esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Therefore, an efficient approach to solving the above deficiency is to combine different damage indices or, even better, various data analysis methods. Recent research has utilized machine learning algorithms to evaluate the performance of RC members due to their high precision and accuracy. [PERSON] published a comprehensive review paper regarding the application of machine learning to structural engineering, and a significant portion of the research papers focused on different aspects of RC structures [59]. Specifically, 38% of the papers analyzed the structural behavior of RC members, 29% examined material properties, 18% were dedicated to SHM, 11% focused on analysis and design, and 4% addressed the assessment of fire-imposed degradation. Recently, researchers have investigated the application of resilient back-propagating neural networks to predict the shear behavior of RC beams strengthened with externally bonded FRP sheets [60]. Furthermore, [PERSON] et al. present an Artificial Neural Network capable of predicting and evaluating the structural responses of externally bonded FRP beams under the combined shear and torsion loading [61]. Machine learning techniques, such as k-means, Fuzzy Logic, principal component analysis, neural networks, and the hierarchical clustering approach (HCA), have demonstrated their ability to identify structural integrity degradation of concrete [9; 48; 62; 63; 64; 65]. [PERSON] et al. introduced a convolutional neural network to assess stress levels and damage severity using raw EMI signatures [23]. [PERSON] et al. also introduced an effective stress monitoring scheme that involves analyzing EMI signals obtained from a smart aggregate sensor using a 1D convolutional neural network [66]. Furthermore, [PERSON] et al. conducted a study integrating the EMI method with the HCA technique to detect premature debonding of FRP laminates from RC beams subjected to bending [48]. Additionally, [PERSON] et al. exploit HCA as a distinguishing procedure between load-induced and damage-induced EMI variations in fiber-reinforced concrete beams under four-point bending [19]. This study introduces an EMI-based approach to monitor the performance of the C-FRP retrofitting technique. Thus, this experimental project investigates the efficacy of a monitoring scheme to assess the performance of real-scale retrofitted BCJs under cyclic loading. The proposed approach integrates the EMI technique with the widely utilized statistical index, RMSD, and HCA to assess the variations in the EMI responses of PZT transducers.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Age Groups** & **OR (Lower CI–Upper CD)** & **Q1/Q2** & **Q1/Q3** & **Q1/Q4** & **Q2/Q3** & **Q2/Q4** \\ \hline U-10 & 1.598 \({}^{*}\) (1.15–2.22) & 1.371 & 1.856 \({}^{*}\) & 0.858 & 1.162 & 1.354 \\  & & (0.99–1.89) & (1.32–2.60) & (0.61–1.21) & (0.81–1.66) & (0.95–1.92) \\ F-12 & 0.958 (0.76–1.21) & 1.324 \({}^{*}\) & 1.324 \({}^{*}\) & 1.383 \({}^{*}\) & 1.383 \({}^{*}\) & 1.000 \\  & & (1.04–1.69) & (1.04–1.69) & (1.08–1.76) & (1.08–1.76) & (0.78–1.29) \\ F-14 & 0.878 (0.72–1.07) & 1.361 \({}^{*}\) & 1.005 & 1.551 \({}^{*}\) & 1.145 & 0.738 \({}^{**}\) \\  & & (1.11–1.68) & (0.82–1.23) & (1.26–1.90) & (0.94–1.39) & (0.60–0.91) \\ F-16 & 0.845 (0.68–1.04) & 1.243 & 1.000 & 1.471 \({}^{*}\) & 1.183 & 0.805 \\  & & (1.00–1.55) & (0.81–1.24) & (1.18–1.83) & (0.96–1.46) & (0.64–1.01) \\ F-18 & 1.412 \({}^{*}\) (1.09–1.83) & 1.607 \({}^{*}\) & 1.483 \({}^{*}\) & 1.138 & 1.050 & 0.923 \\  & & (1.23–2.09) & (1.14–1.93) & (0.86–1.50) & (0.80–1.38) & (0.70–1.22) \\ F-20 & 1.197 (0.87–1.64) & 1.437 \({}^{*}\) & 1.549 \({}^{*}\) & 1.200 & 1.293 \({}^{*}\) & 1.078 \\  & & (1.04–1.98) & (1.12–2.14) & (0.86–1.67) & (0.93–1.80) & (0.77–1.51) \\ F-Elite & 0.731 \({}^{**}\) (0.55–0.97) & 0.947 & 0.881 & 1.296 & 1.205 & 0.929 \\  & & (0.71–1.26) & (0.66–1.17) & (0.98–1.71) & (0.92–1.58) & (0.70–1.23) \\  & & 1.109 & 1.400 \({}^{*}\) & 1.327 \({}^{*}\) & 1.675 \({}^{*}\) & 1.262 \({}^{*}\) \\  & & (0.90–1.36) & (1.13–1.73) & (1.09–1.62) & (1.36–2.06) & (1.02–1.56) \\ M-14 & 0.928 (0.78–1.10) & 1.201 \({}^{*}\) & 1.291 \({}^{*}\) & 1.294 \({}^{*}\) & 1.392 \({}^{*}\) & 1.075 \\  & & (1.01–1.43) & (1.08–1.54) & (1.09–1.54) & (1.17–1.66) & (0.90–1.29) \\ M-16 & 0.992 (0.83–1.19) & 1.099 & 1.139 & 1.108 & 1.148 \({}^{*}\) & 1.036 \\  & & (0.92–1.32) & (0.95–1.37) & (0.92–1.33) & (0.96–1.38) & (0.86–1.25) \\ M-18 & 0.937 (0.76–1.16) & 1.266 \({}^{*}\) & 1.347 \({}^{*}\) & 1.351 \({}^{*}\) & 1.438 \({}^{*}\) & 1.064 \\  & & (1.01–1.58) & (1.08–1.68) & (1.09–1.68) & (1.15–1.79) & (0.85–1.34) \\ M-20 & 0.928 (0.73–1.18) & 1.143 & 1.331 \({}^{*}\) & 1.232 & 1.434 \({}^{*}\) & 1.165 \\  & & (0.89–1.46) & (1.04–1.71) & (0.97–1.57) & (1.12–1.84) & (0.90–1.50) \\  & & 2.054 \({}^{*}\) & 1.301 \({}^{*}\) & 1.578 \({}^{*}\) & 1.000 & 0.634 \({}^{**}\) \\ M-Elite & 1.301 \({}^{*}\) (1.06–1.60) & (1.65–2.56) & (1.06–1.60) & (1.26–1.98) & (0.81–1.24) & (0.50–0.80) \\ \hline \hline \end{tabular} Note. (\({}^{*}\)) Positive statistical significance (95% CI \(>\) 1); (\({}^{**}\)) Negative statistical significance (95% CI \(<\) 1). Q1 = born in the first quarter of the year (January–March; Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).  
\end{table} 
Table 7: Odds ratios (OR) and confidence intervals (95%) for comparisons between birth quartiles in the different age groups studied.
# The NIRS Brain AnalyzIR Toolbox
## 5 Connectivity and Hyper-Scanning Modules
### Graph-Models  
An additional method of connectivity analyses is known as complex network analysis on graph theory. This approach entails defining each measurement channel as a node in a graph, and the relationship between any two measurements as an edge. The edges may be derived from correlation, coherence, or any other similarity metric, and are often thresholded to produce a binary graph of connected and disconnected nodes. Once in this form, a number of algorithms can be employed to determine network-level characteristics, such as node clustering, network efficiency, and small-worldness (the ratio of clustering to mean distance between nodes). The graph theoretic approach allows large connectivity matrices to be characterized by a small number of scalar parameters, making it particularly useful for the development of neural biomarkers. The AnalyzIR toolbox supports the conversion of both first and higher-level connectivity (and hyperscanning) statistical models to graph-based models which are compatible with the Brain Connectivity Toolbox [57].  
