esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ3: What Methods and Techniques Are Utilized to Detect IoT Botnets?
#### 5.3.3 Methods Based on Software Defined Network Technology  
Software defined networking (SDN) is a modern technology that enables the overall behavior of a network to be managed by a central program, named the SDN controller. The controller allows fast security threat responses, granular traffic filtering, and the implementation of complex security policies [95]. SDN can support IoT botnet detection. One of the selected studies that was based on SDN is discussed below.  
In [75], the authors developed a detection and mitigation method using the Mirai botnet as a particular case study for IoT-originating DDoS attacks. By performing the mitigation near the IoT devices, protection contra DDoS attacks from IoT to ICT public services came to be successful. SDN was utilized as a compliant resolution for this purpose in order to impose different flow rules and actively renew them when needed. Even though the proposed work is aimed at Mirai variations, it is possible to easily configure and extend its detection mechanism for various BotNet threats. To enable the proposed solution in this setting, they also used fog computing. The proposed scheme has shown that when forcing attacks the edge first by using fog computing, DDoS attacks can be alleviated by exploiting SDN. This solution can also be offered in the fog computing environment as a security as a service (SECaaS) plan. There are practical problems with the integration of this method into IoT networks, which are characteristically diverse, loosely administered and emergent systems. On the other hand, a huge base of IoT infrastructure previously implemented without SDN and inheritance devices. The vital role of IoT networks as portion of the upcoming Internet is to make these remedies necessary and highly valuable.
# A Decision Tree for Rockburst Conditions Prediction
## 2 Data Characterization  
The rockburst database is collected according to the studies performed by many authors [20; 21; 28; 29; 30; 33; 35; 36; 39; 45]. Several empirical methods have been introduced to evaluate rockburst phenomena [1; 20]. Founded on empirical methods proposed for rockburst, the input database was selected [20; 24; 30; 46; 47]. The input variables consist of depth, uniaxial compressive strength (\(\sigma_{\mathrm{c}}\)), tensile strength (\(\sigma_{\mathrm{t}}\)), maximum tangential stress (\(\sigma_{\mathrm{0}}\)), and the elastic energy index (EEI), which are converted to four variables, namely, depth, EEI, BI--(\(\sigma_{\mathrm{c}}/\sigma_{\mathrm{t}}\)), and SI--(\(\sigma_{\mathrm{0}}/\sigma_{\mathrm{c}}\)), for the prediction of rockburst condition. The depth was chosen because it influences the size of the in-situ stress, distribution, and direction. Uniaxial compressive strength (\(\sigma_{\mathrm{c}}\)), tensile strength (\(\sigma_{\mathrm{t}}\)), and the elastic energy index (EEI) [15] reflect the properties of the surrounding rock, and the maximum tangential stress (\(\sigma_{\mathrm{0}}\)) reflects the virgin geostatic stress condition and the influence of the shape and dimension of the underground space on rockburst. The Stress Index (\(\sigma_{\mathrm{0}}/\sigma_{\mathrm{c}}\)) and Brittle Index (\(\sigma_{\mathrm{c}}/\sigma_{\mathrm{t}}\)) were selected because, when the BI value is small, rockburst occurs intensely, and when large, it is light. Whereas, SI relates directly to BI through Ks [20].  
This paper uses a rockburst dataset with 210 records and predefined classes (Table 1) to predict rockburst using a classification approach. Table 3, Figures 1 and 2 provide the input variables and statistics. In summary, the statistical analysis of the dataset revealed that the weight distribution and variance are high, indicating a wide distribution of data, which may be caused by outliers.  
* Rock type (Igneous--IG, Metamorphic--MT, and Sedimentary--SD)
* Depth (m)
* Brittle Index (BI)
* Stress Index (SI)
* Elastic Energy Index (EEI)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** & **Median** & **Skew** \\ \hline Depth (m) & 100 & 2520 & 730.03 & 354.85 & 700 & 1.56 \\ SI (\(\sigma_{\mathrm{0}}/\sigma_{\mathrm{c}}\)) & 0.1 & 5 & 0.65 & 0.78 & 0.48 & 3.52 \\ BI (\(\sigma_{\mathrm{c}}/\sigma_{\mathrm{t}}\)) & 0.26 & 80 & 19.91 & 14.8 & 14.73 & 2.14 \\ EEI & 0.81 & 30 & 5.23 & 4.44 & 4.4 & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of main input variables considered in this study.  
