esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Workflow Technology for Big Data Analysis Service
## 4 Experiments
### Performance of Offline Batch Analysis Model  
Cloud-based big data intelligent analysis needs to deal with massive data that requires high capacity. Social network analysis is taken as an example. We evaluated the performance of an offline batch analysis framework (as shown in Table 4). Social network analysis mainly solves the network structure data problems, such as discovering tight communities from user communication networks. The analysis includes in/out degree, single-source shortest path, closeness, clustering coefficient, and so on. In this experiment, we set the number of Map and Reduce as 60. The complexities of data replacement, in/out degree, average, maximum, edge/node statistics, single-source shortest path, and closeness were linear. The performances of clustering coefficient and community discovery analysis were easily influenced by the input data. Large local density subgraphs in the input data may lead to unbalanced calculation, which would cost considerable time in task completion. The complexity of the sparse graph was approximately linear.  
Figure 21: The performance of different execution forms.  
\begin{table}
\begin{tabular}{c c} \hline
**The Number of Users** & **Average Response Time (s)** \\ \hline
100 & 3.23 \\
500 & 3.54 \\
1000 & 4.38 \\
5000 & 4.69 \\ \hline \end{tabular}
\end{table}
Table 3: Response time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Dataset** \\ **(million)** \\ \end{tabular} } & \multicolumn{8}{c}{**Workflow Executive Time (Sed)**} \\ \cline{2-11}  & **Data Replace** & **In/Out Degree** & **Average** & **Maximum** & \begin{tabular}{c} **Edges/Nodes** \\ **Statistics** \\ \end{tabular} & \begin{tabular}{c} **Clustering** \\ **Coefficient** \\ \end{tabular} & \
\begin{tabular}{c} **Community** \\ **Discovery** \\ \end{tabular} & **Diameters** &
\begin{tabular}{c} **Single-Source** \\ **Shortest Path** \\ \end{tabular} & **Closeness** \\ \hline
50 & 420 & 88 & 76 & 77 & 125 & 300 & 1800 & 1983 & 641 & 34 \\
100 & 426 & 76 & 79 & 79 & 155 & 356 & 3720 & 2278 & 681 & 33 \\
200 & 423 & 78 & 81 & 79 & 155 & 605 & 4512 & 2312 & 656 & 36 \\
300 & 440 & 108 & 82 & 78 & 158 & 805 & 5452 & 2498 & 679 & 39 \\
400 & 463 & 105 & 79 & 77 & 158 & 602 & 5433 & 2693 & 687 & 40 \\
500 & 513 & 113 & 80 & 79 & 162 & 741 & 6288 & 2944 & 705 & 60 \\
600 & 537 & 113 & 82 & 79 & 163 & 817 & 5831 & 2933 & 711 & 65 \\
800 & 662 & 119 & 84 & 79 & 164 & 1431 & 5044 & 3236 & 732 & 70 \\
1000 & 750 & 121 & 85 & 80 & 169 & 1406 & 5554 & 4337 & 765 & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of social network analysis workflow.
# GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier
## 3 Materials and Methods
### Classifiers Selected for the Experiment
#### 3.1.4 Support Vector Machine  
SVM was originally developed by [PERSON] and [PERSON] for binary classification [35]. It is a non-probabilistic binary linear classifier that constructs a hyperplane or set of hyperplanes high or infinite-dimensional space, which can be used for classification, regression, and similar other tasks [36; 37]. An SVM uses a device called kernel mapping to map the data in input space to a high-dimensional feature space in which the problem becomes linearly separable [38]. The basic idea underlying the SVM for sentiment classification is to find a hyperplane that divides the documents, or in our case, Google app reviews as per the sentiment and the margin between the classes (positive, negative and neutral) being as high as possible. The points that lay on the separation boundaries are called support vectors, as shown in Figure 2.  
Figure 1: The architecture of gradient boosting machine. Rectangles in the figure show the split criteria while the end node is called ‘leaf’.  
