1<!DOCTYPE html> 2<html lang="en" dir="ltr" prefix="og: https://ogp.me/ns#"> 3 <head> 4 <meta charset="utf-8" /> 5<link rel="canonical" href="https://ieee-dataport.org/search" /> 6<meta name="Generator" content="Drupal 10 (https://www.drupal.org)" /> 7<meta name="MobileOptimized" content="width" /> 8<meta name="HandheldFriendly" content="true" /> 9<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" /> 10<meta http-equiv="x-ua-compatible" content="ie=edge" /> 11<link rel="icon" href="/themes/custom/dataport_bootstrap/favicon.ico" type="image/vnd.microsoft.icon" /> 12 13 <title>Search | IEEE DataPort</title> 14 <link rel="stylesheet" media="all" href="/sites/default/files/css/css_GR_4hIP0IpRfNKYOB8kdTQcjYfHgG_FFdYkDt0neyuA.css?delta=0&language=en&theme=dataport_bootstrap&include=eJyNjsEOwjAMQ3-oWz-pSlczipqmSjIGfw8CwQE4cLFk-zlKhjs04TLEUNKhtru1uKJDqYUs4uZKI2VSrRIP0p12mDB-lcrf6dokU5vMr6329btnmNEKS_ux-o-rXQrCIoq4CA_p6G7zJzVNjL79iaVF2sbdnnjRbVCb6USXUMhpiHp6Dz_fH1qNX5uHCXY1B8dMhnCu2C0-dGYpW8MNIQuMiA" /> 15<link rel="stylesheet" media="all" href="/sites/default/files/css/css_jYHjQyKGqOr8FCYreo-Fgl2OMwO_ThenrIXLGy_ISU0.css?delta=1&language=en&theme=dataport_bootstrap&include=eJyNjsEOwjAMQ3-oWz-pSlczipqmSjIGfw8CwQE4cLFk-zlKhjs04TLEUNKhtru1uKJDqYUs4uZKI2VSrRIP0p12mDB-lcrf6dokU5vMr6329btnmNEKS_ux-o-rXQrCIoq4CA_p6G7zJzVNjL79iaVF2sbdnnjRbVCb6USXUMhpiHp6Dz_fH1qNX5uHCXY1B8dMhnCu2C0-dGYpW8MNIQuMiA" /> 16<link rel="stylesheet" media="all" href="//use.fontawesome.com/releases/v5.13.0/css/all.css" /> 17<link rel="stylesheet" media="all" href="/sites/default/files/css/css_kCggqQiTC_0AX1y5MYSr9qXQg3vnKDHa0phxyPBV4bE.css?delta=3&language=en&theme=dataport_bootstrap&include=eJyNjsEOwjAMQ3-oWz-pSlczipqmSjIGfw8CwQE4cLFk-zlKhjs04TLEUNKhtru1uKJDqYUs4uZKI2VSrRIP0p12mDB-lcrf6dokU5vMr6329btnmNEKS_ux-o-rXQrCIoq4CA_p6G7zJzVNjL79iaVF2sbdnnjRbVCb6USXUMhpiHp6Dz_fH1qNX5uHCXY1B8dMhnCu2C0-dGYpW8MNIQuMiA" /> 18<link rel="stylesheet" media="print" href="/sites/default/files/css/css_L0vrHWt2p2tnoG7oo1qlO7qidUWghFbnZn6LaXvK_XE.css?delta=4&language=en&theme=dataport_bootstrap&include=eJyNjsEOwjAMQ3-oWz-pSlczipqmSjIGfw8CwQE4cLFk-zlKhjs04TLEUNKhtru1uKJDqYUs4uZKI2VSrRIP0p12mDB-lcrf6dokU5vMr6329btnmNEKS_ux-o-rXQrCIoq4CA_p6G7zJzVNjL79iaVF2sbdnnjRbVCb6USXUMhpiHp6Dz_fH1qNX5uHCXY1B8dMhnCu2C0-dGYpW8MNIQuMiA" /> 19 20
20<script type="application/json" data-drupal-selector="drupal-settings-json">{"path":{"baseUrl":"\/","pathPrefix":"","currentPath":"search","currentPathIsAdmin":false,"isFront":false,"currentLanguage":"en"},"pluralDelimiter":"\u0003","suppressDeprecationErrors":true,"gtag":{"tagId":"","consentMode":false,"otherIds":[],"events":[],"additionalConfigInfo":[]},"ajaxPageState":{"libraries":"eJyNkFGOwzAIRC_kxEeycExd7xpjAW7a22-UStFumo_9QczMAwkimqEEfHZWTOFW6ibVwzAOOiIVc_EaydhQoLrIbGoCPUQQKexv3AxWVCa8CoU-3Vw5Qp3UXrW0_JkTqkJGDeu92MXWxgndwoJ-YercsJnOZ2qaCNv4JxYWroOaugQGncXCwXlIl_bRXYWnCw9iGWpMnkC-cXv5_p7MnCsGg-zzVs56hi94_jXJdSlKPsnoUOddOH2pIfkIiu5RcFW_1_f4b4M4jYo_mlbPZg","theme":"dataport_bootstrap","theme_token":null},"ajaxTrustedUrl":{"\/search":true},"gtm":{"tagId":null,"settings":{"data_layer":"dataLayer","include_classes":false,"allowlist_classes":"","blocklist_classes":"","include_environment":false,"environment_id":"","environment_token":""},"tagIds":["GTM-NSLFHWSR"]},"views":{"ajax_path":"\/views\/ajax","ajaxViews":{"views_dom_id:c162c8bafef088dadb7072c64970f6e7c47eea47f1dc12204f04076d50722a55":{"view_name":"search","view_display_id":"page_1","view_args":"","view_path":"\/search","view_base_path":"search","view_dom_id":"c162c8bafef088dadb7072c64970f6e7c47eea47f1dc12204f04076d50722a55","pager_element":0}}},"user":{"uid":0,"permissionsHash":"82c92eaa8acc8137dedeabe73a9d50016834ecb207f19cc5049d8ebf261e903c"}}</script>
vendor: 1 bytes, line 20
20
21<script src="/sites/default/files/js/js_nYjOZwfZDugfbWZTdcSVfhAV9yx9Kzri4tqGmJWOylc.js?scope=header&delta=0&language=en&theme=dataport_bootstrap&include=eJx1jlEOwzAIQy-UlSNFZKFRNlIiIFt3-1X9qNSp-7F4xlgkcieNtHYxynGuvKEBDpdoI7XqIV1HCi2kyCGJuLlijwlVq0BhScg38w_XpYSMjl3U4xEEzHZlH9PV8l_rfZhLg4b6pO3nWbSFIlKYomOBsskvT_jA9Wy20LVag6yjI087hFelt8Gu-8kX5FZ3RA"></script>
vendor: 1 bytes, line 21
21
22<script src="/modules/contrib/google_tag/js/gtag.js?tlj9mr"></script>
vendor: 1 bytes, line 22
22
23<script src="/modules/contrib/google_tag/js/gtm.js?tlj9mr"></script>
23 24 25 <link rel="preconnect" href="https://fonts.googleapis.com"> 26 <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> 27 <link href="https://fonts.googleapis.com/css2?family=Open+Sans:ital,wght@0,300..800;1,300..800&family=Roboto:ital,wght@0,100..900;1,100..900&display=swap" rel="stylesheet"> 28
28<script src="https://cmp.osano.com/AzyzptTmRlqVd2LRf/ae923df5-3102-4fd5-823d-d18db6ce5c2f/osano.js"></script>
28 29
29<script type='text/javascript' src='https://platform-api.sharethis.com/js/sharethis.js#property=67e1a10b59793500196aafc8&product=sop' async='async'></script>
29 30 <link rel="stylesheet" href="https://cookie-consent.ieee.org/ieee-cookie-banner.css" type="text/css"/> 31 </head> 32 <body class="layout-no-sidebars page-view-search path-search"> 33 <a href="#main-content" class="visually-hidden-focusable"> 34 Skip to main content 35 </a>
vendor: 72 bytes, lines 35-36
35 36 <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=
36GTM-NSLFHWSR
vendor: 108 bytes, lines 36-39
36" 37 height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript> 38 39
39<div class="dialog-off-canvas-main-canvas" data-off-canvas-main-canvas> 40 41 <nav class="navbar" id="navbar-top"> 42 <section class="container-fluid region region-top-header"> 43 <nav role="navigation" aria-labelledby="block-dataport-bootstrap-ieeemenus-menu" id="block-dataport-bootstrap-ieeemenus" class="block block-menu navigation menu--ieee-menus"> 44 45 <h2 class="visually-hidden" id="block-dataport-bootstrap-ieeemenus-menu">IEEE Menus</h2> 46 47 48 49 <ul class="clearfix nav" data-component-id="bootstrap_barrio:menu"> 50 <li class="nav-item"> 51 <a href="https://www.ieee.org/" target="_blank" class="nav-link nav-link-https--wwwieeeorg-">IEEE.org</a> 52 </li> 53 <li class="nav-item"> 54 <a href="https://ieeexplore.ieee.org/" target="_blank" class="nav-link nav-link-https--ieeexploreieeeorg-">IEEE Xplore Digital Library</a> 55 </li> 56 <li class="nav-item"> 57 <a href="https://standards.ieee.org/" target="_blank" class="nav-link nav-link-https--standardsieeeorg-">IEEE Standards</a> 58 </li> 59 <li class="nav-item"> 60 <a href="https://spectrum.ieee.org/" target="_blank" class="nav-link nav-link-https--spectrumieeeorg
60-">IEEE Spectrum</a> 61 </li> 62 <li class="nav-item"> 63 <a href="https://www.ieee.org/sitemap.html" target="_blank" class="nav-link nav-link-https--wwwieeeorg-sitemaphtml">More Sites</a> 64 </li> 65 </ul> 66 67 68 69 70 </nav> 71<nav role="navigation" aria-labelledby="block-dataport-bootstrap-account-menu-menu" id="block-dataport-bootstrap-account-menu" class="block block-menu navigation menu--account"> 72 73 <h2 class="visually-hidden" id="block-dataport-bootstrap-account-menu-menu">User account menu</h2> 74 75 76 77 <ul class="clearfix nav flex-row" data-component-id="bootstrap_barrio:menu_columns"> 78 <li class="d-none d-md-block btn btn-primary btn-sm my-2 py-1 nav-item"> 79 <a href="/subscribe" class="text-white rounded p-0 nav-link text-white rounded p-0 nav-link--subscribe" target="_blank" data-drupal-link-system-path="node/914">Subscribe</a> 80 </li> 81 <li class="d-none d-md-block cart nav-item"> 82 <a href="https://www.ieee.org/cart/public/myCart/page.html?refSite=https://ieee-dataport.org&refSiteName=IEEE%20DataPort" target="_blank" class="nav-link nav-link-https--wwwieeeorg-cart-public-mycart-pagehtmlrefsitehttps--ieee-dataportorgrefsitenameieee20dataport">Cart</a> 83 </li> 84 <li class="nav-item"> 85 <a href="/saml_login?idp=IEEEDataport&destination=/" class="nav-link nav-link--saml-loginidpieeedataportdestination-" data-drupal-link-query="{"destination":"\/","idp":"IEEEDataport"}" data-drupal-link-system-path="saml_login">Login</a> 