This dataset includes the code needed to reproduce all the results from "Superancillary equations for PC-SAFT" by Ian Bell (NIST) and Ulrich Deiters (Cologne).

It includes C++ code for the development of the formulation, and the scripts and other files needed to build the python package and build all the plots from the publication

See the README.md file for more detailed information of the contents

Data for "Primary measurement of gaseous elemental mercury concentration with a dynamic range of six decades" article for submission

Data for the figures in the manuscript entitled "Time Programmable Frequency Comb: Generation and Application to Quantum-Limited Dual-Comb Ranging"

"High dynamic range electro-optic dual-comb interrogation of optomechanical sensors" to be published in Optics Letters.

Supplemental material to the article "Expansivity of fused quartz glass measured within 6 x 10^-10 /K" by P F Egan in International Journal of Thermophysics 2024. The dataset and proceeding were first presented at the NCSLI Workshop & Symposium 2022 in Grapevine TX.

Three datasets are included, corresponding to three separate runs (cycles of expansivity measurement).

Run 1: cycled specimen FP_333 and FP_152 side-by-side

Run 2: FP_152 was potted with slip-fit tubes at its ends, and the run cycled specimen FP_333 and FP_152Potted side-by-side

Dataset and scripts supporting the article "D-to-A_eff converter", a proceeding presented at the NCSLI Workshop & Symposium 2022, Grapevine TX.The dataset consists of two main parts: dimensional measurements and conversion scripts.

This set corresponds to a (pending) publication, where we attempt to model spectral features appearing in the lab by calculating many segments of an inhomogeneous field. Every data set here is a transmission value, either normalized to 1 in modeled data, or an arbitrary-scaled voltage reading from a photodiode onto an oscilloscope. These are given in scans over coupling photon detuning, delta_C, which is divided by 2 pi, and given in MHz. Arrays are scans over delta_C, and position/fieldstrength, for figure 3.

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

This archive contains the spatial frequency domain Mueller matrix data associated with the paper J. Chue-Sang, M. Litorja, A. M. Goldfain, and T. A. Germer, "Spatial Frequency domain Mueller matrix imaging," J. Biomedical Optics 27(12), 126003 (2022). The paper shows a subset of the data included in this archive. A Python script, analyze.py, is provided to assist the user in reading the data. The script can be run without any arguments from the top folder and will generate all the figures included in this archive.

Data is for "Effects of Non-Sinusoidal Current Phase Relationships on Single Flux Quantum Circuits" and publication in IEEE Transactions on Applied Superconductivity. Data is from WRspice simulations, as well as MALT margin analysis. Further data analysis was done in python. One set of data is the pulse propagation delay of a chain of JTL's with different current phase relationships. The other set of data is the operating margins of T flip-flop circuits that have been optimized for different current-phase relationships.