Search Results (773)

Autonomous inspection of discrete obstacles (e.g., tree trunks in orchards and forests) requires UAVs to visit every target with proper observation distance and heading, while simultaneously exploring the unknown environment. Existing space-guided exploration methods focus on eliminating unknown space and are inherently agnostic to the inspection targets themselves, leading to incomplete coverage and redundant traversal. We observe that the obstacles themselves encode the spatial topology of the environment and can serve as natural planning anchors. Based on this insight, we propose ORACLE, an Object-centric Autonomous Coverage Exploration framework that shifts the planning paradigm from space-guided to target-guided exploration. ORACLE integrates: (1) an online target detection and persistent identification module via occupied-voxel connected component labelling, (2) a density-aware global coverage planner that modulates ATSP costs to prioritize target-dense regions, and (3) a target-guided local planner that replaces frontier viewpoints with direct obstacle observation points in a Sequential Ordering Problem formulation. Experiments in two point-cloud environments reconstructed from real-world forests with contrasting tree densities (Environment I: 50 trunks, $\overline{n}=1.56$; Environment II: 70 trunks, $\overline{n}=2.19$; both with non-uniform spacing) show that ORACLE achieves $98.8\%$ and $99.7\%$ target coverage compared to $22.7\%$ and $25.1\%$ for the space-guided baseline, while reducing the mission overhead ratio from $202.9\%$ to $129.2\%$ (Environment I) and from $176.8\%$ to $126.6\%$ (Environment II). Ablation studies confirm that zone reactivation is the decisive factor for coverage completeness ($-18.8$ and $-17.2$ percentage points when disabled in Environments I and II, respectively) and that density weighting improves path efficiency. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

The Dense-Residual-Connected Transformer (DRCT) has established a new state-of-the-art in single image super-resolution by mitigating the information bottleneck in deep networks. However, its feature aggregation mechanism relies on a suboptimal Static Addition strategy, where residual features are scaled by a fixed, learnable scalar, regardless of the image content. This content-agnostic approach treats high-frequency textures and low-frequency noise indiscriminately, limiting the model’s representational capability. To address this, we propose a Frequency-Aware Adaptive Fusion Gate (FAFG) to replace the static scaling. Unlike spatial-only gating mechanisms, FAFG integrates the Discrete Cosine Transform (DCT) to explicitly perceive the frequency distribution of feature maps. By decomposing features into frequency components, our gate acts as an intelligent valve, dynamically amplifying valid structural details while suppressing redundant background noise. Extensive experiments on standard benchmarks demonstrate that our proposed FAFG-integrated model consistently outperforms the static-scaling and other state-of-the-art methods. Specifically, our method achieves a significant PSNR improvement of 0.31 dB on the texture-rich Urban100 dataset at $\times 4$ scale. Visual results further confirm that our frequency-aware gating mechanism effectively recovers sharper edges and fine textures, providing a superior trade-off between reconstruction accuracy and model complexity. Full article

Prognostic health management of rolling element bearings requires feature representations that reliably track degradation while remaining tractable for real-time deployment. This paper investigates whether uniform time-delay embedding can serve as a near-optimal substitute for computationally expensive non-uniform embedding in recurrence-based vibration analysis. We show empirically that optimally chosen uniform delay vectors yield phase-space reconstructions of bearing vibration signals not significantly inferior to those produced by globally optimized non-uniform delay vectors, compressing the parameter search from a combinatorial optimization to a single scalar selection. Building on this near-optimality result, we construct color recurrence plots from uniformly embedded phase spaces and apply them to remaining useful life (RUL) prediction on the Intelligent Maintenance Systems (IMS) bearing dataset. We further demonstrate that standard binary recurrence plots are poorly suited for RUL estimation: their dense and erratically varying local patterns obscure the degradation trends required for reliable prognostics. Color recurrence plots, by contrast, suppress these local instabilities by averaging recurrence structures across multiple phase-space projections, exposing a globally evolving intensity that tracks bearing health throughout its degradation trajectory. This work establishes uniform delay embedding combined with color recurrence representation as an efficient, principled, and practically deployable approach to recurrence-based condition monitoring in industrial predictive maintenance. Full article

