Resource
2026 EN
Yonghoon Cho · Chulmin Jung
Mine hunting is crucial for ensuring the safety of maritime routes, particularly in areas critical for trade, military logistics, and regional stability. However, it is a hazardous operation involving continuous and exhaustingwork, which is difficult for humans to sustain. This paper presents a mission and path planning algorithm for mine hunting using multiple heterogeneous unmanned maritime vehicles. The complex mine detection search is divided into two stages: area assignment and path planning. In each stage, particle swarm optimization is employed to optimize the objective function, and probabilistic techniques are used to account for the probability of mine detection. To experimentally validate the proposed algorithm, an unmanned surface vehicle (USV) and autonomy modules for autonomous underwater vehicles (AUVs) were developed. The feasibility of the approach was verified through sea trials of collaboration between the AUV and USV, and the results are discussed.
Resource
2026 EN
Segundo Francisco Segura Altamirano · Carlos Leonardo Oblitas Vera · Victor Paul Palacios Paredes
+4 more
This systematic review of 79 CubeSat antenna studies reveals a progressive "workflow attrition cascade" (68%→70%→87%→92%) wherein electromagnetic design sophistication systematically decouples from deployment-ready system integration. While CubeSats demand reliable communication under severe constraints, our PRISMA 2020-compliant synthesis demonstrates that current research workflows rarely translate theoretical optimization into mission readiness. Specifically, 68% of studies omit critical simulation documentation, 70% rely on single-domain experimental validation inadequate for space qualification, 87% fail to achieve full thermal-mechanical integration of feeding elements, and 92% lack environmental qualification during fabrication. Rather than reflecting technical inability, this attrition highlights systemic gaps between component-level design and system-level engineering, exacerbated by academic incentives and resource constraints. Addressing these discontinuities requires a paradigm shift from isolated electromagnetic optimization toward concurrent multiphysics integration, establishing validated workflows essential for transitioning CubeSat payloads from experimental demonstrations to operational multi-year constellations.
Resource
2026 EN
Xianyong Dai · YANBIAO Li · Wentao Zhu
+5 more
Foothold selection for hexapod robots in complex terrain presents a significant challenge, demanding both high computational efficiency and robust adaptability. To address this issue, this paper introduces a foothold selection framework integrating multi-constraint pruning with adaptive evaluation. The framework employs a hierarchical decision-making mechanism. Initially, a Multi-dimensional Hard-Constraint Pruning Architecture (MHCPA) systematically incorporates kinematic, terrain, collision, and stability constraints. This stage efficiently eliminates physically infeasible solutions, thereby drastically reducing the decision space. Subsequently, an Adaptive Weighted Foothold Selection (AWFS) algorithm is applied to the pruned set of candidates. Leveraging a Foot Candidate Evaluation Function (FCEF), the AWFS algorithm dynamically adjusts evaluation weights based on the specific mission context, enabling adaptive decision-making. Simulation results demonstrate that the proposed framework improves decision-making efficiency by up to 90.8% while consistently securing high-quality footholds. Furthermore, in comparative analyses against state-of-the-art methods, our framework exhibits a favorable trade-off between solution quality and computational performance.
Resource
2026 EN
Muhammad Iqbal · Akihiro Nakao
Network slicing supports diverse applications from commercial services to Mission-Critical Services (MCS) such as disaster management. While the Open Radio Access Network (O-RAN) extends slicing through intelligent control in the Radio Access Network (RAN), current 3GPP specifications only define resource allocation at the slice level, without procedures for sub-slice management. This limitation poses a barrier in disaster-oriented MCS systems, where heterogeneous sensors, applications, and services coexist within the same slice. Our approach addresses these challenges by introducing 5ERASI (5G Service-Enabled Resource Allocation and Sub-slice Isolation), a Machine Learning-driven (ML) sub-slice orchestrator that enables per-service resource allocation, isolation, and dynamic policy enforcement. Our evaluation suggests that 5ERASI incurs a minor overhead, increasing User Equipment (UE) initial access time by an average of 23.41 ms, even in multiple-UE scenarios. Comprising Principal Component Analysis (PCA), our ML classification achieves 98.42% accuracy, 19.40 ms prediction time, and 631.58 ms training time, making it a lightweight and effective algorithm for offline classification while keeping 97.96% accuracy in an online setting. In the online slicing evaluation, the isolation level can be maintained at 2.03%, which is 0.85% below the baseline threshold of 2.88%. Finally, leveraging the RAN Intelligent Controller (RIC) architecture, 5ERASI enables dynamic, on-the-fly RAN resource allocation while preserving traffic isolation, and empirically demonstrates that the proposed additional signaling procedure can be feasibly integrated into the 3GPP framework without disrupting the existing 3GPP Initial Access Procedure.
