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Effective processing, interpretation, and management of sensor data have emerged as a critical component of cyber-physical systems. Traditionally, processing sensor data requires profound theoretical knowledge and proficiency in signal-processing tools. However, recent works show that Large Language Models (LLMs) have promising capabilities in processing sensory data, suggesting their potential as copilots for developing sensing systems. To explore this potential, we construct a comprehensive benchmark, SensorBench, to establish a quantifiable objective. The benchmark incorporates diverse real-world sensor datasets for various tasks. The results show that while LLMs exhibit considerable proficiency in simpler tasks, they face inherent challenges in processing compositional tasks with parameter selections compared to engineering experts. Additionally, we investigate four prompting strategies for sensor processing and show that self-verification can outperform all other baselines in 48% of tasks. Our study provides a comprehensive benchmark and prompting analysis for future developments, paving the way toward an LLM-based sensor processing copilot. 

Ab initio molecular dynamics calculations were used to explore the underlying factors that modulate the velocity of hydrogen migration for 1,2 hydrogen shifts in carbocations in which different groups interact noncovalently with the migrating hydrogen. Our results indicate that stronger electrostatic interactions between the migrating hydrogen and nearby π-systems lead to slower hydrogen migration, an effect tied to entropic contributions from the hydrogen + neighboring group substructures.