Efficient Analytical Design Strategy for High-Gain Nonresonant Partially-Reflective-Surface Antennas
Bibliographic record
Abstract
Nonresonant partially-reflective-surface (PRS) antennas (PRSAs) offer high-gain performance, but their design typically requires extensive electromagnetic simulations to obtain near-field phase distributions, leading to time-consuming processes, especially for complex or large antennas. This letter presents a novel analytical design method that significantly simplifies the design of phase-correcting surfaces (PCSs) for nonresonant PRSAs. By introducing a new design strategy based on a ray-tracing approach, we derive a set of analytical formulas for two PCS configurations: one utilizing a single superstrate that integrates both PRS and PCS functionalities, and another employing distinct PRS and PCS layers. Using these formulas, we designed two non resonant PRSAs, and both simulated and experimental results demonstrate comparable gain performance to those obtained using Ansys HFSS. This approach reduces the dependence on computationally intensive simulations, offering a more efficient pathway for the design of high-performance nonresonant PRSAs, thereby advancing the accessibility and practicality of antenna design methodologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".