86 </li> 87 <li class="nav-item"> 88 <a href="https://www.ieee.org/profile/public/createwebaccount/showCreateAccount.html?sourceCode=dataport&signinurl=https://ieee-dataport.org/&url=https://ieee-dataport.org/" target="_blank" class="nav-link nav-link-https--wwwieeeorg-profile-public-createwebaccount-showcreateaccounthtmlsourcecodedataportsigninurlhttps--ieee-dataportorg-urlhttps--ieee-dataportorg-">Create Free Account</a> 89 </li> 90 </ul> 91 92 93 94 95 </nav> 96 97 </section> 98 99 </nav> 100 <div class="container-fluid"> 101 <div class="row py-4 px-0 px-sm-3 mx-auto"> 102 <nav class="navbar navbar-expand-lg" id="navbar-main"> 103 <a href="/" title="Home" rel="home" class="navbar-brand"> 104 <img src="/themes/custom/dataport_bootstrap/logo.svg" alt="Home" class="img-fluid d-inline-block align-top" /> 105 106 </a> 107 <nav role="navigation" aria-labelledby="block-dataport-bootstrap-main-menu-menu" id="block-dataport-bootstrap-main-menu" class="block block-menu navigation menu--main"> 108 109 <h2 class="visually-hidden" id="block-dataport-bootstrap-main-menu-menu">Main navigation</h2> 110 111 112 <ul class="navbar-nav justify-content-between w-100"> 113 <li class="nav-item "> 114 <a href="/datasets" class="nav-link " > 115 Datasets 116 </a> 117 </li> 118 <li class="nav-item "> 119 <a href="/submit-dataset" class="nav-link " > 120 Submit a Dataset 121 </a> 122 </li> 123 <li class="nav-item "> 124 <a href="/competitions" class="nav-link " > 125 Competitions 126 </a> 127 </li> 128 <li class="nav-item active "> 129 <a href="/search" class="nav-link " > 130 Search 131 </a> 132 </li> 133 </ul> 134 135 </nav> 136<div id="block-dataport-bootstrap-ieeelogo" class="block-content-basic block block-block-content block-block-contentbf31f3fb-7ce3-4e1b-bf33-5b4ec9a3fff3"> 137 138 139 <div class="content"> 140 141 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><nav class="navbar"><div><a class="navbar-brand-ieee" href="https://www.ieee.org" target="_blank"><img class="partner-logo" src="https://ieee-dataport.org/themes/custom/dataport_bootstrap/images/ieee-logo.svg" alt="IEEE" width="120" height="35" loading="lazy"></a></div></nav></div> 142 143 </div> 144 </div> 145 146 147 <button class="navbar-toggler navbar-toggler-left" type="button" data-bs-toggle="collapse" data-bs-target="#CollapsingNavbar" aria-controls="CollapsingNavbar" aria-expanded="false" aria-label="Toggle navigation"><span class="navbar-toggler-icon"></span></button> 148 <div class="collapse navbar-collapse" id="CollapsingNavbar"> 149 <nav role="navigation" aria-labelledby="block-dataport-bootstrap-mainnavigation-menu" id="block-dataport-bootstrap-mainnavigation" class="block block-menu navigation menu--main"> 150 151 <h2 class="visually-hidden" id="block-dataport-bootstrap-mainnavigation-menu">Main navigation</h2> 152 153 154 <ul class="navbar-nav justify-content-between w-100"> 155 <li class="nav-item "> 156 <a href="/datasets" class="nav-link " > 157 Datasets 158 </a> 159 </li> 160 <li class="nav-item "> 161 <a href="/submit-dataset" class="nav-link " > 162 Submit a Dataset 163 </a> 164 </li> 165 <li class="nav-item "> 166 <a href="/competitions" class="nav-link " > 167 Competitions 168 </a> 169 </li> 170 <li class="nav-item active "> 171 <a href="/search" class="nav-link " > 172 Search 173 </a> 174 </li> 175 </ul> 176 177 </nav> 178 179 180 </div> 181 </nav> 182 </div> 183 </div> 184 185 <div data-drupal-messages-fallback class="hidden"></div> 186<div id="block-dataport-bootstrap-page-title" class="block block-core block-page-title-block"> 187 188 189 <div class="content"> 190 191 <h1 class="title"> 192 Search 193 </h1> 194 195 196 </div> 197 </div> 198 199 200 201 202 <div id="main" class="container mx-auto"> 203 <div class="row row-offcanvas row-offcanvas-left clearfix"> 204 <main class="main-content col px-4" id="content" role="main"> 205 <section class="section"> 206 <a href="#main-content" id="main-content" tabindex="-1"></a> 207
208 <div id="block-dataport-bootstrap-content" class="block block-system block-system-main-block"> 209 210 211 <div class="content"> 212 <div class="views-element-container"><div class="view view-search view-id-search view-display-id-page_1 js-view-dom-id-c162c8bafef088dadb7072c64970f6e7c47eea47f1dc12204f04076d50722a55"> 213 214 215 <div class="view-filters"> 216 217<form class="views-exposed-form bef-exposed-form" data-bef-auto-submit-full-form="" data-bef-auto-submit="" data-bef-auto-submit-delay="500" data-drupal-selector="views-exposed-form-search-page-1" action="/search" method="get" id="views-exposed-form-search-page-1" accept-charset="UTF-8"> 218 <div class="d-flex flex-wrap"> 219 220 221 222 223 224 225 <div class="js-form-item js-form-type-textfield form-type-textfield js-form-item-search-api-fulltext form-item-search-api-fulltext mb-3"> 226 <label for="edit-search-api-fulltext--2">Search Terms</label> 227 <input placeholder="Search title, author, abstract, category, data format, DOI" data-bef-auto-submit-exclude="" data-drupal-selector="edit-search-api-fulltext" type="text" id="edit-search-api-fulltext--2" name="search_api_fulltext" value="" size="30" maxlength="128" class="form-control" /> 228 229 </div> 230 231 232 233 234 235 236 <div class="js-form-item js-form-type-select form-type-select js-form-item-field-tags form-item-field-tags mb-3"> 237 <label for="edit-field-tags--2">Category</label> 238 239<select data-drupal-selector="edit-field-tags" id="edit-field-tags--2" name="field_tags" class="form-select"><option value="All" selected="selected">- Any -</option><option value="21421">Aging, Longevity, and AgeTech (5)</option><option value="12471">Agriculture (230)</option><option value="2006">Artificial Intelligence (3032)</option><option value="218">Astronomy (36)</option><option value="195">Biomedical and Health Sciences (718)</option><option value="311">Biophysiological Signals (230)</option><option value="199">Climate Change/Environmental (164)</option><option value="2007">Cloud Computing (155)</option><option value="352">Communications (721)</option><option value="666">Computational Intelligence (446)</option><option value="1639">Computer Vision (1031)</option><option value="2744">COVID-19 (106)</option><option value="214">Demographic (35)</option><option value="353">Ecology (32)</option><option value="5646">Education and Learning Technologies (339)</option><option value="222">Financial (123)</option><option value="349">Geoscience and Remote Sensing (485)</option><option value="354">Health (163)</option><option value="11239">IEEEXtreme (249)</option><option value="342">Image Fusion (112)</option><option value="1638">Image Processing (1289)</option><option value="2008">IoT (693)</option><option value="13738">Light, Lighting and Illumination (35)</option><option value="2005">Machine Learning (2768)</option><option value="350">North and South Poles (6)</option><option value="220">Other (1241)</option><option value="191">Power and Energy (836)</option><option value="385">Reliability (190)</option><option value="213">Security (693)</option><option value="203">Sensors (686)</option><option value="207">Signal Processing (953)</option><option value="219">Social Sciences (281)</option><option value="1051">Standards Research Data (603)</option><option value="351">Transportation (439)</option><option value="7560">Wireless Networking (633)</option></select> 240 </div> 241 242 243 244 245 246 247 <div class="js-form-item js-form-type-select form-type-select js-form-item-type form-item-type mb-3"> 248 <label for="edit-type--2">Content type</label> 249 250<select data-drupal-selector="edit-type" id="edit-type--2" name="type" class="form-select"><option value="All" selected="selected">- Any -</option><option value="document">Dataset (12445)</option><option value="open_access">Open Access (1079)</option><option value="data_competition">Data Competition (100)</option><option