esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 HAWT Blade Design
### Tip Speed Ratio  
The tip speed ratio defined as the relationship between rotor blade velocity and relative wind velocity [Equation (2)] is the foremost design parameter around which all other optimum rotor dimensions are calculated:  
\[\lambda=\frac{\Omega r}{V_{w}} \tag{2}\]  
Aspects such as efficiency, torque, mechanical stress, aerodynamics and noise should be considered in selecting the appropriate tip speed (Table 3). The efficiency of a turbine can be increased with higher tip speeds [4], although the increase is not significant when considering some penalties such as increased noise, aerodynamic and centrifugal stress (Table 3).  
A higher tip speed demands reduced chord widths leading to narrow blade profiles. This can lead to reduced material usage and lower production costs. Although an increase in centrifugal and aerodynamic forces is associated with higher tip speeds. The increased forces signify that difficulties exist with maintaining structural integrity and preventing blade failure. As the tip speed increases the aerodynamics of the blade design become increasingly critical. A blade which is designed for high relative wind speeds develops minimal torque at lower speeds. This results in a higher cut in speed [10] and difficulty self-starting. A noise increase is also associated with increasing tip speeds as noise increases approximately proportionately to the sixth power [4,11]. Modern HAWT generally  
\begin{table}
\begin{tabular}{c c c} \hline \hline _Tip Speed Ratio_ & \begin{tabular}{c} _Low_ \\ \end{tabular} & \begin{tabular}{c} _High_ \\ \end{tabular} \\ \hline _Value_ & \begin{tabular}{c} Tip speeds of one to \\ two are considered low \\ \end{tabular} & \begin{tabular}{c} Tip speeds higher than \\ 10 are considered high \\ \end{tabular} \\ \hline _Utilisation_ & traditional wind mills and water pumps & \begin{tabular}{c} Mainly single \\ or two bladed prototypes \\ \end{tabular} \\ \hline _Torque_ & Increases & Decreases \\ \hline _Efficiency_ & \
\begin{tabular}{c} Decreases significantly below \\ five due to rotational wake \\ created by high torque [4] \\ \end{tabular} &
\begin{tabular}{c} Insignificant increases after eight \\ \end{tabular} \\ \hline _Centrifugal_ & Decreases & Increases as a square \\ _Stress_ & & of rotational velocity [4] \\ \hline _Aerodynamic_ & Decreases & Increases proportionally with rotational \\ _Stress_ & Decreases & velocity [4] \\ \hline _Area of Solidity_ & Increases, multiple 20+ blades required & Decreases significantly \\ \hline _Blade Profile_ & Large & Significantly Narrow \\ \hline _Aerodynamics_ & Simple & Critical \\ \hline _Noise_ & Increases to the 6 th power approximately [4] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tip speed ratio design considerations.  
utilise a tip speed ratio of nine to ten for two bladed rotors and six to nine for three blades [1]. This has been found to produce efficient conversion of the winds kinetic energy into electrical power [1, 6].
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 3 Problem Formulation
### Joint Optimisation of Power Control and Time Allocation  
Based on the optimal local CPU frequency, we solve problem P(2.2) to obtain the optimal transmission power and time allocation.  
