Search Results (2,724)

Conventional antenna designs generally have fixed configurations and are difficult to assemble rapidly and flexibly. To address this limitation, this paper proposes a magnetic antenna building block that enables the rapid assembly of functional antennas. Based on this building block design, a kind of reconfigurable frequency scanning leaky wave antenna is implemented. The antenna module consists of the RF structure, dielectric housing, and embedded magnets. Magnets align and attach adjacent modules, while RF signals are transmitted through non-contact capacitive coupling interfaces and radiated by the RF structures on individual modules. The Port module, low-radiation module L, and high-radiation module H are designed. A variable number of cascaded modules can be rapidly cascaded through magnetic attachment to form different antenna arrays. The H module incorporates a pair of symmetric circular patches connected in parallel and exhibits stronger radiation characteristics than the L module, which does not include this parallel structure. Simulation and measurement results showed that the antenna arrays achieved effective port impedance matching in the C-band for cascades of the same type of modules and several mixed cascades of different types of modules. Far-field measurements also showed that arrays with different module combinations produced distinct far-field radiation patterns and allowed adjustment of the main beam gain and direction over 6.30–6.90 GHz. The main beam direction varied over a range of approximately 30°, and continuously covered the region around normal direction. The beamwidth varied with module types and the number of cascaded modules. The 3 dB beamwidth decreased from 48° to 21°, and the highest peak gain reached 6.98 dBi. The proposed magnetic antenna building block supports module cascading, combinations of different module types, and frequency scanning, providing a low-cost and flexible implementation of reconfigurable antenna arrays for communication and sensing applications. Full article

Frequent heat-source drift and environmental disturbances keep hydrogen compressors in a state of thermal non-equilibrium, where transient interactions between thermal transpiration and Poiseuille flows significantly influence system stability and safety. The mechanisms by which time-dependent temperature patterns modulate this coupling remain insufficiently understood. This research addresses this knowledge gap by numerically solving slip-boundary Navier–Stokes equations for six periodic temperature waveforms: rectangular, segmented, square, Gaussian, triangular, and sinusoidal. The results indicate that the heating rate predominantly determines the intensity of forward Poiseuille flow, whereas cooling rate and plateau duration exert comparatively minor effects. Extending the high-temperature plateau from 0.1 to 0.5 s increases both thermal transpiration and backward Poiseuille peaks by more than 100 percent, while slower heating reduces forward Poiseuille peaks by up to 11 percent. Among the six waveforms, the square wave yields the highest peaks for all three flow components. These findings elucidate how waveform structures modulate transient flow responses and offer quantitative guidance for stability assessment and thermal management in hydrogen Knudsen compressors. Full article

Low-frequency, irregular water waves with continuously changing propagation directions are difficult to harvest efficiently using conventional power generation devices. This work proposes a gyroscope-pendulum-coupled multilayer triboelectric nanogenerator (GP-TENG), in which a multi-axis gyroscope mechanism, an inertial pendulum, and a helical-structured power generation module are integrated inside a spherical floating body. The gyroscope joints enable the pendulum to respond to waves arriving from any horizontal direction, while the heave and tilting motions of the floating body jointly drive periodic contact and separation of the multilayer triboelectric materials. Motor-driven platform and water tank experiments were conducted to investigate the effects of the number of generating layers, excitation frequency, translational stroke, swing amplitude, and external resistance on the output performance. In the controlled translational tests, the maximum root-mean-square open-circuit voltage, short-circuit current, and transferred charge reached 98.6 V, 2.3 μA, and 242 nC, respectively, and a maximum output power of 16.3 μW was obtained at a load of 81 MΩ. In the water tank, the GP-TENG showed a stable response near 1.42 Hz, with maximum output power of 3.45 μW at a 60 MΩ load. The generator successfully charged the capacitor, lit up LEDs, and powered a commercial temperature and humidity sensor. These results indicate that the GP-TENG provides a compact and low-cost approach for omnidirectional low-frequency wave energy harvesting and a distributed power supply for low-power marine electronic devices. Full article

