esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Analysis of the polarization dependences of the magnesium electrode, which is corrosive in sodium and magnesium sulfate solutions, makes it possible to conclude that the limiting stage of the electrochemical process is the transfer of charge through the corrosive film. The basis for this statement is the presence of sufficiently thick corrosion films and high values of the Tafel's coefficients, which do not correspond to the regularities of the electrochemical kinetics charge transfer across the interface.  
Analysis of the charge transport equation in high fields demonstrated that the formal Tafel's coefficient determined from polarization curves was directly proportional to the thickness of the corrosion film. Based on the analysis of the change of the Tafel's coefficients of the cathode and anode process in corrosion, it could be concluded that the charge transfer in the primary film formed during the preparation of the electrodes was limiting at the initial stage. Concurrently, parameters of cathode and anode process transfer differed considerably, indicating the transfer of different charged particles.  
The negligible period of primary film growth under parabolic law ended with its transformation into another state, where parameters of cathode and anode process transfer were equalized. The most likely variant of this conversion was the hydration of magnesium oxide with the primary films and its conversion to hydroxide.  
The characteristics of charge transfer through the film were mainly determined by its dense part from magnesium hydroxide. The layers forming above had an incoherent morphology and little determination of polarization.  
In magnesium sulfate solutions of high concentration, the change in the initial state of the films was very rapid due to the higher solubility of magnesium hydroxide in them. The impedance measurements confirmed the two-phase structure of the film and demonstrated good convergence of their calculated values of polarization resistance with the voltammetry data.  
Conceptualization, A.M.A., A.K., and M.W.; Data curation, A.K.A., Y.B., and M.W.; Formal analysis, G.R. and P.B.; Investigation, A.K.A., Y.B., G.R., and P.B.; Methodology, A.K.A., A.M.A., A.K., M.W., and P.B.; Project administration, A.M.A.; Software, Y.B.; Supervision, A.M.A. and M.W.; Validation, A.K., M.W., and P.B.; Visualization, A.K.A. and G.R.; Writing--original draft, A.K.A.; Writing--review & editing, A.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the program of the Ministry of Education and Science of the Republic of Kazakhstan [AP05132488].  
The authors declare no conflicts of interest.  
The following abbreviations were used in this manuscript:  
\begin{tabular}{l l} NHE & Normal Hydrogen Electrode \\ NDE & Negative Difference Effect \\ PGSTAT & Potentiostat-Galvanostat \\ SEM & Scanning Electron Microscope \\ DC & Direct Current \\ EIS & Electrochemical Impedance Spectroscopy \\ CPE & Constant Phase Element \\ \end{tabular}
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 3 Methodology
### Spatial-Temporal Graph Neural Network
#### 3.3.3 Graph Convolution Module  
In wargaming, the movement trajectories of pieces are affected by other pieces. The purpose of introducing the graph convolution module is to fuse the information of a node with that of its adjacent nodes in order to handle the spatial dependency relationships in the graph. We propose a new mix-hop attention propagation layer to adaptively learn the importance of different graph convolution layers, thereby further enhancing the model performance. Figure 4 shows the architectures of the graph convolution module and the proposed mix-hop attention propagation layer.  
The new mix-hop attention propagation layer in this module includes two steps: information propagation and information selection. The information propagation step is formulated as follows:  
\[H^{(l)}=\alpha H_{in}+(1-\alpha)\hat{A}_{t}H^{(l-1)}, \tag{11}\]  
Figure 3: The temporal convolution module structure.  
where \(H_{in}\) represents the input hidden states produced by the previous layer, with \(H^{(0)}=S^{\prime}\) the output from the temporal convolution module; \(\alpha\) is a hyperparameter that controls the ratio of the original root node states to be retained; and \(l\in\{1,\cdots,L\}\) denotes the propagation depth. For the graph convolution, we use the asymmetrically normalized adjacency matrix \(\hat{A}_{t}\), with \(\hat{A}_{t}=\hat{D}^{-1}(A_{t}+l)\), and \(\hat{D}\) is the degree matrix. The asymmetrically normalized adjacency matrix enables the model to effectively utilize the direction information of edges in a directed graph.  
