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1. A detrital zircon test of large-scale terrane displacement along the Arctic margin of North America

   https://doi.org/10.1130/G48336.1  

   Gibson, Timothy M. ; Faehnrich, Karol ; Busch, James F. ; McClelland, William C. ; Schmitz, Mark D. ; Strauss, Justin V. ( January 2021 , Geology)

   Abstract Detrital zircon U-Pb geochronology is one of the most common methods used to constrain the provenance of ancient sedimentary systems. Yet, its efficacy for precisely constraining paleogeographic reconstructions is often complicated by geological, analytical, and statistical uncertainties. To test the utility of this technique for reconstructing complex, margin-parallel terrane displacements, we compiled new and previously published U-Pb detrital zircon data (n = 7924; 70 samples) from Neoproterozoic–Cambrian marine sandstone-bearing units across the Porcupine shear zone of northern Yukon and Alaska, which separates the North Slope subterrane of Arctic Alaska from northwestern Laurentia (Yukon block). Contrasting tectonic models for the North Slope subterrane indicate it originated either near its current position as an autochthonous continuation of the Yukon block or from a position adjacent to the northeastern Laurentian margin prior to >1000 km of Paleozoic–Mesozoic translation. Our statistical results demonstrate that zircon U-Pb age distributions from the North Slope subterrane are consistently distinct from the Yukon block, thereby supporting a model of continent-scale strike-slip displacement along the Arctic margin of North America. Further examination of this dataset highlights important pitfalls associated with common methodological approaches using small sample sizes and reveals challenges in relying solely on detrital zircon age spectramore »for testing models of terranes displaced along the same continental margin from which they originated. Nevertheless, large-n detrital zircon datasets interpreted within a robust geologic framework can be effective for evaluating translation across complex tectonic boundaries.« less
2. An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2020

   https://doi.org/10.5194/essd-12-3653-2020  

   Olsen, Are ; Lange, Nico ; Key, Robert M. ; Tanhua, Toste ; Bittig, Henry C. ; Kozyr, Alex ; Álvarez, Marta ; Azetsu-Scott, Kumiko ; Becker, Susan ; Brown, Peter J. ; et al ( January 2020 , Earth System Science Data)

   Abstract. The Global Ocean Data Analysis Project (GLODAP) is asynthesis effort providing regular compilations of surface-to-bottom oceanbiogeochemical data, with an emphasis on seawater inorganic carbon chemistryand related variables determined through chemical analysis of seawatersamples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019.The major changes are data from 106 new cruises added, extension of timecoverage to 2019, and the inclusion of available (also for historicalcruises) discrete fugacity of CO2 (fCO2) values in the mergedproduct files. GLODAPv2.2020 now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises. Thedata for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate,phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12,CFC-113, and CCl4) have undergone extensive quality control with afocus on systematic evaluation of bias. The data are available in twoformats: (i) as submitted by the data originator but updated to WOCEexchange format and (ii) as a merged data product with adjustments appliedto minimize bias. These adjustments were derived by comparing the data fromthe 106 new cruises with the data from the 840 quality-controlled cruises ofthe GLODAPv2.2019 data product using crossover analysis. Comparisons toempirical algorithm estimates provided additional context for adjustmentdecisions; this is new to this version. The adjustments are intendedmore »toremove potential biases from errors related to measurement, calibration, anddata-handling practices without removing known or likely time trends orvariations in the variables evaluated. The compiled and adjusted dataproduct is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate,4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % inthe halogenated transient tracers. The other variables included in thecompilation, such as isotopic tracers and discrete fCO2, were notsubjected to bias comparison or adjustments. The original data and their documentation and DOI codes are available at theOcean Carbon Data System of NOAA NCEI(https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, lastaccess: 20 June 2020). This site also provides access to the merged dataproduct, which is provided as a single global file and as four regional ones– the Arctic, Atlantic, Indian, and Pacific oceans –under https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). Thesebias-adjusted product files also include significant ancillary andapproximated data. These were obtained by interpolation of, or calculationfrom, measured data. This living data update documents the GLODAPv2.2020methods and provides a broad overview of the secondary quality controlprocedures and results.« less
3. GLODAPv2.2019 – an update of GLODAPv2