value="article">Insights (53)</option><option value="page">Page (36)</option><option value="faq">FAQ (34)</option><option value="organization">Institution (16)</option><option value="help">Help (15)</option><option value="testimonial">Testimonial (12)</option></select> 251 </div> 252<div data-drupal-selector="edit-actions" class="form-actions js-form-wrapper form-wrapper mb-3" id="edit-actions--2"><button data-bef-auto-submit-click="" data-drupal-selector="edit-submit-search-2" type="submit" id="edit-submit-search--2" value="Search" class="button js-form-submit form-submit btn btn-primary">Search</button> 253</div> 254 255</div> 256 257</form> 258 259 </div> 260 261 <div class="view-content row"> 262 <div class="views-row"> 263 264 265<article data-history-node-id="111195" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 266 <div> 267 <div class="row py-2 mx-auto"> 268 <div class="col-12"> 269 <h2> 270 <a href="/documents/data-spatial-action-placement-reveals-predictive-reallocation-scale-image-restoration">Data for âSpatial Action Placement Reveals a Predictive Reallocation Scale in Image Restorationâ</a> 271 </h2> 272 </div> 273 </div> 274 <div class="row mx-auto"> 275 <div class="col-lg-3 pb-4 featured-image"> 276 277 <img src="/sites/default/files/styles/home/public/tags/images/color-2174045_1280.png.
277webp?itok=h4FtP11d" alt="Tag image"> 278 </div> 279 <div class="col"> 280 281 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset contains author-generated derived numerical data supporting the study âSpatial Action Placement Reveals a Predictive Reallocation Scale in Image Restoration.â It includes geometry-level prediction and regret tables, imagewise intervention outcomes, fixed-inventory placement contrasts, finite-reallocation analyses, video-restoration gate records, frozen prediction metadata, and reproducibility manifests. The dataset preserves both positive and adverse outcomes used in the reported analyses, including the final prospective comparison and cross-backbone boundary results.</p></div> 282 283 <dl class="topinfo"> 284 <dt>Categories:</dt> 285 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 286 <ul class='links field__items'> 287 <li><a href="/topic-tags/image-processing" hreflang="en">Image Processing</a></li> 288 <li><a href="/topic-tags/machine-learning" hreflang="en">Machine Learning</a></li> 289 <li><a href="/topic-tags/signal-processing" hreflang="en">Signal Processing</a></li> 290 </ul> 291</div> 292</dd> 293 </dl> 294 </div> 295 </div> 296 </div> 297</article> 298 299 </div> 300 <div class="views-row"> 301 302 303<article data-history-node-id="111191" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 304 <div> 305 <div class="row py-2 mx-auto"> 306 <div class="col-12"> 307 <h2> 308 <a href="/documents/statistical-models-automatic-fingering-annotated-piano-sheet-music-transcription-1">Statistical Models for Automatic Fingering-Annotated Piano Sheet Music Transcription, Supplementary Material</a> 309 </h2> 310 </div> 311 </div> 312 <div class="row mx-auto"> 313 <div class="col-lg-3 pb-4 featured-image"> 314 315 <img src="/sites/default/files/styles/home/public/tags/images/light-567757_1920.jpg.webp?itok=vE3d6DAe" alt="Tag image"> 316 </div> 317 <div class="col"> 318 319 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Machine learning tools have significantly aided automatic piano music transcription; however, this domain has focused primarily on accurately predicting the pitches and timings of played notes. To produce sheet music for the piano, notes must be separated into two staves, one for each hand, and good sheet music often contains fingering annotations to guide the player when sight-reading or learning fast or complex pieces.</p></div> 320 321 <dl class="topinfo"> 322 <dt>Categories:</dt> 323 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 324 <ul class='links field__items'> 325 <li><a href="/topic-tags/signal-processing" hreflang="en">Signal Processing</a></li> 326 </ul> 327</div> 328</dd> 329 </dl> 330 </div> 331 </div> 332 </div> 333</article> 334 335 </div> 336 <div class="views-row"> 337 338 339<article data-history-node-id="111188" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 340 <div> 341 <div class="row py-2 mx-auto"> 342 <div class="col-12"> 343 <h2> 344 <a href="/documents/ssfdf-net-dataset-0">SSFDF-Net Dataset</a> 345 </h2> 346 </div> 347 </div> 348 <div class="row mx-auto"> 349 <div class="col-lg-3 pb-4 featured-image"> 350 351 <img src="/sites/default/files/styles/home/public/image_38.png.webp?itok=HhT1w0Oe" alt="Dataset image"> 352 </div> 353 <div class="col"> 354 355 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset contains 9,200 multibeam forward-looking sonar images for underwater small-target detection. It covers 10 target categories: ball, circle cage, cube, cylinder, human body, metal bucket, plane, remotely operated vehicle (ROV), square cage, and tyre. Each image is accompanied by a TXT annotation file with the same base filename. The annotations follow the YOLO format, where each row contains the class identifier and n
355ormalized bounding-box coordinates in the form of class_id, x_center, y_center, width, and height.</p></div> 356 357 <dl class="topinfo"> 358 <dt>Categories:</dt> 359 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 360 <ul class='links field__items'> 361 <li><a href="/topic-tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></li> 362 </ul> 363</div> 364</dd> 365 </dl> 366 </div> 367 </div> 368 </div> 369</article> 370 371 </div> 372 <div class="views-row"> 373 374 375<article data-history-node-id="111175" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 376 <div> 377 <div class="row py-2 mx-auto"> 378 <div class="col-12"> 379 <h2> 380 <a href="/documents/trace-instruct">TRACE-Instruct</a> 381 </h2> 382 </div> 383 </div> 384 <div class="row mx-auto"> 385 <div class="col-lg-3 pb-4 featured-image"> 386 387 <img src="/sites/default/files/styles/home/public/TraceInstruct_icon_1024_0.png.webp?itok=mEiDJ_Oi" alt="Dataset image"> 388 </div> 389 <div class="col"> 390 391 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>
391TraceInstruct recasts emotional voice conversion as an instruction-following task. Conventional EVC is driven by a categorical label or a reference utterance, which confines the target to a discrete set and cannot express how much the emotion should change. TraceInstruct instead describes each conversion in free-form natural language: "Speak with more edge and a tighter grip on each syllable." It provides 125,704 source-target utterance pairs and 537,631 instructions, each pair carrying a primary instruction plus paraphrases.</p></div> 392 393 <dl class="topinfo"> 394 <dt>Categories:</dt> 395 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 396 <ul class='links field__items'> 397 <li><a href="/topic-tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></li> 398 </ul> 399</div> 400</dd> 401 </dl> 402 </div> 403 </div> 404 </div> 405</article> 406 407 </div> 408 <div class="views-row"> 409 410 411<article data-history-node-id="111173" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 412 <div> 413 <div class="row py-2 mx-auto"> 414 <div class="col-12"> 415 <h2> 416 <a href="/documents/benefora-social-security-tax-bite-index-taxable-social-security-benefits-share-benefits">Benefora Social Security Tax Bite Index: taxable Social Security benefits as a share of benefits paid, 3,140 US counties, TY2023</a> 417 </h2> 418 </div> 419 </div> 420 <div class="row mx-auto"> 421 <div class="col-lg-3 pb-4 featured-image"> 422 423 <img src="/sites/default/files/styles/home/public/tags/images/library-1666702_1920.jpg.webp?itok=Yu45q5u7" alt="Tag image"> 424 </div> 425 <div class="col"> 426 427 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>County-level index of how much Social Security income becomes federally taxable, for 3,143 US counties and county equivalents, tax year 2023. The Tax Bite is the taxable Social Security benefits reported on federal returns filed from the county (IRS Statistics of Income county data, Form 1040 line 6b, fields N02500 and A02500, summed across eight adjusted-gross-income brackets) divided by twelve times the county's December 2023 OASDI benefits in current-payment status (Social Security Administration, OASDI Beneficiaries by State and County, Tables 4 and 5).