esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results  
### Performance Evaluation  
We report our results in terms of the True Positive Rate (TPR), False Negative Rate (FNR), False Positive Rate (FPR), True Negative Rate (TNR), and Accuracy. We briefly define these performance indicators as such:  
* True Positive Rate (TPR): The rate of correct classification of benign samples.
* False Positive Rate (FPR): The rate of incorrect classification of malware samples as benign.
* False Negative Rate (FNR): The rate of incorrect classification of benign samples as malware.
* True Negative Rate (TNR): The rate of correct classification of malware samples.
* Accuracy: The ratio of correct classifications to all the classification attempts. Mathematically, \[\text{Accuracy}=\frac{\text{TPR}+\text{TRR}}{\text{TPR}+\text{FNR}+\text{ FPR}+\text{TNR}}\] (1)
* Receiver Operating Characteristics (ROC): It is a graphical plot between False Positive Rate (on the x-axis) and True Positive Rate (on the y-axis) used to depict the classification ability of a classifier over the different thresholds. The curve starts from coordinates (0,0) and ends at (1,1). The curve closer to coordinates (0,1) shows higher quality.
* Training time: The time taken by the classifier (in s) for training on the training set.
* Decision-making time: The average testing time (in s) taken by the classifier on the test set.
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 3 Experimental Protocol  
The present study evaluates the ability of an OCARI-based WSN to break the limitations observed with a Zigbee-based WSN for heat measurements during fire experiments, especially in terms of data timestamping. To the best of the authors' knowledge, extant studies do not examine experiments involving the use of OCARI as a measurement system, specifically in the case of heat measurements in a real fire. The most similar study was performed by the authors of [6], where strong limitations were noted in the use of Zigbee to replace a wired system. The principle of the evaluation presented here is identical to that in Reference [6] as follows: It consists of comparing data recorded with the new WSN system based on a typical wired solution with the Campbell Scientific CR3000 datalogger. In the following, we first present the devices for heat measurements and wired and wireless solutions to record data as well as the setup of each experiment.
## 2 Related Work
### Reinforcement Learning for Spaced Repetition  
The advent of deep reinforcement learning has marked a significant shift in the optimization of spaced repetition schedules. DRL-based methods model the student as an environment and use agents to learn optimal policies through interactions. These methods have the advantage of learning from data and can adapt to individual learners' behaviors and performance.  
One of the pioneering works in this area is the application of DRL to spaced repetition by [PERSON] [14], who formulated the review scheduling problem as a Partially Observable Markov Decision Process and used Trust Region Policy Optimization (TRPO) to learn an optimal policy. [PERSON] [15] introduced a method that uses a Recurrent Neural Network (RNN) to model the student's memory mechanism, providing pseudo rewards at each learning event to guide the policy learning process. This approach improved upon the assumption of constant intervals by incorporating the student's memory state into the decision-making process. [PERSON] [16] proposed a deep reinforcement learning framework that simultaneously decides the time intervals of reviews and the content for the students, treating the review process as a Marked Temporal Point Process (MTPP). This method, while innovative, assumes that the student is available for learning at the exact times determined by the policy, which may not align with the student's availability. The Time-Aware scheduler with Dyna-Style planning (TADS) [17] approach by [PERSON] et al. addresses some of these limitations by incorporating varying time intervals between learning events and using Time-LSTM networks to learn an optimal content selection policy. TADS integrates Dyna-style planning to improve sample efficiency, leveraging both model-based and model-free reinforcement learning techniques.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Learning-Based** & **Temporal Dynamics** & **Reinforcement Learning** \\ \hline RANDOM & - & - & - \\ ANKI [7] & - & - & - \\ MEMORIZE [20] & - & - & - \\ HLR [8] & \(\checkmark\) & - & - \\ Ours & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Competing methods in this paper for spaced repetition scheduling. A comparison of innovation and features is summarized in this table. \(\checkmark\) denotes that the corresponding feature is equipped.  
