esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Evolution of Stress and Strain  
To further analyze the evolution of stress and strain, the Von Mises effective stress and effective strain field at macro strains of 0.1%, 0.7%, 1.2%, 4%, 7.8%, and 15.5% are shown in Figure 7. Considering the needs of comparison and analysis, the figures were scaled to the same size. It can be found that the tendencies of Von Mises effective stress and effective strain are somewhat different. At the beginning, the initial stress and strain were relatively small. As the strain increases, the stress also increases. When entering the plastic stage, the stress value increases to a certain value and remains unchanged, while the accumulated strain continues to increase.  
Figure 6: Comparison of slip traces between simulation and experiment of the slip systems activated in different grains (the gray figures were SEM images, and the color figures are simulation results).  
was 720 MPa and the average stress of the \(\beta\) phase was 624 MPa. When the macro strain was 1.2%, the average stress of the \(\alpha\) phase was 1035 MPa and the average stress of the \(\beta\) phase was 1055 MPa, which meant the \(\beta\) phase became the main stress carrier. This was mainly due to the fact that when the macro strain was over 0.7%, the slip systems of the \(\alpha\) phase were activated and the simulation entered the stage of plastic deformation. As a result, the ability of the \(\alpha\) phase to bear stress decreased and the stress transferred from the \(\alpha\) phase into the \(\beta\) phase gradually.  
After reaching the macro strain of 0.7%, as shown in Figure 7g,h, the simulation was in the stage of elastic deformation, where the strain within the grains is a constant of 0.7%. After reaching the macro strain of 1.2%, as shown in Figure 7i, the material began to deform by slip and the strain is locally concentrated. In addition, as shown in Figure 7j-l, it can be seen that the localized strain concentration developed with increasing macro strain. Firstly, after reaching the macro strain of 4%, the maximum strain is 11% and the minimum is 2%. At the macro strain of 7.8%, there are regions where the strain is less than 4% and regions where the strain is more than 16%. Finally, when the macro strain was 15.5%, there was no region where the strain was less than 4% and the region where the strain was greater than 16% increases further. The localized concentration mainly related to the grain orientation changes and the grain interactions, which will be specifically analyzed in Sections 3.3.2 and 3.3.3.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### SADRL Applied to Distributed Agents in a Multi-Agent Environment
#### 6.1.2 [PERSON]'s SADRL Approach in a Multi-Agent Environment  
In [72], [PERSON] et al. embedded SADRL in base stations to select physical resource blocks, particularly the available radio resources, for sharing with other base stations efficiently (A.1). The base stations maximize the global reward in a collaborative (X.2.2) manner. The proposed approach addresses the challenge of high dynamicity (C.2) due to the dynamic condition of the environment, whereby the amount of traffic load varies with the transmission power and frequency of the operating channel.  
Each base station \(i\) represents state \(s^{i}_{t}\) with a two-tuple information, including the number of occupied and available physical resource blocks, respectively. The action \(a^{i}_{t}\) represents selected resource blocks. The reward \(r^{i}_{t+1}(s^{i}_{t+1})\) represents the degree to which the network performance requirements are satisfied. The traditional SADRL approach has been shown to increase throughput (P.3).
# Influence of the Surface Texture Parameters of Asphalt
## 3 Results and Discussion
### Linear Models  
After the texture metrics were cross-validated several times using Weka software, it was determined that the model had the highest correlation and the smallest error when three metrics were used for multifactor linear modeling: root mean square slope \(\Delta\)q, skewness R\({}_{\rm{sk}}\), and kurtosis R\({}_{\rm{ku}}\). The quantitative expression of the multifactor linear model is shown as Equation (10), which has a correlation coefficient of 0.8393, a mean error value of 1.543, and a root mean square slope of 1.8132. The linear correlation between variables can be considered high when the absolute value of the correlation coefficient is greater than or equal to 0.8. The results show that the prediction accuracy of the model is high, and the correlation between the texture index and R\({}_{\rm{L}}\) is good.  
\[R_{L}=-5.3671\Delta q-6.7798R_{sk}-2.664R_{ku}+21.1746 \tag{10}\]  
Figure 10: One-factor modeling of the pavement texture index and reverse reflection coefficient: (**a**) S\({}_{1}\) correlates with R\({}_{\rm{L}}\), (**b**) D\({}_{1}\) correlates with R\({}_{\rm{L}}\), (**c**) S\({}_{2}\) correlates with R\({}_{\rm{L}}\), (**d**) D\({}_{2}\) correlates with R\({}_{\rm{L}}\), (**e**) \(\Delta\)q correlates with R\({}_{\rm{L}}\), (**f**) R\({}_{\rm{sk}}\) correlates with R\({}_{\rm{L}}\), and (**g**) R\({}_{\rm{ku}}\) correlates with R\({}_{\rm{L}}\).  
