esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model for the Structural Behavior of Pipelines During Lowering-In
## 6 Conclusions  
A segmental pipeline model was proposed to analyze the structural behavior of the pipeline during lowering-in, considering geometric and boundary conditions. The segmental pipeline model was formulated based on the two parameter BOEF and Euler-Bernoulli beam theory. The conclusions obtained from this study are as follows.  
1. The structural behavior resulting from the segmental pipeline model and the FE analysis showed good agreement, except for being slightly different in terms of the bending moments and shear forces near the TDP and SSP positions. The small difference is caused due to the limitation of applicable boundary conditions and simplification of the modeling of pipeline-soil interaction.  
During lowering-in, the boundary conditions of the pipeline are continuously changed due to the use of pipelayers. The segmental pipeline model can simulate variable boundary conditions, combining several PCS, PSP and PSS elements. This makes it more convenient to expand the segmental model to cover various construction sequences during lowering-in.
* The necessary elements in the modeling process can be intuitively selected by considering the geometric deformations and boundary conditions. The segmental pipeline model is built by combining the necessary elements and the final system equations of the segmental model are nonlinear, which can be solved easily through a numerical approach.
* The ratios of the absolute maximum bending moment and shear force in Case 2 to Case 1 were about 0.91 and about 1.17, respectively. Additionally, the ratios of the absolute maximum bending moment and shear force in the transverse direction to the vertical direction were about 0.56 and 0.68, respectively. This indicates that it is necessary to consider the sectional forces of pipeline in the lateral direction and the shear forces for an increase of pipelayers when analyzing the structural behavior of pipelines.  
Conceptualization, development of analytical model and verification, FE analysis, original draft preparation, data curation; W.H.; writing, review and editing, supervision, project administration, preparing the final version of this manuscript; J.S.L.  
The National Research Foundation of Korea (NRF) grant funded by the Korean government grant number NRF-2017M2B2B1072890.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP: Ministry of Science, ICT and Future Planning) (No. NRF-2017M2B2B1072890).  
The authors declare no conflict of interest.
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes
### Influence of Ore Block Size \(P\)  
The impact of the ore block size on the shaft wall damage volume was investigated via fixing the inclined angle of the chute (\(\alpha=60^{\circ}\)), the ore-pass dip angle (\(\theta=70^{\circ}\)), and the shaft diameter (\(D=2\) m). In addition, the ore block size \(P\) was set within 100-600 mm, and the ore passes were divided into three groups according to their ore-pass depth (60, 120 and 180 m). Figure 13 shows the variation in the shaft wall damage volume determined via Equations (18) and (19) with respect to the ore block size \(P\) across ore-pass depths.  
Figure 12: Variation in total shaft wall damage volume \(Q_{\rm{bol}}\) with respect to the shaft diameter \(D\) across ore-pass dip angles.  
Figure 13: Variation in total shaft wall damage volume \(Q_{\rm{bol}}\) with respect to the ore block size \(P\) across ore-pass depths.  
As shown in Figure 13, the ore-pass depth \(H\) has a slight influence on the \(Q_{\text{tol}}\)-\(P\) relation curve shape: the greater the ore-pass depth \(H\), the greater the total shaft wall damage volume \(Q_{\text{tol}}\). It can also be observed from Figure 13 that \(Q_{\text{tol}}\) and \(P\) are positively correlated, and \(Q_{\text{tol}}\) increases with an increase in \(P\). Furthermore, the growth rate of the \(Q_{\text{tol}}\)-\(P\) relation curve increases continuously, which indicates that \(P\) greatly affects \(Q_{\text{tol}}\).  
Based on Equations (12)-(16), it is evident that as \(P\) increases, the ore indentation depth \(\delta\) and the wear length \(l\) also increase. Hence, \(Q_{\text{tol}}\) coupling with \(\delta\) and \(l\) exhibits an exponential growth trend.  
Therefore, damage to the shaft wall can be reduced via decreasing the ore block size. Mine managers can install grates at the ore discharge port to strictly control the ore block size, thereby preventing collapses in the shaft wall caused by the impact wear of large ore blocks.