Resource
2026 EN
Eashwar Sathyamurthy · Jeffrey W. Herrmann · Shapour Azarm
Although unmanned vehicle fleets offer efficiency in transportation, logistics and inspection, their susceptibility to failures poses a significant challenge to mission continuity. We study the Multi-Depot Rural Postman Problem with Rechargeable and Reusable Vehicles (MD-RPP-RRV) with vehicle failures, where unmanned rechargeable vehicles placed at multiple depots with capacity constraints may fail while serving arc-based demands. To address unexpected vehicle breakdowns during operation, we propose a two-stage real-time rescheduling framework. First, a centralized auction quickly generates a feasible rescheduling solution; for this stage, we derive a theoretical additive bound that establishes an analytical guarantee on the worst-case rescheduling penalty. Second, a peer auction refines this baseline through a problem-specific magnetic field router for local schedule repair, utilizing parameters calibrated via sensitivity analysis to ensure controlled computational growth. We benchmark this approach against a simulated annealing metaheuristic to evaluate solution quality and execution speed. Experimental results on 257 diverse failure scenarios demonstrate that the framework achieves an average runtime reduction of over 95% relative to the metaheuristic baseline, cutting rescheduling times from hours to seconds while maintaining high solution quality. The two-stage framework excels on large-scale instances, surpassing the centralized auction in nearly 80% of scenarios with an average solution improvement exceeding 12%. Moreover, it outperforms the simulated annealing mean and best solutions in 59% and 28% of scenarios, respectively, offering the robust speed-quality trade-off required for real-time mission continuity.
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2026 EN
Qingqing Liu · Peilong Zhang · Lingbing Meng
+1 more
Integrating Multimodal Large Language Models (MLLMs) into Vehicle-to-Everything (V2X) systems represents a fundamental prerequisite for achieving cognitive-level autonomous driving. However, the substantial computational requirements of MLLMs present a significant challenge to the inherent volatility of edge-cloud communication channels, often leading to severe reasoning interruptions during network jitters or stochastic link transitions.To bridge this chasm, we introduce the Resilient Edge-Cloud (REC) framework, a mission-critical orchestration system leveraging multimodal sensor streams (Camera and LiDAR) specifically engineered to perpetuate tactical intelligence continuity. The REC framework operates via three symbiotic components: (1) a RepDistiller (Module A) that executes cross-scale tactical semantic alignment via Knowledge Distillation (KD) to compress the cloud model’s reasoning heuristics into the edge surrogate; (2) a Robust Collaborative Fusion layer (Module B) that provides link-aware feature synchronization by internalizing transmission latency into Delay-aware Positional Encodings (DPE); and (3) an RL-CBF Orchestrator (Module C) that enforces task-offloading within a mathematically rigorous cognitive safety envelope by integrating Reinforcement Learning with Control Barrier Functions. Rigorous evaluations across LLM-CARLA and V2X-Sim 2.0 benchmarks demonstrate that REC achieves a superior trade-off between reasoning accuracy and inference latency. Specifically, REC maintains 86.5% intelligence continuity and achieves a 2.7x speedup relative to standard cloud-centric offloading by eliminating network-induced queuing delays during communication collapse,functioning as a robust contingency mechanism in scenarios where conventional static paradigms exhibit significant performance degradation.The official implementation of the REC framework is available at: https://github.com/peilongzhang88/REC-Framework.
Resource
2026 EN
A. Popescu · A.-M. Badescu
Satellite quantum key distribution (SatQKD) is widely regarded as a promising approach for extending quantum-secure key establishment beyond the distance limitations of terrestrial optical fiber links. However, practical assessment of SatQKD remains difficult because published results rely on heterogeneous assumptions regarding channel loss, orbital visibility, atmospheric conditions, detector behavior, finite-key analysis, and classical post-processing efficiency. As a result, comparisons across protocols, architectures, and mission concepts are often difficult to interpret from an operational engineering perspective. This paper presents an engineering-oriented review of SatQKD in the context of hybrid optical satellite communication systems. The review synthesizes prior results reported across protocol-level, link-budget, orbital, finite-key, and network-level studies. Within this scope, service-level notions such as pass-integrated and availability-limited key delivery are used as an interpretive lens for discussing practical relevance under orbital intermittency, atmospheric attenuation, receiver non-idealities, finite-key constraints, and classical post-processing limitations. The contribution of this review is a cross-layer engineering synthesis of prior SatQKD results reported under heterogeneous assumptions, aimed at supporting more consistent operational interpretation across protocols, architectures, and mission concepts.
