esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
###### Abstract  
The damage of equipment manufactured with ferromagnetic materials in service can be effectively detected by Metal Magnetic Memory Testing (MMMT) technology, which has received extensive attention in various industry fields. The effect of stress or strain on Magnetic Flux Leakage (MFL) signals of ferromagnetic materials has been researched by many scholars for assessing stress concentration and fatigue damage. However, there is still a lack of research on the detection of stress corrosion damage of ferromagnetic materials by MMMT technology. In this paper, the electrochemical corrosion system was designed for corrosion experiments, and three different experiments were performed to study the effect of corrosion on MFL signals. The distribution of MFL signals on the surface of the specimen was investigated. The results indicated that both the normal component \(H_{\text{n}}\) and tangential component \(H_{\text{t}}\) of MFL signals presented different signal characteristics when the specimen was subjected to different working conditions. Finally, two characterization parameters, \(S_{\text{n}}\) and \(S_{\text{t}}\), were defined to evaluate the corrosion degree of the specimen, and \(S_{\text{t}}\) is better. The direct dependence of corrosion depth on the parameter was developed and the average error rates between the predicted and measured values are 8.94% under the same working condition. Therefore, the expression can be used to evaluate the corrosion degree of the specimen quantitatively. The results are significant for detecting and assessing the corrosion defect of ferromagnetic materials.  
metal magnetic memory testing technology; stress corrosion; ferromagnetic materials; characterization parameters +
Footnote †: journal: Applied Sciences
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 3 What Is the Role of Antioxidants in Mutagenic Complications?  
Excessive oxidative stress diminishes the antioxidant level in the body by exhausting or reducing their synthesis in the host cell. The antioxidant status in the body is maintained by macromolecules through enzymatic and non-enzymatic reactions. The antioxidant enzymes are superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), while the non-enzymatic action is performed by reduced glutathione, vitamin C, vitamin E, flavonoids, carotenoids, thiol antioxidants, etc. [30].  
According to the literature, SOD is a family of enzymes containing metallic ions and they convert O2\({}^{\bullet-}\) to H2O2 [31]. The enzyme can be found practically in all aerobic life-forms and can be classified into four families; Cu-SOD, Cu-Zn-SOD, Mn-SOD, and Fe-SOD. The Cu-Zn-SOD enzyme is the human form of SOD [32]. O2\({}^{\bullet-}\) is the only known substrate for SOD and the enzyme reacts by taking its electron. The enzyme is produced in response to oxidative stress. SOD can be identified in a huge number of tissues and organisms and is found to defend cells from harmful effects caused by O2\({}^{\bullet-}\). SOD catalyzes the conversion of O2\({}^{\bullet-}\) to H2O2 in biological systems, and the enzyme works in combination with H2O2-removing enzymes, such as CAT and GPx [33].  
CAT, another abundant enzyme, catalyses the breakdown of hydrogen peroxide into water and oxygen. CAT, a heme-containing proteins is localized primarily in mitochondria and in sub-cellular respiratory organelles. One of the first lines of defence against oxidative stress is the GPx enzyme, and it requires glutathione as a cofactor. GPx is found to catalyse the oxidation of GSH to GSSH at the cost of H2O2. GPx is also reported to contend with CAT for H2O2 as a substrate and is the chief defence against small levels of oxidative stress. Chemically, glutathione is a tripeptide and is considered as the major thiol antioxidant. It is one of the components of the intracellular non-enzymatic antioxidant system. GSH is abundant in the cytosol, nuclei and mitochondria and is the key water-soluble antioxidant in these cellular compartments [34].  
The reduced type of glutathione is known as GSH, and the oxidized form is called GSSH (glutathione disulphide). The oxidized form of glutathione will remain within the cells and the ratio of GSH/GSSH is considered to be an excellent measure of oxidative stress. Extremely high concentrations of oxidized glutathione are found to damage many enzymes oxidatively. On the other hand, glutathione reductase catalyses the NADPH dependent reduction of oxidized glutathione, serving to preserve intracellular glutathione supplies and favouring the redox position [35]. In addition to these enzymes, estimation of malondialdehydehyde is also believed to be a pointer of oxidative stress. Malondialdehydedehyde is one of the end products of lipid peroxidation known as a thiobarbituric acid reactive substance (TBARS) [36].