During the start stage of ship welding, obtaining the three-dimensional coordinates of welding target points is affected by confined installation space, surface reflection, and deployment constraints. This paper proposes a low-complexity point-wise three-dimensional localization method based on two-dimensional visual planar guidance and one-dimensional point-laser distance constraints. A direct computation model of the laser incident point in the robot base coordinate system is established from the tool center point pose, the extrinsic parameters of the point-laser module, and real-time ranging data, enabling target-point coordinate estimation without dense three-dimensional reconstruction. A dual-stage stabilization strategy is introduced by combining ranging-level filtering, spatial coordinate smoothing, and outlier suppression. Image error-based visual closed-loop alignment is further used as a pre-measurement step to ensure that the point laser acts on the target region. Experimental results show that, after workplane-level extrinsic correction, independent validation points achieve a mean three-dimensional Euclidean error of 1.54 mm with a standard deviation of 0.28 mm. The average planar error in closed-loop alignment experiments is 1.124 mm. Passive binocular depth measurement on the current platform still yields an RMSE of 6.16 mm after linear correction. A simulated fillet-weld task verifies the feasibility of the complete perception-to-execution workflow. The proposed method provides a low-complexity coordinate acquisition route for discrete welding target points before arc ignition. Full article

This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture under Mediterranean conditions. Experiments were conducted in Sicily, Italy, on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV photogrammetric data were used to generate canopy models and estimate canopy height, canopy volume, and vegetation density distribution. A voxel-based approach was applied to LiDAR-derived point clouds to quantify internal canopy structure and vegetation density within the canopy volume. Accuracy was assessed by comparing remote sensing-derived canopy metrics with ground-truth field measurements. LiDAR outperformed UAV photogrammetry in canopy height estimation, achieving lower RMSE values than UAV-derived models (0.19–0.21 m vs. 0.52–0.60 m), corresponding to an approximate error reduction of 60–65%. LiDAR also provided more accurate canopy volume estimation, with lower relative errors than UAV photogrammetry (3.5–4.2% vs. 13.7–16.1%). The voxel-based LiDAR approach enabled the quantification of vegetation density distribution within the canopy volume, showing higher sensitivity to internal canopy layers compared with UAV photogrammetry, particularly in the structurally complex Ficus macrophylla canopy. UAV photogrammetry provided reliable estimates of the external canopy surface but underestimated structural parameters in dense vegetation due to canopy occlusion and limited penetration into inner canopy layers. Differences between the two methods were more pronounced in Ficus macrophylla than in Moringa oleifera, confirming the strong influence of canopy complexity on sensing performance. These findings demonstrate that LiDAR-derived structural and voxel-based metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, yield prediction, and canopy management in Mediterranean cropping systems. Full article

Traditional visual SLAM systems often suffer from localization drift in dynamic environments due to interference from moving objects. Although semantic segmentation and depth-based masking methods have improved performance, they may still suffer from boundary under-segmentation and missed detections due to truncation of dynamic objects. To address these challenges, we propose a cascaded framework, DMSG-SLAM, a cascaded visual SLAM system that fuses Depth-Mask, Semantic information and Geometry constraints for dynamic environments. A lightweight object detection network, combined with depth consistency, is first employed to generate instance-like masks for preliminary dynamic feature removal. Then, a rotation-aware local epipolar geometric filtering mechanism is introduced to suppress residual features near object boundaries and mitigate perceptual blind spots caused by occlusion or truncation. Within potential dynamic regions, the epipolar threshold is adaptively switched according to the estimated inter-frame rotation to provide a more conservative filtering effect under challenging motion conditions. In addition, a TSDF-based dense volumetric map is incorporated to reconstruct more consistent surfaces. Experiments on highly dynamic sequences from the TUM RGB-D dataset indicate that DMSG-SLAM achieves competitive accuracy in dynamic environments, with localization performance improving by up to 90% compared to ORB-SLAM2. Full article

To test the performance of COI-based DNA barcoding in species delimitation, we amplified and sequenced the mitochondrial COI gene from six Schizothoracine fishes endemic to Xinjiang, China. We then characterized the COI gene dataset, quantified genetic divergence, and inferred phylogenetic relationships using distance-based approaches. Morphological examination supported clear phenotypic differentiation among taxa. In particular, Diptychus maculatus can be readily distinguished from the densely scaled Schizothorax species by having a single pair of barbels and relatively sparse scales. The COI gene sequences showed an evident AT bias (55.1%), consistent with patterns reported for teleost mitochondrial genomes. Across all samples, 11 haplotypes were identified. Pairwise comparisons indicated a maximum interspecific divergence of 15.296%, whereas the smallest interspecific distance (0.262%) occurred between Schizothorax barbatus and Schizothorax irregularis. Species-delimitation analyses and phylogenetic reconstruction supported D. maculatus and A. laticeps as distinct mitochondrial lineages, whereas S. biddulphi, S. eurystomus, S. irregularis, and S. barbatus were not fully resolved by COI gene. The shared haplotypes and low genetic distances among the four Schizothorax species may reflect recent divergence, incomplete lineage sorting, or historical gene flow. Overall, COI gene barcoding is effective for distinguishing the major lineages in this dataset, especially D. maculatus and A. laticeps. Full article

