Optical Fiber Technology最新文献

Balloon-like micro-displacement sensor based on chaotic correlation fiber loop ring-down system with loss compensation

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-28 DOI: 10.1016/j.yofte.2025.104213

Meiling Wang , Lingzhen Yang , Juanfen Wang , Xiaomin Fu , Xiaohui Chen , Yuxin Bai , Mingxiao Wu

{"title":"Balloon-like micro-displacement sensor based on chaotic correlation fiber loop ring-down system with loss compensation","authors":"Meiling Wang , Lingzhen Yang , Juanfen Wang , Xiaomin Fu , Xiaohui Chen , Yuxin Bai , Mingxiao Wu","doi":"10.1016/j.yofte.2025.104213","DOIUrl":"10.1016/j.yofte.2025.104213","url":null,"abstract":"<div><div>We demonstrate the micro-displacement sensing using the balloon-like optical fiber to the chaotic correlation fiber loop ring down system. The balloon-like fiber is made of the bent single-mode fiber stripped of a length of coating layer to the phase difference between the core and cladding mode by the interferometer. We analyzed the displacement sensing characteristics of the balloon-like fiber used as a sensing unit theoretically and experimentally. Due to the large bending curvature of the balloon-like fiber, the loss compensation chaotic correlation fiber loop ring down system is used for sensing. The sensing characteristics of balloon-like fibers of different sizes are compared and the influence of cavity length is studied. The result shows that the balloon-like structure with a total length of 4.5 cm embodies superior sensing performance, and its maximum sensitivity can reach −0.38 μs<sup>-1</sup>μm<sup>−1</sup> when the cavity length is set as 11.95 m. The balloon-like structure makes up for the shortcomings of the optical fiber loop ring down system in high-sensitivity micro-displacement sensing detection.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104213"},"PeriodicalIF":2.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metallic TaS2: A newly found transition metal dichalcogenide for Yb-doped mode-locked fiber laser

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-27 DOI: 10.1016/j.yofte.2025.104209

Qizheng Zhang, Zhiwan Hu, Xiangxiang Hu, Guohua Zeng, Pengfei He, Lili Tao

引用次数: 0

Enhanced resource allocation in elastic optical network using deep learning and optimization process

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-26 DOI: 10.1016/j.yofte.2025.104210

Subbulakshmi Easwaran, Mehdi Shadaram

{"title":"Enhanced resource allocation in elastic optical network using deep learning and optimization process","authors":"Subbulakshmi Easwaran, Mehdi Shadaram","doi":"10.1016/j.yofte.2025.104210","DOIUrl":"10.1016/j.yofte.2025.104210","url":null,"abstract":"<div><div>The elastic optical network offers several advantages in bandwidth allocation compared to traditional fixed-grid optical networks. These advantages stem from their ability to flexibly and efficiently allocate resources, meeting modern communication networks’ dynamic and diverse demands. It is crucial to handle dynamic traffic loads and proactively manage the resources in an elastic optical network with a productive technique. Deep learning is an effective tool for complex data analysis and real-time decision-making. We address a model that integrates two deep neural networks: generative adversarial network (GAN) for data augmentation; and echo state network (ESN) for network’s requirement prediction. Furthermore, an optimization process is carried out for efficient spectrum allocation. The GAN provides a considerable and reliable quantity of data necessary to train the ESN model that could provide the desired output. The ESN model is further enhanced by optimizing the essential parameters, enabling it to learn diverse traffic patterns and anticipate unusual situations. By using a GAN-ESN approach, there is a substantial benefit in reducing latency, saving energy, and optimizing bandwidth allocation. The simulation results confirm that the proposed scheme can significantly improve the performance of resource management and achieve a high degree of fairness(95%accuracy) in the evaluation metrics.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104210"},"PeriodicalIF":2.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fast MIMO frequency domain equalization for MDM systems

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-25 DOI: 10.1016/j.yofte.2025.104203

Shuchao Mi , Jianyong Zhang , Baorui Yan , Muguang Wang , Guofang Fan , Peiying Zhang

