esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Junctions and Methods
### Junctions
Figure 1b shows only the topography of the film surface, and a substrate level is required to obtain a cross-section. We fabricated thin-film bridges across the bicrystal interface using PMMA-based UV lithography and etching in 0.1% Br/ethanol. Figure 1c shows a topographic image AFM of a 5 um wide YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\mathrm{x}}\) bridge fabricated across the bicrystal boundary on a SrTiO\({}_{3}\) substrate. The combination of two profiles of a thin-film bridge, measured across the bicrystal boundary in the middle of the bridge and the bridge outside the bicrystal boundary, gives the cross-section \(z(x)\) in Figure 1d, where the level z = 0 is the substrate surface. The V-shaped surface profile of YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\mathrm{x}}\) near the bicrystal boundary has slopes at an angle of \(\pm 12^{\circ}\) to the substrate surface. Growth islands in both electrodes form terraces with a step height of \(5\div 20\) nm. One can see from Figure 1d that the average thickness of the YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\mathrm{x}}\) thin film, except for the bicrystal boundary, is (65 \(\pm\) 10) nm, and the film thickness at the boundary is about 20 nm. To increase the oxygen content in JJs after fabrication, these JJs were annealed in an ozone/oxygen mixture at 1 bar under UV irradiation at a temperature of 140 \({}^{\circ}\)C for 30 min [31]. The junction resistances \(R_{n}\) decrease approximately twice after this annealing, while the critical temperature \(T_{c0}\) of 87.5 K remains. Such low-temperature annealing can initiate diffusion of atomic oxygen through the bicrystal boundary but increases only the concentration of oxygen atoms in the Cu-O chains of YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\mathrm{x}}\) electrodes [32]. Thus, the collective modes containing oxygen in the chains in the YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\mathrm{x}}\) adjacent to the boundary are under selective doping. The interaction of Josephson oscillations with these modes in YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\mathrm{x}}\) electrodes reflects on the \(I\)-\(V\) curve, and Josephson admittance spectroscopy [33] can describe this modification.
## 6 Conclusions  
UVLC technology has seen a sharp increase in demand in recent years. This is mostly due to the fact that an increasing number of technological advancements are implementing their communication systems in underwater environments. The UVLC systems are affected by linear and nonlinear distortions, which are problematic. The outcome is that it makes the  
Figure 9: The computation cost of the proposed and existing techniques, namely APNN [33], TFCNN [34], DSANet [35] and CE-LSTM [36].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Computation Cost (\%)** \\ \hline APNN [33] & 86 \\ TFCNN [34] & 95 \\ DSANet [35] & 83 \\ CE-LSTM [36] & 79 \\ AFL-DLE [Proposed] & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of computation cost (%).  
system less effective. Since the UVLC system exhibits linear and nonlinear distortion, we proposed an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) to reduce both effects. The Enhanced Chaotic Sparrow Search Optimization Method (ECSSOA) is an algorithm that was created to boost system performance through optimization. The developed prototype technique considers the received signals' distortion and dispersion. Following that, it divides the constellation's signals into distinct segments, equalized by two differently sized sophisticated networks. The experimental results demonstrate that the recommended AFL-DLE was effective in reaching the bit error rate (55%) and distortion rate (45%), as well as reducing computational complexity (48%), improving transmission rate (99%), and lowering computation cost (75%). The recommended approach outperforms other methods, indicating that it is more effective when compared to them. Internet data processing can now be implemented in high-speed UVLC systems thanks to the AFL-DLE's effectiveness and viability in real-world applications. In a prospective study, a uniformly distributed constellation stochastic structuring method will be investigated to enhance the performance of UVLC systems further.  
Conceptualization, R.R., M.M. and M.E.; Data curation, S.P.; Formal analysis, R.R. and S.P.; Funding acquisition, M.M.; Investigation, R.R. and S.P.; Methodology, R.R., M.M. and M.E.; Software, R.R., M.M. and M.E.; Validation, R.R. and S.P.; Visualization, R.R.; Writing--original draft, R.R. and S.P.; Writing--review & editing, M.M. and M.E. All authors have read and agreed to the published version of the manuscript.  
