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378 results for “RF”
Chang'e 5 RF recording at 8471.2 MHz with Allen Telescope Array on 2020-12-19 (X polarization)
<p>This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-12-19, after the Earth flyby, in a direct trajectory to the Sun-Earth L1 Lagrange point. The recording was done at Allen Telescope Array using antenna 2h in dual XY (linear) polarization.</p> <p>The hardware configuration was as follows: antenna 2h was connected to RFCB LO d, which was tuned to a frequency of 8475 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 30.72 Msps and a GNU Radio flowgraph was used to channelize four frequencies from the spacecraft (8463.7, 8471.2, 8478.6 and 8486.2 MHz), using an IQ sample rate of 480 ksps for each channel. Only the 8471.2 MHz and 8486.2 MHz channels contains signals from the spacecraft.</p> <p>The GNU Radio flowgraph stored the IQ data for each channel as 32-bit floats in the <a href="https://wiki.gnuradio.org/index.php/Metadata_Information">GNU Radio metatata file format</a>, with detached headers. After recording, the data was converted 16-bit integers and <a href="https://github.com/gnuradio/SigMF">SigMF</a> format.</p> <p>This dataset contains the IQ data for the channel at 8471.2 MHz, in SigMF format. Each polarization is in a different file. The original metadata headers are also provided. This dataset contains the X polarization. The Y polarization can be found in "<a href="https://zenodo.org/record/4395190">Chang'e 5 RF recording at 8471.2 MHz with Allen Telescope Array on 2020-12-19 (Y polarization)</a>".</p>
Chang'e 5 RF recording at 8471.2 MHz with Allen Telescope Array on 2020-12-19 (Y polarization)
<p>This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-12-19, after the Earth flyby, in a direct trajectory to the Sun-Earth L1 Lagrange point. The recording was done at Allen Telescope Array using antenna 2h in dual XY (linear) polarization.</p> <p>The hardware configuration was as follows: antenna 2h was connected to RFCB LO d, which was tuned to a frequency of 8475 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 30.72 Msps and a GNU Radio flowgraph was used to channelize four frequencies from the spacecraft (8463.7, 8471.2, 8478.6 and 8486.2 MHz), using an IQ sample rate of 480 ksps for each channel. Only the 8471.2 MHz and 8486.2 MHz channels contains signals from the spacecraft.</p> <p>The GNU Radio flowgraph stored the IQ data for each channel as 32-bit floats in the <a href="https://wiki.gnuradio.org/index.php/Metadata_Information">GNU Radio metatata file format</a>, with detached headers. After recording, the data was converted 16-bit integers and <a href="https://github.com/gnuradio/SigMF">SigMF</a> format.</p> <p>This dataset contains the IQ data for the channel at 8471.2 MHz, in SigMF format. Each polarization is in a different file. The original metadata headers are also provided. This dataset contains the X polarization. The X polarization can be found in "<a href="https://zenodo.org/record/4393318">Chang'e 5 RF recording at 8471.2 MHz with Allen Telescope Array on 2020-12-19 (X polarization)</a>".</p>
Chang'e 5 RF recording at 8486.2 MHz with Allen Telescope Array on 2020-12-19 (X polarization)
<p>This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-12-19, after the Earth flyby, in a direct trajectory to the Sun-Earth L1 Lagrange point. The recording was done at Allen Telescope Array using antenna 2h in dual XY (linear) polarization.</p> <p>The hardware configuration was as follows: antenna 2h was connected to RFCB LO d, which was tuned to a frequency of 8475 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 30.72 Msps and a GNU Radio flowgraph was used to channelize four frequencies from the spacecraft (8463.7, 8471.2, 8478.6 and 8486.2 MHz), using an IQ sample rate of 480 ksps for each channel. Only the 8471.2 MHz and 8486.2 MHz channels contains signals from the spacecraft.</p> <p>The GNU Radio flowgraph stored the IQ data for each channel as 32-bit floats in the <a href="https://wiki.gnuradio.org/index.php/Metadata_Information">GNU Radio metatata file format</a>, with detached headers. After recording, the data was converted 16-bit integers and <a href="https://github.com/gnuradio/SigMF">SigMF</a> format.</p> <p>This dataset contains the IQ data for the channel at 8486.2 MHz, in SigMF format. Each polarization is in a different file. The original metadata headers are also provided. This dataset contains the X polarization. The Y polarization can be found in "<a href="https://zenodo.org/record/4405179">Chang'e 5 RF recording at 8486.2 MHz with Allen Telescope Array on 2020-12-19 (Y polarization)</a>".</p>
Rezultati CNN, LSTM, RF, GRU, XGB modela za modeliranje dnevnih koncentracija čestica u zraku
<p>Rezultati CNN, LSTM, RF, GRU, XGB modela za modeliranje dnevnih koncentracija čestica u zraku u pogleu koeficijenta determinaciej i srednje apsolutne pogreške.</p>
Dataset related to diabetes phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to diabetes phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
CommRad RF: A dataset of communication radio signals for detection, identification and classification
<p>In this age of information, data is the most valuable commodity. In today's world it is easier to acquire millions of standard/known and even non- standard/unknown RF signals. Perhaps, having such huge amount of data can help people to develop novel deep learning techniques. This dataset focuses on communication radios baseband signals. It includes RF signals from multiple radios using different frequencies, in different environments. Over 2700 RF signals from 27 different radios are included in the dataset. An RF receiver was used to capture the dataset, which automatically detects RF signals and records them until they disappear. In order to create a signal library, the receiver is connected to a laptop that pre-processes and stores RF signatures from different emitters.</p>
Adrian Helmet - WW1 RF
davideagan3d.com Source: Objaverse 1.0 / Sketchfab
Dataset related to ADHD phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to ADHD phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Shallow subsurface water-ice distribution in the lunar south pole: Analysis based on Mini-RF and multi-metrics
Open the record for dataset details and reuse information.
CommRad RF: A dataset of communication radio signals for detection, identification and classification
<div> <div> <div> <div> <p>In today’s world, data is one of the most valuable resources. It’s now easier than ever to collect millions of radio frequency (RF) signals, both well-known and unusual. With such a large amount of data, researchers can create advanced deep learning techniques. This dataset focuses on baseband signals from communication radios. It includes RF signals captured from 27 different radios, using various frequencies and recorded in different environments. Altogether, there are over 2,700 signals in this collection. The signals were captured using an RF receiver that automatically detects and records signals until they disappear. To organize these signals into a library, the receiver was connected to a laptop, which processed and saved the unique RF fingerprints from different devices.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
A 2-million-year record of Amazon climate - CDH-79 X-RF Data (Ti, Ca, Fe, K)
<p>X-ray fluorescence ( Ti, Ca, Fe and K) spanning the last 2 million years (myr) from a piston core (CDH-79) collected offshore of the mouth of the Amazon River and Statistical analisys.</p>
Selected processed 5G base station RF-EMF measurement data
<p>This presents the selected processed 5G base station (BS) radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in outdoor environment. This data links to the findings shown in Section 5 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
Selected SSB-based RF-EMF Outdoor Measurement Campaigns data
<p>This presents the selected Synchronisation Signal Block (SSB) based radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in indoor environment. This data links to the findings shown in Section 4.2 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
Selected SSB-based RF-EMF Indoor Measurement Campaigns data
<p>This presents the selected Synchronisation Signal Block (SSB) based radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in indoor environment. This data links to the findings shown in Section 3.3 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset
<p>Data files were used in support of the research paper titled “<em>Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning</em>" which has been submitted to the IET Communications journal.</p> <p>---------------------------------------------------------------------------------------------</p> <p>All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'</p> <ul> <li>'ModeSelect.py': class used to defined the antenna state selection algorithm</li> <li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna</li> <li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning</li> <li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only</li> </ul> <p>---------------------------------------------------------------------------------------------</p> <p>Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar<br> Contact: krd26@drexel.edu</p> <p>---------------------------------------------------------------------------------------------</p> <p>Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.</p> <p>---------------------------------------------------------------------------------------------</p> <p>classifier_training: files used for training classifiers that are integrated into SDR platform</p> <ul> <li>'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)</li> <li>'classTrain.py' is the main parser for training the classifiers</li> <li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'</li> </ul> <p>post_processing_classifier: contains logs of online classifier outputs and processing script</p> <ul> <li>'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario</li> <li>'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'</li> </ul> <p>post_processing_mgen: contains MGEN receiver logs and parser</p> <ul> <li>'configs' contains JSON files to be used with parser for each experiment</li> <li>'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'</li> </ul> <p>ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.</p> <ul> <li>Directory contains detailed 'readme.txt' for understanding.</li> <li>Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. <ul> <li>S. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.</li> </ul> </li> </ul> <p>results: contains results obtained from study</p> <ul> <li>'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'</li> <li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'</li> <li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'</li> </ul> <p>tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study</p> <ul> <li>'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.</li> <li>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.</li> </ul>
Supplementary material 1 from: El-Barougy RF, Cadotte MW, Khedr A-HA, Nada RM, Maclvor SJ (2017) Heterogeneity in patterns of survival of the invasive species Ipomoea carnea in urban habitats along the Egyptian Nile Delta. NeoBiota 33: 1-17. https://doi.org/10.3897/neobiota.33.9968
Table 1S. Comparison of stratified multivariable generalized linear models : Explanation note: Comparison of stratified multivariable generalized linear models that model the relationship between survival probability and different variables. The best model, selected by AIC, p-value of chi square, deviance explained (DE) and (AW). AW is the Akaikes weight which is the probability of the model being the best model explaining the relationship between survival probability and different variables. DE is the percentage of deviance explained (DE) as a measure of the model's goodness-of-fit.
Supplementary material 1 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Table S1. Anastrepha fraterculus male and female characteristic.: Explanation note: Anastrepha fraterculus male (m) and female (f) characteristic cuticular hydrocarbons identified by principal component analyses.
Dataset related to epilepsy phase 1 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 1 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</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.