Search Results (27,019)

Aerial forest firefighting is a critical technology for wildfire suppression. Recent studies have examined suppression agent drop dynamics, deposition patterns, and optimization strategies. This review synthesizes advances from three perspectives: (i) in-flight suppression agent jet dynamics, (ii) ground deposition patterns, and (iii) suppression effectiveness, while outlining future research directions. Flight altitude, velocity, and momentum ratio govern jet behavior—affecting penetration, expansion, and breakup. Momentum ratio, shaped by drop velocity and aircraft speed, is pivotal in penetration depth and fragmentation. Deposition patterns vary with delivery systems and flight parameters: low-altitude/low-speed drops yield higher coverage density over smaller areas, whereas high-altitude/high-speed drops cover larger areas but less densely. Suppression efficacy depends on fire intensity–vegetation interactions, droplet size–coverage requirements, and operational parameters such as response time, aircraft capacity, and real-time intelligence. Large droplets excel in cooling high-intensity flames, while fine droplets provide efficient area coverage. Adequate resources and integrated data enhance outcomes. Future work should couple multi-physics models of terrain, meteorology, and fire plume dynamics, and develop integrated deposition models including wind, thermodynamics, terrain, and fire behavior to optimize aerial dispersion in diverse wildfire scenarios. Full article

Despite over two decades of advancement in object detection, achieving high accuracy for small target detection in practical applications remains an unresolved challenge. This paper proposes a novel small-object detection model to address this issue. The model incorporates three key innovations: first, the RCSOSA module, which optimizes feature information transmission through dynamic channel interaction and multi-scale feature coordination; second, the HFPN module, a three-branch multi-scale feature fusion network that integrates local and global features by combining CNN and Transformer architectures to enhance semantic details; and third, the NWD-CIoU loss function, which dynamically adjusts the weights of NWD and CIoU losses based on the training phase. Experimental results on the COCO dataset demonstrate that our model improves detection accuracy by 4% over YOLOv11 and achieves state-of-the-art performance among mainstream models while maintaining a real-time inference speed of no less than 60 FPS. Furthermore, validation on the VisDrone dataset confirms the model’s strong generalization capability. The proposed algorithm significantly enhances small target detection accuracy, effectively mitigating a critical limitation in current practical object detection applications. Full article

The high-speed and high-Reynolds-number conditions encountered in actual flight, coupled with the performance requirements for both low-speed climb and high-speed cruise, pose challenges for boundary-layer transition prediction and optimization in laminar design. Consequently, there are still relatively few mature and applicable high-speed laminar airfoils available. To address the insufficient validation of Reynolds-averaged Navier-Stokes (RANS) models under actual high-speed and high-Reynolds-number (Re > 10⁷) flight conditions, the practical fidelity of the most commonly used $\gamma -{\tilde{R}e}_{\theta t}$ transition model as well as NASA CFL3D solver is systematically assessed based on NASA HSNLF(1)-0213 and Honda SHM-1 high-speed business jet laminar airfoils. To the best of the authors’ knowledge, since there is no available geometry data for the SHM-1 airfoil, this is the first systematic analysis of this airfoil from a perspective other than the design team. Results demonstrate that the $\gamma -{\tilde{R}e}_{\theta t}$ transition model could accurately capture natural transition and separation-induced transition at Reynolds numbers up to 16.2 × 10⁶, while also exhibiting strong robustness against variations in Mach and Reynolds number. Using the HSNLF(1)-0213 as the baseline airfoil and the design conditions of SHM-1, a multi-objective drag-reduction optimization considering climb and cruise performance was then conducted based on the Isight platform. The optimal airfoil achieves 9.53% climb drag reduction and 9.21% cruise drag reduction, revealing that aft-loading and strong favorable pressure gradients are essential to balance lift characteristics and sustain extensive laminar flow at high Reynolds numbers. Full article

