esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 4 Performance Evaluation
### Evaluation Measures
#### 4.1.1 Retrieval Effectiveness of the Routing Algorithms  
Figure 2 shows the evolution of the cumulative average recall of RLQR-EGNS, RLQR-UCBNS, RLQR-TSNS, CF, and LPS. As expected CF has low and stable recall (around 0.32) since queries are randomly flooded over the P2P network. We note that the recall of LPS is low (around 0.32) during the training phase then it improves after each update of the LPS' knowledge base. During the simulation, we periodically updated the knowledge base of each peer three times (i.e., after sending 1000, 2000 and 3000 queries). Moreover, we observe that the recall of our reinforcement learning algorithms RLQR-EGNS, RLQR-UCBNS and RLQR-TSNS increases rapidly, after sending a few queries, allowing them to outperform CF and LPS. Indeed, the cumulative average recall of CF, LPS, RLQR-EGNS, RLQR-UCBNS, and RLQR-TSNS for all sent queries is 0.17, 0.50, 0.64, 0.67, and 0.77, respectively. These encouraging results are owed to the fact that RLQR relies on reinforcement learning neighbor selection strategies (i.e., Epsilon Greedy, Upper Confidence Bounds, and Thompson Sampling) that efficiently balance between exploration and exploitation. Doing so, RLQR improves the routing performance continuously and, therefore, goes quickly through the cold-start phase. Moreover, we observe that Thompson Sampling-based neighbor selection algorithm RLQR-TSNS outperforms its competitors. This can be explained by the fact that RLQR-TSNS has a more sophisticated exploration strategy.
## 3 Software Design and Implementation
### Configuration Module  
The configuration module as shown in Figure 12 has four sub-modules, the database path and remote server address module, the task module, the communication port number, and the sensor configuration setup module.  
The database path is the link parameter for the MySQL local host database. The local database stores all the temperature information in the local server by default. If the user set the remote server address, the received temperature information will also be send to the remote server. The remote server is identified by and IP address and a port number.  
The task module manages the sensing data collection for each specific task. It allows several tasks to operate simultaneously without interfering each other. In this way, the software is able to handle multi-task in the same time. So for each task the monitoring parameters and temperature curves will not override with each other.  
The communication  
last update time. For the self-diagnostic and self-healing purposes, the software checks the health status of each sensor every 10 min. If any error occurs to the sensor, e.g., the sensor is disconnected, the software try to fix it by itself. If the software is not capable of doing so, e.g., the sensor's battery is running out, the software displays a warning message on the screen. By doing so, the temperature monitoring personnel will be able to fix the errors as quick as possible.
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 3 Methodology
### Experiment Design
#### 3.3.2 Procedure
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ID** & **Question** \\ \hline \(Q1\) & The electric force between point charges of the same sign is: \\  & \((a)\) attractive \((b)\) repulsive \((c)\) depends on the values of the charges \\ \hline \(Q2\) & The electric force between point charges of different sign is: \\  & \((a)\) attractive \((b)\) repulsive \((c)\) depends on the values of the charges \\ \hline \(Q3\) & The magnitude of the electric force between two point charges located at a given distance \(r\) is \\  & proportional to: \\  & \((a)\)\(r^{2}\)\((b)\)\(r\)\((c)\) does not depends on \(r\)\((d)\)\(1/r\)\((e)\)\(1/r^{2}\) \\ \hline \(Q4\) & The magnitude of the electric force between an infinite straight line charge and a point charge \\  & located at a given distance \(r\) is proportional to: \\  & \((a)\)\(r^{2}\)\((b)\)\(r\)\((c)\) does not depends on \(r\)\((d)\)\(1/r\)\((e)\)\(1/r^{2}\) \\ \hline \(Q5\) & The magnitude of the electric force between an infinite plane charge and a point charge \\  & \((a)\)\(r^{2}\)\((b)\)\(r\)\((c)\) does not depends on \(r\)\((d)\)\(1/r\)\((e)\)\(1/r^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Questions of the Pre-test and Post-test applied. At the end of the activity and before students left the haptic device lab, a post-test identical to the pre-test was applied. Students were also given 15-20 min to solve the post-test. Both the pre-test and the post-test were graded on a 0 to 100 point scale, where each of the 5 questions was assigned 20 points.
# A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period
## 2 Literature Review
### The Flow-Based Unknown Internet Traffic Identification with Statistical Features  
The flow-based unknown Internet traffic identification with statistical features is a hot research topic [15]. As far as we know, the statistical features of Internet traffic mainly include the number of packets, the length of each packet, packet size, arrival time, etc. Based on the statistical features, researchers adopt various approaches, such as clustering, deep auto-encoder, etc., to achieve unknown Internet traffic identification.  
