esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Stochastic models use random processes to generate _EEG_ signals, often incorporating elements like autoregressive processes or Gaussian noise. These models can capture more of the variability seen in real _EEG_ data, providing a more realistic approximation of _EEG_ signal dynamics. However, accurately estimating the parameters of these models can be challenging. Additionally, while they can replicate certain aspects of _EEG_ variability, they might still lack some of the intricate patterns present in real signals. Research has shown that stochastic models, such as those based on autoregressive processes, can be quite effective but may still fall short in capturing all the nuances of _EEG_ data [23; 24]. Physiologically inspired models, such as neural mass models or neural field models, aim to replicate the physiological processes underlying _EEG_ signals. By incorporating biological constraints and mechanisms, these models offer a more realistic representation of _EEG_ activity. However, their complexity requires extensive computational resources and detailed knowledge of the underlying physiology, which can be a significant drawback. Studies have demonstrated that physiologically inspired models can provide a closer approximation to real _EEG_ signals by simulating neural dynamics, but they are often computationally intensive and complex to implement [23; 24]. The mentioned methods do not allow us to generate signals with varying directed connectivity over time, nor do they provide the ability to control the level of such connectivity. The proposed method aims to generate synthetic signals with realistic properties and predefined connectivity changes over time. This capability allows for the testing and development of improved methods for dynamic analysis of functional connectivity. By addressing the limitations of previous methods, the proposed approach seeks to create more accurate and reliable synthetic _EEG_ signals, facilitating better evaluation and enhancement of connectivity estimation techniques. The primary contribution of this paper is the development of a method to generate realistic synthetic _EEG_ signals for testing dynamic directed connectivity estimation tech niques. Our approach overcomes the limitations of existing signal generation methods by enabling control over time-varying connectivity. The paper is structured as follows: Section 2 describes the methodology for generating synthetic signals and assessing connectivity. Section 3 presents the results of applying the method to a real dataset. Section 4 discusses the findings, limitations, and future directions.
###### Abstract  
In this study, the effects of crust formation were investigated on the quality properties of pork meat, including texture, colour, cooking loss, pH, water-holding capacity (WHC), and sensory attributes. Two cuts of pork, (1) tenderloin (lean) and (2) collar (fatty), were subjected to three treatments as follows: (1) crusting before cooking, (2) crusting after cooking, and (3) cooking without crusting. Texture was analyzed measuring hardness, cohesiveness, springiness, and chewiness. Colour was analyzed measuring lightness, redness-greenness, yellowness-blueness, and hue angle. Cooking loss and pH and WHC were measured using standard methods and a pressing test. Results showed that meat type and treatment significantly impacted texture, with tenderloin exhibiting greater hardness and chewiness than collar (\(p=0.05\)). Crusting after cooking produced the highest values for hardness and chewiness, while cohesiveness and springiness were unaffected by treatment (\(p=0.05\)). Colour measurements revealed significant treatment-induced differences, with samples crusted after cooking being darker and redder, on average 14% and 33%, respectively. Cooking loss varied considerably, with no significant treatment differences, although a trend suggested lower loss in samples crusted before cooking. pH values were slightly higher in crusted samples, while WHC was notably higher for samples crusted after cooking, likely due to oil retention.  
