An MBSE Architectural Framework for Inter‐Satellite Communication in a Multiorbit Disaggregated System
Bibliographic record
Abstract
Abstract The Multi‐Orbit Disaggregated System (MODS) concept is a disaggregation strategy geared towards improved resiliency and flexibility of space missions by dispersing payloads and/or functionality across multiple small satellites (SmallSats). Although SmallSats make good candidates for deployment as a MOD System, their success hinges on the ability to realize reliable Inter‐Satellite Communication (ISC). To this end, we investigate the efficacy of developing a model‐based systems engineering (MBSE) Architectural Framework for ISC to guide and constrain instantiations of ISC architecture solutions. An MBSE architectural framework is a systems engineering artifact that defines a set of views required to describe an architecture based on MBSE principles and practices. It provides a standardized structure and guidance to capture architectural decisions while maximizing opportunities for commonality, consistency, and interoperability within the Domain of Interest. To achieve well‐defined architecture descriptions, a MODS architectural framework pattern, and a comprehensive architectural framework for the ISC (sub)system are created and presented. The Systems Modeling Language (SysML) (OMG, 2019) serves as the modeling language for the framework design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".