An atmosphere–ocean–wave-coupled model, the UWIN-CM, running in real-time in the Hong Kong Observatory, has adopted radar data assimilation (DA) and been incorporated with a HAILCAST module in WRF for hail size simulation. This paper analyzes the distribution of maximum hail sizes forecast by the UWIN-CM in comparison with the Maximum Estimated Size of Hail (MESH) from radar for six hail cases reported by human or derived from radar in the Pearl River Estuary of Guangdong Province in 2026. The performance of hail forecast with and without radar DA is compared. The model with DA performed simulates hail at the locations that were consistent with the reported data and/or MESHs in five out of six cases. The timings of simulated hail aligned with reports/MESHs in three cases, and these were delayed by approximately 1 to 3 h in the remaining two. For the average hail diameters, those from simulations were smaller than MESH by around 30 to 50% in two of the five successful cases, while the other three cases had larger diameters than MESH by about 90 to 140%. Without radar DA, hails were simulated in only two out of six cases. The rate of detection was enhanced with radar DA, potentially benefiting from the improved moisture distribution and wind field of the initial conditions. The possible factors behind the differences in hail size between the simulations and MESH are discussed, including the choice of microphysics scheme and the differences in climatology between the PRE, and the contiguous US where the HAILCAST parameters were calibrated. For practical use in operation, parameter tuning of HAILCAST based on the hail climatology of the PRE would be needed. This work paves the way for providing forecasters with early alerts about the occurrence of hail in the region. Full article

Rapidly deployable floating bridges face coupled challenges in compact deployment, structural load-bearing, and controllable module maneuvering, which cannot be fully evaluated through a single-stage structural or hydrodynamic assessment. To close this gap, this study proposes a rigid–flexible hybrid floating bridge composed of rigid deck plates, inflatable buoyancy bladders, scissor linkages, and integrated waterjet propulsors. A multi-stage assessment was conducted through inflation and calm-water maneuvering tests, gas–solid coupled finite-element analysis, and hydrodynamic and mooring simulations. The inflation experiment revealed three stages in the inflation process of the rigid–flexible specimen, including filling, transition, and pressurization stages. A remotely controlled scale model completed longitudinal, lateral, rotational, and compound motions, demonstrating the feasibility of module-level maneuvering under manual remote control. The finite-element results showed that increasing the initial internal pressure improved the load-bearing capacity and reduced local plastic deformation of the upper deck, while further improvement became limited above 70 kPa. Under the specified wave–current conditions, the ten-module assembly exhibited maximum mooring tension, horizontal displacement, and rotation of 34.7 kN, 0.276 m, and 5.525°, respectively. These results demonstrate the potential of the proposed configuration for bearing capacity, rapid deployment, and resistance to the investigated current and wave conditions while providing a multi-stage framework for further engineering design. Full article

Non-mechanical beam steering technology based on liquid crystal optical switches and liquid crystal polarization gratings holds significant application prospects in fields such as laser communication, radar detection, and optical information processing. Traditional nematic liquid crystal optical switches exhibit slow response speeds, whereas novel ferroelectric liquid crystal optical switches, despite their fast response, are hampered in engineering applications by complex fabrication processes, the large number of devices required for cascading, and substantial module thickness. To address these issues, this paper proposes and demonstrates a fast non-mechanical beam steering scheme by cascading a stressed polymer network liquid crystal (SPNLC) optical switch with a liquid crystal polarization grating. The SPNLC is fabricated by mechanically shearing a polymerized liquid crystal–polymer composite, enabling sub-millisecond response and continuous linear phase modulation without the need for an alignment layer. A 30-μm-thick SPNLC half-wave plate was prepared, which introduces a phase retardation of 3.6 μm under a driving voltage of 300 V, and the rise time and fall time are measured to be approximately 526 μs and 560 μs at a driving voltage of 20 V with a 1 kHz square wave, and 470 μs and 538 μs at 27 V under the same waveform conditions. Cascaded with a passive polarization grating, the waveplate enables fast electrical switching of the beam between the ±1st diffraction orders. Furthermore, a two-dimensional multi-angle beam deflector was constructed based on a supra-binary cascade scheme. Experimental results confirm that the system possesses sub-millisecond response, large phase retardation, continuous tunability, and an alignment-layer-free fabrication process, demonstrating its feasibility for large-range fast beam scanning. Full article

