esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of Coefficient of Restitution (CoR)  
### Data Processing  
To determine the coefficient of restitution, the experimental force-time curves were approximated because the raw data contained noise from the measurement system, transducer non-linearity and setup vibrations. Such disturbances could compromise data reliability, especially during the integration used to obtain the impulse.  
The approximation expressed the signals as sixth-degree polynomials \(F_{c}(t)\) that were smooth and free of local disturbances, enabling more precise analysis. Polynomial fitting was performed with APPROXYMACJA software (version 1.5.7.2) using the least-squares method, which minimised the influence of noise while preserving the signal shape--Figure 13. This procedure was essential for an accurate determination of the coefficient of restitution (CoR).  
The determination of the coefficient of restitution was based on Equations (9) and (10), which require integrating the force function. Figure 4 illustrates how the approximating polynomials were integrated to obtain the impulse for the two contact phases--compression and restitution. To separate these phases, the dataset was divided at the point of maximum force \(F_{c\_max}\), identified as the location where the first derivative of \(F(t)\) is equal to zero.  
Integration was carried out in a spreadsheet with the rectangular rule by subdividing the time axis into intervals \(\Delta t\) and summing the products \(F(t)\Delta t\). The impulses calculated for the compression and restitution phases were then used to evaluate \((CoR,e)\) in accordance with Equation (11).  
Processing the complete dataset of 400 wheat-grain impacts allowed \((CoR,e)\) to be plotted as a function of impact velocity and collision force for four moisture levels (7%, 10%, 13% and 16%; Figure 14). Under the relatively low velocities studied, \((CoR,e)\) appears  
Figure 13: Approximation of the measurement data.  
to be governed by internal energy dissipation rather than by grain breakage, so damage effects can be neglected for the conditions tested.  
The collected datasets were approximated using polynomial regression. The obtained goodness-of-fit coefficients showed no significant improvement when higher-degree polynomials were applied. Therefore, a quadratic function was deemed an appropriate representation of the relationship between the coefficient of restitution (_CoR_, _e_) and collision velocity (\(v_{c}\)) for grains at all moisture levels. Other functional forms--including exponential, power-law and higher-order polynomials up to the sixth degree--were also tested; however, none of them improved the fit because of the natural scatter in the measurement data, which stems from the inherent morphological heterogeneity of plant materials. The quadratic model is further supported by earlier studies of CoR in seeds such as rapeseed and peas, which display similar nonlinear characteristics.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 6 Training and Detection Time Comparison  
This paper not only provides an evaluation of the detection performance of each model, but also records their training and detection times, which provides an important indicator of the computational efficiency of the models. This comparison not only reveals the differences in processing speed among the models, but also provides researchers with a reference for making trade-offs between time and performance when selecting models. This allows the selection of an optimal model that meets efficiency requirements and guarantees detection accuracy. Table 5 displays the outcomes of the experiment. According to the experimental findings, the training time of the LSTM-BO-SVM model is longer (136.94 s) on the ClaMP dataset, which may be related to the higher computational cost of the LSTM for processing data. On the other hand, the training time of the BO-SVM model is 34.68 s, which demonstrates its optimization efficiency. In terms of detection speed, the BO-SVM model is the fastest with a detection time of 0.22 s, while the BiLSTM-BO-SVM model has the longest detection time (2.89 s), which may be related to the complexity of its structure. On the CICMalDroid-2020 dataset, the training time of all models was extended due to the higher dimensionality of this dataset, but the BO-SVM model still maintained the shortest training time (45.56 s). In terms of detection time, the BO-SVM model remained the fastest at 0.32 s, while the BiLSTM-BO-SVM model had the longest detection time (3.21 s). Figure 14: F1-Score comparisons of six models on CICMalDroid-2020. In conclusion, the BO-SVM model shows higher efficiency in both training and detection speeds and may be more suitable for environments with higher real-time requirements. On the other hand, the LSTM-BO-SVM model may be more suitable for complex data analysis that requires high accuracy, although it takes longer to train. Comparison with Related Work  
Many literature studies on SSC malware detection have been conducted using the ClaMP and CICMalDroid-2020 datasets. [PERSON] et al. [35] and [PERSON] et al. [36] analyzed the CICMalDroid-2020 dataset using various machine learning models. [PERSON] et al. [37] proposed an effective improved deep neural network. [PERSON] et al. [38] used the XGBoost model for detection. [PERSON] et al. [39] proposed feature selection using the ACO algorithm followed by detection using DTs. [PERSON] et al. [40] used RFs for detection. [PERSON] et al. [2] analyzed SSC attacks using Quantum SVM (QSVM) and Quantum Neural Network (QNN) on a quantum platform. This paper presents the accuracy and computation time of each study according to the dataset used, with the results ranked by accuracy as shown in Table 6. Additionally, computation times are compared; \"\(\backslash\)\" indicates that the computation time is not mentioned in the article.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 4 Simulation Results and Discussion
### Environmental Conditions
#### 4.1.1 Air Pressure  
The air at any planetary body constitutes a weight, and it pushes against anything it comes in contact with. This pressure is known as atmospheric or air pressure. Air pressure is the force applied by the air on the planetary body (as gravity draws it towards the  
Figure 3: Block diagram of the prototype UAV model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Mars [91] (Reported)** \\ \hline Volume (km\({}^{3}\)) & 16.318 \\ \hline Mass (10\({}^{24}\) kg) & 0.64171 \\ \hline Gravity (m/s\({}^{2}\)) & 3.711 \\ \hline Air pressure (bars) & \(6.1\times 10^{4}\) \\ \hline Air density & 0.020 \\ \hline Speed of sound (m/s) & 240 \\ \hline Atmospheric temperature (Kelvin) & 273 + 15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters considered for Mars flight.  
