esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Case Study: February-April 2017  
The eruptive activity at Mount Etna was very intensive in the first half of 2017. It started at the end of February 2017, when lava fountains took place at the summit craters, and continued until the end of April 2017, when discontinuous lava emission formed a long, complex lava flow field in the upper south flank of the volcano [79]. In order to investigate this event, Sentinel-2 data was acquired on 9 July 2016 (have been used as pre-eruptive image, while the ones acquired on 30 April 2017 and 8 May 2017 as post-eruptive images). The area of interest was restricted to the region where the eruptive activity took place. In particular, each of the 10 m spatial resolution bands was zoomed to smaller areas of \(321\times 333\) pixels. In Figure 4, the 2016 DEM [33] and RGB composite from Sentinel-2 MSI data (Red--Band 4, Green--Band 3, Blue--Band 2) of the pre- and post-eruptive images are shown.  
In order to build the input dataset, each pixel is considered as characterized by 13 components, namely bands 2, 3, 4, 8 of day 9 July 2016; bands 2, 3, 4, 8 of day 30 April 2017; bands 2, 3, 4, 8 of day 8 May 2017, and DEM. The k-medoids stage receives as input the 106,893 \(\times\) 13 matrix, and it clusters the 106,893 pixels in 50 classes depending on their correlation distances. For the training set, 150 pixels have to be labelled (as described in Section 2.2), and it is noteworthy that, in this case, only 17 pixels out of 150 training pixels belong to the lava flow field, so our results have been obtained labelling only 0.016% of the full dataset size to train our model. In order to test the trained BNN, the remaining 106,743 \(\times\) 13 pixels are used, producing as a result the output map. Finally, the post-processing step is performed on the output, removing some noisy isolated pixels. The final lava flow map after the last post-processing step is shown in Figure 5 together with the contour of the real observed lava flow manually retrieved from multi-view PlanetScope images (four bands: Blue, Green, Red plus Near Infrared with a ground sampling distance of \(\sim\)3.7 m).  
Figure 3: The workflow adopted to map lava flows using the MSI Sentinel-2 data and DEM information. The input data are firstly pre-processed and given as input to the k-medoids unsupervised clustering; then \(n\) pixels for each class are used to train the BNN. The output of the BNN is post-processed by opening the resulting image providing the areal extent of lava flow.  
Figure 4: RGB composition from Sentinel-2 MSI data (Red—Band 4, Green—Band 3, Blue—Band 2) of the pre-eruptive area 9 July 2016 (**a**), post-eruptive area 30 April 2017 (**b**) and 8 May 2017 (**c**) and DEM 2016 (**d**) restricted to the lava flow region.  
Figure 5: Lava flow map resulting from the BNN and the post-processing stages (red) and the contour of the real lava flow (white) observed during the 2017 Etna eruption.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 4 Experimental Results  
### Ablation Study Results  
Visual results from the ablation study are presented in Figure 4, while the quantitative results are shown in Table 1. A visual analysis of Figure 4 shows that the denoise module is effective in removing noise from the original OCT image (raw image). The data in Table 1 demonstrates improvements of 10.59 in PSNR and 0.24 in SSIM, which confirms this module works properly. However, the problem of unclear tissue layers is not addressed.  
The deblur module, on the other hand, removes blur and makes the layers more visible. This can be observed in Figure 4 by comparing the result of the deblur module with the raw image. In this case, the PSNR improved from 8.94 to 17.55, and the SSIM improved from 0.34 to 0.47. However, noise is still present in the image, and this limits the image enhancement performance.  
The proposed method combining both modules provides better enhancement performance than each single module applied separately. A visual inspection of the result in Figure 4 shows that the proposed method was able to effectively enhance the original  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Metrics (Mean \(\pm\) std)**} \\ \cline{2-3}
**Method** & **PSNR** & **SSIM** \\ \hline Original images & \(8.94\pm 2.01\) & \(0.34\pm 0.14\) \\ Denoise module & \(19.53\pm 1.87\) & \(0.58\pm 0.20\) \\ Deblur module & \(17.55\pm 1.52\) & \(0.47\pm 0.12\) \\ OSE & \(\mathbf{26.71\pm 2.21}\) & \(\mathbf{0.81\pm 0.16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study results.  
