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59 results for “hexagonal”
Tungsten Trioxide Adsorption on hexagonal boron nitride in acidic electrolyte
<p>Progressive adsorption of ((WO)<sub>3</sub>)<sub>3</sub> clusters in initially vacant pores of hexagonal boron nitride 2D-crystal. Electrochemical Scanning Tunneling microscopy images taken with tungsten tips in 0.1M H<sub>2</sub>SO<sub>4. </sub>Seen in time-lapse images. Recording of these images commences as soon as tunnelling is established, strong effects of noise are still present. Time stamp in seconds.</p>
Brutal outdoor concrete hexagon flower pot
Brutal outdoor concrete hexagon flower pot public space planter - **test for a community 3D print** https://en.wikipedia.org/wiki/Brutalist_architecture @ Usti nad Labem, regional goverment building https://goo.gl/maps/pGcUzD3mnbHxHVxbA Legendary 1980s design by Prefa Přeštice, more info: https://www.fb.com/architektura489/posts/1214134412117289 Photogrammmetry scan 160x24MP, Free download, 16K texture. Source: Objaverse 1.0 / Sketchfab
Dual Brutal outdoor concrete hexagon flower pot
Dual Brutal outdoor concrete hexagon flower pot public space planter https://en.wikipedia.org/wiki/Brutalist_architecture @ Prague, redisential area street https://goo.gl/maps/HR3kP8ytP5YtneL47 Legendary 1980s design by Prefa Přeštice, more info: https://www.fb.com/architektura489/posts/1214134412117289 Photogrammmetry scan 160x24MP, Free download, 3x8K texture Created in RealityCapture by Capturing Reality Source: Objaverse 1.0 / Sketchfab
The complete set of neighborhood motions maps induced on a hexagonal grid
<p>The complete set of neighborhood motions maps induced on a hexagonal grid. This is a supplementary material for: Pluta K., Romon P., Kenmochi Y., Passat N.: Honeycomb geometry: Rigid Motions on the Hexagonal Grid, Accepted for DGCI17.</p>
HexaLCSeg: Hexagon-based Historical Land Cover Benchmark Dataset
<p>This dataset is a research outcome of a European Research Council, Proof of Concept Grant funded (Grant Number 101100837, A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040, <a href="https://cordis.europa.eu/project/id/101100837">GeoAI_LULC_Seg</a>) project.</p> <p>We introduce a new benchmark dataset derived from very high-resolution historical Hexagon (KH-9) reconnaissance satellite images for use in deep learning-based image segmentation tasks. Our dataset comprises high-resolution monochromatic Hexagon images from the 1970s and 1980s covering Turkish and Bulgarian territories, encompassing a large geographic area.</p> <p>Land cover (LC) classes used in this study:<br>Our dataset is inspired by the European Space Agency (ESA) WorldCover project and includes eight LC classes and related RGB codes were set for each class but we adjusted the 0-pixel value as no data and replaced the 0 values with 1 in the ESA RGB code palette. Additionally, a new sub-class for the trees, named Permanent Cropland is defined and its RGB code was set to 1-207-117. This class is important to differentiate permanent fruit trees from other trees, specifically crucial for past agricultural mapping purposes.</p> <p>The HexaLCSeg dataset comprises eight panchromatic images accompanied by corresponding 3-channel RGB Ground Truth Masks, all with 8-bit radiometric resolution and a spatial resolution of 1 meter. The dataset is organized into a total of 10,000 patches, each sized at 256x256 pixels. We split our dataset into 70% training (7000 patches), 15% validation (1500 patches), and 15% testing (1500 patches).</p> <p>Methodology:<br>In our study, we employed the geographic object-based image analysis (GEOBIA) approach to generate accurate land cover (LC) maps, which serve as the ground truth masks for our dataset.</p> <p>For deep learning-based image segmentation, we employed a total of 9 CNN models, implementing U-Net++ and DeepLabv3+ segmentation architectures with different hyperparameters, paired with SE-ResNeXt50 backbone that pre-trained with weight values from the 2012 ILSVRC ImageNet dataset.