esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parameter Optimization
### Response Surface Analysis
#### 5.4.2 Regression Model Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Sum of Mean Squares** & **Dof** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 0.0049 & 20 & 0.0002 & 20.91 & \textless{}0.0001 & significant \\ A-\(X_{9}\) & 0.0008 & 1 & 0.0008 & 67.46 & \textless{}0.0001 & **\\ B-\(X_{0}\) & 0.0003 & 1 & 0.0003 & 27.67 & \textless{}0.0001 & **\\ C-\(X_{5}\) & 0.0019 & 1 & 0.0019 & 164.58 & \textless{}0.0001 & ** \\ D-\(X_{3}\) & 0.0001 & 1 & 0.0001 & 7.63 & 0.0106 & * \\ E-\(X_{2}\) & 0.0010 & 1 & 0.0010 & 86.17 & \textless{}0.0001 & ** \\ AB & \(2.500\times 10^{-7}\) & 1 & \(2.500\times 10^{-7}\) & 0.0215 & 0.8847 & - \\ AC & 0.0000 & 1 & 0.0000 & 1.82 & 0.1897 & - \\ AD & \(9.000\times 10^{-8}\) & 1 & \(9.000\times 10^{-8}\) & 0.0077 & 0.9306 & - \\ AE & \(3.063\times 10^{-6}\) & 1 & \(3.063\times 10^{-6}\) & 0.2630 & 0.6125 & - \\ BC & 0.0001 & 1 & 0.0001 & 10.77 & 0.0030 & * \\ BD & \(2.103\times 10^{-6}\) & 1 & \(2.103\times 10^{-6}\) & 0.1806 & 0.6745 & - \\ BE & \(2.500\times 10^{-9}\) & 1 & \(2.500\times 10^{-9}\) & 0.0002 & 0.9884 & - \\ CD & 0.0002 & 1 & 0.0002 & 12.99 & 0.0014 & * \\ CE & 0.0001 & 1 & 0.0001 & 7.83 & 0.0097 & * \\ DE & \(1.000\times 10^{-6}\) & 1 & \(1.000\times 10^{-6}\) & 0.0859 & 0.7719 & - \\ A\({}^{2}\) & \(5.638\times 10^{-7}\) & 1 & \(5.638\times 10^{-7}\) & 0.0484 & 0.8276 & - \\ B\({}^{2}\) & \(4.923\times 10^{-7}\) & 1 & \(4.923\times 10^{-7}\) & 0.0423 & 0.8388 & - \\ C\({}^{2}\) & 0.0003 & 1 & 0.0003 & 25.32 & \textless{}0.0001 & ** \\ D\({}^{2}\) & \(1.044\times 10^{-6}\) & 1 & \(1.044\times 10^{-6}\) & 0.0896 & 0.7671 & - \\ E\({}^{2}\) & \(1.095\times 10^{-6}\) & 1 & \(1.095\times 10^{-6}\) & 0.0940 & 0.7617 & - \\ \multicolumn{6}{c}{\(R^{2}\) = 0.9436, \(R^{2}_{\rm adj}\) = 0.8985, \(CV\) = 8.03%, Adeq Precision = 19.7373} \\ \hline \hline \end{tabular} Note: ** is extremely significant (\(p\) < 0.001); * is significant (\(p\) < 0.05)¿- is not significant (\(p\) > 0.1).  
\end{table} 
Table 5: ANOVA of BBD quadratic regression model. The ANOVA of the optimized regression model is shown in Table 6, with CV = 7.05%, \(R^{2}\) = 0.9374, \(R^{2}\) adj = 0.9217, and Adeq Precision = 31.7698. Compared to the pre-optimization period, the resultant regression equations show a significant improvement in terms of fit, reliability, and precision. The software Design-Expert 12 was applied to solve the optimized regression equation for optimization with volume ratio as the objective; the optimal values of the five parameters were obtained: \(X_{9}=-224.759\), \(X_{0}\) = 877.27, \(X_{5}\) = 1.574, \(X_{3}\) = 1498.308, and \(X_{2}\) = 924.974. The values of these five parameters were substituted into Equation (19) to obtain the volume ratio \(\Re=0.064\), which increased by 43.82% compared to the pre-optimization ratio, proving the effectiveness of using the volume ratio as the response value for parameter optimization. Finally, the optimized mechanism parameters were obtained as shown in Table 7.
