Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1664429

Oak Ridge Spoofing and Interference Test Battery (OAKBAT) - GPS

Albright, Austin·Powers, Sarah·Bonior, Jason·Combs, Frank
View original source ↗Read source document ↗Export metadata ↓

Abstract

This dataset contains the RF data recordings for the GPS L1C/A only OAKBAT scenarios. The files are binary data in the format described in the paper: Albright, A., Powers, S. Bonior, J., and Combs, F., 'A Tool for Furthering GNSS Security Research: The Oak Ridge Spoofing and Interference Test Battery (OAKBAT),' in Proceedings of ION GNSS+ 2020, 2020. The metadata, equipment documentation and configuration files, collection procedures and more materials that allow the independent reproduction of the OAKBAT data are https://github.com/oakbat/. Additionally, this site includes a complete listing of all the RF data files, not just this series, available as part of OAKBAT.

Explore related subjects

42 ENGINEERING97 MATHEMATICS AND COMPUTINGGalileoglobal navigation satellite systemsGlobal Positioning SystemGNSSGPSRFspoofing

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Albright, Austin, Powers, Sarah, Bonior, Jason, Combs, Frank. 2020-09-30. Oak Ridge Spoofing and Interference Test Battery (OAKBAT) - GPS. https://doi.org/10.13139/ornlnccs%2F1664429

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