A one-dimensional Thue–Morse graphene photonic crystal (1D TMGPC) composed of alternating identical dielectric layers and graphene sheets is proposed, in which two distinct graphene chemical potentials are arranged according to a Thue–Morse quasi-periodic sequence. Using the transfer matrix method, we demonstrate that this structure effectively modulates terahertz waves and generates multiple abundant photonic bandgaps at both 20 K and 300 K. Notably, a novel splitting of low-frequency bandgaps produces two additional omnidirectional and polarization-insensitive bandgaps centered at approximately 1.45 THz and 1.95 THz. By analyzing the dispersion relations, reflection phase, photonic density of states, and electric field distributions, the boundary-driven modulation mechanism associated with the quasi-periodic chemical potential is elucidated. Furthermore, the proposed structure exhibits excellent multi-dimensional tunability. The bandgap properties can be dynamically tuned via the electrical control of graphene chemical potentials without altering the physical geometry. Structural tailoring provides an additional degree of freedom, as increasing the Thue–Morse sequence order induces passband splitting. Additionally, increasing the number of repeating periods yields comb-like multi-channel narrowband filtering responses. At a cryogenic temperature of 20 K, two distinct multi-channel narrowband comb filtering responses appear in the frequency ranges of 1.20–1.33 THz and 4.10–4.80 THz, with a minimum full width at half maximum (FWHM) of 1.10 GHz. At a room temperature of 300 K, the higher-frequency comb filtering response remains in the range of 4.10–4.80 THz, with a minimum FWHM of 5.70 GHz. Moreover, we evaluate the performance and stability of the structure when employed as filters and electro-optic switches, thereby providing useful insights for terahertz applications. With its simple geometry, abundant bandgaps, and flexible electro-structural tunability, the proposed 1D TMGPC is highly promising for broadband and electrically tunable terahertz devices. Full article

As global photovoltaic (PV) installation capacity grows rapidly, the environmental pollution and resource waste from the large-scale end-of-life (EOL) wave have drawn increasing attention. Traditional disposal methods such as landfilling and incineration are no longer viable, making green recycling a logical path for the PV industry. This paper reviews recent progress in the disassembly and recycling of EOL crystalline silicon (c-Si) PV modules. It first describes the structural material composition of c-Si PV modules and summarizes global recycling policies and regulatory frameworks. It then analyzes the mechanisms and process parameters of major delamination technologies, including mechanical crushing, pyrolysis, thermal cutting, high-voltage pulse fragmentation, solvent-based approaches, and laser peeling. Methods for purifying silicon and recovering precious metals such as silver and copper are also covered. Finally, key challenges in the recycling field and future development trends are discussed, with the aim of supporting the advancement of c-Si PV recycling technologies and the sustainable development of related industrial chains. Full article

To address the challenges of multi-scale target variation, complex background interference, and insufficient feature fusion quality in maritime remote sensing ship detection, this paper proposes MarShip-DET, a frequency-aware multi-scale fusion detection algorithm based on YOLO11n. Three core modules are introduced: Channel-Decoupled Progressive Feature Extraction Module (CDPFEM), which employs asymmetric channel decoupling with dual-statistic channel attention and image-relative-position-encoded multi-head self-attention to enhance discriminative feature extraction; Edge-Aware Region Context Fusion Module (EARCFusion), which integrates learnable Sobel edge sensing and cross-attention correction to achieve precise foreground refinement; and Wavelet-guided Prototype Attention Module (WavePAM), which combines Haar wavelet frequency decomposition with prototype-guided spatial compression attention to strengthen deep semantic representation. Experiments on HRSC2016 demonstrate that MarShip-DET achieves an mAP50 of 94.9% and an mAP50-95 of 84.4%, improving by 3.7% and 4.3% over the baseline, respectively. Zero-shot experiments on HRSID and SSDD, including comparisons with YOLO11n, D-FINE-N, and YOLOv13n, provide additional evidence of cross-domain transferability under the evaluated protocol. Full article

