Orbital Angular Momentum （OAM） beams， featuring spiral phase wavefronts， act as critical new information carriers in optical communication， manipulation， imaging and quantum information. However， ocean turbulence， driven by temperature and salinity fluctuations， induces intensity scintillation， phase distortion and mode crosstalk in OAM beams， hindering their marine application. Traditional identification methods relying on optical components are complex and environment-sensitive， while current machine learning-based static models fail to satisfy marine real-time response requirements due to high retraining costs.

To address these challenges， this study proposes an online OAM identification method based on the Online Sequential Extreme Learning Machine （OS-ELM）. This efficient online sequential learning algorithm has a three-layer structure. The input layer receives feature vectors of OAM beams， with the number of nodes matching the feature dimension. The hidden layer has a fixed number of nodes and supports two activation modes： additive mode and RBF-type mode. Additive mode uses random weights and biases， while RBF-type mode uses random centers and influence factors. The parameters of the hidden layer are fixed after random initialization， eliminating the need for time-consuming gradient iterations. The output layer generates One-Hot encoded labels for OAM modes， and the output weights are solved through Moore-Penrose generalized inverse to ensure fast model training. The implementation of the method in this study consists of two key components： data generation and model construction. In the data generation phase， a numerical simulation model is built using MATLAB to generate an OAM spot dataset affected by oceanic turbulence. Key parameters include the specific inner and outer scales of oceanic turbulence， an appropriate waist radius of Laguerre-Gaussian beams， a wavelength commonly used in optical communications， and fixed spacing of random phase screens. The dataset covers LG modes with topological charges ranging from 1 to 10 and generates intensity images. For data preprocessing， 200 turbulence-affected images are generated for each topological charge， forming a training set of 2 000 images and a test set of 600 images. After adjusting the image size via bicubic interpolation， performing grayscale conversion， double-precision conversion， and normalization to the range of ［-1， 1］， the 2D image matrices are flattened into 1D feature vectors， which are combined with labels to form a feature matrix. In the model construction phase， the dataset is divided into an initial training block with 1 000 samples and 20 online training blocks with 50 samples each. The initial training block is used to calculate the hidden layer output matrix， solve the initial weights by combining the label matrix and Moore-Penrose generalized inverse， and define an intermediate matrix. During online learning， the hidden layer output matrix of the new data block is first computed； the intermediate matrix is then updated using the Woodbury identity without retraining historical data. Finally， the output weights are incrementally updated using the new labels and prediction residuals， ensuring the model dynamically adapts to OAM beam feature drift while maintaining real-time performance.

Based on the same dataset， comprehensive experimental comparisons are conducted between the Online Sequential Extreme Learning Machine OS-ELM and other models， including Support Vector Machine （SVM）， Incremental Naive Bayes classifier （IncNB）， and Naive Bayes classifier （NB）， with performance evaluated using multiple metrics. In weak turbulence scenarios， the Accuracy of OS-ELM reaches 92.33% higher than SVM's 85.33%， IncNB's 87.00%， and NB's 88.67%， with simultaneous leadership in Precision， Recall， and F1-Score. In moderate turbulence， its Accuracy is 71.17% far exceeding SVM's 49.50%， IncNB's 51.83%， and NB's 62.00%. Even in strong turbulence， it maintains an Accuracy of 54.33% higher than SVM's 37.33%， IncNB's 38.83%， and NB's 50.67%. The ROC （Rate of Change） curve of OS-ELM clusters near the top-left corner of the coordinate plane， with AUC （Area Under Curve） values of 0.992， 0.946， and 0.890 under weak， moderate， and strong turbulence respectively， significantly outperforming the comparison models.

The advantages of OS-ELM stem from two core mechanisms： first， the random initialization of hidden layer parameters and analytical solution of output weights avoid the risk of falling into local optima and eliminate the need for complex kernel function calculations required by SVM， enabling efficient approximation of the global optimal solution； second， the online sequential learning mechanism supports real-time incremental updates， accurately adapting to OAM beam feature drift caused by oceanic turbulence and overcoming the inherent limitation of traditional static models with fixed parameters. Looking ahead， the dynamic adaptability， high robustness， and real-time performance of the online sequential extreme learning machine make it highly compatible with complex interference and high-frequency dynamic changes in marine environments. With further engineering development， this method holds significant potential to drive technological upgrades in intelligent manufacturing and industrial communication.

Femtosecond laser filamentation has drawn much attention from researchers recently， which contains fruitful physical processes， such as the self-focusing induced by the optical Kerr effect， plasma defocusing， self-phase modulation and so on. It has shown great potential for many applications in the atmospheric field， such as remote sensing， lightning control， rain making. When propagating in the atmosphere， the influence of the turbulence on the filament spatial distribution has to be taken into consideration. Many studies have been dedicated to the filamentation distance， filament survival rate， transverse wandering， multiple filaments formation induced by turbulence.

