FDTD analysis of resistively loaded broadband "Dark Eyes" antenna
Bibliographic record
Abstract
A perfectly conducting bowtie antenna exhibits broadband properties. Variable resistive loading is the strategy sometimes used to minimize the reflections from the end of the bowtie antenna, but its practical implementation can be difficult and its overall efficiency very low. We continue investigation of an antenna design with a constant resistive loading (Popovic/spl acute/, M. and Popovic/spl acute/, B.D., Proc. URSI XXVIIth General Assembly, 2002; Randa, J. et al., IEEE Int. Symp. on Electromagnetic Compatibility, p.265-6, 1991), where the reflection-minimizing strategy by accurate loading variation is replaced by a favorable change in the antenna geometry. We report results. of FDTD analysis for an "inverted bowtie" antenna geometry. The antenna is wide at its apex, and its width is tapered along the longitudinal axis towards its ends. With a constant surface resistivity, therefore, the resistance per unit length increases along the longitudinal direction. The gradient of resistance per unit length can be controlled by careful design of the taper. The authors nicknamed the design as the "Dark Eyes" antenna, as the geometry is reminiscent of the eye shape. The FDTD simulations demonstrate that broadband antenna behavior in the range of 4 to 8 GHz is improved for lower values of surface resistive loading.
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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 | 0.001 |
| 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 | 0.001 | 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".