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908 results for “Meshes”
MeshRIR: Dataset of Room Impulse Responses on Meshed Grid Points
<p>MeshRIR is a dataset of acoustic room impulse responses (RIRs) at finely meshed grid points. Two subdatasets are currently available: one consists of IRs in a 3D cuboidal region from a single source, and the other consists of IRs in a 2D square region from an array of 32 sources. This dataset is suitable for evaluating sound field analysis and synthesis methods. </p> <p>See the link below for the details:</p> <p><a href="https://sh01k.github.io/MeshRIR/">https://sh01k.github.io/MeshRIR/</a></p>
S-MESh based daily 1km surface PM2.5 over Europe for the years 2021-2022
<p>These datasets consist of estimated daily surface PM<sub>2.5</sub> concentrations over Europe at ~1km spatial resolution for the years 2021-2022 in tiff file format generated using our <em>Satellite and ML-based Estimation of Surface air quality at High resolution</em> (S-MESH) model. The S-MESH model for PM<sub>2.5</sub> generates surface PM<sub>2.5</sub> over Europe by downscaling CAMS regional forecast using satellite AOD and other meteorological parameters through a<em> stacked XGBoost model</em>. The downscaled S-MESH products have accuracies similar to CAMS regional interim reanalysis. More details on this methodology and study can be found in the related research article <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envres.2024.120363" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.envres.2024.120363</span></span></a></p> <p><span>The attached dataset files are zipped into 2 folders each corresponding to a year and can be unzipped from command line using "tar -xvzf filename.tar.gz".</span> Each tiff file represents daily PM<sub>2.5</sub> and is a single band image with an extent of 26°W-45°E and 34°N–72°N in EPSG:4326 - WGS 84 projection. </p> <p>This work was primarily <span>supported by a PhD fellowship from the Norwegian Research Council under grant agreement 321851.</span></p>
Mesh files for WAMIT, NEMOH and HAMS
<p>The dataset includes the mesh files for the simple truncated cylinder, the cylinder with heave plate, the incomplete cylinder and the overlapped cylinder, and the dataset is made available so the similar modelling results can be reproduced </p>
Dataset for Anomaly Detection in a Production Wireless Mesh Community Network
<p>CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24). </p> <p>Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a></p> <p>The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.</p> <p>A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.</p> <p><br> Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:<br> feature.rate are columns computed from feature as<br> feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])<br> feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted<br> where n is the number of samples</p> <p><strong>features</strong><br> - processes number of processes<br> - loadavg.m1 1 minute load average<br> - softirq.rate servicing softirqs<br> - iowait.rate waiting for I/O to complete<br> - intr.rate <br> - system.rate processes executing in kernel mode<br> - idle.rate twiddling thumbs<br> - user.rate normal processes executing in user mode<br> - irq.rate servicing interrupts<br> - ctxt.rate total number of context switches across all CPUs<br> - nice.rate niced processes executing in user mode<br> - nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure<br> - nr_anon_pages anonymous memory pages<br> - swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile<br> - page_tables Memory used to map between virtual and physical memory addresses<br> - swap <br> - eth.txe.rate tx errors over all ethernet interfaces<br> - eth.rxe.rate rx errors over all ethernet interfaces<br> - eth.txb.rate tx bytes over all ethernet interfaces<br> - eth.rxb.rate rx bytes over all ethernet interfaces<br> - eth.txp.rate tx packets over all ethernet interfaces<br> - eth.rxp.rate rx packets over all ethernet interfaces<br> - wifi.txe.rate tx errors over all wireless interfaces<br> - wifi.rxe.rate rx errors over all wireless interfaces<br> - wifi.txb.rate tx bytes over all wireless interfaces<br> - wifi.rxb.rate rx bytes over all wireless interfaces<br> - wifi.txp.rate tx packets over all wireless interfaces<br> - wifi.rxp.rate rx packets over all wireless interfaces<br> - txb.rate tx bytes over all ethernet and wifi interfaces<br> - txp.rate tx packets over all ethernet and wifi interfaces<br> - rxb.rate rx bytes over all ethernet and wifi interfaces<br> - rxp.rate rx packets over all ethernet and wifi interfaces<br> - sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces<br> - sum.xp.rate tx+rx packets over all ethernet and wifi interfaces<br> - diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces<br> - diff.xp.rate tx-rx packets over all ethernet and wifi interfaces</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