Continuous-wave mud pulse telemetry offers high spectral efficiency and transmission rates, making it an important technology for high-speed information transmission under complex well conditions. However, surface-received signals are highly susceptible to periodic pressure pulsations generated by mud pumps, which degrade phase extraction and symbol decision performance. Dual-pressure-sensor delayed differential processing can exploit the correlated propagation characteristics of pump noise between two measurement locations to suppress its correlated components; however, its performance depends on accurately matching the propagation delay and amplitude compensation coefficient. To specifically address the dynamic variation in the pump noise propagation relationship between two measurement locations under actual operating conditions, a joint delay–amplitude compensation method is developed, in which pump noise suppression is formulated as the joint estimation of the signal propagation delay and amplitude compensation coefficient. Built upon LMS-based time delay estimation, the proposed method employs an enhanced time-varying step-size LMS time delay estimation algorithm (HTVSS-LMSTDE) to improve dynamic retracking capability following changes in propagation delay. A sliding-window weighted least-squares method (SWLS) is further introduced to estimate the amplitude compensation coefficient and correct differential mismatch caused by variations in the amplitude transfer ratio. With non-pump interference modeled as additive white Gaussian noise independent of the telemetry signal and pump noise, simulation results demonstrate that, when the propagation delay and amplitude transfer ratio vary simultaneously, the proposed method yields delay estimates and amplitude compensation coefficients close to their theoretically optimal values. Field wellbore tests further verify that the proposed method effectively attenuates low-frequency pump noise interference in continuous-wave mud pulse telemetry signals while preserving the BPSK-modulated information. Full article

Granular collapse-induced wave generation is a critical process in coastal engineering and natural hazards, yet its rapid and complex fluid–solid coupling mechanism poses significant challenges for numerical modeling. This paper presents a comprehensive validations and performance benchmarking study of non-spherical granular collapse-induced wave generation using a GPU-accelerated resolved SPH-DEM coupling framework. Through three benchmark cases with increasing complexity, the numerical accuracy and robustness of the model are thoroughly verified with respect to free-surface flows, multi-body collisions, and intense fluid–solid interactions. Subsequently, the influence of SPH resolution and particle shape on computational efficiency is quantitatively assessed. It is found that the total runtime is dominated by the number of SPH particles, while the GPU acceleration advantage becomes more pronounced as the number of DEM faces increases. Furthermore, in the granular collapse-induced wave case, the temporal evolution of the leading wave amplitude and the difference in granular runout distance under dry and wet conditions are analyzed, revealing from the particle scale how fluid resistance modulates the coupling between wave generation and granular motion. This study not only validates the capability of the model to capture complex particle–wave interactions, but also provides quantifiable performance benchmarks and physical insights for its engineering applications. Full article

Ultrasonic attenuation in particle–cementitious heterogeneous media results from the coupled effects of matrix memory dissipation and particle-induced random heterogeneity, which cannot be readily distinguished using conventional homogeneous-medium models. This study develops a unified stochastic fractional wave framework that couples Caputo fractional dissipation with a random-potential representation of spatial heterogeneity. The main contribution is an analytically tractable amplitude–phase formulation that separates the leading-order roles of the two mechanisms: fractional dissipation primarily governs exponential amplitude attenuation, with $\kappa (\omega )\propto {\omega }^{\alpha -1}$, whereas the random potential mainly modulates local phase propagation and introduces finite scattering-type amplitude corrections. By transforming the governing equation into a frequency-domain Helmholtz form and applying Wentzel–Kramers–Brillouin (WKB) asymptotic analysis, explicit scaling relations are obtained for both attenuation and phase fluctuations. Two-dimensional Helmholtz simulations support the predicted attenuation law and show that the relative $L_2$ error of the WKB phase prediction decreases from 25.23% to 5.36%, while the covariance-based fixed-receiver ensemble phase-variance prediction lies within the 95% confidence interval of 30 independent realizations. Single-frequency ultrasonic transmission experiments provide complementary trend-level evidence, showing reduced tail retention and increased descriptive tail attenuation with increasing particle volume fraction. The proposed framework provides a mechanistically interpretable basis for distinguishing dissipation-dominated and heterogeneity-induced ultrasonic responses. Full article

Slow-time coding techniques, including Code Division Multiple Access (CDMA), Doppler Division Multiple Access (DDMA), and joint CDMA–DDMA coding, are widely used in Multiple-Input Multiple-Output (MIMO) millimeter-wave radar systems to improve transmit-channel isolation, angular resolution, and field of view (FOV). However, traffic radar applications also require a high range resolution and long unambiguous detection range, which cannot be fully achieved by MIMO coding alone. This paper proposes a stepped-frequency Doppler-coded integrated joint division multiple access (SF-DC-JDMA) waveform that embeds stepped-frequency (SF) modulation into a jointly encoded CDMA–DDMA MIMO framework. In the proposed design, inter-group CDMA coding provides group-level transmit separation, intra-group DDMA modulation supports Doppler-domain Tx identification, and stepped-frequency synthesis improves range resolution. The resulting waveform combines multi-Tx orthogonality with synthesized wide-band ranging, enabling simultaneous channel separation, high-resolution range estimation, and long-range detection. The simulations and real-scene measurements demonstrate that, under the same range coverage, the proposed SF-DC-JDMA waveform achieves a significantly improved range resolution relative to the conventional FMCW waveform integrated by CDMA and DDMA modulation, and yields denser point-cloud representations of traffic targets. Full article

