Search Results (214)

High-precision time–frequency systems are essential for low Earth orbit (LEO) navigation satellites to achieve real-time (RT) centimeter-level positioning services. However, subject to stringent size, power, and cost constraints, LEO satellites are typically equipped with oven-controlled crystal oscillators (OCXOs) as the system clock. The inherent long-term stability of OCXOs leads to rapid clock error accumulation, severely degrading positioning accuracy. To simultaneously balance multi-dimensional requirements such as clock bias accuracy, and frequency stability and phase continuity, this study proposes a linear quadratic Gaussian (LQG) frequency precision steering method that integrates a four-dimensional constraint integrated (FDCI) model and hierarchical weight optimization. An improved system error model is refined to quantify the covariance components (Σ₁₁, Σ₂₂) of the LQG closed-loop control system. Then, based on the FDCI model that explicitly incorporates quantization noise, frequency adjustment, frequency stability, and clock bias variance, a priority-driven collaborative optimization mechanism systematically determines the weight matrices, ensuring a robust tradeoff among multiple performance criteria. Experiments on OCXO payload products, with micro-step actuation, demonstrate that the proposed method reduces the clock error RMS to 0.14 ns and achieves multi-timescale stability enhancement. The short-to-long-term frequency stability reaches 9.38 × 10⁻¹³ at 100 s, and long-term frequency stability is 4.22 × 10⁻¹⁴ at 10,000 s, representing three orders of magnitude enhancement over a free-running OCXO. Compared to conventional PID control (clock bias RMS 0.38 ns) and pure Kalman filtering (stability 6.1 × 10⁻¹³ at 10,000 s), the proposed method reduces clock bias by 37% and improves stability by 93%. The impact of quantization noise on short-term stability (1–40 s) is contained within 13%. The principal novelty arises from the systematic integration of theoretical constraints and performance optimization within a unified framework. This approach comprehensively enhances the time–frequency performance of OCXOs, providing a low-cost, high-precision timing–frequency reference solution for LEO satellites. Full article

Creating high-fidelity digital twins (DTs) for Industry 4.0 applications, it is fundamentally reliant on the accurate 3D modeling of physical assets, a task complicated by the inherent imperfections of real-world point cloud data. This paper addresses the challenge of reconstructing accurate, watertight, and topologically sound 3D meshes from sparse, noisy, and incomplete point clouds acquired in complex industrial environments. We introduce a robust two-stage completion-to-reconstruction framework, C2R3D-Net, that systematically tackles this problem. The methodology first employs a pretrained, self-supervised point cloud completion network to infer a dense and structurally coherent geometric representation from degraded inputs. Subsequently, a novel adaptive surface reconstruction network generates the final high-fidelity mesh. This network features a hybrid encoder (FKAConv-LSA-DC), which integrates fixed-kernel and deformable convolutions with local self-attention to robustly capture both coarse geometry and fine details, and a boundary-aware multi-head interpolation decoder, which explicitly models sharp edges and thin structures to preserve geometric fidelity. Comprehensive experiments on the large-scale synthetic ShapeNet benchmark demonstrate state-of-the-art performance across all standard metrics. Crucially, we validate the framework’s strong zero-shot generalization capability by deploying the model—trained exclusively on synthetic data—to reconstruct complex assets from a custom-collected industrial dataset without any additional fine-tuning. The results confirm the method’s suitability as a robust and scalable approach for 3D asset modeling, a critical enabling step for creating high-fidelity DTs in demanding, unseen industrial settings. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