</p></div> 428 429 <dl class="topinfo"> 430 <dt>Categories:</dt> 431 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 432 <ul class='links field__items'> 433 <li><a href="/topic-tags/social-sciences" hreflang="en">Social Sciences</a></li> 434 </ul> 435</div> 436</dd> 437 </dl> 438 </div> 439 </div> 440 </div> 441</article> 442 443 </div> 444 <div class="views-row"> 445 446 447<article data-history-node-id="111166" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 448 <div> 449 <div class="row py-2 mx-auto"> 450 <div class="col-12"> 451 <h2> 452 <a href="/documents/california-traffic-flow-data-and-beijing">California traffic flow data and Beijing </a> 453 </h2> 454 </div> 455 </div> 456 <div class="row mx-auto"> 457 <div class="col-lg-3 pb-4 featured-image"> 458 459 <img src="/sites/default/files/styles/home/public/tags/images/metro-1209556_1920_0.jpg.webp?itok=3mY63hKs" alt="Tag image"> 460 </div> 461 <div class="col"> 462 463 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This benchmark dataset repository contains curated spatiotemporal sensing data used to evaluate privacy-preserving spatial crowdsensing frameworks, specifically supporting the empirical evaluation of the DualDP framework submitted to IEEE TIFS. The repository comprises two real-world physical sensing benchmarks:</p></div> 464 465 <dl class="topinfo"> 466 <dt>Categories:</dt> 467 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 468 <ul class='links field__items'> 469 <li><a href="/topic-tags/transportation-0" hreflang="en">Transportation</a></li> 470 <li><a href="/topic-tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></li> 471 </ul> 472</div> 473</dd> 474 </dl> 475 </div> 476 </div> 477 </div> 478</article> 479 480 </div> 481 <div class="views-row"> 482 483 484<article data-history-node-id="111165" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 485 <div> 486 <div class="row py-2 mx-auto"> 487 <div class="col-12"> 488 <h2> 489 <a href="/documents/agrivision">AgriVision</a> 490 </h2> 491 </div> 492 </div> 493 <div class="row mx-auto"> 494 <div class="col-lg-3 pb-4 featured-image"> 495 496 <img src="/sites/default/files/styles/home/public/ChatGPT%20Image%20Sep%2023%2C%202026%20at%2012_57_53%20PM.png.webp?itok=NsQNE_Om" alt="Dataset image"> 497 </div> 498 <div class="col"> 499 500 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p><strong>AgriVision Maharashtra Agricultural Vegetation and Climate Dataset</strong> is a machine-learning-ready spatiotemporal dataset developed for agricultural vegetation monitoring and short-term vegetation-condition prediction across Maharashtra, India. The dataset integrates satellite-derived Normalized Difference Vegetation Index (NDVI), GSMaP rainfall, temperature, temporal and seasonal variables, spatial coordinates, and engineered historical features.</p></div> 501 502 <dl class="topinfo"> 503 <dt>Categories:</dt> 504 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 505 <ul class='links field__items'> 506 <li>
506<a href="/topic-tags/agriculture" hreflang="en">Agriculture</a></li> 507 <li><a href="/topic-tags/climate-changeenvironmental" hreflang="en">Climate Change/Environmental</a></li> 508 <li><a href="/topic-tags/demographic" hreflang="en">Demographic</a></li> 509 <li><a href="/topic-tags/geoscience-and-remote-sensing" hreflang="en">Geoscience and Remote Sensing</a></li> 510 <li><a href="/topic-tags/machine-learning" hreflang="en">Machine Learning</a></li> 511 </ul> 512</div> 513</dd> 514 </dl> 515 </div> 516 </div> 517 </div> 518</article> 519 520 </div> 521 <div class="views-row"> 522 523 524<article data-history-node-id="111153" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 525 <div> 526 <div class="row py-2 mx-auto"> 527 <div class="col-12"> 528 <h2> 529 <a href="/documents/gucholtyphoonseismicdata">Guchol_typhoon_seismic_data</a> 530 </h2> 531 </div> 532 </div> 533 <div class="row mx-auto"> 534 <div class="col-lg-3 pb-4 featured-image"> 535 536 <img src="/sites/default/files/styles/home/public/tags/images/hong-kong-2287517_1920.jpg.webp?itok=B1V1o8Fp" alt="Tag image"> 537 </div> 538 <div class="col"> 539 540 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Guchol_typhoon_seismic_data contains seismic data associated with the analysis of teleseismic P-wave microseisms generated during Typhoon Guchol in June 2012. The data include: analysis results of microseism data from the Mongolian portable seismic array during Typhoon Guchol in 2012 (i.e., matched-field processing localization spectral data), as well as waveform data of the February 2012 Philippine earthquake event. At the current manuscript submission stage, only a representative subset of the data is provided.</p></div> 541 542 <dl class="topinfo"> 543 <dt>Categories:</dt> 544 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 545 <ul class='links field__items'> 546 <li><a href="/topic-tags/geoscience-and-remote-sensing" hreflang="en">Geoscience and Remote Sensing</a></li> 547 </ul> 548</div> 549</dd> 550 </dl> 551 </div> 552 </div> 553 </div> 554</article> 555 556 </div> 557 <div class="views-row"> 558 559 560<article data-history-node-id="111150" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 561 <div> 562 <div class="row py-2 mx-auto"> 563 <div class="col-12"> 564 <h2> 565 <a href="/documents/ai-based-integrated-building-management-system-multi-service-architecture">An AI-Based Integrated Building Management System Multi-Service Architecture</a> 566 </h2> 567 </div> 568 </div> 569 <div class="row mx-auto"> 570 <div class="col-lg-3 pb-4 featured-image"> 571 572 <img src="/sites/default/files/styles/home/public/dataset_image_31.png.webp?itok=Csb8_t8L" alt="Dataset image"> 573 </div> 574 <div class="col"> 575 576 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset provides one full year of hourly whole-building electrical demand for 74 office buildings in the United States and Switzerland, joined to hourly weather from 17 stations, with the complete outputs of forecasting, transfer-learning, benchmarking, control and fault-detection experiments. It is derived from the Building Data Genome Project 1 (MIT licence) and adds timezone correction, a complete hourly grid, a completeness-filtered office subset, a data-quality audit of all 507 source meters, and reproducible experiment outputs.</p></div> 577 578 <dl class="topinfo"> 579 <dt>Categories:</dt> 580 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 581 <ul class='links field__items'> 582 <li><a href="/topic-tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></li> 583 <li><a href="/topic-tags/electric-utility" hreflang="en">Electric Utility</a></li> 584 <li><a href="/topic-tags/mechanical-sensing" hreflang="en">Mechanical Sensing</a></li> 585 <li><a href="/topic-tags/analog-signal-processing" hreflang="en">Analog signal processing</a></li> 586 <li><a href="/topic-tags/communications-0" hreflang="en">Communications</a></li> 587 <li><a href="/topic-tags/standards-research-data" hreflang="en">Standards Research Data</a></li> 588 </ul> 589</div> 590</dd> 591 </dl> 592 </div> 593 </div> 594 </div> 595</article> 596 597 </div> 598 <div class="views-row"> 599 600 601<article data-history-node-id="111140" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 602 <div> 603 <div class="row py-2 mx-auto"> 604 <div class="col-12"> 605 <h2> 606 <a href="/documents/ara-rural-cots-5g-nr-ue-measurement-dataset">ARA Rural COTS 5G NR UE Measurement Dataset</a> 607 </h2> 608 </div> 609 </div> 610 <div class="row mx-auto"> 611 <div class="col-lg-3 pb-4 featured-image"> 612 613 <img src="/sites/default/files/styles/home/public/ARA_Rural_COTS_5G.png.webp?itok=jmnyW18W" alt="Dataset image"> 614 </div> 615 <div class="col"> 616 617 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset contains 7,144 timestamped, georeferenced user-equipment measurements collected on March 19â20, 2026, using the ARA Wireless Living Lab's commercial Ericsson 5G radio access network in rural central Iowa. The records combine UE position with available serving-cell identifiers, NR-ARFCN, RSRP, RSRQ, SINR, round-trip latency, and paired uplink/downlink throughput results. Base-station coordinates and cell-ID mappings for four ARA sites are provided separately.