The information propagation step recursively disseminates node information according to the given graph structure. However, as the number of layers in the graph convolution network increases, the model often encounter the issue of over-smoothing. To address this problem, we introduce self-attention mechanisms [25] in the information selection step to retain the important information generated at each convolution layer. The information selection step is defined as follows:  
\[H_{out}=\sum_{t=0}^{L}\big{(}Attn^{(l)}(H^{(l)})+H^{(l)}\big{)}, \tag{12}\]  
where \(H_{out}\) represents the output hidden states of the current layer \(l\).  
The graph convolution module consists of two mix-hop attention propagation layers, which process the inflow and outflow information through each node. The final output of the graph convolution module is obtained by summing the outputs of the two mix-hop attention propagation layers.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)
### Results for GHI  
Table 6 gives the values of the error metrics calculated on the test data set (RMSE, MAE, and MBE are given in Wh.m\({}^{-2}\)).  
As the ranking of the model is always identical from a RMSE point of view or a MAE point of view, we only present in Figure 10 the results in terms of RMSE and nRMSE expressed in percentage.  
The smart persistence, a naive model, was used as a reference. This model has a good RMSE and MAE for a time horizon, h+1, but its performances decrease rapidly with the time horizon. The gap in term of performances between ANN and SP increases with the time horizon.
# Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions
## 4 Discussion  
Current approaches focus on evaluating classification solutions with message streams generated from a cross-validation strategy or with different message corpora. This reduces the ability to adequately model dynamic environments with varying latency and limits the representation of different behaviours of message sources such as spammers. In contrast, the present proposal generates the message streams following a profiling approach of the message sending sources together with the use of simulation techniques and queueing theory. This gives the possibility to model the different behaviours that different sources may have, be it different spammers with different adversarial attack strategies, press media or social network sources. Each of these profile instances generates its own stream of messages, which are then combined into the main stream, which is closer to the real stream. For example, in the study case presented, it was found that the evaluated classification solution degrades in its quality results as the latency increases and when message obfuscation strategies are used by spammers. This suggests that the classification solution should be improved to better adapt to message streams where training message latency increases and spammers use message obfuscation strategies. A possible solution to the latency problem could be the use of a semi-supervised or unsupervised classification approach and, for obfuscated messages, the improvement of the text processor used by means of a term similarity approach. However, these sources may have social and proactive behaviours that influence the generation of the stream and are not covered by the proposed solution. An example of this is the spammers themselves, who may proactively vary their attack strategy so that their target cannot evade them, sometimes forming communities of spammers. Furthermore, the proposed solution only considers obfuscation of the message body as an adversarial attack strategy for this type of source, and there are others.  
On the other hand, in the developed study case, the latency in sending training instances and the obfuscation of the body of the messages were identified as the parameters with the greatest impact on the quality of the classification. This case study demonstrated the performance of the proposed framework. However, the dataset used for the evaluation was small, as were the processing and classification solutions used, suggesting a larger-scale evaluation at the level of the volume of messages used and a wider range of classifiers and text processors. Furthermore, the framework was only evaluated in the context of spam detection, although it can be used in other contexts, such as news analysis, where sources are modelled as thematic profiles that may constitute digital news media.
