esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GNSS Position Time Series Conversion and Visualization in PS-NETM  
Since time series are supplied by various data analysis centers in arbitrary formats, PS-NETM supports such formats as *series, *NEU, and *XYZ.  
It is also possible for users to create their own time series based on *pos files made in Pride-PPPAR [15]. If you wish to manually create your own time series, PS-NETM can work with *_pos_ files created in PRIDE PPP-AR v.2.2 Software (also available at GPS Toolbox). In that case, the user is required to choose a folder that contains only pos-files generated with PRIDE PPP-AR. If any other files (or subfolders) are present in the chosen directory, the user is notified with an error message.  
To start working with PS-NETM, the user needs to specify the path to the input data. Once the data are successfully adapted to the software environment, the interface displays time series plots (a time series tab) and histograms of the residual error distribution for each coordinate component N,E,U (Histogram tab).  
The graphs for each coordinate show the trend parameters. On the right side of the screen, a table with statistical indicators and a conclusion about the series is located.  
When the user applies the filter, the histogram, table, and conclusions are updated according to the \"clean\" data, and the graphs are supplemented (Figure 2).  
The result of data processing in PS-NETM is the results table with time series statistics and NETM conclusions according to the NETM. A description of the results table is given in Table 2.  
Also, during the processing, histograms of the residual error distribution with information about the number of degrees of freedom are created (Figure 4): \(k=m-s\), where \(m\) is the number of grouping intervals, and \(s\) is the number of superimposed connections. To visualize the generated histograms, it is enough to activate the Histogram tab.  
As can be seen from Figure 4, a separate histogram is created for each individual coordinate, as they are individually analyzed by PS-NETM. Each bar has a relative frequency on top of it, depicting how many residual values it contains. The red line is used for a more comfortable visualization of distribution, as it clearly depicts whether it is the zero mean or not.  
Based on the data obtained for the construction of histograms, the matching criteria are determined to test the null hypothesis that the empirical law of sample distribution obeys the theoretically predicted law of distribution of the population. Among the well-known [PERSON], [PERSON], [PERSON], [PERSON] statistical criteria of agreement, and Wilcoxon signed-rank test, etc., the first two were chosen. These criteria, in the authors' opinion, are used in mathematical statistics most often.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 1 Introduction
### Background
Section 2 describes the freeway scenario and hybrid traffic flow while Section 3 introduces a traffic breakdown prediction model and a hard shoulder dynamic opening strategy. Section 4 analyzes the traffic breakdown characteristics of the hybrid network scenario and verifies the effectiveness of dynamic hard shoulder running based on HMM (HMMD-HSR) by comparing it to hard shoulder running based on HMM (HMM-HSR), dynamic hard shoulder running based on logistic model (LMD-HSR), and no hard shoulder running (N-HSR). Finally, Section 5 summarizes the research results. The main contribution of this paper is as follows:  
1. Modeling a mixed traffic flow expressway scenario and proposing a traffic breakdown prediction method based on a hidden Markov model;
2. Analyzing the characteristics of traffic breakdown in a hybrid network environment by comparing them with the logistic model-based prediction method;
3. Proposing a dynamic hard shoulder running method (HMMD-HSR) based on the traffic breakdown characteristics and verifying its effectiveness through a combined simulation of SUMO (1.9.2) and MATLAB (R2019b).
## 4 Analysis of Test Results
### Dynamic Displacement  
#### 4.3.1 Variation of Dynamic Displacement  
Figure 17 displays the distribution of the dynamic displacement amplitude and its attenuation coefficient along the depth for various operating conditions obtained from excitation tests. The figure reveals that the dynamic displacement magnitude gradually diminishes with greater depth, and the rate of this decrease gradually decreases. Furthermore, the dynamic displacement attenuation coefficient decreases more rapidly as the vehicle speed increases. In other words, higher vehicle speeds lead to a faster decay of the dynamic displacement amplitude in the depth direction compared to slower vehicle speeds.  
In Figures 18 and 19, the distribution of the dynamic displacement along the depth under different loads is displayed. It is evident that the dynamic displacement magnitude at various depths increases as the load weight increases. Furthermore, the growth of the dynamic displacement decay coefficient corresponds with the increase in the load weight, signifying that greater load weights result in a slower decay of the dynamic displacement amplitude and a deeper influence.  
