esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 4 Discussion
### Tracking the E2E Loss Threshold  
The agent's main task is to select an appropriate subset of the available connections to avoid E2E losses. Since we transmitted the information traffic together with replicas, the data were considered lost if both the information bits and their replicas were lost. The E2E loss threshold was set to 5%. As explained earlier, we set the loss probability for LOS to 0% and that for noLOS to 100%. The results presented in Figure 7 show the E2E loss rate achieved by our AC agent, the optimal policy and an RR scheduling scheme that did not use redundancy and transmitted only over a single path in a round-robin fashion without considering the channel conditions of each path. It is shown that our AC agent was able to track the predefined E2E loss threshold of 5% and achieve an average loss rate of 5.12%, which was very close to the loss rate of 4% achieved by the optimal policy. It outperformed the RR system, which had a loss rate of 8%. In each context, the agent exhibited the typical learning behavior of an RL model. At the beginning of a context, the loss was high because the model was still learning and searching for the optimal scheduling pattern according to the channel conditions of that context. However, after convergence, the loss decreased toward the end of the context until a new context began. As can be seen from Figure 7, in contexts 2, 3 and 4 with satellite elevation angles of 45\({}^{\circ}\) and 60\({}^{\circ}\), the agent experienced higher losses compared with contexts 1, 5 and 6 with elevation angles of 60\({}^{\circ}\) and 70\({}^{\circ}\), despite the small difference in elevation angles. This was because the LOS probabilities were quite low at 45\({}^{\circ}\) and 60\({}^{\circ}\) compared with the probabilities at an elevation angle of 70\({}^{\circ}\). For example, while the minimum LOS duration at 70\({}^{\circ}\) was 118, it was 10 at 60\({}^{\circ}\) and 45\({}^{\circ}\) (see Table 1). According to Equation (8), this affects both the LOS probability and the loss probability. For this reason, the losses in contexts 2, 3 and 4 were high, while the losses in contexts 5 and 6 were well below the threshold in all models.
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 5 Experimental Validations
### Motion Resolution Tests  
The resolution of the movement of the adjustment mechanism was measured using a micrometer with an accuracy of one \(\upmu\)m. The micrometer was placed at the center of the secondary mirror barrel to measure the displacement of the secondary mirror barrel in the z-direction, as shown in Figure 10. According to the design resolution, the adjustment mechanism's resolution was evaluated by starting with 1 \(\upmu\)m and increasing the step size with 1 \(\upmu\)m increments. When the step size reached around 5 \(\upmu\)m, the output motion was more stable than the initial state; therefore, the resolution of the sub-mirror adjustment mechanism in the z-direction was evaluated in 5 \(\upmu\)m steps. A control program along the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modal** & **1** & **2** & **3–4** & **5–8** & **9–10** \\ \hline Frequency/Hz & 61.24 & 74.77 & 157.33 & 348.73 & 434.64 \\ \hline \multirow{3}{*}{Vibration type} & The vibration of the secondary mirror chambers up and down & Secondary mirror chamber rotation & Secondary mirror chamber translation mode & Blade twist vibration & Complex Modes \\ \hline \hline \end{tabular}
\end{table}
Table 1: First tenth-order modal values of the SMAM.  
Figure 8: (**a**) First-order modal vibration diagram; (**b**) Second-order mode vibration diagram.  
Figure 9: Prototype of the SMAM.  
z-direction in 25 steps monitored the gradual movement of the adjustment mechanism. Each step followed the measured displacement output of the SMAM along the z-direction, as shown in Figure 11.  
Figure 11 shows the results of the motion resolution test of the adjustment structure in the z-direction displacement, where the resolution of the adjustment mechanism in the z-direction translational motion is 5 \(\pm\) 1 \(\upmu\)m.