Nonlinear Models  
In this section, the multilayer perceptron model based on the BP algorithm is used to construct a nonlinear model of the effect of the asphalt-pavement macro- and microtexture parameters on the reverse reflection coefficient. The relationships between the input and hidden layers in the model established in this experiment are shown in Figure 11. The model was obtained by regression using the multilayer perceptron module of Weka software using S\({}_{1}\), S\({}_{2}\), D\({}_{1}\), D\({}_{2}\), \(\Delta\)q, R\({}_{\text{sk
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Theoretical Analysis
When \(n=2\), as is shown in Figure 8, we can consider node \(R_{2}\) as a relay node between nodes \(R_{1}\) and D. In this case, there is only one relay node between \(R_{1}\) and D. Therefore, we have \(E_{R_{1}D}>E_{R_{1}R_{2}}+E_{R_{2}D}\), and combined with \(E_{direct}>E_{SR_{1}}+E_{R_{1}D}\), we obtain \(E_{direct}>E_{SR_{1}}+E_{R_{1}R_{2}}+E_{R_{2}D}\), i.e., \(E_{direct}>E_{2\_node}\);  
Assuming that when \(n=k,k\geq 2\), \(E_{direct}>E_{k\_node}\) holds, i.e., \(E_{direct}>E_{SR_{1}}+E_{R_{1}R_{2}}\)\(+\cdots+E_{R_{k}D}\);  
Then, when \(n=k+1\), as node \(R_{k+1}\) can be regarded as a relay node between \(R_{k}\) and D (similarly when \(R_{k+1}\) is positioned between other nodes), we have \(E_{R_{k}D}>E_{R_{k}R_{k+1}}+E_{R_{k+1}D}\). Consequently, we have:  
\[E_{SR_{1}}+E_{R_{1}R_{2}}+\cdots+E_{R_{k}D}>E_{SR_{1}}+E_{R_{1}R_{2}}+\cdots+E_ {R_{k}R_{k+1}}+E_{R_{k+1}D}\]  
which implies \(E_{direct}>E_{k+1\_node}\). Thus, we have shown that \(E_{direct}>E_{n\_node}\) holds. Case 2: When the relay node is positioned between the source node and the destination node but not directly on the line connecting the source and destination nodes. According to the analysis carried out by the authors in [18] regarding the scenario with only one relay node, we can conclude that when \(d_{SR_{1}}^{k}a(f)^{d_{SR_{1}}}+d_{R_{1}R_{2}}^{k}a(f)^{d_{R_{1}R_{2}}}+\cdots+ d_{R_{k}D}^{k}a(f)^{d_{R_{k}D}}>d_{SD}^{k}a(f)^{d_{SD}}\) is satisfied, multi-hop transmission will no longer be energy efficient. In fact, due to nodes having only the information regarding the next hop and not the information regarding all nodes along the entire path, nodes can only choose the next-hop nodes with higher energy efficiency to forward data and maximize energy utilization. Meanwhile, they also need to consider the forwarding success rate and remaining energy of the next-hop nodes.
