esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Digital Representation as a Stage of Creating a Digital Twin  
As mentioned in Section 2.1 of this study, the development of complex technical systems often involves the use of artificial intelligence, machine learning technologies, the Internet of Things, big data, distributed ledgers, and blockchain technologies. A special place in this series is reserved for digital twins, as they can function not only as an individual technology but also as a product of technology application. Digital Twin technology was included in Gartner Inc.'s top 10 strategic technology trends of 2017. This technology is the outcome of the continuous improvement of product development methods and engineering activities [47]. The evolution of these methods began with manual drawings and specifications, then gradually progressed to computer-aided design and finally to model-based system design. Over time, these \"ideological twins\" transformed into sets of mathematical models, describing real-world objects or their individual properties with varying degrees of accuracy [48]. The digital twin concept allows for the creation of a virtual image or a digital representation of real-world equipment, devices, or systems, ensuring maximum synchronization between the real and virtual worlds. A digital representation of the full life cycle of a product, from the design stage to maintenance, can provide businesses with a predictive analysis of potential problems [49].  
According to the classic concept introduced by [PERSON], a digital twin is a set of virtual information structures that fully describe a potential or actual real manufactured product from the microscopic to the macroscopic geometric level. Based on the concept of digital twins, digital marketing author [PERSON] identified four stages of the concept's development, which are clearly presented in the figure below [50] (Figure 2):  
* The first stage implies that there is only a real object or process.
* In the second stage, \"mirroring\" occurs, where a digital version of the real object or process is created, describing the real counterpart with varying degrees of accuracy.
* The third stage begins when a connection is established between a real object or process and their digital version.
* In the fourth stage, there is a convergence and even an intersection of a real object or process with their digital versions.  
In this study, the authors focus on the second stage of digital twin development, where a digital representation of a complex system is created. This digital representation is a \"mirroring\" of the physical system in the virtual world.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Hashing Techniques in Genome Alignment  
Hashing is a cornerstone computational technique used extensively across various domains, including bioinformatics and genomics. In the context of genome alignment, hashing techniques facilitate the rapid mapping of sequencing reads to a reference genome by efficiently indexing sequence data. This method leverages hash functions to convert variable-length sequences into fixed-length hash values, which serve as unique identifiers for subsequences or \"k-mers\" within the genomic data. Through creating hash tables that map these hash values to their corresponding locations in the reference genome, alignment tools can quickly identify potential matching regions, significantly speeding up the alignment process.
# An Efficient Library for Reliability Block Diagram Evaluation
## 3 RBD Computation Library-librbd
### librbd Usage
#### 3.3.2. API for Identical Components  
The following interfaces are used to evaluate the reliability of RBD blocks with identical components:  
* int _rbdSeriesIsdentical_(double *R, double *O, unsigned char **N**, unsigned int **T**)
* int _rbdParallelIdentical_(double *R, double *O, unsigned char **N**, unsigned int **T**)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**RBD Block**} & \multirow{2}{*}{**Enror Function**} & \multirow{2}{*}{**Maximum**} \\  & & \\ \hline Series identical & \(-1.00\times 10^{-13}\) & \(1.00\times 10^{-13}\) \\ Series generic & \(-1.00\times 10^{-13}\) & \(1.00\times 10^{-11}\) \\ Parallel identical & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ Parallel generic & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ KooN identical & \(-1.00\times 10^{-9}\) & \(0.00\times 10^{0}\) \\ KooN generic & \(-1.00\times 10^{-9}\) & \(0.00\times 10^{0}\) \\ Bridge identical & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ Bridge generic & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error function between librbd and SHARPE tool.  
* int _rbdKooNldentical_(double *R, double *O, unsigned char **N**, unsigned char **K**, unsigned int **T**)
* int _rbdBridgelent_(double *R, double *O, unsigned char **N**, unsigned int **T**)  
The capitalized and bold characters identify the following input parameters:  
* **N**: the number of components inside the block. Note that, for the bridge block, the number of components must be equal to 5.
* **K**: the minimum number of components inside the block. Note that this parameter is available for Koo\(N\) blocks.
