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381 results for “electromagnetism”
Discontinuous Galerkin time-domain method for the computation of electromagnetic resonant modes
<p>Data generated for the paper "The application of a high-order discontinuous Galerkin time-domain method for the computation of electromagnetic resonant modes" published in Applied Mathematical Modelling. It includes:</p> <ul> <li>Meshes</li> <li>Time domain signals</li> <li>Spectra</li> <li>Figures</li> </ul>
Effect Of Pulsed Electromagnetic Field And Microwave Therapy On Pain And Physical Function In Older Adults With Knee Osteoarthritis: A Randomized Clinical Trial.
<p><strong><span>Background and purpose:</span></strong><span> </span><span>Pulsed electromagnetic field (PEMF) therapy and microwaves (MW) are two electrotherapy modalities that have shown to improve pain and function in patients with knee osteoarthritis (KOA). Nevertheless, the effectiveness of these therapies is controversial due to diversity in the application parameters and treatment protocols in these patients. The objective is to compare the effectiveness of active PEMF versus MW, as well as sham PEMF, in addressing pain and improving functionality for treating KOA.</span></p> <p><strong><span>Methods:</span></strong><span> </span><span>Double-blind, placebo-controlled, randomized clinical trial. Participants diagnosed with KOA were assigned to an intervention combining an exercise program with active PEMF, MW, or sham PEMF, delivered in three weekly sessions over four weeks. The main outcomes were pain, reported on a 0–10 cm visual analogue scale (VAS), and functionality, as evaluated with the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire, and the Timed Up and Go test. The outcomes were measured at pre-intervention, immediately post-intervention, and one and four months after the intervention.</span></p>
Raw Data Results Figure: Electromagnetic Properties of Indium Isotopes Elucidate the Doubly Magic Character of 100Sn
<p>Raw data for the results figure of the article "Electromagnetic Properties of Indium Isotopes Elucidate the Doubly Magic Character of 100Sn."</p> <p>Preprint at <a href="https://doi.org/10.48550/arXiv.2310.15093">arXiv.2310.15093.</a></p>
Electromagnetic + Hadronic Sampling Calorimeter Shower Images
<p>We include multiple hdf5 files:</p> <ol> <li>The files 'piplus_1.hdf5' and 'piplus_2.hdf5' each contains 100,000 calorimeter showers originating from incoming charged pions with incident energies <strong>uniformly</strong> distributed in the range [1,100] GeV. The file 'piplus_1.hdf5' was used to train our models. The second file 'piplus_2.hdf5' was used in evaluation of generation performance.</li> <li>The file 'piplus_log.hdf5' contains 100,000 calorimeter showers originating from incoming charged pions with incident energies <strong>log-uniformly</strong> distributed in the range [1,100] GeV.</li> <li>The files with names 'piplus_{E_true}.hdf5' each contain 100,000 calorimeter showers originating from incoming charged pions with fixed incident energy at E_true = {10, 20, 30, 40, 50, 60, 70, 80, 90} GeV. These files were used for evaluation of calibration performance.</li> </ol> <p>Each file has the following structure:</p> <p>energy Dataset {100000, 1}</p> <p>layer_0 Dataset {100000, 3, 96}</p> <p>layer_1 Dataset {100000, 12, 12}</p> <p>layer_2 Dataset {100000, 12, 6}</p> <p>layer_3 Dataset {100000, 3, 96}</p> <p>layer_4 Dataset {100000, 12, 12}</p> <p>layer_5 Dataset {100000, 12, 6}</p> <p>overflow Dataset {100000, 6}</p> <p>The sampling calorimeter we built is segmented longitudinally into six layers with different depths and granularities. In units of mm, the six layers have the following (eta, phi, z) dimensions:<br>Layer 0: (5, 160, 90) | Layer 1: (40, 40, 347) | Layer 2: (80, 40, 43) | Layer 3: (20.83, 666.67, 375) | Layer 4: (166.67, 166.67, 667) | Layer 5: (333.33, 166.67, 958)</p>
Landmine Detection Using Electromagnetic Time Reversal-Based Methods. Part 1: Classical TR, Iterative TR, DORT and TR-MUSIC
<p>In this repository, you can find the simulation files used to reproduce the results presented in the paper titled "Landmine Detection Using Electromagnetic Time Reversal-Based Methods." If you find these files useful, please cite the paper as it helps acknowledge the work and support future research.</p> <p>This repository is organized into three main folders, each serving a distinct purpose and containing specific types of files:</p> <p><strong>CST-MWS Files</strong>: This folder contains the necessary files to simulate the examples discussed in the paper using CST Microwave Studio (CST-MWS). These files are essential for setting up and running the simulations that replicate the experimental results. </p> <p><strong>TR-MUSIC</strong>: In this folder, you will find the MATLAB m-file required to implement the TR-MUSIC (Time Reversal-MUltiple SIgnal Classification) method proposed in the paper. This MATLAB script is designed to help users apply the TR-MUSIC algorithm to their data, enabling them to detect landmines effectively. </p> <p><strong>2D-GFB</strong>: The 2D-GFB folder contains the MATLAB m-file necessary to run the 2D TR-MUSIC and DORT (Decomposition of the Time Reversal Operator) methods. These advanced techniques are crucial for analyzing two-dimensional data and enhancing the accuracy of landmine detection. </p> <p><br>By organizing the repository in this manner, we aim to make it easier for researchers and practitioners to access and utilize the simulation files effectively. Whether you are replicating the study's results or applying these methods to new datasets, the files and instructions provided should serve as a valuable resource.