esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### The AQAGS Overview  
Refer to Figure 2, which depicts the proposed AQAGS scheme's design. The proposed AQAGS scheme focuses on vehicles that are within the LTE service area and can directly link to LTE networks. We categorized VANET applications into two types: delay-tolerant (DT) applications (such as FTP) and non-delay-tolerant (NDT) applications such as (VoIP). The average delay requirement values for these applications are shown in Table 2, along with the QoS class identifier (QCI). The LTE-eNB regularly broadcasts its presence and residual load capacity (\(RL_{eNB}\)) to all vehicles within their cell. When the IV needs to communicate with an LTE-eNB, it uses its LTE interface to calculate the received signal strength (\(RSS_{IV}\)) of all sensed eNBs. The IV then builds a diversity set from all detected LTE-eNBs. The IV uses Algorithm 1 to choose the LTE-eNB with the highest \(RL_{eNB}\). The IV determines the best way to connect to the LTE infrastructure network based on the application type and \(RL_{eNB}\). The IV either directly connects to the LTE-eNB or through a given MGW vehicle. If the communication must be handled by MGW, the IV initiates the gateway selection process and uses Algorithm 2 to find the best MGW. The chosen MGW broadcasts itself as a gateway for all its neighbors. The IV initiates packet transmission to the LTE-eNB through the chosen MGW. Simultaneously, the IV starts the gateway maintenance process  
Figure 1: The system model.  
by checking the amount of time left before the link lifetime (LLT) expires. When the LLT is nearing its end, the IV uses Algorithm 3 to transfer the control to another MGW if necessary. In the following sections, we discuss the specifics of the proposed AQAGS.
# \(HM^{3}a1D\): Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model
## Appendix A Mapping a Subsequence of System Calls to Actions  
High-level actions derived from subsequences of system calls are classified into seven sets:  
1. File Actions include \"write file\", \"read file\", \"delete file\", \"execute file\", \"copy file\", and \"move file\".
2. Registry Actions indicate the program behavior regarding the registry of Windows operating system (OS), which includes writing, reading, and deleting in the registry.
3. Service Actions relate to the registered services in Windows OS. These actions include creating, deleting, and executing a service in Windows.
4. Network Actions cover the behavior of executing sample in the transport layer. This Action set is formed based on the state diagram in the TCP protocol, which includes opening and closing a connection (socket), listening on a socket, binding a socket, accepting a socket, sending and receiving on a socket, etc.
5. Internet Actions include all actions that occur during communications of a running program in the Application Layer. These actions include opening a session, session connection, and sending and reading files via the network.
6. System Actions consist of the sequence of system calls which express a system operation to begin the execution. Load library, memory allocation, address allocation to procedures, etc. are some examples of system actions.
7. Process Actions include all the actions related to process and threads, (e.g., creating, executing, and killing a thread.)For example, we map the subsequence of system call {NTCreateFile, NTOpenFile, NTReadFile, NTReadFile, NTReadFile, NTReadFile, NTCloseFile} to the action \(ReadFile\).
## 3 The Penetration Depth of the Capacitive Technique  
For a coplanar capacitance sensor, the penetration depth is often defined as the maximum distance in the vertical direction to the electrodes that produce a detectable variation in the sensor output. It is an important parameter to assess the sensor performance because it indicates how far the sensor can sense [19]. The penetration depth is determined by the material properties (such as permittivity), the noise level of the equipment, and the electrode geometry (shape, size, and arrangement) [9,18]. The material permittivity has certainly an important impact on the penetration depth, the higher the permittivity the faster the decay of the electric field. However, the primary factor having an influence on the penetration depth of a given material is the geometry of the driving and sensing electrodes. In the case of coplanar capacitive sensors, penetration depth determination is difficult due to the different types of electrode geometry and the complexity of the fringing electric field. A practical and straightforward manner to determine the effective penetration depth of the capacitive sensor is to place the probe over the inspected specimen and gradually move it away from its surface while measuring the capacitance between driving and sensing electrodes [9]. This technique is different from the other NDT method such as eddy currents, where the variation of frequency leads to a change in the depth of penetration while the sensing depth of capacitive sensors mainly depends on the probe geometry and properties of the specimen [18].
