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381 results for “electromagnetism”
BeMAGIC_Multiferroic materials as flux guides for electromagnetic waves
<p>BeMAGIC ITN (GA861145)_Multiferroic materials as flux guides for electromagnetic waves. Results from VOXALYTIC.</p>
Datasets for ``Electromagnetic conversion into kinetic and thermal energies''
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Electromagnetic conversion into kinetic and thermal energies" by A. Brandenburg and N. Protiti. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
Raw data for 'Electromagnetic lensing using the Aharonov-Bohm effect'
<p>This repository contains the raw data used for the figures in the manuscript 'Electromagnetic lensing using the Aharonov-Bohm effect' (<a href="https://arxiv.org/abs/2301.09980">https://arxiv.org/abs/2301.09980</a>) and the code for phase profile calculations. The repository is organized in folders for the different experiments and samples. All experimental data is in .dm4 format. Code is MATLAB and Mathematica.</p>
the supplemental data of 'It is the Frequency that Matters - Effects of Electromagnetic Fields on the Release and Content of Extracellular Vesicles.'
<p>This data set is the supplemental data of the manuscript ‘It is the Frequency that Matters - Effects of Electromagnetic Fields on the Release and Content of Extracellular Vesicles.’</p>
Robotic Versus Electromagnetic Bronchoscopy for Pulmonary LesIon AssessmeNT: (the RELIANT Trial)
ClinicalTrials.gov study NCT05705544. IPD Sharing: YES. Countries: 1. Publications: 12.
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
Open the record for dataset details and reuse information.
Hybrid electromagnetic toroidal vortices
Open the record for dataset details and reuse information.
The ELF WERA stations measurements used in the paper "TGF 181010 study using extremely low frequency electromagnetic waves" by V. Marchenko, J. Mlynarczyk, M. Ostrowski, A. Kulak, O. Senchenko, J. Kubisz, A. Michalec, J. Salvador, N. Diaz
<p>The data analyzed in the paper are provided as text files containing measurements of two stations of the WERA project:</p> <p>File "TGF181010_Hylaty_201810101300.txt" - from the Hylaty station in Poland, </p> <p>File "TGF181010_Patagonia_201810101300.txt" - from the Patagonia station in Argentina.</p> <p>In each file the first column provides time, as measured in seconds since October 10, 2018, 13:00 UT. The second and third columns provide signals from NS and EW magnetic antennas. The duration of each data set is 5 min. The time accuracy for the Hylaty station is below 1 ms and for the Patagonia station up to several ms.</p>
Supplementary Material for "Time-domain modelling of 3-D Earth's and planetary electromagnetic induction effect in ground and satellite observations"
<p>1. Magnetic field residuals from Observatory and Swarm data. Details about data origin and pre-processing are given in the main paper.</p> <p>2. Time series of external Spherical Harmonic coefficients estimated from observatory and satellite data as described in the main paper.</p>
The Influence of the Extremely Low Frequency Electromagnetic Field on Clear Cell Renal Carcinoma
<p>Abstract:</p> <p> </p> <p>The development of new technologies and industry is conducive to the increase in the number and variety of electromagnetic field (EMF) sources in our environment. The main sources of EMF are high-voltage lines, household appliances, audio/video devices, mobile phones, radio stations, and radar devices. In the growing use of electronic devices, scientists are increasingly interested in the effects of EMF on human health. Even though many studies on the effects of EMF have already been carried out, none of them has shown a significant effect on mammals, including humans. Moreover, it is not entirely clear how EMF influences cell behavior. The International Agency for Research on Cancer on May 31, 2011, classified PEM as a possible carcinogenic factor. This study aimed to investigate the effect of the electromagnetic field on morphological and functional changes in clear cell renal carcinoma. The research was carried out on in vitro cultures of four cell lines: HEK293, 786-O 769-P, and Caki1. The results of the research showed that the EMF of low frequency had a slight effect on the viability of cells. EMF, which induced cell arrest in the G1 phase, increased the number of early apoptotic cells and decreased the number of viable cells in the 786-O line. EMF did not affect the proliferation and viability of HEK293 cells. Extreme low-frequency EMF (ELF-EMF) also showed an inhibitory effect on the migration and metastatic properties of clear cell kidney cancer cells. Moreover, shortly after the end of ELF-EMF exposure, significant increases in ROS levels were observed in all tested cell lines. As part of the work, it was shown that low-frequency EMF shows an inhibitory effect on the proliferation of primary cancer cells, diminishing their migratory, invasive, and metastatic abilities. It also increases the apoptosis of cancer cells and the amount of reactive oxygen species. Based on the results of our research, we want to point up that the effect of ELF-EMF depends on a specific metabolic state or at a specific stage in the cell cycle of the cells under study.</p> <p> </p> <p> </p>
A Wholly Analytical Method for the Simulation of an Electromagnetic Acoustic Transducer Array
<p>These data sets are for the paper "A Wholly Analytical Method for the Simulation of an Electromagnetic Acoustic Transducer Array" which will be published in the International Journal of Applied Electromagnetics and Mechanics.</p> <p>All of the data are responding to the figures shown in this article.</p>
Electromagnetic Analysis of the HPM Oscillator-Reltron
