Bibliographic record
Abstract
The formulas presented in the paper allow fast and reasonably accurate estimation of printed slot antennas efficiency and driving point impedance. They can be used at the initial stage of the antenna design to choose optimal values for the substrate parameters and slot length. The internal feature of printed slot antennas that is to radiate the most of the input power into a media with a higher dielectric constant, results in their low efficiency, since the dielectric constant of the substrate is usually much higher then that of the atmosphere. The same feature, however, makes the printed slot antennas a very attractive feeder for the DRA, since the dielectric permittivity of the DRA can be chosen much higher then that of the substrate. The generalization of the presented formulas for some practical antenna structures of interest, such as slot antenna driving a hemispherical dielectric resonator or slot antenna on a finite width dielectric substrate is straightforward. Some preliminarily results on these structures are to be reported during the presentation. The accuracy of the method can be increased since the current distribution M (y) = (L/2 − |y|) × cos[k × (L/2 − |y|)] also allows the closed form expression for the antenna impedance. This function together with (1) can be used for very efficient implementation of the MoM for the printed antenna impedance and efficiency calculations.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".