Search Results (66)

Interferometric fiber optic hydrophones (IFOHs) are highly sensitive for underwater acoustic detection but face challenges owing to the trade-off between laser monochromaticity and coherence length. In this study, we propose a pseudo-random binary sequence (PRBS) phase modulation method for laser coherence length control, establishing the first theoretical model that quantitatively links PRBS parameter to coherence length, elucidating the mechanism underlying its suppression of parasitic interference noise. Furthermore, our research findings demonstrate that while reducing the laser coherence length effectively mitigates parasitic interference noise in IFOHs, this reduction also leads to elevated background noise caused by diminished interference visibility. Consequently, the modulation of coherence length requires a balanced optimization approach that not only suppresses parasitic noise but also minimizes visibility-introduced background noise, thereby determining the system-specific optimal coherence length. Through theoretical modeling and experimental validation, we determined that for IFOH systems with a 500 ns delay, the optimal coherence lengths for link fibers of 3.3 km and 10 km are 0.93 m and 0.78 m, respectively. At the optimal coherence length, the background noise level in the 3.3 km system reaches −84.5 dB (re: rad/√Hz @1 kHz), representing an additional noise suppression of 4.5 dB beyond the original suppression. This study provides a comprehensive theoretical and experimental solution to the long-standing contradiction between high laser monochromaticity, stability and appropriate coherence length, establishing a coherence modulation noise suppression framework for hydrophones, gyroscopes, distributed acoustic sensing (DAS), and other fields. Full article

The research and related tests aimed to investigate the effect of different heat inputs on the microstructure and properties of the joint when using laser-backing welding for X80 steel, with the purpose of guiding a reasonable adjustment of heat inputs to obtain a sound and high-quality joint, and ultimately laying the foundation for the engineering application of laser-backing welding. The fiber-laser-backing welding is performed on a 22 mm thick X80 steel, before which a groove is prepared and assembled; joints were obtained under different heat inputs (162, 180, 210, 270 J/mm) with orthogonal combinations of laser power and welding speed. The microstructure and properties of the joints were characterized by using an optical microscope, scanning electron microscope, and microhardness tester. According to this investigation, the morphology of the joint is directly affected by the heat input, and insufficient heat input (<180 J/mm) will lead to an unacceptable weld profile. The width of the weld and heat-affected zone gets bigger as the heat input increases. The hardness nephograms of the joints under different heat inputs show that the weld has the highest hardness, followed by the coarse-grain heat-affected zone and the fine-grain heat-affected zone, sequentially. The less heat input, the lower the joint hardness; when the heat input increases to 270 J/mm, the coarse-grain zone near the fusion line shows obvious hardening. In addition, heat input also affects the impact toughness of the weld. The grain size of X80 steel with a lower content of niobium easily becomes coarse under excessive heat input (270 J/mm), resulting in the degradation of the grain-boundary slip ability; hence, the impact toughness of the joint deteriorates. The optimal heat input of 210 J/mm was identified, achieving a grain size of nearly 14 µm and providing a balanced combination of lower strength and higher impact toughness. Full article

Erbium-Doped Fiber Amplifiers (EDFAs) are fundamental to optical communication networks, providing signal amplification while introducing noise that affects system performance. Accurate noise figure estimation is critical for optimizing link budgets, monitoring optical Signal-to-Noise Ratio (OSNR), and enabling real-time network optimization. Traditional analytical models, while computationally efficient, often fail to capture device-specific variations, whereas machine-learning-based approaches require large training datasets and introduce high computational overhead. This paper proposes a polynomial regression model for real-time EDFA noise figure estimation, striking a balance between accuracy and computational efficiency. The model leverages Generalized Least Squares (GLS) regression to fit a multivariate polynomial function to measured EDFA noise figure data, ensuring robustness against measurement noise and dataset variations. The proposed method is benchmarked against experimental measurements from multiple EDFAs, achieving prediction errors that are within the measurement uncertainty of Optical Spectrum Analyzers (OSAs). Furthermore, the model demonstrates strong generalization across different EDFA architectures, outperforming analytical models while requiring significantly less data than deep-learning approaches. Computational efficiency is also analyzed, showing that inference time is below 0.2 ms per evaluation, making the model suitable for real-time digital-twin applications in optical networks. Future work will explore hybrid modeling approaches, integrating physics-based regression with Machine Learning (ML) to enhance performance in high-variance spectral regions. These results highlight the potential of lightweight polynomial regression models as an alternative to complex ML-based solutions, enabling scalable and efficient EDFA performance prediction for next-generation optical networks. Full article

Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address this issue, we propose a lightweight deep learning model, the Temporal Efficient Residual Network (TEResNet), for the detection of anomalous intrusion. In contrast to the majority of two-dimensional convolutional approaches, which require a deep architecture to encompass both low- and high-frequency domains, our methodology employs temporal convolutions and a compact residual network architecture. This allows the model to incorporate lower-level features into the higher-level feature formation in subsequent layers, leveraging informative features from the lower layers, and thus reducing the number of stacked layers for generating high-level features. As a result, the model achieves a superior performance with a relatively small number of layers. Moreover, the two-dimensional feature map is reduced in size to reduce the computational burden without adding parameters. This is crucial for enabling rapid intrusion detection. Experiments were conducted in the construction environment of the Guangzhou Metro, resulting in the creation of a dataset containing 6948 signal segments, which is publicly accessible. The results demonstrate that TEResNet outperforms the existing intrusion detection methods and advanced deep learning networks, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it provides an efficient and reliable solution for intrusion detection in metro tunnels, aligning with the growing demand for lightweight and robust information processing systems. Full article

With the rapid advancement of information technology, the data demands in transmission rates, processing speed, and storage capacity have been increasing significantly. However, silicon electro-optic modulators, characterized by their weak electro-optic effect, struggle to balance modulation efficiency and bandwidth. To overcome this limitation, we propose an electro-optic modulator based on an all-fiber micro-ring resonator and a p-Si/n-ITO heterojunction, achieving high modulation efficiency and large bandwidth. ITO is introduced in this design, which exhibits an ε-near-zero (ENZ) effect in the communication band. The real and imaginary parts of the refractive index of ITO undergo significant changes in response to variations in carrier concentration induced by the reverse bias voltage, thereby enabling efficient electro-optic modulation. Additionally, the design of the all-fiber micro-ring eliminates coupling losses associated with spatial optical-waveguide coupling, thereby resolving the high insertion loss of silicon waveguide modulators and the challenges of integrating MZI modulation structures. The results demonstrate that this modulator can achieve significant phase shifts at low voltages, with a modulation efficiency of up to 3.08 nm/V and a bandwidth reaching 82.04 GHz, indicating its potential for high-speed optical chip applications. Full article

We propose an observation system based on the Lagrange L1 point of the Earth–Moon system to observe solar spectral radiation reflected from the Earth, enabling continuous hyperspectral observation of the Earth’s hemisphere. The system can observe the solar spectral radiation reflected by the Moon, with its data applicable to on-orbit spectral radiation calibration. In this paper, the spectral irradiance at the entrance pupil of the Earth spectral radiation observation system (ESROS) is analyzed, and the optical design of the ESROS is introduced. An off-axis two-mirror telescope system, a coupling system of a microlens array and a fiber bundle, and an optical splitting system based on concave grating are used to achieve the full field of view hyperspectral splitting and miniaturization of the instrument. Finally, the stray radiation suppression of the instrument is introduced. The results show that the spectral resolution of the system is better than 5 nm in the 380–1000 nm band, and the spectral resolution is better than 10 nm in the 1000–1700 nm band. When observing the Earth, the signal-to-noise ratio is greater than 200. The external stray radiation suppression reaches the order of 10⁻⁹. The ESROS will provide crucial data support for researching global energy balance, climate change, and the spectral characteristics of exoplanets, facilitating planetary science and the exploration of extraterrestrial life. Full article

