esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Microbial Diversity during the Fermentation of Passion Fruit Wine
#### 3.3.1 Analysis of Different Types of Nonvolatile Metabolites during Fermentation  
A total of 256 nonvolatile metabolites were detected in 18 samples from the CF and NF groups, including 59 sugars and derivatives, 50 organic acids, 26 amino acids, 21 fatty acids, 19 alcohols, 7 ketones, 10 phenols, 7 esters, 11 amines, 10 nucleotides and derivatives, and 36 others. Among them, the contents of sugars and derivatives and organic acids were the highest. The sugars and derivatives of CF and NF showed a decreasing trend. The organic acids in the CF first increased and then decreased, while those in the NF changed in an opposite manner to those in the CF. At the late stage of fermentation, the content of organic acids in the NF was slightly higher than that in the CF (Figure 4).  
Figure 3: Species distribution histogram in phylum and genus of bacterial structure. (**a**) Phylum level; (**b**) genus level.  
3.3.2. Screening of Key Nonvolatile Metabolites and Analysis of Metabolic Pathways during Fermentation  
To identify key differential metabolites during the fermentation of CF and NF, the established method of [PERSON] et al. [49] was used to screen the key differential metabolites during fermentation. Metabolites in CF and NF were analyzed by OPLS-DA, and key differential metabolites were screened according to VIP > 1 and \(p\) < 0.05. Three key differential metabolites were identified in CF (Figure 5a), including one sugar and derivative (digitoxose), one ester (D-erythnonolactone), and one other compound (noradrenaline). In total, 15 key differential metabolites were identified in NF (Figure 5b), including 5 sugars and derivatives (allose, xylitol), 3,6-anhydro-D-galactose, alpha-D-glucosamine 1-phosphate, gluconic lactone), 4 organic acids (glutaric acid, aconitic acid, citric acid, dl-p-hydroxyphenyllactic acid), 3 amino acids (N-carbamylglutamate, O-phosphoserine, citruline), 1 alcohol (2-amino-1-phenylethanol), and 2 others (hydroxyurea, trans-3,5-dimethoxy-4-hydroxycinnamaldehyde).  
Figure 4: Heatmap of different types of nonvolatile metabolites during fermentation.  
Figure 5: Venn diagram of nonvolatile differential metabolites during fermentation. (**a**) CF group; (**b**) NF group.  
The above 18 differential metabolites were annotated to 19 metabolic pathways with significant differences, among which the citrate cycle (TCA cycle) and glyoxylate and dicarboxylate metabolism were the main metabolic pathways, and the citrate cycle (TCA cycle) metabolic pathway was enriched significantly (Figure 6).
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 5 Evaluation
### Experimental Settings  
To effectively evaluate the QuoTa mechanism, we conducted simulation experiments on a laptop with an Intel(r) Core(tm) i7-12700H CPU, NVIDIA GeForce RTX 3060 Laptop GPU, and 16G RAM with WIN11 operating system and built a federated learning framework based on PyTorch deep learning framework.  
We use the CIFAR10 and FMNIST datasets and the ResNet18 and LeNet5 models to simulate the federated learning task. We cut the CIFAR10 and FMNIST training sets into 20 sub-datasets of 5*1000, 5*2000, 5*3000, and 5*4000 and 5*500, 5*1000, 5*1500, and 5*2000 to simulate 20 data owners in the federated system. Subsequently, label mislabeling is used to simulate malicious data owners, i.e., the data output labels in the dataset are maliciously labeled as other values. For the simulation of CPU frequency, we use the numerical values to represent the rated CPU frequency and the real CPU frequency of the data owner's device. For example, the rated CPU frequency and the real CPU frequency of a data owner can be represented as 5 GHz and 4.2 GHz, respectively. We use the mathematical expectation of the rated CPU frequency based a normal distribution of variance of 0.2 to simulate the CPU frequency fluctuation.  
For the time consumption calculation of the federated system, as the server's arithmetic power is stronger, the time consumption is negligible. Then, the time consumption of the federated system mainly depends on the slowest participating data owner, where the stronger the data owner's computing capability per unit (CPU frequency/data amount), the shorter the time consumption is. We use a linear function to map the relationship between the computing capability and the time consumption and then use the longest data owner's time consumption as the time consumption of training round. Additionally, we take 80 percent of a data owner's computing capability as the computing capability threshold.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
## 3 Analysis of Results
### Analysis of Experimental Results
#### 3.2.2 Stress Results Analysis  
The stress results for each test point are shown in Figure 20. Since the stress data are constantly changing during the test, the stress data are analyzed in the form of an image display, making the test results and analysis process more intuitive.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Maximum Displacement (mm)** & **Test Value** &
\begin{tabular}{c} **Finite Element** \\ **Analysis Value** \\ \end{tabular} & **Deviation** \\ \hline Large gear ring & 8.313 & 12.9531 & 4.6401 \\ Pinion & 11.215 & 15.0023 & 3.7873 \\ Gear displacement deviation & 2.902 & 2.0492 & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pinion gear and large gear ring displacement test values and finite element analysis values.  