\[\begin{array}{l}\text{P(2.2)}:\min\limits_{\Omega}\quad\sum\limits_{k=1}^{K}E_{k}^{t- 1}\Big{[}\eta_{k}\epsilon_{0}^{t}h_{k}-\kappa_{mob,k}(f_{k}^{t})^{3}T-\epsilon_{ k}^{t}\Big{]}-\sum\limits_{k=1}^{K}Q_{k}^{t-1}\bigg{[}\frac{\tau_{f_{k}^{t}}^{t}}{ \delta_{k}}+B\tau_{k}^{t}\log_{2}(1+\frac{p_{k}^{t}h_{k}^{t}}{B\sigma^{2}}) \bigg{]}\\ \qquad\qquad-E_{S}^{t-1}\bigg{[}\kappa_{mob,S}(f_{MEC}^{t})^{3}\delta_{S}\sum \limits_{k=1}^{K}B\tau_{k}^{t}\log_{2}(1+\frac{\epsilon_{k}^{t}h_{k}^{t}}{ \tau_{k}^{t}B\sigma^{2}})+\epsilon_{0}^{t}\bigg{]}\\ \qquad\qquad-V\left[\begin{array}{c}\sum\limits_{k=1}^{K}\left[\frac{\tau_{f _{k}^{t}}^{t}}{\delta_{k}}+B\tau_{k}^{t}\log_{2}(1+\frac{\epsilon_{k}^{t}h_{ k}^{t}}{\tau_{k}^{t}B\sigma^{2}})\right]\\ \qquad\qquad-\eta_{EE}^{t}\left[\begin{array}{c}\epsilon_{0}^{t}+\kappa_{mob, S}(f_{MEC}^{t})^{3}\delta_{S}\sum\limits_{k=1}^{K}B\tau_{k}^{t}\log_{2}(1+ \frac{\epsilon_{k}^{t}h_{k}^{t}}{\tau_{k}^{t}B\sigma^{2}})\\ \qquad\qquad-\sum\limits_{k=1}^{K}\eta_{k}\epsilon_{0}^{t}h_{k}+\sum\limits_{k =1}^{K}\left(\kappa_{mob,k}(f_{k}^{t})^{3}T+\epsilon_{k}^{t}\right)\end{array} \right]\end{array}\right]\end{array} \tag{30}\]  
Since the transmit power is strongly coupled to time, problem P(2.2) is non-convex and hard to solve. We introduce an auxiliary set of variables \(\epsilon_{k}^{t}=P_{k}^{t}\tau_{k}^{t},k=0,1,2,\ldots,K\) in this section, which converts problem P(2.2) into problem P(2.3).  
\[\begin{array}{l}\text{P(2.3)}:\min\limits_{\{\epsilon_{k}^{t}\tau_{k}^{t}\}} \quad\sum\limits_{k=1}^{K}E_{k}^{t-1}\big{[}\eta_{k}\epsilon_{0}^{t}h_{k}- \epsilon_{k}^{t}\big{]}-E_{S}^{t-1}\bigg{[}\kappa_{mob,S}\big{(}f_{MEC}^{t} \big{)}^{3}\delta_{S}\sum\limits_{k=1}^{K}B\tau_{k}^{t}\log_{2}(1+\frac{ \epsilon_{k}^{t}h_{k}^{t}}{\tau_{k}^{t}B\sigma^{2}})+\epsilon_{0}^{t}\bigg{]} \\ \qquad\qquad-\sum\limits_{k=1}^{K}Q_{k}^{t-1}\bigg{[}B\tau_{k}^{t}\log_{2}(1+ \frac{\epsilon_{k}^{t}h_{
# Moving Mesh Strategies of Adaptive Methods for Solving Nonlinear Partial Differential Equations
## 3 The Numerical Algorithms  
Using the coordinate transformation, (1) can be rewritten as:  
\[u_{t}-u_{x}x_{t}=L(u)u_{x}, \tag{19}\]  
Equations (17) and (19) form a coupled PDE system, and it could be simulated by various numerical methods, such as the finite difference method [20], the finite element method [14],the meshfree methods [30; 31] and so forth. In this manuscript, we apply the method of line in Huang [6; 9] for the numerical solution of the system to compare the proposed moving mesh PDE with [PERSON]'s MMPDEs. Specifically, the PDEs are first discretized in the spatial domain and then the resulting system of ODEs is integrated using an ODE solver.  
Discreting the PDE system applying central finite differences, for \(j=1,\cdots,N-1\), one can get:  
\[\frac{du_{j}}{dt}-\frac{u_{j+1}-u_{j-1}}{x_{j+1}-x_{j-1}}\frac{dx_ {j}}{dt}=L(u_{j})\frac{u_{j+1}-u_{j-1}}{x_{j+1}-x_{j-1}}, \tag{20}\] \[\frac{dx_{j}}{dt}=-\frac{1}{\triangle t}\frac{E_{1,j}}{M_{j}}-L(u _{j}), \tag{21}\]  
where the integration \(E_{1}\) is approximated applying the trapezoidal method,  
\[M_{j}=\sqrt{1+(\frac{u_{j+1}-u_{j-1}}{x_{j+1}-x_{j-1}})^{2}},\] \[E_{1,j}=\sum_{i=1}^{j}\frac{M_{i}+M_{i-1}}{2}(x_{i}-x_{i-1})- \frac{j}{N}\sum_{i=1}^{N}\frac{M_{i}+M_{i-1}}{2}(x_{i}-x_{i-1}),\]  
\(u_{j}(t)\approx u(x(\xi_{j},t),t),x_{j}(t)=x(\xi_{j},t),\xi_{j}=\frac{j}{N},j= 0,1,\cdots,N\). The expression of \(L(u_{j})\) depends on its specific form.  
When applying (17) in the practical problems, to avoid mesh crossing and keep the mesh moving stably, the spatial smoothing techniques proposed in [11] are employed; when treating the boundary nodes in the Lagrange method, the technique in [30] is applied.  
Equations (20) and (21) form an ODE system, the system could be simulated by various schemes, such as the upwinding differencing. In this manuscript, the system is simulated using a MATLAB ODE solver \"ode15i\" as in [6] in order to observe the performances of the moving mesh PDEs. The method use a variable step-size backward differentiation Runge-Kutta schemes of order 5 and the method selects the step size based on the accuracy of the solution [32]. The required input data is the initial and boundary conditions, the output time, the numberer of the knots \(N\) and local time stepping tolerance \(atol\) and \(rtol\). Throughout in this paper we set  
\[N=21,atol=10^{-4},rtol=10^{-5}.\]
# Solar Sail Transfer Trajectory Design for Comet 29P/Schwassmann-Wachmann 1 Rendezvous
## 2 Mathematical Preliminaries and Models
### Spacecraft Dynamics
For example, the upcoming NASA's Solar Cruiser demonstration mission [37] has a characteristic acceleration of about \(0.12\,\mathrm{mm}/\mathrm{s}^{2}\). Moreover, in Equation (14) the normal unit vector \(\hat{\mathbf{n}}\) is the control term in the trajectory design, that is, bearing in mind the natural constraint \(\|\hat{\mathbf{n}}\|=1\), one has two independent scalar control variables in the optimization problem, which is briefly described in the next subsection.
## 3 Model Parameter Estimation
### Tire Noise  
The emission parameters for tire noise were obtained from pass-by measurements with idling engine. For an individual tire type, pass-bys by the same passenger car at different speeds were recorded at a sampling frequency of \(f_{s}\) = 44.1 kHz with a calibrated measurement microphone in a set-up referring to the standard ISO 11819-1 [25] and depicted in Figure 8a. The pass-by speed was measured by radar, and the pass-by time was determined from synchronous video. Under the assumption of constant speed, a time-dependent backpropagation to the source was performed. Thereby, two equal incoherent point sources at the nearby wheels were assumed, _i.e._, placed at the side of the car, horizontally separated by the wheelbase and set on the ground. For the temporal accordance, the sound propagation delay, as well as the filter group delays of the 1/3 octave band filters have to be taken into account. Consequently, emission levels at reference distances \(r_{0}\) were obtained by integration over an emission angle range of 90\({}^{\circ}\). Applying a logarithmic transformation to the measured pass-by speeds, the linear regression parameters \(A_{i}\) and \(B_{i}\) of Equation (4) were fitted in a least-squares sense. Despite the idling engine, some low 1/3 octave bands were contaminated by the engine sound. To correct for this, in the first step, for each band, a quality criterion based on the correlation coefficient and the slope of the regression line was deployed. Adverse bands were identified and imputed based on the values of adjacent valid bands. In the second step, low-frequency peaks of \(A_{i}\) were smoothed by a nonlinear method. Figure 9 contains measured parameters \(A\) and \(B\) in 1/3 octave bands of 13 tires.
# Low-Intensity Whole-Body Vibration: A Useful Adjuvant in Managing Obesity? A Pilot Study
## 1 Introduction
In light of those gaps in the knowledge, we decided to investigate whether the use of low vibration intensities that certainly fall within the existing recommended safety limits would induce any benefit in subjects with obesity where weight loss or gain in muscle strength represent clinically meaningful goals. In fact, some evidence exists that WBV improves body composition [20], muscle strength [6] and cardiovascular function in various populations, including individuals with obesity [21; 22]. WBV seems to yield the potential to induce both mechanical and metabolic adaptive responses. From the mechanical point of view, exposure to 20-30 Hz WBV has shown neuromuscular adaptations [7; 8]. Evidence that body vibrations slow down fat accumulation and reduce adipogenesis in rats [23] suggests a possible clinical use of WBV in the treatment of obesity. Changes in neuromuscular response, testosterone, and growth hormone concentrations have been observed after WBV [24]. An increase in circulating levels of irisin, favoring the browning of the adipose tissue, testosterone and IGF-1 has also been observed [25]. Scant evidence on the effects of WBV in weight loss and reduction in visceral fat exists [26]. It may cause an increase in lipolysis and energy expenditure through enhancement of the aforementioned anabolic hormones [8; 24; 25]. A recent systematic review [19] attempted to define the outcomes of WBV on individuals with obesity, the optimal combination of vibration and exercise, and to identify gaps in the knowledge that may lead to improper use of WBV with consequent harmful effects. It was shown that when combined with dietary intervention, 10 or more weeks of WBV produced significant body weight reduction [26; 27] and improvements in lower limb strength [9; 27]. WBV may also appeal to subjects that are not so prone to engage in regular active exercise as it represents a quick and \"passive\" training modality. The present study aimed at investigating whether WBV performed at lower intensities falling within the safety limits recommended by the ISO-2631 would be able to induce any metabolic or mechanical effect in subjects with obesity. The primary endpoints were: changes in body composition, metabolic syndrome, performance and muscle strength. The secondary endpoints were: modification of irisin, testosterone, GH and IGF1 levels.
## 3 Results and Discussions
### Alfacrux Digital Twin Pilot  
The first elements of the proposed digital twin, shown in Figure 7, were developed and tested in the current phase with synthetic and preliminary telemetry data, specifically the attitude reconstruction, environment, and communication channel models; the telemetry viewer; and risk analysis.
## 4 Case Studies
### Case Study 3: Prestressed Concrete I-Shape Girder Bridge  
The third bridge for the validation of the proposed method was Hyoja 2 Bridge, which is a single-span bridge with a pre-stressed concrete I-shape girder (Figure 9). The bridge has a length of 30.7 m, a width of 21 m and is skewed by 33\({}^{\circ}\). Two accelerometers were installed in the vertical direction at the center of spans 1 and 3. Measurement was conducted over 4 h at a sampling rate of 100 Hz. This case differs from the other two cases by having a small number of sensors.  
Figure 10 plots the results obtained by applying the pre-cleaning criteria to the candidate modes from SSI. This bridge also includes modes other than those presenting a high potential of being real modes while maintaining constant values of the various parameters. Consequently, it can be stated that the pre-cleaning criteria for each individual parameter have been set loosely.  
Figure 9: Shape and sensor layout of Hyoja 2 Bridge (The red dots indicate sensor locations).  