# A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks
## 2 Stability Assessment of Earthworks
### Reliability of Slopes  
Because soils are naturally occurring materials, their properties vary spatially and temporally. Therefore, while simple limit equilibrium analyses such as those described in Section 3.1 can be performed to determine the factor of safety against slope failure, many of the input parameters needed for these analyses are highly variable. As a result, the _FOS_ value obtained is critically dependent on assumptions made by the designer. For the case of shallow translational landslides, the specific uncertainties associated with this failure mode include; the variation of total cohesion when suctions reduce as a result of changes in water content during infiltration and difficulties in assessing the rate at which wetting front development occurs. The wetting front or failure surface depth is a function of many variables. These include the initial suction profile (which depends on the grain size properties and antecedent weather), the geometry of the slope and permeability of the soil, rainfall intensity and duration, among other factors. For the rotational failure mechanism, the key uncertainties are the shear strength parameters and the presence of local soft spots or buried features, stresses on the failure plane and location of the slip surface. For rock wedge failures, the key uncertainty relates to the fracture spacing and the impact of freeze-thaw cycles.  
Probabilistic analysis offers a rational way for engineers to include uncertainty in their designs [32]. Instead of assuming deterministic (unique) values for variables, the full distribution of likely values is considered. The limit-state (performance) function, which compares the capacity (\(C\)) and demand (\(D\)), See Figure 3 is given by Equation (3).  
\[g(X)=(C-D)\begin{cases}&>0,\;safe\;state\\ &=0,\;limit\;state\\ &<0,\;failure\;state\end{cases} \tag{3}\]  
The probability of failure is the area of the curve where demand exceeds capacity and the reliability index \(\beta\) is defined as the distance by which the mean or expected value of the performance function exceeds zero in units of its standard deviation, \(\sigma\) (Equation (4). As entire distributions are being used as inputs, reliability can either be determined through brute force computation using Monte Carlo or else approximated using mathematical optimization approaches such as First Order Reliability Methods [33], Second Order Reliability Methods [34], Genetic Algorithm [29], Particle Swarm Optimisation [35,36], etc. This paper utilizes a genetic algorithm approach, as recommended by [PERSON] and [PERSON] [29], to determine the reliability index.  
\[\beta=\frac{\mu(G(X))}{\sigma(G(X))} \tag{4}\]  
Figure 3: Definition of the probability of failure, P(f) and reliability index, \(\beta\).
## 3 Background on YANG and OPC UA
### Yang  
YANG is a data modeling language commonly used in networks and device configurations to define data structures and relationships for network management protocols, such as NETCONF, which IETF created as a network configuration standard [33; 34]. O-RAN Alliance Working Group 4 standardized the Management Plane (M-Plane) for managing the RU. It defines YANG data models for managing, configuring, and monitoring the RU. The specification defines two architectures for managing the RU. In the hierarchical model, the RU is managed entirely by DUs, whereas in the hybrid model, the RU can be managed by centralized network management systems as well as DUs [35]. O-RAN Alliance Working Group 10 and 3 GPP standardized the operations and management of DU and CU in the O1 specifications [36]. O1 Interface specification for CU defines the interfaces between CU and the centralized network management system by defining the YANG models for management, configuration, and monitoring. O1 Interface specification for DU defines the interfaces between DU and the centralized network management system by defining the YANG models for management, configuration, and monitoring. The yang models for RU, CU, DU, and 5 GC are maintained in the 3 GPP repository [37]. The YANG data model defines the data tree that represents the hierarchical structure of defining components' data. Figure 2 summarizes the YANG's several built-in constructs, allowing data modeling and representation flexibility.  
Figure 2: YANG language construct.
## 2 System Model and Basic Assumptions
### A&F NR-RN Signal Model
In (5), the first term is the desired signal and the second term is the noise, which denotes the noise in the backhaul and access links at the cooperative system, respectively.
## 3 Results and Discussion
### Effect of Air Flow on Arc Evolution  
In this section, the effect of air velocity on the rotating gliding arc was analyzed. The discharge cones used in this section were all No. 5 discharge cones. In order to avoid the effect of accidental arc stretching on the arc length, the threshold value of gray value was set at 20 in the calculation program of the arc length. Pixels with a gray value less than 20 were not included in the calculation of the arc  
Figure 12: The effect of the electrode size on rotational frequency with the extension tube.  
length. In order to visualize the difference in the arc lengths, two light blue dotted lines were drawn at the same position in the image, as shown in Figure 13. Obviously, the arc length increased significantly from 100 SLPM to 150 SLPM and decreased significantly from 150 to 200 SLPM. Then, with the increase of air flow rate, the arc length basically remained unchanged, as shown in Figure 14. When the air flow rate was greater than 200 SLPM, the arc length was between 30 mm and 35 mm, and the arc length did not change significantly with the change of air flow rate. The influence of the air flow rate on arc length was mainly reflected in the small flow range (100-200 SLPM).  