This article was supported by the project SP2023/074 Application of Machine and Process Control Advanced Methods supported by the Ministry of Education, Youth, and Sports, Czech Republic.  
Not applicable.  
The authors state that they have no conflict of interest.
## 3 Results and Discussions
### Extraction of Metal Ions by JNPs  
JNP-bPEI 2 mL was employed in the extraction of metal ions: Cr (VI), Co(II), Ni(II), Cu(II) and Zn(II). The procedure consisted of adding 10 mg/mL JNP-bPEI to a metal ion solution with a concentration of 10 \(\upmu\)mol/mL, the conditions were kept constant throughout all ion extractions as described in the experimental section. The photograph in Figure 3 shows the color of JNP-bPEI 2 mL, after extraction/absorption of Cr (VI), Co(II), Ni(II), Cu(II) and Zn(II). Furthermore, to quantitatively determine the absorption capacity for each metal ion, i.e., metal ion load, the concentrations of metal ions in the supernatant were analyzed using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) and compared to the initial concentration. The metal ion extraction capacity \(q_{\text{e}}\) (mg/g) was calculated with the formula given by the Equation (1) [20].  
Figure 1: The IFT evolution vs. time of the heptane/water interface in the presence of the Janus nanoparticles (50 mg/mL in the aqueous phase) of the homologous series (A) JNP-bPEI 2 mL, (B) JNP-bPEI 3 mL, (C) JNP-bPEI 4 mL.  
Figure 3 shows the color change of the particles occurring upon the absorption of certain metal ions, and Figure 4 shows the measured metal ion extraction capacity \(q_{\rm e}\) of the JNP-bPEI 2 mL. Furthermore, we have determined the number of metal ions that can be recovered, extracted from the JNP-bPEI 2 mL. The recovery was performed in acidic conditions according to the procedures described in the experimental section. The supernatant was analyzed using ICP-OES, and the metal ion recovery capacity \(q_{\rm r}\) (mg/g) was calculated with formula given by the Equation (2).  
Figure 3: Photograph of the JNP-bPEI 2 mL after absorption of Cr (VI), Co(II), Ni(II), Cu(II), Zn(II) and centrifugation.  
Figure 2: Wax colloidosomes obtained with JNP-bPEI 4 mL.  
To test the metal ion absorption capacity and recovery by JNPs with multiple duty cycles, the JNPs were washed as described previously, and the entire procedure was repeated up two or five times for Cu(II). After an initial decrease of roughly 25% after the first cycle, the extraction/recovery capacity of JNP-bPEI 2 mL remains unchanged for at least four subsequent cycles, Figure S10.
# A UML Activity Flow Graph-Based Regression Testing Approach
## 3 Identifying the Changes in Software Design and Classifying Test Cases
### Classifying Test Cases
#### 3.1.4. Algorithm to Classify Test Cases  
When all the above techniques for classifying test cases are combined, a classifying algorithm is created. Given a software design \(S_{o}\) and a test suite \(T_{o}\) for \(S_{o}\) and a modified software \(S_{m}\), Algorithm 2 describes the procedure for classifying the test cases for testing a UML design of the software. This procedure makes use of \"change tables\", \"change categories\", and classification rules. In our algorithm, we make use of arrays as bit vectors for implementation in practice. The entry \(i\) in the array is 1 if there exists a change \(\rho\) which affects a vertex \(v_{i}\). We also have arrays as bit vectors for the sets \(N_{S}\), \(M_{S}\), \(D_{S}\), \(PC\), and \(NC\). Any vertex \(v_{i}\) is a member of these arrays if element \(i=1\). Implementation using arrays and bit vectors makes it easier to compute the retestable, reusable, and obsolete test cases.