Figure 4: Sample images from the ablation study performed using OCT images from a pork larynx to evaluate the denoise module, the deblur module, and the proposed OSE method.  
raw image. This is corroborated by the data in Table 1, which shows the proposed method achieved top performances in terms of PSNR and SSIM.
## 3 SCH Nonlinear Mathematical Model (NMM)
### Modeling According to First Physical Principles
#### 3.1.5 Ducted Fan Model  
The static thrust and power data of the ducted fan were obtained from ground experiments. The results are shown in Figure 5. The ducted fan can produce an x-direction force \(X_{duct}\) and y-axis moment \(M_{duct}\). The delay of the force action was estimated as 0.4 s. The control signal \(\delta_{duct}\) was normalized to [0,1].
# Using Fast Frequency Hopping Technique to Improve Reliability of Underwater Communication System
###### Abstract  
Acoustic underwater communication systems designed to work reliably in shallow coastal waters must overcome major limitations such as multipath propagation and the Doppler effect. These restrictions are the reason for the complexity of receivers being built, whose task is to decode a symbol on the basis of the received signal. Additional complications are caused by the low propagation speed of the acoustic wave in the water and the relatively narrow bandwidth. Despite the continuous development of communication systems using coherent modulations, they are still not as reliable as is desirable for reliable data transmission applications. This article presents an acoustic underwater communication system that uses one of the varieties of the spread spectrum technique i.e., the fast frequency hopping technique (FFH). This technique takes advantage of binary frequency-shift keying (BFSK) with an incoherent detection method to ensure the implementation of a system whose main priority is reliable data transmission and secondary priority is the transmission rate. The compromised choice of parameters consisted of the selection between the narrow band of the hydroacoustic transducer and the maximum number of carrier frequency hops, which results from the need to take into account the effects of the Doppler effect. In turn, the number of hops and the symbol duration were selected adequately for the occurrence of multipath propagations of an acoustic wave. In addition, this article describes experimental communication tests carried out using a laboratory model of the FFH-BFSK data transmission system in the shallow water environment of Lake Wdzydze/Poland. The test results obtained for three channels of different lengths are discussed.  
shallow coastal waters; reliable underwater acoustic communication; fast frequency hopping technique; bit error rate +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Proteolytic Activity of Lactobacillus Strains  
Results of determinations of the initial and overall proteolytic activity of the tested LAB strains are presented in Figure 1. The initial proteolytic activity of all tested LAB strains was similar and did not differ significantly from that determined in the control sample. A balance was maintained at the early stage of fermentation between the consumption of free fatty acids and the degradation of peptides and milk proteins by LABs. In contrast, differences were observed in the overall proteolytic activity measured after 24 h. Among all analyzed LAB strains, the lowest activity was exhibited by _Lb. delbrueckii_ 490, while the highest activity was exhibited by the _Lb. casei_ 2639; the values of absorbance--being an indicator of the ongoing proteolysis--accounted for 0.262 and 0.512, respectively (Figure 1). The proteolytic activity of the _Lb. casei_ 2639 strain was ca. twofold higher than that of _Lb. delbrueckii_ 490. Proteolytic activity of the strains used as adjunct by active cultures in cheesemaking that is too high is undesirable, as it may lead to rapid proteolytic transformations during ripening and, consequently, to changes in the organoleptic traits of cheeses (such as a bitter taste and an untypical aroma).  
[PERSON] et al. (2019) determined the initial and overall proteolytic activity (after 24 h incubation) of the following probiotic strains: _Lb. acidophilus_ LAFTI L10 and _Lb. casei_ LAFTI L26, as well as _Lb. acidophilus_ 4962 and _Lb. casei_ 279. Likewise, in our study we found no differences in the initial proteolytic activity between the tested strains. The lowest overall proteolytic activity was determined for the strain _Lb. casei_ (0.290). In turn, very high activities accounting for 0.460, 0.670, and 1.90 were found for _Lb. acidophilus_ LAFTI L10, _Lb. acidophilus_ La 4962, and _Lb. casei_ LAFTI L26, respectively. The proteolytic activity of _Lb. acidophilus_ 2499 determined in our study at 0.400 is similar to that reported by [PERSON] et al. (2019) for _Lb. acidophilus_ LAFTI L10 (0.460). In turn, the activity of _Lb. casei 2639_ determined in our study at 0.512 is considerably higher from the activity assayed by [PERSON] et al. (2019) for _Lb. casei_ (0.290) and over threefold lower from that determined for _Lb. casei_ LAFTI L26 (1.90).  
