esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
### Results
#### 5.2.4 Computation Latency  
In Table 5, the computation latency of all schemes for DM000 and NM0007 is presented. First, in the first evaluated group, _Spearman_ always showed the highest latency compared with the other schemes, and _J-Index_ showed the next highest latency. Although _Pearson_ was one of the fastest methods, the prediction quality in this scheme was significantly worse than the decision tree-based methods, _Entropy_ and _Gini_. This, again, implies the high efficiency of the decision tree-based methods. Second, the schemes using Spearman's correlation coefficient for attribute reduction always showed an obviously higher latency compared with using Pearson's correlation coefficient. In this case, from the perspective of efficiency, Pearson's correlation coefficient was employed for attribute reduction by default. Third, comparing _Ent_Prs_ with _Gini_Prs_, they had a similar computation latency, where _Gini_Prs_ was 14% slower than _Ent_Prs_ in DM000 and 6% faster than _Ent_Prs_ in NM0007. Thus, with consideration of the prediction quality, _Gini_Prs_ is more recommended. It should be noted that, compared with the modeling latency, the overheads of attribute selection is much lower, especially for deep neural networks such as CNN and LSTM. In addition, once the critical attributes have been selected, they can be used multiple times in disk prediction tasks. Thus, the overheads are negligible.  
Figure 7: Normalized prediction latency for the two models: (**a**) the latency for DM000; (**b**) the latency for NM0007.
## 5 Conclusions and Future Work  
In this paper, a key event detection and event context pulse grooming method is proposed. A method is proposed for the embedding feature representation of the temporal relative position. A temporal non-linear function is also constructed to describe the effect of time on the degree of association between nodes. The subjective use of temporal information in previous studies is avoided. The nodes of the graph are updated using a graph convolutional neural network to extract deep semantic information about individual nodes of a high-quality phrasal graph, thereby enhancing the graph's expressive ability. The summary generation algorithm is applied to each subset of key event news data, which is then sorted according to time span to obtain the final event context, thus avoiding the need to manually generate the relevant descriptions of individual key events. Experiments on the Ebola corpus demonstrate the effectiveness of our proposed approach, achieving higher F-measure and precision values compared to existing baselines methods. There is a little research on this task and the proposed method offers some new ideas and practical implications for current key event detection tasks. For example, it can be used to detect key events in the fields of disease epidemics, natural disasters, etc.  
Considering that the use of the original GCN alone may not be sufficient to extract the deep semantic features of the nodes in the graph, future work will try to use more complex graph networks for extraction. This paper currently only performs key event detection on news documents under the same top-level topic, and future work will continue to explore the task of simultaneous key event detection from a corpus containing events from multiple top-level topics.  
Figure 4: Impact of parameter \(\gamma\).  
**Author Contributions:** [PERSON] proposed overarching research goals and aims. [PERSON] and [PERSON] performed the experiment. [PERSON] and [PERSON] wrote the main manuscript text. [PERSON] proposed the research framework. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Key Research and Development Program of China under Grant 2020 YFB1406702-3, in part by the National Natural Science Foundation of China under Grant 62006257.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset used for this work is the Ebola News dataset published by [PERSON] [7]. It can be found at [[https://github.com/yzhan238/EvMine/tree/main/data](https://github.com/yzhan238/EvMine/tree/main/data)]([https://github.com/yzhan238/EvMine/tree/main/data](https://github.com/yzhan238/EvMine/tree/main/data)) (accessed on 1 January 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Analysis  
The collected data, including both qualitative and quantitative data, were analyzed using [33], a statistical software commonly used for data analysis. The analysis incorporated both inductive and deductive thematic analysis approaches to capture a comprehensive understanding of the research topic. For the quantitative data obtained from the questionnaires, statistical software was employed to perform a descriptive analysis. This involved generating summary statistics, such as means, frequencies, and percentages, to examine the distribution of responses and identify patterns and trends in farmers' knowledge, attitudes, and practices related to drought management in cattle farming. These analyses helped to provide a quantitative overview of the participants' perspectives. The qualitative data, including interview transcripts and focus-group discussions, underwent a rigorous analysis process. Firstly, the data were transcribed to ensure accurate representation. Then, a coding system was developed, whereby segments of the data were assigned specific codes based on their content. These codes helped to categorize and organize the data for further analysis. A thematic analysis was applied to identify key themes and categories that emerged fromthe data. This involved examining the coded segments, identifying recurring patterns, and exploring the relationships between different themes. The analysis aimed to capture the nuances and complexities of farmers' perceptions, beliefs, and experiences related to drought management in their cattle farming systems. By integrating the quantitative and qualitative data, a triangulation approach was adopted to gain a more comprehensive understanding of the research topic. This integration allowed for the validation and convergence of findings from different data sources, enhancing the overall robustness of the study. Additionally, a multiple regression analysis was employed to examine the relationship between the dependent variable--which, in this case, was farmers' willingness to adopt drought-resistant farming practices--and various independent variables. These independent variables may have included factors such as farmers' perception of the severity of drought, education level, income level, and other relevant variables. The multiple regression analysis helped us to assess the impact and significance of these independent variables on farmers' willingness to adopt drought-resistant practices, providing quantitative insights into the factors influencing farmers' decision-making processes. Overall, the data analysis process involved a combination of statistical analysis for quantitative data and thematic analysis for qualitative data, allowing for a comprehensive exploration of the research objectives and facilitating evidence-based conclusions.