## 5 Simulation Verification
### Simulation Model  
For verification of the developed current analysis method of the hybrid MMC, a two-terminal MMC-HVDC topology is constructed (Figure 12), which is developed in PSCAD/EMTDC, with a time step of 50 \(\upmu\)s. Hybrid MMC\({}_{1}\) and MMC\({}_{2}\) operate as sending-end and receiving-end converter stations, respectively. The fault current contributions from these converters are denoted as \(I_{1}\) and \(I_{2}\), referenced from sending-end to receiving-end. The AC outer loop of both hybrid MMCs controls the average SM capacitor voltage. MMC\({}_{1}\) operates in active power control mode, whereas MMC\({}_{2}\) regulates the DC voltage. The hybrid MMC integrates HBSMs and FBSMs in a balanced 1:1 configuration ratio. This configuration enables \(M_{dc}\) to achieve an operational domain of [\(-\)0.1, 1.0] [32]. System parameters are given in Table 1. The 200 km DC overhead line exhibits per-unit parameters of 0.014 \(\Omega\)/km resistance and 0.82 mH/km inductance. A DC fault with 0.01 \(\Omega\) resistance is initiated at \(t_{0}\).
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 2 Framework  
To demonstrate the concept of the theoretical uncertainty approach applied to actual data, we construct an uncertainty model for MODIS LST data. This necessitates generation of new retrieval coefficients, and thus we first present the retrieval algorithm and summarise the steps taken to produce the coefficients.
## 3 The Proposed Distributed SPARQL Query Processing Scheme
### Query Processing Procedure
Once the path of \(GQ\) move is determined, whether it is a join operation \(\left(T^{j}_{GQ1-GQ2}\right)\) or union operation \(\left(T^{t}_{GQ1-GQ2}\right)\) is determined according to \(GQ_{1}\) and \(GQ_{2}\). For example, if \(GQ_{1}\) and \(GQ_{2}\) are not the same \(SG\) and have a join variable called B(X), a query path cost can be calculated through join operation. On the other hand, if \(GQ_{1}\) and \(GQ_{2}\) have the same \(SG\), the query path cost is calculated through the union operation. \[T^{total}_{GQ1-GQ2}=\left\{ \begin{array}{l}T^{wait}_{GQ1-GQ2}+T^{j}_{GQ1-GQ2 }\\ T^{wait}_{GQ1-GQ2}+T^{t}_{GQ1-GQ2}\end{array} \right. \tag{2}\]  
\[T^{wait}_{GQ1-GQ2}=\max\Bigl{\{}\left(T^{l}_{GQ2}+T^{r}_{GQ2-GQ1}\right),\;T^{ l}_{GQ1}\Bigr{\}} \tag{3}\]  
Once the join and union costs between groups by each node are calculated, the calculated costs are stored in the table in ascending order. The least cost of the query path stored in the table is selected first. When all the groups by all nodes are selected, the cost is calculated again through Equation (2) using the selected query path. Finally, this calculation is iterated until all groups by all nodes generate a single query path. Once the iterations are complete, the query paths determined through Equation (2) are sorted out sequentially to create a query execution path. Figure 6 shows the operation of the ETE algorithm. The cost is calculated through the ETE algorithm for the groups in each node according to whether the join operation can be performed. The least \(GQ_{1}\cup GQ_{2}\) is selected after comparing the calculated costs, and \(GQ_{3}\bowtie GQ_{4}\) is selected, which is the least cost calculated with groups other than the selected groups by node. Step 1 in Figure 6a shows the first iteration of the ETE algorithm, which is iterated until the order is determined after all groups are selected. Figure 6b shows the query processing order based on the groups by node. Once the query processing execution path is determined, the master sends the query processing execution path to each node and RDDs to the slave. The slaves that receive the RDDs perform query processing tasks according to the query processing execution path. The node that has the final result sends it to the master again to finish the query processing.
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 5 Conclusions  
In this paper, a leak detection and classification model of a boiler tube was presented. The main objective of this work was to perform data-driven health assessment of boiler tubes that are normally used in thermal power plants. To achieve this, acoustic emission signals were recorded for normal and leaked states (0.63 mm, 1 mm, 2 mm) of a boiler tube. To explore the salient information of the AE signals, wavelet packet decomposition of the signal was carried out. The relative wavelet packet energy and entropy of the nodes were considered as features for leakage detection and the classification model. The wavelet packet decomposition provides a unique pattern for the feature distribution that could easily help differentiate between the health states of a boiler tube. The wavelet features extracted were provided to a deep neural network (DNN). Stacked autoencoders could easily extract salient information from the well clustered input data. The results of the proposed model were superior compared to the other state-of-the-art fault diagnosis algorithms. The proposed model yielded an average classification accuracy of 99.25%, which is at least 4.25% better than any of the state-of-the-art fault diagnosis algorithms to which it was compared. Therefore, it can be concluded that the proposed model can be used to effectively detect and classify leaks in a boiler tube.  