# RFID Authentication System Based on User Biometric Information +
###### 1 Introduction
To address the shortcomings of current user authentication methods, we subdivide biometric information into physiological and behavioral characteristics, and we design and implement RF-Ubia, which is a simple and secure user authentication method for different people, supports both password and passwordless authentication methods, and can achieve high recognition accuracy and at a much lower cost than existing work. RF-Ubia consists of nine passive tags forming a cryptographic array, with each tag acting as a cryptographic button, as shown in Figure 1. Users only need to touch the tag surface once to enter a password number. When the user touches multiple tags continuously, the digital sequence obtained is used as the user authentication password. In the process of touching, we also skillfully incorporate the biometric information into tag signals. When different users touch the RFID tags, distinctive phase changes will occur as a result of users' different body impedance. The closer these tags are in the array, the stronger the coupling effect among them will become [8; 9; 10]. When a tag is touched, not only its signal will change, but also the signals of the other eight tags will change due to the coupling effect. These signal changes are highly correlated with the user's body impedance. Normally, the impedance of the human body is about 300-1000 \(\Omega\). Different users have different body impedance, which leads to different changes in the phase signal of the tags. Combined with user's biometric information, different users who entered the same password can be distinguished. The main contributions of our work are summarized as follows:  
* We propose a low-cost simple user authentication method called RF-Ubia, which allows users to effectively resist password theft even with simple passwords. * We propose an extension of the RF-Ubia system that fuses user impedance and behavioral features, allowing users to authenticate users even if they forget their passwords, and ensures security that is largely immune to environmental interference, achieving an average recognition accuracy of 0.94. We improve the traditional anomaly detection and feature extraction algorithms to obtain more fine-grained feature information to improve accuracy. We used Random Forest in Wake to classify, and RF-Ubia was able to achieve an average recognition accuracy of 0.96 while existing work based on template matching or convolutional neural networks (CNN) was below 0.92 under the same conditions.
# Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models
## 2 Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Mean** & **Std. Dev** & **Min** & **Max** \\ \hline \multicolumn{5}{c}{Barriers with crashes} \\ \hline EPDO & 9.3 & 32.898 & 1 & 302 \\ \hline Shoulder width, categorical, cutting point of 5 feet & 0.5 & 0.4999 & 0 & 1 \\ \hline Barrier height (in) & 30.0 & 2.969 & \textless{}12 & 56 \\ \hline Barrier length (ft) & 683.7 & 1181.515 & 20 & 10276 \\ \hline \multirow{5}{*}{Barrier type} & Box beam & Frequency: 883 & \\ \cline{2-5}  & Cable barrier & Frequency: 5 & \\ \cline{2-5}  & Concrete barrier & Frequency: 51 & \\ \cline{2-5}  & W beam barrier & Frequency: 129 & \\ \hline Restraint condition, restrained as 0 versus 1 as others & 0.110 & 0.258 & 0 & 1 \\ \hline Speed compliance, speed limit was compiled as 0 versus 1 as others. & 0.0969 & 0.229 & 0 & 1 \\ \hline \multicolumn{5}{c}{Barriers with no crash} \\ \hline EPDO & 0 & — & — & — \\ \hline Shoulder width (ft) & 7.1 & 2.977 & 2 & 18 \\ \hline Barrier height (in) & 27.9 & 5.501 & \textless{}12 & 44 \\ \hline AADT (Average annual daily traffic) & 3049 & 1240.090 & 750 & 6019 \\ \hline Barrier length & 358 & 318.185 & 32 & 2711 \\ \hline \multirow{5}{*}{Barrier type} & Box beam & Frequency: 91 & \\ \cline{2-5}  & Cable barrier & Frequency: 172 & \\ \cline{2-5}  & Concrete barrier & Frequency: 7 & \\ \cline{2-5}  & W beam barrier & Frequency: 43 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of barriers with and without crashes. Figure 1: Distribution of traffic barrier equivalent property damage only (EPDO) crashes.
## 5 Conclusions  
Waveform inversion techniques provide information for the study of the dynamic processes of a landslide. In this study, the Taimali landslide of 2009 was chosen for the implementation of waveform inversion using the PFH method. According to the pre- and post-event DEMs, the source area of the Taimali landslide had an area of \(9.3\times 10^{5}\) m\({}^{2}\) and a volume of \(7.5\times 10^{7}\) m\({}^{3}\). PFH inversion presented the complicated force-time functions and two impact timings during the propagation of the landslide. Thus, the 174-s total of the dynamic processes can be divided into three stages for the different sliding directions: northeast for the first stage, northwest for the second stage, and northeast for the third stage. The average sliding velocities of the three stages were 32.5, 29.4, and 7.5 m/s, respectively. The inversion results indicated that the PFH inversion method could extract the dynamic processes of the landslide, even if the directions of landslide movement varied during propagation. The study also proved that the single-station waveform inversion was feasible by using the PFH inversion method with high-quality seismic data.  