```
Input: An array of test cases \(T[x]\), An array of the changes in the software \(\rho[y]\) Output: Classified test cases \(O_{t}\), \(R_{t}\), and \(U_{t}\) \(O_{t}\leftarrow\) empty list; \(R_{t}\leftarrow\) empty list; \(U_{t}\leftarrow\) empty list; for\(i\gets 1\)to\(x\)do for\(j\gets 1\)to\(y\)do if\(\delta_{\rho[j]}(T[i])=1\)then if\(\delta_{\rho[j]}\in PC\)and\(\delta_{\rho[j]}\in D_{S}\)then \(O_{t}.\text{append}(T[i])\);  end if if\(\delta_{\rho[j]}\in PC\)and\(\delta_{\rho[j]}\in M_{S}\)then \(O_{t}.\text{append}(T[i])\);  end if if\(\delta_{\rho[j]}\in NC\)and\(\delta_{\rho[j]}\in N_{S}\)then \(R_{t}.\text{append}(T[i])\);  end if if\(\delta_{\rho[j]}\in NC\)and\(\delta_{\rho[j]}\in M_{S}\)then \(R_{t}.\text{append}(T[i])\);  end if if\(\delta_{\rho[j]}\in NC\)and\(\delta_{\rho[j]}\in D_{S}\)then \(R_{t}.\text{append}(T[i])\);  end if  end if else \(U_{t}.\text{append}(T[i])\);  end if  end for  end for
```
**Algorithm 2** Classifying Test Cases
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Link
#### 4.2.1 Time Slot  
In order to analyze the dynamic behaviors, a specific SN scenario is divided into multiple time slots. The network status is regarded as static and represented by the initial state in each slot. The more time slots are divided, the more accurate an SN environment is emulated.
# Paragraph Boundary Recognition in Novels for Story Understanding
## 3 Technical Background
### Loss Functions
#### 3.2.3 Dice Loss  
One of the indicators used to evaluate the classification model of imbalanced data is \(F1\). Dice coefficient (Sorensen-Dice coefficient: DSC) is the \(F1\)-oriented statistical index. Although the DSC is generally an index for measuring similarity between two sets, it may also be used in the segmentation of affected images in the medical field in connection with the imbalanced classification problem [56]. [PERSON] et al. [48] showed the relationship between the Dice coefficient and \(F1\) as follows: First, given two sets \(A\) and \(B\), the DSC is given as follows:  
\[\text{DSC}=\frac{2|A\cap B|}{|A|+|B|}. \tag{4}\]  
In this study, set \(A\) is the set of samples determined to be positive by the model, and \(B\) is the set of ground-truth samples. Here, using the true-positive (TP), false-positive(FP), and false-negative (FN) rates, the relationship between the Dice coefficient and \(F1\) is expressed as follows:  
\[\begin{split}\text{DSC}&=\frac{2\text{TP}}{2\text{TP}+ \text{FN}+\text{FP}}\\ &=\frac{2\frac{\text{TP}}{\text{TP}+\text{FN}}\frac{\text{TP}}{ \text{TP}+\text{FP}}}{\frac{\text{TP}}{\text{TP}+\text{FN}}+\frac{\text{TP}}{ \text{TP}+\text{FP}}}\\ &=\frac{2\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\\ &=F1.\end{split} \tag{5}\]  
On the basis of the above definition, the value of the DSC for each sample \(x_{i}\) is given as follows:  
\[\text{DSC}=\frac{\sum_{i}2y_{i1}p_{i1}+\epsilon}{\sum_{i}y_{i1}+\sum_{i}p_{i1 }+\epsilon}, \tag{6}\]  
where \(\epsilon\) provides numerical stability to prevent division by zero.  
