This dataset contains Brillouin light scattering (BLS) and low-frequency Raman (LFR) spectroscopy measurements as collected using a custom-built microscope and spectrometer platform. The platform is used to investigate the mechanical properties of semiconductor packaging materials. This dual-mode microscope is capable of both in situ temporal and hyperspectral imaging acquisition modes. The in situ temporal mode is used to monitor the cure of an epoxy sample as well as post-cure temperature cycling to probe the mechanical properties and glass transition temperature of polymers.

This data is associated with the manuscript titled Radio-Frequency Fingerprinting Millimeter-Wave Commercial Off-the-Shelf Cellular User Equipment. The abstract of this manuscript is: We present a measurement test bed and algorithm for non-destructive, millimeter wave over-the-air fingerprinting of commercial off-the-shelf cellular user equipment (UE) at the model level. This test bed has been configured to repeatably collect radiated fields from UEs in a 5G mmWave scenario.

Data to accompany the paper "Compressed Liquid (p-rho-T) Measurements of trans-1,2-Dichloroethene [R-1130(E)]" published in International Journal of Thermophysics. Included are experimental density data for the refrigerant R-1130(E) presented in Table 2 of the above manuscript. Also included are the combined expanded (95% confidence level) uncertainties. Measurements were made using a vibrating-tube densimeter. Details regarding the experimental methods and uncertainty analysis can be found in the manuscript.

The Internet of Things (IoT) is comprised of networks of physical, computational, and human components that coordinate to fulfill time-sensitive functions in a shared operating environment. Development and testing of IoT systems often utilizes modeling and simulation, whether to analyze potential performance gains of new technologies or develop robust digital twins to support future operations and maintenance.

This dataset consists of single source and mixture samples which were genotyped/sequenced with kits targeting Forensic DNA markers. More information specific to the kit and or method used can be found in the README text files included in each zipped file.

The CE-STR kits reported for the single source samples include:
Applied Biosystems GlobalFiler,
Applied Biosystems Y-Filer Plus,
Promega PowerPlex Fusion 6C,
Promega PowerPlex Y23

The CE profiles for single source samples are also included in a spreadsheet.

The 2.4 GHz ISM band is shared by Wi-Fi, Bluetooth, Wireless HART, ISA100.11a, and several other industrial wireless systems. Our dataset contains comprehensive electromagnetic interference (EMI) measurements from machinery taken in various industrial environments. The measurements were taken at two frequencies: 900 MHz, 2.4 GHz.

The increasing amounts of discarded textiles represent a potentially valuable resource that could be reclaimed. This work underscores the significance of advancing chemical recycling techniques for multi-component fabrics under mild reaction conditions. We present a method for nondestructive recovery of cotton (C), elastane, and nylon from polyester (polyethylene terephthalate (P)) textile blends through alkaline hydrolysis, supported with a phase-transfer catalyst.

The data are associated with figures in A. P. Kotula, R. Tao, J. Tu, Y. Lee, G. A. Holmes, Advanced Characterization of the Cure Kinetics of a Liquid Encapsulant, 2025 IEEE 75th Electronic Components and Technology Conference (ECTC), Dallas, Texas, May 27 - 30, 2025.

This dataset accompanies the publication below. Here we provide the results of the analysis, including gridded estimates of methane emissions for the Washington/Baltimore area at 0.01 degrees resolution and monthly from 2017 through 2021. All files available for unrestricted public use with citation of this data set *except* the Observations_XX files. Observations are for non-commercial use only, as they are purchased from a private data provider (AEM, Inc. via Earth Networks, Inc.).

AgentDojo-Inspect is a codebase created by the U.S. AI Safety Institute to facilitate research into agent hijacking and defenses against said hijacking. Agent hijacking is a type of indirect prompt injection [1] in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.

AgentDojo-Inspect is a fork of the original AgentDojo repository [2], which was created by researchers at ETH Zurich [3]. This fork extends the upstream AgentDojo in four key ways: