A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation. The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties. Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential. The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data.

cmomy is a python package to calculate central moments and co-moments in a numerical stable and direct way. Behind the scenes, cmomy makes use of Numba to rapidly calculate moments. cmomy provides utilities to calculate central moments from individual samples, precomputed central moments, and precomputed raw moments. It also provides routines to perform bootstrap resampling based on raw data, or precomputed moments. cmomy has numpy array and xarray DataArray interfaces.

Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool to measure structure in complex, chemically heterogeneous systems. P-RSoXS combines principles of X-ray scattering and X-ray spectroscopy; this combination provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials.

Parameters, input scripts, and molecular dynamics simulation results corresponding to the simulations performed for the paper "Entropy Scaling for Viscosity for Molecular Models of Molten Salts" by J.M. Young, I.H. Bell, and A.H. Harvey, submitted to J. Chem. Phys. in September 2022.

The National Institute of Standards and Technology is currently conducting a series of large compartment fire tests to investigate the behavior and fire-induced failure mechanisms of the full-scale composite floor assemblies with the two-story steel gravity frame, two bays by three bays in plan. This report presents the experimental design and results from the second fire experiment (Test #2) conducted at the National Fire Research Laboratory.

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.

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

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.