esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 5 Results
### Comparative Analysis  
Table 2 shows a comparative analysis between the MVT model and other machine learning techniques reported in the literature. The Decision Tree (DT) and CNN models achieved higher accuracy but were less effective in detecting zero-day threats. MVT, leveraging transformer-based learning, provided a more balanced performance across various classes.  
The MVT demonstrated an acceptable balance between detecting benign and malicious files, outperforming traditional models in scenarios where class imbalance is significant. While the recall for Class 1 was consistently higher, the precision for Class 0 remained more variable. This can be attributed to the inherent class imbalance and the complexity of benign file detection.  
The model's ability to maintain a high F1-Score for malware detection suggests robustness against zero-day threats. However, the slightly lower performance for benign files may indicate that further improvements are needed, possibly by enhancing data augmentation techniques or incorporating additional feature extraction layers.  
In future works, integrating ensemble methods could enhance the model's capability to differentiate between benign and malicious files without sacrificing precision in either class [19]. The MVT model, while effective, has several limitations:  
* Class Imbalance: The high volume of malware samples skews the model towards better malware detection.
* Computational Cost: The transformer-based architecture requires significant computational resources, which could limit real-time deployment.
* Binary Classification Approach: A more granular classification (e.g., distinguishing different malware types) could enhance the model's practical application.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline MVT (Proposed) & 75.2 & 78.6 & 76.8 \\ Decision Tree (DT) [1] & 99.0 & 72.4 & 84.1 \\ Convolutional Neural Network (CNN) [2] & 98.8 & 74.3 & 85.7 \\ Support Vector Machine (SVM) [20] & 96.4 & 71.2 & 82.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative Performance Analysis of MVT and Traditional Models  
Figure 12: Average Recall comparison across experiments.  
In summary, the proposed MVT model provides a promising approach for zero-day threat detection but requires further optimization to achieve robust performance in real-world cloud environments.
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 4 Experimental Results
### Performance Analysis of Multi-Scale-Based End-to-End Rate Control
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **DCVC** & **[PERSON] et al. [22]** & **[PERSON] et al. [9]** & **Proposed** \\ \hline A1 (average) & 29,267.82 & 37,577.63 & 32,325.63 & 29,135.9 \\ \hline A2 (average) & 43,911.46 & 57,200.19 & 49,623.23 & 42,136.14 \\ \hline B (average) & 21,357.6 & 25,076.49 & 23,282.05 & 21,235.88 \\ \hline E (average) & 7418.42 & 8609.4 & 7939.47 & 7408.27 \\ \hline Total average & 100\% & 126\% & 111\% & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Coding time comparisons of DCVC, [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm. Figure 10: Subjective comparisons of BasketballDrive for the second frame @ 2320.8 kps and Cactus for the second frame @ 4261.38 kps. (**a-1,a-2**) are the ground truth images; (**b-1,b-2**) are images from the proposed algorithm; (**c-1,c-2**) are the images from DCVC; (**d-1,d-2**) are the images from [PERSON] et al. [22]; (**e-1,e-2**) are the images from [PERSON] et al. [9]. From Figure 10, it can be observed that the subjective quality of the basket region in Figure 10(b-1) is clearer than the others in Figure 10(c-1), (d-1), and (e-1). Similarly, the subjective qualities of the yellow flower regions presented in Figure 10(c-2), (d-2), and (e-2) are worse than that of the image in Figure 10(b-2). Therefore, we can easily conclude that the proposed algorithm exhibits better subjective performances than the other algorithms.
## 3 Results and Discussion
### Gelling Characteristics
#### 3.4.4 Lipid Oxidation and Fishy Odor  
Lipid oxidation in muscle foods has a significant negative impact on overall quality. It causes a loss of qualitative attributes such as color, flavor, texture, and nutritional value [22]. The TBARS method is a widely used method for measuring lipid oxidation [54]. Table 2 shows the TBARS contents of gels prepared from unwashed mince and surimi with various washing cycles.  
