## 4 Discussion
If changed to 25, the computation time would become16 s.  
Meanwhile, the current MA-MD implementation, which has been repeatedly applied to DAS data, has asymmetrical MA and MD window positions relative to the point under processing. The resulting data loss at the end of the processed time series has already  
Figure 9: Extremely noisy data filtering with AFDA. (**a**) Original noisy data set, SNR = 7.3 dB; (**b**) data processed by the AFDA algorithm, SNR = 9.9 dB. been mentioned. But an important aspect of the feature under discussion is the higher probability of data distortion, since the current value of a point takes into account only the signal values at subsequent points, but not at previous ones. A window that is symmetrical in the area of the point under processing, however, will either result in a loss of data at both ends of the time series or in the problem of filling areas at the beginning and end. In this work, a similar problem was solved for the AFDA algorithm and frequency or time cross sections of DAS data. The upcoming research will, most likely, be aimed at finding a solution to the problem of the MA-MD algorithm. In addition, it should include the calculation of SNR dependence on the frequency of the useful signal provided by the use of each of the algorithms. This will allow for the determination of the most efficient performance range of the MA-MD and AFDA methods. Such an estimate is necessary because, in this work, it was found out that the resulting SNR depended on how the signal frequency correlated with the sampling frequency. Such cost-effective sensors can also be widely used in biology and agriculture, where there is a need for distributed acoustic monitoring, and suitability of fiber optic DAS for this has already been confirmed [48]. It is important to note that pest registration [49] and the monitoring of the state of beneficial insects [50] or plant crops [51] are associated precisely with the specific tracking of frequency bands in the signal spectrum, and therefore, the results of this work can be very useful. The described features of the algorithms and their possible applications are summarized in Table 3.  
Another interesting direction of future work may be the application of the developed method in the time domain. In this case, the time domain refers to the intensity of the acoustic vibration as a function of time at a single point in the optical fiber. In sound engineering, non-linear processing is widely applied to such data. For example, when there is a need to make the sound denser, by compensating the changing distance from the sound source (singer) to the sensor (microphone), a compression procedure is applied. When the task is to make the dynamics of intensity variation more noticeable, the reverse procedure is used--expanding. In classical sound engineering, these processing methods operate on the principle of raising intensity values to a power, and the exponent determines whether the signal becomes dynamically more diverse or not. Processing an audio signal using an inverted activation function is somewhat similar to expanding, since loud sounds will become even louder, and quiet ones even quieter. In this case, quiet sounds will lose their readability due to averaging over time (an effect similar to delay and reverb). It is expected that this will be useful for isolating a voice from extraneous noise (for example, other voices) for the purpose of further speech recognition.