esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ensemble of Convolutional Neural Networks for Audio Classification
## 4 Data Augmentation Methods
### Short Spectrogram Augmentation (SSpA)  
Short spectrogram augmentation (see samples in Figure 3): works directly on spectrograms and generates five transformed versions of each original  
1. applySpectrogramRandomShifts randomly applies pitch shift and time shift;
2. VocalTractLengthNormalization (apply VTLN) creates a new image by applying VTLP [29], which divides a given spectrogram into 10 unique temporal slices. Once so divided, each slice passes through the following transformation: \[G(f)=\left\{\begin{array}{ll}\alpha f&,\ 0\leq f<f_{0}\\ \frac{f_{max}-f_{0}}{f_{max}-f_{0}}(f-f_{0})+\alpha f_{0}&,\ f_{0}\leq f\leq f _{max}\,\end{array}\right.\] (7) where \(f_{0}\), \(f_{max}\) are the basic and maximum frequency, and \(\alpha\in[a,b]\) is randomly chosen. In this study, \(a\) and \(b\) are set to 0.9 and 1.1, respectively;
3. applyRandTimeShift does what its name indicates by randomly picking the shift value T in \([1,\text{M}]\), where M is the horizontal size of the input spectrogram. A given spectrogram is cut into two different images: S\({}_{1}\) and S\({}_{2}\), the first taken before and second after time T. The new image is generated by inverting the order of S\({}_{1}\) and S\({}_{2}\);
4. applyRandomImageWarp creates a new image by applying thin-spline image warping (TPS-Warp) [50] to a given spectrogram. TPS-Warp is a perturbation method applied to the original image by randomly changing the position of a subset S of the input pixels. It adapts pixels that do not belong to S via linear interpolation. In this study, the spectrogram is changed on the horizontal axis only. Also, a frequency-time mask is applied by setting to zero the values of two rows and one column of the spectrogram. In this study, the width of the rows is set to 5 pixels and the width of the column to 15 pixels;  
Figure 3: Spectrogram augmented samples (SSpa and SuSA). The horizontal axis in each image represents time, and the vertical axis represents frequency.  
applyNoiseS applies pointwise random noise to spectrograms. The value of a pixel is multiplied by a uniform random variable of average one and variance one, with a probability 0.3
## 4 Conclusions  
The conducted studies show the possibility of combined using of FS and LDF methods in minimally invasive surgery of abdominal organs. The obtained preliminary data demonstrated statistically significant differences in fluorescence intensity maxima that proves the sensitivity of proposed approach to the properties of the area of interest as well as presence of pathological changes. Moreover, the differences were observed more clearly for excitation wavelength of 450 nm (\(1.43\pm 0.61\) a.u. for common bile duct and 0.93 \(\pm\) 0.49 a.u. for gallbladder; 0.63 \(\pm\) 0.39 a.u. for liver) rather than for excitation wavelength of 365 nm (0.90 \(\pm\) 0.36 a.u. for common bile duct and \(0.66\pm 0.34\) a.u. for gallbladder). The analysis of LDF signals showed similar results as well (\(16.82\pm 6.24\) p.u., 12.46 \(\pm\) 5.03 p.u. and 11.34 \(\pm\) 5.72 for common bile duct, gallbladder and liver, respectively). At the same time, it was concluded that further development of this topic require more detailed studies of variety of influencing factors. Therefore, it is planned to further optimize the technical part and develop new diagnostic criteria based on the results of further research. In total, suggested multimodal approach seems promising and can be used as instrumental method for controlling tissues and mucous membranes in abdominal organs.  
A.D. funding acquisition, supervision; A.M., A.D. conceptualization; E.P., E.Z., V.D. methodology, formal analysis; K.K., A.M., A.A. investigation; A.M., A.A., Y.I., A.B. resources; E.P., K.K. writing--original draft preparation; E.Z., V.D., A.D. writing--review and editing.  
This research was funded by the Russian Science Foundation under grant number 18-15-00201.  
