Search Results (3,124)

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

This study investigates the downstream wake characteristics of a helical hydrokinetic turbine through combined experimental and numerical analyses. A four-bladed helical turbine with a 20 cm rotor diameter and blockage ratio of 53.57% was tested in an open water channel under a flow rate of 180 m³/h, corresponding to a Reynolds number of approximately 90 × 10³. Velocity measurements were collected at 13 downstream cross-sections using an Acoustic Doppler Velocimeter, with each point sampled repeatedly. Standard error analysis was applied to quantify measurement uncertainty. Complementary numerical simulations were conducted in ANSYS Fluent using a steady-state k-ω Shear Stress Transport (SST) turbulence model, with a mesh of 4.7 million elements and mesh independence confirmed. Velocity deficit and turbulence intensity were employed as primary parameters to characterize the wake structure, while the analysis also focused on the recovery of cross-sectional velocity profiles to validate the extent of wake influence. Experimental results revealed a maximum velocity deficit of over 40% in the near-wake region, which gradually decreased with downstream distance, while turbulence intensity exceeded 50% near the rotor and dropped below 10% beyond 4 m. In comparison, numerical findings showed a similar trend but with lower peak velocity deficits of 16.6%. The root mean square error (RMSE) and mean absolute error (MAE) between experimental and numerical mean velocity profiles were calculated as 0.04486 and 0.03241, respectively, demonstrating reasonable agreement between the datasets. Extended simulations up to 30 m indicated that flow profiles began to resemble ambient conditions around 18–20 m. The findings highlight the importance of accurately identifying the downstream distance at which the wake effect fully dissipates, as this is crucial for determining appropriate inter-turbine spacing. The study also discusses potential sources of discrepancies between experimental and numerical results, as well as the limitations of the modeling approach. Full article

Suppose X and Y are Banach spaces, K is a compact Hausdorff space, and $C(K, X)$ is the Banach space of all continuous X-valued functions (with the supremum norm). We will study some strongly bounded operators $T:C(K, X)\to Y$ with representing measures $m:\Sigma \to L(X,Y)$, where $L(X,Y)$ is the Banach space of all operators $T:X\to Y$ and $\Sigma$ is the $\sigma$-algebra of Borel subsets of K. The classes of operators that we will discuss are the Grothendieck, p-limited, p-compact, limited, operators with completely continuous, unconditionally converging, and p-converging adjoints, compact, and absolutely summing. We give a characterization of the limited operators (resp. operators with completely continuous, unconditionally converging, p-convergent adjoints) in terms of their representing measures. Full article

Let $\Sigma$ be a nonempty set of characters, called an alphabet. The run-length encoding $\left(\mathrm{RLE}\right)$ algorithm processes any nonempty string u over $\Sigma$ and produces two outputs: a k-tuple $(b_1,b_2,\dots ,b_k)$, where each $b_i$ is a character and $b_{i+1}\ne b_i$; and a corresponding k-tuple $(q_1,q_2,\dots ,q_k)$ of positive integers, so that the original string can be reconstructed as $u=b_1^{q_1}{b}_2^{q_2}\dots b_k^{q_k}$. The integer k is termed the run-length of u, and symbolized by $\rho \left(u\right)$. By convention, we let $\rho \left(\epsilon \right)=0$. In the Euclidean space $({\mathbb{R}}^n,\parallel ·{\parallel }_2)$, the volume of a sphere is determined solely by the dimension n and the radius, following well-established formulas. However, for spheres of strings under the edit metric, the situation is more complex, and no general formulas have been identified. This work intended to show that the volume of the sphere ${\mathbf{S}}_L(u,1)$, composed of all strings of Levenshtein distance 1 from u, is dependent on the specific structure of the “$\mathrm{RLE}$-decomposition” of u. Notably, this volume equals $(2\mathcal{l}\left(u\right)+1)s-\left(2\mathcal{l}\left(u\right)-\rho \left(u\right)\right)$, where $\rho \left(u\right)$ represents the run-length of u and $\mathcal{l}\left(u\right)$ denotes its length (i.e., the number of characters in u). Given an integer $p\ge 2$, we present a partial result concerning the computation of the volume $\left|{\mathbf{S}}_L(u,p)\right|$ in the specific case where the run-length $\rho \left(u\right)=1$. More precisely, for a fixed integer $n\ge 1$ and a character $a\in \Sigma$, we explicitly compute the volume of the Levenshtein sphere of radius p, centered at the string $u=a^n$. This case corresponds to the simplest run structure and serves as a foundational step toward understanding the general behavior of Levenshtein spheres. Full article

