esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Investigation  
### Comparison and Validation  
To make a comparison with the previous literature [24], we adopted the following parameters: \(l_{a}=l_{b}=10\) nm, \(h=0.34\) nm, \(E=1.06\) TPa, \(\mu=0.25\), \(\rho=2250\) kg/m and \(K_{1}=K_{2}\)= 80 for the axially-moving nanoplate without a foundation. The material properties of the foundation are the [PERSON]'s modulus \(k_{w}=0\), the [PERSON]'s modulus \(k_{G}=0\), and the damping \(c_{t}=0\). According to the literature [39], strip element number \(NE=6\) was selected for GSDTFM in all of the following numerical calculations. The numerical results of the present work were compared with the corresponding results in literature [24], as shown in Table 1. It can be observed from Table 1 that the dimensionless fundamental frequencies for the axially-moving nanoplate in this paper were in excellent agreement with those in [24], demonstrating the accuracy and efficiency of the proposed method for vibration analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\omega_{11}\)} & \multicolumn{2}{c}{\(\omega_{12}\)} \\ \cline{2-5}
**C** & \(\alpha=0.0\) & \(\alpha=0.005\) & \(\alpha=0.0\) & \(\alpha=0.005\) \\ \cline{2-5}  & **Present/Ref. [24]** & **Present/Ref. [24]** & **Present/Ref. [24]** & **Present/Ref. [24]** \\ \hline
0 & 44.3601/44.3709 & 44.3577/44.3687 & 79.8500/79.8941 & 79.9500/79.8753 \\
2 & 43.3402/43.3435 & 43.3361/43.3409 & 78.5746/78.5923 & 78.5598/78.5714 \\
4 & 40.2698/40.2712 & 40.2500/40.2674 & 74.6534/74.6768 & 74.6401/74.6494 \\
6 & 35.1732/35.1804 & 35.1611/35.1745 & 68.1010/68.1130 & 68.0699/68.0746 \\
8 & 28.0989/28.1030 & 28.0899/28.0940 & 58.8110/58.8225 & 58.7618/58.7690 \\
10 & 19.0302/19.0359 & 19.0221/19.0228 & 46.6143/46.6159 & 46.5418/46.5434 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensionless natural frequencies for the axially-moving nanoplate without a foundation.  
For future comparisons with other researchers, the first dimensionless natural frequencies of the axially-moving nanoplate resting on a viscoelastic foundation before the dimensionless velocity C = 10 with small-parameter \(\alpha\) = 0.01 and 0.10 are presented in Table 2. The material properties of the viscoelastic foundation were \(k_{w0}\) = 0.1 GPa/nm, \(k_{\text{G0}}\) = 0.25 GPa-nm and \(c_{t0}\) = \(10^{-4}\) GPa-ns/nm, as referred to in the literature [39]. It can be seen from the tables that the dimensionless natural frequencies of the axially-moving nanoplate were complex numbers when it rested on a viscoelastic foundation.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 3 Design and Implementation
### Overview
Moreover, when data are transmitted through a single point, a bottleneck problem could occur. High-availability and redundancy clusters are the key factors to avoid the single point of failure risk. The set of fog servers is modeled as an undirected graph G = (N, E) where N represents the set of fog servers and E describes the connection-links among them. The fog servers are categorized into two types:  
Figure 2: General architecture of the fog server. (1) an in-domain fog server, and (2) a master fog server. The in-domain fog server represents a server which is located in one cluster and only has connections via short-links with all in-domain servers placed in the same cluster and the master server of that cluster. The master server represents a server located in one cluster and has a connection via short-links with all in-domain servers placed in the same cluster and at the same time has connection via long-links with some master nodes located in other clusters (Figure 3). The long-links (i.e., red lines in the figure) create connections among master nodes and are responsible for achieving the high clustering coefficient in the network. Short-links (i.e., blue lines) create connections among in-domain nodes, and among master servers and in-domain servers. Clustering minimizes the outages of the system components. When a fog server fails, then immediately another server has to process the role of the failed one. The details of clusters' constructions are beyond the scope of this research (more details [23]).
