P0tJSwsLCGmxnMpno3bu3Vh2usffhVMTExBAfH8+BAwe0uVz1xQXewgfH01zx2u129u/fX6eghhBCCCE6p2MrxKmqSkZGBrt372b7zh2k6MLQ+we1ZYhtrsWqw2VmZpKWlkZmZiZut5u0tDTS0tK0b8rT09M544wzOPfcc7nrrrvIy8sjLy+PwsLClgqpQ9Lr9ezcuZMdO3bU25MQFBTEPffcw1//+lfmz5/P/v372bRpEy+99BLz588HvPNYNmzYwI8//siePXv4+9//zvr167VzZGRk8OCDD7J69WoOHTrETz/9xN69e7V5QWeeeSYbNmzgvffeY+/evTz66KN1kqL63HbbbZSUlHDllVeyfv169u/fz48//sgNN9yA2+0mMDCQm266iXvvvZelS5eSnp7OrFmzTrhycWpqKpGRkVoBBoDvv/+ea665hu+//549e/awe/dunnvuORYuXMj06dMbdR+ay+OPP87TTz/Niy++yJ49e9i2bRvz5s3j+eefByA6Oho/Pz9++OEH8vPzKS+vv4TlycT78ssva/OrfNasWYPZbNbmjAkhhBCic/P1BBUfSYKWL1/OZ599xtatW3E7XbhRCfELON4pOr0WS4IeeeQRUlNTefTRR6mqqtJ6GTZs2ADA559/TmFhIe+//z5xcXHaY9SoUS0VUocVHBxMcHBwg/v/+c9/8ve//52nn36a/v37M3XqVBYsWKCVYv7Tn/7EJZdcwh/+8AfGjBlDcXFxrd4Ff39/du3axaWXXkq/fv24+eabue222/jTn/4EwJQpU/j73//Offfdx6hRo6isrOS66647Ydzx8fGsXLkSt9vNueeey+DBg7nzzjsJDQ3VEp1nn32W008/nWnTpnH22Wdz2mmn1ZlbdCy9Xs8NN9xQawHQAQMG4O/vz913382wYcMYO3Ysn376KW+99RbXXntto+5Dc/njH//IW2+9xbx58xg8eDCTJk3i3Xff1X4fBoOBF198kTfeeIP4+HgtSTvWycRbVFRUp7foo48+4uqrr8bf3795XqAQQggh2rVjh8OlpKRgNBoZNWoUEcP6kOWpJLQLrxEEoKjHTvboYCoqKggJCaG8vLxOomC328nIyKBnz571TqQXHVdeXh4DBw5k06ZN9OjRo63DabeKiopITk5mw4YNWhImWpf8HRJCCNHaftu3lb98/ALJMYl8OPsxwFtR2Gw28+LSz5m/ahFXjT6Hu8+9om0DbQHHyw2O1mI9QUK0pNjYWN5++20yMzPbOpR27eDBg7z66quSAAkhhBBdiNYTVP37epq+glm+6nBSGEGIDmrGjBltHUK7N3LkSEaOHNnWYQghhBCiFUUEeJOg0uoK3B4P+qPmW5dZvdXhQrr4cDjpCRJCCCGEEKITCQ8IRqcoeFSVkqN6g0B6gnwkCRJCCCGEEKIT0et0hAV458MUV9euQltu8y4d0tULI0gSJIQQQgghRCcTeSQJOnbB1DKbLJYKkgQJIYQQQgjR6UQGhgJQVPl7EuT2eKg40hMkc4KEEEIIIYQQnYq2YOpRw+Eq7VY8R1bHkZ4gIYQQQgghRKcSccyCqQDlNm9RhACzH0Z91y4SLUmQEEIIIYQQnYy2VlBVmbZNqwznF9AWIbUrkgSJk1JcXEx0dDQHDx5s1vMmJSXxn//8p1nP2ZJOFO8DDzzAHXfc0XoBCSGEEEJwdBL0e09Qmc1XHjuoTWJqTyQJaodmzZpV70Kgy5cvR1EUysrKaj0/9vHwww8DYLVaefDBB+nduzcWi4WoqCgmTZrEN998o50zIyODq666ivj4eCwWCwkJCUyfPp1du3YdN8Ynn3yS6dOnk5SUpG376quvGDt2LCEhIQQFBTFw4EDuvPPOJr329evXc/PNNzfpmKZ69913CQ0NbdFr+Nxzzz3Mnz+fAwcOtMr1hBBCCCHg9+FwxUcPh9N6grr2fCCArj0YsJPYvXs3wcHB2vPAQO8be86cOaxdu5aXXnqJAQMGUFxczKpVqyguLgbA6XRyzjnnkJyczJdffklcXBzZ2dksWrRIS7TqY7Vaefvtt/nxxx+1bT///DN/+MMfePLJJ7noootQFIUdO3awePHiJr2WqKioJrVv7yIjI5kyZQqvvfYazz77bFuHI4QQQoguIjooDICCyjJcHjcGnZ68Cu9nQF8vUVfWJXuCHA4HDocD9Uh1DAC3243D4cDlcjV725YWHR1NbGys9vAlQd9++y0PPfQQ559/PklJSYwYMYI77riDG2+8EYDt27ezf/9+Xn31VcaOHUuPHj2YMGECTzzxBGPHjm3wegsXLsRsNtdq89133zFhwgTuvfdekpOT6devHzNmzOCVV17R2uzfv5/p06cTExNDYGAgo0aNYsmSJbXOfezwMkVReOutt7j44ovx9/enb9++fPvtt8e9HzU1Ndxzzz1069aNgIAAxowZw/LlywFv79kNN9xAeXm51nP22GOP1XuexsTbGNOmTePjjz9u8nFCCCGEECcrNiQcf5MZp9vFoeI8APbkZwPQJzqhLUNrF7pkEjR37lzmzp2LzWbTtq1du5a5c+fW6bl4+eWXmTt3LhUVFdq2TZs2MXfuXBYtWlSr7euvv87cuXMpKirStm3btq2FXsWJxcbGsnDhQiorK+vdHxUVhU6n4/PPP29Ssvbrr78yYsSIOtfavn076enpDR5XVVXF+eefz88//8zmzZuZOnUq06ZNIzMz87jXe/zxx7n88svZunUr559/PldffTUlJSUNtr/99ttZvXo1H3/8MVu3buWyyy5j6tSp7N27l/Hjx/Of//yH4OBgcnNzyc3N5Z577mnWeI81evRosrOzm33+lBBCCCFEQ3SKTkt29uRnAbC3QJIgny6ZBHUE33//PYGBgbUe5513Xr1tExISarXzDXf773//y6pVq4iIiGDUqFH89a9/ZeXKldpx3bp148UXX+SRRx4hLCyMM888k3/+858nnL9y6NAh4uPja2274447GDVqFIMHDyYpKYkrrriCd955h5qaGq3N0KFD+dOf/sSgQYPo27cv//znP+ndu/cJe3ZmzZrFlVdeSZ8+fXjqqaeoqqpi3bp19bbNzMxk3rx5fPbZZ5x++un07t2be+65h9NOO4158+ZhMpkICQlBUZQ6PWfHOtl4j+W7V4cOHWrScUIIIYQQp6JvdCIAe/OzsDrsZJcWeLfHSBLUJecE/fWvfwXAaDRq28aMGcPIkSPR6WrnhbfffnudtsOHD2fo0KF12s6ZM6dO28GDB59UjGeccQavvfZarW1r167lmmuuqdP2119/JSjo9yofYWHeMaATJ07kwIEDrFmzhlWrVvHzzz/zwgsv8Pjjj/P3v/8dgNtuu43rrruO5cuXs2bNGj777DOeeuopvv32W84555x6Y7PZbFgsllrbAgICWLBgAfv372fZsmWsWbOGu+++mxdeeIHVq1fj7+9PVVUVjz32GAsWLCA3NxeXy4XNZjthz8qQIUNqXSc4OJiCgoJ6227btg23202/fv1qba+pqSEiIuK41znWycZ7LD8/P8A7l0oIIYQQorX0i/EmQXvys9l3pBcoKiiUMKkO1zWTIJPJVGebXq9Hr9e3SNuTERAQQJ8+fWpty87Orrdtz549G6x2ZjQaOf300zn99NO5//77eeKJJ/jHP/7B/fffr8UbFBTEtGnTmDZtGk888QRTpkzhiSeeaDAJioyMpLS0tN59vXv3pnfv3vzxj3/kb3/7G/369eOTTz7hhhtu4J577mHx4sU899xz9OnTBz8/P2bOnInD4TjuvTg6qQTvPCGPx1Nv26qqKvR6PRs3bqxz7xvq8WnIycZ7LN/Qvc5W9EEIIYQQ7Vu/Iz0+ewqy2HtkPlBfGQoHdNEkqCsbMGAALpcLu91eb9KmKAopKSmsWrWqwXOkpqby/vvvn/BaSUlJ+Pv7U11dDcDKlSuZNWsWF198MeBNWJp7nkxqaiput5uCggJOP/30etuYTKZGzYFqrnjT09MxGo0MHDiwyccKIYQQQpysPtEJKCgUV5Wz9uAOQJIgH5kT1IlNnjyZN954g40bN3Lw4EEWLlzIQw89xBlnnEFwcDBpaWlMnz6dzz//nB07drBv3z7efvtt3nnnHaZPn97geadMmcL27dtr9QY99thj3HfffSxfvpyMjAw2b97MjTfeqJXhBujbty9ffvklaWlpbNmyhauuuqrBHp2T1a9fP66++mquu+46vvzySzIyMli3bh1PP/00CxYsALzJWVVVFT///DNFRUUNDlM7mXivu+46HnzwwVrbfv31V04//XRtWJwQQgghRGvwN1lICPOORPl1zxYA+h4ZItfVtVgS9OSTTzJ+/Hj8/f1PuDBlcXExCQkJtRYCFaduypQpzJ8/n3PPPZf+/ftzxx13MGXKFD799FPAW1AhKSmJxx9/nDFjxjB8+HBtztDf/va3Bs87ePBghg8frp0HYNKkSRw4cIDrrruOlJQUzjvvPPLy8vjpp59ITk4G4PnnnycsLIzx48czbdo0pkyZwvDhw5v9dc+bN4/rrruOu+++m+TkZGbMmMH69evp3r07AOPHj2fOnDn84Q9/ICoqimeeeabe85xMvJmZmeTm5tba9vHHHzN79uzmeXFCCCGEEE3gmxfkcHuXdpGeIC9FPXpRm2b06KOPEhoaSnZ2Nm+//fZxk5sZM2bgcDhYtGgRpaWlJ0yajlZRUUFISAjl5eW1FgwFsNvtZGRk0LNnzzoT+cWpWbBgAffeey/p6el1CkSI3y1atIi7776brVu3YjDI6NOuSP4OCSGEaEtv/vodr6/4GgCj3sCv972CUd95P5McLzc4WovdgccffxyAd99997jtXnvtNcrKynjkkUfqrLsj2q8LLriAvXv3cvjwYRITpVu1IdXV1cybN08SICGEEEK0iX5HDX/rFRnXqROgpmjTu7Bjxw7+8Y9/sHbt2hOuTeNTU1NTa+2ZoxcxFa3rzjvvbOsQ2r2ZM2e2dQhCCCGE6ML6HTX8rU+0fHHt02bjmGpqarjyyit59tlntbkajfH0008TEhKiPaQXQgghhBBCiPrFhkQQZPEHZJHUozUpCXrggQdQFOW4j127djXqXA8++CD9+/evd/HPEx1XXl6uPbKyspp0vBBCCCGEEF2FoiiM7zUIvaJjdFL/tg6n3WjScLi7776bWbNmHbdNr169GnWupUuXsm3bNj7//HMAfPUZIiMj+dvf/qbNKTqW2WzGbDY3Puijzi2EEK1N/v4IIYRoa3+/cBZ3nHkpcaGRbR1Ku9GkJCgqKqrZVr3/4osvsNls2vP169dz44038uuvv9K7d+9muYbRaATAarXKGi1CiDbhcDgA0Ov1bRyJEEKIrsrPZMbP1LROhM6uxQojZGZmUlJSQmZmJm63m7S0NAD69OlDYGBgnUSnqKgIgP79+zepRPbx6PV6QkNDKSgoAMDf3x9FUZrl3EIIcSIej4fCwkL8/f2lQqAQQgjRjrTYv8qPPPII8+fP156npqYCsGzZMiZPntxSl60jNjYWQEuEhBCiNel0Orp37y5fwAghhBDtSIstltpaGrsgktvtxul0tmJkQggBJpNJFhQWQgghWkmbL5ba3uj1ehmTL4QQQgghhGi7dYKEEEIIIYQQoi1IEiSEEEIIIYToUiQJEkIIIYQQQnQpHX5OkK+uQ0VFRRtHIoQQQgghhGhLvpzgRLXfOnwSVFlZCUBiYmIbRyKEEEIIIYRoDyorKwkJCWlwf4cvke3xeMjJySEoKOik1+GoqKggMTGRrKys45bSE6dG7nPrkXvdOuQ+tw65z61H7nXrkPvcOuQ+t572dK9VVaWyspL4+PjjLlHR4XuCdDodCQkJzXKu4ODgNv/FdQVyn1uP3OvWIfe5dch9bj1yr1uH3OfWIfe59bSXe328HiAfKYwghBBCCCGE6FIkCRJCCCGEEEJ0KZIEAWazmUcffRSz2dzWoXRqcp9bj9zr1iH3uXXIfW49cq9bh9zn1iH3ufV0xHvd4QsjCCGEEEIIIURTSE+QEEIIIYQQokuRJEgIIYQQQgjRpUgSJIQQQgghhOhSJAkSQgghhBBCdCmSBAGvvPIKSUlJWCwWxowZw7p169o6pE7lscceQ1GUWo+UlJS2DqtT+OWXX5g2bRrx8fEoisLXX39da7+qqjzyyCPExcXh5+fH2Wefzd69e9sm2A7sRPd51qxZdd7jU6dObZtgO7Cnn36aUaNGERQURHR0NDNmzGD37t212tjtdm677TYiIiIIDAzk0ksvJT8/v40i7pgac58