<p>In this paper, electromagnetic analysis of the reltron, which is a compact, simple and efficient high power microwave (HPM) source has been presented. The beam wave interaction process of the reltron oscillator has been analyzed to understand the device physics. The split cavity oscillator and relativistic klystron principles have been extended to demonstrate the electric field responsible for beam bunching and the electron beam modulation process in the reltron. The analytical formulation to obtain the RF energy growth and efficiency of the device has also been presented. To validate the analytical results and to evaluate the overall performance of the device, beam present simulation of reltron has been performed using commercial 3D PIC simulation code "CST Particle Studio". With the parameters of a previously reported experimental reltron device, the present analytical calculation provided ~240 MW RF output power with ~38% efficiency while the PIC simulation provided RF output power of ~225 MW with ~36% efficiency at 2.75 GHz frequency. The obtained analytical and simulation results have also been found in agreement of ~6% with this experimental work.</p>
Measured scattering parameters for the coupling of stochastic electromagnetic fields to transmission line networks of single-wire lines above a ground plane in a reverberation chamber
<p>This data set contains the measuremed scattering parameters between two antennas and a transmission line network under test in a reverberation chamber. The purpose of this measurement was an experimental validation of a numerical simulation model for the stochastic field coupling to a transmission line network. For the experiment, an exemplary network consisting of three single-wire lines above a ground plane was created. Different configurations of the network were tested and the average squared magnitude of the coupled voltage at the terminals of the network was analyzed and discussed.</p>
Subnanosecond-electromagnetic-pulse-generated-by-a-long-spark-discharge:-Lightning-implication-data
<p><strong>Data description</strong></p><p>The data is used in the paper "Subnanosecond electromagnetic pulse generated by a long spark discharge: Lightning implication" (M. Gushchin et. al.) submitted in December 2023 in Geophysical Research Letters. Two types of files are presented. First are photos stored in "png" format. Second are waveforms stored in text files. First column is time and second is value. The delimiter is ";".</p><p><strong>Data is used in second figure</strong></p><p>Figure_2a.png -- Photo of the the appearance and growth of leaders with their streamer zones from the upper (HV) electrode</p><p>Figure_2b.png – First flash on the lower (grounded) electrode.</p><p>Figure_2c.png -- Common streamer zone formation after the upward leader starts.</p><p>Figure_2d.png -- Current increase in downward and upward leader channels, reduction in the size of the common streamer zone.</p><p>Figure_2e.png -- Discharge main stage.</p><p>Figure_2f_curve_1.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2a.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_2.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2b.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_3.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2c.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_4.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2e.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_5.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2f.png" photo. Time unit is mks, value unit is a.u.</p><p><strong>Data is used in third figure</strong></p><p>Figure_3b_curve1.dat -- The power waveform from RF analyzer f0 = 6 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve2.dat -- The power waveform from RF analyzer f0 = 5.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve3.dat -- The power waveform from RF analyzer f0 = 4.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve4.dat -- The power waveform from RF analyzer f0 = 3.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve5.dat -- The power waveform from RF analyzer f0 = 2 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve6.dat -- The power waveform from RF analyzer f0 = 1 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3a_curve1.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve1.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve2.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve2.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve3.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve3.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve4.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve4.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve5.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve5.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve6.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve6.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3c.dat -- Waveform obtained using TEMH. Time unit is mks, value unit is V/m.</p><p>Figure_3d.dat – Detailed waveform obtained using TEMH. Time unit is ns, value unit is V/m.</p><p> </p><p><strong>Data is used in fourth figure</strong></p><p>Figure_4b_curve_1.dat – TPMP waveform obtained during calibration. Time unit is ns, value unit is A/m.</p><p>Figure_4b_curve_2.dat – IPPL waveform obtained during calibration. Time unit is ns, value unit is E/m.</p><p>Figure_4c_curve_1.dat – TEMH waveform obtained in shot #104 at 12-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4c_curve_2.dat – IPPL waveform obtained in shot #104 at 12-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4d_curve_1.dat – TEMH waveform obtained in shot #19 at 13-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4d_curve_2.dat – IPPL waveform obtained in shot #19 at 13-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4e_curve_1.dat – TPMP waveform obtained in shot #19 at 25-sept-23. Time unit is ns, value unit is A/m.</p><p>Figure_4e_curve_2.dat – IPPL waveform obtained in shot #19 at 25-sept-23. Time unit is ns, value unit is E/m.</p><p>Figure_4f_curve_1.dat – TPMP waveform obtained in shot #2 at 27-sept-23. Time unit is ns, value unit is A/m.</p><p>Figure_4f_curve_2.dat – IPPL waveform obtained in shot #2 at 27-sept-23. Time unit is ns, value unit is E/m.</p><p><strong>Data is used in fifth figure.</strong></p><p>Figure_5a.png – The photo of negative discharge</p><p>Figure_5b.dat -- Waveform from a capacitive probe. Time units is mks, value units is a.u.</p><p>Figure_5c.dat -- The power waveform from RF analyzer f0 = 0.98 GHz, df = 40 MHz obtained simultaneously with Figure_5b.dat. Time unit is mks, value unit is dB. </p><p>Figure_5d.dat -- UWB EMP waveform obtained using TEMH obtained simultaneously with Figure_5c.dat. Time unit is ns, value unit is V/m.</p>
