## 5 Conclusions  
In this work, we describe a new approach for the non-linear 2D processing of data from a distributed acoustic sensor implemented in both the time and frequency domains. when using an inexpensive radiation source, it allows various types of noise to be excluded, leading to a maximum increase in the signal-to-noise ratio of the received data. Basic information regarding the supposed nature of noises allows us to rank them by intensity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Most Suitable Event Type** & **Limitations** & **Potential Applications** \\ \hline \multirow{3}{*}{MA-MD} & \multirow{3}{*}{Pointwise} & Requires event frequency & Railway monitoring, pest \\  & & information; partial data loss; high & registration, flow metering \\  & & frequencies reduce efficiency & \\ \hline \multirow{3}{*}{AFDA} & \multirow{3}{*}{Distributed; high-frequency} & The signal fraction should be less & Flaw detection, perimeter \\  & & than noise fraction; may require & security, bee and plant \\ \cline{1-1}  & & information on signal characteristics & monitoring \\ \hline \hline \end{tabular}
\end{table}
Table 3: Features and potential applications of the studied algorithms.  
(rather than by frequency, as is usually the case) and to adjust filtering parameters for each type of noise. Through the refinement of simple and undemanding DAS noise reduction algorithms, an improvement in their performance was achieved: the processed data SNR was increased, and spatial distortions and operating time were reduced (compared to the FDDA technique). Thus, the dynamic averaging algorithm began to work up to eight times faster, providing an increase in SNR of 3.7 dB on its own and in combination with MA-MD by 10.8 dB. The differentiation of algorithms by types of processed actions has been proposed. All of this makes it possible to reduce the DAS cost and increase accessibility, hence expanding the scope of their application.  
The SNR enhancement was achieved due to a new method, which involves using the inverted activation function to determine the best averaging parameters. In fact, the key varying calculation options are the parameters of this function, as well as the window sizes when calculating the moving differential and moving averaging. In the future, the selection of these parameters can be entrusted to artificial intelligence. After training the system, this will not require serious additional computing resources. We expect that the proposed combined application of algorithms and artificial intelligence methods will expand the possibilities surrounding distributed acoustic sensor utilization for the registration of voice commands [52; 53; 54] and their origin localization in smart home systems, smart production [55; 56; 57], and other Internet of Things applications [58].  
A.A.F., D.A.K. and C.A.L.-M.: setup arrangement, programming of methods, discussions, writing; A.T.T.: arrangement of Sections 3 and 4 of the paper, adaptation of drawings, editing of all parts; Y.A.K. and F.L.B.: editing, idea, conception, management, Sections 1, 2 and 5. All authors have read and agreed to the published version of the manuscript.  
Section 4 was performed as a part of state assignment No. 122031100058-3. Section 2.1 is supported by the Ministry of Science and Higher Education of the Russian Federation, grant number 075-15-2021-581 (experiment and raw data collection) and the Russian Science Foundation, grant number 23-79-30017 (master laser design and application, data interpretation). Sections 1, 2, 3 and 5 were performed as a part of state assignment No. AAAA-A19-119042590085-2. A.A.F. is supported by the European Union's Horizon 2020 research and innovation program (Individual Fellowship, H2020-MSCA-IF-2020, #101028712).  
Not applicable.