Mobile laser scanning (MLS) enables rapid, high-resolution measurement of forest structure, yet its reliability for estimating stem volume and aboveground biomass (AGB) in dense plantations and its sensitivity to tree height uncertainty remain insufficiently quantified. This study evaluates handheld MLS for tree-level stem volume and AGB estimation in a mature Sitka spruce (Picea sitchensis (Bong.) Carr.) plantation in Ireland, using destructive sampling (n = 12) as a reference. MLS-derived diameter measurements were used to reconstruct stem profiles, with merchantable volume calculated by frustum integration to a 7 cm top-end diameter. The central objective was to quantify how uncertainty in tree height propagates through MLS-derived stem reconstruction and affects volume and AGB estimates. On average, 68.2% of merchantable stem volume was directly measured before upper-stem reconstruction. Under ideal validation conditions using true felled-stem height, MLS-derived merchantable volume and total AGB were estimated with RMSE values of 5.6% and 10.9%, respectively. Across practical height-input scenarios, error increased moderately, indicating that direct measurement of the lower stem constrained the propagation of height uncertainty. Compared with the nationally applied spruce allometric benchmark, the MLS-based workflow showed lower sensitivity to height-input uncertainty under the conditions evaluated. These findings demonstrate the potential of handheld MLS as a tree-level validation and calibration tool for measurement-based biomass assessment while highlighting the need for broader testing across stand types, species and operational plot-level workflows. Full article

Long-distance water pipelines are typically instrumented by engineering convention, producing dense and partially redundant networks. Using 7239 hourly snapshots from a 122 km trunk pipeline in northeast China (72 deployed pressure sensors; 60 retained after a >$30\%$ missing-rate filter), we ask how few sensors are needed to reconstruct the full pressure field. The field has effective rank 15 at the 99% cumulative-variance level, so the cleaned network is over-sampled by roughly $4\times$. We benchmark four selection strategies against a random baseline—spatial farthest-point, PCA leverage, Q-DEIM, and a proposed hybrid that adds a soft spatial-diversity penalty to the Q-DEIM residual score—under a uniform Tikhonov-regularised gappy POD reconstruction. Q-DEIM and the hybrid both reach $R^2=0.982$ (RMSE 0.96 mH₂O) with only 15 sensors (75% reduction of the 60-sensor cleaned network); the stricter $R^2\ge 0.99$ milestone requires $K=26$ for Q-DEIM and $K=42$ for the hybrid. PCA leverage, spatial and random sampling need 19, 32, and 43 sensors for $R^2\ge 0.95$. At $K=20$, the hybrid concedes 0.003 mH₂O of RMSE for a 3.3× improvement in worst-case fill distance (5.86 km vs. 19.37 km). Regime coverage of the training library is the binding deployment constraint. Full article

Atmospheric correction is a critical preprocessing step in optical remote sensing, but repeated high-fidelity radiative transfer simulations remain computationally expensive for dense look-up-table generation, sensitivity analysis, retrieval support, and operational preprocessing. This study presents a physics-guided multi-fidelity surrogate framework for emulating atmospheric correction coefficients using paired 6S and libRadtran simulations. Atmospheric and geometric states are sampled using Latin Hypercube Sampling, and both radiative transfer models are evaluated under matched conditions for Sentinel-2 bands using spectral-response-function-aware coefficient generation. The high-fidelity targets are path reflectance, total transmittance, and spherical albedo. A physics-guided Kolmogorov–Arnold Network, termed pKANrtm, receives the atmospheric state and low-fidelity 6S coefficients, predicts the residual relative to libRadtran, and reconstructs the high-fidelity coefficients. The pKANrtm model uses an Efficient-KAN architecture and is trained with a physics-guided penalty applied in the original coefficient space. The proposed model is evaluated against state-of-the-art regression-based RTM surrogates. Across both standard and out-of-distribution (OOD) evaluation settings, pKANrtm achieves the strongest overall predictive performance among the compared models. Band-wise analysis shows that most Sentinel-2 bands are accurately emulated, while absorption-sensitive bands remain comparatively challenging. Runtime benchmarking demonstrates substantial acceleration relative to libRadtran, with GPU inference providing approximately four orders of magnitude single-sample speedup and batched inference reaching tens of thousands of samples per second. As an initial real-scene validation, the trained pKANrtm correction was applied to a Sentinel-2A acquisition over the Gobabeb RadCalNet site, demonstrating that the learned residual correction improves downstream surface-reflectance retrieval beyond synthetic RTM-to-RTM coefficient emulation. These results indicate that physics-guided multi-fidelity pKANrtm emulation provides an accurate, physically structured, computationally efficient, and practically useful strategy for atmospheric correction coefficient generation. Full article

