Search Results (73)

Wide-field infrared imaging systems are often constrained by detector size, cooling requirements, and payload limitations, leading to the need for multi-FOV detector sharing. However, conventional geometric multiplexing introduces severe spatial aliasing, which significantly degrades target localization performance. This paper proposes a polarization-encoded field-of-view multiplexing method for recovering spatial information from aliased detector measurements. The imaging plane is divided into multiple FOV regions, each assigned a distinct polarization state. After optical folding, the modulated sub-images are superimposed onto a common detector region. Six-channel polarization measurements are used to reconstruct pixel-wise Stokes vectors, and the spatial origin of each pixel is identified through polarization-domain similarity matching and target-level voting. MATLAB-based simulations were conducted using a nine-region multiplexing configuration. The proposed method achieves 97.3% pixel-level classification accuracy under ideal conditions and maintains over 95% accuracy at a noise level of σ = 0.02. The normalized Stokes reconstruction error is below 0.02, and stable performance is observed under polarization modulation deviations within ±10°. By introducing polarization as an additional encoding dimension, the proposed framework enables efficient separation of multiplexed spatial information without increasing detector resources, demonstrating its potential for compact wide-field infrared sensing applications. Full article

Encrypted traffic classification is essential for network management and security, yet payload inspection is ineffective under modern protocols such as Transport Layer Security (TLS) and Quick UDP Internet Connections (QUIC). Existing metadata-based methods perform well for coarse-grained tasks but often fail to distinguish structurally similar applications because they model temporal behavior only implicitly or coarsely. We propose the Bi-Directional Directed Temporal Graph (BiDT), a framework based on a Directed Temporal Interaction Graph (DTIG) and a Bi-Directional GraphSAGE (BiGraphSAGE). The DTIG represents packets as nodes and explicitly encodes inter-arrival times (IATs) as directed edge attributes, preserving both causal structure and communication rhythm. The BiGraphSAGE then aggregates temporal interaction features from forward and backward perspectives. We evaluated the BiDT on the VNAT benchmark and validated it on ISCX-VPN. On the challenging 10-class VNAT dataset, the BiDT achieves 98.57% accuracy and outperforms strong baselines, including complete separation of easily confused protocols such as SCP and SFTP. The results on ISCX-VPN further confirm the effectiveness of the proposed design. These findings show that explicit temporal edge modeling is effective for fine-grained encrypted traffic classification. Full article

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

GNSS reflectometry (GNSS-R) altimetry has been widely used for retrieving surface elevation over oceans, cryosphere, and land. Recently, UAV-borne GNSS-R systems have gained attention due to their flexibility for low-altitude and localized observations. However, lightweight UAV platforms impose strict payload and real-time processing constraints. At low altitudes, the small geometric delay between direct and reflected signals often leads to waveform overlap, degrading conventional altimetry algorithms. In this study, a lightweight UAV-borne GNSS-R receiver and a signal-separation-based altimetry method are proposed. Direct and reflected signals are separated using waveform characteristics without relying on external height information, mitigating the impact of waveform overlap. Simulations and experiments using a SPIRENT 9000 GNSS simulator demonstrate stable height retrieval under dynamic low-altitude conditions while maintaining real-time capability, confirming the feasibility of lightweight UAV GNSS-R altimetry for rapid elevation monitoring. Full article

Zero-day intrusion detection is still a difficult task because of the difference between high laboratory precision and real-time deployability under strict operational constraints. This paper proposes a lightweight two-stage cascade architecture that is specifically designed for CPU-only environments and strict zero-day evaluation. The proposed architecture only uses statistical and flow-level metadata attributes, which are independent of payload analysis, to ensure compatibility with encrypted traffic. The first stage of the proposed architecture is precision oriented to detect potentially malicious traffic with a low decision threshold, and the second stage is precision oriented to enhance classification and remove false positives. To avoid optimistic bias, a strict attack-type separation protocol is employed, where testing attack types are strictly prohibited from training. The proposed method is tested on three benchmark datasets: CSIC 2012 (HTTP level), UNSW-NB15 (intra-domain), and CSE-CIC-IDS2018 (cross-domain). The experimental results show the excellent intra-domain zero-day detection capability (up to 94.81% accuracy with 0.50% FPR), controllable performance degradation in the cross-domain setting (80.53% accuracy with near-zero FPR), and extremely low FP rates on all datasets. The system provides microsecond-level inference latency (0.002–0.006 ms), a throughput of up to 470,000 requests per second, and memory usage below 6.2 MB without GPU support. These results confirm the significance of architectural optimization and thorough evaluation in building efficient zero-day detection systems. Full article

