Search Results (591)

In high-frequency surface wave radar (HFSWR) systems, the retrieval of the directional wave spectrum has remained challenging, especially in the case of echoes from long ranges with a low signal-to-noise ratio (SNR). Therefore, a quadratic programming algorithm based on the regularization technique is proposed with an empirical criterion for estimating the optimal regularization parameter, which minimizes the effect of noise to obtain more accurate inversion results. The reliability of the inversion method is preliminarily verified using simulated Doppler spectra under different wind speeds, wind directions, and SNRs. The directional wave spectra inverted from a radar network with two multiple-input multiple-output (MIMO) systems are basically consistent with those from the ERA5 data, while there is a limitation for the very concentrated directional distribution due to the truncated second order in the Fourier series. Further, in the field experiment during a storm that lasted three days, the wave parameters are calculated from the inverted directional spectra and compared with the ERA5 data. The results are shown to be in reasonable agreement at four typical locations in the core detection area. In addition, reasonable performance is also obtained under the condition of low SNRs, which further verifies the effectiveness of the proposed inversion algorithm. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

In a large deep lake, the generation of internal Kelvin waves and internal Poincaré waves due to wind stress on the lake surface is a significant phenomenon. These internal waves play a crucial role in material transport within the lake and have profound effects on its ecosystem and environment. Our study, which investigated the modes of internal waves in Lake Biwa using the vertical temperature distribution from field observations, has yielded important findings. We have demonstrated the applicability of the frequency equation solutions, considering the Coriolis force. The period of the internal Poincaré waves, as observed in the field, was found to match the solutions of the frequency equation. For example, observational data collected in late October revealed excellent agreement with the theoretical solutions derived from the frequency equation, showing periods of 14.7 h, 11.8 h, 8.2 h, and 6.3 h compared to the theoretical values of 14.4 h, 11.7 h, 8.5 h, and 6.1 h, respectively. However, the periods of the internal Kelvin waves in the field observation results were longer than those of the theoretical solutions. The Modified Mathew function uses a series expansion around $q_i=0$, making it difficult to estimate the periods of internal Kelvin waves under conditions where $\left|q_i\right|>1.0$. Furthermore, in lakes with an elliptical shape, such as Lake Biwa, the elliptical cylinder showed better reproducibility than the circular cylinder. These findings have significant implications for the rapid estimation of internal wave periods using the frequency equation. Full article

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

Accurate gas prediction is crucial for identifying gas-bearing zones in tight sandstone reservoirs. Traditional seismic techniques, primarily grounded in elastic theory, often overlook inelastic dispersion effects inherent to such formations. To overcome this limitation, we introduce a gas prediction approach utilizing a dispersion attribute derived from frequency-dependent inversion based on an AVO equation parameterized by a gas indicator and related properties. Rock physics modeling, based on multi-scale fracture theory, reveals the frequency-dependent gas indicator is highly responsive to variations in porosity and gas saturation. Seismic AVO simulations exhibit distinguishable signatures corresponding to these variations, supporting the potential to estimate reservoir properties from pre-stack seismic data. Synthetic data tests confirm that the values of the proposed dispersion attribute increase with increasing porosity and gas saturation. Additionally, the calculated dispersion attribute exhibits a strong positive correlation with gas content, validating its effectiveness for gas evaluation. Field application results further demonstrate that the proposed dispersion attribute shows prominent anomalies in sandstone reservoirs with high gas content. Compared to the conventional P-wave dispersion attribute, the proposed dispersion attribute exhibits superior reliability in detecting gas-rich zones. These results demonstrate the utility of the method in predicting gas-bearing regions in tight sandstone reservoirs. Full article