Interestingly， it has been demonstrated that the air turbulence can have opposite effects of either increasing or decreasing the filamentation probability and either decreasing or increasing the filament onset distance depending on the initial experiment conditions. However， few studies have been dedicated to explore the mechanism of this phenomenon based on numerical simulation method， which would be significant for understanding the mechanism of the interaction between turbulence and laser filamentation.In this paper， the propagation of a single laser filament was simulated based on the laser filament nonlinear propagation model and the turbulent phase screen model.

The filamentation probability as a function of initial laser beam power was investigated in the turbulent air with turbulence structure constant C_n^{\mathrm{2}} of 10^-13 cm^-2/3， 10^-12 cm^-2/3 and 10^-11 cm^-2/3. The corresponding minimum initial laser powers for the formation of the laser filament with the filamentation probability larger than 0 under the above three turbulent conditions are 0.96 P_cr，0.68 P_cr and 0.14 P_cr， respectively. Moreover， the filamentation probability increases with the turbulence intensity C_n^{\mathrm{2}} when the initial laser beam power is less than P_cr. Thus， the air turbulence can trigger the laser filamentation for an initial laser power below the filamentation threshold. In the next step， the effect of a lens as a focusing optics on the filamentation probability in the turbulent air with C_n^{\mathrm{2}} of 10^-12 cm^-2/3 has been studied. By using a lens with focal length of 5 m as the focusing optics， the laser filamentation probability is always larger than that without any focusing optics in the turbulent air when the initial laser power is less than the filamenting threshold. However， for a lens with focal length of 2 m， the laser filamentation probability is less than that without any focusing optics in the turbulent air. Thus， using a lens with a relative large focal length could promotes the filamentation probability effectively in the turbulent air. Further， the filament onset distances in the turbulent air with different turbulent intensities have been investigated. The initial laser power is chosen to be 1.4 P_cr. The filament onset distance is 354 cm in the calm air. When C_n^{\mathrm{2}} is 10^-14 cm^-2/3， the filament onset distance varies between 349 cm and 358 cm. The average filament onset distance is 353.3 cm. When C_n^{\mathrm{2}} is 10^-11 cm^-2/3， the filament onset distance varies between 107 cm and 290 cm. The average filament onset distance is 149.1 cm. The relationship between the average deviation of the filament onset position 〈\mathrm{\Delta }r〉 and \sqrt[]{C_n^{\mathrm{2}}} has been obtained. For 4×10^-13 cm^-2/3≤C_n^{\mathrm{2}}≤4×10^-12 cm^-2/3， the relationship between 〈\mathrm{\Delta }r〉 and \sqrt[]{C_n^{\mathrm{2}}} can be fitted linearly well.

The interaction mechanism between the air turbulence and the laser beam can be determined by the parameter Q， which is defined by the ratio of the coherent area in the beam and the power it contains over the critical laser power for self-focusing. When C_n^{\mathrm{2}} is larger than 2.25×10^-12 cm^-2/3， Q is larger than 1. Under this condition， the air turbulence is relative strong， leading to the modulation instability of the beam phase， which can promote the filament formation finally. Thus the filament onset distance is always shorter than that without any turbulence. When C_n^{\mathrm{2}} is less than 2.25×10^-12 cm^-2/3， Q is much larger than 1. Under this condition， the air turbulence is relative weak， which will act as a noise and suppress the whole beam self-focusing effect. The filamentation probability will decrease and the filament onset distance will be lengthened. This study will pave the way to understand the mechanism of the influence of air turbulence on the filamentation probability and filament onset distance and would be valuable for many applications of the laser filament in the atmosphere.

Monitoring the finger rehabilitation process is critical for preventing accidental injuries and achieving superior personalized therapeutic outcomes. However， current research focusing specifically on monitoring this process remains limited. To address this gap， this study aims to accurately detect finger joint movement angles and assess contact forces during rehabilitation training. The primary objective is to develop a solution that enables comprehensive monitoring of both non-grip and grip rehabilitation exercises.

This work proposes a novel optical Fiber Bragg Grating （FBG） finger exoskeleton rehabilitation device. The core of the device consists of two specialized FBG fingerstalls： a monitoring fingerstall for tracking movement and a tactile fingerstall for measuring force， both integrated with advanced FBG sensor units. To validate the design， a mathematical model of the rehabilitation device was established， and its rationality was rigorously analyzed. The experimental methodology included extensive calibration and testing： the FBG sensor units underwent sensitivity， connectivity， fit， and creep resistance tests； the monitoring fingerstall was calibrated for motion angle and its angular velocity sensing performance was compared against an IMU sensor using Bland-Altman analysis on two volunteers； and the tactile fingerstall was calibrated for force， with its sensory capabilities further analyzed using arc-length， linear， and BP neural network methods. Finally， volunteers conducted grip rehabilitation training experiments to assess contact force by measuring six different objects.

