esa-sceva/satcom-chunk-collection huggingface dataset:
## Finite Element Model
### Comparison with the Closed-Form Solutions

Table 1: Comparison of maximum deflection and the interface slip of the beam end for two types of load. Figure 5: Details of the simply supported composite beam. (a) uniformly distributed load; (b) point load; and (c) cross-sectional properties  
As shown in Table 2, when the value of connector stiffness increases from 3.46e4 N/mm to 1.0e8 N/mm, i.e., from partial interaction to full interaction, the maximum deflection at the mid-span of the beam decreases by 35.37% or so in the case of a uniformly distributed load; meanwhile, the maximum deflection decreases by as much as 35.94% in the point load case. In addition, it can be seen that the deflection and interface slip of the composite beam decreases with increasing values of stiffness of a shear connector \(k\) for the same composite beam. On the other hand, slip exists at the interface between the two components of the composite beams because of the partial interaction, thus affecting the deformation capacity of the composite beam. Therefore, interface slip has a great influence on deflection. Nevertheless, a great number of composite beams employ partial interaction, since slip always exists at the interface between the concrete slab and the steel beam of a composite beam. As a consequence, it is unsafe to ignore the existing interface slip of a composite beam with partial interaction; in other words, interface slip should be considered in deflection analysis.
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### Design of the Upper-Level NSGA-II Algorithm
#### 4.2.3 Hierarchical Random Mutation Operator  
In the NSGA-II algorithm, the mutation operator introduces \"mutations\" in one or more genes of a chromosome to increase the probability of escaping local optima and maintaining population diversity. Based on the special encoding rules of the chromosome, this paper adopts a hierarchical random mutation method, where a random gene position in different rows of the matrix is selected for mutation. Notably, due to specific encoding constraints (i.e., if the splitting flag is 0, both the splitting level and batch count are set to 1), when a mutation occurs in the splitting flag gene position, the corresponding splitting level and batch count must also be modified accordingly. The detailed process is illustrated in Figure 5.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods  
### Sampling Location and Duration  
The project scope of the Macao Management Work Package in Buildings covers all buildings using secondary water supply systems in Macao, including residential buildings, commercial buildings, industrial buildings, schools, hotels, and casinos. Some school dormitories are selected as sampling points for water quality monitoring based on actual demand, and water quality data will be collected on both regular and irregular bases. The study focuses on five dormitories, with the monitoring period spanning from July to November 2024. Regular sampling is scheduled at three fixed time slots--8:00 a.m., 2:00 p.m., and 8:00 p.m.--to ensure coverage of major water usage periods and capture fluctuations in water quality throughout the day. Additionally, unscheduled sampling is conducted based on specific demands to enhance dataset completeness and representativeness.
# Spectroscopic Studies of Laser-Based Far-Ultraviolet Plasma Light Source
## 3 Computational Model  
To theoretically investigate the spectral characteristics of plasma radiation, we constructed a CR balance-rate equation, including excited states in a detail-level accounting approach [6; 16; 18; 21]. The CR model includes all of the important atomic processes in a typical LPP light source, such as the auto-ionization from the doubly excited states and dielectronic recombination, electron impact ionization and three-body recombination, electron impact excitation and deexcitation, photo-ionization and radiative recombination, photo-excitation and photo-deexcitation. The effect of photo-absorption on a level population is calculated by reducing the radiative decay rate by a factor equal to the escape probability factor. A frequency-averaged escape probability formalism is employed for the Voigt spectral line radiation. Due to the lack of public and validated atomic physics data necessary for solving the CR model, see for example, the NIST database [22], the calculations of the atomic cross-sections and rates for any chosen element were performed using the FAC code [15]. The FAC solves the relativistic Dirac equation, using a single central parametric potential to compute the orbitals. Radiative decay, collisional excitation, and ionization, auto-ionization and photo-ionization cross sections, and rates are computed within the distorted wave approximation. To study the plasma dynamics in LPPs, a radiation-hydrodynamics code FLASH, is used [19]. The FLASH is a three-temperature (electron, ion, and radiation) state-of-the-art radiation-hydrodynamics solver, including the thermal conduction, multi-group radiation diffusion, tabulated equations-of-states (EOS), and laser ray-tracing model. For a given target material, the constructed CR is used to calculate a necessary non-LTE EOS database, including the spectral emissivity and spectral absorption coefficients on a density-temperature grid. In the present study, the calculations were carried only out for an Si plasma. To identify the Si ion charge states responsible for the experimental Si spectral emission in Figure 1 and also to verify the calculated spectral line positions by the FAC code, calculations were done using the CR solver for a homogeneous plasma. In this work, the CR model for Si includes a total of 1050 levels of the neutral to fully ionized Si charge states as listed in Table 2. For simplicity, the CR solver includes main spectral transitions among responsible low-ionized charge states for plasma FUV emission.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 1 Introduction
* We defined energy consumption based dynamic selfishness, which is less harmful but more realistic in MANETs, and its impacts on the performance of network are evaluated from various aspects with comparison to that of static selfish nodes as baselines. * In this work, a static/dynamic selfishness switching functionality is implemented in the routing table module at Network Layer in INET Framework [8], and it is irrelevant to any specific routing protocol implementation. Therefore, there is no need  
Figure 1: An example of network partitioning caused by the presence of selfish nodes in a mobile ad hoc network (MANET). to customize routing protocols to test the network performance with static/dynamic selfish nodes. * Exhaustive computer simulations are conducted on a proper platform to evaluate the impacts of selfish nodes with various randomization in MANETs. The remainder of the paper is organized as follows: In Section 2, we discuss some recent work related to node selfishness in wireless ad hoc networks. The energy consumption based dynamic selfish nodes are defined in Section 3. Evaluation algorithms and evaluation metrics are introduced Section 4. Simulation setup including the platform and parameters used in this work is given Section 5. Section 6 provides the results of the simulations and corresponding explanations. The results obtained from the simulations are discussed in Section 7. Finally, Section 8 concludes our work and points out our future research directions.