</p> <p>Models, metric results and weights:</p> <table> <tbody> <tr> <th>Model No</th> <th>Architecture</th> <th>Loss Function</th> <th>Augmentation</th> <th>Loss</th> <th>Accuracy</th> <th>IoU</th> <th>F-1 Score</th> <th>Precision</th> <th>Recall</th> </tr> </tbody> <tbody> <tr> <td>Model 1</td> <td>U-Net++</td> <td>Focal Loss</td> <td>No Augmentation</td> <td>0.1252</td> <td>0.9734</td> <td>0.8052</td> <td>0.8804</td> <td>0.8805</td> <td>0.8803</td> </tr> <tr> <td>Model 2</td> <td>U-Net++</td> <td>Focal Loss</td> <td>Horizontal Flip</td> <td>0.1253</td> <td>0.9728</td> <td>0.8008</td> <td>0.8776</td> <td>0.8778</td> <td>0.8774</td> </tr> <tr> <td>Model 3</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>No Augmentation</td> <td>0.1255</td> <td>0.9720</td> <td>0.7959</td> <td>0.8739</td> <td>0.8744</td> <td>0.8734</td> </tr> <tr> <td>Model 4</td> <td>U-Net++</td> <td>Focal Loss</td> <td>Random BC</td> <td>0.1256</td> <td>0.9717</td> <td>0.7938</td> <td>0.8725</td> <td>0.8727</td> <td>0.8723</td> </tr> <tr> <td>Model 5</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>Horizontal Flip</td> <td>0.1292</td> <td>0.9714</td> <td>0.7928</td> <td>0.8714</td> <td>0.8717</td> <td>0.8711</td> </tr> <tr> <td>Model 6</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>No Augmentation</td> <td>0.1307</td> <td>0.9711</td> <td>0.7906</td> <td>0.8699</td> <td>0.8702</td> <td>0.8697</td> </tr> <tr> <td>Model 7</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>Horizontal Flip</td> <td>0.1257</td> <td>0.9711</td> <td>0.7897</td> <td>0.8698</td> <td>0.8704</td> <td>0.8692</td> </tr> <tr> <td>Model 8</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>Random BC</td> <td>0.1259</td> <td>0.9704</td> <td>0.7871</td> <td>0.8667</td> <td>0.8673</td> <td>0.8662</td> </tr> <tr> <td>Model 9</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>Random BC</td> <td>0.1401</td> <td>0.9691</td> <td>0.7793</td> <td>0.8608</td> <td>0.8612</td> <td>0.8604</td> </tr> </tbody> </table> <p> </p> <p>System-specific notes and configuration:</p> <p>The code was implemented in Python (3.10) Programming Language.</p> <p>- torch == 2.1.2<br>- segmentation-models-pytorch == 0.3.3<br>- Albumentations == 1.4.0</p> <p>Apart from main data science libraries, RS-specific libraries such as GDAL, rasterio, and tifffile are also required.</p> <p>Citation:<br>Please kindly cite our paper if this code and the dataset used in the study are useful for your research.</p> <p>Elif Sertel et al., “HexaLCSeg: A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation [Software and Data Sets],” <em>IEEE Geoscience and Remote Sensing Magazine</em> 12, no. 3 (September 2024): 197–206, <a href="https://doi.org/10.1109/MGRS.2024.3394248">https://doi.org/10.1109/MGRS.2024.3394248</a>.</p>
Tailoring the Emission Wavelength of Color Centers in Hexagonal Boron Nitride for Quantum Applications
<p>Dataset for all studied defects (sorted by type and by wavelength) in terms of electronic transition type, transition energy, wavelength, and lattice deformation.</p>
NEP potential training and testing files for monolayer quasi-hexagonal-phase fullerene (qHPF)
<p>This dataset contains the force-field parameter files and reference dataset described in the manuscript "Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning molecular dynamics simulations" (to be published).</p> <p>The format of nep.in file is compatible with the GPUMD 3.3.1 (https://github.com/brucefan1983/GPUMD/releases/tag/v3.3.1).</p>
Third-order momentum advection on the quasi-hexagonal C-grid on the sphere: Data and source code
<p>This upload contains data and source code accompanying the paper submitted to JAMES (Journal of advances in modeling Earth systems) under the title 'Third-order momentum advection on the quasi-hexagonal C-grid on the sphere'</p> <p>See README files for further details.</p>
Accommodating a Hexagonal Zeta-phase Mn2N Film on a Cubic MgO (001) Substrate
<p>The figures associated with this paper can be derived from the following raw data set:</p> <p> </p> <p><strong>Figure 2.opju:</strong> This file contains the numerical data that can be used to generate line profiles shown in the Figure 2. It consist of two set of data for Figure 2(a) and Figure2(b). The data set are clearly labelled. This fie can be opened using Origin software.</p> <p> </p> <p><strong>Figure 3.opju: </strong>This file contains three set of raw data for Fig.3(a), Fig.3(b), and Fig.3(c) used in the mauscript. Those data set can be opened using Origin software.</p> <p> </p> <p><strong>Figure 5.opju: </strong>The raw data for XRD and AES are given in this file. This file contains two sheets and are labellled properly. This also can be opend using Origin software.</p> <p> </p> <p><strong>Figure 6(a).SM4/ Figure 6(c).SM4/Figure 6(d).SM4/: </strong>The raw data for STM images shown in Fig. 6 are given in the .SM4 format. It can be opened in WSxM software or other SPM mage processing software. These files are not drift and scale corrected. To do the drift correction in the image we used corel draw sotware and scanner calibration factors were applied for scale correction.</p> <p> </p> <p><strong>Figure 7.xlsx: </strong>The numerical data for surface formation enery plot given in Fig. 7 is given in this file. It can be opened in Excel/Origin software.</p> <p> </p> <p><strong>Figure 8(b).cube/ Figure 8(d).cube: </strong>The raw file for the simulated STm images are given in these files. These files can be opened in Vesta software.</p>