# Boosting of Denoising Effect with Fusion Strategy
## 3 Combination of the NCSR and the DnCNN
### Global Gradient Based Weight Design \((W_{2,n}(i,j))\)  
The image gradient has been widely studied because it conveys rich information regarding image edges and structures. To explore the complementary information provided by the gradient of the image pixels, and further understand how to design an efficient weight function, we study the gradient between the pixel intensity and its frequency. In this subsection, we will discuss how image gradient information can be exploited to compute the weight map for the initial denoised images.  
In a bright image, the pixels values in bright regions are saturated close to 1, whereas they have a small gradient in the dark regions. The opposite relation holds in the case of a dark image. Some methods assign large weights to pixels with large gradient values [39; 40; 41]. However, the pixel gradient value is small in smooth regions regardless of the degree of luminance; thus, emphasizing only the pixel values in regions with large gradients will fail to stress the pixels with a small gradient that are in well-denoised regions.  
In this regard, we design another weight that is based on the gradient of the pixel intensity and its frequency to emphasize the well-denoised regions regardless of their local contrast. As the proposed gradient is not a local one (that is, relative to surrounding pixels) but relative to other remote pixels in a similar frequency range, we refer to the proposed gradient as the global gradient. The global gradient of a dark image is large because many saturated pixel intensities are close to zero. Therefore, we posit that an image pixel is in a well-denoised region when it is in a region with a small global gradient. In other words, pixel values are relatively scarce in this region; thus, the pixels have a large variation in value compared to that of the surrounding pixels. In contrast with dark images, bright images show smaller global gradients at lower pixel values. This also indicates that the pixels with a smaller global gradient are in well-denoised or high-variation regions. Therefore, we give a pixel a larger weight when it has a smaller global gradient. Considering these observations, we design the second weight:  
\[W_{2,n}(i,j)=\frac{G_{n}(\hat{x}_{n}(i,j))^{-1}}{\sum_{n=1}^{2}G_{n}(\hat{x}_ {n}(i,j))^{-1}+\epsilon}, \tag{14}\]  
where \(G_{n}(\hat{x}_{n}(i,j))\) is the global gradient for pixel intensity \(\hat{x}_{n}(i,j)\).
## 4 Results
### Human Error Probabilities Calculation  
A human error probability (HEP) was calculated for the incorrect installation of each of four types of parts: brackets, cushioned loop clamps, cap covers and the generic part fasteners. The calculations followed the guidelines provided by [PERSON] [81] in the original article describing the HEART technique. The generic task type assigned to each part installation was routine, highly practiced, rapid task involving relatively low level of skill, which corresponds to task type E in the HEART nomenclature. The generic error probability for this task type is 0.02. The number of error producing conditions (EPCs, also known as performance shaping factors or PSFs) was limited to no more than five for each of the parts analyzed. Although HEART provides a list of 40 error producing conditions, [PERSON] [86] acknowledges that three to nine PSFs are generally sufficient to arrive at a screening value in the quantification phase. For the sake of simplification and the need to avoid repeated calculations, only an extract of the HEART calculation table for the critical task of installing a bracket is presented in Table 4. The consolidated results of the HEART analysis are presented in Table 5.  
The first and second columns of Table 5 show the identified EPC and the proposed multiplier according to HEART analysis. The percentages in the other columns represent the contribution of each EPC to the probability of failure for each component. Table 5's bottom row shows the total human error probabilities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Generic Task Type E**} & \multicolumn{4}{c}{**Generic Error Probability: 0.02**} \\ \hline \multirow{2}{*}{**EPCs**} & **Maximum** & **Assessed Proportion** & \multirow{2}{*}{**Assessed Effect**} & **\% Contribution** \\  & **Multiplier** & **of Effect** & & **to HEP** \\ \hline \hline (5) No means of conveying spatial & & & & \\ and functional information to & \(\times\)8 & 0.7 & = ((8 - 1) \(\times\) 0.7) +1 = 5.9 & 49\% \\ operators in a form which they can & & & & \\ readily assimilate & & & & \\ \hline \hline (39) Distraction and & \(\times\)4 & 0.3 & = ((4 - 1) \(\times\) 0.3) +1 = 1.9 & 15.6\% \\ task interruptions & & & & \\ \hline (12) A mismatch between perceived & & & & \\ and real risk & \(\times\)4 & 0.2 & = ((4 - 1) \(\times\) 0.2) +1 = 1.6 & 13.2\% \\ \hline (17) Little or no independent & & & & \\ checking or testing of output & & & & \\ \hline (31) Low workforce morale & \(\times\)1.2 & 0.2 & = ((1.2 - 1) \(\times\) 0.4) +1 = 1.08 & 9\% \\ \hline \hline \end{tabular}
\begin{tabular}{c} HEP= 0.02 \(\times\) 5.9 \(\times\) 1.9 \(\times\) 1.6 \(\times\) 1.6 \(\times\) 1.08 = 0.62 \\ \end{tabular}
\end{table}
Table 4: HEART calculation of the human error probability (HEP) for brackets 1.