In an actual concrete dam construction, the efficiency of thermal field reconstruction directly affects the timeliness of temperature control measures. Therefore, using lightweight methods to obtain real-time, accurate thermal fields is crucial for concrete temperature control. To balance both accuracy and efficiency, this study proposes an optimization method for thermal field reconstruction in concrete dams. The method consists of three components: evaluating interpolation algorithms, optimizing the number of monitoring points, and analyzing their positions. Specifically, a distributed temperature sensing system is employed for concrete monitoring, with a “Z-shaped” optical fiber layout. Three interpolation algorithms—Kriging, Natural Neighbor, and Inverse Distance Weighting—are quantitatively evaluated, with Kriging showing the highest accuracy. Sensitivity analysis, combined with the control variable method, is used to assess the impact of the monitoring point number and position. Lightweight application procedures are then proposed, using reconstructed thermal field results to guide strategy formulation and parameter adjustment for the intelligent cooling control system. A case study demonstrates that this method ensures the effectiveness and timeliness of concrete temperature control measures. The proposed approach enables real-time updates of concrete temperature control measures in sync with the progress of the pouring process, providing a valuable reference for similar projects. Full article

This paper explores the ways in which the digital divide affects labor in the context of sustainable development within the digital economy. It discusses the effects of major indicators such as digital infrastructure construction, digital industry development, and digital-inclusive finance on labor mobility. Although existing research has analyzed the ways in which the digital economy enhances economic vitality, there is insufficient research that investigates how the divide between digital access and usage can be effectively reduced to promote sustainable development. Therefore, through empirical analysis and mechanism research, this study used quantitative measurement and regression analysis methods to conduct an in-depth analysis of the dual effects of digital access and usage divides on the long-term marginal impact for labor. The results show that improving digital infrastructure such as broadband and fiber optic networks not only significantly boosts the economic vitality of underdeveloped areas, but also enhances their ability to participate in sustainable development. This enables more laborers to access new job opportunities and resources provided by the digital economy. While narrowing the digital use divide initially increases labor mobility, uneven dissemination may create barriers to information access, thus limiting mobility. Our research indicates that the development of the digital economy promotes cross-regional labor mobility, which is particularly prominent in the digital platform economy, facilitating more sustainable economic growth. After controlling for variables such as the level of economic development, this positive impact remains robust. This paper suggests that digital infrastructure construction and training in digital skills should be strengthened to narrow the digital divide and promote sustainable, balanced regional development and increased economic vitality. Full article

During recent years, the optical-fiber-based simultaneous sensing of strain and temperature has attracted increased interest for different applications, e.g., in medicine, architecture, and aerospace. Specialized fiber layouts further enlarge the field of applications at much lower costs and with easier handling. Today, the performance of many sensors fabricated from conventional fibers suffers from cross-sensitivity (temperature and strain) and relatively high interrogation costs. In contrast, customized fiber architectures would make it possible to circumvent such sensor drawbacks. Here, we report on the development of a high-quality coupled-core fiber and its performance for sensors—from the initial fiber layout via elaboration of the preform and fiber up to the sensor evaluation. A compact, high-speed, and cost-effective interrogation unit using such a specialized coupled-core fiber has been designed to monitor reflectivity changes while even being able to distinguish the direction of the force or impact. Several fiber core material techniques and approaches were investigated, which made it possible to obtain a sufficient volume of material for the required fiber core number and a specialized fiber core geometry in terms of core distances and radial refractive index profile, whilst handling the non-symmetrical fiber architectures of such modeled, complex structures and balancing resources and efforts. Full article

