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113 results for “Radio Frequency”
Large-Scale Dataset for Radio Frequency based Device-Free Crowd Estimation
<p>This dataset serves to estimate the status, in particular the size, of a crowd given the impact on radio frequency communication links within a wireless sensor network. To quantify this relation, signal strengths across sub-GHz communication links are collected at the premises of the Tomorrowland music festival. The communication links are formed between the network nodes of wireless sensor networks deployed in three of the festival's stage environments. </p> <p>The table below lists the eighteen dataset files. They are collected at the music festival's 2017 and 2018 editions. There are three environments, labeled: ‘Freedom Stage 2017’, ‘Freedom Stage 2018’, and ‘Main Comfort 2018’. Each environment has both 433 MHz and 868 MHz data. The measurements at each environment were collected over a period of three festival days. The dataset files are formatted as Comma-Separated Values (CSV).</p> <pre><code class="language-markdown">| Dataset file | Reference file | Number of messages | |-------------------- |------------------------- |-------------------- | | free17_433_fri.csv | None | 393 852 | | free17_868_fri.csv | None | 472 202 | | free17_433_sat.csv | free17_transactions.csv | 996 033 | | free17_868_sat.csv | free17_transactions.csv | 1 023 059 | | free17_433_sun.csv | free17_transactions.csv | 1 007 066 | | free17_868_sun.csv | free17_transactions.csv | 1 036 456 | | free18_433_fri.csv | None | 765 024 | | free18_868_fri.csv | None | 757 657 | | free18_433_sat.csv | free18_transactions.csv | 711 438 | | free18_868_sat.csv | free18_transactions.csv | 714 390 | | free18_433_sun.csv | free18_transactions.csv | 648 329 | | free18_868_sun.csv | free18_transactions.csv | 656 290 | | main18_433_fri.csv | None | 791 462 | | main18_868_fri.csv | None | 908 407 | | main18_433_sat.csv | main18_counts.csv | 863 666 | | main18_868_sat.csv | main18_counts.csv | 884 682 | | main18_433_sun.csv | main18_counts.csv | 903 862 | | main18_868_sun.csv | main18_counts.csv | 894 496 |</code></pre> <p>In addition to the datasets and reference files, a software example is provided to illustrate the data use and visualise the initial findings and relation between crowd size and network signal strength impact.</p> <p>In order to use the software, please retain the following file structure: </p> <pre><code class="language-markdown">. ├── data ├── data_reference ├── graphs └── software</code></pre> <p>The peer-reviewed data descriptor for this dataset has now been published in MDPI Data - an open access journal aiming at enhancing data transparency and reusability, and can be accessed here: <a href="https://doi.org/10.3390/data5020052">https://doi.org/10.3390/data5020052</a>.<br> Please cite this when using the dataset.</p>
A multi-resolution, multi-epoch low Radio Frequency Survey of the Kepler K2 Mission Campaign 1 Field
<p>Data abstract:</p> <p>Contained within are the MWA images used as input data for this study. The production and analysis of these images are described in the linked paper. The final catalogues and light curves are available from VizieR (http://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=J/AJ/152/82).</p> <p>Paper abstract:</p> <p>We present the first dedicated radio continuum survey of a Kepler K2 mission field, Field 1, covering the North Galactic Cap. The survey is wide field, contemporaneous, multi-epoch, and multi-resolution in nature and was conducted at low radio frequencies between 140 and 200 MHz. The multi-epoch and ultra wide field (but relatively low resolution) part of the survey was provided by 15 nights of observation using the Murchison Widefield Array (MWA) over a period of approximately a month, contemporaneous with K2 observations of the field. The multi-resolution aspect of the survey was provided by the low resolution (4‧) MWA imaging, complemented by non-contemporaneous but much higher resolution (20″) observations using the Giant Metrewave Radio Telescope (GMRT). The survey is, therefore, sensitive to the details of radio structures across a wide range of angular scales. Consistent with other recent low radio frequency surveys, no significant radio transients or variables were detected in the survey. The resulting source catalogs consist of 1085 and 1468 detections in the two MWA observation bands (centered at 154 and 185 MHz, respectively) and 7445 detections in the GMRT observation band (centered at 148 MHz), over 314 square degrees. The survey is presented as a significant resource for multi-wavelength investigations of the more than 21,000 target objects in the K2 field. We briefly examine our survey data against K2 target lists for dwarf star types (stellar types M and L) that have been known to produce radio flares.</p>