effects of crusting; tenderloin; collar; fat content +
Footnote †: journal: Applied sciences
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Possible Scenarios
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Field** & **Rule Item** & **SIMEP** & **SIMIP** & **SIMs** & **Score** \\ \hline \multirow{3}{*}{**Study**} & Study\_Home\_Evening & 0.8165 & 0.8135 & 0.7291 & 0.7892 \\ \cline{2-6}  & Study\_Home\_Weekend & 0.8165 & 0.8135 & 0.6188 & 0.7561 \\ \hline \multirow{6}{*}{**Domotics**} & Adjustate\_Water\_Temperature & 0.8165 & 0.8135 & 0.8184 & 0.8160 \\ \cline{2-6}  & Morning\_Preparation & 0.8165 & 0.8135 & 0.8184 & 0.8160 \\ \cline{2-6}  & Morning\_Turn\_Light\_Kitchen & 0.8165 & 0.8135 & 0.8184 & 0.8160 \\ \cline{2-6}  & Enter\_Bathroom\_Light\_On & 0.8165 & 0.8135 & 0.8184 & 0.8160 \\ \cline{2-6}  & Wake\_Up\_All\_Wakes\_Up & 0.8165 & 0.8135 & 0.8184 & 0.8160 \\ \cline{2-6}  & Leave\_Bathroom\_Light\_Off & 0.8165 & 0.8135 & 0.8465 & 0.8244 \\ \cline{2-6}  & Showing\_Light\_Color\_Blue & 0.8165 & 0.8135 & 0.8465 & 0.8244 \\ \hline \multirow{6}{*}{**Security**} & House\_Fire\_Alarm & 0.8165 & 0.8135 & 0.7326 & 0.7902 \\ \cline{2-6}  & Home\_Intrusion\_Alarm & 0.8165 & 0.8135 & 0.6548 & 0.7669 \\ \cline{2-6}  & Home\_Room\_Unlocking & 0.8165 & 0.8135 & 0.6231 & 0.7574 \\ \cline{2-6}  & Security\_Control\_Door & 0.8165 & 0.8135 & 0.7326 & 0.7902 \\ \cline{2-6}  & Night\_Garage\_Close & 0.8165 & 0.8135 & 0.5551 & 0.7370 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of recommended rules with first preferences configuration and context (home). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Field** & **Rule Item** & **SIMEP** & **SIMIP** & **SIMs** & **Score** \\ \hline \multirow{3}{*}{**Driving**} & Path\_Route & 0.4165 & 0.2235 & 0.5996 & 0.4038 \\ \cline{2-6}  & Car\_Map\_Weekday & 0.4165 & 0.2235 & 0.3528 & 0.3298 \\ \hline \multirow{6}{*}{**Shopping**} & Shop\_Al\_Othaim\_Market & 0.4165 & 0.2235 & 0.4898 & 0.3709 \\ \cline{2-6}  & Shop\_Al\_Basem\_Shop & 0.4165 & 0.2235 & 0.4831 & 0.3689 \\ \cline{2-6}  & Shoping\_Order\_Google\_Calendar & 0.4165 & 0.2235 & 0.4048 & 0.3454 \\ \cline{2-6}  & Shop\_Open\_Send\_Email & 0.4165 & 0.2235 & 0.3072 & 0.3161 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of recommended rules with second preferences configuration and context (outdoors). **Third Scenario. ** When a user enters his office, of course, his preference configurations will be loaded (see Figure 11). The system runs the ontology-based context-aware probabilistic recommendation process and the list of recommended rules is shown in Table 4. When meeting time comes, the service deployment module orchestrates the best way to adapt the interactive services after finding available devices. The system removes pen-click services on his desktop PC.
# Metabolism and Regulation of Ascorbic Acid in Fruits
## 1 Introduction  
Ascorbic acid (AsA), also known as vitamin C, is an important antioxidant widely found in plants. AsA is closely related to human health. A lack of AsA can lead to scurvy [1]. In addition, AsA also plays an important role in the defense of cardiovascular diseases, cataracts, cancer, aging, and other diseases [2, 3]. Since the human body lacks the gene encoding L-galuronic acid-1,4-lactone oxidase, the last step in the AsA synthesis pathway, it cannot synthesize AsA [4]. Therefore, fruit is the main source of human AsA intake.  
As an important reactive oxygen species scavenger, AsA plays an important role in plant resistance to biotic stress. The increase in endogenous ascorbic acid content can improve the resistance of potato to Phytophthora infestans [5]. Seed priming with salicylic acid treatment enhanced tomato resistance to Fusarium wilt [6]. In addition to biotic stress conditions, more studies have shown that AsA played an important role in abiotic stress resistance [7, 8]. The reactive oxygen species produced by drought, salinity, freezing, and other abiotic stresses can be eliminated by AsA, thereby protecting cells from damage [9, 10, 11]. Exogenously applied AsA can alleviate the adverse effects of various abiotic stresses and promote plant growth [12, 13].  
AsA plays an important role in the postharvest low-temperature storage of fruits. Exogenous AsA treatment can significantly improve the cold resistance of banana fruit and strawberry fruit under low-temperature storage [14, 15]. Changes in AsA content can also affect fruit ripening [16]. In addition, AsA has a certain relationship with fruit development. After reducing the AsA content in tomato fruit, the size of tomato fruit is significantly reduced [17]. Understanding the metabolism and regulatory mechanism of AsA in fruit hasimportant guiding significance for fruit quality improvement and postharvest preservation. This paper systematically reviews the AsA metabolism and regulatory mechanism in fruits in recent decades.
# Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR
## 2 Constructing Trajectory Planning Problems
### Cost Function  
The cost function is defined as:  
\[J=\int_{\tau=0}^{T}[(x\;(\tau)-x_{ref}(\tau))^{T}w_{x}(x(\tau)-x_{ref}(\tau))+ u(\tau)^{T}w_{u}u(\tau)]\cdot d\tau \tag{2}\]where \(x(\tau)\) denotes the actual state profiles at moment \(\tau\) and represents the reference state profiles at moment \(\tau\). \(u(\tau)\) denotes the control volume at the moment \(\tau\). \(w_{x}\), and \(w_{u}\) denotes the vehicle state and control profiles' weight coefficient matrix.  
The first term, \(x(\tau)-x_{ref}(\tau)\), serves to converge the state of the vehicle at a given time to a given reference state and can be explicitly written as:  
\[\begin{split}& x(\tau)-x_{ref}(\tau)=(x(\tau)-x_{ref}(\tau))+(y(\tau)-y _{ref}(\tau))\\ &+(v(\tau)-v_{ref}(\tau))+(\theta_{1}(\tau)-\theta_{1 ref}(\tau)) \\ &+(\theta_{2}(\tau)-\theta_{2 ref}(\tau))\end{split} \tag{3}\]  
where \(x(\tau)\) and \(x_{ref}(\tau)\) are the actual x-coordinate and reference x-coordinate of the vehicle's reference point at time \(\tau\), respectively. \(y(\tau)\) and \(y_{ref}(\tau)\) are the actual y-coordinate and reference y-coordinate of the vehicle's reference point at time \(\tau\), respectively. \(v(\tau)\) and \(v_{ref}(\tau)\) are the actual and reference velocities of the vehicle at the reference point at time \(\tau\), respectively. \(\theta_{1}(\tau)\) and \(\theta_{1 ref}(\tau)\) are the actual and reference yaw angles of the tract at time \(\tau\), respectively. \(\theta_{2}(\tau)\) and \(\theta_{2 ref}(\tau)\) are the actual and reference yaw angles of the semi-trailer at time \(\tau\), respectively.  
The second term brings the vehicle control profiles to 0, improving the planned trajectory's smoothness. Thus, it improves the stability of the vehicle's driving and saves energy, which can be explicitly written as:  
\[u(\tau)=\mathit{jerk}(\tau)+\omega(\tau) \tag{4}\]  
where \(\mathit{jerk}(\tau)\) is the derivative of the acceleration at moment \(\tau\). \(\omega(\tau)\) is the angular velocity of the front wheel turning angle of the tractor at moment \(\tau\).
## 4 Stepwise Process Enclosed in Proposed Research
### First Stage: Design of Optimal Routing Protocol
#### 4.2.1 Node Deployment in Building  
The first and most significant process in any WSN is node deployment. Eight different sensors were deployed in a building to sense various types of information. The eight sensors deployed in a building include contact, electric current monitoring, temperature, motion, air quality, water quality, humidity, and smoke sensors. Generally, the node that is referred to as the source node initiates communication by broadcasting a Route Request (RREQ) packet to every node [30]. This RREQ packet contains specific details such as ID, source IP address, destination IP address, and location. The nodes within the communication limit will receive this RREQ packet and reply with the Route Reply (RREP) packet, which contains ID, location, and lifetime information. The ultimate objective of these deployed nodes is to transmit sensed information to the base station.
## 5 Results for RQ1  
We first verified the assumptions for normality, homogeneity of variance, sample size, and multicollinearity. Using Kolmogorov-Smirnov tests (\(p<0.010\)) and Shapiro-Wilk tests (\(p<0.010\)), we found that the dependent variable (i.e., green policymaking initiatives) was not normally distributed for each category of the independent variables (i.e., organization location, organization size, and organizational green capabilities). Regarding the homogeneity of variance, we calculated [PERSON]'s test in its traditional version and its nonparametric version, given the fact that our data were not normally distributed [64]. Both versions of [PERSON]'s test gave evidence for unequal variances among all variables (\(p<0.001\)). Third, the contingency tables for each variable showed unequal sample sizes (see Appendix A). Finally, the collinearity diagnostics (correlation, tolerance, and variance inflation factor) did not show problematic values.
