esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
## 4 The Correlation between the Strength and Ultrasonic P-wave Velocity  
For the examination of the physical characteristics of coal based paste fill materials, emphasis was placed on the relationship between ultrasonic p-wave velocity and both compressive and shear strengths. This relationship was probed using correlation analysis, which sought to elucidate the connection between acoustic features and mechanical robustness, offering valuable insights for practical applications.  
The correlation coefficient, particularly the Pearson correlation coefficient, was employed to quantify the linkage between variables during the correlation analysis. This coefficient ranges from \(-1\) to \(1\), with values close to \(1\) (\(-1\)) indicating a strong positive (negative) correlation, while a coefficient near \(0\) suggests no correlation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Groups**} & \multicolumn{3}{c}{**Influencing Factors**} & \multicolumn{3}{c}{**Test Results**} \\  & \(\mathrm{P_{A}}\) & \(\mathrm{P_{B}}\) & \(\mathrm{P_{C}}\) & \(\mathrm{P_{D}}\) & **Wave Velocity/km\(\cdot\)s\({}^{-1}\)** & **Compressive Strength/MPa** & **Shear Strength/MPa** \\ \hline
1 & 2 & 3.00 & 15 & 27 & 1.826 & 2.387 & 0.537 \\
2 & 2 & 4.50 & 20 & 31 & 1.873 & 2.455 & 0.748 \\
3 & 2 & 6.00 & 25 & 35 & 1.891 & 0.333 & 0.135 \\
4 & 5 & 3.00 & 20 & 35 & 1.596 & 1.393 & 0.484 \\
5 & 5 & 4.50 & 25 & 27 & 2.357 & 5.521 & 1.388 \\
6 & 5 & 6.00 & 15 & 31 & 1.790 & 1.879 & 0.565 \\
7 & 10 & 3.00 & 25 & 31 & 1.922 & 2.888 & 0.591 \\
8 & 10 & 4.50 & 15 & 35 & 1.886 & 1.806 & 0.281 \\
9 & 10 & 6.00 & 20 & 27 & 2.190 & 3.601 & 0.690 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of orthogonal test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**Ultrasonic P-wave Velocity/km\(\cdot\)s\({}^{-1}\)**} \\  & \(\mathrm{P_{A}}\) & \(\mathrm{P_{B}}\) & \(\mathrm{P_{C}}\) & \(\mathrm{P_{D}}\) \\ \hline k1 & 1.863 & 1.781 & 1.834 & 2.124 \\ k2 & 1.914 & 2.039 & 1.886 & 1.862 \\ k3 & 1.999 & 1.957 & 2.057 & 1.791 \\ R & 1.358 & 2.569 & 2.225 & 3.334 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Ranged analysis of test data.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 6 Performance Evaluation
### Reputation Value Change Rate  
In the proposed CRMWSL scheme, vehicles calculate the reputation values of candidate nodes based on local opinions and recommendations from other vehicles. The CRMWSL scheme is compared with the traditional subjective logic (TSL) scheme [29] and the MWSL scheme [30], which is a typical model that uses linear functions to calculate reputation.That is \(T_{i\to j}^{l}=(1-k)T_{\text{ave}}+kT_{\text{las}}\), where \(T_{\text{ave}}={{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{  
Assuming that the probability of abnormal behavior of the abnormal vehicle is 70%, and the abnormal vehicle interacts with other normal vehicles through RSUs, the reputation value of the target RSU decreases over time. Figure 5 shows the change in the reputation of the malicious miner candidates for three schemes: (i) MWSL scheme, (ii) TSL scheme, and (iii) CRMWSL scheme. It can be seen that the CRMWSL scheme updates the reputation value more accurately, resulting in a lower reputation value for anomalous RSUs. After 7 min, the reputation value drops to 0.5, which is lower than that of the TSL and MWSL schemes, implying that the probability of anomalous RSUs being detected is higher in the CRMWSL scheme when the trust threshold is 0.5. Due to the weight of interaction frequency, timeliness, and interaction effects on the recommended and local opinions, the reputation value of RSU nodes in the CRMWSL scheme falls below the trust threshold faster than in the TSL scheme and MWSL scheme. Therefore, the proposed scheme achieves more accurate reputation calculation and is more secure and fair during committee elections.
