esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Introduction of FAD Key Parameters
### Stress Concentration Factor Due to Misalignment  
The presence of misalignment can cause additional bending stress when the girth weld is subjected to axial tensile load. Therefore, in the calculation of the load and fracture ratios, it is necessary to consider the stress concentration factor induced by misalignment and the magnitude of the bending stress. According to Appendix D in BS 7910-2019 [5], the calculation equation is as follows:  
\[k_{m}=1+\frac{\sigma_{s}}{P_{m}} \tag{10}\]  
where \(k_{m}\) is the stress concentration factor due to misalignment; \(\sigma_{s}\) is the bending stress caused by the misalignment; and \(P_{m}\) is the primary stress that the ring weld withstands axially.  
The bending stress for axial misalignment at girth welds in pipes with or without thickness changes (Figure 6) could be calculated as follows:  
\[\sigma_{s}=\left\{\begin{array}{cc}\frac{6e}{B_{1}(1-b^{2})}\bigg{(}\frac{1} {1+(B_{2}/B_{1})^{1.5}}\bigg{)}P_{m}&\frac{\sigma_{s}}{P_{m}}<1\\ \frac{2\epsilon e}{B_{1}}\bigg{(}\frac{1}{1+0.7(B_{2}/B_{1})^{1.4}}\bigg{)}P_{ m}&\frac{\sigma_{s}}{P_{m}}\geq 1\end{array}\right. \tag{11}\]  
where \(e\) is the misalignment level; and \(B_{1}\) and \(B_{2}\) is the wall thickness of the pipe on both sides of the misalignment of the girth weld. In this paper, it is assumed that the wall thickness on both sides is the same; that is, \(B_{1}=B_{2}\).
## 3 Results and Discussion
### Nitrogen Jet Profile
Although there is little difference in density between the supercritical jet and the subcritical one at the centerline, the boundary layer of \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Temperature (K)** & **Pressure (MPa)** & **Density (Kg/m\({}^{3}\))** & **Velocity (m/s)** \\ \hline I & 88.2 (0.7) & 5.1 (1.5) & 772.9 & 515 \\ II & 100.8 (0.8) & 5.1 (1.5) & 699.8 & 459 \\ III & 113.4 (0.9) & 5.1 (1.5) & 607.0 & 267 \\ IV & 126.0 (1.0) & 5.1 (1.5) & 473.2 & 208 \\ V & 138.6 (1.1) & 5.1 (1.5) & 250.6 & 107 \\ \hline \end{tabular}
\end{table} 
Table 2: The thermodynamic condition parameters of jet. The corresponding reduced numbers to critical point are in parentheses. Figure 5: Two-dimensional (2-D) axial sectional view of a subcritical jet under condition I (upper) and a supercritical jet under condition IV (lower). the subcritical fluid is thinner, so the density gradient of the boundary layer of the subcritical jet is more severe. Correspondingly, the density gradient of the supercritical fluid is generally smaller since the boundary layer is thicker. That is to say, when approaching the center of the jet, for supercritical conditions, the density begins to rise from a very distant position (point 1 in Figure 6), while for the subcritical condition, it only begins to rise when it is closer to the center (point 2 in Figure 6). Outside the shear layer, the temperature of the liquid quickly approaches the temperature of the nitrogen in the liquid core, while the condition of the gas also quickly becomes close to the state of the ambient mixture. The shear layer can also be seen in Figure 7, which demonstrates the density contour of both a subcritical jet and a supercritical jet. The subcritical jet has a generally larger density gradient in the shear layer, which means that the jet at subcritical conditions has a clearer gas-liquid interface, making it easy to carry out further study on the stability of the jet. In comparison, the density gradient for the supercritical jet is smaller. Figure 6: Radial density profile of a subcritical jet under condition I and a supercritical jet under condition IV. Figure 7: Density profile of a subcritical jet under condition I (upper) and a supercritical jet under condition IV (lower). As stated before, the supercritical jet will not exhibit a clear gas-liquid interface. In Figure 8, we can see that for the subcritical condition, the boundary layer is relatively thin and the gas-liquid interface is very regular and clear, but for the supercritical condition, the boundary layer is much thicker. In order to obtain the growth rate of disturbances, an artificial gas-liquid interface should be determined by the blending rate of the jet and ambient gas. We defined the jet interface as the point where the ambient gas and jet ratio reached 50%, as shown in Figure 8, for both subcritical (upper) and supercritical (lower) conditions. Then we can obtain the spatial growth rate through the development of a perturbed interface.
