NEC (Japan)

Vibration sensing based on forward transmission is an emerging topic for network protection and environmental monitoring, especially in long-haul submarine cables and urban fiber networks. However, previous field trials of this approach have mainly focused on localizing strong events under controlled or relatively quiet conditions. In this work, we investigate the capability of forward-transmission vibration sensing to detect weak signals in noisy environments. We demonstrate a highsensitivity vibration sensing system operating over an 80-km deployed live urban fiber loop without optical amplifiers. The system is enhanced by adaptive time-frequency masking and inband laser phase noise suppression techniques to improve sensitivity and noise robustness. It has successfully identified and localized weak real-world vibration events with peak-to-peak amplitude lower than 20 rad, such as construction activity near a manhole and even footsteps on handhole lids. Field trial results confirm its robust performance under dynamic environments, including road traffic-induced ground vibrations and aerial cable disturbances. To the best of our knowledge, this is the first demonstration of weak vibration event detection using forward transmission in urban fiber networks. It remarks a significant step towards practical distributed vibration sensing in smart city applications.

The demand for 6G wireless networks requires global, high-capacity coverage. Non‑terrestrial networks using the low-earth orbit (LEO) satellites and the high-altitude platform station (HAPS) are promising solutions. Although originally designed for limited applications, evolving applications now require more efficient use of spectrum. Previous works have focused on HAPS relay links, but integrating satellite–ground direct links introduces new coordination challenges and the static nature of HAPS coverage can be inefficient. To overcome these issues, we propose a downlink user association method that leverages LEO satellites and HAPS by optimizing instantaneous link sets and adaptive long‑term switching control to maximize spectral efficiency. Simulation results show our method outperforms conventional distance‑based or fixed‑interval approaches.

We present methods and field trial results demonstrating an integrated distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) system for manhole localization, condition diagnostics, and anomaly detection in pre-deployed telecommunication fiber networks. The proposed system leverages ambient environmental signals, such as vibrational patterns from traffic and day-night temperature fluctuations, and machine learning techniques for automated detection. By combining DAS waterfall traces with temperature measurements from DTS, we achieve improved classification accuracy. Experimental results from three real-world testbeds in Texas and New Jersey show a significant improvement in classification accuracy-from 78.9% and 89.5% using DAS and DTS alone, respectively, to 94.7% via cross-referenced analysis. We propose a structured prediction formulation for manhole localization based on a U-Net architecture with a gated attention mechanism, where the label of each fiber location in the waterfall image is predicted using both its neighboring context and within-patch discriminative features. The method also supports cross-route generalization for manhole localization and enables condition diagnostics, identifying issues such as cable exposure and water ingress. These results highlight the potential for scalable deployment of fiber sensing solutions for real-time, continuous monitoring of telecom infrastructure.

In many sports events, replay footage is commonly used to enhance audience engagement. However, the process of selecting and editing these replays is typically manual, placing a significant operational burden on on-site staff. While recent advancements in computer vision have facilitated sports match analysis, most existing methods lack real-time capabilities, rendering them unsuitable for automatic replay generation. This paper proposes a real-time ball tracking and action classification method using an event camera for volleyball matches, addressing the gap in automatic replay generation. By leveraging the unique advantages of event cameras, our method avoids machine learning techniques, instead utilizing event-based data streams for lightweight computation in ball tracking and action classification. When ball identification fails, temporal and spatial patterns of key events in volleyball are used for localization. The tracked ball trajectory and classified actions are then employed to identify key moments for replay generation. Experimental results demonstrate the effectiveness of the proposed method, achieving real-time operation and successfully identifying moments suitable for replay generation.

This paper addresses source component shift adaptation, aiming to update predictions adapting to source component shifts for incoming data streams based on past training data. Existing online learning methods often fail to utilize recurring shifts effectively, while model-pool-based methods struggle to capture individual source components, leading to poor adaptation. In this paper, we propose a source component shift adaptation method via an offline decomposition and online mixing approach. We theoretically identify that the problem can be divided into two subproblems: offline source component decomposition and online mixing weight adaptation. Based on this, our method first determines prediction models, each of which learns a source component solely based on past training data offline through the EM algorithm. Then, it updates the mixing weight of the prediction models for precise prediction through online convex optimization. Thanks to our theoretical derivation, our method fully leverages the characteristics of the shifts, achieving superior adaptation performance over existing methods. Experiments conducted on various real-world regression datasets demonstrate that our method outperforms baselines, reducing the cumulative test loss by up to 67.4%.

We report the first trial of network tomography over a live network in a multi-domain environment. We visualise end-to-end optical powers along multiple routes across multiple domains solely from a commercial 800 G transponder, enabling performance bottleneck localisation, power and routing optimisation, and lightpath provisioning. ©2025 The Author(s)

In clustered star-forming regions, stellar feedback-such as HII regions/photon-dominated regions (PDRs), and protostellar jets/outflows-shapes cloud structures and influences star formation. Using high-resolution ALMA millimeter and JWST infrared data, we analyze the cloud structure and the impact of stellar feedback in the nearest dense cluster-forming region Oph A. All 6 known Class 0/I and 2 of 6 Flat Spectrum/Class II objects are detected in the 1.3 mm dust continuum. Additionally, we newly detected 7 substellar cores, three of which show compact near-infrared emission, suggesting they are young substellar objects. The remaining cores, with masses of 0.01 Msun and high densities, are likely gravitationally bound. They appear connected by faint CO finger-like structures extending from the triple Class 0 system VLA1623-2417 Aa+Ab+B, suggesting they may have been ejected from the close binary VLA1623 Aa+Ab. 12CO and near-infrared data reveal multiple protostellar outflows. From the comparison, we identified several new outflows/jets, and shocked structures associated to the GSS30 large bipolar bubble. Strong 12CO emission traces the eastern edge of the Oph A ridge, forming part of the expanding HII/PDR bubble driven by the nearby Herbig Be star S1. The northern ridge appears blown out, with warm gas flowing toward GSS 30, injecting additional turbulent momentum. Several C18O striations in the S1 bubble align with magnetic fields, and position-velocity diagrams show wave-like patterns, possibly reflecting magnetohydrodynamic waves. Stellar feedback significantly influences Oph A's cloud structure.