## 1 Introduction
The lifecycle costs of communication technologies, in particular, are an important area, as they tie up capital over a long period of time. Financial analysis, including life cycle costing, is essential to ensure a comprehensive assessment of the costs and benefits associated with the adoption of a new technology [4]. This paper aims to quantify the economic and financial viability of two promising IoT technologies, NB-IoT and LoRaWAN [5], through a comprehensive life cycle cost analysis using pragmatic computational tools. The life cycle cost analysis assesses the full range of costs and benefits associated with the deployment of NB-IoT and LoRaWAN technologies, including not only the upfront costs but also the costs associated with operations, maintenance, and disposal [6]. The analysis will provide decision-makers with an understanding of the total cost of ownership of these technologies and identify potential areas for cost savings [7]. Furthermore, this paper argues that financial analysis should accompany technology decisions to ensure that both aspects are addressed for the successful adoption of innovative technologies. In the context of the \"not-invented-here\" syndrome [8], which hinders good decisions about innovative technologies, a thorough financial analysis becomes critical. The syndrome can lead to the adoption of innovative technologies without proper financial analysis, resulting in inefficient use of resources, high costs, and ultimately, technology adoption failure. Therefore, this paper proposes a methodology for conducting a life cycle cost analysis of NB-IoT and LoRaWAN technologies to quantify their economic and financial viability [9]. The methodology uses pragmatic computational tools to facilitate the analysis and ensure that it is comprehensive and efficient. In conclusion, this paper argues that financial analysis is essential for technology adoption decisions, and a comprehensive life cycle cost analysis can facilitate the decision-making process. Furthermore, the proposed methodology can be applied to other IoT technologies to provide valuable insights into their economic and financial viability. Ultimately, this can enable organizations to make informed technology adoption decisions, maximize the benefits of innovative technologies, and minimize financial risks [10].
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 2 Related Work  
In this section, an overview is provided of relevant studies regarding the influence of network latency on user experience and performance in immersive media. As studies on multi-user experiences are still scarce in the literature, especially with respect to VR, a set of relevant single-user systems will be discussed first. Afterwards, the connection with multi-user systems is considered. Please note that all mentions of the Mean Opinion Score (MOS) refer to a 1-5 scale, with 1 the lowest- and 5 the highest-rated experience.
## 3 Simulation Results
### Multi Objective Optimization
#### 3.2.2 With Considering the Power Exchange Limitation with the Network  
In order to illustrate the effectiveness of the NSIDE approach in solving the issue, this algorithm is implemented in the problem and its Pareto Front is compared with NSDE and NSPSO methods in Figure 7. It is quite clear that NSPSO is quite weak in solving this issue state and is not capable of solving this problem state, and NSDE has been defeated in all NSIDE cases. However, it has been shown in the preceding section that, without limiting the power exchange of different methods, it leads to similar solutions. The highlight of Pareto Front here is that it allows the user to choose the right answer. For example, a sample response from the Pareto Front with a total cost of $ 748.82 and a pollution of $82.81 is shown in Table 14. By comparing the amount of gas emission and cost mentioned with the single-purpose mode ($661 and 449 kg, respectively); that is, although in the multi-objective  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{6}{c}{**Generation**} \\  & **MT (kW)** & **FC (kW)** & **PV (kW)** & **WT (kW)** & **Storage (kW)** & **Grid (kW)** \\ \hline
1 & 6 & 3.01 & 0 & 0 & \(-10\) & 52.99 \\  
01 & 0 & 0 & \(-10\) & 65.98 \\
24 & 6 & 30 & 0 & 0 & 10 & 12.01 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Power distribution in a Pareto Front sample answer without considering the limits of upstream power exchange.  
Figure 6: Comparing Pareto Fronts in different methods without considering the limits of upstream power exchange (cost and emission based).  
mode, each of the answers alone has a higher value than the single-objective mode, but for the intermediate mode, the answer is good, because both the cost and the contaminant are considered.
# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 2 Related Work
The number density of GCPs was about 5.4 times higher than that of GCPs in Korea. In [19], the authors used Phantom 4 Pro, which is a fixed-wing drone, and by conducting tests with varying numbers of GCPs from 4 to 24, a trend of a decreasing error rate with an increasing number of GCPs was confirmed. In [20], the authors conducted a survey without using GCPs, and their results showed that the error of the Z-axis coordinate value was larger than other values. In [21], a rotary-wing drone was used and the number of GCPs was 9, 19, and 33, while the RMSE values of the X, Y, and Z coordinates were compared. In addition, analyses of how different physical properties of GCPs affect the identification of GCPs from orthophotos [22] helped to improve the understanding of the actual impact of GCPs; the results confirmed that the color, material, size, and shape of GCPs can affect their accurate identification in orthophotos [23]. The study by [PERSON] et al. [24] analyzed the effects of the number and distribution of GCPs, as well as bundle adjustment, on the accuracy of SfM-based UAV surveys. This study showed that while increasing the number of GCPs improves accuracy, the improvement effect becomes insignificant above a certain level. Additionally, it confirmed that the spatial distribution of GCPs can greatly influence accuracy, and the bundle adjustment process also contributes to improving accuracy. In the study by [PERSON] et al. [25], they used an RTK-GNSS UAV and SfM techniques to perform large-scale topographic mapping and proposed a GCP distribution strategy for this purpose. They experimentally analyzed the influence of GCP number and placement on terrain accuracy. The study found that an even the distribution of GCPs plays a crucial role in improving accuracy. In particular, a GCP arrangement spanning the entire area was shown to be important. Synthesizing the findings from these prior studies, optimizing both the number and spatial distribution of GCPs is critical for maximizing the accuracy of UAV-based orthophotos and terrain models. It is efficient to devise a placement strategy that can satisfy the required accuracy with a minimum number of GCPs, considering the scale and terrain characteristics of the target area. Based on these previous studies on GCPs, we can see that GCPs play an important role in improving geolocation/positioning accuracy in various applications of mapping and surveying, especially in applications related to aerial or satellite imagery. Furthermore, to achieve a high degree of positioning/geolocation accuracy when building geospatial datasets of a specific target site, it is believed that by adjusting the number and distribution of GCPs, the desired level of accuracy can be achieved when a minimum number of GCPs is installed relative to the area of the target site.
## 3 Results
### Parameters for Performance Evaluation
#### 3.1.1 Mean Square Error (MSE)  
The performance of the techniques that are used to find localization errors is evaluated by MSE. To calculate the MSE, the location of the attacker is calculated \(t\) number of times, so that coordinated population is determined as (\(a_{1}\), \(b_{1}\)...... \(a_{t}\), \(b_{t}\)). The mean of these coordinates is determined by using (10) [23].  
\[MSE\ \left(\overline{a},\overline{b}\right)=E[(\overline{a}-a)^{2}+\left( \overline{b}-b\right)^{2})] \tag{10}\]  
Here:  
(\(a\),\(b\)) = real coordinates, as in (10).  
\[\overline{a}=\frac{1}{t}\sum_{j=1}^{t}A_{j}\hskip 72.27 pt\overline{b}=\frac{1}{ t}\sum_{j=1}^{t}B_{j} \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Secondary Users (N) & 50 \\ Number of PUFA (n)p & 1 \\ Simulation Area & 30 \(\times\) 30 km\({}^{2}\) \\ PUEA Antenna Height (Ah) & 2 m \\ Crossover Rate (CR) & 0.3 \\ Population Size (M) & 50 \\ SNR & \(-\)10 to 20 \\ Iterations & [1, 150] \\ Noise & AWGN \\ BS Location (CRBS) & Center of the Network \\ SU Locations & Randomly Selected \\ PUEA Location & Random \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and values.  
Figure 3: Deployment of SUs in a CRN with area of 30 km \(\times\) 30 km.