## 4 Results  
We assessed the performance of the proposed receiver based on several criteria. These included acquisition latency, the receiver's ability to process Galileo E1b/c signals in high-sensitivity mode, and its capability to simultaneously process Galileo E1b/c signals in high-sensitivity mode alongside Galileo E5a and GPS signals in normal-sensitivity mode. The precision of navigation solutions and the power consumption were also evaluated.  
The receiver's ability to process Galileo E1b/c signals in high-sensitivity mode was tested using three different TCXOs and an OCXO. Two TCXOs caused a CFO in the received signals larger than expected, requiring the user to perform a manual estimation and correction of the CFO before using the receiver in high-sensitivity mode. This procedure was not required when using an OCXO, with a better frequency stability. A detailed explanation is provided in Section 4.2. For this reason, the authors switched to using an OCXO for the remaining tests. However, improving the acquisition algorithm to eliminate the need for manual CFO compensation remains as future work.  
Appendix A.1 provides the receiver configuration used for processing the Galileo E1b/c signals in high-sensitivity mode, while Appendix A.2 offers the configuration for processing the Galileo E5a and GPS signals in normal-sensitivity mode. Appendix A.3 details the receiver's PVT configuration.
# Computational Social Science of Disasters: Opportunities and Challenges
## 3 Computational Social Science
### Social Networks and Geospatial Analysis  
Social network analysis and geospatial analysis are two promising computational methods for studying the structures of social and physical spaces within which humans live and interact, especially with the advent of crowdsourced information and the rise of social media [142; 143]. Social network analysts see the social world as structured by a web of connected agents tied together by specific relationships [144]. Sociograms, which are graphical depictions of social networks, make social structure visible and tangible. Their representation in matrices and other visualizations on computers allow researchers to examine the properties of large networks. There are different relational types of networks, including directed (digraph), signed (valued), weighted, or multiplex, each of which reflect characteristics of social relations. The levels of analysis of networks at different levels (nodal, dyadic, triadic, n-adic, or at network-level) provide insights about the structure of the social system under study (e.g., on concepts, measures, and properties). Examples of these forms of data analysis are found in disaster studies such as [145; 146; 147], which explore the structure of organizational networks and the access and flow of information within networks. Other applications of social network analysis include human cognition and belief systems, decision-making models, organizations and meta-models, supply chains, diffusion, and international relations (for a brief review of these, see [104]).  
Geography adds an important dimension to human interactions with their environments. Humans do not live in a spatial vacuum, and social reality is heavily dependent on spatial features. The gap between geography and social sciences is addressed by geographers [21; 148]. Developments in geographic information systems (GIS), especially in the fields of spatial databases, positioning technologies, remote sensing, and geo-visualization, have made GIS a common tool in criminology, archaeology, public health, anthropology, economics, demography [21], and disaster research [149]. More importantly, we should note that GIS is not simply a set of technological tools; it brings \"spatialthinking\" to the social sciences [148] in the form of geographical information science [150]. For example, [PERSON] et al. [151] developed a technique for grid-based tessellation of space that provides a systematic approach for prioritizing areas needing to be mapped by digital volunteers based on information value theory [152]. In this regard, geography has both benefitted from and contributed to computational social sciences [21].
# Strain Rate Dependencies and Competitive Effects of Dynamic Strength of Some Engineering Materials
## 3 Results
### Comparison of Critical Stresses for Various States of Mortar and Limestone  
Figure 3c-f show the strain/stress rate dependencies of critical stresses for dry, saturated and frozen mortar and limestone under compression and splitting. In the case of quasi-static conditions, the strength of mortar and limestone seriously decreased with water saturation and increased with freezing for both schemes of loading. However, the saturated limestone exhibited maximum critical stresses under compression at strain rates above 600 1/s, and the dry limestone showed maximum critical stresses under splitting stress rates above 100 GPa/s. The frozen limestone, which is the most durable under slow loading, showed the lowest critical stresses under splitting at loading rates over 250 GPa/s. A similar dynamic effect was observed for the mortar. The dry mortar showed the lowest critical stresses under compression at strain rates of more than 200 1/s and under splitting at stress rate of more than 100 GPa/s.  
Figure 3: Comparison of the critical stress dependencies on a strain rate in compression (**a**,**c**,**e**) and splitting (**b**,**d**,**f**) tests of cylindrical specimens of quasi-brittle constructional materials. (**a**,**b**) Different materials; (**c**,**d**) different states of mortar; (**e**,**f**) different states of limestone. \(R^{2}\) is the coefficients of determination.  
