Polarization dynamics in optical ground wire network
Bibliographic record
Abstract
We report the polarization dynamics in an optical ground wire (OPGW) network for a summer period and a fall period for what is believed to be the first time. To better observe the surrounding magnetic fields contribution to modulating the state of polarization (SOP) we installed a Faraday rotating mirror to correct reciprocal birefringence from quasi-static changes. We also monitored the OPGW while no electrical current was present in the towers' electrical conductors. The spectral analysis, the arc length mapped out over a given time interval on a Poincaré sphere, histograms of the arc length, and the SOP autocorrelation function are calculated to analyze the SOP changes. Ambient temperature changes, wind, Sun-induced temperature gradients, and electrical current all have a significant impact on the SOP drift in an OPGW network. Wind-generated cable oscillations and Sun-induced temperature gradients are shown to be the dominant slow SOP modulations, while Aeolian vibrations and electrical current are shown to be the dominant fast SOP modulations. The spectral analysis revealed that the electrical current gives the fastest SOP modulation to be 300 Hz for the sampling frequency of 1 KHz. This has set the upper speed limit for real-time polarization mode dispersion compensation devices.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".