# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 3 Results and Discussion
Different submergence depths of the net and the DHC, different averaging volumes, and different sea states affect the camera shooting quality, as illustrated in Figure 12;
2. The blur of the shape parameter of the _Others_ taxon. The mesh size of the net here is 180 \(\upmu\)m. For the DHC, the minimum size is \(H=200\)\(\upmu\)m adopted in the classification algorithm (Table 1). Unlike other taxa, this group includes organisms of various shapes. In addition, particles of non-living matter may also belong here. \begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Traditional Classification of Plankton Samples Collected by the Net**} & \multicolumn{3}{c}{**Classification of Plankton Using the DHC**} \\ \multicolumn{6}{c}{**(Sampling from a Depth of 32 m, Averaging over 3.2 m\({}^{3}\))**} \\ \hline \begin{tabular}{c} **Organism,** \\ **Type** \\ \end{tabular}  & **Class** & \begin{tabular}{c} **Taxon or** \\ **Group** \\ \end{tabular}  & **Genus** & \begin{tabular}{c} **Number,** \\ **pcs/m\({}^{3}\)** \\ \end{tabular}  & **Taxon** & \begin{tabular}{c} **Number,** \\ **pcs/m\({}^{3}\)** \\ \end{tabular}  & **Holographic Image** \\ \hline \multirow{6}{*}{Arthropoda} & \multirow{2}{*}{Crustacea} & \multirow{2}{*}{Copperoda} & _Oitona_ & 296.9 & \multirow{6}{*}{\ \begin{tabular}{c} \\ **Calcanus/Pseudocalanus** \\ \end{tabular}  } & \multirow{6}{*}{343.8} & \multirow{6}{*}{ \begin{tabular}{c} \\ **343.8** \\ \end{tabular}  } \\ \cline{1-1}  & & & &  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Traditional Classification of Plankton Samples Collected by the Net (Sampling}} & \multicolumn{4}{c}{**Classification of**} \\  & & & **Plankton Using the** & & **Plankton Using the** & & **Object Classification of Holographic Images** \\  & & & & **DIC** (to a Depth of up to 45 m, Averaging over 0.825 m\({}^{3}\)) & & & **(to a Depth of up to 45 m, Averaging over 0.825 m\({}^{3}\))** & \\ \hline \hline
**Organism,** & \multirow{2}{*}{**Class**} & **Taxon or** & **Genus** & **Number,** & **Taxon** & **Number,** & **P**s/m\({}^{3}\)** & **Holographic Image** & **Number,** \\  & & & **Group** & & **Oloan** & 400.0 & & \\ \hline \multirow{4}{*}{Arthropoda} & \multirow{4}{*}{Crustacea} & \multirow{4}{*}{Calumns/Pseudocalanus} & \multirow{4}{*}{500.0} & \multirow{4}{*}{500.0} & \multirow{4}{*}{694.4} \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  
Due to these reasons, the DHC performance error is more accurately established through the comparison of data from the automatic and expert classification of images of plankton individuals reconstructed from holograms. For station No. 6961 (Table 6), this single variation does not exceed 30%, depending on the taxon. Figure 13 shows the comparison of turbidity measurements using the DHC technology and standard turbidimeter data. A good correlation in the data at a significance level of 0.05 indicates the high potential of digital holography for turbidimetry purposes. The correlation coefficient between turbidity measurements using the turbidimeter and the DHC was 75.5%. Data on methane bubbles away from gas flames in the East Siberian Sea at station No. 6962 and in the Laptev Sea at station No. 6947 are not presented in this paper, since the issue of synchronizing acoustic and holographic data on this expedition could not be addressed. The estimated submergence trajectory of the digital holographic camera shown in Figure 9 by the green line is not certain due to the drift in relation to the research vessel. Highly scattered holographic survey data should be averaged not over five samples at one point taken drift of the vessel, but over a large area\(\sim\)1 m\({}^{2}\) of the water surface along the vessel journey. The number of samples in this case increases to 1000. Then the holographic flow measurements will be more representative.