Partial discharge (PD) in insulators will not only lead to the gradual degradation of insulation performance but even cause power system failure in serious cases. Because there is strong noise interference in the field, it is difficult to accurately locate the position of the PD source. Therefore, this paper proposes a three-dimensional spatial localization method of the PD source with a four-element ultra-high-frequency (UHF) array based on improved wavelet packet dynamic threshold denoising and the $G_{xx}-\beta$ generalized cross-correlation algorithm. Firstly, considering the field noise interference, the PD signal is decomposed into sub-signals with different frequency bands by the wavelet packet, and the corresponding wavelet packet coefficients are extracted. By using the improved threshold function to process the wavelet packet coefficients, the PD signal with low distortion rate and high signal-to-noise ratio (SNR) is reconstructed. Secondly, in order to solve the problem that the amplitude of the first wave of the PD signal is small and the SNR is low, an improved weighting function, $G_{xx}-\beta$, is proposed, which is based on the self-power spectral density of the signal and is adjusted by introducing an exponential factor to improve the accuracy of the first wave arrival time and time difference calculation. Finally, the influence of different sensor array shapes and PD source positions on the localization results is analyzed, and a reasonable arrangement scheme is found. In order to verify the performance of the proposed method, simulation and experimental analysis are carried out. The results show that the improved wavelet packet denoising algorithm can effectively realize the separation of PD signal and noise and improve the SNR of the localization signal with low distortion rate. The improved $G_{xx}-\beta$ weighting function significantly improves the estimation accuracy of the time difference between UHF sensors. With the sensor array designed in this paper, the relative localization error is 3.46%, and the absolute error is within 6 cm, which meets the requirements of engineering applications. Full article

Electromagnetic radiation measurement and management emerge as crucial factors in the economical deployment of fifth-generation (5G) infrastructure, as the new 5G network emerges as a network of services. By installing many base stations in strategic locations that operate in the millimeter-wave range, 5G services are able to meet serious demands for bandwidth. To evaluate the ground-plane radiation level of electromagnetics close to 5G base stations, we propose a unique machine-learning-based approach. Because a machine learning algorithm is trained by utilizing data obtained from numerous 5G base stations, it exhibits the capability to estimate the strength of the electric field effectively at every point of arbitrary radiation, while the base station generates a network and serves various numbers of 5G terminals running in different modes of service. The model requires different numbers of inputs, including the antenna’s transmit power, antenna gain, terminal service modes, number of 5G terminals, distance between the 5G terminals and 5G base station, and environmental complexity. Based on experimental data, the estimation method is both feasible and effective; the machine learning model’s mean absolute percentage error is about 5.89%. The degree of correctness shows how dependable the developed technique is. In addition, the developed approach is less expensive when compared to measurements taken on-site. The results of the estimates can be used to save test costs and offer useful guidelines for choosing the best location, which will make 5G base station electromagnetic radiation management or radio wave coverage optimization easier. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the scattered field data. To simplify the training process, a Back Propagation Scheme (BPS) is employed initially to calculate the preliminary permittivity distribution, which is then fed into the GAN with SA for image reconstruction. The proposed GAN with SA offers superior performance and higher resolution compared with GAN, along with enhanced generalization capability. The methodology consists of two main steps. First, TM waves are used to estimate the initial permittivity distribution along the z-direction using BPS. Second, TE waves estimate the x- and y-direction permittivity distribution. The estimated permittivity values are used as inputs to train the GAN with SA. In our study, we add 5% and 20% noise to compare the performance of the GAN with and without SA. Numerical results indicate that the GAN with SA demonstrates higher efficiency and resolution, as well as better generalization capability. Our innovation lies in the successful reconstruction of various uniaxial objects using a generator integrated with a self-attention mechanism, achieving reduced computational time and real-time imaging. Full article

This paper presents a comparative analysis of three outdoor wave propagation models—ITU-R P.1546-6, the SUI model, and ITU-R P.452-17—benchmarked against the deterministic Parabolic Equation Modeling (PEM) method at 3.6 GHz and 6 GHz. The evaluation focuses on prediction accuracy (RMSE, MAE, bias, relative error), terrain sensitivity, and computational efficiency. At 3.6 GHz, ITU-R P.1546-6 shows poor terrain responsiveness and high relative errors, while ITU-R P.452-17 demonstrates strong terrain sensitivity and low errors in flat areas, but decreased accuracy over hilly terrain. At 6 GHz, the SUI model consistently underestimates field strength and exhibits weak terrain sensitivity, limiting its use to rough estimations. In contrast, ITU-R P.452-17 maintains good terrain correlation and acceptable accuracy, although it slightly overestimates field strength in complex environments. The results confirm that prediction accuracy, terrain sensitivity, and bias are highly model- and frequency-dependent. ITU-R P.452-17 emerges as the most reliable and computationally efficient alternative to deterministic methods when terrain effects must be considered without significant computational overhead. Full article

