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1. AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs

   https://doi.org/10.5281/zenodo.6525446  

   D., Adam Cobb; Roy, Anirban; Elenius, Daniel; Michael, F. Heim; Swenson, Brian; Whittington, Sydney; D., James Walker; Bapty, Theodore; Hite, Joseph; Ramani, Karthik; et al (January 2023, Zenodo)

   Dataset accompanying code and paper: AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs We present AircraftVerse, a publicly available aerial vehicle design dataset. AircraftVerse contains 27,714 diverse battery powered aircraft designs that have been evaluated using state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. This repository contains: A zip file "AircraftVerse.zip", where each design_X contains: design_tree.json: The design tree describes the design topology, choice of propulsion and energy subsystems. The tree also contains continuous parameters such as wing span, wing chord and arm length.design_seq.json: A preorder traversal of the design tree and store this as design_seq.json.design_low_level.json: The most low level representation of the design. This low level representation includes significant repetition that is avoided in the tree representation through the use of symmetry.Geom.stp: CAD design for the Aircraft in composition STP format (ISO 10303 standard).cadfile.stl: CAD design for the Aircraft in stereolithographic STL file,output.json: Summary containing the UAV's performance metrics such as maximum flight distance, maximum hover time, fight distance at maximum speed, maximum current draw, and mass.trims.npy: Contains the [Distance, Flight Time, Pitch, Control Input, Thrust, Lift, Drag, Current, Power] at each evaluated trim state (velocity).pointCloud.npy: Numpy array containing the corresponding point clouds for each design. corpus_dic: The corpus of components (e.g. batteries, propellers) that make up all aircraft designs. It is structured as a dictionary of dictionaries, with the high level components: ['Servo', 'GPS', 'ESC', 'Wing', 'Sensor', 'Propeller', 'Receiver', 'Motor', 'Battery', 'Autopilot'], containing a list of dictionaries corresponding to the component type. E.g. corpus_dic['Battery']['TurnigyGraphene2200mAh3S75C'] contains the detail of this particular battery. Corresponding code for this work is included at https://github.com/SRI-CSL/AircraftVerse.  Acknowledgements: This material is based upon work supported by the United States Air Force and DARPA under Contract No. FA8750-20-C-0002.  Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and DARPA.   

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2. Main Cropping System Experiment Field Logs and treatment descriptions at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2020)

   https://doi.org/10.6073/pasta/e5642426f9200404b6ba3af45ad9c423  

   Robertson, G.; Simmons, Joseph (January 2020, Environmental Data Initiative)

   Dataset Abstract This dataset includes information about the LTER main site treatments, agronomic practices carried out on the treatments and approved site use requests. Most long-term hypotheses associated with the KBS LTER site are being tested within the context of the main cropping systems study. This study was established on a 48 ha area on which a series of 7 different cropping systems were established in spring 1988, each replicated in one of 6 ha blocks. An eighth never-tilled successional treatment, is located 200 m off-site, replicated as four 0.06 ha plots. Cropping systems include the following treatments: T1. Conventional: standard chemical input corn/soybean/wheat rotation conventionally tilled (corn/soybean prior to 1992) T2. No-till: standard chemical input corn/soybean/wheat rotation no-tilled (corn/soybean prior to 1992) T3. Reduced input: low chemical input corn/soybean/wheat rotation conventionally tilled (ridge till prior to 1994) T4. Biologically based: zero chemical input corn/soybean wheat rotation conventionally tilled (ridge till prior to 1994) T5. Poplar: Populus clones on short-rotation (6-7 year) harvest cycle T6. Alfalfa: continuous alfalfa, replanted every 6-7 years (converted to switchgrass in 2018) T7. Early successional community: historically tilled soil T8. Mown grassland community: never-tilled soil. For specific crops in a given year see the Annual Crops Summary Table. In 1993 a series of forest sites were added to the main cropping system study to provide long-term reference points and to allow hypotheses related to substrate diversity to be tested. These include: TCF. Coniferous forest: three conifer plantations, 40-60 years old TDF. Decidious forest: three deciduous forest stands, two old-growth and one 40-60 years post-cutting TSF. Mid-successional forest: three old-field (mid-successional) sites 40+ years post-abandonment. All share a soil series with the main cropping system treatments, and are within 5 km of all other sites. For each system (and for a number of microplot treatments nested within the main treatment plots) the following baseline variates are being measured (described in greater detail in other data set descriptors): plant characteristics, including species distributions and abundances, net aboveground productivity by functional group (crop vs. non dominant biomass, selected non dominant biomass), economic yields, tissue C and N contents, seed bank composition; soil chemical and physical characteristics, including soil moisture, pH, inorganic N and P pools, total C, N, and P pools, bulk density; soil biological characteristics, including microbial biomass C and N, N mineralization rates (buried bags), microbial populations, invertebrate populations; and insect and pathogen dynamics, including distributions and abundances of major insect pests and predators and of Fusarium pathogens. original data source http://lter.kbs.msu.edu/datasets/7 