Effect of net direct current on the properties of radio frequency sheaths: simulation and cross-code comparison
<p>The accompanying files contain digital data for figures in the article "Effect of net direct current on the properties of radio frequency sheaths: simulation and cross-code comparison" by J. R. Myra, M.T. Elias, D. Curreli, and T. G. Jenkins, submitted to the journal Nucl. Fusion.</p> <p>Abstract</p> <p>In order to understand, predict and control ion cyclotron range of frequency (ICRF) interactions with tokamak scrape-off layer plasmas, computational tools which can model radio frequency (RF) sheaths are needed. In particular, models for the effective surface impedance and DC rectified sheath potentials may be coupled with full wave RF simulation codes to predict self-consistent wave fields near surfaces and the resulting power dissipation and plasma-material interactions from ion sputtering. In this study, previous work assuming zero net DC current flow through the sheath is generalized to allow the surface to collect net positive or negative current, as is often observed in experiments. The waveforms, DC potential and RF admittance are investigated by means of analytical theory, nonlinear fluid and particle-in-cell (PIC) codes. Cross-code comparisons provide detailed model verification and elucidate the roles of ion and electron kinetics. When the sheath draws negative (positive) DC current, the voltage rectification is reduced (increased) compared with the zero-current case, and both the real and imaginary parts of the admittance are increased (reduced). A previous four-input parametrization of the sheath rectification and admittance properties is generalized to include a fifth parameter describing the DC sheath current. </p>
Physics-based parametrization of the surface impedance for radio frequency sheaths
<p>The accompanying files contain digital data for the figures in the article "Physics-based parametrization of the surface impedance for radio frequency sheaths" by J.R. Myra, Physics of Plasmas 24, 072507 (2017).</p> <p>Filenames correspond to the figures or parts thereof. All data is given as ascii text in csv format. The files may be open and plotted by common spreadsheet programs and may be easily read by procedural programs. The first row gives the column headers. These correspond to the labels on the x and y axes of the figures (as represented by ascii text). Most headers are self-explanatory. Exceptional cases are noted below.</p> <p>Fig. 2<br> w = frequency omega<br> yir20 = real part of ion impedance Re[yi] for Vrf = 20<br> yii20 = imaginary part of ion impedance Im[yi] for Vrf = 20<br> yir10 = real part of ion impedance Re[yi] for Vrf = 10<br> yii10 = imaginary part of ion impedance Im[yi] for Vrf = 10<br> yir5 = real part of ion impedance Re[yi] for Vrf = 5<br> yii5 = imaginary part of ion impedance Im[yi] for Vrf = 5<br> yir0 = real part of ion impedance Re[yi] for Vrf = 0<br> yii0 = imaginary part of ion impedance Im[yi] for Vrf = 0</p> <p>The diagonal blue lines in the published figures for Figs. 3 - 8 indicate the line y = x and are not explicilty tabulated here.</p> <p>Abstract:</p> <p>The properties of sheaths near conducting surfaces are studied for the case where both magnetized plasma and intense radio frequency (rf) waves coexist. The work is motivated primarily by the need to understand, predict and control ion cyclotron range of frequency (ICRF) interactions with tokamak scrape-off layer plasmas, and is expected to be useful in modeling rf sheath interactions in global ICRF codes. Employing a previously developed model for oblique angle magnetized rf sheaths [J. R. Myra and D. A. D’Ippolito, Phys. Plasmas 22, 062507 (2015)], an investigation of the four-dimensional parameter space governing these sheath is carried out. By combining numerical and analytical results, a parametrization of the surface impedance and voltage rectification for rf sheaths in the entire four-dimensional space is obtained.</p>
Corresponding Dataset for "Ganymede's Ionosphere observed by a Dual-Frequency Radio Occultation with Juno"