Figure 1: Matrix scatter plots (SPLOM) for each variable in the prediction of rockburst condition-RBC (with bivariate scatter plots below the diagonal, histograms on the diagonal, Pearson correlation above the diagonal, and boxplots for each RBC based on the input variables).  
Figure 2: Rockburst Data Characterization.
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 8 Conclusions  
This study showed that the UDP DoS attack had no impact on the parameters of the voice and video flows in a VoIP network based on Asterisk FreePBX. Even though the server is flooded with UDP packets, this flooding does not degrade the parameters of the individual VoIP streams. The mean jitter and packet loss, for both VoIP streams, are within normal limits, very far from the maximum allowable levels, exceeding which would result in the degradation of VoIP service quality.  
In addition, during the UDP attack, the Asterisk FreePBX is accessible. Both the server's CPU and memory are lightly loaded despite the flood with UDP packets.  
This study confirms the results obtained from the previous study when Asterisk FreePBX was attacked with various TCP DoS attacks. And in the previous study, it was found that different TCP DoS attacks do not affect the parameters of individual VoIP flows, and regardless of the attacks, the server is accessed, and different VoIP calls can be made.  
Both studies show that the Asterisk FreePBX is designed to be able to resist a variety of DoS attacks that do not impact system performance.  
This research received no external funding.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The researcher would like to thank the South-West University \"Neofit Rilski\" for the APC.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review
## 2 Existing Breast Cancer Detection Techniques & Technologies
### Body Image-Based Technology
#### 2.1.3 Magnetic Resonance Imaging (MRI)  
Magnetic Resonance Imaging (MRI) is a medical imaging technique that records changing strong magnetic fields and radio waves to produce detailed images of the organ and soft tissues of the human body [38; 47; 48]. MRI is the most effective imaging modality that offers precise accuracy and sensitivity in detecting structural abnormalities of the body compared to the other technique, as shown in Figure 6[49; 50; 51]. However, MRI is an expensive technology, and a waiting list is often long. MRI is a painless radiology procedure and does not use harmful ionizing radiation of X-ray. The MRI scanner machine is a large horizontal tube surrounded by a circular superconducting magnet where one patient enters (lie down in bed) [52] and running through the magnet from front to back that assist by the technician or a radiographer. During this procedure that take around 30-45 min, some patients can experience discomfort or fear of enclosed spaces (claustrophobia). MRI creates a powerful magnetic field that forces the protons in the body to align with that field. Then, a radio-frequency current is pulsed through the patient's body that disrupts the proton and forces it into 90-degree or 180-degree realignment with the static magnetic field. The scanner can identify the energy signal from the patient's body when the radio frequency is switched off. On the computer screen, these impulses were used to create visual images [53] as shown in Figure 7. Combining mammography, MRI, and certain clinical techniques yielded the best sensitivity 94.4%. The highest level of accuracy attained was 75.6%, implying that one out of every four diagnoses is incorrect [46]. The comparison of diagnostic performance of different breast tumor detection techniques is present in Table 4.  
Figure 6: Conventional mammogram of breast cancer [46].  
Figure 7: Typical finding of MRI-detected breast cancer (arrow) [50].