## 5 Experiment
### Experimental Result  
#### 5.3.1 Data Processing  
Feature Selection  
The C-MAPSS dataset includes a total of 21 monitored parameters collected by sensors. The trends of raw monitoring parameters from a single engine are illustrated in Figure 5. The trends of \(T_{2}\), \(P_{2}\), \(P_{15}\), \(epr\), \(B_{fa}\), \(N_{fdmd}\), and \(NR_{fdmd}\) are not significant, indicating that these parameters have minimal influence on the remaining life of the engine. To reduce data redundancy, these seven parameters were removed. On the other hand, \(T_{24}\), \(T_{30}\), \(T_{50}\), \(P_{30}\), \(N_{f}\), \(N_{c}\), \(P_{30}\), \(phi\), \(N_{Rf}\), \(N_{Rc}\), \(BPR\), \(Bleed\), \(W_{31}\), and \(W_{32}\) show clear monotonic trends with increasing engine operation cycles, suggesting a certain correlation with the engine degradation process. Therefore, these 14 monitored parameters were selected as input variables for the prediction model.  
Figure 5: Trend of raw monitoring parameters for single engine.  
Data Normalization and RUL Labeling  
Since the experimental data from CMAPSS consists of multiple parameters monitored by different sensors, these parameters exhibit varying units and significantly different scales. To mitigate the errors caused by dimensional discrepancies during model training, this study employed the MinMax scaling method to normalize the 14 monitored parameters in the dataset. The specific formula is as follows [32]:  
\[x_{i}^{\prime}=\frac{x_{i}-x_{i}^{\text{min}}}{x_{i}^{\text{max}}-x_{i}^{\text {min}}} \tag{14}\]  
In the equation, \(x_{i}\) and \(x_{i}^{\prime}\) respectively denote the monitor data and the data after normalization of the ith monitored parameter from the sensor; \(x_{i}^{\text{max}}\) and \(x_{i}^{\text{min}}\) represent the maximum and minimum datas of the ith monitored parameter from the sensor.  
Figure 6 illustrates the effects of normalization on the 14 types of monitoring data for a single engine.  
The paper employed piecewise linear functions to describe the RUL of turbofan engines, as shown in Figure 7. In the initial stages of engine operation, wear was minimal, and the RUL remained stable. As the engine reached a certain degradation threshold, performance started to decline, causing the RUL to gradually decrease until reaching its end of life. The formula for calculating the segmented linear degradation curve of the engine RUL is as follows [33]:  
\[RUL=\left\{\begin{array}{l}RUL_{\text{init}}\,,RUL_{i}\geq RUL_{\text{init} }\\ RUL_{i},RUL_{i}<RUL_{\text{init}}\end{array}\right. \tag{15}\]  
In the equation, \(RUL_{\text{init}}\) represents the initial degradation threshold of the engine, and \(RUL_{i}\) denotes the RUL of the engine in the ith cycle period.  
Figure 6: Effect of normalization on 14 types of monitoring data from a single engine.
