Calibration of Atmospheric Density Model Using Orbital Data of Multiple Satellites
Bibliographic record
Abstract
In orbit dynamics, most perturbations are well modeled, while the inaccuracy of the atmospheric density model turns into the biggest error source in orbit prediction and determination. The commonly used empirical atmospheric density models, such as, Jacchia, MSIS, DTM and Russian GOST, still have a relative error about 10% − 30%. Because of the uncertainty of the density distribution of atmosphere, estimating the atmospheric density by a deterministic model cannot achieve high accuracy. The better way to improve the model precision is calibrating the model with updated measurements. Two-line element set is accessible orbital data of satellite, which can be used in the model calibration. In this paper, an algorithm for calibrating atmospheric density model is developed. First, the density distribution of atmosphere is represented by a power series expansion whose coefficients are denoted by spherical harmonic expansions. Then, the expansion coefficients and the ballistic coefficients of the satellites are identified simultanteneously by solving a nonlinear least squares problem. The simulation results show that the relative error of the atmospheric density is less than 3%, and the relative error of the ballistic coefficient is less than 0.3% after calibration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".