Bibliographic record
Abstract
Synthetic aperture radar (SAR) membranes have very stringent flatness requirements. SAR antennas are designed to be perfectly flat with electrical elements at a stable position one with respect to another. Although membranes open the possibility of greater packaging possibilities and a much lower mass, these flatness and dimensional stability requirements must still be respected. Several phenomena will affect the flatness of the membrane: the quality of the tensioning system, the modes of vibration of the membrane, and the material behaviour. Past research has shown that using a pocket adopting the shape of parabola is an excellent way of uniformly tensioning a membrane. The uniform tension along one side of a membrane is needed to avoid the creation of ripples in areas where the tension would differ. This paper shows the need to take into account the slope the cable takes as it exits the tensioning pocket. If this slope differs from the slope imposed by the geometric constraints, the position of the membrane with respect to the pulley, there will be a decrease in tension in the membrane near the extremities of the pocket, and this will lead to ripples in that area of the membrane. The equation system allowing the design of a tensioning system using parabolas and taking into account the geometry of the membrane antenna and its supporting structure is described. An analysis of the first mode of the membrane shows that the deflection brought be this mode could be much larger than any creases created by a faulty tensioning system. Solutions to this problem using our current tensioning system have not been found yet, but other others have started exploring options which could be applicable to our tensioning system.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".