{"title":"A fast MIMO frequency domain equalization for MDM systems","authors":"Shuchao Mi , Jianyong Zhang , Baorui Yan , Muguang Wang , Guofang Fan , Peiying Zhang","doi":"10.1016/j.yofte.2025.104203","DOIUrl":"10.1016/j.yofte.2025.104203","url":null,"abstract":"<div><div>In mode division multiplexing (MDM) systems, mechanical or acoustic disturbances can cause modal coupling changes on timescales as short as tens of microseconds, which requires multi-input multi-output (MIMO) frequency-domain equalization (FDE) be able to adapt quickly. Based on the least mean squares Newton (LMS-Newton) algorithm, the LMS-Newton–Nesterov accelerated gradient (LMS-Newton–Nesterov) algorithm is proposed for six-mode MDM transmission systems. The Nesterov accelerated gradient method accelerates convergence by adding a correction factor, subtracting the momentum term from the loss function to adjust the current direction, and then updating the parameters based on this gradient. Simulation results suggest that the LMS-Newton–Nesterov algorithm converges faster to a lower asymptotic mean-square error (MSE) than the LMS and LMS-Newton algorithms, and the convergence rate would be correspondingly enhanced by 83.3% and 66.7%, respectively. After the LMS-Newton–Nesterov algorithm, the equalized signal is more ideal than the LMS and LMS-Newton algorithm. In six-mode MDM systems with QPSK, the LMS-Newton–Nesterov algorithm outperforms LMS and LMS-Newton algorithms by <span><math><mrow><mn>1</mn><mo>.</mo><mn>8</mn><mi>d</mi><mi>B</mi></mrow></math></span> and <span><math><mrow><mn>0</mn><mo>.</mo><mn>8</mn><mi>d</mi><mi>B</mi></mrow></math></span> at a symbol error rate (SER) of <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, respectively. The research results have guiding significance for improving the performance of MIMO FDE.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104203"},"PeriodicalIF":2.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coupling properties of asymmetric high index contrast soft glass dual-core fiber in C-band

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-25 DOI: 10.1016/j.yofte.2025.104211

Jozef Chovan , Miroslav Michalka , František Uherek , Dariusz Pysz , Ryszard Buczyński , Ignác Bugár

{"title":"Coupling properties of asymmetric high index contrast soft glass dual-core fiber in C-band","authors":"Jozef Chovan , Miroslav Michalka , František Uherek , Dariusz Pysz , Ryszard Buczyński , Ignác Bugár","doi":"10.1016/j.yofte.2025.104211","DOIUrl":"10.1016/j.yofte.2025.104211","url":null,"abstract":"<div><div>In this study, we investigate the impact of asymmetry and excitation wavelength on the coupling properties of soft glass dual-core optical fibers. Recent technological advancements have led to the development of high index contrast two-component soft glass dual-core fibers with sufficient symmetry, creating a new platform for nonlinear all-optical switching tasks. However, the nonlinear propagation of femtosecond pulses, which has been experimentally demonstrated, is significantly influenced by the linear optical properties of the fibers, including spectral profiles of dispersion, coupling length, and coupling efficiency. Our contribution extends beyond the novel non-destructive experimental investigation of these properties. We also conduct extensive numerical studies on the linear transmission characteristics. The findings we obtained not only deepen the understanding of the nonlinear experimental results but also provide valuable insights for development of the next generation of dual-core fibers, which will be more suitable for all-optical data processing applications.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104211"},"PeriodicalIF":2.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing mixed-grid optical switching networks: A dual-phase approach for resource optimization and security analysis

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-24 DOI: 10.1016/j.yofte.2025.104205

S. Shanthini Devi , N. Kirubanandasarathy

{"title":"Enhancing mixed-grid optical switching networks: A dual-phase approach for resource optimization and security analysis","authors":"S. Shanthini Devi , N. Kirubanandasarathy","doi":"10.1016/j.yofte.2025.104205","DOIUrl":"10.1016/j.yofte.2025.104205","url":null,"abstract":"<div><div>Network operators typically find it challenging to upgrade their network infrastructure due to concerns about cost and service level agreements, particularly when it comes to backbone optical switching networks (OSNs). These days, network operators use backbone OSNs’ flex-grid to fixed-grid node migration process to support a multitude of bandwidth-demanding applications. But without careful planning, it could result in the wasteful use of resources. Also, during the migration process, the networks face security issues specific to optical communication networks including susceptibility to eavesdropping, data interception, unauthorized access, and denial-of-service attacks that compromise data confidentiality, integrity, and availability. This work offers resource allocation optimization methods for mixed-grid OSNs to optimize resource utilization. Modern optical networks feature complex architectures and a variety of technologies, making network management and information distribution challenging. This complexity is exacerbated by diverse optical technologies and service delivery protocols. This research addresses security and resource allocation in optical communication networks using a unique method that combines Siamese Heterogeneous Convolutional Neural Networks (SHCNN) with Triangulation Topology Aggregation Optimizer (TTAO). The introduced method consists of two phases. In the first phase, SHCNN-TTAO is proposed for Resource allocation. In the second phase, Software-Defined Fuzzy Alpine Skiing Neural Network for security analysis. Key performance metrics such as accuracy, Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are comprehensively assessed. The proposed method attains a higher accuracy of 99.7%, and lower RMSE of 0.015329, MSE of 0.000235, and MAPE of 0.000343.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104205"},"PeriodicalIF":2.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2 µm mode-locked pulse generation using nickel metal-organic framework (Ni-MOF) coated arc-shaped fiber

