esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security and Privacy-Preserving Solutions for IoE
### Quantum-Resilient Security Approaches  
With the advancement of quantum computing, existing encryption methods are under threat, making the development of quantum-resilient security solutions mandatory for IoE systems.
# Multi-Disciplinary Optimization of UV-C Filter for Air Disinfection
## 1 Introduction
The MDO (multi-objective optimization) design of the filter was realized by coupling the numerical models with the optimization platform modeFRONTIER, which is used in various industrial fields, such as automotive and aeronautics, for the optimal design of products and systems according to the definition of the objectives and constraints. Optimization algorithms based on genetic algorithms and artificial intelligence guide the exploration of different candidate solutions by the automatic execution of several numerical simulations, until the design objectives are met. The numerical models integrated in the MDO platform include CFD for the simulation of air flow across the filter and ray tracing to evaluate the intensity of UV radiation to which each particle transported by the air flow (as evaluated by particle tracking post-processing analysis over the CFD field) is exposed. By the combination of UV radiation intensity and exposure time on a given number of particles, it is possible to define the average dose absorbed by any pathogenic particle, as the objective of the optimization process to be maximized. In the next sections we first describe the filter numerical models, and then we focus on the set-up and execution of the MDO optimization process, highlighting how the performance of the system was improved following this methodology.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 3 Sparse Array Synthesis
### A Synthesis Example  
Now, let us consider the synthesis of a sparse array with the same specifications, beam-width, side-lobe level and scanning range, used for the regular lattices (all the layouts presented in the following will comply with the same constraints to simplify the comparisons): SLL lower than \(-20\) dB for \(0.067\leq w\leq 1.77\).  
Using IDEA, it is possible to find a layout employing 213 elements only (see Figure 5). All the data (elements' positions and excitations) necessary to reproduce this layout, as well as the data for the layouts that will be presented in the following, are given in Appendix A. The achieved layout shows an excitation dynamic \(\rho=15.9\) dB, the minimum inter-element distance is \(0.49\lambda\). The overall computation time, on an Intel i7-8700 k 4.7 GHz processor, required about 50 min, and was performed using \(P=3\), \(\delta=\lambda/60\), \(\epsilon=\max(\epsilon^{\mathrm{c}})/1000\), \(\mu=\max(\epsilon^{\mathrm{c}})/1000\); some computational remarks are discussed in Appendix B.  
Undoubtedly, the sparsification allows a huge reduction of the number of elements to the number of radiators a regular lattice, from 665 to 213 radiators (a reduction of about 68%), but such a reduction has a cost: the very low directivity of the obtained pattern, which is 23.6 dB only.
## 2 Materials and Methods
### Integrating the Equations of Motion
#### 2.3.1 Euler Integrator  
In the Euler algorithm, a deformation matrix was computed as:  
\[\mathbf{\mu}=\mathbf{I}-\frac{\beta_{T}}{3\tau_{p}}\Big{[}\big{(}P_{0}\mathbf{I}- \mathbf{P}_{\text{int}}\big{)}-\frac{k_{B}T}{V}\mathbf{I}+\frac{\mathbf{h} \Sigma\mathbf{h}^{T}}{V}\Big{]}\Delta t+\sqrt{\frac{2\beta_{T}k_{B}T\Delta t}{ 3V\tau_{p}}}\mathbf{\mathcal{R}}\,. \tag{11}\]  
Here, \(\mathbf{\mathcal{R}}\) is a \(3\times 3\) matrix of standard Gaussian numbers. The cell matrix and atomic positions were then updated as:  
\[\mathbf{h}^{\prime} =\mathbf{\mu}\mathbf{h}\,, \tag{12}\] \[\mathbf{q}_{i}^{\prime} =\mathbf{\mu}\mathbf{q}_{i}\,. \tag{13}\]  
Optionally (see Section 2.2), momenta were updated as:  
\[\mathbf{p}_{i}^{\prime}=\Big{(}\mathbf{\mu}^{-1}\Big{)}^{T}\mathbf{p}_{i}\,. \tag{14}\]  
This integrator resembles the one proposed in the original formulation of the Berendsen barostat [17], with the exception of the scaling of momenta (Equation (14)) and of the noise term in Equation (11), which are not present in the Berendsen barostat. Its implementation was, thus, straightforward for any code already implementing the Berendsen scheme. However, as discussed in Ref. [18], such an algorithm leads to unnecessary violations of time-reversibility that are particularly relevant in the limit of large \(\tau_{p}\) or small \(\Delta t\).