</p></div> 618 619 <dl class="topinfo"> 620 <dt>Categories:</dt> 621 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 622 <ul class='links field__items'> 623 <li><a href="/topic-tags/wireless-networking" hreflang="en">Wireless Networking</a></li> 624 </ul> 625</div> 626</dd> 627 </dl> 628 </div> 629 </div> 630 </div> 631</article> 632 633 </div> 634 <div class="views-row"> 635 636 637<article data-history-node-id="111133" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 638 <div> 639 <div class="row py-2 mx-auto"> 640 <div class="col-12"> 641 <h2> 642 <a href="/documents/lhsdataset">lhs_dataset</a> 643 </h2> 644 </div> 645 </div> 646 <div class="row mx-auto"> 647 <div class="col-lg-3 pb-4 featured-image"> 648 649 <img src="/sites/default/files/styles/home/public/tags/images/power-poles-503935_1920_0.jpg.webp?itok=uxwr3RS0" alt="Tag image"> 650 </div> 651 <div class="col"> 652 653 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset contains the numerical results supporting the design-optimization case study of "Gaussian-Process Surrogate Modeling and Bayesian Optimization for Coupling-Loss-Minimizing Design of Multifilamentary HTS Composite Tapes" (submitted to IEEE Transactions on Magnetics). All values were computed with a validated anisotropic frequency-domain electromagnetic model of inter-filament c
653oupling currents in a multifilamentary high-temperature superconducting (HTS) composite tape; no experimental measurements are included.</p></div> 654 655 <dl class="topinfo"> 656 <dt>Categories:</dt> 657 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 658 <ul class='links field__items'> 659 <li><a href="/topic-tags/power-and-energy" hreflang="en">Power and Energy</a></li> 660 </ul> 661</div> 662</dd> 663 </dl> 664 </div> 665 </div> 666 </div> 667</article> 668 669 </div> 670 <div class="views-row"> 671 672 673<article data-history-node-id="111132" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 674 <div> 675 <div class="row py-2 mx-auto"> 676 <div class="col-12"> 677 <h2> 678 <a href="/documents/cnc-machine-tool-fault-data-0">CNC machine tool fault data</a> 679 </h2> 680 </div> 681 </div> 682 <div class="row mx-auto"> 683 <div class="col-lg-3 pb-4 featured-image"> 684 685 <img src="/sites/default/files/styles/home/public/tags/images/stone-3354928_1920.jpg.webp?itok=oFJLnX7K" alt="Tag image"> 686 </div> 687 <div class="col"> 688 689 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Three coordinate acceleration sensors, temperature sensors, current sensors, and voltage sensors were installed on the CNC machining machine of MLT-YK3132 gear machine. Inner ring faults, outer ring faults, rolling body faults, and their combination faults were performed on the bearings of B-axis. Relevant data was collected according to different speeds and loads. The collection cycle for each data is to process one gear.</p></div> 690 691 <dl class="topinfo"> 692 <dt>Categories:</dt> 693 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 694 <ul class='links field__items'> 695 <li><a href="/topic-tags/reliability" hreflang="en">Reliability</a></li> 696 </ul> 697</div> 698</dd> 699 </dl> 700 </div> 701 </div> 702 </div> 703</article> 704 705 </div> 706 <div class="views-row"> 707 708 709<article data-history-node-id="111124" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 710 <div> 711 <div class="row py-2 mx-auto"> 712 <div class="col-12"> 713 <h2> 714 <a href="/documents/dataset-hierarchical-physics-informed-learning-temporal-dynamics-satellite-orbit">Dataset for Hierarchical Physics-Informed Learning of Temporal Dynamics for Satellite Orbit Prediction</a> 715 </h2> 716 </div> 717 </div> 718 <div class="row mx-auto"> 719 <div class="col-lg-3 pb-4 featured-image"> 720 721 <img src="/sites/default/files/styles/home/public/tags/images/artificial-intelligence-3382521_1920.jpg.webp?itok=R9L-xwxC" alt="Tag image"> 722 </div> 723 <div class="col"> 724 725 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset supports the reproduction and evaluation of hierarchical physics-informed satellite orbit prediction under realistic onboard conditions. It contains processed precise orbit determination (POD) and GNSS-based orbital state sequences from Sentinel-1A, Sentinel-3A, and Sentinel-3B collected over multiple years. The dataset also includes time-aligned multi-source space-weather measurements, covering solar radiation, solar-wind conditions, and geomagnetic activity.</p></div> 726 727 <dl class="topinfo"> 728 <dt>Categories:</dt> 729 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 730 <ul class='links field__items'> 731 <li><a href="/topic-tags/machine-learning" hreflang="en">Machine Learning</a></li> 732 </ul> 733</div> 734</dd> 735 </dl> 736 </div> 737 </div> 738 </div> 739</article> 740 741 </div> 742 <div class="views-row"> 743 744 745<article data-history-node-id="111123" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 746 <div> 747 <div class="row py-2 mx-auto"> 748 <div class="col-12"> 749 <h2> 750 <a href="/documents/suppl-video-mono-avoidance">Suppl. video for mono avoidance</a> 751 </h2> 752 </div> 753 </div> 754 <div class="row mx-auto"> 755 <div class="col-lg-3 pb-4 featured-image"> 756 757 <img src="/sites/default/files/styles/home/public/tags/images/system-2660914_1920.jpg.webp?itok=q2lAUo32" alt="Tag image"> 758 </div> 759 <div class="col"> 760 761 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Supplementary video of A Dynamic Obstacle Avoidance Method based on Monocular Camera for UAVs via Knowledge Distillation. Supplementary video for our paper. This dataset entry provides supplementary video material for our paper. It includes a video of autonomous drone flight recorded in a simulated urban environment built in Gazebo, which corresponds to the four scenarios illustrated in Figure 8 of the paper. These simulated sequences demonstrate the behavior of our proposed method under urban conditions and multi dynamic obstacles environment.</p></div> 762 763 <dl class="topinfo"> 764 <dt>Categories:</dt> 765 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 766 <ul class='links field__items'> 767 <li><a href="/topic-tags/other" hreflang="en">Other</a></li> 768 </ul> 769</div> 770</dd> 771 </dl> 772 </div> 773 </div> 774 </div> 775</article> 776 777 </div> 778 <div class="views-row"> 779 780 781<article data-history-node-id="111118" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 782 <div> 783 <div class="row py-2 mx-auto"> 784 <div class="col-12"> 785 <h2> 786 <a href="/documents/mind-gap-generative-ai-and-context-problem-software-architecture-practice-interview">Mind the Gap: Generative AI and the Context Problem in Software Architecture Practice - Interview Guideline</a> 787 </h2> 788 </div> 789 </div> 790 <div class="row mx-auto"> 791 <div class="col-lg-3 pb-4 featured-image"> 792 793 <img src="/sites/default/files/styles/home/public/tags/images/library-1666702_1920_0.jpg.webp?itok=WDz1OJv6" alt="Tag image"> 794 </div> 795 <div class="col"> 796 797 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>Use of generative AI (GenAI) in domains such as software<br>development, healthcare, or finance is becoming the norm. Potential advantages<br>for software architecture are also clear: automate routine tasks, explore solution<br>spaces, and even challenge design assumptions. The reality, however, is more<br>nuanced and poses a question: how do software architects actually use it and<br>where does it fall short? We conducted semi-structured interviews with ten<br>