## 2 UAV-Enabled ITS Framework
### Cybersecurity for ITS  
An ITS is a complex framework comprising diverse components such as UAVs, road-side infrastructure, sensors, data storage and processing units, vehicles, and pedestrians, all collaborating to enhance the safety, efficiency, and sustainability of transportation networks [4]. Protecting sensitive data and the overall system from unauthorized third-party access is essential to ensuring accurate decision-making while safeguarding user privacy. ITS cybersecurity is critical, as vulnerabilities can directly impact human lives, mainly when affecting emergency services like medical delivery vehicles and ambulances. Implementing artificial intelligence (AI)-driven systems to detect and mitigate cyberattacks can significantly bolster the resilience of transportation infrastructure [77]. Additionally, establishing protocols for rapidly sharing cyber threat intelligence and mitigation strategies is vital. Expanding the workforce skilled in ITS cybersecurity will further reduce the likelihood and impact of cyberattacks [78]. Furthermore, developing contingency plans with the necessary resources and redundancies in high-risk components can help minimize the effects of cyberattacks and facilitate quicker recovery of ITS functionality after an incident [79].  
Advances in cybersecurity for ITSs have been well discussed in Refs. [77; 80; 81; 82]. These efforts summarized innovative approaches to securing the diverse components of ITSs, including the most frequent cyberattacks and their consequences [80], the implementation of advanced encryption techniques, intrusion detection systems, and robust authentication protocols [81], and potential AI-enabled solutions in UAV-assisted IoT applications [82]. These advances also address the importance of collaboration among stakeholders--such as government agencies, industry leaders, and researchers--to develop comprehensive strategies for mitigating cyber threats [81].
# Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials
## 4 Numerical Results
### Effect of Interval Support Stiffness  
Assuming the degree of uncertainty is \(\beta_{K_{b}}\), the support stiffness can be expressed in interval form as follows:  
\[\left[\underline{K}_{b},\overline{K}_{b}\right]=\left[K_{b}-\beta_{K_{b}}K_{b},K_{b}+\beta_{K_{b}}K_{b}\right] \tag{15}\]  
where \(K_{b}\) is the nominal value of stiffness in Support 1 or Support 2.  
Generally, support stiffness comprises three components: the support structure stiffness, the squeeze film damper stiffness, and the bearing stiffness; all of these vary with temperatures, assembly states, and loads [25]. Furthermore, squirrel-cage elastic supports are widely used in jet engines, and unexpected breakage of the cage bars further increases the uncertainty of the support stiffness. Consequently, defining the precise value of support stiffness is challenging. Here, uncertain degrees of 5%, 10%, and 15% for support stiffness are considered, and uncertainty analysis is performed for these three scenarios to determine the range of steady-state responses of the rotor system. The convergence results of the relative generalization error \(\varepsilon_{r}\) are shown in Figure 6.  
As shown in Figure 6, the error \(\varepsilon_{r}\) converges when the Chebyshev orthogonal polynomial is of order 3 (\(p=3\)). Therefore, a third-order polynomial with four integration points in each dimension is employed. Utilizing full factorial numerical integration requires 16 invocations of the system evaluation model. The results of the uncertainty analysis for the steady-state response of Disc 1 are presented in Figure 7.  
As shown in Figure 7, near the critical speed, the range of vibration amplitude broadens as the degrees of uncertainty increase. This phenomenon is more pronounced at the first and second critical speeds compared to the third, indicating that the dynamic response at these speeds is more sensitive to changes in support stiffness. In the range from 1000 to 4000 rpm, the resonance frequency, initially characterized by a single-peak mode, transitions into a broad resonance band. At the upper bound, the distinction between the first and second order becomes very blurred. The steady-state response exhibits less variability when the rotational speed exceeds 4000 rpm.  
