Search Results (106)

Accurate group target association is essential for multi-sensor multi-target tracking, particularly in heterogeneous radar systems where systematic biases, asynchronous observations, and dense formations frequently cause ambiguous or incorrect associations. Existing approaches often rely on strict spatial assumptions or pre-trained models, limiting their robustness when measurement distortions and sensor-specific deviations are present. To address these challenges, this work proposes a robust association framework that integrates deep feature embedding, density-adaptive clustering, and global graph-theoretic matching. The method first applies an autoencoder–HDBSCAN clustering scheme to extract stable latent representations and obtain adaptive group structures under nonlinear distortions and non-uniform target densities. A weighted bipartite graph is then constructed, and a global optimal matching strategy is employed to compensate for heterogeneous systematic errors while preserving inter-group structural consistency. A mutual-support verification mechanism further enhances robustness against random disturbances. Monte Carlo experiments show that the proposed method maintains over 90% association accuracy even in dense scenarios with a target spacing of 1.4 km. Under various systematic bias conditions, it outperforms representative baselines such as Deep Association and JPDA by more than 20%. These results demonstrate the method’s robustness, adaptability, and suitability for practical multi-radar applications. The framework is training-free and easily deployable, offering a reliable solution for group target association in real-world multi-sensor fusion systems. Full article

A fast and stable flight control system is crucial for improving the efficiency of unmanned aerial vehicle (UAV) missions. Focusing on the trajectory tracking control of quadrotor UAVs, this paper proposes a trajectory tracking control method based on the global fast terminal sliding mode control (GFTSMC) algorithm to address the slow response speed and insufficient anti-disturbance capability inherent in the widely used Proportional–Integral–Derivative (PID) control algorithm and conventional sliding mode control (SMC) algorithm. Firstly, considering the gyroscopic moment of a quadrotor UAV’s rotors, an accurate kinematic and dynamic model of a quadrotor UAV is established, and the trajectory tracking problem faced by such UAVs is decoupled into the command tracking problems of the position loop and the attitude loop. Secondly, GFTSMC controllers are designed for these loops, and the Lyapunov principle is adopted to prove the stability of the designed controllers. Finally, simulation verification is carried out. The simulation results show that, compared to PID control, GFTSMC-based trajectory tracking control for quadrotor UAVs exhibits the characteristics of no overshoot, higher tracking accuracy, and stronger anti-disturbance capability. Compared to nonsingular terminal sliding mode control (NTSMC) and SMC, GFTSMC-based trajectory tracking control reduces the steady-state convergence time by 33.8% and 36.5% and the steady-state disturbance error by 83.1% and 97.3%, respectively, demonstrating faster response speed and stronger anti-disturbance capability. Therefore, the application of GFTSMC significantly improves the trajectory tracking control performance of quadrotor UAVs, thereby supporting them in performing operations in scenarios requiring high real-time performance, precision, and anti-disturbance capability. Full article

With the rapid development of the vegetable industry and the accelerating pace of population aging, mechanization in the core production process of vegetable seedling grafting has become an inevitable trend. To address this, various vegetable grafting devices have been developed globally. However, most existing equipment exhibits limited automation and is prone to damaging young plant stems during operation. To effectively reduce seedling injury, improve grafting quality, and increase success rates, this study focused on tray seedlings of cucurbitaceous vegetables as grafting subjects. Based on the bud grafting method, we conducted mechanistic analysis and structural design for the cutting module, the integrated clamping and grafting mechanism, and the clip supply and binding system. Experiments were carried out at the Protected Agriculture Demonstration Base in Ke Township, Yecheng County, Kashgar Prefecture, Xinjiang. The study adopted the multiple-group repeated experiment verification method, and completed verification through cutting tests and grafting efficiency tests. Specifically, 250 rootstocks and 250 scions were selected for the cutting tests, while 500 rootstocks and 500 scions were selected for the grafting efficiency tests; both tests were divided into 5 groups, and the data were analyzed using descriptive statistical analysis. Cutting trials and clamping performance tests demonstrated that the designed mechanism improves the precision of alignment between rootstock and scion cuts while minimizing potential damage during clamping, confirming the rationality of the design. The overall performance was further validated in grafting trials using Qingyan rootstock No. 1 pumpkin and Yongtian No. 5 melon as scions. Results showed that with rootstock and scion cutting angles set at 30° and 25°, respectively, and corresponding cut surface lengths of 6.34 ± 0.18 mm and 6.29 ± 0.14 mm, the device achieved a grafting efficiency of 1400 plants per hour with an average success rate of 90%, and no obvious stem damage was observed during the clamping process. These results demonstrate that the proposed grafting machine design is effective in enhancing both grafting efficiency and quality. Full article

