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Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.

Noise radars can be understood in terms of a correlation coefficient which characterizes their detection performance. Although most results in the literature are stated in terms of the signal-to-noise ratio (SNR), we show that it is possible to carry out performance prediction in terms of the correlation coefficient. To this end, we derive the range dependence of the correlation coefficient. We then combine our result with a previously-derived expression for the receiver operating characteristic (ROC) curve of a coherent noise radar, showing that we can obtain ROC curves for varying ranges. A comparison with corresponding results for a conventional radar employing coherent integration shows that our results are sensible. The aim of our work is to show that the correlation coefficient is a viable adjunct to SNR in understanding radar performance., Comment: 5 pages, 3 figures; accepted for publication in IEEE Access

Global positioning system (GPS) and inertial measurement units (IMUs) are often combined to produce navigation systems for airborne imaging platforms. The current state-of-the-art radar technology allows for radars to pulse at very high rates. GPS and IMU update rates are not fast enough to accurately report the platform position for each radar pulse. Independent GPS and IMUs cannot provide positional accuracy for long term stability. Traditional techniques, such as the Kalman and particle filter, are used to fuse GPS and IMU measurements. The Kalman filter excels for linear and Gaussian systems whereas the particle filter excels at non-linear and non-Gaussian systems. Sensor fusion techniques are used to help correct for IMU errors and provide the positional accuracy required for synthetic aperture radar (SAR) imaging applications. However, SAR requires the fusion algorithms to provide faster update rates. This paper explores the use of an up-sampled particle filter (UPF) for SAR to provide highly accurate position estimates at sampling frequencies comparable to radar pulse rates and overcome the limitations of standard interpolation techniques. This up-sampled particle filter is proven through simulations and instrumentation with a NovAtel GPS and IMU. The UPF technique allows for the GPS/IMU sampling rate to be different from the radar pulse repetition frequency (PRF) while providing accurate position solutions for each radar pulse capable of compensating for the phase history required for focusing a SAR image. The algorithms are instrumented in a SAR system and the position estimates are further validated and demonstrated through captured SAR images.

Sea ice concentration algorithms using brightness temperatures ($T_{B}$) from satellite microwave radiometers are used to compute sea ice concentration ($c_{\text{ice}}$), sea ice extent, and generate sea ice climate data records. Therefore, it is important to minimize the sensitivity of $c_{\text{ice}}$ estimates to geophysical noise caused by snow/sea ice thermal microwave emission signature variations, and presence of WV and clouds in the atmosphere and/or near-surface winds. In this study, we investigate the effect of geophysical noise leading to systematic $c_{\text{ice}}$ biases and affecting $c_{\text{ice}}$ standard deviations (STD) using simulated top of the atmosphere $T_{B}$s over open water and 100% sea ice. We consider three case studies for the Arctic and the Antarctic and eight different $c_{\text{ice}}$ algorithms, representing different families of algorithms based on the selection of channels and methodologies. Our simulations show that, over open water and low $c_{\text{ice}}$, algorithms using gradients between V-polarized 19-GHz and 37-GHz $T_{B}$s show the lowest sensitivity to the geophysical noise, while the algorithms exclusively using near-90-GHz channels have by far the highest sensitivity. Over sea ice, the atmosphere plays a much smaller role than over open water, and the $c_{\text{ice}}$ STD for all algorithms is smaller than over open water. The hybrid and low-frequency (6 GHz) algorithms have the lowest sensitivity to noise over sea ice, while the polarization type of algorithms has the highest noise levels.

Pulse position modulation (PPM) is a key technology for deep space optical communication (DSOC) system to improve the data transmission rate. To reach optimum rate, the modulation parameters of PPM are required to be analyzed. The rate can be improved by choosing appropriate modulation orders and the initial pulse width. In this paper, the meteorological statistical data of our experimental location are analyzed. Then, the analysis formula of the cloud scattering and atmospheric turbulence influence on PPM communication rate are given. The cloud particles are measured through aircraft detector, simulation analysis is carried out by using the measured data of aircraft detector. Finally, the influence on the communication rate of different cloud physical thickness are analyzed.

Atmospheric refraction is an important factor that affects the positioning accuracy of optical high-resolution satellite imaging. Current calculation methods do not consider regional change characteristics in the atmosphere; the atmospheric division model is rough and does not conform to reality. This article studies atmospheric refraction in optical remote sensing satellite observations, provides a formula for calculating the strict refraction of a multilayer atmosphere, and shows a new method for calculating atmospheric refraction error. We use a ray-tracing method to calculate the light propagation path in the atmosphere and continuously layer the atmosphere according to the height difference of 100 m. Different from general calculation methods, we prove that a regional atmospheric model can calculate the atmospheric refraction index more accurately than using the empirical model. As the off-nadir angle gradually increases, the calculation results in this article are better than the current commonly used methods. We use WorldView-2 images of the Qinghai–Tibet Plateau region in China for the experiments. When the off-nadir angle is less than 32°, the positioning accuracy improves by 6–47%. Compared with the standard atmospheric model, the regional atmospheric model improves positioning accuracy by 2–18%. This method reflects the continuous variation in the atmospheric refractive index with the vertical distribution of the atmosphere and amended regional meteorological conditions. Model errors caused by overly simple atmospheric division are avoided, andthe positioning accuracy of optical remote sensing images is increased.

