Siting low-altitude takeoff and landing platforms (vertiports) is a fundamental challenge for developing urban air mobility (UAM). This study formulates this issue as a variant of the capacitated facility location problem, incorporating flight range and service capacity constraints, and proposes SPID, a deep reinforcement learning (DRL)-based solution framework that models the problem as a Markov decision process. To handle dynamic coverage, the designed DRL framework-based SPID uses a multi-head attention mechanism to capture spatiotemporal patterns, followed by integrating dynamic and static information into a unified input state vector. Afterward, a gated recurrent unit (GRU) is used to generate the query vector, thereby enhancing sequential decision-making. The action network within the DRL network is regulated by a loss function that integrates service distance costs with unmet demand penalties, enabling end-to-end optimization. Subsequent experimental results demonstrate that SPID significantly enhances solution efficiency and robustness compared with traditional methods under flight and capacity constraints. Especially, across the social performance metrics emphasized in this study, SPID outperforms the suboptimal solutions produced by traditional clustering and graph neural network (GNN)-based methods by up to approximately 29%. This improvement comes with an increase in distance-based cost that is kept within 10%. Overall, we demonstrate an efficient, scalable approach for vertiport siting, supporting rapid decision-making in large-scale UAM scenarios.

The development of urban air mobility (UAM) systems requires scalable, regulation-aware planning of low-altitude airspace and supporting infrastructure. This study proposes an end-to-end framework for the design, simulation, and iterative optimization of a structured UAM corridor over Brasilia's central road axis (Eixão-UAM), aligned with the Brazilian unmanned aircraft traffic management (BR-UTM) ecosystem. In addition, this study proposes a multilayered aerial configuration stratified by unmanned aerial vehicle class, supported by a modular ground infrastructure composed of vertihubs, vertiports, and vertistops. A takeoff-scheduling simulator is developed to evaluate platform allocation strategies under realistic traffic and weather conditions. Initial experiments compare a round-robin (RR) baseline with a genetic algorithm (GA), and results reveal that RR outperforms GA v1 in terms of the average waiting time. To address this gap, a large language model (LLM) assisted optimization loop is implemented using GPT-4o Mini and Gemini 2.5 Pro. The LLMs act as reasoning partners, supporting the root-cause diagnoses, fitness function redesign, and rapid prototyping of five GA variants. Among these, GA v5 achieves a 59.62% reduction in maximum waiting time and an approximately 10% reduction in average waiting time over GA v1, thereby approaching the robustness of RR. In contrast, GA v2-v4 and GA v6 perform less consistently, showing an importance of fitness function design. These results underscore the role of an iterative, LLM-guided development in enhancing classical optimization, demonstrating that generative artificial intelligence (AI) can contribute to simulation acceleration and the cocreation of operational logic. The proposed method provides a replicable blueprint for integrating LLMs into early-stage UAM planning, offering both theoretical insights and architectural guidance for future low-altitude airspace systems.

Multi-aircraft task allocation (MATA) plays a vital role in improving mission efficiency under dynamic conditions. This paper proposes a novel coevolutionary genetic programming (CoGP) framework that automatically designs high-performance reactive heuristics for dynamic MATA problems. Unlike conventional single-tree genetic programming (GP) methods, CoGP jointly develops two interacting populations, i.e., task prioritizing heuristics and aircraft selection heuristics, to explicitly model the coupling between these two interdependent decision phases. A comprehensive terminal set is constructed to represent the dynamic states of aircraft and tasks, whereas a low-level heuristic template translates developed trees into executable allocation strategies. Extensive experiments on public benchmark instances simulating post-disaster emergency delivery demonstrate that CoGP achieves superior performance compared with state-of-the-art GP and heuristic methods, exhibiting strong adaptability, scalability, and real-time responsiveness in complex and dynamic rescue environments.