The clustering algorithm is currently one of the most common methods for dealing with statistical flow characteristics and realizing the identification of unknown Internet traffic [16]. For example, [PERSON] adopts a semi-supervised clustering method to deal with unknown Internet traffic identification with statistical flow features [17]. It collects a small amount of labeled traffic, clusters the labeled and unlabeled data together, and further marks the clustering results with extended tags. This method finally adopts 20 session-level statistical features and realizes the identification of up to three types of unknown traffic, with the highest accuracy of about 65%.  
In addition to semi-supervised clustering, deep clustering also holds an important role in training statistical characteristics and identifying Internet traffic. For example, [PERSON] analyzes features of data length, control keywords, and address information to establish the eigenvector matrix of unknown traffic and adopts deep clustering to realize the identification [18]. [PERSON] extracts the bottleneck features from traffic flow and realizes unknown traffic identification by combining deep auto-encoder with constrained clustering [19].  
In a word, the statistical features of traffic flow show advantages in helping identify unknown Internet traffic and could adapt to the changing requirements of user privacy protection, continuous updates of protocols, etc. However, it is usually difficult to filter and select efficient statistical features for model training and analysis. In the meantime, the processing of statistical features requires a lot of computing resources, which is somewhat tough to deploy in large networks. Hence, some studies try to use deep learning to automatically extract features for unknown Internet traffic identification.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 3 Time Scale Algorithm Principle  
The principle of the time scale algorithm is to firstly obtain the observed clock difference of each atomic clock in an atomic clock group relative to the reference clock. The observed clock difference is calculated to obtain a virtual clock difference sequence. The virtual clock difference sequence can be regarded as the time on paper. The stability of the time on the paper is better than any clock in the clock group. This section first introduces the KPW time scale algorithm, and then uses the genetic algorithm for optimization to obtain our desired time scale or paper time.
## 4 Simulation Results
### Case 5: Active power loss
#### 4.1.2 Multiobjective OPF  
In the next five cases, we used the proposed metaheuristics to find better solutions for multiobjective OPF problems. Table 10 summarizes the best solutions of the simulation results using the HIC-GWA approach for cases 6-10.  
Cases 1 and 5 have been combined to reduce the fuel cost and the active power losses using the multiobjective function given in Equation (20). Simulation results show that the fuel cost and power loss using HIC-GWA are 856.99 ($/h) and 4.04 (MW). Compared with MSA, MDE, MPSO, FPA, and MFO approaches in Table 11, HIC-GWA has significantly reduced the fuel cost and power loss.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solutions** & **Case 6** & **Case 7** & **Case 8** & **Case 9** & **Case 10** \\ \hline Fuel cost ($/h) & 856.99 & 802.45 & 797.80 & 802.00 & 817.59 \\ Emission (t/h) & 0.23 & 0.36 & 0.37 & 0.36 & 0.27 \\ \(P_{\text{loss}}\) (MW) & 4.04 & 9.95 & 8.75 & 9.67 & 5.29 \\ VD (p.u.) & 1.78 & 0.10 & 1.98 & 1.97 & 0.23 \\ \(L\) index & 0.12 & 0.13 & 0.11 & 0.11 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimal solution of IEEE 30 bus power system for case studies 6 to 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Fuel Cost ($/h)** & **Emission (t/h)** & \(P_{\text{loss}}\) (MW)** & **VD (p.u.)** & \(L\) **Index** \\ \hline MSA & 967.66 & 0.2073 & 3.10 & 0.89 & 0.14 \\ MPSO & 967.65 & 0.2073 & 3.10 & 0.96 & 0.14 \\ MDE & 967.65 & 0.2073 & 3.16 & 0.77 & 0.14 \\ MFO & 967.68 & 0.2073 & 3.11 & 0.92 & 0.14 \\ FPA & 967.11 & 0.2076 & 6.57 & 0.39 & 0.14 \\ ARCBO & 967.66 & 0.2073 & 3.10 & 0.89 & 0.14 \\ GWO & 968.38 & & 3.41 & & \\ DE & 968.23 & & 3.38 & & \\ ABC & 967.68 & 0.2073 & 3.11 & 0.90 & 0.14 \\ DSA & 967.65 & 0.2083 & 3.09 & & 0.13 \\ EEA & 952.38 & & 3.28 & & \\ EGA & 967.93 & & 3.24 & & \\ ALC-PSO & 967.77 & & 3.17 & & \\ HIC-GWA & 1000.30 & 0.2080 & 2.61 & 1.41 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of HIC-GWA solutions with the literature for case study 5.
# Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification
## 3 Proposed Algorithm
### Proposed NIDS  
According to previous experimental results, the proposed algorithm can be designed as follows: First, the entire packet data, rather than partial packet data, of a received packet should be used as the input to the classifier. Additionally, as many packets as possible in a session should be used as the input to the classifier.  