The sludge pyrolysis technology for biochar production delivers dual environmental benefits, addressing both sludge disposal challenges and enabling environmental remediation through the utilization of the resultant biochar. However, the complex multi-step procedures and low catalyst output in previous studies constrain the practical implementation of this technology. A facile sludge pyrolysis method was constructed to achieve the batch production of municipal sludge biochar (MSB) in this study. Compared to municipal sludge (MS), the resultant MSB showed a higher BET surface area, more well-developed pore channel architecture, and plentiful active sites for activating peroxymonosulfate (PMS). Under the optimized conditions (C_MSB = C_PMS = 0.2 g/L), 93.34% of Acid Red G (ARG, 20 mg/L) was degraded after 10 min, posing an excellent rate constant of 0.278 min⁻¹. Additionally, MSB demonstrated excellent broad pH adaptability, ion interference resistance, reusability, and recyclability for ARG elimination. It was primary Fe sites that excited PMS to generate ${\mathrm{O}}_2^{•-}$ and Fe-oxo species (Fe^IV=O) for ARG degradation. The reaction process exhibited minimal heavy metal leaching, indicating limited environmental risk. Therefore, the practical applicability of the sludge biochar production, coupled with its scalable manufacturing capacity and exceptional catalytic activity, collectively demonstrated that this study established a viable pyrolysis methodology for municipal sludge, offering critical insights for sludge disposal and resource reutilization. Full article

Thermokinetic characterization of amorphous carbamazepine was performed utilizing non-isothermal differential scanning calorimetry (DSC) and thermogravimetry (TGA). Structural relaxation of the amorphous matrix was described in terms of the Tool–Narayanaswamy–Moynihan model with the following parameters: Δh* ≈ 200–300 kJ·mol⁻¹, β = 0.57, x = 0.44. The crystallization of the amorphous phase was modeled using complex Šesták–Berggren kinetics, which incorporates temperature-dependent activation energy and degree of autocatalysis. The activation energy of the crystal growth was determined to be >320 kJ·mol⁻¹ at the glass transition temperature (T_g). Owing to such a high value, the amorphous carbamazepine is stable at T_g, allowing for extensive processing of the amorphous phase (e.g., self-healing of the quench-induced mechanical defects or internal stress). A discussion was conducted regarding the converse relation between the activation energies of relaxation and crystal growth, which is possibly responsible for the absence of sub-T_g crystal growth modes. The high-temperature thermal decomposition of carbamazepine proceeds via multistep kinetics, identically in both an inert and an oxidizing atmosphere. A complex reaction mechanism, consisting of a series of consecutive and competing reactions, was proposed to explain the second decomposition step, which exhibited a temporary mass increase. Whereas a negligible degree of carbamazepine degradation was predicted for the temperature characteristic of the pharmaceutical hot-melt extrusion (~150 °C), the degradation risk during the pharmaceutical 3D printing was calculated to be considerably higher (1–2% mass loss at temperatures 190–200 °C). Full article

Folic acid (FA) is used as a targeting ligand for targeted drug delivery to tumor cells, some types of which overexpress folate receptors on their surface. However, while the preparation of conjugates containing FA may comprise a multi-step process, FA presents low photostability under UV irradiation. In addition, FA undergoes radiolysis under the action of ionizing radiation, which is utilized for drug sterilization. In this study, we investigate the stability of FA in a conjugate (FA-PVP-C₆₀) with fullerene C₆₀ and polyvinylpyrrolidone under the action of UV (205–400 nm) and electron irradiation (doses from 2 to 8 kGy) at different pH (4.5, 7.2, 10.7). The degradation of FA is studied using fluorescence and UV–Vis spectroscopy. It is found that the fullerene C₆₀ in the FA-PVP-C₆₀ conjugate suppresses the degradation of FA during both photolysis and radiolysis, which is confirmed by the decrease in the quantum yield of fluorescence and the radiation chemical yield of FA destruction accompanied by increasing fullerene content in the conjugate (from 2.8 to 10 wt.%). Full article