nT55c5z09Z86cNoq4Y3rttdcYMmSItnjkuHHjWLRokbZf3svN50T3Wt7PLeP//u//UBSFO++8U9vWkd7XXT4J+uSTT7jrrrt49NFH2bRpE0OHDmXKlCkUFBS0dWidysCBA8nNzdUev/32W1uH1ClUV1czdOhQXnnllXr3P/PMM7z44ou8/vrrrF27loCAAKZMmYLdbm/lSDu2E91ngKlTp9Z6j3/00UetGGHnsGLFCm677TbWrFnD4sWLcTqdnHvuuVRXV2tt/vrXv/Ldd9/x2WefsWLFCnL+v737j4m6/uMA/sSDuzAv9AC5O+1uxw/PEaICA08Xto6V5JxEa0asIPyxKZTmKmabMxvLZm2mrp9r09oAM4tqrmnMPFYNE65diNIlN5GaXIQOQg+k7t794fx8vyegoQcf7u752G67e9/b8/V57TnYi8/d5y5cQGFhoYxVB5//0mcAWLt2rV+md+7cKVPFwWn27Nl4/fXXYbfb0dzcjAcffBArV67E6dOnATDLgXSrXgPMc6A1NTXh/fffR3p6ut96UOVahLns7GxRXl4uPfZ6vUKv14sdO3bIWFVo2bZtm5g/f77cZYQ8AKKurk567PP5hFarFW+88Ya01tvbK1QqlaitrZWhwtBwY5+FEKKkpESsXLlSlnpCWXd3twAgGhoahBDX8hsVFSU+/fRTaU9bW5sAIBobG+UqM+jd2GchhFi6dKnYuHGjfEWFqBkzZogPP/yQWZ4A13stBPMcaP39/SIlJUXU19f79TbYch3WZ4KGhoZgt9uRl5cnrU2ZMgV5eXlobGyUsbLQc/bsWej1eiQmJqK4uBidnZ1ylxTyzp07B7fb7ZfvmJgY5OTkMN/jwGazYebMmTCbzVi/fj0uXrwod0lBr6+vDwCg0WgAAHa7HX///bdfpufOnQuDwcBM34Eb+3xddXU14uLikJaWhi1btsDj8chRXkjwer04cOAArly5AovFwiyPoxt7fR3zHDjl5eVYvny5X36B4PsZHSl3AXLq6emB1+tFQkKC33pCQgJ++eUXmaoKPTk5Odi/fz/MZjO6urqwfft23H///WhtbYVarZa7vJDldrsBYMR8X3+OAmPZsmUoLCyEyWSCy+XCyy+/jPz8fDQ2NkKhUMhdXlDy+XzYtGkTlixZgrS0NADXMq1UKjF9+nS/vcz07RupzwDw5JNPwmg0Qq/Xo6WlBZWVlXA6nfj8889lrDb4nDp1ChaLBYODg5g2bRrq6uqQmpoKh8PBLAfYaL0GmOdAOnDgAH766Sc0NTUNey7YfkaH9RBEEyM/P1+6n56ejpycHBiNRhw8eBCrV6+WsTKiwHjiiSek+/PmzUN6ejqSkpJgs9lgtVplrCx4lZeXo7W1lZ8fHGej9XndunXS/Xnz5kGn08FqtcLlciEpKWmiywxaZrMZDocDfX19OHToEEpKStDQ0CB3WSFptF6npqYyzwHy22+/YePGjaivr8ddd90ldzl3LKzfDhcXFweFQjHsqhV//PEHtFqtTFWFvunTp2POnDlob2+Xu5SQdj3DzPfES0xMRFxcHDN+myoqKnD48GEcP34cs2fPlta1Wi2GhobQ29vrt5+Zvj2j9XkkOTk5AMBMj5FSqURycjIyMzOxY8cOzJ8/H7t372aWx8FovR4J83x77HY7uru7kZGRgcjISERGRqKhoQF79uxBZGQkEhISgirXYT0EKZVKZGZm4tixY9Kaz+fDsWPH/N5HSoF1+fJluFwu6HQ6uUsJaSaTCVqt1i/ff/31F3788Ufme5z9/vvvuHjxIjM+RkIIVFRUoK6uDt9++y1MJpPf85mZmYiKivLLtNPpRGdnJzM9Brfq80gcDgcAMNN3yOfz4erVq8zyBLje65Ewz7fHarXi1KlTcDgc0i0rKwvFxcXS/WDKddi/HW7z5s0oKSlBVlYWsrOz8dZbb+HKlSt45pln5C4tZLzwwgtYsWIFjEYjLly4gG3btkGhUKCoqEju0oLe5cuX/f6Sde7cOTgcDmg0GhgMBmzatAlVVVVISUmByWTC1q1bodfrUVBQIF/RQehmfdZoNNi+fTsee+wxaLVauFwuvPTSS0hOTsbDDz8sY9XBp7y8HDU1Nfjyyy+hVqul95DHxMQgOjoaMTExWL16NTZv3gyNRoN77rkHzz77LCwWCxYtWiRz9cHjVn12uVyoqanBI488gtjYWLS0tOD5559Hbm7usMvh0ui2bNmC/Px8GAwG9Pf3o6amBjabDUePHmWWA+xmvWaeA0etVvt9dhAA7r77bsTGxkrrQZVruS9PNxns3btXGAwGoVQqRXZ2tjhx4oTcJYWUVatWCZ1OJ5RKpZg1a5ZYtWqVaG9vl7uskHD8+HEBYNitpKRECHHtMtlbt24VCQkJQqVSCavVKpxOp7xFB6Gb9dnj8YiHHnpIxMfHi6ioKGE0GsXatWuF2+2Wu+ygM1KPAYh9+/ZJewYGBsSGDRvEjBkzxNSpU8Wjjz4qurq65Cs6CN2qz52dnSI3N1doNBqhUqlEcnKyePHFF0VfX5+8hQeZsrIyYTQahVKpFPHx8cJqtYpvvvlGep5ZDpyb9Zp5Hl83Xn48mHIdIYQQEzl0ERERERERySmsPxNEREREREThh0MQERERERGFFQ5BREREREQUVjgEERERERFRWOEQREREREREYYVDEBERERERhRUOQUREREREFFY4BBERERERUVjhEERERBNGCIF169ZBo9EgIiICDodj1L1PPfUUXnvttYkr7gZHjhzBggUL4PP5ZKuBiIjGB4cgIiKaMEeOHMH+/ftx+PBhdHV1IS0tbcR9P//8M77++ms899xzfuvt7e0oKyuDwWCASqXCrFmzYLVaUV1djX/++UfaFxERgS+++GLY65aWlqKgoOA/1bps2TJERUWhurr6Px8fEREFBw5BREQ0YVwuF3Q6HRYvXgytVovIyMgR9+3duxePP/44pk2bJq2dPHkSGRkZaGtrw9tvv43W1lbYbDasWbMG7777Lk6fPh3wektLS7Fnz56Avy4REclr5N8+REREAVZaWoqPPvoIwLUzNUajER0dHcP2eb1eHDp0yO8MjBACpaWlmDNnDn744QdMmfK/v+GlpKSgqKgIQogx1dPR0QGTyTRsfenSpbDZbACAFStWoKKiAi6XC0lJSWN6fSIimrw4BBER0YTYvXs3kpKS8MEHH6CpqQkKhWLEfS0tLejr60NWVpa05nA40NbWhtraWr8B6P9FRESMqZ57770XXV1d0mO32428vDzk5uZKawaDAQkJCfjuu+84BBERhRAOQURENCFiYmKgVquhUCig1WpH3Xf+/HkoFArMnDlTWvv1118BAGazWVrr7u5GYmKi9Hjnzp3YsGGD9LioqGjYoHX16lUsX74cAPzqGBwcREFBASwWC1555RW/f6PX63H+/PkxHi0REU1mHIKIiGhSGRgYgEqluuWZndjYWOnqcg888ACGhob8nt+1axfy8vL81iorK+H1eoe9VllZGfr7+1FfXz/sTFN0dDQ8Hs9tHAkREU1WHIKIiGhSiYuLg8fjwdDQEJRKJYBrn/sBAKfTiYULFwK4diYnOTkZAEa8wIJWq5Wev06tVqO3t9dvraqqCkePHsXJkyehVquHvc6lS5cQHx9/x8dFRESTB68OR0REk8qCBQsAAGfOnJHWFi5ciLlz5+LNN98M6Pf2fPbZZ3j11Vdx8ODBET/zMzg4CJfLJQ1eREQUGjgEERHRpBIfH4+MjAx8//330lpERAT27dsHp9OJJUuW4KuvvsLZs2dx5swZvPfee/jzzz9HvdDCaFpbW/H000+jsrIS9913H9xuN9xuNy5duiTtOXHiBFQqFSwWS8COj4iI5MchiIiIJp01a9YM+5LSRYsWwW63w2w2o7y8HKmpqVi8eDFqa2uxa9curF+/fkz/R3NzMzweD6qqqqDT6aRbYWGhtKe2thbFxcWYOnVqQI6LiIgmhwgx1i9WICIiGmcDAwMwm8345JNPZDsL09PTA7PZjObm5hG/T4iIiIIXzwQREdGkEx0djY8//hg9PT2y1dDR0YF33nmHAxARUQjimSAiIiIiIgorPBNERERERERhhUMQERERERGFFQ5BREREREQUVjgEERERERFRWOEQREREREREYYVDEBERERERhRUOQUREREREFFY4BBERERERUVjhEERERERERGHlXyuwR87vRj/6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot S21\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.plot(\n",
