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2,214 results for “Walls”

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zenodo40/100

Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"

Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations" Includes the data for 20 heartbeats. Divided into external and internal walls regions.

opencc-by-4.0Jun 2023View details →

zenodo40/100

Experimental data: Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: Simply supported, rigid, non-rigid

This dataset contains the results of laboratory quasi-static monotonic four-point bending tests conducted at RISE Research Institutes of Sweden on eleven natural-scale unreinforced brick masonry walls. The walls were spanning vertically between two reinforced concrete slabs and were tested under three different support conditions defined according to the American manual UFC 3-340-02: simply supported, rigid, non-rigid. The influence of these support conditions on the out-of-plane behavior of the walls was studied on elements with varying thickness – single and double wythe – and subjected to different levels of axial compression (or overload). The walls were tested inside of a bi-axial test setup that allowed not only the lateral, out-of-plane force but also the axial, arching action to be measured throughout the tests. Optical full-field displacement measurements were also acquired by two systems of cameras making use of the 2D and 3D Digital Image Correlation (DIC) technique. The data generated from these tests are made here available to support further investigations on masonry structures subjected to extreme lateral, out-of-plane actions. The dataset includes 3 compressed folders, ordered from 01 to 03, along with an auxiliary document describing the content and organization of the dataset.  The data presented here are described in the following research article: <blockquote> <a href="https://www.sciencedirect.com/science/article/pii/S0950061823022602?via%3Dihub">Godio M, Flansbjer M, Williams Portal N (2023). Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: simply supported, rigid, non-rigid, Construction and Building Materials</a> </blockquote> To cite this dataset, please refer to the article. The Authors

openother-openJul 2023View details →

zenodo40/100

WALL-E polarization lidar measurements from Cyprus campaign 11/2019

WALL-E lidar measurements during the Cyprus observational campaign, on November 2019.The description of the WALL-E lidar system design and calibration procedures can be found herein: Tsekeri, A., Amiridis, V., Louridas, A., Georgoussis, G., Freudenthaler, V., Metallinos, S., Doxastakis, G., Gasteiger, J., Siomos, N., Paschou, P., Georgiou, T., Tsaknakis, G., Evangelatos, C., and Binietoglou, I.: Polarization lidar for detecting dust orientation: system design and calibration, Atmos. Meas. Tech., 14, 7453–7474, https://doi.org/10.5194/amt-14-7453-2021, 2021. <ul> <li>Orientation flag measurements</li> <li>Measurements for the lidar system calibration</li> </ul>

opencc-by-4.0Jul 2023View details →

zenodo40/100

Stiffness transitions in new walls post-cell division differ between Marchantia polymorpha gemmae and Arabidopsis thaliana leaves

Plant morphogenesis is governed by the mechanics of the cell wall–a stiff and thin polymeric box that encloses the cells. The cell wall is a highly dynamic composite material. New cell walls are added during cell division. As the cells continue to grow, the properties of cell walls are modulated to undergo significant changes in shape and size without breakage. Spatial and temporal variations in cell wall mechanical properties have been observed. However, how they relate to cell division remains an outstanding question. Here we combine time-lapse imaging with local mechanical measurements via atomic force microscopy to systematically map the cell wall’s age and growth, with their stiffness. We make use of two systems, M. polymorpha gemmae, and A. thaliana leaves. We first characterise the growth and cell division of M. polymorpha gemmae. We then demonstrate that cell division in M. polymorpha gemmae results in the generation of a temporary stiffer and slower growing new wall. In contrast, this transient phenomenon is absent in A. thaliana leaves. We provide evidence that this different temporal behaviour has a direct impact on the local cell geometry via changes in the junction angle. These results are expected to pave the way for developing more realistic plant morphogenetic models and to advance the study into the impact of cell division on tissue growth.

opencc-by-4.0Mar 2023View details →

zenodo40/100

WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32

WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32 This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1]. Datasets <ul> <li>DP_LOS - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.</li> <li>DP_NLOS - Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.</li> <li>DA_LOS - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.</li> <li>DA_NLOS - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.</li> </ul> Table 1: Characteristics of presence detection and activity recognition datasets.  <table> <tbody> <tr> <td>Dataset</td> <td>Scenario</td> <td>#Rooms</td> <td>#Persons</td> <td>#Classes</td> <td>Packet Sending Rate</td> <td>Interval </td> <td>#Spectrograms</td> </tr> <tr> <td>DP_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DP_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> <tr> <td>DA_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DA_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> </tbody> </table>   Data Format Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files trainLabels.csv, validationLabels.csv, and testLabels.csv. Label files use the sample format [i c], with i corresponding to the spectrogram index (i.png) and c corresponding to the class. For presence detection datasets (DP_LOS , DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS , DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in meanStd.csv. Download and Use This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1]. [1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" International Conference on Computer Vision Systems. Cham: Springer Nature Switzerland, 2023.  BibTeX citation: <pre>@inproceedings{strohmayer2023wifi, title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={International Conference on Computer Vision Systems}, pages={41--50}, year={2023}, organization={Springer} }</pre>

opencc-by-4.0Sep 2023View details →

zenodo40/100