   https://doi.org/10.5194/essd-11-1437-2019  

   Olsen, Are ; Lange, Nico ; Key, Robert M. ; Tanhua, Toste ; Álvarez, Marta ; Becker, Susan ; Bittig, Henry C. ; Carter, Brendan R. ; Cotrim da Cunha, Leticia ; Feely, Richard A. ; et al ( January 2019 , Earth System Science Data)

   Abstract. The Global Ocean Data Analysis Project (GLODAP) is asynthesis effort providing regular compilations of surface to bottom oceanbiogeochemical data, with an emphasis on seawater inorganic carbon chemistryand related variables determined through chemical analysis of water samples.This update of GLODAPv2, v2.2019, adds data from 116 cruises to the previousversion, extending its coverage in time from 2013 to 2017, while also addingsome data from prior years. GLODAPv2.2019 includes measurements from morethan 1.1 million water samples from the global oceans collected on 840cruises. The data for the 12 GLODAP core variables (salinity, oxygen,nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity,pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive qualitycontrol, especially systematic evaluation of bias. The data are available intwo formats: (i) as submitted by the data originator but updated to WOCEexchange format and (ii) as a merged data product with adjustments appliedto minimize bias. These adjustments were derived by comparing the data fromthe 116 new cruises with the data from the 724 quality-controlled cruises ofthe GLODAPv2 data product. They correct for errors related to measurement,calibration, and data handling practices, taking into account any known orlikely time trends or variations. The compiled and adjusted data product isbelieved to be consistent to better than 0.005 inmore »salinity, 1 % in oxygen,2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in totalalkalinity, 0.01–0.02 in pH, and 5 % in the halogenated transienttracers. The compilation also includes data for several other variables,such as isotopic tracers. These were not subjected to bias comparison oradjustments. The original data, their documentation and DOI codes are available in theOcean Carbon Data System of NOAA NCEI(https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2019/, last access: 17 September 2019). Thissite also provides access to the merged data product, which is provided as asingle global file and as four regional ones – the Arctic, Atlantic, Indian,and Pacific oceans – under https://doi.org/10.25921/xnme-wr20(Olsen et al., 2019). Theproduct files also include significant ancillary and approximated data.These were obtained by interpolation of, or calculation from, measured data.This paper documents the GLODAPv2.2019 methods and provides a broad overviewof the secondary quality control procedures and results.« less
4. Experimentation Framework for Wireless Communication Systems under Jamming Scenarios Dataset

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

   Jacovic, Marko ( January 2021 )

