On the design of high-gain resonant cavity antennas using different highly-reflective frequency selective surfaces as the superstrates
Bibliographic record
Abstract
Resonant cavity antennas (RCAs) are usually used in applications that require high-gain antennas. High-gain is achieved by using highly-reflective frequency selective surfaces (FSSs) as the superstrate and adjusting the height of the RCA. In this paper, different types of FSSs are employed as RCA superstrates. The formulation provided in implies that the RCA directivity only depends on the reflection coefficient magnitude of the FSS superstrate. In this paper, it is shown that the reflection phase plays an important role, too. The importance of the reflection phase lies in the fact that the resonant height of the RCA is directly related to it. It is illustrated that for two RCAs having different FSS superstrates with identical reflection magnitude, the one whose resonant height is larger exhibits higher gain. Some other points, useful for RCA designs, are also indicated and briefly discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".