Special thanks are extended to the patients of the Orel Regional Clinical Hospital, which kindly agreed to take part in the studies in the frame of taking their planned minimally invasive surgical intervention.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Study Area
### Geology and Hydrogeology
Map shows structural separation of the study area from the main rift valley (absence of faulting). Inset map shows the specific study area geology and the line of cross-section (A–B) from Figure 3.  
limited storage where present, however, the lateral extent of permeability remains unknown. Uneven bedrock surfaces create isolated sub-aquifer units which may become increasingly isolated during the dry season when groundwater levels are low. The aquifer exists under semi-confined conditions due to discontinuous clay layers at the base of the laterite layer. Boreholes drilled where there is clay often have resting water levels above the original water strike [34]. Recharge occurs at the base of the Nkhoma and Chilenje Hills (Figure 1), at alluvial fans, places where unaltered and fractured gneiss is exposed as gneiss mounds and at areas where topsoil/laterite is absent exposing saprolite [32] (Figure 3). Higher groundwater levels may facilitate increased sub-aquifer connectivity (flushing) during the wet season, down-hydraulic-gradient from the Nkhoma and Chilenje Hills in northwest and southeast directions, following decreasing altitude (Figure 3), however this is assumed to be minimal based on low hydraulic conductivities [33] and perpendicular flow barriers. Groundwater resource development is mainly boreholes accounting for 66% of water points (incl. surface water). Drilling may be with little geological or hydrogeological knowledge. Boreholes are drilled to an average depth of 47 m (range: 34-66 m) and cover all lithologies. A smaller proportion of hand dug shallow wells (3-10 m depth) exists (19% of water points), 81% of which are protected at the surface by hand-pumps. The unprotected wells have open sections and are often covered simply by a piece of wood. There are also some natural springs (2% of all water points) at the base of the hills. There is a partially functional piped water supply network in the area (12%) where water is transported from gravity-fed systems and a reticulated borehole, via pipes to various kiosks. Piped supplies were not sampled in this study.
# Antibacterial Ceramics for Dental Applications
## 3 Results and Discussion
### Influence of Surface Properties on the Antibacterial Activity of Bioceramics
aureus_ (SA, NBRC 12732) was used as Gram-positive bacteria and _E. coli_ (EC, NBRC 3972) as Gram-negative bacteria. Bacteriophage Q8 (host bacteria: _E. coli_) was used as a non-enveloped virus to test the antiviral activity. Bacteriophage \(\Phi\)6 (host bacteria: _P. syringae_) was used as an enveloped virus. Figure 19 shows that YSZ possesses antibacterial activity against EC and SA. The order of antibacterial activity against EC was Y\({}_{2}\)O\({}_{3}\) > YSZ \(\gg\) ZrO\({}_{2}\) (Figure 19a). The order of antibacterial activity against SA was YSZ \(\approx\) Y\({}_{2}\)O\({}_{3}\) > ZrO\({}_{2}\) (Figure 19b). The antiviral activity of YSZ was observed against both Q8 and \(\Phi\)6. The order of antiviral activity against Q8 was Y\({}_{2}\)O\({}_{3}\) > YSZ \(\gg\) ZrO\({}_{2}\) (Figure 19c), and that against \(\Phi\)6 was Y\({}_{2}\)O\({}_{3}\) > YSZ \(\approx\) ZrO\({}_{2}\) (Figure 19d) [104]. Figure 19: Results of antiviral and antibacterial activity of samples: (**a**) _E. coli_, (**b**) _S. aureus_, (**c**) Q8 (non-developed), (**d**) \(\Phi\)6 (enveloped) [104]. Reprinted with permission from Elsevier. Figure 18: The schematic diagram of bacteria rupture on the microtextured surface [56]. Reprinted with permission from Elsevier. The adsorption of cations on bacterial and virus surfaces can be explained by the electrostatic interactions between the cations and the negatively charged bacteria and viruses. Y\({}_{2}\)O\({}_{3}\) exhibits a high affinity for phosphoric acid, a component of bacteria and viruses, resulting in its high antiviral and antibacterial activity due to strong interactions with phosphoric acid. Due to this interaction, Y\({}^{3+}\) can deform the lipid bilayer membrane by mimicking Ca\({}^{2+}\). The interaction of Zr\({}^{4+}\) with phosphate is weaker than that of Y\({}^{3+}\). The inactivation of the proteins resulting from these interactions disrupts bacterial growth [104]. The authors also attributed the death of bacteria and viruses to the denaturation of bacterial proteins. In addition, the surface shapes of the investigated powders could play a role in their antibacterial and antiviral activity [104]. Cytotoxicity was not observed in any sample. Further studies should be conducted to further investigate the antibacterial and antiviral activities of 3 YSZ powder and 3 YSZ ceramics to better understand the interaction between YSZ and various bacteria and viruses.
## 2 Basic Theories
### Gray Forecasting Theory  
From the original data, the GM finds the law of system change by comparing the similarities and differences between the data characteristics. Thus, the GM weakens the randomness of the original data and improves the prediction accuracy. The gray prediction model predicts the growth trend of the data by establishing a differential equation model from the original data [32]. These advantages make it suitable to predict the long-term RUL of the lithium-ion battery. The following is a brief introduction of the GM.  
Assuming the original data sequence can be defined as \(X^{(0)}=\Big{\{}x_{1}^{(0)},x_{2}^{(0)},\ \cdots,x_{n}^{(0)}\Big{\}}\). In terms of battery RUL prediction, \(X^{(0)}\) represents the sequence of the battery SOH value, and \(n\) is the number of training dataset or the length of the sequence. First of all, we should accumulate the original data to generate the gray sequence \(X^{(1)}=\left\{x_{1}^{(1)},x_{2}^{(1)},\cdots,x_{n}^{(1)}\right\}\), where \(x_{k}^{(1)}=\sum\limits_{i=1}^{k}x_{i}^{(0)}\), \(k=1,2,\cdots,n\). Then, we can establish a first-order differential equation based on the gray sequence \(X^{(1)}\) as follows:  
\[\frac{x_{t}^{(1)}}{\text{d}t}+ax_{t}^{(1)}=b, \tag{14}\]  
where \(a\) and \(b\) are the parameters of the gray model to be calculated. According to the discretized Equation (14), \(a\) and \(b\) can be calculated by the least square method:  
\[\left[\begin{array}{c}\hat{a}\\ \hat{b}\end{array}\right]=\left[B^{T}B\right]^{-1}B^{T}Y, \tag{15}\]  
\[B=\left[\begin{array}{ccc}-z_{2}^{(1)}&1\\ -z_{3}^{(1)}&1\\ \vdots&\vdots\\ -z_{n}^{(1)}&1\end{array}\right] \tag{16}\]  
\[z_{k}^{(1)}=\frac{1}{2}\left(x_{k}^{(1)}+x_{k-1}^{(1)}\right), \tag{17}\]  
\[Y=\left[x_{2}^{(0)},x_{3}^{(0)},\cdots,x_{n}^{(0)}\right]^{T}. \tag{18}\]  
Therefore, the sequence generation model can be derived by submitting the starting condition \(x_{1}^{(1)}=x_{1}^{(0)}\) into Equation (16), as follows:  
\[\hat{x}_{k}^{(1)}=\frac{\hat{b}}{\hat{a}}+\left(x_{1}^{(0)}-\frac{\hat{b}}{ \hat{a}}\right)\exp(-\hat{a}(k-1)). \tag{19}\]  
Under the starting condition \(\hat{x}_{1}^{(0)}=x_{1}^{(0)}\), the estimation model of the original sequence is as follows:  
\[\hat{x}_{k}^{(0)}=(1-\exp(\hat{a}))\left(x_{1}^{(0)}-\frac{\hat{b}}{\hat{a}} \right)\exp(-\hat{a}(k-1)),k=2,3,\cdots,n, \tag{20}\]  
Finally, if \(k>n\), we can get the prediction of the original sequence by Equations (19) and (20).
## 4 Initial Trajectory Generation
### B-Spline-Based Trajectory Generation
#### 4.7.2 Continuity  
B-spline-based trajectory generation offers several advantages over piece-wise-based trajectory generation [35; 37]. In piece-wise-based trajectory generation, the boundary constraints must be explicitly satisfied to ensure continuity. The smoothness of the trajectory depends solely on how the control points are formed. In contrast, B-spline-based trajectory generation can neglect boundary constraints because the entire trajectory can be treated as one segment. Moreover, as explained in Section 4.7.1, B-spline-based trajectories can be controlled locally without affecting the rest of the trajectory.
## 3 Algorithms
### Contour Parallel Algorithms
#### 3.2.3 Geodesic Spiral Around Seed-Point (Geo-Spiral)  
The core focus in this algorithm is the continuous curvature of a circular path which was studied in [35], and thus, is better understood than the rapid and irregular direction changes in more classical path planning methods. By selecting a single seed point and creating a spiral that continually increases with geodesic distance, the observed high curvatures when changing directions incorporated in the preceding algorithms are prevented. The big drawback hereby being the inability to completely fill the surface with fiber, taking a hard toll on the fiber area fraction. Nevertheless, this algorithm serves to establish a good control for further testing and experimentation, when checking the material quality, as the induced gaps and the curvature are expected to be minimal.  
The input to the algorithm is a point \(p_{send}\in\mathcal{T}\) on the surface mesh, which is to be selected by a user, the triangular surface mesh \(\mathcal{T}\), the spacing \(d\) and the travel height \(h_{r}\). The selected point \(p_{send}\) serves as the seed \(\gamma\) for calculating the geodesic distance function \(\phi\) via the heat method. As before, the isocontours \(c_{i}\) are extracted from \(\phi\). For this algorithm, the procedure is terminated once a multiplicity is detected, resulting in the advantage of no travel motions and discontinuities, while sacrificing fiber area fraction. To this end, only the \(c_{i}\) up to the first i are kept for which \(|\{s_{i,j}\}_{j=1,\ldots,m}|>1\). These path segments are then reindexed to have their starting point with minimal distance to the boundary. This way, once the first contour is discontinuous due to hitting the boundary, it can be rerouted at the intersection point, including the open contours with no multiplicity in the path. If there are such open contours they are continued to be traversed with reversing directions until no further connections can be made.
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 4 Performance Analysis for KPS  
### Satellite Visibility  
As one of the critical indicators for the system performance, we considered the number of visible satellites. To compare the average number of visible satellites on global coverage, the computations were performed for two different constellation combinations (KPS-only and KPS+GPS). The elevation  
Figure 4: The averaged orbit RMS errors in the along-track, cross-track, and radial directions for 24 GPS satellites.  
Figure 3: The averaged orbit root mean square (RMS) errors in the along-track, cross-track, and radial directions for 7 KPS satellites.  
cutoff angle of satellites is set to 10\({}^{\circ}\). The coordinate grids have a spatial resolution with 2.5\({}^{\circ}\) in latitude and 5\({}^{\circ}\) in longitude. Consequently, we calculate the average number of visible satellites at each grid point.  
Figure 5 shows the average number of visible satellites on a global scale for KPS-only, KPS-only (\(\geq\)4 SV), and KPS+GPS, respectively. As shown in Figure 5a, KPS satellites aim to provide its services in the Asia-Oceania region. The area that can receive all seven KPS satellites is located between 35\({}^{\circ}\)S and 35\({}^{\circ}\)N latitude and 90\({}^{\circ}\)E and 162\({}^{\circ}\)E longitude. In addition, the area plotted in Figure 5b can track more than four KPS satellites. The positioning service for KPS is available only in this area. The combination of KPS and GPS can utilize the signals of many satellites in the Asia-Oceania region. In such areas, the maximum number of visible satellites is 16. The minimum number of visible satellites on average is 10.  
Figure 5: The average number of visible satellites on a global scale for two different constellation combinations over 24 h (UTC) in doy 140, 2018. (**a**) KPS-only; (**b**) KPS-only (\(\geq\)4 SV); (**c**) KPS+GPS. The ground-track of KPS IGSO satellites is the analemma. The red crosses denote the ground-track of three KPS GEO satellites.
# Statistical Analysis and Modeling for Optical Networks
## 2 Literature Review on Statistical Analysis and Modeling for Networks
### Essence of Statistical Analysis and Modeling for Communication Networks  
In today's world, telecommunications networks play a fundamental role in both personal and business communications. With the rapid expansion of the Internet, telecommunications now support essential global interactions [1; 2; 3; 4]. The backbone of this connectivity is high-speed core networks, which are predominantly optical due to their unparalleled bandwidth capacity and ability to handle massive traffic loads [1; 2; 3; 4]. Long-distance optical communication systems have evolved into various forms, supporting core networks, high-speed metro and access networks, and inter-cloud connectivity. Given their critical role, optical networks must be systematically analyzed in terms of both structure and behavior. Statistical analysis and modeling of network parameters are essential for understanding network performance, traffic patterns, and fault prediction [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. These techniques reveal key behavioral insights and enable accurate estimations of network parameters, even when complete data is unavailable. By leveraging statistical methodologies, researchers and engineers can optimize network design, resource allocation, and efficiency, ensuring that optical networks continue to support the ever-growing demands of modern digital communication [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15].  
Dimensioning and cost estimation of optical networks are crucial for design, planning, and optimization [3; 4]. During the early planning stages, network upgrades, and performance optimization, accurate estimation of network parameters is essential for efficiency. However, in many cases, complete data is unavailable, and detailed estimations can be complex and time-consuming. Early-stage available parameters include coverage area and node count, but additional statistical insights, such as distribution patterns and first and second moments of network parameters, greatly enhance network planning and design [26; 27; 28; 29; 30]. These insights aid in determining modulation and demodulation schemes, compensation techniques, repeater and amplifier placements, and fiber length requirements. To achieve these goals, statistical models must be developed to accurately predict link length distributions and shortest path lengths [31; 32; 33]. In optical networks, fast and reliable statistical estimations are widely used due to their effectiveness in cases where gathering additional data is impractical. These estimations play a critical role in network planning, enabling efficient resource allocation, infrastructure development, and operational decision-making in modern optical communication systems.
# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 4 Experimental Conditions and Results
Since actors do not move across the camera's field of view, feet positions do not influence the results. Furthermore, the same accuracy is achieved for the 'lower-occlusion' database and the database without any occlusions. It is also important to notice that different shape descriptors are used; however, the size of action representation is similar. In some cases, the number of DFT coefficients is smaller than the number of images in an input sequence. Therefore, the proposed approach offers data reduction, both by representing each image by a single value and each action by a selected number of the absolute spectral domain coefficients. Figures 5-7 present confusion matrices for selected experiments which yielded the highest accuracy results. In each subgroup, there are 45 action sequences--five action classes, nine sequences in each class. Each figure contains two confusion matrices: the left one refers to 'actions performed in place' and the other one to 'actions with changing location of a silhouette'. Values in matrices correspond to the number of classifications, where correct classifications are on a grey background, and misclassifications are on a white background. Among the occluded actions,'skip' is most often confused with other classes. However, when there is no occlusion, this class is recognized correctly, but other action types are confused with'skip'. In the experiment with the 'lower-occlusion' database, besides'skip', jump in place ('pjump') was less accurate classified, with 4 false classifications out of 9 in total. Among actions without occlusion, jumping forward ('jump') is the least distinguishable class. Appendix A contains additional confusion matrices for the experimental results which are given in Tables 1 and 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Shape Descriptor** & \multicolumn{2}{c}{**Actions Performed in Place**} & \multicolumn{2}{c}{**Actions with Changing Location of a Silhouette**} \\  & **EU** & **C1** & **EU** & **C1** \\ \hline Lower-occlusion’ & Elongation & Elongation & ShorterMBR & ShorterMBR \\ database & 91.11\% (57) & 91.11\% (55) & 71.11\% (30) & 73.33\% (71) \\ \hline Upper-occlusion’ & Elongation & Elongation & Area & Rectangularity \\ database & 82.22\% (47) & 88.89\% (51) & 84.44\% (70) & 84.44\% (46) \\ \hline Database & ShorterMBR & Perimeter & Area & Area \\ without occlusion & 86.67\% (56) & 91.11\% (51) & 86.67\% (31) & 84.44\% (33) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the results obtained using databases with or without occlusions.