Space and Position Management of Wideband Conformal Vivaldi Antenna Array With Sidelobe Reduction
Bibliographic record
Abstract
This work systematically studies conformal Vivaldi antenna (VA) arrays in a spherical volume. A VA with gain varying from 5 to 9 dBi and$S_{11} \leq -10$dB from 4.3 to 9.8 GHz is considered. A$1 \times 7$VAs are placed on a correctional area of the cylinder to scan from$\theta = 0^{\circ}$to 45° at$\phi = 90^{\circ}$. The results show that the sidelobe level (SLL) has a higher amplitude, almost equivalent to the main lobe. To reduce the SLL, the phase center to phase center distance of antenna elements is reduced by bringing the radius correctional area of the cylinder leading to the development of a modified VA (MVA) without disturbing the$S_{11}$parameters of the VA. To verify, the MVA as a radiating element a$1 \times 7$circular MVA array is developed with the same scanning capability, the results show a high reduction of SLL. Finally, a 49-element MVA array arranged in a spherical volume is developed to obtain 3-D-beam scanning. The antenna elements are designed to be conformal to the periphery of a sphere to achieve a 360° beam scanning in the azimuthal plane and ±45° in the elevation angle. The 3-D array has a realized gain of 18.57 dBi at$\theta = 0^{\circ}$and an approximately 10 dB difference in the magnitude of the major lobe and side lobe levels at all the scan angles. The validation is carried out using ANSYS HFSS full wave solver and achieved a good agreement with the theoretical considerations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".