Although only a small range of air flow had an effect on the arc length, the air flow had a significant effect on the rotation frequency and regeneration frequency of the rotating gliding arc in all experimental conditions. Figure 15 shows the effect of air flow on the two characteristic frequencies in one chart where it was obvious that the higher the velocity, the higher the frequency, but the change trend of the two characteristic frequencies was different. The rotating frequency was more sensitive to the increase of air flow, and its growth rate was much faster. From 150 SLPM, the ratios of rotation frequency to regeneration frequency were 1.07, 1.09, 1.63, 1.80, 1.75, and 2.0, respectively. Basically, when the flow rate was low (150-200 SLPM), the gliding arc rotated exactly one cycle during a tensile fracture period. With the increase of air velocity, the effect of air swirl was strengthened, and the number of gliding arc rotating circles increased gradually in a tensile fracture period. When 400 SLPM was reached, the gliding arc rotated two circles in a tensile fracture period.  
Figure 14: The influence of air flow rate on arc length (No. 5 discharge cone, with the extension tube).  
Figure 13: Side photographs of the arc taken under different air flow rates.
## 2 Stability Analysis of Haptic Interaction
### Virtual Spring-Damper  
If the haptic interaction consists of an elastic force with stiffness coefficient \(K\) plus a viscous effect with damping coefficient \(B\), then the characteristic equation of this system will be a third-order polynomial:  
\[1+\left(K+B\frac{z-1}{Tz}\right)\frac{z(e^{-\delta}-1+\delta)+1-(1+\delta)e^{- \delta}}{(z-1)(z-e^{-\delta})\frac{b^{2}}{m}}=0 \tag{23}\]  
\[Tz(z-1)(z-e^{-\delta})\frac{b^{2}}{m}+[(KT+B)z-B]\left[z(e^{-\delta}-1+\delta) +1-(1+\delta)e^{-\delta}\right]=0 \tag{24}\]  
The condition that is required in order to preserve stability [17] is:  
\[0<K<\frac{b(B+b)(e^{\delta}-1)(Bm-Bme^{\delta}+Tb^{2}e^{\delta}+BTb)}{(me^{ \delta}-m-Tb)(Bm-Bme^{\delta}+Tb^{2}e^{\delta}+BTbe^{\delta})} \tag{25}\]  
Figure 2 shows the stability boundary (25) in the dimensionless (\(\alpha,\beta\))-plane for two values of \(\delta\). The shape and size of the stability region only changes with parameter \(\delta\). However, typical values of \(\delta\) are so low [15] that the influence of this parameter on the shape of the complete stability region is barely noticeable.  
The abscissa axis of Figure 2, for \(\alpha=0\), meets the previous condition (15), although the lower limit is so low as compared to the upper one that the stability boundary would seem to start at the origin \((0,0)\) instead of \((-\delta,0)\). Similarly, the ordinate axis for \(\beta=0\) meets condition (9). Again, the stability boundary would seem to start at the origin \((0,0)\) instead of passing through the point \((0,2\delta)\).  
Figure 2: Stability boundaries for virtual spring-damper effects.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 2 Materials and Methods
### Image Acquisition  
In order to develop and validate the proposed crack width measurement method, reinforced concrete beams were subjected to four-point bending loading until failure in the laboratory. The width of the cracks on the beams was manually measured in millimeters. Images of the cracks were captured using two different image acquisition devices. The first device was an iPhone 11 Pro Max smartphone equipped with a 12 mega-pixel f/1.8 aperture wide lens. The second device was a Trust webcam with a resolution of 640 \(\times\) 480 pixels. The reason for choosing these two devices was their difference in image resolution, with the iPhone producing high-quality images while the Trust webcam produced low-quality images. The distance at which some of the images were captured is shown in Table 1, along with details of the image acquisition devices. This measurement and acquisition process was repeated on cracks in buildings for validation purposes.
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods
### CoHo-Pol Model for Right Circular-Horizontal (RH) Polarization  
To build the compact RH polarized wind speed retrieval model, denoted CoHo-Pol, we employ the parameterization method from [PERSON] et al. (2018). Thus, we use a quadratic relationship in a regression model between buoy wind speed, as a dependent variable, and RH-polarized NRCS, along with incidence angles as independent variables:  
\[V=a_{0}+a_{1}\sigma_{RH}^{0}+a_{2}\theta+a_{3}\Big{(}\sigma_{RH}^{0}\Big{)}^{2 }+a_{4}\theta^{2}+a_{5}\sigma_{RH}^{0}\theta \tag{6}\]  
where \(V\) is the wind speed (m/s), \(\theta\) is the radar incidence angle (degree), and \(\sigma_{RH}^{0}\) is the RH-polarized NRCS (dB). Table 2 presents the parameters for the model.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 3 Implementation
### Dee-Km
#### 3.3.2 Methodology  
The proposed DEEC-KM protocol introduces a more adaptive and energy-efficient approach to clustering in WSNs. The algorithm starts by identifying all live nodes in the network and dynamically adjusting the number of clusters based on the current network size. This ensures that clusters remain balanced and efficient as nodes die over time. The K-Means algorithm is then applied to the spatial coordinates of the nodes, grouping them into spatially balanced and well-distributed clusters across the network area.  
The node with the highest residual energy in the network will be selected as the CH within each cluster. This energy-aware selection ensures that leadership roles are assigned to nodes best equipped to handle the additional communication load, thereby prolonging the network's operational life. The number of clusters in K-Means was not fixed but dynamically adjusted based on the number of active nodes in the network at each round. Specifically, we used the heuristic Number of Clusters = \(max(2,\textit{floor}(\textit{sqrt}(n_{\textit{alitve}})))\), where \(n_{\textit{alitve}}\) is the number of currently active nodes. This approach ensures that clusters remain balanced as the network evolves, which is particularly important in WSNs with node failures. We clarify this rule in the methodology section and provide a brief discussion of its impact. The K-Means algorithm was set to terminate when cluster assignments no longer changed between iterations or when a maximum of 100 iterations was reached, whichever came first. This stopping criterion is standard in clustering applications and ensures convergence without excessive computation.  
Using the K-Means clustering algorithm improves the spatial organisation of clusters and allows for adaptive resizing, which helps maintain optimal cluster sizes and load balancing as the network evolves.
## 2 Materials and Methods
### Statistical Analysis  
The results of all tests were processed and subsequently converted into a table using Microsoft Excel (Microsoft Corp., Redmond, WA, USA). The statistical analysis of the data was conducted using the data analysis software Statistica (Version 14.0, StatSoft, Tulsa, OK, USA). Descriptive statistics, including mean, standard deviation, median and interquartile range, were calculated. The Shapiro-Wilk test was applied to assess the normality of data for all observed variables, revealing that normality was not confirmed across variables. To compare differences among groups classified by maturity status (early, average, late) for each variable, we employed the non-parametric Kruskal-Wallis ANOVA test. Subsequently, to assess differences between maturity status groups, multiple comparisons of mean ranks were used. Statistical significance was established at \(p<0.05\).
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
In Section 3, the closed-form analyses for outage probability and throughput, which are the main performance metrics, are conducted. Computer simulations are performed in Section 4 and the conclusion is provided in Section 5. **Notation**: \(\Pr\{\cdot\}\) stands for a probability. \(f_{X}(\cdot)\), \(F_{X}(\cdot)\), and \(\mathbb{E}\{\cdot\}\) represent the probability density function (pdf), the cumulative distribution function (CDF), and the expectation of a random variable \(X\), respectively. \(\Gamma(\cdot)\) denotes a Gamma function.
# Genetic Algorithm for the Retailers' Shelf Space Allocation Profit Maximization Problem
## 6 Computational Experimental Results
### Tuning of the Genetic Algorithm  
A tuning process on small instances preceded GA testing. This helps to figure out control parameter values, which guarantee the highest efficiency for the chosen metaheuristic method. For this reason, each set of products on a four-shelf planogram with the shelf length equal to 250 cm are used.  
The following GA parameters were set:  
* Number of tests = 20;
* Population size = 3 \(\times\) 13 (13 algorithms, take the 1 st, the 2 nd, the 3 rd product according to the algorithm);
* Number of products = 10;
* Number of shelves = 4;
* Shelf length = 250 cm;
* Termination condition:
* Maximum number of iterations = 50;
* Number iterations without improvement = 0.
* While performing GA tuning such control parameters are tested:
* Crossover rate = {0.1, 0.2... 1.0};
* Mutation rate = {0.01, 0.02... 0.1};
* Selection method = {Two Rankings, Roulette Wheel, Tournament}.  
