Design of reconfigurable frequency‐selective surfaces including the PIN diode threshold region
Bibliographic record
Abstract
Reconfigurable frequency‐selective surfaces (RFSSs) are of significant interest in applications, such as secure communication systems or tunable radomes, to improve indoor communication and smart antennas. In order to change the frequency‐selective surface (FSS) characteristics, and therefore its frequency response, conventional methods include loading with active semiconductor devices such as varactors, PIN diodes, Schottky diodes and radio‐frequency microelectromechanical system. Another possibility is the use of mechanical adjusts, such as spring resonators or mechanical rotation. When PIN diodes are used, commonly only the reverse and forward regions, OFF and ON states, respectively, are considered. In this study, the implementation of an RFSS is described, using PIN diodes as active components. The RFSS is based on the four‐arms star geometry, and initial design equations and procedures are presented. Numerical and measured results are shown for different project stages, with a very good agreement. In addition to obtaining two distinct resonant frequencies, due to the OFF and ON states, a third situation is included, considering the PIN diode threshold region when the FSS becomes practically transparent, which is an interesting feature with potential applications.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".