esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Production Process Capability Assessment for Non-Normal Data
## 4 Statistical Background of the Study
### Normality Test  
To test the normality of the distribution, among the available tests, [60] the Jarque-Bera test was chosen [61]. For the sample \(\left\{x_{i}\right\}_{1\leq i\leq n}\) of feature \(X\) at the level of significance \(0<\alpha<1\), we define a null hypothesis:  
\[H_{0}:\ X\sim N\Big{(}m,\sigma^{2}\Big{)} \tag{4}\]  
against an alternative hypothesis  
\[H_{0}:\ X\
sim N\Big{(}m,\sigma^{2}\Big{)} \tag{5}\]  
The Jarque-Bera test is based on measures of skewness and kurtosis. At the same time, it takes into account deviations from the normality of the distribution caused by both the coefficient of the skewness as well as flattening. The test statistics are described by following the formula:  
\[JB=\frac{n}{6}\left(S^{2}+\frac{(K-3)^{2}}{4}\right) \tag{6}\]  
The estimator of skewness \(S\) is calculated as follows:  
\[S=\frac{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{3}}{\left(\frac{1}{n} \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\right)^{3/2}} \tag{7}\]  
whereas kurtosis \(K\) is:  
\[K=\frac{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{4}}{\left(\frac{1}{n} \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\right)^{2}} \tag{8}\]  
where the estimator of the mean \(\overline{x}\) is equal:  
\[\overline{x}=\frac{1}{n}\sum_{i=1}^{n}x_{i}. \tag{9}\]  
The test statistic (6) has \(\chi^{2}\) distribution with two degrees of freedom. Test probability for Jarque-Bera test is equal \(p.val=1-F_{\chi^{2}(2)}(JB)\), where \(F_{\chi^{2}(2)}\) denotes the \(\chi^{2}\) distribution function with two degrees of freedom.
###### Abstract  
The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.  
blockchain; federated learning; intelligence transportation system; vehicular internet of things (IoT); vehicular ad hoc network (VANET) +
Footnote †: journal: Sensors
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 3 Methodology
### Influence Factors
#### 3.2.3 Ground Loss  
[PERSON] [58] presented an analytical solution for calculating the ground surface movement in the isotropic homogeneous and incompressible ground condition. The equations are as follows:  
\[u_{s}=-\frac{V}{2\pi}\frac{1}{\left(x^{2}+y^{2}+h^{2}\right)^{1/2}} \tag{33}\]  
\[v_{s}=-\frac{V}{2\pi}\frac{y}{y^{2}+h^{2}}\left[1-\frac{x}{\left(x^{2}+y^{2}+h ^{2}\right)^{1/2}}\right] \tag{34}\]  
\[w_{s}=\frac{V}{2\pi}\frac{h}{y^{2}+h^{2}}\left[1-\frac{x}{\left(x^{2}+y^{2}+h ^{2}\right)^{1/2}}\right] \tag{35}\]  
Figure 5: Overlaying coefficients in section direction and penetrating direction.  
where, \(u_{s}\) (\(v_{s}\), \(w_{s}\)) is the ground movement along \(x\) (\(y\), \(z\)) axis in Sagaseta solution, \(V\) is the ground loss per unit length (m\({}^{3}\)/m).  
[PERSON]'s solution can be used as the vertical displacement, whereas the lateral and longitudinal deformations of the inner soil are not satisfied with the measured ones. Finding that the ground loss due to lining deflection and lining gap are not uniform, [PERSON] and [PERSON] (2009) proposed the gap should be oval shaped and gave the equation for calculating the equivalent ground loss, which is shown as follows:  
\[V^{\prime}=V\times\exp\left[-\frac{1.38y^{2}}{\left(h+R\right)^{2}}-\frac{0.69 z^{2}}{h^{2}}\right] \tag{36}\]  
\[V=\pi R^{2}\eta \tag{37}\]  
where, \(\eta\) is the empirical coefficient, which has the relationship with the pipe-jacking face and the geotechnical conditions, for instance \(\eta\) is 0.5-2.5% for silty soil layers.  
