Feed-reflector design for large adaptive reflector antenna (LAR)
Bibliographic record
Abstract
A novel feed-reflector system for large Cassegrain antennas of radio astronomy and deep-space communication applications is investigated. This feed-reflector is used to illuminate a hyperboloid subreflector with a 5-10 m diameter located 500 m above the ground. Because the subreflector is located in the near field of the feed-reflector antenna, a theory based on the near field focusing properties of paraboloid reflectors is established. The focusing at near distance is formed by moving the feed horn away from the focal point of the feed-reflector. In this theory, the properties of axial defocused paraboloid reflectors at near distance are investigated in more detail. By using equivalence path law, the subreflector shape is obtained. It is found that the hyperbola can approximate the subreflector well. A detailed ray tracing is performed on the entire system which reveals that the feed system uses some part of the subreflector three times. The gain, side lobe level, cross polarization, and aperture distribution are calculated for different feed horn locations and taper at the edge of the feed-reflector and also for different sizes and eccentricities of the subreflector. Peak efficiency in excess of 74.8% and side lobe level around -20 dB are obtained for an unshaped system. The performance of the system over the operating band (1-22 GHz) is also studied and shown that the lower-frequency limit is dependent on subreflector and feed-reflector sizes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".