[PERSON] et al. [19] proposed an objective function that squares each term of the denominator in the Dice coefficient, and defined the dice loss as a loss function to maximize it as follows:  
\[\text{DL}=\frac{1}{N}\left(1-\frac{\sum_{i}2y_{i1}p_{i1}+\epsilon}{\sum_{i}y_{ i1}^{2}+\sum_{i}p_{i1}^{2}+\epsilon}\right). \tag{7}\]
# Bearing Capacity of a Concrete Grouting Pad on the Working Surface of a Highway Tunnel Shaft
## 2 Grouting Pad Failure Mode  
Compared with mine shafts, highway tunnel shafts have larger diameters, usually ranging from 7 to 10.5 m. The grouting pad thickness typically ranges from 2 to 4 m, resulting in a thickness-to-diameter ratio close to 0.2. Therefore, it is treated as a circular thin plate problem [21]. Based on the analysis of failure modes in simply supported circular plates, it is observed that tangential bending moments act along the radial direction, while radial bending moments act annularly along different coefficients multiplied by the radius (less than 1) [24]. Tangential bending moments generate radial cracks, while radial bending moments generate annular cracks.  
Based on the above discussion, the failure mode of a shaft grouting pad is analysed. Regarding the grouting pad on the working surface of the shaft, the grouting holes are generally arranged along the periphery of the grouting pad at a certain distance from the circumferential spacing, and all the grouting holes are used for high-pressure grouting on the rock body of the working surface. After closing the grouting valve, the load acting on the bottom surface of the grouting pad consists of two parts, one of which is the pressureised water of the working surface, and the other one is the lifting force generated by the diffusion of the grout from the grouting holes [25]. Both forces act in the same direction on the bottom of the grouting pad, as shown in Figure 1.  
To facilitate the installation of the orifice pipe, the grouting hole must pass through the full thickness of the grouting pad during the hole forming process. This results in the formation of a ring hole \"weak ring\" around the grouting pad. It is worth noting that this \"weak ring\" is caused by the grouting hole cracking at a certain distance around the grouting pad. This \"weak ring\" provides a stress concentration area for cracks caused by tangential and radial bending moments [26]. However, its ring is more conducive to the propagation and extension of circumferential cracks. During grouting construction, it is important to balance the peripheral pressure in the relative direction of the grouting pad to prevent ring cracking on one side of the pad. It is also important to consider the limited ability of the grout to spread after closing the grouting valve. This can often result in punching shear failure within a certain range from the mouth of the hole, as shown in Figure 2.  
Figure 1: The force on the shaft grouting pad.
## 3 Results
### Linking MOC to Clay Minerals and Other Factors  
The MOC content was positively correlated with the environmental factor MAP (r = 0.53), clay (r = 0.67), silt (r = 0.47), bacteria (r = 0.67), and fungi (r = 0.56). MOC content with pH (r = \(-\)0.58), Sa (r = \(-\)0.40), Saf (r = \(-\)0.42), and sand (r = \(-\)0.58) was negatively correlated (Table 3). The correlation between the above indicators and MOC was stronger than SOC (except bacteria and fungi). The correlation between the MOC and minerals gradually increased with decreasing temperature (Table 4). Only Ver (r = 0.42) was significantly correlated with MOC in the highest temperature LP. Ver was the next most significantly correlated with MOC in the MP (r = 0.66). Chl (r = 0.75), Sme (r = 0.83), and It (r = 0.41) were significantly correlated with the MOC in the TP with the lowest temperature, and I/Sme (r = \(-\)0.73) was significantly negatively correlated with MOC. The findings of the partial correlation study confirmed the significance of the MAP and MAT (Table 5). After controlling the effect of MAP, the relationship between the MOC and MAT changes from an insignificant to a significant negative correlation. As for the spatial variation in different grassland transects, MOC accounted for 75% (Figure 5). The direct effect of the MAT and MAP on MOC is greater than the indirect effect. The MAT indirectly affects soil carbon stability by affecting CaO, clay particles, bacteria, etc. pH and Sa had negative effects on MOC. MAP, clay particles, and bacteria had \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Kao** & **Chl** & **Sme** & **It** & **Ver** & **I/Sme** \\ \hline MP & \(-\)0.17 & 0.47 & \(-\)0.24 & \(-\)0.01 & 0.66 \({}^{\star}\) & 0.14 \\ TP & 0.62 & 0.75 \({}^{\star}\) & 0.83 \({}^{\star\star}\) & 0.41 & \(-\)0.05 & \(-\)0.73 \({}^{\star\star}\) \\ LP & 0.32 & \(-\)0.13 & 0.12 & 0.10 & 0.42 \({}^{\star}\) & \(-\)0.22 \\ \hline \hline \end{tabular} \({}^{\star}\)\(p\) < 0.05, \({}^{\star\star}\)\(p\) < 0.01, significant correlations. Sme: smectite; It: illite; Ver: vermiculite; I/Sme: mixed-layer illite/smectite; Kac: kaolinite; Chl: chlorite.  
