We propose a method for localizing a fire source using an optical fiber distributed temperature sensor system. A section of two parallel optical fibers employed as the sensing element is installed near the ceiling of a closed room in which the fire source is located. By measuring the temperature of hot air flows, the problem of three-dimensional fire source localization is transformed to two dimensions. The method of the source location is verified with experiments using burning alcohol as fire source, and it is demonstrated that the method represents a robust and reliable technique for localizing a fire source also for long sensing ranges.

We propose an accelerometer based on a micro-cantilever fabricated onto the side of a single mode optical fiber using a combination of ps-laser machining and focused ion beam (FIB) processing. FIB machining is also used to fabricate a 45° mirror onto the end of the fiber that is accurately aligned with optical fiber core. This provides a reliable means to optically address components attached to the side of the fiber, such as the cantilever. Using interferometry to accurately monitor the deflection of the side cantilever results in a device that is capable of measuring two-axis acceleration. Acceleration up to 6 g is measured with a resolution of ~0.01 g and a frequency range from dc to 500 Hz has been demonstrated. The cross-axis sensitivity is below −32 dB.

This paper proposes a long distance pipeline threat event identification method based on Gramian Angular Field (GAF) and deep learning networks for phase sensitive Rayleigh distributed fiber optic vibration system. Eleven types of threat events of vibration signals along a 28.9 km natural pipeline are collected and converted to two-dimensional images using GAF. The performance of five deep learning networks: GoogLeNet, VGG, AlexNet, ShuffleNet, and DenseNet are compared for events classification. The results show that DenseNet achieved the highest classification accuracy within the eleven types of threat events. Moreover, the false alarm rates of manual digging and mechanical excavation among the eleven events were 0.31% and 0.18%, respectively. The proposed method is suitable for complicated pipeline safety monitoring niche and offers fast and accurate recognition solution for long distance pipeline safety monitoring.

We propose a method based on Synchrosqueezing Transform (SST) for vibration event analysis and identification in Phase Sensitive Optical Time-Domain Reflectometry (Φ-OTDR) systems. SST has high time-frequency resolution and phase information, which can distinguish and enhance different vibration events. We use six tap events with different intensities and six other events as experimental data and test the effect of attenuation. We use Visual Geometry Group (VGG), Vision Transformer (ViT), and Residual Network (ResNet) as deep classifiers for the SST transformed data. The results show that our method outperforms the methods based on Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT), while ResNet is the best classifier. Our method can achieve high recognition rate under different signal strengths, event types, and attenuation levels, which shows its value for Φ-OTDR system.

This article presents a novel method for monitoring leakage in buried natural gas pipelines. The method is based on phase sensitive time-domain distributed reflection detection (Φ-OTDR) technology, which can classify and identify small leakage signals under different pressures. The article compares and evaluates three time-frequency analysis methods for leakage signals: Horizontal Synchrosqueezing Transform (HST), Time-reassigned Synchrosqueezing Transform (TSST), and Synchronous Wavelet Transform (SST). It also applies You Only Look Once (YOLO) to classify small leakage events under different pressures. The experimental results demonstrate that the HST method outperforms other methods in both feature extraction and event classification. It can effectively distinguish small leakage signals under different pressures, achieving a classification accuracy of 89.87%. Therefore, this article offers an effective technical solution for monitoring and early warning of leaks in buried natural gas pipelines.

Chiral metasurfaces with enhanced chiroptical effects have drawn extensive attentions due to their potential applications from chiral molecule detection to low-threshold chiral emission. However, pursuing strong circular dichroism (CD) and high quality (Q) factors of chiroptical resonances simultaneously still remains problematic. Here, we present a novel design of all-dielectric chiral metasurface which supports enhanced chiroptical responses driven by toroidal dipole (TD) resonances. By introducing geometrical perturbations and breaking the in-plane mirror symmetry, the proposed chiral metasurface can excite high-Q factor ( $\sim 2938$ ) chiroptical resonance with maximal CD ( $\sim -0.995$ ) at terahertz frequencies. Interestingly, the magnitude of CD signals can be freely modulated via tuning geometrical parameters, while the value of Q factor almost remains stable. These results may pave a new avenue for manipulating chiroptical responses for potential applications which require strong CDs accompanied by enhanced light-matter interaction.