This study investigates the effects of key geometric parameters on the hydrodynamic and cavitation characteristics of two-element hydrofoil systems for fully submerged unmanned hydrofoil craft, aiming to solve their active stabilization problems. Using STARCCM+ software, the RANS method, and the SST k-ω turbulence model, the research analyzes the impacts of flap deflection angle (α), main wing-to-flap chord ratio (c1/c2), and spacing (g). Results show that when the spacing is fixed, increasing the chord ratio reduces the lift and drag coefficients. When the chord ratio is fixed, increasing the spacing causes the lift and drag coefficients to first rise and then fall. With increasing flap deflection angle (α), cavitation intensifies, but it can be suppressed by increasing the chord ratio, reaching a minimum at g = 2.4%c1. The optimal configuration is c1/c2 = 1.5 and g = 2.4%c1, which can balance the lift–drag performance and anti-cavitation capability. This study provides a scientific basis for solving the active stabilization problems of fully submerged unmanned hydrofoil craft and insights for enhancing their seakeeping performance. Full article

The complex interplay between the cardiac and the respiratory systems, termed cardiorespiratory coupling (CRC), is a bidirectional phenomenon that can be affected by pathologies such as heart failure (HF). In the present work, the potential changes in strength of directional CRC were assessed in HF patients classified according to their cardiac rhythm via two measures of coupling based on k-nearest neighbor (KNN) estimation approaches, cross-entropy (CrossEn) and state space correspondence (SSC), applied on the heart period (HP) and respiratory (RESP) variability series, while also accounting for the complexity of the cardiac and respiratory rhythms. We tested the measures on 25 HF patients with sinus rhythm (SR, age: 58.9 ± 9.7 years; 23 males) and 41 HF patients with ventricular arrhythmia (VA, age 62.2 ± 11.0 years; 30 males). A predominant directionality of interaction from the cardiac to the respiratory rhythm was observed in both cohorts and using both methodologies, with similar statistical power, while a lower complexity for the RESP series compared to HP series was observed in the SR cohort. We conclude that CrossEn and SSC can be considered strictly related to each other when using a KNN technique for the estimation of the cross-predictability markers. Full article

In the present study, three-dimensional turbulent, subcritical open-channel flow (Fr = 0.19) through submerged rigid vegetation is numerically investigated using the ANSYS FLUENT solver (v. 22. 1). The Volume of Fluid (VOF) method is applied for free-surface tracking, while the standard k-ε turbulence model is employed for turbulence closure. Vegetation is modeled as vertical rigid cylinders fixed at the bottom of the channel. Regarding the arrangement of the stems, two cases are examined. In the first case, a linear arrangement of three equally spaced vegetative stems is located transversely at the center of the channel, while in the second case, a parallel arrangement of fifteen equidistant vegetative stems is located downstream of the channel center. In both cases, the vertical velocity profile within the submerged vegetation layer deviates significantly from that of the upper non-vegetated layer, which generally adheres to the logarithmic velocity distribution. In the second case, flow field repeatability is observed after the third stem series, particularly in terms of velocity profiles. Additionally, the structure of turbulence is noticeably affected in the vicinity of the stems, resulting in higher eddy viscosity values near each stem’s crest area. Furthermore, a localized drop in the free surface is recorded above the vegetated region, while a slight rise is observed upstream of each stem series, consistent with subcritical open-channel flow. Full article

The increasing presence of non-cooperative targets poses significant challenges to the space environment and threatens the sustainability of aerospace operations. Accurate on-orbit perception of such targets, particularly those without cooperative markers, requires advanced algorithms and efficient system architectures. This study presents a hardware–software co-design framework for the pose estimation of non-cooperative targets. Firstly, a two-stage architecture is proposed, comprising object detection and pose estimation. YOLOv5s is modified with a Focus module to enhance feature extraction, and URSONet adopts global average pooling to reduce the computational burden. Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. Secondly, a customized FPGA-based accelerator is developed with an instruction scheduler, memory slicing mechanism, and computation array. Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. Finally, a heterogeneous DSP–FPGA system is implemented, where the DSP manages data pre-processing and result integration, and the FPGA performs core inference. The system is deployed on a Xilinx X7K325T FPGA operating at 200 MHz. Experimental results show that the optimized model achieves a peak throughput of 399.16 GOP/s with less than 1% accuracy loss. The proposed design reaches 0.461 and 0.447 GOP/s/DSP48E1 for two model variants, achieving a 2× to 3× improvement over comparable designs. Full article

