Wind speed and direction estimation from wave spectra using deep learning

High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information about 40 min previously because the high-frequency portion of the wave spectrum integrates preceding wind conditions. The overall root-mean-square error (RMSE) of estimated wind speed is ∼1.1 m s−1, and the RMSE of the wind direction is ∼ 14∘ when wind speed is 7–25 m s−1. This model can be used not only for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

Modelling Ocean Waves and an Investigation of Ocean Wave Spectra for the Wave-to-Wire Model of Energy Harvesting

Ocean wave energy is an abundant and clean source of energy; however, its potential is largely untapped. Although the concept of energy harvesting from ocean waves is antiquated, the advances in wave energy conversion technologies are embryonic. In many major studies related to wave-to-wire technologies, ocean waves are considered to be regular waves with a fixed amplitude and frequency. However, the actual ocean waves are the sum of multiple frequencies that exhibit a particular sea state with a significant wave height and peak period. Therefore, in this paper, detailed modelling of the ocean waves is presented and different wave spectra are analyzed. The wave spectra will eventually be used for the generation of wave elevation time series. Those time series can be used for the wave-to-wire model-based studies for improved investigations into wave energy conversion mechanisms, mimicking the real ocean conditions.

Coalescence of black hole–neutron star binaries

We review the current status of general relativistic studies for coalescences of black hole–neutron star binaries. First, high-precision computations of black hole–neutron star binaries in quasiequilibrium circular orbits are summarized, focusing on the quasiequilibrium sequences and the mass-shedding limit. Next, the current status of numerical-relativity simulations for the merger of black hole–neutron star binaries is described. We summarize our understanding for the merger process, tidal disruption and its criterion, properties of the merger remnant and ejected material, gravitational waveforms, and gravitational-wave spectra. We also discuss expected electromagnetic counterparts to black hole–neutron star coalescences.

Iron-Oxide Magnetic Materials for Millimeter-Wave Absorption

High-performance electromagnetic-wave absorbers are required for the control of millimeter-wave spectra, which will play a big role in future 5G and 6G wireless networks. Traditional absorbing materials comprised of metals or soft ferrites have been developed but their lack of ability to absorb at extremely high frequencies continues to hinder their practical applications. Thus, this article briefly introduces several iron-oxide magnetic materials with millimeter-wave absorbing capability.

Wind Speed and Direction Estimation from Wave Spectra using Deep Learning

High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information ~1 h ago, because the wave spectra contain wind information a short period before. The overall root-mean-square error (RMSE) of estimated wind speed is ~1.1 m/s, and the RMSE of wind direction is ~14° when wind speed is 7~25 m/s. This model can not only be used for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

The Approach for Studying Variability of Sea Wave Spectra in a Wide Range of Wavelengths from High-Resolution Satellite Optical Imagery

The development and validation of a method for remote measurement of the spectra of sea waves, which significantly expands the capabilities to study surface waves in large water areas in a wide range of wavelengths, is described. The applied approach is based on the use of retrieving operators, which are constructed by the method of numerical simulation, taking into account the nonlinear nature of brightness field modulation by the slopes of the sea surface. Retrieving operators have a set of parameters that are adapted to the real conditions of aerospace imaging. To assess the adequacy of the retrieving of wave spectra recorded from satellite images, they are compared with the spectra obtained by ground-based means under controlled conditions. The studies have shown the adequacy of remote measurement of the spectra of slopes and elevations of sea waves with wavelengths in the range 0.1–1 m. The possibility of using the developed method for studying the variability of sea waves in the coastal zone under conditions of limited fetch, including in the presence of anthropogenic disturbances, is shown.