Search Results (258)

Submarine cable systems, serving as the critical backbone of global communications, face evolving resilience threats. This work proposes a novel sensing attack that utilizes ultra-narrow-linewidth lasers to surveil these infrastructures. First, the Narrowband Jamming Attack (NJA) is introduced as an evolution of conventional physical-layer jamming. NJA is divided into three categories according to the spectral position, and the non-overlapping class represents the proposed sensing attack. Its operational principles and the key parameters determining its efficacy are analyzed, along with its deployment strategy in submarine cable systems. Finally, the sensing capability is validated via OptiSystem simulations. Results demonstrate successful localization of vibrations within the 50–200 Hz range on a 1 km fiber, achieving a spatial resolution of 1 m, and confirm the influence of vibration parameters on sensing performance. This work reveals that the proposed sensing attack has the potential to covertly monitor environmental data, thereby posing a threat to information security in submarine cable systems. Full article

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. Full article

To address the challenge of efficiently identifying and providing early warnings for typical structural damages in small and medium-sized bridges during long-term service, this paper proposes an intelligent monitoring and recognition method based on ultra-weak fiber Bragg grating (UWFBG) array sensing. By deploying UWFBG strain-sensing cables across the bridge, the system enables continuous acquisition and spatial analysis of multi-point strain data. Based on this, a series of experimental scenarios simulating typical structural damages—such as single-slab loading, eccentric loading, and bearing detachment—are designed to systematically analyze strain evolution patterns before and after damage occurrence. While strain distribution maps allow for visual identification of some typical damages, the approach remains limited by reliance on manual interpretation, low recognition efficiency, and weak detection capability for atypical damages. To overcome these limitations, machine learning algorithms are further introduced to extract features from strain data and perform pattern recognition, enabling the construction of an automated damage identification model. This approach enhances both the accuracy and robustness of damage recognition, achieving rapid classification and intelligent diagnosis of structural conditions. The results demonstrate that the integration of the monitoring system with intelligent recognition algorithms effectively distinguishes different types of damage and shows promising potential for engineering applications. Full article

Cable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) with a single monocular camera, asking whether simple global static Jacobians (GSJ) can be sufficient and how an adaptive Jacobian estimator behaves. Two platforms are evaluated: a three-cable (3C) platform and a redundant six-cable du-al-plane platform (RC). Motion-capture (MoCap) validation shows that redundancy improves stability and tracking by reducing platform tilt and making image errors correspond more directly to Cartesian motions. Across static and low-speed tracking tasks, GSJ proved reliable, while a baseline recursive least-squares (RLS) estimator without safety triggers was often unstable. These findings suggest that improving mechanical conditioning may be as important as adding algorithmic complexity, and that carefully estimated global models can suffice in practice. Limitations include the use of a single camera, laboratory conditions, and a baseline RLS variant; future work will evaluate event-triggered adaptation and higher-speed trajectories. Full article

Slope stability monitoring and evaluation are key means to ensure the safety of engineering projects. Firstly, the classification, principles, and characteristics of distributed fiber optic sensing technology for slope engineering are introduced, and the significant advantages of this technology in slope monitoring are analyzed. Secondly, taking the Three Gorges Reservoir landslide as a case study, laboratory experiments of slopes were conducted using spatiotemporally continuous fiber optic neural sensing technology. Through the slope physical model experiment under loading excavation and rainfall conditions, it is found that (1) the strain changes monitored by vertically laid sensing cables are more sensitive to loading (with a peak strain of about 1400 με), while horizontally laid optical cables are more sensitive to excavation processes (with a peak strain of about 8900 με). Specifically, the tension–compression strain transformation in horizontally laid sensing cables can be used to identify slope failure in advance. (2) Rainfall infiltration significantly weakens the strength of the slope soil. Only considering the loading situation, the slope experiences instability and failure under a load of 120 kg. Under the premise of the soil saturation caused by rainfall infiltration, the slope experienced instability and failure under a load of 20 kg. Therefore, compared to human engineering activities, rainfall has a more significant impact on the stability of the slope. This study sheds light on the slope failure mechanism and provides a scientific basis for early warning. Full article

Fiber optic technologies have strong potential to augment and improve existing areas of sensor performance across many applications. Magnetic sensing, in particular, has attracted significant interest in structural health monitoring and ferromagnetic object detection. However, current technologies such as fluxgate magnetometers and inspection gauges rely on measuring magnetic fields as single-point sensors. By using fiber optic distributed strain sensors in tandem with magnetically biased magnetostrictive material, static and dynamic magnetic fields can be detected across long lengths of sensing fiber. This paper investigates the relationship between Fiber Bragg Grating (FBG)-based strain sensors and the magnetostrictive alloy Metglas^® 2605SC for the distributed detection of static fields for use in a compact cable design. Sentek Instrument’s picoDAS system is used to interrogate the FBG based sensors coupled with Metglas^® that is biased with an alternating sinusoidal magnetic field. The sensing system is then exposed to varied external static magnetic field strengths, and the resultant strain responses are analyzed. A minimum magnetic field strength on the order of 300 nT was able to be resolved and a variety of sensing configurations and conditions were also tested. The sensing system is compact and can be easily cabled as both FBGs and Metglas^® are commercialized and readily acquired. In combination with the robust and distributed nature of fiber sensors, this demonstrates strong promise for new means of magnetic characterization. Full article

Distributed Acoustic Sensing (DAS) is a new oceanographic measurement technology that exploits the physical sensitivities of fiber-optic communication cables to changes in pressure, allowing time series measurements of pressure at meter-scale spacing for ranges up to 150 km. The along-cable measurement locations, called channels, are evenly distributed, but the specific locations of each are initially unknown. In terrestrial applications, channel locations are often found by the “tap test” where acoustic transients are created at surveyed locations along the cable. For submarine installations, tap tests are inconvenient or logistically impossible. Here we describe a new method for submarine channel geolocation by comparing DAS signals to ambient ocean wave time series using a variety of cross-spectral methods. Ground truth data were derived from two remote sensing sources: marine radar (X-band) and shore-based cameras. The methods were developed and tested at two coastal locations and showed an ability to geolocate DAS channels to within 10 m at ranges of up to 3 km (radar) or within 1.0 m at ranges up to 600 m (optical). Full article

Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article

Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a dual innovation: a real-time break detection method and a low-cost multi-core parallel sensing system design based on ACS712 Hall sensors, achieving high detection accuracy (100% with zero false positives in tests). Unlike conventional techniques, the approach leverages current differential (ΔI) monitoring across parallel cores, triggering alarms when ΔI exceeds I_rate/2 (e.g., 0.3 A for 0.6 A rated current), corresponding to a voltage deviation ≥ 110 mV (normal baseline ≤ 3 mV). The core innovation lies in the integrated sensing system design: by optimizing the parallel deployment of ACS712 sensors and LMV324-based differential circuits, the solution reduces hardware cost to USD 3 (99.99% lower than fiber optic systems), payload by 18%, and power consumption by 23% compared to traditional methods. Post-fault cable temperatures remain ≤56 °C, ensuring safety margins. The 4-core architecture enhances mean time between failures (MTBF) by 83% over traditional systems, establishing a new paradigm for low-cost, high-reliability sensing systems in terrestrial tethered UAV cable health monitoring. Preliminary theoretical analysis suggests potential extensibility to underwater scenarios with further environmental hardening. Full article

This study investigates the performance of Distributed Acoustic Sensing (DAS) for detecting gas pipeline leaks under controlled experimental conditions, using multiple fiber cable types deployed both internally and externally. A 21 m steel pipeline with a 1 m test section was configured to simulate leakage scenarios with varying leak sizes (¼”, ½”, ¾”, and 1”), orientations (top, side, bottom), and flow velocities (2–18 m/s). Experiments were conducted under two installation conditions: a supported pipeline mounted on tripods, and a buried pipeline laid on the ground and covered with sand. Four fiber deployment methods were tested: three internal cables of varying geometries and one externally mounted straight cable. DAS data were analyzed using both time-domain vibration intensity and frequency-domain spectral methods. The results demonstrate that leak detectability is influenced by multiple interacting factors, including flow rate, leak size and orientation, pipeline installation method, and fiber cable type and deployment approach. Internally deployed black and flat cables exhibited higher sensitivity to leak-induced vibrations, particularly at higher flow velocities, larger leak sizes, and for bottom-positioned leaks. The thick internal cable showed limited response due to its wireline-like construction. In contrast, the external straight cable responded selectively, with performance dependent on mechanical coupling. Overall, leakage detectability was reduced in the buried configuration due to damping effects. The novelty of this work lies in the successful detection of gas leaks using internally deployed fiber optic cables, which has not been demonstrated in previous studies. This deployment approach is practical for field applications, particularly for pipelines that cannot be inspected using conventional methods, such as unpiggable pipelines. Full article