```
1:input data \((x,y)_{i=1}^{N}\)
2:number of iterations \(M\)
3:choice of the loss-function \(\Psi(y,f)\)
4:choice of the base-learner model \(h(x,\theta)\)
5:initialize \(\widehat{f_{0}}\) with a constant
6:for\(t=1\) to \(M\)do
7:compute the negative gradient \(g_{t}(x)\)
8:fit a new base-learner function\(h(x,\theta_{t})\)
9:find the best gradient descet step-size \(\rho_{t}\)
10:\(p_{t}=argmin_{\rho}\sum_{i=1}^{N}\Psi\{y_{i},\widehat{f_{t-1}}(x_{i}),\rho h(x_ {i},\theta_{t})\}\)
11:update the function estimate : \(\widehat{f_{t}}\longleftarrow\widehat{f_{t-1}}+\rho_{t}h(x,\theta_{t})\)
12:endfor
```  
**Algorithm 1** Friedman's Gradient Boosting Algorithm [31].  
The class separability depends upon the distance between the samples of the classes. In other words, the higher the distance between the support vectors (margins) is, the more distinguished the classes are. The hyperplanes are originated using quadratic programming optimization problem [39]. The decision function of an SVM is related not only to the number of SVs and their weights but also to the a priori chosen kernel that is called the support vector kernel [40; 41]. For this purpose, various kernels like radial, polynomial, and neural kernels, etc., are used with SVM [42]. The working principle of SVM is given in Algorithm 2[43; 44].
## 5 Discussion
This includes the operation simulation for different satellite components, the simulation of observation data generation and transmission, and the simulation of the power consuming processes of the components. According to a finer-grained simulation condition, the simulation results can be utilized to improve the scheduling algorithm;
3. Research can be conducted on mission allocation and resource distribution for multiple satellites and orbits. Scheduling on more orbits will bring target observation opportunities in different orbit periods, reduce the pressure of observation and data transmission missions on a single orbit by selecting targets in different orbits, and hence obtain more profits. It is also crucial to perform research on how to coordinate limited resources among multiple satellites to obtain better multi-satellite mission scheduling results;
4. Targets can be merged in the preprocessing step. When the field of view of the satellite's camera is relatively wider and the distances between targets are shorter, multiple targets can be observed with a single observation. If this merging preprocess needs to be integrated into our proposed method, the neighboring targets are required to be grouped according to their satellite orbits and the frame widths of the camera in the target preprocessing step. The roll angle, time window, and scanning time duration also need to be calculated so that these grouped targets can be added to the methodological framework proposed in this paper.
## 3 Results
### Molecular Docking Validation  
Four active compounds (oleic acid, methyl linoleate, leguminol, and sterol) were subjected to molecular docking with six key targets (PPARG, PTGS2, BDNF, GCG, PPARA, and EDN1), and the binding energies after docking are presented in Table S1. Lower AutoDock docking scores indicate a higher ability of the molecules to bind to the targets compared to the target itself. A lower AutoDock score signifies a stronger binding ability between the molecule and the target, as well as a reduced energy requirement for binding. It is evident that certain active components of _C. japonicum_ (such as sterol and stigmasterrol) exhibit a robust affinity towards several key targets, including PPARA, PPARG, PTGS2, and EDN1. Molecular docking results demonstrate that sitosterol binds to the active sites of PPARA and PTGS2 proteins while forming hydrogen bond interactions with ASN-299, THR-60 and LYS-546 (Figure 5a,b). Vanillin binds to the active sites of BDNF and PPARA proteins while forming hydrogen bond interactions with ARG-88, TY-52, ARG-87, ARG-409, and ASP-419 (Figure 5c,d). Stigmasterrol binds specifically to the active site of PPARA protein while forming a hydrogen bond interaction with GLY-296 (Figure 5e).  
Figure 4: KEGG enrichment analysis and key pathway network. Each data point represents a KEGG pathway, with the corresponding pathway names indicated on the left side of the axis. The x-axis displays the enrichment factor, which quantifies the ratio of differentially expressed proteins annotated to that specific pathway compared to all annotated proteins in the species. A higher enrichment factor signifies a more robust indication of significant enrichment for differential proteins within that particular pathway.  
Figure 5: Molecular docking patterns of key targets and specific active components of _C. japonicum_. (**a**) Sitosterol and PPARA (AutoDock Docking score: \(-8.67\)); (**b**) sitosterol and PTGS2 (score: \(-6.09\)); (**c**) vanillin and BDNF (score: \(-5.54\)); (**d**) vanillin and PPARA (score: \(-4.82\)); (**e**) stigmasterol and PPARA (score: \(-8.2\)).
