Analysis and Optimum Design of Sequential-Rotation Array for Gain Bandwidth Enhancement
Bibliographic record
Abstract
The gain bandwidth constraint of sequential-rotation arrays (SRAs) has been an open issue since it was first pointed out in 1989. This paper reveals the reason, provides some useful conclusions and proposes an optimum design. Firstly, a first-order analytical formula is derived for the array factor (AF) of the 4-element sub-SRA. The formula embraces the polarization properties of the antenna elements. The AF is found to be dependent on the elements' polarization properties and frequency, which is not found in conventional regular arrays. Based on the theoretical results and full-wave simulations, these dependencies are analyzed in detail. Secondly, an optimum design is proposed to enhance the gain bandwidth: to constitute SRA with elliptically polarized elements (this type of SRA is denoted as EP-SRA) rather than the conventional circularly polarized elements (this type of SRA is denoted as CP-SRA). Detailed design guidance is presented for this new method. Finally, as an example, a CP-SRA and an EP-SRA are designed and tested for comparison. According to the example, by employing EP-SRA, the 1 dB AF bandwidth is improved by 80%, the measured 1 dB/3 dB gain bandwidth is enhanced by 114%/76%. In fact, the conventional CP-SRA and LP-SRA (SRA with linearly polarized elements) are two special cases of the proposed EP-SRA.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".