Applying Model Based Systems Engineering (MBSE) to a standard CubeSat
Bibliographic record
Abstract
Model Based Systems Engineering (MBSE) is an emerging technology that is providing the next advance in modeling and systems engineering. MBSE uses Systems Modeling Language (SysML) as its modeling language. SysML is a domain-specific modeling language for systems engineering used to specify, analyze, design, optimize, and verify systems. An MBSE Challenge project was established to model a hypothetical FireSat satellite system to evaluate the suitability of SysML for describing space systems. Although much was learned regarding modeling of this system, the fictional nature of the FireSat system precluded anyone from actually building the satellite. Thus, the practical use of the model could not be demonstrated or verified. This paper reports on using MBSE and SysML to model a standard CubeSat and applying that model to an actual CubeSat mission, the Radio Aurora Explorer (RAX) mission, developed by the Michigan Exploration Lab (MXL) and SRI International.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".