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2,214 results for “Walls”
Painted wall fragment 5
3D model created from a 2D public domain image. The imagery is superimposed on both sides to emphasis the fragments colour. Dimensions are relatively accurate. [Wall painting fragment](https://www.metmuseum.org/art/collection/search/253335?searchField=All&sortBy=Relevance&ao=on&showOnly=openAccess&ft=roman+wall+fragment&offset=0&rpp=80&pos=7) via The Metropolitan Museum of Art is licensed under CC0 1.0 Source: Objaverse 1.0 / Sketchfab
Stone Wall Nr.4
Stone Wall with some plants and moss, 94 photos processed with Photoscan Source: Objaverse 1.0 / Sketchfab
Photos of a White Wall in Varying Illumination Settings using Sony A6400
<p>This dataset contains photos of a plain white wall, taken with the Sony A6400 camera with the Sony SEL18135 lens, under varying exposure levels, for the purpose of parameter estimation for natural steganography. The zoom level is set 135mm (maximum) and the lens is out-of-focus. The ISO and shutter speed are manually varied to generate different exposure levels. All other settings of the camera are default. The scene is illuminated by four Four Lupa Superpanel Dualcolour LED panels placed 1.5m away from the wall, with power set to 50% and colour temperature 5600K. A detailed documentation of the creation and analysis of this dataset is published in an article titled Dataset, Noise Analysis, and Automated Parameter Estimation for Natural Steganography by Woo, Yiu, Yin, and Lai in IH&MMSec'24.</p>
Dataset for the manuscript "Magneto-Ionic Control of Coercivity and Domain-Wall Velocity in Co/Pd Multilayers by Electrochemical Hydrogen-Loading"
<p>Data used to create Figures 1-5 in the manuscript "Magneto-Ionic Control of Coercivity and Domain-Wall Velocity in Co/Pd Multilayers by Electrochemical Hydrogen-Loading" and S1-S18 in the supporting information of the same manuscript. Video files belong to data for Figure 4 (b,d) and are described in the correspondig procedure file in the folder for Figure 4 .</p>
Fig. 2 in A New Locality of the Italian Wall Lizard Podarcis siculus (Rafinesque-Schmaltz, 1810) from Turkey
Fig. 2. Photo of the discovered specimen of Podarcis siculus hieroglyphicus. Photo: I. Mollov.
Adaptative Survival of Aspergillus fumigatus to Echinocandins Arises from Cell Wall Remodeling Beyond β-1,3-glucan Synthesis Inhibition
<p>Unprocessed Solid-state NMR and Molecular Dynamics data sets for the manuscript titled "Adaptative Survival of Aspergillus fumigatus to Echinocandins Arises from Cell Wall Remodeling Beyond β-1,3-glucan Synthesis Inhibition"</p>
Data from: Sexual color ornamentation, microhabitat choice, and thermal physiology in the common wall lizard (Podarcis muralis)
<p>Common wall lizards (<em>Podarcis muralis</em>) in Italy show a striking variation in body coloration across the landscape, with highly exaggerated black and green colors in hot and dry climates and brown and white colors in cool and wet climates. Males are more intensely colored than females, and previous work has suggested that the maintenance of variation in coloration across the landscape reflects climatic effects on the strength of male–male competition, and through this sexual selection. However climatic effects on the intensity of male–male competition would need to be exceptionally strong to fully explain the geographic patterns of color variation. Thus, additional processes may contribute to the maintenance of color variation. Here we test the hypothesis that selection for green and black ornamentation in the context of male–male competition is opposed by selection against ornamentation because the genes involved in the regulation of coloration have pleiotropic effects on thermal physiology, such that ornamentation is selected against in cool climates. Field observations revealed no association between body coloration and microhabitat use or field active body temperatures. Consistent with these field data, lizards at the extreme ends of the phenotypic distribution for body coloration did not show any differences in critical minimum temperature, preferred body temperature, temperature‐dependent metabolic rate, or evaporative water loss when tested in the laboratory. Combined, these results provide no evidence that genes that underlie sexual ornamentation are selected against in cool climate because of pleiotropic effects on thermal biology.</p>
Μονή Θάρρι Moni Thari, Rodos. Interior. Mary and donor, north wall under dome.