There is a growing need for precise and rapid detection methods in fields such as biomedical diagnostics, environmental monitoring, and chemical analysis. Surface plasmon resonance (SPR) sensors have been used for the detection and quantification of a wide range of analytes, including biomolecules, chemicals, and gases, in real-time. Despite the promising capabilities of SPR sensors, there remains a gap in creating a balance between having a large enough area to capture a significant number of analytes for detection and being small enough to ensure high sensitivity. This research aims to explore the design of a D-shaped SPR-based optical fiber sensor, focusing on the use of copper, gold, and silver thin films at optimized width and thickness of 10 µm and 45 nm, respectively, to improve the sensor’s performance. Employing a computational approach, this study examines the influence of the optimized width and refractive indices of metallic films on the sensor’s characteristics. The 10 µm width of the metallic thin film has been found to produce an optimal balance between the sensitivity and the dynamic range of the sensor. Leveraging on the ratio of the real and imaginary parts of the dielectric constant of the thin film metal provides insight into the optical properties and sensitivity at certain wavelengths. Within an analyte refractive index range of 1.37–1.42 and a wavelength range of 650–1200 nm, results indicate that silver outperforms gold and copper at the optimized width with a wavelength sensitivity, and detection accuracy of 12,300 nmRIU⁻¹, and 3.075, respectively. By optimizing the width of the metal thin film at 10 µm, a highly sensitive D-SPR is designed, allowing for enhanced sensor detection capabilities for a wide range of bioanalytes. Full article

The power transformer is one of the most crucial pieces of high-voltage equipment in the power system, and its stable operation is crucial to the reliability of power transmission. Partial discharge (PD) is a key factor leading to the degradation and failure of the insulation performance of power transformers. Therefore, online monitoring of partial discharge can not only obtain real-time information on the operating status of the equipment but also effectively predict the remaining service life of the transformer. Meanwhile, accurate localization of partial discharge sources can assist maintenance personnel in developing more precise and efficient maintenance plans, ensuring the stable operation of the power system. Dual partial discharge sources in transformer oil represent a more complex fault type, and piezoelectric transducers installed outside the transformer oil tank often fail to accurately capture such discharge waveforms. Additionally, the sensitivity of the built-in F-P sensors can decrease when installed deep within the oil tank due to the influence of oil pressure on its sensing diaphragm, resulting in an inability to accurately detect dual partial discharge sources in transformer oil. To address the impact of oil pressure on sensor sensitivity and achieve the detection of dual partial discharge sources under high-voltage conditions in transformers, this paper proposes an optical fiber ultrasonic sensor with a pressure-balancing structure. This sensor can adapt to changes in oil pressure environments inside transformers, has strong electromagnetic interference resistance, and can be installed deep within the oil tank to detect dual partial discharge sources. In this study, a dual PD detection system based on this sensor array is developed, employing a cross-positioning algorithm to achieve detection and localization of dual partial discharge sources in transformer oil. When applied to a 35 kV single-phase transformer for dual partial discharge source detection in different regions, the sensor array exhibits good sensitivity under high oil pressure conditions, enabling the detection and localization of dual partial discharge sources in oil and winding interturn without obstruction. For fault regions with obstructions, such as within the oil channel of the transformer winding, the sensor exhibits the capability to detect the discharge waveform stemming from dual partial discharge sources. Overall, the sensor demonstrates good sensitivity and directional clarity, providing effective detection of dual PD sources generated inside transformers. Full article

A delicate balance between dissipative and nonlinear forces allows traveling waves termed solitons to preserve their shape and energy for long distances without steepening and flattening out. Solitons are so widespread that they can generate both destructive waves on oceans’ surfaces and noise-free message propagation in silica optic fibers. They are naturally observed or artificially produced in countless physical systems at very different coarse-grained scales, from solar winds to Bose–Einstein condensates. We hypothesize that some of the electric oscillations detectable by scalp electroencephalography (EEG) could be assessed in terms of solitons. A nervous spike must fulfill strict mathematical and physical requirements to be termed a soliton. They include the proper physical parameters like wave height, horizontal distance and unchanging shape; the appropriate nonlinear wave equations’ solutions and the correct superposition between sinusoidal and non-sinusoidal waves. After a thorough analytical comparison with the EEG traces available in the literature, we argue that solitons bear striking similarities with the electric activity recorded from medical conditions like epilepsies and encephalopathies. Emerging from the noisy background of the normal electric activity, high-amplitude, low-frequency EEG soliton-like pathological waves with relatively uniform morphology and duration can be observed, characterized by repeated, stereotyped patterns propagating on the hemispheric surface of the brain over relatively large distances. Apart from the implications for the study of cognitive activities in the healthy brain, the theoretical possibility to treat pathological brain oscillations in terms of solitons has powerful operational implications, suggesting new therapeutical options to counteract their detrimental effects. Full article