## 4 Applications
### Effect of Through-the-Thickness Distribution of CNTs
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Elastic Foundation:**\(\boldsymbol{\rho}^{(-)}=1800\,kg/m^{3}\), \(\boldsymbol{h}_{f}^{(-)}=0.1\,m\), \(\boldsymbol{k}_{3f}^{(-)}=75\times 10^{7}\,N/m^{3}\)} \\ \hline \(f\)**[Hz]** & \(\boldsymbol{p=0}\) & \(\boldsymbol{p=1/5}\) & \(\boldsymbol{p=1/2}\) & \(\boldsymbol{p=1}\) & \(\boldsymbol{p=2}\) & \(\boldsymbol{p=5}\) & \(\boldsymbol{p=10}\) & \(\boldsymbol{p=15}\) & \(\boldsymbol{p=20}\) \\ \hline  & & & & & & & & & \\ \hline
1 & 341.557 & 341.153 & 340.590 & 339.761 & 338.431 & 336.089 & 334.456 & 333.695 & 333.244 \\
2 & 379.719 & 378.036 & 375.701 & 372.262 & 366.774 & 357.249 & 350.783 & 347.791 & 345.955 \\
3 & 379.719 & 378.036 & 375.701 & 372.262 & 366.774 & 357.249 & 350.783 & 347.791 & 345.955 \\
4 & 430.007 & 426.774 & 422.278 & 415.633 & 404.978 & 386.395 & 373.862 & 368.213 & 361.622 \\
5 & 469.071 & 464.709 & 458.633 & 449.631 & 435.144 & 409.749 & 392.584 & 372.274 & 361.622 \\
6 & 470.711 & 466.315 & 460.190 & 451.114 & 436.502 & 410.867 & 392.772 & 372.274 & 364.919 \\
7 & 528.685 & 522.691 & 514.326 & 501.897 & 481.810 & 446.124 & 392.772 & 384.850 & 380.340 \\
8 & 528.685 & 522.691 & 514.326 & 501.897 & 481.810 & 446.124 & 393.522 & 385.704 & 381.145 \\  
Figure 6: Frequency variations of a CCCC square plate (Figure 3a) made of one lamina of constant thickness \(h=0.1\,\mathrm{m}\); reinforced by CNTs distributed as in Figure 5a for the different parameters \(p^{(k)}\). The mass fraction was \(w_{r}=0.05\), and the agglomeration parameters \(\mu_{1}=\mu_{2}=1\). The Chebyshev-Gauss-Lobatto grid distribution was employed, with \(I_{N}=I_{M}=21\). The following mode shapes are considered: (**a**) 1 st mode; (**b**) 2 nd mode; (**c**) 3 rd mode; (**d**) 4 th mode; (**e**) 5 th mode; (**f**) 6 th mode; (**g**) 7 th mode; (**h**) 8 th mode.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Segmentation with 2D Architectures  
In this subsection, we will discuss five deep learning architectures for image segmentation: UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet. These architectures all share a common foundation, characterized by a U-shaped architecture. This lineage of U-shaped architectures, originally inspired by UNet, has been meticulously adapted to address specific nuances within the field of medical imaging. In this paper, we will apply these U-shaped architecture variants for lung infection segmentation. Departing from the foundational UNet architecture, each of the developed algorithms incorporates distinctive elements. In the subsequent sections, we will explore the key features of each architecture.
# HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks
## 4 HinPhish  
The core idea of HinPhish was motivated by the observation that every webpage on the Internet cannot exist independently; it will always be connected with other pages or resources, such as forwarding pages, loading resources, and so on, for a variety of reasons. For normal sites, the relationships among links in the pages are cohesive; namely,  
Figure 3: Example of a HIN instance (**a**) and its network schema (**b**).  
the domain names of links in pages are the same or related to the visited domain name. However, phishers may ignore this relationship when deploying mimic sites, in order to reduce the cost of attacks. So, for phishing sites, there are often unrelated domain names, leading to a divergent relationship among the links in pages.  