<p>Μονή Θάρρι Moni Thari, Ρόδος Rodos. Interior. Mary and donor, north wall under dome.</p>
LFA experiments on Geant network, virtuall wall 1, rlite
<p>This contains perf flows and rtt reports.</p> <p>See ARCFIRE D4.4 for data analysis.</p>
Virtual wall 1, 3 metro nets, 3 metro nodes, 2 end nodes
<p>Contains rtts and summaries.</p> <p>See ARCFIRE D4.4 for data analysis.</p>
Virtual wall 1, scaling time, 2 metro nets, 3 metro nodes, 2 end nodes
<p>This contains rtts and summaries thereof.</p> <p>See ARCFIRED D4.4 for data analysis.</p>
Virtual wall 1, 3 metro nets, 5 metro nodes, 2 end nodes
<p>Contains rtts and summaries.</p> <p>See ARCFIRE D4.4 for data analysis.</p>
Bhitārī भितारी (Uttar Pradesh). Site 2, north wall of temple
<p>Bhitārī भितारी (Uttar Pradesh). Site 2, north wall of temple.</p>
Kalograia (Καλογραία/Bahçeli), Cyprus/Kıbrıs. Church of Christ Antiphonitis (Χριστός Ἀντιφωνητής), interior, north wall.
<p>Kalograia (Καλογραία/Bahçeli), Cyprus/Kıbrıs. Church of Christ Antiphonitis (Χριστός Ἀντιφωνητής), interior, north wall under dome, fresco showing Last Judgement. As documented 1973; scanned 2018.</p>
LIBS-wallscanner dataset related to "Scanning laser-induced breakdown spectrometer for mine walls" thesis
<p>This is a LIBS dataset published alongside the thesis in related identifiers. For better description see the thesis publication.</p>
Dataset of "3D generative adversarial networks for turbulent flow estimation from wall measurements"
<p>Dataset of the article 'Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements' (https://doi.org/10.1017/jfm.2024.432). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020. R.V. acknowledges financial support from ERC (grant agreement no. 2021-CoG-101043998, DEEPCONTROL).</p>
Raw Data and Codes for the Article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion"
<p>Dataset for the article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion", containing:</p> <ul> <li>The data and the codes used to generate the figures</li> </ul>
SWDD: Sonar Wall Detection Dataset
<p>This repository contains three side scan sonar datasets:</p> <ul> <li><strong>SWDD: Sonar Wall Detection Dataset,</strong> which is part of the paper <a href="https://arxiv.org/abs/2403.09313"><strong>Knowledge Distillation in YOLOX-ViT for Side Scan Sonar Object Detection</strong></a>. </li> <li><strong>SWDD-Validation </strong>is an extension version and<strong> </strong>part of the paper <strong><a href="https://arxiv.org/abs/2410.10554">ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</a></strong>.</li> <li><strong>SWDD-Adversarial</strong> is another extended version and<strong> </strong>part of the paper <a href="https://arxiv.org/abs/2410.10554"><strong>ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</strong></a>.</li> </ul> <p><strong>SWDD Dataset</strong></p> <p>SWDD has been recorded with a <em>lightweight autonomous underwater vehicle</em> (LAUV), operated by OceanScan-MST and carrying a Klein 3500 side scan sonar (SSS). The AUV has been deployed in the Porto de Leixões harbor following the harbor's walls while collecting SSS raw data. The SSS operated at a high frequency of 900kHz with a range of 75m for a total range of 150m from port to starboard. This setup produced a resolution of 4.168 pixels per line. The data were processed using Neptus software, transforming the raw data into waterfall images. The 216 images have been manually annotated with two different classes, <em>wall</em> and <em>noWall</em>. Data augmentation such as noise, flips, and combined noise-flip transformations have been applied, increasing the data to 864 images. Across the 864 images, there are a total of 2,616 labeled samples. To ensure robust training, with respect to the data quality, the original dataset has been mixed with the augmented images. The dataset is divided into 70% for training, 15% for validation, and 15% for testing. The authors chose to generate images with 500 lines, meaning the images have a resolution of 4.168x500. Finally, the images have been resized to 640 × 640 to use this data in specific computer vision algorithms. The dataset is annotated following the COCO annotation format.</p> <p>YOLOX and YOLOX-ViT have been trained and compared using the SWDD dataset. A 6-minute 57-second video from another survey was used for model comparison. From this video, 6243 frames have been extracted with its manually annotated ground truth. </p> <p>Thus, this dataset repository offers an SSS dataset, a 6-minute 57-second SSS video, and 6243 extracted frames from this video with its manually annotated ground truth. </p> <p>The Knowledge Distillation in YOLOX-ViT code using the SWDD dataset is publicly available at <a href="https://github.com/remaro-network/KD-YOLOX-ViT">KD-YOLOX-ViT.