</p> <p>If you encounter any issues or have questions regarding the use of these files, please do not hesitate to reach out for support. We hope this repository aids your research and contributes to advancements in landmine detection technologies.</p> <p> </p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".
<p>Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".</p>
Data files and electromagnetic simulation software used in the paper "Radar evidence of subglacial liquid water on Mars" By Orosei et al. (2018)
<p>This archive contains radargrams, geometric information and visualizations of MARSIS radar observations over the area centered at 193°E, 81°S within Planum Australe, Mars, together with scripts and results of electromagnetic propagation simulations used to interpret the data. This archive contains everything needed to reproduce the results presented in the paper "Radar evidence of subglacial liquid water on Mars" by Orosei et al. (2018). The software made available in this archive consists of scripts written in the Matlab© computing language, and is provided "as is" without warranty of any kind, either express or implied. Queries on the content of the archive can be sent to Roberto Orosei (roberto.orosei@inaf.it).</p> <p>Three types of MARSIS data files are contained in this archive:</p> <p>* orbit_XXXXX_frequency_Y_MHz_radargram.csv, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file contains an ASCII table of real numbers separated by commas. The table has as many columns as the number of radar echoes acquired during the orbit (usually 3200), and 980 lines, one for each echo sample. Values are samples of the uncalibrated echo voltage, without phase information (i.e. positive real numbers instead of complex echo samples). Echo samples are acquired every 0.3571 microseconds (2.8 MHz sampling rate). The first sample of an echo is located at a round-trip time corresponding to an altitude of 25 km above the Martian IAU ellipsoid.</p> <p>* orbit_XXXXX_frequency_Y_MHz_geometry.csv, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file contains an ASCII table of real numbers separated by commas. The table has as many rows as the number of radar echoes acquired during the orbit (usually 3200), and contains the following auxiliary parameters:</p> <p> - EPHEMERIS TIME - Number of seconds elapsed since Jan 1, 2000, 12:00 UTC corresponding to the time at which data collection for the current echo started.</p> <p> - MARS SOLAR LONGITUDE - Angle between the Mars-Sun line at the time corresponding to EPHEMERIS TIME and the Mars-Sun line at the vernal equinox, in degrees.</p> <p> - MARS SUN DISTANCE - Distance from the centre of Mars to centre of the Sun at the time corresponding to EPHEMERIS TIME, in Km.</p> <p> - SPACECRAFT ALTITUDE - Distance from the Mars Express spacecraft to the reference surface of the target body measured normal to the surface at the time corresponding to EPHEMERIS TIME, expressed in Km.</p> <p> - SUB-SPACECRAFT LONGITUDE - East longitude of the point on the target body that lies closest to the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed in degrees and in the [ 0 -360 ] range.</p> <p> - SUB-SPACECRAFT LATITUDE - Planetocentric latitude of the point on the target body that lies directly beneath the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed in degrees.</p> <p> - RADIAL VELOCITY - Radial component of the Mars Express spacecraft velocity vector in the reference frame of the target body at the time corresponding to EPHEMERIS TIME, expressed in Km/s.</p> <p> - TANGENTIAL VELOCITY - Tangential component of the Mars Express spacecraft velocity vector in the reference frame of the target body at the time corresponding to EPHEMERIS TIME, expressed in Km/s.</p> <p> - LOCAL TRUE SOLAR TIME - Angle between the extension of the vector from the Sun to Mars and the projection on Mars' ecliptic plane of a vector from the center of the target body and the point on the target body surface that lies directly beneath the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed on a 24-hour clock with decimal fractions of the hour.</p> <p>* orbit_XXXXX_frequency_Y_MHz.png, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file is a visualization of the corresponding radargram, of the spacecraft ground track during the observation, and of surface and subsurface echo power.