4DMRI moving meshes
<p><strong>Moving lung and ribcage meshes extracted from 4DMRIs</strong></p> <p>The newest version of the dataset and the documentation can be found on <a href="https://gitlab.psi.ch/4DCT-MRI_updated/4DMRI_moving_meshes">https://gitlab.psi.ch/4DCT-MRI_updated/4DMRI_moving_meshes</a>.</p> <p>The moving lung and ribcage meshes can for example be used to generate synthetic 4DCT(MRI)s. The code for generating 4DCT(MRI)s using a reference CT and the moving mesh data can be found on <a href="https://gitlab.psi.ch/4DCT-MRI_updated/4DCT-MRI">https://gitlab.psi.ch/4DCT-MRI_updated/4DCT-MRI</a>.</p> <p>Please cite the following publications when using the data:</p> <p>Jenny, T., Duetschler, A., Weber, D.C., Lomax, A.J. and Zhang, Y. (2022), <em>Technical Note: Towards more realistic 4DCT(MRI) numerical lung phantoms</em>. Manuscript submitted for publication.</p> <p>Duetschler, A., Bauman, G., Bieri, O., Cattin, P.C., Ehrbar, S., Engin-Deniz, G., Giger, A., Josipovic, M., Jud, C., Krieger, M., Nguyen, D., Persson, G.F., Salomir, R., Weber, D.C., Lomax, A.J. and Zhang, Y. (2022), <em>Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations.</em> Med. Phys.. <a href="https://doi.org/10.1002/mp.15591">https://doi.org/10.1002/mp.15591</a></p>
Supplementary material for the paper: "Mesh-based topology, shape and sizing optimization of ribbed plates"
<p>Source code and results, accompanying the paper "Mesh-based topology, shape and sizing optimization of ribbed plates", published in Structural and Multidisciplinary Optimization, 2024. </p>
Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during 2022 at the monthly sampling observatory of Blanes Bay Microbial Observatory.
<p><span>Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seasonally resolved seawater measurements. Mediterranean surface seawater sampling at the Blanes Bay Microbial Observatory was carried out monthly during 2022. January, March and April were not sampled due to technical difficulties. Seawater was hand-collected in glass sampling bottles around 11:00 am each day. The samples were kept in the dark and analysed at the ICM-CSIC for MeSH and DMS with the Vocus-PTR coupled to SFCE within 3 h of sample collection. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature and sea surface salinity). Sea surface temperature and salinity were determined using a CTD profiler Model SD204 (SAIV A/S). The chlorophyll from the Blanes 2022 timeseries was measured on a fluorometer (10AU Turner fluorometer).</span></p> <p><span>Details on the measurement methodology can be found here:</span></p> <p><span>Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011</span></p> <p><span> </span></p> <p><span>Definitions of acronyms, site abbreviations, or other project-specific designations:</span></p> <p><span>Lat=latitude (negative indicates south)</span></p> <p><span>Lon=longitude (negative indicates west )</span></p> <p><span>Hourlybin = The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as DD/MM/YYYY HH:MM</span></p> <p><span>MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3)</span></p> <p><span>DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3)</span></p> <p><span>fluo = chlorophyll a concentration in mg m^(-3)</span></p> <p><span>SST= sea surface temperature in degrees Celsius </span></p> <p><span>SSS= sea surface salinity in practical salinity units (PSU)</span></p>
Fig. 1. A — a mesh netting covering a in Morphometric characteristics of juvenile growth in Molipteryx fuliginosa (Uhler) (Heteroptera, Coreidae) from the South of the Russian Far East
Fig. 1. A — a mesh netting covering a raspberry bush (Rubus idaeus L.) with nymphs of the squash bug Molipteryx fuliginosa; B — imago of M. fuliginosa; С — nymphal molt in M. fuliginosa: instar II nymph emerging from the exuviae; D — M. fuliginosa instar III nymph (photos by M. V. Maslov) Рис. 1. А — саΔок, покрывающий цеΛые кусты маΛины (Rubus idaeus L.) с Λичинками кΛопа Molipteryx fuliginosa; B — имаго M. fuliginosa; С — Λинька Λичинки M. fuliginosa: высвобожΔение Λичинки II возраста от экзувия; D — Λичинка M. fuliginosa III возраста (фото М. В. МасΛов)
Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during POLAR-CHANGE Antarctic expedition 2023 on board the BIO Hesperides.