Deep-learning-based approaches, particularly those utilizing reinforcement learning, have shown promise in creating more flexible and personalized spaced repetition schedules. These methods can learn from individual learning patterns, although they come with challenges such as naive simulation environments, improper problem definition, and the use of ineffective DRL algorithms.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 2 Related Works
### Overview of the Multi-Task Learning  
The advent of deep learning has led to significant performance improvements in CV and NLP, as well as network traffic classification. The typical approach is to learn these tasks in isolation, where a separate neural network is trained for each individual task [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Nevertheless, deep learning-based methods suffer from a number of limitations in terms of time and memory. Recently, research has been conducted on MTL techniques, which have shown promising results in terms of performance, computational, and/or memory efficiency [41; 42; 43]. MTL is the joint handling of multiple tasks through a learned shared representation. In [41], the author introduces hard parameter sharing and soft parameter sharing and discusses techniques such as deep relationship networks and fully adaptive feature sharing. In [42], the authors investigate various aspects of MTL. First, we provide a definition of MTL, and then we categorize supervised MTL models into five main approaches and discuss their characteristics The authors note that outlier tasks that are unrelated to other tasks are known to degrade the performance of all tasks when learning collaboratively, and they present this as a challenge. In [43], the authors present an overview of architectural and optimization-based strategies for MTL within the scope of deep neural networks. They also introduce how to set weights for each task in an MTL. In summary, MTL leverages useful information from multiple related tasks with the goal of improving the generalization performance of any task. MTL is efficient in terms of performance, time, and memory as it can handle multiple tasks using a single model. However, it is important to consider the correlation between tasks, the structure of the model, and optimization because certain tasks can degrade the performance of others.  
With the rising interest in MTL, there is a gradual increase in research applying MTL to traffic classification studies [48; 49; 50]. In [48], the authors claim to be the first to apply MTL in network traffic classification research and utilize CNNs to perform malware detection. In [49], the authors employ three time-series features and utilize CNN for multi-task classification on QUIC and ISCX 2016 VPN/Non-VPN datasets. However, the detection performance appears relatively low with an accuracy range of 82-92%. The classification task is configured slightly differently compared to previous studies. In [50], the authors perform multi-task classification using transformer and 1D-CNN, achieving an accuracy of 97-98% on the ISCX 2016 VPN/Non-VPN dataset. Our work is similar to their work.  
Their study is similar to ours, but we demonstrate accuracy exceeding 99% across all three tasks. Additionally, we evaluate the efficiency of multi-task classification, an aspect not addressed in their work.
# An Analysis of Mechanical Properties for Ultrasonically Welded Multiple C1220-Al1050 Layers
## 3 Results of the Experiment
### The Optimization of Welding Conditions
#### 3.2.3 Analysis of the Interfacial Shape  
In the ultrasonic welding of aluminum and copper sheets, tearing occurs in aluminum because copper is harder. Looking at the two interfaces, there was an indentation pattern of a circle on the opposite side of the aluminum. Both interfaces showed vibration marks in the transverse direction, and the aluminum tissue was observed on the copper interface, as shown in Figure 17b. The distribution of aluminum on the copper interface was observed to determine the cause of the difference in weld strength between the case with and without the optimized conditions.  
Figure 18 presents an observation of the degree to which aluminum settled on the copper plate using the contrast ratio difference depending on the mass of the tissue component. The settlement amount of aluminum cannot be identified quantitatively, but the approximate amount of settlement can be determined by observing the distribution of aluminum in the same area. After the shear tensile test, the surface was observed by SEM and the distribution of the compressed aluminum on the surface of the copper sheet was confirmed. It was observed in the order of x100, x200, and x500 times for surface observation. The settlement amount is related to the welding performance of the two metals. The greater the settlement amount, the better the bonding performance, which leads to a strength improvement. By comparing the settlement amount according to the two welding conditions, it was found that the distribution size of aluminum was larger when the optimized welding conditions were used compared to when they were not used. In addition, when compared according to the location of the weldment, weld zone 2, which was close to the indentation, was confirmed to have a larger aluminum distribution compared to weld zone 3.  
Figure 17: Comparison of the interface shape of Al and Cu: (**a**) aluminum interface, (**b**) copper interface, (**c**) enlarged aluminum interface, and (**d**) enlarged copper interface.