</p> <p>Electromagnetic propagation model and results consist of several files:</p> <p>* model_Pss_over_Ps_ratio.m is a script written in the Matlab© computing language. It simulates the propagation of a MARSIS radar pulse through a model stratigraphy representing the Martian South Polar Layered Deposits (SPLD). The simulation is based on the solution of Maxwell's equations for a plane parallel stratigraphy, and it is thus one-dimensional. The SPLD are represented as an uniform layer of ice mixed with dust.</p> <p>* figure_S5.m is a script written in the Matlab© computing language. It simulates the propagation of a MARSIS radar pulse through a model stratigraphy representing the Martian South Polar Layered Deposits (SPLD). The simulation is based on the solution of Maxwell's equations for a plane parallel stratigraphy, and it is thus one-dimensional. The SPLD are represented as an uniform layer of ice mixed with dust, either overlaid by or overlaying a layer of pure CO2 ice of variable thickness.</p> <p>* fftvars.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes vectors containing the correct time and frequency values for FFT computations.</p> <p>* genrefl.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the frequency-dependent complex electromagnetic reflectivity of a plane parallel stratigraphy at normal incidence.</p> <p>* GPR_1D_simulation.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It simulates the radar echo produced by the propagation of a broadband signal in a plane parallel stratigraphy.</p> <p>* matzler.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the relative dielectric permittivity of pure water ice (ice Ih) for any temperature and frequency according to formulas provided in Matzler (1998).</p> <p>* maxgarmix.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the effective dielectric constant of a medium containing intrusions of different dielectric properties according to the Maxwell-Garnett dielectring mixing model.</p> <p>* Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv is a comma-separated value file containing model results for subsurface to surface echo power ratio as a function of central frequency, basal temperature, dust content and basal permittivity. One file is produced for every value of the central frequency and of the dust fraction. The value of these two parameters for a given files are reported in the file name, where X is the frequency in MHz, and Y.YY is the dust fraction. Each file contains a matrix in which there is a row for every value of basal temperature from 170 K to 270 K, and a column for every value of basal permittivity from 4 to 100 with logarithmic spacing. Basal temperature and permittivity values are reported in different comma-separated value files called "basal_temperatures.csv" and "basal_permittivities.csv".</p> <p>* basal_permittivities.csv is a comma-separated value file containing the values of basal permittivity corresponding to the columns of the model result matrixes contained in the files "Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv".</p> <p>* basal_temperatures.csv is a comma-separated value file containing the values of basal temperature corresponding to the rowso of the model result matrixes contained in the files "Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv".</p>
Modeling energetic electron nonlinear wave-particle interactions with electromagnetic ion cyclotron waves
<p>Simulation data set for the JGR paper "Modeling energetic electron nonlinear wave-particle interactions with electromagnetic ion cyclotron waves". Data formats are in ASCII and Matlab mat file. Data contents are self-explanatory by their file names.</p>
Product Datasheets of MIDE Piezoelectric Energy Harvesters V20W & V25W And Product Datasheet of Brüel & Kjaer LDS 406-408 Electromagnetic Shaker
<p>Product datasheets containing technical information used for products' technical analyses as vibration energy harvesters:</p> <ul> <li>Product Datasheet LDS V406 and V408 shakers, Brüel & Kjaer, Nærum, Denmark (2012).</li> <li>Product Datasheet Volture Piezoelectric Energy Harvesters (including V20W and V25W), Midé Technology Corporation, Massachusetts, USA, rev. no. 002 ed. (2013).</li> <li>Product Datasheet for Materials Properties of Volture Piezoelectric Products (including V20W and V25W), Midé Technology Corporation, Massachusetts, USA (Retrieved 2013).</li> </ul>
Data for "Controlling Plasmonic Catalysis via Strong Coupling with Electromagnetic Resonators"
<p>This upload includes the data presented and analyzed in the article "Controlling Plasmonic Catalysis via Strong Coupling with Electromagnetic Resonators" by Jakub Fojt, Paul Erhart, and Christian Schäfer.</p> <p>The codes for reproducing the data are provided at <a href="https://doi.org/10.5281/zenodo.13374591">doi:10.5281/zenodo.13374591</a>.</p> <p>See <em>README.md</em> in <em>data.zip</em> for a detailed description.</p>
data_set for "Time-resolved sensing of electromagnetic fields with single-electron interferometry"
Open the record for dataset details and reuse information.
