The dataset corresponds to the findings detailed in the paper titled "ProSAS: An O-RAN Approach to Spectrum Sharing between NR and LTE," to be included in the proceedings of the 2024 IEEE International Conference on Communications (ICC).

This repository contains the collected resources submitted to and created by the NIST Collaborative Research Cycle (CRC) Data and Metrics Archive. The NIST Collaborative Research Cycle (CRC) is an ongoing effort to benchmark, compare, and investigate deidentification technologies. The program asks the research community to deidentify a compact and interesting dataset called the NIST Diverse Communities Data Excerpts, demographic data from communities across the U.S. sourced from the American Community Survey.

This dataset consists of high signal-to-noise ratio (SNR) in-phase and quadrature (I/Q) radio frequency (RF) recordings of long-term evolution (LTE) uplink emissions from a commercial-off-the-shelf (COTS) handset in a fully conducted environment. A base station emulator was used to configure the handset to statically transmit over various LTE physical resource block configurations for 1.4 MHz, 3 MHz, 5 MHz, and 10 MHz uplink channels centered at 1770 MHz in LTE Band 66, which is a frequency-division duplex (FDD) band.

This dataset contains information from investigating the effects of micro-computed tomographic imaging irradiation on vinyl nitrile foam and code for a finite element user material to model these effects. More details are available in the README.txt and data_summary.txt files in the main directory.Data in the repository includes exposure and dosage calibration files (.csv, .dat, .mat, .in, .png. .jpg), imaging data from optical microscopy (.tif, .zvi) and scanning electron microscopy with energy-dispersive spectroscopy (SEM EDS) (.tif, .png, .docx).

The National Institute of Standards and Technology (NIST) maintains the United States' primary standards for traceable RF and mm-wave power measurements. In the 2.4 mm connector type, the specialized sensors that NIST currently uses as primary standards are not commercially available, which raises concerns about the long-term sustainability of NIST's measurement capabilities. These concerns motivated us to explore the option of using commercially available thermoelectric power sensors.

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles.

Delta-sigma algorithms are used to determine the desired sequence of quantum-based voltage pulses used by the Josephson Arbitrary Waveform Synthesizer (JAWS) to create calculable voltage waveforms. This data set contains a comparison (Fig 1. of the CPEM 2024 abstract) of a 1 kHz pulse pattern with rms amplitude of 2 mV without post-processing and with post-processing which modifies the problematic pulse pattern to remove incorrect pulses at the 0.75 ms pattern wrap point.

Dataset for multiple publishable figures in the paper entitled "Stability Study of ac Voltage Source using Josephson Voltage Standards"in CPEM 2024 abstract

Rydberg atom-based receivers of modulated radio frequency (RF) fields are promising systems for measurements. These systems are self-calibrating, widely tunable, nearly transparent to RF fields, and can be electrically small. However, the instantaneous bandwidth of current Rydberg atom receivers is typically less than 1 MHz.

Dataset for multiple publishable figures in the paper entitled "Digitizer Linearity Measurement with a Josephson Arbitrary Waveform Synthesizer"in CPEM 2024 abstract