Search Results (925)

Glow-discharge plasma (GDP) poling is revisited as an electrode-free method for activating piezoelectricity in poly(vinylidene fluoride) (PVDF) films. Although this method was proposed several decades ago, its effect on the properties of PVDF films has remained poorly understood. In this work, we demonstrate that GDP enables efficient poling of oriented PVDF films without pre-deposited electrodes and investigate the relationship between plasma treatment time, structural evolution, and piezoelectric response. Commercially available 25 μm-thick oriented PVDF films (PolyK) were treated in a DC glow discharge for 15 s to 15 min and characterized using FTIR, DSC, piezoresponse force microscopy, UV–Vis–NIR spectrophotometry, quasi-static d₃₃ measurements and water contact-angle measurements. GDP poling produced a side-averaged piezoelectric coefficient d₃₃ of up to ~25 pC/N within 1–5 min, with local maxima at approximately 1, 2.5, and 5 min. This behavior was accompanied by pronounced changes in the domain structure, including an increase in the ferroelectric domain size from 86 to 552 nm, while the crystallinity and electroactive phase fraction changed only moderately. Plasma treatment also increased the wettability of the plasma-facing surface, reducing the water contact angle from about 85° to 42° within 3 min. At longer treatment times (>5 min), however, the piezoelectric response decreased and the optical transparency deteriorated because of increased haze and turbidity, most likely associated with plasma-induced chemical modification of the surface layers. These results indicate that GDP poling has an effective processing window of 1–5 min. The proposed approach provides a vacuum-compatible and electrode-free route for preparing PVDF films with increased surface wettability for flexible piezoelectric sensors, wearable electronics, and integrated polymer-based devices, because it is compatible with electrode deposition on an already activated polymer surface within a single vacuum cycle. Full article

Traditional Low Earth Orbit (LEO) satellite constellation design methods, primarily driven by geometric coverage, fail to satisfy the non-uniform and dynamic tracking requirements of moving targets. To address this, a multi-objective optimization framework for large-scale satellite constellations is proposed. This framework is task-driven, constraint-guided, and integrates space and ground segments. A quantitative model is established to characterize the multi-target tracking capability of space-based sensing systems. The model explicitly links the constellation revisit period, payload detection and positioning performance, target maneuverability, and the maximum trackable target density. These relationships are then formulated as optimization objectives and constraints. To capture temporal consistency in observation performance, the coefficient of variation of revisit time is introduced as an independent optimization objective. This yields a three-objective optimization problem that addresses tracking performance, coverage uniformity, and system cost, enabling Pareto-optimal constellation design solutions. Simulation results demonstrate that the proposed method improves track association performance in representative maritime target tracking scenarios when compared with conventional coverage-driven constellation designs. The proposed framework provides a systematic and implementable approach for constellation design by integrating capability modeling with multi-objective optimization at the system level. Full article

Time-series analysis is important for various scientific and industrial fields, such as remote sensing where observations may be disturbed by clouds, have irregular revisits and different sensors. Current transformer-based approaches have enhanced the modeling of distant time relationships, but they are still constrained by specific task designs and poor adaptability to various time-series patterns. To solve these problems, we present TimeHome, a universal sparse transformer basic model for handling heterogeneous time series. TimeHome incorporates a Heterogeneous Mixture-of-Experts (H-MoE) component, where different expert types are chosen dynamically based on a low-rank temperature-controlled gating mechanism to fit various sequence features. Moreover, a hybrid local–global attention mechanism is designed to consider both short-term variations and long-distance correlations, while specific heads are used for unified prediction, missing value estimation and abnormal event detection. TimeHome is pretrained on TS-200B, a huge database of time series including various temporal patterns from different domains. Comprehensive tests on several benchmark datasets and remote sensing extended evaluations show that TimeHome performs well in long-term prediction, missing value replacement and abnormal event detection. The model also exhibits good zero-shot adaptation ability and fast inference speed by adjusting experts dynamically. The source code and pre-training data will be released publicly. Full article