## 2 Materials and Methods
### Susceptibility Modeling
to choose the best splits at the nodes, and advanced handling of missing values without requiring explicit imputations. The model's performance is assessed using accuracy as a key metric. It was further improved through hyperparameter tuning to boost its performance using the Randomized Search CV method. This approach randomly selects combinations of values from predefined ranges and evaluates them through cross-validation. In the context of an XGBoost classification model, this strategy enables efficient exploration of the extensive hyperparameter space, including parameters like tree depth, learning rate, and the number of trees. By avoiding the need to evaluate all possible combinations, this method enhances performance while significantly reducing computational costs. Consequently, the tuning process is expedited, allowing for the swift identification of the optimal configuration for accurately classifying the areas in question. Following the workflow shown in Figure 4, after hyperparameter optimization, the classification model is reapplied, and its performance is reevaluated. Additionally, a classification report and a confusion matrix are used to assess classification results by identifying true positives, true negatives, false positives, and false negatives, thus providing a thorough analysis of the model's predictive capability. Furthermore, an error analysis is performed to gain a deeper understanding of the model's performance. Finally, a feature importance analysis is conducted to identify the most influential factors. 5. Lastly, the weight of each variable to the model's predictions is assessed by determining the extent to which each independent variable influences the target variable. This is achieved using the SHAP method [32, 33, 34], a popular approach in Explainable Artificial Intelligence (XAI) [35], commonly used in game theory. The SHAP method calculates the Shapley value for each predictor in the model, and the relation is written as follows. \[\phi_{i}= \sum_{S\subseteq N\setminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{|N|!} \cdot[v\;(S\cup\;\{i\})-v(S)]\] (1) where \(\phi_{i}\) is the Shapley value for each predictor \(i\); \(N\) is the set of all predictors; \(S\) is a subset of predictors that does not include \(i\); \(v(S)\) is the value function (model prediction) for subset \(S\). The main objective of XAI methods is to make the functioning of artificial intelligence models transparent, avoiding so-called \"black box\" processes. Through XAI methods, the importance of each observed variable can be evaluated to determine its influence on the predicted values. Based on this hypothesis, it is possible to estimate the reliability of the model. Additionally, Bee Swarm plots are applied to obtain a clear idea of the most significant variables and their corresponding values (global interpretability).
###### Abstract  
While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental results reveal that AFF_CGE substantially outperforms traditional methods, improving F1-scores by 5.3% through 22.8%. The framework achieves F1-scores ranging from 0.903 to 0.929 across various classifiers, exceeding the performance of state-of-the-art techniques. These results underscore the effectiveness and robustness of AFF_CGE in detecting encrypted malicious traffic, demonstrating its superior performance.  
encrypted malicious traffic; representation learning; attention; graph embedding +
Footnote †: journal: applied sciences  
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Footnote †: journal: applied sciences
# RFID Technology for Management and Tracking: e-Health Applications
#### Aim and Scope  
This contribution aims to provide detailed information concerning the implementation and testing of a basic RFID system for e-Health applications, in particular, for tracking and managing assets (medication and patients) in hospitals. While the architecture of the proposed system is quite similar to those already deployed in hospitals worldwide, the aim here is to provide additional details about implementation features that must be taken into account to ensure proper functionality: (i) choice of the antennas depending on the coverage area and cost; (ii) item traceability depending on the RFID tags' detectability; and (iii) simplicity and ease of use of the graphical user interface (GUI). Although the scope of the RFID system described in this contribution is restricted to a small area of the hospital, the architecture is fully scalable to cover the needs of the different medical services in a hospital.  
The implemented system allows the tracking of medical assets that are tagged with RFID tags within a hospital. UHF RFID technology is selected over the most extended NFC and HF RFID technology to minimize the hardware infrastructure. The former also allows ease of coverage/reading area conformation by simply using different kind of antennas. Information is stored in a database, which can be accessed from end-user mobile devices (tablets, smartphones) where the position and status of the assets to be tracked is displayed.
# Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes
## 1 Introduction
### Related Work  
Phenotypic characteristics, such as the degree of fruit coloration and concentration, are important indicators for judging table grapes' maturity and quality [2, 3]. Grape color is caused by the accumulation of anthocyanins in the peel. Anthocyanians are a type of water-soluble pigment, and their presence is positively correlated with fruit coloration [2].  
Red light irradiation during plant growth increases the degree of fruit coloring, and the sugar content increases accordingly [3]. This demonstrates how the color concentration of grape peel has a positive correlation with sugar content. The correlation coefficients of anthocyanin content and fruit color index of the four grapes \"Jingxiu,\" \"Juxing,\" \"Fujiminori,\" and \"Yongyou\" were 0.821, 0.946, 0.996, and 0.857, respectively. Additionally, the correlation coefficients of anthocyanins and sugar content were 0.804, 0.953, 0.932, and 0.911, respectively [3]. Visual characteristics of table grapes can reflect the physiological characteristics of a bunch of grapes during the growth process; hence, this provides a research method for predicting the sugar content of grapes using visual features.  