## 2 System Modeling
### DFIG Model  
Usually, the frequency of the oscillation modes with frequencies between 0.2 and 2.5 Hz in the system is lower than the grid frequency. Fast dynamics of the DFIG can be disregarded for the reduction of the model orders. The DFIG machine equations have been described in [15; 16]. Then, the DFIG model can be presented by the following algebraic equation in the dq stationary reference frame:  
\[\left\{\begin{array}{l}\overset{\rightarrow}{u}_{s}=R_{s}\overset{ \rightarrow}{i}_{s}+\overset{\rightarrow}{\psi}_{s}+\overset{\rightarrow}{j \psi}_{s}\omega_{s}\\ \overset{\rightarrow}{u}_{r}=R_{r}\overset{\rightarrow}{i}_{r}+\overset{ \rightarrow}{\psi}_{r}+\overset{\rightarrow}{j\psi}_{r}(\omega_{s}-\omega_{g })\end{array}\right., \tag{8}\]  
\[\left\{\begin{array}{l}\overset{\rightarrow}{\psi}_{s}=L_{s}\overset{ \rightarrow}{i}_{s}+L_{m}\overset{\rightarrow}{i}_{r}\\ \overset{\rightarrow}{\psi}_{r}=L_{r}\overset{\rightarrow}{i}_{r}+L_{m} \overset{\rightarrow}{i}_{s}\end{array}\right., \tag{9}\]  
\[T_{e}=\frac{3n_{p}}{2}R\bigg{(}\overset{\rightarrow}{j\psi}_{s}\overset{ \rightarrow}{i}_{s}\bigg{)}, \tag{10}\]  
where \(\overset{\rightarrow}{u}_{s}=u_{ds}+ju_{qs}\), \(\overset{\rightarrow}{u}_{r}=u_{dr}+ju_{qr}\) are the stator and rotor voltage vectors; \(\overset{\rightarrow}{i}_{s}=i_{ds}+ji_{qs}\) is the stator current vector; \(\overset{\rightarrow}{i}_{r}=i_{dr}+ji_{qr}\) is the rotor current vector; \(\overset{\rightarrow}{\psi}_{s}=\psi_{ds}+ji_{qs}\) is the stator flux vector; \(\overset{\rightarrow}{\psi}_{r}=\psi_{dr}+j\psi_{qr}\) is the rotor flux vector; \(L_{s}\), \(L_{r}\), and \(L_{m}\) are the stator, rotor, and magnetizing inductance, respectively; \(R_{s}\) and \(R_{r}\) are the stator and rotor resistance, respectively; \(\omega_{s}\) is the grid frequency; and \(n_{p}\) is the pole pair of the generator.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 3 Data Preparation and Models Setup
### Data Preparation
On the other hand, cleaned and high-quality datasets can cause reliable models to provide outstanding results. There are many data cleaning methods, for this research, we adopted the method of removing irrelevant values and taking care of some missing values using the interpolation between the nearest two values. Hence, after cleaning and analyzing, the datasets are ready for the next step. After carefully selecting the input features, normalization of the data was applied for some of the ML methods. This was performed by first computing the mean value for each feature, then subtracting this mean value over the entire dataset feature to centralize the data, finally, calculating the standard deviation and dividing the subtracted values by the standard deviation. After that, the processed data were applied to each model and divided into training and testing datasets based on a reliable hyperparameter tuning technique and cross-validating the developed model to provide accurate and stable results. More specifically, of the 865 collected samples, 80% of these samples were used for training and cross-validating each model by dividing these datasets 7-fold to evaluate the model's stability, as to be detailed in Section 3.3. The other 20% of the datasets were used for validating the models' prediction accuracy. Figure 1 depicts the flow chart of the adopted ML-based path loss prediction strategy. Figure 1: Flow chart of the adopted ML-based path loss prediction modeling technique.
## 3 Methodology
### Automatic Dialect Identification
#### 3.2.3 Feature Selection  
High dimensionality in MFCCs may result in higher training and prediction computation costs. Overfitting occurs when the model picks up on the data's noise instead of its underlying patterns. Feature choice aims to find the most pertinent geographies that significantly impact the prototype's action. Reducing the number of features can increase interpretability, decrease training time, and improve model accuracy. The produced MFCCs were subjected to four distinct feature selection techniques. Features were chosen using a statistical approach that combines K-best with ANOVA, based on statistical significance determined using ANOVA. Features with indices 2, 3, 5, 7, and 10 were selected. Two filter-based techniques, [PERSON]'s correlation and [PERSON]'s Tau, were used to evaluate the relationship between features and the target variable. Features 6, 9, 2, 4, and 7 were selected using both approaches. Finally, features 2, 4, 6, 7, and 9 were selected using a hybrid feature selection method that uses RFE to iteratively remove features based on how important they are to a model's performance. We determined that features 2, 4, 6, 7, and 9 were the best MFCC features; these features were used for subsequent analysis.  
