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378 results for “RF”
Daily RF AOD dataset in the Sichuan Basin, China (2015-2020)
<p>The random forest (RF) machine learning method and multiple datasets are used to establish aerosol optical depth (AOD) dataset in the cloudy Sichuan Basin. Multiple datasets include ground-based PM10 and PM2.5, the AOD from the Sun-sky radiometer Observation Network (SONET) and the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) aerosol reanalysis, and several meteorological variables.</p> <p> </p> <p> </p> <p> </p>
Database of RF fingerprinting on use case IoT devices
<p>This document is a dataset of radiofrequency signals. It is composed of 1000 signals coming emitted by 10 different devices. This dataset was developped for benchmarking machine learning methods on an Internet of Things classification task: recognizing which device emitted a signal.</p> <p>This dataset is in an adaptation of the dataset collected by Basak et al. in “Drone classification from RF fingerprints using deep residual nets” (IEEE COMSNETS conference, 2021).</p> <p>Basak et al. collected signals from six commercial drones, three drone radio-controllers and one WiFi router. The conducted the measurements in an anechoic chamber, using a universal software radio peripheral (USRP X310) placed seven meters apart from the devices . The signals were all in the 2.4 GHz ISM band and the whole 100 MHz band was received instantaneously using a receiving sampling rate of 100 MSps (i.e. the system down-converted the signal frequencies to the 0-100 MHz band to sample them correctly).</p> <p>While the original dataset by Basak et al. consisted in spectrograms of 256 frequency bins by 256 time frames, we have converted in into averaged spectra of 256 frequency bins. Furthermore, while Basak et al. have considered several noise levels, here we only consider the lowest noise level available (-60 dBm).</p> <p>The database is stored in an h5 file, a format adapted to databases. Inside the file there are two datasets: the signals (‘Signals’) and the targets (‘Targets’). The targets correspond to the ten different classes of signals: Parrot Disco (0), Q205 (1), Tello (2), MultiTx (3), Nine Eagles (4), Spektrum DX4e (5), Spectrum DX6i (6), Wltoys (7), S500 (8) and Linkys router (9).</p> <p>This dataset corresponds to the Deliverable D6.2 of the RadioSpin EU funded project.</p>
MRI raw data: RF-spoiled radial FLASH sequence without cardiac gating and during free breathing
<p>RF-spoiled radial FLASH sequence without cardiac gating and during free breathing<br> Sequence parameters:<br> TR 2.0ms<br> TE 1.3 ms<br> Flip angle 8°<br> Matrix Size 128x128 (two-fold oversampling, resulting in 256 sample points for each radial spoke)<br> Radial Spokes: 125<br> In Plane Resolution 2mmx2mm<br> Slice Thickness 8mm<br> 3T System<br> 32 channel body array coil, compressed to 12 virtual channels using svd based coil compression<br> ote: This data set was acquired with a radial acquisition. The corresponding k-Space trajectory is also included (256x125 matrix k). The Non-Uniform-FFT Toolbox is needed for reconstruction of this data set.<br> Florian Knoll (florian.knoll@tugraz.at) Date: 2.2.2011</p>
RF UAV
<p>We gathered signals from two DJI M100 UAVs using a USRP X310 in an anechoic chamber. I/Q samples were exclusively collected in the downlink channel. At each distance, we captured I/Q samples for 2 seconds, followed by a pause of 10 seconds. </p>
Gaia and Prot crossmatch dataset for RF SRMP project
Open the record for dataset details and reuse information.
