Search Results (3,541)

This paper presents a simplified matching network using coupled transmission lines (CTLs) for broadband power amplifiers. The proposed structure consists of a CTL with an electrical length shorter than $\lambda$/4 and a single shunt component, exhibiting excellent frequency characteristics across a wide bandwidth at both the input and load networks of the transistor. The reactance variation of the non-Foster elements in the equivalent circuit of the CTL with respect to frequency was analyzed, and the external reactive components were accordingly optimized to extend the bandwidth of the matching network. The proposed network was applied to the input and load networks of a GaN HEMT-based power amplifier. It was designed to maintain required performances over a wide frequency range of 1.9–4.9 GHz, covering both LTE and sub-6 GHz 5G bands, thereby achieving a fractional bandwidth (FBW) of 88.2%. The CTLs were fabricated on a two-layer printed-circuit board (PCB), and the additional shunt components were designed using surface-mount devices (SMDs). The overall power-amplifier module occupied a small area of 40 × 35 mm². Using the continuous-wave (CW) signal, the proposed power amplifier exhibited a power gain of 10–14.8 dB and a drain efficiency (DE) of 47.5–60% at a saturated output power of 7.1–9.3 W across the entire operating frequency band. Using a 5G New Radio (NR) signal with a 100 MHz bandwidth and a peak-to-average power ratio (PAPR) of 7.8 dB, the amplifier achieved an average output power of 30 dBm, a DE of 20–27.5%, and an adjacent-channel leakage power ratio (ACLR) better than −30 dBc. Full article

This study investigates the hydrodynamic characteristics and pollutant transport in vegetated seepage channels, with a particular focus on the impacts of seepage and vegetation density on flow velocity and pollutant dispersion. The primary innovation of this research lies in the novel integration of the Lattice Boltzmann Method (LBM) and the Random Displacement Method (RDM) to establish a numerical model for simulating vertical flow velocity and pollutant transport in such channels. To enhance simulation accuracy, the sediment bed was treated as a porous medium. The findings reveal that higher seepage rates significantly increase pollutant infiltration, and denser vegetation further amplifies this effect by enhancing turbulent diffusion and mechanical dispersion within the vegetated zone. These insights are critical for sustainable groundwater protection and the design of vegetated buffer zones in river management. Furthermore, treating the sediment layer as a porous medium yielded more accurate flow velocity predictions. These results provide new insights into the complex interactions between seepage, vegetation, and pollutant transport, and offer a valuable theoretical basis for optimizing sustainable vegetation planting schemes and management practices in vegetated seepage rivers to protect groundwater quality. Full article

Cloud Top Height (CTH), defined as the altitude of the highest cloud layer above mean sea level, is a crucial geophysical parameter for quantifying cloud radiative effects, analyzing severe weather systems, and improving climate models. To enhance the accuracy of CTH retrieval from Fengyun-4A (FY-4A) satellite data, this study proposes a multi-stage deep learning framework that progressively refines cloud parameter estimation. The method utilizes cloud information from the FY-4A/AGRI (Advanced Geosynchronous Radiation Imager) Level 1 calibrated scanning imager radiance data product to construct a multi-source data fusion neural network model. The model inputs combine multi-channel radiance data with cloud parameters, including Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). We used the CTH measurement data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite as a reference to verify the model output. Results demonstrate that the proposed multi-stage model significantly improves retrieval accuracy. Compared to the official FY-4A CTH product, the Mean Absolute Error (MAE) was reduced by 49.12% to 2.03 km, and the Pearson Correlation Coefficient (PCC) reached 0.85. To test the applicability of the model under complex weather conditions, we applied it to the CTH inversion of the double typhoon event on 10 August 2019. The model successfully characterized the spatial distribution of CTH within the typhoon regions. The results are consistent with the National Satellite Meteorological Centre (NSMC) reports and clearly reveal the different intensity evolutions of the two typhoons. This research provides an effective solution for high-precision retrieval of high-level cloud CTH at a large scale, using geostationary meteorological satellite remote sensing data. Full article