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-24 DOI: 10.1016/j.yofte.2025.104212

H. Ahmad , J.W. Chiam , M.Z. Samion , M.F. Ismail , Z. Radzi , S. Mutlu , S. Savaskan Yilmaz , N. Arsu , B. Ortaç , K. Thambiratnam

{"title":"2 µm mode-locked pulse generation using nickel metal-organic framework (Ni-MOF) coated arc-shaped fiber","authors":"H. Ahmad , J.W. Chiam , M.Z. Samion , M.F. Ismail , Z. Radzi , S. Mutlu , S. Savaskan Yilmaz , N. Arsu , B. Ortaç , K. Thambiratnam","doi":"10.1016/j.yofte.2025.104212","DOIUrl":"10.1016/j.yofte.2025.104212","url":null,"abstract":"<div><div>Metal-organic framework (MOF) is an emergent material that consists of metal ions and highly porous organic ligands. In this study, we demonstrate a 2 μm all-fibre mode-locked thulium/holmium-doped fiber laser (THDFL) by utilizing a nickel-based MOF (Ni-MOF) as a saturable absorber (SA) material. For the fabrication of the SA, the material was drop-casted on an arc-shaped fiber. The SA showed a 19.98 MW/cm<sup>2</sup> saturation intensity and an exceptionally high modulation depth of 15.3 %, surpassing previously reported materials in this wavelength range. The laser output centered at 1907.66 nm with an average power reaching up to 2.057 mW. The fundamental pulse rate and width were 14.30 MHz and 1.664 ps, respectively. Consequently, the THDFL generates pulses with an energy of 175 pJ and a peak power of 105 W. Mode-locked operation proved stable as the laser was observed over 2 h and showed a signal-to-noise ratio (SNR) of about 48 dB. Notably, this work represents the first demonstration of Ni-MOF as an SA in a THDFL, opening new avenues for ultrafast fiber laser development.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104212"},"PeriodicalIF":2.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Securing SDON with hybrid evolutionary intrusion detection system: An ensemble algorithm for feature selection and classification

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-20 DOI: 10.1016/j.yofte.2025.104206

Benitha Christinal J. , Ameelia Roseline A.

{"title":"Securing SDON with hybrid evolutionary intrusion detection system: An ensemble algorithm for feature selection and classification","authors":"Benitha Christinal J. , Ameelia Roseline A.","doi":"10.1016/j.yofte.2025.104206","DOIUrl":"10.1016/j.yofte.2025.104206","url":null,"abstract":"<div><div>Software-Defined Optical Network (SDON) is a modernized approach to manage optical networks by integrating the principles of Software-Defined Networking (SDN). This integration allows for enhanced programmability, flexibility, and automation in network operations, addressing the growing demand for efficient and adaptable network infrastructures. SDON is powered by a centralized controller, where the entirety of network intelligence is integrated into the control plane. However, this centralization introduces significant challenges related to data privacy and network security. To address these security issues, this paper proposes an adaptive IDS-SDON-EFSC (Intrusion Detection System for Software-Defined Optical Network with Ensemble Feature Selection and Classification) framework. In the proposed framework, during the initial phase, the gathered data is pre-processed to remove noise, and Adaptive Synthetic (ADASYN) sampling is implemented to balance imbalanced datasets. Overfitting is mitigated using Root Mean Square Deviation (RMSD) regularization, which reduces the loss between training and test data. In the second phase, the Deep Convolutional Neural Network Attention-based Bi-directional Long Short-Term Memory (DCNN-AttBiLSTM) system is employed for classification. The hybrid metaheuristic-Adam optimizer is used to optimize hyperparameter selection. Experiments demonstrate the efficacy of the IDS-SDON-EFSC model, which was trained and evaluated on the most current and practical datasets in the SDON domain: InSDN, SDN-IoT, and SDNFlow. The model achieved excellent performance, discerning various forms of intrusion with 100% accuracy on training data and 99.71% accuracy on test data. Additionally, it achieved a minimal latency rate of 1.6%, reduced controller overhead, and a minimal degradation rate of 2.1% leading to improved throughput performance when executed in real-time networks.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104206"},"PeriodicalIF":2.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MT-SCUNet: A hybrid neural network for enhanced mode decomposition in optical fibers

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-18 DOI: 10.1016/j.yofte.2025.104196