Figure 5: The steady\(-\)state unbalance response for Disc 1.
# Freshness Monitoring of Packaged Vegetables
## 3 Factors Indicating the Freshness of Packaged Vegetables
### Oxygen and Carbon Dioxide Effects
#### 3.1.2 Respiratory Metabolism  
Respiration generally affects all the metabolic pathways occurring in vegetables. Where through breathing, the consumption of oxygen together with the metabolic materials such as sugars and acids will lead to reduced shelf life of vegetables. This biological process has an effect in suppressing or enhancing the meristematic activity in many vegetables such as asparagus, broccoli, and onions [16]. The meristematic region is where the tissues responsible for the growth are found. For example, controlling the range of oxygen in packaged onions to 3 kPa will result in suppressing the activity of this region, thus preventing sprout and bulb weight loss which will prolong the storage time [16; 26].  
Elevating carbon dioxide concentration was found to reduce chlorophyll degradation [16]. At the same time, increasing its concentration above certain limits depending on the vegetable type may induce the fermentation process [16]. Hence, keeping CO\({}_{2}\) percentage inside the package within the right amount can control the deterioration of chlorophyll content for many vegetables as in broccoli, cabbage, asparagus, and spinach when the partial pressure is kept in the range of 3-5 kPa. Moreover,  
Figure 3: Polyphenol oxidase (PPO) reaction responsible for browning in vegetables.  
elevating carbon dioxide concentration would also suppress to a lesser extent the respiration and metabolic materials consumption compared to oxygen [16; 27].
## 5 Performance Evaluation
### Results  
This section presents and discusses the results of all experiments including the execution time, acceptance ratio, embedding cost, ratio of average cost to average revenue, and the impact of decomposition selection cost parameters. The overall results show a significant enhancement in execution time for the optimal solution (ILP-P), as shown in Figure 5, compared to average execution time equals to 459.519 s for optimal solution of [12] at 110 nodes. The best gap between heuristic and optimal solutions was in [47] which was equal to 25%. In our approach, the gap between DcPSM (heuristic) and ILP-P (optimal) was reduced to 9% which outperforms previous proposed works.
## 4 Experiments  
### Experiment Materials and Setup  
#### 4.1.1 Dataset  
We conducted experiments on the weather time series dataset collected from the Max Planck Institute for Biogeochemistry, Jena, Germany in tabular format. The datasets contained 21 different meteorological variables, including temperature, humidity, and wind speed, as listed in the Appendix A. The variables were recorded every 10 min over the whole year of 2020. Using a ratio of 7:1:2, we split the dataset into a training set for training the model, a validation set for tuning the hyperparameters, and a test set for evaluating the model's performance after training.  
As demonstrated in Figure 4, some of the meteorological variables exhibit unstable and nonstationary properties with noise and fluctuations, making it challenging for general forecasting models to capture useful features precisely and resulting in less effective performance on forecasting tasks.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 3 Performance Analysis  
In this section, we analyze the SE of the rate-splitting based massive MIMO system with low-resolution ADCs/DACs. We consider the results in the asymptotic regime where \(K\) and \(M\) grow to infinity but the ratio \(\theta=K/M\) remains constant. Firstly, we derive the asymptotic SINRs of the common and private messages, respectively. The asymptotic results enable us to draw useful insights into the impact of the various system parameters such as the number of BS antennas, quantization bits, and the number of users, respectively. Next, to obtain the full benefits of the RS scheme, the power allocation to the common and private messages needs to be carefully considered. Using the asymptotic SINR results, we derive an analytic power allocation solution and the optimal regularization parameter. We show that the power allocation depends on the low-resolution ADCs/DACs and highlights the rate gain of the RS scheme over the conventional non-RS scheme in the massive MIMO system.
## 3 Methodology and Data
### Results of Qualitative Interviews
#### 3.2.2 Organizational Context  
The organizational context includes the internal factors that may influence a company's intention to adopt innovative technologies [28].  