In low-Earth-orbit (LEO) satellite constellation remote sensing for surface maritime target detection, the inherent characteristics of discontinuous detection epochs, non-uniform temporal intervals, and clutter contamination invariably cause conventional data association algorithms to suffer from validation gate degradation, covariance divergence, and combinatorial explosion. To circumvent these limitations, this paper proposes a multi-target, time-adaptive fast association method tailored for discontinuous sparse observations. Within the joint probabilistic data association (JPDA) framework, the proposed method analyzes the mismatch between the Kalman filter prediction covariance and the actual error under discontinuous observations. A time-interval adaptive gating mechanism maintains the gate detection probability across arbitrary revisit intervals. Secondly, to resolve the massive connected cluster problem triggered by the densification of the validation matrix, a progressive clustering strategy inspired by simulated annealing is designed, which recursively decomposes the global, exponentially scaling association graph into independent subgraphs of manageable sizes. Building upon this, a depth-first search (DFS) heap pruning technique is integrated with the Hungarian hard assignment algorithm as a safety-degradation mechanism to safeguard numerical robustness in extreme scenarios. Comparative experiments demonstrate that the proposed method significantly enhances both tracking accuracy and track completeness across various constellation coverage characteristics and maritime clutter intensities. Furthermore, its execution efficiency satisfies real-time simulation requirements, effectively supporting engineering application for surface maritime target detection. Full article

Accurate and robust localization remains the fundamental bottleneck for truly autonomous robotic systems, despite decades of progress in probabilistic estimation and SLAM. This paper provides a critical and comprehensive review of mobile robot localization across sensing modalities, estimation paradigms, and deployment domains, covering ground, aerial, and underwater platforms. Beyond a descriptive survey, we explicitly analyze the limitations and trade-offs of existing approaches. We introduce an updated taxonomy that spans classical proprioceptive and exteroceptive sensors, emerging technologies such as 4D imaging radar and event cameras, and infrastructure-based positioning systems including GNSS and Ultra-Wideband. We revisit the evolution of localization algorithms, from Bayesian filtering techniques (EKF, UKF, and particle filters) to modern graph-based SLAM frameworks and tightly coupled multi-sensor fusion systems. Particular emphasis is placed on the recent paradigm shift toward learning-based and neural implicit approaches, including NeRF-SLAM and Gaussian Splatting, highlighting both their transformative potential and their current impracticality for real-time deployment. Unlike previous surveys, this work provides a unified cross-domain perspective while critically examining scalability, robustness, computational cost, and real-world deployability. We identify key unresolved challenges, including long-term consistency, operation in degraded environments, and the integration of semantic understanding into localization pipelines. Furthermore, we propose standardizing evaluation metrics with a formal Trajectory Completeness formulation to expose tracking brittleness. Finally, we outline future research directions toward resilient, certifiable, and truly autonomous localization systems, emphasizing the critical transition from passive estimation to Active SLAM in unstructured environments. Full article

The increasing frequency of natural disasters such as floods has led to severe casualties and substantial economic losses. Consequently, near-real-time flood disaster monitoring has become critical for effective disaster response and accurate damage assessment. However, traditional approaches relying on daytime optical imagery and SAR data often suffer from long satellite revisit cycles and data acquisition latency, while nighttime Earth observation remains limited by the absence of adequate illumination. This study proposes an innovative framework that integrates moonlight Earth observation to enable near-real-time flood monitoring and loss assessment and is applied to the 7.20 Zhengzhou rainstorm event. Compared to conventional disaster monitoring techniques, integrating moonlight observations shortens the effective data acquisition period to approximately one day, significantly improving temporal responsiveness compared with conventional techniques. The innovative framework identifies a flood-affected area of 722.93 km² and estimated economic losses of 107.96 billion yuan in the disaster-affected region. The results indicate that the moonlight data source has significant capacity for near-real-time disaster monitoring and disaster loss assessment and highlight the potential broad impact of this study in relevant fields. Full article

This paper revisits a delayed tumor–immune model by replacing the discrete delay with weak and strong Gamma distributed memories, reflecting the realistic spread of immune-response times. Because the Gamma kernels are normalized, the biologically relevant equilibria of the reference model are preserved. The local stability problem, however, changes substantially: a careful linearization shows that the characteristic equation contains both the first and the second power of the memory transfer function, since delayed immune and tumor variables enter coupled feedback terms. Consequently, the weak Gamma chain leads to a quintic characteristic polynomial, whereas the strong Gamma chain leads to a seventh-degree polynomial. Routh–Hurwitz conditions and explicit Hopf bifurcation tests are derived for both memory structures, including simplicity and transversality requirements. Numerical simulations performed with the parameter sets of the reference study show that distributed memory reproduces the main biological regimes while shifting stability thresholds and modifying transient oscillations. The results indicate that not only the mean immune-response time but also the shape of its distribution can influence tumor–immune dynamics. Full article