Memristive circuits based on multilayer hexagonal boron nitride for millimetre wave radiofrequency applications - Source data
<p>Source data for the paper "Memristive circuits based on multilayer hexagonal boron nitride for radiofrequency and millimetre wave applications"</p> <p>Find online at:</p> <p>https://doi.org/10.1038/s41928-024-01192-2</p>
SAXS Data of Phospholipid Mixtures forming the Inverted Hexagonal Phase
<h1>Lipids and Experimental Info</h1>
Dataset for publication High-capacitance BiPO4 material with monoclinic-hexagonal crystalline phase heterostructure for aqueous asymmetric supercapacitors
<p>The dataset contains all relevant data and figures regarding the manuscript "High-capacitance BiPO4 material with monoclinic/hexagonal crystalline phase heterostructure for aqueous asymmetric supercapacitors".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Cubic, hexagonal and tetragonal FeGex phases (x = 1, 1.5, 2): Raman spectroscopy and magnetic properties
<p>Raw data associated to the manuscript “Cubic, hexagonal and tetragonal FeGe<sub>x</sub> phases (x = 1, 1.5, 2): Raman spectroscopy and magnetic properties", <em>CrystEngComm</em> 2021, XXXX, XXX, XXX-XXX</p> <p>Abstract:</p> <p>There is currently an emerging drive to computational materials design and fabrication of the predicted novel materials. One of the keys for developing appropriate fabrication methods is determination of the composition and phase. Here we explore the FeGe system and establish reference Raman signatures for the distinction between the FeGe hexagonal and cubic structures, as well as FeGe<sub>2</sub> and Fe<sub>2</sub>Ge<sub>3</sub> phases. The experimental results are substantiated by first principles lattice dynamics calculations as well as by complementary structural characterizations such as transmission electron microscopy and X-Ray diffraction, along with the magnetic measurements.</p>
Dataset from J. Am. Chem. Soc. paper: Off-Stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane
<p>Dataset of the article "Off-Stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane" accepted in J. Am. Chem. Soc.</p> <p>- armchair.db: The ASE database of all unique surface states from the grand canonical genetic algorithm (GCGA) searches of the restructured armchair edge of h-BN.</p> <p>- zigzag.db: The ASE database of all unique surface states from the GCGA searches of the restructured zigzag edge of h-BN.</p> <p>Other key info such as DFT-calculated energies and magnetic moments are included in the metadata of each entry in the ASE databases.</p> <p>GCGA searches are performed using the workflow in the GOCIA package (https://github.com/zishengz/gocia)</p>
Data from: Exploring density and frequency dependent interactions experimentally: an R program for generating hexagonal fan designs.
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Direct bandgap quantum wells in hexagonal Silicon Germanium
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Observation of carrier cooling in direct band gap hexagonal silicon-germanium
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MAP. Distribution of Chinese Skleroprotopus species. Star, S. confucius; triangle, S. laticoxalis laticoxalis; square, S. laticoxalis longus; circle, S. membranipedalis; hexagon, S. securifer sp. nov.; diamond, S. serratus. in The millipede genus Skleroprotopus Attems, 1901 (Diplopoda, Julida, Mongoliulidae) in China, with description of a new species
MAP. Distribution of Chinese Skleroprotopus species. Star, S. confucius; triangle, S. laticoxalis laticoxalis; square, S. laticoxalis longus; circle, S. membranipedalis; hexagon, S. securifer sp. nov.; diamond, S. serratus.
Magnetic imaging with spin defects in hexagonal boron nitride
<p>Data relative to the publication arXiv:2207.10477</p>
Hexagonal Silicon-Germanium Nanowire Branches with Tunable Composition
<p>Hexagonal SiGe-2H has been recently shown to have a direct bandgap, and holds the promise to be compatible with silicon technology. Hexagonal Si (Ge) has been grown on an epitaxial lattice matched template consisting of wurtzite GaP (GaAs). Here, we present the growth of hexagonal Si and SiGe nanowire branches grown from a wurtzite stem by the Vapor-Liquid-Solid (VLS) growth mode, which is substantiated by <em>in-situ</em> transmission electron microscopy. We show that the composition can be tuned through the whole range of stoichiometry from Si to Ge, and the possibility to realize Si and SiGe heterostructures in these branches. </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.