To deal with the flexibility in the electric aircraft design, this study develops a structural parameter optimization method. The quad tilt-wing aircraft exhibits rotary-wing flight and fixed-wing flight modes by tilting its wings. This study focuses on the domain of attraction, which is a class of state vectors that converge to a static equilibrium point after infinite time. The method indirectly estimates the size of domain of attraction of an aircraft by calculating the radius of a sphere that is encompassed by the domain. In addition, after investigating the relationship between change in structural parameters and the size of the domain of attraction, the structural parameters that maximize the size of the domain are surveyed. The effectiveness of the proposed method is demonstrated for the optimization of the quad tilt-wing aircraft. The quad tilt-wing aircraft exhibits rotary-wing flight and fixed-wing flight modes by tilting its wings. As the characteristics of the dynamics of the quad tilt-wing aircraft demonstrate a significant nonlinear change depending on the tilt angle of the wing, we propose a method to optimize the structural parameters for an easy control of the aircraft. Simulation results show that the optimization scheme identifies the parameters that enable the aircraft to perform agile control to address a disturbance moment. Further, although the same controller is used in the original and optimized aircraft, the optimized aircraft can handle a disturbance in a shorter time when compared with the original aircraft.

We introduce a framework to dynamically combine heterogeneous models called \texttt{DYCHEM}, which forecasts a set of time series that are related through an aggregation hierarchy. Different types of forecasting models can be employed as individual ``experts'' so that each model is tailored to the nature of the corresponding time series. \texttt{DYCHEM} learns hierarchical structures during the training stage to help generalize better across all the time series being modeled and also mitigates coherency issues that arise due to constraints imposed by the hierarchy. To improve the reliability of forecasts, we construct quantile estimations based on the point forecasts obtained from combined heterogeneous models. The resulting quantile forecasts are coherent and independent of the choice of forecasting models. We conduct a comprehensive evaluation of both point and quantile forecasts for hierarchical time series (HTS), including public data and user records from a large financial software company. In general, our method is robust, adaptive to datasets with different properties, and highly configurable and efficient for large-scale forecasting pipelines., Comment: ICDM 2022 Workshops, 10 pages, 6 figures

Control of warm-water supply for dialysis treatment in a hospital environment is typical of safety-critical control problems. In order to guarantee the continuity of warm-water supply satisfying physical specifications for a wide range of operating conditions, it is inevitable to consider the nonlinearity involved in a dynamic model of a warm-water supply system for the control design. In this paper, we propose to incorporate control specifications described by signal temporal logic, which is a temporal logic with semantics over finite-time signals in formal methods, into the so-called Koopman-Model Predictive Control (MPC) as a novel technique of nonlinear MPC based on the Koopman operator framework for nonlinear systems. This enables us to generate a sequence of optimal inputs such that the controlled state of a nonlinear system can satisfy the specifications. The proposal is applied to the temperature regulation of warm-water supply, and its effectiveness is established numerically., 12 pages, 5 figures

The Rains relative entropy of a bipartite quantum state is the tightest known upper bound on its distillable entanglement -- which has a crisp physical interpretation of entanglement as a resource -- and it is efficiently computable by convex programming. It has not been known to be a selective entanglement monotone in its own right. In this work, we strengthen the interpretation of the Rains relative entropy by showing that it is monotone under the action of selective operations that completely preserve the positivity of the partial transpose, reasonably quantifying entanglement. That is, we prove that Rains relative entropy of an ensemble generated by such an operation does not exceed the Rains relative entropy of the initial state in expectation, giving rise to the smallest, most conservative known computable selective entanglement monotone. Additionally, we show that this is true not only for the original Rains relative entropy, but also for Rains relative entropies derived from various R\'enyi relative entropies. As an application of these findings, we prove, in both the non-asymptotic and asymptotic settings, that the probabilistic approximate distillable entanglement of a state is bounded from above by various Rains relative entropies., Comment: v2: 10 pages, no figures, published version

Accurate wind speed forecasting is a fundamental requirement for advanced and economically viable large-scale wind power integration. The hybridization of the quaternion-valued neural networks and stationary wavelet transform has not been proposed before. In this paper, we propose a novel wind-speed forecasting model that combines the stationary wavelet transform with quaternion-valued neural networks. The proposed model represents wavelet subbands in quaternion vectors, which avoid separating the naturally correlated subbands. The model consists of three main steps. First, the wind speed signal is decomposed using the stationary wavelet transform into sublevels. Second, a quaternion-valued neural network is used to forecast wind speed components in the stationary wavelet domain. Finally, the inverse stationary wavelet transform is applied to estimate the predicted wind speed. In addition, a softplus quaternion variant of the RMSProp learning algorithm is developed and used to improve the performance and convergence speed of the proposed model. The proposed model is tested on wind speed data collected from different sites in China and the United States, and the results demonstrate that it consistently outperforms similar models. In the meteorological terminal aviation routine (METAR) dataset experiment, the proposed wind speed forecasting model reduces the mean absolute error, and root mean squared error of predicted wind speed values by 26.5% and 33%, respectively, in comparison to several existing approaches.

Polarimetric Doppler frequency-modulated continuous-wave weather radar produces a tremendous amount of data during the observation of atmospheric conditions. Using conventional signal processing method, the beat signals are sampled at Nyquist rate, so that the range signal can be reconstructed perfectly. We propose compressive sampling (CS) technique to sample and reduce the data simultaneously by exploring the sparsity of the beat signal in transform domain. Reducing the number of the beat signal and the transform coefficients are natural choices, because sparse signals will be obtained by inverse Fourier transforming the beat signals. The proposed techniques are evaluated by constructing the plan position indicator of reflectivity and mean Doppler velocity measurements from real weather polarimetric data. Compared to the conventional method, the CS polarimetric Doppler processing significantly reduce the number of the data while pertaining important weather information. We show that the proposed method works properly to quantify and classify the precipitation, mainly when the number of the samples is above 25% of its original.