Figure 11 plots the AOMA results by means of the stabilization diagram and modal check diagram. The pre-cleaned modes indicated in the stabilization diagram appear in the form of poles and show that the pre-cleaning process was appropriately conducted. In addition, the natural frequencies of the first and second modes are seen to be fairly close to each other, with values of 6.35 Hz and 6.94 Hz, respectively. It is difficult to verify that these two modes are identical modes by the stabilization diagram only, but an observation of the MAC index chart in the modal check diagram easily shows that these two modes are different with different mode shapes.  
In view of the MAC index chart in the modal check diagram, it appears that high MAC indices were computed even for other modes that are not identical modes. This is because of the small number of sensors installed on the bridge. If sensors were installed in sufficient numbers to allow distinguishing of the mode shapes according to the mode, this situation would not occur. However, the proposed method was able to distinguish different modes through the use of not only the MAC index but also the frequency for assessing the identicality of modes.  
Figure 10: Pre-cleaning results for Hyoja 2 Bridge (The red dots indicate the modes satisfying the pre-cleaning criteria).
## 5 Results and Discussion
### SmartPLS Results for Structural Model  
To evaluate the indicator's significance of this model, bootstrapping was employed. The number of bootstrap samples that SmartPLS typically creates is about 5000. For this research, SmartPLS selected the samples 5000 times, randomly from 200 cases, to provide the results shown below in Figure 2.  
The t-value is known as the indicator's weight, and can be obtained from bootstrapping results. The numerical values on the lines between indicators, as shown in Figure 3, represent the t-values. The t-values are used to indicate the path's significance (\(p\)). For a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{Cross-Correlations} & \multicolumn{1}{c}{Item} & \multirow{2}{*}{AVE \(>\) 0.50} & \multirow{2}{*}{CR \(>\) 0.07} & \multirow{2}{*}{R2} & \multirow{2}{*}{**CA \(>\) 0.07**} \\ \cline{2-2} \cline{7-13}  & **CA** & & & & & & & & & & & & \\ \hline CA & 0.9137 & - & - & - & - & - & - & - & - & - & 0.891–0.927 & 0.963 & 0.981 & - & 0.964 \\ \hline SI & - & 0.9247 & - & - & - & - & - & - & - & - & 0.915–0.934 & 0.950 & 0.983 & - & 0.973 \\ \hline PE & - & - & 0.954 & - & - & - & - & - & - & - & 0.946–0.964 & 0.969 & 0.990 & - & 0.982 \\ \hline ROF & - & - & - & 0.964 & - & - & - & - & - & - & 0.928–1 & 0.964 & 0.982 & - & 0.961 \\ \hline TA & - & - & - & - & 0.934 & - & - & - & - & 0.868–1 & 0.934 & 0.966 & - & 0.926 \\ \hline PEU & - & - & - & - & - & 0.943 & - & - & - & - & 0.910–0.975 & 0.957 & 0.989 & - & 0.985 \\ \hline PU & - & - & - & - & - & - & 0.941 & - & - & - & 0.912–0.972 & 0.956 & 0.989 & - & 0.984 \\ \hline BI & - & - & - & - & - & - & - & 0.983 & - & - & 0.965–1 & 0.982 & 0.991 & - & 0.981 \\ \hline COM & - & - & - & - & - & - & - & - & 0.963 & - & 0.922–0.973 & 0.963 & 0.981 & - & 0.959 \\ \hline ATU & - & - & - & - & - & - & - & - & 0.923 & 0.886–0.975 & 0.949 & 0.982 & 0.736 & 0.973 \\ \hline ASU & - & - & - & - & - & - & - & - & - & - & - & - & 0.991 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Factor correlations, loadings, average variances extracted, R2s of AVE, Cronbach’s alphas, and composite reliabilities of developed research model.  
Figure 2: SmartPLS bootstrap results.  
two-tailed test, the critical t-value significance level = 1% or 0.01 is equal to 2.58; 0.05 or 5% is equivalent to 1.96; and the level 10% or 0.10 is equal to 1.65.  