Figure 16: Variation of acceleration decay coefficient with load: (**a**) 90 km/h; (**b**) 100 km/h.  
Figure 17: Distribution of dynamic displacements along the depth for different vehicle speeds (P = 30 t): (**a**) Variation of the dynamic displacement with the depth; (**b**) Variation of the attenuation coefficient with the depth.
## 4 Materials and Methods
### Simulation Platform
#### 4.1.5 Simulator Outputs  
The SimSMILE simulator produces the following outputs. Their granularity is the same as the simulation step-size.  
* \(P_{PV}(t)\): The amount of power being produced by the solar PV system over time \(t\).
* \(P_{L}(t)\): The total consumption of the loads (L) over time \(t\).
* \(Grid(t)\): The amount of power being requested or injected in the grid over time \(t\). If power is requested, \(Grid(t)\) will have a positive sign. Otherwise, \(Grid(t)\) will have a negative sign.  
Figure 4: Cycling and calendric battery degradation models.  
Figure 3: Inverter efficiency as a function of its size and the amount of power to be transferred.  
* \(Bat(t)\): The amount of power being stored or withdrawn from the Battery over time \(t\). If power is withdraw, \(Bat(t)\) will have a negative sign. Otherwise, \(Bat(t)\) will be positive.
* \(P_{PV\_G}(t)\): The power from PV going to the Grid at time \(t\).
* \(P_{PV\_Bat}(t)\): The power from PV going to the BESS at time \(t\).
* \(P_{PV\_L}(t)\): The power from PV going to the Loads at time \(t\).
* \(P_{G\_L}(t)\): The power from the Grid going to the Loads at time \(t\).
* \(P_{Grid\_Bat}(t)\): The power from the Grid going to the Battery at time \(t\).
* \(cycles(t)\): The number of battery cycles at time \(t\).
* \(SOH(t)\): The battery State of Health at time \(t\).
* \(SOC(t)\): The battery State of Charge at time \(t\).
###### Abstract  
Expanded graphite significantly improves the tribological properties of materials in friction pairs, but there is a lack of research in the literature on its cooperation with metals and the effect of water on friction and wear mechanisms. It is particularly important to understand the phenomenon of graphite layer formation on the steel surface and its effect on tribological properties. The aim of this study is to evaluate the tribological properties of an expanded graphite-alloy steel combination operating under selected loads in both dry and humid conditions. The tests were carried out on a block-on-ring tribological tester (where the blocks were made of expanded graphite and the rings were made of AISI 4130 steel) at a rotational speed of 150 rpm, with loads of 200 and 650 N. The frictional behavior was analyzed on the basis of the measured values of the friction torque and the coefficient of friction (COF) calculated from it (and the applied load). In dry conditions, the friction torque was stable, while in humid conditions it showed cyclical changes. An increase in load from 200 to 650 N caused an increase in the average friction torque by 235% in dry conditions and by 209% in humid conditions. The presence of water reduced the friction pair temperature by 12% at 200 N and by 18% at 650 N; however, it simultaneously increased graphite consumption-by 1179% at 200 N and by 100% at 650 N. The amount of graphite deposited on the steel surface depended on the load-in humid conditions, it increased by 114% at 200 N, while it decreased by 250% at 650 N. The conducted research expanded the understanding of the influence of operating conditions on the tribological properties of the expanded graphite-alloy steel pair. It also provided new data on the friction and wear mechanisms of this material combination in humid conditions, which may have significant engineering applications.  
expanded graphite; alloy steel; material transfer; dry and humid conditions; load +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000]K.A.A.