## 1 Introduction
The use of floating cables allows to cover a wider area; however, measurement errors may be higher due to the stacking suppression, off-line array movement induced by boat navigation, wind, or wave action, electrode cavitation at high boat speeds, and vegetation entrainment on electrodes. Usually, streamer electrodes can be made of steel or graphite; however, the latter are more fragile but more resistant to saltwater corrosion [24]. Less common are electrical resistivity measurements in water-covered areas such as wetlands, ponds, and lakes [31; 32; 33; 34]. One strategy to improve the resolution of electrical resistivity tomograms in water covered areas is to incorporate constraints on the water-column resistivity and thickness. In particular, the electrical resistivity of the water, in the margins of eligible error, can be considered constant [35]. In this framework, the main goal of our research consists of the preliminary analysis of the contribution of the two different geophysical methods, direct current (DC) and GPR offer for archaeogeophysical purposes, unsaturated and underwater scenarios (i.e., lacustrine, wetlands, underwater landslide, and fast natural erosion coast). For these aims, an archaeological site was reconstructed at the Hydrogeosite laboratory [36] with buried full-scale archaeological remains and different water content conditions were reproduced. The archaeological test site was characterized by remains simulating structures of the Lucanian and Roman times (walls, tombs, roads, etc.) covered by sediments [37; 38; 39; 40]. The approach adopted is based on the cooperative use of GPR and electrical resistivity tomography (ERT), adopting 2D and 3D data acquisition strategies. The paper defines different GPR and DC conventional and non-conventional acquisition procedures, in order to highlight the best setting in water saturated/quasi-saturated subsoil and flooded conditions, and a first level of integration is shown. In particular, we applied GPR on a boat and ERT on the water to demonstrate the potentiality of the methods applied in tandem for shallow underwater research. Further, the effectiveness of the non-conventional loop-loop array for ERT analyses was analyzed and compared with classical setting. With the loop-shaped acquisition scheme, we have tried to overcome the limitations analysing the effective capacity of the DC method. The qualitative integration of the GPR and ERT data has been tried in order to reduce the uncertainties for the archaeological features detection and preliminary quantitative analyses of the geophysical data regarding the real sizes and positions of the buried objects, which are discussed in this paper. The outline of this paper is as follows. After a brief overview of GPR and ERT is applied to the archaeological field, a description of the archaeological test site is shown. Then, the results obtained in two main phases of the experiments are presented and discussed. Finally, conclusions and future perspectives are debated in the last paragraph.
## 3 Results and Discussion
The data presented in Figure 9 demonstrates that the \(\sum_{\mathrm{R}}\) varied between 0.01945 and 0.01841 cm\({}^{2}\cdot\)g\({}^{-1}\). The highest effective removal cross-section was achieved for the Inconel-718 (\(\sum_{\mathrm{R}}\) = 0.01945 cm\({}^{2}\cdot\)g\({}^{-1}\)) and Nimocost-75 (\(\sum_{\mathrm{R}}\) = 0.01940 cm\({}^{2}\cdot\)g\({}^{-1}\) ) alloys. The alloys also possessed the lowest \(Z_{\mathrm{eff}}\) among the selected samples. The lowest effective removal cross-section was achieved by the MAR-302 alloy (\(\sum_{\mathrm{R}}\) = 0.01841 cm\({}^{2}\cdot\)g\({}^{-1}\)) because it possessed the highest \(Z_{\mathrm{eff}}\) among the studied alloys. Finally, the SRIM program was applied to evaluate the ability of the studied alloys to shield charged particles. Figure 10 shows the variation of the mass stopping power (MSP) and the projected range (PR) versus the kinetic energy of the incident charged particles. The MSP of the charged particles mainly depends on two factors. The first is the kinetic energy of