From the hypothesis analysis shown in Table 2, it was found that out of the total nine hypotheses, nine are supported, which were comfortability (H1), social influence (H2), perceived enjoyment (H3), fear of failure (H4) perceived ease-of-use (H6), behavioral intention (H7), perceived usefulness (H8), compatibility (H9), and attitude towards use (H10); all of these resulted in intense significance levels, with coefficients = 0.930, 7.726, 2.353, 2.269, 6.523, 2.988, 4.962, 4.970, and 64.37, respectively. Technology awareness was not supported by this model for the 45-65+ age groups.
# Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
## 6 Conclusions  
This survey has presented a systematic review of the literature, which was conducted by employing the PRISMA methodology, which aimed to identify the principal and recent artificial intelligence technologies that have been applied in the agricultural domain. This research selected 176 papers for bibliometric analysis and 17 papers for quality appraisal.  
It was possible to identify seven main agriculture applications: crop management, water management, soil management, fertigation, crop prediction, crop classification, and disease and pest management. Beyond these, twenty-four different artificial intelligence technics were identified. The technics most used were machine learning, deep learning with a convolutional neural network, robotics, and the Internet of Things. The main benefits of this included the optimization of agricultural management systems, irrigation, and the  
Figure 6: Top 10 most frequent technologies and terms resulting from the analysis of 176 papers.  
identification of diseases and pests. It was observed that an increase in intelligence in agriculture could be related to the digitization and manipulation of large volumes of data, enabling the use of intelligent techniques in system optimization and planning.  
In this context, a big challenge, especially for small and medium agricultural production units, is the mapping and digitization of production processes. Recently, the hardware and software costs required have decreased; however, these values are still prohibitive for many farmers. Labor qualification is also a challenge. In food-producing countries, public policies are necessary for the development of competitive technologies and workforce qualifications.  
In these trends, computer vision has been used with robotics and unmanned aerial vehicles (UAVs) for classifying crops and identifying diseases and pests. New technologies like digital twins are promising for optimizing agricultural processes. And frequently used terms such as precision agriculture has been sharing a space with frameworks such as smart farming; agriculture 4.0, which uses telecommunications and data infrastructure for the agricultural supply chain; and agriculture 5.0, which incorporates AI and UAVs for more information.  
This article provides a synthesis of the recent studies and technologies analyzed. This review has some limitations, such as the selection of articles from academic journals written in English and the findings being related to the reviewers' experiences. Some threats to the validity of these results are biases in the selection of digital libraries and the number of studies selected. For future work, the inclusion of other databases is planned.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Case Study  
A fully mechanized U-type working face (15116 lineway) in Sijiazhuang coal mine was selected as the physical prototype to implement the GIS-coupled spatiotemporal modeling, which is located in Shanxi province, China. This mine working face is selected for access to precisely and continuously recorded mine data. The layout of the working face lineway is shown in Figure 1. It has a working face zone, wind intake lineway, and wind outtake lineway with the rectangular cross-section of 4.2 m wide and 4 m high. The working face is 220 m long, while the wind intake and outtake lineways are all 600 m long.  
According to the safety operation regulations in Sijiazhuang coal mine, the airflow quantity in the 15116 working face lineway is 53.3 m\({}^{3}\)/s, which is calculated based on the requirement of the mine gas and carbon dioxide emission quantity, the temperature and the number of maximum miners in the working face area, and it has been checked by the maximum and minimum wind speed. The estimated methane emission flow rate is about 0.2 m\({}^{3}\)/s. During the procedure of coal mining in the U-type lineway system, the working face is the main source of the methane gas emission. A large amount of the methane gas generated from the working face disperses with the pressing air from the wind inflow side, finally, part of it is removed through the wind inflow lineaway, while the rest stays in the lineway space. During the delivering process, movement of methane coupled with the airflow exhibits a species transportation flow. Here, we have three model assumptions consistent with the published research [39]: (i) the airflow in the working face zone belongs to the turbulent flow; (ii) the airflow and methane gas are continuous medium gas; (iii) heat and mass transfer are ignored during the process of air flow.