Figure 19: Displacement waveform after secondary integration.  
As can be seen from Figure 20, under the 40 t load test condition, the stress curve of each measurement point shows a certain trend of change with the action of the crane. At the initial stage, the stress value of each measurement point fluctuates around \"0\". In the process of lifting the 40t load to the maximum amplitude, the stress data of each measurement point gradually increased, and the stress data stabilized after the load reached the maximum amplitude hovering. When the boom was retracted to the minimum amplitude and the load landed, the stress data at each measurement point gradually decreased from the stable state to approximately \"0\"; meanwhile, the stress state returned to the initial position. The trend of the whole stress curve was essentially in line with the door operation process.  
In the graph of the stress curve at each measurement point, when the boom is lifted to the maximum suspension (after 100 s), the structure stress value reaches the steady state. Comparing the steady state value of the stress test with the FEA results, as shown in Table 4, it can be found that the maximum deviation of the stress value corresponding to each measurement point is 11.3 MPa, which is within the acceptable range.  
According to the test results, the test gantry crane cylinder structure strength increased to meet the load, so its strength on the mesh of the large gear ring of the slewing bearing essentially has no effect. However, the problem of tooth breakage still exists. As a consequence, the relative displacement between the pinion and the large gear ring is the main reason for the bad meshing of the gears and the fatigue tooth breakage problem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Point** & **Test Value** & **Finite Element Analysis Value** & **Deviation** \\
**Number** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline J1 & \(-\)23.9 & \(-\)35.2 & 11.3 \\ J2 & \(+\)17.2 & \(+\)21.4 & 4.2 \\ K1 & \(-\)47.9 & \(-\)44.8 & 3.1 \\ K2 & \(+\)21.4 & \(+\)28.0 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Steady-state values of stress at maximum amplitude at each measurement point.  
Figure 20: Stress test results graph.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design
### Link Weight Calculation Using Multi-Attribute Decision Making
#### 4.2.1 Standardize Decision Attribute Parameter Values  
Establish a multi-attribute decision matrix for each of the \(n\) potential inter-satellite links of the satellite node, which can be represented as:  
\[D=\left\{\begin{array}{ccc}a_{1}&b_{1}&c_{1}\\ \vdots&\vdots&\vdots\\ a_{n}&b_{n}&c_{n}\end{array}\right\}. \tag{14}\]  
The meanings and measurements of different attributes are not the same. To facilitate the unified calculation of link weights and improve the accuracy of the calculation, it is necessary to eliminate the differences between data through standardization. The multi-attribute decision matrix can be standardized by Formula (6) to achieve this:  
\[r_{ij}=\frac{\frac{1}{x_{ij}}}{\underset{i\in N}{\max}\left(\frac{1}{x_{ij}} \right)}=\frac{\underset{i\in N}{\max}(x_{ij})}{x_{ij}}=\frac{x_{j}^{-}}{x_{ ij}}. \tag{15}\]  
Here, \(X_{ij}\) represents the value of the original decision matrix, and \(r\) represents the value of the standardized decision matrix after normalization. Formula (15) can solve the incommensurability problem among multiple attributes and obtain the normalized standard decision matrix \(R\). It can be represented as:  
\[R=\left[\begin{array}{ccc}r_{11}&\cdots&r_{1m}\\ \vdots&\ddots&\vdots\\ r_{n1}&\cdots&r_{nm}\end{array}\right]. \tag{16}\]
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 3 Results
### Cohesion
#### 3.2.2 Relationship Between Cohesion and Degree of Cementation  
Figure 6b illustrates the relationship between the cohesion (\(c\)) of fault gouge and the degree of cementation under varying water content conditions. Overall, as the degree of  
Figure 6: Cohesion relationship curves: (**a**) relationship between cohesion and water content; (**b**) relationship between cohesion and degree of cementation.  
cementation increases, the cohesion of the fault gouge exhibits an increasing trend across different water contents. However, for samples with water contents of 10%, 15%, and 20%, the cohesion exhibits a slight decrease when the degree of cementation increases from 0% to 1%, decreasing by 9.3%, 28.9%, and 15.6%, respectively. It then begins to increase with further increases in cementation degree. This occurs because at a cementation degree of 1%, the amount of cementing material within the samples is relatively low. Additionally, at lower water contents, the cementing materials are not fully wetted, and their cementing effect is not fully developed. The presence of unwetted cementing materials may even increase the overall rigidity of the samples, thereby reducing the cohesion [28].  
