Bibliographic record
Abstract
The threats of space debris are enormously high, which are increasing due to launch of multi-satellite constellations, especially in low-Earth orbit, with millions of pieces of junk there. Different passive and active debris removal methods are being developed like self-deorbiting of used satellites, drag sails, mechanical grasps, tethers and nets, also directed energy, lasers including. Space junk is the responsibility of the whole mankind, and the problem of managing space debris is both the international challenge and the opportunity to preserve the space environment for future space exploration missions. The paper shows how self-organized constellation networks of deorbiting satellites can organize multiple cleaning operations autonomously under the developed Spatial Grasp Technology (SGT), with cooperative involvement of the whole network and minimum interaction with costly ground antennas and stations. It also offers a unique solution where most dangerous junk items can themselves be treated as active virtual-physical items freely moving through terrestrial and celestial environments and ultimately finding, by their own initiative, the proper cleaning satellites. This can effectively organize the global junk management and removal problem, where the active junk items can keep initiative of self-removal for any time needed and using any distributed resources. A combined solution is also offered with initial global search for approximate satellite-debris matching, after which the junk is delegated its own initiative to find the absolute match by traveling around the globe as far and as long as required. The paper shows and explains different practical cleaning scenarios in the high-level Spatial Grasp Language (as key element of SGT) and possibilities of quick implementation of the approach.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".