A hybrid technique based on combining ray tracing and FDTD methods for site-specific modeling of indoor radio wave propagation
Bibliographic record
Abstract
The paper presents a hybrid technique based on combining ray tracing and finite-difference time-domain (FDTD) methods for site-specific modeling of indoor radio wave propagation. Ray tracing is used to analyze the wide area and FDTD is used to study areas close to complex discontinuities where ray-based solutions are not sufficiently accurate. The hybrid technique ensures improved accuracy and practicality in terms of computational resources at the same time since FDTD is only applied to a small portion of the entire modeling environment. Examples of applying the method for studying indoor structures and penetration of wave from outdoor to indoor are given at 2.4 GHz. Numerical results are compared with known exact solutions or results of the full wave analysis or traditional ray model to demonstrate the accuracy, efficiency, and robustness of the novel method. Numerical results are also compared with reported measurement results for waves at 1.29 GHz penetrating an external wall with metal-framed windows. Cumulative distributions of field envelope obtained from the hybrid method show close resemblance to the Rayleigh distribution, which conforms to the reported measurement results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".