esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comprehensive Cost Computation Model
### Security Costs Regardless of Data Breach  
An organization takes steps to reduce the risk of data breaches based on the organization's size and the potential security risks. The indirect data breach costs comprise recurring costs of the security measures and security upgrades. Upgrading means bridging internal security holes to protect the organization against data loss or harm. Such security upgrades minimize the data breach probability, and then the cyber insurance at one time.  
In general, it is difficult to obtain the essential information on the costs due to security upgrades because these upgrades happen internally, and organizations do not disclose the details. As a result, developing a preliminary economics model to estimate the cost of a security upgrade for an organization is still an open issue. However, some security-related cost information can be obtained from some occasionally published articles. For example, LinkedIn spent between USD 2-3 million in 2012 to prevent password theft [41].  
These costs can be hard to calculate for departments in small organizations because each staff member spends time, which is hard to measure, engaging in serval tasks such as installing security patches, configuring systems and applications to improve security, and managing system behavior in response to a security breach.  
In order to calculate the cost regardless of a data breach (CRDB), we should add the following costs: the security maintenance cost (SM), security upgrade cost (SU), and an insurance premium (IP). Equation (11) shows how to compute the elements that comprise general security costs for any organization.  
\[\text{CRDB}=\text{SM}+\text{SU}+\text{IP} \tag{11}\]  
The effectiveness of security processing and automation of SU on data breach costs enhance the SM. Deployment of security levels varies with many factors; some of these factors are IP, security tools, trust security models, etc.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Mahalanobis-Taguchi System (MTS) Procedures
#### 2.2.1 The Role of the Orthogonal Array (OA) in MTS  
Orthogonal array (OA) is a type of fractional factorial design of experiment introduced by [PERSON] in 1947 [24]. It is different from the traditional fractional factorial DOE in the sense that it tries to balance the combination or interaction of factors equally with the minimum number of experimental runs. In MTS, the orthogonal array structure is represented by Latin symbology as \(L_{a}\) (\(b^{\prime}\)) where \(L\) is the Latin Square, \(a\) is the number of runs, \(b\) is the number of factor levels and \(c\) is the number of main factors. Table 1 illustrates an example of an OA structure for seven factors with eight runs and two factor levels.  
The name \"orthogonal\" is suggested not because of the perpendicular attribute of the structure but rather it is defined as any pairs of columns with the same repetition number of combinations of factors [24]. To illustrate further, using the OA in Table 2 as an example, take a pair between column 1 and column 2, the repetition number of each level of combinations in this column pair is the same (which is twice in this case). The same number of repetitions should be obtained for the rest of the column pairs thus the \(L_{8}\) (\(2^{\prime}\)) array depicted by Table 1 can be said to be orthogonal. Table 2 illustrates the number of repetitions in level combination for another three more column pairs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline
**Run** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
3 & 1 & 2 & 2 & 1 & 1 & 2 & 2 \\ \hline
4 & 1 & 2 & 2 & 2 & 2 & 1 & 1 \\ \hline
5 & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\ \hline
6 & 2 & 1 & 2 & 2 & 1 & 2 & 1 \\ \hline
7 & 2 & 2 & 1 & 1 & 2 & 2 & 1 \\ \hline
8 & 2 & 2 & 1 & 2 & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of an Orthogonal Array (OA) structure of type L8 \(2^{7}\) array.  
In MTS, OAs are used to select the features of importance by minimizing the different combinations of the original set of features. The features are assigned to the different columns of the array. Since the features have only two levels, a two-level array is used in MTS as illustrated in Table 2. For each run of an OA, MDs corresponding to the known abnormal conditions are computed. The importance of features is judged based on their ability to measure the degree of abnormality on the measurement scale [25]. This is where the signal to noise ratio metric is deployed. Further discussion on OA concepts can be found from [24; 26; 27; 28].
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### COVID-19 Infection Detection Using Deep Learning
#### 3.3.4 Heatmap Using GRAD Class Activation Mapping  
Gradient-weighted Class Activation Mapping (Grad-CAM) is a powerful visualization technique that enhances the interpretability of deep learning models, particularly Convolutional Neural Networks (CNNs) [31]. Grad-CAM enables us to gain visual insights into the regions of the X-ray scans most relevant for COVID-19 infection detection, thereby offering transparency and evidence-based information about the model's predictions [17; 31].  
The Grad-CAM technique operates by capturing the gradients of each target class during the model's forward pass. It then uses these gradients to produce a coarse localization map by traversing through the last convolutional layer. This localization map highlights the significant regions in the X-ray image that contribute most to the model's prediction of COVID-19 infection. An example of the Grad-CAM visualization's impact is shown in Figure 4. The arrow marks or arrows added to a chest X-ray image serve as annotations, which are commonly used to highlight or draw attention to specific structures, abnormalities, or areas of interest within the X-ray image. They can be employed to emphasize particular abnormalities such as tumors, nodules, fractures, or other conditions that are of significance for clinical analysis and diagnosis.  
Integrating Grad-CAM with our CNN-based COVID-19 detection model, we generate high-resolution visualizations of class discrimination, providing precise insights into the regions affected by the COVID-19 infection in X-ray scans. By superimposing the Grad-CAM heatmap onto the original X-ray image, medical professionals and researchers can gain valuable insights into the areas of abnormality detected by the model [17].  
This visualization technique offers several advantages for our COVID-19 detection approach. First, it enhances the model's transparency, providing visual evidence of the regions that the model relies on to make its predictions. This transparency is especially critical in medical applications, where the interpretability of AI-based systems is essential for building trust among healthcare practitioners.  
Second, the Grad-CAM visualization aids in the fine-grained interpretation of the CNN model's decisions. By focusing on the localized regions in the X-ray images, medical experts can gain deeper insights into the specific features that the model identifies as indicative of COVID-19 infection. This can lead to a better understanding of the disease and improve the model's diagnostic accuracy.  
Incorporating Grad-CAM into our COVID-19 detection system enhances its interpretability, accuracy, and potential for further research in the medical domain. The ability to visualize and understand the model's decision-making process fosters confidence in the system's performance and supports medical professionals in making well-informed decisions.
## 3 Materials and Methods
### Normalization
#### 3.2.8 EfficientNe-B0  
EfficientNet-B0 is part of the EfficientNet family of neural network architectures, which are designed to achieve state-of-the-art accuracy while being computationally efficient in terms of parameters and computation cost. EfficientNet was proposed by [PERSON] and [PERSON]. It serves as the foundational model within the EfficientNet family, which comprises eight variants denoted as B0 to B7. Each variant differs in terms of dimensions and performance. EfficientNet-B0, for instance, contains 5.3 million parameters and achieves a top-1 accuracy of 77.3% on the ImageNet dataset [30].  
EfficientNet introduces a compound scaling method that optimally scales the model's depth, width, and resolution simultaneously. This method allows EfficientNet models to achieve better accuracy than traditional models with a similar number of parameters [31]. The scaling factors are controlled by three parameters: \(\varnothing\) for depth, \(\beta\) for width, and \(\gamma\) for resolution. EfficientNet-B0 has \(\varnothing=1,\ \beta=1,\ and\ \gamma=1\).  
EfficientNet-B0 builds upon the inverted bottleneck residual blocks of MobileNetV2. Additionally, it incorporates squeeze-and-excitation blocks, which bolster the model's ability to learn expressive features by adaptively recalibrating the channel-wise feature responses [32; 33]. EfficientNet-B0 uses the Swish activation function, which is a smooth, non-monotonic activation function designed to improve model performance. The network ends with a global average pooling layer, followed by a fully connected layer for the final classification. EfficientNet-B0 includes regularization techniques like dropout to prevent overfitting.  
A simplified representation of the mathematical model for a basic building block in EfficientNet-B0 is as follows:  
1. Depthwise Separable Convolution: \[X_{d}=DepthwiseConv2D(X,W_{d},\ padding=\text{'same',\ activation}=None)\] (14) Batch Normalization after Depthwise Convolution: \[X_{b}n_{d}=BatchNorm Normalization(X_{d},\gamma_{d},\beta_{d})\] (15)
2. Swish Activation after Depthwise Batch Normalization: \[X_{s}\ wish=\sigma\big{(}X_{bn_{d}}\big{)}\] (16)
3. Pointwise Convolution: \[X_{p}=Conv2D\big{(}X_{s}wish,W_{p},\ adding=\text{'same',\ activation}=None\big{)}\] (17)
4. Batch Normalization after Pointwise Convolution: \[X_{b}n_{p}=BatchNorm Normalization\big{(}X_{p},\ \gamma_{p},\beta_{p}\big{)}\] (18)
5. Skip Connection (Identity Connection): \[X_{out}=X+X_{bn_{p}}\] (19)  
This block, often referred to as a depthwise separable convolution block with Swish activation, is a fundamental component repeated multiple times in the EfficientNet-B0 architecture.
## 3 Plant-Based Biopolymers
### Starch-Based VMAs
On the other hand, the starch derivatives explored in another study, one assessing the properties of self-leveling mortars incorporating several VMAs, exhibit an intermediate behavior between a natural polysaccharide and microsilica [42]. It was found that while maintaining a constant water/binder ratio (W/B), VMAs, including starch, reduce mortar flow and increase flow time, simultaneously leading to an increase in yield stress and plastic viscosity. However, it is essential to mention that the composition of cement mortars is influenced by the type of starch modification and chain length. For example, in the study, hydrolyzed starch, with a short polymer chain, reduced the yield stress and the plastic viscosity, resulting in larger flow diameters. Meanwhile, unhydrolyzed potato starch thickened the cement mixture and increased the yield stress [43]. In addition, starch and modified starch have been compared with conventional VMAs, not only for comparative purposes but also to assess their affinity with different superplasticizers. For example, it has been found that starch has a low affinity with polycarbonate (PCE)-based HRWRs, unlike Welan gum [44]. Another example can be found in the study by [PERSON] et al. where modified starch was combined with a polycarbonate ether-based HRWR [9]. Modified starch alone led to a reduction in the yield stress, slightly affecting plastic viscosity. However, when combined with the HRWR, viscosity, segregation resistance, and toughness increased further. It should also be noted that the demand for the HRWR proved to be limited, demonstrating its effectiveness. Figure 8: Chemical structure of (**a**) amylose and (**b**) amylopectin [from PubChem Database]. On the other hand, starch has proven its potential in improving shear-thinning behavior, particularly at high dosages. It has been shown to have a significant effect on improving apparent viscosity and yield stress, as well as on reducing the spreading diameter [44]. In the context of research into alternatives to conventional VMAs, notably Welan gum, starch has proven its potential for increasing segregation resistance [45]. Furthermore, starch ether has also been shown to be effective in eliminating bleeding, unlike Welan gum, which only reduces it. It is important to mention, however, that starch ethers have been used at high dosages due to inherent distinctions in its structure [46]. Furthermore, it has been shown that the behavior of starch in concrete can vary according to its origin. For example, corn starch improves workability and density at a rate of 1% due to its gelatinous properties. Nevertheless, when a certain dosage of starch is reached, the density and the compressive strength decrease while compressive strength remains higher than that of ordinary concrete [47]. In contrast to corn starch, cassava starch reduces workability and increases the setting time. The versatility of cassava starch lies in its modification of viscosity in parallel with setting delay without compromising the compressive strength of the concrete [48].
## 3 Route for Non-Isocyanate PUs (NIPUs)
A polyaddition of bifunctional cyclic carbonates with primary di- or polyamines containing poly(hydroxy urethanes) (PHUs) with other primary or secondary hydroxyl groups in the side chain, shown in Figure 4, is the most examined and the best possible synthesis path for preparing NIPUs. [PERSON] and [PERSON] first researched this route in 1957 [158]. The group's hydroxyl can form inter- and intra-molecular hydrogen connections with the groups of urethane carbonyl [14; 150; 154; 157]. The use of ecologically benign bio-based materials and carbon dioxide fixation is also a benefit of the polyaddition process. Furthermore, without catalysts and solvents, synthesis could be achieved [14]. This generates enhanced chemical resistance to organic solvents in obtained PHUs. Additionally, PHUs have increased thermal stability due to a lack of labile biurets and allophanate thermally unstable groups [159]. PHUs also benefit from the ability to react with various chemical functional groups of constituted hydroxyl groups [160]. In contrast with standard polyurethanes, literature studies show inconsistent evidence of PHU water intake during exposure to water [150; 152; 154]. The chemical structures of both traditional isocyanate-based and hydroxy-functional isocyanate-free polyurethanes are presented in Figure 5.  
Figure 3: Possible pathways for synthesis of NIPUs [15]. The first successful application of NIPUs in the industry was achieved by Nanotech Industries, Inc (Daly City, CA, USA). Under the trade name Green Polyurethane, the company patented phospene and isocyanate-free commercially available materials obtained using bio-based resources by the cyclic carbonate, epoxy oligomer, and aliphatic or cyclo aliphatic polyamines reaction. These materials are characterized by increased adhesion  
Figure 4: Comparison of NIPU structure produced by polyaddition of bifunctional cyclic carbonate and diamine as well as polycondensation [80,149]. Figure 5: The conventional method of polyurethane synthesis in comparison with a route to hydroxyl-functional NIPUs [149]. properties and hydrolytic stability, as well as improved corrosion and wear resistance compared with the traditional, commercially available PUs [152]. Gradually reducing the number of fossil resources could lead to a predicted scarcity of polymer synthesis substrates. Recent years demonstrated the use of bio-based and renewable monomers and their derivatives, e.g., vegetable oils, fatty acids, sugar, terpen, and even carbon dioxide [161]. The preparation of PU-based adhesives comprising biosourced, sustainable raw materials involves the synthesis of non-isocyanate PUs. This approach, excluding the use of harmful isocyanates, has gained a significant research interest in the recent years.