## 4 Performance Evaluation
### Simulation Environment  
For the performance analysis, we consider a star topology network where the nodes send data to the hub over a lossy channel under saturated traffic scenarios. Initially, we consider 10 nodes in the network; however, the values of throughput and energy consumption are presented against several nodes in priority class 5, class 3, and class 1. The sizes of the minimum and maximum contention windows are taken according to [10]. For priority class 5, class 3, and class 1, the values of \((CW_{i,min},CW_{i},max)\) are set to (4,8), (8, 16), and (16, 32). The different values of contention windows allow nodes to obtain priority access to the channel depending on their traffic type. We assume a heterogeneous network with multiple nodes priorities. The operation of the proposed protocol is implemented in an independent C++ simulator by closely following all details and parameters of IEEE 802.15.6. The size of the data packet is 1560 bits. The narrow band physical layer considered in our analysis is 2400 MHz to 2483.5 MHz with 971.4 kbps data rate. The physical layer preamble and header are transmitted at a symbol rate (600 ksps) and a header rate (91.9 kbps) in the aforementioned physical layer, while the data packet is transmitted at 971.4 kbps. The size of RTS, CTS, and ACK packets is 193 bits. Table 1 shows values of all parameters used to derive the results. According to the IEEE 802.15.6, the value of \(\rho\) is calculated as \(\rho\) = CCA time + 40\(\upmu\)s, where CCA time is given by 63/symbol rate. We assume five power levels for high priority nodes, i.e., the power is incremented five times only. As we consider a lossy channel, the results are derived for BER = \(10^{-5}\) and BER = \(10^{-4}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Values** & **Parameter** & **Values** \\ \hline RTS & 193 bits & CTS & 193 bits \\ Preamble & 90 bits & PHY & 31 bits \\ MH & 72 bits & \(\psi\) & 1 \(\upmu\)s \\ Initial \(P_{tx}\) & 33 mW & \(P_{rx}\) & 8 mW \\ pSIFS & 75 \(\upmu\)s & \(L\) & 1560 bits \\ ACK & 193 bits & Power levels & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard parameters for 2400 MHz-2483.5 MHz.
## 1 Introduction
Therefore, the aim of the present paper was to develop a mathematical model for predicting the effectiveness of the soil decontamination process by washing with chitosan depending on the studied variables affecting this process: the stirring time of the polluted soil with the investigated washing agent, washing solution concentration and solid/liquid ratio (S/L ratio). The model was based on the results obtained in a laboratory test regarding the efficacy of using commercial chitosan to extract copper and lead from heavy metal-contaminated soil collected from the abandoned mining perimeter of \"Larga de Sus\" from Zlatna (Alba County, Romania). During laboratory investigations, a series of soil washing experiments were performed in order to determine the effects of stirring time, washing solution concentration, and solid/liquid ratio (S/L ratio) in decimal fractions (dec. fr.) on the extraction yield of copper and lead. The purpose of the proposed mathematical model was to show the correct layout of the interdependence among the variables of the process with the scope of helping in the estimation, based on the investigated parameters, of the effectiveness of the soil decontamination process by washing with chitosan. Also, the mathematical model can be used as a tool for modelling Cu and Pb removal by soil washing with the studied washing agent.
# AR in the Architecture Domain: State of the Art
## 5 AR System
### Devices
#### 5.1.4 Network Connectivity  
This topic is crucial for MAR systems. There are two different aspects to evaluate, intertwined among them: the type of network, and the data transmission capacity [99]. Regarding the former, MAR can work with local-based or stream-based applications. In the first case, the digital model's database is downloaded to the same AR device, suggesting a self-consistent system. The database is disconnected from the AR device in the second case, so it is necessary to stream digital data via Wi-Fi, Bluetooth, or mobile network. In general, MARs present different network interfaces for connecting directly with a remote server through a mobile network or Wi-Fi. Moreover, wearable devices usually do not have a network interface, but have Bluetooth connections with an external device. The data rate is the consequence of the connection type and the distance from the routers/antennas. As for mobile networks, there are great expectations with the advent of the 5G system [102]. Two quick concepts descend from this topic: On the one hand, mobile network systems are currently favored for outdoor applications, and 5G predictions suggest that they will alsoboost indoor applications. On the other hand, the type of connection heavily conditions the type of information conveyed, the tracking, and the real environment's recording activities. Digital data such as videos and 3D models in markerless tracking require a consistent data stream, with the risk of increasing the latency time and the correct AR perception.
## 3 Results  
### Mechanical Response Characteristics  
Under the action of radial load, the tensile stress (\(\sigma_{x}\)) at the center of the Brazilian split test sample is:  
\[\sigma_{x}=-\frac{2P}{\pi DL} \tag{1}\]  
Figure 3: Arrangement of acoustic emission probes.  
Figure 2: Test system.  
where \(P\) is axial load, \(D\) is the diameter of the Brazilian splitting specimen, \(L\) is the thickness of the Brazilian splitting specimen.  