Thus, considering the oval shaped ground movement and the Poisson's ratio for different soils, we modified [PERSON]'s solution and have proposed the following equations:  
\[u_{4}=-\frac{2(1-\mu)V^{\prime}}{\pi}\frac{1}{\sqrt{x^{2}+y^{2}+h^{2}}} \tag{38}\]  
\[v_{4}=-\frac{2(1-\mu)V^{\prime}}{\pi}\frac{y}{y^{2}+h^{2}}\left(1-\frac{x}{ \sqrt{x^{2}+y^{2}+h^{2}}}\right) \tag{39}\]  
\[w_{4}=\frac{2(1-\mu)V^{\prime}}{\pi}\frac{h}{y^{2}+h^{2}}\left(1-\frac{x}{ \sqrt{x^{2}+y^{2}+h^{2}}}\right) \tag{40}\]  
where, \(u_{4}\) (\(v_{4}\), \(w_{4}\)) is the ground movement along the \(x\) (\(y\), \(z\)) axis caused by ground loss.
# Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
## 4 Methodology
### Evaluating Global and Local Serviceability Measures  
The global serviceability measures comprise the \(\alpha_{L}\) and \(\alpha_{T}\) angular distortions. To arrive at these measures, longitudinal and transversal lines are established along the MSE wall face. A fitting technique establishes the longitudinal lines through the corners of the horizontal sides of the MBRs enclosing the panels along the above and below rows adjacent to these lines. Once the 3D line parameters have been derived, the \(\alpha_{L}\) value is defined through the dot product between the directional line parameters and the \(y\)-axis components of the \(LF_{cs}\). The transversal line for a column of panels is determined using the centers of the horizontal sides of the MBRs enclosing the uppermost and lowermost panels for that column. To derive the \(\alpha_{T}\) value, a dot product of the transversal line and \(z\)-axis components of the \(LF_{cs}\) is then performed. An example of the defined longitudinal and transversal lines for a planar MSE Wall face is shown in Figure 13.  
Figure 11: An illustration of three manually established faces along an MSE wall with piece-wise planar facade.  
Figure 12: An illustration of segmented panels along an MSE wall face with different panels shown in distinct colors.  
In contrast to the standard/global serviceability measures, the recently-developed local measures by [PERSON] et al. [5] are used to evaluate the differential rotations and displacements for a given panel relative to the \(LF_{cs}\) of its enclosing face. The translational and rotational relationships between the \(LF_{cs}\) and \(P_{cs}\), as seen in Figure 8, are used to derive these serviceability measures. Thus, the location of the origin of the Pcs relative to the \(LF_{cs}\) (denoted as Xo, Yo, and Zo) defines the panel position. On the other hand, the rotation angles (denoted as \(\theta_{yp}\), \(\theta_{yp}\), and \(\theta_{zp}\)) that need to be applied to the \(LF_{cs}\) to make it parallel to the \(P_{cs}\) are used to define the panel orientation. The last serviceability measure is the normal displacement between the corners of each panel and the fitted planes through neighboring panels. These normal distances are established using the derived MBR corners as shown in Figure 14, where a total of eight normal distance estimates (shown by the black lines) from the four corners of a given panel to its adjoining panels is evaluated.
## 3 Problem Description and Methodology
### Mixed Integer Programming (MIP) Model
 \tag{12}\]  
For each job, tardiness can be given by  
\[T_{j}\geq\left(C_{j}-d_{j}\right)\quad\forall\quad j=1,2,3,\ldots,n \tag{13}\]  
In this model, our goal is to minimize total weighted tardiness, which is given by  
\[Min\,\sum_{j=1}^{n}(w_{j}\cdot T_{j}) \tag{14}\]  
It is worth noting from Figure 1 that the considered problem here involves two interrelated sets of decisions: how to sequence the jobs and when to execute PM activities. Implicitly, the decision of PM activities may affect the objective value, even for the same job sequence. To describe this phenomenon, suppose that the job sequence of \(j_{1}\)-\(j_{3}\)-\(j_{4}\)-\(j_{5}\)-\(j_{2}\) is given for the 5-job instance in Table 2, and two different PM decision methods are used for the job sequence. The first PM decision method is inspired by the first fit (FF) concept for bin packing problems [33]. Thus, the jobs are assigned to the machine in orders as long as the working time of the machine does not exceed the threshold value (\(L\)). Based on the FF concept, the obtained _TWT_ value is 51, according to the Gantt chart in Figure 2. The second PM decision method is motivated by the dynamic programming (DP) method for batch problems [34; 35]. Using the DP method, the obtained _TWT_ value is 41 to the Gantt chart in Figure 3. Additionally, the detailed steps of the DP method are demonstrated in Appendix A for the sake of brevity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(r_{j}\) & \(p_{j}\) & \(d_{j}\) & \(w_{j}\) \\ \hline \(J_{1}\) & 0 & 2 & 4 & 1 \\ \hline \(J_{2}\) & 8 & 8 & 17 & 3 \\ \hline \(J_{3}\) & 5 & 2 & 9 & 2 \\ \hline \(J_{4}\) & 7 & 4 & 12 & 2 \\ \hline \(J_{5}\) & 13 & 4 & 19 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Five-job instance where \(L=10\) and \(MT=5\). Figure 2: Gantt chart obtained by FF method. From this example, adopting the DP method as the PM decision method is better than the former. Moreover, the above two sets of decisions affect each other. In this paper, the sequence of jobs is in the form of chromosomes, and then the DP method is used as a decoding method to obtain an objective value for any chromosome in our GA.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 3 Optimization Algorithm
### Genetic Algorithm  
The cost function utilized in this method (as demonstrated in Section 3.1) creates landscapes that tend to be complex, nonlinear, often non-convex, and (more importantly) not continuous, posing formidable challenges for optimization.  