## 5 Numerical Results and Discussion  
In this section, the numerical results calculated for the velocity and temperature profiles are presented through graphs and tables. The computations are performed to study the effects of the variation of permeability parameter \(k^{*}\), nanofluid volume concentration parameter \(\phi\), velocity slip parameter \(\delta\), thermal slip parameter \(\Delta\), suction and injection parameter \(S\), viscosity parameter \(A\) and variable thermal conductivity \(\epsilon\) on the velocity and temperature profiles of the Cu-water nanofluid. The behavior of the skin friction coefficient and Nusselt number with the variation in physical parameters is also shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Physical Properties**} & \multirow{2}{*}{**Units**} & **Base Fluid** & **Nanoparticles (Solid)** \\  & & **Water** & **Cu (300-K)** \\ \hline Density \(\rho\) & (kg/m\({}^{3}\)) & 997.1 & 8933 \\ Specific heat \(Cp\) & (J/kg\(\cdot\)K) & 4179 & 385 \\ Thermal conductivity \(\kappa\) & (W/m\(\cdot\)K) & 0.613 & 401 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermophysical properties of base fluid and nanoparticles [44]. Figure 2: Comparison of the results for the velocity \(f^{\prime}(\eta)\) profiles for different values of permeability parameter \(k^{*}\), with [PERSON] et al. [43]. The influence of the permeability parameter \(k^{*}\) on the velocity and temperature profiles in the presence of slip at the boundary is depicted in Figures 3 and 4. The velocity and temperature profiles are plotted for several values of permeability parameter \(k^{*}\) for the Cu-water nanofluid. It is observed that the velocity of the nanofluid increases with the increase in the permeability of the medium and consequently decreases the thickness of the momentum boundary layer. This is due to the fact that the increase in permeability reduces the magnitude of the Darcian body force (inversely proportional to the permeability) and enhances the motion of the fluid in the boundary layer. In other words, progressively less drag is experienced by the flow, and flow retardation thereby decreases. From Figure 4, it is noticed that the temperature \(\theta(\eta)\) at a fixed distance from the plate decreases with the increase in \(k^{*}\). The permeability parameter is inversely proportional to the density of the base fluid, hence the increase in \(k^{*}\) causes a decrease in the density and temperature of the nanofluid within the boundary layer. In conclusion, the increase in the permeability of the porous medium decreases the thickness of momentum and thermal boundary layers and eventually increases the heat transfer rate.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 4 Simulation of Voltage Mitigation
### Fast Quasi-Static Time Series Analysis
#### 4.2.2 Distribution Systems Tool  
The developed algorithm is intended to deal with distribution systems which are characterized by both system and load unbalances as well as high R/X ratio. To increase the speed of computation, hence, an accelerator is used to speed up the solution process. The main packages of the simulator include the following:  
Accelerator: The developed algorithm utilizes various predictor methods including linear and non-linear predictors to predict the initial guess of the voltage magnitude and phase angle. The initial guess for a specific function can be evaluated as follows [45]:  
\[y^{m}(x)=\sum_{i=0}^{n}y^{m}(x_{i})\prod_{\begin{array}{c}j=0\\ j\
eq i\end{array}}^{n}\frac{x-x_{j}}{x_{i}-x_{j}} \tag{6}\]  
where \(m\) refers to the phase \(a,b,\ or\ c,\ y^{m}\) is the voltage magnitude \(V^{m}\) or phase angle \(\delta^{m}\), the variable \(x\) represents the independent variable which could be the time or active or reactive power according to the prediction technique adopted for calculating the initial guess [44; 46; 47]. If the adopted technique is based on time variation instead [44; 47], a linear approximation of (6), \(n=1\), is expressed as follows:  
\[V^{m}(t)=V^{m}(t_{0})\frac{t-t_{1}}{t_{0}-t_{1}}+V^{m}(t_{1})\frac{t-t_{0}}{t_ {1}-t_{0}} \tag{7}\]  
\[\delta^{m}(t)=\delta^{m}(t_{0})\frac{t-t_{1}}{t_{0}-t_{1}}+\delta^{m}(t_{1}) \frac{t-t_{0}}{t_{1}-t_{0}} \tag{8}\]  
An approximation used for (3) can also be utilized for evaluating the initial guess during the solution of a time series considering the voltage magnitude and phase angle as a function in the load variation [46].  
Power-flow engine: The load flow analysis utilized with the FQSTS is based on the forward-backward sweep analysis method [44; 48]. The method includes mainly three basic steps [47]:  
(1) calculating the nodal current injection for phase \(p=a,\ b,\ or\ c\):  
\[I_{i}^{p}=\left(\frac{S_{i}^{p}}{V_{i}^{p}}\right)^{*} \tag{9}\]  
(2) calculating the branch current vector \(\mathbf{J}_{l}\) which flows in a line segment \(l\) connected between upstream node \(i\) and downstream node \(j\) nodes:  
\[\mathbf{J}_{l}=\mathbf{I}_{j}+\sum_{k\in L}^{L}\mathbf{J}_{k}, \tag{10}\]  
where \(k\) is the set of lines connected to the downstream node \(j\) of the line segment \(l\).  
