Self-Sustained Rigid Fully Metallic Metasurfaces to Enhance Gain of Shortened Horn Antennas
Bibliographic record
Abstract
This paper presents mechanically robust, low-cost, lightweight, and polarization-independent metallic metasurfaces (MMs) to enhance the gain and directivity of shortened horn antennas. The MM was designed based on the strategy of correcting the actual phase errors probed on the aperture of the horn using the near-field phase-transformation principle. The fundamental unit cell of the MM is made of a pair of cross slots created in a monolithic thin conductive sheet and is entirely free from high-cost dielectrics. The lack of dielectrics makes MM lightweight, cost-efficient and easy to fabricate the prototype for mass production. The MM has a 2D array of unit cells arranged to increase the gain of the shortened horn by improving aperture efficiency through local phase transformation in a wide frequency band. The concept is demonstrated by designing MMs for shortened horns with different heights and the same physical aperture at the center operating frequency of 12.5 GHz. The maximum gain-bandwidth with MM is achieved for the shortest horn, which is validated by measuring the physical prototype. The results indicate that horn gain with MM increases by 9.2 dB (from 11.1 to 20.3 dBi) and has a 3-dB fractional gain-bandwidth of 10.4%. The weighted density of the fabricated MM is only 0.87 g/cm2. Including MM, the total antenna height is around 61% shorter than a conventional air-filled horn having a similar peak gain.
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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) | 0.000 | 0.000 |
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".