In federated learning, secure aggregation is essential to protect the confidentiality of local model updates, ensuring that the server can access only the aggregated result without exposing individual contributions. However, conventional secure aggregation schemes lack mechanisms that allow participating nodes to verify whether the aggregation has been performed correctly, thereby raising concerns about the integrity of the global model. To address this limitation, we propose V-MHESA (Verifiable Masking-and-Homomorphic Encryption–combined Secure Aggregation), an enhanced protocol extending our previous MHESA scheme. V-MHESA incorporates verification tokens and shared-key management to simultaneously ensure verifiability, confidentiality, and authentication. Each node generates masked updates using its own mask, the server’s secret, and a node-only shared random nonce, ensuring that only the server can compute a blinded global update while the actual global model remains accessible solely to the nodes. Verification tokens corresponding to randomly selected model parameters enable nodes to efficiently verify the correctness of the aggregated model with minimal communication overhead. Moreover, the protocol achieves inherent authentication of the server and legitimate nodes and remains robust under node dropout scenarios. The confidentiality of local updates and the unforgeability of verification tokens are analyzed under the honest-but-curious threat model, and experimental evaluations on the MNIST dataset demonstrate that V-MHESA achieves accuracy comparable to prior MHESA while introducing only negligible computational and communication overhead. Full article

Environmental information fraud, such as greenwashing, severely impedes the achievement of global Sustainable Development Goals (SDGs). Blockchain technology, as an innovation tool with a sustainability orientation, offers new possibilities for improving the reliability of supply chain information oversight. However, its practical application mechanisms and policy value in green supply chain governance remain unclear. This study focuses on the greenwashing behavior of core enterprises and constructs an incomplete information game model to compare and analyze the inherent mechanisms of traditional regulation (TR) and blockchain-based digital supply chain regulation (DSCR). By simulating the strategic choices of enterprises between “genuine production” and “greenwashing” within a supply chain network, this research finds that when the quality of on-chain information reaches a certain threshold, the blockchain consensus mechanism can more accurately reveal corporate moral hazards, such as information manipulation, significantly reducing the incidence of greenwashing. As the number of enterprises participating in the blockchain network increases, the reliance on high-quality information in the DSCR model decreases, and regulatory efficiency is further enhanced through network effects. The findings provide theoretical support for designing regulatory strategies against greenwashing: Blockchain technology can build a trustworthy supply chain ecosystem through cross-enterprise data verification, directly supporting the SDG 12 goal of “Responsible Production.” Its decentralized nature helps optimize industrial infrastructure (SDG 9) and indirectly promotes climate action (SDG 13). This study suggests that regulatory agencies use policy tools such as “establishing on-chain information quality standards” and “incentivizing enterprises to join the blockchain network” to strengthen the practical application of the model, while also addressing implementation challenges such as data authenticity and digital infrastructure compatibility. Full article

The increasing integration of High-Voltage Direct Current (HVDC) systems and renewable energy challenges traditional grid strength assessment. This paper proposes a comprehensive framework that combines a composite strength index with an interpretable importance analysis to address this issue. First, a composite index is developed using the AHP-CRITIC method to fuse structural and fault withstand metrics. Then, to identify the factors influencing this index, SHapley Additive exPlanations (SHAP) is employed, accelerated by a high-fidelity Gaussian Process Regression (GPR) surrogate model that overcomes the computational burden of large-scale simulations. This GPR-SHAP approach provides both global parameter rankings and local, scenario-specific explanations, overcoming the limitations of conventional sensitivity analysis. Validated on a detailed model of the Southwest Power Grid in China, the framework successfully quantifies grid strength and pinpoints key vulnerabilities. Verification through a typical scenario demonstrates that implementing coordinated increases in both generation and load (each by 1000 MW) in the Chengdu area, as guided by local SHAP explanations, significantly improves the grid strength index from 33.73 to 47.61. It provides operators with a dependable tool to transition from experience-based practices to targeted, proactive stability management. Full article