To this end, LSTM is used. However, since LSTM requires the same shape as the input of each cell, one packet must be divided into equal sizes and input to LSTM (i.e., packet classifier). Then, each received packet is converted into a feature for each packet using a packet classifier.  
The packet classifier processes packets in a session as a classifier composed of a double LSTM, regardless of the number of packets, and therefore converts each received packet into a packet feature that has the same size regardless of the size of the packet. The packet features converted in this manner are sequentially provided as inputs to the session classifier. Similar to the packet classifier, session classification also consisted of one  
Figure 5: Detection rate of LSTM in terms of session according to the total LSTM cell size with the CIC-IDS2017 dataset.  
LSTM classifier. In this way, regardless of the number of packets in the session, the session classifier can classify the session using the packet features generated from the packets.  
Figure 6 shows the entire classification process for the proposed classification model. According to the figure, packet features are created by the packet classifier, and these packet features are integrated into one session feature by the session classifier, and then the final classifier uses it to detect network intrusion. The packet and session classifiers will now be described in detail.
## 3 Method
### Results and Verification  
#### 3.5.1 Feature Selection Algorithm Verification  
All the extracted multimodal physiological features are subjected to Principal Component Analysis (PCA) feature dimension reduction, and then input to XGBoost for feature classification. A total of 10 folds of independent experiments are carried out, and the samples are randomly scrambled in each fold. Taking Valence as the classification label, the recognition accuracy of the two feature dimension reduction methods is  
It can be seen from the Figure 3 that at the beginning of dimension reduction, the recognition effects of the two algorithms are not much different. With the decline in later features, the recognition rates of both have increased. For the PCA algorithm, the best recognition effect is when the feature dimension is 150, the recognition rate is 75.3%; for the JMI-Score feature selection algorithm. The best recognition effect is when the feature dimension is only 120, the recognition rate is 81.8%.
## 2 Methodology of the Inversion
### Inverse Problem  
The inverse problem given by Equation (9) is written as follows:  
\[\mathbf{I}\,\Omega=\theta. \tag{12}\]  
In practice, \(\mathbf{I}\) is not square. Its number of lines is much greater than its number of columns. Therefore, \(\mathbf{I}\) is not invertible. The algorithm used for the studied problem is therefore based on the Moore-Penrose pseudo-inversion method [12; 13]. The pseudo-inverse \(\mathbf{I}\) is obtained by using singular value decomposition (SVD) method [14; 15; 16; 17]. The principle of SVD is to decompose the matrix \(\mathbf{I}\) of size \(m\) by \(n\) into a product of three matrices where \(\mathbf{U}\) is an orthogonal matrix of size \(m\) by \(m\), orthogonal \(\mathbf{V}\) of size \(n\) by \(n\) and \(\mathbf{S}\) a \"diagonal\" matrix of size \(m\) by \(n\). The diagonal elements of \(\mathbf{S}\) are called singular values. They are positive and ranked from the largest to the smallest.  
If only the first \(p\) singular values are different from zero \((p<n)\), the SVD can then be further reduced. Thus, the pseudo-inversion of Equation (12) gives:  
\[\Omega=\sum_{k=1}^{p}\frac{\mathbf{U}_{\cdot,k}^{T}\theta}{s_{k}}\mathbf{V}_{ \cdot,k}. \tag{13}\]  
The reconstruction problem studied here responds well to the existence and uniqueness requirements of a well-posed inverse problem (cf. [PERSON] [18]). Unfortunately, the condition of stability is not respected. One of the methods to study this stability is to observe singular values of operator matrix \(\mathbf{I}\): the closer they are to zero, the more the problem is ill-posed. If, in addition, their decay follows a power law such that if \(s_{i}\approx i^{-n}\), with \(n\) strictly positive, the problem is described as ill-posed. On the other hand, if their decay follows an exponential law such as \(s_{i}\approx e^{-i}\) (which is the case here), then it is strongly ill-posed. Then to overcome such problems, it becomes necessary to use regularization methods.
## 5 Energy Efficiency and Sustainability
### Energy Efficiency in the Context of RIS and HAP Integration
#### 5.1.3 Minimizing Losses Through Passive Elements  
RIS consists of numerous low-cost, passive elements that do not require power for signal processing or active radiation. This passive nature significantly reduces the system's energy consumption [72; 116]. Unlike active antenna systems, where each element might require its power source for operation, RISs can manipulate the EM waves impinging upon them without any active energy input, leading to substantial energy savings [126].