This paper addresses the generation of time-efficient, collision-free cooperative motions for a dual-arm robotic system transporting a shared payload in constrained industrial workspaces. Trajectory generation is formulated as a constrained optimization problem and solved through bio-inspired metaheuristic search, where candidate solutions are evaluated with a safety-first cost function that first enforces feasibility by heavily penalizing collisions and then minimizes total execution time among collision-free trajectories. Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Gazelle Optimization Algorithm (GOA) are evaluated under identical bounds and stopping conditions, showing that all three reliably discover feasible cooperative trajectories; however, clear differences emerge in feasibility discovery and final trajectory quality: PSO reaches feasibility earlier and achieves the lowest final objective value and the shortest trajectory execution time (6.825 s), followed by WOA (7.330 s) and GOA (8.525 s). Overall, this work contributes an object-centric optimization methodology for constrained dual-arm bimanipulation using bio-inspired metaheuristics, a feasibility-first cost structuring that explicitly separates safe motion discovery from time-optimal refinement, and a controlled benchmarking of PSO/WOA/GOA that quantifies their distinct convergence and late-stage refinement behaviors. Full article

Characterizing both the CO₂ distribution and wind dynamics in the Martian mesosphere and lower thermosphere is vital for planetary atmospheric science and mission planning. In this work, we propose a novel dual-channel passive limb-viewing imaging system designed to simultaneously observe partial CO₂ column density and line-of-sight (LOS) wind speed from ultraviolet and visible airglow emissions under dayside and terminator illumination conditions. A dichroic beam splitter separates the ultraviolet and visible channels, ensuring high optical throughput and independent optimization of both subsystems. The ultraviolet channel targets O(¹S) 297.2 nm emission, a well-established Martian limb emission driven by CO₂ photodissociation under solar Lyman-α flux. By applying narrow-band imaging and brightness inversion, this channel provides quantitative constraints on CO₂ column density with a stable and well-defined response function. In the visible channel, we introduce a lens array-based compact static Michelson interferometer optimized for the O(¹S) 557.7 nm green line emission, which has been observed in the Martian dayside limb, providing Doppler wind measurements in the 60–180 km altitude range. Radiative transfer simulations using Mars Climate Database indicate retrieval precisions of ±6~8% for CO₂ column density and better than ±5 m/s for wind speed within the primary emission layer (approximately 60–160 km) under representative dayside limb conditions. This dual-parameter remote sensing concept simultaneously constrains the composition and dynamics of the Martian mesosphere and lower thermosphere region, addressing a long-standing observational gap. The compact and modular design of the system makes it well suited for future Mars orbiter payloads under nominal dayside and terminator observation geometries, providing critical data for validating global circulation models and supporting future entry, descent, and landing system design. Full article

