esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Comparative Analysis Based on Recent Literature
These techniques performed well in terms of increasing network longevity and energy efficiency but failed spectacularly in terms of improving QoS and security. A comparison of the various methodologies and performance metrics is shown in Figure 4.  
In Figure 4, researchers tried to reduce the packet loss and overhead. Moreover, they have made an effort to enhance the lifetime of the network i.e., it depends on energy consumption. [PERSON] et al. [57] reduced the packet loss by 11%. In comparison to LEACH and DEEC, network lifespan increased by 50% and 30%, respectively, to [PERSON] et al. [58]. In comparison to LEACH and EAUCF, Prolonged Energy increased by 155.18% and 35.75%, respectively, to [PERSON] et al. [59]. [PERSON] et al. [60] and [PERSON] et al. [61] also improved the overall performance with a high reduction in delay and overhead, respectively. In Figure 5, researchers tried to improve the lifespan of the network by taking energy and throughput into their account. The authors also compared their results with the existing algorithms. [PERSON] et al. [62] and [PERSON] et al. [63] improved in energy and overall performance by 43% and 45%, respectively, as compared to LEACH. [PERSON] et al. [64] improved the maximum energy residual and network longevity, according to the results. [PERSON] et al. [65] and [PERSON] et al. [66] also boosted the maximum energy and network longevity. The algorithm improved the life of the network and preserved residual energy in the search space. Figure 4: Comparison of the various methodologies and performance metrics [57; 58; 59; 60; 61].
## 3 Preliminary
### Mobile Edge Computing  
Edge computing (EC) is an ecosystem characterized by capability openness, resource integration, and architectural flexibility. Mobile Edge Computing (MEC) refers to the deployment of cloud computing's computing and storage resources to the edge of mobile communication networks, providing an IT service environment and cloud computing capabilities [14]. It features bandwidth savings, reduced latency, strong perception, energy efficiency, high isolation, and wide distribution.  
As shown in Figure 3, MEC adopts a distributed approach, typically deployed closer to users in terms of geographical space and network location, on the access network side. Service data are offloaded to the network edge for processing and delivery, not only avoiding the detour of data back to the core network and alleviating the pressure on link load, significantly reducing the latency of service delivery but also saving terminal computing energy consumption and extending the CPU lifecycle of the terminal. Secondly, based on the characteristic of MEC network capability openness, it enables interaction and integration with 3 GPP networks. The MEC API gateway can invoke user location, network context, cell information, and bearer link capacity from the RAN side. Its network perception capability supports a variety of location-based and network information-based application services. Lastly, MEC can work with the user plane gateways pushed down (5G UPF gateway and 4G GW-U gateway) to offload and process service data locally, thereby achieving secure isolation needs without data leaving the premises. MEC has a wide range of application scenarios. Ref. [15] introduces the application methods of 5G and edge computing technologies in fields such as transportation, healthcare, and smart cities, evaluating MEC resource allocation and 5G and MEC integration deployment methods. Ref. [16] describes the computation offloading framework of 5G MEC in the power robot inspection scenario and proposes a collaborative optimization algorithm for path planning and task offloading based on deep neural networks.
# Malicious Powershell Detection Using Graph Convolution Network
## 2 Related Work  
AI-based malicious file detection involves two steps. The first step involves extracting feature data from the files. The second step involves training the AI model for malicious file detection using feature data [5; 6].  
Feature data can be extracted by two methods. The first is to use a static analysis [6] and the second is to use a dynamic analysis [7]. Static analysis extracts feature data from the string information of the file. In the PE files, we used tokens of assembly codes of PE files as feature data. In powershell scripts, there are 20 types of tokens and we use these tokens as feature data. However, if a file is encrypted or encoded, it is difficult to analyze.  
Dynamic analysis uses system call information as the feature data after we run a file. It analyzes encrypted or encoded files. However, it is not executed in a virtual machine environment and it takes a long time to analyze because it must run for several minutes for each file.  
There are two models in the AI model for malicious file detection. The first uses a CNN model that is mainly used for image recognition [6], while the second uses an RNN model that is mainly used for natural language processing [7]. The first method involves  
Figure 1: Graph Convolution Network (GCN) for Recommendation System.  
transforming a file into an image. Eight bits can be transformed into gray image pixels. Then, we can determine whether it was a malicious file image using a CNN. The second method involves transforming a file into a sentence. Afterwards, we determine whether it was a malicious sentence using an RNN.  
In addition, studies have been conducted to detect malicious powershells. They extracted feature data from the powershell used static analysis and detected malicious powershells by using the CNN and RNN models in combination [2; 3]. Using the PSParser library, they extracted token data from powershell scripts and used them as feature data. In related research, six types of tokens were used as feature data from a total of 20 types of tokens [3].  
