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1. Enhancing Understanding of the Hydrological Cycle via Pairing of Process‐Oriented and Isotope Ratio Tracers

   https://doi.org/10.1029/2021MS002648  

   Fiorella, Richard P. ; Siler, Nicholas ; Nusbaumer, Jesse ; Noone, David C. ( October 2021 , Journal of Advances in Modeling Earth Systems)

   The hydrologic cycle couples the Earth's energy and carbon budgets through evaporation, moisture transport, and precipitation. Despite a wealth of observations and models, fundamental limitations remain in our capacity to deduce even the most basic properties of the hydrological cycle, including the spatial pattern of the residence time (RT) of water in the atmosphere and the mean distance traveled from evaporation sources to precipitation sinks. Meanwhile, geochemical tracers such as stable water isotope ratios provide a tool to probe hydrological processes, yet their interpretation remains equivocal despite several decades of use. As a result, there is a need for new mechanistic tools that link variations in water isotope ratios to underlying hydrological processes. Here we present a new suite of “process‐oriented tags,” which we use to explicitly trace hydrological processes within the isotopically enabled Community Atmosphere Model, version 6 (iCAM6). Using these tags, we test the hypotheses that precipitation isotope ratios respond to parcel rainout, variations in atmospheric RT, and preserve information regarding meteorological conditions during evaporation. We present results for a historical simulation from 1980 to 2004, forced with winds from the ERA5 reanalysis. We find strong evidence that precipitation isotope ratios record information about atmospheric rainout and meteorological conditions during evaporation, but little evidence that precipitation isotope ratios vary with water vapor RT. These new tracer methods will enable more robust linkages between observations of isotope ratios in the modern hydrologic cycle or proxies of past terrestrial environments and the environmental processes underlying these observations.

    

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2. Atmospheric river contributions to ice sheet hydroclimate at the Last Glacial Maximum

   https://doi.org/10.5281/zenodo.7339985  

   Skinner, Christopher ; Lora, Juan ; Tabor, Clay ; Zhu, Jiang ( January 2022 , Zenodo)

   This dataset contains the atmospheric river catalogues and the associated precipitation and temperature data for the Preindustrial and Last Glacial Maximum CESM2 simulations presented in the GRL manuscript:  Atmospheric river contributions to ice sheet hydro climate at the Last Glacial Maximum. The atmospheric river catalogue files (zipped) are in netcdf format and organized by year. There are 100 years of data for both simulations.  The Preindustrial simulation catalogue begins in model year 41 and ends in model year 140.  The LGM simulation catalogue begins in model year 1 and ends in year 100. Each yearly file has a temporal resolution of 6 hours (1460 time steps each file) and a spatial resolution of 0.9° x 1.25° (the native resolution of the CESM simulation). A variable in the file called "ar_binary_tag" indicates whether an atmospheric river is present at each grid cell and each tilmestep: 1 indicates an atmospheric river is present; 0 indicates an atmospheric river is not present.  The precipitation and temperature files are 100-year annual or 100-year seasonal averages of atmospheric river precipitation/temperature. See the Methods section of the article for more details on the atmospheric river detection algorithm and precipitation/temperature calculations.

   Associated article abstract:

   Atmospheric rivers (ARs) are an important driver of surface mass balance over today’s Greenland and Antarctic ice sheets. Using paleoclimate simulations with the Community Earth System Model, we find ARs also had a key influence on the extensive ice sheets of the Last Glacial Maximum (LGM). ARs provide up to 53% of total precipitation along the margins of the eastern Laurentide ice sheet and up to 22-27% of precipitation along the margins of the Patagonian, western Cordilleran, and western Fennoscandian ice sheets. Despite overall cold conditions at the LGM, surface temperatures during AR events are often above freezing, resulting in more rain than snow along ice sheet margins and conditions that promote surface melt. The results suggest  ARs may have had an important role in ice sheet growth and melt during previous glacial periods and may have accelerated ice sheet retreat following the LGM.

    

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3. A biodiversity dataset graph: DataONE

   https://doi.org/10.5281/zenodo.1486279  

   Poelen, Jorrit H. ( January 2018 , Zenodo)

   The intended use of this archive is to facilitate meta-analysis of the Data Observation Network for Earth (DataONE, [1]). 

   DataONE is a distributed infrastructure that provides information about earth observation data. This dataset was derived from the DataONE network using Preston [2] between 17 October 2018 and 6 November 2018, resolving 335,213 urls at an average retrieval rate of about 5 seconds per url, or 720 files per hour, resulting in a data gzip compressed tar archive of 837.3 MB .  

