This paper tackles denial-of-service (DoS) attacks and output constraints in secondary voltage control of islanded microgrids. While existing literature predominantly addresses communication security, it largely overlooks the operational boundaries of power converters. This oversight may result in control saturation, device failure and ultimately, compromised system stability. Furthermore, traditional consensus-based voltage regulation fails to facilitate necessary power interchange among sub-microgrids, limiting its practical applicability. To overcome these limitations, a novel distributed voltage control scheme based on the inclusion principle is proposed. Firstly, a distributed control system model incorporating output constraints is established. The nonlinearities of the system are effectively handled via a barrier Lyapunov function approach, ensuring strict adherence to the output voltage amplitude limits. Secondly, an adaptive state estimator with a switching mechanism is developed to mitigate intermittent communication failures induced by DoS attacks. The controller parameters are then obtained by solving a set of linear matrix inequalities, guaranteeing the semi-global uniform ultimate boundedness (SGUUB) of all closed-loop signals and the ultimate convergence of errors to a neighborhood of the origin. Simulation results verify that the proposed strategy effectively ensures voltage stability while simultaneously satisfying both output constraints and cybersecurity requirements.

Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks.Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly.While this underlying assumption is frequently adopted, it is not universally applicable, which can result in potential shortcomings in learning effectiveness.In this paper, for the first time, we transfer the prevailing concept of "one node one receptive field" to the heterophilic graph.By constructing a proxy label predictor, we enable each node to possess a latent prediction distribution, which assists connected nodes in determining whether they should aggregate their associated neighbors.Ultimately, every node can have its own unique aggregation hop and pattern, much like each snowflake is unique and possesses its own characteristics.Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond.We conduct comprehensive experiments including (1) main results on 10 graphs with varying heterophily ratios across 10 backbones; (2) scalability on various deep GNN backbones (SGC, JKNet, etc.) across various large number of layers (2,4,6,8,16,32 layers); (3) comparison with conventional snowflake hypothesis; (4) efficiency comparison with existing graph pruning algorithms.

We propose a novel mixed analog-to-digital converter (ADC) architecture for direction-of-arrival (DOA) estimation using a uniform linear array, where the in-phase and quadrature-phase channels can be independently sampled by different ADCs. We derive the Cramér-Rao bound (CRB) and utilize its lower bound to optimize the placement of different ADCs via a swap-based method. Numerical examples are provided to demonstrate the superiority of the optimized placement of different ADCs over existing schemes.

We consider a mixed analog-to-digital converter (ADC) based architecture for direction of arrival (DOA) estimation using a uniform linear array (ULA). We derive the Cramér-Rao bound (CRB) of the DOA under the optimal time-varying threshold, and find that the asymptotic CRB is related to the arrangement of high-precision and one-bit ADCs for a fixed number of ADCs. Then, a new concept called "mixed-precision arrangement" is proposed. It is proven that better performance for DOA estimation is achieved when high-precision ADCs are distributed evenly around the edges of the ULA. This result can be extended to a more general case where the ULA is equipped with various precision ADCs. Simulation results show the validity of the asymptotic CRB and better performance under the optimal mixed-precision arrangement.