<p> Corresponding Dataset for "Ganymede’s Ionosphere observed <br> by a Dual-Frequency Radio Occultation with Juno"<br> README FILE<br> VERSION 2<br> Dustin Buccino<br> April 22, 2024<br> Jet Propulsion Laboratory<br> California Institute of Technology</p> <p>=============================================================================<br>VERSION 2 INFORMATION<br>=============================================================================</p> <p> Version 2 of this dataset separates the Electron Density profile from the<br>main data files and makes a correction to the egress profile that was<br>discovered. Differences in egress profile are very small and within<br>the uncertainties. Furthermore egress is statistically a non-detection<br>(zero densities), but for sake of accuracy they are reposted to be<br>consistent with the publication.</p> <p>=============================================================================<br>INTRODUCTION<br>=============================================================================</p> <p> This dataset contains processed radio science data and results of the<br>Juno Ganymede radio occultation. This dataset is provided in order to <br>supplement the submitted article to the "Geophysical Research Letters"<br>journal:</p> <p> Buccino, D.R., et al (2022), Ganymede’s Ionosphere observed by a <br> Dual-Frequency Radio Occultation with Juno, Geophysical Research <br> Letters, submitted February 2022.</p> <p><br> Please note the raw data used in this analysis are not provided in this<br>supplementary dataset. The raw Juno Gravity Science Data may be found at <br>the Planetary Data System:</p> <p> Buccino, D. R. (2016). Juno jupiter gravity science raw data set <br> V1.0, JUNO-J-RSS-1 JUGR-V1.0, NASA planetary data system (PDS). <br> Retrieved from https://atmos.nmsu.edu/PDS/data/jnogrv_1001/<br> </p> <p>=============================================================================<br>ARCHIVE INFORMATION<br>=============================================================================</p> <p> This archive contains two files within the root directory.<br> <br> ROOT<br> `- JunoG34OccData_Egress_v2.csv</p> <p> This data file contains the EGRESS data relevant to the radio<br> occultation. The data is a timeseries of impact parameter, sky<br> sky frequency at X-band and Ka-band, the dual-frequency <br> combination, the calibrated dual-frequency, Total Electron <br> Content.</p> <p> `- JunoG34_GRL_Egress_Profile_v2.csv</p> <p> This data file contains the EGRESS Electron density, and <br> 1-sigma electron density uncertainty.</p> <p> `- JunoG34OccData_Ingress_v2.csv</p> <p> This data file contains the INGRESS data relevant to the radio<br> occultation. The data is a timeseries of impact parameter, sky<br> sky frequency at X-band and Ka-band, the dual-frequency <br> combination, the calibrated dual-frequency, Total Electron <br> Content.</p> <p> `- JunoG34_GRL_Ingress_Profile_v2.csv</p> <p> This data file contains the INGRESS Electron density, and <br> 1-sigma electron density uncertainty.</p> <p>=============================================================================<br>FILE FORMAT<br>=============================================================================</p> <p> This dataset contains only a comma-separated text files which are<br>given with the "*.csv" extension.</p> <p><br> CSV FILES<br> -------------------------------------------------------------------------</p> <p> The Comma-Separated Value (CSV) files are plain-text files. Values in<br> each data file are separated using a comma ",". Each column is defined <br> by a header row which provides a description of each column.<br> </p> <p>=============================================================================<br>ACKNOWLEDGMENTS<br>=============================================================================</p> <p>This work was carried out at the Jet Propulsion Laboratory, <br>California Institute of Technology, under contract with the National <br>Aeronautics and Space Administration. Government sponsorship acknowledged.</p> <p>EG, LGC, PT, MZ and AC are grateful to the Italian Space Agency (ASI) for <br>financial support through Agreement No. 2018-25-HH.0 in the context of ESA's <br>JUICE mission, and Agreement No. 2017-40-H.1-2020, and its extension <br>2017-40-H.02020-13-HH.0, for ESA’s BepiColombo and NASAs Juno radio science <br>experiments. EG is grateful to "Fondazione Cassa dei Risparmi di Forlì" for <br>financial support of his PhD fellowship.