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 6 Conclusions and Future Work  
This paper studies the UA problem and proposes a novel embedding-based model, JARUA, which incorporates attributes and structure features into a unified vector space. JARUA first captures the characteristics of multi-type attributes through multi-granularity attribute embedding. Then, the proposed model adopts a GAT to embed the network structure. A unified objective function optimizes the attribute embedding component and structure embedding component. Finally, JARUA utilizes unlabeled data with an iterative training algorithm with quality filters. The experimental analysis demonstrates that: (1) leveraging both features of multi-type user attributes and network structure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Weibo-Douban**} & \multicolumn{3}{c}{**DBLP17-DBLP19**} \\ \cline{2-7}  & **k = 1** & **k = 3** & **k = 5** & **k = 1** & **k = 3** & **k = 5** \\ \hline \(Z=1\) & 0.385 & 0.418 & 0  
can significantly improve UA performance; (2) the iterative training algorithm developed in JARUA significantly improves model performances; (3) JARUA is expected to be well adapted to annotating constrained scenarios; (4) the proposed approach is robust to hyperparameter tuning.  
The insights into model construction gained from this study may be of assistance to cross-network user profile and network integration. Our future work seeks to extend the ideas of JARUA to other promising applications, such as link prediction, cross-network information diffusing, and network dynamics analysis.  
Conceptualization, M.Y. and B.C.; methodology, M.Y.; software, B.C.; validation, X.C.; formal analysis, M.Y.; investigation, M.Y. and B.C.; resources and data curation, B.C.; writing--original draft preparation, M.Y.; writing--review and editing, B.C. and X.C.; visualization, B.C.; supervision, X.C.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Science and Technology Program of Sichuan Province (Grant No. 2023 ZDYF2732) and National Natural Science Foundation of China (Grant No. 61902324).  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/[PERSON]/JARUA](https://github.com/ChenBaiyang/JARUA)]([https://github.com/ChenBaiyang/JARUA](https://github.com/[PERSON]/JARUA)), accessed on 4 November 2022.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Proposed Methodology  
A single sink network model is assumed in the proposed scheme. As illustrated in Figure 1, it is made up of anchored nodes, relay nodes, and sink nodes. The anchored nodes are located on the seafloor and are primarily used to collect underwater data, while the relay nodes are located at a different underwater location and are responsible for transporting data to the sink and forwarding the collected packets [29]. For the sink node, there are sound and radio modems. For acoustic communication, the former is used, while the latter enables radio communication outside the aquatic environment. The sink node, which is mostly on the water's surface, sends data packets to the satellite, which then sends them to the control center.
## 4 Empirical Evaluations Utilizing Open Source Systems
### Results And Discussions  
Using our tools, we applied the proposed approach on the two considered systems. Tables 4 and 5 report the results for the ten classes selected from Tomcat system and the ten classes selected from Ant system, respectively. We set the refactoring threshold (i.e., _Threshold_ used in Algorithm 1) for each considered class to the median of the values of the client-based similarities between all the pairs of the methods in the class excluding the zero values and the main diagonal entries. We set the minimum number of methods each extracted class can have to 2, i.e., \(minNumMethods\) used in Algorithm 2. This is due to unreasonability to extract classes that have only one method. The results in Tables 4 and 5 lead to the following observations. * The cohesion (i.e., \(CBCS\) values) of the extracted classes suggested by our approach are higher than the original classes for all the selected classes. * In most of the cases, the \(CBCS\) values of the extracted classes increased to more than two times that of the \(CBCS\) value in original class. * In some cases, the extracted classes have relatively low \(CBCS\) values (e.g., classes extracted from _NoBodyResponse_ class in Tomcat). We investigated these cases and we found the main reason behind that is the values of the client-based similarities between most of the pairs of methods in the original class are 0. * The number of extracted classes varies from cases to cases. Two factors can influence the number of the extracted classes: the refactoring threshold and the number of methods in the original class. The higher the value of the refactoring threshold is, the more likely the number of extracted classes will increase (e.g., the cases of the classes _Extension_ and _VAJBuildInfo_ in Ant system). Similarly, the higher the number of methods in the original class, the more classes will probably be extracted (e.g., the case of the class Request in Tomcat system). We further conducted the paired samples \(t\)-test using the \(R\) software [32] in order to test our null \(\mathbf{H}_{0}\) and alternative \(\mathbf{H}_{1}\) hypotheses (see Table 6). The paired samples \(t\)-test is a statistical procedure used to compare the means between two related sets of samples of the same size. In our case, the two sets are the \(CBCS\) values of the original classes and the averages of \(CBCS\) values of the extracted classes suggested by our approach for each original class (the reader is directed to data presented in Tables 4 and 5). In other words, a pair of samples has the \(CBCS\) of an original class and the average of the \(CBCS\) values of the classes extracted from that original class. We considered the averages of \(CBCS\) values of extracted classes instead of \(CBCS\) value of each extracted class because the total number of