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3. The NANOGrav Search for Signals from New Physics: MCMC chains

   https://doi.org/10.5281/zenodo.8083620  

   NANOGrav, The Collaboration (January 2023, Zenodo)

   {"Abstract":["MCMC chains for the GWB analyses performed in the paper "The NANOGrav 15 yr Data Set: Search for Signals from New Physics<\/em>". <\/p>\n\nThe data is provided in pickle format. Each file contains a NumPy array with the MCMC chain (with burn-in already removed), and a dictionary with the model parameters' names as keys and their priors as values. You can load them as<\/p>\n\nwith open ('path/to/file.pkl', 'rb') as pick:\n temp = pickle.load(pick)\n\n params = temp[0]\n chain = temp[1]<\/code>\n\nThe naming convention for the files is the following:<\/p>\n\nigw<\/strong>: inflationary Gravitational Waves (GWs)<\/li>sigw: scalar-induced GWs\n\tsigw_box<\/strong>: assumes a box-like feature in the primordial power spectrum.<\/li>sigw_delta<\/strong>: assumes a delta-like feature in the primordial power spectrum.<\/li>sigw_gauss<\/strong>: assumes a Gaussian peak feature in the primordial power spectrum.<\/li><\/ul>\n\t<\/li>pt: cosmological phase transitions\n\tpt_bubble<\/strong>: assumes that the dominant contribution to the GW productions comes from bubble collisions.<\/li>pt_sound<\/strong>: assumes that the dominant contribution to the GW productions comes from sound waves.<\/li><\/ul>\n\t<\/li>stable: stable cosmic strings\n\tstable-c<\/strong>: stable strings emitting GWs only in the form of GW bursts from cusps on closed loops.<\/li>stable-k<\/strong>: stable strings emitting GWs only in the form of GW bursts from kinks on closed loops.<\/li>stable<\/strong>-m<\/strong>: stable strings emitting monochromatic GW at the fundamental frequency.<\/li>stable-n<\/strong>: stable strings described by numerical simulations including GWs from cusps and kinks.<\/li><\/ul>\n\t<\/li>meta: metastable cosmic strings\n\tmeta<\/strong>-l<\/strong>: metastable strings with GW emission from loops only.<\/li>meta-ls<\/strong> metastable strings with GW emission from loops and segments.<\/li><\/ul>\n\t<\/li>super<\/strong>: cosmic superstrings.<\/li>dw: domain walls\n\tdw-sm<\/strong>: domain walls decaying into Standard Model particles.<\/li>dw-dr<\/strong>: domain walls decaying into dark radiation.<\/li><\/ul>\n\t<\/li><\/ul>\n\nFor each model, we provide four files. One for the run where the new-physics signal is assumed to be the only GWB source. One for the run where the new-physics signal is superimposed to the signal from Supermassive Black Hole Binaries (SMBHB), for these files "_bhb" will be appended to the model name. Then, for both these scenarios, in the "compare" folder we provide the files for the hypermodel runs that were used to derive the Bayes' factors.<\/p>\n\nIn addition to chains for the stochastic models, we also provide data for the two deterministic models considered in the paper (ULDM and DM substructures). For the ULDM model, the naming convention of the files is the following (all the ULDM signals are superimposed to the SMBHB signal, see the discussion in the paper for more details)<\/p>\n\nuldm_e<\/strong>: ULDM Earth signal.<\/li>uldm_p: ULDM pulsar signal\n\tuldm_p_cor<\/strong>: correlated limit<\/li>uldm_p_unc<\/strong>: uncorrelated limit<\/li><\/ul>\n\t<\/li>uldm_c: ULDM combined Earth + pulsar signal direct coupling \n\tuldm_c_cor<\/strong>: correlated limit<\/li>uldm_c_unc<\/strong>: uncorrelated limit<\/li><\/ul>\n\t<\/li>uldm_vecB: vector ULDM coupled to the baryon number\n\tuldm_vecB_cor:<\/strong> correlated limit<\/li>uldm_vecB_unc<\/strong>: uncorrelated limit <\/li><\/ul>\n\t<\/li>uldm_vecBL: vector ULDM coupled to B-L\n\tuldm_vecBL_cor:<\/strong> correlated limit<\/li>uldm_vecBL_unc<\/strong>: uncorrelated limit<\/li><\/ul>\n\t<\/li>uldm_c_grav: ULDM combined Earth + pulsar signal for gravitational-only coupling\n\tuldm_c_grav_cor: correlated limit\n\t\tuldm_c_cor_grav_low<\/strong>: low mass region  <\/li>uldm_c_cor_grav_mon<\/strong>: monopole region<\/li>uldm_c_cor_grav_low<\/strong>: high mass region<\/li><\/ul>\n\t\t<\/li>uldm_c_unc<\/strong>: uncorrelated limit\n\t\tuldm_c_unc_grav_low<\/strong>: low mass region  <\/li>uldm_c_unc_grav_mon<\/strong>: monopole region<\/li>uldm_c_unc_grav_low<\/strong>: high mass region<\/li><\/ul>\n\t\t<\/li><\/ul>\n\t<\/li><\/ul>\n\nFor the substructure (static) model, we provide the chain for the marginalized distribution (as for the ULDM signal, the substructure signal is always superimposed to the SMBHB signal)<\/p>"]} 