Reinforcement learning (RL) has emerged as a powerful paradigm for enabling autonomous coordination in multi-UAV systems operating in complex and uncertain environments. However, the effectiveness of learned policies is strongly influenced by how actions are implemented at the control level, an aspect that has received limited attention in the literature. This paper presents a comparative study of three control methods (heading-based, waypoint-based, and deterministic) within a unified hybrid-AI architecture, in which the same RL policy structure is used across two of the three configurations. By isolating the control method as the sole variable, the study evaluates how different action abstractions affect learning efficiency, robustness, and operational performance in cooperative surveillance missions. A statistically rigorous Monte Carlo evaluation, supported by non-parametric hypothesis testing, demonstrates that heading-based control consistently achieves superior performance in terms of revisit period, target acquisition time, and tracking continuity. The analysis further reveals that these gains arise from improved reactivity and constraint handling rather than from differences in policy learning. The results highlight the critical role of control-level design in RL-based multi-agent systems and provide practical guidelines for selecting action abstractions in aerial swarm applications. Full article

Dinosaur footprints are among the most abundant trace fossils, but they are not direct records of anatomy, behaviour or faunal composition. They preserve locomotion, substrate interaction and occurrence data only after those signals have been filtered by foot anatomy, movement, sediment properties and preservation. Classical dinosaur ichnology has relied on two-dimensional outlines, linear and angular measurements, qualitative ichnotaxonomy and influential hypotheses about trackmaker identity, speed, social behaviour and evolutionary timing. Here we review how these hypotheses are being reassessed with three-dimensional digitisation, geometric morphometrics, supervised and unsupervised machine learning, and biomechanical simulation. We first consider how different footprint representations, including interpretive outlines, landmarks, silhouettes, depth maps and three-dimensional models, shape the questions that track data can answer. We then assess analytical approaches ranging from multivariate statistics and landmark-based classifiers to convolutional neural networks and β-variational autoencoders. Against this methodological background, we revisit four linked problem domains: ornithopod–theropod discrimination and the Grallator–Anchisauripus–Eubrontes plexus; speed and gait reconstruction; ecological and behavioural interpretations of track abundance, sauropod gauge and trackway arrangement; and macroevolutionary claims about body-size trends, functional morphotypes and avian-like pedal morphologies. Across these cases, newer methods rarely remove ambiguity. They more often show where classical interpretations are robust, where they depend on representation or prior labels, and where competing explanations remain hard to separate. We argue that footprint-based inference is strongest when tracks are treated as preservationally filtered products of anatomy, motion and substrate mechanics, and when they are integrated with skeletal data, experimental analogues and forward models in explicit, uncertainty-aware frameworks. Full article

As AI research advances, various performance optimization techniques have been studied for TinyML models on microcontrollers with very limited resources. In TinyML frameworks such as TFLM (TensorFlow Lite Micro) and NNOM (Neural Network on Microcontrollers), their execution engines mainly depend on CMSIS-NN for inference acceleration, which is an ARM’s back-end library to execute optimized kernel functions for performance optimization. But we often find that CMSIS-NN is not invincible for inference time optimization on TinyML. In this paper, we examine TinyML frameworks and their CMSIS-NN libraries and consider how to improve CMSIS-NN in terms of runtime. Then, we propose the D2I technique to reduce the overhead of memory operations that occur while performing the Im2col procedure within the convolution function, which takes most of the inference time in CMSIS-NN. The proposed technique creates a necessary index table, finds the location of the input with the corresponding index, and performs direct operations between filters and inputs. Thus, it can quite mitigate data copy operations in Im2col with a small additional amount of memory compared to Im2col. In extensive experiments using an Arduino nano 33 BLE board with Cortex-M4 and an STM32F746G-DISCO board with Cortex-M7, D2I was found to achieve about 16.3% and 14.5% inference time improvements against the Im2col in TFLM’s and NNOM’s CMSIS-NNs, respectively, for the SqueezeNet model. And the additional memory usage was shown to be identically 11.52 kB. Full article