</p> <p>PS and AH were supported by NASA Contract NNM06AA75C from the Marshall <br>Space Flight Center under subcontract 699054X from Southwest Research <br>Institute.</p> <p><br>=============================================================================<br>PRIMARY POINT OF CONTACT<br>=============================================================================</p> <p>Dustin Buccino<br>Jet Propulsion Laboratory<br>Planetary Radar and Radio Sciences<br>(818) 393 - 1072<br>Dustin.R.Buccino@jpl.nasa.gov</p> <p>=============================================================================<br>ACRONYMS AND ABBREVIATIONS<br>=============================================================================</p> <p> ASCII American Standard Code for Information Interchange<br> DOY Day of year<br> DSN Deep Space Network<br> JPL Jet Propulsion Laboratory<br> NAIF Navigation Ancillary Information Facility<br> NASA National Aeronautics and Space Administration<br> PDS Planetary Data System<br> RS Radio Science<br> RSS Radio Science Subsystem<br> SIS Software Interface Specification<br> TXT Text file<br> UTC Universal Time, Coordinated</p>
Volcanic Lightning and Continual Radio Frequency Impulses at Sakurajima Volcano: A Multiparametric Dataset
<p>This is a multiparametric data set of volcanic activity at Sakurajima volcano in Japan. The data set was collected in May and June 2015. The data set includes the following types of data: Lightning Mapping Array data, slow and fast electric field waveforms, log-RF VHF data, infrasound data, plume height, velocity, and temperature data.</p>
Radio Frequency Interference (RFI)
<p>In this dataset, Signal of Interest (SoI) is a real-time video stream that is transmitted using DVB-S2 standards in four modulation types including (QPSK, 8/16/32 APSK). Further, this SoI combined by three well-known jamming signals namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). This dataset includes 300 samples per modulation type for each type of signal. Therefore, totally there are 4800 samples in the dataset and each sample is a vector of size 1 by 32488 (8ms) at sample frequency 40 Hz. Also, AWGN power is -140 dBm which is approximately equal to SNR=9 dB.</p> <p>More importantly, SoI is modulated and processed by GNU radio and transmitted using a Universal Software Radio Peripheral (USRP-N210). In GNU radio, the modulation type and amplitude of the transmitted signal can be easily adjusted. A SatCom Emulator (RTLogic T400) is used for modeling a real-time communication channel. The programmatic control of the channel simulator is facilitated over an Ethernet connection using a control protocol or optional plugin to STK software. The Channel Simulator produces IF/RF signals with extracting signal characteristics for any scenario. The Kratos STK plugin provides real-time, phase-continuous control of the channel simulator when playing STK scenarios. Further, the generated jamming signals (CWI, MCWI, and CI) are transmitted using a NanoBee modem and combined to SoI by a combiner. Finally, the combined signal is received by a MegaBee modem. </p> <p>Notably, in this dataset, each jammer indicates a combination of SoI with that jammer, as an instance "CWI_16APSK " refers to SoI (16APSK)+CWI.</p> <p> </p>
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
<p>The WIDEFT data corpus has been created to provide bursts from wireless devices in the spectrum of Bluetooth, WiFi, and other RF signals to further research of the acquisition and usage of wireless device fingerprints. WIDEFT was developed through the efforts of the Physical Science Laboratories (PSL) at New Mexico State University (NMSU). Data collection was recorded at PSL and at NMSU main campus and cataloged, maintained, and prepared for release at PSL.</p> <p>Please cite the following article:</p> <p>A. Bucker Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research,” <em>IEEE Int. Symp. Technol. Homel. Secur. (HST)</em>, 2021.</p>
Solar eclipse radio frequency measurements
<p>Measurements of the carrier frequency of the NIST radio station WWV on 10 MHz, as performed in north suburban Milwaukee, Wisconsin, during the solar eclipse of August 21, 2017. Details are in the file "readme.pdf".</p> <p>Steven Reyer, WA9VNJ, approx. Lat/Long = 43.218, -87.951. WAV file start time = 1400 UTC. Antenna is a DX Engineering RF-PRO-1B aimed north-south, receiver is a Yaesu FT-857D locked to a Trimble Thunderbolt GPS via an XRef-FT oscillator interface. I tuned the radio to 9999.00 kHz USB and listened for the resulting nominal 1000 Hz tone, which was measured by Spectrum Lab software, doing 512k-point FFTs, overlapping 75%, resulting in a measurement every 12 seconds. </p> <p> </p> <p> </p>
Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023
<p>Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023</p>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</p>
Radio-frequency C-V measurements with subattofarad sensitivity: data and analysis script
<p>The attached files include:</p> <ul> <li>QCoDeS database containing all of the raw data underlying the results presented in the publication "Radio-frequency CV measurements with subattofarad sensitivity" by F.K. Malinowski at al. published in Physical Review Applied in 2022</li> <li>Jupyter Notebook file with Python scripts, that processes the raw data and outputs the figures embedded in the publication (except for the schematics of the devices and the rf circuitry).</li> </ul>
Radio frequency wave interactions with a plasma sheath: the role of wave and plasma sheath impedances
<p>The accompanying files contain digital data for figures in the article "Radio frequency wave interactions with a plasma sheath: the role of wave and plasma sheath impedances" by J.R. Myra and H. Kohno, to be submitted to the journal Physics of Plasmas.</p> <p><br> Abstract:<br> RF sheaths form near surfaces where plasma and strong RF fields coexist. The effect of these RF sheaths on wave propagation near the boundary can be characterized by an effective sheath impedance that includes both resistive and capacitive contributions describing RF sheath rectification and RF power absorption in the sheath [J. R. Myra and D. A. D'Ippolito, Phys. Plasmas 22, 062507 (2015)]. Here we define a dimensionless parameter, the ratio of incoming wave impedance to the sheath impedance, which determines the characteristics of the interaction, ranging from quasi-conducting to quasi-insulating, or in the case of matched impedances, to a sheath-plasma resonance. A semi-analytical analysis is carried out for electrostatic slow waves in the ion cyclotron range of frequencies (ICRF). For the propagating slow wave case, where the incident wave is partially reflected, the fraction of power dissipated in the sheath is calculated. For the evanescent slow wave case, which admits a sheath-plasma resonance, an amplification factor is calculated. Using the impedance ratio approach, RF sheath interactions are characterized for a range of RF wave and plasma parameters including plasma density, magnetic field angle with respect to the surface, wave frequency and wave-vector components tangent to the surface. For a particularly interesting example case, results are compared with the rfSOL code [H. Kohno and J. R. Myra, Comput. Phys. Commun. 220, 129 (2017)]. Finally electromagnetic effects, absent from the semi-analytical analysis, are assessed.</p>
Photometric Measurements of Lightning Phenomena with Simultaneous Radio Frequency Data
<p>This data was collected with a novel multi-band optical instrument alongside simultaneous radio frequency measurements for 16 lightning flashes.</p> <p>The transmissivity of 3 narrowband optical filters used to collect photometric data for temperature analysis are stored in the filterData folder along with the responsivity of the photodiode used to collect the data. </p> <p>The flash data is stored by flash in folders labeled with the UTC day and time in a MonthDDYYY_HHMMSS format. For each flash, there is an overview flashFigure.pdf that quickly summarizes the flash by plotting the Lightning Mapping Array (LMA) data and some of the data stored in PicoScope.csv including the Fast Antenna data, the Slow Antenna data and the 777 nm data measured at the ground. Some of the flashes were also seen by the Geostationary Lightning Mapper (GLM), this data is included on the flashFigure.pdf file when available as well as fully reproduced in the GLM.csv file. For each flash, the LMA data is stored in the LMA.dat file, the photodiode and electric field antenna data are stored in the PicoScope.csv file as well as the time in milliseconds after the trigger occurs in UTC time (the millisecond precise data is stored in the name of the time column in the PicoScope.csv). Each PicoScope file has some pre-trigger data, this is denoted with a negative time value in the time column as it is before the trigger event. Finally, the temperature is reported in the Temperature.csv file, it was based off of a decimated version of the PicoScope file to reduce the computational time but it's time units are still milliseconds after the trigger occurs in UTC time like the data in PicoScope.csv.</p>
