The Optimization of Satellite Landing Site based on Particle Swarm Optimization
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.
Bibliographic record
Abstract
Hazard avoidance is one of the key stages in the satellite's soft landing process, during which the selection of the landing site would have a huge influence on the satellite safe landing. In this paper, a new design method is presented for determining the landing site selection using Particle Swarm Optimization based on the Moon’s grounds image taken by moon satellite. Applying this method, the problem of hazard avoidance can be converted into the optimization of the value of “obstacle function” after processing the image employing the median filter algorithm. In order to discuss and to compare the efficiency of the different optimization methods, three algorithms including Particle Swarm Optimization, Genetic Algorithm and Global Search Algorithm are tested to solve this problem. The results show that Particle Swarm Optimization has the more satisfactory optimization results and the quickest optimization speed in the landing site selection. The detailed comparisons are introduced in the body paragraph.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it