In order to simulate realistic traffic scenarios, a radar target simulator must be able to generate multiple radar targets with different directions of arrival. The presented concept is able to generate an arbitrary amount of targets with individual directions of arrival for the radar under test. By measuring the radar channel, the novel approach enables target simulators to simulate arbitrary directions of arrival, while minimizing the required hardware. The optimum setup is derived for radars with a uniform linear receive antenna array. The compensation of placement errors for automotive chirp-sequence frequency modulated continuous wave radars is demonstrated. Finally, the calibration for the setup is provided, and the performance of the presented approach is validated., publishedVersion

Accurate forecasting methods for wind and solar power are important for power systems because of their potential to improve the economic and environmental performance. For this purpose, an ensemble neural network framework composed of LSTM, SVM, BP neural network, and ELM is proposed for wind and solar power forecasting in China. Three common methods for improving the prediction accuracy were adopted. First, unstable wind and solar power time series are decomposed into smooth subsequences by VMD, which reduces the undesirable effects caused by the volatility of the original series. Then, based on the decomposed subsequences, four basic models that are optimized based on the EOSSA algorithm are used to forecast wind and solar power. Finally, the prediction results of ENN were reconstructed by weighting the prediction results of the four models. The proposed ENN model was compared to nine state-of-the-art prediction models for wind and solar power forecasting. The results showed that the ENN model had the lowest MAPE, MAE, MSE, and RMSE for both wind and solar power forecasting. These comparison results also show that the ENN model not only has the best prediction accuracy, but also the most reliable prediction performance.

In this article, airborne broadband (maximum 400 MHz bandwidth) C-band circularly polarized synthetic aperture radar (SAR) is proposed and developed for further study on airborne compact polarized synthetic aperture radar (CP SAR) system using circular polarization. This article explains the scattering characteristic of circular polarization as the concept of circularly polarized SAR, system configuration, RF system and antenna, ground test, and flight test of circularly polarized SAR in Hinotori-C2 (Firebird-C2) mission onboard CN235MPA aircraft on 14 and 15 March 2018 at South Celebes, Indonesia. The result of the flight test depicts multipath full polarimetric circularly polarized images that show good performance of circularly polarized SAR and matched well to the result of the ground test of a multipolarized single pulse. The circularly polarized scattering clarification using trihedral, cylindrical, and omni corner reflectors (TCR, CCR, and OCR), assessment of circularly polarized image analysis, and image classification using the conventional axial ratio (AR), ellipticity (ε), and polarization ratio (ρ) are discussed. The proposed circularly polarized SAR will enrich the existing CP SAR systems and could be employed in further study of CP SAR calibration technique, also applications development for the environment and disaster monitoring using CP SAR.

This letter presents a systematic approach to estimate the annual energy production (AEP) of variable-speed wind turbines erected at high-altitude sites. Compared with the existing empirical-model based approaches, the proposed approach models the influence of the air density on the power production while employing the theoretical power curve. Consequently, the proposed approach provides a precise estimation of AEP, which can serve as a foundation of the optimum turbine-site matching design at different-altitude sites.

This article focuses on improving the understanding of the electromagnetic (EM) scattering from water waves modulated by shoaling effect arising from different upslope submarine topography in the nearshore zone. Simulation of EM bistatic scattering from water waves in the nearshore zone where the water depth becomes gradually shallower is presented. The shallow water waves with periodic sinusoidal type are simulated by the model that is based on Boussinesq approximation theory, which can accurately construct the detailed wave structure in the nearshore zone, and the analytical model-the second-order small slope approximation is applied to modeling the EM bistatic scattering from the shallow water waves. Comparisons of the bistatic scattering coefficient (BSC) between water waves corresponding to different submarine plane-upslope topography in the nearshore zone have shown that the angular position of harmonics of BSC will change as upslope varies: higher-order harmonics for plane-upslope cases would be far away from the specular peaks for flat topography case. Moreover, the wave height of the shallow water waves and nonlinear effect will greatly impact changing trend of the BSC. Simulations for TMA spectrum-based water waves also support the fact that the transfer of wave energy from low frequency to higher frequency wave components would be intensified in the shallow water zone with the upslope submarine topography. The results in this article indicate that shallow water waves would reflect some interesting scattering features that traditional deep-sea waves did not show, which will be helpful to the further quantitative investigation of scattering characteristics of nearshore waves.

The concerns over the increase in carbon footprints and global climatic changes have given momentum to find ways that reduce the use of fossil fuels to generate electricity. Offshore and onshore windmills for electricity generation are viable options for bulk power generation. A wind generation system has a variable frequency variable voltage supply at the induction generator terminals in response to the changes in the wind velocity and in turn, the speed of the wind turbine. There is a need for an additional power electronic interface to maintain the frequency and voltage at the terminals of the wind generator constant. This paper introduces a novel three-phase Bifurcated Winding Induction Generator (BWIG) comprising a bifurcated set of stator windings. A prototype model has been developed by bifurcating the stator windings of a three-phase induction machine. One part of the winding acts as the excitation winding, and the other half is used as output winding capable of producing three-phase voltages at a constant frequency at all speeds of the prime over. The machine is tested for sub and super synchronous speeds and the characteristics plotted. The flux analysis of the machine is simulated using Ansys Maxwell software. The voltage is controlled by a simple off-on control implemented using a digital controller, TMS320F28335.