Polar-orbiting microwave radiometers provide indispensable all-weather measurements of the atmospheric state, yet revisit intervals of many hours leave critical gaps during rapidly evolving weather events. To address this limitation, we developed MIDAS (Microwave Inference via Diffusion Across Satellites), a probabilistic framework that estimates microwave brightness temperature (BT) fields across the geostationary full-disk domain from infrared observations at 10 min intervals. This study focuses on the five Microwave Humidity Sounder-2 (MWHS-2) humidity-sounding channels near 183 GHz, which provide vertically resolved water vapor information. MIDAS achieves relative errors below 0.5% for the majority of cases, with a channel-averaged mean absolute error of 1.15 K, outperforming a deterministic U-Net baseline (1.43 K). Beyond per-sample evaluation, MIDAS reproduces large-scale climatological patterns across the full-disk domain over a three-month summer period, consistent with Radiative Transfer for TOVS–Scattering (RTTOV-SCATT) simulations. In deep convective scenes where reconstruction is most difficult, the ensemble spread naturally tracks reconstruction difficulty, providing a built-in indicator of prediction confidence. Notably, MIDAS incorporates real-time polar-orbiting observations as physical constraints via a merge-sampling mechanism, reducing ensemble RMSE by over 20% and improving probabilistic calibration by more than 30%. Proof-of-concept assimilation experiments for two high-impact weather cases show that MIDAS-generated fields yield forecast improvements comparable to those from real satellite observations, reducing tropical cyclone track errors from approximately 110 km to 40 km and improving heavy precipitation forecasts at extreme rainfall thresholds where direct infrared assimilation shows no benefit. Overall, our framework demonstrates the potential of generative models to supplement sparse observational coverage and provide physically plausible microwave humidity fields for downstream applications. Full article

If one compares the continuous-time convolution outputs with their sampled discrete counterparts, one may observe slight differences even when the sampling process itself is otherwise straightforward. This issue becomes noticeable when one or both continuous-time signals have a discontinuity at the sampling instant, such as t = 0. In this paper, we revisit this issue and explain its root causes: the treatment of midpoint values at discontinuities and the sampling-period scaling that appears when a continuous-time convolution is approximated in discrete time. Although the midpoint rule is not new, we show how this classical result can be used systematically to construct sampled sequence representations that are consistent with inverse-transform reconstruction at discontinuities. Based on this viewpoint, we derive midpoint-consistent sampled sequence representations for quite a few representative functions, and we show the corresponding implications for sampled convolution formulae. Several examples are used to compare conventional discrete formulae with midpoint-consistent sampled formulae and with samples of the continuous-time results. The proposed formulation is intended for sampled continuous-time signals at discontinuities; it is not meant to replace standard native discrete-time conventions used in digital signal processing. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

Post-disaster scenarios such as tsunamis require rapid terrain assessment that cannot wait for the next satellite synthetic aperture radar (SAR) revisit, yet a readily deployable system remains lacking. We present an off-the-shelf K-band drone-borne dual circular polarimetric (DCP) SAR and a processing pipeline for on-demand terrain classification. Compared with fully polarimetric (FP) SAR, DCP requires only a single transmit polarization and two receive channels, providing a wider swath than FP for the same acquisition, while still separating odd-bounce and even-bounce scattering mechanisms, which dual linear polarimetric modes with the same channel count provide with greater ambiguity due to their sensitivity to target orientation angle. To compensate for platform motion, we implemented RTK global navigation satellite system (GNSS) guided time-domain backprojection (TDBP) with phase gradient autofocus (PGA), yielding an 11.98 dB improvement in peak amplitude. We then applied single-target wire calibration to correct a measured 8.91 dB inter-channel complex gain difference between co-polarization and cross-polarization. As a result, H/α decomposition of the calibrated DCP data classifies canonical reflectors, artificial structures, gravel roads, vegetation, and a pond surface. These field experiments extend compact polarimetric H/α decomposition to drone-borne SAR data for terrain discrimination, establishing a practical pathway toward rapid post-disaster terrain assessment. Full article

Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload. Full article