\end{table} 
Table 4: Pearson’s correlation coefficients (r) among the MOC and clay minerals in different grassland transects.
## 5 Conclusions  
This study proposed an optimal generalized Bingham number (GBN) control method to improve the soft-landing control accuracy of the drop-induced shock mitigation system using an MREA. The primary innovation was to recognize that post-yield damping for a high-impact speed linear stroke MREA is most likely quadratically proportional to piston velocity due to turbulent transition and minor loss effects. Our prior efforts assumed that post-yield damping was linearly proportional to piston velocity. An MREA was designed, modeled, fabricated, and tested to investigate the MREA damping force characteristic under a shock load. The dynamic equation of the single-degree-of-freedom shock mitigation system considering quadratic damping was established. A corresponding optimal GBN control strategy to achieve the soft-landing control objective was proposed, that is, to come to a rest after fully utilizing the available stroke with minimal or no end stop impact. The optimality of the GBN control algorithm was demonstrated via both simulation and experiments. The key conclusions are as follows:  
1. The GBN control can achieve a soft landing accurately.
2. The damping force of an MREA is proportional to the square of piston velocity at high sink rate.
3. The quadratic GBN control strategy is superior to the linear BN control strategy in terms of soft-landing control accuracy. Limited by the accuracy of the damping force model, the BN control caused either an end-stop impact or incomplete exploitation of the available stroke.  
Conceptualization, M.W. and N.M.W.; methodology, M.W.; software, D.L. and Y.G.; validation, Y.X. and Y.G.; formal analysis, D.L.; investigation, M.W.; resources, D.L., Y.G. and B.L.; data curation, M.W.; writing--original draft preparation, M.W., Y.X. and N.M.W.; writing--review and editing, M.W., Y.X. and N.M.W.; visualization, D.L.; supervision, D.L.; project administration, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Science and Technology Program of CSEI (Grant No. 2021 XKTD013) and the National Nature Science Foundation of China (Grant No. 12102319).  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare they have no conflict of interest.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Angle (\(\circ\))**} \\ \hline  & \multicolumn{3}{c}{**Healthy**} & \multicolumn{8}{c}{**Infected**} \\ \hline  & \multicolumn{3}{c}{A1} & \multicolumn{8}{c}{A1} \\ \hline Number & Avg. & Min. & Max. & StdDev. & Number & Avg. & Min. & Max. & StdDev. \\ \hline
1 & 106.2 & 104.4 & 108.3 & 1.6 & 1 & 137.6 & 124.6 & 147.2 & 8.6 \\
2 & 93.3 & 92.4 & 95.2 & 1.1 & 2 & 143.4 & 141.2 & 146.6 & 1.9 \\
3 & 117.9 & 115.1 & 120.7 & 2.3 & 3 & 131.4 & 128.9 & 134.4 & 1.8 \\
4 & 117.6 & 115.4 & 121.1 & 5.7 & 4 & 131.8 & 127.1 & 135.3 & 2.6 \\
5 & 110.6 & 110 & 111.4 & 2.9 & 5 & 129.8 & 126.8 & 133.7 & 2.5 \\ \hline \hline \multicolumn{8}{c}{A2} & \multicolumn{8}{c}{A2} & \multicolumn{8}{c}{A2} \\ \hline Number & Avg. & Min. & Max. & StdDev. & Number & Avg. & Min. & Max. & StdDev. \\ \hline
1 & 80.3 & 77.7 & 83.4 & 2.3 & 1 & 93.3 & 89.8 & 98.2 & 3.3 \\
2 & 92.5 & 90.5 & 95.6 & 1.7 & 2 & 112.9 & 112.1 & 114.1 & 0.8 \\
3 & 86.6 & 85.7 & 88.4 & 1 & 3 & 102.4 & 98.1 & 107.2 & 3.3 \\
4 & 75.3 & 71.7 & 79.9 & 3 & 4 & 94.4 & 87.3 & 98.8 & 4.1 \\
5 & 74.6 & 72.6 & 75.5 & 1 & 5 & 97.5 & 93.1 & 100 & 2.4 \\ \hline \hline \multicolumn{8}{c}{A3} & \multicolumn{8}{c}{A3} & \multicolumn{8}{c}{A3} \\ \hline Number & Avg. & Min. & Max. & StdDev. & Number & Avg. & Min. & Max. & StdDev. \\ \hline
