Dielectric Loaded Frequency Tunable Fabry–Perot Resonator Antenna With PRS for X-Band Applications
Bibliographic record
Abstract
In this article, a reconfigurable Fabry–Perot resonator antenna with a microstrip feed is proposed for X-band applications. The proposed radiator comprises a slotted circular patch with a single layer of a partially reflective surface (PRS). This PRS is positioned on top of the radiator at a distance of 9 mm. This arrangement exhibits a wide operating bandwidth from 8 to 12.4 GHz with electrical dimensions of2.34λg×2.34λg×0.23λg, where λgthe guided wavelength is calculated at the center frequency of 10.2 GHz. The proposed antenna has an average gain of 7.01 dBi and covers an impedance bandwidth of 40% relative to the center frequency of 10.2 GHz. Moreover, it has the distinctive feature of frequency tuning from a wideband to a narrowband by filling different dielectric materials in the slots etched on the substrate. Distilled water, vinegar, salt, and dry wood powder are used in the slots that tune the band from 7.9 to 8.4, 8.5 to 10.5, 10 to 10.5, and 10.15 to 10.7 GHz, respectively. Thus, the frequency tunability of the proposed radiator makes it highly adaptable for various X-band applications. With precise frequency tuning capability, the antenna can mitigate interference in point-to-point telecom systems, enhancing the target detection in weather radar for small aircraft, and improving the sensitivity and range in radar motion detectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".