# Voltage Optimization in PV-Rich Distribution Networks--A Review
## 3 Voltage Optimization in PV-Rich Distribution Networks--Objectives and Variables  
The OPF concept was proposed in the early 1960s [43] as an enhancement of economic dispatch to find the optimal solution for controlling variable settings under different constraints. The OPF is used as a universal term for problems associated with network optimization [44; 45; 46; 47]. The OPF is ordinarily modeled to the appliance on transmission level considering large generating units. Besides the fundamental variables, the OPF model may contain ancillary generation units and variables representing the other segments of the power system used for optimal operation.  
The transmission network (TN) diverges from DN in topology, nature, electrical parameters, power flow values, and a number of control devices. Unlike TNs, DNs are inherently unbalanced and more complex [48]. The reason for the imbalance is that the DN supplies unequal single-phase loads and contains unequal conductor interspace of three-phase segments [49; 50]. The \(R\)/\(X\) ratio is high in DNs and contributes to the complexity of  
Figure 5: Voltage profile in a distribution feeder obtained using the fixed power factor control mode.  
Figure 6: Voltage profile in the distribution feeder obtained using the volt–VAR control mode.  
control and optimization. In contrast, the \(R\)/\(X\) ratio is low for TNs. Compared to DNs, TNs have a few direct consumers. The simple control and a well-built communication system of TNs are the main reasons why OPF has applied only at the transmission level. The integration of DGs and flexible loads such as EVs makes OPF feasible in DN optimization. To incorporate unpredictable DG and to exploit the potential of flexible loads, OPF became imminent for DNs [51]. Although there is no official record in the literature of the beginning of the application of OPF in DNs, it can be said it started with the integration of different types of prosumers in DNs [48].
## 3 Results and Discussion
### Oxide Cross-Section Evaluation  
#### 3.2.1 Oxide Thickness Evaluation  
The representative cross-sectional thickness values for each oxide group are shown in Figure 6. This examination revealed each oxide group to comprise bi-layered oxides. Therefore, we separated the analyses of the oxide thickness values for the inner and outer layer portions, as shown in Figure 6A,B. Interestingly, TiMo \(\beta\) and TiMo \(\alpha+\beta\) oxides had significantly thicker inner oxide values compared to CPTi and TAV oxides (\(p<0.0001\)). Furthermore, CPTi oxides showed significantly thinner outer oxide values compared to each of the other titanium alloy oxides (\(p<0.0001\)). Overall, the total thickness of the CPTi oxide group was found to be the thinnest among the oxide groups in the study. Previous anodization studies involving the development of hydroxyapatite have shown similar bi-layered oxide formation, consisting of a titanium dioxide-rich inner layer and a hydroxyapatite-rich outermost layer [29; 62; 67; 68; 69].
## Notations  
The following notations are used in this manuscript:  
AIC Akaike Information Criterion  
ANOVA Analyze of Variance  
AR(1) Auto-Regressive of order 1  
ARMA AutoRegressive-Moving-Average  
ARIMA AutoRegressive--Integrated-Moving-Average  
BM Block Maxima  
CEVT Conditional Extreme Value Theory  
CSL Cycle Service Level  
EVT Extreme Value Theory  
FED Forecast Errors Estimation  
FHS Filtered Historical Simulation  
GARCH Generalized Auto-Regressive Conditional Heteroskedasticity  
GEV Generalized Extreme Value  
GPD Generalized Pareto Distribution  
LT Lead Time  
iid Independent and Identically Distributed  
KDE Kernel Density Estimation  
MEF Mean Excess Function  
MSE Mean Squared Error  
MTO Make-To-Order  
MTS Make-To-Stock  
SSE Simple Exponential Smoothing  
SS Safety Stock  
TLF Tick Loss Function  
POT Peack Over Threshold
# GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
## 3 Android Malware Detection Method  
### The GA-StackingMD Framework  
In consideration of the high feature dimension and low detection efficiency of single classifiers, an Android malware detection method GA-StackingMD based on Stacking is presented, and GA is used to optimize the hyperparameters of the base models. The GA-StackingMD Framework is shown in Figure 2.  