1 & 112.1 & 110 & 113.4 & 1.2 & 1 & 128 & 123 & 126.6 & 1.3 \\
2 & 108.9 & 106.2 & 110.2 & 1.4 & 2 & 146.9 & 144.9 & 148.2 & 1.2 \\
3 & 125.1 & 122.9 & 127.1 & 1.6 & 3 & 148.2 & 141.9 & 155.6 & 4.7 \\
4 & 109.8 & 107.8 & 112.8 & 2.1 & 4 & 132.5 & 129.5 & 136 & 2.3 \\
5 & 98.7 & 92.7 & 102.6 & 3.2 & 5 & 136.8 & 134.4 & 140.8 & 2.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerically evaluated average, minimum, and maximum three types of angular information compared between healthy and infected specimens with standard deviations. averaged using the mean of three replicates as representative. The leaf rolling index is determined by dividing the distance of the cross axis perpendicular to the axis of the rolling leaf midrib. The main drawback of these methods is abnormalities can be identified only at a relative late stage, when treatments are no longer possible. As a non-destructive clarification, OCT-based quantifications were obtained from representative healthy and partially abnormal specimens after inoculation, which can be used for an approximate classification between healthy and abnormal specimens at a relatively early stage. Since the experiment was demonstrated to investigate feasible applicability of OCT to differentiate healthy and abnormal specimens, the precision of the thickness and angular parameters will be enhanced through succeeding field trials based time-resolved measurements based experimental attempts.
## 5 Role of IoT Technology in Education
### The Security Aspects
#### 5.3.2 Privacy Preservation in ULE  
One of the main security risks in U-learning is the potential for unauthorized access to sensitive data. This can occur when learners access learning materials on public Wi-Fi networks or other unsecured connections. To mitigate this risk, it is important for educators and learners to use secure networks and implement strong authentication measures, such as two-factor authentication.  
Another concern is the potential for data breaches or cyberattacks on learning platforms or mobile devices. This can result in the theft of personal information, financial data, or intellectual property. To address this risk, organizations should implement robust security protocols, such as encryption and firewalls, to protect their systems and data. Lastly, there is a risk of social engineering attacks, where cybercriminals attempt to trick learners into divulging sensitive information through phishing scams or other tactics. To prevent these attacks, learners should be educated on how to identify and avoid suspicious emails or messages, and organizations should implement policies and procedures to detect and respond to such incidents [20; 58].  
While U-learning offers many benefits, it is important to address the security risks associated with this approach. By implementing strong security measures and educating learners on best practices, organizations can ensure that their U-learning initiatives are both effective and secure. The benefits of IoT technology in education are significant, but it is important to prioritize cybersecurity and privacy to ensure a safe and secure learning environment. By implementing a multi-layered approach that includes education, proper configuration and maintenance of devices, access controls, and monitoring systems, schools, and universities can enjoy the benefits of IoT technology while minimizing the risks. The use of IoT can help to improve the security of educational institutions, as it can be used to monitor and control access to the premises and the sensitive data stored within.