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Network operators are aiming to overcome the envisioned data traffic crunch by relying on already installed fiber cables. Multiband optical line systems (LSs) will thus be the solution, starting from the extension to C+L band transmission. Such a bandwidth extension also speeds up network disaggregation to avoid an increase in costs. Thus, LS controllers need quality-of-transmission estimator (QoT-E) modules capable of quickly estimating the merit of lightpaths, and so analytical models for the transmission layer are mandatory. We review the generalized Gaussian noise (GGN) model for wideband prediction of nonlinear interference generation and validate its accuracy by comparing C+L simulative results to model predictions. Results are obtained for the case of amplification using a hybrid Raman and erbium-doped fiber amplifier, and display excellent conservative accuracy. We also present an experimental validation performed on commercial equipment that confirms how the GGN model is the most feasible solution for QoT-E modules in multiband LS controllers, enabling a frequency-resolved minimization of system margins. [ABSTRACT FROM AUTHOR]

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

Conventionally processed precipitation hardening aluminum alloys are generally treated with T6 heat treatments which are time-consuming and generally optimized for conventionally processed microstructures. Alternatively, parts produced by laser powder bed fusion (L-PBF) are characterized by unique microstructures made of very fine and metastable phases. These peculiar features require specifically optimized heat treatments. This work evaluates the effects of a short T6 heat treatment on L-PBF AlSi7Mg samples. The samples underwent a solution step of 15 min at 540 °C followed by water quenching and subsequently by an artificial aging at 170 °C for 2-8 h. The heat treated samples were characterized from a microstructural and mechanical point of view and compared with both as-built and direct aging (DA) treated samples. The results show that a 15 min solution treatment at 540 °C allows the dissolution of the very fine phases obtained during the L-PBF process; the subsequent heat treatment at 170 °C for 6 h makes it possible to obtain slightly lower tensile properties compared to those of the standard T6. With respect to the DA samples, higher elongation was achieved. These results show that this heat treatment can be of great benefit for the industry.