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814 results for “radio”
Fig. 6 in Appraisal of sperm dynamics as a crucial trait of radio- sterilized Spodoptera litura (Lepidoptera: Noctuidae) and its F progeny for evaluation of the 'inherited sterility technique' for pest suppression
Fig. 6. Effect of gamma irradiation on (a) the percentage of active apyrene sperm, and (b) the intensity of sperm activity (no. of undulations /s) in mated irradiated parental (P) male Spodoptera litura and their F1progeny.
gellum black. Femora all black or forefemur ferruginous in apicoventral half; foretibia brown or ferruginous, midtibia brown ferruginous, hindtibia brown; tarsi varying from brown to ferruginous. ♂.– Unknown. GEOGRAPHIC DISTRIBUTION.– Known only from higher elevations (1020-1130 m above sea level) of Ranomafana National Park, Madagascar. RECORDS (Fig. 29).— All specimens were collected in Ranomafana National Park, Fianarantsoa Province. Holotype: ♀, Belle Vue at Talatakely at 21º15.99'S 47º25.21'E, alt. 1020 m, 14-21 Jan 2002, M. Irwin and R. Harin 'Hala (CAS). Paratypes: Radio tower at forest edge at 21º15.05'S 47º24.43'E, alt. 1130 m, 23 Aug – 7 Sept 2006 and 1-11 Nov 2006, M. Irwin and R. Harin 'Hala (2 ♀, CAS); same data as holotype except 22-28 Nov 2001 and R. Harin 'Hala alone (1 ♀, CAS); Vohiparara at 21º13.57'S 47º22.19'E, alt. 1110 m, 22-28 Nov 2001, R. Harin 'Hala (1 ♀, CAS). FIGURE 29. Collecting localities of Tachytes melanogaster sp. nov. in A Review of the Wasp Genus Tachytes Panzer, 1806 of Madagascar (Hymenoptera: Crabronidae)
gellum black. Femora all black or forefemur ferruginous in apicoventral half; foretibia brown or ferruginous, midtibia brown ferruginous, hindtibia brown; tarsi varying from brown to ferruginous. ♂.– Unknown. GEOGRAPHIC DISTRIBUTION.– Known only from higher elevations (1020-1130 m above sea level) of Ranomafana National Park, Madagascar. RECORDS (Fig. 29).— All specimens were collected in Ranomafana National Park, Fianarantsoa Province. Holotype: ♀, Belle Vue at Talatakely at 21º15.99'S 47º25.21'E, alt. 1020 m, 14-21 Jan 2002, M. Irwin and R. Harin 'Hala (CAS). Paratypes: Radio tower at forest edge at 21º15.05'S 47º24.43'E, alt. 1130 m, 23 Aug – 7 Sept 2006 and 1-11 Nov 2006, M. Irwin and R. Harin 'Hala (2 ♀, CAS); same data as holotype except 22-28 Nov 2001 and R. Harin 'Hala alone (1 ♀, CAS); Vohiparara at 21º13.57'S 47º22.19'E, alt. 1110 m, 22-28 Nov 2001, R. Harin 'Hala (1 ♀, CAS). FIGURE 29. Collecting localities of Tachytes melanogaster sp. nov.
