Search Results (26)

Accurate prediction of radio wave propagation in evaporation ducts is critical for radar systems but faces significant environmental uncertainties. This study presents an uncertainty quantification and global sensitivity analysis framework comparing three surrogate models: Polynomial Chaos Expansion, Ordinary Kriging, and Polynomial-Chaos Kriging. Using a parabolic equation solver, we quantify how five parameters—mean duct height, duct height slope, potential refractivity gradient, frequency, and root mean square (RMS) wave height—affect propagation loss. We assess predictive accuracy, perform Sobol-based sensitivity analysis, and explore how surrogate performance relates to the normalized frequency V, a parameter characterizing modal complexity. Results show that Kriging consistently outperforms the others: its local interpolation capability proves essential for capturing rapid spatial oscillations caused by multimode interference. We observe a statistically significant negative correlation between Kriging’s prediction error and V, suggesting that its local interpolation becomes increasingly advantageous as the modal complexity of the field (quantified by V) increases. This provides a physically interpretable, though not yet predictive, link between surrogate model choice and the underlying propagation physics. Sensitivity analysis reveals that mean duct height dominates uncertainty at short-to-medium ranges, while the potential refractivity gradient becomes increasingly influential at longer ranges. RMS wave height exhibits localized effects near multipath nulls, particularly at higher frequencies. These findings provide quantitative guidance for prioritizing environmental measurements and offer a physically interpretable basis for surrogate model selection in evaporation duct problems. Full article

Non-invasive, real-time biomass estimation is critical for smart aquaculture, yet high computational latency and the cost of specialized optical sensors remain significant bottlenecks. This study proposes an ultra-low-cost, edge-deployable stereo-vision framework utilizing a dual-webcam architecture synchronized with a lightweight YOLOv11n-pose model. To address the spatial uncertainties in non-rigid fish locomotion, we integrated advanced spatial loss functions to achieve precise anatomical keypoint extraction. These coordinates are processed through a three-point Bézier curve interpolation and a mathematically derived Dynamic Shape Factor (K) to correct for optical refraction and morphological variations. As a proof-of-concept, the proposed system was validated on a live multi-species cohort (N = 10), achieving a Mean Absolute Percentage Error (MAPE) of 8.64% and an R² of 0.92 under strict Leave-One-Out Cross-Validation (LOOCV), drastically outperforming traditional naive volumetric baselines (MAPE > 54%). Requiring only 6.7 GFLOPs and 5.5 MB of memory, the model achieves 111.6 FPS. These results demonstrate the feasibility of highly efficient, cost-effective AI solutions for precision aquaculture while clearly defining the validity boundaries and statistical constraints for future large-scale deployment. Full article

A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method’s robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. Full article

Atmospheric refractivity governs the propagation behavior of electromagnetic waves in the lower troposphere. Accurate spatial characterization of this parameter is essential for optimizing communication, radar, and navigation systems. This study presents a geostatistical framework for generating high-resolution refractivity maps using Universal Kriging (UK) applied to meteorological observations from a dense network of automatic weather stations in the Galician region (NW Spain). The methodology explicitly models the non-stationary vertical structure of the atmosphere by decomposing the refractivity field into a deterministic altitude-dependent drift and a stochastic residual component characterized by an exponential variogram. Validation, performed using independent test stations bounding the regional vertical profile, demonstrates that the UK approach significantly outperforms Ordinary Kriging (OK). UK not only reduces mean errors and improves linear agreement, but critically minimizes systematic bias and extreme outlier occurrences ($P_{95}$). Beyond accurate spatial interpolation, the dynamically estimated vertical drift retrieves the macroscopic refractivity gradient, serving as a direct, real-time diagnostic tool to classify anomalous radio-frequency (RF) propagation regimes (e.g., super-refraction and ducting) and supporting robust decision-making in complex topographies. Full article

Objectives: We devised a simple and practical method for optimizing intraocular lens (IOL) constants using linear interpolation, based on the IOL power calculation study protocol proposed by Hoffer et al., and evaluated its effectiveness. Methods: This retrospective study included 188 eyes from 188 Japanese patients who underwent cataract surgery with the implantation of CNA0T0 (Alcon) between June 2022 and September 2024. Preoperative biometric data were obtained using ARGOS (Alcon) and OA-2000 (Tomey). Predicted refractions were calculated using the European Society of Cataract and Refractive Surgeons’ (ESCRS) IOL Web Calculator with the EVO, Hill-RBF 3.0 (Hill), and Kane formulas, using both A-constants of 119.1 and 119.33. The mean prediction error (MPE) was calculated as the difference between the predicted and postoperative spherical equivalent at 3 months. Linear interpolation was applied to the paired results to derive optimized A-constants yielding MPE = 0 and to correct each case’s predicted refraction values (“corrected values”). Additionally, predicted refractions were recalculated using the optimized A-constants with the ESCRS IOL Web Calculator to obtain “actual values”. Both corrected and actual values achieved an MPE of 0 and were compared using the Friedman test and Cochran’s Q test. Results: The optimized A-constants for ARGOS were 119.540 (EVO), 119.733 (Hill), and 119.563 (Kane); for OA-2000, they were 119.388, 119.532, and 119.417, respectively. No significant differences were found between corrected and actual values under any condition. Conclusions: This method is simple, accurate, and applicable to new IOLs, devices, and formulas, with potential to improve the precision of clinical IOL power calculations. Full article

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored. Full article

Parking path optimization is the principal problem of automatic vertical parking (AVP); however, it is difficult to determine a collision avoiding, smooth, and accurate optimized parking path using traditional parking reference trajectory optimization methods. In order to implement high-performance automatic parking reference trajectory optimization, we establish an automatic parking reference trajectory optimization model using cubic spline interpolation, and we propose an improved immune shark smell optimization (IISSO) to solve it. Firstly, we take the length of the parking reference trajectory as the optimization objective, and we introduce an intelligent automatic parking path optimization model using cubic spline interpolation. Secondly, the improved immune shark optimization algorithm combines the immune, refraction, and Gaussian variation mechanisms, thus effectively improving its global optimization ability. The simulation results for the parking path optimization experiments indicate that the proposed IISSO has a higher optimization accuracy and faster calculation speed; hence, it can obtain a parking path with higher optimization performance. Full article

This paper presents a Kriging interpolation method for constructing a database of atmospheric refractivity in Korea. To collect as much data as possible for the interpolation, meteorological data from 120 regions of Korea, including both land and sea areas, are examined. Then, the normalized atmospheric refractivity N_n is calculated for an altitude of 0 m, because the refractivity tends to vary depending on the altitude of the observation site. In addition, the optimal variogram model to obtain the spatial correlation required for the Kriging method is investigated. The estimation accuracy of the Kriging interpolation is compared with that of the inverse distance weighting (IDW) and the bi-linear methods. The average and maximum estimation errors when using the Kriging method are 0.24 and 1.32, respectively. The result demonstrates that the Kriging method is more suitable for the interpolation of atmospheric refractivity in Korea than the conventional methods. Full article

To analyze the intensity of atmospheric turbulence in a satellite–ground laser communication link, it is important to consider the effect of increased atmospheric turbulence caused by wind speed. Atmospheric turbulence causes a change in the refractive index, which negatively impacts the quality and focusing ability of the laser beam by altering its phase front. To simulate the changes in amplitude and phase characteristics of laser beam propagation in atmospheric turbulence caused by wind speed, a transverse translation phase screen is used. To better understand and address the influence of atmospheric wind speed on the phase of optical signals in satellite–ground laser communication links, this paper proposes a Monte Carlo simulation method. This method utilizes the spatial and temporal variations in the refractive index in the atmosphere and integrates the principles of optical signal propagation in the atmosphere to simulate changes in the phase of optical signals under different wind speed conditions. By analyzing the variations in the received optical signal’s power, the Monte Carlo method is employed to simulate phase screens and logarithmic amplitude screens. Additionally, it models the probability density of the statistical behavior of received optical signal’s fluctuations, as well as the time autocorrelation coefficient of optical signals. This paper, under the coupling condition in satellite–ground laser communication links, conducted a Monte Carlo simulation experiment to analyze the characteristics of the optical signal’s fluctuations in the link and discovered that atmospheric wind speed affects the shape of the power spectral density model of the received optical signal. Increasing wind speed leads to a decrease in the time autocorrelation coefficient of the received optical signal and affects the coupling efficiency. The paper then used a cubic spline interpolation fitting method to verify the models of the power spectral density and the autocorrelation time coefficient of the optical signal. This provides a theoretical foundation and practical guidance for the optimization of satellite–ground laser communication systems. Full article

The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot and dry air mass from the southwest desert area. An accurate knowledge of the water vapor spatio-temporal variability in the lower part of the atmosphere is crucial for a better understanding of the evolution of the dryline. The tropospheric refractivity, directly related to water vapor content, is a proxy for the water vapor content of the troposphere. It has already been demonstrated that the refractivity and the refractivity vertical gradient can be jointly estimated from radar phase measurements. In fact, it has been shown that using kriging interpolation techniques, accurate refractivity maps within the coverage area of the radar can be obtained with high temporal resolution. In this paper, a detailed analysis of the time series of radar-based refractivity maps obtained during a dryline that occurred on the afternoon of 22 May 2002 during the International $H_{2}O$ Project ($IHOP\_2002$) is presented. Comparisons between the time series of radar refractivity maps, obtained with the NCAR S-Pol radar, and the refractivity measurements derived from automatic ground-based weather stations and the AERI instrument, placed at different locations within the coverage area of the NCAR S-Pol radar, demonstrate the accuracy of radar refractivity estimates even for highly variable conditions, both in time and space, in the troposphere. Correlation coefficients higher than 0.95 are obtained in all weather station locations. Regarding the RMSE, errors less than 6 N-units are obtained for all cases, being even as low as 2.92 N-units at some locations. Full article

Refraction has to be eliminated or mitigated for medium- or long-range applications requiring high accuracy (such as deformation monitoring). The high variability of meteorological parameters, and thus refraction, makes mitigation complicated. This study explores the possibility of direct refractivity interpolation with different algorithms for a nine-station meteorological sensor network in Cortes de Pallás (Spain). Our Multiple Linear Regression (MLR) model can potentially contribute to improving the refraction correction of the monitored area. MLR provides, on average, an RMSE of 0.6 (dimensionless) compared to 1.5 obtained with Inverse Distance Weighting (IDW). For future improvements, the previous smoothing of meteorological data will be considered, and the possibility of using GNSS for vertical atmospheric information will be studied. Full article

The metropolitan community of Montreal (MMC) is located in Eastern Canada and included in the western Quebec seismic zone characterized by shallow crustal earthquakes and moderate seismicity. Most of the urbanized areas are settled close to the Saint-Lawrence River and its tributaries and within the region, delimiting the extension of the clay deposits from the Champlain Sea. The influence of these recent and soft deposits on seismic waves has been observed after the 1988 M5.8 Saguenay earthquake and has proven to be crucial in seismic hazard analysis. The shear-wave velocity Vs averaged over the 30 m of soil, abbreviated V_s30, is one of the most used parameters to characterize the site condition and its influence on seismic waves. Since 2000, a site condition model has been developed for the municipalities of Montreal and Laval, combining seismic and borehole data for risk mitigation purposes. The paper presents an extended version of the V_s30 mapping for the entire region of the MMC, which accounts for half of the population of Quebec, including additional ambient noise recordings, recently updated borehole datasets, geological vector map and unpublished seismic refraction data to derive Vs profiles. The estimated V_s30 values for thousands of sites are then interpolated on a regular grid of 0.01 degrees using the inverse distance weighted interpolation approach. Regions with the lowest estimated V_s30 values where site amplification could be expected on seismic waves are in the Northeastern part and in the Southwest of the MMC. The map expresses in terms of site classes is compared with intensity values derived from citizen observations after recent felt. In general, the highest reported intensity values are found in regions with the lowest V_s30 values on the map. Areas where this rule does not apply, should be investigated further. This site condition model can be used in seismic hazard and risk analysis. Full article

Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons. Full article

Atmospheric refraction is one of the most significant factors that affect the geolocation accuracy of high-resolution remote sensing images. However, most of the current atmospheric refraction correction methods based on empirical data neglect the spatiotemporal variation of pressure, temperature, and humidity of the atmosphere, inevitably resulting in poor geometric positioning accuracy. Therefore, in terms of the problems mentioned above, this study proposed a spatiotemporal atmospheric refraction correction method (SARCM) based on global measured data to avoid the uncertainty of traditional empirical models. Initially, the atmosphere was stratified into 42 layers according to their pressure property, and each layer was divided into 1,042,560 grid cells with intervals of 0.25 longitude and 0.25 latitude. Then, the atmospheric refractive index of each grid in the imaging region was accurately calculated using the high-precision Ciddor formula, and the result was interpolated using three splines. Subsequently, according to the rigorous geometric positioning model, the line-of-sight of each pixel and the viewing zenith angle outside the atmosphere in WGS84 were derived to provide input for atmospheric refraction correction. Finally, the coordinates of the ground control points were corrected with the calculated atmospheric refractive index and Snell’s law. The experimental results showed that the proposed SARCM could effectively improve the positioning accuracy of the image with a large viewing zenith angle, and especially, the improvement percentage for a viewing zenith angle of 34.2426° in the x-direction was 99.5%. Moreover, the atmospheric refraction correction result of the SARCM was better than that of the primary state-of-the-art methods. Full article

Global Navigation Satellite System (GNSS) radio occultation (RO) technology has been widely used in Earth atmospheric detection and has a significant impact on numerical weather prediction (NWP), climate detection, and other fields. Cosmic-2 LEO-1 (C2E1) is a well-known RO data provider; however, its observations are confined to 45°S and 45°N. Recently, the Binhu meteorological observation test satellite (BH) has provided global coverage of RO data, including refractivity, specific humidity, and temperature data. In this study, RO data from BH and C2E1 are analyzed and compared from 8 February 2022 to 17 February 2022. Employing the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) data as a reference, both BH and C2E1 RO data agree with the ERA data, with the refractivity, temperature, and specific humidity profiles reflecting the real conditions of the natural atmosphere. In addition, BH data are comparable to C2E1 data at low and middle latitudes (0–45°), and BH data at middle and high latitudes (45–90°) are of better quality than those at low and middle latitudes (0–45°). For example, without considering the errors introduced by the interpolation of the ERA-5 data for comparative analysis, the BH refractivity profiles show a mean absolute bias of 0.73 N at low and middle latitudes and only 0.23 N at middle and high latitudes, and that for BH specific humidity profiles at middle and high latitudes is 0.015 g/kg, which is only half of that at low and middle latitudes. The BH temperature and specific humidity data show promising data accuracy. Therefore, BH RO data may provide important supplementary data at higher latitudes and may improve future NWPs through assimilation. Full article