Wet-snow avalanches constitute a major geomorphic hazard in southeastern Tibet, where warm, humid climatic conditions and a steep, high-relief terrain generate failure mechanisms that are distinct from those in cold, dry snow environments. This study investigates the snowpack conditions underlying avalanche initiation in this region by integrating UAV-based multi-sensor surveys with field validation. Ground-penetrating radar (GPR), infrared thermography, and optical imaging were employed to characterize snow depth, stratigraphy, liquid water content (LWC), snow water equivalent (SWE), and surface temperature across an inaccessible avalanche channel. Calibration at representative wet-snow sites established an appropriate LWC inversion model and clarified the dielectric properties of avalanche-prone snow. Results revealed SWE up to 1092.98 mm and LWC exceeding 13.8%, well above the critical thresholds for wet-snow instability, alongside near-isothermal profiles and weak bonding at the snow–ground interface. Stratigraphic and UAV-based observations consistently showed poorly bonded, water-saturated snow layers with ice lenses. These findings provide new insights into the hydro-thermal controls of wet-snow avalanche release under monsoonal influence and demonstrate the value of UAV-based surveys for advancing the monitoring and early warning of snow-related hazards in high-relief mountain systems. Full article

In intelligent meter reading and other applications, power line communication can use relay technology to solve the problem of cross-station or long-distance reliable communication. This study investigates the combined impact of the physical and Media Access Control (MAC) layers on power line relay communication system performance. To this end, cross-layer modeling, optimization, and simulation analysis integrating both layers are conducted. Based on the CSMA algorithm of IEEE 1901 protocol, a cross-layer performance analysis model of two-hop relay power line communication system is established considering the influence of non-ideal channel transmission at physical layer and competitive access at MAC layer on system performance. In order to reduce the high collision probability caused by two competitions of packets in the above scheme, an improved two-hop transmission algorithm based on CSMA-TDMA is proposed. The cross-layer performance of the system under different single-hop and two-hop schemes is compared, and the mechanism of how parameters such as the MAC layer and the physical layer affect the cross-layer performance of the power line communication system is analyzed. And the optimal power allocation factor is obtained by using the sequential quadratic programming method for the joint system throughput and packet loss rate optimization model with the two-hop power constraint. Simulation results show that the two-hop transmission scheme based on CSMA-TDMA can avoid the second-hop competition and backoff process, and has better performance in terms of throughput, packet loss rate, and delay. Full article

Junctionless (JL) thin-film transistors (TFTs) are promising candidates for low-cost, large-area electronic devices, but improvements in mobility and bias stability are still required. In this study, the effects of independent annealing of the indium oxide (InO_x) channel layer and the aluminum oxide (AlO_x) capping layer (CL) on the performance and reliability of InO_x/AlO_x heterostructure JL TFTs are examined. Devices were fabricated via solution deposition and photopatterning, and the InO_x and AlO_x layers were annealed at 250 °C and 400 °C. Increasing the annealing temperature from 250 °C to 400 °C, the InO_x layer crystallized and densified. The AlO_x layer remained amorphous at both temperatures, but its metal-hydroxyl content decreased with higher annealing. For both layers, JL TFTs annealed at 400 °C exhibited the best electrical performance (threshold voltage = 1.82 ± 0.40 V, subthreshold swing = 0.50 ± 0.07 V dec⁻¹, saturation mobility = 1.57 ± 0.37 cm² V⁻¹ s⁻¹). The threshold voltage shift under positive bias stress was 1.70 V, which demonstrates excellent bias stability. These results show that simultaneous high-temperature annealing of the channel and CL is essential to reduce trap-assisted scattering and stabilize electrostatics in JL TFTs, providing practical process guidelines for bias-stable and high-performance oxide electronics. Full article