As the degree of cementation and water content continue to increase, the amount of cementing materials within the samples rises. The enhanced bonding effect of the cementing materials leads to an increase in cohesion. When the water content reaches 25% or 30%, the cohesion consistently increases with the degree of cementation, which indicates that the role of cementing materials becomes more significant under high-water-content conditions.
## 3 Results
### Histological and Morphological Changes in the Prostate  
In histological analysis, prostate tissues of NC rats showed normal cell morphology, whereas those of BPH rats exhibited extensive glandular hyperplasia, including multilayered epithelial cells, and a reduced glandular luminal area (Figure 5C). Such TP-induced Histological alterations were improved by treatment with finasteride and BenPros. Moreover, as shown in Figure 5D, BPH rats had significantly increased prostate epithelial thickness compared to rats in the NC group, but the finasteride or BenPros 400 mg/kg treated group markedly reduced this increase. The saw palmetto and BenPros 200 mg/kg groups showed only a slight decrease, but there was no significant difference. These results  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**Prostate Weights (g)**} & \multicolumn{2}{c}{**Body Weights (g)**} \\ \cline{3-4}  & & & **Initial** & **Final** \\ \hline NC & 1.40 \(\pm\) 0.14 & & 270.50 \(\pm\) 2.67 & 378.33 \(\pm\) 8.57 \\ BPH & 2.74 \(\pm\) 0.15 *** & & 270.83 \(\pm\) 2.99 & 340.50 \(\pm\) 8.61 ** \\ Finasteride (5 mg/kg) & 1.83 \(\pm\) 0.05 *** & 67\% & 273.17 \(\pm\) 4.21 & 345.00 \(\pm\) 4.33 * \\ BenPros (200 mg/kg) & 2.22 \(\pm\) 0.09 * & 38\% & 270.33 \(\pm\) 3.90 & 326.17 \(\pm\) 7.96 *** \\ BenPros (400 mg/kg) & 1.99 \(\pm\) 0.10 *** & 55\% & 270.33 \(\pm\) 2.63 & 333.67 \(\pm\) 5.51 ** \\ Saw palmetto (400 mg/kg) & 2.14 \(\pm\) 0.05 * & 44\% & 270.33 \(\pm\) 1.71 & 329.00 \(\pm\) 6.98 *** \\ \hline \end{tabular}  
* The data are presented as the mean \(\pm\) standard error of the mean of 6 rats per group.
* \(p\) < 0.05, *
* \(p\) < 0.01 and **
* \(p\) < 0.001 vs. NP; \(\#\)\(p\) < 0.05, *
* \(p\) < 0.01 and **
* \(p\) < 0.001 vs. BPH.  
\end{table}
Table 1: Prostate growth inhibition ratio in each group.  
Figure 5: Histopathological analysis of BPH rats. (**A**) The changes in the rat prostate index. (**B**) Representative photomicrographs (ventral prostatic lobes, seminal vesicles, and bladder) of the dissected prostate. (**C**) Representative histological results based on hematoxylin and eosin staining in prostate tissues (magnification, \(\times\)200). (**D**) The epithelial thickness of prostate tissues. The data are presented as the mean \(\pm\) standard error of the mean of 6 rats per group. *** \(p\) < 0.001 vs. NC; \(\#\)\(p\) < 0.05, \(\#\)\(\#\)\(p\) < 0.01 and *** \(p\) < 0.001 vs. BPH.  
suggest that the decreased prostate weight by BenPros treatment was due to the alleviation of abnormal histological changes in the prostates of BPH mice.