Remote sensing semantic segmentation encounters several challenges, including scale variation, the coexistence of class similarity and intra-class diversity, difficulties in modeling long-range dependencies, and shadow occlusions. Slender structures and complex boundaries present particular segmentation difficulties, especially in high-resolution imagery acquired by satellite and aerial cameras, UAV-borne optical sensors, and other imaging payloads. These sensing systems deliver large-area coverage with fine ground sampling distance, which magnifies domain shifts between different sensors and acquisition conditions. This work builds upon DeepLabV3+ and proposes complementary improvements at three stages: input, context, and decoder fusion. First, to mitigate the interference of complex and heterogeneous data distributions on network optimization, a feature-mapping network is introduced to project raw images into a simpler distribution before they are fed into the segmentation backbone. This approach facilitates training and enhances feature separability. Second, although the Atrous Spatial Pyramid Pooling (ASPP) aggregates multi-scale context, it remains insufficient for modeling long-range dependencies. Therefore, a routing-style global modeling module is incorporated after ASPP to strengthen global relation modeling and ensure cross-region semantic consistency. Third, considering that the fusion between shallow details and deep semantics in the decoder is limited and prone to boundary blurring, a fusion module is designed to facilitate deep interaction and joint learning through cross-layer feature alignment and coupling. The proposed model improves the mean Intersection over Union (mIoU) by 8.83% on the LoveDA dataset and by 6.72% on the ISPRS Potsdam dataset compared to the baseline. Qualitative results further demonstrate clearer boundaries and more stable region annotations, while the proposed modules are plug-and-play and easy to integrate into camera-based remote sensing pipelines and other imaging-sensor systems, providing a practical accuracy–efficiency trade-off. Full article

Remotely operated underwater vehicles (ROUVs) have been attracting more attention lately as they are considered to be operationally versatile, capable of real-time communication, and can be fitted with various sensor payloads for environmental monitoring purposes. This study presents the design, development, and field validation of a sensor-enhanced ROUV platform tailored for environmental monitoring and aquaculture applications. The vehicle is equipped with a modular set of sensors for temperature, pH, dissolved oxygen (DO), and electrical conductivity (EC) along with separate signal-conditioning circuits for each sensor and real-time data acquisition from tethered architecture. The general system concept is modularity, reproducibility, and robustness in a marine environment. In situ measurements were performed at an active aquaculture site in the North Aegean Sea throughout several seasons during 2025. Using this system, depth-resolved measurements were obtained with sensor accuracies of ±0.1 °C (temperature), ±0.05 pH units, ±0.05 mg/L (dissolved oxygen), and ±2% (electrical conductivity). The following sections describe the development and aquaculture testing of the platform, which yielded stable and repeatable operation in real conditions. Full article

Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstruction-error scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments. Full article

Planetary remote sensing super-resolution aims to enhance the spatial resolution and fine details from low-resolution images. In practice, planetary remote sensing is inherently constrained by sensor payload limitations and communication bandwidth, resulting in restricted spatial resolution and inconsistent scale factors across observations. These constraints make it impractical to acquire uniform high-resolution images, thereby motivating the need for arbitrary-scale super-resolution capable of dynamically adapting to diverse imaging conditions and mission design restrictions. Despite extensive progress in general SR, such constraints remain under-addressed in planetary remote sensing. To address those challenges, this article proposes an arbitrary-scale super-resolution (SR) model, the Adaptive Frequency–Spatial Neural Operator (AFSNO), designed to address the regional context homogeneity and heterogeneous surface features of planetary remote sensing images through frequency separation and non-local receptive field. The AFSNO integrates Frequency–Spatial Hierarchical Encoder (FSHE) and Fusion Neural Operator in a unified framework, achieving arbitrary-scale SR tailored for planetary image characteristics. To evaluate the performance of AFSNO in planetary remote sensing, we introduce Ceres-1K, the planetary remote sensing dataset. Experiments on Ceres-1K demonstrate that AFSNO achieves competitive performance in both objective assessment and perceptual quality while preserving fewer parameters. Beyond pixel metrics, sharper edges and high-frequency detail enable downstream planetary analyses. The lightweight, arbitrary-scale design also suits onboard processing and efficient data management for future missions. Full article

Multirotor UAVs powered by batteries face limitations due to the low energy density of their energy source, which constitutes a significant portion of the total weight. During missions, the high battery mass remains constant, necessitating high required power. This leads to reductions in payload capacity and endurance constraints. This study developed a design tool for multirotor UAVs that sequentially detach used batteries during missions to reduce weight and extend endurance. The developed tool consists of a battery weight prediction model and a required power prediction model. It accurately predicts endurance by considering changes in weight, thrust, RPM, motor-propeller efficiency, and required power at each battery separation point. Using the developed tool, the battery separation technology was applied to a quadcopter with total weights of 7, 15, and 25 kg, and the extended endurances were quantitatively compared. The results showed endurance improvements of 127.3%, 122.0%, and 127.0% for the 7, 15, and 25 kg quadcopters, respectively, compared to using a single battery. In addition, the method was applied to the commercially available industrial UAV DJI Matrice 300 RTK. With a 2.7 kg payload, the two-stage battery configuration extended the endurance by 12.5% compared to the single-battery case. Under no-payload conditions, a three-stage configuration achieved a 16.7% improvement. These results confirm the effectiveness of staged battery detachment even in real-world UAV platforms. Full article