High-speed photodetectors with extended dynamic ranges are critical for emerging optical systems like LiDAR. This paper presents a design methodology for a modified uni-traveling-carrier photodetector (MUTC-PD) that integrates a physics-based analytical model with numerical simulations. The existing analytical models for MUTC-PDs rely on approximations that may not hold under high injection levels and high frequencies, leading to discrepancies between theoretical predictions and practical observations. To address this limitation, we re-examine the governing equations and derive a corrected frequency response analytical model based on drift–diffusion theory by decomposing the device into distinct transport regions, enabling a physically meaningful optimization of the epitaxial layer structure to maximize theoretical intrinsic bandwidth. The calculated results closely match the simulated bandwidth (maximum error less than 6%), demonstrating consistent peak positions and trends. Subsequently, numerical simulations reveal the dynamic evolution of the device’s bandwidth under varying incident optical intensities. The results demonstrate that the intrinsic bandwidth initially increases to a peak of 325.82 GHz at $7\times {10}^4\mathrm{W}/{\mathrm{cm}}^2$ under −3.5 V, attributed to the drift-enhancement effect driven by the self-induced quasielectric field. Beyond this optimal regime, the space charge effect causes internal field collapse and significant bandwidth degradation. This study establishes bandwidth maintenance capability under high injection as a key metric for linearity, offering a transparent theoretical and practical framework for designing a high-speed MUTC-PD. Full article

C. F. Hansen’s NASA TR R-50 published in 1959 remains one of the most widely used analytic approximations for the thermodynamic and transport properties of high-temperature air. Although modern equilibrium and nonequilibrium models extend the temperature range and species sets, Hansen’s expressions continue to provide a transparent, closed-form representation valuable for hypersonic aerothermodynamics, preliminary design, and code verification studies up to 15,000 K. In this work, we reconstruct the full Hansen model from his source equations, implement the formulation in a consistent modern notation, and derive all thermodynamic and transport quantities explicitly. The transport-property model developed by Hansen is discussed in comparison to research by Thompson et al., Gordon and McBride, and D’Angola et al. The resulting implementation provides a clean, analytic 7-species-air model for high-speed/hypersonic applications where rapid evaluations of thermodynamic and transport properties are required. Full article

Container machining plays a key role in the finishing of workpieces. The aim of this article was to compare the effectiveness of vibratory and high-speed rotational machining. Mass loss and selected changes in surface geometric structure parameters were assessed. To obtain a porous structure, the samples were prepared by sandblasting. The novelty of this work is the use of high rotational speeds for rotational machining and the use of a planned experiment to limit the number of samples. The innovative nature of the comparison of vibratory and high-speed rotational machining allowed the development of mathematical models of the influence of process parameters on the final results. A two-factor planned experiment with five levels of process variables was used to investigate a wide range of process input variables. Based on the RSM response surface, mathematical models of changes in mass losses MRR, arithmetic mean surface roughness Ra, maximum height of the highest elevation (peak) of the roughness profile Rp, and surface skewness Ssk as a function of input parameters were developed. Working containers with a volume of 25 dm³ were used for the tests, and the test material was samples made of PA38/EN AW 6060 aluminum. Studies have shown that, for similar machining times, greater MRR changes were achieved with rotary machining. Rotary machining using the same machining media and similar machining times was characterized by up to 15% greater MRR than vibratory machining after 75 min of container machining. The reason for this high efficiency is the use of high rotational speeds. Comparing the effectiveness of reducing surface geometric structure parameters between rotational and vibration machining processes depends primarily on the machining time. The work proves that the use of rotational machining and high rotational speeds allows for shorter machining times compared to vibration machining. Full article