## 3 System Overview
### Energy Model for MEC Servers  
The central processing units (CPUs) endeavors 80% of the electrical energy in edge servers compared to other resources [38]. Furthermore, when operating at full speed, idle CPUs account for 70% of the overall energy utilized by the server. As a result, an increase in CPU use is proportional to energy consumption [35]. We employ the energy model proposed in [39] as seen in Equations (8) and (9).  
\[P(u)=k\times P_{m}+(1-k)\times P_{m}\times u \tag{8}\]  
\[E_{s}=\int\limits_{t0}^{t1}P(u(t))dt \tag{9}\]  
where \(P(m)\) is the amount of energy that a server consumes when it is fully loaded, and \(P(u)\) is the estimated energy consumed by a server. The \"\(k\)\" represents the percentage of power drawn by an idle server, while the \"\(u\)\" represents the server's utilization at any given time. Being a benchmark value for modern servers, the value of \(P(m)\) is set to 250 joules per second in this scenario [40]. Due to the varying workload, CPU usage fluctuates with time, becoming a function of time denoted by \(u(t)\). Finally, we can compute a server's energy consumption \(E_{s}\) using Equation (9).
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Theoretical Insights and Practical Applications
#### 5.1.3 Collaborative Emergency Management and Communication Systems  
Cluster #3 explores blockchain integration with emerging technologies such as 6G, UAVs, and vehicular ad hoc networks. Theoretical contributions include multi-agent systems, decentralized governance models, and secure consensus mechanisms for disaster communication [1; 92; 93; 94; 95; 96; 98]. These frameworks demonstrate how blockchain can enhance coordination, accountability, and decision-making in complex emergency scenarios.  
In practice, blockchain ensures secure task assignment and data exchange between heterogeneous agents (e.g., drones, vehicles, and command centers) during crises [94; 95; 98].
## 4 Implementation
### BER Estimation for Optical OOK Modulation  
The electrical amplitude of the received signal in the receiver side of the communication system is calculated as follows:  
\[r(t)=I(t)+\sum_{i=-\infty}^{+\infty}I(t)\times a_{i}\times g(t-iT_{symbol})+n(t) \tag{4}\]  
where \(a_{i}\) is the level of the \(i\)th OOK symbol and \(a_{i}\)\(\in\) {0,1}. We assumed that the probabilities of bit 0 and 1 are \(P_{0}\) and \(P_{1}\), respectively, \(g(t)\) is rectangular function; and \(T_{symbol}\) is the symbol duration. Because of only additive white Gaussian noise (AWGN) in the channel model, the BER can be presented [26] in Equation (5).  
\[P_{e}=\frac{1}{2}\times erfc(\sqrt{\frac{E_{b}}{2\times\sigma_{n}^{2}}}) \tag{5}\]  
Figure 10: SNR measurement with different communication distances.  
Figure 9: SNR measurement with a Point Grey rolling shutter camera at a communication distance of 20 m with 100 μs.  
The received signal on the receiver side for bits 0 and 1 in the AWGN channel is as follows:  
\[r(t)=\left\{\begin{array}{l}n(t),a_{i}=0\\ 2\times I(t)+n(t),a_{i}=1\end{array}\right. \tag{6}\]  
Figure 11 presents the estimation results with different SNR values for the matched filter and zero-crossing filter, which is compared with the theoretical bit error probability of OOK modulation. The performance of our proposed scheme is better than the zero-crossing filter. Based on the information given in Figures 10 and 11, we can achieve a BER of \(10^{-4}\) at an SNR of 11 dB, with an exposure time of 100 us, and a communication distance of 20 m if the matched filter is used. We achieved a BER of \(10^{-4}\) at an SNR of 16 dB, which corresponds to a communication distance of 10 m with a 100-us exposure time. From this, it is evident that the performance of the OCC system can be improved by applying a matched filter. The exposure time can be adjusted to increase the SNR, but the exposure time needs to be carefully considered because a long exposure time can decrease the system bandwidth.