<p>Global emissions of methanethiol (MeSH) are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seawater concentration measurements from the Southern Ocean. The POLAR-CHANGE cruise was conducted in February and March 2023 on board the BIO RV Hesperides A33 in waters of the Antarctic Peninsula and the Weddell Sea. A Vocus PTR-ToF (TOFWERK AG, Thun, Switzerland, Vocus Scout) coupled to a segmented flow coil equilibrator (SFCE) was used to measure DMS and MeSH in seawater from the underway seawater inlet. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature (SST) and sea surface salinity (SSS)). Sea surface temperature (SST) and sea surface salitity (SSS) were measured in seawater from the underway water inlet located at 4-5 m depth. There was good agreement between the underway SST, SSS and the CTD sensors, confirming that the underway sensors were well calibrated. Underway seawater fluorescence measurements were not calibrated for total chlorophyll a (Chl a) concentration by frequent discrete measurements of Chl a from the on-board 10AU Turner fluorometer. For calibrated Chl a data, please get in touch with the autors.</p> <p> </p> <p>From the beginning of the POLAR-CHANGE cruise we observed extremely high MeSH concentrations which immediately dropped down to within previously observed ranges, after cleaning the equilibrator daily with 10 % HCl. We decided to exclude all MeSH measurements before regular cleaning commenced from 26/02/2023 onwards as these datapoints are likely affected by biofouling. MeSH seawater concentrations prior to this date are replaced by -999 in the dataset shared here. Other gaps in the timestamp of data acquisition are due to calibrations or measuring other samples.</p> <p> </p> <p>Details on the measurement methodology can be found here:</p> <p>Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011</p> <p> </p> <p>Definitions of acronyms, site abbreviations, or other project-specific designations:</p> <p>Lat=latitude (negative indicates west and south)</p> <p>Lon=longitude (negative indicates west and south)</p> <p>Date= The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as DD/MM/YYYY HH:MM</p> <p>MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3)</p> <p>DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3)</p> <p>fluo = surface fluorescence, a proxi for chlorophyll a concentration in mg m^(-3)</p> <p>SST= sea surface temperature in degrees Celsius</p> <p>SSS= sea surface salinity in practical salinity units (PSU)</p>
A Layer-averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation and Sensitivity Exploration
<p>Selected model output data for supporting this paper.</p> <p>List of Files:</p> <p>2dtracer.tar.gz: correlated tracer test</p> <p>rh3d.tar.gz: 3D Rossby-Haurwitz Wave</p> <p>modon.tar.gz: Colliding Modons</p> <p>jwss.tar.gz: Jablonowski-Williamson Baroclinic Steady State</p> <p>jwbw_1d.tar.gz: 1D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>jwbw_2d.tar.gz: 2D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>dcmip31.tar.gz: DCMIP3-1 nonhydrostatic gravity wave</p> <p>Klemp15.tar.gz: Nonhydrostatic Mountain Waves in Klemp et al. 2015</p> <p>held-suarez.tar.gz: Held-Suarez dry climate (post-processed data for plotting, the raw daily data are too large to upload)</p> <p>jwbwvr.tar.gz: Variable-Resolution modeling of the Jablonowski-Williamson Baroclinic Wave</p> <p> </p> <p>see https://doi.org/10.5281/zenodo.3544795 for a companion work</p> <p>References:</p> <p>Zhang, Y., J. Li, R. Yu, S. Zhang, Z. Liu, J. Huang, and Y. Zhou, 2019: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration. <em>Journal of Advances in Modeling Earth Systems</em>, <strong>11,</strong> 1685-1714.</p>
Micro-CT scans, whole-test meshes, and internal chamber segments of planktonic foraminifera for three-dimensional analysis of inter- and intra-specific variation in ontogenetic growth trajectories