Road accidents could result in severe or fatal neck injuries. A few surrogate necks are available to develop and test neck protectors as countermeasures, but each has its own limitations. The objective of this study was to develop a surrogate neck compatible with the Hybrid III dummy, focused on tunable flexural stiffness and integrated angular sensors for kinematic feedback during impact tests. The neck features six 3D-printed surrogate vertebral bodies interconnected by rubber surrogate discs, providing a baseline flexibility to the surrogate fundamental spinal units. An adjustable inner cable and elastic elements hooked on the sides of vertebral elements allow to increase the flexural stiffness of the surrogate and to simulate the asymmetric behavior of the human neck. Neck flexural angles and axial compression are measured using a novel system made of wires, pulleys, and rotary potentiometers embedded in the neck base. A motion capture system and a load cell were used to determine the bending and torsional stiffness of the neck and to calibrate the sensors. Results showed that the neck flexural stiffness can be tuned between 3.29 and 5.76 Nm/rad. Torsional stiffness was 1.01 Nm/rad and compression stiffness can be tuned from 39 to 193 N/mm. Sensor flexural angles were compared with motion capture angles, showing an RMSE error of 1.35° during static testing and of 3° during dynamic testing. The developed neck could be a viable tool for investigating neck braces from a kinematic and kinetic perspective due to its inbuilt sensing ability and its tunable stiffness. Full article

Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This study proposes a novel two-step method to address this. The first step employs Variational Mode Decomposition (VMD) combined with Short-Time Energy Entropy (STEE) for the adaptive extraction of impact signal from noisy data. STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. The second step utilizes the Pruned Exact Linear Time (PELT) algorithm for accurate signal segmentation, followed by an unsupervised learning method combining Dynamic Time Warping (DTW) and clustering to identify the impact segment and precisely pick the arrival time based on shape similarity, overcoming the limitations of traditional pickers under conditions of complex noise. Field tests on an operational water pipeline validated the method, demonstrating the consistent localization of manual impacts with standard deviations typically between 1.4 m and 2.0 m, proving its efficacy under realistic noisy conditions. This approach offers a reliable framework for pipeline safety assessments under operational conditions. Full article

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch parallel collaborative architecture: two branches employ Gramian Angular Summation Field (GASF) and Recursive Pattern (RP) algorithms to convert one-dimensional intrusion waveforms into two-dimensional images, thereby capturing rich spatial patterns and dynamic characteristics and the third branch utilizes a Gated Recurrent Unit (GRU) algorithm to directly focus on the temporal evolution features of the waveform; additionally, a Transformer component is integrated to capture the overall trend and global dependencies of the signals. Ultimately, the terminal employs a Bidirectional Long Short-Term Memory (BiLSTM) network to perform a deep fusion of the multidimensional features extracted from the three branches, enabling a comprehensive understanding of the bidirectional temporal dependencies within the data. Experimental validation demonstrates that the GRT-Transformer achieves an average recognition accuracy of 97.3% across three typical intrusion events—illegal tapping, mechanical operations, and vehicle passage—significantly reducing false alarms, surpassing traditional methods, and exhibiting strong practical potential in complex real-world scenarios. Full article

This article presents a review on the applications of basalt fibers and their composites in infrastructures. The characteristics and advantages of high-performance basalt fibers and their composites are firstly introduced. Then, the article discusses strengthening using basalt fiber sheets and BFRP bars or grids, followed by concrete structures reinforced with BFRP bars, asphalt pavements, and cementitious composites reinforced with chopped basalt fibers in terms of mechanical behaviors and application examples. The load-bearing capacity of the strengthened structures can be increased by up to 60%, compared with those without strengthening. The lifespan of the concrete structures reinforced with BFRP can be extended by up to 50 years at least in harsh environments, which is much longer than that of ordinary reinforced concrete structures. In addition, the fatigue cracking resistance of asphalt can be increased by up to 600% with basalt fiber. The newly developed technologies including anchor bolts using BFRPs, self-sensing BFRPs, and BFRP–concrete composite structures are introduced in detail. Furthermore, suggestions are proposed for the forward-looking technologies, such as long-span bridges with BFRP cables, BFRP truss structures, BFRP with thermoplastic resin matrix, and BFRP composite piles. Full article