* Top Management Support: Respondents P1 and P3 indicated that a large part of the past successful cases should be attributed to the decision by a company's top-level management. As for those cases without management's or owners' participation, a supervisor was responsible for the decision, and the supervisor often needed to later consult the upper management or owner to modify the decision, or the transaction failed because of a budget issue. Some respondents reported that many employees within their enterprise were not accustomed to the operating modes of the adopted broadband mobile applications, such as video conferencing. As a result, leadership must display full determination to all colleagues in the process of comprehensive implementation (A1-2).
* Employees' Knowledge: Respondent P1 pointed out that if the employees of clients' companies have only little knowledge and awareness, it is critical to aid them in fully understanding the importance of broadband mobile applications. Many respondents said that staff levels within information departments are inadequate. Companies are willing to increase their employment and focus on the application and implementation of innovative information technology. However, it is hard to find suitable talent, especially information professionals; SMEs' pay and future vision are difficult to compare with the large electronics companies (A2-3, I1-2, N1-2).
* Absorptive Capability: Some of the respondents stated that they are worried about whether there is sufficient additional time and capacity for colleagues to learn and familiarize themselves with the new adopted broadband mobile applications when capacity is already maximized due to existing workloads and responsibilities. (A2, II). Respondent I2 said that, although leadership had communicated with each department's head before the adoption and was expected to strengthen the provision of education and training if there were problems during the adoption process, the follow-up results are yet to be observed. Respondent N2 stated that, considering work-time and location, it is difficult to ask all the branches and franchisees to send staff to participate in education and training courses together.
# Evaluation of Survivability of the Automatically Obfuscated Android Malware
## 3 Results
### Findings: Impact of Obfuscation on Static Analysis  
A random sample of 2000 applications from the benign and malicious sample sets was selected for the static analysis. Each APK was decompiled using Quark [30] to extract the following five features: (1) permissions requested; (2) native API calls; (3) certain combinations of native API calls; (4) sequence of native API calls; and (5) API calls that handle the same register. In the total 2000 APKs submitted for static analysis, Quark detected all the malware APKs in the original files. However, the detection ratio decreased to 82% for monomorphic obfuscation techniques while producing the lowest detection ratio for High obfuscation of 72%, in which polymorphic obfuscation strategies had been enabled. Table 8 shows the detection ratio of the static analysis for varying levels of obfuscation.  
The manifest files were compared to determine the impact of obfuscation on the permissions listed in the original APKs. Random APKs were selected from a dataset of 30,000 APKs to check if the permissions were added or deleted from the obfuscated file compared to the original file. Meld [31], a static-analysis tool, was used to perform two-way and three-way comparisons of the files. It was confirmed that all APK's from the sample set have the same permissions in both the original and obfuscated manifest files. However,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Obfuscation Technique** & **Detection Ratio** & **Percentage** \\ \hline Encryption & 3498/5299 & 66.03\% \\ \hline Code & 3602/5299 & 67.98\% \\ \hline Rename & 3815/5299 & 72.00\% \\ \hline High & 3443/5299 & 64.99\% \\ \hline Medium & 3867/5299 & 72.99\% \\ \hline Low & 4132/5299 & 77.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Detection Ratio based on Obfuscation Strategies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Obfuscation Method** & **APKs Tested** & **APKs Detected** & **Percentage** \\ \hline Encryption & 5299 & 4371 & 82.50\% \\ \hline Rename & 5299 & 4398 & 83\% \\ \hline Code & 5299 & 4191 & 79.10\% \\ \hline High & 5299 & 3831 & 72.30\% \\ \hline Medium & 5299 & 3974 & 75\% \\ \hline Low & 5299 & 4451 & 84\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Detection Ratio based on Static Analysis.  
in some APKs, the obfuscation impacted the number of times each permission was used. Table 9 provides a sample of such applications.  