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
#### 3.1.3 Results and Analysis of Simulation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Attitude \(\theta\), \(\varphi\), \(\gamma\prime\)** & \begin{tabular}{c} **Velocity \(V_{E}\), \(V_{N}\)**, \\ **V\({}_{\text{ul}}\)/m/s** \\ \end{tabular} & \
\begin{tabular}{c} **Horizontal** \\ **Position \(\lambda\), L/m** \\ \end{tabular} &
\begin{tabular}{c} **Vertical Position** \\ **H/m** \\ \end{tabular} \\ \hline SHAKF & 21.2, 19.9, 30.4 & 7.64, 5.57, 2.42 & 47.3, 73.7 & 20.8 \\ STF & 48.4, 18.8, 67.9 & 0.27, 1.04, 0.13 & 6.22, 5.14 & 9.05 \\ H\({}_{\infty}\) & 26.4, 9.01, 47.5 & 10.2, 7.06, 2.15 & 35.4, 39.9 & 17.3 \\ SAKF & 30.4, 17.5, 59.6 & 0.49, 0.27, 0.31 & 5.15, 5.19 & 6.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The root mean square (RMS) obtained by the output of four algorithms in scene 1 (250 s\(\sim\)300 s). Figure 4: The diagrams of errors obtained by four types of filtering algorithms in the scene 2 designed in 3.1.2. (**a**) SHAKF; (**b**) STF; (**c**) H\({}_{\infty}\) filter; (**d**) SAKF.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Blockchain
#### 5.1.2 Blockchain for Mobile Edge Computing  
Another use case for blockchain in 5G is in the MEC space, which was introduced earlier. MEC enables more efficient usage of network resources but also provides for a better end-user experience. However, there are significant challenges for reliability, data privacy, and security with MEC. The authors of [113] outline the risk of attacks on edge nodes disrupting an entire system and consequently showing potential issues that can severely degrade MEC reliability. The incorporation of blockchain with MEC--both being architecturally decentralized systems--can address many of the safety and security issues within such a system, as the survey in [113] shows. For example, blockchain technology can help alleviate the challenge in MECs of ensuring the secure exchange of configuration information during MEC service scaling, migration, and adaptation. During those changes to the edge deployments of MEC services, configuration and authentication information needs to be exchanged between the orchestrator and the edge deployments, posing a significant risk of intercepts, falsification, and more. By using distributed ledgers, this information can be securely provided and accessed, and risks to their authenticity can be greatly reduced. Another significant benefit is to the data integrity, using data compute operations at the mobile edge. Here, ledgers can be used to ensure the immutability of the data--an inherent property of blockchain technology [113].  
Additionally, multiple works have been completed on improving MEC's reliability through blockchain technology. These works include the dynamic offloading of individual transactions and computational tasks [114; 115; 116]. For data privacy, the authors of [117] use smart contracts to complete and register distributed authentications without a central authority.  
Meanwhile, the authors of [118] speak to the fact that typically, the privacy of most MEC systems only prioritizes user security at the expense of network topology privacy. Keeping the network's inner workings hidden from the user landscape increases the confidentiality of the network and thereby makes it more difficult for a threat actor to identify potentially viable attack vectors. Having a trusted MEC system enables the system to provide more reliable services and achieve lower latency in certain applications. The authors propose a method of using blockchain technology in order to verify different routes in cross-domain MEC architectures using different known ledgers. Their findings show that it is a feasible solution, as their simulation results demonstrate similar or better latency compared to normal routing algorithms.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 8 Considerations for Energy Consumption  
The optimization of energy consumption was not addressed in this study. However, in the domain of WSNs, energy consumption can be of paramount importance. In the sequel, we discuss aspects of our solution that contribute to the topic of energy consumption. Given that our protocol operates on probability density functions, in contrast to conventional protocols, an increased energy expenditure is anticipated. This can be attributed, on one hand, to the more intricate calculations and, on the other hand, to the larger packets required for transmitting routing information. Additionally, probes are employed for monitoring individual links, contributing to higher energy consumption.  
However, the protocol effectively mitigates energy consumption by promptly discarding packets that are unable to meet their deadlines, thereby improving the overall energy balance. Additionally, passive monitoring contributes to reduced energy consumption. In situations where data traffic is high in relation to the routing overhead, the positive energy effects of our presented protocol may outweigh the associated costs. This remains an open topic for future analysis.  
Nevertheless, for deployment in energy-constrained scenarios, an optimization focusing on energy is crucial. The probability functions employed in our approach allow integration with additional metrics that could be leveraged to reduce energy consumption or achieve a more balanced load distribution. Furthermore, it is conceivable to apply the proposed approach to exploit different transmission rates and performance levels in order to select the most energy-effective option for meeting a given deadline. Optimization strategies for energy efficiency should be explored for practical applications in resource-constrained environments.