Data and code repository for the research "Assessing the use of Airborne Electromagnetic Data for nitrate vulnerability assessment in the Central Valley, California"
<p>Data and code for "Assessing the use of Airborne Electromagnetic Data for nitrate vulnerability assessment in the Central Valley, California". This repository contains the analysis and post-processing code for generating results and figures used in the manuscript.</p>
Positioning of lightning electromagnetic radiation sources with satellite constellations: simulation and preliminary validation
<p>The dataset contains simulation calculation codes, satellite trajectories, and transmission time estimates. The simulation calculation is based on the version of matlab2024a,</p> <p>The observation_data_processing file contains the satellite orbit information and time difference information.</p>
Dataset for electromagnetic holding force of split core transformer
<p>Dataset for electromagnetic holding force of split core transformer grasping mechanism. Dataset is separated in two documents with "coil_test1" containing data from a single test at a primary AC current of 1000 A. "coil_test2" contain eight test samples at different secondary currents denoted by 1-8. </p>
3-D synthetic near surface data set with frequency-domain electromagnetic induction data
<p>Realistic three-dimensional exhaustive data set that mimics a near surface mining landfill deposit of waste fine-shaly sands. The data set is composed by petrophysical properties and frequency domain electromagnetic induction (FDEM) data and was created with the purpose of testing algorithms for near-surface modeling and characterization using electromagnetic data.</p> <p>The set of petrophysical properties include porosity, water saturation, particle density and density. Each property corresponds to a single geostatistical realization. The three-dimensional model has a dimension of 150 by 200 by 4 meters (i.e., length, width, depth) with a cell size of 0.5 m by 0.5 m by 0.1 m, respectively (grid size of 300 x 400 x 40). The model grid has 4.8 million cells.<br> Porosity and particle density were modelled based on samples of fine-shaly sands collected at a mine tailing in Portugal for which we investigated porosity, specific weight and particle density. The results of these investigations were used to generate three-dimensional models of subsurface rock properties with unconditional stochastic sequential simulation (Deutsch & Journel, 1998).<br> Porosity was modelled with an omnidirectional spherical variogram model in the horizontal direction. The variogram model has a horizontal range of 10 m, a vertical range of 1 m and a nugget effect of 0.2 % of the total variance of the data. This variogram model describes the expected spatial distribution of this property in the mine tailing.</p> <p>To ensure plausibility between rock properties, particle density and water saturation models were generated with stochastic sequential co-simulation (Deutsch & Journel, 1998) conditioned to the porosity model. For particle density we imposed an omnidirectional spherical variogram model in the horizontal direction with a range of 10 m, a vertical range of 1 m and a nugget effect of 0.2 % of the total variance of the data, and the correlation between porosity and particle density from the lab measurements. For water saturation we imposed an omnidirectional spherical variogram model in the horizontal direction with a range of 16 m, a vertical range of 2 m and a nugget effect of 0.1 (%). For the co-simulation we imposed a correlation between porosity and water content, borrowed from Bhanbhro et al. (2013) and Dumont et al. (2016).</p> <p>The pore fluid was defined as consisting in 80% of water and 20% of leachate, having a density of 0.99114 g/cm3 at a temperature of 30ºC (Souza et al., 2014). The density was mathematically calculated from porosity and particle density models and the density of the pore fluid by using a simple volumetric average of the geological material densities and its relationship to porosity (Mavko et al., 2009), <em>d</em><sub><em>b</em> </sub>= (1 - Ø) <em>d<sub>0</sub></em> Ø <em>d<sub>fl</sub></em> , where <em>d<sub>0</sub></em> is the density of the mineral grains, <em>d<sub>fl</sub></em> is the density of the pore fluids, and Ø is porosity.