Antenna Array Based on Fabry–Perot Cavity with Mechanoelectrical Beam Steering
Bibliographic record
Abstract
Introduction. Introduction. Low-profile effective antenna systems (AS) with maintained directional characteristics in a wide sector of scanning angles are required for satellite communication at mobile objects. This article investigates the directional characteristics of a subarray based on a Fabry–Perot cavity and an antenna array with mechanoelectrical beam steering. Aim. To investigate a Fabry–Perot based antenna array with mechanoelectrical beam steering and to estimate its gain and directivity at different scanning angles. Materials and methods. Computer simulations were carried out using the finite element method (FEM), finite difference time domain (FDTD) method and template based post-processing. Results. A subarray based on a Fabry–Perot cavity for an antenna array with mechanoelectrical beam steering was simulated. The efficiency of the subarray comprised at least 65 % in the 11.9…12.5 GHz frequency band. An antenna array based on a Fabry–Perot cavity with mechanoelectrical beam steering was developed and investigated. The calculated characteristics of the developed antenna array agreed well with those obtained experimentally. The gain degradation did not exceed 2.5 dB in the 0…70° scanning angle range. The advantages of using antenna elements based on a Fabry–Perot cavity and developing on their basis mobile satellite antenna systems with wide-angle scanning are noted. Conclusion. The use of a radiator based on a Fabry–Perot cavity and the development on it basis an antenna array with mechanoelectrical beam steering provides an antenna efficiency of no less than 0.5 with a gain degradation of no more than 2.5 dB in the scanning angle range 0…70° from 11.9 to 12.5 GHz.
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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".