   Abstract

   <p>Data files were used in support of the research paper titled &#34;“Experimentation Framework for Wireless<br /> Communication Systems under Jamming Scenarios&#34; which has been submitted to the IET Cyber-Physical Systems: Theory &amp; Applications journal. </p> <p>Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar<br /> Contact: krd26&#64;drexel.edu</p> <p>---------------------------------------------------------------------------------------------</p> <p>Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. </p> <p>--------------------------------</p> <p>logs:    - data logs collected from devices under test<br />     - &#39;defenseInfrastucture&#39; contains console output from a WARP 802.11 reference design network. Filename structure follows &#39;*x*dB_*y*.txt&#39; in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples &#61; 1 ms). &#39;noJammer.txt&#39; does not include the jammer and is a base-line case. &#39;outMedian.txt&#39; contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script. <br />     - &#39;uavCommunication&#39; 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. <br />     - &#39;vehicularTechnologies&#39; contains MGEN logs at the car receiver for different scenarios. &#39;rxNj_5rep.drc&#39; does not consider jammers present, &#39;rx33J_5rep.drc&#39; introduces the periodic jammer, in &#39;rx33jSched_5rep.drc&#39; the device under test uses time scheduling around the periodic jammer, in &#39;rx33JSchedRandom_5rep.drc&#39; the same modified time schedule is used with a random jammer. </p> <p>--------------------------------</p> <p>parsers:    - scripts used to collect or process the log files used in the study<br />         - &#39;defenseInfrastructure&#39; contains the &#39;xputFiveNodes.py&#39; 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). <br />         - &#39;uavCommunication&#39; contains a &#39;readMe.txt&#39; 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. <br />         - &#39;vehicularTechnologies&#39; contains the &#39;mgenParser.py&#39; script and supporting &#39;bfb.json&#39; configuration file which also require TRPR to be installed and directories to be updated. </p> <p>--------------------------------</p> <p>rayTracingEmulation:    - &#39;wirelessInsiteImages&#39;: images of model used in Wireless Insite<br />             - &#39;channelSummary.pdf&#39;: summary of channel statistics from ray-tracing study<br />             - &#39;rawScenario&#39;: scenario files resulting from code base directly from ray-tracing output based on configuration defined by &#39;*WI.json&#39; file <br />             - &#39;processedScenario&#39;: pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by &#39;*DYSE.json&#39; file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired<br />             - 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)<br />             - &#39;nodeMapping.txt&#39;: mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required<br />             - &#39;uavCommunication&#39; directory additionally includes &#39;antennaPattern&#39; which contains the RALA pattern data for the omni-directional mode (&#39;omni.csv&#39;) and directional state (&#39;90.csv&#39;)</p> <p>--------------------------------</p> <p>results:    - contains performance results used in paper based on parsing of aforementioned log files<br />  </p>
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5. 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 )

   Abstract

   <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>&#34; 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 &#39;dragonradio/scripts/:&#39;</p> <ul><li>&#39;ModeSelect.py&#39;: class used to defined the antenna state selection algorithm</li><li>&#39;standalone-radio.py&#39;: SDR implementation for normal radio operation with reconfigurable antenna</li><li>&#39;standalone-radio-tuning.py&#39;: SDR implementation for hyperparameter tunning</li><li>&#39;standalone-radio-onmi.py&#39;: 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&#64;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>&#39;logs-8-18&#39; directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)</li><li>&#39;classTrain.py&#39; is the main parser for training the classifiers</li><li>&#39;trainedClassifiers&#39; contains the output classifiers generated by &#39;classTrain.py&#39;</li></ul> <p>post_processing_classifier: contains logs of online classifier outputs and processing script</p> <ul><li>&#39;class&#39; directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario</li><li>&#39;classProcess.py&#39; parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in &#39;results-&gt;classifier_performance&#39;</li></ul> <p>post_processing_mgen: contains MGEN receiver logs and parser</p> <ul><li>&#39;configs&#39; contains JSON files to be used with parser for each experiment</li><li>&#39;mgenLogs&#39; contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by &#39;mit&#39; for mitigation used, &#39;nj&#39; for no jammer, and &#39;noMit&#39; 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 &#39;results-&gt;mitigation_performance&#39;</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 &#39;readme.txt&#39; for understanding.</li><li>Please note: the processing files and data logs present in &#39;validation&#39; 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, &#34;Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna,&#34; 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>&#39;classifier_performance&#39; contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using &#39;post_processing_classifier.&#39;</li><li>&#39;mitigation_performance&#39; contains figures generated by &#39;post_processing_mgen.&#39;</li><li>&#39;validation&#39; contains RTE and OTA performance comparison obtained by &#39;ray_tracing_emulation-&gt;validation-&gt;matlab-&gt;outdoor_hover_plots.m&#39;</li></ul> <p>tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study</p> <ul><li>&#39;dataCollect&#39; 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>&#39;data_summary.txt&#39;this file contains the summaries from all the CSV files for convenience.</li></ul>
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