esa-sceva/satcom-chunk-collection huggingface dataset:
# International Airline Alliance Network Design with Uncertainty
## 4 Case Study
### Computational Results
The model proposed in this paper can select gateway locations and network configurations, and can also provide more decision-making options for airline partners. Through our simulations, we obtained more results. A large number of gateways contributes to excessive diversion and flow dispersion, leading to a discount capacity for inter-hub transportation. A small number of gateways leads to limited choices of gateways for the origin-destination (OD) flow transfer, resulting in serious amounts of bypass transportation. Consequently, the gateway number should be seriously considered for the airlines and airline partners when constructing an alliance route network. Specifically, when multiple regional hubs have better knowledge of uncertain parameters, the gateway number can be increased so as to improve the capability for coordination among airlines. Moreover, by having a robust optimization formulation, the deviation under uncertain costs can be reduced, which makes an optimized route network more resistant to risks and economic decisions.
# Development of Potato Mass Estimation System Based on Deep Learning
## 2 Materials and Methods
### Mass Estimation Equation  
In the present study, we intend to calculate potato mass after first estimating the volume of potatoes for the estimation of potato mass using the regression equation. Accordingly, the length of the major axis, the length of the minor axis, and the volume were examined to develop a potato volume estimation model, and the density was examined to estimate the mass from the volume.  
The samples used for the examination were Superior, Atlantic, and Jopung, which are the three varieties that are most widely cultivated in Korea. In total, 240 samples--80 of each variety--were examined by selecting twenty samples from each of the four groups of each variety, of which the masses were 100 g or less, 100 to 150 g, 150 to 200 g, and 200 g or more. In the present study, potato mass is estimated using real-time images of potatoes that are conveyed during harvest. Since each potato can be assumed to have an oval figure [23], the lengths of the major axis and minor axis in the acquired image vary depending on the direction in which the conveyed potato is laid. Accordingly, the major axis length and minor axis length of each sample are examined from the front and the side of the potato, respectively, as shown in Figure 5.  
Potato volume is measured using the seed replacement method by filling a beaker with millet seeds, emptying the seeds and keeping them separate, placing a potato in the beaker, filling the empty space of the beaker with the seeds, and finally measuring the volume of the remaining seeds [24]. The mass of the potato is measured using a scale (WZ-3A, CAS, Korea). The methods used to measure the volumes and masses of the potatoes are shown in Figure 6.  
Figure 5: Measurement of major and minor axis lengths: (**a**) top view; (**b**) side view.  
Figure 6: Measurement of the volume and mass of a potato: (**a**) volume; (**b**) mass.  
The density of the potato is calculated using the ratio of the measured volume and mass (1).  
\[\rho=\frac{m}{V} \tag{1}\]  
where \(\rho=\) density of potatoes, kg m\({}^{-3}\), \(m=\) mass of potatoes, kg, and \(V=\) volume of potatoes, m\({}^{3}\).  
To estimate the volume of the potato using the measured lengths of the major axis and the minor axis, the data obtained by examining the lengths of the major axis and the minor axis from the front and the side are mixed together utilizing the statistical analysis software SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and is used as an independent variable, while the volume is used as a dependent variable. A linear regression analysis is carried out for each of the three varieties, and a linear regression analysis is carried out by summing up the values obtained by measuring the three varieties.
# Design and Performance Evaluation of eLoran Monitoring System
## 2 Principle and Structure
### Far-Field Monitoring Station  
The propagation characteristics of eLoran signals have large path variability, so the distance from the BPL time service station and the difference in geographical direction will cause the difference in the propagation characteristics of timing signals, including the propagation delay, field strength, signal-to-noise ratio changes, etc. Therefore, this design mainly considers the construction of four monitoring stations with different distances from the BPL time service station. Therefore, this design mainly considers the construction of four monitoring stations with different distances in four different directions of the BPL station to monitor the quality of the BPL signal in real time, and the monitoring data are transmitted to the BPL monitoring data processing center.  