Hydrogen-rich gas (HRG) injection is a promising low-carbon solution for blast furnace ironmaking. This study conducted numerical simulations in the lower part of a blast furnace to analyze the combustion behavior of coinjected coke oven gas (COG) and pulverized coal (PC) within the raceway and the associated thermal load on the tuyere. A three-dimensional computational fluid dynamics model incorporating fluid–thermal–solid coupling and the GRI-Mech 3.0 chemical kinetic mechanism (validated for 300–2500 K) was established to simulate the lance–blowpipe–tuyere–raceway region. The simulation results revealed that moderate COG injection accelerated volatile release from PC and enlarged the high-temperature zone (>2000 K). However, excessive COG injection intensified oxygen competition and shortened the residence time of PC, ultimately decreasing the burnout rate. Notably, although COG has high reactivity, its injection did not cause an increase in tuyere temperature. By contrast, the presence of an unburned gas layer near the upper wall of the tuyere and the existence of a strong convective cooling effect contributed to a reduction in tuyere temperature. An optimized cooling water channel was designed to enhance flow distribution and effectively suppress localized overheating. The findings of this study offer valuable technical insights for ensuring safe COG injection and advancing low-carbon steelmaking practices. Full article

This study introduces an energy-aware hybrid security framework that safeguards embedded systems against code theft, closing a critical gap. The approach integrates bitstream encryption, dynamic key generation, and Dynamic Function eXchange (DFX)-based memory obfuscation, yielding a layered hardware–software countermeasure to Read-Only Memory (ROM) scraping, side-channel attacks, and Man-in-the-Middle (MITM) intrusions by eavesdropping on communications on pins, cables, or Printed Circuit Board (PCB) routes. Prototyped on a Xilinx Zynq-7020 System-on-Chip (SoC) and applicable to MicroBlaze-based designs, it derives a fresh Authenticated Encryption with Associated Data (AEAD) key for each record via an Ascon-eXtendable-Output Function (XOF)–based Key Derivation Function (KDF) bound to a device identifier and a rotating slice from a secret pool, while relocating both the pool and selected Block RAM (BRAM)-resident code pages via Dynamic Function eXchange (DFX). This moving-target strategy frustrates ROM scraping, probing, and communication-line eavesdropping, while cryptographic confidentiality and integrity are provided by a lightweight AEAD (Ascon). Hardware evaluation reports cycles/byte, end-to-end latency, and per-packet energy under identical conditions across lightweight AEAD baselines; the framework’s key-derivation and DFX layers are orthogonal to the chosen AEAD. The threat model, field layouts (Nonce/AAD), receiver-side acceptance checks, and quantitative bounds are specified to enable reproducibility. By avoiding online key exchange and keeping long-lived secrets off Programmable Logic (PL)-based external memories while continuously relocating their physical locus, the framework provides a deployable, energy-aware defense in depth against code-theft vectors in FPGA-based systems. Overall, the work provides an original and deployable solution for strengthening the security of commercial products against code theft in embedded environments. Full article

Accurate remaining useful life (RUL) prediction of tantalum capacitors is essential for enhancing the reliability and maintainability of power electronic systems. However, online RUL prediction remains a challenging task due to the difficulty of accessing internal degradation states and the non-stationarity of operating conditions. This paper presents a novel CNN-LSTM-Attention-based deep learning framework for accurate online RUL prediction of tantalum capacitors, leveraging infrared surface temperature measurements and ambient thermal compensation. The proposed framework initiates with the collection of degradation temperature data under controlled accelerated aging experiments, where true degradation indicators are extracted by eliminating ambient temperature interference through dual-sensor compensation. The resulting preprocessed data are used to train a hybrid deep neural network model that integrates convolutional layers for local feature extraction, long short-term memory (LSTM) units for sequential dependency modeling, and a soft attention mechanism to selectively focus on the critical degradation patterns. A channel attention module is further embedded to adaptively optimize the importance of different feature channels. Experimental validation using three groups of aging data demonstrates the effectiveness and superiority of the proposed method over conventional LSTM and CNN-LSTM baselines. The CNN-LSTM-Attention model achieves a substantial improvement in prediction accuracy, with mean absolute percentage error (MAPE) reductions of up to 60.97%, root mean squared error (RMSE) reductions of up to 65.63%, and coefficient of determination (R²) increases of up to 68.67%. The results confirm the ability to deliver precise and robust online RUL predictions for tantalum capacitors under complex operational conditions. Full article

