Synthesising of reactionless flexible mechanisms for space applications
Bibliographic record
Abstract
Often, one achieves the dynamic balancing condition by resorting to counter-devices approach, however, by doing this, one adds extra weight and therefore the inertia are increased inside the whole system, which is not cost-effective when the system is sent into space and later used in space. In this study, it is suggested one is able to achieve the reactionless condition through combining the self-balanced system. For example, the dynamic balancing condition can be realised via the reconfiguration concept. Extra counter-mass is not employed but through reconfiguring the whole structure, in this way, the system will not get to be heavy and therefore, reduce the energy costs and make the system more applicable and flexible for space applications. Based on this concept, first and foremost, one needs to balance a single component through the reconfiguration approach (i.e., decomposition process) and after that integrate the above balanced components to build the entire system (i.e., integration process). Finally, with the mechanical reconfiguration, the control laws governing the operation of the mechanism also need to be changed, so as to make whole systems more flexible when they are used in space.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".