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1. 25yr observations of the sdB star AQ Col

   https://doi.org/10.26093/cds/vizier.19260017  

   Otani, T; Lynas-Gray, AE; Kilkenny, D; Koen, C; Von_Hippel, T; Uzundag, M; Vuckovic, M; Pennock, CM; Silvotti, R (September 2023, Centre de Donnees Strasbourg (CDS))

   VizieR online Data Catalogue associated with article published in journal Astrophysical Journal with title 'Anomalous orbital characteristics of the AQ Col (EC 05217-3914) system.' (bibcode: 2022ApJ...926...17O) 

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2. 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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3. 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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4. Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2000-2010)

   https://doi.org/10.5281/zenodo.7406675  

   Porter, Nicholas; Silver, Adrienne; Gangopadhyay, Avijit (January 2022, Zenodo)

   {"Abstract":["This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( https://doi.org/10.5281/zenodo.6436380) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).<\/p>\n\nThe format of the dataset is similar to that of  Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring\u2019s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting.  For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, \u201cLon\u201d- the ring center\u2019s weekly longitude, \u201cLat\u201d- the ring center\u2019s weekly latitude, \u201cArea\u201d - the rings weekly size in km^{2<\/sup>, and \u201cDate\u201d in days - representing the days since Jan 01, 0000. <\/p>\n\n}The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark\u2019s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). <\/p>\n\n <\/p>\n\nSilver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380<\/p>\n\nSilver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans<\/em>, 127<\/em>(8), e2022JC018737. https://doi.org/10.1029/2022JC018737 <\/p>\n\nGangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.<\/p>\n\nGangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020.  A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.<\/p>\n\nQGIS Development Team. QGIS Geographic Information System (2016).<\/p>\n\nDecker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).<\/p>\n\n <\/p>"],"Other":["Funded by two NSF US grants OCE-1851242, OCE-212328","{"references": ["Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380", "Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea.\\u00a0Journal of Geophysical Research: Oceans,\\u00a0127(8), e2022JC018737.\\u00a0https://doi.org/10.1029/2022JC018737", "Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.", "Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.", "QGIS Development Team. QGIS Geographic Information System (2016).", "Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986)."]}"]} 

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5. Data for: Development of a learning community focused on sea-level rise and coastal habitat change

   https://doi.org/10.5061/dryad.ghx3ffbvz  

   Watson, Elizabeth; Freyland, Christopher; Sylla, Habibita; Payne, Andrew; Njie, Daouda; Echiejile, Faith; Gale-Cottries, Jah'ya; Wiley, Alexis; Brittany, Wilburn; Joshua, Barufaldi; et al (January 2024, Dryad)

   Rapid habitat changes are occuring in salt marshes located in the Northeastern United States, including expansion of ponded areas on the marsh platform, die off of coastal forests, and subsequent colonization of 'ghost forests' by marsh vegetation. This work focuses on two main areas: (1) environmental conditions along the marsh forest border undergoing rapid transitions; and (2) environmental conditions and plant stress in marsh platforms with extensive ponding, with three study sites: in Long Island and Southern New England, where there are often significant slope breaks along the upland (slope ~0.01), and in southern New Jersey on the Atlantic Coastal plan (slope ~0.003). To better understand drivers of environmental change in marsh-forest borders undergoing rapid transitions, we measured shallow groundwater levels,  soil salinity, and forest health and structure along the salt marsh-upland border at three sites with varying slopes using installation of shallow groundwater wells, drone imagery and associated image processing, and geophysical methods. To better understand drivers of environmental change on the marsh platform, we measured used piezometers to understand vertical gradients in marsh groundwater levels, and measured photosynthesis and plant biomass and used drone imagery to map plant stress indices, as indicators of plant stress. While we anticipate that this data will be published in journal articles of the next 2 years, we archive collected data to facilitated data sharing, as required by NSF. 

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