</p> <p>The electrical conductivity (EC) was created based on the well-known empirical relationship of Archie’s law (Archie, 1942). We first calculate electrical conductivity using the following equation, <em>R<sub>t</sub></em> = <em>a</em> <em>S<sub>w</sub><sup>-n</sup></em> Ø<sup><em>-m</em></sup> <em>R<sub>w</sub></em> , where <em>a</em> is the tortuosity constant, assumed as 0.88, <em>S<sub>w</sub></em> is the water saturation, <em>n</em> is the saturation exponent, assumed as 2, Ø is the porosity, <em>m</em> is the cementation exponent, assumed as 1.37, and <em>R<sub>w</sub></em> is the electrical resistivity of the pore fluid, assumed as 0.25. From the lithology and range of porosity values of the mining landfill model, the values of <em>a</em>, <em>n</em> and <em>m</em> were defined from Keller (1987). The electrical resistivity of the pore fluid was defined based on its composition and density (Keller, 1987). The EC was calculated based on Archie´s second law (Archie, 1942), where conductivity of the partially saturated rock (<em>c<sub>t</sub></em>) is the inverse of its resistivity (<em>R<sub>t</sub></em>), <em>c<sub>t</sub></em> = 1 / <em>R<sub>t </sub></em> (Mavko et al., 2009).</p> <p>Since the relationship between magnetic minerals and the magnetic properties of the rocks depends primarily of the composition and grain size of them (Butler, 2005), the magnetic susceptibility (MS) was modelled using the common range of magnetic susceptibility for unconsolidated sediments (Hudson et al., 1999) with unconditional stochastic sequential simulation (Deutsch & Journel, 1998), imposing an omnidirectional spherical variogram model in the horizontal direction with a range of 20 m, a vertical range of 4 m and a nugget effect of 0.1 % of the total variance.</p> <p>From the resulting three-dimensional models of EC and MS, we retrieved nine equally spaced boreholes along the same yz profile. These borehole data might be used as experimental data for modelling workflows, including geophysical inversion.</p> <p>FDEM data, both the in-phase (IP) and quadrature-phase (QP), were calculated using a 1-D forward model (Hanssens et al., 2019). The acquisition configuration replicates one of the most common sensors for FDEM near-surface surveys, namely the DUALEM-421S (DUALEM Inc., Milton, Canada). It considers two loop-loop coil orientations, a horizontal coplanar (HCP) and a perpendicular one (PRP), with the normal 3 offsets per coil orientation for this equipment, 1, 2 and 4 meters for HCP, and 1.1, 2.1 and 4.1 meters for PRP, plus an extra offset per coil orientation, 10 meters for HCP and 10.1 meters for PRP, ensuring a theoretical larger depth of investigation. The FDEM data were calculated defining the operating frequency of the sensor as 9000 Hz, with an elevation to the surface of 0.15 m.</p>
Obfuscation Revealed: Leveraging Electromagnetic Signals for Obfuscated Malware Classification
<p>Data used in the paper: "Obfuscation Revealed: Leveraging Electromagnetic Signals for<br> Obfuscated Malware Classification". The paper has been accepted at <a href="http://acsac.org/">ACSAC-2021</a>.</p> <p>Two dataset are available:</p> <ul> <li>traces_selected_bandwidth.zip: the extracted bandwidth (40) of spectrograms from the testing dataset to reproduce the classification results presented in the paper,</li> <li>raw_data_reduced_dataset.zip: a reduce set of the raw electromagnetic traces to reproduce the end-to-end process (pre-processing and classification).</li> </ul> <p>Due to storage constraints we did not upload the full datasets, fill free to contact us to get the remaining ones.</p>
Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures
<p>Traffic-induced vibration of transportation infrastructures is a reliable source of kinetic energy, which can be harvested to power conventional monitoring sensors and peripherals installed on bridges, thereby reducing some dependence on non-renewable energy. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5 billion. This is a massive source of kinetic energy that lies unused in the national transportation network. This study focuses on the design and field testing of a high-power density electromagnetic energy harvester (EMEH) to convert such a kinetic energy into electrical energy for powering ubiquitous sensors installed on transportation infrastructures. The principal investigators have been investigating the design of the EMEH using analytical and finite element simulations, as well as, its laboratory prototype fabrication and testing in the first phase. The proposed EMEH utilizes the innovative concept of creating planar array of large number of small permanent magnets through certain optimization criteria to achieve strong and focused magnetic field in a particular orientation. The proposed EMEH has a compact design, such that it can be integrated into the power circuit of wireless sensor nodes (WSNs) and installed at suitable part of a transportation infrastructure without elaborate wiring. It is capable of continuously charging the rechargeable battery of a WSN, thereby extending the lifespan of the monitoring system, almost, indefinitely. For the next phase of this research, the principal investigators propose the development and field implementation of a larger scale and more compact version of the EMEH with a minimum