* **T**: the number of time instants over which the reliability curve is computed.
* **R**: the reliability curves for the input components. Since this is the identical components case, this parameter underlies an array of **T** elements.
* **O**: the computed reliability curve, returned as an array of **T** elements.  
All interfaces return 0 in case of successful computation, a negative number otherwise.
## 2 Design Methodology
### Modeling of Compliant Finger  
As already stated, the PIP joint of the index finger will be provided as a reference for modeling the flexure hinge. Figure 4 shows a human finger that will serve as an example for modeling. The lengths given in the figure represent the lengths of the finger bones together with the knuckles and, as such, will serve as limits for the given design (Figure 4a,b). In a state of relaxation (e.g., while a person is sleeping), the forearm is minimally pronated,  
Figure 3: Curved flexure hinge: (**a**) Model and (**b**) Geometrical representations.  
Figure 2: Types of compliant mechanisms with basic flexure hinges: (**a**) notch flexure hinges, (**b**) leaf springs joints, and (**c**) rod-shaped joints.  
and the wrist is kept neutral or with minor flexion and ulnar deviation. From the index through the little fingers, the MP joints are kept flexed in a gradient (45-70 degrees), while the Interphalangeal joints (IP) are in various degrees of flexion. This position is called the resting hand position [26].  
In the paper [27], the resting position of the PIP joint varies from 20-47 degrees depending on the position of forearm posture and shoulder flexion. In our case, it can be noticed that the finger takes a natural resting position which, with the completely straightened position of 180 degrees, forms an angle of 21 degrees (180\({}^{\circ}\) \(-\) 159\({}^{\circ}\)= 21\({}^{\circ}\)) (Figure 4c). This finger position was used as a reference for designing its compliant counterpart in SolidWorks version 2021 SP05.1 for modeling [28].  
As the natural curvature of the finger was followed for modeling the compliant finger shown in Figure 4c, the PIP joint, in this case, must be modeled using a curved flexure hinge. This results in the two models shown in Figure 5 with their given dimensions of compliant fingers:  
\[\text{L}_{1}=53.70\text{ mm}\]  
\[\text{L}_{2}=50.17\text{ mm}\]  
\[\text{H}=10\text{ mm}\]  
\[\text{w}=10\text{ mm}\]  
\[\text{h}=1\text{ mm}\]  
\[\text{r}=1\text{ mm}\]  
\[\alpha_{1}=21^{\circ}\]  
\[\alpha_{2}=90^{\circ}\]  
The first model fully follows the 21-degree bend of the finger, while the second represents an idealized model of 90-degree curved flexure hinges.  
The idea is that both mechanisms will be analyzed in the following sections to determine which model is more plausible for the application considered in the paper.  
Figure 4: Measurements of the index finger: (**a**) Combined length of distal and intermediate phalanges bone and (**b**) Length of proximal phalanges bone. The angle of the PIP joint in: (**c**) A resting position, (**d**) Hyperextension, and (**e**) Flexion.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 5 Analysis of Examples
### Optimization Results under Different Confidence Levels  
As shown above in Table 4, where the wind power installed capacity is 180 MW and the wind speed correlation is moderate, with an increasing confidence level the EENS decreases; however, the spinning reserve capacity and the total cost rise. Therefore, it is important to choose an appropriate confidence level to provide the trade-off between security and economy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**EENS/MW\(\cdot\)h**} \\ \cline{2-5}
**Wind Power Installed Capacity/MW** & **High** & **Moderate** & **Low** & **Negative** \\  & **Correlation** & **Correlation** & **Correlation** & **Correlation** \\ \hline
90 & 3.74 & 2.91 & 1.44 & 1.03 \\
180 & 5.63 & 3.48 & 2.16 & 1.45 \\
270 & 9.51 & 5.49 & 4.52 & 2.17 \\
360 & 15.36 & 9.64 & 6.20 & 4.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of wind speed correlation on expected energy not served (EENS).  
