Bibliographic record
Abstract
Photonic Bandgap (PBG) structures have been successfully applied to antenna design in order to raise the antenna radiation efficiency. In application of PBG structure in microstrip antennas design, mainly two techniques are used. One is to introduce a periodic lattice in the substrate layer. The seemingly simplest way to achieve this is to drill a periodic lattice of holes on the substrate slab. It is reported that for a single microstrip patch antenna, the radiation efficiency, backlobe and endfire radiation have been greatly improved by using a PBG substrate of drilled holes over the conventional bulk substrate [1]. It has also been revealed that PBG structures improve the antenna performance by suppressing the propagation of surface waves. This has been experimentally verified by measuring the mutual coupling between two distant microstrip antenna elements built on PBG substrate [1], or superstrate [2]. An alternative approach is focused on construction of a planar periodic lattice of metallic patch array on top of a dielectric substrate layer, and similar improvement was observed [3, 4]. By appropriately design of the element geometry and the lattice constant, an equivalent perfect magnetic conductive (PMC) surface was achieved over a limited band of frequency. Such a PMC surface can be applied in antenna design to suppress the flow of surface electric currents, which is the major source of edge diffraction, therefore improves the radiation property of the antenna.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".