All of the authors contributed equally to the conception of the idea, the design of experiments, the analysis and interpretation of the results, and the writing of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20161120100350). The authors declare no conflicts of interest.
## 4 Results and Discussion
### Machine Learning Results Discussion
Finally, to demonstrate that the chosen approach of data augmentation has allowed the numerical modeling to preserve the meaning of the experimental data, Figure 11 shows the contour maps of the percentage variation (\(PV\)) of the seven target variables between the asphalt mixes with SF and OPC, for the TanH-SNN model, and the maxima interpolation of experimental results. By way of example:  
\[PV_{MS}(\%)=\frac{MS_{SF}-MS_{OPC}}{MS_{OPC}}.100 \tag{8}\]  
Figure 11: _Cont._  
Figure 11: Contour maps of the percent variation of air voids (**a**), voids in the mineral aggregate (**b**), and Marshall stability (**c**) between SF and OPC mixes for the TanH-SNN model and the maxima  
interpolation of experimental results. Contour maps of the percent variation of Marshall stability after aging (**d**), Marshall quotient (**e**), indirect tensile strength (**f**), and water sensitivity (**g**) between SF and OPC mixes for the TanH-SNN model and the maxima interpolation of experimental results. In particular, the two plots are quite similar from a qualitative and quantitative point of view. The TanH-SNN not only succeeds in reproducing the same percentage variation in the experimental grid points (black cross markers in Figure 11) but allows the empirical trend of the \(PV\) variable in the domain of interest to be properly represented. Moreover, the results of these maps further confirm that silica fume leads to slightly better mechanical performance when used as filler compared to ordinary Portland cement.
## 2 Impact Resistance Theories  
Since the composite structure of SC walls consists of a front concrete plate attached to a rear steel plate, the ballistic performance of SC walls should be roughly separated into three parts: penetration of the front concrete, perforation of the steel plate and interaction with both concrete and steel. When a projectile perforates a concrete panel with a certain thickness it goes through three response stages: front crater scabbing, stable tunneling, and rear crater plugging (spalling) [16]. The impact perforation response of thin steel plates has been successfully analyzed by the petaling theory proposed by [PERSON] [17].  
This work developed a semiemperical analytical model of projectile penetration of steel plate reinforced concrete walls based on five penetration stages, as plotted in Figure 1. Stage 1 is projectile cratering on the front concrete impact face. Stage 2 represents stable tunneling inside the concrete block. Afterwards, rear face plugging occurs in Stage 3 until the projectile starts to hit the rear steel plate. Stage 4 occurs when the projectile interacts with the steel plate and fragments the concrete rear face. Finally, the projectile head has interaction only the steel plate in stage 5. Hence, the penetration resistance consists of concrete resistance and steel plate resistance. Classical resistance equations are introduced below.
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
## 1 Introduction
[13] extended the realizable \(k-\varepsilon\) model to account for the effects of streamline curvature and system rotation. The coefficients in the \(\varepsilon\)-equation and the damping function for eddy viscosity were modified based on the Richardson number defined by [PERSON] [12]. For more detailed descriptions of the solutions related to the effects of streamline curvature and rotation, interested readers may refer to the review articles [14; 15]. The purpose of this study is to extend an elliptic blending model (the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model)--which has been shown to perform well in several turbulent flows ranging from a two-dimensional (2D) channel to an impinging jet--in order to sensitize it to streamline curvature effect. Two methods are introduced, and their performances are assessed concerning typical benchmarks. The turbulence modeling and its corrections for streamline curvature are briefly described in the following section. Then, the performance of the models is assessed through three typical flow configurations: 2D infinite serpentine passage flow, 2D U-turn duct flow, and flow over the 2D periodic hill. Our conclusions are presented in the final section.