Offshore wind turbines are rapidly scaling in size, which amplifies the need for credible integrated load analyses that consistently resolve the coupled dynamics among rotor–nacelle–tower systems and their support substructures. This study presents a comprehensive ultimate limit state (ULS) load assessment for a fixed jack-up-type substructure (hereafter referred to as K-wind) coupled with the IEA 15 MW reference wind turbine. Unlike conventional monopile or jacket configurations, the K-wind concept adopts a self-installable triangular jack-up foundation with spudcan anchorage, enabling efficient transport, rapid deployment, and structural reusability. Yet such a configuration has never been systematically analyzed through full aero-hydro-servo-elastic coupling before. Hence, this work represents the first integrated load analysis ever reported for a jack-up-type offshore wind substructure, addressing both its unique load-transfer behavior and its viability for multi-MW-class turbines. To ensure numerical robustness and cross-code reproducibility, steady-state verifications were performed under constant-wind benchmarks, followed by time-domain simulations of standard prescribed Design Load Case (DLC), encompassing power-producing extreme turbulence, coherent gusts with directional change, and parked/idling directional sweeps. The analyses were independently executed using two industry-validated solvers (Deeplines Wind v5.8.5 and OrcaFlex v11.5e), allowing direct solver-to-solver comparison and establishing confidence in the obtained dynamic responses. Loads were extracted at the transition-piece reference point in a global coordinate frame, and six key components (Fx, Fy, Fz, Mx, My, and Mz) were processed into seed-averaged signed envelopes for systematic ULS evaluation. Beyond its methodological completeness, the present study introduces a validated framework for analyzing next-generation jack-up-type foundations for offshore wind turbines, establishing a new reference point for integrated load assessments that can accelerate the industrial adoption of modular and re-deployable support structures such as K-wind. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

The malicious contamination of food has been recognized by the World Health Organization (WHO) as a real and current threat that must be integrated into food safety systems to ensure preparedness for deliberate attacks. Traditional approaches, such as HACCP, effectively address unintentional hazards but remain insufficient against intentional contamination and sabotage. Food defense frameworks such as HACCP (Hazard Analysis and Critical Control Points), VACCP (Vulnerability Assessment and Critical Control Points), and TACCP (Threat Assessment and Critical Control Points) represent complementary methodologies, addressing unintentional, economically motivated, and deliberate threats, respectively. This review critically examines food defense frameworks across the European Union, the United States, and the United Kingdom, as well as standards benchmarked by the Global Food Safety Initiative (GFSI), drawing on peer-reviewed and grey literature sources. In the United States, the Food Safety Modernization Act (FSMA) mandates the development and periodic reassessment of food defense plans, while the European Union primarily relies on general food law and voluntary certification schemes. The United Kingdom’s PAS 96:2017 standard provides TACCP-based guidance that also acknowledges cybercrime as a deliberate threat. Building on these regulatory and operational gaps, this paper proposes the Cyber-FSMS model, an integrated framework that combines traditional food defense pillars with cyber risk management to address cyber–physical vulnerabilities in increasingly digitalized supply chains. The model introduces six interconnected components (governance, vulnerability assessment, mitigation, monitoring, verification, and recovery) designed to embed cyber-resilience into Food Safety Management Systems (FSMS). Priority actions include regulatory harmonization, practical support for small and medium-sized enterprises (SMEs), and the alignment of cyber-resilience principles with upcoming GFSI benchmarking developments, thereby strengthening the integrity, robustness, and adaptability of global food supply chains. Full article

Nuclear energy plays a critical role in global decarbonisation, but its expansion raises concerns about the proliferation risks associated with conventional fuel cycles. This study addresses this challenge by evaluating Am-241 doping as a method to enhance the intrinsic proliferation resistance of nuclear fuel. Using full-core simulations across Pressurised Water Reactors (PWRs), Boiling Water Reactors (BWRs), and Molten Salt Reactors (MSRs), the research assesses the impact of Am-241 on isotopic composition, reactor performance, and safety. The results show that Am-241 reliably increases the Pu-238 fraction in spent fuel above the 6% threshold, which significantly complicates its use in nuclear weapons. Additionally, Am-241 serves as a burnable poison, reducing the need for conventional absorbers without compromising operational margins. Economic modelling indicates that the levelised cost of electricity (LCOE) increases modestly, with the most notable impact observed in MSRs due to continuous doping requirements. The project concludes that Am-241 doping offers a passive, fuel-intrinsic safeguard that complements existing verification regimes. Adoption of this approach may require adjustments to regulatory frameworks, particularly in fuel licencing and fabrication standards, but could ultimately support the secure expansion of nuclear energy in regions with heightened proliferation concerns. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