Conventional applications of the Galvanostatic Intermittent Titration Technique (GITT) and EIS for estimating chemical diffusivity in battery electrodes face issues such as insufficient relaxation time to reach equilibrium, excessively long pulse durations that violate the short-time diffusion assumption, and the assumption of sequential electrode reaction and diffusion processes. In this work, a quasi-equilibrium criterion of 0.1 mV h⁻¹ was applied to NMC622 electrodes, yielding 8–9 h relaxations below 3.8 V, but above 3.8 V, voltage decayed linearly and indefinitely, even upon discharging titration, showing unusual nonmonotonic relaxation behavior. The initial 36-s transients of a 10-min galvanostatic pulse and diffusion impedance in series with the electrode reaction yielded consistent diffusivity values. However, solid-state diffusion in spherical active particles within porous electrodes, where ambipolar diffusion occurs in the pore electrolyte with $t_{+}=0.3$, requires a physics-based three-rail transmission line model (TLM). The corrected diffusivity may be three to four times higher. An analytic two-rail TLM approximating the three-rail numerical model was applied to temperature- and frequency-dependent EIS data. This approach mitigates parameter ambiguity and unphysical correlations in EIS. Physics-based EIS enables the identification of multistep energetics and the diagnosis of performance and degradation mechanisms. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for collaborative search missions in complex environments. However, in the presence of interference, communication disruptions between UAVs and ground control stations can severely degrade coordination efficiency, leading to prolonged search times and reduced mission success rates. To address these challenges, this paper proposes a novel multi-agent deep reinforcement learning (MADRL) framework for joint spectrum and search collaboration in multi-UAV systems. The core problem is formulated as a combinatorial optimization task that simultaneously optimizes channel selection and heading angles to minimize the total search time under dynamic interference conditions. Due to the NP-hard nature of this problem, we decompose it into two interconnected Markov decision processes (MDPs): a spectrum collaboration subproblem solved using a received signal strength indicator (RSSI)-aware multi-agent proximal policy optimization (MAPPO) algorithm and a search collaboration subproblem addressed through a target probability map (TPM)-guided MAPPO approach with an innovative action-masking mechanism. Extensive simulations demonstrate superior performance compared to baseline methods (IPPO, QMIX, and IQL). Extensive experimental results demonstrate significant performance advantages, including 68.7% and 146.2% higher throughput compared to QMIX and IQL, respectively, along with 16.7–48.3% reduction in search completion steps versus baseline methods, while maintaining robust operations under dynamic interference conditions. The framework exhibits strong resilience to communication disruptions while maintaining stable search performance, validating its practical applicability in real-world interference scenarios. Full article

This study employs a multi-model coupling and multi-scenario simulation approach to construct a framework for identifying and optimizing avian ecological corridors in the urban core of Kunming. The framework focuses on the ecological needs of resident birds (64.72%), woodland-dependent birds (39.87%), and low-mobility birds (47.29%) to address habitat fragmentation and enhance urban biodiversity conservation. This study identifies 54 core ecological corridors, totaling 183.58 km, primarily located in forest–urban transition zones. These corridors meet the continuous habitat requirements of resident and woodland-dependent birds, providing a stable environment for species. Additionally, 55 general corridors, spanning 537.30 km, focus on facilitating short-distance movements of low-mobility birds, enhancing habitat connectivity in urban fringe areas through ecological stepping stones. Eighteen ecological pinch points (total area 5.63 km²) play a crucial role in the network. The northern pinch points, dominated by forest land, serve as vital breeding and refuge habitats for woodland-dependent and resident birds. The southern pinch points, located in wetland-forest ecotones, function as critical stopover sites for low-mobility waterbirds. Degradation of these pinch points would significantly reduce available habitat for birds. The 27 ecological barrier points (total area 89.79 km²), characterized by urban land use, severely impede the movement of woodland-dependent birds and increase the migratory energy expenditure of low-mobility birds in agricultural areas. Following optimization, resistance to resident birds in core corridors is significantly reduced, and habitat utilization by generalist species in general corridors is markedly improved. Moreover, multi-scenario optimization measures, including the addition of ecological stepping stones, barrier improvement, and pinch-point protection, have effectively increased ecological sources, met avian habitat requirements, and secured migratory pathways for waterbirds. These measures validate the scientific rationale of a multidimensional management strategy. The comprehensive framework developed in this study, integrating species needs, corridor design, and spatial optimization, provides a replicable model for avian ecological corridor construction in subtropical montane cities. Future research may incorporate bird-tracking technologies to further validate corridor efficacy and explore planning pathways for climate-adaptive corridors. Full article

