Circularly Polarized Stepped Reflectarray: A new design approach for bandwidth enhancement
Bibliographic record
Abstract
A planar reflectarray (planar RA) with a small focal-to-diameter ratio (F/D) suffers from limited bandwidth. The primary factor hindering bandwidth is increasing the planar RA’s spatial path delay from the center to the edge, which introduces substantial phase variations that cannot be adequately compensated for by the RA elements away from the design frequency. A faceted RA has been proposed, but the structure becomes more complicated, particularly for a small F/D. Here, a new, simple RA design approach is proposed to enhance the bandwidth. A planar RA is cut to annular rings of subreflectarrays (sub-RAs), with the center sub-RA being circular. The sub-RAs are displaced to lower levels below the outer sub-RA, which is kept at the same position from the feed to reserve feed-edge illumination. The proposed structure is referred to as astepped RA. Cross-bow-tie elements are used to design circular polarization (CP) planar and stepped RAs with an aperture diameter of${25}{.}{25}\,{\lambda}$. Element rotation is employed for phase compensation. The stepped RA reduces the relative path delay as the ray moves toward the edge. A parametric study is performed, and a simple, compact stepped RA is designed. The performance of the stepped RA is compared to the planar RA. The two RA configurations are fabricated and measured. The stepped RA exhibits a matching bandwidth of 33.4%, a 1-dB gain bandwidth of 23.2% (13% broader than the planar RA), a 1-dB axial ratio (AR) bandwidth of 33.4%, and an aperture efficiency of 51% (at 30 GHz). Based on the results, the stepped RA’s 1-dB gain bandwidth is improved by 13% over the conventional planar RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".