This article presents the measurement results of maritime wireless propagation channel at 8GHz in the South China Sea region. Large-scale path loss and small-scale fading of channels between a marine observation platform and a moving vessel are investigated. For large-scale path loss, comparisons are conducted between the free space model, two-ray model, parabolic equation model and log-distance model with the measured path loss data. The results show that the log-distance model agrees well with the general trend of the measured data, whereas the parabolic equation model has a better ability to capture the variations of the interference nulls. In addition, the free space model and two-ray model have larger deviation from the empirical data, implying that they are not suitable for the desired scenario. For small-scale fading, various distributions are considered to fit the fading. Based on the criteria of Kolmogorov–Smirnov statistics, the Rician distribution has the best goodness of fit, compared with the Rayleigh, Nakagami-m, Welbull, or Lognormal distributions. These research can provide good guidance for efficient designing and future deployments of distributed buoy networks in the South China Sea region.

The evaporation duct could significantly affect the work status of maritime microwave communication systems in the South China Sea. Therefore, the exact forecasting of the evaporation duct is vital for the normal operation of the systems. This study presents a stochastic modeling approach to predict the future trends of the evaporation duct over the South China Sea. The autoregressive integrated moving average (ARIMA) model has been used for modeling the monthly evaporation duct height estimated from the Climate Forecast System Reanalysis dataset released by the National Centers for Environment Prediction. The long-term evaporation duct height data were collected for a period of 10 years from 2008 to 2017. The analysis of correlation function reveals the existence of seasonality in the time series. Therefore, a seasonal ARIMA model with the form as ARIMA (0,0,1) × (0,1,2)${}_{12}$ is proposed by fitting the monthly data optimally. The fitted model is further used to forecast the evaporation duct variation for the year 2018 at 95% level of confidence, and high-accuracy results are obtained. Our study demonstrates the feasibility of the proposed stochastic modeling technique to predict the future variations of the evaporation duct over South China Sea.

In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.

The evaporation duct is a particular type of atmospheric structure that always appears on the open ocean. Predicting the evaporation duct height (EDH) accurately and in a timely manner is of great significance for the practical application of marine wireless communication equipment. Understanding the characteristics of EDH time series is an essential prerequisite for establishing an appropriate prediction model. Moreover, the sampling timescales of EDH data may influence the dynamic characteristics of the EDH time series as well. In this study, EDH time series datasets at three timescales, hourly, daily, and monthly, were constructed as the case study. Statistical methods, namely the augmented Dickey–Fuller test and Ljung–Box test, were adopted to verify the stationary and white noise characteristics of the EDH time series. Then, rescaled range analysis was applied to calculate the Hurst exponent to study the fractal characteristics of the EDH time series. An extensive analysis and discussion of the chaotic dynamics of the EDH time series are provided. From the perspective of nonlinear dynamics, the phase space was constructed from the time delay $\tau$ and embedding dimension m, which were calculated from the mutual information method and the Grassberger–Procaccia algorithm, respectively. The maximum Lyapunov exponent was also calculated by the small data volume method to explore the existence of chaos in the EDH time series. According to our analysis, the EDH time series are stationary and have a non-white noise characteristic. The Hurst exponents for all three timescales were greater than 0.5, indicating the predictability of the EDH time series. The phase space diagrams exhibited strange attractors in a well-defined region for all the timescales, suggesting that the evolution of the EDH time series can possibly be explained by deterministic chaos. All of the maximum Lyapunov exponents were positive, confirming the chaos in the EDH time series. Further, stronger chaotic characteristics were found for the finer-resolution time series than the coarser-resolution time series. This study provides a new perspective for scholars to understand the fluctuation principles of the evaporation duct at different timescales. The findings from this study also lay a theoretical and scientific foundation for the future application of chaotic prediction methods in the research on the evaporation duct.

The characterization of targets by electromagnetic (EM) scattering and underwater acoustic scattering is an important object of research in these two related fields. However, there are some difficulties in the simulation and measurement of the scattering by large targets. Consequently, a similarity study between acoustic and EM scattering may help to share results between one domain and the other and even provide a general reference method for the simulation of scattering characteristics in both fields. Based on the method of physical optics, the similarity between the EM scattering of conductors and the acoustic scattering of soft/hard targets and the similarity between the EM scattering of dielectrics and the acoustic scattering of elastics are studied. In particular, we derive how to transfer quantities from one domain into another so that similar scattering patterns arise. Then, according to these transfer rules, the EM scattering and acoustic scattering of three typical targets with different types of boundaries were simulated and measured, and the simulated EM scattering and acoustic scattering curves were found to be in perfect agreement, with correlation coefficients above 0.93. The correlation coefficients between the electromagnetic and acoustic scattering patterns were above 0.98, 0.91, and 0.65 for three typical targets. The simulated and measured scattering results verify the proposed similarity theory of EM and acoustic scattering, including the transfer from one domain into the other and the conditions of EM and acoustic scattering, and illustrate that the acoustic scattering characteristic of the target can be simulated using the EM scattering based on the derived conditions and vice versa.