Bibliographic record
Abstract
Many governmental agencies and private companies of different countries are now rushing into space around Earth in hope to provide smart communication, industrial, security, and defense solutions. This often involves massive launches of cheap small satellites which are also contributing to the growth of space debris. The current paper discusses how the developed high-level system philosophy and model can effectively organize distributed space-based systems on different stages of their development and growth. The briefed Spatial Grasp Technology, based on parallel pattern-matching of distributed environments with high-level recursive mobile code, can effectively provide any networking protocols and important applications of large satellite constellations, especially those on Low Earth Orbits. The paper contains examples of technology-based solutions for establishing basic communications between satellites, starting from their initial, often chaotic, launches and distributing and collecting data in the growing constellations with even unstable and rapidly changing connections between satellites. It describes how to organize and register networking topologies in case of predictable distances between satellites, and how the fixed networking structures can help in solving complex problems. The latter including those related to the new Space Development Agency multiple-satellite defense-oriented architecture and allowing for effective integration of its continuous earth custody observation and cooperative missile tracking and elimination layers, based on self-spreading mobile intelligence. Earlier versions of the technology, described in many papers, six books including, were prototyped and used in different countries, with the current one quickly implementable too, even in university-based environments.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".