## 3 Damage Analysis
### Numerical Modeling
In order to improve the overall computational efficiency and ensure the computational accuracy, the local mesh refinement method is used to divide the model into two density meshes, where the impact penetration part is encrypted mesh, and the two are connected by the trapezoidal transition mesh co-node method, as shown in Figure 20.  
The fragment is set up with * CONTACT_AUTOMATIC_SURFACE_TO_SURFACE automatic face-to-face contact and * CONTACT_AUTOMATIC_SINGLE_SURFACE automatic single-sided contact between the fragment and the structure. Typical damage modes of the composite bottom skin and stringer obtained by experiments and simulation are shown in Figures 21 and 22, respectively. In terms of characteristic damage size, the diameter of openings and penetrations obtained from the simulation is close to that of the test. Since the composite simulation model uses 2D shell unit modeling, it cannot simulate the damage morphology of fiber fracture and spalling, and the Mat_Composite_Damage model does not consider the effect of temperature on overall damage. However, there is a small amount of fiber-melting phenomena in the actual test. Therefore, the characteristic damage size of a composite obtained from simulation is relatively small compared with the actual one, but the relative error is not big, and it can meet the requirements of battle damage size prediction to some extent. In terms of the Al alloy side damage morphology, the simulated results are in high agreement with the test, and the difference in feature size is small, as shown in Figure 23. \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Symbol** & **T300/QY8911** \\ \hline Density & R0 & 1.6 \\ Elastic modulus along the a direction & EA & 1.32 \\ Elastic modulus along the b direction & EB & 0.073 \\ Elastic modulus along the c direction & EC & 0.073 \\ ba/ca Poisson’s ratio & PRBA/PRCA & 0.03 \\ cb Poisson’s ratio & PRCB & 0.31 \\ Shear strength & SC & 0.00079 \\ Tensile strength along the a direction & XT & 0.049 \\ Tensile strength along the b direction & YT & 4.8 \\ Compressive strength along the b & YC & 0.002 \\ direction & & \\ \end{tabular}
\end{table} 
Table 2: T300/QY8911 material parameters [14]. Figure 19: Finite element model of spherical fragments. Figure 23: Damages of metallic top skin. Figure 22: Damages of composite stringer. Figure 20: Finite element model of the structure. Figure 21: Damages of composite bottom skin damage. Figure 24 shows the kinetic energy curve of the fragment at a speed of 2400 m/s (kinetic energy = 6 kJ) from the composite side (Situation 2) and the metal side (Situation 3), respectively. The fragment is almost always linearly decaying during the intrusion. At a constant thickness, the kinetic energy dissipation of the fragment is greater for the carbon fiber composite layer, while the Al alloy layer is insensitive to the kinetic energy dissipation of the secondary penetration of the fragment, and the kinetic energy of the fragment decays rapidly to 0 during the secondary penetration of the composite layer.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 4 Technical Advantages of Our Solution
### Mobile Edge Computing (MEC) Accelerating Signal Processing and Real-Time Ranging  
One of the core innovations of this solution is offloading signal processing tasks to MEC servers, which use the low-latency and high-bandwidth edge computing capabilities to process signal data and perform real-time ranging computations. Unlike traditional centralized computing models, MEC places computation tasks on edge nodes close to the users, significantly reducing signal processing latency and improving the real-time performance of ranging. MEC not only alleviates the computational burden on the core network but also optimizes resource scheduling and load balancing, ensuring the system operates efficiently in complex environments. Especially in 5G ultra-dense network scenarios, MEC better supports the concurrent ranging demands of large-scale devices, enhancing overall system response speed and accuracy.
## 3 Modelling and Simulation
### Mode #3 Non-Harmonic Supply and Linear R-L Load
#### 3.3.3 2-Phase Inverter Type of VSI with Linear RL Load  
There is a considered system having two 1- phase inverters with PWM and supplying linear RL load, as in Figure 21c.  
