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Record W1979670993 · doi:10.1109/aero.2012.6187339

Applying Model Based Systems Engineering (MBSE) to a standard CubeSat

2012· article· en· W1979670993 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsSystems Modeling LanguageCubeSatComputer scienceUnified Modeling LanguageSystems engineeringSystems modelingModeling languageDomain (mathematical analysis)SpacecraftSatelliteSoftware engineeringAerospace engineeringEngineeringProgramming languageSoftware

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.261
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it