There have been studies on detecting malicious PE files using GCN [12; 13]. These studies generated an adjacency matrix from the system call graph of a PE file and determined whether it was malicious. However, in the current study, we generated an adjacency matrix using Jaccard similarities between powershells.  
By contrast, trace abstraction was proposed in [14]. The authors used a longest common subsequence (LCS) technique to determine whether two program traces were similar. However, since LCS methods require a relatively long processing time, they are not appropriate for malware detection, which should be performed quickly. In addition, Func2 Vec was proposed in [15]. This method generated sentences using a random walk over a control-flow graph to find function synonyms. However, in this study, generating sentences from a control-flow graph is not the subject of our research.
## 1 Introduction
The framework integrates target detection and semantic segmentation capabilities, addressing challenges in multi-scale feature fusion and ambiguous boundary identification. Experimental validation demonstrates the enhanced network's effectiveness in improving feature representation and model convergence, providing a robust technical foundation for precision agriculture applications.
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
## 5 Prediction of the Shear Strength of the DSRC Beam
### Comparison of Experimental Data and Predictions  
The comparison of the experimental results of the DSRC beams and 10 previous studies [46; 48; 49; 50; 51; 52; 53; 54; 55; 59] with the predictions of the selected four national codes are presented in Figure 9a-d. The ratios between the experimental results and predictions (\(V_{\rm u}/V_{\rm pred}\)) and corresponding mean, standard deviation, and coefficient of variation (COV) are also reported in Figure 9. The diagonal line (\(y=x\)) indicated that the predicted and experimental values were equal. In the upper region of the diagonal line, the predicted value was greater than the experimental value, and correspondingly, in the lower region of the diagonal, the predicted value was less than the experimental value.  
Most of the scatter of the NRC beams and DSRC beams were distributed below the diagonal line \(y=x\), indicating that all four codes underestimated the experimental values, producing mean values of 1.15-1.75. Among them, CSA offered conservative estimates  
Figure 9: Comparison of experimental results of this paper and the included studies [46; 48; 49; 50; 51; 52; 53; 54; 55; 59] with the predictions of (**a**) ACI318–11; (**b**) GB50010–2010; (**c**) EN 1992–1–1; (**d**) CSA A23.3–04.  
and the maximum dispersion for NRC and DSRC deep beams and predicted a mean value and standard deviation of 1.75 and 0.58 for NRC beams and a mean value and standard deviation of 1.38 and 0.26 for DSRC beams. Additionally, the predictions for DSRC beams provided by GB50018 were less accurate and more discrete than the predictions provided by the other three codes, with an average strength ratio and standard deviation of 1.57 and 0.10, respectively. The data points were highly scattered around the diagonal line \(y=x\) for ACI and EC2 (see Figure 9a,c). The shear strength of NRC beams was rationally expected by ACI and EC2 with mean values of 1.15 and 1.19, respectively. However, the predicted values obtained by EC2 greatly underestimated the experimental values for DSRC beams. In contrast, when predicting the shear strength of DSRC beams, the mean value, standard deviation and COV of the ACI are lower than those of EC2, which equal 1.17, 0.10 and 0.09, respectively. It is believed that ACI exhibited better predictive performance than the other three national codes.
## 4 Limitations and Future Work  
Cell differentiation from MDSCs into cardiomyocytes is important for MI repair. In this work, cell differentiation from MDSCs into cardiomyocytes was not experimentally tested, as we only focused on optimizing the cell survival rate by adjusting the hydrogel formulation. In future work, we will study how MDSCs differentiate into cardiomyocytes and compensate for diminished viable cell numbers in MI hearts. Different stimulations inducing cell differentiation will be analyzed and the beating ability of differentiated cells will also be evaluated.