Based on the above observations, our work assumes that different types of relations among domain names influence the importance of domain names in the proposed HIN model. Here, we name the importance score the phish-score. According to different purposes, we divide links into two types of nodes: domain nodes and resource nodes. Further, for the four categories of links, we designed three relationships: outlier, local, and relative. In addition, we apply a modified algorithm to leverage the character of different link types to calculate the phish-score of the node objects in the webpage. Finally, the node attributes in HIN are used as input to machine leaning algorithms, in order to detect phishing attacks.  
As shown in Figure 4, there are four main components in the proposed HinPhish model: Information Extraction, Model Construction, HinPhish-score Computation, and Website Classification. After extracting links from webpages (step 1), a HIN model is built (step 2). Then, we calculate the feature matrix (step 3). Based on these features and label information, the machine learning classifier classifies the website set (step 4). Finally, HinPhish outputs the final detection results (step 5). In the following, we introduce each component in detail.
# Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm
## 2 Optimization Problem and Methodology
### Fuzzy-Based Method  
After determining the Pareto front, the fuzzy set theory is used to choose the best candidate solution for the VPP [22; 23]. The methodology has the following steps:1. Definition of the maximum and minimum values for each objective function.
2. Definition of the membership function for each objective function (see Equation (16)).
3. Definition of the normalized membership function (see Equation (17)).
4. Order the Pareto solutions according to the normalized membership function.  
In Step 2, a membership function \(\mu_{o}^{S}\) related to each objective function (represented by index '\(o\)') of each solution '\(s\)' in the Pareto front is calculated as  
\[\mu_{o}^{S}=\left\{\begin{array}{ll}1&,F_{o}\leq F_{o}^{min}\\ \frac{F_{o}^{max}-F_{o}^{min}}{F_{o}^{max}-F_{o}^{min}}&,F_{o}^{max}<F_{o}<F_{ o}^{min}\\ 0&,F_{o}\geq F_{o}^{max}\end{array}\right. \tag{16}\]  
where, \(F_{o}^{max}\) and \(F_{o}^{min}\), defined in Step 1, are the maximum and minimum values of the objective function o, respectively. The membership function can be represented as exemplified in Figure 3.  
In Step 3, for each nondominated solution \(s\), the normalized membership function \(\mu^{s}\) is calculated as:  
\[\mu^{s}=\frac{\sum\limits_{o=1}^{N_{obj}}\mu_{o}^{S}}{\sum\limits_{s=1}^{M} \sum\limits_{o=1}^{N_{obj}}\mu_{o}^{S}} \tag{17}\]  
where \(M\) and \(N_{obj}\) are the total number of nondominated solutions and the total number of objective functions, respectively. The best compromise solution is the one with the maximum \(\mu_{o}^{S}\) value. It is noted that, for the decision-maker, it would be interesting to arrange all Pareto solutions in descending order according to their membership function and having a priority list of Pareto solutions (Step 4).
## 4 Simulation Results
### Comparison of the QoS of the Node Deployment Strategies  
In this section, we mainly compare the node deployment strategy proposed in this paper with the random node deployment strategy, the uniform node deployment strategy, and the 1-fold coverage-based node deployment in a cellular network in terms of coverage, information integrity, information validity, and information redundancy to reflect the advantages of the node deployment strategy proposed in this paper.
# Target Image Mask Correction Based on Skeleton Divergence
###### Abstract  
Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms.  