Dataset for Radio frequency interference scan at APD, SVNIT, Surat(IN)
<p>RFI scan data with 13 observation runs; collected using a RTL-SDR RTL2832U (Software defined radio).</p>
Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)
<p>Selecting a suitable nest site is critical to the survival and reproduction of birds. Prospecting allows individuals to gather information on the local quality of potential future breeding sites, which may help them make the best nest site selection decision. However, few studies have focused on the direct links between the prospecting activity of breeders and subsequent nest site selection. In this study, we investigated the prospecting pattern of Japanese tits (<em>Parus minor</em>) during the pre-breeding period of the first breeding attempt and whether nest site characteristics influence their nest box visiting behaviour and occupied nest site. We used radio frequency identification (RFID) to track the movements of Japanese tits visiting nest boxes and compared nest site characteristics between visited and unvisited (control) nest boxes, as well as between visited and occupied nest boxes. We found that Japanese tits started visiting nest boxes approximately 20 days before breeding, visited an average of 6 nest boxes and eventually chose the most visited nest box for breeding activities. Japanese tits were more likely to visit nest boxes that had less canopy cover and lower shrub density but a greater total number of surrounding trees and ultimately chose breeding nest boxes with a smaller entrance inclination, in nesting trees with a larger diameter at breast height (DBH) which were surrounded by trees with a larger DBH. Our results suggest that Japanese tits visit several potential breeding sites before choosing breeding nest boxes and that nest site characteristics can influence their prospecting activity and nest site selection.</p>
Datasets for Deep Learning Based Radio Frequency Side-Channel Attack on Quantum Key Distribution
<p>The dataset contains measurements of radio-frequency electromagnetic emissions from a home-built sender module for BB84 quantum key distribution. The goal of these measurements was to evaluate information leakage through this side-channel. This dataset supplements our <a href="https://link.aps.org/doi/10.1103/PhysRevApplied.20.054040">publication</a> and allows to reproduce our results together with the source code hosted at <a href="https://github.com/XQP-Munich/EmissionSecurityQKD">GitHub</a> (and also on <a href="https://doi.org/10.5281/zenodo.7965628">Zenodo</a> via integration with GitHub).<br><br>The measurements are performed using a magnetic near-field probe, an amplifier and an oscilloscope. The dataset contains raw measured data in the file format output by the oscilloscope. Use our source code to make use of it. Detailed descriptions of measurement procedure can be found in our paper and in the metadata JSON files found within the dataset.</p> <p><strong>Commented list of datasets</strong></p> <p>This file lists the datasets that were analyzed and reported on in the paper. The datasets in the list refer to directories here. Note that most of the datasets contain additional files with metadata, which detail where and how the measurements were performed. The mentioned Jupyter notebooks refer to the source code repository https://github.com/XQP-Munich/EmissionSecurityQKD (not included in this dataset). Most of those notebooks output JSON files storing results. The processed JSON files are also included in the source code repository.</p> <p>In naming of datasets,</p> <ul> <li><em>Antenna</em> refers to the log-periodic dipole antenna. All datasets that do not contain `Antenna` in their name are recorded with the magnetic near-field probe.</li> <li><em>Rev1</em> refers to the initial electronics design, while `rev2` refers to the revised electronics design which contains countermeasures aiming to reduce emissions.</li> <li><em>Shielding</em> refers to measurements where the device is enclosed in a metallic shielding and the measurement takes place outside the shielding.