The experimental results comprehensively demonstrated the high performance of the proposed system. The FBG sensor units exhibited excellent connectivity， with an average sensitivity exceeding 0.911 98 nm/N， an average fit greater than 0.995 5， and outstanding creep resistance （maximum wavelength shift of 0.9 pm）. The monitoring fingerstall achieved an average angular sensitivity greater than 9.5 pm/（°）. Critically， its performance in sensing finger angular velocity was nearly identical to that of a commercial IMU sensor， and it successfully recognized six different hand gestures. For the tactile fingerstall， sensitivity measurements at symmetric points were nearly identical， with average sensitivity at points 1/2， 3/4， and 5/6 being higher than 19.5 pm/N， 13.45 pm/N， and 9.1 pm/N， respectively. The arc-length and linear analysis methods yielded low errors （8.45% and 8.38%）， confirming model reliability， while BP neural network analysis resulted in an extremely low mean squared error of 0.000 003 309 6. The grip experiments revealed that object volume had a more significant impact on the tactile fingerstall's readings than object mass.

This study successfully developed and validated a sophisticated FBG-based finger exoskeleton rehabilitation device. The system demonstrated high accuracy， reliability， and multi-functionality in monitoring finger joint angles， assessing contact forces， and recognizing complex hand gestures. The findings confirm that the proposed device and its underlying mathematical model are effective for comprehensive finger rehabilitation monitoring. Consequently， this work provides substantial theoretical and technical support for the practical implementation of FBG intelligent sensing technology in clinical finger rehabilitation applications， paving the way for more data-driven and personalized therapeutic interventions.

All-time laser ranging to space debris is a critical direction in the development of future laser ranging systems. During Earth's shadow periods， however， debris objects receive no solar illumination and are invisible to optical cameras. In these conditions， telescope pointing corrections cannot rely on imaging and must instead depend on echo signal strength. Because debris moves rapidly and orbit prediction errors remain， even targets briefly captured within the field of view quickly drift out. This prevents stable tracking and reduces ranging success rates.

To address this challenge， this study introduces a novel approach by incorporating a fiber array into the laser ranging system. The array consists of a central fiber surrounded by six uniformly arranged multimode fibers. Each fiber is divided into two outputs： one reference fiber of equal length and one guiding fiber of varying lengths. The fourteen outputs are fused into a single fiber， which is then connected to a Superconducting Nanowire Single-Photon Detector （SNSPD）. By measuring the relative delays among echo signals， the incident fiber can be identified， enabling calculation of the target's offset direction in the field of view and guiding the telescope for tracking.

Experiments were conducted using the 1.2 m coaxial laser ranging system at Yunnan Observatory during May-June 2025. Cooperative targets with small orbit prediction errors were first selected as observation objects to verify the effectiveness of the fiber array method. After validation， the fiber array method was applied to space debris ranging experiments during Earth's shadow periods. The results demonstrate that the fiber array can effectively enhance the tracking capability of the ranging system for invisible targets. For cooperative targets equipped with retro-reflectors， the system efficiently extracted echo signals and accurately identified the incident fiber， thereby deriving the offset direction and achieving stable and reliable ranging. For non-cooperative debris， the current system shows certain limitations. As shown in Fig. 6（d）， debris with NORAD ID 28480 produced strong echoes that allowed identification of the incident fiber as F5. In contrast， debris with NORAD IDs 22220 and 21393， shown in Figs. 6（e）-（f）， yielded more dispersed echoes， making it difficult to accurately invert the incident fiber from time-delay information. This phenomenon mainly arises from temporal overlap of signals received by adjacent fibers. Specifically， debris generally exhibits a size effect， where differences in reflection depth across the surface broaden echo signals in the time domain. At the same time， the low surface reflectivity of most debris weakens the echoes and increases their susceptibility to background noise. Together， these factors cause temporal overlap among the multichannel data， which appears in the residual plots as clearly dispersed echo patterns and prevents reliable fiber identification based on inherent time delays.

To improve performance for non-cooperative targets， fiber parameters require targeted optimization. First， the delay difference between reference and guiding fibers should be increased so that the minimum channel separation exceeds three times the ranging precision， thereby reducing signal overlap. Second， the spatial arrangement of the array should be optimized to maximize the average channel delay difference， further suppressing inter-channel interference. Additionally， the current multimode fibers use a core diameter of 62.5 µm and cladding diameter of 125 µm， with the core occupying only 25% of the cross-sectional area. Under weak signals， this may limit detection efficiency. Future designs could reduce cladding thickness to increase area utilization while maintaining low crosstalk， thereby improving overall system performance. In summary， the proposed fiber array method provides a new solution for high-precision tracking of invisible targets and constitutes an important technical step toward all-time space debris laser ranging. With further optimization of fiber parameters， the method offers strong engineering potential and prospects for practical application.