To analyze the mechanical response characteristics of the samples under different loading rate conditions, the failure load of each group of samples was statistically analyzed. Using Equation (1), the loading rate was converted into the change rate of tensile stress at the center of the sample, and the failure load was converted into tensile strength. The results are shown in Table 1. In the following discussion, the median tensile strength of the results from each group will be used as the subject of analysis (corresponding sample numbers are sequentially 1-2, 2-1, 3-3, 4-3).  
The variation curve of tensile stress at the center of the sample under different loading rate conditions during the loading process is shown in Figure 4. It is observed that, before the peak failure point, the tensile stress at the center of the sample gradually increases with the increase in loading displacement. At the moment of specimen failure, the tensile stress measured at the center represents its tensile strength. The relationship between tensile strength and the change rate of tensile stress is illustrated in Figure 5. Through fitting analysis, they approximately exhibit a linear relationship, with a correlation coefficient reaching 0.89029 (>0.8), indicating a good fit of the regression model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Loading** & **Rate of Change of Tensile** & **Failure Load/kN** & **Tensile** & **Median Tensile Strength/MPa** \\
**Number** & **Rate/kN s\({}^{-1}\)** & **Stress/MPa s\({}^{-1}\)** & **Failure Load/kN** & **Strength/MPa** & **Median Tensile Strength/MPa** \\ \hline
1-1 & & & 16.04 & 6.81 & \\
1-2 & 0.1 & 0.04 & 18.05 & 7.66 & 7.66 \\
1-3 & & & 18.84 & 8.00 & \\ \hline
2-1 & & & 18.24 & 7.75 & \\
2-2
## 4 Simulation Results
### Analysis on Average Required Data Rate  
The effect of the average required data rate \(\overline{R}\) on the average system throughput is shown in Figure 7. The performance of the proposed algorithm is stable at 3.97 Gbps. This is because the average system throughput is mainly related to the quality of communication channels and \(\overline{R}\) has less influence on communication channels. Compared with the RDR-PA method, the proposed algorithm has a performance improvement of 1.45 Gbps with an improvement ratio of 58.0%.  
Figure 5: The average system throughput versus the density of access points.  
Figure 6: The average ratio of satisfied terminals versus the density of access points.  
In Figure 8, we studied the effect of \(\overline{R}\) on the average ratio of satisfied terminals. The performance of the proposed algorithm decreases slowly while the performance of the RDR-PA method decreases rapidly. For example, when \(\overline{R}\) increases from 20 Mbps to 100 Mbps, the performance of the proposed algorithm decreases from 0.235 to 0.196 and the performance degradation ratio is only 16.9%. In contrast, the performance of the RDR-PA method ranges from 0.447 to 0.024 and the degradation ratio is 94.7%. When \(\overline{R}\) is greater than 40 Mbps, the proposed algorithm has better performance than the RDR-PA method. This is because the resource shortage become worse as \(\overline{R}\) increases and the advantage of the proposed algorithm is highlighted under the high required data rate scenario. When \(\overline{R}\) is 100 Mbps, the performance of the proposed algorithm is 8.2 times that of the RDR-PA method. The simulation analysis indicates that the proposed algorithm has better QoS guarantee under a high required data rate and is more suitable for scenarios with high traffic requirements.
## 4 Experimental validation
### Experiment I: three activities  
A first attempt to solve the problem will be done using a set of sequences containing sequences 1, 2 and 3. It contains three different activities: _stopped_, _walking_ and _noise_. Results obtained with GA and BFS can be seen on Table 4. Relative confusion matrix for the solution obtained by BFS is shown on Table 5 and relative confusion matrix of the solution obtained by GA is shown on Table 6. Relative confusion matrices show the frequency of predicting errors per class. The HMM induced can be seen on Figure 6  
The solution found by GA is better than the solution found by BFS according to the global accuracy. Also, taking a look in the confusion matrices of both classifiers (Tables 5 and 6) reveals that the solution found by GA is better than the solution found by BFS for the accuracy of all the classes to predict. In both cases, the hardest class to predict is the _error_ class, maybe because it is the class with less examples.  