## 8 Results
### Qualitative Results: Assisted Learning Performance Through Multi-Modalities
Generally speaking, they are classified according to specific learning tasks, such as video courses, online Q&A, question discussions, and question solving software, etc._  
Comparing the learners' responses, we also discovered that many apps pertain to diverse subjects or majors, functions, tasks, or exams, or are game-based apps like Duolingo. The classification is shown in Figure 2.  
The fourth discovery is associated with the modality that is most useful or suitable for particular learning apps, as demonstrated in Figure 3. Because classifying learning apps is difficult and multifaceted, the responses in this part were heterogeneous. Still, some common points stand out. Of the numerous varieties of learning apps, language learning apps or English exam apps appeared repeatedly in the college students' comments. They believed that, in these exam- and function-based apps, all five modalities are important, especially the visual mode and aural mode, as in Interviewee 17's response:  
Figure 3: Modality importance in different types of learning apps. Figure 2: Educational app categories. Function-based apps, such as word memorization apps, dictionary apps, and video course apps, I think they require more visual and aural modalities. For exam apps like the CET-4 and CET-6 exams, the aural and reading modes are more important. For apps with spoken language functions, the verbal function is even more important. It really depends. The detailed analysis of these data revealed that most university learners believe that multi-modalities in mobile learning applications have a positive influence on their learning performance, owing to their many ways of displaying information and knowledge. From the five modalities, the visual mode, such as videos and pictures, was most preferred by college learners for its entertaining and visual impact, improving memory retention. In terms of classifying the learning applications, although the answers were multifarious, commonalities could still be identified. The categories were subject-based, major-based, function-based, task-based, exam-based, and game-based apps, and, among them, game-based apps were the least mentioned.
## 3 Methods
### Attention  
The channel attention mechanism can enhance the weighting factor of important channels, while the spatial attention mechanism can highlight important information in the feature map. However, using these two mechanisms at the same time will increase the computational cost of the model. To deal with this problem, the shuffle attention (SA) is proposed by Zhang et al. [43], with the structure shown in Figure 7. The SA combines the advantages of channel-wise attention and spatial-wise attention, while maintaining the efficiency of the network. We introduce this light-weight attention mechanism module for the higher feature extraction ability. The SA separates the input feature maps \(X\in R^{C\times H\times W}\) into G groups, which is \(X=[X_{1},\dots,X_{C}]\), \(X_{k}\in R^{C/C\times H\times W}\). Each group splits the channel and spatial attention pathways, i.e., \(X_{k1},X_{k2}\in R^{C/2G\times H\times W}\), and the outputs of both attention mechanisms are:  
\[X_{k1}^{\prime}=\sigma(\mathcal{F}_{c}(s))\cdot X_{k1}=\sigma(W_{1}s+b_{1}) \cdot X_{k1} \tag{12}\]  
\[X_{k2}^{\prime}=\sigma(W_{2}\cdot GN(X_{k2})+b_{2})\cdot X_{k2} \tag{13}\]  
where s indicates the global averaging pooling, \(GN\) is the group norm [45], and \(W_{1}\), \(b_{1}\), \(W_{2}\), and \(b_{2}\) are within \(R^{C/2G\times 1\times 1}\). The results of the attention process are connected. The whole G groups are processed parallelly and fused with the channel shuffle operator for communication between each group. Note that the SA outputs are the same size as input \(X\), making it convenient for CNN integration.  
Figure 6: The distance cost.