## 6 Interactions among MEC Domains
\\ \hline Integration at platform & & & & Business–PaS model for Domain B; SLAs related to \\ level (infrastructure & & & & the MEP from Domain B remotely, \\ owned by Domain B) & & & VM could be instantiated on-demand by & including platform and resource abstractation data models for \\  & & & Domain A. & resources and platform; multi-domain awareness retained \\  & & \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Multi-Domain Scenarios** & **Phase 1** & **Phase 2** & **Phase 3** & **Phase 4** \\ \hline Integration at & Domain A provides to Domain B the & Domain B is instructed for the allocation & & \\ infrastructure level & configuration of the infrastructure, & of resources and the configuration of the & N/A & N/A \\  & including the application images. & infrastructure & & \\ \hline Integration at platform & & Domain B configures the virtualization & Domain B’s MEP is able to update & \\ level with infrastructure & N/A & Infrastructure from Domain A & client’s DNS & \\ \hline Integration at platform & Domain A provides to Domain B the & Domain A receives fault and & Domain B’s MEP is able to update & \\ level with infrastructure & configuration of the infrastructure, & performance information from Domain B & client’s DNS & \\  & including the application images. & & & \\ \hline Integration at service level & Domain A passes to Domain B the image & Full delegation on Domain B for the & Domain B’s MEP is able to update & N/A \\  & and selects Domain B VIMs & instantiation of the application & client’s DNS & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of interactions between MEC domains in the deployment of an MEC application per multi-domain alternative scenarios.
# New Security Improvements in Next-Generation Passive Optical Networks Stage 2 +
## 4 Our Proposal of Security Improvements in NG-PON2
In this case, an attacker could connect a malicious  
Figure 3: The process of the calculation and verification of the Registration_ID_PUF in SI-NG-PON2. device to the infrastructure and could impersonate an OLT CT. Subsequently, the malicious OLT CT can acquire data from an ONU. We propose the mandatory use of mutual authentication between an ONU and an OLT CT in NG-PON2. If mutual authentication is not performed between an ONU and an OLT CT, the ONU will not further respond to the nonauthenticated OLT CT.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Spark
#### 4.2.1 Architecture  
Apache Spark has a layered architecture in which all Spark components and layers are loosely interconnected, as shown in Figure 6. Spark has a rich set of high-level libraries, which includes SparkSQL [36] for the processing of structured data, Spark streaming [37] for real-time stream processing, MLlib [38] for machine learning algorithms, GraphX [39] for graph processing and SparkR [40] for big data analysis from R shell. Spark core is responsible for task scheduling, fault recovery, interacting with storage systems and memory management. The spark core engine is based on the master slave architecture. The master node has the driver program that executes the main function of the user application. The driver program creates the spark context. The spark context connects with the cluster manager and acquires the executors on worker nodes and distributes tasks to the executors. The worker nodes are salve nodes that actually executes the tasks and return the result to spark context. Every worker node launches a process known as executor to run tasks and provide in-memory storage for Resilient distributed datasets (RDDs). RDDs are in-memory partitioned sets of data that are distributed over multiple nodes across the cluster. Spark can be deployed as a Standalone server or run on top of a cluster manager like Mesos [41] or YARN. Spark does not have its storage mechanism rather. It can work with any Hadoop compatible data sources such as HBase, HDFS, Casandra and Hive etc. [34].
## 3 Materials and Methods
### Coalescence Rate Modeling
#### 3.2.2 Collision Efficiency  
The collision between two bubbles can result either in coalescence or bouncing. Hence, in addition to the collision frequency, one has to consider the \"coalescence efficiency\", named \(\lambda_{ij}\). \(\lambda_{ij}\) is a function of the contact time between the bubbles (\(\tau_{ij}\)) and the time needed by the liquid film to dry out (\(t_{ij}\), drainage-time) and, accordingly with [16], reads as follows:  
\[\lambda_{ij}=\exp\left(-\frac{t_{ij}}{\tau_{ij}}\right) \tag{19}\]  
The drainage time, \(t_{ij}\), between bubbles \(i\) and \(j\) is estimated as follows:  
\[t_{ij}=\left[\frac{\left(d_{C,ij}/2\right)^{3}\rho_{L}}{16\sigma}\right]\ln \left(\frac{h_{0}}{h_{f}}\right) \tag{20}\]  
where \(d_{C,ij}\) is the equivalent radius for the bubble coalescence, equal to [17]:  
\[d_{C,ij}=\left(\frac{2}{d_{eq,i}}+\frac{2}{d_{eq,j}}\right)^{-1} \tag{21}\]  
where \(h_{b,0}\) and \(h_{b,t}\) are the initial thickness and the final thickness of the liquid film at which rupture and coalescence occur. In air-water systems, they are approximately \(h_{b,0}=1\times 10^{-4}\) m [18] and \(h_{b,t}=1\times 10^{-8}\) m [19]. The drainage time, \(\tau_{ij}\), between bubbles \(i\) and \(j\) is estimated as follows [20]:  
\[\tau_{ij}=\frac{\left(d_{ij}/2\right)^{2/3}}{\varepsilon^{1/3}} \tag{22}\]