Metabolic state strongly shapes social and reproductive behaviors, yet the neural circuits that convert internal energy signals into behavioral responses remain poorly defined. The ventral premammillary nucleus (PMv) of the hypothalamus has been implicated in this process, particularly through leptin receptor-expressing (LepRb) neurons, but its brain-wide circuit organization is still unclear. Here, we used Cre-dependent retrograde (RV) and anterograde (HSV) viral tracing techniques in LepRb-Cre mice to construct a comprehensive, single-cell-resolution input–output map of PMv^LepRb neurons. 3D reconstruction showed that these neurons receive dense convergent inputs, mainly from hypothalamic and forebrain regions involved in energy balance, motivation, and limbic processing. In contrast, their outputs extend not only back to several input regions but also prominently to midbrain and pontine autonomic centers, including the periaqueductal gray (PAG) and parabrachial nucleus (PB). Quantitative analysis revealed that forebrain regions were more likely to participate in reciprocal connectivity, whereas brainstem regions were dominated by outgoing projections. This organization suggests that PMv^LepRb neurons are positioned to integrate metabolic and motivational signals and relay them to downstream systems controlling instinctive behavioral and autonomic responses. These findings provide a structural basis for understanding how energy state can influence decisions related to social competition and reproduction. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Three-dimensional (3D) ear morphology is critical for the design of in-the-ear hearing aids, earphones, transcutaneous auricular vagus nerve stimulation (taVNS) electrodes, and auricular reconstruction, yet most existing ear shape models still rely on manually placed landmarks. Here, a fully annotation-free pipeline is presented for constructing a 3D ear atlas and statistical shape model (SSM) of the auricular bowl from 50 surface meshes. Individual ears are iteratively registered to a current atlas using rigid the iterative closest point (ICP) algorithm followed by a bidirectional thin-plate spline (BiTPS) deformation, and dense surface correspondences are established by nearest-neighbour mapping. Registration quality is quantified using mean and maximum nearest-neighbour distance, symmetric Chamfer-$L_2$ distance and coverage. Furthermore, SSM-derived bowl height and width are validated against manual 3D mesh measurements in Geomagic Design X. Across five atlas iterations, the BiTPS pipeline substantially reduces registration errors and increases coverage, and principal component analysis (PCA) derived dimensions show excellent agreement with manual measurements (Pearson $r\ge 0.98$, ICC $\ge 0.98$). The proposed framework yields a stable, anatomically plausible ear atlas and an interpretable low-dimensional SSM without manual landmarks, providing a computational basis for the geometric optimization of ear-related medical and wearable devices. Full article

Uplink emergency communications for LEO satellites are vulnerable to severe co-channel interference from dense terrestrial networks. This paper proposes a database-aided robust adaptive beamforming framework that treats database angles as coarse priors and compensates for spatial mismatches. The method includes three stages: (1) local spatial refinement via constrained Capon search to accurately locate interferers while protecting the desired signal; (2) truncated adaptive amplitude (TAA) estimation to recover true interference powers and suppress noise artifacts; and (3) reconstruction of the interference-plus-noise covariance matrix (INCM) for MVDR beamforming. By avoiding global angular scanning and improving spatial statistics estimation, the approach achieves near-optimal SINR under limited snapshots and strong interference. Simulations show consistent performance gains over DL-MVDR, ESB, IAA, and existing database-assisted methods across varying SNR, INR, and mismatch conditions, demonstrating strong robustness and practical applicability. Full article