In this article, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a simple subimage selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple subimages, and the subimages with relatively clear wave signatures are identified based on the random-forest-based classification model. Then, wave direction is estimated by performing the Radon transform on each valid subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected subimage using the gray-level co-occurrence matrix and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by $\text{6.9}^\circ$, $\text{6.0}^\circ$, $\text{4.9}^\circ$, and $\text{1.0}^\circ$ for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13, 0.20, 0.30, and 0.20 s under those four rainfall intensity levels, respectively.

Passive microwave satellite observations provide critical information for global forecast models, particularly in cloudy and/or precipitating conditions. The limited temporal sampling provided by current operational polar orbiters cannot capture rapidly changing conditions such as the development of convective storms. This is a significant issue for open-ocean weather systems such as tropical cyclones and hurricanes that can only be effectively monitored from satellites. The recent development and demonstration of miniaturized microwave radiometers on-board low-cost CubeSat satellites has the potential to dramatically improve the temporal and spatial sampling of all-sky microwave observations by deploying a substantial constellation of satellites in low Earth orbit. Two constellations of 60 CubeSats in 550 km orbits are compared to the current operational microwave sensors. One approach employs all polar orbiters, while the other approach uses multiple inclination orbits for increased sampling over convective storm regions. Both approaches reduce average revisit times to approximately 20–30 min globally, and the multi-inclination approach also provides irregular 5–10 min sampling over selected latitudes. Improved global temporal sampling would provide all-sky observations to global forecast models over rapidly changing environments, while millimeter-wave observations over convective storm regions would be valuable for both forecasting and studying the development of convective storms. This article demonstrated that a constellation of low-cost CubeSats with microwave radiometers has the potential to provide equivalent temporal resolution to that observed from sensors on geostationary orbit .

The magnetic field of end windings affects the leakage reactance of end windings and the eddy current losses of end structures in the large electrical machine. With the rising of single-machine capacity, such effect becomes more obvious. However, due to the complexity of end structures of large electrical machine, it will cause difficulty in meshing and long calculation time to calculate the magnetic field of end windings by using 3-D finite element method (FEM). In order to calculate the magnetic field of end windings quickly and accurately, an improved analytical algorithm (IAA) is proposed based on the mirror image principle, which takes into account the influence of stator back core and air-gap on the magnetic field of end windings by adding the fictitious boundary current and air-gap current, respectively. Taking a 300Mvar synchronous condenser (SC) as an example, the boundary current, air-gap current and magnetic field of end windings are calculated. Then, the influence of stator back core and stator involute segment current on the magnetic field of end windings are analyzed, which provides a theoretical support for the design and optimization of SC. To validate the accuracy of IAA, the calculation result is compared with 3-D FEM.

A photonic radar has recently received considerable interest as a next-generation radar due to its capability to generate wideband signals beyond the technical limitations of the conventional radar. In this paper, we design and implement an ${X}$ -band photonic radar for a low radar cross-section (RCS) target detection and high-resolution image acquisition in real-time. The implemented photonic radar utilizes the photonic frequency quadrupling for the transmitter and detects the dechirped signal by using a delay interferometer and a balanced photo-detector to improve the signal-to-noise ratio. The performance of the photonic radar is verified using a commercial hexacopter having a diagonal wheelbase of 1133 mm as a low-RCS target in the field experiments. We show that the implemented photonic radar can successfully detect the drone on the move even though it is 2.7 km away from the radar. In addition, a high-resolution image of the drone, which is far away 1.1 km from the radar, can be obtained in real-time.

To overcome the problems of wind power forecast for the current deterministic wind power turbine output model cannot accurately describe the statistical characterization of wind turbine output, this article proposes a novel wind power stochastic cloud model. There are three steps to build this model. Firstly, it is necessary to analyze the power data and to filter out the disturbed data by utilizing the improved Bin method and data fitting. Secondly, it is available to obtain respectively two groups of the expectation, entropy and hyper entropy of the waist and upper data of stochastic cloud model by the known and unknown membership of backward cloud generators. Finally, the wind-speed data can be transformed into a stochastic cloud model, which is composed of X condition cloud generator and the positive cloud generator component. In short, the comparative data of the wind power cloud droplets and the measured data of wind power show that the line correlation coefficient of the wind power frequency of the proposed model reaches to 0.8868, which could simulate the statistical characterization of wind turbine output effectively.

Permanent magnet synchronous motors with the formed winding can not only improve the slot fill factor and heat dissipation capacity but also effectively reduce the additional loss in the winding. Formed winding permanent magnet synchronous motors will become the future development tendency. Due to inherent features of the formed winding, the circulating current loss is a non-negligible issue. This paper proposes using the formed winding in permanent magnet synchronous motors and analyzes the circulating current loss in the formed winding. First, the finite element models of integer slot and fractional slot motors are established, and the magnetic field analysis are carried out. Then two commonly used methods of calculating circulating current loss are introduced in detail, and their calculation results are compared. Moreover, the high-precision field-circuit coupled finite element method is used to calculate the circulating current loss of integer and fractional motors. And circulating currents of the hairpin winding and the formed winding are compared. The analysis indicates that the circulating current in the formed winding cannot be ignored. Finally, comprehensive and in-depth research on the influencing factors of circulating current loss in the formed winding has been conducted. The results clarify that the number of parallel strands, the width of the slot opening, and the stator current have a considerable influence on the circulating current loss.