## 2 Materials and Methods
### Transport Experiments  
#### 2.6.1 Cytotoxicity Assay  
The MTT assay was used to examine the toxicity of NMN, blank liposomes (KB lips), drug-loaded liposomes (NMN-lips), ANG-NMN-lips, and RVG-NMN-lips on hCMEC/D3 cell lines. The hCMEC/D3 cells were seeded in 96 well plates at a density of 1 \(\times\) 10\({}^{4}\) cells per well with 200 mL of culture medium for 24 h. The stock solutions of NMN, KB-lips, NMN-lips, and ANG-NMN-lips were diluted with ECM complete culture medium to the testing concentrations of 0.02 mmol/L, 0.05 mmol/L, 0.1 mmol/L, 0.2 mmol/L, 0.5 mmol/L, 1 mmol/L, 2 mmol/L, and 5 mmol/L, respectively. And these testing media were added into the wells (with 4 replicates for each concentration). No treated cultures served as controls. After 24 h treatment, the medium was removed, and 0.1 mL of MTT solution (5 mg/mL) was added to each well to react for 45 min at 37 \({}^{\circ}\)C. The supernatant was removed, and 0.1 mL DMSO was added to dissolve the MTT formazan crystals. The plates were gently shaken for 10 min until the complete solvation of the purple formazan crystals. Absorbance at 570 nm was measured and the results were normalized by controls.  
\[\text{Cell viability (\%)}=(\text{OD}_{\text{drug}}/\text{OD}_{\text{average value of blank group}})\times 100\% \tag{3}\]
## 5 Performance Evaluation
### Average Response Time  
RLS is considered for applications in which the average response time is a vital metric, such as for disaster prevention like tsunamis [54]. The period T is defined as the average response time where it is started from the time the event is detected by a sensor to estimating the location of the same sensor in the sink. In fact, the average response time is the average time to get a location estimate for the first time. Localization can be updated and refined in time but we consider the delay in estimating the location for the first time [7]. Figure 10 illustrates the average response time terms to different depths  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Messages** & **Description** \\ \hline D’N’R & 3 & Anchor \\ MSL & 3–6 & Anchor(3) + Reference node(3) \\ UPS & 4 & Master-Anchor(1) + Anchor(3) \\ AAL & 9 & Anchor-weak up(3) + Sensor-Req(3) + Anchor(3) \\ LDB & 2 & AUV arrive to range + AUV exit from range \\
3 DUL & 7 & Anchor-Req(3) + Sensor-Ack(1) + Anchor(3) \\ SLMP & 2–3 & Buoy + Referenced node + (Anchor) \\ RLS & 1 & Applicant Sensor \\ \hline \hline \end{tabular}
\end{table}
Table 3: Communication cost.  
of water. In this simulation metric, we set the number of sensor nodes to 250. The localization time will increase with increasing depth of water. It is a logical relation, because the propagation delay of sound is high and will be increased by increasing the distance, so this is due to propagation delays. Higher anchor rates reduce the average response time because the number of anchors in communication range of detector sensor will be increased and they help to accomplish localization fast. Unlike all existing localization methods such as [2], RLS does not need to wait to receive anchor location information. It broadcasts its data, as soon as it senses something, so if the mobility of water current changes the node location, its position information is available in the sink to estimate its coordinates in real time when detecting the event.  
Average response time is a vital component for estimating the performance of localization. In some applications like for an effective tsunami warning system, time is of the essence, while, it is not so crucial for ocean sampling applications. The time constraint for a tsunami early warning system is specified as [54]:  
\[T_{1}+T_{2}+T_{3}\leq T_{4} \tag{16}\]  
where \(T_{1}\) is detection time, \(T_{2}\) is assessment time, \(T_{3}\) is evacuation time, and \(T_{4}\) is tsunami travel time. It is clear that to make the warning system effective, the time needed for detection should be minimized. On the other hand, most of the localization methods cannot reduce the detection time, because they must wait to receive the beacon messages. RLS is considered to reduce detection time and also reduce the response time via triggering of the localization process by the detector sensor. Delays in the RLS scheme only include the propagation delay and it is increased by increasing the communication range.
## 4 Experimental Design
### Experimental Setup  
The experimental system was developed as shown in Figure 5. The shaking table was the Quanser single-axis shaking table II. The actuator was the Maxon DC motor with a planetary gearbox. The data acquisition devices were the US Digital encoder and Maxon encoder, employed to collect the swing angle and motor rotation angle. The sampling resolutions of the encoder were 0.0879\({}^{\circ}\) and 0.18\({}^{\circ}\), respectively. The parameters of the experimental setup are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Stroke (Shaking table)/(cm) & \(\pm\)7.5 \\ The maximum acceleration (Shaking table)/(g) & 2.5 \\ The most frequency (Shaking table)/(Hz) & 10 \\ Rated voltage (Motor)/(V) & 24 \\ No-load speed (Motor)/(rpm) & 8810 \\ Rated speed (Motor)/(rpm) & 8050 \\ Rated torque (Motor)/(mN-m) & 85.6 \\ Moment of inertia (Motor)/(g-mm\({}^{2}\)) & 3350 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the experimental setup.  