</a></p> <p><strong>For (re-)using/publishing SWDD-Validation, please include the following copyright text:</strong></p> <p><em><strong>SWDD</strong> is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> <p> </p> <p><strong>⚠️The two following datasets (SWDD-Validation and SWDD-Adversarial) are parts of the paper <em>ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</em>, submitted to the 2025 IEEE Symposium on Maritime Informatics & Robotics (MARIS 2025). The paper is still under review.<em> </em>⚠️</strong></p> <p> </p> <p><strong>SWDD-Validation</strong></p> <p>The SWDD dataset has been recorded using the LAUV with the Klein 3500 side scan sonar deployed in the Porto de Leixões harbor following the harbor's walls. The SWDD-Validation comports three field-collected detected: <em>SWDD-Clean</em>, <em>SWDD-Surface</em>, and <em>SWDD-Noisy</em>. The SWDD-Clean dataset, which includes data from the same mission as the original SWDD dataset; the SWDD-Surface dataset, captured while the LAUV was on the surface during windy weather, featuring a non-straight wall and wave-induced variations; and the SWDD-Noisy <em>dataset,</em> collected under stormy conditions, where the SSS transducer intermittently exited the water, resulting in data loss represented by black lines in the images.</p> <p>The SWDD-Validation dataset provides three datasets collected under different weather and sonar setups aiming to study object detection models' robustness variation under different sonar and noise conditions. The metadata of the SWDD-Validation dataset is depicted in the following table.</p> <div> <div> <p> </p> <table> <tbody> <tr> <td><em>Dataset</em></td> <td><em># Image</em></td> <td><em># Bbox</em></td> <td><em>Freq. (kHz)</em></td> <td><em>Range (m)</em></td> <td><em>Resolution</em></td> </tr> <tr> <td>SWDD-Clean</td> <td>148</td> <td>248</td> <td>900</td> <td>50</td> <td>4168 x 500</td> </tr> <tr> <td>SWDD-Surface</td> <td>98</td> <td>153</td> <td>900</td> <td>75</td> <td>6552 x 500</td> </tr> <tr> <td>SWDD-Noisy</td> <td>551</td> <td>800</td> <td>455</td> <td>100</td> <td>4168 x 500</td> </tr> </tbody> </table> </div> <div> </div> <div> <p><strong>For (re-)using/publishing SWDD-Validation, please include the following copyright text:</strong></p> <p><em><strong>SWDD-Validation</strong> is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> </div> <div> </div> <div> </div> <div><strong>SWDD-Adversarial</strong></div> <div> </div> <div> <p>The SWDD-Adversarial dataset has been generated by the ROSAR framework proposed in the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection, which, by leveraging adversarial PGD and Patch attack on the SWDD dataset, generates the three following datasets: P1-SWDD, P2-SWDD, and Patch-SWDD. The ROSAR framework is publicly available in our GitHub [<a href="https://github.com/remaro-network/ROSAR-framework">repository</a>].</p> <p><strong>P1 and P2-SWDD</strong> datasets result from adversarial PGD attacks, each resulting from different safety properties (<em>P1</em> and <em>P2</em>), resulting in 1017 and 1462 images for <em>P1</em> and <em>P2</em>. </p> <p><strong>Patch-SWDD</strong> dataset results from the adversarial patch attack, resulting in 151 images provided by the following repository: <a href="https://github.com/SamSamhuns/yolov5_adversarial" target="_blank" rel="noopener">Patch Attack.</a></p> </div> <div> </div> <div><strong>For (re-)using/publishing SWDD-Adversarial, please include the following copyright text:</strong></div> <div> <p><em><strong>SWDD-Adversarial</strong> is a public dataset based on the original SWDD dataset, which was collected using a Light Autonomous Underwater Vehicle by Oceanscan-MST and is within the scope of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> </div> </div>
Dataset of "Some effects of limited wall-sensor availability on flow estimation with 3D-GANs"
<p>Dataset of the article 'Some effects of limited wall-sensor availability on flow estimation with 3D-GANs' (https://doi.org/10.1007/s00162-024-00718-w). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020.</p>
Transcripts (+attributions) from a mixed-presence user study with two wall-sized displays
<p>Transcripts from a mixed-presence experiment with two wall-sized displays.<br>Automatic transcription (and translation when necessary) using Whisper large v3. Resulting sentences were then attributed to individuals.<br>Comes from a study ran in Q4 2023. Accompanies a paper.</p> <p>As for the abbreviations/acronyms used in the file:</p> <ul> <li>Conditions C0 and C1 correspond respectively to "no cues" and "cues enabled" (see paper)</li> <li>Sides A and V respectively correspond to Arena (=circular display) and Viswall (flat display)</li> <li>Speakers CEO, FIN, ICU and LOG respectively correspond to the roles given to these speakers (i.e. CEO, Head of Finance, Head of Intensive Care Unit, and Head of Logistics)</li> <li>Speakers TECH_VIZWALL and FACILITATOR_VIZWALL correspond to members of the research team (see protocol)</li> </ul>
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