# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 4 Experiment Results
### Sea Clutter Generation Using GCC-FLOW with Global Condition  
Using the global condition variable \(\alpha\), a single GCC-FLOW can be trained on two different datasets: high sea state data with \(\alpha=1\) and low sea state data with \(\alpha=0\). Figures 10 and 11 plot the magnitude histogram and magnitude autocorrelation of the generated sea clutter data using GCC-FLOW with \(\alpha=1\) and \(\alpha=0\), respectively, at three different time points (beginning, middle, and end) from the \(262,144\) samples. The three time points are designated by the beginning (1\(\sim\)10,000), middle (130,001\(\sim\)140,000), and end (252,145\(\sim\)262,144) of the sequence.  
Figure 8: Distribution (**1**) and autocorrelation (**2**) of the generated data and 11 th range bin of Grimsby #155 data : beginning (**a**), middle (**b**), end (**c**).  
Figure 7: Distribution (**1**) and autocorrelation (**2**) of the generated data trained on low sea state data: beginning (**a**), middle (**b**), end (**c**).  
Figure 9: Autocorrelation comparison of block-by-block method and the proposed GCC-FLOW: original data (**1-(a)**,**2-(a)**), block-by-block method (**1-(b)**,**2-(b)**), and GCC-FLOW (**1-(c)**,**2-(c)**).  
In Figure 10, the histograms of generated high sea state data were plotted for 10,000 samples at three different time points (beginning 1-(a), middle 1-(b), end 1-(c)), and the autocorrelations of generated high sea state data were plotted for 512 samples corresponding to each time point (beginning 2-(a), middle 2-(b), end 2-(c)). In Figure 11, the magnitude histograms and autocorrelations of low sea state were plotted in a similar fashion to Figure 10. The statistical characteristics, including distribution and autocorrelation, remain consistent across all time points, exhibiting a resemblance to those observed in the original data. Figures 10 and 11 demonstrate that both high and low sea state data are successfully aligned with the corresponding global conditions in a single GCC-FLOW.  
The benefit of this single-mode training on two different datasets is the feature interpolation, i.e., by adjusting \(\alpha\) between 0 and 1, the GCC-FLOW can generate data with interpolated statistical characteristics between the high and low sea state.  
Figure 12 shows the magnitude histogram of generated data for \(\alpha=0.2,0.5,0.8\) at three different time points (beginning, middle, and end) of 262,144 samples. The magnitude histograms were plotted for 10,000 samples for all time points (beginning (a), middle (b), end (c)). Figure 13 presents the autocorrelation of generated data for \(\alpha=0,2,0.5,0.8\) at three different time points (beginning, middle, and end) of 262,144 samples. The autocorrelations were plotted for 512 samples corresponding to each time point (beginning (a), middle (b), end (c)) and each value of \(\alpha\) (0.2 (1), 0.5 (2), 0.8 (3)).  
In both figures, as \(\alpha\) increases from 0 to 1, the characteristics of magnitude distribution and autocorrelation transforms from the low sea state characteristics to the high sea state characteristics. It is observed that, as the value of the \(\alpha\) increases, the distribution becomes increasingly concentrated, and the correlation becomes increasingly fast-fluctuating for all  
Figure 11: Distribution (**1**) and autocorrelation (**2**) of the generated data by GCC-FLOW with \(\alpha=0\): beginning (**a**), middle (**b**), end (**c**).  
Figure 10: Distribution (**1**) and autocorrelation (**2**) of the generated data by GCC-FLOW with \(\alpha=1\): beginning (**a**), middle (**b**), end (**c**).  
time points. This suggests that, by using GCC-FLOW with a global condition variable tuned to sea state, various sea state sea clutter can be generated without the need for the dedicated training datasets for different sea states. As a result, data exhibiting consistent statistical characteristics at all time points can be generated as intended with global sea state, not only for high and low sea states, but also for intermediate sea states through interpolation.