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1. Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset

   https://doi.org/10.5281/zenodo.6304194  

   Jacovic, Marko; Rivas, Xaime Rey (January 2022, Zenodo)

   {"Abstract":["Data files were used in support of the research paper titled \u201cMitigating RF Jamming Attacks at the Physical Layer with Machine Learning<\/em>" which has been submitted to the IET Communications journal.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAll 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>\n\n'ModeSelect.py': class used to defined the antenna state selection algorithm<\/li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna<\/li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning<\/li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only<\/li><\/ul>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAuthors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar\nContact: krd26@drexel.edu<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nTop-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nclassifier_training: files used for training classifiers that are integrated into SDR platform<\/p>\n\n'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)<\/li>'classTrain.py' is the main parser for training the classifiers<\/li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'<\/li><\/ul>\n\npost_processing_classifier: contains logs of online classifier outputs and processing script<\/p>\n\n'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario<\/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>\n\npost_processing_mgen: contains MGEN receiver logs and parser<\/p>\n\n'configs' contains JSON files to be used with parser for each experiment<\/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>\n\nray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.<\/p>\n\nDirectory contains detailed 'readme.txt' for understanding.<\/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. \n\tS. 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>\n\t<\/li><\/ul>\n\nresults: contains results obtained from study<\/p>\n\n'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'<\/li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'<\/li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'<\/li><\/ul>\n\ntuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study<\/p>\n\n'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>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.<\/li><\/ul>"]} 

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2. Experimentation Framework for Wireless Communication Systems under Jamming Scenarios Dataset

   https://doi.org/10.5281/zenodo.4749668  

   Jacovic, Marko (January 2021, Zenodo)

   {"Abstract":["Data files were used in support of the research paper titled "\u201cExperimentation Framework for Wireless\nCommunication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. <\/p>\n\nAuthors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar\nContact: krd26@drexel.edu<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nTop-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. <\/p>\n\n--------------------------------<\/p>\n\nlogs:    - data logs collected from devices under test\n    - 'defenseInfrastucture' contains console output from a WARP 802.11 reference design network. Filename structure follows '*x*dB_*y*.txt' in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples = 1 ms). 'noJammer.txt' does not include the jammer and is a base-line case. 'outMedian.txt' contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script. \n    - 'uavCommunication' contains MGEN logs at each receiver for cases using omni-directional and RALA antennas with a 10 dB constant jammer and without the jammer. Omni-directional folder contains multiple repeated experiments to provide reliable results during each calculation window. RALA directories use s*N* folders in which *N* represents each antenna state. \n    - 'vehicularTechnologies' contains MGEN logs at the car receiver for different scenarios. 'rxNj_5rep.drc' does not consider jammers present, 'rx33J_5rep.drc' introduces the periodic jammer, in 'rx33jSched_5rep.drc' the device under test uses time scheduling around the periodic jammer, in 'rx33JSchedRandom_5rep.drc' the same modified time schedule is used with a random jammer. <\/p>\n\n--------------------------------<\/p>\n\nparsers:    - scripts used to collect or process the log files used in the study\n        - 'defenseInfrastructure' contains the 'xputFiveNodes.py' script which is used to control and log the throughput of a 5-node WARP 802.11 reference design network. Log files are manually inspected to generate results (end of log file provides a summary). \n        - 'uavCommunication' contains a 'readMe.txt' file which describes the parsing of the MGEN logs using TRPR. TRPR must be installed to run the scripts and directory locations must be updated. \n        - 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated. <\/p>\n\n--------------------------------<\/p>\n\nrayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite\n            - 'channelSummary.pdf': summary of channel statistics from ray-tracing study\n            - 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file \n            - 'processedScenario': pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by '*DYSE.json' file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired\n            - DYSE scenario file format: time stamp (milli seconds), receiver ID, transmitter ID, main path gain (dB), main path phase (radians), main path delay (micro seconds), Doppler shift (Hz), multipath 1 gain (dB), multipath 1 phase (radians), multipath 1 delay relative to main path delay (micro seconds), multipath 2 gain (dB), multipath 2 phase (radians), multipath 2 delay relative to main path delay (micro seconds)\n            - 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required\n            - 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')<\/p>\n\n--------------------------------<\/p>\n\nresults:    - contains performance results used in paper based on parsing of aforementioned log files\n <\/p>"]} 

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3. Data complementing the publication: "Does total column ozone change during a solar eclipse?"

