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908 results for “Mesh”
Mesh Tinning St Photoscan
_Mesh Tinning Street Source: Objaverse 1.0 / Sketchfab
Mesh[maria - Cloud] (level 10)
Monument of Maria made from point data Source: Objaverse 1.0 / Sketchfab
Khan As'ad Pasha, Syria Mesh
Constructed at a major point of economic activity of the Ottoman Empire's rule in Damascus, governor As'ad Pasha al-Azem oversaw the project which was completed in 1752 and served as a guesthouse, commerce center, and storage facility for merchants traveling through the city. The structure follows the typical layout of a khan or caravanserai and was situated in the heart of the historic trading center along the Suq al-Buzuriyyah. Collected: CyArk 2019: Khan As'ad Pasha - LiDAR - Terrestrial . Collected by Directorate General of Antiquities and Museums . Distributed by Open Heritage 3D. https://doi.org/10.26301/wvvm-ck87 Source: Objaverse 1.0 / Sketchfab
Ananias Chapel, Syria Lidar Mesh
Lidar data, mesh, clean and edit, and decimate The Chapel of Saint Ananias, also known as the House of Saint Ananias, is a small underground chapel located in the ancient city of Damascus, Syria. The site is connected to the biblical story of Jesus's disciple, Saul of Tarsus, later known as St. Paul the Apostle. Over many centuries, non-Christian rulers have continually destroyed the chapel, rebuilt as a pagan temple and mosque before Franciscans rebuilt it in 1814. More info: https://doi.org/10.26301/j4g0-ek82 Cyark 2019 Ananias Chapel - LiDAR - Terrestrial . Collected by Directorate General of Antiquities and Museums . Distrubuted by Open Heritage 3D. Source: Objaverse 1.0 / Sketchfab
Security Fences: Stainless Steel Mesh is Very Strong and Will Not Bend.
<p>Stainless steel mesh is the greatest material for security fences since it is long-lasting and dependable. While more traditional options such as chain-link or wrought iron receive more attention, stainless steel mesh exemplifies how far modern engineering has progressed and how much individuals want to keep their houses secure.</p> <h1>Keeping People Safe with Strength: Security Fences</h1> <p><a href="https://www.dukesmetal.com/" target="_blank" rel="noopener"><strong>Security fences</strong></a> are today's unsung heroes, quietly guarding homes, businesses, and key structures. Among the several materials used to construct them, stainless steel mesh is the strongest and most lasting.</p> <p>Stainless steel mesh may not appear impressive at first glance, but its composition tells a different story. This mesh is made of fine stainless steel wire that has been expertly woven into a grid-like shape, making it highly robust while remaining elegant and unobtrusive.</p> <p>Stainless steel mesh is quite valuable since it may be utilized in a variety of applications. Whether employed in factories, government buildings, or private places, it provides a great combination of protection and aesthetic appeal. Its open structure allows you to keep an eye on things and let air flow through, but it also prevents people from entering.</p> <p>Unlike conventional fence materials, which rust and degrade over time, stainless steel mesh resists the elements. Because it does not rust naturally, it will last a long time and cost you less in the long run. Furthermore, advances in manufacturing techniques have improved its ability to withstand harsh weather conditions ranging from extremely hot to extremely cold.</p> <h2>Forging the Backbone: Companies That Make Steel Wire</h2> <p>Steel wire manufacturers are responsible for the production of stainless steel mesh. Their expertise and inventiveness influence the field of security fencing. These industry pioneers harness steel's raw force to create a flexible material capable of withstanding high stress and pressure.</p> <p>Steel wire manufacturers utilize cutting-edge tools and procedures to produce stainless steel wire that is stronger and more consistent than any other wire available on the market. Each strand is thoroughly tested to ensure that it satisfies strict quality standards. This ensures that the final product is trustworthy.</p> <p>It's not just about strength, though. <a href="https://www.dukesmetal.com/product/wire-rope-and-cable/" target="_blank" rel="noopener"><strong>Steel wire manufacturers</strong></a> understand the importance of being able to adapt their goods to satisfy their customers' diverse needs. They work hard to develop solutions that meet specific security requirements, whether that means modifying the wire thickness or weave pattern.</p> <p>Furthermore, everything they do is based on environmental sustainability. Steel wire manufacturers limit their environmental effect without losing quality or performance by optimizing resource utilization and implementing eco-friendly methods. This commitment to sustainability benefits the environment while also making stainless steel mesh appear to be a more responsible option for security fences.