Diclofenac (DCF), a widely consumed non-steroidal anti-inflammatory drug, presents significant environmental challenges due to its persistence and toxicity in aquatic ecosystems. This study investigates the pH-dependent ozonation of DCF in aqueous media, focusing on degradation kinetics, transformation pathways, and effects on key water quality indicators. Ozonation experiments were conducted across a broad pH range (2.0–13.0), using a multi-scale analytical approach combining UV/Vis spectroscopy, colorimetry, turbidity, and aromaticity measurements. The results show that pH strongly influences DCF degradation efficiency: acidic conditions favor selective reactions with molecular ozone, while an alkaline pH enhances non-selective oxidation via hydroxyl radicals. Spectroscopic analyses revealed the progressive breakdown of aromatic structures, the transient formation of quinonoid and phenolic intermediates, and eventual mineralization to inorganic by-products such as nitrate. Low-pH conditions also induced turbidity due to precipitation of neutral DCF species. These findings underline the importance of pH control in optimizing ozonation performance and minimizing toxic by-products. Furthermore, this study proposes ozonation as a viable pre-treatment step within Nature-Based Solutions (NBSs), potentially improving the performance of downstream biological systems such as constructed wetlands. The results contribute to the development of integrated and sustainable water treatment strategies for pharmaceutical contaminant removal and water reuse. Full article

Accurate cloud detection is a critical preprocessing step in remote sensing applications, as cloud and cloud shadow contamination can significantly degrade the quality of optical satellite imagery. In this paper, we propose CDWMamba, a novel dual-domain neural network that integrates the Mamba-based state space model with discrete wavelet transform (DWT) for effective cloud detection. CDWMamba adopts a four-direction Mamba module to capture long-range dependencies, while the wavelet decomposition enables multi-scale global context modeling in the frequency domain. To further enhance fine-grained spatial features, we incorporate a multi-scale depth-wise separable convolution (MDC) module for spatial detail refinement. Additionally, a spectral–spatial bottleneck (SSN) with channel-wise attention is introduced to promote inter-band information interaction across multi-spectral inputs. We evaluate our method on two benchmark datasets, L8 Biome and S2_CMC, covering diverse land cover types and environmental conditions. Experimental results demonstrate that CDWMamba achieves state-of-the-art performance across multiple metrics, significantly outperforming deep-learning-based baselines in terms of overall accuracy, mIoU, precision, and recall. Moreover, the model exhibits satisfactory performance under challenging conditions such as snow/ice and shrubland surfaces. These results verify the effectiveness of combining a state space model, frequency-domain representation, and spectral–spatial attention for cloud detection in multi-spectral remote sensing imagery. Full article

This study focuses on Neogene red-bed soft rock tunnels in the Huicheng Basin, China. Through engineering geological investigation, remote wireless monitoring systems, and total station multi-parameter monitoring, the deformation characteristics of red-bed soft rock surrounding rock under high in situ stress environments and their influencing factors were systematically analyzed. The findings reveal that the surrounding rock deformation follows a three-stage evolutionary pattern of “rapid, slow, and stable”. Construction disturbances can disrupt the stable state, leading to “deep V-shaped” anomalies or double-step responses in deformation curves. Spatially, the deformation exhibits significant anisotropy, with the haunch area showing the maximum deformation (95 mm) and the vault the minimum (65–73 mm). Deformation stabilization requires 30–42 days, and a reserved deformation of 10 cm is recommended based on specifications. Mechanical behavior analysis indicates that the stress–strain curves of red-bed argillaceous sandstone are stepped, with increased confining pressure enhancing both peak and residual strengths, validating the necessity of timely support. The study elucidates a multi-factor coupling mechanism: rock mass classification, temporal–spatial effects (excavation face constraints and rheological properties), construction methods, in situ stress levels, and support timing (timely support during the rapid phase inhibits strength degradation) significantly influence deformation evolution. The spatiotemporal distribution of surrounding rock pressure shows that invert pressure increases most rapidly, while vault pressure reaches the highest magnitude, with construction disturbances triggering stress redistribution. This research provides theoretical and practical guidance for the design, construction optimization, and disaster prevention of red-bed soft rock tunnels. Full article

