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Record W2329221686 · doi:10.2514/6.2011-7273

An Iterative Subsystem-Generated Approach to Populating a Satellite Constellation Tradespace

2011· article· en· W2329221686 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 institutionsCOM DEV International
Fundersnot available
KeywordsConstellationSatellite constellationSatelliteComputer scienceAerospace engineeringEngineeringPhysicsAstronomy

Abstract

fetched live from OpenAlex

‡Tradespace exploration provides a structured, concept neutral means of considering a large number of design alternatives while avoiding a premature focus on point designs. However, the process can be difficult to implement in the design of complex space systems when considering practical partitioning and allocation of design tasks. Complicating the effort further is the existence of many subsystem interactions that must be considered in order to assemble a system level tradespace. This paper presents a process used to populate and analyze the tradespace for an example constellation of Earth observation satellites, by analyzing individual subsystem level trades in parallel and capturing system level interactions at key nodal checkpoints. The process is shown to allow for practical development of tradespace knowledge, leveraging subsystem-level design expertise, as well as identifying novel design alternatives at the intersection of subsystem proposed alternatives.

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: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.612

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 head0.119
GPT teacher head0.272
Teacher spread0.153 · 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