Unwashed gels with the greatest lipid and endogenous prooxidants (e.g., myoglobin) (Table 1) had the highest TBARS content, indicating that lipid oxidation can be enhanced to a greater extent when heated. Surimi gels had a lower content of TBARS than unwashed mince gels (Table 2). Some lipid and endogenous prooxidants that can be oxidized during heating were eliminated by cold water washing. According to [PERSON] et al. [55], fish tissue, particularly dark-fleshed fish, contains iron-binding protein, which can be dissociated during thermal gelation. Free iron may operate as a prooxidant, speeding up the oxidation of lipids. Lower TBARS levels may be obtained as a result of removing lipids and oxidative constituents from mince [56]. In general, lipid and myoglobin oxidation in fish muscle occurred at the same time, and one process seemed to promote the other [22; 25]. W2 and W3 had the same TBARS level, which was found to be the lowest content (\(p<0.05\)) in the study. This was most likely owing to the identical lipid and myoglobin contents found in W2 and W3 surimi (Table 1).  
Table 2 shows the fishy odor scores of unwashed mince and surimi gels. Undoubtedly, the gel from the unwashed mince had the strongest fishy odor (\(p<0.05\)). The findings  
were consistent with [PERSON] et al. [57], who discovered that unwashed hybrid catfish mince had a stronger fishy odor than conventional surimi. After washing, the fishy odor seemed to reduce as the number of washing cycles increased. However, sensory trained panelists did not perceive a significant difference in the fishy odor of surimi after two (W2) and three (W3) washing cycles. As a result, two-cycle washing can be utilized to make mackerel surimi with a good odor score.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 5 Reinforcement Learning  
Reinforcement learning (RL) is a specialised area within the realm of machine learning that concerns itself with determining appropriate actions to be taken by an intelligent agent in a given environment. The objective of this methodology is to maximise the cumulative reward obtained by the agent by selecting the most optimal action. It places emphasis on finding a balance between exploration and exploitation, thereby continually refining the agent's decision making capabilities. The agent must actively gather information about possible states, actions, transitions, and rewards. Unlike supervised learning, where evaluation is separate, in RL, the agent is evaluated while learning. The agent interacts with the environment through perception and action. The agent receives input in the form  
Figure 6: Interference mitigation system functional blocks.  
of the current state of the environment and selects an action to alter the environment's state. The agent then receives a scalar reinforcement signal or reward based on the value of the state transition. The agent's goal is to maximise the long-run sum of the reinforcement signal's values by selecting actions that tend to increase the reward.  
Table 2 provides definitions for various elements involved in the training phase that determine interaction between the agent and states and provide expected discounted reward value. Figure 7a illustrates inter-element interaction. The agent changes its state from \(s\) to \(s^{{}^{\prime}}\) by taking an action \(a\) in an environment and receives reward \(w\) for it.  
In this work, the agent is the DQL algorithm, which exists in a given state \(s\), i.e., a specific power value. It takes a suitable action, i.e., increase or decrease the power value, to optimise the SINR value. This interaction takes place in a 5G cellular environment. Finally, these commands are sent to the UAV. This is depicted in Figure 7b.
## 1 Introduction  
### Motivation  
The control and modeling of batches or networks of multiple vessels (air and sea) is a new topic that has received much attention in recent years from the community of control researchers. The idea under discussion is that a group of vessels in a given category or network, performance and capability are much better than moving alone on any of the vessels and can perform more tasks: autonomous surface vessels are robotic boats or ships that respond to environmental changes and perform various tasks with minimal manpower intervention. Like many advanced systems capable of civilian use, the development of autonomous surface vessels for military applications has also begun.  
Control of multiple autonomous surface vehicles (ASVs) to operate together as a team was first received attention of control engineers in 1991 [1]. Since the early 1990s, ASVs attracted great attention in system and control. Autonomous surface vessels are robotic ships that can react to environmental changes and fulfill different tasks with minimal human intervention. Individualsurface vehicles that are connected as a network with a specific shape, can perform various tasks. Relevant applications include automatic ocean exploration, environmental monitoring, disaster search and rescue, surveillance of territorial waters, underway ship replenishment, and so on. In general, formation control is the coordination of a group of robots to create and maintain a seal arrangement with the specified format [2; 3; 4; 5]. To achieve the desired formation between UAVs, several methods have been proposed. Among these control schemes, the leader-follower strategy seems to be much preferred in practice due to its simplicity and scalability and has been studied by many researchers [6; 7]. In a formation with leader-follower configuration, one or more ASVs is selected as leaders, which are responsible for guiding the formation, and the rest of the ASVs are controlled to follow the leaders. The control objective is to make the follower ASVs track the leaders with some prescribed offsets. In recent years, many visual object tracking techniques based on correlation filters [8; 9] and deep learning [10; 11] have emerged. Other advantages of the leader-follower formation are simplicity, understandability and its easy implementation.  