Parameters for steady states at \(t\in\langle 0.0;0.1\rangle\) and at \(t\in\langle 0.1;0.2\rangle\) are given in the Table 33:  
Power component average values, and classical calculus-assuming harmonic currents, are given in Table 34.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Load** & \(R\) [\(\Omega\)] & \(L\) [mH] & \(Z\) [\(\Omega\)] & \(\tau\) [ms] & \(\varphi\) [\({}^{\circ}\)el.] & \(\cos\varphi\) [\({}^{-}\)] & \(\sin\varphi\) [\({}^{-}\)] \\ \hline before & 18.4 & 43.93 & 23 & 2.39 & 36.76 & 0.8 & 0.6 \\ after & 9.2 & 43.93 & 16.59 & 4.78 & 56.33 & 0.5545 & 0.8322 \\ \hline \hline \end{tabular}
\end{table}
Table 33: Load parameters for steady states before and after load change.  
Figure 23: VSI inverter voltages (**a**) and currents (**b**) in \(a,b,c,d,e\)- coordinate system during the transient.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & \(S_{av}\) [VA] & \(P_{av}\) [W] & \(Q_{av}\) [Var] & \(D_{av}\) [VAd] & \(PF\) [\({}^{-}\)] & \(THD_{i}\) [\%] & \(P_{1 av}\) [W] & \(P_{0 ap}\) [W] \\ \hline t = 0.1 s & 11,154.5 & 8159.6 & 6126.0 & 4507.3 & 0.7315 & 0.0 & 8106 & 0 \\ \hline t = 0.2 s & 15,472.0 & 7847 & 11,778.8 & 6251.2 & 0.5071 & 0.0 & 7846 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 32: Simulation results before and after the change (at steady states).  
The simulation was performed under the same load phase parameters; therefore, the magnitudes are proportionately different.  
The waveforms of apparent, active, and reactive (blind) power components during transient states for 3-, 5-, and 2-phase systems look similar to those under harmonic supply (Figure 10(a)-d). However, the distortion component of power is different substantively and features nearly 42% of total power. Therefore, the supply voltages of the inverters were adapted to be the total apparent power the same as under harmonic supply (approx. 16 kVA/5-phase system, Figure 24(a) vs. Figure 10(a)). The reason was an easier comparison between inverter- and harmonic supply. During the transient state, the distortion component is characterized by a typical overshoot, Figure 24(d). Note to create the 2-phase inverter network. Two single full-bridge inverters were used. Distortion components of all connections are rather high due to the impulse character of the voltages produced by VSI inverters. Therefore, the power factors are also rather poor.  
The reference currents \(i_{\mathit{aref}}(k)\) and \(i_{\mathit{Ref}}(k)\) for compensating by power active filter (PAF compensator) are calculated using Equation (27a,b). Before this, they should be back-transformed into \(a,\ b,\ c\)-phase system.
## 3. Results and Discussion  
### Yield, Neutral Sugar, Glucuronic Acid and Protein Content of FPs  
The extraction rate, total sugar content and protein content of FPs were analyzed, and the results are shown in Table 1. The extraction rates of FPs after FD, VD, FID and HD were 2.49%, 1.66%, 1.94% and 1.57%, respectively. The results showed that there were significant differences in the extraction rate of FPs after four drying methods, and the extraction rate of freeze-drying was the highest. This result is in consist with [PERSON] et al. [16], who reported the highest extraction rate of polysaccharides after freeze-drying lychee pulp. [PERSON] et al. [34] also reported that the extraction rate of polysaccharides from freeze-dried mulberry leaves was higher than that of hot air and air-dried. The extraction rate of polysaccharides is closely related to the tissue structure of the sample itself [14]. The increase in temperature tends to cause aggregation of the tissue structure of figs. Therefore, the low-temperatureenvironment of the FD process allows figs to maximally maintain their tissue structure state, leading to the highest extraction rate compared to HD, VD and FID.  
As shown in Table 1, the neutral sugar and uronic acid contents of different FPs varied significantly (\(p<0.05\)). FPH had the highest neutral sugar content (63.29%), followed by FPV (62.84%), FPF (60.04%) and FPFI (58.09%). The contents of uronic acid in FPF, FPV, FPFI and FPH were 25.56%, 14.34%, 19.84 and 13.51%, respectively. This indicates that FPF has the highest content of uronic acid and FPH has the lowest. The uronic acid content of FPF was 12.05% higher than that of FPFI, showing a significant difference (\(p<0.05\)). The total sugar content of FPF was 85.6%, which was higher than that of FPFI (77.93%), FPV (77.18%) and FPH (76.80%). According to previous reports, the sugar and uronic acid contents are influenced by environmental temperature and oxygen concentration during the drying process, as well as by the activity of certain polysaccharide hydrolases and uronic acid enzymes [16; 35]. It can be inferred that the activity of polysaccharide hydrolase and glucuronidase in figs may have been suppressed by the low-temperature and low-oxygen environment during the freeze-drying process, leading to the highest retention of total sugar and glucuronic acid content.  