In the Internet of Things (IoT), data distribution among diverse terminals exhibits substantial statistical heterogeneity. This imbalance can lead to skewness and accuracy degradation, ultimately affecting the generalization ability and robustness of Federated Learning (FL) models. Our work addresses these critical challenges by proposing a novel method, Meta-Equilibrized Federated Learning (MEFL), which integrates meta-learning with gradient-descent preservation and an equilibrated optimization aggregation mechanism based on gradient similarity and variance weighted adjustment. By alleviating the gradient biases caused by multi-step local updates from the source, MEFL effectively resolves the issues of inconsistency between global and local optimization objectives. MEFL optimizes trade-offs between local and global models, and provides an efficient solution for cross-domain data security deployment in IoT scenarios. Comprehensive experiments conducted on real-world datasets demonstrate that MEFL achieves at least 3.26% improvement in final test accuracy, and substantially lowers communication overhead, compared to the existing state-of-the-art baseline methods. The results demonstrate that MEFL exhibits superior performance and generalization capability in addressing personalization challenges with imbalanced non-IID data distributions. Full article

Image segmentation is a crucial step in image processing and analysis, with multi-level thresholding being one of the important techniques for image segmentation. Existing approaches predominantly rely on metaheuristic optimization algorithms, which frequently encounter local optima stagnation and require extensive parameter tuning, thereby degrading segmentation accuracy and computational efficiency. This paper proposes a Shannon entropy-based multi-level thresholding method that utilizes composite contours. The method selects appropriate multiscale multiplication images by maximizing the Shannon entropy difference and constructs a new Shannon entropy objective function by dynamically combining contour images. Ultimately, it automatically determines multiple thresholds by integrating local contour Shannon entropy. Experimental results on synthetic images and real-world images with complex backgrounds, low contrast, blurred boundaries, and unbalanced sizes demonstrate that the proposed method outperforms six recently proposed multi-level thresholding methods based on the Matthew’s correlation coefficient, indicating stronger adaptability and robustness for segmentation without requiring complex parameter tuning. Full article

This work reports a study on the structural characterization, evaluation of thermal stability, and non-isothermal decomposition kinetics of urea–formaldehyde (UF) resin modified with hydrochar (obtained by the hydrothermal carbonization of spent mushroom substrate (SMS)) (UF-HC). The structural characterization of UF-HC, performed by scanning electron microscopy (SEM), Fourier transform infrared (FTIR), and X-ray diffraction analyses, showed that UF-HC consists of a large number of spheroidal particles, which are joined, thus forming clusters. It constitutes agglomerates, which are composed of crystals that have curved plate-like forms, including crystalline UF structure and graphite lattices with an oxidized face (graphene oxide, GO). The measurement of inherent thermal stability and non-isothermal decomposition kinetic analysis was carried out using simultaneous thermogravimetric–differential thermal analyses (TGA-DTA) at various heating rates. Parameters that are obtained from thermal stability assessment have indicated the significant thermal stability of UF-HC. Substantial variation in activation energy and the pre-exponential factor with the advancement of decomposition process verifies the multi-step reaction pathway. The decomposition process takes place through three independent single-step reactions and one consecutive reactions step. The consecutive stage represents a path to the industrial production of valuable heterocyclic organic compounds (furan) and N-heterocyclic compounds (pyrroles), building a green-protocol trail. It was found that a high heating rate stimulates a high production of furan from cellulose degradation via the ring opening step, while a low heating rate favors the production of urea compounds (methylolurea hemiformal (HFn)) by means of methylene ether bridges breaking. Full article