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This dataset provides a GraphML‐based representation of an interdependent infrastructure system, covering power, water, transportation, and community infrastructures. Each node is annotated with key attributes such as resource type, criticality, societal vulnerability score (SVS), and capacity—while directed edges capture the flow or dependency relationships among these resources. The dataset facilitates resilience and recovery analysis by enabling repair order simulation, based on certain metrics‐based prioritization.

Contains data supporting the IEEE TC submission titled "General Principles for Implementing Branch Prediction in Fully Adiabatic, Reversible, and SuperScalar (FARS) Processors". Paper abstract: Adiabatic and reversible logic families take advantage of Landauer's principle to provide a more efficient means of computation using conventional MOSFETs. However, there are performance drawbacks inherent in the way such devices physically operate. Such drawbacks can be overcome by scaling the number of devices to meet performance needs.

The dataset used in this study is sourced from benchmark datasets~\cite{marcelli2022machine} for binary similarity detection and was decompiled using \textit{IDA Pro 7.5}. We selected the following datasets for evaluation: \textit{Coreutils-ARM-32}, \textit{Curl-MIPS-32}, \textit{ImageMagick-ARM-32}, \textit{OpenSSL-X86-32}, \textit{Putty-X86-32}, and \textit{SQLite-X86-32}. These datasets are commonly used, but their application scenarios are relatively limited.

Architected materials, which are designed and engineered with specific microstructures, hold great promise for healthcare applications. In particular, tensile samples based on architected materials exhibit unique properties that can address key challenges in medical devices and implants. By precisely controlling the architecture at the micro- and nano-scale, these materials can be optimized for high strength-to-weight ratio, tunable stiffness, and enhanced biocompatibility.