Keywords:skeleton divergence; edge merging; TOPSIS; simultaneous optimization binary; mask correction +
Footnote †: journal:
## 3 Enhanced Fault Current Limiting Circuit
### Fault Current Interruption with DC Circuit Breaker  
Considering that fault relay involves FCL behavior and DCCB operation, it is necessary to investigate the coordination between EFCLC behavior and DCCB operation in this paper. Hence, the fault interruption progress could be determined for further parameter setting of EFCLC. The DCCB topologies in articles and patents can be classified into passive and active resonance DCCB, hybrid DCCB (HCB), and solid-state DCCB (SCB) [39]. The resonance-based DCCB spend a relatively long time in fault current interruption and may lead to overcurrent. The HCB and SCB have a rapid operation speed. Although the detailed topologies of the HCB and SCB are different from the nominal path, these topologies can be equivalent to the ideal breaker in the nominal path and a metal oxide arrestor (MOA) when proactive switching control is applied [14; 24]. A residential current breaker is used to cut a residential current in the MOA, which is also modeled as an ideal breaker. The topologies of the DCCB and equivalent simulation model [14] are exhibited in Figure 14a,b, correspondingly. The main breaker demonstrated in Figure 14b will wait for the operation delay in ultrafast disconnector switch (Figure 14a) operation, and the delay time is normally 2 ms [33]. The fault current with a hybrid DCCB operation is exhibited in Figure 15.  
Figure 16 displays the fault current interruption sequence of the EFCLC with DCCB. \(T_{1}\) and \(T_{2}\) are opened during the normal state, whereas \(T_{3}\), \(T_{4}\), and the DCCB main breaker are closed. When a fault occurs, \(T_{1}\) and \(T_{2}\) remain open before a fault is detected, and the main breaker of the DCCB remains closed (Figure 16a). When a fault is detected, \(T_{1}\) and \(T_{2}\) are switched off to insert an FCL branch in the fault circuit loop, and the DCCB main breaker remains closed due to operation time delay [33] (Figure 16b). Thus, the fault current level and accelerating speed are limited by the FCL branch, which releases the overcurrent pressure on the IGBTs and diodes in the converter and reduces the requirement of the DCCB in an interruption capacity. Once the converter is blocked under an overcurrent protection, \(T_{3}\) and \(T_{4}\) are switched on to bypass \(L_{FCL}\), and the DCCB main breaker is opened (Figure 16c). Consequently, \(R_{FCL}\) shares the fault current with the MOA in the DCCB, and the fault circuit inductance is reduced to quickly extinguish the fault current. Afterward, when the current  
Figure 13: Proposed enhanced fault current limiting circuit.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT Key Technologies
#### 2.2.4 Data Transmission Scheme  
Compared with LTE and for simplicity, the NB-IoT supports only one hybrid automatic repeat request (HARQ) that is asynchronous and adaptive for both uplink and downlink to support scheduling flexibility of the base station [14]. Figure 4 shows the physical channel and timing relation for uplink and downlink data transmission. A narrowband physical dedicated control channel (NPDCCH) carries the downlink control information (DCI) that includes scheduling delay, modulation coding scheme, and repetition number for both the narrowband physical uplink shared channel (NPUSCH) and narrowband physical downlink shared channel (NPDSCH). The NPUSCH conveys not only a medium access control (MAC) protocol data unit (PDU), but also HARQ acknowledgment (ACK)/negative acknowledgment (NACK) information, unlike LTE. The repetition number of each channel is determined from the base station based on the coverage-enhancement (CE) level of the UE, which is determined through the random-access procedure. In addition, the time interval between each channel can be distributed for the flexibility of scheduling policy. Since the NB-IoT is a half-duplex system, a transmission switching time above 3 ms is required.