surface). The ability to fly vehicles to achieve lift is due to air pressure. According to Bernoulli's Principle, faster-moving air has lower pressure while slower-moving air has higher pressure. That means that the air pressure on the bottom would be higher, pushing the plane upward. The air pressure value at the surface of Mars is approximately 610 Pascal [68].  
This means the air pressure on Mars is less than 1% of that on Earth. As a result, the air on Mars is significantly leaner than it is on Earth. As a result, the critical source of concern when developing a prototype UAV is whether there would be enough lift. The UAV is possibly heavier than air. For a UAV to fly successfully in a planets' atmosphere, four forces are obligatory: lift, drag, weight, and thrust. Figure 4 shows the aerodynamics of the UAV. A coordinated system is used in the UAV flight. The coordinate system allows keeping track of an aircraft or Spaccraft's position and orientation in space. Here, three coordinate systems are used in the UAV's flight mechanism. These coordinate systems are:  
* Inertial System: Inertial system is attached to the planetary surface, does not move.
* Fixed Body Frame: This frame is attached to the airframe and moves with the UAV.
* Aerodynamic frame: The average velocity of the aircraft's centre of mass defines this frame. The UAV is also equipped with a dynamic frame.  
The three axes on the UAV prototype are \(X_{b}\), \(Y_{b}\), and \(Z_{b}\). These represent the forward, right, and positive downward axis, respectively. The engine of a flying vehicle generally provides thrust. Thrust must surpass the vehicles' drag for a successful flight. The wings provide the lift of the vehicle. UAV's lift should equal its weight for the flight to flourish. The UAV's smooth shape will probably reduce drag, and the materials it is made up of will affect its weight.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 3 Methodology
### Experimental Design  
For the sake of convenience, the researchers performed experiments directly in the exhibition space. A within-subjects design was adopted, and the visitors could choose between the exhibition-viewing and message modes. The experiment process is shown in Figure 6. Based on the experiment duration coordinated with the exhibition period (28 October 2022, to 30 December 2022), all the participants who attended the exhibition were from the general public, deciding on their own whether to use the app or not. Consequently, convenience sampling was employed. The participants scanned the QR code on the guide brochure, promotional materials, or the website of the exhibition to download our app and operate it as instructed by the system. No staff members provided any aid. With their consent, the app collected visitors' coordinates, usage duration, smartphone model, and operating system using anonymous random encoding. Outliers that were significantly deviated due to potential mobile hardware factors or environmental factors causing unstable AR positioning quality were excluded. In the exhibition-viewing mode, out of the 225 data points collected, 93 were identified as outliers. In the message mode, out of the 188 data points collected, 58 were identified as outliers. The final usable samples consisted of 132 visitors in the exhibition-viewing mode and 130 visitors in the message mode. To better understand the differences between the behaviors of visitors using the two  
Figure 5: Messages left by visitors in the exhibition space (The Chinese characters displayed on the wall in the image are introductions to the three centers, while the floating Chinese characters represent messages left by visitors at the exhibition.).  
modes, we removed samples in which only one mode was used and invalid samples where severe AR positioning shifts occurred due to personal hardware issues or other external factors. This resulted in 61 usable samples, in which 8 participants used Android devices and 53 participants used iOS devices.
# Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm
## 3 BiLSTM Attack Prediction Model Based on Improved Gray Wolf Algorithm
### Threshold Selection  
In real life, people use the Internet in roughly the same way, so there will be a large number of users online at the same time. In this case, network congestion occurs, but the network congestion is different from that caused by DoS and DDoS. Because the fluctuating network traffic features extracted by IPDCF may be mistaken by the model as attack traffic, it is particularly critical to select an appropriate threshold for a given data set. If the threshold is too large, the DoS and DDoS alarm may be delayed or missed. If the threshold is too small, false positives may occur.  