Solution obtained by BFS algorithm includes different meassures of the optical flow. Solution obtained by GA includes velocity along \(y\) axes and different optical flow meassures.  
\begin{table}
\begin{tabular}{l|l|l} Algorithm & Accuracy & Selection \\ \hline BFS & 0.8699 & \(f\left(t,F\right),R_{\Sigma_{\vec{P_{4}}}}\left(t,F\right)_{1},R_{\Sigma_{ \vec{P_{3}}}}\left(t,F\right)_{1},R_{\Sigma_{\vec{P_{4}}}}\left(t,I\right)_{2 },R_{\Sigma_{\vec{P_{5}}^{\prime}}}\left(t,I\right)_{1}\) \\ GA & 0.9046 & \(\frac{\partial y(t)}{\partial t},R_{\Sigma_{\vec{P_{3}}}}\left(t,F\right)_{1}, f_{8}\left(t,F\right)_{1},\) \\ \end{tabular} \\ \end{tabular}
\end{table}
Table 4: Features found by by Best First Search (BFS) and Genetic Algorithm(GA) for classificating three activities  
\begin{table}
\begin{tabular}{l|l|l|l|} class prediction & error & walking & stopped \\ \hline error & 0.5869 & 0.4161 & 0 \\ walking & 0.0505 & 0.8984 & 0.0511 \\ stopped & 0.0531 & 0.0796 & 0.8673 \\ \end{tabular}
\end{table}
Table 5: Relative Confusion matrix for the HMM generated using BFS to classify the activities _walking_, _stopped_ and _error__Algorithms_**2009**, 2  
\begin{table}
\begin{tabular}{l|c|c|c|} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} \\ class & error & walking & stopped \\ \hline error & 0.7521 & 0.0427 & 0.2051 \\ walking & 0.0238 & 0.9260 & 0.0502 \\ stopped & 0.0828 & 0.0251 & 0.8921 \\ \end{tabular}
\end{table}
Table 6: Relative Confusion matrix for the HMM generated using GA to classify the activities _walking_, _stopped_ and _error_  
Figure 6: Structure of the HMM used in experiment 1  
confusion matrices of both classifiers (Tables 8 and 9) reveals that the solution found by BFS is better for predicting classes _error_, _stopped_ and _falling_, while the solution found by GA is better for _walking_, _lyed down_ and _rising_.  
Solution found by BFS includes optical flow and trajectory eccentricity measures. GA includes also a shape descriptor.
# Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine
## 3 Methodologies
### Gaussian Process Model
As for the Matern correlation function, two most interesting cases for the GPM are \(v=3/2\) and \(v=5/2\):  
\[R\bigg{(}x_{i},x_{j}\bigg{|}\frac{3}{2},\theta\bigg{)}=\prod_{k=1}^{d}\left(1 +\frac{\sqrt{3}h_{k}}{\theta_{k}}\right)\exp\left(-\frac{\sqrt{3}h_{k}}{\theta _{k}}\right), \tag{12}\]  
\[R\bigg{(}x_{i},x_{j}\bigg{|}\frac{5}{2},\theta\bigg{)}=\prod_{k=1}^{d}\left(1 +\frac{\sqrt{5}h_{k}}{\theta_{k}}+\frac{5h_{k}^{2}}{3\theta_{k}^{2}}\right) \exp\left(-\frac{\sqrt{5}h_{k}}{\theta_{k}}\right), \tag{13}\]  
Equation (9) just reflects the prior knowledge about the relationship between inputs and output of a simulator. It must be updated using the observation data. In general, the output data are collected at a series of design sites \(\mathbf{D}=\left\{\mathbf{x}^{(1)},\mathbf{x}^{(2)},\ldots,\mathbf{x}^{(n)}\right\}^{T}\) which are usually selected based on a certain criterion, e.g., the maximin criterion. According to the design sites \(\mathbf{D}\), a vector of observed outputs \(\mathbf{Y}^{n}\) can be obtained by running the simulator. Considering the assumptions of the GPM, \(\mathbf{Y}^{n}\) follows a multivariate normal distribution \(N\big{(}F_{D}(\mathbf{x})^{T}\mathbf{\beta},\Sigma_{D}\big{)}\), where \(\mathbf{F}_{D}(\mathbf{x})=\Big{[}\mathbf{F}\Big{(}\mathbf{x}^{(1)}\big{)},\mathbf{F}\Big{(}\mathbf{x}^{ (2)}\Big{)},\ldots,\mathbf{F}\Big{(}\mathbf{x}^{(n)}\Big{)}\Big{]}\) is a matrix of regression functions, \(\Sigma_{D}=\sigma_{z}^{2}R_{D}\) is the covariance matrix, and \(\mathbf{R}_{D}=\Big{(}R\Big{(}\mathbf{x}^{(i)},\mathbf{x}^{(j)}\Big{)}\Big{)}_{i,j=1,2,\ldots n}\) is defined in terms of the correlation function \(R(\cdot,\cdot)\). Let \(\mathbf{\beta}\) have a noninformative prior \(p(\mathbf{\beta})\propto 1\). Given a new site \(\mathbf{x}^{*}\), a joint probability distribution can be expressed as:  