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>
Radio emission from a pulsar's magnetic pole revealed by general relativity: a data archive
<p>This repository contains all pulsar data presented as part of the Science paper: "Radio emission from a pulsar's magnetic pole revealed by general relativity". These data consist of pulse profiles archives of PSR J1906+0746 observed by the Nancay and Arecibo radio telescopes between 2005 and 2018, that are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p>
Predicted UV-to-Radio SEDs of little red dots: AGN and SF
<p>Predicted Dust SEDs for JWST's Little Red Dots<br>17-Sep-2024</p> <p>Here are the average UV through radio spectral energy distributions predicted for the LRD population from Figure 2 of Casey et al. (2024b, https://arxiv.org/abs/2407.05094, ApJL in press). There are two SEDs: one predicting the emission is due to AGN and one that it is due to star-formation. Wavelength is given as log10 of rest-frame wavelength in microns. Flux density is log10 Fnu in mJy. Email cmcasey.astro@gmail.com with questions.</p>
FRGADB - FIRST Radio Galaxy Anomaly Detection Benchmark
<p>This dataset is a combination of samples from the MiraBest, FRGMRC and LRG catalogues. It is intended to serve as a benchmark for models' performance with respect to anomalous source detection in radio astronomy.</p>
Reproduction package for the paper "A Nançay Radio Telescope study of the hyperactive repeating FRB 20220912A"
<p>This is a reproduction package for the paper "A Nançay Radio Telescope study of the hyperactive repeating FRB 20220912A" by Konijn et al., soon to be published in MNRAS, 2024. The package provides Jupyter-notebooks and the accompanying data to reproduce the figures from the paper. </p>
NCSRD-DS-5GDDoS: 5G Radio and Core metrics containing sporadic DDoS attacks
<p><strong>NCSRD-DS-5GDDos v3.0 Dataset</strong><br>===<br>NCSRD-DS-5GDDos is a comprehensive dataset recorded in a real-world 5G testbed that aligns with the 3GPP specifications. The dataset captures Distributed Denial of Service (DDoS) attacks initiated by malicious connected users (UEs). </p> <p>The setup comprises of 3 cells with a total of 9 UEs connected to the same core network. The 5G network is implemented by the Amarisoft Callbox Mini solution (cell 2), and we further employ a second cell using the Amarisoft Classic (cell 1 & 3), that also hosts the 5G core.</p> <p>The setup utilizes a broad set of UE devices comprising a set of smart phones (Huawei P40), microcomputers (Raspberry Pi 4 - Waveshare 5G Hat M2), industrial 5G routers (Industrial Waveshare 5G Router), a WiFi-6 mobile hotspot (DWR-2101 5G Wi-Fi 6 Mobile Hotspot) and a CPE box (Waveshare 5G CPE Box). All UEs are being operated by subsidiary hosts which are responsible for the traffic generation, occurring from scheduled communications times.</p> <p>All identifiers are artificially generated and do not represent or based on personal data. We identify each UE through its ‘imeisv’ ID, that corresponds to the device in use, due to vendor implementation, that uses the same IMSI for all UEs.</p> <p>This dataset captures attack data from a total of 5 malicious User Equipment (UE) devices that initiated various flooding attacks on a 5G network. Each record includes key identifiers such as the IMEISV (International Mobile Equipment Identity Software Version number) and IP address of the attacking UE, along with the device type. The file "summary_report.csv" summarizes this information. The traffic types used in the attacks include syn flooding, UDP flooding, ICMP flooding, DNS flooding, and GTP-U flooding. The benign users stream YouTube and Skype traffic.</p> <p>The dataset is recorded through the use of a data collector that interfaces with the 5G network and gathers data regarding UEs, gNBs and the Core Network. The data are recorded in an InfluxdB and pre-processed into three separate tabular .csv files for more efficient processing: “amari_ue_data.csv”, “enb_counters.csv” and “mme_counters.csv”. In this version, we use an Amarisoft Classic (cells 1 & 3, Core Network) and an Amarisoft Mini (cell 2) (more information on the products can be found in https://www.amarisoft.com/).</p> <p>The ”amari_ue_data.csv” provides information on the UEs regarding identification (“imeisv”, “5g_tmsi”, “rnti”), IP addressing, bearer information, cell information (“tac”, “ran_plmn”), and cell information (“ul_bitrate”, “dl_bitrate”, “cell_id”, retransmissions per user per cell “ul_retx” as well as aggregated bit rates for each cell).</p> <p>The ”enb_counters.csv” focuses on cell-level information, providing downlink and uplink bitrates, usage ratio per user, cpu load of the gNB.</p> <p>We provide separate files of ”amari_ue_data.csv” and ”enb_counters.csv” generated from each gNB (Amarisoft Classic and Mini).</p> <p>The “mme_counters.csv” provides information on the Non-Access Stratum (NAS) of the 5G Network and focuses on session status reports (e.g., number of PDU session establishments, paging, context setup. This part gives an overview of the connection management throughout the recording session, and provides information on features suggested by 3GPP for abnormal user behavior.