(3) after calculating the branch currents, nodal voltages are updated starting from the substation as follows:  
\[\mathbf{V}_{j}=\mathbf{V}_{i}-\mathbf{Z}_{ij}\mathbf{J}_{l}, \tag{11}\]where \(\mathbf{V}\) is the voltage vector, \(\mathbf{Z}_{ij}\) is the line impedance matrix of a line segment \(l\) connected between nodes \(i\) and \(j\).  
The above three steps are repeated until the convergence of the power flow solution is realized.
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 5 Conclusions
### Discussion
In particular, Samsung has laid a solid technological foundation in optical communications, such as that related to fiber optic cables as well as other optical communication components, modules, and equipment. The company's research focus on photonic technologies may be further applied to the deployment of 5G in the future. The results of this study also demonstrate the importance of optical components in the construction of 5G technologies in the future. Finally, this study found that 5G photonic technology-related patents have not focused solely on a single field; rather, they are indicative of cross-domain technological development. Studies have noted that an open innovation system is conducive to the development of new technologies, the improvement of innovative performance, and the accumulation of knowledge among different stakeholders [47; 48; 49].Therefore, the government can assist in the integration of related industries in technology development in the future and establish a platform for collaboration between stakeholders in different industries to facilitate cooperation and exchange in order to enhance technological development and competitiveness in said industries.
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
###### Abstract  
As density of a wireless LAN grows, per-user throughput degrades severely, deteriorating user experience. To improve service quality, it is important to increase system spectral efficiency. Controlling carrier-sense threshold is one of the key techniques to achieve the goal, because frequently transmissions are unnecessarily blocked by carrier sensing, even though these transmissions can take place without causing packet losses. Using high carrier-sense threshold and allowing nodes to transmit aggressively may increase the system throughput, but this approach can lead to unfair channel share and cause starvation for the edge nodes. In this paper, we propose a medium access control protocol where transmitters include the carrier-sense threshold required to protect its packet in the preamble. Nodes receiving the preamble only transmit concurrently, when they are confident that their own transmission as well as the on-going transmission will both be successfully received at the respective receivers. The simulation results show that this dual-threshold approach can achieve higher system throughput compared to using a single carrier-sense threshold, without penalizing edge nodes.  
wireless LAN; spatial reuse; carrier-sense threshold control Article
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 3 HGSA Algorithm
### Initialization
#### 3.1.2 Population Initialization  
Complete the population initialization by the following five steps:  
1. Draw a \"\(T_{\max}^{k}\) circle\" with the depot of UAV \(k\), \(D_{k}\), as the center and the endurance of UAV \(k\), \(T_{\max}^{k}\), as the radius. Then delete the indices of potential targets outside the \(T_{\max}^{k}\) circle from the target set \(\mathbf{T}\) and obtain set \(\mathbf{T}_{k}\);
2. Randomly arranged the indices in set \(\mathbf{T}_{k}\) to obtain a random array. Then add '0' to the front and back of the random array to obtain a temporary routing scheme of UAV \(k\), denoted as \(A_{k}\);
3. Randomly generate the same number of heading angle indices according to the number of elements in \(A_{k}\), place them in the second row of \(A_{k}\), and place UAV index, \(k\), in the third row of \(A_{k}\), then the routing scheme of UAV \(k\), denoted as \(R_{k}\) is obtained;
4. Repeat steps 2 and 3 according to the number of UAVs, \(K\), until all UAVs obtain a routing scheme. An initial chromosome is obtained by combining all \(R_{k}\) by row.
5. Repeat steps 2-4 according to the population size to obtain the initial population.  
It is not necessary that all chromosomes of the initial population satisfy all constraints of the MURARP model. Therefore, constraint checks should be performed on the population and the chromosomes that do not satisfy the constraints must be adjusted.
# Scale-Free Random SAT Instances
## 3 Scale-Free Random Formulas
### Implementation of the Generator  
The generation method is formalized in Algorithm 1.  
The function sampleVariable(\(\beta\),n) may be implemented in two ways.  
