A PTD-MM hybrid formulation for scattering from flat plates
Bibliographic record
Abstract
Hybrid methods which combine the Moment Method (MM) with high frequency techniques such as Geometrical Theory of Diffraction (GTD) or Physical Optics (PO) have been the subject of research for many years. Ray-based hybrid methods are useful for problems involving a small object such as an antenna located in front of a large screen for which canonical asymptotic solutions are available. In the past, GTD has been successfully combined with MM for analyzing wire antennas in the presence of curved or planar surfaces [1]. In GTD/MM hybrid, MM is applied only over the antenna while GTD is employed to account for the presence of the nearby scattering centers such as edges. Edge diffracted fields are then considered to be effective sources in the MM solution. For antenna radiation problems (e.g., monopole mounted on a disc), it is usually possible to find a specular diffraction/reflection point on the surface of the reflecting screen. Then, diffracted fields can be calculated accurately without ray tracing. However, an extensive ray tracing is usually necessary for scattering problems.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".