## 2 Design
### Mechanical Design and Modelling  
The vibration frequency is a function of the material properties, the particle size and, to a lesser extent, the surrounding media. We have assumed that only the main breathing mode of vibration is excited. This is justified because the particle is small and uniformly heated on a time scale that is short compared with the period of vibration. Moreover, we do not expect to detect the presence of higher order modes of vibration, and they would be heavily damped compared with the breathing mode.  
The acoustic properties of a nanosphere can be described in terms of vibrations of a homogeneous elastic body embedded in an infinite homogeneous medium [12; 13; 28]. This macroscopic model is valid for particles with diameter larger than a few nanometers where continuum mechanics and bulk elastic constants can be used [29; 30]. The complex radial frequency modes, \(\tilde{\omega_{n}}(R)=\xi_{n}\
u_{L}^{(s)}/R\), depend on \(\
u_{L}^{(s)}\), the longitudinal sound velocity of the sphere; \(R\), the radius of the sphere; and \(\xi_{n}\), the eigenvalues, given by Equation (7) [31]:  
\[\xi_{n}\cot(\xi_{n})=1-\frac{\xi_{n}^{2}(1+i\xi_{n}/\alpha)/\eta}{\xi_{n}^{2}-4 \alpha^{2}\gamma^{2}(1-1/\eta\beta^{2})(1+i\xi_{n}/\alpha)} \tag{7}\]  
where the parameters are defined by:  
\[\alpha=\
u_{L}^{(m)}/\
u_{L}^{(s)};\quad\beta=\
u_{T}^{(m)}/\
u_{T}^{(s)}\]  
\[\gamma=\
u_{T}^{(m)}/\
u_{L}^{(m)};\quad\eta=\rho^{(m)}/\rho^{(s)} \tag{8}\]  
where \(\rho^{(m)(s)}\) and \(\
u_{L,T}^{(m)(s)}\) are the density and the longitudinal (L) or transverse (T) sound velocity of the medium (m) and sphere (s).  
[PERSON] et al. have assumed a weak coupling between the sphere and the medium (\(Im(\xi_{n})\ll Re(\xi_{n})\)) and weak damping [28]. Therefore, the radial mode frequencies, \(\omega_{n}\), can be related to the real part of \(\xi_{n}\) (\(Re(\xi_{n})\approx(n+1)\pi\)) and the damping of the sphere modes, \(\gamma_{n}\), is proportional to the imaginary part of \(\xi_{n}\) (\(Im(\xi_{n})\approx\eta\alpha\)). Hence, the main breathing mode frequency (\(n=0\)) of a nanosphere and the damping between it and the medium are expressed in Equations (9) and (10), respectively:  
\[f_{n}=Re(\xi_{n})\frac{\
u_{L}^{(s)}}{2\pi R}\approx(n+1)\frac{\
u_{L}^{(s)}} {2R} \tag{9}\]  
\[\gamma_{n}=Im(\xi_{n})\frac{\
u_{L}^{(s)}}{R}\approx\frac{\rho^{(m)}}{\rho^{(s )}}\frac{\
u_{L}^{(m)}}{R} \tag{10}\]Figure 2f shows the theoretical main breathing mode frequency in air (lines) and water (dots) as a function of size for a nanoparticle made of silica, silver and gold. It can be seen there that, for all the materials, the frequency generated is inversely proportional to the particle size. It can be seen also that a faster material (in terms of the longitudinal speed of sound), like silica, generates higher frequencies than a slower material like silver or gold. Furthermore, the breathing mode frequency for a gold particle will be slightly damped in a water surrounding media (\(<\)1 GHz).
## 1 Introduction  
### A Brief Description of Wireless Sensor Networks  
Recent dramatic developments in micro-electro-mechanical (MEMS) systems, wireless communications and digital electronics have already led to the development of small in size, low-power, low-cost sensor devices. Such extremely small devices integrate sensing, data processing and wireless communication capabilities. Current devices have a size at the cubic centimeter scale, a CPU running at 4 MHz, some memory and a wireless communication capability at a 4 kbps rate. Also, they are equipped with a small but effective operating system and are able to switch between \"sleeping\" and \"awake\" modes to save energy. Pioneering groups (like the \"Smart Dust\" Project at Berkeley, the \"Wireless Integrated Network Sensors\" Project at UCLA and the \"Ultra low Wireless Sensor\" Project at MIT) pursue further important goals, like a total volume of a few cubic millimeters and extremely low energy consumption, by using alternative technologies, based on radio frequency (RF) or optical (laser) transmission.  
Examining each such device individually might appear to have small utility, however the effective _distributed co-ordination_ of large numbers of such devices may lead to the efficient accomplishment of large sensing tasks. Large numbers of sensor nodes can be deployed in areas of interest (such as inaccessible terrains or disaster places) and use _self-organization and collaborative methods_ to form a sensor network.  
We note that a single, universal and technology independent model is missing in the state of the art, so in each case we precisely state the particular modeling assumptions (weaker or stronger) needed. Also, in Section 1.3 we make an attempt to define a hierarchy of models and relations between them.  
Their wide range of applications is based on the possible use of various sensor types (i.e. thermal, visual, seismic, acoustic, radar, magnetic, _etc._) in order to monitor a wide variety of conditions (e.g. temperature, object presence and movement, humidity, pressure, noise levels _etc._). Thus, sensor networks can be used for continuous sensing, event detection, location sensing as well as micro-sensing. Hence, sensor networks have important applications, including (a) military (like forces and equipment monitoring, battlefield surveillance, targeting, nuclear, biological and chemical attack detection), (b) environmental applications (such as fire detection, flood detection, precision agriculture), (c) health applications (like telemonitoring of human physiological data) and (d) home applications (e.g. smart environments and home automation). For an excellent survey of wireless sensor networks, see [1; 15; 29].
###### Abstract  
In order to ensure the blade strength of large-scale wind turbine, the blunt trailing edge airfoil structure is proposed, aiming at assessing the impact of the trailing edge shape on the flow characteristics and airfoil performance. In this paper, a Joukowsky airfoil is modified by adding the tail thickness parameter \(K\) to achieve the purpose of accurately modifying the thickness of the blunt tail edge of the airfoil. Using Ansys Fluent as a tool, a large eddy simulation (LES) model was used to analyze the vortex structure of the airfoil trailing edge. The attack angles were used as variables to analyze the aerodynamic performance of airfoils with different \(K\)-values. It was found that when \(\alpha=0^{\circ}\), \(\alpha=4^{\circ}\), and \(\alpha=8^{\circ}\), the lift coefficient and lift-drag ratio increased with increasing \(K\)-value. With the increase in the angle of attack from \(8^{\circ}\) to \(12^{\circ}\), the lift-drag ratio of the airfoil with the blunt tail increased from \(+70\%\) to \(-7.3\%\) compared with the original airfoil, which shows that the airfoil with the blunt trailing edge has a better aerodynamic performance at a small angle of attack. The aerodynamic characteristics of the airfoil are affected by the periodic shedding of the wake vortex and also have periodic characteristics. By analyzing the vortex structure at the trailing edge, it was found that the value of K can affect the size of the vortex and the position of vortex generation/shedding. When \(\alpha=0^{\circ}\), \(\alpha=4^{\circ}\), and \(\alpha=8^{\circ}\), the blunt trailing edge could improve the aerodynamic performance of the airfoil; when \(\alpha=12^{\circ}\), the position of vortex generation changed, which reduced the aerodynamic performance of the airfoil. Therefore, when designing the trailing edge of an airfoil, the thickness of the trailing edge can be designed according to the specific working conditions. It can provide valuable information for the design and optimization of blunt trailing edge airfoil.  
Joukowsky airfoil; blunt trailing edge; LES; modification; aerodynamic performance 2021
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 4 The D2D Loosely Coupled Model
### UE Departure or Disconnection  
UE disconnection from the D2D network can be performed according to three different scenarios:  
* Alert before leaving: An alert can be generated by the UE before leaving the D2D network. This can happen when the battery reaches a critical level, as soon as the CQI level drops continuously and tends toward zero, or if the user sends a disconnection request before closing the application.