Figure 4: Experimental system design: (**a**) Experimental system sketch; (**b**) suspended structure with the ARID system; (**c**) analytical model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Mass (Motor)/(g) & 260 \\ Reduction ratio (Gearbox) & 3.7:1 \\ Moment of inertia (Gearbox)/(g\(\cdot\)mm\({}^{2}\)) & 150 \\ Mass (Gearbox)/(g) & 118 \\ Length (Structure)/(mm) & 650 \\ Radius (Rotary inertia mass)/(mm) & 45 \\ Rotary inertia (Rotary inertia mass)/(g\(\cdot\)mm\({}^{2}\)) & 1,498,413.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 5: Photo of the ARID experiment system: (**a**) Photo of the ARID actuator; (**b**) schematic of the experimental system.  
The natural structural frequency was close to 0.64 Hz, according to the equation \(\omega_{m}=\sqrt{g/l}\). The rotary inertia was calculated by the software. The total mass of the ARID system was about 815 g. The suspended structural steel bracket was fixed on the shaking table and the bracket stiffness was regarded as infinity, and the excitation at the lifting point can be equivalently regarded as the shaking table excitation. The experimental control model was designed by Simulink. It was compiled and run by the Quanser software.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
## 4 Parametric Analysis
### Effects of the Slenderness Ratio
#### 4.3.2 Soil Displacement around the Pile  
The values of the slenderness ratio (\(\gamma\)) studied in this section are 15, 9.35, and 6.25, and the values of seawater depth variation are \(H\) = 44.78 m, \(H\) = 32.12 m, and \(H\) = 23 m, while other parameters are kept constant. Figure 13 shows the horizontal displacement of the foundation on the left and right sides of the center pile for the nine conditions of \(\gamma\) and \(H\). The immersed tunnel has a high density and large mass. Fixed contact is used between the sand compaction pile, gravel cushion, and tunnel in the numerical model, thus forming a top-heavy and bottom-light structure with SCP. Seismic injury mainly produces acceleration differences in the structure, which is then subjected to shear. The mass distribution of top-heavy and bottom-light will enlarge this acceleration difference, which makes the shear near the connection between the SCP and the tunnel larger, and thus, a larger displacement occurs at a depth of 2 m. The pile embedment depth below 2 m depth is large, and the soil densification degree is high enough to resist the lateral load, so the displacement below 2 m gradually decreases.  
Figure 12: \(p\)-\(y\) curves of the piles with different seawater depths: (**a**) plum blossom piles, (**b**) diagonal piles, (**c**) rectangular piles.  
Figure 13: _Cont._  
In the case of the same seawater depth, when the embeddent depth \(L\) decreases from 15 \(D\) to 6.25 \(D\), the horizontal displacement difference between the two sides of the pile increases. The area of the difference extends from 7 m below the top of the pile to the end of the pile. It can be seen that when using SCP to strengthen the lateral deformation resistance of the foundation, it should be avoided that the embedding depth is too deep, leading to the destruction of the tip of the pile, and the embedding is too shallow, leading to the instability of the end of the pile.  
In the case of the same embedding depth \(L\), when the seawater depth \(H\) increases from 23.12 m to 44.78 m, the horizontal displacements of the foundations on the left and right sides of the pile are shifted to the right, forming a transformation process from curved parabola to flat parabola. The piled foundation produces a horizontal displacement away from the direction of the earthquake source, which is related to the resonance of the structure away from the source end. At this time, the horizontal displacement of the foundation on both sides of the pile shifted to the right as a whole without any significant change in the difference. It is worth noting that the maximum depth of foundation horizontal displacement that exists below the end of the pile is within 1 m.
## 4 Weighting Factor Estimation Method from Scheduling Results
### Feature Extraction
#### 4.2.1 Small Scale Problems with 2 Objectives Optimization Problems  
First, the features used for the small-scale problems with 2 objectives are explained [28]. In the 2 objectives case, the maximum completion time and the sum of setup costs are used as objective functions. The objective function of the scheduling problem is expressed by Equation (2).  
