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.

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.

Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and resource-demanding but also fails to fully harness the creative potential of expansive diffusion models. To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training steps. The scheme proposes a Feature Normalization Mapping Module with Cross-Attention Mechanism (INN-FMM) based on the dual-path diffusion model. This module employs soft attention to extract style features and integrate them with content features. Additionally, a parameter-free Similarity Attention Mechanism (SimAM) is employed within the image feature space to facilitate the transfer of style image textures and colors, while simultaneously minimizing the loss of structural content information. The fusion of these dual attention mechanisms enables us to achieve style transfer in texture and color without sacrificing content integrity. The experimental results indicate that our approach exceeds existing methods in several evaluation metrics.

An early warning on the highway will effectively reduce traffic accidents. Considering the influence of an abandoned object on driving behavior, a Visual-based Asymmetric Two-lane Cellular Automata model with Abandoned Object (V-ATCA-AO) and an Internet-of-Vehicles-based Asymmetric Two-lane Cellular Automata model with Abandoned Object (IoV-ATCA-AO) are proposed. Based on the two models, two types of traffic accidents caused by an abandoned object are analyzed: rear-end collision caused by the abandoned object ahead and collision of the vehicle with the abandoned object. Simulation results show the following: (1) the accidents occur when the road density is smaller, while the accidents will not occur when the density is larger. The results are different from the rear-end collision rate curve without abandoned object in a single lane. (2) Compared with the visual-based avoidance pattern in V-ATCA-AO, the Internet-of-Vehicles-based avoidance pattern in IoV-ATCA-AO can create an early warning for the abandoned object and tell the vehicle to make an earlier lane change and decelerate in advance, thereby significantly reducing the accident rate. (3) Spatiotemporal characteristics in front of the abandoned object directly affect the accident rate: the less the “stability” of a traffic jam in front of the abandoned object, the higher the accident rate.