Data associated with the publication: "Two-dimensional imaging of electromagnetic fields via light sheet fluorescence imaging with Rydberg atoms"Abstract:The ability to image electromagnetic fields holds key scientific and industrial applications, including electromagnetic compatibility, diagnostics of high-frequency devices, and experimental scientific work involving field interactions. Generally electric and magnetic field measurements require conductive elements which significantly perturb the field.

These data will appear in [1]. The abstract for that paper is given below:This paper presents an in situ S-parameter calibration technique using on-chip superconducting transmission lines at 4 K. The method uses coplanar waveguide (CPW) models based on various impedance states of niobium (Nb), which has zero dc resistance below 9 K and a monotonically increasing resistance from 10 K to room temperature.

This data includes a four alchemical processes with data files generated with the python package: generate_alchemical_lammps (DOI from MIDAS) and the resulting output to be used for the calculation of free energies using Multi-state Bennett Acceptance Ratio (MBAR), BAR, or Thermodynamic Integration (TI). These input files are only applicable for LAMMPS versions after April 2024. The four cases can be separated into two systems, benzene solvated in water, and a Lennard-Jones (LJ) dimer in solvent.

This library make the process of fitting highly accurate thermodynamic mixture models much easier, facilitating their use by a large swath of the community. A modular Python-based library is developed, with methods for a number of the common operations (loading of data files, adding additional helper data, building a cost function, plotting results, etc.). The user is required to define the cost function, and an example showing how that can be achieved is presented.

This document is part of a series of reports describing experimental property measurements completed at the National Institute for Petroleum and Energy Research (NIPER) in Bartlesville, Oklahoma, in the 1980s and 1990s. Members of the Bartlesville Thermodynamics Group included William D. "Bill" Good, William V. "Bill" Steele, Bruce E. Gammon, Norris K. Smith, Stephen E. Knipmeyer, An "Andy" Nguyen, Timothy D. Klots, I. A. "Alex" Hossenlopp, Aaron P. Rau, William B. Collier, John F. Messerly, Ann G. Osborn, Susan Lee-Bechtold, Donald G. Archer, Ian R.

This dataset contains results represented in the work titled 'A Robust, Over-the-Air Test Bed for Radio-Frequency Fingerprinting of Cellular Devices', whose abstract sample is below. We present a characterized test bed and algorithms for non-destructive, over-the-air fingerprinting of commercial cellular user equipment (UE). This test bed is designed to repeatably collect radiated fields from cellular devices in a 4G long term evolution (LTE) network configuration.

The ad hoc retrieval task investigates the performance of systems that search a static set of documents using new questions (called topics in TREC). This task is similar to how a researcher might use a library - the collection is known but the questions likely to be asked are not known. NIST provides the participants approximately 2 gigabytes worth of documents and a set of 50 natural language topic statements. The participants produce a set of queries from the topic statements and run those queries against the documents.

ETSpy is a HyperSpy extension package package for the processing, aligment, and reconstruction of electron tomography data from TEM/STEM instruments. Tools are provided for basic tilt series data processing, stack alignment, and reconstruction using the ASTRA Toolbox.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of RL agents operating in the Safety Gymnasium environment. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of instruction fine tuned LLMs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for mitigating that trigger behavior in the trained AI models.