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Record W2321911563 · doi:10.2514/6.2015-1589

Design and Evaluation of a Semi-Empirical Piece-wise Exponential Atmospheric Density Model for CubeSat Applications

2015· article· en· W2321911563 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

VenueAIAA Modeling and Simulation Technologies Conference · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsCubeSatAtmospheric modelExponential functionComputer scienceMeteorologyAerospace engineeringMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the theory and design of a semi-empirical atmospheric density model based on data from the MSIS-86 model. Created as part of ongoing research into optimal guidance laws for nanosatellite applications, this model focuses on being computationally lightweight, while providing reasonably accurate atmospheric density predictions at geometric altitudes ranging from 0 - 1000 km. The model is validated against data from existing analytical and empirical atmospheric models. It is then implemented in a variety of orbit and attitude propagation environments in Matlab-Simulink to assess its stability, validity, and computational footprint. The orbital elements from each simulation were compared against those obtained from baseline simulations run using the Naval Research Lab (NRL) MSISE-00 model. The results show good agreement with the baseline simulations, while indicating a signicant reduction in computational run time.

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 score0.561
Threshold uncertainty score0.552

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 head0.101
GPT teacher head0.326
Teacher spread0.225 · 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