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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. Occurrence of animals along five transects at the Jornada Basin LTER site from 1989-1994

   https://doi.org/10.6073/pasta/6179ef160f753a6229d8e6a2c3a0569b  

   Lightfoot, David C; Whitford, Walter G (January 2023, Environmental Data Initiative)

   This dataset contains data on the occurrence of rabbits, birds, and lizards observed along the Jornada Basin LTER (II) animal transects in southern New Mexico, USA. Five, 1 km transects were established, each in a different vegetation zone, near the current NPP study locations C-CALI, G-IBPE, M-NORT, P-COLL, and T-EAST. An observer walked each transect once every two weeks from early 1989 through 1994 recording animals observed along the transects. The data consists of species names, numbers of individuals, and perpendicular distances observed from transects, as well as weather and other context observations. Observation history and species codes are described in additional files. This study is complete. 

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3. High frequency turbidity data and storm event characteristics of three urban streams in Cleveland, Ohio

   https://doi.org/10.4211/hs.a249f3100f924ad09600c9d3de2183b6  

   Safdar, Suffiyan; Jefferson, Anne; Costello, David; Blinn, Andrew (August 2024, Hydroshare)

   This dataset contains turbidity data and storm event characters of three urban watersheds in Cuyahoga County, Ohio. Turbidity data were collected at a frequency of 10 minutes using in-situ Cyclop-7 turbidimeters designed by Turner Designs and integrated with a Cyclops-7 logger by Precision Measurement Engineering, Inc. Data were collected for three years from September 2018 to 2021. Turbidity data is harmonized with instantaneous discharge data from USGS stream gages. Event characteristics contains runoff, precipitation and antecedent characteristics. The data support the findings of the study titled "Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization using Concentration-Discharge Hysteresis". 

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4. On the v -Picard Group of Stein Spaces

   https://doi.org/10.1093/imrn/rnae185  

   Ertl, Veronika; Gilles, Sally; Nizioł, Wiesława (September 2024, International Mathematics Research Notices)

   Abstract We study the image of the Hodge–Tate logarithm map (in any cohomological degree), defined by Heuer, in the case of smooth Stein varieties. Heuer, motivated by the computations for the affine space of any dimension, raised the question whether this image is always equal to the group of closed differential forms. We show that it indeed always contains such forms but the quotient can be non-trivial: it contains a slightly mysterious $$\mathbf{Z}_{p}$$-module that maps, via the Bloch–Kato exponential map, to integral classes in the pro-étale cohomology. This quotient is already non-trivial for open unit disks of dimension strictly greater than $$1$$. 

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5. Responding to the housing crisis in the Arctic: A transdisciplinary approach across physical, natural, and social systems, Organizational surveys - Alaska, Fall 2020 to Spring 2021.

   https://doi.org/10.18739/A27S7HT8R  

   Poleacovschi, Cristina; Taylor, Jessica (January 2023, NSF Arctic Data Center)

   This dataset contains de-identified survey data from 26 organizations and 36 people involved in housing services and projects in rural Alaska. These organizations included state and federal government organizations (9 organizations), regional housing authorities (4 organizations), and non-governmental organizations (13 organizations). The file contains questions and data on organizations’ ability to enact adaptation of housing, frequency of inter-organizational communication, and organizational attributes. The data was collected from Fall 2020 to Spring 2021. This study aims to understand the role of organizations in housing in rural Alaska. Through surveys with stakeholders from government organizations, non-profits, and professional firms, this study will provide a deeper understanding of housing concerns, collaboration between organizations and participatory practices of organizations with community-level stakeholders. 

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