This paper addresses the urgent need to detect low, slow, and small (LSS) unmanned aerial vehicles (UAVs) in complex and critical environments, proposing an active low-altitude target detection method based on the cat's eye effect. The detection system incorporates a control module, a laser emission component, a co-optical path panoramic scanning optical mechanism structure, an echo reception component, target detection, and visualization processing to achieve small target detection. The light source is emitted by a near-infrared laser, and the scanning optical path is realized using micro-electro-mechanical system (MEMS) mirrors and servo mechanisms. The echo reception signal is received by an avalanche photodiode (APD) and the target detection module, which captures the reflected signal and distance information. The detection software integrates the local pyramid attention (LPA) module and the field pyramid network (FPN) through the UAV micro lens identification algorithm. It eliminates false alarms by incorporating SKNet21 and uses the APD to collect echo intensity and flight time, thereby reducing the false alarm rate. The results demonstrate the feasibility of the proposed target detection method, which achieves a mean average precision of 0.809 at an intersection over union (IoU) of 0.50, a mean average precision of 0.324 at an IoU of 0.50-0.95, and a throughput of 49.8 Giga floating-point operations per second (GFLOPs), indicating that it can address the current limitations in LSS target detection.

The rapid advancement of the low-altitude economy (LAE) necessitates a fundamental shift from fragmented systems toward deeply integrated communication, sensing, navigation, and control capabilities. To this end, this paper proposes a low-altitude digital-intelligent network (LADIN) as an overarching architecture, with integrated sensing and communication (ISAC) serving as the core enabling technology that pervasively unifies its three layers. At the heterogeneous infrastructure layer, we detail an ISAC waveform design based on orthogonal frequency division multiplexing, enabling dual-purpose hardware to simultaneously achieve high-speed data transmission and high-precision environmental sensing. Within the intelligent data fusion layer, ISAC's role expands into a multimodal fusion paradigm, providing the crucial electromagnetic sensing modality. This layer constructs a unified spatiotemporal feature space by introducing pluggable back-projection adapters and spatiotemporal modeling. These adapters systematically integrate heterogeneous data from ISAC, optical cameras, and light detection and ranging (LiDAR) by inverting their respective observation models, thereby overcoming representational disparities and association ambiguities. At the service and management layer, this coherent representation directly drives algorithmic processes and control policies. ISAC resources are virtualized into dynamically allocable assets, enabling closed-loop control that responds to the real-time state of the feature space, such as reconfiguring base station operational modes based on live situational awareness. Validation through multi-frequency collaborative sensing and multimodal fusion use cases demonstrates significant performance gains in tracking robustness, detection of near-zero radar cross-section targets such as balloons, and seamless urban airspace governance, conclusively establishing the transformative potential of a deeply integrated, ISAC-centric approach for future LAE systems.

Millimeter-wave (mmWave) communication is the key to increasing the demand for high data rates and low latency resulting from the rapid evolution of wireless communications, especially in the fifth generation (5G) of wireless communication systems and beyond. The mmWave band suffers from high path loss and obstacle blockage, significantly reducing the transmission range. Note that high-directional beams are required to perform well in the mmWave band. Hence, beam alignment is crucial for high-data-rate transmission between the transmitter (Tx) and the receiver (Rx). One of the drawbacks is getting an accurate beam alignment when the transceiver (Tx, Rx, or both) is mobile. Beam tracking plays a considerable role in 5G communications, especially in vehicular communications, due to the repeated change of the transceiver (Tx, Rx, or both) position. This work presents an overview of the different beam-tracking methods used in mmWave communications, focusing on hybrid beamforming techniques. We also compare the various tracking techniques in a recommendation table. This overview suggests that some tracking methods used in the sub-6-GHz band, such as least mean squares (LMS), recursive least squares (RLS), and Kalman filter, are unsuitable for the mmWave band (due to higher frequency and shorter coherence time), and it recommends faster tracking strategies.

