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Record W1598883659 · doi:10.5539/mas.v9n6p128

Research on Satellite Orbit Simulation Based on Recursive Algorithm

2015· article· en· W1598883659 on OpenAlexvenueno aff
Zihong Li, Yuanbiao Zhang, Ma Xueming

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsOrbit (dynamics)Computer scienceAlgorithmMoment (physics)SatelliteSet (abstract data type)Point (geometry)Orbit determinationSoft landingMathematicsAerospace engineeringPhysicsGeometryEngineering

Abstract

fetched live from OpenAlex

In this paper, recursive algorithm is presented for the simulation of the soft landing orbit of Chang'e-3, for making sure the location of the landing orbit and the perilune point. Recursive algorithm is a method of derivation from the state in a moment to the next moment through their relationship. Frist, the coordinate model of soft landing and the relevant parameters are set up. Second, the soft landing orbit of Chang'e-3 derivate by recursive algorithm. Third, the soft landing orbit and the perilune point are carried out after analyzing the result from the simulation. The simulation of the soft landing orbit shows that recursive algorithm is innovative in the simulation of orbit in high probability.

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.

How this classification was reachedexpand

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.079
GPT teacher head0.342
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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