## 7 Performance Evaluation
### Mesh Antenna Array
This is because when directional antennas are used, the probability of connections increases for various distances among the collaborative sensors because of optimizing the mainlobe of the antenna. We believe that the connectivity solutions in the realm of IoT aim not only at supporting the needs imposed by the several practical applications (such as logistics, agriculture, and asset management), but also to support network self-management for these applications. Thus, a part of the use of the performance of CB is to improve the connectivity solutions for a certain application depends on how well its features solve the specific needs of the end application. There are several studies that address the comparison of connectivity solutions for IoT applications by covering many possible measurement domains with relevant parameters for topologies in terms of power or signal strength-based, time-based, and space-based [45; 46; 47; 48].
## 4 Related Work
The only methods [33; 38; 44; 46] obtaining performance slightly higher than the one we obtained consider the binary detection between healthy patients and patients affected by lung disease. Differently, the method we propose is aimed at discriminating between healthy and disease-affected patients, but it is also devoted to the identification of the specific lung disease. From the analysis of the current state-of-the-art literature, it emerges that researchers are mostly focused on the binary discrimination between healthy patients and patients with lung diseases, while the proposed method is also devoted to detecting the lung disease with a feature set never previously considered in the lung disease detection context. Moreover, the performances are lower in comparison to the ones we achieved by using the _neural network_ classification algorithm. Another novelty is represented by the lung disease characterisation, i.e., the automatic detection of the specific lung disease. We highlight also that, to the best of the authors' knowledge, the proposed feature set has not been exploited in the previous literature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Features** & **Performance** \\ \hline [PERSON] et al. [32] & IMF & N.A. \\ [PERSON] et al. [33] & BP-NN & 98.33\% \\ [PERSON] et al. [42] & ELM, SVM & 92.86\% \\ [PERSON] et al. [43] & fractal & N.A. \\ [PERSON] et al. [44] & width & 98.3\% \\ [PERSON] et al. [45] & K-NN & N.A. \\ [PERSON] et al. [46] & Lacunarity & 99\% \\ [PERSON] et al. [47] & AR coefficient & 67\% \\ [PERSON] et al. [48] & Time-variant AR & N.A. \\ [PERSON] et al. [49] & MFCC & 83\% \\ [PERSON] et al. [38] & NMF & 95\% \\ [PERSON] et al. [39] & MEL & 71\% \\ [PERSON] et al. [40] & CNN & 93\% \\ [PERSON] et al. [41] & NN & 92.5\% \\ Our method & CR,RMS,SC,SR,ZCR,MEL,T,P & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: State of the art comparison in lung disease classification; (N.A. stands for data not available).