Small satellites in low Earth orbit (LEO) are typically organized as a satellite constellation. The satellite number of a LEO satellite constellation would be several hundred or even tens of thousands to realize a fully global coverage. This imposes a big challenge on ground station for satellite ordinary management and control operation. Phased array antennas are considered as attractive candidates for future satellite ground station. The primary reason is that phased array antennas can produce numbers of beams by the beamforming network thus support multiple satellites simultaneously. This paper aims to provide a tutorial-like review on phased array antennas as satellite ground station. The advantages of phased array antennas for this application are summarized by comparing them with traditional reflector antennas. Several array geometries for realizing the hemispherical coverage required for satellite tracking are presented. System architecture are given, and some considerations for engineering practice about phased array antenna based satellite ground station are also discussed.

In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors. The multi-instrument inversion and data assimilation preprocessing system, artificial intelligence version, or MIIDAPS-AI for short, is valid for both polar and geostationary microwave and infrared sounders and imagers as well as for pairs of combined infrared and microwave sounders. The algorithm produces vertical profiles of temperature and moisture as well as surface temperature, surface emissivity, and cloud parameters. Additional products from hyperspectral infrared sensors include selected trace gases. From microwave sensors, additional products such as rainfall rate, first year/multiyear sea ice concentration, and soil moisture can be derived from primary products. The MIIDAPS-AI algorithm is highly efficient with no noticeable decrease in accuracy compared to traditional operational sounding algorithms. The automatically generated Jacobians from this deep-learning algorithm could provide an explainability mechanism to build trustworthiness in the algorithm, and to quantify uncertainties of the algorithm's outputs. The computation gain is estimated to be two orders of magnitude, which opens the door to either 1) process massively larger amounts of satellite data, or to 2) offer improvements in timeliness and significant saving in computing power (and therefore cost) if the same amount of data is processed. Here, we present an overview of the MIIDAPS-AI implementation, discuss its applicability to various sensors and provide an initial performance assessment for a select number of sensors and geophysical parameters.

In this article, a water vapor density inversion model based on the long short-term memory network is proposed for E-band commercial microwave links (CMLs). A full-duplex E-band microwave link located in Prague (two sublinks with frequencies of 73.5 and 83.5 GHz, both vertically polarized) was used to verify the performance of the model. The results show that the model inversion results are in good agreement with the water vapor density calculated by temperature and humidity sensors. Compared with previous water vapor inversion methods based on CMLs, this model has a higher temporal resolution and can realize real-time monitoring.

In this study, an energy spectrum (ES) algorithm is proposed to retrieve wind direction from X-band marine radar image sequences. This algorithm is based on utilizing the occlusion area zero-pixel percentage to distinguish rain-free and rain-contaminated radar data. And then the rain-contaminated images are detected and discarded. The effect of radar radial attenuation in radar image sequences is modified by the piecewise fitting technique. Wind direction is determined from rain-free and radial correction data, based on the ES of small-scale wind streaks. The ES of small-scale wind streaks is obtained by establishing an ES scale separation filter. Based on the wind streak characteristics, a 2-D fast Fourier transform is used to obtain the ES of radar images. The wind streak characteristics are derived from the distribution of the azimuth normalization radar cross section. The proposed algorithm is tested using data collected from X-band radar images and in situ anemometer data from the coast of the East China Sea. Compared with the anemometer data, after using the proposed algorithm, the root-mean-square difference for wind direction is 12.13°, which is an acceptable result for engineering application.

The estimation of the degree of polarization of backscattered light and target light is the key to realize scattering removal in underwater polarimetric imaging. In this paper, a new polarization scattering removal method is proposed by realizing polarimetric calculation of the target light with each pixel of the scene automatically, which is conducive to the restoration of the details of underwater scene. The global model of pixel level is established by utilizing the gradient prior of the polarization information of the total radiance image. Different from the previous study that the degree of polarization of the target light is a constant, the global calculation method can solve the global variable image of the degree of polarization of the target light and it is used for reconstruction experiments of underwater real-world scene to verify the effectiveness of the algorithm. The experimental results demonstrate that the global pixel calculation method has obvious effect on image detail recovery of the underwater scene to realize the clear underwater vision. The influence of the backscattered light on underwater imaging is well suppressed and image contrast has been significantly improved, which provides a new method for enhancing performance of underwater polarimetric imaging with better vision.

We present the design of a novel K-band radar architecture for short-range target detection. Applications include direction finding systems and automotive radar. The developed system is compact and low cost and employs substrate-integrated-waveguide (SIW) antenna arrays and a $4\times 4$ Butler matrix (BM) beamformer. In particular, the proposed radar transmits a frequency modulated continuous-wave (FMCW) signal at 24 GHz, scanning the horizontal plane by switching the four input ports of the BM in time. Also, in conjunction with a new processing method for the received radar signals, the architecture is able to provide enhanced resolution at reduced computational burden and when compared to more standard single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems. The radar performance has also been measured in an anechoic chamber and results have been analyzed by illuminating and identifying test targets which are 2° apart, while also making comparisons to SIMO and MIMO FMCW radars. Moreover, the proposed radar architecture, by appropriate design, can also be scaled to operate at other microwave and millimeter-wave frequencies, while also providing a computationally efficient multi-channel radar signal processing platform.