Water and sand inrush is frequently accompanied by surface subsidence, which severely constrains the sustainable development of coordinated coal mining and ecological environment. This study investigated four key influencing factors based on a water and sand inrush test system: fracture width, aquifer thickness, sand particle size composition and stratigraphic sedimentary structure. It obtained the morphological evolution characteristics of collapse funnels and revealed the evolution mechanism of collapse funnels induced by water and sand inrush. The results indicate that fracture width and aquifer thickness mainly affect the range of collapse funnel, and both show a positive correlation with the radius of collapse funnels. Sandy particle size composition plays a dominant role in the morphology of collapse funnels induced by disasters: as the size of the soil skeleton particles increases, the morphology of collapse funnels changes sequentially from a bowl shape to an inverted cone shape and then to a funnel shape with a sunken center and raised slopes. The stratigraphic sedimentary structure has a significant impact on the morphology and damage induced by disasters in collapse funnels. The upper clay layer of the underlying aquifer inhibits the water and sand inrush processes to some extent. An increase in the thickness and number of clay layers effectively prevents the water and sand mixture from flowing into the fracture channel from the lateral direction. This reduces the damage range of collapse funnels and decreases the rate of water and sand inrush. This study clarifies the formation mechanism of surface collapse funnels under the influence of the disaster-causing factors of water and sand inrush, and provides theoretical guidance for the prevention and control of such disasters. Full article

Electrowetting display technology is increasingly prevalent in modern microfluidic and electronic paper applications, yet it remains susceptible to micro-scale defects such as screen burn-in, charge trapping, and pixel wall deformation. These defects often exhibit low contrast, irregular morphology, and scale diversity, posing significant challenges for conventional detection methods. To address these issues, we propose ASAF-Net, a novel lightweight network incorporating adaptive attention mechanisms for real-time electrowetting defect detection. Our approach integrates three key innovations: a Multi-scale Partial Convolution Fusion Attention module that enhances feature representation with reduced computational cost through channel-wise partitioning; an Adaptive Scale Attention Fusion Pyramid that introduces a dedicated P2 layer for micron-level defect detection across four hierarchical scales; and a Shape-IoU loss function that improves localization accuracy for irregular small targets. Evaluated on a custom electrowetting defect dataset comprising seven categories, ASAF-Net achieves a state-of-the-art mAP@0.5 of 0.982 with a miss detection rate of only 1.5%, while operating at 112 FPS with just 9.82 M parameters. Comparative experiments demonstrate its superiority over existing models such as YOLOv8 and RT-DETR, particularly in detecting challenging defects like charge trapping. This work provides an efficient and practical solution for high-precision real-time quality inspection in electrowetting display manufacturing. Full article

The Late Holocene flood history of the Cauvery River floodplain in the Poompuhar region was reconstructed using a multiproxy sedimentological approach applied to three trench cores. Lithostratigraphy, loss on ignition (LOI), magnetic susceptibility (MS), sand–silt–clay textural analysis, granulometric statistics (Folk and Ward), Passega CM diagrams, and grain angularity provide complementary evidence to differentiate high-energy flood deposits from background slackwater sediments. Grain-size processing and statistical analyses were carried out in R using the G2Sd package, ensuring reproducible quantification of mean size, sorting, skewness, kurtosis, and transport signatures. We identified 10 discrete high-energy event beds. These layers are characterised by >80% sand content, low LOI (<3.5%), and low frequency-dependent MS (χfd% < 2%), confirming rapid, mineral-dominated deposition. A tentative chronology, projected from the regional aggradation rate, suggests two major flood clusters: a maximum-magnitude event at ~3.2 ka and a synchronous cluster at ~1.6–1.8 ka. These events chronologically align with the documented phases of channel avulsion in the adjacent Palar River Basin, supporting the existence of a synchronised Late Holocene climato-tectonic regime across coastal Tamil Nadu. This hydrological evidence supports the hypothesis that recurrent high-magnitude flooding triggered catastrophic channel avulsion of the Cauvery distributary, leading to the fluvial abandonment and decline of the ancient port city of Poompuhar. Securing an absolute chronology requires advanced K-feldspar post-IR IRSL dating to overcome quartz saturation issues in fluvial deposits. Full article