This study establishes a high-precision measurement methodology for determining the polarization vector and the circular-to-linear birefringence ratio of Spun Fiber， addressing the critical need for accurate parameter decoupling in Fiber Optical Current Transformers （FOCT）. By leveraging the fixed-point principle within the framework of quaternion polarization optics， the proposed method identifies the intrinsic rotation axis of the fiber on the Poincaré sphere， thereby circumventing the systematic errors induced by polarization axis misalignment and the complexities of prior axis calibration. The research focuses on the 1 310 nm wavelength band to provide a robust physical basis for optimizing FOCT sensitivity and stability through the precise quantification of residual linear birefringence and inherent circular birefringence. The methodology is centered on the application of quaternion polarization optics， where any polarization-dependent system is characterized by a Jones-Mueller Quaternion （JMQ） whose vector part defines the polarization vector W.

Utilizing an experimental configuration comprising an eight-wavelength discrete light source （1 284~1 318 nm）， an electric tunable Polarization Controller （PC） with 0.01 V resolution， and a high-accuracy Polarization Analyzer （PA）， the measurement process exploits the invariance of the output State of Polarization （SOP） when the input SOP is aligned with the fiber's polarization vector. During the direction-finding phase， continuous wavelength scanning is performed while the PC iteratively adjusts the input SOP； the polarization vector's orientation is confirmed when the output SOP trajectory on the Poincaré sphere converges to a stationary “fixed point”， minimizing the rotation radius to near zero. Following the identification of this vector， the magnitude of the birefringence is determined by shifting the fixed point to the Poincaré sphere's pole and moving the SOP to the equator via an output PC. By recording the angular displacement \mathrm{\Delta }\varphi of the SOP along the equatorial plane during subsequent wavelength scans， the total beat length L_p is calculated using the variational phase-shift formula L_p=(L\cdot \mathrm{\Delta }\lambda )/({\lambda }_{\mathrm{0}}\cdot (\mathrm{\Delta }\delta /\mathrm{2}\mathrm{\pi }))， where L is the fiber length and \mathrm{\Delta }\delta is the phase change derived from the trajectory arc. The circular beat length （L_c） and linear beat length （L_l） are then extracted by projecting the synthesized polarization vector onto the Z-axis and the equatorial plane， respectively， enabling a direct physical separation of the two birefringence components. Empirical validation was conducted using Fibercore SHB1250 elliptical polarization-maintaining Spun Fiber across multiple specimens with lengths of 1.20 m， 0.80 m， 0.53 m， and 0.46 m to ensure consistency and repeatability. For the 1.2 m sample， the polarization vector was precisely located at Stokes coordinates.

Across the experimental trials， the average circular beat length was measured at \mathrm{93.347}\mathrm{m}\mathrm{m} and the average linear beat length at \mathrm{497.451}\mathrm{m}\mathrm{m}， yielding a circular-to-linear birefringence ratio of \mathrm{5.33}. These results are highly congruent with the manufacturer's specified circular beat length range of 63~125 mm， verifying the accuracy of the fixed-point detection. Comprehensive error analysis indicates a systematic error of approximately \mathrm{0.063}， primarily stemming from the discrete resolution of the polarization controller and the intrinsic measurement uncertainty of the analyzer. The random error was restricted to \mathrm{0.002}， demonstrating the method's superior immunity to environmental perturbations， such as thermal drift and mechanical vibration， facilitated by the trajectory-fitting approach.

The fixed-point principle offers a physically intuitive and mathematically rigorous solution for the characterization of complex optical fibers， providing a significant advantage over Mueller matrix decomposition by eliminating the need for coordinate-dependent calibration. This methodology facilitates the high-precision decoupling of birefringence components， which is essential for suppressing zero-point drift and improving the long-term reliability of FOCT systems in high-voltage sensing applications. The ability to directly output the polarization vector and beat length parameters under varying environmental conditions establishes this approach as a candidate for the standardized testing of complex polarization-maintaining components. Furthermore， the robust performance of the fixed-point identification under broad wavelength scanning demonstrates its potential for broader applications in the standardized detection of non-uniform birefringence in advanced optical waveguides.

In the modern electronic countermeasure environment with strong electromagnetic interference， traditional wireless communication methods such as radio frequency， Wi-Fi， and millimeter waves face challenges like unstable connections and vulnerability to interference or detection. These issues hinder their ability to satisfy the high requirements for stealth and strong anti-interference needed by rear combat formation Unmanned Aerial Vehicles （UAVs）. Ultraviolet Communication （UVC）， with its non-line-of-sight transmission capability， low detectability， and high anti-interference performance in the “solar-blind”band， has become an important means of new-generation covert communication.