797software architects at a large industrial company and applied a coding process</p></div> 798 799 <dl class="topinfo"> 800 <dt>Categories:</dt> 801 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 802 <ul class='links field__items'> 803 <li><a href="/topic-tags/standards-research-data" hreflang="en">Standards Research Data</a></li> 804 </ul> 805</div> 806</dd> 807 </dl> 808 </div> 809 </div> 810 </div> 811</article> 812 813 </div> 814 <div class="views-row"> 815 816 817<article data-history-node-id="111115" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 818 <div> 819 <div class="row py-2 mx-auto"> 820 <div class="col-12"> 821 <h2> 822 <a href="/documents/openlane-sky130a-parameter-sweep-dataset-1440-run-rtl-gdsii-timing-area-and-power-results">OpenLane SKY130A Parameter Sweep Dataset: 1,440-Run RTL-to-GDSII Timing, Area, and Power Results</a> 823 </h2> 824 </div> 825 </div> 826 <div class="row mx-auto"> 827 <div class="col-lg-3 pb-4 featured-image"> 828 829 <img src="/sites/default/files/styles/home/public/tags/images/system-2660914_1920.jpg.webp?itok=q2lAUo32" alt="Tag image"> 830 </div> 831 <div class="col"> 832 833 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset contains the complete results of a 1,440-run automated parameter <br>sweep of the OpenROAD/OpenLane RTL-to-GDSII flow on the SkyWater 130 nm PDK <br>(SKY130A). Three benchmark designs were evaluated: the SPM 32-bit serial-parallel <br>multiplier, the PicoRV32 RISC-V CPU core, and a custom 4Ã4 CNN MAC array <br>accelerator (ai_accel). Each design was swept across an 8Ã5Ã3Ã4 Cartesian grid <br>of four configuration parameters: CLOCK_PERIOD (4.0â10.0 ns), FP_CORE_UTIL </p></div> 834 835 <dl class="topinfo"> 836 <dt>Categories:</dt> 837 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 838 <ul class='links field__items'> 839 <li><a href="/topic-tags/other" hreflang="en">Other</a></li> 840 </ul> 841</div> 842</dd> 843 </dl> 844 </div> 845 </div> 846 </div> 847</article> 848 849 </div> 850 <div class="views-row"> 851 852 853<article data-history-node-id="111113" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 854 <div> 855 <div class="row py-2 mx-auto"> 856 <div class="col-12"> 857 <h2> 858 <a href="/documents/rsc-dataset-smartphone-based-sensor-data-road-surface-condition-monitoring">RSC Dataset: Smartphone-Based Sensor Data for Road Surface Condition Monitoring</a> 859 </h2> 860 </div> 861 </div> 862 <div class="row mx-auto"> 863 <div class="col-lg-3 pb-4 featured-image"> 864 865 <img src="/sites/default/files/styles/home/public/RSC%28dataset%29.PNG.webp?itok=pHfjrxsE" alt="Dataset image"> 866 </div> 867 <div class="col"> 868 869 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset, referred to as the Road Surface Condition (RSC) dataset, was collected for research on road surface condition monitoring and road anomaly detection using smartphone-based motion sensors. The dataset contains sensor measurements acquired from a smartphone mounted inside a moving vehicle, enabling the analysis of vehicle vibrations and motion associated with different road surface conditions.</p></div> 870 871 <dl class="topinfo"> 872 <dt>Categories:</dt> 873 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 874 <ul class='links field__items'> 875 <li><a href="/topic-tags/sensors-0" hreflang="en">Sensors</a></li> 876 <li><a href="/topic-tags/transportation-0" hreflang="en">Transportation</a></li> 877 <li><a href="/topic-tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></li> 878 </ul> 879</div> 880</dd> 881 </dl> 882 </div> 883 </div> 884 </div> 885</article> 886 887 </div> 888 <div class="views-row"> 889 890 891<article data-history-node-id="111110" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 892 <div> 893 <div class="row py-2 mx-auto"> 894 <div class="col-12"> 895 <h2> 896 <a href="/documents/agecare-sim-synthetic-daily-activity-and-well-being-dataset-older-adults">AgeCare-Sim: Synthetic Daily Activity and Well-Being Dataset for Older Adults</a> 897 </h2> 898 </div> 899 </div> 900 <div class="row mx-auto"> 901 <div class="col-lg-3 pb-4 featured-image"> 902 903 </div> 904 <div class="col"> 905 906 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>AgeCare-Sim is a synthetic dataset designed to support research and benchmarking in AgeTech, with a focus on independent living and healthy aging. The dataset represents simulated daily observations for older adults across demographic, activity, mobility, sleep, well-being, medication adherence, and synthetic fall-risk attributes. It contains 1,000 synthetic participants and 10,000 observation records.<
906/p></div> 907 908 <dl class="topinfo"> 909 <dt>Categories:</dt> 910 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 911 <ul class='links field__items'> 912 <li><a href="/topic-tags/aging-longevity-and-agetech" hreflang="en">Aging, Longevity, and AgeTech</a></li> 913 </ul> 914</div> 915</dd> 916 </dl> 917 </div> 918 </div> 919 </div> 920</article> 921 922 </div> 923 <div class="views-row"> 924 925 926<article data-history-node-id="111109" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 927 <div> 928 <div class="row py-2 mx-auto"> 929 <div class="col-12"> 930 <h2> 931 <a href="/documents/rtk-gps-dataset">RTK GPS DATASET</a> 932 </h2> 933 </div> 934 </div> 935 <div class="row mx-auto"> 936 <div class="col-lg-3 pb-4 featured-image"> 937 938 <img src="/sites/default/files/styles/home/public/ataport_abstract.png.webp?itok=RVU-Hu7L" alt="Dataset image"> 939 </div> 940 <div class="col"> 941 942 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset provides high-precision RTK-GNSS trajectories collected for the development and evaluation of IMU-based positioning methods in GNSS-challenged environments. A total of 33 independent trajectories are included, comprising 22 two-wheeler and 11 pedestrian walking trajectories. Data were collected using a Septentrio PolaRx5e GNSS receiver along with smartphone IMU data (acceleration, gyroscope, magnetometer) across diverse urban environments, including highways, flyovers, tunnels, intersections, and densely built-up corridors.</p></div> 943 944 <dl class="topinfo"> 945 <dt>Categories:</dt> 946 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 947 <ul class='links field__items'> 948 <li><a href="/topic-tags/sensors-0" hreflang="en">Sensors</a></li> 949 </ul> 950</div> 951</dd> 952 </dl> 953 </div> 954 </div> 955 </div> 956</article> 957 958 </div> 959 <div class="views-row"> 960 961 962<article data-history-node-id="111095" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 963 <div> 964 <div class="row py-2 mx-auto"> 965 <div class="col-12"> 966 <h2> 967 <a href="/documents/distrimuse-ds-vru-distrimuse-driving-simulations-hazardous-scenarios-involving-vulnerable">DistriMuSe DS - VRU (DistriMuSe Driving Simulations in Hazardous Scenarios Involving Vulnerable Road Users)</a> 968 </h2> 969 </div> 970 </div> 971 <div class="row mx-auto"> 972 <div class="col-lg-3 pb-4 featured-image"> 973 974 <img src="/sites/default/files/styles/home/public/tags/images/artificial-intelligence-3382521_1920.jpg.webp?itok=R9L-xwxC" alt="Tag image"> 975 </div> 976 <div class="col"> 977 978 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>The DistriMuSe DS - VRU (DistriMuSe Driving Simulations in Hazardous Scenarios Involving Vulnerable Road Users) contains synchronized driving-simulator and physiological HR/RR observations, derived cardiovascular activation, model-generated Vulnerable Road Users (VRUs) detections, distraction-detection outputs, emotion estimates, head-pose estimates, behaviour-only Fitness-to-Drive (FtD) annotations, participant-reported questionnaires, and three environment-facing simulator scene views.</p></div> 979 980 <dl class="topinfo"> 981 <dt>Categories:</dt> 982 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 983 <ul class='links field__items'> 984 <li><a href="/topic-tags/machine-learning" hreflang="en">Machine Learning</a></li> 985 <li><a href="/topic-tags/transportation-0" hreflang="en">Transportation</a></li> 986 <li><a href="/topic-tags/artificial-intelligence" hreflang="en">Artificial Intelligence</a></li> 987 <li><a href="/topic-tags/sensors-0" hreflang="en">Sensors</a></li> 988 </ul> 989</div> 990</dd> 991 </dl> 992 </div> 993 </div> 994 </div> 995</article> 996 997 </div> 998 <div class="views-row"> 999 1000