## 2 Materials and Methods
### Modeling with ANFIS  
The data used in the development of the ANFIS model were divided into two sets: 70% for training and 30% for testing the data sets. The membership function parameters were then adjusted and used in the creation of the fuzzy inference system.  
After the data were introduced in the system, the network training proceeded by sequentially selecting the membership functions to be used. The model underwent multiple training sessions utilizing triangle, trapezoidal, and Gaussian membership functions, with each membership function having three functions and a fixed number of iterations set at 1000.  
After all parameters are determined, the training of the model is performed. The training interface of the ANFIS model is shown in Figure 6.  
Figure 6: ANFIS training interface.  
After the prediction values were produced with ANFIS, the predicted values were compared with the actual data.  
The model's error performance values were calculated for the predicted values produced by various functions. As a performance measure, the RMSE, MAPE, and R\({}^{2}\) methods were used. The model's error performance values are given in Table 7. When the error performance values were compared, it was seen that the ANFIS model, which produced the closest prediction value to the real values, was the model trained using the Gaussian membership function.  
Figure 7 shows the comparison of the best single-hidden layer ANN, multi-hidden layer ANN, and ANFIS results with linear fitting results. As can be seen from the figure, the differences between the ANN and ANFIS models in the linear direction are close to each other.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Power** & **TriMF** & **TrapMF** & **GaussMF** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: ANFIS model prediction values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Membership Function** & **RMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline TriMF & 41.40 & 19.82 & 0.9977 \\ TrapMF & 26.39 & 10.39 & 0.9991 \\ GaussMF & 12.94 & 6.02 & 0.9998 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance measures of ANFIS models.
## 3 Methodology and Implementation
### Research Design
#### 3.1.2 Preprocessing Module  
First, data are retrieved from the cloud database and presented in a format suitable for processing. The data are divided into sets according to their nature, namely users, generation, new pictograms, updated pictograms, and deleted pictograms. The user table includes information related to the origin and the number of interactions from each type that the user has generated. The remaining tables include the data described in the previous subsection, adding the corresponding user identifier to each entry to guarantee its traceability.  
For this research, only Spanish-speaking users are considered in the analysis, so the tables are filtered by language. We made this decision as Spanish is the only language represented in the data set with enough samples to conduct an exhaustive study (see Table 2). Free text fields (generated sentences, pictogram names, etc.) undergo minor modifications, like letter case change or graphic accent removal, to maximize coincidences. Major modifications such as lemmatization or stop-word removal are counterproductive, as the phrases' length and complexity are relevant to this research.  
Each entry is associated with a predefined category according to its meaning. The categorization requires manually labeled phrases in order to ensure consistency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entry Type** & **Global** & **Spanish** \\ \hline \multirow{2}{*}{Sentence generations} & Punctual & 79,793 & 72,308 \\  & Cumulative & 887 & 801 \\ \multirow{2}{*}{New pictograms} & Punctual & 2180 & 2096 \\  & Cumulative & 435 & 435 \\ \multirow{2}{*}{Deleted pictograms} & Punctual & 1939 & 1922 \\  & Cumulative & 157 & 157 \\ \multirow{2}{*}{Updated pictograms} & Punctual & 610 & 595 \\  & Cumulative & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global and Spanish distribution of samples by event type.
###### Abstract  
Fiber-reinforced polymers (FRP) are commonly used as internal reinforcement in RC structures in aggressive environments. The design of concrete elements reinforced with FRP bars is usually ruled by serviceability criteria rather than the ultimate limit state. Six continuous concrete beams over two spans with longitudinal and transverse glass FRP (GFRP) reinforcement were investigated until failure to estimate the effects of different reinforcement arrangements on the limit states of continuous beams. The ratio of longitudinal reinforcement between the midspan and middle support sections (i.e., the design moment redistribution) and the type of GFRP reinforcement were the main parameters. The experimental results were compared to prediction models and other code formulations under serviceability and ultimate limit states. The bond-dependent coefficient \(k_{b}\) was investigated to assess adhesion conditions for GFRP reinforcement and concrete. The results showed that moment redistribution in continuous beams with GFRP reinforcement happens with slippage between the reinforcement and concrete in the middle support without the load capacity being reduced. A modified model was suggested for better deflection prediction of continuous beams reinforced with GFRP bars. Based on deformability factors, the tested continuous beams, although containing GFRP reinforcement that has brittle behavior, showed a certain kind of ductile behavior.  