   The archive associates 325,757 unique metadata urls [3] to 202,063 unique ecological metadata files [4]. Also, the DataONE search index was captured to establish provenance of how the dataset descriptors were found and acquired. During the creation of the snapshot (or crawl), 15,389 urls [5], or 4.7% of urls, did not successfully resolve. 

   To facilitate discovery, the record of the Preston snapshot crawl is included in the preston-ls-* files . There files are derived from the rdf/nquad file with hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f . This file can also be found in the data.tar.gz at data/8c/67/e0/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f/data . For more information about concepts and format, please see [2]. 

   To extract all EML files from the included Preston archive, first extract the hashes assocated with EML files using:

   cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep "hash://" | sort | uniq > eml-hashes.txt

   extract data.tar.gz using:

   ~/preston-archive$ tar xzf data.tar.gz 

   then use Preston to extract each hash using something like:

   ~/preston-archive$ preston get hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa
   <eml:eml xmlns:eml="eml://ecoinformatics.org/eml-2.1.1" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:stmml="http://www.xml-cml.org/schema/stmml_1.1" packageId="doi:10.18739/A24P9Q" system="https://arcticdata.io" scope="system" xsi:schemaLocation="eml://ecoinformatics.org/eml-2.1.1 ~/development/eml/eml.xsd">
     <dataset>
       <alternateIdentifier>urn:x-wmo:md:org.aoncadis.www::d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>
       <alternateIdentifier>d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>
       <title>Airglow Image Data 2011 4 of 5</title>
   ...

   Alternatively, without using Preston, you can extract the data using the naming convention:

   data/[x]/[y]/[z]/[hash]/data

   where x is the first 2 characters of the hash, y the second 2 characters, z the third 2 characters, and hash the full sha256 content hash of the EML file.

   For example, the hash hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa can be found in the file: data/00/00/2d/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa/data . For more information, see [2].

   The intended use of this archive is to facilitate meta-analysis of the DataONE dataset network. 

   [1] DataONE, https://www.dataone.org
   [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . DataONE was crawled via Preston with "preston update -u https://dataone.org".
   [3] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep -v "hash://" | sort | uniq | wc -l
   [4] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep "hash://" | sort | uniq | wc -l
   [5] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep  "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep -v "hash://" | sort | uniq | wc -l

   This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

    

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4. Hydrogen and oxygen stable isotope variations of daily precipitation from 2014-2018 in the central United States

   https://doi.org/10.1594/PANGAEA.895300  

   Tian, Chao ; Wang, Lixin ( January 2018 , PANGAEA)

   Stable isotopes of hydrogen and oxygen (δ2H, δ18O and δ17O) serve as powerful tracers in hydrological investigations. To our knowledge, daily precipitation isotope record especially 17O-excess is rare in the mid-latitudes. To fill such knowledge gap, daily precipitation samples (n = 446) were collected from June 2014 to May 2018 in Indianapolis, Indiana, U.S. A Triple Water Vapor Isotope Analyzer (T-WVIA) based on Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) technique was used to concurrently measure precipitation isotopic variations (δ2H, δ18O and δ17O). Meanwhile, 17O-excess and d-excess as second-order isotopic variables were calculated to provide additional information on precipitation formation and transport mechanisms. This study presents a four-year daily precipitation isotope dataset for mid-latitudes, and makes it available to researchers around the world who may use it as a reference for site comparisons and for global hydrological modeling validation. 

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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 , Zenodo)

   Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.

   ---------------------------------------------------------------------------------------------

   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 'dragonradio/scripts/:'

   • 'ModeSelect.py': class used to defined the antenna state selection algorithm
   • 'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna
   • 'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning
   • 'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only

   ---------------------------------------------------------------------------------------------

   Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
   Contact: krd26@drexel.edu

   ---------------------------------------------------------------------------------------------

   Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.

   ---------------------------------------------------------------------------------------------

   classifier_training: files used for training classifiers that are integrated into SDR platform

   • 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
   • 'classTrain.py' is the main parser for training the classifiers
   • 'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'

   post_processing_classifier: contains logs of online classifier outputs and processing script

   • 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
   • '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'

   post_processing_mgen: contains MGEN receiver logs and parser

   • 'configs' contains JSON files to be used with parser for each experiment
   • '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'

   ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.

   • Directory contains detailed 'readme.txt' for understanding.
   • 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. 
     • S. 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.

   results: contains results obtained from study

   • 'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'
   • 'mitigation_performance' contains figures generated by 'post_processing_mgen.'
   • 'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'

   tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study

   • '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.
   • 'data_summary.txt'this file contains the summaries from all the CSV files for convenience.

    

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