Figure 5a demonstrate the connectivity of simultaneously measured brain activity using fNIRS on two subjects (A and B) during a cooperative puzzle-solving task. The toolbox provides facilities to calculate and visualize connectivity between two or more subjects. The page rank centrality of a single subject is shown in Figure 5b. Page rank centrality is a variant of eigenvector, which was developed by Google search to rank websites in their search engine results [58; 59; 60]. In brain network, researchers use this technique to describe the connectivity patterns of nodes (or channels) and links of a network based on adjacency matrix. The color bar in the right corner shows the degree of connection.  
Figure 5: Examples of connectivity analyses employed by AnalyzIR: (**a**) Connectivity between two subjects during cooperation on a puzzle task; (**b**) relative PageRank centrality for a single subject.
## 5 Performance Evaluation
### Simulation Results
As the number of nodes in the network increased, Figure 11 shows that the variation in the number of packets remained essentially constant across all the different schemes. Following the cluster-based and mesh schemes in terms of the number of packets used for varying numbers of nodes in the IoT sensor area comes the newly proposed SFPCR scheme, which used the fewest packets overall. The FSRC Scheme, on the other hand, had the most packets since it broadcast packets from the source nodes, relay nodes, and normal nodes. This caused a redundancy in the network as a result of the increased number of duplicate packets that were broadcast. Similarly, Figure 12 demonstrates that the SFPCR scheme forwarded the fewest data packets to the GW. Furthermore, in the SFPCR scheme, the number of sent packets was more important than their size. Consequently, there was always a limit at which increasing the packet size would no longer increase performance and may instead decrease it. Cluster-based and mesh schemes had roughly the same number of packets as the payload size grew because their topologies were nearly identical. In contrast to the SFPCR scheme, the broadcast phase of the FSRC scheme resulted in a large number of duplicate packets in the network. As depicted in Figure 13, the SFPCR scheme demonstrated a single packet transmission at near ranges from the GW to approximately 3000 m, beyond which the number of packets  
Figure 10: Energy consumption as a function of LoRa devices’ distance from the GW (m) with a comparison between the proposed SFPCR scheme and other protocols. Figure 9: Energy consumption as a function of payload size (B) with a comparison between the proposed SFPCR scheme and other protocols. increased modestly due to the employment of relay nodes in the LSFZ. In contrast, the CHs in the HSFZ aggregated data packets from all remote source nodes within the HSFZ, hence lowering network traffic flow. Similarly, the FSRC scheme from the GW to a distance of approximately 3000 m demonstrated a single packet transfer because no relay nodes were utilized in that zone. Beyond this stage, relay nodes that duplicated the sent packets were deployed. In the mesh and cluster-based schemes, each node obtained data from one node while passing data to the next node, resulting in an increase in packets. Consequently, as the distance from the GW rose, so did the number of node connections and hops. Figure 11: Variation in the number of packets according to the number of nodes in the network with a comparison between the proposed SFPCR scheme and other protocols. Figure 12: Variation in the number of packets according to the payload size (B) with a comparison between the proposed SFPCR scheme and other protocols.