# Layout Model of Bike-Sharing Facilities in the Transfer-Influenced Area of a Subway Station
## 3 Results and Discussion
### Layout and Configuration Results
Compared with the original plan, the total number of facilities increased by 4, and the riding volume is 1121, increasing by 17.4%. Transfer cost is reduced by 6.4%, and facility-operating cost is increased by 13.6%, which is mainly due to the increasing number of bike-sharing facilities and bikes. Although the total operating costs of the facility increased, the average facility operating costs declined by 9.1%. These results show that the facility layout and bike-sharing allocation of the optimal scheme are more reasonable and scientific. The principle of the optimization results includes the following two main aspects:  
1. The model optimization effect is related to the multi-objective weight of the facility layout in the transfer-influenced area. Theoretically, to maximize riding volume and minimize the number of facilities, it is necessary to reduce the transfer costs and choose the facility location with the shortest walking distance wherever possible, and provide sufficient shared bikes to meet demand. On the one hand, if the number of facilities is too small, the accumulation of a large number of shared bikes will increase the overload penalty corresponding to the number of transfer rides, while on the other hand, too many facilities will trigger resource waste penalties due to oversupply. Therefore, the optimal layout scheme needs to consider the comprehensive effect of multi-objective optimization at the same time, and solve the layout scheme with optimal comprehensive benefit. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **First-Generation Scheme** & **Optimal Scheme** & **Change Rate** \\ \hline Number of facilities & 16 & 20 & 25\% \\ Riding volume & 955 & 1121 & 17.4\% \\ Total transfer cost & 32300 & 30235 & \(-\)6.4\% \\ Facility operating cost & 17600 & 20000 & 13.6\% \\ Average facility operating cost & 1100 & 1000 & \(-\)9.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of various indicators between the schemes. 2. The optimization effect is related to the input parameters of the model. The bi-level programming model is an optimization model in which the upper-level location plan and the lower-level division interact each other and dynamically change. Assuming that the overall demand remains unchanged, changes in the single-use cost parameters of bike-sharing and bus will lead to changes in transfer cost, which will cause changes in the sharing rate of transfer modes, and indirectly affect the layout scheme. The total number of facilities supplied and the number of basic bikes allocated will directly affect the number of facilities and bikes allocated, which in turn has an impact on the final result of the sharing rate of transfer mode division.
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## 5 Results and Discussion
### Effects of Random Number Update Rate and Modulation Index
This conclusion is confirmed also in terms of Channel Capacity and Channel Capacity Loss, whose plots are omitted for the sake of brevity.
# Estimation and Adjustment of Interface Stiffnesses for Machine-Tool
## 2 Theoretical Foundation
### Frequency Response Function  
Dynamic stiffness is one of the six expressions for the frequency response function (FRF). Figure 3 shows a typical single-input and single-output system. The time-domain input waveform is the impact force \(f(t)\), and the time-domain output waveform by the system is \(x(t)\). The frequency domain signals obtained by Fourier transform for the input and output time-domain waveforms are represented by \(F(\omega)\) and \(X(\omega)\), respectively.  
The frequency response function \(H(\omega)\) can be defined as the ratio of the system output \(X(\omega)\) to the system input \(F(\omega)\) under harmonic excitation. It can also be called the transfer function. The common expression is as follows:  
\[H(\omega)=\frac{Output}{Input}=\frac{X(\omega)}{F(\omega)} \tag{3}\]  
The frequency response function is a characteristic of a linear dynamic system, and it will not change with the type of the input excitation function. The excitation function can be a sine function, a random function, or a transient function in the time domain. The test results obtained by any kind of the excitation can be used to judge the response of the system by the excitation. Using the impulse excitation generated by the hammer is the simplest and fastest technique to obtain accurate structure frequency response function. However, impulse technology has a very high ratio of peak level to total energy, so it is not suitable for exploring the structural rigidity of the non-linear damping caused by the assembly tolerance, tightness, or mechanical joints between structural components.  
The overall structure of a machine-tool is composed of several large castings and related subsystems, so it can be regarded as a multi-degree-of-freedom system composed of several masses m, spring k, and damping c. Assuming that an external force \(f_{n}(t)\) is applied to the mass block, the system will generate a single degree of freedom displacement \(x_{n}(t)\). The frequency response function diagram will show multiple natural frequencies, damping ratios, and dynamic stiffness values. These values will be presented in diagrams such as amplitude versus frequency, real part versus frequency, and imaginary part versus frequency. Each frequency response function diagram needs to cooperate with the appropriate judgmental method to find the natural frequency of the multi-degree-of-freedom system  
Figure 3: A typical single-input and single-output system.  
Figure 2: Signal exhibited in frequency domain.  
structure. The amplitude diagram is judged by the peak value of the wave, as shown in Figure 4a; the real part graph meets \(\text{Re}=0\), corresponding to the frequency position as referred to in Figure 4b; the imaginary part of the graph is the frequency corresponding to the extreme value of the natural frequency value, as referred to in Figure 4c.
## 5 Conclusions  
The optimization of planar two-dimensional arrays has been achieved in this paper where the unwanted sidelobes are reduced using recursive multibeam pattern subtraction (MPS) algorithm. The proposed beamforming technique has been demonstrated and analyzed where the convergence to the desired maximum SLL is guaranteed by including control factor along with the proper number of multibeams in the reduction process. The adoption of multibeams in SLL reductions for planar arrays resulted in significant reduction in the processing time compared with the recent related techniques and can provide deeper SLL values relative to the conventional efficient tapered windows. In addition, the simulation results show that the MPS beamformer can operate with rounded corner structures as it depends on the presence of identified sidelobes in the original array radiation pattern. On the other hand, the optimization can be achieved only one time and the obtained amplitude weights can be applied at any other mainlobe direction after inserting the suitable phase shifts. In addition, the proposed MPS beamformer provides the same SLL over the whole angular ranges from broadside to endfire directions which is very important for beam scanning applications.  
