As the underlying foundation of a digital twin network (DTN), digital twin channel (DTC) can accurately depict the electromagnetic wave propagation in the

Blast furnace gas (BFG) produced from steel industries is generally one of the most important energy supplies in enterprises. Owing to a great deal of output, fluctuation and heterogeneity in data, it is very difficult to provide profound insights into its internal dynamic. In this paper, a novel analysis framework is developed for the BFG data processing, considering recurrence plot (RP) and recurrence quantification analysis (RQA). The specific aim is to investigate relationship between BFG output and its potential influencing factors. This framework can be deemed as an uniform and consistent system with functional components of qualitative visualization and quantitative analysis. Concretely, the BFG outputs related to five factors are separately projected to high-dimensional spaces, followed by that their internal dynamics can be embodied through a 2-D recurrence representation of states. Finally, five RQA parameters are used to quantify the influence of these factors on the BFG output. This is the first attempt revealing the relations among BFG data from the qualitative and quantitative perspectives. Experimental results show that RP can discover the BFG output patterns of laminar state, chaos and instability over given three states of influencing factors, and the ranked influential degree can be given by a two-stage standard deviation of all considered RQA measures. Besides, we also demonstrate that the temperature of the hot-blast stove is most relevant to the BFG output, while the influence of considered other factors directly depends on the selected series length.

Due to its significant efficiency, semantic communication emerges as a promising technique for sixth-generation (6G) networks. The wireless propagation channel plays a crucial role in system design, as it directly impacts transmission performance and capability. Given the increasingly complex communication scenarios, the channel exhibits high dynamism and poses challenges in acquisition. In such cases, sensing-based methods have drawn significant attention. To enhance system robustness, we propose a predictive channel-based semantic communication (PC-SC) system tailored for sensing scenarios. The PC-SC system is designed with an orientation toward applications by directly taking semantic targets into account. It comprises three modules: transmitter, predictive channel, and receiver. Firstly, at the transmitter, instead of employing global semantic coding, the scheme emphasizes preserving semantic information through target-based semantic extraction. Secondly, the channel prediction module predicts the dynamic wireless channel by utilizing the extracted target-based semantic information. Finally, at the receiver, the target-based semantic information can be utilized to meet specific application requirements. Alternatively, pre-captured background and semantic targets can be composited to fulfill complete image reconstruction needs. We evaluate the proposed approach by using a sensing image transmission scenario as a case study. Experimental results demonstrate the superiority of the PC-SC system in terms of image reconstruction performance and cost savings of bit. We employ beam prediction as a channel prediction task and find that the targets-based method outperforms the complete image-based approach in terms of efficiency and robustness, which can provide 32% time-saving.

Deep learning has become a mainstream method in marine data processing field. However, the raw marine data, characterized by fluctuations, outliers and noise, is a serious obstacle to its performance improvement. To address this problem, a hybrid deep computing model is developed for marine sensor data prediction, combining smoothing and deep belief echo state network (DBEN). The proposed structure adopts a two-stage data processing mode to deal with the complex characteristics of marine time series. In the preprocessing stage, four smoothing methods are considered for fluctuation reduction and outlier handling, so that purer data conducive to achieving a higher prediction accuracy can be gained. In the prediction stage, DBEN equipped with efficient feature learning serves as a nonlinear approximator. The effectiveness of the constructed hybrid model is tested on real-world marine time series of different sources and traits, and its superiority in prediction accuracy is demonstrated by readout comparisons and statistical measures.