1001<article data-history-node-id="111083" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 1002 <div> 1003 <div class="row py-2 mx-auto"> 1004 <div class="col-12"> 1005 <h2> 1006 <a href="/documents/sub-10-milliseconds-multi-constraint-routing-based-contraction-hierarchies-large-scale">Sub-10 Milliseconds Multi-Constraint Routing Based on Contraction Hierarchies in Large Scale Optical Networks</a> 1007 </h2> 1008 </div> 1009 </div> 1010 <div class="row mx-auto"> 1011 <div class="col-lg-3 pb-4 featured-image"> 1012 1013 <img src="/sites/default/files/styles/home/public/tags/images/earth-2254769_1920_0.jpg.webp?itok=UsdDJjJj" alt="Tag image"> 1014 </div> 1015 <div class="col"> 1016 1017 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>In optical network planning, the routing problem for a single connection under complex constraints (such as wavelength continuity and optical nonlinearities) is a critical challenge. As network scales expand, these constraints significantly increase the complexity of the problem. Consequently, it is difficult to find the shortest path that satisfies all the constraints with several milliseconds in a large-scale optical network. To address this issue, we propose a multi-constraint routing algorithm based on contraction hierarchies (MCR-CH) for large-scale optical networks.</p></div> 1018 1019 <dl class="topinfo"> 1020 <dt>Categories:</dt> 1021 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 1022 <ul class='links field__items'> 1023 <li><a href="/topic-tags/communications-0" hreflang="en">Communications</a></li> 1024 </ul> 1025</div> 1026</dd> 1027 </dl> 1028 </div> 1029 </div> 1030 </div> 1031</article> 1032 1033 </div> 1034 <div class="views-row"> 1035 1036 1037<article data-history-node-id="111082" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 1038 <div> 1039 <div class="row py-2 mx-auto"> 1040 <div class="col-12"> 1041 <h2> 1042 <a href="/documents/dataset-power-electronics-si-igbt-devices-600-6500v-30-3600a">Dataset for Power Electronics: Si-IGBT Devices 600-6500V, 30-3600A</a> 1043 </h2> 1044 </div> 1045 </div> 1046 <div class="row mx-auto"> 1047 <div class="col-lg-3 pb-4 featured-image"> 1048 1049 <img src="/sites/default/files/styles/home/public/dataset1.jpg.webp?itok=tdS34USP" alt="Dataset image"> 1050 </div> 1051 <div class="col"> 1052 1053 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset contains electrical and thermal specifications, loss characteristics, and package-price information for 21 IGBT devices with antiparallel diodes from ABB/Hitachi Energy and Infineon. The devices cover rated blocking voltages of 600â6500 V and nominal IGBT currents of 30â3600 A. Data were compiled from manufacturer datasheets, PLECS models, and an existing IEEE DataPort dataset, Negri et al. (DOI: 10.21227/qtbj-0z69).</p></div> 1054 1055 <dl class="topinfo"> 1056 <dt>Categories:</dt> 1057 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 1058 <ul class='links field__items'> 1059 <li><a href="/topic-tags/power-and-energy" hreflang="en">Power and Energy</a></li> 1060 </ul> 1061</div> 1062</dd> 1063 </dl> 1064 </div> 1065 </div> 1066 </div> 1067</article> 1068 1069 </div> 1070 <div class="views-row"> 1071 1072 1073<article data-history-node-id="111076" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 1074 <div> 1075 <div class="row py-2 mx-auto"> 1076 <div class="col-12"> 1077 <h2> 1078 <a href="/documents/constraint-generation-kron-reduced-opf-unbalanced-hybrid-acdc-distribution-networks-data">Constraint Generation for Kron-Reduced OPF in Unbalanced Hybrid AC/DC Distribution Networks: Data and Results</a> 1079 </h2> 1080 </div> 1081 </div> 1082 <div class="row mx-auto"> 1083 <div class="col-lg-3 pb-4 featured-image"> 1084 1085 <img src="/sites/default/files/styles/home/public/fig_sec2_framework.png.webp?itok=JK09xDdh" alt="Dataset image"> 1086 </div> 1087 <div class="col"> 1088 1089 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset accompanies the manuscript "Constraint Generation for Exact Converter-Consistent Kron-Reduced OPF in Unbalanced Hybrid AC/DC Distribution Networks" and contains the data, results, figure source data, and regeneration scripts behind every number reported in the paper.</p></div> 1090 1091 <dl class="topinfo"> 1092 <dt>Categories:</dt> 1093 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 1094 <ul class='links field__items'> 1095 <li><a href="/topic-tags/smart-grid" hreflang="en">Smart Grid</a></li> 1096 </ul> 1097</div> 1098</dd> 1099 </dl> 1100 </div> 1101 </div> 1102 </div> 1103</article> 1104 1105 </div> 1106 <div class="views-row"> 1107 1108 1109<article data-history-node-id="111075" class="node node--type-document node--view-mode-teaser clearfix mb-3"> 1110 <div> 1111 <div class="row py-2 mx-auto"> 1112 <div class="col-12"> 1113 <h2> 1114 <a href="/documents/image-dataset-historical-heritage-sites-lal-mahal-and-ram-mandir">Image Dataset of Historical Heritage Sites: Lal Mahal and Ram Mandir</a> 1115 </h2> 1116 </div> 1117 </div> 1118 <div class="row mx-auto"> 1119 <div class="col-lg-3 pb-4 featured-image"> 1120 1121 <img src="/sites/default/files/styles/home/public/Lal_Mahal_783.jpg.webp?itok=-aEr_Zbp" alt="Dataset image"> 1122 </div> 1123 <div class="col"> 1124 1125 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>This dataset is a curated image dataset of<strong> historical heritage sites</strong>, initially focusing on <strong>Lal Mahal</strong> and <strong>Ram Mandir</strong>, developed as part of <em>
1125ANCESTRA (An Intelligent Heritage Informatics and Predictive Conservation Framework through Opportunistic Crowdsourced Visual Intelligence)</em>. The dataset is designed to support computer vision-based documentation, visual analysis, temporal comparison, and AI-assisted heritage conservation.</p></div> 1126 1127 <dl class="topinfo"> 1128 <dt>Categories:</dt> 1129 <dd><div class="field field--name-field-tags field--type-entity-reference field--label-hidden clearfix"> 1130 <ul class='links field__items'> 1131 <li><a href="/topic-tags/computer-vision" hreflang="en">Computer Vision</a></li> 1132 <li><a href="/topic-tags/image-processing" hreflang="en">Image Processing</a></li> 1133 </ul> 1134</div> 1135</dd> 1136 </dl> 1137 </div> 1138 </div> 1139 </div> 1140</article> 1141 1142 </div> 1143 1144 </div> 1145 1146 1147 </div> 1148</div> 1149 1150 </div> 1151 </div> 1152 1153 1154 </section> 1155 </main> 1156 </div> 1157 </div> 1158 1159 <div class="container-fluid py-4 px-4 featured-bottom"> 1160 <section class="row region region-featured-bottom"> 1161 <div id="block-dataport-bootstrap-marketonewslettersignup" class="block block-dataport-custom block-marketo-newsletter-block"> 1162 1163 1164 <nav class="py-1 pl-0"> 1165 <a href="/"> 1166 <img class="logo" src="/themes/custom/dataport_bootstrap/images/dataport-logo-white.svg" alt="IEEE DataPort" width="300" height="53" loading="lazy"> 1167 </a> 1168 </nav> 1169 <div class="content"> 1170 <div class="marketo-newsletter-form"><div class="marketo-newsletter-title">Newsletter Signup:</div><form class="marketo-pending"></form></div> 1171 </div> 1172 <div class="mb-3"> 1173 <a href="https://x.com/IEEEDataPort" target="_blank"> 1174 <img class="mr-2" src="/themes/custom/dataport_bootstrap/images/x-logo.svg" alt="X" width="21" height="21" loading="lazy"> 1175 </a> 1176 <a href="https://www.youtube.com/@ieeedataport7743" target="_blank"> 1177 <img style="margin-left:6px;" src="/themes/custom/dataport_bootstrap/images/youtube.svg" alt="Youtube" width="32" height="22" loading="lazy"> 1178 </a> 1179 <a href="https://www.linkedin.com/company/ieee-dataport" target="_blank"> 1180 <img style="margin-left:6px;" src="/themes/custom/dataport_bootstrap/images/InBug-White.png" alt="LinkedIn" width="24" height="22" loading="lazy"> 1181 </a> 1182 </div> 1183 </div> 1184<nav role="navigation" aria-labelledby="block-dataport-bootstrap-whyieeedataport-menu" id="block-dataport-bootstrap-whyieeedataport" class="block block-menu navigation menu--why-ieee-dataport"> 1185 1186 <h2 class="visually-hidden" id="block-dataport-bootstrap-whyieeedataport-menu">Why IEEE DataPort?