We can compute a vector \(p\) such that \(p[i]=\sum_{i=1}^{i}j^{-\beta}/\sum_{i=1}^{n}j^{-\beta}\) at the beginning of the algorithm. Then, every time we call sampleVariable, we compute a random number \(r\) uniformly distributed in \([0,\,1)\), using a dichotomic search, look for the smallest \(i\) such that \(p[i]>r\), and return such \(i\).  
Alternatively, if \(n\) is big we can use the following approximated algorithm. If we want to generate numbers \(x\) with probability density \(f(x)\), we can integrate \(F(x)=\int f(x)\,\mathrm{d}x\), find the inverse function, and compute \(F^{-1}(y)\), where \(y\) is a uniformly random number in \([0,\,1]\). Our probability function is discrete. However, when \(0<\beta<1\), and both \(X\rightarrow\infty\) and \(n\rightarrow\infty\), we can approximate it as  
\[P(x\leq X)=\frac{\sum_{i=1}^{X}i^{-\beta}}{\sum_{i=1}^{n}i^{-\beta}}\approx \frac{\zeta(\beta)+1/(1-\beta)\,X^{1-\beta}}{\zeta(\beta)+1/(1-\beta)\,n^{1- \beta}} \tag{5}\]  
Therefore, computing the inverse, sampleVariable may be computed as  
\[X=\left\lfloor\left(\left(n^{1-\beta}+(1-\beta)\zeta(\beta)\right)Y-(1-\beta) \,\zeta(\beta)\right)^{1/(1-\beta)}\right\rfloor+1 \tag{6}\]  
where \(Y\) is a uniform random variable in \([0,\,1)\). This way, avoiding the use of the vector \(p\) and the dichotomic search, we save a \(\mathcal{O}(\log n)\) factor in the time-complexity and a \(\mathcal{O}(n)\) factor in the space-complexity of the generator.
## 3 Verification Method
### Data Acquisition and Transformation Methodology
This method also attenuates spectral leakage and suppresses side lobes in the frequency domain, thereby enhancing the clarity of dominant spectral components such as those induced by cardiac rhythms. Figure 3: Raw vs. filtered signal (time domain). Figure 2: Angular position of six-axis sensing device. The preprocessed data was then downsampled from 255 Hz to 25 Hz following a low-pass FIR filter (cutoff: 10 Hz) to prevent aliasing. Downsampling to 25 Hz was selected to retain sufficient resolution for HR detection while reducing computational load and noise. For spectral analysis, we adopted the Welch method using a 4 s Hanning window with 50% overlap (see Figure 5). This approach reduces the variance of the PSD estimate and enhances the reliability of spectral peak detection. Figure 5 shows the PSDs of both the six-axis signal and the PPG reference, where the dominant spectral components align within the 0.8-2.0 Hz range. This alignment supports the feasibility of frequency-based heart rate detection using inertial signals. The dominant peak in this band was identified and converted to beats per minute (bpm) to estimate heart rate (HR), as shown in Equation (5). \[\mathrm{HR}=60\times\mathrm{f}_{\mathrm{max}} \tag{5}\]  
The above pipeline--filtering, downsampling, windowing, PSD computation, and peak detection--was consistently applied to both the six-axis sensor and PPG data. The effectiveness of each analysis stage was validated by comparing results using paired \(t\)-tests and Bland-Altman plots across data from 20 participants. Figure 4: PSD before vs. after filtering. Figure 5: Comparison of PSD: six-axis vs. PPG (Welch method).
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 4 Linking Genetics and Imaging
### Imaging Biomarkers and Genetic Data Correlation  
Imaging biomarkers, such as Wall Shear Stress (WSS), provide a non-invasive means to assess the functional consequences of genetic mutations. Abnormal WSS patterns, which can be quantified using 4D Flow MRI, are associated with different BAV phenotypes and related aortopathies [40]. High WSS regions correlate with aortic dilation and aneurysm formation, while low WSS regions might indicate areas prone to thrombosis [40]. In addition to WSS, other imaging biomarkers have emerged as valuable tools in understanding the genetic impact on BAV pathology. Flow helicity and eccentricity, measurable via 4D Flow MRI, reflect disturbed hemodynamics often associated with genetic mutations such as NOTCH1 and SMAD6. These biomarkers highlight asymmetric and turbulent blood flow, which can further exacerbate aortic wall stress and promote localized remodeling [52; 53].