Conceptualization, G.-W.L. and C.H.; Formal analysis, G.-W.L. and C.H.; Funding acquisition, G.-W.L. and C.H.; Validation, G.-W.L. and C.H.; Writing--original draft, G.-W.L. and C.H.; Writing--review and editing, G.-W.L. and C.H. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The correlation between distance to the landslide and residual of single-station inversion.  
**Funding:** This research was funded by the Ministry of Science and Technology of Taiwan.  
**Acknowledgments:** The authors gratefully acknowledge the financial support of the Ministry of Science and Technology of Taiwan and the Soil and Water Conservation Bureau, Council of Agriculture, Executive Yuan of Taiwan. The source of all seismic and rainfall information included in this paper was the Institute of Earth Sciences, Academia Sinica of Taiwan, and the Seismology Center, Central Weather Bureau (CWB), Taiwan. This research was, in part, supported by the Ministry of Education, Taiwan, ROC, and the headquarters of University Advancement to the National Cheng Kung University (NCKU).  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.
## 2 Materials and Methods
As an alternative, machine learning methods are devised to learn directly from the data. The initial challenge associated with text classification is transforming the text into a representation suitable for machine learning models, a process comprising the tasks of feature extraction and feature engineering. Rule-based feature extraction faces similar challenges as rule-based classification methods. At the same time, general text classification and representation learning benefit highly from deep learning approaches [24, 25]. Thus, for this research, we opted for an end-to-end deep learning classification model. Our text classification model consists of a preprocessing pipeline, an embedding layer, a recurrent network layer, and a fully connected layer as an output layer. Namely, we test four recurrent neural networks layers: LSTM [26], GRU [27], and their bidirectional variation [28]. The classifier is trained on a multilabel, multiclass classification using weighted binary cross-entropy loss to account for the classes' imbalance in the training dataset. The loss function is calculated using the equation:  
\[dL=-\Big{[}w_{p}\times y_{true}\times\log\Big{(}y_{pred}\Big{)}+w_{n}\times(1- y_{true})\times\log\Big{(}1-y_{pred}\Big{)}\Big{]}\]  
where \(w_{p}\) and \(w_{n}\) are positive and negative class weights, respectively, for each concept class. Our classification model is achieved as follows: First, the FMEA dataset is split into three datasets: training, testing, and development. The training and development datasets are used in the training phase. During the training phase, preprocessing is learned on the training dataset. The preprocessing pipeline is responsible for text data cleaning and transformation into a numerical representation using techniques of abbreviation substitution, text cleaning, and text tokenization. The resulting text representation is sparse. Next, the embedding layer and the recurrent neural network layer try to learn the mapping function of the sparse text representation to the target labels based on the loss function. Here, to  
Figure 3: Concept classes forming a causal chain. There are two types of documents (Process FMEA, Unit Process FMEA), each containing causal relations between concepts. One of the concept classes is shared between the two FMEA types, allowing for linking between the otherwise disjoint documents, finally forming a causal chain of five distinct classes. control the training of the model, the model performance on the development dataset is used for an early stopping approach. Finally, the testing dataset is used to evaluate and report the model performance. In our case, we noticed that the model performance is affected by the dataset splits. Thus, we opted for k-folds cross-validation to adjust for biases induced by dataset splits. Hence, the average model performance is reported.
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 3 Resource Profiling and Performance Modeling
### Resource Profiling and Prediction Models
#### 3.2.4 Adaptive Filter-Based Moving Average (AFMV)  
In order to address more steep fluctuation of resource availability and reflect the recent trend, we have devised Adaptive Filter-based Moving Average (AFMV) model by employing the moving average technique (as discussed in Section 3.2.2). AFMV prediction model applies similar rationale as AFOLR model so that it estimates the previous value \(Y(k-1)\) using moving average technique over \(w\) intervals. AFMV model can be expressed in the following generalized form:  
\[Y(k)=(1-\alpha)*Y_{MV}^{w}(k-1)+\alpha*u_{scout}(k) \tag{16}\]  
Generally speaking, it is difficult to set a magic number for this kind of filtering technique (i.e., \(\alpha\)). In our experiments, we use a heuristic approach based on the standard deviation (STDEV) of profiled data. For example, with a higher STDEV, more weight is given on the recent profiled information. More sophisticated and automated approach to determine the \(\alpha\) value will be one of our future work.  