This work presents the initial experimentation of a real-time 5G mmWave downlink positioning testbed deployed at Airbus premises. This experimentation is part of a first-of-a-kind testbed for hybrid Global Navigation Satellite Systems (GNSS), fifth-generation (5G) new radio (NR) and sensor positioning, called the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed. The mmWave 5G base station (BS) exploits the 5G standard positioning reference signal (PRS) to support positioning capabilities within the 5G NR downlink transmissions. Outdoor field results are used to characterize the received power levels and beam-based angle-of-arrival (AoA) estimation accuracy of this 5G mmWave PRS platform. The goal is to assess the suitability of this platform to enhance the positioning performance thanks to the 5G downlink mmWave transmissions. To the best of the authors’ knowledge, this paper presents the first AoA results using OpenAirInterface (OAI) PRS mmWave signal transmissions at 27 GHz for positioning. These initial field results indicate a maximum coverage of 30 m and an AoA accuracy limited by the reduced array size. The limitations and potential enhancements of this platform are provided as future recommendations. Full article

In this study, a correction procedure for ship mass and its longitudinal location of center of gravity suitable for a simulation environment is proposed in OpenFOAM v6.0. The concept is implemented ensuring static equilibrium and an approximately zero-pitch moment on the ship before the simulation. The viscous flow field around the ship in calm water is simulated using the VOF (Volume of Fluid) free surface two-phase and SST (Shear Stress Transport) k–ω turbulence models. Using static mesh, the resistance error of medium and fine grids is 4%, on average, against the experimental value. As the sinkage and trim are predicted using dynamic mesh, the increasing ship’s resistance causes larger errors, except for the container ship. Through the proposed correction, the ship’s vertical motions are significantly improved, and the resistance error decreases for the dynamic simulation. For the container ship, the error of resistance and motion achieved is less than 1%. The sinkage and trim errors improve tremendously for the tanker and bulk carrier, and the resistance errors are reduced slightly, by less than 3%. In the end, the detailed flow field is analyzed, as well as the ship wave-making pattern and the nominal wake velocity distribution, and these are compared with the measurement data available. The characteristics of the flow phenomena are successfully modeled. The resistance value for each hull form satisfies the requirement of Verification and Validation, and the uncertainty values are estimated. Full article

Based on the field data of laser interference devices obtained on the shelf of the Sea of Japan, the interaction of internal sea waves with the bottom and the transfer of energy from the sea wave to the seismic acoustic wave were studied. It has been established that when internal waves move from the depth dump to the surf zone, they transform, and their period decreases. When the energy of the internal wave is transformed into elastic bottom vibrations, the flow density is estimated to spread evenly over a shelf about 30 km wide. Taking into account the maximum amplitudes of elastic bottom vibrations caused by offshore internal waves, the density of the seismic energy flux will increase by 2–3 orders of magnitude and will be comparable to the density of the seismic energy flux caused by surface sea waves. Full article

Climate changes have increased heavy rainfall, intensifying flood damage, especially along small streams with steep slopes, fast flows, and narrow widths. In Korea, nearly half of flood-related casualties occur in these regions, underscoring the need for effective flood early warning systems. However, predicting flood depths is challenging due to the complex channels and rapid flood wave propagation in small streams. This study developed a flood early warning framework (FEWF) tailored for small streams in Korea, optimizing rainfall–discharge nomographs using hydro-informatic data from four streams. The FEWF integrates a four-parameter logistic model with real-time updates with a nomograph using a robust constrained nonlinear optimization algorithm. A simplified two-level early warning system (attention and severe) is based on field-verified thresholds. Discharge predictions estimate the water depth in unmeasured cross-sections using the Manning formula, with real-time data updates allowing for the dynamic identification of the flood depth. The framework was validated during the 2022 flood event, where no inundation or bank failures were observed. By improving flood prediction and adaptive management, this framework can significantly enhance disaster response and reduce casualties in vulnerable small stream areas. Full article

We introduce the Whistler Identification by Spectral Power Estimation and Recognition (WhISPER) algorithm, a novel automated technique for detecting whistler waves in the top side of the Earth’s ionosphere. WhISPER is the first step towards a comprehensive system designed to accumulate and analyze a large dataset of whistler observations, which has been developed to advance our understanding of whistler generation and propagation. Unlike conventional image-correlation-based techniques, WhISPER identifies whistlers based on their energy content, enhancing computational efficiency. This work presents the results of applying WhISPER to four years (2019–2022) of top-side ionospheric magnetic field data. A statistical analysis of over 800,000 detected whistlers reveals a strong correlation with lightning activity and (as expected) higher occurrence rates during local summer months. The presented results demonstrate the excellent performance of the WhISPER technique in identifying whistler events. Full article