</p> <p>We also provide a separate pre-processed dataset, that merges the two "amari_ue_data_*.csv" file, including labeling of the malicious/benign samples, and may be more flexible for interested data scientists.</p> <p>Please refer to README.txt for the features included in each file.</p> <p>If you use this dataset, please also cite the following papers:</p> <p>M. Christopoulou, A. Garos, A. Vekraki, D. Santorinaios, I. Koufos, S. Karamitsiani, G. Xilouris, M.-A. Kourtis, G. Gardikis, and P. Trakadas, “User Terminals as Attackers: An Open Dataset Analysis of DDoS Attacks in 5G Networks,” in Proc. 2024 IEEE Conf. Standards Commun. Netw. (CSCN), 2024, pp. 301–307, doi: 10.1109/CSCN63874.2024.10849694.</p> <p>G. Xylouris, A. Vekraki, M. Christopoulou, M. A. Kourtis, E. K. Markakis and P. Trakadas, "Advancing Predictive Security for Consumer Applications in Beyond 5G/6G Networks With Annotated Datasets," in IEEE Transactions on Consumer Electronics, doi: 10.1109/TCE.2025.3567151.</p>
Complete figure set for 'Radio Morphology of Gamma-ray Sources: Double-Lobed Radio Sources'
<p>This file is the online supplementary material for the data that will be published in the manuscript title "Radio Morphology of Gamma-ray Sources: Double-Lobed Radio Sources" by the Astrophysical Journal.</p>
Data from "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154"
<p>This includes the data and scripts used to generate the plots in the paper "A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154."</p>
A candidate coherent radio flash following a neutron star merger
<p>This is a reproduction file for the paper ``A candidate coherent radio flash following a neutron star merger``. The sources of the raw data are provided, the link to the calibration and imaging pipeline used, and the scripts required to reproduce the results.</p>
Ice Anatomy: A Benchmark Dataset and Methodology for Automatic Ice Boundary Extraction from Radio-Echo Sounding Data
<p>The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of their bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data, to facilitate straightforward comparisons of deep learning models in the future. The ``IceAnatomy'' dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45,000km of observations. The RES data originates from three sources: FAU, CReSIS, and AWI. The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data was acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Icefield. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train-test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the ``omni'' model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve Mean Meter Errors of 2.1m, 23.1m, and 4.9m for the ice surface and 9.1m, 78.2m, and 29.3m for the ice bottom. In relation to the mean measured ice thickness, these errors equate to 1.2%, 3.1%, and 0.3% for the ice surface and 4.9%, 10.4%, and 1.5% for the ice bottom.</p> <p> </p> <p> For more information, please read the following paper:</p> <p>[Coming soon. Currently under review.]</p> <p>Please also cite this paper if you plan on using the dataset.</p> <p> </p> <p>For the implementation of a baseline model please visit:</p> <p>[Coming soon]</p> <p> </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>
ET1008 - VE3KG Radio Association of Canada - CHU 14.670 MHz from April 8, 2024 Solar Eclipse
<p>See <a href="https://doi.org/10.5281/zenodo.13293306">10.5281/zenodo.13293306</a> for system documentation. <br>See <a href="https://doi.org/10.5281/zenodo.14004715">10.5281/zenodo.14004715</a> for python code to parse these files. </p> <p>Location: Ottawa, ON.</p> <p>Lat/lon: </p> <table> <tbody> <tr> <td>45.14417295</td> <td>-76.16592324</td> </tr> </tbody> </table> <p>Data collected by <a href="https://www.qrz.com/db/ve3kg">Dave Goodwin VE3KG</a>. </p>
Expedición Seaflower - Radio y Televisión De Colombia - Metales Pesados
<p>Entrevista de la Radio y Televisión de Colombia durante el desarrollo de la expedicipon Seaflower 2018, publicada en 2019</p>
Radio Interview: dissemination of the project
<p>Radio Interview at "Live Social", Radio Roma Capitale. Recorded on May 16th 2021, on air in June 28th 2021.</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>
Fig. 4 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 4. Movements of Prochilodus lineatus (fish no 1 and 4) and Pseudoplatystoma fasciatum (fish no 9, 10 and 13) in the lateral channel located near Itaipu Dam (detections in the fixed stations, in number of hours post-release, are shown in the white circles).
Fig. 3 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 3. Radio-telemetry fixed station at "Lago das Grevilhas", in the lateral channel near Itaipu Dam.
Fig. 1 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 1. Locations of the fixed-station receivers (numbers inside the white circles) during the pilot study conducted in the lateral channel located near Itaipu Dam.
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.