In the context of information warfare, command and control (C2) networks are exhibiting increasingly prominent multi-network dependency characteristics, leading to a growing interest in the study of interdependent C2 network models. We propose a modeling approach for interdependent C2 networks based on integrated reconnaissance-strike and OODA loop, addressing the limitations of existing edge connection strategies in effectively capturing the interdependent coupling relationships within the network. First, the interdependent relationships within the network are described, and a dual-layer structural model of interdependent C2 networks is abstracted. Second, based on the local efficiency of nodes, an edge connection strategy for sensing and firepower nodes under integrated reconnaissance-strike is proposed. Third, interdependence strength and link balance are defined, and an inter-layer coupling edge connection strategy based on interdependence strength and link balance is proposed. Finally, algorithmic simulations are designed to analyze the network properties of the model and network performance under different edge connection strategies. Simulation results demonstrate that the proposed modeling method effectively captures the interdependent characteristics of C2 networks while exhibiting enhanced network resilience against destruction.

As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the $LS{E}^{FSTE100}$ and $LS{E}^{S\&P500}$ are higher than $LS{E}^{SZI}$, which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy.

Lane detection is a common task in computer vision that involves identifying the boundaries of lanes on a road from an image or a video. Improving the accuracy of lane detection is of great help to advanced driver assistance systems and autonomous driving that help cars to identify and keep in the correct lane. Current high-accuracy models of lane detection are mainly based on artificial neural networks. Among them, CLRNet is the latest famous model, which attains high lane detection accuracy. However, in some scenarios, CLRNet attains lower lane detection accuracy, and we revealed that this is caused by insufficient global dependence information. In this study, we enhanced CLRNet and proposed a new model called NonLocal CLRNet (NLNet). NonLocal is an algorithmic mechanism that captures long-range dependence. NLNet employs NonLocal to acquire more long-range dependence information or global information and then applies the acquired information to a Feature Pyramid Network (FPN) in CLRNet for improving lane detection accuracy. Using the CULane dataset, we trained NLNet. The experimental results showed that NLNet outperformed state-of-the-art models in terms of accuracy in most scenarios, particularly in the no-line scenario and night scenario. This study is very helpful for developing more accurate lane detection models.

Due to the inadequacy of edge importance identification methods for single network connectivity or transmission capability, a novel approach to edge importance ranking: balancing network structure and transmission performance is proposed. First of all, a new definition for network energy intensity and network constraint coefficient is presented, and an improved structure hole method is proposed and verified. It solves the problem that the traditional structural hole method cannot be used to identify important edges directly. Second, the edge information entropy of the command and control network is defined by introducing the ego-network betweenness centrality, and the complexity of information entropy calculation is reduced. Furthermore, the novel edge importance ranking method based on the combination of the improved structure hole and information entropy is proposed. This method solves the problem of edge importance evaluation which takes into account network structure and transmission performance. Finally, the performance simulation experiments are designed based on different network models and edge importance ranking methods. Simulation results show that the proposed method has higher recognition accuracy and better universality for important edges.

Most existing cascading failure models for command and control networks (C2Ns) primarily focus on node-level failures while overlooking edge-level failures. To address this issue, this paper proposes a novel edge-based cascading failure model that integrates edge importance and hierarchical structure. First, both local and global edge importance are quantified through edge degree and ego network edge betweenness, and an improved definition of edge importance is introduced by incorporating an enhanced bridging coefficient. Second, based on the hierarchical structure of network nodes, edge hierarchy is defined, and a new initial edge load formulation is proposed by combining edge hierarchy and importance. Third, to reflect load imbalance in realistic networks, a nonlinear edge capacity model is constructed, and a non-uniform, adjustable load redistribution strategy based on the residual capacity of neighboring edges is developed to accommodate network hierarchy. Simulation experiments are conducted to validate the proposed model. The results show that, in two representative C2N networks, the degradation rates of average efficiency and connectivity coefficient are 22.42% and 73.70% for Network 1, and 13.84% and 14.52% for Network 2, respectively. These results demonstrate that the proposed model significantly improves the robustness and resilience of C2Ns under cascading failure scenarios.