The IPDCFs of normal and predicted values were statistically analyzed to obtain their intervals \([a,b]\) and \([c,d]\). \(Z\) is the maximum value of the IPDCF predicted value interval \([c,d]\), \(A\) is the average error between the predicted value and normal value, and the threshold is \(U=Z+A\). According to Equation (10), when the predicted value \(X\) exceeds the preset threshold value \(U\), it is considered abnormal traffic, and the existence of network attack behavior can be determined. When the deviation of the predicted value is large but does not exceed the preset threshold value \(U\), normal network congestion except abnormal is considered to have occurred.  
\[f\left\{\begin{array}{l}X\geq U,\text{DOS attacks or }\text{DDOS attacks}\\ \text{otherwise},\text{normal congestion}\end{array}\right.. \tag{10}\]
## 3 Traffic Models  
### Intelligent Driver Model  
The ID model is a microscopic traffic model that characterizes driver response considering desired speed and distance headway. The driver response is based on the ratio of vehicle speed to desired speed. As this ratio approaches 1, traffic flow becomes smooth and acceleration is minimal. The driver response is defined as [32]  
\[a_{k}=a_{max}^{k}\left(1-\left(\frac{V_{k}}{V_{k}^{{}^{\prime}}}\right)^{ \delta}-\left(\frac{S^{\prime}}{S}\right)^{2}\right), \tag{2}\]  
where \(a_{max}^{k}\) is the maximum acceleration of vehicle \(k\), \(V_{k}\) and \(V_{k}^{{}^{\prime}}\) are the corresponding speed and desired speed, \(\delta\) is the acceleration exponent, and \(S\) is the distance headway as shown in Figure 2. \(S^{\prime}\) is the desired distance headway and is given by [32]  
\[S^{\prime}=j+V_{k}T_{k}+\frac{V_{k}\Delta V}{2\sqrt{a_{max}^{k}b_{min}^{k}}} \tag{3}\]  
where \(j\) is the spacing between vehicles during congestion, \(T_{k}\) is the time headway, \(b_{min}^{k}\) is the deceleration, and \(\Delta V=V_{k}-V_{k-1}\) is the difference in velocity between the leading and subject vehicles.  
The driver response with the ID model depends on the constant \(\delta\). Thus, driver behavior is the same for all traffic conditions, regardless of whether the traffic is moving freely or experiencing a bottleneck, which is not realistic. Actual driver behavior varies according to the traffic conditions. Near a bottleneck, drivers become cautious and reduce their speed, whereas in free-flow traffic, drivers are more aggressive and can maintain high speeds. Therefore, \(\delta\) should vary with changes in flow and density to reflect real-world traffic behavior. Further, the ID model was developed to simulate human-driven vehicle dynamics and so does not adequately account for the advanced communication and cooperative abilities of CAVs, which is crucial in complex traffic environments. Thismodel also does not capture the decision-making and coordination behavior of CAVs in scenarios like bottlenecks.
## 5 System Design  
The first part of developing the proposed scheme is constructing a matrix to store the transactional records, which will act as a shared database system. The novelty of this initial step is that the proposed model keeps the transactional records of data being exchanged by mobile nodes in an encoded form in a distributed yet highly connected database system. The proposed scheme implements a strategic game model to control node misbehaviour, which deploys an on-demand ad hoc protocol. The proposed concept of the strategic game model allocates two discrete roles of a mobile node in IoT, the CIN and RIN. The complete implementation of the proposed security model is carried out in three stages, viz. primary, secondary, and tertiary. The primary stage is responsible for determining the role played by each TRM, while the secondary stage performs the selection of the payoff for the RCN. The tertiary stage enhances the gain for the RCN. It should be noted that the operation carried out for the primary and secondary stages is carried out over the private cloud, which is not accessible to all nodes, while the tertiary stage is carried out in the public cloud, which is accessible to every IoT node in the environment. The scheme ensures that TRM is introduced in mobile IoT systems to perform various operation sequences, viz. (i) consistent validation of GTC scores exchanged by any mobile IoT nodes, (ii) efficient selection of CIN, and (iii) reliable allocation of payoffs for the CIN. The implementation is carried out using both public and private cloud ecosystems to retain better cost-effectiveness of the proposed strategic game model.  
The study considers the deployment of TRM within an IoT hotspot, a type of an access point mounted within the environment with a coverage range slightly more than that of mobile IoT nodes. The realisation of the proposed model is carried out by developing a unique agreement system responsible for automating the predefined execution of programs within TRM. The two essential actors in this process are the GTC and CSC. The GTC is responsible for storing and updating the global trust value of all the mobile IoT nodes publicly. It will eventually mean that the proposed scheme assists in validating the shared value of global trust through any communicating IoT nodes. The CSC is responsible for aggregating the game strategies adopted by RIN and CIN, in order to select the upcoming CIN, thereby keeping the network updated. Furthermore, CSC also performs the aggregation and dispatching of the computed payoff from normal IoT nodes to their respective CIN to permit cooperation with each other. Further operation is discussed as follows.