3D bird pose estimation plays a pivotal role in ecological conservation research. However, it remains a formidable challenge due to extensive joint deformation, severe self-occlusion, and the scarcity of 3D ground truth data. Therefore, practical solutions typically rely on accurate 2D keypoint detection from monocular images and subsequent 3D lifting. Although the High-Resolution Network (HRNet) has established a benchmark in 2D pose estimation by preserving high-resolution feature representations, its architecture, which relies on small convolution kernels, faces difficulties in capturing the global long-range dependencies necessary to resolve severe occlusions. To address these deficiencies, the core contributions of this work are summarized as follows: (1) We design a Gated LS-Block with a partial channel gating strategy to decouple channel mixing from spatial mixing, and extract global long-range dependencies via the proposed Large–Small Convolution (LSConv) to minimize feature redundancy. (2) We embed this block into Stage 2 of HRNet, enhancing multi-scale feature learning while slightly reducing model parameters and computational overhead; (3) To alleviate the ill-posed nature of monocular 3D lifting without paired supervision, we develop an unsupervised 3D reconstruction algorithm. Experimental results on the Animal Kingdom dataset demonstrate that our method achieves a 0.9% improvement in PCK@0.05 while reducing GFLOPs by 3.3%. These results verify that the proposed architecture enhances the model’s representation capability for bird poses while ensuring efficient inference. Meanwhile, we validate the applicability of the proposed 3D reconstruction algorithm via qualitative experiments, and further demonstrate that our unsupervised 3D lifting algorithm successfully preserves low symmetry error and robust bone length consistency with proxy metrics. Full article

Passive Optical Networks (PONs) play a vital role in providing high-speed broadband access in the 5G and F5G generation. However, their shared nature makes them vulnerable to physical-layer attacks like fiber bending, tapping and fiber cut. The problem is more serious in high-density PONs, where high split ratios result in high optical loss and overlapping back-scattered light, making it difficult to distinguish small attacks from background noise. Contrary to most existing works that neglect class imbalance and signal interference in high-density networks, this paper proposes a robust hierarchical two-stage attack detection scheme. First, we employ a binary classifier to distinguish eavesdropping attacks from normal traffic. Then, a second stage focuses on the specific eavesdropping categories (C1–C4). To address the small amount of attack samples, SMOTE is utilized for oversampling the minority class, and PCA-SVM is used to refine feature selection. Finally, the output of both stages is combined using probability score to obtain reliable decision. The experimental results show the effectiveness of our approach, achieving a classification accuracy of 89.07%. When evaluated on the same data, it has shown superior results in comparison to conventional machine learning algorithms, including decision tree (86.3%), k-nearest neighbors (79%), logistic regression (60%), and Naïve Bayes (52.6%). Full article

This study presents an integrated technological approach for improving the service life and operational stability of a P6 die used in the cold-forging production of automotive brake connectors. The work was conducted in an industrial environment characterized by high production volumes and recurrent premature die failure. A hybrid methodology combining Shainin’s dominant-variable methodology with controlled experimentation and finite element analysis (FEA) was implemented to identify and optimize the dominant process variables affecting die durability. The attack angle, chamfer length, and machine rotational speed were determined to be the primary factors influencing stress distribution and fatigue behavior. The optimized configuration (16° attack angle, 1.4 mm chamfer length, and 88 RPM) increased die service life by 416%, improving production throughput from approximately 60,000 to over 250,000 parts per cycle. Numerical simulations confirmed that the geometric redesign effectively reduced localized Von Mises stress concentrations, contributing to enhanced structural reliability. The results demonstrate that integrating empirical industrial methodologies with numerical modeling provides a practical and replicable framework for technological improvement in high-volume cold-forging operations. The proposed approach is transferable to similar tooling optimization challenges in the automotive manufacturing sector. Full article