<p> </p> <p>Here, we release tomographic reconstructions of 42 planktonic foraminifera from plankton tows and sediment traps, along with meshes and shrinkwrap meshes the whole tests and internal meshes of segmented chambers. Shrinkwrap meshes are test meshes that have been modified to close all pores and apertures in the test. Additionally, we have provided sample metadata for each specimen and volumetric measurements for the tests and chambers. This dataset was used in a study of ontogenetic growth in planktonic foraminifera and its variation within and among species.</p> <p> The CT-scans and reconstructions were obtained at Naturalis Biodiversity Center in Leiden, the Netherlands with a Zeiss Xradia 520 Versa micro-CT scanner. The meshes and segments were created at Yale University.</p> <ol> <li>Sample_Metadata.csv: Spreadsheet containing information on the sampling localities and dates for all specimens.</li> <li>Scan_data.csv: Spreadsheet containing metadata for all micro-CT scans including current strength, pixel size, voltage, image height, image width, and the number of images taken.</li> <li>Whole_Test_Measurements.csv: Spreadsheet containing measurements of linear dimensions (axis1, axis2, axis 3), total number of chambers, calcite test volume, calcite test surface area, shrinkwrap volumes, and and shrinkwrap surface areas for all specimens.</li> <li>Chamber_Measurements.csv: Spreadsheet containing measurements of individual internal chamber segments, including position from the final chamber (F-chamber), position from the first chamber (Chamber), volume, and surface area.</li> <li>CT_Scan_Stacks.zip: reconstructed micro-CT image stacks (.tif files) for each specimen.</li> <li>Meshes.zip: Meshes of the test calcite, the shrinkwrap, and the internal chamber segments for each specimen (.stl 3D mesh files). Regular test meshes are named with the format “SampleID.stl”, and shrinkwrap meshes are named “SampleID-WRAP.stl”. Chamber meshes are named “SampleID-CH#.stl” and “SampleID-CH#-Wrap.stl”. Chambers are numbered in relation to their position from the final chamber, with “CH1” being the final chamber and “CH2” being the penultimate chamber.</li> </ol> <p>This data is described and analyzed in the manuscript “Three-Dimensional Analysis of Inter- and Intraspecific Variation in Ontogenetic Growth Trajectories of Planktonic Foraminifera” submitted to the journal <em>Marine Micropaleontology.</em></p>
Finite element mesh of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Image-Based Simulation (IBSim) mesh:<br> A finite element mesh of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The FE mesh data uses the EnSight Gold file format and may be visualised using Paraview (<a href="https://www.paraview.org">https://www.<strong>paraview</strong>.org</a>).</p> <p>The CT data used for the mesh is available as a separate dataset:</p> <p>This data was used originally for the following publications (please cite if re-using the data):<br> Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh
<p>Contains figures used in + the data with scripts to generate figures for the manuscript (in preparation)</p> <p>Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh</p>
Sphere meshed with 925 million tetrahedra
<p>A sphere meshed with 925,056,781 tetrahedra with GHS3D, renumbered through a Hilbert SFC and compressed with XZ.</p>
Accompanying dataset for the paper "Mesh Density and Geodesic Tortuosity in Planar Triangular Tesselations Devoted to Fracture Mechanics"
<h2>Contributions</h2> <ul> <li>Author #1 is the major contributor to the paper writing. Author #1 has created most of the Figures in the paper and has provided all the results concerning the geodesic tortuosity of planar triangular meshes.</li> <li>Author #2 and Author #3 have provided the results concerning the density of planar triangular meshes.</li> </ul> <h2>Funding sources</h2> <ul> <li>This work was funded by the French Institute for Radiation Protection and Nuclear Safety (IRSN) and the University of Montpellier, France.</li> </ul> <h2>Data structure and information</h2> <p>All the results and figures presented in the paper have been obtained with a python script available in the <code>workflows</code> folder. </p> <p>Data files processed by that script are provided in the <code>data</code> folder. The pickle files <code>t_{nb}.pkl</code> inside the <code>data/pkl</code> folder contain lists of tortuosity values computed on a <code>gmsh</code> mesh for several paths containing various number of edges (<code>nb</code>).