Figure 2: Spinning reserve comparisons under wind speed correlation: (**a**) up spinning reserve capacity with wind speed correlation under a wind power installed capacity of 180 MW; (**b**) down spinning reserve capacity with wind speed correlation under a wind power installed capacity of 180 MW; (**c**) up spinning reserve capacity with wind speed correlation under a wind power installed capacity of 360 MW; (**d**) down spinning reserve capacity with wind speed correlation under wind power installed capacity of 360 MW.
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Practical Cases  
#### 5.2.1 The 5G-RealTime-HART Module  
Figure 6 shows the process of building a 5G-RealTime-HART module based on the HART network. In the implementation principle of the HART protocol, the wireless part will be replaced with a 5G module and necessary adjustments to the 5G communication protocol shall be made according to the actual needs. This means that the application layer of HART remains unchanged, while the underlying layer will be adjusted accordingly. By introducing a 5G HART module, effective integration between 5G and HART can be achieved.  
The HART standard [52] has been in existence since the late 80's. In its initial release, the HART Field Communications Protocol was superimposed on a 4-20 mA signal providing two-way communications with smart field instruments without compromising the integrity of the measured data. During its years of existence, the HART protocol has evolved from a simple 4-20 mA-based protocol to the current wired and wireless-based technology with extensive features supporting security, unsolicited data transfers, event notifications, block mode transfers, and advanced diagnostics. The complete HART network layers are shown in Figure 6.  
Figure 5: Comparison and correlation between the OSI protocol and TCP/IP protocol.  
Figure 6: The RealTime-5G-HART structure.
## 4 Optimization Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **System** & **E\({}_{\text{f}}\) [m\({}^{3}\)/d]** & **E\({}_{\text{c}}\) [kWh/m\({}^{3}\)]** & **E [kWh/cycle]** \\ \hline \multirow{3}{*}{I} & CU & 1203 & 0.4192 & 751 \\  & HE & 2266 & 0.3269 & 317.5 \\  & OP & 1846.7 & 0.2184 & 212.1 \\ \hline \multirow{3}{*}{II} & CU & 1203 & 0.6023 & 1080 \\  & HE & 2021 & 0.4427 & 430.4 \\ \cline{1-1}  & OP & 1249.3 & 0.4152 & 403.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of efficiency, energy cost and energy to perform a single work cycle.
# Planar UWB Monopole Antenna with Tri-Band Rejection Characteristics at 3.5/5.5/8 GHz
## 3 Parametric Study
### Variation of Patch Parameters  
The size of the patch slot is critical to the first notched-band. Figure 4 shows the effect of the parameter \(\alpha\) changing from 30 degrees to 60 degrees on the simulated return loss, and other parameters are the same as those listed in Table 1. It can be seen from Figure 4 that when \(\alpha\) increased from 30 degrees to 60 degrees, the lower notched band moved towards the low frequency. In addition, it was found that \(\alpha\) slightly affected the third notched-band, and hardly affected the middle notched-band. Thus, Figure 4 indicates that the optimum value of \(\alpha\) is 45 degrees. Another important parameter R1 is studied and shown in Figure 5. It can be seen from the simulation results that the low notched-band moved to the low frequency when the value of R1 increased from 7.0 mm to 9.0 mm, and it had little effect on the middle notched-band and third notched-band. Therefore, the value of R1 was chosen to be 8.0 mm, moving the low notched-band to 3.8 GHz.  
Figure 4: Effect of parameter \(\alpha\) on the simulated return loss.  
Figure 3: Simulated surface current distributions at (**a**) 3.8 GHz, (**b**) 5.6 GHz, and (**c**) 8.1 GHz.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 2 General Approaches for the Synthesis of Organoboron Derivatives  
The three most popular synthetic approaches to 1,3,4-oxadiazole-containing organoboron derivatives were identified in the literature:  
1. Halogen-metal-boron exchange in aromatic compounds (method A),
2. Miyaura borylation of aryl and vinyl halides using bis(pinacolato)diboron (method B),
3. formation of the 1,3,4-oxadiazole ring from acyclic reagents already containing a boronic ester moiety (method C).