The overall design scheme of the far-field monitoring station is shown in Figure 3, and the equipment components of the far-field monitoring station mainly include a BPL monitoring receiver, time and frequency traceability keeping unit, time interval counter, and data processing and transmission platform.  
Its main working principle:  
1. The time-frequency traceability keeping unit realizes time traceability to China standard time UTC (NTSC) through BDS (BeiDou Navigation Satellite System) common-view comparison, and provides a high-precision 1 PPS reference signal (measurement reference), a 10 MHz frequency standard signal, and UTC time code information;
2. The BPL monitoring receiver outputs a 1 PPS timing signal, a 10 MHz frequency calibration signal, and time code data information by receiving a BPL signal;
3. The time interval counter receives the time difference data by measuring the 1 PPS and reference 1 PPS output from the BPL monitoring receiver, and receives the time code monitoring comparison data by comparing the time code information;
4. The monitoring receiver transmits the time difference data and time code data obtained from the measurement comparison to the monitoring data processing center through the network.  
Figure 2: Component structure of near-field monitoring station.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 5 5G Wireless Network Requirements  
In the previous section, the limitations on 4G networks to provide for each service scenario were examined, and the new technical requirements were suggested to overcome these limitations. Based on this analysis, the requirements of 5G networks in terms of wireless technologies to provide for each service are summarized in Table 4. In this table, seven required wireless technologies are included along with a detailed description. These are closely related with the KPIs of 5G. Additionally, Table 5 (C): first-tier requirement, \(\odot\): second-tier requirement, \(\circ\): third-tier requirement, MCS: massive contents streaming, UCC: user-centric computing, CA: crowded area, ST: smart transportation, DM: disaster monitoring, PS/PS: private security/public safety, ES:emergency services) shows the relationship between 5G services in different categories and the required wireless technologies. For example, the requirements of IoT services can be explained as follows. Technology for massive connections are required to support the huge number of sensors in a single cell. Because most of these sensors can operate for at most several years with the battery that is initially equipped, energy efficiency in wireless transmissions is a significant requirement. Furthermore, the implementation cost of such sensors should be minimized. In addition, some of the data delivered through the smart grid networks and intelligent transportation networks in smart cities should be transmitted with high reliability and low latency. Thus, as the first three requirements mentioned are needed in every IoT scenario, they are placed in the first tier. The remaining two are in the second and third tiers because they are limited to certain scenarios.  
Figure 11: Private security and public safety services.
###### Abstract  
The comparison of the results obtained using two methods for measuring the radio disturbance emissions produced by compact lighting drivers in the frequency range of 30 to 300 MHz has been presented in this paper. Any electrical and electronic equipment used within the EU must comply with Directive 2014/30/EC and harmonised standards. For lighting equipment, the dedicated standard is EN-IEC 55015:2019-11E. In this standard, for tests in the frequency range of 30 to 300 MHz, two equivalent test methods are allowed for lighting drivers, i.e., the traditional method in which disturbance emissions are measured in a semianechoic chamber SAC and the alternative CDNE (Coupling Decoupling Network Emission) method. Each method is characterised by a different measurement technique. For this reason, this paper aims to compare the results obtained by the two methods and to find out whether the CDNE and SAC methods, despite the difference in measurement technique, can be considered equivalent. The theoretical part of this study presents the results of an analysis of the literature on the subject and the technical aspects of measuring disturbance emission using both measurement methods. The practical part describes the construction of measurement stands for selected LED lamps and presents the measurement results and their statistical analysis.  
LED lamp; electromagnetic compatibility; statistical analysis Article
## 3 Results
### Effects of FF on Liver Function  
To assess liver function improvements due to FF, serum ALT and AST levels were measured in HFD-induced mice (Figure 7). Serum ALT levels showed a significant increase in the HFD group to 60.5 \(\pm\) 7.6 U/L compared to the NFD (36.5 \(\pm\) 6.2 U/L) group, with a dose-dependent decrease observed in the FF administration group. The GTE (40.0 \(\pm\) 11.6 U/L) and FF 250 (43.3 \(\pm\) 5.4 U/L) groups showed significant decreases compared to the HFD group (Figure 7A). Additionally, serum AST levels decreased in a dose-dependent manner with FF administration. The GTE and FF 250 groups showed significant decreases to 68.3 \(\pm\) 8.9 U/L and 66.0 \(\pm\) 8.5 U/L, respectively, compared to the HFD (84.0 \(\pm\) 4.1 U/L) group (Figure 7B). These findings indicate the potential of FF to improve liver function in HFD-induced mice.