With the increasing number of vulnerabilities exposed on the Internet, autonomous penetration testing (pentesting) has emerged as a promising research area. Reinforcement learning (RL) is a natural fit for studying this topic. However, two key challenges limit the applicability of RL-based autonomous pentesting in real-world scenarios: the training environment dilemma—training agents in simulated environments is sample-efficient while ensuring that their realism remains challenging; poor generalization ability—agents' policies often perform poorly when transferred to unseen scenarios, with even slight changes potentially causing a significant generalization gap. To address both challenges, we propose a generalizable autonomous pentesting framework termed GAP, which aims to achieve efficient policy training in realistic environments and train generalizable agents capable of drawing inferences about other cases from one instance. GAP introduces a real-to-sim-to-real pipeline that enables end-to-end policy learning in unknown real environments while constructing realistic simulations and improves agents' generalization ability by leveraging domain randomization and meta-RL learning. We are among the first to apply domain randomization in autonomous pentesting and propose a large language model-powered domain randomization method for synthetic environment generation. We further apply meta-RL to improve agents' generalization ability in unseen environments by leveraging synthetic environments. Combining the two methods effectively bridges the generalization gap and improves agents' policy adaptation performance. Simulations are conducted on various vulnerable virtual machines, with results showing that GAP can enable policy learning in various realistic environments, achieve zero-shot policy transfer in similar environments, and achieve rapid policy adaptation in dissimilar environments.

Inferring protocol state machines from observable information presents a significant challenge in protocol reverse engineering (PRE), especially when passively collected traffic suffers from message loss, resulting in an incomplete protocol state space. This paper introduces an innovative method for actively inferring protocol state machines using the minimally adequate teacher (MAT) framework. By incorporating session completion and deterministic mutation techniques, this method broadens the range of protocol messages, thereby constructing a more comprehensive input space for the protocol state machine from an incomplete message domain. Additionally, the efficiency of active inference is improved through several optimizations for the L_M⁺ algorithm, including traffic deduplication, the construction of an expanded prefix tree acceptor (EPTA), query optimization based on responses, and random counterexample generation. Experiments on the real-time streaming protocol (RTSP) and simple mail transfer protocol (SMTP), which use Live555 and Exim implementations across multiple versions, demonstrate that this method yields more comprehensive protocol state machines with enhanced execution efficiency. Compared to the L_M⁺ algorithm implemented by AALpy, Act_Infer achieves an average reduction of approximately 40.7% in execution time and significantly reduces the number of connections and interactions by approximately 28.6% and 46.6%, respectively.

Enhancement of service function chain (SFC) security ability by composing virtual network functions (VNFs) and allocating resources considering their security attributes can address the vulnerability threats in cloud environments, which is an important means of attempting to secure SFCs at the deployment stage. However, existing works do not consider the vulnerability correlation of the multi-step attack chains when completing SFC deployment based on trustworthiness. This results in existing security orchestration methods ignoring the differences in trustworthiness among network entities and focusing only on local trust optimization; these steps effectively disrupt the attack chains to secure SFCs. In this article, an innovative hierarchical trust model is proposed to assess the differentiated trustworthiness among network entities caused by vulnerability correlation. On the basis of trustworthiness assessment, both virtual trust of VNF combinations at the SFC composition stage and physical trust of physical node (PN) selections at the SFC placement stage are globally considered to disrupt the attack chains in SFCs as much as possible. To this end, the security-aware and cost-efficient SFC composition and placement (SCSCP) problem is formulated as an integer linear programming (ILP) problem, which is NP-hard. To tackle the SCSCP problem, the joint trust and cost global optimization (JTCGO) algorithm is proposed to dynamically update the trustworthiness and globally find the SFC deployment solutions including the VNF combination schemes and PN selection schemes. Simulation results demonstrate that our proposed algorithm can provide the optimal SFC deployment solutions for requests and can guarantee the SFC trustworthiness at a controllable cost, thereby protecting SFCs from network attacks in complex security environments.

Avoidance of topic drift and enabling crossing tunnels are two main difficulties in focused crawling. To overcome the problem of topic drift, we design a comprehensive priority evaluation (CPE) method based on the web text, anchor text, and context of hyperlinks, which improves the topic-relevance evaluation of unvisited hyperlinks. Subsequently, we propose an improved Bayesian classifier with weights (BCW), which adds label weights to the feature words of the Bayesian classifier to enhance the accuracy of webpage classification. To cross tunnels through which some topic-relevant webpages can be reached from low-relevance webpages, we construct a content block segmentation (CBS) technology for webpages based on the backtracking method, which segments a webpage into multiple blocks and then judges the relevance of every content block, extracting hyperlinks with high comprehensive relevance. Finally, a BCW-based focused crawling strategy combining the CPE and CBS strategies (BCW_CC) is proposed and experimentally evaluated for focused crawling in two domains: rainstorm disasters and sports. The results demonstrate the effectiveness of the developed BCW_CC method.