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 3 Drilling Fluids for Extended-Reach Wells
### Equivalent Circulating Density  
The equivalent circulating density (ECD) is a combination of the static drilling fluid density and annular pressure loss. The ECD and hole cleaning are inter-related: poor hole cleaning can increase the ECD. The amount of particles present in the drilling fluid increases its density and thus the ECD. In addition, extended-reach wells are characterized by their long horizontal displacement. The annular pressure loss increases when increasing the annulus length, resulting in a continuous increase in the ECD with the measured depth (MD). This is a problem when the formation pressure gradient/fracture gradient window is narrow. The flow rate can be limited to control the annular pressure loss and reduce the ECD, but this can affect the quality of cuttings transport and cause a fluctuation in the ECD. which can cause formation fracturing and a loss of circulation. The presence of cuttings increases the pressure drop due to the reduction in the flow area inside the wellbore. An increase in the annular pressure loss was observed with a high pipe rotation speed in an eccentric annulus [59]. In the absence of cuttings, frictional pressure losses increase as the pipe rotation speed increases, but, in the presence of cuttings, due to a reduction in the stationary area of the cuttings bed, frictional pressure losses may decrease [71].  
The open-hole limit of an extended-reach well (the greatest measured depth of the horizontal ERW) mainly depends on the pressure drop in the annulus and the fracture pressure of the drilled formation [72].
# A State of the Art Review of Systems of Linear Inequalities and Related Observability Problems
## 6 Examples of Applications
### Applications to Algebra Problems  
In this subsection, the following algorithms and methods are given:  
1. A pivoting transformation or algorithm, to obtain the orthogonal linear subspace to a given linear subspace is presented in [6], by explaining the meaning of the tables resulting after introducing each generator of the initial linear subspace. In addition, this algorithm allows us to solve many problems of linear algebra avoiding some painful classical methods, such as calculating the rank of a matrix, its inverse and determinant, updating inverses and determinants of a matrix after changing rows or columns, identifying linearly dependent vectors, etc.
2. The \(\Gamma\) algorithm, to obtain the dual cone of a given cone, is presented in some detail in [17]. This algorithm obtains, at the \(i\)-th step, the dual cone of the cone generated by the first generators of the initial cone. This algorithm is useful to solve a wide collection of algebra problems that can be solved if one uses the point of view of polarity, such as checking if a vector belongs to a cone, obtaining minimal representations of cones as the sum of a linear subspace and an acute cone, obtaining the cone intersection of two given cones, etc.
3. Finally, the proposed methods and applications are illustrated with some examples.
4. An interesting method that implies systems of linear equations subject to data with rounding errors is presented in [41], where, due to the errors, the linear system becomes incompatible. Thus, instead of solving the system, a least squares problemis solved, leading to another linear system. The methods mentioned in the present paper suggest alternative solutions to the problem.
4. The problem of systems of linear equations when the less than or equal to, equal to or greater than, or equal to relations can be selected is solved in [20]. The proposed algorithm provides the necessary information to solve this problem, where the operators in each linear relation are chosen as wished. In addition, the algorithm permits obtaining the dual cone of the cone defined by any subset of constraints. Some examples illustrate the methodology.
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
## 3 New Node-Based Tomographic Approach
### Determination of a Tomographic Region
#### 3.1.2 Minimum Bounding Box Algorithm  
After the above process, the tomographic region is initially discretized by convex hulls at different tomographic planes (see Figure 3). The next step is to build the secondary approximation region by a minimum bounding box algorithm. As shown in Figure 4, the minimum bounding boxes of these convex hull polygons are defined by the theorem defined by [28] that the rectangle of the minimum area enclosing a convex polygon has a side collinear with one of the edges of the polygon.  
As an example, the convex hull polygon at the top of the tomographic region in Figure 4 is used to define the corresponding minimum bounding boxes. This process is illustrated in Figure 5 and includes three steps: (1) First, each edge of the convex hull is set as an axis to find the bounding box and the ordinate origin of the axis is the middle of the edge (e.g., top panel). This is due to the fact that each edge of the minimum bounding box must be collinear with the one of the convex hull's edge. Second, the point with the longest vertical distance between the edge and the points of the convex hull (e.g., R1) and the two points with the absolute values of the maximum positive and negative  
Figure 2: The procedure of the Graham scan algorithm; the illustration of the Graham scan algorithm comprises the following steps in turn: (**a–d**); the solid nodes are those on the border of the convex hull and the hollow nodes are not on it.  
coordinates (e.g., R2 and R3) are used to build the bounding box, which is also collinear with the one of convex hull's edge. Third, all the areas of the bounding box are calculated and then the lowest area is selected as the minimum bounding box. In the bottom panel, the blue rectangle is the minimum bounding box comparing with the other bounding boxes (dotted rectangles).  