    "    freqs_exp / 1e9,\n",
    "    20 * np.log10(np.abs(S21_exp)),\n",
    "    label=\"RF photonics solver (Flexcompute)\",\n",
    "    color=\"#328362\",\n",
    ")\n",
    "ax.plot(\n",
    "    freqs_sain_measured, S21dB_sain_measured, \"--\", color=\"#888888\", label=\"Measured (Sain et al.)\"\n",
    ")\n",
    "ax.plot(freqs_sain_hfss, S21dB_sain_hfss, \":\", color=\"#888888\", label=\"HFSS (Sain et al.)\")\n",
    "ax.set_title(\"S21 (dB)\")\n",
    "ax.set_xlabel(\"f (GHz)\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071360d6-77d7-4102-b812-6724db6b27b8",
   "metadata": {},
   "source": [
    "We observe a good match with the data from Sain et al. \n",
    "\n",
    "Remaining discrepancy with the measured data, as noted by Sain et al., can be attributed to metal surface roughness as well as deviations in the fabrication measurements. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc52215-6884-4ccb-bec3-dadbdc3aa12e",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208c17b2-b5a0-42de-b29c-63edc07368ca",
   "metadata": {},
   "source": [
    "[1] Sain, Arghya, and Kathleen L. Melde, \"Impact of ground via placement in grounded coplanar waveguide interconnects.\" IEEE Transactions on Components, Packaging and Manufacturing Technology 6.1 (2015): 136-144."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Microwave and RF devices"
  ],
  "description": "In this notebook, we demonstrate using via fences in a grounded CPW to suppress higher order modes. We also compare simulation results with measured data.",
  "feature_image": "./img/gcpw_via_fence_render.png",
  "features": [
   "Lumped port",
   "Smatrix plugin"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "co-planar waveguide, cpw, transmission line, via, interconnect",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  },
  "title": "Grounded co-planar waveguide with via fence"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