Baorui Yan , Jianyong Zhang , Shuchao Mi , Muguang Wang , Chenyu Wang , Guofang Fan , Peiying Zhang

{"title":"MT-SCUNet: A hybrid neural network for enhanced mode decomposition in optical fibers","authors":"Baorui Yan , Jianyong Zhang , Shuchao Mi , Muguang Wang , Chenyu Wang , Guofang Fan , Peiying Zhang","doi":"10.1016/j.yofte.2025.104196","DOIUrl":"10.1016/j.yofte.2025.104196","url":null,"abstract":"<div><div>In few-mode and multimode fibers, data-driven mode decomposition (MD) leveraging deep learning has achieved notable progress and demonstrated significant advantages in simulated environments. However, when applied to experimental scenarios, the practicality of MD is hindered by substantial challenges, primarily due to inherent noise and alignment errors in optical profile image acquisition systems. Therefore, MT-SCUNet: a multitasking hybrid neural network model by integrating Swin-Transformer and Convolutional Neural Network architectures is proposed in this paper to address these limitations. It is capable of performing image restoration, classification and modal coefficient-based image reconstruction tasks simultaneously. Furthermore, accurate predictions on real-world images are attained upon the convergence of training, facilitated by the meticulous processing of pure simulation data with additive white Gaussian noise (AWGN) and mismatch errors to align with experimental conditions. The model’s effectiveness is verified using both simulation and experimental data on a few-mode fiber supporting up to 10 modes. The results show that the model performs well in terms of image restoration and reconstruction accuracy, with average peak signal-to-noise ratio (PSNR), structured similarity index measure (SSIM), and Pearson correlation coefficient (PCC) values of 66.03 dB, 0.9824, and 0.9910 for simulated data and 60.03 dB, 0.9694, and 0.9733 for experimental data, respectively. Additionally, the model also achieves 99.11% classification accuracy on the validation set. This work provides a solid foundation for advancing data-driven deep learning algorithms in MD, while also opening up new possibilities for applications in optical communications, sensing, and imaging systems.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104196"},"PeriodicalIF":2.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Few-Mode Erbium-Doped Fiber Amplifiers for high-capacity optical networks using a multi-objective optimization algorithm

IF 2.6 3区计算机科学

Optical Fiber Technology Pub Date : 2025-03-18 DOI: 10.1016/j.yofte.2025.104186

Rym Regaieg , Mohamed Koubàa , Abdullah S. Karar , Kaboko Jean-Jacques Monga , Ehsan Adibnia , Hafedh Mahmoud Zayani , Mohamed Salhi , Faouzi Bahloul

{"title":"Optimizing Few-Mode Erbium-Doped Fiber Amplifiers for high-capacity optical networks using a multi-objective optimization algorithm","authors":"Rym Regaieg , Mohamed Koubàa , Abdullah S. Karar , Kaboko Jean-Jacques Monga , Ehsan Adibnia , Hafedh Mahmoud Zayani , Mohamed Salhi , Faouzi Bahloul","doi":"10.1016/j.yofte.2025.104186","DOIUrl":"10.1016/j.yofte.2025.104186","url":null,"abstract":"<div><div>In this paper, an optimized design for a Few-Mode Erbium-Doped Fiber Amplifier (FM-EDFA) is presented, using a Genetic Algorithm (GA) for multi-objective optimization of gain, noise figure (NF), and differential modal gain (DMG) across multiple modes. The GA explored a three-layer erbium doping profile structure to support four mode groups (<span><math><msub><mrow><mtext>LP</mtext></mrow><mrow><mn>01</mn></mrow></msub></math></span>, <span><math><msub><mrow><mtext>LP</mtext></mrow><mrow><mn>11</mn><mi>a</mi><mo>/</mo><mi>b</mi></mrow></msub></math></span>, <span><math><msub><mrow><mtext>LP</mtext></mrow><mrow><mn>21</mn><mi>a</mi><mo>/</mo><mi>b</mi></mrow></msub></math></span>, and <span><math><msub><mrow><mtext>LP</mtext></mrow><mrow><mn>02</mn></mrow></msub></math></span>). Results demonstrated that an optimized two-layer configuration was sufficient to achieve low DMG, low NF, and consistent high gain across modes, essential for long-haul space-division multiplexing (SDM) systems. MATLAB simulations validated the FM-EDFA performance under varying doping profiles, revealing a robust balance of gain, DMG, and NF for multimode amplification. This optimized FM-EDFA model supports high-capacity SDM transmission with stable, uniform amplification, offering valuable insights into efficient amplifier design for next-generation optical networks.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"93 ","pages":"Article 104186"},"PeriodicalIF":2.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0