The rapid development of spacecraft on-orbit services has increased the requirements for docking technology, especially for large-scale payloads that exceed the launch envelope. Docking technology based on astronaut extravehicular activities is one of the most promising directions for on-orbit services. In view of this, this paper designs and characterizes a handheld double-point docking mechanism for assembling large-scale payloads that is suitable for extravehicular activity (EVA) in dual-astronaut collaborative operations. It achieves the functional decoupling of docking, locking, unlocking, and separation throughout the whole process. The mechanism also has excellent design for human factors engineering, allowing astronauts to change hands, operate with one hand, and apply limited force. The mechanism adopts a dual-point probe–drogue configuration, while the misalignment tolerance design guarantees the docking accuracy and the operating range, and forms a rigid structural connection through a force amplification mechanism. Theoretical analysis and numerical simulations are implemented to estimate the dynamics, statics, and kinematics of the docking process. Corresponding experiments of the prototype are also conducted, including high–low temperature dynamics, docking tests, and kinematic tolerance experiments. The experiments validate the finite element analysis and verify the actual performance of the mechanism. The designed handheld dual-point docking mechanism was successfully applied for the first time by the Shenzhou 15 crew on China’s Space Station in March 2023. This paves a new road for spacecraft on-orbit service of large-scale payloads by EVAs, providing guidance as well as a technical foundation for the on-orbit construction of large spacecraft in the future. Full article

This study investigates the cooperative transport of a cable-suspended payload by two multirotor unmanned aerial vehicles (UAVs). A compact nonlinear control law that allows to simultaneously (i) track a slow reference trajectory, (ii) hold a prescribed inter-vehicle geometry, and (iii) actively damp load swing is developed. The model treats the two aerial robots and the payload as three point masses connected by linear-elastic cables, and the controller is obtained through a Newton–Euler formulation. A singular-perturbation analysis shows that, under modest gain–separation conditions, the closed-loop system is locally exponentially stable: fast dynamics govern formation holding and swing suppression, while slow dynamics takes into account trajectory tracking. Validation is performed in a realistic simulation scenario that includes six-degree-of-freedom rigid-body vehicles, Blade-Element theory rotor models, and sensor noise. Compared to an off-the-shelf, baseline controller, the proposed method significantly improves flying qualities while minimizing hazardous payload oscillations. Owing to its limited parameter set and the absence of heavy optimization, the approach is easy to tune and well suited for real-time implementation on resource-limited UAVs. Full article

In response to the challenges faced by unmanned aerial vehicles (UAV) in cluttered environments such as forests, ruins, and pipelines, this study introduces a ground–air amphibious UAV specifically designed for personnel search and rescue in complex environments. By innovatively designing and applying a separation cage structure, the UAV’s capabilities for ground movement and aerial flight have been enhanced, effectively overcoming the limitations of traditional single-mode robots operating in narrow or obstacle-dense areas. This design addresses the occlusion issue of sensing components in traditional caged UAVs while maintaining protection for both the UAV itself and the surrounding environment. Additionally, through the innovative design of an H-shaped quadcopter frame skeleton structure, the UAV has gained the ability to perform steady-state aerial flight while also better adapting to the separation cage structure, achieving a reduced energy consumption and significantly improving its operational capabilities in complex environments. The experimental results demonstrate that the UAV prototype, weighing 1.2 kg with a 1 kg payload capacity, achieves a 40 min maximum endurance under full payload conditions at the endurance speed of 10 m/s while performing real-time object detection. The system reliably executes multimodal operations, including stable takeoff, landing, aerial hovering, directional maneuvering, and terrestrial locomotion with coordinated steering control. Full article