# A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
## 2 Related Work
### Process Mining and MOOCs  
Although MOOC systems generate a large amount of data, research on process mining approaches is still in its early stages [12]. Several authors, however, have attempted to describe or investigate student processes using this data. One of the first works on the subject, [15], uses process discovery and conformance techniques to categorize behavioral differences among groups of students. More recent studies such as [7] assess the behavioral differences between students who both complete and do not complete a MOOC from the specific perspective of time commitment. In the same context, [16] investigates the differences in the process between three different sets of students depending on whether they have completed all, some, or none of the MOOC activities. [17] compares the use of process mining and sequence mining to predict student dropout from a MOOC, while process mining provides useful tools for analysis, sequence mining provides better predictive results. Dotted charts, fuzzy miner, and social miner are used in [18] to establish differences between groups of students with high and low performance.  
By combining clustering approaches with process mining, [19] identifies four sets of students, ranging from those who drop out at the very beginning of the course to those who successfully complete it. Their research shows that students in the cluster of individuals who successfully completed the course tended to watch videos in successive batches. Furthermore, using clustering techniques and process mining, [20] proposes an approach to measuring the difficulty and importance of videos in a MOOC.  
[5] examines the event logs of three MOOC Coursera courses and discovers six patterns of student interaction. According to the behavior described, these patterns were also classified into three clusters: sampling learners, comprehensive learners, and targeting learners. These findings are expanded and refined in [21]. Furthermore, the idea of \"session\" has been used as a unit of analysis in both studies. In the same regard, [2] investigates the behavior of students in work sessions in greater depth based on eight different possible interactions, segmenting them into those who complete and those who do not complete the course. Their research found that students who complete the course exhibit more dedicated behavior and attend a greater number of sessions during the course. [6], like our study, aims to uncover high-level patterns in low-level events. Their research looks at student behavior in MOOCs via the lens of nine previously identified learning actions [4], which are subsequently utilized to find higher-level patterns known as learning tactics.
# Study of the Preparation and Dielectric Property of PP/SMA/PVDF Blend Material
## 1 Introduction  
Requirements for polymer materials have gradually increased with the development of technology [1; 2]. However, there are many limitations, such as source of raw material, synthesis technology, and cost of production, to the development of a new kind of polymer [3]. Therefore, researches focus on the study of polymer blends which combine the performances of the blended polymers. Polymer blends are easy to process and shape, and have great flexibility [4; 5]. Meanwhile, the application prospects of polymer blends is extensive, and they can typically be adopted by the modern electronic industry [6; 7]. In our previous study, polyamide (PA) 11/PVDF and PA66/PVDF blends were reported as novel blends for capacitors [8; 9; 10]. Polyvinylidene fluoride (PVDF) is known as the polymer with the best dielectric properties, and has stable chemical and mechanical performance. The blends comprised of PVDF and other organic materials have also demonstrated that the organic-polymer composites possess very distinct dielectric properties that can be applied in electrics, such as hydroacoustic sensors and medical applications [8; 9; 10; 11]. Nevertheless, most of the high-molecular weight polymers are incompatible due to the lack of effective interactions with each other between mixed phases and the strong surface tension when melting [12]. Consequently, a compatibilizer is introduced in this research to promote the compatibility between polymers.  
Because of the excellent piezoelectricity and robust tensile strength of PVDF and polypropylene (PP), respectively [13; 14], this research prepared PP/PVDF through machinery blending. Then, styrene maleic anhydride copolymer (SMA) was adopted as compatibilizer and added into PP/PVDF blends. SMA has been widely applied as a compatibilizer since it has grafting reactions with some polymers. There are anhydride groups on the backbone structure of SMA which result in various reactions with other groups and produce derivatives that enhance the interactions between two polymers [8]. As aresult, the mechanical and dielectric properties of the blends are significantly improved. Although there are some studies about the dielectric properties of PP/PVDF blends [15; 16], few correlate the results with the microstructures of blends. Thus, this research studies the correlation between blend microstructures and dielectric properties. Besides, PP/SMA/PVDF blends are investigated to verify the compatibilization of PP/PVDF blends by SMA. The paper proceeds as follows: firstly, the preparation method of PP/PVDF and PP/SMA/PVDF blends is introduced. Then, a description of the structural analyses of these blends as conducted based on XRD and SEM is provided. Next, we present the results of the evaluation of the dielectric properties of blends tested by the dielectric spectrum tester. Finally, some discussions about the relationships between microstructures and dielectric properties of blends are presented and are then followed by conclusions of the research.