Replication Data for: Self-Strengthening Tape Junctions Inspired by Recluse Spider Webs
Abstract
The raw data for the associated manuscript is organized here into three categories: 1) relating to the measurement and analysis of the native recluse spiders loop junctions, 2) raw- Publisher:
- Harvard Dataverse
- Publication Year:
- NSF-PAR ID:
- 10383119
- Sponsoring Org:
- National Science Foundation
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Abstract
Raw data of optical microscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and diameter measurements of the exfoliated and self-assembled nanofibrils for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Data analysis results were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. ReadMe.md This description in Markdown format. Figure 2 - Microscopy Raw Data Figure 2 - panel a.jpg (7.2 MB) Optical micrograph (JPEG format) Figure 2 - panel b.jpg (6.1 MB) Optical micrograph (JPEG format) Figure 2 - panel c f.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel d.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel e - Exfoliated Fibrils.gwy (32.0 MB) AFM raw data (Gwyddion format) Figure 3 - -
Abstract
This dataset contains raw data, processed data, and the codes used for data processing in our manuscript from our Fourier-transform infrared (FTIR) spectroscopy, Nuclear magnetic resonance (NMR), Raman spectroscopy, and X-ray diffraction (XRD) experiments. The data and codes for the fits of our unpolarized Raman spectra to polypeptide spectra is also included. The following explains the folder structure of the data provided in this dataset, which is also explained in the file ReadMe.txt. Browsing the data in Tree view is recommended. Folder contents Codes Raman Data Processing: The MATLAB script file RamanDecomposition.m contains the code to decompose the sub-peaks across different polarized Raman spectra (XX, XZ, ZX, ZZ, and YY), considering a set of pre-determined restrictions. The helper functions used in RamanDecomposition.m are included in the Helpers folder. RamanDecomposition.pdf is a PDF printout of the MATLAB code and output. P Value Simulation: 31_helix.ipynb and a_helix.ipynb: These two Jupyter Notebook files contain the intrinsic P value simulation for the 31-helix and alpha-helix structures. The simulation results were used to prepare Supplementary Table 4. See more details in the comments contained. Vector.py, Atom.py, Amino.py, and Helpers.py: These python files contains the class definitions used in 31_helix.ipynb and a_helix.ipynb. See more details -
Abstract
<p>Data files were used in support of the research paper titled "“Experimentation Framework for Wireless<br /> Communication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. </p> <p>Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar<br /> Contact: krd26@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 /> - '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. <br /> - '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 -
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>" 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 'dragonradio/scripts/:'</p> <ul><li>'ModeSelect.py': class used to defined the antenna state selection algorithm</li><li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna</li><li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning</li><li>'standalone-radio-onmi.py': 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@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>'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)</li><li>'classTrain.py' is the main parser for training the classifiers</li><li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'</li></ul> <p>post_processing_classifier: contains logs of online classifier outputs and processing script</p> <ul><li>'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario</li><li>'classProcess.py' parses