Electromagnetic Sampling Calorimeter Shower Images
<p>We include two files: "gamma_1.hdf5" and "gamma_2.hdf5". The first file was used to train CaloFlow and the second file was used during evaluation. Each file has the following structure:</p> <p>energy Dataset {100000, 1}<br>layer_0 Dataset {100000, 3, 96}<br>layer_1 Dataset {100000, 12, 12}<br>layer_2 Dataset {100000, 12, 6}<br>overflow Dataset {100000, 3}</p> <p>Each file is contains 100,000 calorimeter showers originating from incoming photons with incident energies uniformly distributed in the range [1,100] GeV.</p> <p>The sampling calorimeter we built is segmented longitudinally into three layer with different depths and granularities. In units of mm, the three layers have the following (eta, phi, z) dimensions:<br>Layer 0: (5, 160, 90) | Layer 1: (40, 40, 347) | Layer 2: (80, 40, 43)</p> <p>In the hdf5 files, the `energy' entry specifies the incident energy of the incoming photon in units of GeV. `layer_0', `layer_1', and `layer_2' represents the energy deposited in each layer of the calorimeter in an image data format. Given the segmentation of each calorimeter layer, these images have dimensions 3x96 (in layer 0), 12x12 (in layer 1), and 12x6 (in layer 3). The `overflow` contains the amount of energy that was deposited outside of the calorimeter section we are considering.</p> <p>We also include the files containing signal showers used in the paper "Anomaly detection with flow-based fast calorimeter simulators". The signal showers originating from chi particles decaying at fixed displacements are included in "files_fixed_disp.zip", and the signal showers originating from chi particles with fixed decay lifetimes are included in "files_fixed_lifetime.zip".</p>
A deep learning-based parametric inversion for forecasting water-filled bodies position using electromagnetic method
<p>We design a tunnel electromagnetic joint scan observation system and present a deep learning-based parametric inversion for improved tunnel electromagnetic imaging, designed specifically for tunnel prediction of water filled structures. It utilizes a configuration wherein transmitters scan along the surface while receivers are positioned within the tunnel, employing time-domain and frequency-domain transmitters and a multi-component receiver. The DL model for the first time provides parametric imaging of two different view, forming a self-checking mechanism, which can help constrain the predictions and reduce the non-uniqueness of the inversion. Trained by synthetic data, our system shows impressive adaptability to predict the 3D spatial position of water-filled anomalies and strong robustness in the tunnel environment with metal interference.</p> <p> </p> <p>Before prediction, you need to download the pre-trained weight model which contain UNet_model/FTEM.ckpt.data-00000-of-00001, UNet_model/FTEM.ckpt.index, UNet_model/FTEM.h5. Then place the directory containing the weight model in the same directory as the prediction code. Then run: python predi.py</p>
On the electromagnetic-electron rings originating from the interaction of high-power short-pulse laser and underdense plasma
<p>This repository contains data for the paper P. Valenta et al., Phys. Plasmas 28, 122104 (2021); <a href="https://doi.org/10.1063/5.0065167" target="_blank" rel="noopener">https://doi.org/10.1063/5.0065167</a>. The data were obtained by the EPOCH (v4.18-devel) particle-in-cell code (<a href="https://epochpic.github.io" target="_blank" rel="noopener">https://epochpic.github.io</a>). The data analysis can be found on GitHub (<a href="https://github.com/valenpe7/5.0065167" target="_blank" rel="noopener">https://github.com/valenpe7/5.0065167</a>).</p>
Data Regarding Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis
<p> </p> <div>The following data files were used for the analysis presented in the paper </div> <div>"Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis"</div> <div> </div> <div>The files include details of the infrasonicly detected evnents, and features extracted from electromagnetic signals, as explined in the README file.</div>
HPC geophysical electromagnetics: a synthetic VTI model with complex bathymetry
<p>Castillo-Reyes, O., de la Puente, J., Cela, E. J.M. (2022) HPC geophysical electromagnetics: a synthetic VTI model with complex bathymetry. Submitted to Energies Journal</p>
Electromagnetic immune phosphor-tipped fibre-optic thermometers
<p>Calibration traceability can be broken where there are significant, unquantified uncertainties. Thermometry in harsh environments using electrical sensors, such as thermocouples or resistance thermometers, can be unpredictably affected by electro-magnetic sources. For this application, phosphor based temperature sensors with fibre-optic connection have been made following two different approaches – phosphor decay time changes and phosphor emission spectral changes – and tested by bombardment with high energy electrons and by measurements in large magnetic fields. Both sets of tests showed good immunity to exposure, and with magnetic field tests significantly better than a thermocouple. Phosphor thermometry therefore has potential to retain traceability in situations where conventional sensors might fail.</p>
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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.