A sample formation is shown in Figure 1. As can be seen, one leader (red) and four followers (blue and green) arranged in a particular formation to form a triangular motion without conflict to each other and with predetermined moving.
## 3 Results and Discussion
### HPLC-PDA Method Validation
#### 3.2.5. Stabilities of Analyte Solutions  
The stabilities of the analytes in solution were evaluated by calculating the %RSD and %difference for the peak areas of L7 GC, CDR, C7 dGlu, and DSM after storage at room temperature or 4 \({}^{\circ}\)C for 0, 6, 24, 48, and 72 h. The peak areas of L7 GC, CDR, C7 dGlu, and DSM in solution decreased over time at both temperatures. The %difference values of the four compounds after 72 h were as follows: L7 GC, \(-\)10.89% (R.T.) and \(-\)7.65% (4 \({}^{\circ}\)C); CDR, \(-\)5.65% (R.T.) and \(-\)6.15% (4 \({}^{\circ}\)C); C7 dGlu, \(-\)5.80% (R.T.) and \(-\)7.86% (4 \({}^{\circ}\)C); DSM, \(-\)39.80% (R.T.) and \(-\)5.66% (4 \({}^{\circ}\)C). DSM showed a greater decrease in peak area over time at room temperature than the other compounds (RSD, 18.95%). In addition, at room temperature CDR showed a low RSD of 2.42%, while under refrigerated conditions DSM showed a low RSD of 2.13%. The %RSD values of the peak areas for the two conditions (room temperature or 4 \({}^{\circ}\)C) fell in the range of 2.42-18.95% for the room temperature condition and 2.13-3.66% for the 4 \({}^{\circ}\)C condition, respectively, indicating that solutions at both room temperature and 4 \({}^{\circ}\)C were stable below 18.95%. However, we recommend using sample solutions within 6 h of preparation (Table 4). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Compound a**} & \multirow{2}{*}{**Temp**} & \multicolumn{5}{c}{**Peak Area (Mean AU \(\pm\) SD)**} & \multirow{2}{*}{**RSD**} \\ \cline{3-3} \cline{5-7}  & & **0 h** & **6 h** & **24 h** & **48 h** & **72 h** & **(\%)** \\ \hline \multirow{2}{*}{L7 GC} & R.T. & 19,437 \(\pm\) 193 & 18,357 \(\pm\) 155 & 18,510 \(\pm\) 127 & 17,857 \(\pm\) 92 & 17,321 \(\pm\) 238 & 4.03 \\  & 4 \({}^{\circ}\)C & 19,437 \(\pm\) 193 & 18,314 \(\pm\) 136 & 18,051 \(\pm\) 109 & 17,542 \(\pm\) 183 & 17,951 \(\pm\) 138 & 3.66 \\  & R.T. & 51,346 \(\pm\) 340 & 49,122 \(\pm\) 424 & 48,587 \(\pm\) 181 & 48,438 \(\pm\) 195 & 48,443 \(\pm\) 615 & 2.42 \\  & 4 \({}^{\circ}\)C & 51,346 \(\pm\) 340 & 48,789 \(\pm\) 483 & 49,070 \(\pm\) 334 & 48,710 \(\pm\) 335 & 48,188 \(\pm\) 270 & 2.38 \\  & R.T. & 95,923 \(\pm\) 1816 & 90,583 \(\pm\) 881 & 89,581 \(\pm\) 441 & 88,643 \(\pm\) 1548 & 90,358 \(\pm\) 1121 & 3.12 \\  & 4 \({}^{\circ}\)C & 95,923 \(\pm\) 1816 & 91,128 \(\pm\) 650 & 89,458 \(\pm\) 1438 & 88,591 \(\pm\) 663 & 88,384 \(\pm\) 830 & 3.35 \\  & R.T. & 357,036 \(\pm\) 1145 & 340,928 \(\pm\) 1062 & 324,260 \(\pm\) 5259 & 250,171 \(\pm\) 4572 & 214,936 \(\pm\) 637 & 18.95 \\  & 4 \({}^{\circ}\)C & 357,036 \(\pm\) 1145 & 339,828 \(\pm\) 1173 & 341,246 \(\pm\) 1379 & 341,845 \(\pm\) 1559 & 336,843 \(\pm\) 1513 & 2.13 \\ \hline \end{tabular}  
* Luteolin-7-O-\(\beta\)-D-glucurononopyranosyl (1 \(\rightarrow\)2)-O-\(\beta\)-D-glucurononopyranosyl, L7 GC; clerodendrin, CDR; chrysoreiol-7-O-diglucuronide, C7 dGlu; diosmin, DSM.  