Figure 3: Determination of the convex hull polygons of pierce points (blue points); **(a)** Vertical division of tomographic region; **(b)** The convex hull polygons for the tomographic region; the black frames represent the tomographic area at each of the tomographic layers; the red lines are the satellite signals used in tomographic modeling at a tomographic epoch.  
Figure 4: The minimum-area encasing rectangle for GNSS tomographic region; the blue boxes are the boundaries of the minimum-area encasing rectangle; the black boxes are the boundaries of the convex hull; the red lines are the satellite signals used in tomographic modeling at a tomographic epoch.  
Figure 5: Process of the minimum bounding box algorithm; (**a**) Determination of the coordinate systems of each edge (**b**) Determination of the minimum rectangle; the red polygon frame is the convex hull; the dotted black boxes are the bounding box; the solid blue box is the boundary of the minimum-area encasing rectangle.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Failure Rate (FR)
The FR distribution function is as follows:  
\[G_{p}(x)=\frac{1}{p}\sum_{i=1}^{k}I_{[-\infty,x]}(X_{i}) \tag{11}\]  
where \(I_{[-\infty,x]}\) denotes the indicator function that differentiates between malicious clients whose \(F_{r}\) is greater than the threshold \(\tau\) and a benign client whose \(F_{r}\) is less than the threshold \(\tau\). Thus, the indicator function \(I_{[-\infty,x]}\) is 1 when the given random variable \(x\) is greater than \(X_{i}\), ranging from \([-\infty,x]\); otherwise, it is 0. \[I_{[-\infty,x]}= \begin{cases}1,&\text{if }x\geq X_{i}\\ 0,&\text{otherwise}\end{cases}  \tag{12}\]  
To quantify the distance among client nodes' FRs, we apply the Kolmogorov-Smirnov (KS) two-sided test statistic [55] based on the difference between the empirical distribution function and cumulative distribution function. \[D_{k}=\sup_{x}|G_{k}(x)-G(x)| \tag{13}\]  
where \(\sup_{x}\) of a subset \(X_{S}\) in a partially ordered set \(X_{i}\) is the least element in \(X_{i}\), which is greater than or equal to all elements of \(X_{S}\subseteq X_{i}\), and \(G(x)\) represents the cumulative distribution function. Finally, FR is classified as malicious if  
\[FR_{\text{C}}= \begin{cases}malicious,&\text{if }D_{k}\geq\tau\\ benign,&\text{otherwise}\end{cases}  \tag{14}\]  
To better understand the context of these equations, Equation (11) calculates the empirical distribution of failure rates across network client nodes to help in visualizing and understanding the distribution of connection failures, which is critical for identifying patterns that may indicate malicious activity. By assessing how these failure rates spread across the network, the model can better distinguish between normal and suspicious behaviors. Additionally, we use the indicator function Equation (12) to classify network connections based on their failure rates, comparing them against a predefined threshold. This aids in filtering out benign activities from potentially malicious ones. If a connection's failure rate exceeds the threshold, it is flagged for further inspection, ensuring that the model focuses on connections that more likely represent genuine threats. In KS test statistic, Equation (13) measures the maximum deviation between the empirical distribution of observed network behaviors and the expected distribution under normal conditions to assess the significance of observed anomalies and confirm whether observed patterns of network behavior deviate from what is typically expected in a way that suggests malicious activity. Finally, Equation (14) applies the KS test results to make a decision on whether a network behavior is considered normal or anomalous by providing a concrete criterion that flags nodes as potentially malicious based on statistical evidence. This enables our mode to take action on connections that most strongly deviate from normal behavior patterns.