Resource
2026 EN
Nour El-Din Safwat · Kavindu Ranasinghe · Kester Leibhardt
+2 more
The increasing demand for autonomous robotic systems in extended space exploration missions necessitates robust health management frameworks to ensure operational reliability in challenging and extreme extraterrestrial environments. This paper proposes an Integrated Vehicle Health Management (IVHM) framework designed to support NASA's RESOURCE Lunar Mission, with a focus on enabling the sustained operation of an autonomous lunar polar rover tasked with exploiting ice deposits at the lunar poles. The framework employs artificial intelligence in fault detection and diagnostic reasoning modules, using real-time sensor data to identify, predict, and mitigate subsystem faults. This approach enhances system reliability, safety, and mission success rates while reducing operational costs. Emphasis is placed on mission-critical communication and navigation subsystems, evaluated through digital twin simulations and structured fault injection techniques under nominal and degraded conditions. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed as the IVHM reasoning engine to deliver interpretable fault diagnosis, early degradation detection, and rapid subsystem reconfiguration in dynamic operational scenarios. The results demonstrate the feasibility and efficacy of the proposed IVHM framework in advancing autonomous fault management for planetary exploration vehicles, providing a pathway for safer, more resilient, and cost-effective space missions.
Resource
2026 EN
Oskars Bormanis · Janis Zakis · Frede Blaabjerg
+2 more
Power modules in industrial systems are exposed to variable load and thermal stress, which drives cumulative degradation and complicates lifetime prediction. Probabilistic modeling of this degradation is essential for predictive maintenance, risk management, and compliance with functional safety standards. This paper presents a Monte Carlo–based workflow for estimating the lifetime degradation behavior of IGBT power modules by combining measured current profiles, electro-thermal simulation, Rainflow cycle counting, Miner’s rule, and global sensitivity analysis. The framework improves traditional mission-profile methods by adding uncertainty to the key thermal parameters. This makes it possible to see skewed lifetime distributions that single-point deterministic methods cannot show. Current profiles from a six-axis industrial robot are used to obtain junction-temperature traces, count thermal cycles, and generate cumulative damage distributions. Applied Sobol sensitivity analysis identifies thermal-path impedance as the dominant source of variability, providing guidance for derating and robustness evaluation. The resulting probabilistic outputs provide relative stress indicators that can support maintenance scheduling and form a basis for reliability evidence consistent with the evidence structure required by the functional safety standards family derived from IEC 61508, once calibration data are available. The framework runs efficiently on multi-core hardware platforms, produces data directly usable within digital-twin or reliability toolchain architectures, and shows that heavily loaded robot axes accumulate substantially more damage than lightly loaded ones. These findings confirm the feasibility of a workflow that bridges mission-specific operating data with probabilistic reliability modeling in power electronics. The results are interpreted as indicators of relative stress and comparative degradation, rather than as absolute lifetime or failure probability predictions.
Resource
2026 EN
Bingxu Yao · Xin Chen · Bowen Yang
The rapid growth of low-Earth-orbit (LEO) satellites has intensified orbital congestion and collision risks, wherein accurate orbit prediction is critical for space situational awareness and mission safety. Atmospheric drag, represented by the BSTAR parameter in two-line element (TLE) sets, dominates the orbital uncertainty of LEO targets below 800 km. However, traditional statistical methods and shallow neural networks struggle with long-sequence nonlinear time-varying perturbations and error accumulation in long-horizon extrapolation. To address these limitations, this study proposes a local residual multi-scale temporal convolutional network (LR-MS-TCN) model for BSTAR parameter correction to enhance LEO orbit prediction accuracy. The model integrates a multi-scale feature fusion module to extract perturbation features at different temporal scales, adopts a dilated causal TCN backbone to capture long-range temporal dependencies, and introduces a local residual compensation branch with a gated fusion mechanism to strengthen the representation of short-term sensitive variations near the current epoch. Experiments are conducted on 16 years of TLE data of the FENGYUN-3A satellite, with MAE and RMSE as evaluation metrics. Results show that the proposed model achieves 1.6%, 17.1%, 23.7%, and 27.6% 3D RMSE improvement at 24 h, 48 h, 72 h, and 96 h prediction horizons, respectively, outperforming MSF-TCN, PlainTCN, SingleScaleTCN, LSTM, and GRU. It also maintains stable performance under quiet, storm, and strong geomagnetic storm conditions and exhibits favorable cross-target generalization on the FENGYUN-3B satellite. The LR-MS-TCN model effectively improves BSTAR correction accuracy and long-horizon orbit prediction stability, providing a reliable data-driven approach for high-precision LEO orbital propagation.