Dental caries remains one of the most prevalent oral health problems worldwide, yet automated detection in intraoral photographs is challenging because of variable lighting, specular reflections, saliva, restoration margins, and subtle early demineralization. We propose WaveViT-YOLO, a hybrid architecture built on a YOLOv9m backbone and integrating (i) a discrete wavelet transform (DWT) preprocessing stage, (ii) learnable WaveletAttention modules at the feature-pyramid scales, and (iii) ViT-based MultiScaleCrossAttention fusion. On the publicly available Annotated Intraoral Image Dataset (6313images; patient-level 70/15/15 split; three independent seeds), YOLOv9m is the strongest standalone YOLO model by mAP@50 (mAP@50 = $0.807\pm 0.003$; mAP@50–95 = $0.642\pm 0.004$). WaveViT-YOLO achieves the highest measured mAP@50 ($0.814\pm 0.005$; $0.007$ absolute and $+0.87$% relative), mAP@50–95 ($0.647\pm 0.004$), F1 ($0.831\pm 0.005$), and PR-AUC ($0.845$) among the evaluated models. The model contains 26.3 M parameters, a $30.8$% increase over the 20.1 M YOLOv9m baseline. Model-only inference is $27.3\pm 1.6$ ms on an NVIDIA T4 GPU, while the current CPU DWT stage adds $235.2\pm 8.0$ ms, giving approximately 262.5 ms/image end-to-end; therefore, the current pipeline is not real-time end-to-end. Using the displayed seed-averaged mAP@50 values, the isolated relative changes are $+0.62$% for DWT and $+0.37$% for either WaveletAttention or ViT fusion, whereas the full configuration reaches $+0.87$%. The paired three-seed comparison against YOLOv9m yields $t\left(2\right)=6.06$, $p=0.026$, and Cohen’s $d_z=3.50$; because $n=3$, this analysis is treated as exploratory. Small lesions (<$0.098$% image area) remain the principal limitation (recall = $0.477$). Because evaluation uses clinician-provided annotations from one retrospective dataset and no independent external or prospective validation was completed, the system is presented as a research-stage screening architecture rather than a clinically validated diagnostic tool. Full article

Accurately assessing the fatigue levels of air traffic controllers is crucial for reducing human errors in ATC and ensuring the safe and orderly operation of the civil aviation transportation system. In remote tower scenarios, air traffic controllers’ work environments and task interaction modes differ significantly from those in traditional towers, and traditional fatigue detection approaches relying on physiological monitoring can cause intrusive disruptions to ATC operations. To overcome these limitations, this study proposes a scenario-based, non-invasive assessment framework for accurate and low-interference fatigue recognition. Taking three key scenario elements (traffic load, main operation screen brightness, and core work area illuminance) as the basis for measuring fatigue, the framework bridges the mapping from scenario elements to fatigue status, thereby enabling the transition of assessment inputs from physiological metrics to scenario features. In this mapping, fatigue labels are determined using a fusion strategy. Specifically, objective fatigue labels are derived from optimal wave features extracted from electroencephalogram data using one-way analysis of variance (OW-ANOVA), which are then fused with subjective labels based on the Karolinska Sleepiness Scale (KSS) self-reports through fuzzy C-means (FCM) clustering. Ultimately, a hybrid intelligent classification model integrating the Gannet optimization algorithm (GOA) and random forest (RF) is constructed to perform the primary assessment task. The experimental results indicate that the proposed framework achieves a recognition accuracy of 95.00%, outperforming six other commonly used classification or combination models. Ablation experiments and robustness tests validate the effectiveness of the fused labeling strategy and GOA modules, as well as the method’s excellent stability in resisting data noise. Furthermore, feature interpretability analysis reveals the quantitative influence of the three core fatigue drivers used. The research findings confirm the feasibility of non-invasive fatigue assessment for remote tower controllers leveraging scenario-based elements, which can offer intelligent decision support for controller shift scheduling, visual environment optimization, and targeted safety interventions. Full article