## 2 Dead-Time Effect of the Auxiliary Resonant Snubber Inverter (ARSI)
### Principle
#### 2.1.1 Heavy Load Condition
The ideal pole voltage, on the other hand, should be as follows:  
Figure 1:8  
The voltage error during the resonant stage \(t_{H6}\)-\(t_{H7}\) can be calculated as follows:  
Figure 1:9  
For Equation (16) \(v_{ab}(t)=V_{s}\), the resonant time can be obtained:  
\[v_{ab,id}(t)=-V_{s} \tag{2}\]0  
According to the analysis of dead-time effect in the heavy load condition, the voltage error in a switching cycle can be obtained from Equations (3) and (18):\[v_{err}=\left\{ \begin{array}{ccc}2V_{s}-\frac{i_{s}}{C_{r}}(t-t_{H1})&t_{H1} \leq t\leq t_{H2}\\ -V_{s}-V_{s}\text{cos}\omega_{A}\left(t-t_{H6}\right)+Z_{A}I_{boost1}\text{sin} \omega_{A}\left(t-t_{H6}\right)&t_{H6}\leq t\leq t_{H7}\\ 0&t_{H0}\leq t<t_{H1}\text{ \ or \ \ }t_{H2}<t<t_{H6}\text{ \ or \ \ }t_{H7}<t<t_{H10}\end{array} \right. \tag{20}\]  
Thus, the average voltage error in a switching cycle can be calculated as follows:  
\[V_{err}=\frac{1}{T_{s}}\int_{t_{H0}}^{t_{H10}}v_{err}dt=\frac{\Delta t_{H12}- \Delta t_{H67}}{T_{s}}V_{s}=\frac{V_{s}}{T_{s}}\left(\left|\frac{2C_{r}V_{s}}{ i_{o}}\right|-\left|\frac{2}{\omega_{A}}\text{arcsin}\frac{V_{s}}{\sqrt{V_{s}^{2}+Z _{A}^{2}\,l_{boost1}^{2}}}\right|\right) \tag{21}\]  
where \(T_{s}\) is the switching period.
## 2 Methods
### Parameter Selection  
For the TE methods, all parameters were estimated before extracting the phase and amplitude time series, that is to say, from the initial, real-valued time series data. The value of the embedding delay \(\tau\) was set to 1 autocorrelation time (ACT) [31]. The embedding dimension \(d\) was automatically selected using [PERSON]'s criterion [36; 42] from the range \(d=\{1,2,\ldots,10\}\)[22]. The parameter \(u\) was selected as the delay producing the largest TE from the following ranges : \(u=\{4,8,\ldots,40\}\) for the simulated data, and \(u=\{50,60,\ldots,250\}\) for the WM data. The parameter \(\alpha\) in [PERSON]'s entropy was set to 2, which is neutral to weighting, an a reasonable, a priori choice [16; 34]. A radial basis function (RBF) kernel with Euclidean distance [43], defined as:  
\[\kappa(\mathbf{a}_{i},\mathbf{a}_{j})=\exp\left(-\frac{d^{2}\left(\mathbf{a}_ {i},\mathbf{a}_{j}\right)}{2\sigma^{2}}\right), \tag{12}\]  
was employed as kernel function, where \(d^{2}(\cdot,\cdot)\) is a distance operator, and \(\sigma\ \in\ \mathbb{R}^{+}\) is the kernel's bandwidth. The bandwidth \(\sigma\) was set in each case as the median distance of the data [44]. For CFD estimation, we used a sliding window 5 frequency bins long. Furthermore, for all measures, the required phase and amplitude decompositions we carried out by convolving the real-valued data with a Morlet wavelet, defined as:  
\[h(t,f)=\exp(-t^{2}/2\varsigma_{t}^{2})\exp(i2\pi ft), \tag{13}\]where \(f\) is for the filter frequency, \(\xi_{t}=m/2\pi f\) is the standard deviation of the wavelet in the time domain, and \(m\) controls the time/frequency resolution [21]. The parameter \(m\) was varied from 3 to 10 in a logarithmic scale, according to the selected frequency of the filter. Finally, all connectivity values were obtained through in-house implementations of the algorithms for the different measures studied. The Python implementation of the proposed \(T\mathrm{E}_{\kappa\kappa}^{\theta\theta^{c}}\) approach is available at [[https://github.com/ide2704/Directed_PAC_through_kernel-based_Phase_Transfer_Entropy](https://github.com/ide2704/Directed_PAC_through_kernel-based_Phase_Transfer_Entropy)]([https://github.com/ide2704/Directed_PAC_through_kernel-based_Phase_Transfer_Entropy](https://github.com/ide2704/Directed_PAC_through_kernel-based_Phase_Transfer_Entropy)) (accessed on 7 September 2021).