The question about the mechanisms of such competitive behavior of critical stresses for the various states of the materials remains open. It is necessary to consider the theoretical problems of the influence of liquid and ice in the pores and micro-cracks on the strength of rocks and mortar. For example, a liquid medium can break the bonds between structural components or act as a wedge, but under dynamic loads, it allows better transfer of stresses in the material by filling in micro-defects. However, it is already clear that the competition effect of critical stresses is of serious importance for practical calculations of structures. For instance, such strength behavior of various states of rock may be important in engineering calculations, for example, in deep-sea mining or mining in permafrost areas. Mortar is often used in climates where one condition may prevail or all climates equally. In both cases, dynamic loads can significantly affect the ratios of a design and its real strength.
## 2 Materials and Methods
Rockwell tests (HRB) were executed using a 5 mm spaced grid on the top of the specimen according to ISO 6508, while Vickers tests (HV) were executed using a 1 mm spaced grid on the transversal section of the specimen according to ASTM E92-17 and using a load of 1000 gf to limit the elastic recovery of the material that could modify the measured values. Both hardness tests were carried out moving from the center of the welded joints until the hardness of the base material was reached. Tensile tests (5 repetitions) were conducted according to UNI EN ISO 6892-1:2016 using a testing machine Galdabini with a load cell of 50 kN. The tests were carried out orthogonally to the welding direction, with the welding center placed in the middle of the specimen, according to EN 10002-1:2001, under speed control (0.13 mm/s) and with a pre-load equal to 0.5 kN. The microstructure of both the base materials and the cross-section specimens (Figure 5) was observed at the optical microscope only for precipitation-hardening alloys since on their microstructure the welding process is more damaging. The samples were mounted in a cold-working resin, polished up to 0.05 \(\upmu\)m with alumina suspension and etched by Keller (for AA2024 and AA7075) and Weck solutions (for AA6082). It is important to remark that all the tested specimens were extracted from a region of the welded sheet in which the steady state conditions of the process were achieved. The average size of the grain in the base materials and also in correspondence of both the nugget and the Thermo Mechanically Altered Zones (TMAZs) was determined according to the Heyn lineal intercept procedure (ASTM E 112-13) on images obtained with an optical microscope with 400\(\times\) magnification for the nugget and 200\(\times\) for the TMAZs and for the base metals. For each line drawn, the intercepted grain boundaries were counted and then the final average size of the single grain was calculated according to Equation (2). \[d=\ A_{int}/M \tag{2}\]  
\[A_{int}=l_{line}/n_{i} \tag{3}\]  
where:  
\(d\): average grain diameter;  
\(A_{int}\): average line length intersected;  
\(M\): image magnification;  
\(l_{line}\): length of the line;  
\(n_{i}\): average numbers of grain boundaries intersected by the line. To consider the anisotropy of the TMAZs, three lines of 300 mm parallel to the rolling direction and three lines of 200 mm perpendicular to the rolling direction were drawn. Due to the presence of equiaxial grains in the nugget zone, the evaluation of the average grain size in this zone was performed by tracing three parallel lines having a length equal to 100 mm.
## 2 Materials and Methods
### Regression Model
#### 2.2.1 Reflectance Estimation with Associated Uncertainties  
The regression problem is posed as the estimation of one or several spectral bands as a nonlinear combination of a disjointed set of the available bands. For the prediction of a single-band, this can be written as:  
\[\widehat{\rho_{i}}=f(\{\rho_{j\
eq i}\},\vec{\theta}),\]  
that is, prediction of reflectance of band \(i\) is a function of the measured reflectances of the other bands and a set of parameters \(\vec{\theta}\) containing other pertinent information, such as solar and sensor angles. The regression can jointly estimate several spectral bands in a set \(I\):  
\[\{\widehat{\rho_{i}}\}_{i\in I}=f(\{\rho_{j}\}_{j\
otin I},\vec{\theta}) \tag{1}\]  
The regression procedure should also produce a credibility interval of the estimation of the target variable. (We use the term _credibility interval_ instead of _confidence interval_ because we adopt a Bayesian point of view: we consider the estimated value is a random variable and the bounds of the interval are fixed, while the use of confidence intervals considers the bounds as random variables that result from repeated measures.) In order to do this, instead of regressing over the expected mean, we can implement a regression of the mean and the variance of the target variable. Estimating a mean and a variance means that we are assuming a Gaussian error model.  