In this paper， a joint optimized clustering method （MOPSO-DE） integrating Multi-Objective Particle Swarm Optimization （MOPSO） and Differential Evolution （DE） is proposed to solve the problems of uneven node energy consumption and limited data transmission of traditional clustering algorithms in order to meet the demands for efficient and cooperative communication of formation UAVs in combat missions under UV covert communication.In this paper， we first develop models for UAV inter-copter Ultraviolet （UV） non-line-of-sight communication links and UAV energy consumption. We then propose four key metrics for selecting cluster heads： the node's remaining energy， stability of the UV link's Received Signal Strength Indicator （RSSI）， the node's normalized distance to the leader UAV， and consistency in speed. For the above multi-objective optimization problem， this paper introduces the multi-objective particle swarm optimization algorithm into the cluster head election process， constructs the Pareto optimal solution set by the non-dominated sorting mechanism， and filters out the global optimal cluster head set by combining with the congestion distance strategy， and at the same time introduces the differential evolution mechanism to prevent the algorithm from falling into the local optimum. In the data transmission phase， a communication framework based on the three-layer structure of ‘leader-cluster leader-member’ is designed， and an intra-cluster fast reconfiguration mechanism is proposed to cope with the failure of the cluster leader nodes due to energy depletion， deterioration of the link， or positional deviation. By constructing a single-objective weight function to achieve rapid cluster head replacement without global reclustering， the dynamic adaptive capability and control overhead of the network are effectively improved.

In the simulation validation this paper deploys 100 UAV nodes in a 1 000 m×1 000 m area based on Matlab platform， and comprehensively compares the network performance of MOPSO-DE with five algorithms： LEACH， WCA， LEACH-X， DSWCA， and CNNUC， under different simulation rounds. The results show that MOPSO-DE has significant advantages in terms of node energy consumption， network survival time and data transmission volume. Compared with the LEACH algorithm， WCA algorithm， LEACH-X algorithm， DSWCA algorithm and CNNUC algorithm， the MOPSO-DE algorithm respectively reduced the average energy consumption by 25.8%， 23.6%， 17.1%， 15.3% and 11.1%， and increased the data transmission volume by 91.6%， 54.42%， 29.3%， 12.8% and 9.6%. The article further simulates and analyses the effects of different node numbers， different packet lengths and different node movement speeds on the performance of the algorithm， and the results show that the increase of node density increases the overall communication load of the system； the increase of packet length improves the amount of data transmission per unit of time， but it also significantly accelerates the energy consumption of the nodes； and the effects of different movement speeds on the performance of the network show a ‘pre-favorable， post-favorable’ effect. The impact of different mobile speeds on the network performance shows the stage characteristics of ‘favorable in the early stage and unfavorable in the later stage’.

In summary， the MOPSO-DE multi-objective clustering algorithm proposed in this paper effectively integrates multi-factor optimization strategies and intelligent optimization methods， and achieves significant results in improving clustering efficiency， extending network life cycle， reducing energy consumption and enhancing link robustness， which provides theoretical support and algorithmic basis for the efficient deployment of UAV swarm self-organized communication networks in the future under the complex battlefield environment.

Calcium ions and dissolved oxygen are critical physiological indicators： Calcium ions regulate nerve conduction， muscle contraction， and cardiac function， while dissolved oxygen reflects tissue oxygenation status via blood oxygen partial pressure. Both parameters are implicated in diverse physiological and pathological processes， including respiratory， cardiovascular， and renal disorders.While traditional electrochemical methods can simultaneously detect calcium ions and dissolved oxygen， they have limitations such as the need for blood sampling， high maintenance requirements for electrodes， and long detection cycles. Existing multi-parameter sensing technologies based on fiber optics still have shortcomings in terms of stability and biocompatibility， limiting their application in long-term in vivo monitoring. In contrast， hydrogels， as a novel sensing material， offer the potential for monitoring physiological parameters within the body due to their excellent biocompatibility. Changes in calcium metabolism and oxygenation status interact in many physiological and pathological processes. Therefore， developing a technology capable of simultaneously detecting calcium ions and dissolved oxygen concentrations is crucial for comprehensively understanding an organism's physiological state and disease progression.

In response to the above problems， a hydrogel-based dual-parameter fibre-optic fluorescence sensing probe was proposed in this paper based on the principles of fluorescence enhancement and fluorescence quenching. The probe aims to provide an innovative clinical tool for early disease diagnosis， condition monitoring， and treatment evaluation. The probe was made of 80% w/v and 40% w/v polyethylene glycol diacrylate as core and cladding materials， respectively， and the core was doped with calcein and dichlorotris （1，10-phenanthroline） ruthenium （Ⅱ）， which acted as fluorescent indicators for calcium ions and dissolved oxygen， respectively. The preparation process of the dual-parameter sensing probe was optimized， and the sensitivity and accuracy of the probe for the dual-parameter detection were optimal when the concentration of calcein was 0.05 mg/ml， the concentration of dichlorotris （1，10-phenanthroline） ruthenium （Ⅱ） was 2.5 mg/ml， and the pre-soaking time of the probe was 4 h. The double peaks of the probe were obvious in the extreme concentration of the dual-parameter detection.