In this paper, we present a new scheme to generate ultrawide tunable random fiber lasers (RFLs) covering the S-, C- and L-band by combining the broadband Raman gain in tellurite fibers and the active gain in erbium-doped fibers. A numerical simulation based on the power-balance model is conducted to verify the feasibility of the ultrawide tunable random fiber lasing generation. Pumped by a 1450 nm laser, the tunable random Raman fiber laser in the ranges of 1480–1560 nm and 1590–1640 nm can only be realized with a tellurite fiber. To further fill in the emission gap in the range of 1560–1590 nm, the erbium-doped fiber is incorporated in the cavity, which can provide efficient erbium-doped gain in the C- and L-band. By combining a 100 m long tellurite fiber and an 8 m long erbium-doped fiber, an ultrawide tunable RFL based on hybrid erbium–Raman gain can be realized with a wavelength tuning range (1480 nm–1640 nm) covering the S-, C- and L-band at 3.5 W pump power. Such a widely tunable RFL is of great importance in applications such as optical communication, sensing and imaging. Full article

This work is a follow-up to previous research by our team and is devoted to studying a dual-sinusoidal placement of distributed fiber-optic sensors (FOSs) that are embedded inside an adhesive joint between two composite laminates. The constructed smart continuous fiber-reinforced polymer composite structure is well suited to the structural health monitoring (SHM) system for offshore wind turbine blades. Three main drawbacks of SHM through embedded distributed FOSs, however, have been identified in this article, so their impact must be analyzed. Despite existing research, the influence of the dual-sinusoidal placement under various loading conditions on structural mechanical behavior and sensing functionality has not been considered yet since its introduction. Thus, this study aims to identify the resulting strain patterns and sensing capabilities from an optimized dual-sinusoidal placement of FOSs in various loading cases through finite element modeling. Ultimately, this work illustrates the strain-measuring advantages of dual-sinusoidal FOSs, explains the correspondence between the strains measured by FOSs and that of host structures, and discusses the balance among mechanical influences, sensing functions, and monitoring coverage. It is worth noting that the current work is a still introductory concept that aims at refining key parameters that have been emphasized in previous research, before starting an applied study that will consider both numerical and validation steps on real large smart composite structures. Full article

With the penetration of distributed resources into power distribution networks, power distribution networks are transforming into active distribution networks with a high proportion of distributed generations and power electronic equipment. Efficient modeling and simulation methods are essential to perform dynamic response analysis. In order to satisfy the fast/steady/slow multiple time-scale simulation requirements of active distribution networks, a fast/medium/slow time partition model and a network decoupling method for short line characteristic lines is proposed in this paper. Through the decomposition coordination simulation method, the network is decomposed into multiple regions that can be simulated in parallel. Based on the interconnection of fiber optic network cards, a multi-rate parallel simulation and synchronization strategy is proposed, which significantly improves the simulation speed of active distribution networks while ensuring simulation accuracy. The numerical experiments have been conducted based on a modified IEEE 33-bus and a PG&E 69-bus, and simulation results show the feasibility of the proposed method. The verification results of the example show that using adaptive variable-step-size multi-rate parallel simulation technology can increase the subnet computation-time balance rate and simulation acceleration ratio to 119.90% and 121.31% in the same rate-parallel mode. Full article