# Projection Pursuit Multivariate Sampling of Parameter Uncertainty
## 2 Sampling Techniques
### Projection Pursuit Multivariate Transform
Transform \(\mathbf{Y}^{\prime}\) to the standard Gaussian values \(\hat{\mathbf{Y}}\) so that the projection \(\hat{\mathbf{p}}=\mathbf{\theta}\hat{\mathbf{Y}}\) is univariate Gaussian. The steps for Gaussian transformation along a projection vector of \(\mathbf{Y}^{\prime}\) can be found in [PERSON] et al. [33]. 9. Return to step 7 until the projection index \(I(\mathbf{\theta})\) reaches convergence. The stopping criteria for the optimization can be found in [24]. 10. Establish the final PPMT scores as a matrix \(\hat{\mathbf{Y}}\) where \((\hat{y}_{ij})\in\mathbb{R}^{5x2}\). 11. Draw the probabilities from the standard Gaussian distribution and establish them in a matrix \(\mathbf{D}=F^{-1}[G(\hat{\mathbf{Y}})]\), where \((d_{ij})\in\mathbb{R}^{5x2}\), where \(\mathbf{D}\) indicates a PPMT sampling design. 12. End. The steps given above generate a sampling design through the projection pursuit iterations. In step 1, the random realizations are generated using MCS. In steps 2-4, the realizations are transformed into the standard Gaussian values and its variance-covariance matrix is diagonalized. In step 5, the normalized realizations are sphered. The projection pursuit iterations are carried out in steps 6-9. In step 10, the final PPMT scores are generated, and in step 11, the probabilities \(p_{i}\in[0,1]\), \(i=1,\ldots,n\) are drawn from the standard Gaussian distribution. The Cholesky decomposition (Equation (1)) can be used to impose the linear correlations (or target correlation matrix) among the independent variables. A flowchart indicating the steps of the aforementioned algorithm is presented in Figure 4.  
Considering the two-dimensional sampling space \((k=2)\) and \((n=5)\) realizations, a sampling design generated by PPMT mapping the probabilities according to the given CDF are shown in Figure 5a,b, respectively. Figure 4: A flowchart indicating the steps for generating a sampling design using PPMT. Figure 5: (**a**) A sampling design with \(n=5\) realizations generated by PPMT in a two-dimensional sampling space where the random variables are independent, (**b**) Mapping the quantiles according to the standard Gaussian distribution using the inverse transform method.
# Parasitic Motions of 3-PRS Parallel Mechanisms with Two Different Branch Chain Arrangements
## 1 Introduction
[34] established the workspace of a 3--PRS PM through the stroke limit of the sliding rod and the rotating angle constraint condition of the spherical pair and the revolute pair. [PERSON] et al. [35] analyzed a UPS-UPR-S PM with two degrees of freedom and concluded that the PM has no parasitic motion. As an inherent kinematic feature of a PM, parasitic motion leads to complex kinematics, requires real-time compensation, and hinders calibration. For applied 2R1T PMs as the spindle head of CNC machine tools, it is greatly practically significant to obtain 2R1T PMs with an accurate attitude adjustment ability and a large workspace through kinematic analysis. Many scholars have studied the minimization and elimination of the parasitic motions of 2R1T PMs. The performance of 2R1T PMs is closely related to its two shafts, and parasitic motions may affect the accuracy of end effectors for 2R1T PMs. Since there are countless kinds of 3-PRS PMs under different structural and branched-chain arrangements, it is necessary to study the inherent relation between the parasitic motions and configuration of 2R1T PMs. Parasitic motion varies with the configuration of the PM. On the basis of the relative relationship between the three branch chain planes and the three centers of the spherical joints, this paper analyzes the kinematics of two 3-PRS 2R1T PMs that had different structures and the same branch chains under specific circumstances. Therefore, for the PM with precision requirements, such as machine tool processing, parasitic motion must be analyzed. If a parasitic motion exists and cannot be ignored, topological optimization or precision compensation is needed to eliminate its influence and render the end-effector motion more accurate. Last, two improved 3-PRS PMs without the parasitic motion of different branch chain arrangements were established, which provides a reference for 2R1T PMs used as the spindle head of five-DOF CNC machine tools to provide a high speed and high precision in machining in industrial fields. The remainder of this paper is organized as follows. Section 2 first establishesthe mathematical model of the 3-PRS PM I and the parasitic motions by establishing a closed-vector loop equation. Second, according to the parasitic motion equation, the influence factors of the parasitic motion are analyzed. Lastly, an improved 3-PRS PM I without parasitic motion is presented via the comprehensive optimization of structural and kinematic parameters. In Section 3, the mathematical model and parasitic motions of the 3-PRS PM II are derived, and the influence factors of parasitic motion and the improved 3-PRS PM II are analyzed. Conclusions are drawn in Section 4.