</p> <h2>Use Concrete Reinforcing Mesh to Strengthen Foundations</h2> <p>Concrete reinforced mesh is an essential component of construction because it strengthens structures against natural pressures. It is composed of meticulously placed stainless steel wires in a grid pattern. This offers modern infrastructure the necessary strength to withstand the strains of modern living.</p> <p>Reinforcing mesh for concrete is the foundation of concrete structures. It distributes loads evenly and reduces the possibility of cracks and structural collapse. Its high tensile strength and rust resistance ensure that buildings, bridges, and roads last a long period while remaining structurally sound.</p> <p>However, concrete reinforcing mesh has a purpose other than strengthening; it also speeds up and reduces the cost of construction. It reduces the need for excessive concrete and accelerates the construction process, decreasing costs and project timeframes without sacrificing quality.</p> <p>Furthermore, concrete reinforcing mesh is an excellent example of how effectively stainless steel and concrete function together. Steel is strong, but concrete is flexible, therefore they function well together. Together, they can create long-lasting structures.</p>
3D meshes of H1 and H6 footprints at PK23 track site, Northern Chile
<p>We report new ichnites corresponding to a trackway composed of three tetradactyl pedal footprints and plant remains near Southeastern Guatacondo, Tarapacá Region, within the transition of Majala and Chacarilla Formations (Late Jurassic-Early Cretaceous). The fossils were found in the context of a Copper Mining road construction at the PK23 site, Quebrada Blanca Phase 2 project, by the Teck Mining Company Quebrada Blanca S.A.</p> <p>We performed stratigraphical, paleobotanical, and paleoichnological studies, along with the footprint characterization and comparisons by Principal Component Analysis, and Discriminant Analysis of I-II, II-III and III-IV interdigital angles, using a large tetradactyl footprint morphological database of potential producers (dinosaurs, crocodiles, lepidosaurs, and pterosaurs). Additionally, we performed 3D pedal computer modeling kinematic analysis of the <em>Chilesaurus diegosuarez</em>i's 3D tetradactyl foot under two different sedimentary conditions (i.e. hard soil with superficial impression, and soft sediment with a penetrative impression), to test it as a potential trackmaker.</p> <p>The stratigraphic section reveals an association of siliciclastic facies, formed by the intercalation of fine-grained sandstones and wackes, with ripple marks, cross and horizontal lamination, evidencing intermittent moderate to low sedimentation energy.</p> <p>Among the plant material, several organs of the family Cheirolepidiaceae were identified such as, leaves and leafy branches of <em>Brachyphyllum</em> type, <em>Classostrobus</em> pollen cone, ovuliferous scales, and wood fragments related to <em>Cupressinoxylon</em>. The monodominance of Cheirolepidiaceae among the preserved macroflora allows inferring the characteristics of the environment in which the plants lived, highlighting halophytic, and temporarily arid conditions. Evidence as a whole, permits to interpret the depositional environment as an arid deltaic-littoral plain, temporarily covered with an ephemeral water layer associated with a stunted woody vegetation, in which terrestrial vertebrates walked by.</p> <p>Despite the low predictive power of the multivariate analysis, due to the various sources of morphological variability inherent to footprint formation, the PK23 footprints seem to better correspond to Dinosauria or Avialae as the most likely producers, rather than to Lepidosauria or Crocodylia, discarding the dinosaur <em>Chilesaurus </em>due to very low interdigital mediolateral rotation capabilities.</p>
Video supplement for publication "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain"
<p>This is the video supplement for the manuscript "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain". It includes compiled videos from allsky cameras at two different field sites (CLOUDLAB (hilly terrain, HT) and CROSSINN (complex terrain, CT)) for in total four case studies (named by their dates). For each case study, we compiled a video of the cloud cover from the model for the 1 km and 65 m resolution, both with a one-moment (1M) and two-moment (2M) microphysics scheme. </p> <p> </p>
Industrially relevant characterisation of a Ni mesh anode in alkaline water electrolysis
<p>A dataset on the characterisation of a 1 cm² Ni mesh anode in alkaline water electrolysis is provided herein. Setup wise, a three electrode beaker cell setup was used and industrially relevant conditions (<em>e.g.</em> 80°C & 30 wt.% KOH) were applied. Further details on the setup can be found in a previous <a href="https://doi.org/10.1002/celc.202300432">publication.</a></p>