## 3 Results and Discussion
### Hydroxyl Radical scavenging Activity and Fe\({}^{2+}\) Chelating Activity
 } & 62.5 & 29.81 \(\pm\) 0.12 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 15.69 \(\pm\) 1.31 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 13.51 \(\pm\) 2.14 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 15.04 \(\pm\) 4.25 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 0.464 \\  & 125 & 48.84 \(\pm\) 0.41 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 24.06 \(\pm\) 1.20 \({}^{\mathrm{\SIUnitSymbol b}}\) & 18.79 \(\pm\) 6.19 \({}^{\mathrm{\SIUnitSymbol a}}\) & 14.60 \(\pm\) 1.12 \({}^{\mathrm{\SIUnitSymbol a}}\) & 4.926 \\  & 250 & 74.86 \(\pm\) 0.52 \({}^{\mathrm{\SIUnitSymbolMicro c}}\) & 35.88 \(\pm\) 2.47 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 22.14 \(\pm\) 3.10 \({}^{\mathrm{\SISymbolMicro a}}\) & 22.69 \(\pm\) 6.58 \({}^{\mathrm{\SIUnitSymbol a}}\) & 9.299 \\  & 500 & 88.38 \(\pm\) 3.69 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 46.07 \(\pm\) 5.31 \({}^{\mathrm{\SISymbolMicro a}}\) & 35.43 \(\pm\) 3.68 \({}^{\mathrm{\SISymbolMicro c}}\) & 30.74 \(\pm\) 13.28 \({}^{\mathrm{\SISymbolMicro a}}\) & 2.543 \\  & 1000 & 99.72 \(\pm\) 0.24 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 58.91 \(\pm\) 7.23 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 53.54 \(\pm\) 1.96 \({}^{\mathrm{\SISymbolMicro a}}\) & 57.66 \(\pm\) 5.77 \({}^{\mathrm{\SI  
this particularly regards to the effects of antioxidant activity on the reduction of oxidative stress, which is induced by reactive oxygen species that have a high level of reactivity. In this study, the hydroxyl radical scavenging activity in different organs of the ice plant was lower compared to the reference material: L-ascorbic acid at 1 mg/mL. Meanwhile, the activity was higher than that of the 16.0% inhibition, caused by 1 mg/mL of the ethanol extract of a fresh sample of _Salicornia herbacea_, a similar halophyte, in the study by [PERSON] [25]. This suggests that hydroxyl radical scavenging could vary across plants with similar characteristics. The hydroxyl radical scavenging activity in this study was the highest in the leaves, followed by the stem and cotyledon, indicating that for ice plant extracts, despite a relatively low activity, the effect on and removal of hydroxyl radicals varies according to the plant organs (Table 3). The Fe\({}^{2+}\) chelating activity leads to the generation of hydroxyl radicals caused by the reduction processes of reactive oxygen species and involves the transfer of hydrogen peroxide. Thus, the chelating of Fe\({}^{2+}\) indicates the level of inhibition of free radicals generated via the Fenton reaction. In this study, the Fe\({}^{+}\) chelating activity was not high in each organ of the ice plant; however, significant differences were observed in activity, with the highest activity in the cotyledon, followed by the leaves and stems.
# The Use of Free Weight Squats in Sports: A Narrative Review--Terminology and Biomechanics
## 6 Basic Squat Biomechanics
The eccentric/concentric action of the squat primarily results from hip (gluteus maximus) and knee extensor (quadriceps) activation, and it is clear that hamstring muscles are not primary contributors for squat tasks [82; 119; 149; 151]. Data indicate that the gluteus maximus activation level is dependent upon the squat depth [66]. Increasing the strength of the gluteus maximus can be paramount for improving performance in many sport activities. For example, in sprinting, major functions of the gluteus maximus include control of trunk flexion on the stance-side and deceleration of the swing leg. Contractions of the stance-side gluteus maximus may also help to control flexion of the hip and to extend the thigh [156]. As such, performing deeper squatting, perhaps periodically including front squats, may be of benefit to those sports in which gluteus maximus' maximum strength is of particular importance. Examining the EMG of the gluteus maximus is a technique necessary to understand the activation of such a strong hip extensor muscle. However, caution is necessary to minimize measurement error associated with electrode placement and electrical activity readings, which can be related to the muscle's fiber alignment (diagonal) and the density of adipose tissue surrounding the gluteus maximus. Without proper precautions taken in electrode placement, conductivity factors, etc., high variance between and within subjects is typically reported, and all investigators should be aware of these problems in future studies [18; 139]. It is also important to note that the degree of intra-muscular activation may not be consistent throughout a muscle [157; 158], and this could influence inhomogeneous adaptions [45; 159]. It is apparent that a biomechanical approach to understanding the underlying mechanisms of the squat can be a vital resource for coaches and practitioners. The following sections further elaborate on biomechanical findings to help understand the safety and benefits of performing squat exercises.