MIMO-DFS for detection-localization-identification of mechanical threats over existing telecom networks
Bibliographic record
Abstract
Multiple input multiple output (MIMO) distributed fiber Sensing (DFS) is an innovative digital approach to phase-OTDR derived from technologies used in long-haul transmission over both terrestrial and submarine telecom networks. The probing signal is composed of digital codes that modulate in phase a highly coherent laser source over two orthogonal polarization axes, and the Rayleigh backscattered optical field is captured by a coherent mixer set in a homodyne configuration. This technique estimates, after digital processing, the backscattered signal without polarization fading effect and under a Jones matrix form, so giving access to the intensity, the local phase and the state of polarization along the fiber link under test. The link is continuously probed, which allows to monitor the local phase changes induced by mechanical events occurring in the fiber vicinity over a maximal bandwidth given by length of the monitored fiber link. We demonstrated the MIMO-DFS capabilities over links up to 100km with a gauge length of 10 meters and a capture of events linearly to disturbance pressure, in a microphone-like way, over a 200Hz bandwidth.  The detection threshold, or noise floor, increases along the fiber length and was shown to be drastically lowered compared with standard DFS techniques which probe the fiber over one polarization axis only. The 100km figure achieved will be further improved soon by enhancing the stability of the laser source. The MIMO-DFS technique has been designed to be compliant with telecom networks in a way so that a fiber can be monitored using one WDM channel without impacting the traffic rate propagating over adjacent WDM channels. We recently conducted a field trial using a telecom operator active network in Saudi Arabia. The purpose was to early detect mechanical threats that may lead to a traffic disruption. We provoked mechanical events by means of jackhammer and excavator in the vicinity of a 57km deployed cable buried two meters depth, both in a city area and in the desert, at distance of 15 and 30km from the fiber start respectively.  The aim of the paper is to highlight the ability to detect, localize and even recognize the various noise sources under test in the context of a realistic deployed network, thanks to the outstanding sensitivity of the sensing technique. Beyond early detection of human induced threats for telecom networks integrity, the MIMO-DFS approach is also suited to detect much more critical environmental threats such as seisms with potential impact on people safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".