The sensing performance of the probe was investigated experimentally and the results showed that in the range of 0~2 mmol/L for calcium ions， the sensitivity of the system was 0.340 2 （mmol/L）^-1 with an R² of 0.997 8， and the lower limit of detection reached 0.095 mmol/L with a response time of 11 min. In the range of 0~41.6 mg/L for dissolved oxygen， the sensitivity of the system reached 0.020 03 （mg/L）^-1 with an R² of 0.996 82， and the lower limit of detection was 0.44 mg/L， with a response time of 12 min. The stability of this sensing probe was 99.31% and 99.4%， respectively， over a period of 3 days. The reproducibility of the probe reached 98.47% and 99.48%， respectively， after five sets of replicated experiments. Within the physiological temperature range， the probe has reliable detection performance. Compared with other substances in the blood， the probe has high selectivity for calcium ions and dissolved oxygen.

The probe takes advantage of the good biocompatibility and flexibility of hydrogel to overcome the rigidity limitations of traditional fiber optic materials and is more suitable for long-term monitoring in vivo. At the same time， the probe can also realize electromagnetic radiation-free detection， featuring a simple preparation process， low cost and high integration. By simultaneously detecting calcium ions and dissolved oxygen concentration， the probe can provide more comprehensive physiological data， overcoming the shortcomings of single-parameter detection. Based on this， the prepared dual parametric hydrogel fiber-optic probe for calcium ions and dissolved oxygen has the advantages of implantability， high sensitivity， and real-time online monitoring， which provides an innovative solution for biomedical detection. Especially in clinical scenarios， it is valuable for early prevention and real-time monitoring of diseases， accurate diagnostic decision-making， treatment planning and efficacy assessment， and other key aspects.

Pulse diagnosis， as a core diagnostic method in traditional Chinese medicine， has long faced challenges such as strong subjectivity and difficulty in standardization. To address these issues， this paper proposes a pulse diagnosis sensing system based on a tri-channel U-shaped Micro-Nano Fiber （MNF） sensor. The system can synchronously acquire pulse signals from the cun， guan， and chi positions at different pressing depths （floating， middle， and sinking）， comprehensively capture subtle variations in pulse waveforms， and integrate deep learning models to achieve accurate pulse recognition， thereby enhancing the objectivity and digitization of traditional Chinese medicine diagnosis.

To construct a synchronous signal acquisition system， three well matched U-shaped MNFs were embedded in a Polydimethylsiloxane （PDMS） film and fixed on a Polycarbonate （PC） substrate to form sensing units， which were arranged on the cun， guan， and chi parts respectively. MNF was prepared using flame heating cone method， with a diameter of about 3 μm and a bending radius of 2 mm. Its structural parameters were optimized through COMSOL simulation to achieve a balance between evanescent field effect and mechanical strength， ensuring both adaptation to radial artery pulsation and stable and accurate detection. The PDMS package thickness was optimized through ANSYS finite element simulation and ultimately determined to be 100 μm to achieve the best balance between mechanical strength and sensitivity. Three sensing units are integrated into a customized 3D printed silicone platform， which has good biocompatibility and can adapt to individual physiological differences. In the experimental section， a tri-channel Photoelectric Detection （PD） module was used to convert the optical signals into electrical signals， and the dynamic waveforms were displayed in real time on an oscilloscope. To simulate the three pulse conditions of “floating， middle， and sinking” in traditional Chinese medicine， an airbag cuff loading system was designed to apply different levels of pressure to the pulse positions， thereby enabling signal acquisition. Through signal processing， multiple physiological parameters including Rise Wave Transit Time （RWTT）， Upstroke Time （UT）， Pulse Transit Time （PTT）， Left Ventricular Ejection Time （LVET）， etc. are extracted. In terms of intelligent recognition， a dataset containing 1 260 annotated pulse wave data was constructed， covering seven typical pulse types： normal pulse， forceful pulse， rapid pulse， flooding pulse， slippery pulse， tense pulse， and thready pulse. A deep learning model combining one-dimensional Convolutional Neural Network （1D-CNN） and Bidirectional Gated Recurrent Unit （BiGRU） is proposed， where CNN is used for local feature extraction and BiGRU is used to capture temporal dependencies， thereby achieving efficient classification of multi-channel pulse signals.