## 1 Introduction
### Atmospheric Turbulence  
Atmosphere can be described as a fluid with two distinct motion states. One with uniform motion characteristics, the laminar, and another where uniformity disappearsand random sub-flows occur, called turbulent eddies [12]. This motion state is a result of temperature and moisture gradients that cause changes in the refractive index of the atmosphere. This phenomenon, called optical turbulence, is highly non-linear and is described by the Navier-Stokes equations [13]. Turbulence theory, as it is known nowadays, is based on [PERSON] approach based on dimensional analysis and approximations of Navier-Stokes equations. According to the [PERSON]'s cascade energy theory, turbulence can be described by unstable air masses, called eddies, which by inertial energy transfer begin from the macroscale \(L_{0}\), i.e., outer scale and end at the microscale \(l_{0}\), i.e., inner scale. An important assumption of this theory is that eddies smaller than \(L_{0}\) are statistically homogeneous and isotropic [13]. Due to this characteristic, the covariance of the refractive index reduces to a function of solely a scalar distance \(R=|\,\mathbf{R}_{1}-\mathbf{R}_{2}\,|^{2}\), where \(\mathbf{R}_{1}\) and \(\mathbf{R}_{2}\) are two random points in space. Then, the structure function within the inertial subrange, i.e., \(l_{0}<<R<<L_{0}\), is [13],  
\[D_{n}(R)=C_{n}^{2}R^{2/3} \tag{1}\]  
where \(C_{n}^{2}\) is the refractive index structure parameter. A point estimate of \(C_{n}^{2}\) can be easily obtained by measuring the temperature difference within two points with known separation distance. This can be done by using a set of thermocouples, with various numbers of fine wires. Another important parameter is the temperature structure parameter, \(C_{T}^{2}\), which is related to \(C_{n}^{2}\) as follows [13],  
\[C_{n}^{2}=\left(79\times 10^{-6}\frac{P}{T^{2}}\right)^{2}C_{T}^{2} \tag{2}\]  
where \(P\) and \(T\) the related pressure and temperature of that specific point. Path-averaged \(C_{n}^{2}\) measurements can also be taken using an instrument called scintillometer. A large number of experimental research works have led to the construction of many empirical models, for any kind of terrain, range and geometry of measurements, which can give good estimates for the refractive index structure parameter along the path, assuming constant values. A comprehensive list of \(C_{n}^{2}\) predictive models can be found in Tab. 2.5 [12].