FPH had the highest protein content at 1.33%, while the protein contents of the other three FPs ranged from 0.96% to 1.13%, showing no significant differences, as shown in Table 1. Our results are consistent with the previous report by [PERSON] et al. [36], which indicated that tea polysaccharides treated with the Sevage method still retained detectable protein content. It is speculated that natural polysaccharides are often bound to proteins to exhibit biological activity, and the Sevage method denatures free proteins in polysaccharides, but cannot remove bound proteins.
## 4 Proposed Hybrid FVIMDE Algorithm
### Hybrid FVIMDE Algorithm Stages  
The Hybrid FVIMDE Algorithm as shown in Algorithm 1, consists of two main stages, each designed to complement the other in optimizing complex functions by effectively balancing exploration and exploitation. The source code is available at [[https://www.mathwo.rks.com/matlabcentral/fileexchange/171204-hybrid-four-vector-intelligent-metaheuristic-c-with-de](https://www.mathwo.rks.com/matlabcentral/fileexchange/171204-hybrid-four-vector-intelligent-metaheuristic-c-with-de)]([https://www.mathwo.rks.com/matlabcentral/fileexchange/171204-hybrid-four-vector-intelligent-metaheuristic-c-with-de](https://www.mathwo.rks.com/matlabcentral/fileexchange/171204-hybrid-four-vector-intelligent-metaheuristic-c-with-de)) (accessed on 17 July 2024).  
```
1:Initialize population \(\mathbf{P}\) of size \(N\times D\) within the bounds \([\text{lb},\text{ub}]\) using Equation (4).
2:Evaluate the fitness of each individual in \(\mathbf{P}\).
3:Set\(\text{DE\_iter}=\frac{\text{Max\_iter}}{2}\) (number of iterations for DE phase).
4:for each iteration \(l=1\) to DE_iterdo
5:for each individual \(i=1\) to \(N\)do
6:Generate mutant vector \(\mathbf{V}_{i}\) using Equation (5).
7:Generate trial vector \(\mathbf{U}_{i}\) using crossover as shown in Equation (6).
8:Apply boundary handling to \(\mathbf{U}_{i}\).
9:Evaluate the fitness of \(\mathbf{U}_{i}\).
10:Perform selection between \(\mathbf{P}_{i}\) and \(\mathbf{U}_{i}\) using Equation (7).
11:endfor
12:Update the best solution \(\mathbf{P}_{\text{best}}\) if a better solution is found.
13:endfor
14:Initialize positions for FVIM with \(\mathbf{P}_{\text{best}}\) as the alpha particle's position.
15:Set FVIM_iter = Max_iter - DE_iter (number of iterations for FVIM phase).
16:for each iteration \(itr=1\) to FVIM_iterdo
17:Update the coefficient \(a\) using Equation (10).
18:for each individual \(i=1\) to \(N\)do
19:for each dimension \(j=1\) to \(D\)do
20:Calculate the position updates \(\mathbf{X}_{i,j}^{1},\mathbf{X}_{i,j}^{2},\mathbf{X}_{i,j}^{3},\mathbf{X}_{i,j}^ {4}\) using Equation (9).
21:Update the position \(\mathbf{X}_{i,j}\) using Equation (8).
22:endfor
23:Evaluate the fitness of \(\mathbf{X}_{i}\).
24:Update Alpha, Beta, Gamma, and Delta positions if necessary.
25:endfor
26:Record the best solution \(\mathbf{P}_{\text{best}}\).
27:endfor
28:Return the best solution \(\mathbf{P}_{\text{best}}\) and its fitness value.
```  
**Algorithm 1** Hybrid FVIMDE Algorithm Pseudocode