Time-sensitive networking (TSN) is an emerging topic for the advancement of wireless networking for industrial applications. TSN, as defined under the umbrella of IEEE 802.1 working group standards, addresses issues related to providing deterministic communications over IEEE 802-based Local Area Networks (LANs). TSN was originally designed to support real-time audio/video applications over Ethernet providing better reliability and lower, more deterministic latency with traffic shaping capabilities.

A set of 6 experiments was performed on poly(methyl methacrylate), PMMA. In each test, samples (i.e., 2.44 m tall, 0.61 m wide slabs of PMMA mounted in a parallel panel configuration) were exposed to a propane burner (nominal heat release rate, HRR = 60 kW), which was turned off after sustained flaming was observed across the panel walls.

Broadband S-parameter measurements of Parylene C microwave microfluidic devices from 100 MHz - 110 GHz. The Parylene C devices consisted of 400 nm platinum coplanar waveguides (CPW) (50 um center conductor, 5 um gaps, 200 um ground planes) deposited on fused silica with 6.5 um of Parylene C on top, capped with a PDMS microfluidic layer aligned over the CPWs. The dimensions of the PDMS microfluidic channels were approximately 210 um wide by 100 um deep. The total CPW line length was 10.000 mm and the channel length was 4.160 mm, where the channel was aligned over the center of the CPW.

The testing dataset used at TRECVID for the DSDI task in 2020-2022.The dataset includes public videos, ground truth and features of the DSDI task. As the task is continuing, the dataset will be continually updated.There are 32 features across 5 main categories (Environment, Vehicles, Water, Infrastructure, Damage). All videos are airborne low altitude from natural disaster events.

The data files provide the literature experimental data used to plot the figures, develop and/or verify the models in the following article:A. Bazyleva, W. E. Acree Jr., V. Diky, G. T. Hefter, J. Jacquemin, M. C. F. Magalhães, J. W. Magee, D. K. Nordstrom, J. P. O'Connell, J. D. Olson, I. Polishuk, K. A. G. Schmidt, J. M. Shaw, J. P. M. Trusler, R. D. Weir. Reference Materials for Phase Equilibrium Studies. 2. Solid-Liquid Equilibria (IUPAC Technical Report). Pure Appl. Chem., 2022, accepted for publication.

This dataset includes three dimensional schematics (in the form of Revit files, .RVT) for architectural updates and plumbing system designs for seven reference buildings, which were drawn from NIST's suite of homes (collection of dwellings developed to represent 80 % of the U.S. housing stock at the time of their development) for residential reference buildings and the Department of Energy's Prototype Building Models for commercial reference buildings.

This dataset provides RF data from software defined ratio (SDR) measurement results of a few cases:  A. one 4G LTE link, B. two LTE links, C. one LTE link and one Wi-Fi link. The LTE links were emulatedby USRP B210 units and an open-source software (srsRAN), and the Wi-Fi link was emulated by a pair ofWi-Fi commercial development boards.  This dataset includes metadata and performance results (ina spreadsheet format) and  I/Q baseband sample data (in a binary float point format). Though specifictrade names are mentioned, they should not be construed as an endorsement of that product.

This repository contains scripts and supporting information for a set of demonstrations related to the 2022 AM-Bench challenge series.In particular, the data used in this demonstration comes from the 2018 AM-Bench challenge documented at https://www.nist.gov/ambench/amb2018-02-description.The script queries the AM-Bench 2018 repository located at https://ambench.nist.gov/, then processes the returned XML-based results, does some image processing, and generates plots of melt pool dep

This tool computes the expected link capacity in data rate (Mbit/s) for Sidelink considering 5G New Radio (NR) and Long Term Evolution (LTE) communication standards, as defined by the The 3rd Generation Partnership Project (3GPP). Users can input the respective configuration parameters and evaluate the resulting capacity.The tool provides side-by-side chart plotting between NR and LTE Sidelink capacities for easy comparison.

`analphipy` is a python package to calculate metrics for classical models for pair potentials. It provides a simple and extendable api for pair potentials creation. Several routines to calculate metrics are included in the package. The main features of `analphipy` are 1) Pre-defined spherically symmetric potentials. 2) Simple interface to extended to user defined pair potentials. 3) Routines to calculate Noro-Frenkel effective parameters. 4) Routines to calculate Jensen-Shannon divergence.