# Green IoT: A Review and Future Research Directions
## 5 Energy-Efficient and Eco-Sustainable Wireless Sensor Networks
### Sleep/Wake-Up Techniques  
Turning off (entering sleep mode) unused wireless resources and devices has become a widely used method for reducing power consumption in the field of information and communications technology because it can save a significant amount of energy. To determine the system's average power consumption, we need to use Equation (1), which involves multiplying the sleep power by the percentage of time the system is in sleep mode, multiplying the active power by the percentage of time the system is active, and then adding the two products together before dividing the result by 100.  
\[P_{avg}=\frac{(P_{sleep}\times\%time\;asleep)+(P_{active}\times\%time\;active )}{100} \tag{1}\]  
The idea behind the proposed approach is to take advantage of the dense deployment of sensor nodes in a small coverage area, to make sensor nodes more energy efficient with extended battery life by turning sensor nodes on and off. When a system has a large sleep power compared to its active power, in this case, the system can be saving a large amount of energy. However, in some cases, the active power may be much larger than the sleep power, either because the power per event is high or because active power events occur frequently. However, in all cases, it is important to consider coverage in this approach, and ensure that it is still sufficient by the remaining active nodes. [PERSON] et al. [52] proposed a solution that aims to minimize the energy consumption of the network while maintaining network coverage by activating only a subset of nodes with minimal overlap in their coverage areas. [PERSON] et al. [53] modeled the issue of energy efficiency as a subset selection problem for collecting correlated data payload from active connected sensors. This approach takes advantage of spatial correlation to gather sensor information from non-active nodes through active nodes, making it a useful strategy.  
Duty cycle-based protocols are generally considered to be very energy-efficient [54; 55]. The duty cycle approach, which switches a sensor node on and off depending on network activity, can be categorized into three types: (i) on-demand, (ii) asynchronous, and (iii) scheduled rendezvous. However, it is important to keep in mind that while a low-duty cycle can save a significant amount of energy, it may also result in lengthy communication delays. To minimize delays, protocol parameters can be adjusted before deployment for simplicity, although this approach may lead to reduced flexibility, or they can be dynamically altered to reflect current traffic conditions. Moreover, to optimize power consumption, the active period of nodes may depend on various factors such as traffic load, buffer overflows, delay requirements, or harvested energy, as discussed in [56].
## 7 Experiments and Results  
In this section, the series of experiment and its results will be divided into two parts: simulation and experiment. All experiments are applied to over-constrained system with two redundant actuators because of their high popularity and application. Other parameters of the robot are mentioned in Table 1. In order to confirm the availability of the proposed model for any spatial CDPM, we considered a simulation test with two CDPM. They have the same number of cables and related parameters as summarized in Table 2, but they have different a configuration, illustrated in Figure 17. From that, we can compare the performance of the proposed model in these configurations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Experiment** & **Simulation** \\ \hline Fixed frame size & 3 m x 6 m x 3 m & 15 m x 11 m x 6 m \\ End-effector size & 0.3 m x 0.3 m x 0.5 m & 0.3 m x 0.3 m x 0.5 m \\ End-effector load & 50 kg & 50 kg \\ \(\rho\) & 0.09 kg/m & 0.067 kg/m \\ \(E\) & 3.5 GPa & 2 GPa \\ \(A\) & 4 mm\({}^{2}\) & 2 mm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of CDPMs used in experiment and simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Desired Positions (m)** & **Actual Position (m)** & **Positioning Error (mm)** \\ \hline \(\mathbf{P_{1}}\)(0.22, \(-\)0.45, 1.43) & (0.2220, \(-\)0.4510, 1.4470) & 17.1464 \\ \(\mathbf{P_{2}}\)(\(-\)0.13, 1.42, 0.68) & (\(-\)0.1290, 1.4380, 0.7210) & 44.7884 \\ \(\mathbf{P_{3}}\)(\(-\)0.29, \(-\)0.25, 1.52) & (\(-\)0.2920, \(-\)0.2510, 1.5340) & 14.1774 \\ \(\mathbf{P_{4}}\)(0.30, 1.26, 2.31) & (0.3000, 1.2610, 2.3150) & 5.0990 \\ \(\mathbf{P_{5}}\)(\(-\)0.47, 0.80, 2.17) & (\(-\)0.4710, 0.8000, 2.1760) & 6.0828 \\ \(\mathbf{P_{6}}\)(0.48, \(-\)0.85, 0.72) & (0.4860, \(-\)0.8550, 0.7460) & 27.1477 \\ \(\mathbf{P_{7}}\)(\(-\)1.08, \(-\)0.75, 2.54) & (\(-\)1.0810, \(-\)0.7510, 2.5410) & 1.7321 \\ \(\mathbf{P_{8}}\)(0.26, 1.06, 0.44) & (0.2640, 1.0760, 0.4790) & 42.3438 \\ \(\mathbf{P_{9}}\)(0.45, \(-\)1.73, 1.71) & (0.4500, \(-\)1.7340, 1.7270) & 17.4642 \\ \(\mathbf{P_{10}}\)(0.67, 0.52, 2.02) & (0.6710, 0.5200, 2.0280) & 8.0623 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of model in our CDPM based on set of static poses.  