the charged particle. At low energies, the calculated MSP values gradually increase with the increase in the kinetic energy of the incident particles. Around 0.1 and 0.8 MeV, the MSP reaches its maximum values for proton and alpha particles, respectively. It should be noted that the MSP for protons reaches its peak at a lower kinetic energy than alpha particles because the mass of a single proton is lower than one alpha particle. The maximum MSP varied between 0.218 and 0.242 MeV\(\cdot\)cm\({}^{2}\cdot\)mg\({}^{-1}\) for protons, while the maximum MSP for alpha particles varied between 0.676 and 0.743 MeV\(\cdot\)cm\({}^{2}\cdot\)mg\({}^{-1}\). As the kinetic energy keeps increasing, the MSP values for both proton and alpha particles drastically decrease, which continues at higher energies. The second factor is the chemical composition of the selected alloys. The alloys with high contents of heavy elements such as W, Hf, and Ta (MAR-247, MAR-302, and WI-52) possessed lower MSP values, while the alloys with light elements (Inconel-625, Inconel-718, and Nimocast-75) possessed much lower MSP values at all kinetic energies for both protons and alpha particles. Figure 3.3 The fast neutron effective removal cross-section of the studied alloys. Figure 10 illustrates the variation of the projected range (PR) for protons and alpha particles against the kinetic energy of the incident particles. The calculated PR increased exponentially with the increase of the kinetic energy of the charged particles. The highest PR was obtained at a kinetic energy of 10 MeV for the MAR-247 alloy (a PR equal to 275.42 \(\upmu\)m for both proton and alpha particles, respectively), while the lowest PR was obtained for the Nimocast-75 alloy (with a PR equal to 241.49 and 25.83 \(\upmu\)m for both particles, respectively).
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 6 Conclusions  
In conclusion, RPL-CGA can comprehensively assess the five proposed routing metrics (QL, EED, RER, HC, and ETX) and combine these metrics according to Equation (9). Meanwhile, their respective weighting factors are determined by a chaotic genetic algorithm. Then, the selection of preferred parent can based on the minimum value of Equation (19). Finally, performance evaluation proves the effectiveness and reliability of RPL-CGA. Meanwhile, RPL-CGA is significantly superior to other improvements of RPL such as ETXOF, 0.8 \(\times\) ETX + 0.2 \(\times\) RER, and 0.6 \(\times\) HC + 0.4 \(\times\) RER in the aspect of average end-to-end delay, average success rate, etc. Further analysis should be focused on multiple instances of routing RPL to process different kinds of traffic flows in the network.  
Conceptualization, Y.C. and M.W.; methodology, Y.C. and M.W.; software, Y.C.; supervision, Y.C. and M.W.; writing-original draft preparation, Y.C.; writing-review and editing, Y.C. and M.W.  
This research received no external funding.  
This work was supported by the Beijing Laboratory of Advanced Information Networks.  
The authors declare no conflict of interest.
# Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
## 2 Materials and Methods
### Mathematical Models
#### 2.3.3 Turbulence Method and Liquid Level Capture  
In this paper, the improved realizable _K-epsilon_ two-layer model (RKE--2L) is selected to close the Reynolds time average equation [51]. This method was first proposed by [PERSON]. The realizable _K-epsilon_ model introduces the variable damping function \(f_{\mu}\) (expressed as turbulence and uniform flow properties) to apply to the critical coefficient \(c_{\mu}\) of the model.  
Therefore, the turbulent eddy viscosity \(\mu_{t}\) can be expressed as:  
\[\mu_{t}=\rho C_{\mu}f_{\mu}\frac{k^{2}}{\varepsilon} \tag{15}\]  
\[f_{\mu}=\frac{1}{C_{\mu}\Big{\{}4+\sqrt{6}\cos\Big{[}\frac{1}{3}\cos^{-1} \Big{(}\sqrt{6}\frac{\xi^{*3}}{\sqrt{5^{*}};5^{*}}\Big{)}\Big{]}\frac{k}{ \varepsilon}\sqrt{S:S+W:W}\Big{\}}} \tag{16}\]  
\[S^{*}=S-\frac{1}{3}\text{tr}(S)\text{I} \tag{17}\]  
where S represents the average strain rate tensor; W represents the mean vortex tensor; I represents the identity matrix.  
