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Analysis of Degree of Controllability based on Gramian for Solar Sails in Artificial Lagrangian Orbits

2018· article· en· W3009996116 on OpenAlex
Bin Du, George Vukovich, Jian Guo

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

Venue2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsYork University
Fundersnot available
KeywordsControllabilityControllability GramianDegree (music)LagrangianSatelliteLagrangian pointPoint (geometry)MathematicsAerospace engineeringComputer scienceSolar sailControl theory (sociology)Applied mathematicsArtificial intelligenceControl (management)PhysicsEngineeringGeometrySpacecraftClassical mechanics

Abstract

fetched live from OpenAlex

The model of the solar sail in the Sun-Earth system is established, the concept of degree of controllability based on controllability Gramian is introduced as well. The degree of controllability of solar sail at five artificial Lagrangian points near the sun-earth L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> point, and the degree of controllability of solar sail at one chosen artificial Lagrangian point under different cases of control inputs are evaluated. The results reveal that the controllability of the solar sail is various due to the different locations and the distinct control inputs. The results could give us more quantitative information about the controllability of solar sail, rather than it is simply controllable or not, which may provide more effective suggestions for the design of the solar sail in the future space missions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.289
Teacher spread0.250 · 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