## 3 Results
### biocompatibility with Colon Epithelial Cells  
The biocompatibility of the microcapsules was confirmed in a culture assay with colonic epithelial cells (CCD841 CoN). The cells were treated with microcapsules with CE and PE cores given at three concentrations, as well as their counterparts without extract (Figure 2). After 24 h incubation, the cell viability ranged from 101.8 (A) to 120.4% (K), from 90.2 (E) to 109.1% (K), and from 73.4 (G) to 105.2% (D) in the control group and the groups with microcapsules given at concentrations of 100, 1000 and 5000 \(\upmu\)g/mL, respectively. This means that the cells proliferated properly in contact with the microcapsules, indicating their good biocompatibility. Analyzing the individual groups of microcapsules, microcapsules with CE, at the highest concentration given, inhibited cell viability by 5.3% (C, Alg + WPI). In turn, the addition of Pect and WPI increased cell viability by 5.2% (D). The same tendency was noted for microcapsules with PE, where also microcapsules Alg + WPI (G) inhibited cell proliferation to the greatest extent (by 26.5%). In this group, the addition of Pect and WPI (H) also had a positive effect on cell growth. The same relationship was observed for the control sample. The demonstrated relationships can be explained by the composition of the carrier mixture, where the simultaneous addition of Alg, Pect, and WPI to the culture medium, i.e., enrichment of the culture medium with polysaccharides and protein, allowed the creation of an environment suitable for the growth of colonic epithelial cells. This fact is confirmed by the studies of [PERSON] et al. [51], who, in vivo studies, demonstrated the ability of Alg and Pect oligosaccharides to protect the integrity of intestinal epithelial cells while reducing the permeability and the possibility of using them as agents alleviating ulcerative colitis. Furthermore, for microencapsulated (chitosan and carrageenanan) 5-aminosalicylic acid, a drug used in the therapy of inflammatory bowel diseases, [PERSON] et al. [52] noted a slight
## 2 Materials and Methods
### Statistical Analysis  
Data related to the pilot study are expressed as medians (range). Considering the ordinal nature of the variables and the small sample size, non-parametric tests were used. Specifically, to test for baseline difference, the two groups were compared by means of the Mann-Whitney U test. To assess the effects of the rehabilitation approaches separately (within-group analysis), data obtained at T0 and T1 in the two groups were compared by means of Wilcoxon signed rank tests. Finally, to compare the effects obtained in the two groups (between-group analysis), for each variable we computed the change score (T1-T0); then, we compared the change score obtained in the two groups by means of the Mann-Whitney U tests. For all the statistical analysis, a \(p\)-value of 0.05 was deemed significant. Statistical analysis was performed with SPSS (IBM, version 25).
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 2 Materials and Methods
### Emergency Response Protocol Integration for Exceeding Safe AQI Levels  
Based on the AQIF model, the population and authorities are informed about the evolution of _AQI_ values up to five days before. This enables proactive measures to be proposed when the forecasted _AQI_ values exceed 100, corresponding to the \"Unhealthy for Sensitive Groups\" category, as outlined in Figure 2. _AQI_ values may be suddenly altered due to a spontaneous, unforeseen event. The MA-AQI model will detect the _AQI_ value in these cases and propose an alternative emergency assessment protocol.  
The proposed intervention protocols (PIP) based on _AQI_ categories are integrated using Azure services to enhance AQ management. The PIP is integrated using Azure  
Figure 9: The graphical user interface of the RTPF application.  
Communication Services, which facilitates real-time communication through automated SMS and email notifications. This service enables the seamless integration of smart notifications, community action alerts, and public information campaigns, ensuring that relevant stakeholders receive timely alerts and updates about AQ events.  
Figure 10 presents the Azure Communication Service integrated for PIP. After testing, the two SMS and email services were implemented using C# language, including a custom message for the population.  
Using these communication channels enhances communities' responsiveness to AQ conditions, contributing to the protection of public health. Emails will inform the public by delivering detailed information, updates, and general announcements to a broad audience through the warning message category. At the same time, SMS messages will be reserved for urgent notifications for immediate, time-sensitive alerts. This last trigger is associated with the alert category.  
The flowchart presented in Figure 11 provides a decision-making process for handling _AQI_ notifications based on real-time data from the MA-AQI model. It outlines the steps to determine whether to send informative emails or SMS alerts, depending on the _AQI_ levels.  
Figure 11: Decision flow for _AQI_ notifications based on real-time data using PIP.  
Figure 10: Azure communication services for PIP. (Note: * stands for required field).  
The process triggers informative emails if the _AQI_ is between 101 and 150, indicating moderate _AQI_. When the _AQI_ exceeds 150, indicating an unhealthy _AQI_, an SMS alert is sent instead of emails due to urgency. Afterward, the _AQI_ is displayed in the real-time platform.  
This system ensures communication emails for general updates and SMS for immediate alerts when AQ reaches critical levels.
