<inline-formula> <tex-math notation="LaTeX">$W$ </tex-math> </inline-formula>-Band Low-Profile Monopulse Slot Array Antenna Based on Gap Waveguide Corporate-Feed Network
Bibliographic record
Abstract
This paper presents a gap waveguide-based compact monopulse array antenna, which is formed with four unconnected layers, for millimeter-wave tracking applications at W-band (85-105 GHz). Recently developed gap waveguide technology removes the need for galvanic contact among metallic layers of waveguide structures, and thereby, makes the proposed antenna suitable for easy and low-cost manufacturing. In this context, a low-loss planar Magic-Tee is designed to be used in a monopulse comparator network consisting of two vertically stacked layers. The gap waveguide planar monopulse comparator network is integrated with a high-efficiency 16× 16 corporate-fed slot array antenna. The measured results of the comparator network show the amplitude and phase imbalance values to be less than 0.5 dB and 2°, respectively, over the frequency band of interest. The fabricated monopulse array antenna shows relative impedance bandwidths of 21% with input reflection coefficients better than -10 dB for the sum and difference ports. The null in the difference radiation pattern is measured to be 38 dB below the peak of the sum radiation pattern at 94 GHz. The measured gain is about 30 dBi for the same frequency. The low-loss performance of the comparator network and the feed network of the proposed array, together with the simple and easy manufacturing and mechanical assembly, makes it an excellent candidate for W-band compact direction-finding systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 0.051 |
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".