Keywords:GFRP bars; continuous beams; moment redistribution; serviceability; cracks; deflection; bond strength; deformability +
Footnote †: journal: Applied Sciences
## 3 Construction of the DT Model
### Modeling of Power Battery Based on Data-Driven  
#### 3.3.1 Power Battery Mathematical Model  
The solar car power battery is a lithium-ion battery, and the basis of the battery modeling is to determine the characteristic functions of the electric potential and internal resistance of the lithium-ion battery, which are obtained based on the relationship of the battery state of charge value SOC (state of charge) variation [40; 41]. This model includes three parts: calculation of available battery capacity, calculation of battery SOC value, and calculation of battery terminal voltage.  
The available battery capacity is calculated by the following equation:  
\[Q_{u}(t)=Q_{0}-\int_{0}^{t}\lambda Idt \tag{7}\]  
where \(Q_{u}(t)\) is the remaining available capacity at time \(t\), \(Q_{0}\) is the initial capacity, \(\lambda\) is the charge/discharge efficiency, and \(I\) is the battery current. The \(SOC\) value is calculated by the following equation:  
\[SOC(t)=SOC(0)-\frac{\int_{0}^{t}\lambda Idt}{C_{N}} \tag{8}\]  
where \(SOC(t)\) is the \(SOC\) value at time \(t\), \(SOC(0)\) is the initial \(SOC\) value, and \(C_{N}\) is the rated capacity of the battery. The battery terminal voltage is calculated by the following equation:  
\[u(\tau,SOC)=E(\tau,SOC)-IR(\tau,SOC) \tag{9}\]  
where \(E(\tau,SOC)\) denotes the open-circuit voltage as a function of temperature \(\tau\) and \(SOC\); \(R(\tau,SOC)\) denotes the internal resistance of the battery as a function of temperature \(\tau\) and \(SOC\). The correspondence between \(R\) and \(E\) is obtained through experiments, as shown in Figure 6a,b below, respectively. It can be seen that it is difficult to establish  
Figure 5: Motor efficiency MAP.  
the relationship between the terminal voltage of a lithium battery and its measurable parameters by pure mechanistic modeling, which includes temperature, voltage, current, internal resistance and SOC at the current moment. Therefore, the support vector machine (SVM) is introduced to solve this problem.
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 6 Discussion
### Effect of Residential Solar PVs on Systems Efficiencies  
There is a worldwide focus on the installation of clean, green and environmentally friendly renewable energy resources. In line with the argument, we employed solar PV panels on rooftops of homes and studied the power efficiency comparison of the two paradigms, i.e., AC and DC distribution  
Figure 4: System efficiency for 24 h of the AC and DC system.  
systems. The topological designs of the two remained the same in this part of the analysis with a slight modification of supplying 'D' and 'I' category loads with solar PV panels.  
The idea is to supply 'D' and 'I' category loads with a 1-kW solar system on the rooftop, accompanied with the maximum power point tracking (MPPT) converter and a battery bank, and study the comparative analysis of both AC and DC distribution systems. The priority scheme is as follows. When solar energy is available, loads are driven via solar and extra power is used to charge the battery bank. During the periods when solar energy is insufficient to drive loads, the rest of the power shall be taken from the battery storage. When both solar and battery power are insufficient, power is taken from the utility to drive the loads. In order to fit solar in the mathematical model, the solar irradiance data curve [38] was utilized, as shown in Figure 10. The expression for solar power against hours of the day was fitted in the mathematical model for analysis of solar inclusion on the system's efficiencies. The solar power can be given as  
\[P(t)=0.969e^{\frac{-(t-12.24)}{3.075^{2}}}+0.403e^{\frac{-(t-15.74)}{2.162^{2} }}+0.3471e^{\frac{-(t-8.846)}{2.021^{2}}} \tag{18}\]  
where '_P (t)_' is the solar power at time '_t_' of the day. The system's efficiencies were plotted against time as shown in Figure 5.