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4. 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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5. Effects of Long-Term Cover Cropping on Weed Seedbanks

   https://doi.org/10.3389/fagro.2020.591091  

   Nichols, Virginia; English, Lydia; Carlson, Sarah; Gailans, Stefan; Liebman, Matt (November 2020, Frontiers in Agronomy)

   null (Ed.)

   Cool-season cover crops have been shown to reduce soil erosion and nutrient discharge from maize ( Zea mays L.) and soybean [ Glycine max (L.) Merr.] production systems. However, their effects on long-term weed dynamics are not well-understood. We utilized five long-term research trials in Iowa to quantify germinable weed seedbank densities and compositions after 10+ years of cover cropping treatments. All five trials consisted of zero-tillage maize-soybean rotations managed with and without the inclusion of a yearly winter rye ( Secale cereal L.) cover crop. Seedbank sampling was conducted in the early spring before crop planting at all locations, with three of the five trials having grown a soybean crop the preceding year, and two a maize crop. Two of the trials (both previously soybean) showed significant and biologically relevant decreases (4,070 and 927 seeds m −2 , respectively) in seedbank densities in cover crop treatments compared to controls. In another two trials, one previously maize and one previously soybean, no difference was detected in seedbank densities. In the fifth trial (previously maize), there was a significant, but biologically unimportant increase of 349 seeds m −2 . All five trials' weed communities were dominated by common waterhemp [ Amaranthus tuberculatus (Moq.)], and changes in seedbank composition from cover-cropping were driven by changes in this species. Although previous studies have shown that increases in cover crop biomass are strongly correlated with weed suppression, in our study we did not find a relationship between seedbank changes and the mean amount of cover crop biomass produced over a 10-years period (experiment means ranging from 0.5 to 2.0 Mg ha −1 yr −1 ), the stability of the cover crop biomass production, nor the amount produced going into the previous crop's growing season. We conclude that long-term use of a winter rye cover crop in a maize-soybean system has the potential to meaningfully reduce the size of weed seedbanks compared to winter fallows. However, identifying the mechanisms by which this occurs requires further research into processes such as seed predation and seed decay in cover cropped systems. 

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