Mechanical performance tests show that the sensitivity of the three channel MNF sensor in the pressure range of 0~7 kPa is 11.49% kPa^-1、11.64% kPa^-1 and 11.13% kPa^-1， respectively. The response time and recovery time are 22.7 ms/9.4 ms、31.2 ms/10.6 ms and 29.8 ms/8.5 ms， demonstrated favorable sensitivity and a high level of consistency. After 600 cycles of loading unloading， the signal remained stable， verifying the reliability of the packaging structure. Compared with existing flexible pulse sensors， this device has advantages in sensitivity， channel count， and response speed.In the pulse diagnosis experiment， the system can simultaneously obtain pulse signals from the cun， guan， and chi parts， and clearly characterize the waveform differences at different compression depths. As the pressure increases from shallow to deep， the pulse wave gradually flattens， UT shortens， and the rate of rise accelerates， indicating physiological changes in reduced vascular wall compliance. Through waveform analysis， objective parameters of seven common pulse patterns were successfully extracted， which were highly consistent with clinical manifestations in volunteer experiments. For example， diarrhea patients showed thready pulse， while cold patients showed tense pulse， demonstrating the effectiveness of the system in capturing pulse patterns. In the intelligent recognition experiment， the deep learning model achieved 91.92% and 92.01% accuracy in classifying left and right hand pulse signals， respectively， when only using intermediate data； When integrating data from floating， medium， and sinking phases， the accuracy significantly improves to 96.61% （left hand） and 96.07% （right hand）. Compared with existing classification methods based on manual features or single networks， this method exhibits higher classification performance on similar data scales， demonstrating the superiority of combining 1D-CNN and BiGRU in complex pulse recognition.

The flexible pulse diagnosis and perception system based on three channel U-shaped MNF proposed in this study can achieve synchronous acquisition of pulse waves from multiple parts and depths of compression， and combine deep learning to achieve high-precision pulse recognition. This method effectively improves the objectivity and standardization level of traditional Chinese medicine pulse diagnosis， and has broad application prospects in remote medical care， digital diagnosis and treatment， and chronic disease management.

Vehicle-to-Vehicle Visible Light Communication （V2V-VLC） offers significant advantages in intelligent transportation systems， such as leveraging existing vehicular lighting， spectrum license exemption， low latency， and resistance to multipath interference. By transmitting critical information like vehicle position and driving instructions via LED light sources， V2V-VLC facilitates collaborative decision-making among vehicles. However， the open nature of optical transmissions exposes V2V-VLC to interception risks， leading to privacy breaches and traffic hazards. Current security measures primarily rely on higher-layer encryption and authentication， which fail to address vulnerabilities caused by physical-layer signal exposure. Physical Layer Security （PLS） complements existing methods by providing transparent protection across multiple data types. Yet， PLS research in VLC has been predominantly limited to static indoor environments， which are unsuitable for dynamic road scenarios characterized by high-speed mobility， variable topologies， and wide-beam light coverage. Particularly at intersections， extensive signal exposure and unpredictable eavesdropper locations exacerbate security risks.

This study proposes an Artificial Noise （AN） interference mechanism based on traffic infrastructure collaboration to minimize eavesdroppers' Signal-to-Interference-plus-Noise Ratio （SINR） under worst-case conditions， thereby enhancing V2V-VLC security. To improve the physical-layer security of V2V-VLC in dynamic road environments， a collaborative AN jamming scheme coordinated by traffic infrastructure was introduced. As illustrated in Fig. 2， the source vehicle （S） transmits confidential data to the target vehicle （D） via LED headlights， while the traffic signal （J） concurrently emits controlled artificial noise. The jamming signals are optimized to avoid interference with legitimate vehicles while disrupting eavesdroppers' reception. An optimization problem is formulated to minimize the worst-case SINR of eavesdroppers while maintaining the quality of legitimate communication. The concave-convex procedure （CCP） is employed to derive the optimal solution， ensuring effective suppression of eavesdropper signals and improving the system's secrecy rate without degrading communication performance. Simulations validate the proposed scheme's effectiveness in enhancing V2V-VLC security. Vehicle parameters are configured as follows： the source vehicle's LED headlights are mounted 0.8 m above the ground and spaced 1.6 m apart， while the receiver's photodetector （PD） is installed on the target vehicle's rear bumper， 0.8 m above the ground， with a field of view （FOV） of 60°. Detailed parameters are listed in Table 2. Results indicate that eavesdroppers' SINR is highest near the source vehicle's LED and decreases with distance due to path loss （Fig. 3）. Localized SINR peaks occur near traffic signals due to the directional characteristics of their LEDs. The maximum SINR of eavesdroppers reaches-7.07 dB， 8.77 dB lower than the reference scheme （Table 3）. Compared to traditional spatial jamming methods， the proposed scheme achieves superior SINR suppression across all regions. For near-field eavesdroppers， SINR is reduced by 11.25 dB and 10.61 dB in Scenarios 1 and 2， respectively. For far-field eavesdroppers， reductions of 17.12 dB and 17.41 dB are observed （Fig. 4）. Furthermore， the system's average secrecy capacity improves by 0.68 bits/s/Hz （Fig. 5）. These findings confirm that minimizing the worst-case SINR effectively disrupts eavesdropper reception while preserving legitimate communication quality.