Pressures applied to horses via nosebands are of growing concern. The current study applied noseband pressure to the head of a dead horse. Pressure sensors were placed on the left nasal bone to record pressures as the noseband was progressively tightened. Tightness increased as predicated by holes in the strap of the noseband (as supplied) through eight steps from two fingers’ space, assessed using the standard International Society for Equitation Science Taper Gauge through to zero space. Sensors were also placed at the midline frontal plane and intra-orally at the level of the second premolar tooth. A strain gauge integrated into the noseband recorded tensions within the noseband at each tightness level, and a digital taper gauge under the noseband recorded forces on the face. Pressures at the left nasal bone rose to 403 kPa, while those at the frontal nasal plane reached 185 kPa. Pressures rose rapidly once the noseband was tightened at the equivalent of 1.4 fingers’ space under the noseband. These findings may help to explain cases of bone and skin damage at the noseband location and indicate the need to ensure that nosebands can accommodate more than the equivalent of 1.4 fingers beneath them in the nasal midline. Given that pressures are expected to rise from those reported here when horses wear bits, locomote, and when the reins are under tension, we conclude that the traditional provision of two fingers’ space should be retained. Full article

Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was necessary. To establish the clusters, four different algorithms were tested: K-Means, Hierarchical Agglomerative Clustering, dual-space clustering (DSC), and clustering by autocorrelation statistics. The chosen method was DSC, which can consider the multivariate and spatial aspects of data simultaneously. To better understand each cluster’s mineralogy, an XRD analysis was conducted, shedding light on why each cluster performs differently in the plant: cluster 0 contains high magnetite content, explaining its strong magnetic yield; cluster 3 has low pyrochlore, resulting in reduced flotation yield; cluster 2 shows high pyrochlore and low gangue minerals, leading to the best overall performance; cluster 1 contains significant quartz and monazite, indicating relevance for rare earth elements. A hierarchical indicator kriging workflow incorporating a stochastic partial differential equation (SPDE) trend model was applied to spatially map these domains. This improved the deposit’s circular geometry reproduction and better represented the lithological distribution. The elaborated model allowed the identification of four geometallurgical zones with distinct mineralogical profiles and processing behaviors, leading to a more robust model for operational decision-making. Full article

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. The proposed methodology comprises the following steps: First, a RANSAC algorithm is employed to filter out noise from tunnel objects, such as facilities, tracks, and bolt holes, which exhibit intensity values similar to leakage. Next, intensity features are extracted to facilitate the initial separation of leakage regions from the tunnel lining. Subsequently, geometric features derived from the k neighborhood are incorporated to complement the intensity features, enabling more effective segmentation of leakage from the lining structures. The optimal neighborhood scale is determined by selecting the scale that yields the highest F_1-score for leakage across various multiple evaluated scales. Finally, the XGBoost classifier is applied to the binary classification to distinguish leakage from tunnel lining. Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. The consistent performance across four heterogeneous datasets indicates the robust generalization capability of the proposed methodology. Comparative analysis further shows that XGBoost outperforms other classifiers, such as Random Forest, AdaBoost, LightGBM, and CatBoost, in terms of balance of accuracy and computational efficiency. Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency. Full article

This paper studied the general mechanism of spatial-frequency-shift (SFS) super-resolution imaging based on multiplex illumination modulation. The theory of SFS joint intensity was first proposed. Experiments on parallel slots with discrete spatial frequency (SF) distribution and V-shape slots with continuous SF distribution were carried out, and their real-space images and k-space images were obtained. The influence of single illumination with different SFS and mixed illumination with various combinations on SFS super-resolution imaging was analyzed. The phenomena of sample SF coverage were discussed. The SFS super-resolution imaging characteristics based on low-coherence illumination and highly localized light fields were discovered. The phenomenon of image magnification during SFS super-resolution imaging process was discussed. The differences and connections between the SF spectrum of objects and the k-space images obtained in SFS super-resolution imaging process were explained. This provides certain support for optimization of high-throughput SFS super-resolution imaging. Full article

