# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 2 Atmospheric Sensing Trials: Collection of ATM Datasets
The insert shows the propagation path altitude profile. 19,336 (\(\approx\)20.0K) short-exposure scintillation images and 322 corresponding \(C_{n}^{2}\) values ranging from 6\(\cdot\)10\({}^{-17}\) m\({}^{-2/3}\) to 1.7\(\cdot\)10\({}^{-14}\) m\({}^{-2/3}\). To enrich the dataset and better understand the potential impact of optical receiver characteristics on DNN-based data processing, the second (ATM#2) dataset was collected using an optical receiver with a larger aperture (\(D\) = 14.4 cm). The camera integration time was reduced from 0.05 ms to 0.01 ms, while the frame rate was increased two-fold (\(N_{\Delta T}\ =\ 120\)). The ATM#2 dataset was composed of data collected during three measurement trials conducted on 27 April, 28 April and 29 April (trials 2A, 2B and 2C). The dataset included a total of 115,000 (115K) intensity scintillation images and 958 synchronously captured \(C_{n}^{2}\) values ranging from 5\(\cdot\)10\({}^{-17}\) m\({}^{-2/3}\) to 1.6\(\cdot\)10\({}^{-14}\) m\({}^{-2/3}\). Since the data collection trials were performed at different times and each ATM dataset included data from different trials, it is more convenient to use the frame number \(m\) to identify both sequentially captured scintillation images and the corresponding \(C_{n}^{2}(m)\) values within each dataset. To have a matching number of captured scintillation images (frames) and \(C_{n}^{2}\) values, we artificially included \(N_{\Delta T}\) additional \(C_{n}^{2}\) values between the scintillometer sequential measurements. These \"extra\" \(C_{n}^{2}\) values were computed using a simple linear approximation of the \(C_{n}^{2}\) data between each two sequential measurements. Note that the generation of additional instances from existing data (data augmentation) is commonly used in machine learning to increase the training set size [17]. The diversity of atmospheric turbulence conditions during the measurement trials and the impact of turbulence strength (variability of \(C_{n}^{2}\)) on the characteristic spatial structure of acquired scintillation images are illustrated in Figure 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Trial \#** & **Date and Time** & **\# Frames** \\ \hline
1A & 09 March 11:44 am. to 12:52 p.m. & 3.8 K \\
1B & 09 March 05:46 p.m. to 09:15 p.m. & 9.2 K \\
1C & 13 March 12:10 p.m. to 10:20 p.m. & 7.0 K \\
2A & 27 April 12:54 p.m. to 09:05 p.m. & 55 K \\
2B & 28 April 01:16 p.m. to 07:51 p.m. & 45 K \\
2C & 29 April 05:29 p.m. to 07:11 p.m. & 12 K \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ATM dataset collection trials characteristics. Figure 2: Graphical representation of the refractive index structure parameter \(C_{n}^{2}\) (logarithmic scale) for the measurement trial 1A, 1B, and 1C (ATM#1 dataset) (**a**) and 2A, 2B, and 2C (ATM#2 dataset) (**b**). Each dataset is comprised of intensity scintillation images and \(C_{n}^{2}\) values synchronously measured by the BLS-2000 scintillometer during the atmospheric sensing trial. Index \(m\) corresponds to image frame number (frame stamp) in each dataset. Grey scale images below represent exemplary (selected from the ATM#1 dataset) scintillation patterns captured under different atmospheric turbulence conditions (different \(C_{n}^{2}\) values).