Stable phase‐centre horn antenna using 3D printed dielectric rod for aperture efficiency improvement of space‐fed antennas
Bibliographic record
Abstract
Abstract The design strategy to stabilise the phase centre (PC) of the conventional horn antenna using a shaped dielectric rod is presented for aperture efficiency improvement of space‐fed antennas. In most practical horn antennas, PC location has significant variations with frequency leading to phase error loss and aperture efficiency reduction over the bandwidth of interest. To overcome this drawback, based on physical reasoning, shaped dielectric rods with appropriate excitation mode are designed and placed on the aperture of conventional rectangular and conical horn antennas. The simulation results show that using a shaped dielectric rod inside the horn antenna leads to a decrease in phase‐centre variations versus frequency without disturbing the radiation pattern and reflection coefficient. To investigate the performance, a conical corrugated horn antenna loaded by a conical tapered dielectric rod is used as a feed of the transmitarray antenna. Using a shaped dielectric rod fabricated by 3D printing technology, the PC variations of the proposed horn decrease from 1.06 λ to 0.08 λ over the X‐band ( λ at 8 GHz). The measurement of the fabricated transmitarray antenna shows an improvement of maximum aperture efficiency from 55% to 64.7% at 10 GHz. The aperture efficiency bandwidth above 50% level is also increased from 1 to 2.2 GHz.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".