Mini-RF S-band Radar Characterization of a Lunar South Pole-Crossing Tycho Ray: Implications for Sampling Strategies
<p>Data behind the figures for the publication in the Planetary Science Journal. Data is in .mat format, which is a Matlab save file which can also be read by open languages such as Python. The accompanying code, in .m format, is a Matlab code that will recreate the figures. The .m code can be read by any text editor application. All figures are also provided as pngs. Figures 7 and 8 are provided as GeoTiffs, where the first channel is S1, second channel S2, third channel S3, and the fourth channel S4 (i.e., the four Stokes parameters).<br>Figures.zip is a zip file with all of the figures in png format.<br>FiguresData.zip includes two files: MakeFigures.m, which is the matlab code that will recreate the figures, and RiveraValentinETAL_2024_PSJ_AccompanyingData.mat, which is that matlab data needed to recreate the figures. The .m file contains a header describing each variable in the .mat file. <br>GeoTiffs.zip contains two files, newton_stokes.tiff and haworth_stokes.tiff. These are GeoTiffs of Figures 7 and 8, respectively. </p>
(Dataset) Analysis code for the paper "RF shimming in the cervical spinal cord at 7T"
Dataset provided for NeuroLibre preprint. Author repo: https://github.com/shimming-toolbox/rf-shimming-7t NeuroLibre fork:https://github.com/roboneurolibre/rf-shimming-7t <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/25">NeuroLibre technical screening.</a></p> <p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
Datasets generated in the ConvAE-RF modelling of grain yield in the mid-lower Yangtze plains
<p><em>formatted_grid.zip </em>is the preprocessed input meteorological dataset to a ConvAE-RF model proposed by the author.</p> <p><em>final_output.zip</em> is 2016-2100 output yield projections from ScenarioMIP experiments SSP126, SSP245, SSP370 and SSP585 of 25 AMES NEX GDDP CMIP6 GCMs downscaled by <a title="NASA Global Daily Downscaled Projections, CMIP6" href="https://www.nature.com/articles/s41597-022-01393-4" target="_blank" rel="noopener">Thrasher et al., 2022.</a></p> <p><em>coldwave_order.csv</em> contains 4 lists of 25 AMES NEX GDDP CMIP6 GCMs (one for each ScenarioMIP experiment) ranked according to mean coldwave frequency predicted in a unit area (0.25° * 0.25°) in the mid-lower Yangtze plain provinces.</p> <p><em>heatwave_order.csv</em> contains 4 lists of 25 AMES NEX GDDP CMIP6 GCMs (one for each ScenarioMIP experiment) ranked according to mean heatwave frequency predicted in a unit area (0.25° * 0.25°) in the mid-lower Yangtze plain provinces.</p> <p><em>tmax_thr90.nc</em> is 2d and records the threshold daily maximum temperature for each point on the spatial grid above which a day would be qualified as a heatwave candidate.</p> <p><em>tmax_thr90.nc</em> is 2d and records the threshold daily minimum temperature for each point on the spatial grid below which a day would be qualified as a coldwave candidate.</p> <p>Please reference the README in this <a href="https://github.com/zjmagou/MLYPGrain2024">GitHub Repository</a> for data usage.</p> <p> </p>
Beat Pilot Tone (BPT): Simultaneous MR Imaging and RF Motion Sensing at Arbitrary Frequencies
<p>This data is for the manuscript "Beat Pilot Tone (BPT): Simultaneous MR Imaging and Radio-Frequency Motion Sensing at Arbitrary Frequencies" submitted to the journal Magnetic Resonance in Medicine. It is divided into one subfolder for each figure, with each folder containing the data necessary to reproduce the figure. Most of the data are saved in "cfl" format as used by the BART Computational Magnetic Resonance Imaging Toolbox (https://mrirecon.github.io/bart/). Other data are saved as Comma Separated Values (csv) or plain text files.</p> <p>Keywords and Categories: Magnetic Resonance Imaging, MRI, medical imaging, radio frequency, microwave, motion sensing, motion correction</p>
Figure data for "RF-induced heating dynamics of non-crystallized trapped ions"
<p>Figure data of the paper RF-induced heating dynamics of non-crystallized trapped ions.</p>
RF recording from the QB50-ISS cubesats from 2017-05-29
<p>This dataset contains an IQ recording of the telemetry signals of many of the QB50 project cubesats. The recording was done on 2017-05-29 18:25:29 UTC, shortly after several cubesats were released from the ISS. The recording was done from 40.5962º N, 3.6964º W, 700 m ASL, near Madrid (Spain). A 7 element yagi antenna built by Arrow and a LimeSDR were used to record. The antenna was handheld in vertical polarization and connected to the SDR with a short (~1 metre) piece of coaxial cable.</p> <p>The recording is 3 Msps IQ and uses a centre frequency of 436.5 MHz and WAV format.</p> <p>More information about this recording can be found in the post "<a href="https://destevez.net/2017/05/a-tour-of-qb50/">A tour of QB50</a>" by the author.</p>
RF recording of PSLV 2018-004 launch cubesats
<p>This dataset contains an IQ recording of the telemetry signals of the cubesats from the PSLV 2018-004 launch from 2018-01-12. The recording was done on 2018-01-13 09:54:46 UTC, some 30 hours after the launch. The recording was done from 40.5962º N, 3.6964º W, 700 m ASL, near Madrid (Spain). A 7 element yagi antenna built by Arrow and a LimeSDR were used to record. The antenna was handheld in vertical polarization and connected to the SDR with a short (~1 metre) piece of coaxial cable.</p> <p>The recording is 4 Msps IQ and uses a centre frequency of 436.5 MHz and WAV format.</p> <p>More information about this recording can be found in the post "<a href="https://destevez.net/2018/01/decoding-satellites-from-the-pslv-2018-004-launch/">Decoding satellites from the PSLV 2018-004 launch</a>" by the author.</p>
RF Coil Design for Accurate Parallel Imaging on 13C MRSI using 23Na Sensitivity Profiles
<p>This upload contains data for the article "RF Coil Design for Accurate Parallel Imaging on 13C MRSI using 23Na Sensitivity Profiles" published in Magnetic Resonance in Medicine, https://doi.org/10.1002/mrm.29259.</p> <p>Data are organized with respect to the figures in the article. To read and process the GE MR raw files (p-files) GE software tools are required including Matlab software from the GE MNS Research Pack. For the human data, the raw data are provided in mat-files without header information.</p> <p>All data were processed in Matlab to produce the results presented in the article. Please do not hesitate to reach out, if you are interested in any data processing methods or details, we will be happy to share relevant source code.</p>
RF recording of Vega-C MEO cubesats 436 MHz telemetry beacons
<p>This dataset contains an IQ recording of the 436 MHz telemetry signals of four of the MEO cubesats launched in the Vega-C maiden flight on 2022-07-13: CELESTA, MTCube2, Greencube and AstroBio Cubesat. The recording was done on 2022-07-24 from 40.5958º N, 3.6991º W, 800 m ASL, near Madrid (Spain). This is a urban location, so the RFI is quite high.</p> <p>A 7 element yagi antenna built by Arrow and a USRP B205mini were used. The antenna was held on a tripod and aimed manually. The antenna polarization was horizontal with respect to the local horizon (parallactic angle rotation should be taken into account to derive the sky polarization). It was connected to the SDR with a short (~1 metre) piece of coaxial cable.</p> <p>The signals have rather low SNR, due to the high RFI and the small antenna used. Weak packets from CELESTA and very weak long bursts from Greencube can be seen. Signals from MTCube2 and AstroBio were not detected.</p> <p>The USRP digitized at 4 Msps IQ with a centre frequency of 436.52 MHz. A GNU Radio flowgraph was used for Doppler correction and channelization. A channel of 40 ksps IQ was recorded around the nominal frequency of each satellite. Two different channels were used for AstroBio Cubesat, due to the uncertainty in its transmit frequency caused by a reset of the satellite's radio to factory settings. Each channel is given in a different file, identified by the name of the corresponding satellite.</p> <p>The recordings of the satellites are contained in the files identified as 2022-07-24T18_47_38. The files with later timestamps (2022-07-24T19_25_49 and 2022-07-24T19_29_02) are short recordings of a CW carrier injected into the receiver for absolute amplitude calibration.</p> <p>The data was recorded using <a href="https://wiki.gnuradio.org/index.php/File_Meta_Sink">GNU Radio metadata format</a> with detached headers and converted to <a href="https://github.com/gnuradio/SigMF">SigMF format</a> in postprocessing. The detached headers of the GNU Radio metadata format are provided with the .hdr extension.</p> <p>The Doppler correction files are also included in this dataset. These are text files that contain tabulated timestamps in seconds of UNIX time and Doppler frequency in Hz. They were generated using the TLEs from Celestrak and the Skyfield Python library.</p> <p>More information about this recording, including the details of the absolute amplitude calibration, can be found in the post "<a href="https://destevez.net/2022/07/trying-to-observe-the-vega-c-meo-cubesats/">Trying to observe the Vega-C MEO cubesats</a>" by the author.</p>
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
High Altitude Balloon by Julián Fernández RF recording 2019-05-19
<p>This is a recording of an Amateur high altitude balloon released by Julián Fernández EA4HCD on 2019-05-19. The balloon carried a LoRa and RTTY transmitter on 434.5MHz and a LoRa transmitter on 868MHz. The balloon was released near Madrid (Spain) and reached and altitude of 24km and distance of 180km. This recording was done from shortly after release until 15 minutes before burst.</p> <p>Amateur radio callsign: EA4GPZ</p> <p>Station location: 40.620697,-3.6936182, in Tres Cantos, near Madrid.</p> <p>Antenna: 7 element 435MHz yagi from Arrow Antennas, vertical polarization, roughly aimed to the balloon location, 3m above ground level.</p> <p>Receiver: FUNcube Dongle Pro+, LNA and mixer gain enabled.</p> <p>Frequency: 434.5MHz at the start of the recording, changed later to 434.502MHz without interruption of the recording.</p> <p>Sample rate: 192ksps.</p> <p>Recording start: 2019-05-19 15:37:19 UTC</p> <p>Format: Linrad raw format: 41 byte header followed by little-endian int16 IQ data.</p>
Histological Study on Safety and Efficacy of a RF Device Flexible Applicator for Non-Invasive Lipolysis
ClinicalTrials.gov study NCT04881175. IPD Sharing: NO. Countries: 1. Publications: 1.
RF Surgical Sponge-Detecting System on the Function of Pacemakers and Implantable Cardioverter Defibrillators
ClinicalTrials.gov study NCT02111980. IPD Sharing: Not stated. Countries: 1. Publications: 5.
RF Power, LSI and Oesophageal Temperature Alerts During AF Ablation (PiLOT-AF Study)
ClinicalTrials.gov study NCT02619396. IPD Sharing: NO. Countries: 1. Publications: 5.
PV-Isolation With the Cryoballoon Versus RF:a Randomized Controlled Prospective Non-inferiority Trial (FreezeAF)
ClinicalTrials.gov study NCT00774566. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.