# A Three-Dimensional Elastoplastic Constitutive Model for Geomaterials
## 2 Geometric Representation of Stress Space  
The three-dimensional space defined by principal stresses \(\sigma_{1},\sigma_{2},\sigma_{3}\) is usually called Haigh-Westergaard stress space. Figure 1 provides a traditional failure surface for an isotope pressure-sensitive material. In geotechnical materials, the mechanical compression is usually considered to be positive. The stress point \(P\) in the Haigh-Westergaard stress space is shown in Figure 1. A line with the same angle as all the coordinate axes is called a hydrostatic line. The \(\pi\) plane is also called a deviatoric plane that is perpendicular to the hydrostatic line. Three parameters \((\xi,\rho,\theta)\) represent the stress point in the plane. The following are these parameters' expressions [33]:  
\[\xi=\frac{I_{1}}{\sqrt{3}} \tag{1}\]  
\[\rho=\sqrt{2I_{2}} \tag{2}\]  
\[\theta=\frac{1}{3}\cos^{-1}\left(\frac{3\sqrt{3}}{2}\frac{J_{3}}{I_{2}^{3/2}}\right) \tag{3}\]  
where \(I_{1}=\) first invariant of the stress tensor, \(I_{1}=\sigma_{ii}\); \(J_{2}=\) second invariant of the stress deviator tensor, \(J_{2}=s_{ij}\xi_{ij}/2\); \(J_{3}=\) third invariant of the stress deviator tensor, \(J_{3}=s_{ij}\xi_{jk}/3\); \(s_{ij}=\sigma_{ij}-\sigma_{ij}\delta_{ij}/3\); and \(\delta_{ij}=\) Kronecker delta. The stress point's location on the hydrostatic line is represented by the symbol \(\xi\), its distance from the line, by the symbol \(\rho\), and its angular direction by the symbol \(\theta\). The range of the Lode angle is \([0,\pi/3]\).  
Figure 1: _Cont._  
The principal stress and \(\xi_{r},\rho,\theta\) have the following relationship [16]:  
\[\left\{\begin{array}{c}\sigma_{1}\\ \sigma_{2}\\ \sigma_{3}\end{array}\right\}=\left\{\begin{array}{c}\frac{\xi_{r}}{\sqrt{3}} \\ \frac{\xi_{r}}{\sqrt{3}}\\ \frac{\xi_{r}}{\sqrt{3}}\end{array}\right\}+\sqrt{\frac{2}{3}}\rho\left\{ \begin{array}{c}\cos\theta\\ \cos(\theta-\frac{2}{3}\pi)\\ \cos(\theta+\frac{2}{3}\pi)\end{array}\right\} \tag{4}\]
# A Public Infrastructure for a Trusted Wireless World
## 2 Background
### Fifth-Generation Cellular Communications Technology (5G)  
Fifth-generation cellular communications technology is referred to as 5G. Fourth-generation cellular communications technology (4G) is still widely used, and in fact, many parts of the world are still using 3G and 2G. It will take some time before 5G is available universally [16], and 5G improves upon previous cellular technologies by improving a mix of existing access radio technologies combined with newer technologies such as utilizinga new spectrum band. These updates will allow 5G subscribers to access services from anywhere and anytime, with high data rates, low latency, and increased quality of amenities. There will be a new range of services available for the first time such as virtual reality, augmented reality, ultra-high definition, 3D videos, and autonomous driving [17].
## 4 Discussion
### Stability of the Results  
Some experts have suggested that PSLB treatments may be more stable due to the reduced force required for orthodontic movement. This may result in more natural tooth movements without overburdening the muscles or damaging the periodontal blood vessels [2; 3]. Maintaining arch form and preventing relapse are crucial aspects of orthodontic treatment. However, in non-extraction orthodontic treatment, increasing the arch perimeter leads to transverse expansion of the arches and incisor proclination. This approach is known to affect prolonged stability since changes in arch dimensions can significantly impact posttreatment results. Unstable outcomes may be produced by widening the inter-cuspid dimension and labially tilting the incisors.  
Recent studies evaluated the long-term effects of PSLBs on the transverse dimensions of the maxillary and mandibular arches. According to [PERSON] et al. (2014) [74], PSLBs can effectively correct crowding by proclining and protruding incisors and expanding dental arches, while maintaining stable transverse dimensions of the arches for up to two years after treatment without significant changes to the face's hard and soft tissues. [PERSON] et al. (2019) [75] reported significant increases in transverse arch dimensions and torque values following passive self-ligating fixed therapy, with a tendency towards transverse diameter restriction in the maxillary and mandibular premolars in the first 2 years after treatment, while torque values remained mostly unchanged. Lastly, a 6-year follow-up study by [PERSON] et al. (2022) [11] found that PSLB treatment resulted in significant increases in interplanar and intercanine width, with no significant relapse in expansion achieved with the therapy, except for upper and lower premolars. The study also observed that most of the relapse occurred in the first year after debonding and that the type of retainer could influence the amount and timing of relapse after PSLB treatment.  
Recent studies comparing PSLB to conventional and Damon systems [24; 57] found similar long-term stability of incisor position and maxillary arch transverse dimension changes, with some significant relapse in intercanine and interpremolar width but no relapse in incisor position. PSLB treatment resulted in significant increases in intermidar and intercanine width, with most of the relapse occurring in the first year after debonding and the type of retainer influencing the amount and timing of relapse after PSLB treatment.