As a result of the tuning process, the configuration parameters listed in Table 2 were found. We provide clarification of several parameters in Table 2, where the following abbreviation are used: TR--Two Rankings; RW--Roulette Wheel; T--Tournament.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### System-Level Integration of Dynamic Memory Management
#### 4.2.2 Dynamic Memory Management Related Code  
This section describes the dynamic memory allocation strategy for storing real-time GPS trajectory data. The pseudo-code Algorithm 1 below initializes a linked list, allocates memory for new nodes upon data arrival, and handles allocation failures:  
```
Input: GPS data stream Output: Trajectory linked list
1: Initialize empty linked list
2: while system running do
3: if new GPS data available then
4: allocate new_node \(\leftarrow\)malloc(Node)
5: if allocation successful then
6: store coordinates in new_node
7: insert new_node into linked list
8: else
9: trigger memory warning
10: end if
11: end if
12: wait for next sampling interval
13: end while
```  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Member** & **Description** \\ \hline longitude & record longitude \\ latitude & record latitude \\ next & pointer to the next Node structure \\ \hline \hline \end{tabular}
\end{table}
Table 3: Node structure members.
###### Abstract  
(1) Background: Dynamic knee valgus (DKV) is a common biomechanical risk factor for knee injuries, particularly in sports involving high-intensity movements, such as basketball. While neuromuscular control and structural alignment contribute to DKV, recent evidence indicates that lower limb muscle power (LLMP) and cardiorespiratory fitness (CRF) may significantly influence DKV. This study aims to examine the relationship among LLMP, CRF, and DKV in adolescent basketball athletes. (2) Methods: A total of 104 adolescent basketball athletes (63.5% boys), 12 to 17 years old (13.87 \(\pm\) 1.46 years) participated in this study. Anthropometric and demographic characteristics such as sex, age, height, weight, and body mass index (BMI) were recorded. The Counter Movement Jump (CMJ) was used for the evaluation and prediction of the LLMP, the 20 m shuttle run test (20 mSRT) was used for the evaluation and prediction of CRF, and the single-leg drop jump (SLDJ) was used for the evaluation of DKV via a two-dimensional (2D) kinematic analysis. Statistical analysis included Pearson and Spearman correlations, as well as multiple linear regression, to determine the relationship among LLMP, CRF, and DKV. (3) Results: A statistical analysis revealed strong correlations among LLMP, CRF, and DKV. [PERSON]'s correlation coefficients demonstrated significant associations between the VO\({}_{2}\)max and frontal plane projection angle (FPPA) (r = 0.78, \(p<0.001\)), as well as between LLMP and FPPA (r = 0.82, \(p<0.001\)). Multiple linear regression analysis showed that VO\({}_{2}\)max and LLMP together accounted for 85% of the variance in FPPA (R\({}^{2}\) = 0.85, \(p<0.001\)). (4) Conclusions: The findings highlight that both aerobic capacity and lower limb muscle power significantly contribute to knee valgus control among adolescent basketball players. Implementing training programs focused on improving lower limb muscle power and cardiorespiratory fitness may enhance knee stability and reduce the risk of lower limb injuries. Given the strong predictive value of VO\({}_{2}\)max and LLMP for knee control, targeted training programs focusing on neuromuscular conditioning and aerobic capacity may be effective for injury prevention.  
f _Applied sciences_  
1
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
###### Abstract  
Structural damage inspection after an earthquake is essential for safety assessment of the affected wood-frame buildings and for making knowledgeable decision regarding their repair, renovation, or replacement. We present a polarimetric radar system for sensing the concealed wood-frames damaged by earthquakes. This system employs an antenna array consisting of four linearly polarized Vivaldi antennas recording full-polarimetric radar echoes in an ultra-wideband ranging from 1 to 20 GHz. The detailed design of the system and the signal processing algorithms for high-resolution 3D imaging are introduced. We conducted a number of surveys on damaged wooden wall specimens in laboratory. The experiment results indicate that the high-frequency radar waves can penetrate the wooden walls. Deformations of wooden structures (about 2 cm displacement) inside the wall, as well as the concealed small metal nails (about 3 mm in diameter and less than 2 cm in length) and bolts can be clearly imaged. The shape and orientation of the wooden members have shown a great sensitivity to the radar polarization. It is concluded that radar polarimetry can provide much richer information on the condition of concealed building structures than the conventional single-polarization subsurface penetrating radar.  
ground-based synthetic aperture radar (GB-SAR); radar polarimetry; wood-frame building; earthquake damage; non-destructive testing (NDT) Article