esa-sceva/satcom-chunk-collection huggingface dataset:
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 3 Results
### Analysis of Surfaces  
The Trimble Realworks(r) ver. 11.3. software was used for the twin surface comparison as a coloured mesh with differential scale (Figure 8). The comparison expresses the compliance rate of the compared surfaces.  
Regarding the large number of evaluated points, the compliance differences of the compared surface residuals were evaluated graphically as a frequency histogram, where on the \(X\) axis, the intervals of residuals are displayed, and on the \(Y\) axis, the frequency of residuals in intervals from \(-0.2\) m to \(+0.2\) m are displayed (Figure 9). The single bin size was set to \(5\) mm; the number of intervals was therefore \(80\). The values of residuals of TLS vs. AP (Figure 9a,c) were determined as processed for two separate heaps. Then, as a result, the correlation coefficient \(0.96\) was calculated. For TLS vs. GNSS values of residuals (Figure 9b,d), the correlation coefficient value was \(0.75\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **No. of Points** & **No. of Triangles** & **Density of Points** & **Average Distance** \\ \hline GNSS RTK & 534 & 1 034 & 0.1/m\({}^{2}\) & 3 m \\ \hline TLS & 449 404 & 898 551 & 196/m\({}^{2}\) & 0.07 m \\ \hline AP & 446 629 & 891 591 & 100/m\({}^{2}\) & 0.10 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of point clouds: Heap No. 2.  
Figure 8: Coloured mesh twin surface comparison.: (**a**) TLS vs. AP—Heap No. 1; (**b**) TLS vs. GNSS—Heap No. 1; (**c**) TLS vs. AP—Heap No. 2; (**d**) TLS vs. GNSS—Heap No. 2.  
Tables 3 and 4 show the percentage frequency of points at intervals up to \(\pm\)20 mm, \(\pm\)40 mm, \(\pm\)60 mm, and \(\pm\)80 mm based on residual differences of surfaces obtained by the selected surveying methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Heap 1** & \multicolumn{3}{c}{**Frequency of Residuals for Compared Surfaces in Intervals**} \\ \hline  & up to \(\pm\)20 mm & up to \(\pm\)40 mm & up to \(\pm\)60 mm & up to \(\pm\)80 mm \\ \hline TLS vs. AP & 53\% & 73\% & 78\% & 92\% \\ \hline TLS vs. GNSS & 18\% & 30\% & 53\% & 72\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface differences evaluation—Heap No. 1.  
Figure 9: Histogram of frequency of surface differences of residuals in intervals. (**a**) TLS vs. AP—Heap No. 1, (**b**) TLS vs. GNSS—Heap No. 1, (**c**) TLS vs. AP—Heap No. 2, (**d**) TLS vs. GNSS—Heap No. 2.  
Figure 10: _Cont._  
Figure 10: Graphical evaluation of the surface differences in cross-sections without removal of peak values. **(a)** TLS vs. AP—Heap No. 1, **(b)** TLS vs. GNSS—Heap No. 1, **(c)** TLS vs. AP—Heap No. 2, **(d)** TLS vs. GNSS—Heap No. 2.
## 4 Discussion
### Changes in Skin Microbiome: Phylum  
[PERSON] et al. reported that Proteobacteriaxia, Actinobacteria, Firmicutes, Bacteroidetes, Cyanobacteria, and Acidobacteria are the predominant skin microbiome [28], and the same was observed in the present study. In addition, the phylum Planctomycetota, Bacteroidetes, and Verrucomicrobiota was detected in the control samples, and Acidobacteria, Bacteroidota, Planctomycetota, Verrucomicrobiota, Desulfobacterota, Myxococcota, Chloroflexi, Armatimonadota, Gemmatimonadota, Spirochaetota, Patescibacteria, and Nitrospirota was identified in the treatment samples. The RF values showed the differences in phylum between the control and treatment samples. However, according to the power analysis (two-sample paired means test), statistically, the phylum level changes are not considered significant (Figures 3A and 4A), possibly due to the smaller sample size.  
[PERSON] et al. observed that the abundance of Proteobacteriaxia was higher in older women (54 to 69 years old) compared to younger women (21 to 31 years old) on their skin [29]. Compared to healthy skin, firmicutes are predominant in psoriatic lesions [30; 31]. The abundance of Proteobacteriaxia was less in Tpost samples and higher in Cpost samples compared to the respective pre-samples. The abundance of Firmicutes was increased in the Cpost samples, and its abundance was controlled in Tpost samples (Figures 3A and 4A).  
