A High-Order Mode-Based Multifunctional Antenna Using Substrate-Integrated Waveguide Technology for Millimeter-Wave Applications
Bibliographic record
Abstract
To address the growing demand for multifunctional antennas, this paper presents a novel antenna design that integrates high-order mode theory with substrate integrated waveguide technology. The proposed antenna leverages liquid metal to dynamically control and reconfigure excitation conditions, enabling multiple functionalities such as multi-frequency difference-pattern radiation, dual-frequency high-gain directional radiation, quad-band multi-beam radiation, and beam-switching. The proposed multifunctional antenna can support a total of five distinct operating states, significantly enhancing multifunctionality across frequency, radiation, and polarization domains. Additionally, an equivalent resonant mode analysis method is introduced to examine the internal electric field distribution within a square ring resonant cavity, alongside the development of a radiation slot structure that enhances mode utilization and overall antenna performance. The proposed single-layer, low-profile antenna offers key advantages such as light weight, cost-effectiveness, and high robustness. By achieving multifunctionality with reduced design complexity, this antenna provides an innovative solution for widespread deployment in wireless systems, optimizing spatial resource utilization while minimizing system complexity.
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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".