Accurate estimation of the background error covariance matrix denoted as B remains a critical challenge in numerical weather prediction (NWP), directly influencing data assimilation (DA) performance and forecast accuracy. Although hybrid ensemble-variational (EnVar) methods combine static and flow-dependent matrices to improve assimilation, their effectiveness is constrained by empirically fixed weights. To address this limitation, we propose DRL-EnVar, an adaptive hybrid EnVar DA method enhanced with deep reinforcement learning. DRL-EnVar integrates deep learning (DL) components, including a novel cyclic convolution module to extract abstract features from data, and employs reinforcement learning (RL) to dynamically optimize hybrid weighting strategies. The system adaptively combines multiple ensemble-based flow-dependent matrices with one or more static matrices to construct a time-varying hybrid matrix B that better reflects real-time background errors. Experimental results demonstrate that DRL-EnVar performs better than the traditional ensemble Kalman filter (EnKF) and hybrid covariance DA (HCDA) methods, especially under sparse observations or transitional changes in state variables. It achieves competitive or superior assimilation accuracy with lower computational cost, and can be flexibly integrated into both three-dimensional variational assimilation (3DVar) and four-dimensional variational assimilation (4DVar) frameworks. Overall, DRL-EnVar offers a novel and efficient approach to adaptive DA, particularly valuable for improving forecast skill during transitional weather regimes.

In recent years, physics-informed neural networks (PINNs) have shown remarkable potential in modeling conservative systems of rigid-body dynamics. However, when applied to practical interaction tasks of manipulators (e.g., part assembly and medical operations), existing PINN frameworks lack effective external force modeling mechanisms, resulting in significantly degraded prediction accuracy in dynamic interaction scenarios. Additionally, because industrial robots (including UR5 and UR10e robots) are generally not equipped with joint torque sensors, obtaining precise dynamics training data remains challenging. To address these issues, this study proposes two enhanced PINNs that integrate motor dynamics and external force modeling. First, two data-driven Jacobian matrix estimation methods are introduced to incorporate external forces: one learns the mapping between end-effector velocity and joint velocity to approximate the Jacobian matrix, while the other first learns the system's kinematic behavior and then derives the Jacobian matrix through analytical differentiation of the forward kinematics model. Second, current-to-torque mapping is embedded as physical prior knowledge to establish direct correlations between system motion states and motor currents. Experimental results on two different manipulators demonstrate that both models achieve high-precision torque estimation in complex external force scenarios without requiring joint torque sensors. Compared with state-of-the-art methods, the proposed models improve overall modeling accuracy by 31.12% and 37.07% on average across various complex scenarios, while reducing joint trajectory tracking errors by 40.31% and 51.79%, respectively.

In recent years, multi-label zero-shot learning (ML-ZSL) has garnered increasing attention because of its wide range of potential applications, such as image annotation, text classification, and bioinformatics. The central challenge in ML-ZSL lies in predicting multiple labels for unseen classes without requiring any labeled training data, which contrasts with conventional supervised learning paradigms. However, existing methods face several significant challenges. These include the substantial semantic gap between different modalities, which impedes effective knowledge transfer, and the intricate and typically complex relationships among multiple labels, making it difficult to model them in a meaningful and accurate manner. To overcome these challenges, we propose a graph-augmented multimodal chain-of-thought (GMCoT) reasoning approach. The proposed method combines the strengths of multimodal large language models with graph-based structures, significantly enhancing the reasoning process involved in multi-label prediction. First, a novel multimodal chain-of-thought reasoning framework is presented which imitates human-like step-by-step reasoning to produce multi-label predictions. Second, a technique is presented for integrating label graphs into the reasoning process. This technique enables the capture of complex semantic relationships among labels, thereby improving the accuracy and consistency of multi-label generation. Comprehensive experiments on benchmark datasets demonstrate that the proposed GMCoT approach outperforms state-of-the-art methods in ML-ZSL.