[PERSON] et al. reported that when the skin is exposed to UV-A and UV-B, the skin's microbiome might alter, which increases the phylum Cyanobacteria level on the skin [32]. The use of APM reduced the Cyanobacteria compared to the baseline values, indicating that APM might protect the skin against UV (Figure 4A).  
Planctomycetes have been reported to produce antibiotics, recognized for their biomedical applications [33]. The abundance of Planctomycetes was increased after APM usage (Figure 4A), indicating that the prepared APM might help develop natural skin antibiotics to prevent other microbial growth.  
Chloroflexi was found on the skin of the dominant hand of healthy Chinese people [34]. Chloroflexi load was increased in the Tpost samples (RF = 7.24%), indicating that APM could improve normal skin microbial flora (Figure 4A).  
The abundance of Verrucomicobia was higher in Tpost compared to Cpost samples (Figures 3A and 4A). A lower abundance of Verrucomicrobia was reported in the psoriatic patients [35]. Appropriately, the result of the present study suggested that the APM might prevent the formation of skin lesions.  
[PERSON] et al. reported that Gemmatimonadetes, Planctomycetes, and Nitrospirae are more prevalent phylum in healthy teenagers [1]. The phylum Gemmatimonadota and Nitrospirota are newly developed in the Tpost samples (Figure 4A and Table S3), which indicates that the APM could improve the healthy skin microbiome.  
Some of the predicted phylum Desulfobacterota, Myxococcota, Armatimonadota, Spirochaetota, and Patescibacteria and their role in skin health and diseases are not elucidated.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 4 Results and Discussion
### Unlinkability
#### 4.2.2 Dependent Cases
However, MEC could delay and reorder messages to hide which messages from the MNO side relate to the messages on the MEC application side and vice versa. Therefore, we can argue that the solution could be enhanced to partially meet R2, R3, R11, R12, R13, and R14 in this case. In summary, our solution fulfills the applicable privacy requirements for the MNO and MEC combination in the same way as for the case where MNO, MEC, and APPIFY are independent. Additionally, in the case where MEC and APPIFY share information, the requirements are fulfilled similarly to the independent case, except for one requirement, which is still partially fulfilled in the dependent case. However, when MNO and APPIFY share information, our solution does not fulfill six of the privacy requirements unless the solution is enhanced for the MEC host. Sharing information between honest-but-curious parties increases their power to attack the user privacy while remaining honest but curious. Thus, a protocol that fulfills a set of privacy requirements when the parties are independent does not necessarily fulfill them in the dependent cases.
# The Interaction of Fitness and Fatigue on Performance in Football
## 5 Results  
A total of forty matches were included in the analyses. An average of 17 \(\pm\) 12 observations per player were recorded. The mean and standard deviation of the physical and tactical parameters recorded during the first half and second half of the matches are presented in Table 1. \begin{table}
\begin{tabular}{c c c} \hline Physical Parameters & First Half & Second Half \\ \hline TD [m] & 5260 \(\pm\) 505 & 4643 \(\pm\) 1007 \\ D14.4 [m] & 1093 \(\pm\) 299 & 880 \(\pm\) 290 \\ D19.8 [m] & 319 \(\pm\) 188 & 248 \(\pm\) 107 \\ D25.2 [m] & 63 \(\pm\) 46 & 45 \(\pm\) 40 \\ MW [cnt] & 34 \(\pm\) 9 & 28 \(\pm\) 10 \\ \hline Tactical Parameters & & \\ \hline FT (\%) & 58 \(\pm\) 17 & 57 \(\pm\) 22 \\ PPDA (a.u.) & 13.7 \(\pm\) 7.5 & 12.2 \(\pm\) 7.9 \\ TDO (\%) & 59 \(\pm\) 14 & 56 \(\pm\) 19 \\ xT (a.u.) & 0.51 \(\pm\) 0.23 & 0.58 \(\pm\) 0.26 \\ \hline \end{tabular} TD [m] = total distance; D14.4 [m] = distance covered above 14.4 km/h; D19.8 [m] = distance covered above 19.8 km/h; D25.2 [m] = distance covered above 25.2 km/h; MW [cnt] = sum of accelerations above 3.5 m/s\({}^{2}\) and below \(-\)3.5 m/s\({}^{2}\); FT (\%) = percentage of field tilt; PDDA (a.u.) = passes per defensive actions; TDO (\%) = percentage of territorial domination; xT (a.u.) = expected threat.  
\end{table} 
Table 1: Mean and standard deviation of the physical and tactical parameters collected in the first and second half of the matches. The match-by-match percentage distribution of players across the three performance conditions over the two seasons is presented in Figure 2. The linear mixed model analysis revealed a statistically significant higher z-25.21 stHalf in the intermediate performance condition compared to the poor performance condition (\(p<0.05\), ES = 0.32). No significant differences were observed for z-TD\({}_{\rm 1 stHalf}\), z-D14.4\({}_{\rm 1 stHalf}\), z-19.8\({}_{\rm 1 stHalf}\), and z-MW\({}_{\rm 1 stHalf}\) across the performance groups. Analyzing the second half, z-TD\({}_{\rm 2 ndHalf}\) (optimal vs. intermediate performance condition [\(p<0.05\), ES = 0.29]), z-D14.4\({}_{\rm 2 ndHalf}\) (optimal vs. intermediate performance condition [\(p<0.05\), ES = 0.25]), optimal vs. poor performance condition [\(p<0.01\), ES = 0.52]), and z-MW\({}_{\rm 2 ndHalf}\) (optimal vs. poor performance condition [\(p<0.05\), ES = 0.43]) showed significant differences, with the highest values registered in the optimal performance condition group. All the results are presented in Figure 3. The Pearson product-moment correlation analysis identified a significant relationship between the PSteam and %FT\({}_{\rm 2 ndHalf}\) (\(p<0.05\), r = 0.396), PDPA\({}_{\rm 2 ndHalf}\) (\(p<0.05\), r = \(-0.375\)), and %TDO2 ndHalf (\(p<0.01\), r = 0.431). The correlation results are shown in Figure 4.  
Figure 3: Conf. Figure 3: Box plots with kernel density estimation for the physical parameters’ variation (first half (a) and second half (b)) in relation to the three different performance groups, 4 denotes sig. difference vs poor performance condition: \"denotes sig.
## 2 Dimensional Analysis of Cutterhead Rotation Speed
### Variable Selection  
In TBM excavation data, key operational and mechanical factors influencing cutterhead rotation speed (\(\omega\)) include the cutterhead diameter (\(D\)), conveyor speed (\(Vc\)), cutterhead torque (\(T\)), advance speed (\(v\)), total thrust (\(F\)), gripper force (\(Fs\)), and penetration depth (\(P\)). The dimensional expressions of these parameters are summarized in Table 1.  
The cutterhead rotation speed can be expressed as follows:  
\[\omega=f(D,V_{c},T,v,F_{s},p) \tag{1}\]  
Organize the parameters into a column vector \(\mathbf{q}\), where:  
\[\mathbf{q}=[\omega,D,V_{c},T,v,F_{s},P]^{\mathrm{T}} \tag{2}\]  
This can be reformulated into a vector form as:  
\[\mathbf{q}=f(\omega,D,V_{c},T,v,F_{s},p) \tag{3}\]  
Equation (3) can be expressed through the dimensional matrix \(\mathbf{\Omega}\) as follows:  
\[\mathbf{\Omega}=\begin{bmatrix}1&0&2&1&1&1&1&1\\ 0&0&1&0&1&1&0&0\\ -1&-1&-2&-1&-2&-2&0&0\end{bmatrix} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Parameter & \(\omega\) & \(D\) & \(Vc\) & \(T\) & \(v\) & \(F\) & \(F_{S}\) & \(P\) \\ \hline Dimension & [T\({}^{-1}\)] & [L] & [LT\({}^{-1}\)] & [ML\({}^{2}\)T\({}^{-2}\)] & [LT\({}^{-1}\)] & [MLT\({}^{-2}\)] & [MT\({}^{-2}\)] & [L] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensional Expressions of Parameters.  
Figure 1: Dimensional analysis modeling workflow.  
In this paper, \(\Omega\) represents the parameter dimension matrix, which is used to describe the dimensional decomposition of each parameter in terms of the basic physical quantities (length [L], mass [M], time [T]). Each column of the matrix corresponds to a parameter, such as \(\omega\), \(D\), \(Vc\), \(T\), etc., while each row represents the exponent of the respective parameter in the length, mass, and time dimensions. Since the rank of dimensional matrix \(\Omega\) is 3, three independent variables must be selected as base variables to perform dimensionless analysis on the remaining parameters [30]. In this study, the selection of base variables considers their critical influence on the target variable, dimensional simplicity, and mutual independence [31]. Advance thrust (\(F\)), cutterhead diameter (\(D\)), and advance speed (\(v\)) were selected as the base variables. These variables not only directly impact TBM performance but also minimize potential blind spots in data analysis. The cutterhead diameter (\(D\)) represents a fundamental structural parameter with a simple dimensional property, facilitating the extraction of relevant features for rotation speed prediction through dimensional analysis. The advance speed (\(v\)), as a key operational parameter in tunneling, is directly correlated with cutterhead rotation speed, making it essential for constructing models that reflect actual operational performance. Selecting \(F\), \(D\), and \(v\) adheres to the principles of dimensional analysis while enhancing the interpretability and predictive capability of the model. This selection ensures the development of a robust and physically meaningful model for cutterhead rotation speed prediction.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 3 Field Blasting Vibration Monitoring and Analysis
To enhance the theoretical understanding of blasting vibrations, this study includes a detailed analysis of vibration frequencies and waveform propagation. Specifically, we examined the potential resonance risks between the blasting-induced vibration frequencies (40-80 Hz) and the natural frequencies of structural components (50-200 Hz). If there is overlap between the blasting vibration frequencies and the natural resonance frequencies of the tunnel structure, resonance may occur. Resonance could be a concern, particularly if the lower end of the blasting vibration range (40-50 Hz) coincides with the natural frequencies of the structure. The natural frequency range of concrete tunnel structures typically spans from 50 Hz to 200 Hz, depending on various factors such as the structure's design and materials. Therefore, while the majority of the recorded blasting vibrations (40-80 Hz) fall within a safe range, any potential overlap with the structure's resonance frequencies should be carefully considered. Additionally, vibrations above 100 Hz, which are often seismoacoustic in nature, require proper filtering to isolate the relevant frequencies that could affect structural integrity. In contrast, vehicle-induced vibrations typically range from 0.5 to 25 Hz, which are lower than the frequencies generated by blasting, making resonance with these vibrations unlikely. The placement of multiple monitoring points along the diaphragm wall and central pillar provides a unique dataset to analyze vibration attenuation with increasing distance from the blast source. This study reveals a clear diminishing trend in PPV values, confirming the inverse relationship between vibration impact and distance. Additionally, a comparative analysis of structural responses demonstrates significant differences in how the diaphragm wall, central pillar, and roof respond to identical vibration sources. This finding fills a gap in the existing literature, offering new insights into the spatial dynamics of blast-induced vibrations. A statistical analysis of the vibration frequencies recorded by the vibrometer (Figure 11) indicates that around 95% of the data fall within the 40-80 Hz range. While these frequencies generally fall within a safe range for typical tunnel structures, resonance risks should be evaluated based on each structural component's individual resonance frequencies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monitoring** & **PPV of Numerical** & **PPV of Field** & **Error** \\
**Points** & **Simulation (cm/s)** & **Monitoring (cm/s)** & **Rate (\%)** \\ \hline No. 1 & 1.656 & 1.424 & 14 \\ No. 2 & 1.504 & 1.375 & 8.5 \\ No. 3 & 1.450 & 1.213 & 11.8 \\ No. 4 & 1.385 & 1.236 & 10.7 \\ No. 5 & 1.160 & 1.087 & 6 \\ No. 6 & 1.763 & 1.482 & 16 \\ No. 7 & 1.610 & 1.362 & 15.4 \\ No. 8 & 1.428 & 1.271 & 14 \\ No. 9 & 1.336 & 1.158 & 13 \\ No. 10 & 1.181 & 1.074 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: On-site measurement.
## 4 Discussion and Conclusions
### Conclusions  
This study proposes a land cover classification method for accurately mapping the spatial distribution of forest resources in Czech. The proposed method fully utilizes 3D convolutional neural networks (3 DCNN) and ensemble learning, taking into account feature selection. Initially, the method extracts multidimensional features from remote sensing images, including spectral, texture, morphological, and topographical features. After feature selection, we employ a 3 DCNN model to extract deeper semantic information, thereby improving the discrimination of different land cover categories in the imagery. Finally, an AdaBoost-based ensemble learning model is constructed, utilizing the extracted rich features of surface objects as input to achieve the ultimate land cover classification. Two typical regions in Czech are selected as experimental areas for testing the proposed method. This process produces the land classification map in 2018 and obtains the spatial distribution of the Czech forest resources. This study indicates that the utilization of deep neural network models enhances feature extraction capabilities, while ensemble classification methods improve classification results to some extent. This study can serve as a valuable reference for the application of deep learning methods, especially in land cover classification research that incorporates feature selection.  
The main conclusions of this study are as follows:  
* Feature optimization is particularly crucial for deep learning classification. Our method demonstrates that texture and morphological features enhance remote sensing image classification. These features excel at identifying texture and shape relationships among pixels, thus enhancing image classification within deep learning frameworks. Neural networks excel at extracting features.
* In multi-band remote sensing image classification methods, random forest outperforms support vector machine and naive Bayes. Random forest inherently handles multi-feature input classification well, resulting in superior classification outcomes in this study compared to support vector machines and significantly outperforming naive Bayesian methods.
* Ensemble learning methods improve the classification of remote sensing images. In two experimental areas, there were varying degrees of improvement. This improvement is attributed to AdaBoost's automatic adjustment of results in each round, enhancing classification accuracy.  
Conceptualization, C.W. and T.H.; methodology, C.W. and Q.Z.; software, C.W., C.Z. and T.H.; validation, C.W. and C.Z.; investigation, C.Z. and T.H.; resources, C.W.; data curation, C.W. and T.H.; writing--original draft preparation, C.W. and T.H.; writing--review and editing, C.Z. and Q.Z.; visualization, C.W.; supervision, C.W. and Q.Z.; project administration, Q.Z.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 4 Security Analysis
### Anonymity  
The user obtains multi-level identity pseudonyms \(ID_{1},ID_{2},\ldots,ID_{k-1}\) and \(ID_{A}\), along with the communication identifiers \(ID_{B}\) from the VKGC, and flexibly combines the private key components \(ds_{Bi}\) corresponding to \(ID_{B}\) for signing. The first-level VKGC generates the identity pseudonym \(ID_{1}=H(U||salt||timestamp)\) for the user. Subsequent VKGCs generate multi-level identity pseudonyms and communication pseudonyms in the same manner. Considering that the length of the pseudonym chain is \(k\), and the final VKGC additionally generates \(ID_{B}\) for the user, an attacker wishing to deduce the user's real identity from \(ID_{B}\) would need to reverse \(k+1\) one-way hashes. Assuming each VKGC has a leakage probability of \(p\), and the user preferentially requests identity pseudonyms from VKGCs with which they have not interacted, the probability of the user's real identity being leaked is:  
\[P(leakage)=\begin{cases}1/p^{k},\ k\leq j\\ 1/p^{j},\ k>j\end{cases}\]  
In this mechanism, each level of VKGC only holds the relationship between the pseudonyms at levels 1 to 2. Unless all VKGCs collude, no single VKGC can obtain the user's real identity. Therefore, only the user knows the association between \(ID_{B}\) and their real identity \(U\), ensuring complete anonymity.
## 3 Radius of Plastic Zone and Peripheral Displacement
### Effect of Support Resistance  
The effect of supporting, is equivalent to exerting a resistance around the tunnel to prevent the deformation of rock mass. The result is that the support resistance changes the stress state of the rock mass which inevitably accompanies the development of deformation. The variation curve of the radius of the plastic zone with support resistance (tunnel radius = 4 m, internal friction angle = 23.5\({}^{\circ}\), cohesion = 0.2 MPa) is shown in Figure 3a. It can be seen that radius of the plastic zone decreases with the increase of support resistance. For example, when the tunnel burial depth is 300 m, the radius of the plastic zone of the tunnel is 20.7 m at support resistance of 0.3 MPa, while at support resistance of 0.8 MPa it decreases to 9.7 m.  
The variation curve of peripheral displacement with cohesion of rock mass (tunnel radius = 4 m, internal friction angle = 23.5\({}^{\circ}\), cohesion = 0.2 MPa) is shown in Figure 3b. It can be seen from Figure 3b that the peripheral displacement decreases sharply with the increase of support resistance. For example, when the tunnel burial depth is 300 m, the peripheral displacement of the tunnel is 268.9 mm without support, while with support resistance of 0.8 MPa it decreases to 58.8 mm. It can be seen that the influence of support resistance on the radius of the plastic zone and surrounding displacement is also great, especially in soft rock tunnel. In soft rock tunnel construction, reasonable supporting means are the main measure to control the deformation of soft rock.  
Figure 2: (**a**) Variation curve of radius of plastic zone with rock mass strength; (**b**) Variation curve of peripheral displacement with rock mass strength.  
The above theoretical analysis of tunnel mechanics reveals the stress and deformation law of rock mass in the process of excavation and support of soft rock tunnel in a two-dimensional state, and shows the influence of tunnel burial depth (initial stress field), rock mass strength (\(c\), \(\varphi\)) and support resistance on the plastic zone and peripheral displacement after excavation and support of the soft rock tunnel. However, the rock mass of soft rock tunnel has obvious expansibility and rheological characteristics, and the deformation of rock mass will continue to change with time; thus, tunnel construction is a process of intersecting space and time effects, which are manifested in the space-time effect. Therefore, it is necessary to further analyze the excavation and support of soft rock tunnel according to the space-time effect.