Conceptio › Archive › DigitalCommons@USU
DigitalCommons@USU metadata only

Toward Trainable AI Agents for Satellite Operations

Calderon, Haley et al.
DigitalCommons@USU · Papers
Open Source ↗
aerospace-engineeringengineering
Low Earth orbit is increasingly crowded, and a growing share of it is flown by universities and small operators. These teams must detect and respond to on-orbit anomalies with far less staff, expertise, and tooling than large operators, and a single missed anomaly can end a mission. We present COMET
Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗

Related documents

Record · ID 814137
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive