Search Results (34)

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset. Full article

The use of wide band gap (WBG) semiconductor switches in power converters is increasing day by day due to their superior chemical and physical properties, such as electrical field strength, drift speed, and thermal conductivity. These new-generation power switches offer advantages over traditional induction cooker systems, such as fast and environmentally friendly heating. The size of passive components can be reduced, and the decreasing inductance value of induction coils and capacitors with low ESR (equivalent series resistance) values contributes to total efficiency. Other design parameters, such as passive components with lower values, heatsinks with low volumes, cooling fans with low power, and induction coils with fewer turns, can offset the cost of WBG power devices. High-frequency operation can also be effective in heating non-ferromagnetic materials like aluminum and copper, making them suitable for heating these types of pans without complex induction coil and power converter designs. However, the use of these new generation power switches necessitates a re-examination of induction coil design. High switching frequency leads to a high resonance frequency in the power converter, which requires lower-value passive components compared to conventional cookers. The most important component is the induction coil, which requires fewer turns and magnetic cores. This study examines the induction heating equivalent circuit, discusses the general structure and design parameters of the induction coil, and performs FEM (finite element method) analyses using Ansys Maxwell. The results show that the induction coil inductance value in new-generation cookers decreases by 80% compared to traditional cookers, and the number of windings and magnetic cores decreases by 50%. These analyses, performed for high-power applications, are also performed for low-power applications. While the inductance value of the induction coil is 90 μH at low frequencies, it is reduced to the range of 5 μH to 20 μH at high frequencies. The number of windings is reduced by half or a quarter. The new-generation cooker system experimentally verifies the coil design based on the parameters derived from the analysis. Full article

Sea surface salinity (SSS) observed by satellite has been widely used since the successful launch of the first salinity satellite in 2009. However, compared with other oceanographic satellite products (e.g., sea surface temperature, SST) that became operational in the 1980s, the SSS product is less mature and lacks effective validation from the user end. We employed an unsupervised machine learning approach to classify the Level 3 SSS bias from the Soil Moisture Active Passive (SMAP) satellite and its observing environment. The classification model divides the samples into fifteen classes based on four variables: satellite SSS bias, SST, rain rate, and wind speed. SST is one of the most significant factors influencing the classification. In regions with cold SST, satellite SSS has an accuracy of less than 0.2 PSU (Practical Salinity Unit), mainly due to the higher uncertainty in the cold environment. A small number of observations near the seawater freezing point show a significant fresh bias caused by sea ice. A systematic bias of the SMAP SSS product is found in the mid-latitudes: positive bias tends to occur north (south) of 45°N(S) and negative bias is more common in 25°N(S)–45°N(S) bands, likely associated with the SMAP calibration scheme. A significant bias also occurs in regions with strong ocean currents and eddy activities, likely due to spatial mismatch in the highly dynamic background. Notably, satellite SSS and in situ data correlations remain good in similar environments with weaker ocean dynamic activities, implying that satellite salinity data are reliable in dynamically active regions for capturing high-resolution details. The features of the SMAP SSS shown in this work call for careful consideration by the data user community when interpreting biased values. Full article

Surface wind speed measurements from a satellite radar altimeter are used to adjust altimeter sea level measurements via sea state bias range correction. We focus here on previously neglected ocean radar backscatter and subsequent wind speed variations due to sea surface temperature (SST) change that may impact these sea level estimates. The expected error depends on the radar operating frequency and may be significant at the Ka band (36 GHz) frequency chosen for the new Surface Water and Ocean Topography (SWOT) satellite launched in December 2022. SWOT is expected to revolutionize oceanography by providing wide-swath Ka band observations and enhanced spatial resolution compared to conventional Ku band (14 GHz) altimetry. The change to the Ka band suggests a reconsideration of SST impact on wind and sea level estimates, and we investigate this in advance of SWOT using existing long-term Ku and Ka band satellite altimeter datasets. This study finds errors up to 1.5 m/s in wind speed estimation and 1.0 cm in sea level for AltiKa altimeter data. Future SWOT data analyses may require consideration of this dependence prior to using its radar backscatter data in its sea level estimation. Full article

Millimeter wave fixed wireless systems in future backhaul and access network applications can be affected by weather conditions. The losses caused by rain attenuation and antenna misalignment due to wind-induced vibrations have greater impacts on the link budget reduction at E-band frequencies and higher. The current International Telecommunications Union Radiocommunication Sector (ITU-R) recommendation has been widely used to estimate rain attenuation, and the recent Asia Pacific Telecommunity (APT) report provides the model to estimate the wind-induced attenuation. This article provides the first experimental study of the combined rain and wind effects in a tropical location using both models at a frequency in the E band (74.625 GHz) and a short distance of 150 m. In addition to using wind speeds for attenuation estimation, the setup also provides direct antenna inclination angle measurements using the accelerometer data. This solves the limitation of relying on the wind speed since the wind-induced loss is dependent on the inclination direction. The results show that the current ITU-R model can be used to estimate the attenuation of a short fixed wireless link under heavy rain, and the addition of wind attenuation via the APT model can estimate the worst-case link budget during high wind speeds. Full article

Deep learning models have been widely used in time-series numerical prediction of atmospheric environmental quality. The fundamental feature of this application is to discover the correlation between influencing factors and target parameters through a deep network structure. These relationships in original data are affected by several different frequency factors. If the deep network is adopted without guidance, these correlations may be masked by entangled multifrequency data, which will cause the problem of insufficient correlation feature extraction and difficult model interpretation. Because the wavelet transform has the ability to separate these entangled multifrequency data, and these correlations can be extracted by deep learning methods, a hybrid model combining wavelet transform and transformer-like (WTformer) was designed to extract time–frequency domain features and prediction of air quality. The 2018–2021 hourly data in Guilin was used as the benchmark training dataset. Pollutants and meteorological variables in the local dataset are decomposed into five frequency bands by wavelet. The analysis of the WTformer model showed that particulate matter (PM_2.5 and PM₁₀) had an obvious correlation in the low-frequency band and a low correlation in the high-frequency band. PM_2.5 and temperature had a negative correlation in the high-frequency band and an obvious positive correlation in the low-frequency band. PM_2.5 and wind speed had a low correlation in the high-frequency band and an obvious negative correlation in the low-frequency band. These results showed that the laws of variables in the time–frequency domain could be found by the model, which made it possible to explain the model. The experimental results show that the prediction performance of the established model was better than that of multilayer perceptron (MLP), one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), long short-term memory (LSTM) and Transformer, in all time steps (1, 4, 8, 24 and 48 h). Full article

The Doppler shift of microwave radar sea surface echoes serves as the foundation for sea surface current field retrieval; it includes the shift caused by satellite platform motion, ocean waves, and sea surface currents. The Doppler shift caused by ocean waves is known as the wave-induced Doppler velocity (U_WD), and its removal is critical for the accurate retrieval of sea surface current fields. The low-incidence Ka-band real-aperture radar rotary scan regime has the capability of directly observing wide-swath two-dimensional current fields, but as a new regime to be further explored and validated, simulation and analysis of U_WD in this regime have a significant influence on the hardware design and currently observed applications of this satellite system in its conceptual stage. In this study, we simulated and investigated the impacts of radar parameters and sea-state conditions on the U_WD obtained from small-incidence-angle Ka-band rotational scanning radar data and verified the simulation results with the classical analytical solution of average specular scattering point velocity. Simulation results indicate that the change in the azimuth direction of platform observation affects U_WD accuracy. Accuracy is the lowest when the antenna is in a vertical side-view. The U_WD increases slowly with the incidence angle. Ocean waves are insensitive to polarization in the case of small-incidence-angle specular scattering. The increase in wind speed and the development of wind waves result in a substantial increase in U_WD. We classified swell by wavelength and wave height and found that U_WD increases with swell size, especially the contribution of swell height to U_WD, which is more significant. The contribution of the swell to U_WD is smaller than that of wind waves to U_WD. Furthermore, the existence of sea surface currents changes the contribution of ocean waves to U_WD, and the contribution weakens with increasing wind speed and increases with wind wave development. Full article

An algorithm to retrieve the wind speed within a wide swath from the normalized radar cross section (NRCS) is developed for the data of Ku-band radar operating in scanning mode installed onboard the Global Precipitation Measurement (GPM) satellite. NRCS at the nadir is calculated within a wide swath and is used to obtain the wind speed. The scatterometer data are used to obtain the dependence between NRCS at the nadir and the wind speed for gale winds. The algorithm was validated also using the Advanced Scatterometer (ASCAT) data and revealed good accuracy. Full article

In recent years, environmental problems caused by natural disasters due to global warming have seriously affected human production and life. Fortunately, with the rapid rise of the Internet of Things (IoT) technology and the decreasing power consumption of microelectronic devices, it is possible to set up a multi-node environmental monitoring system. However, regular replacement of conventional chemical batteries for the huge number of microelectronic devices still faces great challenges, especially in remote areas. In this study, we developed a rotating hybrid nanogenerator for wind energy harvesting. Using the output characteristics of triboelectric nanogenerator (TENG) with low frequency and high voltage and electromagnetic generator (EMG) with high frequency and high current, we are able to effectively broaden the output voltage range while shortening the capacitor voltage rising time, thus obtaining energy harvesting at wide frequency wind speed. The TENG adopts the flexible contact method of arch-shaped film to solve the problem of insufficient flexible contact and the short service life of the rotating triboelectric generator. After 80,000 cycles of TENG operation, the maximum output voltage drops by 7.9%, which can maintain a good and stable output. Through experimental tests, the maximum output power of this triboelectric nanogenerator is 0.55 mW at 400 rpm (wind speed of about 8.3 m/s) and TENG part at an external load of 5 MΩ. The maximum output power of the EMG part is 15.5 mW at an external load of 360 Ω. The hybrid nanogenerator can continuously supply power to the anemometer after running for 9 s and 35 s under the simulated wind speed of 8.3 m/s and natural wind speed of 5.6 m/s, respectively. It provides a reference value for solving the power supply problem of low-power environmental monitoring equipment. Full article

