{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Germanium Fano metasurface \n",
    "\n",
    "In this example, we reproduce the findings of Campione et al. (2016), which is linked [here](https://pubs.acs.org/doi/abs/10.1021/acsphotonics.6b00556?casa_token=v7Cq9VMW40UAAAAA:AWrBfYCHrwGQ9PYBYgdprrQ8X8i-nOairIplRs1Ejo2sbDmFT9nsV1M6UEXpvOfYSnwMjagD9IT97Ph2).\n",
    "\n",
    "This notebook was originally developed and written by Romil Audhkhasi (USC). \n",
    "\n",
    "The paper investigates the resonances of Germanium structures by measuring their transmission spectrum under varying geometric parameters.\n",
    "\n",
    "The paper uses a different commercial finite-difference time-domain software, which matches the result from Tidy3D.\n",
    "\n",
    "<!-- <img src=\"img/Ge_struct.png\" alt=\"diagram\" width=\"300\"/> -->\n",
    "\n",
    "To do this calculation, we use a broadband pulse and frequency monitor to measure the flux on the opposite side of the structure.\n",
    "\n",
    "Please check out another case study where we investigated a high-Q [silicon Fano resonator](https://www.flexcompute.com/tidy3d/examples/notebooks/HighQSi/). For more simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8WfOAGgkyh3K",
    "outputId": "f02b3adf-9117-42b1-a69c-b5f4f3f0a6c5",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# tidy3D import\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "gy7J23c5yh3S",
    "tags": []
   },
   "outputs": [],
   "source": [
    "Nfreq = 1000\n",
    "wavelengths = np.linspace(8, 12, Nfreq)\n",
    "freqs = td.constants.C_0 / wavelengths\n",
    "freq0 = freqs[len(freqs) // 2]\n",
    "freqw = freqs[0] - freqs[-1]\n",
    "\n",
    "# Define material properties\n",
    "n_BaF2 = 1.45\n",
    "n_Ge = 4\n",
    "BaF2 = td.Medium(permittivity=n_BaF2**2)\n",
    "Ge = td.Medium(permittivity=n_Ge**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "mGFiHbvhyh3T",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# space between resonators and source\n",
    "spc = 8\n",
    "\n",
    "# geometric parameters\n",
    "Px = Py = P = 4.2\n",
    "h = 2.53\n",
    "L1 = 3.036\n",
    "L2 = 2.024\n",
    "w1 = w2 = w = 1.265\n",
    "\n",
    "# resolution (should be commensurate with periodicity)\n",
    "dl = P / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Ol9GwSGUyh3U",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# total size in z and [x,y,z]\n",
    "Lz = spc + h + h + spc\n",
    "sim_size = [Px, Py, Lz]\n",
    "\n",
    "# BaF2 substrate\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, 0, -Lz / 2],\n",
    "        size=[td.inf, td.inf, 2 * (spc + h)],\n",
    "    ),\n",
    "    medium=BaF2,\n",
    "    name=\"substrate\",\n",
    ")\n",
    "\n",
    "# Define structure\n",
    "\n",
    "cell1 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[(L1 / 2) - L2, -w1 / 2, h / 2],\n",
    "        size=[L1, w1, h],\n",
    "    ),\n",
    "    medium=Ge,\n",
    "    name=\"cell1\",\n",
    ")\n",
    "\n",
    "cell2 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[-L2 / 2, w2 / 2, h / 2],\n",
    "        size=[L2, w2, h],\n",
    "    ),\n",
    "    medium=Ge,\n",
    "    name=\"cell2\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YO2yKasKyh3U",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=freq0, fwidth=freqw)\n",
    "\n",
    "# plane wave source\n",
    "source = td.PlaneWave(\n",
    "    source_time=gaussian,\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, Lz / 2 - spc + 2 * dl),\n",
    "    direction=\"-\",\n",
    "    pol_angle=0,\n",
    ")\n",
    "\n",
    "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
    "run_time = 8e-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4TZn_s8kyh3V",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# monitor fields on other side of structure (substrate side) at range of frequencies\n",
    "monitor = td.FluxMonitor(\n",
    "    center=[0.0, 0.0, -Lz / 2 + spc - 2 * dl],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=freqs,\n",
    "    name=\"flux\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Case Studies\n",
    "\n",
    "Here we define the two simulations to run\n",
    "\n",
    "- With no resonator (normalization)\n",
    "- With Ge resonator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fnpi5BiZyh3W",
    "outputId": "d25c2f44-d5a2-4241-b69b-0ffe5369ec5b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_spec = td.GridSpec(\n",
    "    grid_x=td.UniformGrid(dl=dl),\n",
    "    grid_y=td.UniformGrid(dl=dl),\n",
    "    grid_z=td.AutoGrid(min_steps_per_wvl=32),\n",
    ")\n",
    "\n",
    "# normalizing run (no Ge) to get baseline transmission vs freq\n",
    "# can be run for shorter time as there are no resonances\n",
    "sim_empty = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=[substrate],\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time / 10,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")\n",
    "\n",
    "# run with Ge nanorod\n",
    "sim_actual = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
    "    structures=[substrate, cell1, cell2],\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "id": "T8YFcftUyh3X",
    "outputId": "93b60986-ceb3-4064-eb8d-71a75495c11d",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Structure visualization in various planes\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 6))\n",
    "sim_actual.plot_eps(x=0, ax=ax1)\n",
    "sim_actual.plot_eps(y=-0.1, ax=ax2)\n",
    "sim_actual.plot_eps(z=0.1, ax=ax3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IG0QZ-PDyh3Y",
    "outputId": "332212da-3f04-4920-e593-801457a5e339",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5dcfaadee8884edcb1884895a51aaa15",