It was concluded that the analyzed bacterial strains from the genus _Lactobacillus_ exhibited various overall proteolytic activities. Thus, large differences in proteolytic activity may result from the various activities of their proteolytic systems, and resultantly, from the various number of amine groups released in RSM during incubation, as well as from their various nutritional needs.
## 3 Results and Discussion
### The Synergistic Effect of Tri-Fuel  
The \(\Delta TG\) plots of different mixing ratios of the tri-fuels are shown in Figure 4. \(\Delta TG\) is equal to 0 only at 265.2 \({}^{\circ}\)C, which indicates that the three raw materials are not simply linearly superimposed when they are blended and burned; rather, they interact with each other during the combustion process. According to the algorithm mentioned previously, a value of \(\Delta TG\) equal to zero indicates that the experimental residual mass of combustion  
Figure 3: TG (**A,B**), DTG (**C,D**) and DSC (**E,F**) curves of tri-fuel under different mixing ratios.  
is less than the calculated value. This also indicates that the weight loss of real blended combustion is larger than the weight loss calculated by proportion. Therefore, it can be known that the three raw materials' combustion together is not a simple linear superposition but there is a synergistic effect to promote combustion when \(\Delta TG\) is less than zero. This situation exists when SC:HTB:OS are 50%:35%:15%, 50%:40%:10%, 45%:45%:10%, 40%:40%:20%, 40%:50%:10% and 35%:45%:20%.  
Under most mixing ratios, the \(\Delta TG\) peaks of tri-fuel are at 370.0 and 510.0 \({}^{\circ}\)C, which are not far from the value where the ultimate peak of TG occurs. The main reason for the peak's appearance near 510.0 \({}^{\circ}\)C is the combustion of fixed carbon in SC and HTB. During 400.0 and 510.0 \({}^{\circ}\)C, the combustion of fixed carbon is completed in advance, and the absolute TG value is increased. During 510.0-610.0 \({}^{\circ}\)C, the experimental curve gradually converges to the calculated value because the inhibition of the product becomes the main factor affecting the reaction rate with the increasing temperature. Thus, HTB has a facilitating effect in the combustion of tri-fuel. When the OS mixing ratio is greater than 10%, \(\Delta TG\) is mostly greater than zero. It can be seen that the introduction of OS inhibits the combustion of tri-fuel. As a raw material with poor combustion characteristics, moisture and ash accounted for 39.8% and 34.24%, respectively. The ash produced by its combustion has an inhibitory effect on the reaction. \(\Delta TG\) is less than zero when the OS mixing ratio is 20% and the HTB mixing ratios are 40% and 45%. The tri-fuel combustion performance is the best among all mixing ratios when the HTB mixing ratio is 45%. This is different from previous  
Figure 4: The \(\Delta TG\) curves of tri-fuel under different mixing ratios (high proportion of mixed wood pellets (**A**) and low proportion of mixed wood pellets (**B**)).  
studies [40]. Due to the numerous OS particles, the tri-fuel combustion process may occur in a relatively small unit. Under the same heating conditions, HTB and SC with the dense particle arrangement will be more exposed to oxygen [41]. In the case of a high HTB mixing ratio, due to a suboptimal combustion performance, the impact of OS on promoting combustion outweighs its inhibitory effect, resulting in a synergistic effect among the three raw materials.
## 2 Materials and Methods
### Methods
#### 2.2.2 Three Dimensional Chain Coding
After assigning the codeword values for all voxels along the boundary, we need to detect the points that have convexity changes. These points are called the inflection points (denoted by the red stars in Figure 6b. They can be detected by calculating the differential operations of the codeword values. \[f(i)\ \leftarrow\ i\ if\ \ \
abla\ c(i)\ \
eq 0, \tag{5}\]  
where \(i\) is the \(i^{th}\) voxel of the lung boundary and \(c(i)\) is the corresponding codeword value of that voxel. The non-zero points after the differential operation of \(c(i)\) can then be defined as inflection points \(f(i)\). Once inflection points are detected, we calculate and fill the volume of the gap surrounded by the inflection points, as demonstrated in Figure 6. Unlike the hole filling using SE [4; 7; 10; 12; 20; 21], our border reconstruction using 3D chain coding can accurately fill the volume of the gap, i.e., the exact region of juxta-pleural nodules is filled without resulting in under- or over-filling.
## 3 Automatic Language Identification Using Speech Rhythm Features
### Rhythm Metrics  
Previous studies on linguistics and phonetics reported that speech rhythm effectively represents the prosodic characteristics of speech signals. Speech rhythm was firstly introduced with the concept of isochrony [25; 26], which insisted that languages are classified into stress-timed languages and syllable-timed languages. According to the studies, each of the two groups of languages has identical time intervals in stressed units and the syllable units, respectively. After some research studies advanced the concepts of speech rhythm [27; 28], studies quantifying them were conducted from the 1990s onward. A representative measurement of speech rhythm is known as rhythm metrics [11; 12; 13; 14]. Rhythm metrics concentrate on differences of languages in terms of the duration of consonantal and vocalic intervals. [PERSON] et al. [11] introduced some rhythm metrics including %V, \(\Delta\)C, and \(\Delta\)V. %V denotes the proportion of vocalic intervals within one utterance, and \(\Delta\)C and \(\Delta\)V refer to standard deviation values of vocalic and consonantal intervals, respectively, reflecting the fluctuation of intervals. In [13] and [14], other rhythm features were proposed: Varco-C and Varco-V. They reduce the effect of the speech rate by normalizing \(\Delta\)C and \(\Delta\)V, respectively, with mean values. The Raw Pairwise Variability Index (rPVI) for consonantal intervals and normalized PVI (nPVI) for vocalic intervals were additionally introduced in [12] to identify differences in consecutive intervals.
## 6 Python Implementation and Usage
### Data Filtering (Lines 3608-3846)  
The blurring filter is defined by the _DataFilter_ class, which determines how each element is influenced by its neighborhood. The neighborhood of each element is defined by the elements that are fully within a maximum search radius defined by the user. The code Lines 3760-3762, intentionally left commented to reduce computational cost, generate element sets that allow an easy visual interpretation of the neighborhood of each element.  
Optimization Algorithms: Optimality Criteria, Method of Moving Asymptotes, Sequential Least-Squares Quadratic Programming, and Trust-Constr (Lines 3847-5622)  
The present code provides five optimization algorithms, described in Section 5, which can be used to solve the topology optimization problem.  
The discrete and continuous versions of the OC algorithm, implemented in the functions _oc_discrete_ and _oc_continuous_, are only suitable for compliance-minimization problems. The remaining optimization algorithms are suitable for all problem statements included.  
The _nma_ function is a decorator that links ABAQUS(r) to the _mmusub_ and _subsolv_ functions implemented by [PERSON] (2016). Therefore, the main purpose of the _nma_ function is the processing of the inputs necessary to apply the MMA (2016) as a function of the problem statement selected by the user.  
The functioning of the _oc_discrete_, _oc_continuous_, and _nma_ functions is managed by the main program loop, described in Section 6.11. However, this is not the case for the SLSQP and Trust-constr algorithms available in this code, as they belong to the SciPy module (2016). To be fully functional, the SLSQP and Trust-constr algorithms require the additional freedom to decide when and how many times to call the objective, sensitivity, and constraint functions. Furthermore, the problem statement must be defined using unique data structures. For these reasons, class _SciPyOptimizer_ is responsible for:  
* Calling the SLSQP and Trust-constr methods from the SciPy module (2016).
* Re-defining the problem statement with the correct data structure required by the algorithm selected.
* Providing the freedom for these algorithms to call the objective, sensitivity, and constraint functions as many times and in any given order necessary.  
* Trying to record the data of each iteration as accurately as possible. However, it is to be expected that the data recorded may include information from intermediate evaluation points.  
Finally, there are two details that should be considered before using the SLSQP and Trust-constr algorithms. One is that both have internal convergence criteria. To change them, the user may be required to edit the code provided with this article. The second detail is that the initial solution must be feasible; otherwise, the optimization process will stop. This is particularly relevant when defining the initial material distribution and/or when using a p-norm continuation approach (described in Section 6.11). The OC and MMA methods implemented do not have these restrictions for the initial guess.
# An Improved CZT Algorithm for High-Precision Frequency Estimation
## 4 Experimental Analysis  
In this section, the proposed improved CZT algorithm is verified using simulation studies and field test results for the frequency estimation in the FMCW radar. The performance of the improved CZT algorithm is compared with that of the FFT algorithm, the Candan algorithm [19], the A&M algorithm [20], the QSE algorithm [21], the CZT Candan algorithm [22], and the CZT-based algorithm [23]. In simulations, to ensure that the various algorithms are being compared under the same conditions, the initial values of the parameters of the various algorithms are kept consistent. The sampling rate and sampling points are, respectively, set to \(f_{s}=92\),783 Hz and \(N=1024\). The parameters \(q\) and \(M\) are the key parameters of the basic CZT algorithm and the proposed improved CZT algorithm. The parameter \(q\) determines the frequency band of the CZT algorithm, and \(M\) represents the number of points of the CZT algorithm. The smaller the \(q\) and the bigger the \(M\), the more accurate the frequency estimation. The value of \(q\) is determined by the required frequency resolution. In order to verify the effect of \(q\) on the frequency resolution, the frequency estimates of the proposed method versus various values of \(q\) are provided in Figure 1. As can be seen from Figure 1, the smaller the \(q\), the more accurate the frequency estimation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Computational Complexity** \\ \hline FFT & \(O(N\log N)\) \\ Candan & \(O(N\log N)\) \\ A\&M algorithm & \(O(N\log N)\) \\ QSE algorithm & \(O(N\log N)\) \\ CZT-based algorithm & \(O(PN\log N+PM\log N)\) \\ CZT Candan & \(O(N\log N+M\log N)\) \\ Proposed improved CZT & \(O(N\log N+M\log N)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The computational complexity of various methods.  
In order to keep the same conditions for the methods based on the CZT algorithm in later simulations and the experimental analysis, the parameters \(q\) and \(M\) are set to \(q=1\) and \(M=32\). In addition, all results are obtained over 5000 independent Monte Carlo trials.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 6 Conclusions  
In this work, we have investigated the joint optimization of the active and passive beamforming vectors for a single cellular network assisted by multiple IRSs, intending to minimize the transmit power of the BS. Based on the ML approaches, a new GRNN-based algorithm was developed for the static UE scenario, and a DDPG-based algorithm was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach\(N\) 10** & **12** & **14** & **16** & **18** & **20** \\ \hline
**AO** & 8.5039 & 9.2905 & 11.0527 & 13.2385 & 15.3093 & 19.8383 \\
**AO Heuristic** & 5.5492 & 6.5612 & 6.5886 & 8.1265 & 9.9036 & 12.7005 \\
**DDPG-Based** & 0.0092 & 0.0094 & 0.0095 & 0.0096 & 0.0097 & 0.0098 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average prediction run time (seconds) for different numbers of reflecting elements in dynamic UE case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach\(SINR\)** & \(-\)**10 dB** & **0 dB** & **10 dB** \\ \hline
**AO Heuristic** & 10.6562 & 10.9562 & 12.9007 \\
**AG RNN-Based** & 3.8671 & 3.9377 & 4.5948 \\
**GRNN-Based** & 0.2237 & 0.2366 & 0.2392 \\
**DDPG-Based** & 0.0091 & 0.0092 & 0.0092 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average prediction run time (seconds) for different SINR thresholds in static UE scenario.  
proposed for the dynamic UE scenario. It was demonstrated that the proposed approaches could conduct real-time beamforming predictions in various channel conditions while maintaining performance according to measured criteria. The hyper-parameters of both models were fine-tuned to provide robust stability and convergence and minimize overfitting issues in real-time predictions. Furthermore, the presented algorithms surpassed the traditional alternating optimization techniques in terms of time complexity and optimized transmit power. Note that we restrict our study to a single-cell scenario in this work. In future work, we can study the large-scale system in a multiple-cell scenario. Moreover, imperfect CSI and advanced IRS technologies such as STAR-RIS can also be considered.  
Conceptualization, Z.F.; Software, M.F.; Validation, M.S.A.; Formal analysis, M.F. and J.G.; Investigation, M.F.; Data curation, J.G.; Writing--original draft, M.F.; Writing--review and editing, J.G.; Visualization, M.S.A.; Supervision, Z.F.; Project administration, J.G.; Funding acquisition, Z.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China under Grant 62001029 and in part by Beijing Natural Science Foundation under Grant L202015.  
The authors declare no conflict of interest.