With the rise of linguistic steganography, the robustness to withstand minor perturbations like textual edits or tokenization shifts remains a critical yet crucial challenge. To address this, we propose a novel robustness-enhancing method, Position-Agnostic Probabilistic Generation, which combines semantic clustering with probabilistic generation control. During decoding, the model is softly guided to prefer or avoid specific token sets through time-aware probability boosting, enabling robust bit embedding without relying on fixed token positions. These token sets are constructed from semantically coherent clusters derived from the language model's vocabulary and expanded to ensure fluency. A dynamic, time-aware boosting strategy is then applied to gradually amplify the likelihood of valid tokens throughout the generation. Experimental results demonstrate that our method consistently outperforms baselines in preserving hidden information under 10 types of perturbations.

Channel State Information (CSI), characterizing real-time optical channel conditions, is critical for system optimization in visible light communication (VLC) systems. In VLC networks, CSI acquisition typically relies on uplink feedback mechanisms that require frequent updates due to rapid channel variations. As access point (AP) density increases, this feedback process incurs substantial overhead that scales with network size, becoming a major bottleneck for system scalability. ‌By decoupling static channel characteristics from dynamic user positions‌, this paper proposes a dual-step strategy decoupling offline channel quantization from online uplink positioning. This enables efficient downlink CSI reconstruction via pre-trained lookup tables, bypassing real-time computations. The feedback overhead scales as 1/M of conventional methods in M-AP systems, with experiments showing less than 3% spectrum efficiency loss for precoding-sensitive applications in high speed mobile scenarios. The framework’s adaptability to varying LED densities further supports practical deployment in large-scale networks.‌ This work resolves the CSI acquisition bottleneck, enabling scalable VLC systems with minimal performance degradation.

Abstract Since the quality of steel is of paramount importance in modern production, the defects detection of steel surface is significantly crucial. In this field, two‐stage detection algorithms have encountered issues about low detection speed, while one‐stage detection algorithms have room for improvement in detection accuracy. How to trade‐off between accuracy and speed of detection to better meet the demands of industrial production remains a challenge. To address this problem, this paper proposes a You Only Look Once version 5 (YOLOv5)‐based improved method ACD‐YOLO. ACD‐YOLO model incorporates anchors optimization, context augmentation module, and efficient convolution operators. In anchor optimization, the boundaries of anchors are optimized using an improved genetic algorithm. Moreover, to improve detection accuracy, a context augmentation module is incorporated into both the head and the backbone end of the network. Additionally, efficient convolution operators are adopted to address the increase of computation complexity caused by the context augmentation module. Experimental results show that ACD‐YOLO achieves mean average precision of 79.3%, with frames per second of 72. Compared to reference methods, ACD‐YOLO achieves the best balance between accuracy and speed of detection, and is more suitable for practice industrial production.