Deployable mechanisms for small to medium sized space debris removal
Bibliographic record
Abstract
This research aims to propose a new paradigm in orbital debris removal systems. While most groups addressing this \nissue focus on the high priority of removing the largest debris, we consider the threat posed by those smaller than 10cm as \nserious. A 2cm paint chip is known to be able to render useless most spacecraft as well as being way more difficult to follow \nand thus to avoid. We propose a mission to sweep the main Low Earth Orbits with a 100 to 500 metre diameter cupola \nthat would collect small debris. The cupola consists of a deployable mechanism supporting a membrane. The deployment \nmechanism has to reach a minimal expansion ratio of about 30, in order to create a very wide collecting surface. The \nmembrane covering its surface would be rigid enough to capture most small debris and to at least slow down the mediumsized \nones, accelerating their fall. An overview of our almost-rigid-link mechanism proposed to support the collecting surface \nis presented. The proposed mechanisms is based on previous work on deployable mechanisms developed for a variety of \napplications. However, conventional deployable rigid-link mechanisms typically produce expansions that are much smaller \nthat the ratio required in the present application. Therefore, cable or belts systems are included in order to further increase the \nexpansion ratio while maintaining a low mass. While in operation, the cupola will obviously be subjected to many ultra-highvelocity \nimpacts and its orientation will likely undergo deviations from the targeted optimum. Therefore, another important \nfeature of the proposed debris removal concept is the development of means of reorienting and stabilizing the cupola using \ninternal electric deployment actuators instead of fuel thrusters. The concept of reorientation using internal actuators is \npresented and simple examples are provided. Finally, the performance of the concept is confronted to data analysis from \nMASTER09 and with the recent release of ORDEM3. This data serves as the basis for the design specifications. Space \ndebris removal is a high priority for the future of space missions and space exploration. The approach proposed here is \nbelieved to be one of the pieces of the puzzle.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".