Characterization of dense focal plane array feeds for parabolic reflectors in achieving closely overlapping or widely separated multiple beams
Bibliographic record
Abstract
In the advent of modern mobile satellite communications requiring rapid and adaptive multiple beams, this work studies the ability of reflector antennas fed by dense focal plane arrays (FPA) in achieving arbitrarily shaped and sized footprints to meet the demands. In this paper, the efficiencies of single off‐axis as well as multiple beams of FPA‐fed paraboloids are investigated. The offset FPA considered here comprises hard rectangular waveguides. The focal plane field, which the FPA samples, is synthesized by integration of the physical optics induced electric currents over the reflector surface caused by the off‐axis incident plane wave arriving at that incidence angle of interest. Full mutual coupling analysis has been performed in the FPA sampling, thereby taking into account mutual coupling losses in the arrays. The fields over the tilted elliptical aperture of off‐axis beams needed for calculation of the aperture efficiency are obtained by projecting the usual focal plane fields to this tilted aperture using geometrical optics. Results show that the total efficiency of the offset FPA‐fed reflector decreases with increasing beam angle and increases with larger number of FPA elements. It is also found that the maximum directive gain of the reflector radiation patterns falls noticeably with beam angle when the FPA population is low, but the directivity can be maintained well when an adequate number of FPA elements are used. Multiple beams that are either closely overlapping or widely separated are also successfully investigated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".