* Suddenly without alerting: This situation is not very frequent, and it is due to the voluntary closure of the terminal connection or signal interference. Otherwise, it could be caused by an obstacle that suddenly blocks the radio transmission.  
Figure 10: Flowchart of the network update procedure.  
* General disconnection from the network: When the rescue mission ends, the PLMN terminates the public safety network mode. In this case, the UE is no longer authorized to use direct D2D communication functions.  
Depending on the position on the topology, if the UE was a \"Leaf node\" (holding only one connection), in this case, no damage to the network tree can be caused by this departure. In other cases, a D2D network using the loosely coupled approach will be affected, and the recovery procedure should then be launched in the proactive or reactive mode to find alternatives. Fixing the CQI threshold close to a value of 1 or 2 can, in many cases, resolve the departure problem by proactively changing the network topology in a manner that places the leaving UE as a leaf node.
## 4 Supercontinuum Generation
### Discussion  
Figure 6a shows the dispersion curves of the fundamental TE\({}_{0,0}\) mode for different widths of the waveguide. Figure 6b displays the experimental spectrum obtained in two other TiO\({}_{2}\) waveguides with distinct widths as well as the corresponding numerical simulations. Once again, a good agreement is obtained. The broader waveguide (width = 1.8 \(\upmu\)m) exhibits an all-normal dispersion regime. Therefore, the self-phase modulation and wave-breaking dynamics are dominant effects governing the spectrum expansion [2; 51; 52].  
On the contrary, the 1.4-\(\upmu\)m-wide waveguide exhibits a similar dispersion curve as the 1.5-\(\upmu\)m-wide waveguide but the dispersion is now shifted to higher values, which increases the gap between the two zero-dispersion wavelengths. As a consequence, the dispersive waves are also moved away: the blue edge of the supercontinuum is now shifted below 1 \(\upmu\)m leading to more than an octave SC generation (at \(-\)20 dB). Those results (Figure 6b1) show that with a dispersion engineered waveguide, a two-octave spanning SC and larger is conceivable.  
Figure 5: (a) Phase matching curve for a pump wavelength at 1.64 \(\upmu\)m (initial pump) and at 1.82 \(\upmu\)m (shifted soliton). (b) Numerical simulations of supercontinuum generation obtained in a 2.2-cm-long 1.5-\(\upmu\)m-wide TiO\({}_{2}\) waveguides for different input energies of the femtosecond pulses. For the clarity of the figure, the various spectra were vertically offset. The areas corresponding to the phase matched dispersive waves are coloured.
## 3 Construction of Local and Global Approximations
### Local Approximation of Zero Order  
The local approximations are built on the physical patches, so the local approximation can be influenced by discontinuities. In Figure 2, the goal function to be approximated has two parts  
\[\varphi(x)=\begin{cases}\varphi_{1}(x)&x<x_{d}\\ \varphi_{2}(x)&x\geq x_{d}\end{cases} \tag{3}\]  
where \(x_{d}\) represents discontinuous position.  
The discontinuity at \(x_{d}\) cut the physical patch \(P2\) into \(P2-1\) and \(P2-2\) and cut \(P3\) into \(P3-1\) and \(P3-2\).  
We first consider the local approximation on the ordinary physical patches, such as \(P5\) in Figure 2, in which no discontinuities exist. There are many options we can choose, such as MLS-based function [31]. Since polynomials can uniformly converge to any continuous function, the polynomials are chosen. The zero-degree Legendre polynomial can be used, namely, \(L_{ij}=1\). The global approximation turns out to be linear interpolation  
\[\phi^{*}=\sum_{ij}w_{i}a_{ij} \tag{4}\]  
If the physical patch consists of many parts, such as \(P2-1\) and \(P2-2\) in Figure 2, the local approximation can be also cut into the same parts. As above, local approximation on \(P2\) is first established, and then the parts both \(P2-1\) and \(P2-2\) inherit the origin function on interval \([x_{1},x_{d}]\) and \([x_{d},x_{3}]\) respectively. The number of star point has increased and the value of star points is different at same position. This property can be used to describe the discontinuity, in which the value of two sides from the interface is different.