</p> <p><code>pkl</code>files are produced via a function call to <code>main.py:comp_real_tortuosities(nb)</code></p> <h2>Data structure and information</h2> <ul> <li><code>workflows/</code> - folder containing plotting scripts<ul> <li><code>reproduce.sh</code> - bash script launching python and compressing figures</li> <li><code>main.py</code> - python script for figure creation.</li> <li><code>/utils</code> - folder containing additional python scripts for figures creation</li> <li><code>/figures</code> - folder containing the figures produced by the python script</li> </ul> </li> <li><code>data/</code> - data folder<ul> <li><code>gmsh</code> - folder containing mesh in gmsh information</li> <li><code>pkl</code> - folder containing the pickled vectors "t_{nb}.pkl".</li> </ul> </li> </ul> <h2>Paper Description</h2> <p>In fracture mechanics, the mesh sensitivity is a key issue. It is particularly true concerning cohesive volumetric finite element methods in which the crack path and the overall behavior are respectively influenced by the mesh topology and the mesh density. Poisson-Delaunay tessellations parameters, including the edge length distributions, were widely studied in the literature but very few works concern the mesh density and topology in Delaunay type meshes suitable for finite element simulations, which is of crucial interest for practical use.Starting from previous results concerning Poisson-Delaunay tessellations and studying in detail the Lloyd relaxation algorithm, we propose estimates for the probability density functions of the edge length and triangle top angles sets. These estimates depend both on the intensity of the underlying point process and on an efficiency index associated to the global quality of the mesh. The global and local accuracies of these estimates are checked for various standard mesh generators. Finally the mesh density and geodesic tortuosity are estimated for standard random or structured triangular meshes typically used in finite element simulations.These results provide practical formulas to estimate bias introduced by the mesh density and topology onthe results of cohesive-volumetric finite element simulations.</p>
Mesh and configuration files to perform coupled heat+fluid simulations on a realistic human eyeball geometry with Feel++
<h1>Run the simulation</h1> <h2>With slurm</h2> <p>Set up position and desired mesh in the `run.slurm` file. Then, submit the job with the following command:</p> <p><code>sbatch run.slurm</code><br><br></p> <h2>Without slurm</h2> <p>Run by hand the command of the <code>run.slurm</code> file.<br><code>POSITION=prone # prone supine standing</code><br><code>SOLVER_TYPE=simple # simple lsc</code><br><code>MESH_INDEX=M4 # M1 M2 M3 M4 M5</code></p> <p><code>mpirun -np 128 feelpp_toolbox_heatfluid \</code><br><code> --config-files eye-${POSITION}.cfg pc_${SOLVER_TYPE}.cfg \</code><br><code> --heat-fluid.json.patch='{ "op": "replace", "path": "/Meshes/heatfluid/Import/filename", "value": "$cfgdir/mesh/Mr/'${MESH_INDEX}'/Eye_Mesh3D_p$np.json" }' \</code><br><code> --heat-fluid.scalability-save=1 --heat-fluid.heat.scalability-save=1 --heat-fluid.fluid.scalability-save=1</code></p> <h2>Available meshes</h2> <p>The meshes are available and are already partitioned for parallel computing:</p> <p><code>M0 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M1 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M2 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M3 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M4 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M5 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M6 : 128, 256, 384, 512, 640, 768</code></p>
Dataset for article: Emerging materials and technologies for advancing bioresorbable surgical meshes
<p>The dataset contains all the data discussed in the related paper: Mechanical and biological performances of bioresorbable meshes, clinical complications associated with meshes. Separate folders include data on ideal surgical meshes (Tables), advanced manufactering technologies such as 3D and 4D printing, methods of surface modifications in jpg files. </p> <p> </p>
Linked collectors and determiners for: Abundance of zooplankton during the polar night (cruise in January 2017) at 13 stations using a 64um-mesh Multinet.