\end{table} 
Table 4: Stabilities of L7 GC, CDR, C7 dGlu, and DSM in sample solutions at room temperature or 4 \({}^{\circ}\)C for 0, 6, 24, 48, and 72 h (\(n\) = 5).
# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 2 Materials and Methods
### Docker  
Docker is a technology that enables container virtualization, which can be compared to a highly efficient virtual machine due to its lightweight nature [58; 59]. It is characterized by a modular architecture comprising multiple integral components that interact harmoniously to facilitate the process of \"Containerization\". These components are articulated as follows: At the core of Docker is the Docker Engine, which provides the runtime environment for containers [59]. Docker Images, read-only templates that serve as container building blocks, utilize a layered file system and copy-on-write mechanism for efficient image management [59]. When a Docker Image is instantiated, it becomes a Docker Container, which offers a lightweight and secure execution environment [59]. Docker Containers can be easily created, started, stopped, and deleted, providing flexibility in managing application instances [60]. To facilitate image sharing and distribution, Docker Registries, such as Docker Hub, host a vast collection of prebuilt images [61]. Additionally, organizations can establish private registries tailored to their specific image requirements [61]. The modular architecture of Docker, along with its components, enables scalable and flexible application deployment across various environments.
## 4 Conclusions  
In this study, the mechanical properties, permeability and abrasion resistance of innovative recycled pervious concrete (IRPC) with three RCA quality grades and five replacement rates under sulfate attack was investigated. Based on the experimental results and microscopic analysis, the following conclusions can be drawn:  
* IRPC can not only reach a higher compressive strength but also meet a higher permeability coefficient at the same time, even at a 100% replacement rate. After 60 sulfate wetting-drying cycles, the compressive strength and permeability coefficient of IRPC slightly decreased. With the increase of the replacement rate and the decrease of aggregate quality, their drops continued to increase. However, overall, the decline of compressive strength and permeability coefficient of IRPC was modest, representing the sulfate resistance of IRPC was fine.
* The abrasion resistance of IRPC was mainly influenced by the RCA replacement rate. The abrasion loss of IRPC increased with the increase of the replacement rate. Yet, up to a 50% RCA replacement rate could be adopted without significantly affecting the abrasion resistance of IRPC. However, when the RCA quality grade was high (attached mortar content < 25%), this threshold replacement rate can be increased to 100%.
* The influence of sulfate attack on the compressive strength and abrasion resistance of IRPC could be divided into two stages. The first stage was the enhancement or slow declining stage. In this stage, the sulfate erosion products could fill the micro pores and cracks in the ITZ and the attached mortar. This filling effect could improve the compactness of IRPC; thus, improving its strength and abrasion resistance. The next stage was the declining stage. In this stage, the volume of sulfate erosion products gradually expanded and made the ITZ and matrix of IRPC loose and fragmented, resulting in a reduction in the strength and abrasion resistance of IRPC.  
The experimental results, in terms of compressive strength, permeability and abrasion resistance, show that IRPC made with even 100% RCA was acceptable under sulfate attack. When the replacement rate was less than 50%, the performance of IRPC was very close to that of INPC. With the improvement of RCA quality, this threshold replacement rate could be further increased. The existing traditional pervious concrete can only be used in applications with low abrasive forces. However, IRPC can be used for applications with medium or even high abrasive forces, which greatly expands the application range of pervious concrete.  
Conceptualization, M.H. and H.L.; Data curation, M.H. and B.C.; Formal analysis, B.C. and C.C.; Funding acquisition, H.L. and P.Z.; Investigation, B.C. and X.W.; Methodology, P.Z.; Project administration, B.C. and H.L.; Resources, Y.L. and H.L.; Supervision, P.Z.; Validation, M.H., Y.L. and H.L.; Visualization, M.H.; Writing--original draft, M.H.; Writing--review and editing, M.H. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51878081.  