At inference (estimation) time, the mean will be used for variable estimation (in a Gaussian model the mean is the value with the highest probability), and the variance will be used to give the credibility interval.  
Given a target value \(y\) (in our case \(\rho_{i}\)) and the estimates \(\hat{\mu}\) and \(\hat{\sigma}\), the predictive likelihood of the target value given the estimates is the Gaussian distribution whose probability density function is  
\[p(y|\hat{\mu},\hat{\sigma})=\frac{1}{\sqrt{2\pi\hat{\sigma}^{2}}}e^{\frac{(y- \hat{\mu})^{2}}{2\hat{\sigma}^{2}}}\]We can therefore pose the regression problem as the minimization of a cost function given by the negative log-likelihood [26]. The log-likelihood takes the form:  
\[log(p(y|\hat{\mu},\hat{\sigma}))=-\frac{1}{2}\log(2\pi)-\frac{1}{2}\log\hat{ \sigma}^{2}-\frac{(y-\hat{\mu})^{2}}{2\hat{\sigma}^{2}}\]  
Therefore, after removing the constant term and a multiplicative factor, the cost function to be minimized is:  
\[\mathcal{L}=\sum_{i}\log\hat{\sigma}_{i}^{2}+\frac{(y_{i}-\hat{\mu}_{i})^{2}}{ \hat{\sigma}_{i}^{2}}\]  
where the sum is taken over the training samples.  
Beyond being the correct theoretical loss under a Gaussian error model, this penalty function can be interpreted as follows :  
* the term \((y_{i}-\hat{\mu}_{i})^{2}\) penalizes the errors between the target value and the estimated mean;
* these errors are weighted by the uncertainty estimation: larger errors will need larger values of \(\sigma_{i}\) to lower the penalty;
* in order to avoid allowing large errors on \(\mu_{i}\) by always estimating large values of \(\sigma_{i}\), large values of \(\sigma_{i}\) are also penalized by the first term in the loss.
# Analysis on Flow and Temperature Field of High-Power Magnetorheological Fluid Transmission Device
## 4 Factors Affecting Distribution of Coolant Flow Field  
Computational Fluid Dynamics (CFD) is a method for solving differential equations of fluid flow by numerical calculation to obtain the discrete distribution of flow field in a continuous area, and then simulate various physical phenomena related to fluid flow. The flow of any fluid conforms to three fundamental laws, and these three fundamental laws can be described by mathematical equations in integral/differential form.  
The law of conservation of mass:  
Integral form:  
\[\frac{\partial}{\partial t}\iiint_{\Omega}\rho d\Omega+\oiint_{S}\rho\textbf{ V}\cdot\textbf{n}ds=0 \tag{1}\]  
Differential form:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\textbf{V})=0 \tag{2}\]  
Figure 4: Meshing results of MR transmission device: (**a**) Internal grid; (**b**) Local grid of magnetic column coolant.  
where, \(\rho\) is the density, \(\Omega\) is the control body of the integral equation, \(d\Omega=dxdydz\), \(S=\partial\Omega\) is the surface of the control body, \(\mathbf{V}\) is the velocity vector of the fluid movement.  
The law of momentum conservation:  
Integral form:  
\[\frac{\partial}{\partial t}\iiint\limits_{\Omega}\rho\mathbf{V}d\Omega+\iiint \limits_{S}(\rho\mathbf{V}\mathbf{V}+p\mathbf{I})\cdot\mathbf{n}ds=\iiint \limits_{\Omega}\rho\mathbf{F}d\Omega+\iiint\limits_{S}\mathbf{\tau}\cdot\mathbf{n}dS \tag{3}\]  
Differential form:  
\[\frac{\partial(\rho\mathbf{V})}{\partial t}+\
abla\cdot(\rho\mathbf{V}\mathbf{ V}+p\mathbf{I})=\rho\mathbf{F}+\
abla\cdot\mathbf{\tau} \tag{4}\]  
where, \(\mathbf{F}\) is the external force, \(\mathbf{I}\) is the unit tensor, \(p\) is the pressure, \(\mathbf{\tau}\) is the viscous stress tensor.  