## 4 Results and Discussion
The success of evolutionary algorithms cannot be judged by the outcome of a single run due to their randomness. To draw a clear conclusion about the success of the algorithms, several trials with various initializations should be run. It should be noted that an algorithm is only called stable if it can provide suitable results under a variety of operating conditions, since the algorithms OGSA, GSA, PSO, and GA are all random. Therefore, for each method, the bidding data were run 20 times. Table 8 provides a comparative analysis of the results of various techniques for robustness and validation of the OGSA method. It can be deduced that the standard deviation is lowest for the OGSA technique in comparison to others for the IEEE 30-bus and IEEE 57-bus methods, respectively. Moreover, OGSA produces better outcomes in terms of best, worst, and mean. Therefore, the accuracy of the proposed method is better and allows producers to earn a \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PSs**} & \multirow{2}{*}{\(\pi_{m}\)} & \multicolumn{3}{c}{**GA [22]**} & \multicolumn{3}{c}{**PSO [23]**} & \multicolumn{3}{c}{**GSA [16]**} & \multicolumn{3}{c}{**OGSA**} \\ \cline{3-14}  & & \(\phi_{m}\) & **PG** & **Profit** & \(\phi_{m}\) & **PG** & **Profit** & \(\phi_{m}\) & **PG** & **Profit** & \(\phi_{m}\) & **PG** & **Profit** \\ \hline
1 & 1.7365 & 0.019718 & 530.23 & 5065.65 & 0.020393 & 520.21 & 5058.66 & 0.021819 & 510.26 & 5238.34 & 0.022239 & 505.16 & 5241.2 \\
2 & 10 & 0.095015 & 23.07 & 45.23 & 0.098836 & 23.73 & 50.02 & 0.092598 & 30.99 & 79.34 & 0.076760 & 38.7 & 99.99 \\
3 & 7.1429 & 0.078495 & 64.32 & 295.35 & 0.082602 & 62.98 & 299.48 & 0.081198 & 70.53 & 368.62 & 0.088860 & 65.58 & 351.7 \\
4 & 10 & 0.095015 & 23.07 & 45.23 & 0.098836 & 23.73 & 50.02 & 0.092598 & 30.99 & 79.34 & 0.076760 & 38.7 & 99.99 \\
5 & 1.8 & 0.020843 & 498.08 & 4724.32 & 0.021487 & 490.3 & 4732.82 & 0.02278 & 485.52 & 4945.47 & 0.023240 & 480.23 & 4944.7 \\
6 & 10 & 0.095015 & 23.07 & 45.23 & 0.098836 & 23.73 & 50.02 & 0.092598 & 30.99 & 79.34 & 0.076760 & 38.7 & 99.99 \\
7 & 2.4390 & 0.028839 & 338.18 & 3023.63 & 0.02788 & 355.3 & 3216.81 & 0.030615 & 340.71 & 3275.32 & 0.031635 & 332.92 & 3240.2 \\ MCP & & 12.19 & & 12.35 & & 12.87 & & & & & & & & & \\ Total Profit & & 13,244.65 & & & 13,457.81 & & & & & & & & & \\ Total PG & & 1500 & & & 1500 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal bidding results for the IEEE standard 57-bus without including wind power.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 5 Enhanced SCAFFOLD Protocol
### SCAFFOLD Algorithm
#### 5.4.2 Correlation Analysis  
In addition to the ensemble classifier, Enhanced SCAFFOLD makes use of correlation analysis to identify relationships between the flows that are flagged as assaults. Finding relationships, patterns, and commonalities between various attack scenarios is the aim of correlation analysis.  
The SDN controller receives flow information and applies a range of correlation techniques to identify the links between suspicious flows. Common methods for correlation include the following:  
1. The coefficient of Pearson correlation: With values ranging from -1 (a perfect negative correlation) to +1 (a perfect positive correlation), this gauges the linear correlation between two variables. It assists in locating flows that behave similarly with regard to traffic volume, packet size, or other characteristics.
2. Cross-correlation examines the distances between two time series to determine similarity. It helps to discover time-delayed links between attack flows, which might indicate planned or coordinated assaults.
3. The rule of association identifying significant correlations between variables involves analyzing vast datasets via mining. In order to highlight the co-occurrence of certain flow features in attack situations, it may identify frequently occurring item sets and provide association rules.  
Correlation analysis on labeled attack flows helps Enhanced SCAFFOLD find linked attacks and hidden patterns. This information improves the mitigation strategies by increasing our awareness of the attacks' scope and kind.  
Using correlation analysis and ensemble classifiers, Enhanced SCAFFOLD can discover and assess IoT hazards. The ensemble classifier effectively identifies threats, and the correlation analysis reveals attack flow patterns. The multidimensional technique increases the system's ability to identify complex and intentional attacks via precise SDN reconfiguration responses.  
Advanced machine-learning technologies like Enhanced SCAFFOLD will be needed to secure and avoid emerging threats in increasing IoT networks. These methods' flexibility and learning ability enable the system to stay ahead in a changing threat scenario while improving diagnostic accuracy.