Gradient methods, Bayesian optimization, simulated annealing, or and colony optimization techniques [29; 30; 31; 32; 33; 34; 35; 36] are well-known methods that work well in scenarios with smooth and well-behaved objective functions.  
In contrast, GAs present a compelling set of characteristics that make them the ideal choice for this problem. Their ability to navigate high-dimensional solution spaces, coupled with their resilience in dealing with noisy or irregularly sampled objective functions, positions them as a robust and versatile optimization tool. The adaptive nature of GAs, which merge the best solutions and introduce mutations, facilitates extensive exploration of the solution space, greatly increasing the likelihood of finding globally optimal configurations. In addition, GAs are amenable to parallelization, leveraging the computational power of modern resources to accelerate the optimization process.  
GAs have demonstrated remarkable efficacy in tackling real-world challenges [37; 38; 39; 40]. They operate within a population-based framework, where each member represents a potential solution. The fitness of individuals gauges their problem-solving capabilities. Highly fit individuals reproduce by mating with others, passing on their advantageous traits. This orchestrated progression births a new generation with the promise of enhanced solutions.  
GAs conduct this evolutionary process by combining genetic material from the most promising individuals through recombination (crossover) and introducing occasional mutations. As generations unfold, GAs tend toward optimization. Top-performing individuals propagate favorable traits and information, while mutations prevent stagnation. Consequently, GAs adeptly navigate complex search spaces, as depicted in Figure 3.  
A well-designed GA can converge on optimal solutions, offering strength in diverse problem domains. Although GAs cannot guarantee global optimality due to inherent randomness, they often quickly find practical solutions that suffice for real-world applications.  
Figure 2: Cost function associated with each possible combination of \(d_{tx}-d_{rx}\).
## 2 Background  
### MICP through Ureolytic Hydrolysis  
MICP mediated by bacterial urease is well studied in the literature. Several different reactions occur during this process. These include ureolysis (Equation (1)), the protonation of NH\({}_{3}\) (Equation (2)), CO\({}_{2}\) hydrolysis (Equation (3)), the dissociation of carbonic acid (Equation (4)), the dissociation of bicarbonate (Equation (5)) and CaCO\({}_{3}\) precipitation/ dissolution (Equation (6)).  
\[\text{CO(NH}_{2})_{2}+\text{H}_{2}\text{O}\to 2\text{NH}_{3}+\text{CO}_{2} \tag{1}\]  
\[2\text{NH}_{3}+2\text{H}_{2}\text{O}\longleftrightarrow 2\text{NH}_{4} ^{+}+2\text{OH}^{-} \tag{2}\]  
\[\text{CO}_{2}+\text{H}_{2}\text{O}\longleftrightarrow\text{H}_{2}\text{CO}_{3} \tag{3}\]  
\[\text{H}_{2}\text{CO}_{3}+\text{OH}^{-}\longleftrightarrow\text{HCO}_{3} ^{-}+\text{H}_{2}\text{O} \tag{4}\]  
\[\text{HCO}_{3}^{-}+\text{OH}^{-}\longleftrightarrow\text{CO}_{3} ^{2-}+\text{H}_{2}\text{O} \tag{5}\]  
\[\text{CO}_{3}^{2-}+\text{Ca}^{2+}\rightarrow\text{Ca}\text{CO}_{3}\downarrow \tag{6}\]  
Many factors control and guide the ureolytic MICP process. These include the type of microbe (in terms of urease activity produced and extracellular polymeric substances (EPS) components), calcium ion concentrations, urea concentration, other chemical elementswithin the surroundings (for example Mg\({}^{2+}\)), cell number in terms of nucleation sites, temperature, etc.
