Search NASA⌕ Search

DOE OSTI · 1735081

High-density fabric systems interconnected with multi-port aggregated cables

Abstract

A multi-port aggregated cable includes: a plurality of duplex optical fibers, each duplex optical fiber having a first end and a second end; a first optical interface attached to each of the duplex optical fibers at the first end thereof and defining multiple ports, one for each of the duplex optical fibers, the first optical interface aggregating the duplex optical fibers at the first end thereof; and a second optical interface attached to each of the duplex optical fibers at the second end thereof and defining multiple ports, one for each of the duplex optical fibers, the second optical interface aggregating the duplex optical fibers at the second end thereof.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Leigh, Kevin B., McDonald, Nicholas George. 2020-07-28. High-density fabric systems interconnected with multi-port aggregated cables. https://www.osti.gov/biblio/1735081

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

KEEP EXPLORING

Related reports

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↗