Bibliographic record
Abstract
Abstract Dielectric media affect the wave propagation by their reflective index. Their use in antennas can provide a number of diverse benefits. In applications such as high‐gain antennas, they influence the phase of the propagating waves, and thus influence the direction and gain of the transmitted waves. Lens antennas are typical examples that can be used alone, or in conjunction with other antennas, such as horn and microstrip antennas, to enhance their gain. In other applications like dielectric loaded horns and waveguides the use of dielectric inside them controls the excitation of various modes and thus the performance of the horns. Hybrid and multimode horns are examples that can improve the horn performance for reflector feed applications. Similarly, dielectric resonator antennas can control and improve the radiation performance by controlling the excitation and the phase of the wave propagating through them. Dielectrics being non‐conducting can also be used to insulate and, thus, isolate the antennas from living tissues, in medical treatment and studies. Large permittivity of some dielectrics reduces the signal wavelength and in areas of small antennas can be used to reduce the antenna dimensions. Finally, the dielectric permittivity also influences the wave impedance and can be used to match the impedance of antennas to the radiation media and enhance their gain. This article provides a brief review of some of these applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.040 | 0.031 |
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".