Gaussian-beam analysis of a large adaptive reflector antenna (LAR) using a feed-reflector
Bibliographic record
Abstract
A novel approach for analysing the quasi-optical large adaptive reflector Cassegrain system is described. In this system a feed-reflector is used to illuminate a hyperboloid sub-reflector with 5-10 m diameter, located 500 m above the ground. In the proposed method of analysis the feed-reflector aperture field distribution is expanded into a set of Gaussian–Laguerre modes. These modes propagate from the feed-reflector aperture in a simple and well defined way. The feed-reflector near-field radiation pattern is calculated at the subreflector location. The subreflector parameters in this system are found by maximising the LAR aperture efficiency, which includes phase and taper efficiencies, and minimising the LAR spillover loss. This process is computationally more efficient than the physical-optics current-distribution method and more accurate than the ray-tracing approach. It also provides a new insight into the operation of the feed-reflector system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".