Based on a correlation matrix of the selected features, features 2, 4, 6, 7, and 9 were a good pick in terms of redundancy. Most of the chosen features showed very low to moderate absolute correlations, ranging from \(-0.22\) to \(0.23\). The greatest value, \(0.54\), was observed between features 6 and 7, as shown in Figures 5 and 6.  
Figure 4: MFCC feature extraction.  
SHAP charts (Figures 7 and 8) indicate that our model has strong interpretability. Since the selected features (2, 4, 6, 7, and 9) have a substantially greater magnitude and spread across the unselected features, the charts visually verify that these features are, in fact, the most important in the model's decision-making process. Thus, our feature selection process is supported by this visualization, which also clarifies the \"black box\" nature of deep learning models.  
Figure 5: Correlation matrix for all features.  
Figure 6: Correlation matrix for selected features.
## 4 Experiment and Analysis
### Performance Testing and Analysis
#### 4.3.2 Control Group Experiment  
Table 3 shows the performance impact of the sample associative learning on TSMASAM. The precision, recall and F1-score of stacking are 0.8059, 0.8893, and 0.8390. The sample associative learning improves TSMASAM by an average of 15.37% on each index.  
Table 4 shows the performance of the selected base learners (CNN, LSTM, and LENET) in the ensemble learning model on the [14] dataset. It can be seen from the table that LSTM and LENET have better performance in terms of recall rate, while CNN achieves 99.73% in precision, but the recall rate is 0.34% lower than that of LSTM and LENET. On the other hand, our method achieves an average improvement of 7.28% in F1-score over base learners (CNN, LSTM, and LENET).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Stacking** & 0.8059 & 0.8893 & 0.8390 \\
**TSMASAM** & **0.9972** & **0.9996** & **9984** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of the Sample Associative Learning on the model.  
Table 6 shows the impact of different base learners on the detection results of our method. In addition, we also show the average computation time of TSMASAM for each traffic datum in the table. From it, we can see that our method achieves the best performance when the Kernel_size of CNN is 5, the number of hidden layers of LSTM is 3, and the number of hidden layers is 128.  
The analysis of the control group experiment is as follows: introducing a self-attention mechanism in ensemble learning to capture long-term dependencies between data samples can improve the model's detection performance. After adding the self-attention mechanism, the method in this paper improves the original detection index by 15.37% on average. In the experiments of base learners, our method integrates the detection capabilities of each base learner well. In experiments on different datasets, our method shows good generalization ability and performs well on KDD99 and UNSW-NB15. Since the method in this paper seeks to improve the detection performance, such as introducing a self-attention mechanism, the computational complexity is increased. Therefore, it is not suitable for application scenarios with high response speeds.
## 3 Experimental Setup  
In our previous work, we have established some real-time photonics-aided THz seamless integration transmission systems [12; 13; 14; 15]. In order to verify the effectiveness of our proposed algorithms in this paper, we further perform an experimental demonstration for 144\(-\)Gbps photonics-assisted THz wireless transmission at 500 GHz enabled by J-DBN equalization. A detailed description of the experimental setup is shown in Figure 3, including the optical and THz transmitter modules, THz 2 \(\times\) 2 MIMO wireless link, THz receiver module, and the off-line DSP blocks. For a fair comparison, there were four alternative algorithms included in the experiment at the off-line DSP blocks.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control in UAV Development
#### 5.2.3 Advancements in Nonlinear Control  
The 1990s saw a significant advancement in the application of nonlinear control techniques to UAVs. Techniques like backstepping control, sliding-mode control, and Lyapunov-based control were researched and applied to UAVs. These methods aimed to handle the nonlinearities and uncertainties in UAV dynamics [97].