Urine-derived and plant-derived benzoic acid can accumulate within soil, and it likely changes the soil microbial community as well as N₂O emissions; however, its mechanism is not clear. This study conducted an incubation experiment to monitor N₂O emissions under low moisture (40% water-filled pore space (WFPS)) and high moisture (85% WFPS) conditions. Metagenomic sequencing and q-PCR methods were used to determine the link between N₂O emissions and the composition and functions of soil microbiota. Benzoic acid (BA) was found to significantly, yet dose-dependently, impact N₂O emissions; that is, low BA concentrations increased N₂O, whereas high BA decreased N₂O. However, this was only found under high moisture conditions. In contrast, BA had little impact on N₂O emissions under low moisture conditions. Under high moisture conditions, BA increased the gene copy number of bacteria and fungi, and decreased the ratio of bacteria to fungi. Similarly, BA significantly increased the abundance of denitrification functional genes, but reduced the (NirS + NirK)-to-NosZ ratio at the peak of emission. This is in agreement with the observation of the increased relative abundance of genes encoding N₂O reductase (EC 1.7.2.4) and NO₃⁻ heterotrophic reductase (EC 1.7.1.15, EC 1.7.2.2) in the metagenomic analysis. In summary, high concentrations of benzoic acid reduce N₂O emissions by promoting the reduction of N₂O. This study revealed the impact of BA on soil microbiota and highlighted the favorable conditions and underlying mechanism behind BA’s significant impact on soil N₂O emissions. This study’s novelty lies in the fact that it deepens our understanding of the complicated role of root exudates and metabolites of animals and plants in soil. Full article

Low Earth orbit (LEO) satellite networks deliver superior real-time performance and responsiveness compared to conventional satellite networks, despite technical and economic challenges such as high deployment costs and operational complexity. Nevertheless, rapid topology changes and severe energy constraints of LEO satellites make real-time routing a persistent challenge. In this paper, we employ field-programmable gate arrays (FPGAs) to overcome the resource limitations of on-board computers (OBCs) and to manage energy consumption effectively using the Eclipse-Aware Routing (EAR) algorithm, and we implement the K-Shortest Paths (KSP) algorithm directly on the FPGA. Our method first generates multiple routes from the source to the destination using KSP, then selects the optimal path based on energy consumption rate, eclipse duration, and estimated transmission load as evaluated by EAR. In large-scale LEO networks, the computational burden of KSP grows substantially as connectivity data become more voluminous and complex. To enhance performance, we accelerate complex computations in the programmable logic (PL) via pipelining and design a collaborative architecture between the processing system (PS) and PL, achieving approximately a 3.83× speedup compared to a PS-only implementation. We validate the feasibility of the proposed approach by successfully performing remote routing-table updates on the SpaceWire-based SpaceWire Brick MK4 network system. Full article

Special vehicle detection in intelligent surveillance, emergency rescue, and reconnaissance faces significant challenges in accuracy and robustness under complex environments, necessitating advanced detection algorithms for critical applications. This paper proposes HSF-DETR (Hypergraph Spatial Feature DETR), integrating four innovative modules: a Cascaded Spatial Feature Network (CSFNet) backbone with Cross-Efficient Convolutional Gating (CECG) for enhanced long-range detection through hybrid state-space modeling; a Hypergraph-Enhanced Spatial Feature Modulation (HyperSFM) network utilizing hypergraph structures for high-order feature correlations and adaptive multi-scale fusion; a Dual-Domain Feature Encoder (DDFE) combining Bipolar Efficient Attention (BEA) and Frequency-Enhanced Feed-Forward Network (FEFFN) for precise feature weight allocation; and a Spatial-Channel Fusion Upsampling Block (SCFUB) improving feature fidelity through depth-wise separable convolution and channel shift mixing. Experiments conducted on a self-built special vehicle dataset containing 2388 images demonstrate that HSF-DETR achieves mAP50 and mAP50-95 of 96.6% and 70.6%, respectively, representing improvements of 3.1% and 4.6% over baseline RT-DETR while maintaining computational efficiency at 59.7 GFLOPs and 18.07 M parameters. Cross-domain validation on VisDrone2019 and BDD100K datasets confirms the method’s generalization capability and robustness across diverse scenarios, establishing HSF-DETR as an effective solution for special vehicle detection in complex environments. Full article