Drones have proven effective for acquiring aerial imagery, and when equipped with onboard analysis tools, they can automatically identify objects of interest. Neural-network methods for image analysis typically require large training datasets and substantial computational resources. By contrast, algorithmic techniques can detect objects using simple features, such as pixel colors, thereby reducing the need for extensive training and computational resources. Once trained, both types of system can analyze images in a short time. In our experiments, each approach has distinct strengths. The YOLO-based detector is more accurate for complex-shaped objects, such as trees, whereas the pixel-color approach performs better on sparser objects. This paper proposes YOLO-C3, a hybrid system designed for onboard drone image processing. By leveraging the strengths of both YOLO-based and pixel-based approaches, YOLO-C3 balances detection accuracy with estimation confidence. Trained on Mediterranean imagery dataset, the system is optimized for identifying natural objects, including citrus groves and trees.To assess the robustness of the image classifier, a K-fold cross-validation is performed.Compared to existing models, YOLO-C3 detects a wider range of natural objects with high accuracy and minimal latency, achieving a processing speed of 0.01 s per image. By performing object detection locally, drones can adapt their trajectories to support emergency response, helping to map safe corridors and locate buildings where people may be awaiting rescue after a natural disaster. Full article

In this work, the deformation behavior of a long-span steel–concrete composite girder cable-stayed bridge under temperature loads and its subsequent impact on ballastless track systems were investigated. An integrated finite element model (FEM) of the bridge–track system was developed by taking the Taiziping Wujiang River Bridge (with a main span of 300 m) in Chongqing, China, as a case study. The model incorporates composite girders, pylons, stay cables, rails, and double-block slab tracks. Then, the integrated FEM systematically analyzed structural responses to various temperature loading scenario, namely uniform temperature change, differential temperatures among key components (girder, deck, pylons, and cables), and deck–girder temperature difference. The results show that the girder’s maximum vertical displacement linearly correlates with the temperature variations of the composite girder, upper pylon, and cables, with corresponding temperature sensitivity coefficients of 2.3 mm/°C, 2.78 mm/°C, and −5.8 mm/°C. While the ballastless track coordinates well with the composite girder in vertical deformation, the maximum longitudinal relative displacement occurs between rail and track at the ends of the bridge. Moreover, field monitoring data were used to establish a high-precision relationship between ambient temperature and structural temperatures of key components, enabling successful prediction of girder’s vertical deformation. The findings provide a theoretical basis for the control of thermal deformation during the operation and maintenance of similar long-span composite girder cable-stayed bridges. Full article

In the realm of intelligent transportation systems (ITS), achieving optimal system performance relies heavily on the acquisition of comprehensive and high-quality spatio-temporal traffic data. In practical data-gathering processes, factors such as sensor malfunctions or communication interruptions often lead to incomplete or missing data records, which in turn substantially hinder the advancement of ITS applications. To address missing spatio-temporal data, a widely adopted paradigm involves the Latent Factorization of Tensors (LFT) model. Traditional LFT frameworks often employ the standard L₂ metric in their learning objective, making them easily affected by abnormal data points. Moreover, impulse noise frequently arises in sensors and communication scenarios. To address these limitations, this paper develops an Adaptive L${}_{p,ϵ}$-norm-incorporated Latent Factorization of Tensors (L${}_{p,ϵ}$LFT) model founded on two-fold concepts: (a) constructing a generalized objective function grounded in the L${}_{p,ϵ}$-norm distance to enhance robustness against outliers; (b) realizing the self-adaptation of model hyper-parameters through a fuzzy controller to enhance model practicality. Experimental evaluations on six traffic speed datasets derived from multiple metropolitan traffic networks demonstrate that the proposed L${}_{p,ϵ}$LFT model yields significantly higher imputation accuracy and superior computational efficiency compared with seven state-of-the-art approaches. Full article