# Stability analysis of [PERSON] [PERSON]'s iterative method
If one eigenvalue \(|\lambda_{j_{0}}|>1\), \(j_{0}\in\{1,2,\cdots,n\}\) then \(x^{*}\) is unstable, that is, a repelling or a saddle point. 3. If all eigenvalues \(\lambda_{j}\) have \(|\lambda_{j}|>1\), then \(x^{*}\) is repelling. Also, a fixed point is called hyperbolic if for all eigenvalues \(\lambda_{j}\) of \(G^{\prime}(x^{*})\), we have \(|\lambda_{j}|\
eq 1\). If there exists an eigenvalue such that \(|\lambda_{j}|<1\) and an eigenvalue that \(|\lambda_{i}|>1\) the hyperbolic point is called saddle point. Let us note that, the entries of \(G^{\prime}(x^{*})\) are the partial derivatives of each coordinate function of the vectorial rational operator that defines the iterative scheme. When the calculation of spectrum of \(G^{\prime}(x^{*})\) is difficult the following result which is consistent with the previous theorem, can be used. **Proposition 1**. : _(See [11]) Let \(x^{*}\) be a fixed point of \(G\) then,_  
1. _If_ \(\frac{\partial g_{i}(x^{*})}{\partial x_{j}}\mid<\frac{1}{n}\) _for all_ \(i,j\in\{1,...,n\}\)_, then_ \(x^{*}\in\mathbb{R}^{n}\) _is attracting._
2. _If_ \(\frac{\partial g_{i}(x^{*})}{\partial x_{j}}\mid=0\) _for all_ \(i,j\in\{1,...,n\}\)_, then_ \(x^{*}\in\mathbb{R}^{n}\) _is superattracting._
3. _If_ \(\frac{\partial g_{i}(x^{*})}{\partial x_{j}}\mid>\frac{1}{n}\) _for all_ \(i,j\in\{1,...,n\}\)_, then_ \(x^{*}\in\mathbb{R}^{n}\)__\(x^{*}\) _is unstable and lies at the Julia set._  
In this paper, we only use Theorem 2 to investigate the stability of the fixed points. Let us consider an iterative method for finding the roots of a nonlinear systems \(F(x)=0\). This generates a multidimensional fixed point operator \(G(x)\). A fixed point \(x^{*}\) of \(G(x)\) is called a strange fixed point if it is not a root of the nonlinear function \(F(x)\). The basin of attraction of \(x^{*}\) (which may be a root of \(F(x)\) or a strange fixed point) is the set of pre-images of any order such that  
\[\mathcal{A}(x^{*})=\{x^{(0)}\in\mathbb{R}^{n}:G^{m}(x^{(0)})\to x^{*},m \rightarrow\infty\}.\]  
**Definition 2**. : _A point \(x\in\mathbb{R}^{n}\) is a critical point of \(G(x)\) if the eigenvalues \(\lambda_{j}\) of \(G^{\prime}(x)\) are null for all \(j=1,2,\ldots,n\)._  
The critical points play an important role in this study since a classical result of [PERSON] and [PERSON] establishes that, in the connected component of any basin of attraction including an attracting fixed point, there is always at least a critical point. As it is obvious, a superattracting fixed point is also a critical point. A critical point that is not a root of function \(F(x)\) is called free critical point. The motivation of this work is to analyze the stability of the Jacobian-free variants of [PERSON]'s method for the most simple nonlinear equations. Certainly, it is known that, in general, divided differences are less stable than Jacobian matrices, but we study how the increasing of the precision in the estimation of the Jacobian matrix affects to the stability of the methods and in the wideness of the basins of attraction of the roots.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 5 Proposed Solution  
The slice-selection task in the network runtime in a heterogeneous environment is a difficult problem since there is still no fully accepted solution or technique in this field. The reason for this is the number of variables and scenarios that exist, such as the case of solutions that consider or do not consider the inter-slice mobility process.  