The functioning prototype of the AFMV model is described in Algorithm 3. It calculates the moving average based on the length of the window size (\(w\)-length). Then, based on the most recent profile information \(u_{scout}\), the model predicts value using the Equation (16).  
```
Result: Return AFMV Input: List of CEs \(L\), window length \(w\), \(i\), and Data (\(D\)) of each CE initialization \(mvSum\); forEach CE in \(L\)do for\(j=i\) to \(w+i-1\)do \(mvSum=mvSum+x_{j}\)  end for \(MV_{w+1}=mvSum/\)w  Calculate: AFMV using the Equation (16)  end for
```  
**Algorithm 3**Adaptive Filter-based Moving Average (AFMV)
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Technical Requirements
This development is expected to achieve a level of coverage that is universally pervasive, transcending terrestrial-only networks to incorporate a three-dimensional (3D) coverage model that integrates terrestrial, satellite, and aerial network systems. * 13. **Cost efficiency:** This metric describes the relationship between the value obtained from a user's data usage and the cost of the data traffic involved. In 5G systems, the cost efficiency is approximately 10 Gb/USD [27], whereas in 6G systems, it is expected to reach 500 Gb/USD, representing a 50-fold improvement. * 14. **Battery life:** This indicates the duration an IoT device's battery can last before needing replacement or recharging. In 5G systems, the typical battery life of IoT devices is around 10 years [28], whereas in 6G systems, it is projected to extend to 20 years, representing a twofold improvement. * 15. **Sensing:** This refers to the ability to capture and process visual information with high precision and detail. In 5G systems, the sensing resolution is typically around 1 m [29], whereas in 6G systems, it is expected to improve to 1 millimeter, achieving a 1000-fold enhancement in precision. * 16. **Security capacity:** This refers to the transmission rate of reliable data while minimizing the risk of interception by unauthorized parties. In 5G systems, security capacity is considered low, whereas in 6G systems, it is anticipated to be significantly higher, ensuring enhanced protection and reliability of transmitted data. Indicators related to this metric have been discussed in [27; 30; 31; 32; 33; 34]. * 17. **Intelligence level** This represents the sophistication of information processing and decision-making methods. In 5G systems, the intelligence level is relatively low, whereas in 6G systems, it is expected to be high, enabling more advanced and autonomous operations across various applications. As AI continues to advance, the intelligence level of the 6G communication system is anticipated to see significant improvements, as discussed in [32; 33].
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 3 Proposed Method
### Point-Cloud Rearrangement  
We propose a process to rearrange point-cloud spatial coordinates in voxel units. When using vertex information expressed in real number units, the real number unit has a form in which error values due to devices and algorithms remain. Thus, it is important to rearrange the point-cloud based on the voxel to remove any duplicate vertex. This procedure is shown in the following figure.  
Figure 2 demonstrates the point-cloud rearrangement process of the proposed method in order. The box means voxel. The voxel has a size of about \(k^{3}\), and the voxel is composed of \(n\) pieces according to the expressed range of the point-cloud. The vertex is represented by the voxel number index and voxel internal coordinates. The range of voxel internal coordinates is from 0 to \(k\).  
Figure 2(a) illustrates one of the original point-cloud vertices. Figure 2(b) shows the result of moving to the \(O\) point of voxel. The \(O\) point means the origin of voxel. The relative position of the vertex is obtained by modular operation. Figure 2(c) indicates the operation of moving the vertex to the center of the voxel. Therefore, the formula for rearrangement of vertex by the proposed method is as follows.  
\[F(v)=v-v\;mod\;k+\frac{k}{2}\;,\;k\;\in\;R \tag{4}\]  
Figure 3: Data changes according to voxel size (k) and the rendering results for each: (**a**) k = 1.26, (**b**) k = 1.55, and (**c**) k = 2.0.  
Figure 2: Visualization of rearrangement procedure.  
The rearrangement equation proposed in Equation 4 has the result of removing the error by \(\frac{k}{2}\). In other words, if the vertex is data recorded in units of 0.1, it is possible to judge the duplicate vertex even for errors that occur at a value smaller than 0.1 in the \(k=0.2\) and \(F(v)\) operations.