Figure 17: (**a**) The first configuration; (**b**) the second configuration used in simulation test.
# Novel Processing Methods of Low-Clinker Multi-Component Cementitious Materials--A Review
## 3 Magnetic Field Treatment (MF-T) Methodology
### MF-T of Mineral Admixtures  
[PERSON] et al. [83] demonstrated that M-Water increased the fc of concrete and mortar containing GGBFS by 9-19% depending on the MF-T intensity used for M-Water (0.8, 1.2, or 1.35 T). A similar study by [PERSON] et al. [84] revealed an improved fc in concrete and mortar containing FA mixed with M-Water, with the best results at 0.8-1.2 T MF-T. [PERSON] et al. [85] found that M-Water, combined with 10 or 20% pozzolanic materials (SF, metakaolin, rice husk ash, and FA), enhanced the flowability and viscosity of S-CC. At the age of 28 days, when fc and fst had increased by 49% and 41%, respectively, the water absorption had decreased by 55%.  
In another study, [PERSON] et al. [90] examined the use of GWD as a partial cement substitute in concrete. Up to 10% GWD replacement increased concrete strength, but higher percentages reduced strength and durability. Regardless of the GWD ratio, M-Water led to  
Figure 9: Impact of the properties of M-Water on the amount of superplasticizer dosage reduction (with permission from [91]).  
reduced water absorption compared to tap water. The effect of M-Water on mortar mixes containing MWD as a partial cement substitute was investigated by [PERSON] et al. [92]. The best performance was observed with 10% marble dust replacement, while higher amounts resulted in reduced strength and durability due to increased porosity.  
[PERSON] [93] studied the use of EN-Al as a cement substitute in concrete and found that M-Water improved the strength (13%) and reduced the capillary porosity (27%) for specimens with 1% EN-Al. The properties of concrete with VA and M-Water were evaluated by [PERSON] et al. [94]. M-Water improved the workability and increased the fc by 43% at 7 days and 36% at 28 days without VA and by 22% at 7 days and 33% at 28 days with 5% VA. The addition of M-Water improved both the workability and the strength, allowing for more environmentally friendly concrete using less cement.  
[PERSON] and [PERSON] [95] presented the fracture behavior of self-compacting lightweight concrete (S-CLC) with different cement replacement materials. M-Water improved the fracture energy, fracture toughness, tensile strength, and fc. [PERSON] et al. [96] examined the impact of M-Water and SF content on concrete's fc and bond strength. A 5% SF level resulted in the highest fc and bond strength, while higher SF levels led to strength loss. M-Water increased bond strength with different bar diameters by enhancing the concrete fluidity and reducing the void content.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 2 Mathematical Model of the Rolling Element Bearing
### Nonlinear Effect--Hertzian Contact Theory  
Interactions between rolling surfaces in ball bearings are described with the Hertz contact law and its effect has a significant impact on the dynamic response. The shape of the contact in ball bearings depends on the subjected load at most, but the above-mentioned features influence the contact in lesser impact. We can define the elliptical contact in the loaded zone and the point contact in the unloaded zone. Arisen varying deformations result in the nonlinear output of ball bearings and the stronger contact is, the more nonlinear effect is obtained. In the mathematical models of rolling element bearings, the defects of specific elements are also introduced by the variable contact [52; 53].  
