Modified Convolutional Perfectly Matched Layers in the Presence of Moving Sources
Bibliographic record
Abstract
In modern times, antennas are ubiquitous and interact in dynamic environments where they have the freedom to move in three-dimensional space. To accurately model the dynamic interactions, both frequency and time domain solvers are employed. The frequency domain solver computes fields before motion commences, while the time domain (transient) solver evolves the fields over time, accommodating the movements of antennas. The integration of both solvers becomes imperative, and in this context, we briefly delve into the necessary modifications required for the radiation boundary that models free (open) space. Our implementation incorporates Convolutional Perfectly Matched Layers (CPML), where electromagnetic fields interact with the CPML operator that is capable of effectively “absorbing” EM waves across a broad frequency spectrum and with arbitrary angles of incidence and polarizations. Traditional CPML methods assume null fields before time$t=0$; however, when initial fields are already present, such as the fields of antennas before$t=0$, their contribution should be included. This paper highlights the necessary modifications of CPML to encompass initial steady-state fields and demonstrates how the Finite Difference Time Domain (FDTD) update equations is modified by the inclusion of the initial components within the FDTD framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".