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 3 Results and Discussion  
### Streamline  
Flow patterns of case Q20 h120 d12 (\(Q=0.020\) m\({}^{3}\)/s, \(h_{0}=0.12\) m, \(d_{50}=1.2\) mm) in the stabilized/equilibrium scoured hole at 245 h later were revealed in the previous study by [PERSON] [18]. From the results, stream-wise flow velocity was faster in the vertically upper part (where \(z>\) initial bed elevation) than in the lower part (where \(z<\) initial bed elevation). In the lower part of upstream scour slope, a reverse and circulating movement of sediment particles that were relatively much slower than the depth-averaged flow velocity was captured (Figure 4).  
The flow circulation was also presented in the first plot of Figure 5. Both OpenFOAM and Ansys Fluent showed their ability of capturing this property of the flow by producing a circulation flow right after the transition point of \(x/h_{0}=0\), where the channel bed was set abruptly to change in the roughness. This flow separation phenomenon plays key role in moving or transporting the sediment particles of the channel bed and leads to the development of the scour hole.  
Figure 4: Flow patterns in the upstream scour hole of the equilibrium state (modified from [18]). (**a**) Snapshot of particle movement and (**b**) vertical velocity vector profile.  
As shown in the Figure 5, there is a backflow occurs near the bed in a region called deceleration zone. The size of flow circulation was measured to reach \(x/h_{0}=6.9\) in the laboratory experiment case. However, the numerical results from both models show a little over-prediction, with the separation point located at \(x/h_{0}=9.7\) in the OpenFOAM case and \(x/h_{0}=10.4\) for the Ansys Fluent simulation, which are 1.4 and 1.5 times longer than the experimental case, respectively. This result suggests that both OpenFOAM and Ansys Fluent seem to estimate higher velocities of the flow in this circulation zone than the experimental data. Though both models fail in calculating the exact circulation size, they are in good agreement with each other, with the difference of only 6.7%.
# RCM: A Remote Cache Management Framework for Spark
## 3 Problem Analysis and Modeling
### Data Weight Generation Model  
In the actual process, the result could be from multiple data blocks. After the user initiates a query, different database servers will be accessed. The query of separate servers can be as a sub-request. Then, let \(Query=\{SubReq1,SubReq2,\ldots,SubReqk\}\) be a set of sub-requests of a query. Because all the sub-requests are parallel execution, the response time of the result is the longest time of sub-requests. Therefore, the response time of the result is formalized as shown in Equation (1).  
\[T_{Query}=Max(T_{SubReq1},T_{SubReq2},...;T_{SubReqk}) \tag{1}\]  
where \(T_{SubReq1}\) represents the response time of the first sub-request, \(T_{SubReq2}\) represents the response time of the second sub-request, \(T_{SubReqk}\) represents the response time of the \(k\)-th sub-request. In a period of time, the result may be queried repeatedly. The number of queries is defined as \(NumQ\); then, the total time cost of quering the result is shown in Equation (2).  
\[T_{cost}=NumQ\times T_{Query} \tag{2}\]  
Because the I/O speed of the remote cache server is higher than that of the database server, if the result is hit, the time of querying the result will be reduced. Therefore, the total time cost of querying the result is a positive factor when we consider which should be remained. On the contrary, the data size is a negative factor for the sake of the limited cache resource. The larger the data size, the fewer free resources there are. In the end, the original weight is shown in Equation (3).  
\[weight\_ori=\frac{T_{cost}}{size} \tag{3}\]  
where _size_ represents the data size, and \(T_{cost}\) represents the total time cost of querying the result. By monitoring a large number of jobs, we found that the old data have a lower hit probability than before in the cache. So, the time loss of \(weight\_ori\) can be described with a time loss function. In the weight generation model, the time loss function is designed as shown in Equation (4).  
\[F(weight\_ori)=weight\_ori\times\exp\frac{-y\times(t_{list}-t_{start})}{T_{ recent}} \tag{4}\]  
Figure 2: The process of quering data.  
