In industrial networks, more and more intelligent applications put forward new requirements for deterministic guarantee ability of end-to-end transmission. Most of current research works on deterministic technologies focus on guarantee of their own network certainty, but ignore end-to-end determinism requiring cooperation of multiple deterministic technologies. Based on application scenario of nuclear power industry, a wide-area end-to-end deterministic network architecture with Flexible Ethernet (FlexE) and Time-Sensitive Networking (TSN) fusion is proposed, and an architecture is elaborated from two aspects of control plane function and data plane cooperative scheduling. In real wide area network environment, based on the dual-motor collaboration and industrial machine vision application scenario, the performance of the architecture is tested and verified. Results show that the architecture can meet the needs of applications for wide-area end-to-end deterministic networks, and offer good application value.

Chaos-based communication technology has emerged as a research hotspot in recent years due to its superior resistance to multipath fading and robust security features. Differential Chaos Shift Keying (DCSK), as a non-coherent digital modulation scheme, has attracted widespread attention. However, in practical communication scenarios, the increasing demand for reliable data transmission has revealed the limitations of traditional DCSK systems, such as low transmission rates and high Bit Error Ratios (BER), highlighting the urgent need to enhance system reliability. Considering the significant advantages of polar codes, including low complexity and near-capacity performance, this paper delves into the integration of polar coding algorithms with chaos modulation techniques based on channel polarization principles, aiming to further improve the reliability of chaos-based communication systems. Experiment results show that the proposed solution significantly improves the reliability of chaos-based communication systems and keeps its feature of low complexity.

Seepage monitoring is crucial for the safe operation and maintenance of dams. Traditional dam observation methods suffer from significant random errors and insufficient inspection frequency during flood seasons. To address these limitations, this study proposes an infrared thermography-based unmanned aerial vehicle inspection system for detecting surface seepage on dam bodies. First, an image dataset of seepage-affected areas on the dam surface was collected and established using an infrared camera. Then, an improved Mask Region-based Convolutional Neural Network(Mask R-CNN) framework was employed to extract seepage region data, enabling rapid detection of surface seepage. Subsequently, binary processing was applied to quantify the seepage area. Finally, the proposed method was validated on the downstream face of a hydropower station. Experimental results demonstrate that the proposed approach reduces the inspection cycle by 80% compared to traditional methods while maintaining sufficient accuracy for routine dam monitoring. This study provides a novel technique for seepage detection and quantitative analysis, offering a new solution for dam leakage inspection and seepage-related damage assessment.

Focusing on the channel estimation accuracy degradation caused by the beam splitting effect in near-field wideband Extremely-Large Scale Multiple Input Multiple Output (XL-MIMO) systems, this paper proposes a Bi-Directional Integrated Multi-Subcarrier Augmented Bilinear Pattern Detection (BDI-MSABPD) algorithm. Built upon the polar-domain sparse representation framework, the proposed method addresses both the sparse support set misalignment and parameter estimation bias induced by beam splitting through a dual mechanism combining explicit polar-domain resolution enhancement and implicit multi-subcarrier joint optimization. Simulation results demonstrate that the BDI-MSABPD achieves an average reduction of 2 dB in Normalized Mean Squared Error (NMSE) compared with conventional Bilinear Pattern Detection (BPD) algorithm.

To address the challenges of data scarcity, stylistic diversity, and complex textures in art image classification, a novel self-supervised learning framework is proposed——Frequency-Masked Contrast (F-MaCo). Built upon a dual-branch contrastive learning paradigm, F-MaCo leverages a two-dimensional Discrete Wavelet Transform (DWT) to project images into the frequency domain, enabling dynamic frequency-domain masking augmentation. Additionally, a perceptual loss-driven weighting mechanism is introduced to effectively capture the multi-scale features and rich textures information of art images. Experimental results demonstrate that F-MaCo achieves state-of-the-art performance on four art image datasets—MAMe, Kaokore, Artbench10, and ArtDL—with Top1 accuracies of 73.72%, 77.38%, 58.38%, and 68.31%, respectively, validating its effectiveness and robustness in art image representation learning.

Driven by the ever-increasing supply of computational resources, the parameter size of Large Language Models(LLMs) continues to expand and their task performance in natural language processing has become more superior. However, there are still limitations when faced with reasoning problems, especially in common-sense reasoning or mathematical problems. Chain of Thought(CoT) significantly improves its ability to solve problems in different domains by guiding the model to generate reasoning steps. In this paper, we not only sort out the theoretical foundation system and technical evolution of CoT from the perspective of training method, but also further discuss application scenarios such as government service and enterprise digitalisation. Finally, in the light of the development trend of Artificial Intelligence (AI), the paper discusses the essential role of CoT in the development of LLMs towards a higher cognitive level from the perspective of the degree of AI, and points out the challenges and technical bottlenecks that need to be solved at the present time.

Reconfigurable Intelligent Surface (RIS) and Rate Splitting Multiple Access (RSMA) technologies are two emerging communication techniques with broad prospects in future wireless systems. Active RIS (ARIS) has the advantage of overcoming the effects of multiplicative fading compared to Passive RIS (PRIS). The paper addresses the outage performance of ARIS-assisted RSMA systems under random deployment. Under Nakagami-m small-scale fading model in downlink, multiple users are randomly distributed within a semicircular region around ARIS and sorted by the distances between the users and ARIS. Taking the optimal phase shift for the furthest user as the entry point, a moment matching approach is employed to derive the shape and scale parameters associated with the cascaded channel power characteristic for each RSMA user. We derive a closed-form expression for the outage performance of the users, analyze the effect of the power splitting coefficients on the Outage Probability (OP), and obtain the diversity order and an expression for the approximate OP. Simulation results show that the OP of a non-perfect phase-shifted user under ARIS-assistance is re duced by 58% compared to the passive RIS-assisted system under a power budget of 50 dBm and a moderate amount of RIS elements. In addition, the power consumption of the user with the optimal phase shift is reduced by 53 dBm.