This Python notebook enables users to calculate air-broadened absorption cross sections for HITRAN database molecules at user-defined pressures and temperatures. Users can also specify altitude simulation points, where the pressure and temperature are calculated based on the US Standard Atmosphere, 1976. The spectroscopic reference data used to generate the tables was provided by HITRAN 2020, while the HITRAN Application Programming Interface was used to interface with the HITRAN database and simulate the absorption cross sections.

The chemical gel point of thermoset composites is crucial to the design of manufacturing parameters in processes and industries that utilize these materials, like additive manufacturing and semiconductor packaging. However, many industrially relevant thermoset composite resins react too quickly and are too fragile to capture this behavior using traditional rheological techniques. Here, we pair Optimally Windowed Chirp (OWCh) measurements with rheology-conversion relationships to apply time-cure superposition.

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.

Energy dispersive X-ray spectroscopy (EDX) tomographic tilt series of a needle-shaped sample extracted from NIST SRM 2135c.

Sample description: This SRM consists of alternating layers of nickel and chromium on a silicon substrate (see SRM certificate for further detail).

Data description. The scan size for each spectrum image (SI) is 128 pixels x 100 pixels and 4000 spectral channels deep. EDX data were collected using the following parameters:

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.

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.