## 2 Principle  
The concept of DBP is that the signal at the receiver is back-propagated through virtual fibers with inverse characteristics compared with the transmission fibers, thereby compensating the distortions caused by chromatic dispersion and nonlinearity. Theoretically, polarization-multiplexed optical signals in a single-mode fiber can be analytically described by the inverse Manakov equation [7] and then the equation can be numerically solved by using the SSFM [32]. The Wiener-Hammerstein model of the SSFM is accomplished by applying the dispersion operator in two equal parts, before and after the nonlinear operator, which means that the nonlinear operator is executed in the middle of each step [33], as  
\[A_{x,y}(z+h,t)=\exp\left(\frac{h}{2}\hat{D}\right)\exp\left(h_{\text{eff}}\hat {N}\right)\exp\left(\frac{h}{2}\hat{D}\right)A_{x,y}(z,t) \tag{1}\]  
where \(h_{\text{eff}}\) represents the effective step size involved with step size \(h\)[32], and the linear operator and the nonlinear operator are given as:  
\[\hat{D}=\frac{\alpha}{2}A_{x,y}+\text{j}\frac{\beta_{2}}{2}\frac{\partial^{2} A_{x,y}}{\partial t^{2}} \tag{2}\]  
\[\hat{N}=-\text{j}\frac{8}{9}\xi\gamma\left(\left|A_{x}\right|^{2}+\left|A_{y} \right|^{2}\right) \tag{3}\]  
where the nonlinear compensation factor \(\xi\) is a free parameter that controls the optimum compensation of the nonlinear effect [14]. The nonlinear scaling factor \(k\) in the nonlinear operator is defined as:  
\[k=\xi\gamma \tag{4}\]However, the inaccurate estimation of \(k\) in the conventional DBP degrades nonlinear compensation performance and leads to the extra complexity caused by repeated steps of the SSFM. Hence, A-DBP is required to search for the optimal \(k\) using gradient-dependent algorithms to achieve the best nonlinear compensation performance and reduce the complexity with fewer SSFM steps. As the widely used algorithm in A-DBP, the GDA suffers from slow convergence or excessive fluctuations when compensation parameters vary. A momentum term is added in the GDAM to accelerate convergence, but it brings more intensive fluctuations. Neither of these two algorithms can always maintain a small number of iterations in practical systems, which results in extra computational efforts to apply them practically. Therefore, for gradient-dependent algorithms employed in A-DBP, fast convergence and fluctuation resistance are essential requirements. In order to achieve low-complexity and high-robustness nonlinear compensation based on A-DBP, we propose the A-DBP-LTM algorithm, where the learning rate \(\mu\) is adaptively adjusted. The main purpose is that, at the beginning of the iteration approaching the convergence goal fast and jumping out of the local optimum and at the end of the iteration avoiding crossing the convergence goal, fluctuations are avoided. There are some improved gradient-dependent algorithms with the learning rate \(\mu\) adaptively adjusted, which can be used for implementing the A-DBP-LTM scheme, such as RMSProp, Adam, AdaDelta, and AdaGrad. Here, taking RMSProp as an example, we theoretically demonstrate its advantages over the GDA and GDAM.
# EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching
## 4 Conclusions  
This work has demonstrated the strong potential of EBIC for visualization of the conductive paths formed in graphene oxide as a result of resistive switching. It is shown that the EBIC method is capable of revealing both the generation and elimination of multiple conductive channels, while these processes cannot be detected by I-V measurements. The EBIC study can provide additional information on the individual conductive channel resistance and stability, thus helping better understand the mechanisms of resistive switching in graphene oxide and other insulators with resistive switching properties. Evaluation of the current components contributing to the induced current confirmed the dominating role of the Si substrate. Formation of the rectifying contacts implies that the conductive path formation leads to an increase in work function, or that the conductive paths contain negatively charged traps. As the EBIC signals reflecting the formation of the conductive paths can be observed at zero bias, it is concluded that the conductive paths formed during  
Figure 8: Dependence of FWHM of the conductive channel EBIC profile on \(E_{b}\).  
Figure 7: Profiles of the EBIC image of the same bright spot measured at a few \(E_{b}\) values. A solid line marks the half maximum position.  
resistive switching in graphene oxide play a dual role; that is, they form rectifying nanocontacts at the graphene oxide/Si interface and connect them with the top Al electrode. The collection efficiency of these nanocontacts decreases with the beam energy, which is in good agreement with the theoretical predictions for the behavior of the Schottky nanocontact. The concentration of the generated excess carriers is found to be orders of magnitude higher than that of the primary electrons, thus providing the strong advantage of EBIC sensitivity for metal-insulator-semiconductor devices as compared with metal-insulator-metal structures. EBIC studies using a similar analysis can provide valuable information about the mechanisms of the formation and nature of conductive paths formed as a result of resistive switching in other oxide materials.  
Conceptualization, E.B.Y. and S.K.; methodology, E.B.Y., S.K. and O.K.; investigation, E.B.Y., S.K. and O.K.; writing--original draft preparation, E.B.Y. and O.K.; writing--review and editing, E.B.Y. and S.K. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.