High‐gain end‐fire bow‐tie antenna using artificial dielectric layers
Bibliographic record
Abstract
This study presents a high‐gain bow‐tie antenna for applications in next generation base‐stations operating from 2.5 to 3.9 GHz. The proposed structure consists of three circular sector patches that are attached laterally to the microstrip feed‐line, which is etched on both sides of the common dielectric substrate to create symmetrical bow‐tie antenna. By loading the antenna with a 2 × 5 array of enhanced end‐coupled split‐ring (EECSR) unit‐cells results in significant enhancement in the antenna gain performance. This is because the EECSR unit‐cells behave as parasitic radiators, which is analogous to Yagi–Uda antennas. The EECSR unit‐cells provide a medium of high effective permittivity that effectively reduces the spacing between parasitic directors. As a consequence, a compact and miniature structure is achieved compared with conventional quasi‐Yagi–Uda planar designs. The dimension of EECSR unit‐cell is 33 × 36 mm 2 with inter‐element spacing of 0.075 λ 0 at 3.5 GHz. A prototype of the antenna was fabricated and its performance was measured to validate the simulation results. The measured peak gain of the antenna with 4 × 5 array of EECSR is 12.65 dBi at 3.73 GHz, constituting a peak gain enhancement of 7.45 dBi in the Worldwide Interoperability for Microwave Access band compared with an equivalent conventional bow‐tie antenna.
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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 | 0.001 |
| 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".