Conceptualization, Y.A.; methodology, Y.A.; software, Y.A. and F.A.; validation, Y.A. and F.A.; formal analysis, Y.A.; investigation, F.A.; writing--original draft preparation, Y.A.; writing--review and editing, F.A. and Y.A.; visualization, Y.A.; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/161), Taif University, Taif, Saudi Arabia.  
Figure 11: Normalized array power pattern comparison between the MPS beamformer and tapered windows.  
**Acknowledgments:** The authors would like to thank Taif University Researchers Supporting Project number (TURSP-2020/161), Taif University, Taif, Saudi Arabia, for supporting this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experiments and Results
### Subjective Evaluation  
Although it can be inferred in the previous section, that voice personality was one of the major factors affecting the PESQ, it was worthwhile to verify whether the PESQ results were consistent with those from a subjective listening test. Figure 8 lists the results of the methods, other than the proposed method, that yielded the highest score for each conversion pair. Such results are consistent with those obtained from the objective evaluation, including PD and PESQ. This confirms that PD and PESQ well predicted the perceptual quality of the converted speech and potentially replace the subjective listening tests. The listeners indicated that the voices converted by the proposed method sounded more clear than those from the MMSE, JGMM, and JDGMM methods. A common characteristics of these three methods is that the converted features are given by a linear combination of some representative vectors (e.g., mean vectors of each Gaussian component). This resulted in ambiguous and unclear voices, due to the averaging effects. Such undesired effects were alleviated by adopting the global variance (GV) compensation method [21]. The proposed method yielded the perceptually preferred voices without GV compensation. It was not clearly verified whether perceptually more pleasant quality of the proposed method came from the properties of the DNN-based estimator or from the adopted objective function. Considering the fact that the MLP-dep (proposed) method yielded higher preferences than MLP-ind, it can be said that using conversion rules based on perceptual distance is one of the contributions of improvements in perceptual quality. Consequently, although the evaluations were carried out on the limited number of speakers, one language, and the limited  
Figure 7: Scatterplot of PESQ values. Top: Mel-cepstral distance. Bottom: Perceptual distance.  
number of the subjects, the results appear to be somewhat promising in that some improvements over the conventional MSE-based VC methods especially in perceptual quality could be achieved by employing the perceptual distance measurement.
## 3 Results and Discussion
### Indoor Experimental Results
#### 3.1.1 Calibration of Capacitive Soil Moisture Sensor  
The capacitive soil moisture sensors were compared to soil moisture content sensors using soil samples ranging from 0% to 55%, dry and saturated conditions, respectively. It was found that capacitive soil moisture sensors with higher moisture content had better measurement accuracy compared with the moisture content sensor. The low-cost sensors used in this study were capable of detecting the general trend of the soil-water changes. As can be seen from the calibration illustrations (Figure 9), strong correlations (\(\mathbb{R}^{2}\geq 0.92\)) have been achieved. However, their measurements among the sensors varied, and this influence might be due to the texture of the soil [35; 36]. It is also found that the sensors with the same physical appearance had different reading values. This problem could be overcome by conducting detailed in-situ calibrations. It was found that employing the low-cost capacitive soil moisture sensor minimized the cost and made the irrigation scheduling process efficient. Similarly, the same recommendation was made by other studies due to ease of implementation and importance [13; 36].  
Additionally, the developed seed germination system provided consistent light of 15 h/day, to prepare the seedlings to adapt without any challenge whenever transplanted to outdoor conditions where the temperature might be different compared to the indoor environment. The light system helped to prevent transplanting shock, which tomato seedlings usually face when taken to outdoor environments without preparing them for outdoor conditions. This is one of the first steps that has been taken toward cutting the period that is required for tomato seedlings to be prepared and be able to adapt well to the new environment or colder temperature. Another key attempt that we made was to assess which irrigation systems and soil moisture thresholds were ideal for growing healthy tomato seedlings. By repeating the experiment twice, the results indicated that applying a 12% soil moisture threshold through a subsurface drip irrigation system increased overall tomato seedling growth. The selected agronomical parameters had a positive trend with increasing soil moisture regardless of what type of irrigation systems were used. This was mainly due to the high-water holding capacity of the soil; in the presence of greater soil moisture, plants were able to absorb the required proportion of water.