A Proposed System with Negative Input Impedance
Bibliographic record
Abstract
In this paper, we configured a system with negative input impedance by a pair of Yagi-Uda antennas. The two Yagi antennas were placed face to face feeding by the same source. The input impedance is the sum of self-impedance and mutual impedance. Since Yagi antenna has significantly reduced self-impedance and amplified mutual impedance, it is anticipated that this system present negative input impedance. Description In the course of pursuing negative input impedance [1-5], we designed a new system composed of two Yagi antennas. Let’s first consider two dipole antennas in parallel, as shown in Fig. 1. U1 = Z1I1 + Z12I2 (1) U2 = Z2I2 + Z21I1 (2) Z1 and Z2 are the self-impedance of each dipole respectively. Z12 and Z21 are the mutual impedance between dipoles, Z12 = Z21. I1 and I2 are the current in each antenna. The graphic curve of mutual impedance vs. distance between dipoles are shown in Fig. 2 [6]. The mutual impedance at zero distance is the self-impedance. Since the mutual impedance is always less than the self-impedance, we cannot obtain negative input impedance by merely two dipole antennas. Figure 1: Two dipole antennas face in face feed by the same power source. Figure 2: Graphic curves of mutual impedance vs. distance between dipoles [6]. Now we change the dipole antennas to Yagi antennas, as shown in Fig. 3. Since the two antennas have symmetrical configuration, The equations 1 and 2 become
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".