An FDTD/ray-tracing analysis method for wave penetration through inhomogeneous walls
Bibliographic record
Abstract
A novel method of studying wave penetration through inhomogeneous walls using the hybrid technique based on combining finite-difference time-domain (FDTD) and ray tracing methods is presented . The FDTD method is used to analyze the transmission characteristics of inhomogeneous walls. Using the knowledge of the tangential electric and magnetic field distributions along the borders of the FDTD computation domain, rays are sent out to cover the rest of the environment so that prediction of signal coverage can be made more efficiently without compromising the accuracy. Numerical results of the method have been compared and shown to agree very well with those of measurement and those of full wave analysis. Examples have shown the inadequacy of the traditional ray tracing method in the presence of walls made of concrete blocks. However, the proposed method can accurately predict signal coverage by taking into account the scattered fields by the inhomogeneity inside the walls. The method does not add much to computational complexity. Reduction in computation time is even more significant when the incident waves can be approximated to be plane waves and the wall structure is periodic.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".