The conventional OLSR protocol faces substantial challenges in highly dynamic and interference-intensive UAV environments, including high mobility, frequent topology changes, and insufficient adaptability to electromagnetic interference. This paper proposes a cross-layer improved OLSR protocol, OLSR-LCN, that integrates three evaluation metrics—link lifetime (LL), channel interference index (CII), and node load (NL)—to enhance communication stability and network performance. The proposed protocol extends the OLSR control message structure and employs enhanced MPR selection and routing path computation algorithms. LL prediction enables proactive selection of stable communication paths, while the CII helps avoid heavily interfered nodes during MPR selection. Additionally, the NL metric facilitates load balancing and prevents premature node failure due to resource exhaustion. Simulation results demonstrate that across different UAV flight speeds and network scales, OLSR-LCN protocol consistently outperforms both the OLSR and the position-based OLSR in terms of end-to-end delay, packet loss rate, and network efficiency. The cross-layer optimization approach effectively addresses frequent link disruptions, interference, and load imbalance in dynamic environments, providing a robust solution for reliable communication in complex FANETs. Full article

Explosive welding technology is crucial for the production of large-area plates composed of materials with varying plastic and physical properties. Severe plastic deformation processes increase the mechanical strength of the plates by refining grains and increasing dislocation density. The aim of the research presented in this paper was to analyze the effect of Equal Channel Angular Pressing (ECAP) on the mechanical properties and microstructure of an Al/Cu (EN AW-1050/Cu-ETP) bimetallic plate produced by the explosive welding technology. The ECAP process was carried out at room temperature. The ECAP experiments consisted of 1–3 passes using a die with a channel angle of 90°. The ram speed was 40 mm/min. The study also considered various sample cutting orientations (longitudinal, transverse) and various positions of the bimetallic sample in the die entry channel. Rotating the sample by an angle of 180° between consecutive passes was also considered. To achieve the research objective, static tensile tests, Vickers hardness tests at a load of 4.9 N, and microstructural analysis of the samples using scanning electron microscopy and energy dispersive spectroscopy were carried out. It was found that each subsequent pass in the ECAP process led to a gradual, severe change in the morphology of the Al/Cu interfacial transition layer. The orientation of the cutting plane of the samples was shown to have no effect on the hardness of the bimetallic material. Vickers hardness tests preceded by the ECAP process revealed a more uniform hardness distribution compared to the base material. The orientation of the Al/Cu plate layers in the ECAP die channel clearly influenced the character of the hardness distribution. Full article

To address the current challenges of low single-sensor recognition accuracy for coal and rock cutting states, redundant channel feature responses, and poor performance in traditional neural network models, this paper proposes a new multi-sensor coal and rock cutting state recognition model based on a graph neural network (GNN). This model, consisting of a feature encoder, an information exchange module, and a feature decoder, enhances the communication of feature responses between filters within the same layer, thereby improving feature capture and reducing channel redundancy. Comparative, ablation, and noise-resistance experiments on multi-sensor datasets validate the effectiveness, versatility, and robustness of the proposed model. Experimental results show that compared to the baseline models, CNN3, ResNet, and DenseNet achieve improvements of 2.47%, 2.78%, and 1.50%, respectively. With the addition of the ACI block, the ResNet model achieves the best noise-resistance performance, achieving an accuracy of 93.27% even in 6 dB noise, demonstrating excellent robustness. Embedded deployment experiments further confirmed that the proposed model maintains an inference time of less than 216.1 ms/window on the NVIDIA Jetson Nano, meeting the real-time requirements of actual industrial scenarios and demonstrating its broad application prospects in resource-constrained underground working environments. Full article