\[\min_{\{\delta_{ijk}\}}\quad w_{1}\max(C_{ik})+w_{2}\sum_{k=1}^{M}\sum_{i=1}^{ N}\delta_{ijk}S_{ij} \tag{2}\]  
where \(w_{f}\) represents the weighting factor of objective function \(f\), \(C_{ik}\) represents completion time of job \(i\) at machine \(k\), \(\delta_{ijk}\) represents 1 when job \(j\) is processed next to job \(i\) at machine \(k\), 0 otherwise, \(S_{ij}\) represents setup cost when switching from job \(i\) to job \(j\). We considered the following features: 1. The values of the maximum completion time, 2. The values of the sum of setup costs, 3. The variance of completion time for each machine, and 4. [PERSON]'s rank correlation coefficients (\(\tau_{s,i}\) and \(\tau_{p,i}\)). The objective function values are extracted because they are related to the weighting factors. If the objective of the maximum completion time is emphasized, the difference between the completion times between machines is small. Therefore, it is used as a feature of the variation of the completion time \(\sigma_{c}^{2}\) expressed by Equation (3).  
\[\sigma_{c}^{2}=\frac{1}{M}\sum_{k=1}^{M}(C_{k}-\overline{C})^{2} \tag{3}\]  
where \(M\)\((k=1,2,\ldots,M)\) represents number of machines, \(C_{k}\) represents maximum completion time of machine \(k\), \(\overline{C}\) represents mean of completion time of each machine. Figure 3 shows Gantt charts with different weighting factors. The vertical dashed lines represent processing times of 300 and 600. In Figure 2(a), the maximum completion time is emphasized, and in Figure 2(b), it is not emphasized. In this case, the variance of the maximum completion time is effective.  
Figure 3: Gantt charts with different weighting factor: (**a**) maximum completion time is emphasized; (**b**) maximum completion time is not emphasized.  
\(\tau_{s,i}\) represents starting time order of job \(i\) in ascending order, \(\tau_{p,i}\) represents processing time order of job \(i\) in ascending order. The orders \(\tau_{s,i}\) are indexed by an integer value such as 1, 2, 3,..., \(N\) according to the value of starting time of job \(i\). Others \(\tau\) are the same. [PERSON]'s rank correlation coefficient, which represents the correlation between the ranks of the two indicators, is used. Since the maximum completion time is used as the objective function, a rank correlation is generated from the processing sequence of the job and the job processing time order. The obtained rank correlation value \(r_{s}\) is represented by Equation (4). \(N\)\((i=1,2,\ldots,N)\) represents number of jobs, \(X_{i}\) is \(\tau_{s,i}\), \(Y_{i}\) is \(\tau_{p,i}\).  
\[r_{s}=1-\frac{6\sum_{i=1}^{N}(X_{i}-Y_{i})^{2}}{N^{3}-N} \tag{4}\]
# R-Curve Behavior of Polyhedral Oligomeric Silsesquioxane (POSS)-Epoxy Nanocomposites
## 2 Materials and Methods
### Characterization of Properties
#### 2.1.2 Fracture Toughness: Elastic-Plastic Fracture Mechanics  
Fracture toughness of the ductile sample was determined by single-edge notch bend (SENB) test as per ASTM D-6068 [13]. This test method is to characterize the slow stable crack growth resistance of the bend-type specimen. Samples were machined from the cast plate as per ASTM standard. A notch was cut by using a diamond precision saw and crack initiated by tapping a razor blade on the tip of the notch. The length of the notch (\(a_{o}\)) was kept between 11.4 and 12.7 mm according to the ASTM standard. The specimens were loaded under three-point bending by using the universal testing machine. The specimen was loaded to a selected displacement level that is used to produce a crack extension in the desired position. After that, the specimen was unloaded, and the crack front was marked and broken to expose the specimen's fracture surface.  
The resulting crack fronts were marked (\(a_{p}\)) and the crack extension (\(\Delta a_{p}=a_{p}-a_{o}\)) were measured from fractured surface by looking under microscope. For one set of results, a minimum of seven samples was used to generate a complete \(J\)-\(R\) curve. All specimens must be machined to the same dimensions. The energy required to extend the crack, \(U\), was used to calculate \(J\). \(U\) is determined from the area under the load-displacement curve obtained from the test. \(J\) is calculated as follows,  
\[J=\frac{\eta U}{B(W-a_{o})}, \tag{3}\]  
where \(\eta=2\), \(B\) is the specimen thickness, \(W\) is the specimen width, and \(a_{o}\) is the original crack length.  
The \(J\)-\(R\) curve was constructed from \(J-\Delta a_{p}\) as follows.  
1. Construct a minimum crack extension line at \(\Delta a_{p}=0.05\).
2. Construct a maximum crack extension line at \(\Delta a_{p}=0.1(W-a_{o})\).
3. Divide the interval between the minimum and maximum crack extension lines into four equally paced regions.
4. The data points shall be evenly spaced throughout the interval with at least three data points in the first row, two data points in the second quadrant, and at least one data falling in each of the two remaining regions.