The law of conservation of energy:  
Integral form:  
\[\frac{\partial}{\partial t}\iiint\limits_{\Omega}\rho Ed\Omega+\iiint\limits_{ S}(\rho E+p)\mathbf{V}\cdot\mathbf{n}dS=\iiint\limits_{\Omega}\rho\mathbf{F} \cdot\mathbf{V}d\Omega+\iiint\limits_{S}(\mathbf{\tau}\cdot\mathbf{V}+k\
abla T) \cdot\mathbf{n}dS \tag{5}\]  
Differential form:  
\[\frac{\partial\rho E}{\partial t}+\
abla\cdot[(\rho E+p)\mathbf{V}]=\rho \mathbf{F}\cdot\mathbf{V}+\
abla\cdot(k\
abla T)+\
abla\cdot(\mathbf{\tau}\cdot \mathbf{V}) \tag{6}\]  
where, \(E\) is the total energy, and \(k\) is the heat conduction coefficient.  
In this paper, ANSYS CFX software is used to analyze the flow field and temperature field of high-power MR transmission device. In this section, the influence characteristics of rotation velocity, inlet velocity, inlet position, diameter and number of magnetic columns and other parameters on flow field distribution are investigated. The working domain is divided into fluid domain, solid domain and rotating solid domain. The parameters settings are shown in Table 1.
## 3 Results and Discussion
### XRD Results and Discussion  
Table 3 shows the XRD lattice parameters that agree with the previously published values for stoichiometric LiNiO\({}_{2}\)[6, 7, 15]. Specifically, [PERSON] et al. [15] showed XRD lattice parameter values for different Li\({}_{1\pm 1\mathrm{x}}\)NiO\({}_{2}\) compositions that agree well with our values for stoichiometric LiNiO\({}_{2}\). LN670 presented the highest c/3a value, indicating samples with the lowest cation-anion mixing in a structure [2]. Despite this, the values for the different samples were notably close to each other. The crystallite size increases with the temperature, as can be expected.  
Table 5 shows the lattice occupancies of the Li, Ni, and O sites determined by Rietveld refinement. The refinement was done by Rietveld refinement, the FP (Fundamental Parameter peak shape) method with continuous scan, and the Cheary-Coelho Axial model, using experimental geometry and optics. Constraints on total occupancy of Wyckoff sites 3a and 3b being equal to 1 (Ni + Li = 1). The ESD was the standard deviation from the refinement process, which means the estimated error from the iterative, least squares method computation. From the Ni1(3b) value, which is the ratio of lithium sites occupied by nickel, it showed that LN670 has the lowest amount of cation mixing (occupancy of 0.0128), which actually supports 670 \({}^{\circ}\)C being the best lithiation temperature. [PERSON] et al. [16] claimed that under 2% of cation mixing does not affect the electrochemical performance of the LiNi\({}_{0.33}\)Mn\({}_{0.33}\)Co\({}_{0.33}\)O\({}_{2}\) (NMC111) cathode material.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Charge 4.3 V 0.1 C** & **DC 3.0 V** & **DC 2.6 V** & **0.1 C Eff** & **0.1 C Eff.** & **DC 3.0 V** & **Retention After** \\  & **+ 0.015 C (1 st)** & **0.1 C (1 st)** & **0.1 C (1 st)** & **3.0 v (1 st)** & **2.6 v (1 st)** & **0.1 C (62)** & **62 Cycles** \\ \hline  & **mAh/g** & **mAh/g** & **mAh/g** & **\%** & **\%** & **mAh/g** & **\%** \\ \hline LN650 & 250.9 & 218.4 & 223.1 & 87.0 & 89.0 & 193.3 & 88.5 \\ LN670 & 253.8 & 227.2 & 231.7 & 89.5 & 91.3 & 188.0 & 82.8 \\ LN690 & 252.0 & 221.9 & 225.6 & 88.1 & 89.5 & 165.5 & 74.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: First cycle performance and capacity retention after 62 cycles.  
Figure 3: XRD pattern for the prepared LiNiO\({}_{2}\) materials.