where \(y\) represents an accommodation coefficient to control the influence of time loss, \(t_{start}\) represents the time saved in the cache, \(t_{last}\) represents the last time of hitting the cache, and \(T_{recent}\) represents the time interval, which is from saving in cache to now.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Design of GA for Exploring Optimal Hyperparameters of LSTM
#### 3.3.5 Updating Population and Termination Criteria of GA  
Since there are \(P\) chromosomes in the original population and \(P\) offspring are generated by crossover and mutation operators, there are a total of \(2P\) chromosomes. We choose only \(P\) chromosomes with high fitness values in order to maintain the size of the population. Specifically, we apply a roulette wheel selection, which assigns each chromosome with a probability to be chosen as a parental chromosome proportional to its fitness value. Furthermore, we define the termination criteria of the GA by using the number of iterations without improvement. This means that we stop the iteration if no enhancement of the solution is observed after a pre-determined number of iterations. This termination condition can prevent inefficient iterations from providing little improvement of the solution while causing a waste of computational resources [36].
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 3 Reptile Search Optimization (RSA) Based MPPT Algorithm
### RSA Algorithm for MPPT
#### 3.1.3 Hunting Phase  
The exploiting behavior shows that crocodiles use two strategies to hunt their prey (hunting coordination and hunting cooperation). These techniques are performed at this stage of optimization to conduct the local search and ensure reaching the optimal solution. The hunting techniques are executed according to following conditions; where: hunting coordination technique is performed when \(\mathbf{t}>\frac{2\mathbf{T}}{4}\mathbf{and}\mathbf{t}\leq\frac{3\mathbf{T}}{4}\) and hunting cooperation is executed when \(\mathbf{t}>\frac{3\mathbf{T}}{4}\mathbf{and}\mathbf{t}\leq\mathbf{T}\)[22]. The duty cycle then; is updated based on the following equation:  
\[\mathbf{D_{i}}(\mathbf{t}+\mathbf{1})=\left\{\begin{array}{ll}\mathbf{D_{Best}}(\mathbf{t}) \times\mathbf{\alpha}\times\mathbf{rand}&\mathbf{t}\leq\mathbf{3}\mathbf{{}^{T}_{4}}\mathbf{and}\mathbf{t}> \mathbf{2}\frac{\mathbf{T}}{4}&(\mathbf{a})\\ \mathbf{D_{Best}}(\mathbf{t})-\mathbf{\eta_{i}}(\mathbf{t})\times\mathbf{\varepsilon}-\mathbf{R_{i}}( \mathbf{t})\times\mathbf{rand}&\mathbf{t}\leq\mathbf{T}\text{ and}\mathbf{t}>\mathbf{3}\frac{\mathbf{T}}{4}&(\bm {b})\end{array}\right. \tag{6}\]  
The evaluation of computational complexity is crucial in determining an algorithm's efficiency, as it influences the execution time and memory resources required for implementation. In this context, the proposed RSA-based MPPT algorithm's computational complexity has been analyzed in detail by [33]. The time complexity of the RSA algorithm is expressed as \(\mathbf{O}(\mathbf{T}\times\mathbf{N})+\mathbf{O}(\mathbf{T}\times\mathbf{N}\times\mathbf{D})\). Where \(\mathbf{T}\) is the number of iterations, \(\mathbf{N}\) is the population size, and \(\mathbf{D}\) is the solution size. This linear growth with the increase in input size makes it suitable for real-time applications. Compared to other MPPT techniques, such as DOA, WOA, GWO, and PSO, the RSA algorithm exhibits lower time complexity, resulting in faster execution times and quicker convergence to the optimal solution. It is important to note that the efficiency of the RSA algorithm might be affected by parameter choices, such as population size, number of iterations, and sensitive parameters (\(\mathbf{\beta}\) and \(\mathbf{\alpha}\)); thus, further optimization may be necessary for optimal performance in various applications.