With ongoing advancements toward 6G networks, the terahertz (THz) band is expected to serve as an essential platform for realizing integrated sensing and communication (ISAC). In particular, maintaining high-data-rate communication while ensuring highly responsive, real-time radar operation in dynamic environments is a critical requirement. This study presents a THz-band ISAC architecture that utilizes a high-speed wavelength-tunable laser for photomixing, enabling simultaneous generation of a fast frequency-swept frequency-modulated continuous-wave (FMCW) radar signal and amplitude-shift keying (ASK) communication. The wavelength-tunable laser enables sub-microsecond frequency sweeps and supports high repetition rates suitable for real-time operation. To address the limitations in waveform design efficiency in conventional time-division ISAC, we experimentally investigate two transmission strategies for simultaneous operation. The first is a frequency-division scheme that reduces mutual interference between radar and communication signals, and the second is a joint-waveform scheme in which both functions share the same THz carrier. Using a single THz transmitter, the proposed system achieves sub-centimeter ranging accuracy together with 15-Gbit/s data transmission. These findings demonstrate that the presented ISAC approach enables efficient integration of radar and communication functions while lowering overall system complexity and implementation cost, offering substantial potential for deployment in future 6G infrastructures. Full article

Accurate and rapid detection of anatomical structures, such as the glottis, is critical during tracheal intubation (TI) to ensure patient safety and procedural success. However, it remains a challenge due to the limited field of view and computational resources of video laryngoscopy, especially for difficult airway situations. Existing deep learning (DL) models struggle to balance high accuracy and real-time clinical deployment. To address these issues, we propose TI-YOLO (TI-You Only Look Once), a lightweight and efficient object detection model built upon the YOLOv11 architecture. TI-YOLO introduces the Bidirectional Feature Pyramid Network (BiFPN) module for multi-scale feature fusion, effectively enhancing the ability to detect anatomical structures of different sizes. TI-YOLO integrates the Deformable Attention Transformer (DAT) module to enhance the perception of crucial regions, improving detection accuracy and robustness. To further reduce the consumption of computational resources while maintaining efficiency, TI-YOLO is optimized by reconstructing the backbone based on MobileNetV4. Furthermore, TI-YOLO employs the Slide Weight Function (SWF) as a loss function during model training to mitigate the class imbalance within the dataset. One self-built dataset is used to validate the effectiveness of TI-YOLO. Compared to the original YOLOv11, TI-YOLO achieves mean Average Precision at IoU 0.50 (mAP50) scores of 0.902, with improvements of 3.8%. Meanwhile, TI-YOLO balances detection accuracy and computational efficiency with a 10.5% reduction in floating-point operations (FLOPs) and a 28.9% reduction in parameters, and the model weight is only 4.6 MB. Additionally, to evaluate TI-YOLO real-time inference capability, we quantize and deploy it on a low-cost embedded OrangePi 5 platform. The inference speed reaches over 50 frames per second (FPS), meeting real-time clinical requirements. Full article

Rapid and robust trajectory planning for the Terminal Area Energy Management (TAEM) phase of horizontal-landing Reusable Launch Vehicles (RLVs) is critical but challenging due to large initial deviations, stringent terminal constraints, and strong model nonlinearities. To address the limitations of existing methods in convergence reliability and computational speed, this paper proposes a novel online trajectory optimization framework based on analytical lateral planning and equivalent dynamic decoupling. First, a cubic Bézier curve is employed to parameterize the lateral ground track, enabling the rapid generation of analytical expressions for the lateral states that strictly satisfy boundary constraints. Leveraging these analytical solutions, the original six-degree-of-freedom dynamics are exactly decoupled and reduced to a lower-dimensional model governing only the longitudinal motion. To further mitigate nonlinearity, the third derivative of height with respect to range is introduced as a virtual control variable, transforming the problem into a smoother form. The resulting equivalent longitudinal optimization problem is then efficiently solved using the Gauss Pseudospectral Method. Numerical simulations demonstrate that the proposed method significantly outperforms traditional approaches in computational efficiency: it generates feasible trajectories satisfying all constraints within 0.26 s (3$\sigma$ value). Furthermore, the method exhibits remarkable insensitivity to initial guesses, achieving stable convergence even with simple linear initialization. This approach provides a robust and real-time capable solution for complex TAEM trajectory optimization problems characterized by high nonlinearity and multiple constraints. Full article