Cross-border Fifth Generation Mobile Communication (5G) roaming requires secure N32 connections between network operators via Security Edge Protection Proxy (SEPP) interfaces, but current Transport Layer Security (TLS) 1.3 implementations face a critical trade-off between connection latency and security guarantees. Standard TLS 1.3 optimization modes either compromise Perfect Forward Secrecy (PFS) or suffer from replay vulnerabilities, while full handshakes impose excessive latency penalties for time-sensitive roaming services. This research introduces Zero Round Trip Time Forward Secrecy (0-RTT FS), a novel protocol extension that achieves zero round-trip performance while maintaining comprehensive security properties, including PFS and replay protection. Our solution addresses the fundamental limitation where existing TLS 1.3 optimizations sacrifice security for performance in international roaming scenarios. Through formal verification using ProVerif and comprehensive performance evaluation, we demonstrate that 0-RTT FS delivers 195.0 $\mathsf{\mu }$s handshake latency (only 17% overhead compared to insecure 0-RTT) while providing full security guarantees that standard modes cannot achieve. Security analysis reveals critical replay vulnerabilities in all existing standard TLS 1.3 optimization modes, which our proposed approach successfully mitigates. The research provides operators with a decision framework for configuring sub-millisecond secure handshakes in next-generation roaming services, enabling both optimal performance and robust security for global 5G connectivity. Full article

Background/Objectives: Cancer remains a leading global cause of death, with breast, lung, colorectal, and prostate cancers being among the most prevalent. The integration of Artificial Intelligence (AI) into cancer imaging research offers opportunities for earlier diagnosis and personalized treatment. However, the effectiveness of AI models depends critically on the quality, standardization, and fairness of the input data. The EU-funded INCISIVE project aimed to create a federated, pan-European repository of imaging and clinical data for cancer cases, with a key objective to develop a robust framework for pre-validating data prior to its use in AI development. Methods: We propose a data validation framework to assess clinical (meta)data and imaging data across five dimensions: completeness, validity, consistency, integrity, and fairness. The framework includes procedures for deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance. Results: The pre-validation process identified key data quality issues, such as missing clinical information, inconsistent formatting, and subgroup imbalances, while also demonstrating the added value of structured data entry and standardized protocols. Conclusions: This structured framework addresses common challenges in curating large-scale, multimodal medical data. By applying this approach, the INCISIVE project ensures data quality, interoperability, and equity, providing a transferable model for future health data repositories supporting AI research in oncology. Full article

Accurate calculation of embodied carbon emissions in buildings (ECE) is crucial to achieving global carbon neutrality. However, fragmented data, inconsistent regional standards, and low computational efficiency have long hindered existing methods. This study innovatively integrates large language models (LLMs) with retrieval-enhanced generation (RAG) technology to establish a new intelligent accounting paradigm for embodied carbon in buildings. Through a systematic evaluation of three basic models—Kimi, Doubao, and DeepSeek-R1—in a five-level progressive input scenario, the study quantitatively reveals the “information sensitivity” patterns of LLMs. To address the illusion errors of general models in professional scenarios, an innovative three-stage closed-loop architecture of “knowledge retrieval—calculation embedding—trustworthy generation” is proposed. By dynamically invoking domain knowledge bases and embedded computing modules, zero-error verification of benchmark data is achieved. The core contributions include the following: (1) It has been clarified that the basic large model has application potential in calculating the implicit carbon emissions of buildings, but the reliability of the results is limited. (2) The influence of data elements on calculation accuracy is revealed. (3) The application path for integrating RAG with large models has been pioneered, and the results show that the RAG technology can enhance the performance of large models in calculating the implicit carbon emissions of buildings by approximately 25%. (4) The significant efficiency improvement of RAG technology is verified. (5) A supporting theoretical and application system is established. Full article

The 2015 Paris Agreement paved the way for the carbon trade economy, which has since evolved but has not attained a substantial magnitude. While carbon credit exchange is a critical mechanism for achieving global climate targets, it faces persistent challenges related to transparency, double-counting, and verification. This paper examines how Distributed Ledger Technology (DLT) can address these limitations by providing immutable transaction records, automated verification through digitally encoded smart contracts, and increased market efficiency. To assess DLT’s strategic potential for leveraging the carbon markets and, more explicitly, whether its implementation can reduce transaction costs and enhance market integrity, three alternative approaches that apply DLT for carbon trading were taken as case studies. By comparing key elements in these DLT-based carbon credit platforms, it is elucidated that these proposed frameworks may be developed for a scalable global platform. The integration of existing compliance markets in the EU (case study 1), Australia (case study 2), and China (case study 3) can act as a standard for a global carbon trade establishment. The findings from these case studies suggest that while DLT offers a promising path toward more sustainable carbon markets, regulatory harmonization, standardization, and data transfer across platforms remain significant challenges. Full article