Natural history specimen data linked to collectors and determiners held within, "Abundance of zooplankton during the polar night (cruise in January 2017) at 13 stations using a 64um-mesh Multinet". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/76ef1883-c32a-49bb-a36c-2752af1b4e95">https://bionomia.net/dataset/76ef1883-c32a-49bb-a36c-2752af1b4e95</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/76ef1883-c32a-49bb-a36c-2752af1b4e95">https://gbif.org/dataset/76ef1883-c32a-49bb-a36c-2752af1b4e95</a>. Formatted as a Frictionless Data package.
A discrete 3D mesh of the thorax of a newborn with region segmentation
<p># Newborn mesh</p> <p>## Description:<br> This dataset contains a labelled mesh of the thorax of a newborm premature human baby with the sectionning of different region of the body. The mesh is composed of 229363 nodes that are grouped into 1356069 tetrahedron elements.</p> <p>## Original data<br> The original DICOM (Digital Imaging and communications in medicine) images belong to the Cork University Hospital (CUH) data base. The hospital contacted the parents who approved the academic use of the anonymised CT scan of their child, under the ethical approval ECM 4 (gg) 07/03/18 from the Clinical Research Ethics Committee of the Cork Teaching Hospitals. The thoracic CT scan consist of a stack of 367 cross sectional slices, 0.625 mm thick. The area of each image is 512 x 512 pixels with a pixel size of 0.3555 mm. The neonate was born at 36 weeks gestational age with 3.58 kg weight.</p> <p>## Sectioning process<br> The segmentation of nine main organs (skin, fat, muscle, bone, cartilage, heart, artery, trachea and lung) was done using NIRFASTSlicer2.0. Each DICOM image exhibits the different organs in an especific Housenfield Unit (HU, quantitave scale of radiodensity); dense organs like bone have whitish color, in contrast with less dense organs which appereance is dark. A distinctive colour was assigned to each organ by direct drawing over the CT images one by one. The HU were used to define the boundaries between organs. The organs were considered homogeneous, therefore fine structures present in the human body were ignored.</p> <p>For our study, we subdivided the lungs into 3 different segments (inner, middle and external). On completion of segmentation, the thoracic CT was subdivided in the 11 regions.</p> <p>## Regions<br> The mesh has been divided in 11 different regions number from 1 to 11 in the following way:</p> <ul> <li>1 -> lung (external segment)</li> <li>2 -> bone</li> <li>3 -> cartilage</li> <li>4 -> heart</li> <li>5 -> muscle</li> <li>6 -> artery</li> <li>7 -> fat</li> <li>8 -> skin</li> <li>9 -> trachea</li> <li>10 -> lung (middle segment)</li> <li>11 -> lung (inner segment)</li> </ul> <p>## Data format<br> The mesh data is written in 3 different file format:</p> <ul> <li> mesh.mat: MATLAB .mat format that contains 5 variables: <ul> <li>dimension: Number of dimension in the mesh</li> <li>nodes: Matrix of size 229363 x 3 containing the (x,y,z) coordinates of each node in the mesh in milimetres (mm)</li> <li>elements: Matrix of size 1356096 x 4 containing the indices of the nodes corresponding to each element (tetrahedron) of the mesh</li> <li>region: Vector size 229363 x 1 indicating to which region the nodes belongs.</li> <li>bndvtx: Vector of size 229363 x 1 indicating whether the node is on the boundary of the mesh.</li> </ul> </li> <li>mesh.vtk: Mesh in the vtk Datafile Version 2.0 format. The only data field in the VTK file corresponds to the region variable in the .mat file.</li> <li>mesh_csv.zip: Mesh in a .csv format. The zip file contains 4 csv files containing the nodes list, the elements list, the region list and the bndvtx list.</li> </ul> <p>## Licence<br> The mesh is published under the creative common CC-BY licence. You are free to use this data as you wish, but please cite this dataset using its DOI for example.</p> <p>## Digital Object Identifier:<br> DOI: 10.5281/zenodo.4916863</p> <p>## Authors<br> Mesh sectionning: Andrea Pacheco<br> Data formating: Baptiste Jayet</p> <p>## Funding<br> The research leading to these results was funded by Science Foundation Ireland project no. SFI/15/RP/2828</p>
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