       "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\">09:59:52 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m09:59:52 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                       \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\">09:59:54 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.050</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m09:59:54 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.050\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "507b1363e1c94ac88ae5a759e7a821f8",
       "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\">10:06:41 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:06:41 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \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": "dc7a763c23484efa864f08d8ff1b5764",
       "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": [
    "# run all simulations, take about 2-3 minutes each with some download time\n",
    "batch = web.Batch(simulations={\"norm\": sim_empty, \"actual\": sim_actual}, verbose=True)\n",
    "batch_data = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normalizing run computes the transmitted flux for an air -> SiO2 interface, which is just below unity due to some reflection.\n",
    "\n",
    "While not technically necessary for this example, since this transmission can be computed analytically, it is often a good idea to run a normalizing run so you can accurately measure the *change* in output when the structure is added.  For example, for multilayer structures, the normalizing run displays frequency dependence, which would make it prudent to include in the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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\">10:06:45 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #800000; text-decoration-color: #800000\"> files have already been downloaded and will be skipped. </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">To forcibly overwrite existing files, invoke the load or download  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">function with `</span><span style=\"color: #808000; text-decoration-color: #808000\">replace_existing</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #800000; text-decoration-color: #800000\">`.                             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:06:45 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[31m files have already been downloaded and will be skipped. \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mTo forcibly overwrite existing files, invoke the load or download  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfunction with `\u001b[0m\u001b[33mreplace_existing\u001b[0m\u001b[31m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[31m`.                             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8b20ddc6e8a41978ff10a9d55d1f646",
       "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": [
    "batch_data = batch.load(path_dir=\"data\")\n",
    "transmission = batch_data[\"actual\"][\"flux\"].flux / batch_data[\"norm\"][\"flux\"].flux\n",
    "reflection = 1 - transmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "id": "x10TjU0uyh3a",
    "outputId": "b74cd9e6-f5a4-468f-f1d0-90c8d9861ed9",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BgwcD8O6771KvXj1tA7JhWSuN/vzzz+zfv58yZcpoFJEQeWdcUTsuLu6+f/v+++8B+OCDD/K1flSzZs2YNm0a165dY9myZYSGhqIoCqtXr6Z9+/bUqlWLlStXmmafCOfg4eFBqVKlCAwMpFu3boSGhrJ582atw8qRjDmxAePGjSMmJobq1aszZswYrcOxaVlbTvz9/TWKRIj8M9bryZqc3Lx5k507dwIUuBK0h4cHzz//PM8//zz//PMPs2fP5vvvv+fEiRM888wzVK5cmdGjR9OrVy+nmB5rboqimFaFtzYvL68CjSU6duwYu3fvpkKFCmaMyrwkOdHYiRMnmDVrFgDffPNNtt0WIl3W10emzwp7lFNyEhYWhsFgoFq1apQvX95sz1enTh2++eYbJk2axMSJE5kzZw4REREMGDCAKVOm8N5779G3b18ZPJsHd+7cwdvbW5PnvnXrFoULF87TfdauXYu3tzcpKSkkJiai1+uZMWOGhSIsOOnW0djbb79NamoqXbt2zXaFZpGZJCfCERiTk5s3b2bab5ym3q5dO4s8r4+PDxMnTiQqKopPPvmEgIAATp06xSuvvMKjjz7K77//bpHnFdpr27Ythw8fZu/evfTp04d+/frRvXt3rcPKkbScaGjTpk2sX78eV1dXGdCZS5KcCEeQU8uJsZ6OpZITI29vb95//30GDx7MjBkz+OSTTzhw4AAdO3akVatWzJo1i5o1a1o0Bnvn5eVlWohUi+fOq8KFC/PII48A8N1331G3bl3mz59P//79zR2eWUjLiUYUReHdd98F1JomVapU0Tgi+5CxlLJOp5OS7MIuZZecxMbGcvLkSQDatGljtTg++OADzp07x7Bhw3B1deXPP/8kODiY11577b6WHZFOp9NRuHBhTS4F7X7T6/W8//77jBkzJlO1bVsiyYlG1qxZw+HDh/H29pZBsHmQsYCZn5+fFDQTdim75MS41k5AQIDVB3qXKFGCL774goiICLp27YqiKMydO5egoCC+/fZbUlJSrBqPsLznnnsOFxcXZs6cqXUo2ZIzuwYURWHChAkADB06lGLFimkckf3I+Ish67RiIeyFcSpxxpaJ8+fPAxAYGKhBRKry5cuzatUqtmzZQnBwMHFxcQwcOJBmzZpx7NgxzeIS5ufq6sqQIUOYMmUKt2/f1jqc+0hyooG1a9dy6NAhvL29GTlypNbh2JWMLSUuLi4aRiJE/mXXcmJcF0rL5MSoXbt2HD58mIkTJ+Ln58f+/fupU6cO48aNK9AilkIbCxYsYNWqVfftf++997hy5UqeZ/5YgyQnGpg2bRoAgwYNklaTPMqYnEiXjrBXxgGNGfv7jS0n5pxCXBAuLi689957HD16lG7duqEoCh999BG1a9dm9+7dWocnHJyc3a3swIED/Pnnn7i6ujJ06FCtw7E7kpwIR2Ac2J2cnGzaZ1yl2LgwoK0oV64cK1euZPHixZQpU4bTp0/TvHlzRo4cKa0owmLk7G5ln3/+OQAvvPACZcuW1Tga+yPJiXAErq5qFYeMycm1a9cAKFmypCYxPcxLL73EP//8Q+/evQH1XBYcHMzx48c1jkw4Ijm7W9HFixdZtmwZACNGjNA4GvskyYlwBNm1nMTGxgLYdFdvsWLF+OGHH1i9ejVly5YlIiKC+vXrM3XqVAwGg9bhCQciZ3cr+u6770hJSaFFixY0aNBA63DskiQnwhFkl5xcv34dsI/1op566ikOHDjAk08+SVJSEqNGjaJly5bExMRoHZpwEHJ2txKDwcD8+fMBeO211zSOxn5JciIcgb22nGQUEBDAunXrmDVrFt7e3uzevZtatWqxZs0arUMTDkDO7layefNmzp07h5+fn02vZ2DrJDkRjsCYnBiLmyUnJxMfHw/YR8uJkU6n4/XXX2f//v3UrVuX69ev061bN4YNG5Yp8RIir+TsbiXz5s0DoFevXlI8rAAkORGOIGvLyY0bN0z/Zo/rRVWvXp39+/fz6quvoigKX331Fe3atePs2bNahybslJzdrSAmJobVq1cDMHDgQI2jsW8ZK8RKciLsVdbkxNil4+fnZ7fFBd3c3JgzZw5r1qyhcOHC7NixgxYtWvDHH39oHZqwQ3J2t4KFCxeSkpJCSEgItWvX1jocuyYtJ8IR5JSc2FOXTk66dOnCkSNHqFGjBpcuXeLxxx/n66+/RlEUrUMTdkTO7lbw448/AvDKK69oHIn9k+REOIKsyUlCQgKAw6yyXblyZfbu3UufPn1ITU1l6NChvPHGGzIOxcp0Ot0DLx9++KHWIebIVesAHN3Ro0c5evQo7u7uPPfcc1qHY/ckORGOIGtycuvWLQC8vb01i8ncihQpwvfff09wcDCjRo1izpw5nDp1il9++cVuZiTZu8uXL5u2ly1bxrhx4wgPDzfts+X3m5zdLWzx4sUAdOzY0S4HutkaSU6EIzAmJ4qikJqaaloV1hYXYCsInU7H22+/zZo1a/D29mbbtm2EhIRw8uRJrUNzCqVKlTJdfH190el0mfZJcuKkDAYDS5YsAaBnz54aR+MYJDkRjsCYnIDaeuKILScZde7cmd27dxMUFERERARNmjRh48aNWodlHrdv53zJuvbQg47NsAjkA491EnJ2t6CdO3dy/vx5fHx86Ny5s9bhOARJToQjMCYnrQC3Zs0oktaS4GgtJxnVrl2bffv20aJFC+Li4ujYsSNfffWV/Q+U9fbO+ZK1plXJkjkf++STmY8NCsr+OCchZ3cLWrRoEQDdu3fH09NT42gcgyQnwhEYk5PtgMuhQzw/Zw7g2MkJQIkSJdiyZQv9+vXDYDAwbNgwXn/9dRkoK+4jA2ItJDk5meXLlwPSpWNOkpwIR5C1lolHUhLguN06GXl4eDB//nxq1arFO++8w9y5czl9+jRr1661zwKVaV1y2cpas+bKlZyPzXo+i4zMd0iOQM7uFhIWFkZsbCwlSpSgTZs2WofjMCQ5EY5Ap9Ph5ubGL2m3t9esCTh+y4mRTqfjrbfeMg2U/eOPP2jTpk2m2SV2o3DhnC9ZW8wfdGzWxCyn45yEnN0txNhq0q1bN7ut+GiLJDkRjsLV1ZWradvX084RztByklHnzp1Zv349vr6+7Nu3jzZt2kjJewFIcmIRqamprFy5EoBnn31W42gcS8by9Rm3hbA3bm5uGN/BSWljLpyl5SSjli1bcuDAAcqXL8+pU6cICQnJVItDOCdJTixg165dXLlyhaJFi9K2bVutw3Eo0nIiHIWbmxv10raDrl0DnK/lxKhy5crs3LmTunXrcvXqVR599FF27dqldVgOpW/fvty8eVPrMHJNzu4WYOzSeeqppzLVMxAFJ8mJcBRubm7sTNs+4+4OOGfLiVFgYCAbNmygefPmJCQk8OSTT7J+/XqtwxIakbO7mRkMBlasWAGoU4iFeUlyIhyFm5sbxgofyU7crZNRqVKl2LRpE4899hgJCQk8/fTT/Pbbb1qHJTQgZ3cz+/vvv7lw4QLe3t60b99e63AcjiQnwlFkTE6U1FRAnWbr7Ly8vPj999/p1q0bSUlJdOvWjaVLl2odlrAyObub2dq1awF48sknpfCaBUhyIhyFm5sb76ZtP3vjBqDO4BHg7u7O0qVL6du3LwaDgZdffpm5c+dqHZawIjm7m5kxOZFy9ZYhyYlwFBnHo3mnlXCX5CSdh4cH3377Lf369SM1NZU33niDb7/9Vuuw7L/cfi7Ywt8oZ3czunjxIocOHUKn0/Fk1nUShFlIciIcRXaD5SU5yczFxYX58+fz6quvYjAYGDhwIPPmzdMsFoCktGq+jsz4N2pZo0s+CWa0bt06AJo2bUqJEiU0jsYxSXIiHIUkJ7mj0+mYNWsWvr6+TJ06lVdffZV79+7x5ptvWjUOV1dXvLy8uHr1Km5ubg57/jEYDFy9ehUvLy9N34/ySTAj6dKxPElOhKNwc3NjI9ABmOfpCffuSXKSA71ez+TJk0lJSeHzzz9n+PDhuLi4MGjQIKvFoNPpKF26NGfPnuXcuXNWe14t6PV6ypcvr2mhS/kkmMndu3fZsmULIMmJJWX8sEhyIuyZm5sb0WnbFzLsE9nT6XRMnz6dxMREvvnmGwYPHoyHhwf9+/e3Wgzu7u5UqVLF4bt23N3dNT+/SnJiJn/88Qd3796lfPnyBAcHax2Ow5KWE+EoXF1dTeXrUw0G0z6RM51Ox4wZMyhcuDBTp05l4MCB6HQ6XnnlFavFoNfrZSamFcjZ3UyMlQw7deoka75YkCQnwlHodDpqpG03TkkBJDnJDZ1Ox+TJkxkyZAiKovD666/z448/ah2WMDM5u5vJpk2bAGSWjoVlTEgkCRT2TK/XszZt+5JMJc4TnU7HV199Rc+ePUlOTqZv376sXr1a67CEGUlyYgZnzpzh9OnTuLq60qZNG63DcWiSnAhHodPpTBVikeQkz3Q6HQsXLqR///4YDAaef/55Nm7cqHVYwkwkOTGDzZs3A9CsWTOKFCmicTSOTbpyhKPQ6/UY0raNabYMiM0bvV7P7NmzefbZZ0lKSqJHjx6EhYVpHZYwAznTm4GxS+fxxx/XOBLHJ8mJcBQ6nY4P07ZfSLuWlpO8c3V1ZfHixbRq1Yq4uDiefvppTpw4oXVYooDkTF9AKSkpbN26FZDkxBokORGOQq/Xm6ZL+qZdS3KSP+7u7mzcuJEmTZpw8+ZN2rZty6lTp7QOSxSAnOkLaP/+/cTFxeHv70+DBg20DsfhSXIiHEV2Y6YkOck/T09P1q5dS926dYmJiaFLly5ERkZqHZbIJznTF5CxSyc0NFTTdQichSQnwlFk916Wc0jBFCtWjI0bN1KuXDlOnTpFt27diI+P1zoskQ9ypi8gGW9iXTJDRzgKnU7H7rTtL1GTFUm+Cy4gIIDdu3dTokQJjhw5QocOHbhz547WYYk80vyTMHPmTIKCgvD09CQkJIR9+/Y98PgvvviCatWqUahQIQIDAxkxYgT37t2zUrSZ3bx5k7179wLQvn17TWJwNjKVWDgKvV7PxbTtU8hMHXMKDAxkw4YNFC1alL/++ouXXnqJ1NRUrcMSeaBpcrJs2TJGjhzJ+PHjOXjwIHXr1qVDhw5cuXIl2+OXLFnCe++9x/jx4zlx4gTz589n2bJlvP/++1aOXPXHH3+QmppKtWrVKF++vCYxOJuMyYmiKA84UgjbptPpTCdgBRlvYm4NGjRgzZo1eHh4sHr1aoYNGybnDDuiaXLy2WefMXDgQPr160fNmjWZPXs2Xl5efPfdd9kev3v3bpo3b85LL71EUFAQjz/+OC+++OJDW1ss5Y8//gCk1cSapNlbOAq9Xs8jadttkOTEElq0aMGiRYvQ6XTMnDmTadOmaR2SyCXNzvRJSUkcOHCA0NDQ9GD0ekJDQ9mzZ0+292nWrBkHDhwwJSNnzpxh/fr1dOzY0SoxZ7Vt2zYA2rZtq8nzOyNJToSj0Ol0/JC2nYQkJ5by7LPPMn36dABGjRrFTz/9pHFEIjc0+zRcu3aN1NRUAgICMu0PCAjg5MmT2d7npZde4tq1a7Ro0QJFUUhJSeH1119/YLdOYmIiiYmJptvmGrl95coVjh8/DkCrVq3M8pji4WSciXAUer3eVL5ehyQnljRixAiioqL44osv6NWrF/7+/nTo0EHrsMQD2NXP0LCwMD799FO++eYbDh48yIoVK1i3bh0fffRRjveZOHEivr6+pktgYKBZYtm+fTsAderUoXjx4mZ5TCGE88i4to4OGRBradOnT6dnz56kpqby0ksv8ffff2sdkngAzZKT4sWL4+LiQkxMTKb9MTExlCpVKtv7jB07ll69ejFgwABq167N008/zaeffsrEiRMxGAzZ3mf06NHExcWZLufPnzdL/MYuHVnoTwiRH3q9nv+lbXdGWk4sTa/XM2/ePBo3bkxsbCzPPvssV69e1ToskQPNkhN3d3caNmxoKv0OYDAY2Lp1K02bNs32Pnfu3LlvzIGxaFFOo7A9PDzw8fHJdDEH4+JSMt5ECJEfOp0O49nIB0lOrKFQoUJs3ryZSpUqce7cOZ588kkSEhK0DktkQ9NunZEjRzJv3jx++OEHTpw4wRtvvMHt27fp168fAL1792b06NGm47t06cKsWbNYunQpZ8+eZfPmzYwdO5YuXbpYtbJiTEwMJ06cQKfTyXgTIUS+ZP2hJcmJdfj4+LB+/XqKFy/OgQMH6Nu3L8nJyVqHJbLQ9NPQo0cPrl69yrhx44iOjqZevXps2LDBNEg2Kioq0wd4zJgx6HQ6xowZw8WLFylRogRdunThk08+sWrcxlaTOnXq4O/vb9XnFkI4hqyDuyU5sZ5q1aqxevVqWrduzYoVKxg1ahSff/651mGJDDT/NAwZMoQhQ4Zk+2/GJMDI1dWV8ePHM378eCtEljPp0rENMnNH2DO9Xs9RoDbwBZKcWFuzZs1YtmwZ3bt354svvqBixYoMHTpU67BEGruarWMrZDCsEKKgdDodxuH5h5HZOlp45plnGDduHKBON844BlJoS5KTPLp8+TLh4eEy3kQIUSB6vR5j25+Ur9fOhx9+SN++fTEYDDzzzDMcO3ZM65AEkpzkmbG+Sb169ShatKjG0Qgh7JVOp6NS2vZjSHKiFZ1Ox6xZs2jZsiXx8fH06NHjvhIXwvokOckj6dIRQpiDXq9netq2L5KcaMnT05NffvmFgIAAjh8/zksvvZRj7SxhHZKc5JEMhhVCmEPWCrGSnGgrICCAbdu2UahQIf744w9GjRqldUhOTZKTPLh06RKnTp1Cr9fTsmVLrcMRQtgxWVvH9tSoUYNvv/0WUMvdf/fddxpH5LwkOckDY6tJ/fr18fPz0zQWIYR90+l0pvL1bZDZOrbipZdeYsKECQC89tprbN68WeOInJMkJ3lgTE5kvIkQoqD0ej1l0rZ9wKpVrsWDjRkzhhdeeIGUlBT69u1LRESE1iE5HUlO8kAGwwohzCVrEcGs5eyFdvR6PfPnz6d69epcunSJZ555hsTERK3DciryacilCxcucPr0aRlvYkOkQqywZ1mTEUlObIuXlxdbt26lePHi/PPPPwwYMCDHBWaF+cmnIZeM9U0aNGiAr6+vxtEIIeydTqfjTNr2l0iybYvKlCnDkiVLcHFxYdGiRbL+jhVJcpJLxi4dmUIshDAHvV5vKl+/C2k5sVXt27fnyy+/BGDUqFH89ttvGkfkHOTTkEsyGNb2SBOrsGc6nS5T+XpJTmzXoEGD6Nu3L6mpqQwYMIAzZ848/E6iQOTTkAvnz58nIiICFxcXWrRooXU4QggHoNfrqZC23Rbp1rFlOp2O2bNnExwczJUrV+jYsSO3bt3SOiyHJslJLhhbTRo2bIiPj4+2wQghHIJOp+P9tO3qSMuJrfPw8GDTpk2ULVuW8PBwXnvtNWm9tSD5NOSCdOkIIcxNp9ORmuW2sG2lS5dm6dKluLi4sGTJEmbMmKF1SA5LkpNckMGwQghzy1q+XlpO7EOLFi2YPl1dsvGdd95hy5YtGkfkmOTT8BDnzp3j7NmzuLi40Lx5c63DEUI4iIzl6xsjyYk9efPNN02F2fr27cvVq1e1DsnhyKfhIYxdOo0aNaJIkSLaBiOEcBh6vZ5qaduFkW4de6LX61m0aBGVK1fm4sWLdOrUiaSkJK3DciiSnDyEMTmRLh3bIydzYc+kfL19K1SoEOvWraNo0aLs37+f9957T+uQHIp8Gh5CBsMKISxBytfbv2rVqrFgwQIAPv/8c37++WdtA3Ig8ml4gMjISCIjI3F1dZXxJkIIs9LpdESnbX+FtATaq6eeeop3330XgDfeeIPjx49rHJFjkOTkAYyzdB599FG8vb01jkYI4Uj0ej0X0rY3Ii0n9uyjjz6icePGxMbG8txzz8n4EzOQT8MDyBRi21SnTh0AevXqpXEkQuRf1vL10nJiv9zc3Fi3bh3Fixfn+PHj9O7dWwq0FZAkJzlQFEWSExu1d+9eTp06xWOPPaZ1KELkm16vp1Tadkuk5cTeFS9enMWLF+Pm5sayZcuYPXu21iHZNfk05CAiIoILFy7g5uZGs2bNtA5HZODp6UmVKlW0DkOIAtHpdIxM234cSU4cweOPP87kyZMBGDFiBAcOHNA4Ivsln4YcGFtNmjRpgpeXl8bRCCEcjV6v53badirSreMohg0bRufOnUlMTKR79+5cu3ZN65DskiQnOZAuHSGEJel0Oilf74D0ej0//vgjlStX5ty5c/Tr10/Gn+SDfBqyIeNNhBCWptfrGZ+2XQtJThyJn58fK1euxM3NjbVr1zJr1iytQ7I78mnIxqlTp4iOjsbDw4MmTZpoHY4QwgHpdDoap217Id06jqZ27dqMH6+mn8OGDeOff/7ROCL7IslJNoytJk2bNsXT01PjaIQQjkgqxDq+999/n6effpqUlBR69OhBfHy81iHZDfk0ZEO6dIQQlpa1pURaThyPTqdj1qxZlC1blpMnT/Lqq69qHZLdkOQkC0VRZLE/IYTF6fV64tK2ZyAtJ44qICCAX3/9FRcXF5YtW8ZXX32ldUh2QT4NWRw/fpwrV65QqFAhGjdu/PA7CCFEPuh0Oi6mbf+KJCeOrEmTJkyaNAmAd955R9bfyQX5NGRh7NJp3rw5Hh4eGkcjhHBUer1eytc7kbfeeosOHTqQlJTEE088QUJCgtYh2TRJTrKQ8SZCCGvQ6XT4p203QVpOHJ1Op2PhwoVUqlSJ8+fP8+abb0r9kweQT0MGBoOB7du3A5KcCCEsS6/XMyhteyCSnDiDkiVLMmfOHPR6PT/88APz5s3TOiSbJZ+GDI4cOcL169cpXLgwjRo10jocIYQD0+l0GAubJyPdOs4iNDSUKVOmAOr6OydPntQ4ItskyUkGmzdvBtRWEzc3N42jEUI4Mr1eL+XrndSIESNo164dd+7coXv37iQmJmodks2RT0MGxuSkffv2GkcihHB0Op2OsWnblZCWE2ei1+tZvHgxAQEBHD9+nOHDh2sdks2R5CTN3bt32bFjByDJiRDC8vR6PcYzjTvScuJsAgICmDNnDjqdjtmzZ/Pbb79pHZJNkU9Dmp07d5KYmEjZsmWpXr261uEIIRxc1pYSSU6cT9euXXn77bcBePnll4mIiNA4Itshn4Y0Gbt0pHlVCGFpWZMROe84pw8//JCmTZsSHx/P66+/jsFg0DokmyDJSRoZbyKEsCadTodxGOQspOXEWXl5ebFgwQIKFSrEli1bmDhxotYh2QT5NAAxMTEcPnwYUKd5CSGEpen1eqLTtr9DkhNnVrVqVWbOnAnAuHHj2LNnj8YRaU8+DcDWrVsBqFevHiVLltQ4GiGEM9DpdFK+Xpj069ePnj17YjAY6NWrF7du3dI6JE1JcoJ06QghrE+v1+Odtt0QaTkRMGPGDAIDA4mIiODVV1916vEnTv9pUBRFkhMhhNXpdDoGpG3/D2k5EeDn58fChQtxcXHhp59+Ys6cOVqHpBmnT06OHj3KxYsXKVSoEC1atNA6HCGEk9Dr9ZxL205CWk6Eqk2bNkybNg2AUaNGmcZDOhun/zSsW7cOgHbt2lGoUCGNoxFCOAudTifl60W2Bg8eTNu2bbl16xb9+/cnOTlZ65Cszuk/DcbkpFOnThpHIoRwJnq9nvfTtssg3ToinZubGz/99BN+fn4cPHiQDz74QOuQrM6pk5Pr16+bpmx17NhR42iEEM5Ep9PxbNq2Hmk5EZkFBATw7bffAjB16lQ2bdqkcUTW5dSfho0bN2IwGKhduzbly5fXOhwhhBPJmoxIciKy6t69O4MHDwbglVde4caNGxpHZD1O/WmQLh0hhFayduNIt47IzpQpU6hSpQoXL15k0KBBKIry8Ds5AKdNTlJTU9mwYQMgyYkQwvoytpTMQ1pORPa8vLxYuHAher2epUuXMnXqVK1Dsgqn/TTs27eP2NhY/P39adKkidbhCCGcjE6n41ra9udIy4nIWZMmTfj6668B+Oijjzh69KjGEVme0yYna9asAdSBsK6urhpHI4RwNnq9PlP5emk5EQ/y+uuv07p1a27dusXzzz/v8NOLnfbTYExOunfvrnEkQghnpNPp8EzbDkaSE/Fger2eFStWUKJECU6ePMmHH36odUgW5bSfhgsXLlC4cGE6dOigdShCCCek1+t5OW37B6RbRzycv7+/afXiiRMnmpZecUSaJyczZ84kKCgIT09PQkJC2Ldv3wOPv3nzJoMHD6Z06dJ4eHhQtWpV1q9fn6/n7tSpk1SFFUJoQqfT8W/atpSvF7n13HPP8frrr6MoCv369XPY6cWafhqWLVvGyJEjGT9+PAcPHqRu3bp06NCBK1euZHt8UlIS7du3JzIykl9//ZXw8HDmzZtH2bJl8/X80qUjhNCKXq+X8vUiX6ZPn26aXjx06FCtw7EITT8Nn332GQMHDqRfv37UrFmT2bNn4+XlxXfffZft8d999x2xsbGsWrWK5s2bExQUROvWralbt26en9vDw0OqwgohNKPT6RiVtu2LdOuI3Ms4vXjRokWmmTyORLPkJCkpiQMHDhAaGpoejF5PaGioqaR8VmvWrKFp06YMHjyYgIAAgoOD+fTTT0lNTc3xeRITE4mPj890AQgNDcXb29u8f5QQQuSSXq9nYJbbQuRWkyZNmDhxIqCuXnz27FmNIzIvzT4N165dIzU1lYCAgEz7AwICiI6OzvY+Z86c4ddffyU1NZX169czduxYpk+fzscff5zj80ycOBFfX1/TJTAwEICuXbua748RQog8kgqxoqDeeecdWrZsyb179+jUqRNJSUlah2Q2BU5O7t27Z444csVgMFCyZEnmzp1Lw4YN6dGjBx988AGzZ8/O8T6jR48mLi7OdDl//jwAnTt3tlbYQghxH1lbRxSUTqdj8eLFBAQEcOLECT766COtQzKbfH0aDAYDH330EWXLlsXb25szZ84AMHbsWObPn5+rxyhevDguLi7ExMRk2h8TE0OpUqWyvU/p0qWpWrUqLi4upn01atQgOjo6x4zRw8MDHx+fTBeAwoUL5ypOIYSwhIwtJT8gyYnIn8DAQKZNmwbAxx9/7DCrF+fr0/Dxxx+zYMECpkyZgru7u2l/cHCwaYnnh3F3d6dhw4Zs3brVtM9gMLB161aaNm2a7X2aN2/O6dOnMRgMpn2nTp2idOnSmeIQQghbp9fruZO2PQ7p1hH59/LLL2davfjmzZvaBmQG+UpOFi5cyNy5c+nZs2emVoy6dety8uTJXD/OyJEjmTdvHj/88AMnTpzgjTfe4Pbt2/Tr1w+A3r17M3r0aNPxb7zxBrGxsQwbNoxTp06xbt06Pv30U9N/ihBC2AudTkfGdERaTkRBZFy9+NVXX7X71YvztajMxYsXeeSRR+7bbzAY8lTvv0ePHly9epVx48YRHR1NvXr12LBhg2mQbFRUVKYPbGBgIBs3bmTEiBHUqVOHsmXLMmzYMN599938/BlCCKEZvV6PW9p2ZSQ5EQXj5eXFokWLaN68Ob/88gvt27dn4MCBD7+jjcpXclKzZk127NhBhQoVMu3/9ddfqV+/fp4ea8iQIQwZMiTbfwsLC7tvX9OmTfnrr7/y9BxCCGFrdDodzwKrgD+A36VbRxRQ48aN+fTTTxk1ahTDhg3jiSeeMM1QtTf5Sk7GjRtHnz59uHjxIgaDgRUrVhAeHs7ChQtZu3atuWMUQgiHo9frMS7WkYK0nAjzeOutt1izZg07d+7k5ZdfZvPmzXY5JjNfn4auXbvy22+/sWXLFgoXLsy4ceM4ceIEv/32G+3btzd3jEII4XB0Ol2m8vUyIFaYg16vZ9asWfj4+PDnn3+aFgq0N/lqOQFo2bKlQ6+IKIQQlqTX6xmetu2CtJwI8wkODmbq1Km89tprjBo1ipCQEJo1a6Z1WHkinwYhhNCATqcj41B+SU6EOQ0YMIDu3buTkpLCoEGDuHPnzsPvZENy3XJStGjRXDc7xsbG5jsgIYRwBlmTEenUEeak1+v55ptv2LZtG0eOHGHo0KG5rkNmC3KdnHzxxRem7evXr/Pxxx/ToUMHU8G0PXv2sHHjRsaOHWv2IIUQwtFk/bGnlzEnwsxKlizJr7/+Srt27Zg/fz7PPPMMHTt21DqsXNEp+ajU0r17d9q2bXvfFOAZM2awZcsWVq1aZa74zC4+Ph5fX1/i4uJMpeyFEMLajv/7LzWDgwFYAgSGhdGydWttgxIOacSIEXzxxRcEBARw5MiR+xbczQtrfYfmq5Nz48aNPPHEE/ftf+KJJ9iyZUuBgxJCCEeXseVkGKDLUG1bCHP69NNPCQ4OJiYmhr59+2ZaAsZW5Ss5KVasGKtXr75v/+rVqylWrFiBgxJCCEeXsRtHQQbECsspVKgQS5cuxdPTkw0bNjB37lytQ3qofE0lnjBhAgMGDCAsLIyQkBAA9u7dy4YNG5g3b55ZAxRCCEeUcYRJaWRArLCsWrVqMWnSJIYPH87o0aNp1aoVNWvW1DqsHOUrVe/bty+7du3Cx8eHFStWsGLFCnx8fNi5cyd9+/Y1c4hCCOGA9Hq6pW0eBVxSUjQMRjiDwYMH06RJE27evEn//v1tunsn30XYQkJCWLx4sTljEUIIp6HT69ma4bbM1hGW5urqyvLly6lSpQp//fUXH3zwARMnTtQ6rGzlq+UkKirqgRchhBAPlrF8PUhyIqyjTJkyzJ49G4CpU6dy+PBhbQPKQb5aToKCgh5YkC01NTXfAQkhhDPQpabyeobbkpwIa+nVqxfLly9n9erVdO3alePHj1O4cGGtw8okX8nJoUOHMt1OTk7m0KFDfPbZZ3zyySdmCUwIIRyZPjWVaRlvS3IirOj777+nfv36nDt3jg8++CBToVVbkK/kpG7duvfta9SoEWXKlGHq1Kk888wzBQ5MCCEcWdZURFITYU1Fixblm2++oVOnTnz55Ze0b9+eTp06aR2WiVkn1lerVo39+/eb8yGFEMIhZU1GpMqJsLaOHTsydOhQAPr378+1a9c0jihdvj4P8fHxmS5xcXGcPHmSMWPGUKVKFXPHKIQQDidjcrIEwM1No0iEM5s8eTI1a9YkJiaGN954g3ysaGMR+UpO/Pz8KFq0qOni7+9PzZo12bNnD7NmzTJ3jEII4XAyTioYAOhsbECicA6enp78+OOPuLq68uuvv/LTTz9pHRKQzzEn27Zty3Rbr9dTokQJHnnkEVxd8106RQghnEbGlhMpXy+01KBBA8aNG8e4ceMYPHgwrVu3pmzZsprGlK9MQqfT0axZs/sSkZSUFP78809atWplluCEEMJRZUxOfACdjTSnC+c0evRofvvtN/bv30+nTp3Ys2cPhQoV0iyefKXqbdu2JTY29r79cXFxtG3btsBBCSGEwytUiK5pmzGAa3y8ltEIJ+fq6sqSJUsoUaIER44cYfLkydkfmM2iv5aQr+REUZRsi7Bdv37d5gq5CCGELdK5u7Mmw22pcyK09sgjj/DZZ58B6gK/mzdvznzA9evw6qtWiSVP3TrG+iU6nY6+ffvi4eFh+rfU1FT++ecfmjVrZt4IhRDCAWX9gSepibAFPXv2JCwsjPnz5zNkyBB2795NsWLF1H8sVgx++QW6dLF4HHlqOfH19cXX1xdFUShSpIjptq+vL6VKleLVV19l0aJFlopVCCEchi4piV4ZbkvLibAFOp2OKVOmULZsWU6dOsWwYcMyH2ClMaV5ajn5/vvvAXVtnbffflu6cIQQIp/0d+6wMONtzSIRIjN/f39WrlxJSEgIixcvZpCvL80GDYJatawWQ74+D+PHj5fERAghCkDK1wtb9uijjzJ69GhqAQ2/+QalYUM4dsxqz5/rlpMGDRqwdetWihYtSv369R+4KvHBgwfNEpwQQjgqKV8vbN2EDz6g95df4nH7Nn95e9O4Rg24fdsqz53r5KRr166mAbDdunWzVDxCCOEUspavb6thTQkhsuP6/vtUu32b68DT16/z/jff0KdPH6s8t06xlUL6VhIfH4+vry9xcXH4+PhoHY4QwknFnjiBf82agJqoXLp0idKlS2sblBBGO3aYBr+uf/VVOs2di4eHB+vWrSM0NNTi36EFqjWflJTElStXMBgMmfaXL1++QEEJIYSjM7acGM+eUr5e2IwrV6B7d3V7wACemDWLbleusGrVKt58802rhJCv5OTUqVP079+f3bt3Z9pvLM6WmppqluCEEMJRGZMTPeqJWAbECpuxfj1cvQrBwTB9Onq9nvnz5/Pnn39y4sQJq4SQr+SkX79+uLq6snbtWkqXLv3AwbFCCCGy4efHC8BSIBm4HhkJAQHaxiQEQJ8+cPy4WmwtrevG39+fX375hXbt2lklhHyNOSlcuDAHDhygevXqlojJomTMiRDCFsTFxeHn58cNwA+I/esv/ENCNI5KiAfr0aMHP//8s8W/Q/PVyVmzZk2uXbtm7liEEMJpGFucjb8OZVVioanERBg/Hh6yAOVHH31klXDylZxMnjyZUaNGERYWxvXr14mPj890EUII8WC6u3d5BihqvK1lMEJ8+CH8738QGgoPSJRLlSpllXDyNeYkNDQU4L6+JxkQK4QQuaOPjWV5htvSciI0s3s3TJmibo8eDTYwjjRfycm2bdvMHYcQQjiV+yrE2sAXgnBCt29D795gMKjXTz+tdURAPpOT1q1bmzsOIYRwKvetrSMtJ0ILw4dDRASUKwdffql1NCb5Sk7++eefbPfrdDo8PT0pX768qdS9EEKI+2VMTlYD7YoU0SoU4axWrIBvv1W7cX74Afz8tI7IJF/JSb169R5Y28TNzY0ePXowZ84cPD098x2cEEI4KuMZ9DbQDUgICtIsFuGkZs1Sr997Dx57TNtYssjXbJ2VK1dSpUoV5s6dy+HDhzl8+DBz586lWrVqLFmyhPnz5/PHH38wZswYc8crhBCOIa0bxzSVWMacCGtbuhReegnGjdM6kvvkq+Xkk08+4csvv6RDhw6mfbVr16ZcuXKMHTuWffv2UbhwYd566y2mTZtmtmCFEMJRGFMRSU6EZooVg8WLtY4iW/lqOTl69CgVKlS4b3+FChU4evQooHb9XL58uWDRCSGEg9IFBNAfKAIkAfoDBzSOSDiFc+fg118fWMvEFuQrOalevTqTJk0iKSnJtC85OZlJkyaZStpfvHiRAFknQgghsqXz9eU74AzghszWEVaQmAhdu8Jzz8GwYVpH80D56taZOXMmTz31FOXKlaNOnTqA2pqSmprK2rVrAThz5gyDBg0yX6RCCOFAspavz9cvRSHy4vPP4cgRKFHC5pOTfC38B5CQkMDixYs5deoUANWqVeOll16iiI1Ph5OF/4QQtsAQH08nX19+T7udsn07rq1aaRqTcGAHDkDTppCcDN9/D3375uthrPUdmq+WE4AiRYrw+uuvmzMWIYRwGroLF0yJCaBW6BTCEm7dghdfVBOTp5+GPn20juih8p2cABw/fpyoqKhMY08AnnrqqQIFJYQQji7rGBOZqyMs5u234b//1CqwxqJrNi5fycmZM2d4+umnOXr0KDqdDmPPkLEPVRb+E0KIh8ianMiAWGEJERFqQgKwcCH4+2sbTy7lawzWsGHDqFixIleuXMHLy4t///2XP//8k0aNGhEWFmbmEIUQwgFl6MbZDOiKFtUuFuG4KleGXbtg8mRo21braHItXy0ne/bs4Y8//qB48eLo9Xr0ej0tWrRg4sSJDB06lEOHDpk7TiGEcCxpLSWXgMcBpV49LaMRjiwkRL3YkXy1nKSmpppm5RQvXpxLly4BahG28PBw80UnhBCOKq3lRDpzhEWsXQv//qt1FPmWr5aT4OBgjhw5QsWKFQkJCWHKlCm4u7szd+5cKlWqZO4YhRDC8aS1nMgcHWF2f/8Nzz+vFl3buhXatNE6ojzLV8vJmDFjMKRl/RMmTODs2bO0bNmS9evX8+WXX5o1QCGEcEjlyvEmEAjcANiyRdt4hGNISYFBg+DuXXj8cWjRQuuI8iVfLScZF/yrUqUKJ0+eJDY2lqJFi8riVUIIkRslSzID6As0BLUGhRAF9fHHsH8/FCmiFltzLVDFEM3kKepXXnklV8d99913+QpGCCGcjWnMiRRhEwW1fTt89JG6PWsWlCqlbTwFkKfkZMGCBVSoUIH69euTz6r3QgghAOLjaQU0Mt6Wc6ooiLg46N1bTXL79oWePbWOqEDyNObkjTfeIC4ujrNnz9K2bVvmz5/PypUr77vk1cyZMwkKCsLT05OQkBD27duXq/stXboUnU5Ht27d8vycQgihqWPH2J7xtrSciIL49luIioJKleDrr7WOpsDylJzMnDmTy5cvM2rUKH777TcCAwN5/vnn2bhxY75bUpYtW8bIkSMZP348Bw8epG7dunTo0IErV6488H6RkZG8/fbbtGzZMl/PK4QQmsp6zpSWE1EQI0bAzJmwYAF4e2sdTYHlebaOh4cHL774Ips3b+b48ePUqlWLQYMGERQUxK1bt/IcwGeffcbAgQPp168fNWvWZPbs2Xh5eT1w3Epqaio9e/ZkwoQJMnVZCGGfsraUSMuJKAi9Xp2l4yA/2PM1ldh0Z73etLZOftbTSUpK4sCBA4SGhmZ6zNDQUPbs2ZPj/f73v/9RsmRJ+vfv/9DnSExMJD4+PtMFgB078hyvEEKYTYaWkt0AUr5e5JXBAF98ATdvah2J2eU5OUlMTOSnn36iffv2VK1alaNHjzJjxgyioqLwzmNT0rVr10hNTSUgICDT/oCAAKKjo7O9z86dO5k/fz7z5s3L1XNMnDgRX19f0yUwMFD9h6++ylOsQghhVmktJceB5mCXhbKExj78UO3OqV/f4aai5yk5GTRoEKVLl2bSpEl07tyZ8+fP88svv9CxY0f0+gI1wuRKQkICvXr1Yt68eRQvXjxX9xk9ejRxcXGmy/nz59V/2LZNHd0shBBaSGs5kZEmIl/CwtKnDY8dC25umoZjbnmaSjx79mzKly9PpUqV2L59O9u3b8/2uBUrVuTq8YoXL46LiwsxMTGZ9sfExFAqm/nZERERREZG0qVLF9M+Y6VaV1dXwsPDqVy5cqb7eHh44OHhcf+TJyeraw/Y+XQrIYSdkvL1Ir/i49XpwgADB0Iua5DZkzwlJ7179zZrBVh3d3caNmzI1q1bTdOBDQYDW7duZciQIfcdX716dY4ePZpp35gxY0hISODLL79M77LJreXLJTkRQmijUiXeAyYBFwBWroSnn9Y2JmEfhgyBc+egYkWYPl3raCwiz0XYzG3kyJH06dOHRo0a0bhxY7744gtu375Nv379ADUhKlu2LBMnTsTT05Pg4OBM9/fz8wO4b3+u/P473LrlENOuhBB2JiiIyUA7oD3AnTvaxiPsww8/wI8/qrNzFi5Uy9Q7IM2L7vfo0YOrV68ybtw4oqOjqVevHhs2bDANko2KirLMeJaKFeHsWTVBee458z++EELkgmnMidQ5EQ+TmgqffqpuT5hgt4v65YZOcbI69PHx8fj6+hI3bBg+X34JQ4eCrKQshLC2uDga+fnxKxAE6i/i3r21jUnYvhs3YPZsGDUKXFys/vSm79C4OHx8fCz2PJq3nGjm9ddh8GCoUkXrSIQQzmjXLv7OeFuKsIncKFoURo/WOgqLs/z8X1tVpowkJkII7Uj5epFb27fDN9841XvEeVtOMlIUMOMsJCGEeCgpXy9yIz4e+vRRZ+ckJcHw4VpHZBXO23ICEBkJ3btD06ZaRyKEcDYZfgUfBvD31yoSYcuGD0+fNpyLJVschXO3nPj5werV6gjos2fV/3whhLCGtJaSXUALQJEaJyKrH3+E779XW/Z/+MFhpw1nx7lbTvz80qdirVunaShCCCeTlpw4zygCkScXLqirDAOMH+8wqw3nlnMnJwCdOqnXa9dqG4cQwrlI+XqRk8REtf7WrVvqsIMxY7SOyOokOencWb0OC4PbtzUNRQjhRKpX539AfSAcYNEibeMRtuPPP2HfPnXa8Pz5mtQz0ZokJ9WrQ4UKaqb6559aRyOEcBa1ajEeWA9UBbW4lhAA7dvDkSOwdCnUqKF1NJqQ5ESnU98IAJs3axuLEMLpSPl6ka3gYHj8ca2j0IwkJwBPPgnNmztthiqE0EBcHNWBssbbUufEuSkKvP02/P33w491As49ldjomWfUixBCWMvatZzIeFuSE+c2cyZMnw6zZql1TYoX1zoiTUnLiRBCaEHK1wujo0dh5Eh1++OPnT4xAUlOMrtxA/bv1zoKIYQzkPL1AtLL0ycnQ5cuTlOe/mEkOTHav1/NVp96Sn7BCCEsL8N55j9Qi0IK5/P++3DokPr9M2OGrPOWRpITozp1wMMDoqPh2DGtoxFCOLq05GQdaVOJBw7UMhqhhZ9+UseagDptuHx5beOxIZKcGHl4QOvW6rZMKRZCWJqUrxe//aZev/02tGunbSw2RpKTjKTeiRDCWqR8vVi8WF189tNPtY7E5khykpExOdm+Xa0YK4QQlhIczHQgGDgEMGeOtvEI6zGON9Lp1HGObm7axmODJDnJKDgYSpaEu3fVdQ2EEMJSQkJ4G9gI1AN1vJtwfL/9Bh07ylpuDyHJSUY6HbRpo25v26ZpKEII5yDl653IhQswaBBs2KAWWxM5kgqxWb36qtq948RrGgghrCAhgUDA33hb6pw4NkWBl19WE5RHHlGTFJEjSU6ykhHTQghrWLiQqIy3peXEsU2YoI5nLFQINm4ELy+tI7Jp0q0jhBBayJqMSMuJ4woLg//9T92eMQMqVdI0HHsgLSfZiYxUBy2VLAk9emgdjRDCEUn5eudw4QI8/7yajPbrB6+8onVEdkFaTrKzZQsMHapmuEIIYQkZWk4uARQpolkowoIuXIDChaFePflOyQNpOclO27bq9d69cOeO9A0KIcwvraVkMfAyoLz/vqbhCAtp0gT+/Rdu3pTvkjyQlpPsVKoE5cqpq0Tu2qV1NEIIRyQVYh3bvXvp215eUKaMdrHYIUlOsqPTpbeehIVpGooQwkGltZxIcuKArl6FWrXgq69kFlY+SXKSE2NyIsXYhBCWULcus1BXJN4J8OWX2sYjzCM1FV58Ec6cUceY3LmjdUR2SZKTnBiTk/374dYtbWMRQjie9u0ZBGwDmgOcPattPMI8Jk2CrVvVQbArV6rXIs8kOclJUJB6ATh6VMtIRFbx8XDggDSXCodg6taR97P9W7UKxo9Xt7/5Ru3aEfkiycmDrF0LN25A06ZaRyIyqlMHGjWC33/XOhIh8u/OHfyBQsbbUufEvl26pNYxSU2F/v2hVy+tI7Jrkpw8SK1a4O2tdRQiq3Pn1OtfftE2DiEK4rPPuA68ZbwtyYn9uncPnnhCnS5cvz7Mnq1OrBD5JsmJsD8+Puq1tGgJeybl6x2Hm5vaalKxIvz6K7hKCbGCkuTkYaZNky4EW1OvnnpdtKimYQhRIFmTExlzYr9cXGDECDh5UtbNMRNJTh4mPFwdfPnHH1pHIoz0aW9b+aUp7FmG928cgIeHZqGIfPrnH7U8vZG7u3axOBhJTh6mTRv1Wuqd2A5jYbzISC2jEKJg0lpKZgB+IHVO7M2tW9C5s1p24vJlraNxOJKcPIwxOTl0SB3sJGyH1IUQ9iyt5UQ6c+xQaio8+yycP69uy5o5ZifJycOULQtVqqgnkh07tI5GAAQHq9fPP69tHEIUhKytY78mT4aNG9WkZNEi8PXVOiKHI8lJbhhbT2SdHdtg7KvXy9tX2LF69VgIVAA2AkyZom08Inc2bYIxY9Ttr76CZs20jcdBydk9N2SdHdsiyYlwBM8+Sx/UdXUeBzh2TNt4xMNFRsLLL6utXgMGwCuvaB2Rw5Kze260bg2lS0PNmjJDxBacPKley/Ru4QCkfL0d6d1bXXG4bl211UQKrVmMVIrJjTJl4OJFeSPamuhorSMQIv+SkvAkwy9E+eFj+1avhk8/heHDoVChhx4u8k+Sk9ySxEQIYU7vv89d4J7xtiQntq9oUZg6VesonIJ06+SFokBEhNZRiJIl1etq1bSNQ4iCyDpbR7p1bNOaNepgZfn/sSpJTnLr1i113EmVKhAbq3U0zq1VK/XauMaOEPYoraUkJcttYUPOnlXHmbz7LixZonU0TkWSk9zy9gZ/fzV7/vNPraNxblK+XjiCtF/iKUAyyOwzWxMfD489BnFx0Lix1FWyMvk05IXUO7ENP/+sXl+9qm0cQhREWnL9DeAOsHSpltGIjFJSoFcvdepwUBAsX66uPCysRpKTvJDkxLbIehbCnqW1nMhIBhv0wQfqWBNPT/jxRyhXTuuInI4kJ3lhTE6OHIHr1zUNxakFBKjXb76pbRxCFERay4l0TtqYefPSq/UuWAAtWmgajrOS5CQvSpZUC7GBjDvRklSIFY6gXj2WA8WBFQDjxmkbj1DduQMeHjB+PPTooXU0TkvO7nklXTvaMyYnUntG2LOBA3kW2AE8DbBzp7bxCNWwYRAeriYnQjOSnOTVs8+q08pk5LZ2jF1qv/6qbRxCmIFpKnFKyoMOE5Z065Y6+NWoQgX58aMxqRCbV23bpi8EKLR15ozWEQhRYMnGDUlOtKEo0L8/7NoFW7dKcUcbIcmJEEJoYcAAUoHdxtuSnGjjrbfU8gSurnDtmiQnNkK6dfLj1i3YsEFdBEpYn3Fan7GMvRD2yGBAj7ScaOqHH+Dzz9UunNmzoXlzrSMSaSQ5yY+NG+HJJ2HMGK0jcU5PPaVee3lpG4cQBZFW50SSE41s3Qr9+qnbo0erXTvCZthEcjJz5kyCgoLw9PQkJCSEffv25XjsvHnzaNmyJUWLFqVo0aKEhoY+8HiLaN1avT52TKqUakHK1wtHkPb+TTLeluTEeo4cge7d1QSxVy/46COtIxJZaJ6cLFu2jJEjRzJ+/HgOHjxI3bp16dChA1euXMn2+LCwMF588UW2bdvGnj17CAwM5PHHH+fixYvWC7p4cahdW93evt16zytUxjLft25pG4cQBZHWcvIn4AFw9KiW0TiX4cPVNXNatIA5c6Rmkg3S/H/ks88+Y+DAgfTr14+aNWsye/ZsvLy8+O6777I9fvHixQwaNIh69epRvXp1vv32WwwGA1u3brVu4MZ6J9u2Wfd5hTpoDWR1aGHfMqxKnATg4qJlNM7lt9/g/ffV60KFtI5GZEPT5CQpKYkDBw4QGhpq2qfX6wkNDWXPnj25eow7d+6QnJyMv79/tv+emJhIfHx8potZSDE27U2apHUEQuSfrK1jXUqGV9rbGz75BPz8NAtHPJimycm1a9dITU0lwLhWSpqAgACio6Nz9RjvvvsuZcqUyZTgZDRx4kR8fX1Nl8DAwALHDaSPOzl+HGJizPOYIm+kKVbYs7p1+R215eRHgNdf1zYeR6Yo8OqratVXRdJBe2DXZ/dJkyaxdOlSVq5ciaenZ7bHjB49mri4ONPl/Pnz5nnyYsWgTh11e8cO8zymeLiMJxap4Cjs2ahRdAS2Ai+DVDy2pI8/hm+/Va8PHdI6GpELmhZhK168OC4uLsRkaXmIiYmhVKlSD7zvtGnTmDRpElu2bKGOMUnIhoeHBx4eHmaJ9z4zZqhJSo0alnl8cb+MycmPP8LIkdrFIoQZSPl6C1uwIH1RxZkzoUEDTcMRuaNpy4m7uzsNGzbMNJjVOLi1adOmOd5vypQpfPTRR2zYsIFGjRpZI9TstWyprlIsv+CtJ+P04X/+0S4OIcxEkhML2rQJBg5Ut997T7rO7Ijm5etHjhxJnz59aNSoEY0bN+aLL77g9u3b9EsrjtO7d2/Kli3LxIkTAZg8eTLjxo1jyZIlBAUFmcameHt74+3trdnfIaxEapsIR/Hyy8QDnxlvS3JiXjt3qrVMUlKgZ091AKywG5qPOenRowfTpk1j3Lhx1KtXj8OHD7NhwwbTINmoqCguX75sOn7WrFkkJSXx7LPPUrp0adNl2rRp2vwBv/8OL7wAixZp8/zOxsUFKldWty3VXSeENdy5Q5GMt5OTczpS5FVysrpy/K1b8Nhj8N13MoDezmjecgIwZMgQhgwZku2/hWWZqhuZcVlrW3DgACxbBklJ8PLLWkfj+Fxc1Nd5wgR1oS4h7FXWCrEGg3qRL9GCc3ODyZPVcYFr14K7u9YRiTyST0FBPf64er11qzTLWouUrxeOIOvaOgCpqZqE4pB69VLPy1JkzS5JclJQDRtC0aIQHw/WXuPHGSUnw6pV6rYkg8KepSXXNwB/UMupS2tg/t26pdYyybiUiYxDtFuSnBSUiwsYC8Bt3qxtLM7g9u30OgXSRy/sWVrLSSpqgoKPj8z8y6+7d6FtW5g3D554QlpVHYAkJ+Zg7NrZtEnbOJxBxpPON99oF4cQBZX2XpZ6pQWUmKjOyvn7b3VR1m+/lXE7DkD+B83BmJzs3Qs3b2oaisPLmJzkUBVYCLsQHMwOIBqYBdC/v9o9LHIvJQX69FFnTXp5wfLlEBKidVTCDHSK4lwLDcTHx+Pr60tcXBw+Pj7me+BataBwYfjhB6kYa0lXroBxLSaDQZrBhV3Tpb1/k0mbOnnpEpQurWVI9sNggKefhjVr1LE669dD+/ZaR+XwLPYdmoW0nJjLwYPqgFhJTCwrYy49d652cQhhRqah3TKOKvfGj1cTE09P+PlnSUwcjCQn5iIFwawjY7fOn39qF4cQZiQl7PNh6FBo2lRdO+fpp7WORpiZzFszt7g4tVaBv7/WkTimjMmJjMgX9qxrVy4DryLJSa4ZW051OihRAnbtkq5dByUtJ+Y0frw6WnzGDK0jcVx+fmBchVqSE2HPrl2jFOovRFNnjnTr5MxgUBfxmzQpfZ8kJg5LkhNzKl9e/eWzdq3WkTiuwoXVQksgyYmwb2mJSDLScvJQqakweDDMn6/+CIyK0joiYWGSnJhTp07q9f79kLZasrAAKV8vHIEkJ7ljMEDv3jB7ttpS8sMP6g9B4dAkOTGnUqXg0UfV7fXrtY3FUd25A1u2qNvONQteOJoMyUkIqNOIjV2WQpWcDD16wJIl6nThJUvgxRe1jkpYgSQn5ta5s3r922/axuGoLl6EFSvUbWk5EfYsQ3JyGdT6Jm5uWkZkW27fhpdegl9/VVcVXrgQXnhB66iElUhyYm7G5GTTJvVXvjCvjAnJDz9oF4cQBZUhORHZ+OUXNTFxdVV/kEiLiVOR5MTc6teHoCA1MdmwQetoHI8xOfH3B19fbWMRoiCCgzkEJABDAYYMgWPHtI3JlvTtq/4A2bQpfTyfcBpS58TcdDoYPVod2NaihdbROB7jOBNZ2EvYuzVraJA2FXYOwMyZ0K4dBAdrGpamoqLU7hxjpe3evbWNR2hGkhNLME51FeZnbDm5dg2++kqtEimEnbtt3Lh1S8swtPXff9ClCxQqpC6i6u6udURCQ/LzU9iXjGNOZNCxcBCmlOT27Qcd5riOHYOWLSE8HK5ehRs3tI5IaEySE0u5dg1mzYLPPtM6Esci5euFo6hendNAGZy85WTTJmjcGGJioG5dtU6UceVx4bQkObGUY8dg0CD45BMpSW1OZcpAkybqtiQnwp5FRFA5bdNpW05++gm6doW7d6FtW9i6VZ1SLZyeJCeW0rKlujBVbKz6gRPmUbIkjBihbksRNmGvFMVUDTYZJ205mT9frWNy7x489ZQ6u7FYMa2jEjZCkhNLcXGB555Tt5cs0TYWRyPl64W9y1CmPhlQChVSbzhTy0nnzmoZ+lGj1DomMgBWZCCzdSypZ0/45htYuVKte+LlpXVE9i8+HvbsUbclORH2KkNXb/NWrXjyww8hMNDxWw4SEqBIEXU7IACOHFFXGhciC2k5saSmTaFiRbWpds0araNxDMeOpQ8yluRE2KsMycnajRsJbtsWHnkEihbVMCgL27cPqlVTy9AbSWIiciDJiSXpdGrrCcDixdrG4iiMCYmrKyxfrm0sQuRXxtWHnWE9nZ9+gtat4fJlmDEDUlO1jkjYOElOLK1nT/WL1NVVfumbg/E1rFxZRvUL+1azptqS4OKi1vcYPRq++ELrqMzLYICxYzMPfN26Vf2bhXgAGXNiadWrq/P3/f21jsQxSPl64QiKFYN//02/HRkJkyapdT6GD9cqKvO6fl2tlm1cRXzUKPj0U0lMRK7IGd4aJDExH2PLyYkT8Pnn2sYihLn4+KjX8fHaxmEuigKtWqXPwlmwACZPlsRE5JokJ9Z09iycO6d1FPYtY9fYDz9oF4cQ5mT8ARMbq20c5qLTwfPPQ6VK8Oef0KeP1hEJOyPJibVMnKh+UCdO1DoS+ybl64UjOHVKXXm3TRv1tjE5iYvLPFjWnty9q3blGI0aBTt3QkiIdjEJuyXJibUYS64vWeJcVSDNrXJldVl5kAqxwn7dugUnT6pJCmSeQmyPi94dOqSOl2nXLr31p1AhGbQu8k2SE2tp00atY5CQAD//rHU09qtSJRgzRt2WlhNhr4x1TozTiF1dwddX3banrh2DQR1L0rQp/PefuqKwMeESogAkObEWnQ4GDFC358zRNhZ7J+Xrhb0zdt1krHFirA6bsWvElkVFqbVL3nsPEhOhQwd1BpKxlViIApDkxJr69lVHru/bl16CXeTNzZtqyWuQ5ETYr6wtJ6BWkT59Gho21CamvFi0CBo0UMeUFC6s/uD6/Xep+CrMRpITawoISK8YK9Ng82f7dhg6VN2W5ETYq+ySk1q11DFVHh7axJRbiqJWvL5+HerUUZeUePVVtXVYCDOR5MTaRoxQr7dvl4Gx+WFMSHx91V9qQtij7JITW6Yo6V1ROh38+CNMmQIHDkBQkKahCcckyYm11a4Nq1apNU+8vbWOxv4Yk5PatdUBxkLYI3d3qFABypRJ3/fXX/D+++oXvy357z8IDYVXXkmfIVe8OLzzjjqQVwgLkOREC127gpeX1lHYJ2NyIuXrhT0LDVVL1v/2W/q+AwfUOkjGcu9aS0pSk6V69eCPP9TF+/bv1zoq4STkDK8lgwGOHtU6CvtiTE7+/BOmT9c2FiHMqXx59fr8eW3jALV1t1o1NVm6c0cthXDqFDRurHVkwklIcqKVq1fVXyQhIerCgCJ3MhZe+/hj7eIQwtwCA9XrqCjtYoiOhrZt4emn1ZadgAC1LtPWrVCxonZxCacjyYlWihdXu3bu3oUJE7SOxn5knKETFyczdoR9+uwzaNQIZs5M32ccWHr1Kly7pklYFC2q/lhyc4O334YzZ+C556QbVVidvOO0otOpo91BrRGQcfl0kbPgYPWkCWorSlyctvEIkR8REeoYkytX0vf5+anvb4Bt26wTx9WralkD4+whDw9YulTtwpk6VcbGCc1IcqKlVq3gmWfUX/9vvaV1NPahTh31pFmokHrbHtchEeLmTfXaWLLeyLhu1JYtln3+69fVyq5VqsDIkTB2bPq/1akj04OF5iQ50dqUKeq0wo0bYf16raOxH8ZVXCU5EfbI2OKXU3Jy8qRlnjc6GgYNUse3TJ6sxlGvngx0FTZHkhOtVa6cXvH0zTfh9m1t47F1V65AeHj6WBNJToQ9MiYnWcu9t2unvr/Dwsz/nKNHQ7lyMGuWOtatXj11sOvff6stuELYEElObMHYseovmVKl5Mv2YWbNgurV4fJl9ba8XsIe5dRy4uUFVauapxR8XJxaq8QoIQFSU9UZglu3wsGD6mBXF5eCP5cQZibJiS3w8VHrduzYof6yETm7e1e9bt9eLV8/caJ6Mj93Ttu4hMiLnMacZPTff3D4cN4e12BQu4cHDoQSJTKPXenVC/buVSvRPvaYrIUjbJokJ7YiKCjzdD2ZIpu9e/fU60aN1NVbPTzUk7gMKBb2QlHUMVPe3lCyZPbHrFypLgT43HPqj5YHPdaVK7BsGbz8svpDp1Mn+PZbdQbO3r3px4aEyNgSYTdkYQRbk5iorllx+zbMn691NLbHmJx4eqq/DOfNU9fZWblSbUWZOxdatFCvCxVSp2sGBsKRI+p2u3ZqDYfVq9XjjAMQz52DXbvUJeB9fdXqmHv3qgljaKhalArUZvEhQ9R6EGXKqGskBQSoXzLR0dCkiVpN0+jIEfU+jzyiNrOXKKHGLpyXTpfeIpKxqGBGLVuq75XTp9VZfY8/riYXRYuqCUj//upx//6rvv8z8vZWE5XnnlNbSISwQ5Kc2JqDB9XCTAaD+uXZr5/WEdkWY7eOcSpxcLBa0XLbNnUdEICePdUEwGCAPn1yriHz88/p2zNnqlOUs7N2LXTrpn6puLioK0qfOJH9sa1bZx7M2Lx55kHOOp2azCQlqV8cv/yi7k9JUad2Bgaq/+7mpnZXFS+uDpo0/r3CseTUtVK8uJosjxihlpLftEm9gPq+MCYnwcHw5JNqktyli7puV+PG9rPasRA5kOTE1jRtqlaMHTsWXntNXblUfv2ky9hyYjRlivq6paSoJ+cJE9ST/rVr6YP93N2hWTO1ZQTUpm9jawioCURwsPorNS4OLlyA+vXV+50+DfHx6eMDPvxQTYYuXVJLel+5ol5Klrx/pemqVdW1Uq5dU1thDAa1hQUyfzG5uOS8VpBer87o+vzz9H3jx6sDqH191YSmfHkoW1ZWiXUkQUHqIoC7d6tj0vbtU1vhSpXKfNxvv8mgVuFwdIqSU7uiY4qPj8fX15e4uDh8fHy0Did7BgO8+KL6y97HR/2lXq+e1lHZhq5dYc0atdtm4MD0/cePq60qDRtmPj4lRZ0qWaEClC6tzljQ66FwYevFrCjq83p7qxU5L15Uv0zc3KBmzfTjRo9WW3ni49UkLDxc3Ta+H5YsUY+LjYVixe5/HhcXNVF56SX45JP05961S/2iK11avsRswQ8/wBdfwLPPwgcfaB2NEHlire9Q+Zlli/R69QQWHa3+YnrsMbVJt1EjrSOzLoNBbdEoUiR9X9euamtFnTqZj834JZ+Rq6s6DsQo42NZi06nJpmgdtkEBGR/3MSJ9+9TFHXa9J076fuSk9WaOOfOqcnL+fPqJSlJXawtISH92Bs31PELoCZDFSqoiUrFiup18+ZqV5Swnr//VsectG2rdSRC2CxJTmyVp6faQvDkk7BnjzrmISJCnZ3iiO7cgW++gcWL1WbrNm1g0SI4dkztflm6VO1ieeUVrSO1Lp1OHXibUUAAfPVV5n0Gg5rEREamV88FtUx5xYrqSrfJyWoX1enT6f8+ZEh6chIbqyYrxsQlKEgdyFulirqtRWLniP76S70OCdE2DiFsmCQntszXVy1r37UrDBtm34lJUpLa9bJypVrjwdUVXn1VnRUD6uKH77yTfvyGDenbiqLOYjIYYOFCtV6DDBDNTK9Xx5yULZt5f5Uq6sqyKSlqd1JkpDp40nhtbFUBdd/JkzmXTh8xQl1NF9QWrRUr1Lo8lSur17Jy7cNdv54+Uydji54QIhNJTmxdkSJqIaWMJ/7du9Uv9ezGHdiSEyfUL7STJ9Vuh6y1W1JT1b53UE/UFSqo9UpiYtRWo9q11XVAmjVTWxCaN1f/9qgoGDdObUkRuePqqr6+FSrk3I1TpYr6XjMmLmfPqjVkzpxRv1QDA9OPDQ+H3r3Tb7u7qy0ulSqpycrTT6cP5DYY1P8/KfqlrmeTkgINGqiDmIUQ2ZLkxB5kTEyio+Gpp9TxA59+qn5BaDnI0WBQk4XDh9WBu1WqqAkFqHUaNm9OT0q8vNSaIbVqqQM+H300/XGaNFG/DI1fYB9/fP9zdemiJieffKJ2dzVvbtE/zekUKZJe9yWr+PjMyYWiqMnHhQvq/1tSkpqwhIer/x4UlJ6cHD6s/l9VqKAmL4GB6qVcOfUSHHz/DBRHdOQIfP21uv3RR5KsCfEANpGczJw5k6lTpxIdHU3dunX5+uuvafyASoa//PILY8eOJTIykipVqjB58mQ6duxoxYg1FBurTlk9cUIdfzF1qto68fLL1uvqWLcuvQz2vn3p64SAmmQYk5PixdUxJOXLq7+mS5bM+YScmxN1xmRGCplZV9ZR+Q0bquuzgNoScP68mqScOaOOjcqYOJ47lz77yJi8ZDR9OowcqW7/+6/aemZMXMqWVd9HxYqpY2nKl78/FluVmqpejC18X36pvg5t2qjJtRAiR5pPJV62bBm9e/dm9uzZhISE8MUXX/DLL78QHh5OyWxKO+/evZtWrVoxceJEOnfuzJIlS5g8eTIHDx4kODj4oc9nF1OJHyYxEWbMUFsXjGt0FC2qNqWPH2+e5uLr19Uvkn//VU+wr7+e/m8VKqitJUbu7mo9D2PF1WefLfjzZ+faNbU1BtTESAYU2ofk5PTkJTIyfXbRhQvq5ZNP1AHfoBYcy1h/Jqsvv0xfxfvvv9Xp5MbEJet106bpY5ru3FEHDLu4pF9cXdO3vbzu7yZMSVGTicRE9dq4XaJE+vvwwgV1Zt2NG+mXq1fVv+/iRfVz+tpr6rGXLqljx+bMyTxoWQg7Yq3vUM2Tk5CQEB599FFmzJgBgMFgIDAwkDfffJP33nvvvuN79OjB7du3Wbt2rWlfkyZNqFevHrNnz37o8zlEcmIUFwfffafO3IiMVLt/oqPTT5wLFqgn5AoV1F+b3t5qi0Nqqnps06bpjzVpEhw6pJ5sw8PV5MTIWJrd2Lrx2mvqibpJE/USHGy9ipTVqqmxnD9vP7+gRe5FRaktMhcuqF/uFy+q70Xj5euv1ZovoI5L6to158f66it1yjWojxkamvOx06alr8+0d++DB6tOngyjRqnbf/+duUUvqzfeUGehCeEgnKLOSVJSEgcOHGD06NGmfXq9ntDQUPbs2ZPtffbs2cNIYxNwmg4dOrBq1SpLhmqbfH3VLp2hQ9WS6f/8k56YAHz/vVonJTs+Ppm7YzZvhj/+yHxMuXJQo4Y6MDUxMb0rZc4cs/4ZeXL0qFpsTRITx1S+fO6XbGjaVF2ZOjY2PXkxbsfGqq15RjqdOqYmJUVNzlNSMg/QzjhuKyXl/udyc1NnyxmXRTAqV04tJV+0aPqlWLH0qr3OMJZGCAvQNDm5du0aqampBGQpShUQEMDJHKYzRkdHZ3t8tLEkeBaJiYkkJiaabselfSHHx8cXJHTb8+ij6iXj39Wxo1oV9NIluHVLvSQmqidib+/Mx778srq4WECAWtuicuXMVVSTktSLLdDpMscunJOHhzqT60GM75NGjdTWmIwURU00UlLUz4Tx2OrV1VlKrq5qMuLhcf+gc+OxXl7p06uzk3FdJSEcgPG709KdLjYxINaSJk6cyIQJE+7bH5hxWqSzMq4VI4QQQuTB9evX8bXgd4imyUnx4sVxcXEhJiYm0/6YmBhK5dAcWqpUqTwdP3r06EzdQAaDgdjYWIoVK4bOSlP54uPjCQwM5Pz58/Y/zsVC5DV6OHmNHkxen4eT1+jh5DV6sLi4OMqXL4+/hQd1a5qcuLu707BhQ7Zu3Uq3tNH6BoOBrVu3MmTIkGzv07RpU7Zu3crw4cNN+zZv3kzTjIM7M/Dw8MAjS2VVPz8/c4SfZz4+PvJmfwh5jR5OXqMHk9fn4eQ1ejh5jR5Mb+GK0Jp364wcOZI+ffrQqFEjGjduzBdffMHt27fplzYornfv3pQtW5aJaYuiDRs2jNatWzN9+nQ6derE0qVL+fvvv5k7d66Wf4YQQgghzETz5KRHjx5cvXqVcePGER0dTb169diwYYNp0GtUVFSmDK1Zs2YsWbKEMWPG8P7771OlShVWrVqVqxonQgghhLB9micnAEOGDMmxGycsLOy+fc899xzPPfechaMyHw8PD8aPH39f95JIJ6/Rw8lr9GDy+jycvEYPJ6/Rg1nr9dG8CJsQQgghREayxrkQQgghbIokJ0IIIYSwKZKcCCGEEMKmSHIihBBCCJsiyYkZJCQkMHz4cCpUqEChQoVo1qwZ+/fvf+B9wsLCaNCgAR4eHjzyyCMsWLDAOsFqJK+vUVhYGDqd7r5LTmso2Zs///yTLl26UKZMGXQ63X0LVyqKwrhx4yhdujSFChUiNDSU//7776GPO3PmTIKCgvD09CQkJIR9+/ZZ6C+wLEu8Ph9++OF976fq1atb8K+wrIe9RitWrODxxx83VcM+fPhwrh73l19+oXr16nh6elK7dm3Wr19v/uCtxBKv0YIFC+57H3kaF0W1Mw96fZKTk3n33XepXbs2hQsXpkyZMvTu3ZtLly499HHNcR6S5MQMBgwYwObNm/nxxx85evQojz/+OKGhoVy8eDHb48+ePUunTp1o27Ythw8fZvjw4QwYMICNGzdaOXLryetrZBQeHs7ly5dNl5IlS1opYsu6ffs2devWZebMmdn++5QpU/jqq6+YPXs2e/fupXDhwnTo0IF79+7l+JjLli1j5MiRjB8/noMHD1K3bl06dOjAlStXLPVnWIwlXh+AWrVqZXo/7dy50xLhW8XDXqPbt2/TokULJk+enOvH3L17Ny+++CL9+/fn0KFDdOvWjW7dunHs2DFzhW1VlniNQK0em/F9dO7cOXOEa3UPen3u3LnDwYMHGTt2LAcPHmTFihWEh4fz1FNPPfAxzXYeUkSB3LlzR3FxcVHWrl2baX+DBg2UDz74INv7jBo1SqlVq1amfT169FA6dOhgsTi1lJ/XaNu2bQqg3LhxwwoRagtQVq5cabptMBiUUqVKKVOnTjXtu3nzpuLh4aH89NNPOT5O48aNlcGDB5tup6amKmXKlFEmTpxokbitxVyvz/jx45W6detaMFLtZH2NMjp79qwCKIcOHXro4zz//PNKp06dMu0LCQlRXnvtNTNEqS1zvUbff/+94uvra9bYbMGDXh+jffv2KYBy7ty5HI8x13lIWk4KKCUlhdTU1Pua9QoVKpTjr7I9e/YQGhqaaV+HDh3Ys2ePxeLUUn5eI6N69epRunRp2rdvz65duywZps04e/Ys0dHRmd4jvr6+hISE5PgeSUpK4sCBA5nuo9frCQ0Ndbj3VX5eH6P//vuPMmXKUKlSJXr27ElUVJSlw7UrznZuyq9bt25RoUIFAgMD6dq1K//++6/WIVlFXFwcOp0ux/XpzHkekuSkgIoUKULTpk356KOPuHTpEqmpqSxatIg9e/Zw+fLlbO8THR1tKs9vFBAQQHx8PHfv3rVG2FaVn9eodOnSzJ49m+XLl7N8+XICAwNp06YNBw8etHL01mccV5PdeySnMTfXrl0jNTU1T/exV/l5fQBCQkJYsGABGzZsYNasWZw9e5aWLVuSkJBg0XjtSU7nJkd7DxVEtWrV+O6771i9ejWLFi3CYDDQrFkzLly4oHVoFnXv3j3effddXnzxxRwXRDTnecgmytfbux9//JFXXnmFsmXL4uLiQoMGDXjxxRc5cOCA1qHZjLy+RtWqVaNatWqm282aNSMiIoLPP/+cH3/80VphCwfy5JNPmrbr1KlDSEgIFSpU4Oeff6Z///4aRibsSdOmTWnatKnpdrNmzahRowZz5szho48+0jAyy0lOTub5559HURRmzZplleeUlhMzqFy5Mtu3b+fWrVucP3+effv2kZycTKVKlbI9vlSpUsTExGTaFxMTg4+PD4UKFbJGyFaX19coO40bN+b06dMWjNI2lCpVCiDb94jx37IqXrw4Li4uebqPvcrP65MdPz8/qlat6hTvqdzK6dzkaO8hc3