A Broadband and High-Aperture-Efficiency Multilayer Transmitarray Based on Aperture-Coupled Slot Unit Cells
Bibliographic record
Abstract
A broadband high-gain multilayer transmitarray operating in the X-band is proposed in this work. Unlike most transmitarrays achieving phase compensation by changing their element sizes, the length of striplines of the proposed transmitarray is adjusted to control a wider range of phase responses of identical array elements. By reducing the mutual coupling between adjacent units using surrounded metallic vias, the performance of the proposed transmitarray cell units can be improved significantly. In addition, aperture-coupled bowtie slots with stacked patches are adopted for wideband matching and high gain. By exploiting the true stripline phase compensation and aperture coupled technique with mutual coupling improvement, the proposed transmitarray with a flat gain response is implemented. The designed transmitarray accomplishes an enhanced matching bandwidth covering 8.0–12.0 GHz while adopting the decoupling technique between adjacent unit cells. A square transmitarray of$10\times10$cells forming an aperture area of$25{\lambda }_{o}^{\mathbf {2}}$at 10 GHz is fabricated and measured. The measured realized gain is 22.05 dBi, achieving 50% aperture efficiency at 10 GHz. The 1- and 3-dB gain bandwidths are 17.6% (9.35–11.15 GHz) and 19.5% (9.25–11.25 GHz), respectively. The flat gain characteristic with high aperture efficiency candidates the proposed design for radar and satellite 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 | 0.001 |
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".