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
###### Abstract  
Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.  
situational awareness; environmental awareness; location awareness; geometry-based channel modeling; gaussian process regression; single-anchor positioning; multipath-assisted positioning; UWB radios; mm-wave radios +
Footnote †: journal: _Sensors_  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
# Variables Affecting Peri-Implant Radiographic Bone Loss-8-23 Years Follow-Up
## 4 Results
### Implant Data  
A total of 142 implants were followed up to 23 years. Six implant brands were installed, which can be categorized in two main groups: the external hex group, consisting of 5 Branemark(r)(r)(r) MKIII RP implant, Nobel Biocare AB, Goteborg, Sweden, (external hex, machined)], 8 3I(r)(r) [Biomet 3i, Inc, Palm Beach Gardens, Florida, USA, (external hex, hybrid- machined top, dual acid etched bottom)], 5 Implant Direct(r) [Implant Direct, Valencia, CA, USA, (external hex, soluble blast medium blasted)], 11 MIS(r) [MIS Implant Technologies Ltd, Bar-Lev Industrial Zone, Israel, (external hex, sand-blasted, acid-etched)], 43 Steri Oss(r) [SteriOss Yorba Linda, CA, USA, (external hex, etched)], and the internal hex group, consisting of 70 Zimmer(r) [Zimmer Biomet Dental, Palm Beach Gardens, Florida, USA, (Internal hex, HA blasted)]. Implant length ranged between 10 mm and 16 mm, and their diameters ranged between 3.7 mm and 5 mm. Seventy-five implants (52.8%) had an internal hex connection, whereas 67 (47.2%) had an external hex. One hundred thirty-six implants (96%) were placed in healed ridges (late implantation). Early and immediate implantation corresponded to 4 (2.67%) and 2 (1.33%) implants, respectively. Fifty-four implants (38%) received additional GBR, of which 12 (8.5%) had trans-crestal sinus augmentation and 32 (22.5%) had lateral window sinus augmentation. Ninety-nine (70%) were 10-12 mm long, and eighty-eight (62%) implants were 3.7-4 mm in diameter. A total of 53 (37%) implants were both 10-12 mm long and 3.7-4 mm in diameter.  
All implant restorations were delayed loaded, predominantly fixed-multiple restorations (108 (76%)), with properly fitting margins (112 (78.9%)). Six (14.3%) presented overhanging margins, and 24 (16.9%) were ill-fitted showing radiographic gaps between the crown and the supra-structure.  
In all cases, antagonists were existing. Fifty-four (38%) of the antagonists were intact teeth or teeth with plastic restorations (amalgam/composite), 23 (16.2%) were implants, and 58 (40.8%) were fixed restorations on either teeth or implants. Seven (5%) removable antagonists were recorded. (Table 3).
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 4 Dynamic Performance Analysis
### Generator Inlet Temperature (\(T_{gin}\))
#### 4.1.2 Different Step Change of Generator Inlet Temperature (\(\Delta T_{gin}\))  
The former analysis is just with one step change of \(T_{gin}\), Figures 6-8 demonstrate the effects of different \(\Delta T_{gin}\), which means the generator inlet temperature suddenly becomes 93, 95, 100 or 105 \({}^{\circ}\)C from 90 \({}^{\circ}\)C at time 0. Meanwhile, the cooling water inlet temperature \(T_{cin}\) and the evaporator inlet temperature \(T_{cin}\) still keep at 32 \({}^{\circ}\)C and 12 \({}^{\circ}\)C, respectively.  