Computer vision technology has been widely used in agricultural production as it is fast and non-destructive compared to manual detection [4; 5]. Computer vision detection methods are based on grape color and shape, feature fusion, and deep learning. Information on the color channels in the L*a*b color space translated from RGB images was applied to extract color features, which has the potential for apple sugar content prediction [6]. A combination of visible-range image processing, image feature extraction, a hybrid imperialist competitive algorithm, and artificial neural network regression yielded a squared correlation coefficient (R\({}^{2}\)) on the pH value of 0.843 \(\pm\) 0.043 on a test set of [PERSON] navel oranges [7]. Experiments show that color features can determine the internal fruit quality. In [8], an RGB image of oranges was obtained using a camera, and the color, shape, and texture features were extracted from the image and used as the input value for the neural network model that predicted the sugar content and pH value. The results found that neural networks could determine sugar content and pH with high accuracy solely from the appearance of the fruit. A multiple linear regression model of the Red Globe grape's color features and sugar content was established to classify boxes of Red Globe grapes in [9], where the R\({}^{2}\) was 0.84 and the RMSE was 0.82%. The color, shape, and texture characteristics of an orange, taken from an image, were used to predict the sugar content and pH value. The model was able to predict the sugar content and pH value accurately. These methods mentioned above demonstrate the feasibility of using computer vision methods to detect the internal quality of fruits non-destructively.
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 3 Backstepping-Based Quadrotor Formation Control
### Attitude Control of a Quadrotor
\begin{split} e_{\theta i,1}&=\chi_{i,9}-\theta_{ d,fi},\\ \dot{e}_{\theta i,1}&=\hat{\chi}_{i,9}-\dot{\theta} _{d,fi}=\chi_{i,10}-\dot{\theta}_{d,fi},\\ e_{\theta i,2}&=\chi_{i,10}-\hat{\chi}_{i,10},\\ \dot{e}_{\theta i,2}&=\hat{\chi}_{i,10}-\hat{\chi} _{i,10}=f_{i,2}(\chi_{i,8},\chi_{i,12},\Omega_{r})+\frac{u_{i,3}}{I_{yy}}- \hat{\chi}_{i,10},\\ e_{\varphi i,1}&=\chi_{i,11}-\dot{\psi}_{d,fi},\\ e_{\varphi i,1}&=\hat{\chi}_{i,11}-\dot{\psi}_{d,fi }=\chi_{i,12}-\dot{\psi}_{d,fi},\\ e_{\varphi i,2}&=\chi_{i,12}-\hat{\chi}_{i,12},\\ e_{\varphi i,2}&=\hat{\chi}_{i,12}-\dot{\chi}_{i,1 2}=f_{i,3}(\chi_{i,8},\chi_{i,10})+\frac{u_{i,4}}{I_{zz}}-\dot{\hat{\chi}}_{i,12}.\end{split}  \tag{43}\]  
The proof of the attitude dynamics of the \(i\)-th follower in (7) controlled by the backstepping based attitude control inputs of the \(i\)-th follower in (40) and (41) is the same as the Theorem 1. The [PERSON] function candidate is as follows:  
\[ \begin{split} V_{\phi}(e_{\varphi i,1},e_{\varphi i,2})& =\frac{1}{2}\left(e_{\varphi i,1}^{2}+e_{\varphi i,2}^{2}\right), \\ V_{\theta}(e_{\theta i,1},e_{\theta i,2})&=\frac{1}{ 2}\left(e_{\theta i,1}^{2}+e_{\theta i,2}^{2}\right),\\ V_{\psi}(e_{\varphi i,1},e_{\varphi i,2})&=\frac{1}{ 2}\left(e_{\varphi i,1}^{2}+e_{\varphi i,2}^{2}\right).\end{split}  \tag{44}\]  
Differentiating \(V_{\phi}(e_{\varphi i,1},e_{\varphi i,2}),V_{\theta}(e_{\theta i,1},e_{\theta i,2})\), \(V_{\psi}(e_{\varphi i,1},e_{\varphi i,2})\) with respect to the time and substituting (37)-(44), we finally obtain:  
\[ \begin{split}\dot{V}_{\phi}(e_{\varphi i,1},e_{\varphi i,2})& =-k_{i,7}e_{\varphi i,1}^{2}-k_{i,8}e_{\varphi i,2}^{2}<0,\\ \dot{V}_{\theta}(e_{\theta i,1},e_{\theta i,2})&=-k_{ i,9}e_{\theta i,1}^{2}-k_{i,10}e_{\theta i,2}^{2}<0,\\ \dot{V}_{\psi}(\varphi_{\varphi i,1},e_{\varphi i,2})& =-k_{i,11}e_{\varphi i,1}^{2}-k_{i,12}e_{\varphi i,2}^{2}<0.\end{split}  \tag{45}\]  
Therefore, by the Lyapunov stability theorem [31], the formation error systems related to the attitude of the \(i\)-th follower in (37), (39), (43) and (44) are asymptotically stable with the actual attitude control inputs of the \(i\)-th follower in (40) and (41).