of 500 mW output power to be installed on selected transportation infrastructures for the evaluation of its energy harvesting efficiency and capability to derive different types of monitoring sensors and peripherals. Three different highway bridges with different fundamental frequencies, ideally between 2Hz to 8Hz, will be selected for the field testing of the EMEH. An acceleration sensor will be used to record the traffic-induced vibration of each bridge during a normal daily traffic that after signal processing is used to measure the fundamental frequency of that bridge. The dynamic characteristics of the proposed EMEH (i.e. tip mass and spring stiffness) will be modified to put it into a resonant condition with the bridge by matching their natural frequencies. The output power will be monitored and used to continuously charge a rechargeable battery powering a wireless sensor. The focus is on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.</p>
A dataset on in-situ electromagnetic wave integrity control of selective laser fusion printed parts using Machine Learning
<p><strong>DeepSLM Dataset</strong></p> <p>This dataset contains signals from metal parts printed in 3D using SLM (Selective laser melting) technology, produced as part of the DeepSLM project:<em> In-situ monitoring of Selective-Laser-Melted Ti6Al4V Parts Using Eddy Current Testing and Machine Learning</em>.</p> <p><em>Sensor and data collection</em></p> <p>An impedance-based non-destructive testing device is installed on the LPBF coating device. It is in the form of sensors with the following characteristics:</p> <p><em>Spectrum of analysis of the sensors</em></p> <ul> <li>Minimal length: 3-5 mm</li> <li>Minimal width acquisition: 2-3 mm (Without borders effects)</li> <li>Minimal number of layers: 50 layers</li> <li>Signal minimal penetration depth (TiAl<em>6</em>V<em>4</em>/875kHz) = 1.5 mm</li> </ul> <p>The sensors record the signal during printing, while simultaneously sending it to a computer via Bluetooth. The signals are first stored in the database. Next, a dataset is constructed from the extracted signals related to each part, and from the added semi-automatic annotations which describe the porosity of the part and layers.</p> <p><em>Annotations</em></p> <p>In the annotation process, there are 2 different types of labels and a metadata summary for these prints:</p> <p>1. Archimedean porosity measurement for the whole part<br>2. Layer porosity obtained after image processing applied on the resulting metallography images<br>3. Print-related metadata such as print parameters, part dimensions, layer thickness, etc</p> <p>Size: 66 parts<br>Set of printing parameters: 250</p> <p> --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Dataset organization</strong></p> <p>The dataset is composed of 7 different printings and is organized as follows:<br>- signals: contains collected signals<br>- metallography: contains porosity extracted by metallography<br>- archimedean: contains porosity extracted by archimedean weighing<br>- metallography-images: contains metallography process images<br>- metadata.csv: contains print metadata (print parameter, part name, strategy, etc.)</p> <p>Each printed part has a unique ID ranging from 0 to 65. Each sub-part has an ID which is the ID of the base part suffixed with the sub-part position: 14_1 is the first sub-part of part 14. This nomenclature is uniform throughout the dataset.</p> <p>The README.md file in the dataset provides more detailed information on the structure of the dataset and the format of each of the files making up the dataset.</p> <p> </p> <p><strong>Relative article</strong></p> <p><a title="https://doi.org/10.1007/978-3-031-47784-3_18" href="https://doi.org/10.1007/978-3-031-47784-3_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-47784-3_18</a></p> <p>Sallem, H., Ghorbel, H., Goffinet, E., Cinna, A., Pralong, J., Wicht, J., & Revaz, B. (2023, May). In-Situ Monitoring of Selective Laser Melted Ti–6Al–4V Parts Using Eddy Current Testing and Machine Learning. In <em>Advances In Additive Manufacturing Conference</em> (pp. 139-148). Cham: Springer Nature Switzerland.</p>
Frequency Tunable Electromagnetic Vibration Energy Harvester Utilizing Piecewise Linear Nonlinearity Dataset
<p>This is the data gathered during the experiments referenced in the manuscript. All files are .mat and native to Matlab. The variable "time' is the time array gathered during operation corresponding to the 'data' array of the same length. 'data' contains 2-3 columns depending on the file. The first column is always the base displacement voltage output, the second is the mass displacement voltage output, and if a third column is included then this is the measured voltage at the load applied to the harvester.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.