In Figure 6, the \(COP\) and the cooling capacity in steady-state becomes high when the generator inlet temperature increases, because the refrigerant mass flow rate in the cycle grows. Besides, the dynamic responses of the cooling capacities under different \(\Delta T_{gin}\) are demonstrated in Figure 7, showing that the time reaching a new steady-state is later when \(\Delta T_{gin}\) is higher. When \(\Delta T_{gin}\) becomes from 5 \({}^{\circ}\)C to 15 \({}^{\circ}\)C, the time increases 11.11-35.42% compared with \(\Delta T_{gin}\) = 3 \({}^{\circ}\)C. According to Equation (31), this means the relaxation time is longer, since the relative increment of the cooing capacity \(\Delta Q_{e,g}\) is larger, as shown in Figure 8. And \(\Delta Q_{e,g}\) is calculated with the data from Figure 6:  
\[\Delta Q_{e,g}=\frac{Q_{e,(90+\Delta T_{gin})}-Q_{e,90}}{Q_{e,90}}\cdot 100\% \tag{32}\]  
where \(Q_{e,(90+\Delta T_{gin})}\) is the cooling capacity in steady-state when the generator inlet temperature is 90 + \(\Delta T_{gin}\), kW; \(Q_{e,90}\) is the cooling capacity in steady-state when the generator inlet temperature is 90 \({}^{\circ}\)C, kW.  
Figure 6: The effect of different \(\Delta T_{gin}\) on the \(COP\) and cooling capacity.  
Figure 7: The dynamic responses of the cooling capacities under different \(\Delta T_{gin}\).
## 2 Theoretical Background  
### Skin Effect  
The \"skin effect\" refers to the phenomenon that occurs when a high-frequency current flows through the surface of a conductor and reduces in strength as it moves toward the center of the conductor. This surface impact must be considered to facilitate the precise computation of the eddy currents occurring in the transformer tank. It is important to generate a mesh that considers the surface depth to incorporate the surface effect into  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Existing Methods** & **Proposed Method** \\ \hline \multirow{2}{*}{Referenced loss example} & \multirow{2}{*}{Entire tank wall [3; 27; 32]} & Short, long, and entire \\  & & tank walls \\ \multirow{2}{*}{Size–loss relationship} & Not achieved & Achieved \\  & Multiple shunt packages & \\ \multirow{2}{*}{Number of magnetic shunts} & [3; 27; 32] & Reduces shunt packages \\ \multirow{2}{*}{Number of parametric variables} & 1 [27] & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of existing methods and the proposed method.  
the finite element approach. According to the expression, the skin thickness at which the leakage flux acts on the metal component is written as  
\[\delta=\frac{1}{\sqrt{\pi\mathrm{f}\mathrm{\mu}\mathrm{\sigma}}}=\sqrt{\frac{2}{ \omega\mathrm{\mu}\mathrm{\sigma}}} \tag{1}\]  
where  
\(\delta\) = the skin depth (m);  
f = the frequency (Hz);  
\(\mathrm{\mu}\) = the permeability of the material (H/m);  
\(\mathrm{\sigma}\) = the electrical conductivity of the material (S/m).  
There is a significant relationship between the material's electromagnetic characteristics and the skin thickness. With a frequency of fifty hertz for the mains, this thickness falls in the millimeter region. Considering that the skin thickness is relatively insignificant compared with the dimensions of the transformer's structural components, applying the finite element method necessitates utilizing a dense mesh structure in addition to a substantial quantity of finite elements. As a result, it necessitates a high processing capacity and extends the amount of time needed to solve the problem. The surface impedance approach is utilized to model the tank wall to overcome this disadvantage. Consequently, the solution time and the number of finite elements formed in the tank construction are both greatly reduced. The surface impedance boundary condition is used to accurately calculate the stray losses, along with the relationship between the tangential component of the electric and magnetic fields [33].  
\[Z_{\mathrm{S}}=\frac{\mathrm{E}_{\mathrm{S}}}{\mathrm{H}_{\mathrm{S}}}=\frac{ 1+\mathrm{j}}{\mathrm{\sigma}\mathrm{\delta}}=(1+\mathrm{j})\sqrt{\frac{\pi \mathrm{f}\mathrm{\mu}}{\mathrm{\sigma}}} \tag{2}\]  
In this equation, the electric field is denoted as Es, and the peak value of the tangential magnetic field is denoted as Hs.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 3 Traveling Wave Head Detection Algorithm Based on CEEMDAN-TEO
### Algorithm Adaptation Analysis  
The algorithm's relevance and ranged accuracy were determined through comparison experiments conducted on different grounding resistances and fault types.