In this article, a spectral polarimetric filter named moving spectral depolarization ratio (MsDR) filter is proposed to mitigate the effects of clutter and noise for dual-polarization weather radar without cross-polar measurements. The filter is based on a spectral polarimetric variable termed as spectral depolarization ratio (sDR). sDR can be estimated by any polarimetric weather radar, making the MsDR filter widely applicable. Taking advantage of the spectral polarimetric features and the range-Doppler continuity of precipitation, the MsDR filter is implemented in the range-Doppler spectrogram. The performance of the MsDR filter is assessed using data collected by 1) an X-band full-polarimetric radar interfered by narrow-band clutter (both stationary and moving) and sidelobe wind turbine clutter; 2) a C-band operational dual-polarization radar affected by radio frequency interference. In addition, the implementation of the MsDR filter is straightforward, and the computational complexity is relatively low. Hence, the MsDR filter has great potential to be applied in real-time for operational radar systems at different frequencies.

A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this article to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors ( $\text{T}, \text{H}_2\text{O}, \text{O}_3$ ) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss, and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared against fast line-of-sight atmospheric analysis of hypercubes-infrared (FLAASH-IR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate eight times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real world, time sensitive tasks.

This article provides a survey of recent results on exploiting parametric auto-regressive (AR) models for adaptive radar target detection. Specifically, three types of radar systems are considered, including phased-array radar with multiple co-located transmitters and receivers, distributed multi-input multi-output (MIMO) radar with widely and spatially separated transmitters and receivers, and passive radar which uses existing sources as illuminators of opportunity (IOs). These radar systems are of significant interest for a wide range of military and civilian applications. For each of the three types of radars, we discuss how AR processes can be employed to succinctly model the underlying signal correlation and efficiently estimate it from limited data, thus enabling effective target detection in complex non-homogeneous environments when training data is limited. We illustrate the performance of such parametric model assisted detectors relative to conventional non-parametric approaches by using computer simulated and experimental data.

Medium- and long-term wind power output time series are required in stochastic programming model for power system planning. Hidden Markov model (HMM) is a common method to generate wind power output time series, which can simultaneously consider the temporal and spatial correlation of multiple wind farms. However, the existing HMM methods use discrete matrix or Gaussian distribution to describe the output distribution of multiple wind farms, which usually leads to a relatively large error in statistical indices between the generated time series and the historical time series. Therefore, this paper proposes a method for generating medium- and long-term correlated output time series of multiple wind farms based on the Gaussians mixture model-Hidden Markov model (GMM-HMM). The discrete state variable in the hidden Markov model is used to describe the meteorological state. The Markov chain between discrete state variables is used to describe the temporal correlation of wind power output. The wind power output vector of multiple wind farms is used as the observation variable, and the mixed Gaussian probability distribution mapping relationship between the state variable and the multidimensional wind power output vector is established. Based on the Monte Carlo sampling method, the multi-wind farm output series satisfying the spatiotemporal correlation of historical output series are generated monthly. In the calculation example, the monthly wind power output series generated by five wind farms in Jilin Province are analyzed. The results show that the main statistical characteristics of the multi-wind power output time series generated by the proposed method are generally superior to those obtained with the traditional wind power output modeling method, which proves the superiority of the proposed method.

A wideband and fully optically-transparent water patch antenna is proposed. Both the patch and the ground plane are made of distilled water. Different from the previous water patch antennas, this proposed design features the utilization of an L-shaped distilled water probe as the feed structure. Thus, substantial improvement in the transparency is achieved. To excite the water patch antenna, a short inner probe of a coaxial cable is inserted into the water probe. The electromagnetic (EM) waves excited by the inner probe can propagate along the L-shaped water probe and also resonant in the middle substrate (air) under the water patch, causing radiation into the free space. The L-shaped water probe can significantly improve the impedance bandwidth, which has the same function as the reported L-shaped metallic probe. The results show that the proposed water patch antenna has a wide impedance bandwidth of 42.6% (from 1.48 to 2.28 GHz with $|{\mathrm{ S}}_{11}| < - 10$ dB), a gain up to 7.5 dBi, a radiation efficiency up to 67% and broadside radiation pattern across the bandwidth. In addition, some parametric studies on the distilled water probe have been carried out for the design purpose. The proposed water patch antenna has confirmed the possibility of designing a patch antenna fully made of liquid or solid dielectric materials with high dielectric constant such as water, instead of using metal.

The operation of offshore wind power plants with respect to power quality and grid code compliance must be verified for different conditions in the design phase using harmonic analysis methods. This paper addresses this verification by first considering a scenario in which wind turbines operate with a typically used modulation strategy, carrier-based pulse width modulation. The studies are performed at several operating points, not only at the rated condition, and at two points of the electrical infrastructure, at the point of connection of the wind turbine and at the point of common coupling of the offshore wind farm. For this type of modulation technique and according to the resonance conditions of the studied offshore system, the harmonic distortion of current signals is relatively high and the compliance of the German – grid code is not achieved. To tackle this, wind turbine manufacturer oriented solutions are proposed with the aim of improving the harmonic emission of the offshore wind farm. The implementation of a particular solution of selective harmonic elimination is presented together with complementary solutions to further improve the harmonic emission of the wind power facility. Furthermore, this paper discloses the modeling approach to account for an adequate harmonic assessment of the wind power plant under study.

We address the optimization problem of antenna placement on satellite-mounted interferometric synthetic-aperture instruments. In classic designs, the antennas on satellites are aligned regularly on the satellite’s frame. Inspired by methods proposed for the placement of antennas in astronomical interferometers, such as ALMA or SKA, we explore irregular layouts and show that they are a valid alternative in terms of spatial resolution and reconstruction error. We formalize mathematically the optimization problem of irregularly placed antennas and we show that this kind of arrays can still be calibrated with the same methods used for regular arrays. Finally, this strategy is evaluated in the context of soil moisture and ocean salinity (SMOS) follow-up concepts, such as SMOS-HR (high resolution), for which the new optimized irregular configurations are compared to the regular ones.