</li> <li><em>Rotation</em> refers to orientation of the magnetic near-field probe at the same spacial location</li> </ul> <p><strong>Datasets collected with near-field probe for Rev1 electronics</strong></p> <ul> <li><strong>Rev1Distance</strong>: contains measurements at different distances from the Rev1 electronics performed above the FPGA. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`. The amplitude is analyzed in `get_raw_data_RMS_amplitude.ipynb`.</li> <li><strong>Rev12D</strong>: different locations on a 2d grid at a constant distance from the electronics. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`.</li> <li><strong>Rev130meas2.5cm</strong>: 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset.</li> <li><strong>Rev1Rotation10deg</strong> contains a measurement for varying orientation of the probe at the same location. This is not mentioned in the paper and is only included for completeness. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`.</li> <li><strong>Rev1TEMPESTShieldingFPGA</strong> Measurements with and without shielding at 4cm above the FPGA.</li> <li>- <strong>Rev1TEMPESTShieldingUSBHole</strong> Measurements with shielding in front of a hole of size about 2cm x 2cm. The deep learning attack is analyzed in `TEMPEST_ATTACK*.ipynb`.</li> </ul> <p><strong>Datasets collected with near-field probe for Rev2 electronics</strong></p> <ul> <li><strong>Rev2Distance</strong> contains measurements at different distances from the Rev2 electronics performed above the FPGA.</li> <li><strong>Rev22D</strong> and <strong>Rev22Dstart_7_0</strong> contain measurements on a 2d grid performed on the revised electronics. The dataset is split in two directories because the measurement procedure crashed in the middle. This split structure was kept in order to maintain consistency with the automatic metadata.</li> <li><strong>Rev230meas2.5cm</strong> 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset.</li> </ul> <p><strong>Other datasets</strong></p> <ul> <li><strong>BackgroundTuesday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 21st.</li> <li><strong>BackgroundSaturday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 11th.</li> <li><strong>AntennaSpectra</strong> Dataset of spectra directly recorded by the oscilloscope. Used to demonstrate ability of telling apart the situation of sending QKD key (standard operation) and having the device turned on but not sending any key at a distance. Analyzed in notebook `Comparing_KeyNokey_Measurements.ipynb`.</li> <li><strong>Rev2ShieldingAntenna</strong> Raw amplitude measurements with log-periodic dipole antenna on Rev2 electronics including shielding enclosure, collected at various distances. None of our attacks against this scenario were successful. The dataset represents a challenge to test more advanced attacks using improved data processing.</li> </ul> <p> </p>
Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)
Open the record for dataset details and reuse information.
The scalogram of the Radio Frequency Interference (RFI) signals
<p>This dataset includes the scalogram of RFI signals for RFI classification and Modulation recognition application.</p> <p>In this dataset, SoI is a video stream transmitted based on DVB-S2 standards in a real-time Satellite to Ground communication.</p> <p>CWI refers to a combination of SoI with Continous Wave Interference (CWI)</p> <p>MCWI is also a combination of SoI with Multi-CWI</p> <p>and CI indicates the combination of SoI with Chirp Interference.</p> <p>Moreover, SoI has been transmitted in four different modulation types namely QPSK, 8APSK, 16APSK and 32APSK.</p> <p> </p>
Dataset: Radio Frequency Characteristics of Volcanic Lightning
<p>This data set contains broadband VHF waveforms of vent discharges and volcanic lightning<br> flashes collected during an explosive eruption of Sakurajima volcano in Japan on November 8,<br> 2019. Each file in the dataset is a 3 microsecond waveform. The amplitude values of the<br> waveform are in arbitrary voltage units. The sampling rate is 180 MS/s. There are two tar<br> archive files included in this data set: one for all of the vent discharge waveforms and one for all<br> of the flash waveforms. The UTC time of the waveform, corresponding to the time of the peak<br> of the waveform is included in each file name in the tar archives. The UTC time is given as<br> seconds of the day on November 8, 2020.</p>
ScienceDex guides
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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.