Porous Subvolume - Cylindrical Specimen - Fine Mesh
<p><strong>Contains 44 porous subvolumes (quarter discs).<br></strong></p> <p><strong>Nominal load levels: </strong>20, 30, 40, 50, 60, 70, 80, 90, 100 MPa.</p> <p><strong>QoIs (stabilized cycle 20):</strong> (Δϵeq)/2, Δp, V</p> <p><strong>Specimen type:</strong> Cylindrical</p> <p><strong>Mesh type:</strong> Fine mesh</p> <p><strong>Notes:</strong> Version 2: (E*(Δϵeq)/2 = Σ0) is verified</p>
Meshes, volume fractions, and normal vectors for paraboloid fit verification
<p>These datasets are used to verify the results of the paper "A Paraboloid Fitting Technique for Calculating Curvature from Piecewise-Linear Interface Reconstructions on 3D Unstructured Meshes" by Z. Jibben, N. N. Carlson, and M. M. Francois.</p>
Neonate head mesh for Electrical Impedance Tomography - coarse
<p>A head mesh for EIT made by https://github.com/EIT-team/Mesher for https://github.com/EIT-team/PEITS.</p> <p>This archive is used for Travis CI</p>
Aeroelastic Prediction Workshop PUMI meshes and EnGPar graphs
<p>PUMI meshes and EnGPar graphs from the Aeroelastic Prediction Workshop.</p>
Finite element meshes of graphite foam samples for Image-Based Simulation (IBSim) of experimental laser flash analysis
<p>Image-Based Simulation (IBSim) meshes:<br> Finite element mesh of laser flash analysis (LFA) disc samples made of a graphite foam material (KFoam). The IBSim meshes are generated directly from a 3D volumetric image of a graphite foam block. 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). Segmentation of the data into a binarized image was achieved with ImageJ. Conversion of the segmented 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 graphite foam has anisotropic properties partly due to its microstructure. This dataset contains three meshes, one for each alignment along cartesian axes.</p> <p>The FE meshes use the EnSight Gold file format and may be visualised using Paraview (<a href="https://www.paraview.org">https://www.paraview.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>
The 4D mesh generated by the 'satori' algorithm, and Voronoi regions of its nodes
<p>The data contained in the archive is the 4D mesh containing 5^4 translation units, that are 2^4 points of tesseract shifted with 'satori' algorithm (total 10 000 nodes), and Voronoi diagram calculated using the qhull package for these points.</p> <p>It is used to demonstrate the shape uniqueness of the 4096 diagram's inner regions. The tessellation of inner regions is a candidate to be a solution of Kelvin problem in 4D version, offering possible minimal interface hyper-area to hyper-volume ratio. The structure posesses PT-symmetry so it might have applications in particle physics. For instance, its 3D layers can be used as CPT-compatible cellular automata for 3D simulations in chiral space.</p> <p>Presumably, the structure can be self-assembling in conditions near to critical point of second order phase transitions.<br> </p>
EMI-Meshing: High-quality extracellular-membrane-intracellular meshes of the mouse visual cortex
<p>This repository features a family of tetrahedral meshes of a dense reconstruction of the mouse visual cortex at extreme resolution. Both the extracellular space (ECS) and the main cellular structures are explicitly represented and labelled.</p> <p>The dataset is based on the <a href="https://www.microns-explorer.org/cortical-mm3">Cortical MM^3 dataset</a> centred at position <a href="https://ngl.microns-explorer.org/#!%7B%22dimensions%22:%7B%22x%22:%5B4e-9%2C%22m%22%5D%2C%22y%22:%5B4e-9%2C%22m%22%5D%2C%22z%22:%5B4e-8%2C%22m%22%5D%7D%2C%22position%22:%5B225182.5%2C107314.5%2C22000.5%5D%2C%22crossSectionScale%22:11.406101410482504%2C%22projectionOrientation%22:%5B0.1528419554233551%2C0.49656152725219727%2C0.39075320959091187%2C0.7598538994789124%5D%2C%22projectionScale%22:40961.499900183306%2C%22layers%22:%5B%7B%22type%22:%22image%22%2C%22source%22:%7B%22url%22:%22precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/em%22%2C%22subsources%22:%7B%22default%22:true%7D%2C%22enableDefaultSubsources%22:false%7D%2C%22tab%22:%22source%22%2C%22annotationColor%22:%22#7d7d7d%22%2C%22shaderControls%22:%7B%22normalized%22:%7B%22range%22:%5B86%2C172%5D%7D%7D%2C%22name%22:%22img%22%7D%2C%7B%22type%22:%22segmentation%22%2C%22source%22:%7B%22url%22:%22precomputed://gs://iarpa_microns/minnie/minnie65/seg%22%2C%22subsources%22:%7B%22default%22:true%2C%22mesh%22:true%7D%2C%22enableDefaultSubsources%22:false%7D%2C%22tab%22:%22segments%22%2C%22annotationColor%22:%22#949494%22%2C%22selectedAlpha%22:0.3%2C%22segments%22:%5B%22864691134947427836%22%2C%22864691135337771494%22%2C%22864691135393949941%22%2C%22864691135462270365%22%2C%22864691135474669888%22%2C%22864691135617729935%22%2C%22864691135718476593%22%2C%22864691136024102713%22%2C%22864691136390364287%22%2C%22864691136436690846%22%5D%2C%22segmentQuery%22:%22864691136194301772%2C%20864691136814938734%22%2C%22colorSeed%22:3728349837%2C%22name%22:%22seg%22%7D%5D%2C%22showAxisLines%22:false%2C%22showSlices%22:false%2C%22selectedLayer%22:%7B%22visible%22:true%2C%22layer%22:%22seg%22%7D%2C%22layout%22:%7B%22type%22:%224panel%22%2C%22orthographicProjection%22:true%7D%2C%22selection%22:%7B%22layers%22:%7B%22seg%22:%7B%22annotationId%22:%22data-bounds%22%2C%22annotationSource%22:0%2C%22annotationSubsource%22:%22bounds%22%7D%7D%7D%7D">225182-107314-22000</a> with