The proposed malware detection framework consists of three parts.  
1. Data set construction. The Android application is decompiled and static features are extracted, including Permission, API, Dalvikopcode, Intent, and Hardware. Then the extracted features are digitized to construct the original feature set with high dimensions;  
Figure 1: Two-layer Stacking Structure.  
Figure 2: The GA-StackingMD Framework.  
Feature dimension reduction. A two-step feature selection method is proposed to reduce the original feature dimension. InfoGain is used for the primary election, then Chi-square Test is applied for further reduction to remove redundant and irrelevant features. The final key feature subset contains the selected features with low dimensional and good differentiation;
3. GA-StackingMD. An optimization algorithm based on GA is presented to select the hyperparameters of base classifiers of Stacking. After selecting the better combination of base classifiers, GA is used to adaptively adjust the hyperparameters in a given scope, and finally, improve the malware detection performance.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Acoustic Emission Sensors in Port Crane Maintenance
#### 4.2.1 Working Principle of Acoustic Emission Sensors  
AE sensors detect the acoustic waves generated by materials during stress or deformation. These acoustic emissions are caused by microstructural changes within the material, such as crack propagation, dislocation movement, or phase transformation. When an acoustic wave reaches the sensor, it converts the mechanical wave into an electrical signal, which can then be amplified, processed, and analyzed. There are two main types of AE sensors based on their working principles: piezoelectric transducers and fiber-optic sensors. Piezoelectric transducers generate electrical charges when they are subjected to mechanical stress. When an acoustic wave reaches a piezoelectric transducer, it generates an electrical signal proportional to the amplitude and frequency of the wave.  
Fiber-optic sensors detect AEs by measuring changes in the properties of optical fibers caused by acoustic waves. These changes are then converted into electrical signals for further analysis. In addition, [PERSON] et al. [105], AE sensors are based on the physical principle that elastic waves are generated within a material when stress fields are redistributed. This makes AE testing an inherently in-process method, owing to the need for stress redistribution. This technique is commonly used in machining for tool condition monitoring because acoustic signals, such as rake face pitting and flank face abrasion, are generated through changes to the condition of the tool.  
Research has shown that AE testing can be used to monitor the condition of a tool and simultaneously detect anomalous surface features generated on the workpiece surface [105]. This is owing to the large redistribution of stress fields associated with these examples of severe plastic deformation.  
Figure 4 presents the acoustic emission signal pattern in crack propagation. By analyzing these patterns, engineers and researchers can gain a deeper understanding of how cracks initiate, grow, and become unstable.
## 4 Optimization Approach
### Two-Level PSO-Based Approach to Aircraft Climb Path Optimization
The averaged convergence histories are shown in Figure 10 and the mean and standard deviation values of the final solutions are included in Table 3. These indicate that the proposed approach displayed consistently faster convergence over the basic MOPSO method, which is initialized using a homogeneous random distribution of the particles in the design space. As expected, the injected optimal solutions from the E-M calculations were sub-optimal when evaluated by means of the aircraft simulation; the average hypervolume of the injected front was rather low when compared to the converged solutions and was equaled by MOPSO after only a few iterations. Despite this, the large number of well-placed solutions that were injected to the initial population did consistently boost convergence speed, leading to better fronts for a given amount of fitness function evaluations (Figure 11). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Hypervolume Indicator**} \\ \cline{2-3}  & **Mean** & **Std. Dev.** \\ \hline _MOPSO_ & \(5.142\times 10^{5}\) & \(3.876\times 10^{3}\) \\ _MOPSO 2-level_ & \(5.234\times 10^{5}\) & \(5.825\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Convergence of the proposed 2-level MOPSO vs. standard MOPSO after 100 iterations; Statistics have been derived from 10 optimization runs for each method. Figure 10: Convergence of the proposed 2-level MOPSO vs. standard MOPSO in a supersonic climb path optimization problem, using a population of 20 particles; results are averaged from 10 two-objective optimization runs. Iteration counts for the two-level method have been shifted to account for the computational cost of the low-level optimization run. The hypervolume indicator quantifies the part of the objective space (up to a user-defined “nadir” point) dominated by the Pareto front. Higher indicator values correspond to better-placed and/or better-populated fronts.