# v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers +
## 1 Introduction
To overcome cumbersome resource allocation challenges, the present paper proposes, presents and evaluates a novel paradigm in the field of cloud resource management named v-Mapper. v-Mapper employs applied graph theory and Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) techniques to optimize network performance behavior. This helps in providing a clean representation of VMs on a  
Figure 1: An overview of cloud services and applications. cloud mesh. It enables system administrators to fully realize the potential of application-awareness concepts by putting them under automated control and help service providers to deliver best service packages to small and medium enterprises (SMEs) [11]. The specific aim of this paper is to establish the necessary baseline for a tool-supported decision support method aimed at facilitating the selection of cloud services in a multi-cloud environment. Herein, we propose v-Mapper, a new scheme that implements application-aware features to oversee VM placement, services admission and task scheduling functions in a cloud environment. The rest of this paper is organized as follows. Section 2 discusses the related work in conventional VM management approaches. Section 3 presents background information on application-awareness and related concepts in existing cloud data centers. In Section 4, we elaborate v-Mapper's functions and features in detail. Section 5 provides a comprehensive discussion on the test bed and simulation settings. In Section 6, we present the performance evaluation results and carefully evaluate our solution. Section 7 concludes this paper and discusses the future work.
## 2 Study Area and Data Sources
### Remote Sensing Data
#### 2.3.2 Sentinel-2 Data  
Sentinel-2 is an optical satellite composed of two constellations S2A and S2B launched by ESA in 2015 and 2017, respectively. With a revisit time of 5 days (using S2A and S2B), Sentinel-2 provides images composed of 13 bands among which the bands 4 and 8 are used in the computation of the Normalized Difference Vegetation Index (NDVI). The products corrected from atmospheric effects are processed by the CESBIO's MAJA processor [51] and distributed as Level 2A products by the National Centre for Space Studies (CNES) via the open access platform ([[https://peps.cnes.fr/](https://peps.cnes.fr/)]([https://peps.cnes.fr/](https://peps.cnes.fr/)), accessed on 3 July 2021) or directly from the THEIA website ([[https://theia.cnes.fr/](https://theia.cnes.fr/)]([https://theia.cnes.fr/](https://theia.cnes.fr/)), accessed on 3 July 2021). The NDVI is computed for D1, D2, F2, F3 and R1 (seasons 2016-2017 and 2017-2018) from S2. For 2014-2015 season on R1, the MOD13A2 products used in [52] are also used here. The canopy cover fraction is derived from NDVI using the relationship developed by [53].
## 3 Hypothesis Development and Research Model
### Technology Acceptance Factors and Psychological Experience Factor for Users
#### 3.2.4 Consumer Purchase Intention  
Intention refers to adopting and accepting new technologies that will be used in future purchases. Understanding the factors that drive this decision can go a long way in facilitating the design of technologies with practical applications [48]. TAM has shown that PEU and PU influence behavioral intentions, which influence actual use behavior. Behavioral intention has been identified as a key predictor of actual use behavior [49]. [PERSON] and [PERSON] [34] argued that consumers who experienced mindfulness showed more positive subjective intentions than those who did not experience online mindfulness. [PERSON] and [PERSON]'s [50] study further supported the influence of online flow experience, which mediates the idea that interactivity influences behavioral responses. Therefore, based on the previous review, the following hypothesis is proposed:  
Hypothesis 6: _Consumers' perceived usefulness positively affects consumers' purchase intentions for cultural heritage products._  
Hypothesis 7: _Consumers' perceived ease of use positively affects consumers' purchase intentions for cultural heritage products._  
Hypothesis 8: _Consumers' flow experience positively affects consumers' purchase intentions for cultural heritage products._