Finite Substrate Microstrip Transmission Line Analysis Using the Characteristic Green's Function-Complex Images Technique
Bibliographic record
Abstract
Quasi-static characteristic impedance of a microstrip transmission line on a finite dielectric substrate is calculated by implementing a novel closed-form Green's function in the method of moment (MOM). The Green's function is derived by using the Characteristic Green's Function-complex images (CGF-CI) technique. In this technique, the original 2-D structure is separated into two 1-D layered media surrounding the source. The 1-D Helmholtz's equations are then solved for each of the layered media with proper boundary conditions to find the respective characteristic Green's functions. Combining these 1-D characteristic Green's functions in an integral form gives the spatial Green's function for the original structure. The complex images technique is then applied to this integral form to derive the closed form representation of the spatial Green's function. Since the structure is non-separable, the derived Green's function is an approximate solution especially in the corners. Nevertheless the calculated characteristic impedances show good agreements with other numerical techniques.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".