Radio occultation (RO) is a powerful tool for remotely sensing the atmosphere, producing globally distributed soundings with both high vertical resolution and high temperature retrieval accuracy, especially in the stratosphere. The spatial distribution of the soundings typically prevents the use of these measurements for studying atmospheric effects with small horizontal or temporal scales. However, careful arrangement of a dedicated RO constellation can yield sounding clusters useful for the tomographic reconstruction of internal gravity waves with horizontal wavelengths in the tens of kilometers. This article presents occultation cluster quality metrics predictive of internal gravity wave tomographic reconstruction error and uses these metrics to compare the performance of two alternative RO constellation geometries: mutual orbit groups (MOG) and a spread in right ascension of the ascending node (RAAN). MOG constellations have better overall performance and yield more consistent cluster quality across all sampled latitudes, while RAAN-spread constellations have improved equatorial quality and a trend toward reduced quality at the edges of the latitude range. Additionally, analysis of clusters by latitude, ray azimuth, and quality is performed in order to examine the trends in the outlier best- and worst-performing clusters for each constellation type.

The microwave scatterometer onboard the China-France Oceanography Satellite (CFOSAT Scatterometer, CSCAT) is the first rotating fan-beam scatterometer (RFSCAT) in operation. It is used to obtain the ocean surface wind field by measuring the backscattering coefficient over a large observation swath. To estimate the backscattering coefficient, the scatterometer needs to measure the scattered signal accurately. The process of removing noise from the echo signal (scattered signal plus noise) is called noise subtraction. Noise subtraction has a great influence on the measurement of the backscattering coefficient and subsequent wind field retrieval. Therefore, accurate noise subtraction is necessary and important. In this article, an improved noise subtraction method for CSCAT is proposed. According to the distribution characteristics of backscattering coefficients, noise correction factors are introduced to improve the noise subtraction of CSCAT. The ocean surface wind fields are obtained using the improved backscattering coefficient data, and the deviation of wind speed and direction are calculated by comparison with the European Centre for Medium Range Weather Forecasts wind fields. The results show that the improved noise subtraction method can lead to improvement of the quality of wind field effectively. In addition, based on the improved method of noise subtraction, a method of estimating the noise correction factor is proposed at a large incidence angle, furtherly improving the noise subtraction method. The results show that the proposed method is effective.

In this article, an automatic method is proposed for ship velocity estimation based on the two-dimensional spectrum pattern of Kelvin wakes in synthetic aperture radar (SAR) images of which the effectiveness is proved with both simulation and experimental results. The proposed method has the following advantages. First, the velocity can be estimated automatically, which will not introduce extra error caused by manual operations. Second, the prior knowledge of SAR parameters is not needed and the only factor that matters is the pixel spacing of the SAR image. Then, the effects of ship speed, wind speed, the angle between ship heading, and SAR platform flight direction on the spectrum of Kelvin wakes, and the ability of the proposed method are studied. It is found that ship velocity can be estimated precisely with the proposed spectrum-based method with a larger ship speed or a low wind speed. In addition, it is easier to estimate ship speed when the SAR platform flies across the ship heading.

Multimode microwave remote sensors are a new type of radar system that operates in SAR (synthetic aperture radar), scatterometer, altimeter, and spectrometer modes in a time-sharing manner according to the current task scheduled on small satellites. High flexibility through mode switching is the outstanding advantage of this new microwave remote sensor. Two or more small satellites operating in different operation modes can be networked to perform observations synchronously to provide excellent remote sensing performance. This paper focuses on a method to determine the radar parameters of the scatterometer mode since the frequency band and the platform height of this new of kind sensor are determined according to the requirement of the SAR mode, and their values are evidently different from the values of current scatterometers in orbit. The proposed method systematically presents the complete steps and related formulae of the radar parameter design of scatterometers and determines the signal bandwidth by optimization. A system simulation method of the scatterometer mode is further studied to determine the wind field inversion error and thus evaluate the rationality of the parameter design results. In contrast to the existing methods, which directly add noise to the backscatter coefficients, our method simulates the noisy echo of the scatterometer and then retrieves the wind field by estimated backscatter coefficients from the noisy echo. The performed simulation shows that, by using the designed system parameters, the multimode microwave remote sensor obtains wind field inversion accuracy, resolution and swath width comparable to those of the HY-2 satellite and the QuikSCAT satellite.

Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is crucial for planning - for example when making production capacity or inventory allocation decisions. We propose a practical and robust distribution forecast framework that relies on backtest-based bootstrap and adaptive residual selection. The proposed approach is robust to the choice of the underlying forecasting model, accounts for uncertainty around the input covariates, and relaxes the independence between residuals and covariates assumption. It reduces the Absolute Coverage Error by more than 63% compared to the classic bootstrap approaches and by 2% - 32% compared to a variety of State-of-the-Art deep learning approaches on in-house product sales data and M4-hourly competition data., ICASSP 2022 - "Copyright 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising/promotional purposes, creating new collective works, for resale/redistribution to servers/lists, or reuse of any copyrighted component of this work in other works."