Thus, the implementation of new slice selection techniques becomes quite viable, including the demand for the growing use of vehicular networks, smart cities, robotics, agriculture 4.0, healthcare, remote surgery, Unmanned Aerial Vehicles (UAVs), IoT, and Internet of Vehicles, among other technologies and scenarios involving network convergence.  
Overall, the literature suggests approaches that consider the following situation: Considering a given set of criteria or network parameters, verify at any given time and among the available slices, which one better fits the user needs, supporting the network exchange (handover process) for the mobile [8; 36]. In this case, the process of choosing the slice is subject to certain criteria [5; 17; 18].  
This work presents a novel approach that employs several techniques aiming at the integration and interoperability between RAN networks and the core of the proposed orchestration architecture, based on an efficient and robust NSSF that provides compatibility with the specification standards (e.g., 3 GPP, ETSI NFVI, and 5G PPP).
## 2 Materials and Methods  
### Sample Fabrication  
All materials including the liquid polymer, photo initiator (PI), and photo absorber (PA) are purchased from Sigma-Aldrich (St. Louis, MO, USA) and used in the as-received condition. Poly (ethylene glycol) diacrylate (PEGDA) (Mn 250) is used as a base polymer. Phenylbis (2,4,6-trimethylbenzoyl) phosphine oxide as PI and Sudan I as PA are added into the polymer at concentrations of 2 wt.% and 0.02 wt.%, respectively.  
To examine the sidewall profile of AM part, CAD models of struts with angles of 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\) with respect to a horizontal plane were designed in SolidWorks. All struts are 6 mm in height and exhibit a diameter of 800 \(\upmu\)m. Each 3D model is digitally sliced into a series of two-dimensional (2D) bitmap images with a layer thickness of 100 \(\upmu\)m.  
We used a custom-built mask projection stereolithography AM system in the study [15,16]. An ultraviolet (UV) digital projector (CEL5500, Digital Light innovations) generates spatially patterned light for each cross-sectional image, and this is projected through a 2\(\times\) microscope objective lens and focused on the top surface of the liquid polymer mixture. When a layer is photopolymerized with an exposure time of 20 s, the sample holder on which the object rests is lowered by a linear stage (MTS50-Z8, Thorlabs) by a layer thickness of 100 \(\upmu\)m. The subsequent cross-sectional image is projected to photopolymerize the next layer on top of the preceding layer. The process is repeated until all layers are completed. Each sample is individually fabricated at the center of the build area for consistency. Process parameters of the AM process, including light intensity, layer thickness, and curing time, are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Total Number of Layer** & **60** \\ \hline Layer thickness (\(\upmu\)m) & 100 \\ Curing time per layer (sec) & 20 \\ Light intensity (mW/cm\({}^{2}\)) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mask projection stereolithography process parameters.  
Figure 1: Schematic description of probabilistic modeling framework for an additively manufactured part: (**a**) Manufacturing process of a microlattice structure: a hierarchical structure of a unit cell consisting of multiple struts; (**b**) A probabilistic modeling framework for surface roughness estimation and mechanical behavior of an additively manufactured geometry.  
Fabricated samples are rinsed in ethanol to remove the uncured polymer from the object. The prepared samples were optically inspected via a microscope. Images of the side profile of the samples were captured using a digital camera attached to the microscope.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Choice of Parameters
#### 6.3.3 Choice of Frequency Threshold \(\omega_{0}\)  
Thus, we fix the threshold values \(T_{0}=0.2\) and \(T_{1}=0.5\). With the chosen values of the parameters, the considered methods are quite stable to the change of the frequency threshold \(\omega_{0}\).  
Let us demonstrate this using the same Example (7).  
