## 3 Hardware and Software Sub-Framework for Active LIF Spectroscopy Sensing (HSFW2)
The registration channels are separated by a shutter that closes the first channel at the moment of splashdown and directs the radiation collected in the underwater channel to the spectrometer. Collimator--3 is installed in such a way that its optical axis is brought together with the radiation axis of the source of inducing radiation--2 at a distance of 1.2 m from the receiving window for remote registration of fluorescence from slicks on the water surface. The optical axes of the receiving collimator--4 and the LED--2 are oriented in such a way as to ensure the registration of radiation scattered by water at the shortest possible distance. Radiation is directed to the spectrometer by a UV/Visible Fiber Splitter (OceanInsight, USA). To suppress the frequency of elastic scattering of LED radiation in water, 10 CGA-295 filters (Newport, Irvine, CA, USA)--8 are used. To control the parameters of the elements included in the measuring complex, a Jetson Nano processor board (Nvidia, Santa Clara,  
Figure 4: The emission spectrum of LED LEUVA66H70 HF00. CA, USA)--6 is used. The interaction of the computer with the spectrometer--1 is carried out by the installed libraries Seabreeze and PyUSB. The spectrometer is integrated into the sealed drone body--7. Software components with elements of artificial intelligence were developed to identify the types of oil products contained in the form of slicks or solutions on the sea surface. They automatically classify the fluorescence spectra of solutions according to their types. Initially, sequences of spectra were obtained for each type of oil slick with a thickness of about 100 microns and the solution under study with different concentrations in laboratory. The total number of spectra obtained for the formation of training datasets for slick and the solutions of oil product was more than 8000 units. Python with scikit-learn, NumPy, and matplotlib libraries was used as a means of implementation. The spectra were preprocessed, normalized, and reduced to a single format. Furthermore, arrays of spectra of various types of oil products were used to compile datasets to extract features from them. The data matrix formed from the feature descriptions was used to train the linear support vector classification (linear SVC) method [32]. Thus, the resulting models can be used to classify the spectra of eight types of heavy and light types of marine fuel solutions and slicks. The spectral classification dataset is a matrix of spectral values and class labels, which contains sequences of values of the normalized intensities of the LIF spectrum for each type of fuel (a sequence of 83 spectra for each concentration for solutions and 900 spectra for slicks), in the range from 200 to 900 nm, measured in the spectral interval 0-47 nm (in Figure 5, the distance between the points of the horizontal axis corresponds to 0.47 nm). As a demonstration example of the work of the created model, Figure 5a shows a dataset that includes 83 different spectra for five concentrations of DMA solution (in the upper right part), obtained in the laboratory. Figure 5b shows a dataset that includes 300 different spectra for DMA slicks with a thickness of about 100 microns, also obtained in the laboratory and used in the training process. The vertical axis is the value of the fluorescence intensity in relative units, while the horizontal axis is the wavelength in nm. It is necessary to collect proper datasets for the training procedure to automatically identify the type of oil slick type. The LIF spectra from slicks and solutions of the same products may differ [31]. Figure 5: _Cont._