In this article, a dual-stage proportional integral–proportional derivative with filter (PI–PDF) controller has been proposed for a hybrid two-area power system model having thermal-, hydro-, gas-, wind-, and solar-based power generating sources. Superconductor magnetic energy storage (SMES) units to cope with the transient power deviations have been incorporated in both areas. Governor dead-band (GDB) is considered in the governor model of thermal, and a generation rate constraint (GRC) is considered in the thermal and hydro turbine models to analyze the impact of system nonlinearity. The parameters of the proposed control strategy are optimally tuned by deploying a newly developed bull–lion optimization (BLO) to maintain optimal frequency and power response during system load deviations. Variations in wind speed and PV solar irradiance data have been included to examine the effectiveness of the BLO-based PI–PDF controller with system uncertainties and variability of renewable energy sources. The obtained results are validated by comparison with recently developed existing optimization techniques. The results revealed that the proposed control strategy is efficient for regulating the frequency and tie-line power of renewable integrated power systems. Further, the BLO-based PI–PDF control strategy improved the performance in terms of performance indices like settling time and peak overshoot/undershoot in wide scale. Full article

The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone’s eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone’s eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a <2 m s⁻¹ and >0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s⁻¹. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s⁻¹ with a 0.45 COR ranged from 330° to 360°. Full article

Total suspended matter (TSM) is one of the most widely used water quality parameters, which can influence the light transmission process, planktonic algae, and ecological health. A comprehensive field expedition aiming at water quality assessment was conducted for Lake Qinghai in September 2019. The in-situ measurements were used to support the calibration and validation of TSM concentration using Landsat images. A regional empirical model was established using the top-of-atmosphere (TOA) radiance of Landsat image data at the red band with a wavelength range of 640–670 nm. The coefficient of determination (R²), mean relative error (MRE), and root mean square error (RMSE) of the TSM estimation model were 0.81, 17.91%, and 0.61 mg/L, respectively. The model was further applied to 87 images during the periods from 1986 to 2020. A significant correlation was found between TSM concentration and daily wind speed (r = 0.74, p < 0.01, n = 87), which revealed the dominance of wind speed on TSM concentration. In addition, hydrological changes also had a significant influence on TSM variations of lake estuaries. Full article

Compared with co-polarized (HH/VV) normalized radar cross-section (NRCS) backscattered from the sea surface, there is no saturation phenomenon in cross-polarized (HV/VH) NRCS when wind speed is greater than about 20 m/s, so cross-polarized synthetic aperture radar (SAR) images can be used for high wind speed monitoring. In this work, a new geophysical model function (GMF) is proposed to describe the relation of the C-band cross-polarized NRCS with wind speed and radar incidence angle. Here, sixteen ScanSAR wide mode SAR images acquired by RADARSAT-2 (RS-2) under tropical cyclone (TC) conditions and the matching wind speed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Stepped-Frequency Microwave Radiometer (SFMR) are collected and divided into datasets A and B. Dataset A is used for analyzing the effects of the wind field and radar incidence angle on the reference noise-removed cross-polarized NRCS, and for proposing the new GMF for each sub-swath of the SAR images, while dataset B is used to retrieve wind speed and evaluate the validity of the new GMF. The comparisons between the wind speeds retrieved by the new GMF and the collocated ECMWF and SFMR data demonstrate the excellent performance of the new GMF for wind speed retrieval. To analyze the universality of the new GMF, wind speed retrievals based on 32 Sentinel-1A/B (S-1A/B) extra-wide-swath (EW) mode images acquired under TC conditions are also compared with the collocated wind speeds measured by the Soil Moisture Active Passive (SMAP) radiometer, and the retrieved wind speeds have RMSE of 3.667 m/s and a bias of 2.767 m/s. The successful applications in high wind speed retrieval of different tropical cyclones again supports the availability of the new GMF. Full article

The need for oil spill monitoring systems has long been of concern in an attempt to contain damage with a rapid response time. When it comes to oil thickness estimation, few reliable methods capable of accurately measuring the thickness of thick oil slick (in mm) on top of the sea surface have been advanced. In this article, we provide accurate estimates of oil slick thicknesses using nadir-looking wide-band radar sensors by incorporating both C- and X-frequency bands operating over calm ocean when the weather conditions are suitable for cleaning operations and the wind speed is very low (<3 m/s). We develop Maximum-Likelihood dual- and multi-frequency statistical signal processing algorithms to estimate the thicknesses of spilled oil. The estimators use Minimum-Euclidean-Distance classification problem, in pre-defined multidimensional constellation sets, on radar reflectivity values. Furthermore, to be able to use the algorithms in oil-spill scenarios, we devise and assess the accuracy of a practical iterative procedure to use the proposed 2D and 3D estimators for accurate and reliable thickness estimations in oil-spill scenarios under noisy conditions. Results on simulated and in-lab experimental data show that M-Scan 4D estimators outperform lower-order estimators even when the iterative procedure is applied. This work is a proof that using radar measurements taken from nadir-looking systems, thick oil slick thicknesses up to 10 mm can be accurately estimated. To the best of our knowledge, the radar active sensor has not yet been used to estimate the oil slick thickness. Full article

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas. Full article