In the derived mathematical model, the elastic contact deformation \(\delta_{i}\) is calculated for each \(i\)-th rolling element corresponding to its angular position \(\psi_{i}\). In the derived equation  
Figure 3: The graphics presenting the shape error (waviness) on rolling surfaces of the inner and outer ring.  
(Equation (6)), the effect of radial internal clearance and waviness on rolling surfaces are taken into account:  
\[\delta_{i}=\delta_{\text{xs}}\cos(\psi_{i})+\delta_{\text{ys}}\sin(\psi_{i})-r_{c }-U_{inner}\sin(N_{inner}\psi_{0})-U_{outer}\sin(N_{outer}\psi_{0}), \tag{6}\]  
where \(\delta_{\text{xs}}\), \(\delta_{\text{ys}}\) are relative displacement between inner and outer rings in the \(x\)- and \(y\)-directions, respectively. For the \(\delta_{i}<0\), there is no contact between rolling surfaces.  
Considering the 2-DOF mathematical model, Hertzian contact force [54; 55] acts in horizontal (\(F_{y}\)) and vertical (\(F_{x}\)) direction and can be expressed in the following way:  
\[K_{x}=k_{b}\sum_{i=1}^{n}H(\delta_{i})\delta_{i}^{\gamma-1}\cos(\psi_{i}), \tag{7}\]  
\[K_{y}=k_{b}\sum_{i=1}^{n}H(\delta_{i})\delta_{i}^{\gamma-1}\sin(\psi_{i}), \tag{8}\]  
where, \(k_{b}\) is the rolling element stiffness, \(\gamma\) is the contact coefficient (point contact in ball bearings \(\gamma=3/2\), linear contact in roller bearing \(\gamma=10/9\)) [56], while \(H(\cdot)\) denotes the Heaviside function.  
The value of Heaviside, \(H(\cdot)\), depends on the contact between rolling surfaces and is formulated as follows:  
\[\left\{\begin{array}{c}0\;for\;x<0-no\;contact\\ 1\;for\;x\geq 0-in\;contact\end{array}\right., \tag{9}\]
## 2 Input and Output Parameters for the Forward Radiative Transfer Problem  
### Input Parameters  
The following input parameters must be specified (i) the physical properties of each of the two slabs that constitute the coupled system, (ii) the radiative energy input at the top of the upper slab (top-of-the-atmosphere, TOA), and (iii) the boundary conditions at the bottom of the lower slab (water bottom). Each of the two slabs is assumed to be a plane-parallel, vertically stratified structure in which the scattering and absorption properties, i.e., the IOPs are defined in Section 3. To resolve changes in the IOPs as a function of vertical position \(z\), each slab can be divided into several adjacent layers such that the IOPs are constant within each layer, but allowed to vary from one layer to the next. The impact of a wind-roughened air-water interface is described in Section 5.3.  
To specify the IOPs, we will use the concept of materials, which are radiatively significant constituents in the atmosphere-water system. Examples of such materials are atmospheric gases, aerosols, clouds, snow, ice, pure water and water impurities. These materials can be designed to account for the wavelength dependence of the IOPs so that all one needs to do is to decide which of them to include in each layer. More specifically the following input parameters must be specified: (i) solar beam irradiance [W\(\cdot\)m\({}^{-2}\)]; (ii) wavelength range, number of center wavelengths and widths [nm]; (iii) solar zenith angle(s) in degrees; (iv) the number of \"discrete ordinate streams\" used to solve the radiative transfer equation (RTE) as described in Section 4; (v) IOPs of materials used. Specifications required for the upper slab include (i) layer boundaries, ground-level altitude (sea-level is default); (ii) atmospheric type; (iii) aerosol particle types (e.g., a bimodal lognormal volume distribution) for each layer as described in Section 3.4.2; (iv) cloud particles as described in Section 3.4.3; (v) snow particles as described in Section 3.5. Specifications required for the lower slab include (i) layer boundaries; (ii) ice material as described in Section 3.5; (iii) refractive index as a function of wavelength in the lower slab (water)--set to \"one\" in the upper slab (atmosphere); (iv) water impurities; (v) water bottom albedo.