Signal-to-noise ratio (SNR) fluctuations significantly affect spectrum sensing performance in wireless communications. Traditional convolutional neural network (CNN) exhibits limited feature extraction capabilities and inefficient feature utilization at low SNR levels, leading to suboptimal spectrum sensing performance. This paper proposes a spectrum sensing method based on a multi-scale feature fusion network (MSFFNet) to address this issue. First, the proposed method employs a multi-scale feature extraction block (MSFEB) to capture multi-scale information from the input data comprehensively. Next, an adaptive feature screening strategy (AFSS) highlights key features while suppressing redundant information. Finally, a multi-level feature fusion mechanism (MLFFM) optimizes and integrates features across scales and levels, enhancing spectrum sensing performance. Simulation results demonstrate that compared to other methods, the proposed approach achieves superior performance in low-SNR communication scenarios. At an SNR of -14 dB, the detection probability P_d reaches 0.936, while the false alarm probability P_fa is only 0.1. Furthermore, this paper constructs a multi-level mixed-SNR dataset to simulate real communication environments and enhance the robustness of spectrum sensing.

In practical intelligent reflecting surface (IRS)-assisted multiuser communication systems, inevitable imperfections such as hardware impairments, imperfect channel state information (CSI), and the limited resolution of the IRS phase shifts would introduce interference and thus cause significant performance degradation. As an interference management strategy, rate-splitting multiple access (RSMA) employs the rate-splitting (RS) principle to partition user information into common and private parts, thereby offering enhanced robustness. Accounting for practical imperfections, this study investigates robust beamforming design in IRS-assisted multiuser systems under the RSMA architecture. First, we introduce a system model that captures these non-ideal factors and evaluate their impacts on communication performance. To enhance the performance of the considered system, a weighted sum rate maximization problem is formulated, for which a sample average approximation (SAA)-based robust algorithm is proposed to jointly optimize the IRS phase shifts and the beamforming matrix at the base station (BS). Simulation results demonstrate that the IRS-assisted RSMA system exhibits superior robustness compared to the IRS-assisted space division multiple access (SDMA) system in the presence of inevitable imperfections. Furthermore, the proposed SAA-based robust algorithm outperforms existing benchmark algorithms, highlighting its effectiveness and robustness.

As human exploration of the ocean expands, the demand for continuous, high-quality, and ubiquitous maritime communication is steadily increasing. However, the dynamic nature of the marine environment and resource constraints present significant challenges for traditional heuristic resource allocation methods, complicating the balance between high-quality communication and limited network resources. This results in suboptimal system throughput and an over-reliance on specific problem structures. To address these issues, in this paper, we introduce a joint resource allocation method based on knowledge embedding. The proposed approach includes an action distribution alignment module designed to improve resource utilization by preventing unreasonable action-output combinations. Furthermore, by integrating knowledge embedding with meta-reinforcement learning techniques, a physical guidance loss function is formulated, which effectively reduces the sample size required for model training, thereby enhancing the algorithm's generalization capabilities. Simulation results show that the proposed method achieves an increase in average system throughput of 31.19% compared to the model-agnostic meta-learning proximal policy optimization (MAML-PPO) algorithm and 80.91% compared to the RL2 algorithm, across various channel environments.

This study proposes a method for analyzing synchronization in oscillator systems, illustrated by modeling the dynamics of a circuit of two resistively coupled pulse oscillators. The dynamic characteristic of synchronization is the fuzzy entropy (FuzzyEn), which is calculated from a time series composed of the ratios of the number of pulse periods (subharmonic ratio, SHR) at phase-locking intervals. Low and high entropy values indicate strong and weak synchronization between the two oscillators, respectively. The proposed method effectively visualizes synchronized modes of the circuit using entropy maps of synchronization states. In addition, a classification of synchronization states is proposed based on the dependency of FuzzyEn on the embedding vector length of the SHR time series. An extension of this method for analyzing non-pulse (non-spike) signals is demonstrated using the example of phase-phase coupling rhythms of the local field potential of the rat hippocampus. The proposed entropy-statistical approach, using integers and pulse signal forms, is well-suited for signal synchronization analysis and can be implemented on digital mobile platforms.