</h2> 1187 1188 1189 1190 <ul class="clearfix nav" data-component-id="bootstrap_barrio:menu"> 1191 <li class="nav-item"> 1192 <a href="/why-ieee-dataport" class="nav-link nav-link--why-ieee-dataport" data-drupal-link-system-path="node/2567">Why IEEE DataPort?</a> 1193 </li> 1194 <li class="nav-item"> 1195 <a href="/datasets" class="nav-link nav-link--datasets" data-drupal-link-system-path="node/20">Access Datasets</a> 1196 </li> 1197 <li class="nav-item"> 1198 <a href="/submit-dataset" class="nav-link nav-link--submit-dataset" data-drupal-link-system-path="node/782">Submit a Dataset</a> 1199 </li> 1200 <li class="nav-item"> 1201 <a href="/subscribe" class="nav-link nav-link--subscribe" data-drupal-link-system-path="node/914">Subscribe</a> 1202 </li> 1203 <li class="nav-item"> 1204 <a href="/contact-us" class="nav-link nav-link--contact-us" data-drupal-link-system-path="node/93991">Contact Us</a> 1205 </li> 1206 </ul> 1207 1208 1209 1210 1211 </nav> 1212<nav role="navigation" aria-labelledby="block-dataport-bootstrap-ieeedataportresources-menu" id="block-dataport-bootstrap-ieeedataportresources" class="block block-menu navigation menu--ieee-dataport-resources"> 1213 1214 <h2 class="visually-hidden" id="block-dataport-bootstrap-ieeedataportresources-menu">IEEE DataPort Resources</h2> 1215 1216 1217 1218 <ul class="clearfix nav" data-component-id="bootstrap_barrio:menu"> 1219 <li class="nav-item"> 1220 <a href="/news" class="nav-link nav-link--news" data-drupal-link-system-path="node/3906">IEEE DataPort Resources</a> 1221 </li> 1222 <li class="nav-item active"> 1223 <a href="/search" class="nav-link active nav-link--search is-active" data-drupal-link-system-path="search" aria-current="page">Dataset Search</a> 1224 </li> 1225 <li class="nav-item"> 1226 <a href="/news" class="nav-link nav-link--news" data-drupal-link-system-path="node/3906">Newsletters and Insights</a> 1227 </li> 1228 <li class="nav-item"> 1229 <a href="/use-cases-and-additional-resources" class="nav-link nav-link--use-cases-and-additional-resources" data-drupal-link-system-path="node/4591">Use Cases</a> 1230 </li> 1231 <li class="nav-item"> 1232 <a href="https://supportcenter.ieee.org/" class="nav-link nav-link-https--supportcenterieeeorg-">IEEE Support Center</a> 1233 </li> 1234 <li class="nav-item"> 1235 <a href="/help" class="nav-link nav-link--help" data-drupal-link-system-path="node/744">Help & FAQs</a> 1236 </li> 1237 </ul> 1238 1239 1240 1241 1242 </nav> 1243<nav role="navigation" aria-labelledby="block-dataport-bootstrap-businesssolutions-menu" id="block-dataport-bootstrap-businesssolutions" class="block block-menu navigation menu--business-solutions"> 1244 1245 <h2 class="visually-hidden" id="block-dataport-bootstrap-businesssolutions-menu">Business Solutions</h2> 1246 1247 1248 1249 <ul class="clearfix nav" data-component-id="bootstrap_barrio:menu"> 1250 <li class="nav-item">
1251 <a href="/institutional-subscriptions" class="nav-link nav-link--institutional-subscriptions" data-drupal-link-system-path="node/9127">Business Solutions</a> 1252 </li> 1253 <li class="nav-item"> 1254 <a href="/institutional-subscriptions" class="nav-link nav-link--institutional-subscriptions" data-drupal-link-system-path="node/9127">Institutional Subscriptions</a> 1255 </li> 1256 <li class="nav-item"> 1257 <a href="/corporate-programs" class="nav-link nav-link--corporate-programs" data-drupal-link-system-path="node/12786">Corporate Partnerships</a> 1258 </li> 1259 <li class="nav-item"> 1260 <a href="https://ieee-dataport.org/sites/default/files/2025-09/IEEE_DataPort_Media_Kit_2025_Rev2.pdf" class="nav-link nav-link-https--ieee-dataportorg-sites-default-files-2025-09-ieee-dataport-media-kit-2025-rev2pdf">Advertising/Media Kit</a> 1261 </li> 1262 <li class="nav-item"> 1263 <a href="/ieee-dataport-data-competition-sponsorship-opportunities" class="nav-link nav-link--ieee-dataport-data-competition-sponsorship-opportunities" data-drupal-link-system-path="node/15019">Data Competition Sponsorship</a> 1264 </li> 1265 </ul> 1266 1267 1268 1269 1270 </nav> 1271 1272 </section> 1273 1274 </div> 1275 1276 <footer class="site-footer py-3"> 1277 <section class="row region region-footer mx-3"> 1278 <div id="block-dataport-bootstrap-footerlinks" class="block-content-basic block block-block-content block-block-contentb4865bc9-dce6-425e-977d-076a76a6da91"> 1279 1280 1281 <div class="content"> 1282 1283 <div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><div class="row py-2 mx-auto justify-content-center"><p class="text-center"><a href="http://www.ieee.org/index.html" target="_blank">Home</a> | <a href="http://www.ieee.org/sitemap.html" target="_blank">Sitemap/More Sites</a> | <a href="http://www.ieee.org/about/contact_center/index.html" target="_blank">Contact</a> | <a href="http://www.ieee.org/accessibility_statement.html" target="_blank">Accessibility</a> | <a href="http://www.ieee.org/p9-26.html" target="_blank">Nondiscrimination Policy</a> | <a href="http://ieee-ethics-reporting.org/" target="_blank">IEEE Ethics Reporting</a> | <a href="http://www.ieee.org/security_privacy.html" target="_blank">IEEE Privacy Policy</a> | <a href="https://www.ieee.org/about/help/site-terms-conditions.html" target="_blank">Terms & Disclosures</a></p><p class="text-center">© Copyright
1283<script type="text/javascript">document.write((new Date()).getFullYear());</script>
1283 IEEE - All rights reserved. A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.<br> </p></div></div> 1284 1285 </div> 1286 </div> 1287 1288 </section> 1289 1290 </footer> 1291 1292 </div> 1293 1294 1295
1295<script src="/sites/default/files/js/js_V66wx7OMR0qxw4jwO9CbqYzuKdDZ9hsxF5dr7bvAe0Q.js?scope=footer&delta=0&language=en&theme=dataport_bootstrap&include=eJx1jlEOwzAIQy-UlSNFZKFRNlIiIFt3-1X9qNSp-7F4xlgkcieNtHYxynGuvKEBDpdoI7XqIV1HCi2kyCGJuLlijwlVq0BhScg38w_XpYSMjl3U4xEEzHZlH9PV8l_rfZhLg4b6pO3nWbSFIlKYomOBsskvT_jA9Wy20LVag6yjI087hFelt8Gu-8kX5FZ3RA"></script>
vendor: 1 bytes, line 1295
1295
1296<script src="https://securepubads.g.doubleclick.net/tag/js/gpt.js" async crossorigin="anonymous"></script>
vendor: 1 bytes, line 1296
1296
1297<script src="/sites/default/files/js/js_nwqgUAQNN8W8EbObxjXPfYjuuXqD87imdDYM6ZLHu6U.js?scope=footer&delta=2&language=en&theme=dataport_bootstrap&include=eJx1jlEOwzAIQy-UlSNFZKFRNlIiIFt3-1X9qNSp-7F4xlgkcieNtHYxynGuvKEBDpdoI7XqIV1HCi2kyCGJuLlijwlVq0BhScg38w_XpYSMjl3U4xEEzHZlH9PV8l_rfZhLg4b6pO3nWbSFIlKYomOBsskvT_jA9Wy20LVag6yjI087hFelt8Gu-8kX5FZ3RA"></script>
vendor: 1 bytes, line 1297
1297
1298<script src="//engage.ieee.org/js/forms2/js/forms2.min.js"></script>
vendor: 1 bytes, line 1298
1298
1299<script src="/sites/default/files/js/js_AHJn9vnZ0z_HJmSgsZQBS0gKuSCxD3Z4TtV6ftCMvRo.js?scope=footer&delta=4&language=en&theme=dataport_bootstrap&include=eJx1jlEOwzAIQy-UlSNFZKFRNlIiIFt3-1X9qNSp-7F4xlgkcieNtHYxynGuvKEBDpdoI7XqIV1HCi2kyCGJuLlijwlVq0BhScg38w_XpYSMjl3U4xEEzHZlH9PV8l_rfZhLg4b6pO3nWbSFIlKYomOBsskvT_jA9Wy20LVag6yjI087hFelt8Gu-8kX5FZ3RA"></script>
1299 1300 1301 </body> 1302</html>
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.