Indeed (Figure 5), the range of \(\omega_{0}\) values at which method errors do not change is quite large.  
As for the recommended value, in the case where trend frequencies and periodic frequencies are known, one should take the value \(\omega_{0}\) between the highest trend frequency and the lowest periodic frequency; e.g., \(\omega_{0}=\frac{1}{24}\) for seasonal monthly time series. In the general case, one should choose as the value of \(\omega_{0}\) the upper bound of frequencies, which we consider to be trending.  
Figure 4: MSE (log) vs threshold \(T_{1}\). Example (7).  
Figure 5: MSE (log) vs frequency threshold \(\omega_{0}\). Example (7).
## 4 Conclusions  
In this study, the influence of the working pressure and pre-compression ratio of the rubber O-ring on the contact stress of the combined seal ring was studied, and the influence of the chamfer R, thickness H, and width L of the wear ring on the performance of the composite seal was discussed. The following conclusions can be drawn: (1) The maximum stress on the sealing ring is proportional to the working pressure. (2) In the process of using the combined sealing ring, it is not that the greater the compression ratio of the O-ring is, the better the sealing effect is. It is important to select a compression ratio that is suitable for the working pressure. (3) Under a certain working pressure and compression ratio, friction can be effectively reduced by increasing the chamfer R of the wear ring or decreasing the width L of the wear ring, but at the same time, sufficient contact stress must be guaranteed. (4) When the working pressure and compression ratio are determined, the friction can be effectively reduced by increasing the chamfer R of the wear ring or decreasing the width L.  
It is important to note that the simulation analysis carried out in this article considered the sealing and lubrication effects under ideal conditions; thus, only relevant simulation analyses at conventional pressure were carried out. In the future, the influence of high temperatures, high pressures, and wear ring material needs to be studied.  
Figure 8: Distribution of contact stress along the contact path at I at different widths L (mm).  
**Author Contributions:** Software, H.C. and X.C. ([PERSON]); formal analysis, H.C. and H.L.; investigation, H.C.; writing--original draft, H.C.; writing--review and editing, X.C. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors are thankful for financial support from Wuhan Enterprise Technology Innovation Project, No. 2020020602012133, Key Laboratory Open Fund (Study on the Mechanism of Gap Sealing Hydraulic Cylinder Based on Intelligent Materials No.MECOF2021B05), Central Guidelines Local Science and Technology Development Project of Hubei (Modular Refactoring Intelligent Manufacturing Key Technology and Verification Platform R & D and Application, No. 20202Y2 YD018), Guiding project of Hubei Provincial Department of Science and Technology (35 KV and below electromagnetic voltage transformer fault detection and analysis, No. B2016211).  
**Institutional Review Board Statement:** Not applicable.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data provided in the article is not public data.  
**Acknowledgments:** Thanks to WUST to support in software simulation. Thanks to [PERSON] to provide analysis and guidance.  
**Conflicts of Interest:** No conflict of interest.
###### Abstract  
This study presents a method to predict the flow velocity in a fluid-conveying pipe using vibration signals from the pipe surface. The flexural vibration of a fluid pipe is investigated through wave propagation. The wavenumbers and mode shapes of the pipe are determined based on its mechanical properties and flow velocities. The transient components of wavenumbers at low frequencies vary and converge on all values at higher frequencies as the flow velocity is increased. While the stationary fluid pipe exhibits symmetrical mode shapes, pipes with increasing flow velocities exhibit an asymmetric mode shape distribution skewed on one side of the axis. The resonant frequencies shift to the low frequency side as the flow velocity increases. The analytical results of the vibration analysis are used in the transfer function method to predict the flow velocities. To validate the accuracy of the prediction method, numerical vibration signals simulated by the finite element model are used. The actual input flow velocity is compared with the numerical results regarding the same to gauge the accuracy of the prediction method. This method can be used to monitor the flow rate without using flow meters, and thus protect pipelines from sudden malfunction.  
pipeline system; wave propagation; flow measurement; flexural vibration; flow velocity 10.3390/app1135779 Applied Sciences