Not applicable.
# Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
###### Abstract  
This study reviews the recent progress of generative artificial intelligence for gastrointestinal disease (GID) from detection to diagnosis. The source of data was 16 original studies in PubMed. The search terms were ((gastro* [title]) or (endo* [title])) and ((GAN [title/abstract] or (transformer [title/abstract]). The eligibility criteria were as follows: (1) the dependent variable of gastrointestinal disease; (2) the interventions of generative adversarial network (GAN) and/or transformer for classification, detection and/or segmentation; (3) the outcomes of accuracy, intersection of union (IOU), structural similarity and/or Dice; (3) the publication period of 2021-2023; (4) the publication language of English. Based on the results of this study, different generative artificial intelligence methods would be appropriate for different tasks for the early diagnosis of gastrointestinal disease. For example, patch GAN (accuracy 91.9%) in the case of classification, bi-directional cycle GAN (structural similarity 98.8%) in the case of data generation and semi-supervised GAN (Dice 89.4%) in the case of segmentation. Their performance indicators reported varied within 87.1-91.9% for accuracy, 83.0-98.8% for structural similarity and 86.6-89.4% for Dice. Likewise, vision transformer (accuracy 96.9%) in the case of classification, multi-modal transformer (IOU 79.5%) in the case of detection and multi-modal transformer (Dice 89.5%) in the case of segmentation. Their performance measures reported registered a variation within 85.7-96.9% for accuracy, 79.5% for IOU and 77.8-89.5% for Dice. Synthesizing different kinds of generative artificial intelligence for different kinds of GID data would further the horizon of research on this topic. In conclusion, however, generative artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of gastrointestinal disease from detection to diagnosis.  
gastricintestinal disease; early diagnosis; artificial intelligence
## 4 Review: Slant Links Rain Fade Models
### Learning-Based Model  
#### 4.5.1 Ahuna Model  
The basis of this model [100] is the backpropagation neural network (BPNN), with the SST and ITU-R models. The BPNN network is composed of three input layers (I), three hidden layers (H), and one output layer (O), where a sigmoid function is used as the activation function, and the whole structure can be considered a \"black box.\" While backpropagating the signal through the network, the derivative of the error can be defined as:  
\[\frac{\partial E_{\text{total}}}{\partial O_{a}}=-(O_{t}-O_{a}) \tag{67}\]  
where \(O_{a}\) is the actual output and \(O_{t}\) is the desired output. In this study, the gradient descent algorithm was used to update the weights of the neurons as:  
\[\Delta w_{i}=\eta\frac{\partial E}{\partial O_{i}}\quad i=1,2,\ldots,I \tag{68}\]  
where \(\eta\) is the learning rate, \(i\) is the input, \(\Delta w_{i}\) is the weight change on the \(i\)-th input and \(O_{i}\) is the output contributed by the \(i\)-th input. Finally, the minimum error gradient was achieved with a correlation of actual to predicted outputs at 91%, the optimized weighting and bias matrices were obtained as follows:  
\[w_{\text{HI}}=\left[\begin{array}{ccc}-0.1974&-1.1467&-7.8568\\ \\ -0.0115&0.0086&-0.0476\\ \\ 4.0797&6.1838&6.0543\end{array}\right] \tag{69}\]  
\[w_{\text{OH}}=\left[\begin{array}{ccc}-78.4395&-32.0736&31.939\end{array}\right] \tag{70}\]  
\[b_{\text{H}}=\left[\begin{array}{ccc}-11.4485\\ \\ 0.4845\\ -17.3805\end{array}\right] \tag{71}\]  
\[b_{0}=\left[-32.1027\right] \tag{72}\]  
While millimeter-wave frequency information is unavailable from the models, the regional climatic parameter is simply the rainfall rate obtained from local rainfall data used to train the BPNN networks. The model is not validated with the standard DBSG3 or CCIR rain databanks.  
Advantages  
According to the validation outcome, the model can predict the rain attenuation at the accepted accuracy level.  
Limitations  
This model used rainfall data to predict future rainfall rate and hence the rain attenuation. Nevertheless, the rain attenuation does not depend on the rainfall rate only. There is research scope to add frequency, polarization, and rainfall distribution to enhance the model. However, in the BPNN system description, the name of the three input parameters was not documented.