   https://doi.org/10.5281/zenodo.14201523  

   Bernhard, Germar H; Bernhard, Germar H; Janson, George T; Simpson, Scott; Cordero, Raúl R; Sepúlveda_Araya, Edgardo I; Jorquera, Jose; Rayas, Juan A; Lind, Randall N (November 2024, Zenodo)

   Data published in this zip file complement the publication "Does total column ozone change during a solar eclipse?" by Germar H. Bernhard, George T. Janson, Scott Simpson, Raúl R. Cordero,  Edgardo I. Sepúlveda Araya, Jose Jorquera, Juan A. Rayas, and Randall N. Lind, which will be published in the journal "Atmospheric Chemistry and Physics". A DOI of the publication will be added to this meta data description when available. The DOI of the publication's pre-print (paper under review) is: https://doi.org/10.5194/egusphere-2024-2659 The contents of the zip file are organized in the following four subdirectories: - Figures: This directory contains the figures of the paper in PDF and PNG format plus the data used for plotting the figures. - GUVis-3511 Data Processor: This directory contains the software for processing the raw data collected during the solar eclipses described in the publication as well as ancillary data used for processing and manuals describing the software. - Limb darkening functions: This directory contains the functions expressing the change in the spectral irradiance during the eclipses discussed in the publication as a function of time and wavelength. - Raw data: This directory contains the raw data measured during the eclipses discussed in the publication. Each subdirectory and subdirectories nested therein contains "readme.txt" (in English) and "léeme_Espanol.txt" (in Spanish) files with further information of the contents of each subdirectory. 

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4. Glitch subtraction with adaptive spline -- Part 1

   https://doi.org/10.5281/zenodo.7592442  

   Mohanty, Soumya D (January 2023, Zenodo)

   This dataset contains the data used in the paper (arXiv:2301.02398) on the estimation and subtraction of glitches in gravitational wave data using an adaptive spline fitting method called SHAPES . Each .zip file corresponds to one of the glitches considered in the paper. The name of the class to which the glitch belongs (e.g., "Blip") is included in the name of the corresponding .zip file (e.g., BLIP_SHAPESRun_20221229T125928.zip). When uncompressed, each .zip file expands to a folder containing the following. An HDF5 file containing the Whitened gravitational wave (GW) strain data in which the glitch appeared. The data has been whitened using a proprietary code. The original (unwhitened) strain data file is available from gwosc.org. The name of the original data file is the part preceding the token '__dtrndWhtnBndpss' in the name of the file.A JSON file containing information pertinent to the glitch that was analyzed (e.g., start and stop indices in the whitened data time series).A set of .mat  files containing segmented estimates of the glitch as described in the paper.  A MATLAB script, plotglitch.m, has been provided that plots, for a given glitch folder name, the data segment that was analyzed in the paper. Another script, plotshapesestimate.m, plots the estimated glitch. These scripts require the JSONLab package. 

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5. StreamStats Data Query

   https://doi.org/10.5281/zenodo.3902476  

   URycki, Dawn; Good, Stephen (January 2020, Zenodo)

   This Python script queries the USGS StreamStats Service API for a list of available basin characteristics, and the values for those characteristics, for each input site. The script takes as input a matrix of site identifiers and location coordinates and returns 1) a matrix of values for available basin characteristics obtained from StreamStats for each input location and 2) a matrix of basin characteristic variable names and definitions. To run this script exactly as written, create 3 columns of data in comma-separated format: 1) 'Site,' which are the study site identifiers, 2) 'lonSS,' the longitudinal coordinates, and 3) 'latSS,' the latitudinal coordinates (in decimal degrees). Name the input file 'ssLocs.csv' and store it in a subfolder named 'Data.' Otherwise, the pathnames for input and output files can be modified within the script. The output files 'ssDats.csv' and 'Descriptions.csv' will also be saved to the subfolder 'Data'. Multiple code runs may be necessary to obtain information for all sites; as long as the output file 'ssDats.csv' remains in the 'Data' folder, the script will only query for sites with missing information. If the program returns an error or is unable to obtain data for a site after several attempts, it may be that the input coordinates do not point to a cell defined as water in the StreamStats application. A solution is to check the coordinates manually in the StreamStats web application (http://streamstats.usgs.gov). This script was developed as part of the analysis described in: URycki DR, Good SP, Crump BC, Chadwick J and Jones GD (2020) River Microbiome Composition Reflects Macroscale Climatic and Geomorphic Differences in Headwater Streams. Front. Water 2:574728. doi: 10.3389/frwa.2020.574728 

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