We propose a metric called the bistatic radar detection coverage probability to evaluate the detection performance of a bistatic radar under discrete clutter conditions. Such conditions are commonly encountered in indoor and outdoor environments where passive radars receivers are deployed with opportunistic illuminators. Backscatter and multipath from the radar environment give rise to ghost targets and point clutter responses in the radar signatures resulting in deterioration in the detection performance. In our work, we model the clutter points as a Poisson point process to account for the diversity in their number and spatial distribution. Using stochastic geometry formulations we provide an analytical framework to estimate the probability that the signal to clutter and noise ratio from a target at any particular position in the bistatic radar plane is above a predefined threshold. Using the metric, we derive key radar system perspectives regarding the radar performance under noise and clutter limited conditions; the range at which the bistatic radar framework can be approximated to a monostatic framework; and the optimal radar transmitted power and bandwidth. Our theoretical results are experimentally validated with Monte Carlo simulations.

Due to the low dispatchability of wind power, the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the forecasting hour) chosen for the input layer parameters; and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecasting horizon, the greater the degree of improvement.

The wind field has a great influence on the control stability of Tilt-rotor unmanned aerial vehicle (UAV), especially during the take-off and landing phase. The airspeed of UAV is so small during these phases that it cannot generate stable aerodynamic forces, which will significantly reduce the wind robustness of Tilt-rotor UAV. In this article, the disturbance of wind field is analyzed from two perspectives: the wind field acting on the UAV fuselage, which is regarded as external interference, and the wind acting on the propeller, which is considered as modeling error. After analyzing the interference mechanism of wind field, a generalized extended state observer (GESO) and a $H\infty $ robust control method with mixed sensitivity are proposed, which could empower the Tilt-rotor UAV with good interference suppression ability as well as better performance tracking ability. Finally, the laboratory simulation and flight experiment are studied. The results validate the theory and prove that the proposed method could resist the interference of wind field and shows excellent control effect.

The uncertain and stochastic output of the wind farm results in a lot of problems when it is connected to the power grid. In order to improve the wind power’s friendship to the grid, the wind farm should has a certain self-discipline level. In this paper, it is studied from the perspective of the wind farm self-discipline interval. First, the concept of wind farm self-discipline interval is proposed, followed by a comprehensive index which is used to evaluate the wind farm self-discipline level by comprehensively considering the interval width and the interval accuracy. Second, an optimization method is discussed to obtain the optimal self-discipline interval. This method has general applicability, not only suitable for normal distribution and other known distributions but also for arbitrary distributions (such as non-symmetric, multi-peak distributions). Finally, the size of the energy storage system (ESS) in the wind farm is optimized to guarantee a suitable wind farm self-discipline level. Simulation results show that the proposed method not only effectively improves the self-discipline level of the wind farm but also has general applicability.

With the wide application of vegetable insulating oil in power transformers, it is essential to investigate the influence of moisture on the frequency domain dielectric spectrum (FDS) analysis in order to propose an accurate moisture assessment method for vegetable oil-paper insulation system. In this regard, various vegetable oil-paper samples are prepared in a laboratory environment. FDS curves for all samples are obtained at different moisture contents and operating temperatures. A new method to estimate the moisture content in insulation paper is proposed based on the low-frequency characteristic quantity. Also, the effect of operating temperature and water migration between the oil and paper insulation on the FDS analysis is investigated. Based on this analysis, a compensation factor is introduced to eliminate the influence of temperature on paper moisture assessment. Furthermore, an improved normalization method for the temperature effect on the FDS of vegetable oil-paper insulation with different moisture contents is proposed. The proposed moisture assessment and temperature normalization methods are validated thorough their application on an operating power transformer.

Synthetic aperture radar (SAR) is an important method for observing ocean waves, and inversion of the ocean wave spectrum is an important part of SAR ocean wave detection. For most wave spectrum inversion methods, the first guess spectrum is crucial to the accuracy of SAR wave spectrum inversion. The first guess spectrum can be provided by some existing fixed wave spectrum models, but the applicability of these wave spectrum models as the first guess spectrum has not been systematically studied. This paper focused on the applicability of the Elfouhaily spectrum (E spectrum) as a full wavenumber spectrum and the Pierson-Moscowitz spectrum (PM spectrum) as a wind wave spectrum as the first guess spectrum for inverting the wave spectrum. Based on the SENTINEL-1A and RADARSAT-2 SAR satellite data of the Maritime Silk Road, using the E spectrum and PM spectrum as the first guess spectrum by the Max Planck Institute (MPI) method, the ability of the E spectrum and PM spectrum as the first guess spectrum to retrieve the wave spectrum and wave parameters was systematically evaluated. The significant wave height (HS) and zero-crossing period (TZ) wave parameters obtained from SAR inversion were compared with the European Center for Medium-Range Weather Forecasts (ECMWF) wave data. By the comparison results, we analyzed the best wind speed and significant wave height ranges for the inversion of the PM spectrum and E spectrum as the first guess spectrum. The results showed that the E spectrum as the first guess spectrum had a good effect on the inversion at medium sea situation. It was suitable as the first guess spectrum of the SAR wave spectrum inversion.

The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.

Wind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new information, the accuracy of ultra-short-term wind power prediction (WPP) is still not satisfactory. Therefore, a combined prediction method based on the day-ahead Numerical Weather Prediction (NWP) location technology is proposed. Firstly, the time points with low forecasting accuracy of rolling WPP are approximately located by the NWP information and time windows, and then the hybrid approach combined with neural network and persistence method is presented to predict the future wind power output. The results of the case study show that compared with other classical prediction methods, this method can effectively improve the ultra-short-term prediction accuracy of wind power and verify the effectiveness of the proposed method.