In summary， this study addresses physical-layer security challenges in V2V-VLC systems under dynamic road conditions. An infrastructure-assisted AN interference scheme is proposed to mitigate interception risks， particularly in high-risk areas like intersections. By optimizing interference generation from both source vehicles and traffic signals， the scheme achieves multidimensional suppression of eavesdropper signals while maintaining legitimate communication quality. Simulations show that the proposed method suppresses eavesdroppers' worst-case SINR to-7.07 dB， 8.77 dB lower than traditional spatial jamming methods， with maximum SINR reductions of 11.25 dB and 17.41 dB for near-field and far-field eavesdroppers， respectively. The scheme also improves system secrecy capacity by 0.68 bits/s/Hz.

Compared to conventional methods， this approach offers superior physical-layer security for V2V-VLC systems in complex and dynamic road environments. Key innovations include： 1） Addressing security challenges in V2V-VLC systems with unknown and randomly distributed eavesdroppers at intersections. 2） Proposing a joint jamming scheme utilizing traffic infrastructure as a cooperative interference source， extending the spatial dimension of security. 3） Formulating and solving an optimization problem to minimize the worst-case SINR of eavesdroppers without interfering with legitimate users. This study provides theoretical and technical solutions for enhancing V2V-VLC security， laying a foundation for future research.

In space-based gravitational wave detection systems， ultra-long laser interferometry arms are established between three communication satellites to detect the gravitational wave， requiring the space telescopes to perform both laser transmission and reception functions. However， the extreme weakness of gravitational wave signals imposes stringent requirements on optical path stability and backward stray light suppression in these telescopes， that is， the backward stray light must be less than 10^-10 of the output laser. The primary source of backward stray light from space telescopes arises from surface scattering. Even in ultra-smooth optical elements （roughness less than 1 nm）， the surface scattering inevitably occurs during laser transmission， generating backward-scattered light that affects the gravitational wave detection. Therefore， precise characterization of ultra-smooth optical surfaces is critical for analyzing and suppressing telescope backward stray light levels. However， conventional non-contact measurement methods face significant challenges， including complex instrument structure， high experimental cost， and insufficient accuracy， making it difficult to achieve rapid and high-precision measurement of ultra-smooth optical surface. Consequently， there is an urgent need to develop a surface quality assessment method for ultra-smooth optical elements to meet the extraordinary precision requirements of gravitational wave space telescopes.

For high-precision scattering measurement of ultra-smooth optical elements， Li B C's team proposed a method based on multi-channel cavity ring down technique. This method obtains the surface scattering rate by analyzing multi-channel ring-down signals， offering advantages including absolute measurement， immunity to laser source intensity fluctuation， and exceptional measurement accuracy. This work further combines the cavity ring-down-based scattering measurement technique with the Generalized Beckmann-Kirchhoff （GBK） scalar scattering model， to establish a method for estimating the surface parameters of ultra-smooth optical elements. In this method， the GBK scattering model is used to establish the scattering rate database of the optical elements， and the scattering rate of the ultra-smooth optical elements at different solid angles is measured based on the cavity ring down technique. Then， the scattering rate distribution measured by cavity ring down method is numerically fitted with the scattering rate database to obtain the surface parameters of the ultra-smooth optical elements， which provides a reference for the scattering characteristics analysis and stray light suppression of the ultra-smooth optical elements of space telescopes.

The scattering rate distribution of ultra-smooth optical element measured by the cavity ring down technique was simulated and calculated， with the measured data numerically fitted through our proposed surface parameter estimation approach. As shown in Figure 3， the fitting achieves remarkable precision with an error of 0.16% and a goodness of fit of 0.999 8. Further evaluation of the method's performance across different parameters demonstrates that within the applicable range of GBK theory， the method can achieve an error of below 4% （typically<0.5%）， while the goodness of fit is greater than 0.96 （often>0.999）. In addition， to address measurement errors induced by scattering overlap during bidirectional beam propagation in standing-wave cavity ring-down experiments， we analyzed key influencing factors. By integrating these findings with our predictive model， we quantified scattering overlap effects at various measurement angles and derived an optimized angular selection scheme to minimize interference.

This work proposes a novel method for estimating surface parameters of ultra-smooth optical elements by combining GBK scalar scattering theory with cavity ring-down scattering measurement techniques. The simulation analysis shows that， within the scope of application of GBK scalar theory， the relative error of this method is less than 4%， and the fitting degree is greater than 0.96， confirming its high accuracy for rapid and precise measurement of surface quality parameters. In addition， aiming at the error caused by the scattering overlap of the back and forth propagation of the beam in the scattering measurement experiment of the standing wave cavity ring down method， we propose a measurement angle selection scheme considering both the signal-to-noise ratio and the measurement accuracy， so as to reduce the influence of the scattering overlap of the forward and backward propagation on the scattering measurement and subsequent surface quality estimation.