\[P\Big{(}\mathbf{Y}^{n},Y(\mathbf{x}^{*})|\mathbf{\beta},\sigma_{z}^{2},\mathbf{\theta}\Big{)} \sim N\Bigg{(}\left[ \begin{array}{c}\mathbf{F}_{D}(\mathbf{x})^{T}\\ \mathbf{F}(\mathbf{x}^{*})^{T}\end{array} \right]\mathbf{\beta},\sigma_{z}^{2}\Bigg{[} \begin{array}{cc}\mathbf{R}_{D}&\mathbf{r}(\mathbf{x}^{*})\\ \mathbf{r}(\mathbf{x}^{*})^{T}&1\end{array} \right]\Bigg{)}, \tag{14}\]  
where \(\mathbf{r}(\mathbf{x}^{*})=\Big{(}R\Big{(}\mathbf{x}^{(1)},\mathbf{x}^{*}\Big{)},R\Big{(}\mathbf{ x}^{(2)},\mathbf{x}^{*}\Big{)},\ldots,R\Big{(}\mathbf{x}^{(n)},\mathbf{x}^{*}\Big{)} \Big{)}^{T}\).
###### Abstract  
Among the several tetratricopeptide (TPR) repeat-containing proteins encoded by the _Arabidopsis thaliana_ genome, AtTPR10 exhibits an atypical structure with three TPR domain repeats at the C-terminus in addition to seven ankyrin (ANK) domain repeats at the N-terminus. However, the function of AtTPR10 remains elusive. Here, we investigated the biochemical function of AtTPR10. Bioinformatic analysis revealed that _AtTPR10_ expression is highly enhanced by heat shock compared with the other abiotic stresses, suggesting that AtTPR10 functions as a molecular chaperone to protect intracellular proteins from thermal stresses. Under the heat shock treatment, the chaperone activity of AtTPR10 increased significantly; this was accompanied by a structural switch from the low molecular weight (LMW) protein to a high molecular weight (HMW) complex. Analysis of two truncated fragments of AtTPR10 containing the TPR and ANK repeats showed that each domain exhibits a similar range of chaperone activity (approximately one-third of that of the native protein), suggesting that each domain cooperatively regulates the chaperone function of AtTPR10. Additionally, both truncated fragments of AtTPR10 underwent structural reconfiguration to form heat shock-dependent HMW complexes. Our results clearly demonstrate that AtTPR10 functions as a molecular chaperone in plants to protect intracellular targets from heat shock stress.  
ankyrin; tetratricopeptide; heat shock tolerant; holdase chaperone; structural switching Article
## 4 Sample Preparation
### Extraction Techniques
For instance, divinylbenzene (DVB)/carboxen (CAR)/polydimethylsiloxane (PDMS) fibers are frequently used, which can extract a wide range of volatile compounds due to their double polarity. For example, DVB/CAR/PDMS fibers were used to extract several volatiles and odorous compounds from recycled PP [48]. In the work conducted by [PERSON] et al. [24], aldehydes (e.g., octanal, nonanal, decanal, etc.) and hydrocarbons were detected in bottled water also using a DVB/CAR/PDMS fiber at 60 \({}^{\circ}\)C, while [PERSON] et al. [39] identified 1,247 volatile organic compounds (VOCs) in rPET with a PDMS/DVB/Carbon Wide Range fiber at 110 \({}^{\circ}\)C. Immersion SPME, where the fiber is directly exposed to migration solutions, proved effective for less volatile compounds. In this regard, [PERSON] et al. [53] quantified migrant substances from expanded polystyrene (EPS) and recycled EPS containers by immersing a DVB/CAR/PDMS SPME fiber in the migration solutions obtained using food simulants A and B. However, the recovery efficiency in SPME can vary significantly depending on the physicochemical properties of the NIAS, such as volatility and polarity. Additionally, issues such as fiber saturation and competitive adsorption can limit extraction capacity and selectivity, potentially affecting quantification accuracy. Therefore, the reviewed studies demonstrate that the choice of sample preparation method depends on the polymer type, the physicochemical properties of the NIAS under observation, and the intended application of the FCM. While migration tests provide regulatory compliance data under simulated use conditions, extraction techniques offer deeper insights into the chemical complexity of polymers, including oligomers, additives, and degradation products.