A special feature of Android since API Level 23 is dynamic permission support [32], which allows apps to request, acquire, and revoke permissions as they run. According to this new runtime-permission mechanism, static approaches will not be able to detect abnormal permission requests and grants made during runtime. In addition, users may revoke dangerous permissions after their apps are installed, which could lead to a false positive from anti-malware software.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 6 Results and Analysis  
### Results and Analysis of WSF  
The performance of various ML models for wind speed forecasting (WSF) and wind power forecasting (WPF) was assessed using evaluation metrics such as MAPE, MSE, and adjusted R\({}^{2}\). For very short-term WSF (10 min scale), Extra Trees, Random Forest (RF), Decision Tree (DT), and XGBoost were the most accurate models. Extra Trees achieved the lowest MAPE (3.485%) and MSE (0.254), while DT and RF had the highest adjusted R\({}^{2}\) values of 0.989 and 0.984 (Figure 6). In contrast, Deep Neural Networks (DNNs) and Linear Regression performed poorly, with MAPE values of 42.343% and 46.724%, respectively.  
At the 30 min scale (Figure 7), DT and Extra Trees remained the top performers, achieving MAPE values of 7.8% and 8.635%. Bagging and RF also showed strong results, with MAPE values of 11.439% and 13.484%. Other models did not meet acceptable accuracy thresholds. For 6 h ahead WSF (Figure 8), DT, Bagging, Extra Trees, and RF excelled with MAPE values of 4.103%, 4.171%, 4.6%, and 4.9%, respectively, consistently outperforming regression and DNN models.  
Figure 6: Results of ML models for VSTWSF 10 minutes (10 min).  
For 24 h ahead WSF (Figure 9), DT, Bagging, Extra Trees, and RF again led the rankings, achieving MAPE values of 4.645%, 5.179%, 6.502%, and 6.538%. While models like XGBoost and polynomial regression performed satisfactorily, DNN and SVM models showed poor accuracy. Finally, at the 36 h scale (Figure 10), Extra Trees, DT, Bagging, and RF continued their strong performance, with MAPE values around 9%, reinforcing their reliability across time scales. Overall, tree-based and ensemble models, particularly Extra Trees and DT, consistently outperformed others for WSF, while DNNs underperformed across all time scales.  
Figure 8: ML results for 6 h ahead of WSF.  
Figure 7: ML results for 30 min ahead of WSF.
## 3 Background Theory  
### Graph Convolutional Networks  
Graph convolutional neural networks (GCNs) are a type of neural network architecture designed to operate graph-structured data. The key idea of GCNs is to generalize the convolution operation from regular grids, such as images, to irregular graph structures. So, it can work directly on graphs and takes features of their structural information, and graph convolution is defined as Equation (1) [8].  
\[H^{(k)}=\sigma(\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}}H^{(k-1)}W^{( k)}) \tag{1}\]  
where \(H^{(k)}\) and \(H^{(k-1)}\) are the \(k\)-th \(k-1\)st layer hidden representation, respectively, \(\hat{A}\) is the graph adjacency matrix which A added with self-connections I, and \(\hat{D}\) is the degree matrix of \(\hat{A}\) with the identity matrix. \(\hat{A}\) is normalized using \(\hat{D}\). \(\sigma\) is a non-linear activation function and \(W\) is a trainable weight matrix for the \(k\)-th layer with the dimension of a number of features and weights.  
The GCN equation consists of three parts. Graph convolution, normalization, and non-linearity. The graph convolutional operation is defined as a weighted sum of the neighboring nodes feature vectors, with the weights being determined by the graph structure. The normalization step ensures the outputs have a unit norm, which is important for the stability of the learning process. Non-linearity introduces non-linearities into the model, allowing it to capture complex relationships in the data.  
Equation (2) shows the graph neural network (GNN) [8]. A major difference between the graph neural network (GNN) and graph convolutional neural networks (GCNs) is weight parameter sharing.  
\[H^{(k)}=\sigma(AH^{(k-1)}W^{(k)}_{neigh}+H^{(k-1)}W^{(k)}_{self}) \tag{2}\]  
The GCN has a common weight matrix for both the self and neighbor node, but the GNN has a separate weight for them. The GCN down-weights on the high-degree neighbors by normalizing the adjacency matrix, including self-connections.