In this way, the numerical model and the realizable physical model can have the same mathematical constraints, so the results of this model are better than the standard _K-epsilon_ model in many applications. In this paper, the fluid volume method (VOF) is selected as the liquid level tracking method. The fluid volume (VOF) multiphase model is suitable for simulating multiple immiscible fluid flows on the numerical grid (which can solve the interface between the phases of the mixture). The model is suitable for each phase with a large structure and relatively small contact area between each phase.  
The traditional high-order schemes, such as the central difference scheme and the upwind scheme, have poor calculation results when the phase volume fraction changes greatly. In this paper, the high-resolution interface capture (HRIC) method is used to track the interface between the two phases, which can effectively reduce the error caused by the liquid level capture.  
The phase distribution and position of the interface are described by the field of phase volume fraction \(\alpha_{i}\). The volume fraction of the phase is defined as follows:  
\[\alpha_{i}=\frac{V_{i}}{V} \tag{18}\]  
where \(V_{i}\) is the volume of phase I in the grid cell, and \(V\) is the volume of the grid cell. The sum of the volume fractions of all phases in the grid cell must be 1:  
\[\sum_{i=1}^{N}\alpha_{i}=1 \tag{19}\]\(N\) is the total number of phases. According to the value of volume fraction, whether there are different phases or fluids in the grid cell can be distinguished.  
\(\alpha_{i}=0\) represents that the grid element has no phase i at all;  
\(\alpha_{i}=1\) represents that the grid cell is filled by phase I;  
\(0<\alpha_{i}<1\) represents that the grid element has an interphase interface.
## 3 The Proposed Protocol
### Three Proposed Authentication Methods
#### 3.3.1 Transmitter Centric Authentication (TCA)
Algorithm 1 illustrates the signature generation and verification procedure of the proposed TCA authentication method. The signature verification depends on the bilinear pairing algebraic properties described in Equations (2)-(4). To verify a message under given \(pk_{i},\sigma_{i}\), it checks if Equation (5) is held. ```
1:-One transmitter to many receivers in the same zone (single-zone)
2:-One transmitter to many receivers in different zones (multi-zone)
3:Signature Generation:
4: Prepare a message m = \(\{L_{ZID},\;pid_{i},m_{i},T_{s}\}\)
5: Generate a signature \(\sigma_{i}\) over hashed message \(H\) (\(m\)) using secret \(sk_{i}\):
6:\(\sigma_{i}=sk_{i}.H(m)\)
7: Broadcast \(\{L_{ZID},\;pid_{i},m_{i},T_{s},pk_{i},\sigma_{i}\}\) to all zones
8:Signature Verification:
9: All receivers use the transmitter zone parameters \((\;g_{2 ZID})\) to verify \(\sigma_{i}\) over m = \(\{L_{ZID},\;pid_{i},m_{i},T_{s},pk_{i},\sigma_{i}\}\)
10: Check If \(e\) (\(\;g_{2 ZID},\sigma_{i}\;)=e(pk_{i},\;H(m))\), accept the message, else reject the message. 11:\(e\) (\(\;g_{2 ZID},\sigma_{i}\;)=e(pk_{i},\;H(m))\)
```  
**Algorithm 1**Transmitter-Centric Authentication (TCA) Method  
Figure 7: Multi-zone communication in TCA. If Equation (5) is satisfied, the receiver accepts the message, otherwise, it rejects it. The proof of bilinear pairing verification can be given by Equation (6):  
\[ \begin{array}{l}e\left(\ g_{2 ZID},\sigma_{i}\right)=e\ \left(\ g_{2 ZID},sk_{i}\,.H(m)\right)\\ =e\ (\ sk_{i}.g_{2 ZID},H(m))\\ =e(pk_{i},\ H(m))\end{array}  \tag{6}\]  
Here,  
\[sk_{i}.g_{2 ZID}=pk_{i} \tag{7}\]  
In the TCA method, the transmitter vehicle uses the current zone parameters and allows receivers to find the correct zone parameters required to verify the message. The TCA method can allow vehicles in a boundary area to continue using the old zone security parameters. However, the zone parameters might be updated individually, making the verification process for receivers invalid, if some receivers are not updated in a timely manner with the transmitter's parameter. The next proposed method can provide an alternative solution to this problem.