a resolution of 32 x 32 x 40 nm^3. It contains a total of 20 meshes. The meshed domains are cubes with side lengths 5000, 10000, 20000 and 40000 nm and include the largest 5, 10, 50, 100 and 200 cells in the respective tissue volume, respectively. </p> <p><strong>Data</strong></p> <p>The dataset has the following content:</p> <ul> <li> <p>surface meshes: The surfaces of the extracted cells in `.ply` format, suitable for visualization with ParaView or usage in other meshing or simulation software</p> </li> <li> <p>volume meshes: The generated volumetric meshes in `.xdmf` format, containing labels for the extracellular space (label 1) and increasing integer values (2,..., N) for all cells. The file `facet.xdmf`contains facet marker, where the label *l* corresponds to the boundary between ECS and cell *l*. The outer boundaries are marked as `l + offset`, where `offset` is the next higher power of ten of the number of cells (`offset=int(10 ** np.ceil(np.log10(N_cells)))`).</p> </li> </ul> <p><strong>Usage</strong></p> <p>The meshes are intended for usage with FEniCS (see code below), but can equally be read and used with other Software.</p> <pre><code>from fenics import * import numpy as np mesh = Mesh() infile = XDMFFile("mesh.xdmf") infile.read(mesh) gdim = mesh.geometric_dimension() labels = MeshFunction("size_t", mesh, gdim) infile.read(labels, "label") infile.close() # get all local labels np.unique(labels.array()) # array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=uint64) infile = XDMFFile("facets.xdmf") infile.read(mesh) gdim = mesh.geometric_dimension() boundary_marker = MeshFunction("size_t", mesh, gdim - 1) infile.read(boundary_marker, "boundaries") infile.close() # get all local facet labels np.unique(boundary_marker.array()) # array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 101, 102, # 103, 104, 105, 106, 107, 108, 109, 110, 111], dtype=uint64)</code></pre> <p> </p>
Concept relevance metrics in the Medical Subject Headings (MeSH)
<p>In the MeSH concept relevance dataset, we define metrics to quantify relevance within this knowledge organization system and have applied them to the MeSH hierarchy. The research uses the 2022 PubMed collection, which contains over 33 million articles, and builds citation networks from January 2014 to December 2021. We quantify the relevance of MeSH concepts based on disruptiveness, influence, informativeness, usefulness, and concept relevance, using metrics such as disruption, graph centrality, entropy, category utility, and reciprocal rank fusion.</p>
GEOLAB-PEBSTER-TU Darmstadt: Large-scale model test on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<h1><strong>Large-scale model test on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB PEBSTER TU Darmstadt project on Zenodo! This repository contains the measurement data of the large-scale model test on Piled Embankments with a Basal Steel Reinforcement (PEBSTER). Pile-supported embankments with basal geosynthetic reinforcement are commonly built in soft soil areas. Steel mesh reinforcement is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from a large-scale test on steel-reinforced piled embankments, conducted at the Institute of Geotechnics of the Technical University of Darmstadt (Schneider et al., 2024a,b,c). This test is part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes four small-scale test (van Eekelen et al., 2024), conducted at Deltares, Delft, Netherlands.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Deltares, Netherlands, Keller (Germany, France, Poland), and SHM System, Poland.</p>
mesh
<p>This is the mesh code for the percentage of block volume (or area in 2D) within the central mixture region, corresponding to 10%, 30%, 50%, and 60%.</p>
The data of the mesh used in: Pan M, Zou R, Jüttler B. Algorithms and Data Structures for Cs-smooth RMB-splines of Degree 2s+ 1. Computer Aided Geometric Design, 2024: 102389.
Open the record for dataset details and reuse information.
Simple 2D mesh consisting of 2 regions.
<p>Simple 2D mesh with two regions. It has been designed in GMSH.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.