Bibliographic record
Abstract
Abstract The basic ideas of the method of moments (MoM) technique, based on the surface integral equations, are described by analogy with the numerical integration. Such description makes it easy for a nonexpert to have some understanding of the MoMs based on surface integral equations. Then, a simple 2D scattering from an infinite conducting cylinder is considered to show a beginner how to formulate a problem and obtain the integral equations to be solved using the method of moments. Formulations of the problem of multi‐homogeneous dielectric materials are considered. To ease the construction of the MoM matrix for any problem composed of different materials, the surface integral equations are based on the actual boundary condition on each boundary in an operator form that is translated to a matrix, which is looked at as a composition of different impedance or admittance matrix in a partitioned matrix. Different surface integral equations can even be constructed at the matrix level. Discussions related to different possible formulations are considered. The problems involved in some formulations for conducting objects and how to overcome them are discussed. The literature review at the end is given for more detailed reading, which is related to different 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".