Jzc6N+/foO+z4yJibnzp1j8+bNObaagHnPQ5KcmFHhwoUpXbo0N27cYOPGjXTt2jXb45o2bcrWrVsz7du8eXOmbNxR5fY1ys7hw4cpXbq0BaOzDRUrVqRUqVKZ3iPx8fHs3bs3x/eIu7s7DRs2zHQfg8HA1q1bHe59lZ/XJzu3bt0iIiLCKd5TueXM56b8Sk1N5ejRow75PjImJv/99x9btmyhWLFiDzzerOehPA2fFdnasGGD8vvvvytnzpxRNm3apNStW1cJCQlRkpKSFEVRlPfee0/p1auX6fgzZ84oXl5eyjvvvKOcOHFCmTlzpuLi4qJs2LBBqz/B4vL6Gn3++efKqlWrlP/++085evSoMmzYMEWv1ytbtmzR6k8wq4SEBOXQoUPKoUOHFED57LPPlEOHDplGwU+aNEnx8/NTVq9erfzzzz9K165dlYoVKyp37941PcZjjz2mfP3116bbS5cuVTw8PJQFCxYox48fV1599VXFz89PiY6OtvrfV1CWeH3eeustJSwsTDl79qyya9cuJTQ0VClevLhy5coVq/995vCw1+j69evKoUOHlHXr1imAsnTpUuXQoUPK5cuXTY/Rq1cv5b333jPd3rVrl+Lq6qpMmzZNOXHihDJ+/HjFzc1NOXr0qNX/PnOwxGs0YcIEZePGjUpERIRy4MAB5YUXXlA8PT2Vf//91+p/X0E96PVJSkpSnnrqKaVcuXLK4cOHlcuXL5suiYmJpsew1HlIkhMzWLZsmVKpUiXF3d1dKVWqlDJ48GDl5s2bpn/v06eP0rp160z32bZtm1KvXj3F3d1dqVSpkvL9999bN2gry+trNHnyZKVy5cqKp6en4u/vr7Rp00b5448/NIjcMoxTpbNe+vTpoyiKOl127NixSkBAgOLh4aG0a9dOCQ8Pz/QYFSpUUMaPH59p39dff62UL19ecXd3Vxo3bqz89ddfVvqLzMsSr0+PHj2U0qVLK+7u7krZsmWVHj16KKdPn7biX2VeD3uNvv/++2z/PeNr0rp1a9PxRj///LNStWpVxd3dXalVq5aybt066/1RZmaJ12j48OGmz1hAQIDSsWNH5eDBg9b9w8zkQa+PcXp1dpdt27aZHsNS5yGdoihK3tpahBBCCCEsR8acCCGEEMKmSHIihBBCCJsiyYkQQgghbIokJ0IIIYSwKZKcCCGEEMKmSHIihBBCCJsiyYkQQgghbIokJ0IIIYSwKZKcCCGEEMKmSHIihMi3Nm3aMHz4cK3DMMlvPNevX6dkyZJERkaaPaaMXnjhBaZPn27R5xDCEUhyIoSwO+ZOij755BO6du1KUFCQ2R4zO2PGjOGTTz4hLi7Oos8jhL2T5EQI4dTu3LnD/Pnz6d+/v8WfKzg4mMqVK7No0SKLP5cQ9kySEyHsxNq1a/Hz8yM1NRWAw4cPo9PpeO+990zHDBgwgJdffhmADRs20KJFC/z8/ChWrBidO3cmIiICgLlz51KmTBkMBkOm5+jatSuvvPKK6bbBYGDixIlUrFiRQoUKUbduXX799dccY3zY8W3atGHo0KGMGjUKf39/SpUqxYcffpjpMRISEujZsyeFCxemdOnSfP7555laSvr27cv27dv58ssv0el06HS6TN0xBoPhgY+f1fr16/Hw8KBJkyamfTt37sTNzY179+6Z9kVGRqLT6Th37lym28uXL6dVq1YUKlSIRx99lKioKHbs2EGTJk3w8vKiXbt23Lx50/Q4Xbp0YenSpQ+MSQinV6D1loUQVnPz5k1Fr9cr+/fvVxRFUb744gulePHiSkhIiOmYRx55RJk3b56iKIry66+/KsuXL1f+++8/5dChQ0qXLl2U2rVrK6mpqUpsbKzi7u6ubNmyxXTf69ev37fv448/VqpXr65s2LBBiYiIUL7//nvFw8NDCQsLUxRFXU5+2LBheTrex8dH+fDDD5VTp04pP/zwg6LT6ZRNmzaZHmPAgAFKhQoVlC1btihHjx5Vnn76aaVIkSKm57l586bStGlTZeDAgcrly5eVy5cvKykpKbl+/KyGDh2qPPHEE5n2ff3110rt2rUz7VuxYoVStGhR0+1Vq1YpgNKuXTtlx44dysGDB5XAwEClZcuWSseOHZX9+/crf/31l1KsWDHls88+M93v999/V9zd3ZV79+7lGJMQzk6SEyHsSIMGDZSpU6cqiqIo3bp1Uz755BPF3d1dSUhIUC5cuKAAyqlTp7K979WrVxVAOXr0qKIoitK1a1fllVdeMf37nDlzlDJlyiipqamKoijKvXv3FC8vL2X37t2ZHqd///7Kiy++qChK5uQkt8e3aNEi078/+uijyrvvvqsoiqLEx8crbm5uyi+//GL695s3bypeXl6ZkqCsSVHG/Q96/OxkfR0URU2QevfunWnfuHHjlDZt2phuf/jhh4q/v79y7do1076XX35ZCQoKUm7fvm3a98QTTyijRo0y3T5y5IgCKJGRkTnGJISzk24dIexI69atCQsLQ1EUduzYwTPPPEONGjXYuXMn27dvp0yZMlSpUgWA//77jxdffJFKlSrh4+NjGuwZFRUFQM+ePVm+fDmJiYkALF68mBdeeAG9Xj0tnD59mjt37tC+fXu8vb1Nl4ULF5q6hzLK7fF16tTJdL/SpUtz5coVAM6cOUNycjKNGzc2/buvry/VqlXL9Wv0oMfPzt27d/H09My07/Dhw9SrVy/TvkOHDmXad+TIEZ5++mmKFStm2hcVFUWPHj3w8vLKtK9ixYqm24UKFQLUsS5CiOy5ah2AECL32rRpw3fffceRI0dwc3OjevXqtGnThrCwMG7cuEHr1q1Nx3bp0oUKFSowb9480/iS4OBgkpKSTP+uKArr1q3j0UcfZceOHXz++eem+9+6dQuAdevWUbZs2UxxeHh43Bdbbo93c3PL9G86ne6+sS8FkdfHL168ODdu3DDdTk1N5dixY9SvXz/TcQcPHqR79+6m24cPH2b06NGZjjly5AgjRoww3b537x7h4eHUrVvXtC82NhaAEiVK5OGvEsK5SHIihB1p2bIlCQkJfP7556ZEpE2bNkyaNIkbN27w1ltvAWrdjvDwcObNm0fLli0BdZBnRp6enjzzzDMsXryY06dPU61aNRo0aGD695o1a+Lh4UFUVFSmpCcneT0+O5UqVcLNzY39+/dTvnx5AOLi4jh16hStWrUyHefu7m4aGFxQ9evXzzR7Jjw8nHv37lGmTBnTvj179nDx4kVTy0l8fDyRkZGZEpizZ88SFxeXad/Ro0dRFIXatWub9h07doxy5cpRvHhxs8QvhCOS5EQIO1K0aFHq1KnD4sWLmTFjBgCtWrXi+eefJzk52ZQUFC1alGLFijF37lxKly5NVFRUplk9Rj179qRz5878+++/plk+RkWKFOHtt99mxIgRGAwGWrRoQVxcHLt27cLHx4c+ffoU6PjsFClShD59+vDOO+/g7+9PyZIlGT9+PHq9Hp1OZzouKCiIvXv3EhkZibe3N/7+/qbuqLzq0KEDo0eP5saNGxQtWpTDhw8D8PXXXzN06FBOnz7N0KFDAUytTkeOHMHFxYXg4GDT4xw+fBh/f38qVKiQaV/lypXx9vY27duxYwePP/54vmIVwlnImBMh7Ezr1q1JTU2lTZs2APj7+1OzZk1KlSplGpuh1+tZunQpBw4cIDg4mBEjRjB16tT7Huuxxx7D39+f8PBwXnrppfv+/aOPPmLs2LFMnDiRGjVq8MQTT7Bu3bpMYygKcnx2PvvsM5o2bUrnzp0JDQ2lefPm1KhRI9O4kLfffhsXFxdq1qxJiRIlTONo8qN27do0aNCAn3/+GVATig4dOnDmzBlq167NBx98wIQJE/Dx8eGrr74C1OSkWrVqmWI6cuTIfV1BR44cydSlc+/ePVatWsXAgQPzHa8QzkCnKIqidRBCCJGT27dvU7ZsWaZPn26xQmnr1q3jnXfe4dixYzz55JM8+uijfPzxx2Z/nlmzZrFy5Uo2bdpk9scWwpFIt44QwqYcOnSIkydP0rhxY+Li4vjf//4HqAXiLKVTp078999/XLx4kSNHjmQqRGdObm5ufP311xZ5bCEcibScCCFsyqFDhxgwYADh4eG4u7vTsGFDPvvss0yDSi0lOjqa0qVL8++//1KzZk2LP58QInuSnAghhBDCpsiAWCGEEELYFElOhBBCCGFTJDkRQgghhE2R5EQIIYQQNkWSEyGEEELYFElOhBBCCGFTJDkRQgghhE2R5EQIIYQQNkWSEyGEEELYFElOhBBCCGFTJDkRQgghhE35P1DsvtZYttgsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot transmission, compare to paper results, look similar\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
    "plt.plot(wavelengths, reflection, \"k\", label=\"R\")\n",
    "plt.plot(wavelengths, transmission, \"r--\", label=\"T\")\n",
    "plt.xlabel(r\"wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Magnitude\")\n",
    "plt.xlim([8.8, 12])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
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    "<!-- <img src=\"img/Ge_plot.png\" alt=\"diagram\" width=\"300\"/> -->"
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    "Besides the metasurface demonstrated in this notebook, in Tidy3D's example library we have demonstrated a [dielectric metasurface absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/DielectricMetasurfaceAbsorber/), a [gradient metasurface reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GradientMetasurfaceReflector/), a [metalens at the visible frequency](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/), and a [graphene metamaterial absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/GrapheneMetamaterial/). "
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   "name": "Ge_HighQ.ipynb",
   "provenance": []
  },
  "description": "This notebook demonstrates how to model a germanium Fano metasurface in Tidy3D FDTD.",
  "feature_image": "./img/fano_metasurface.png",
  "features": [],
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