esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Potential of Wind Energy Resources in Magallanes Region
Heat Waves and Droughts Effects:
* Reduction in air density (lower energy production). * Overheating of electrical components (inverters, transformers). Figure 4: Average hourly wind speed profile at different heights registered in the Northeast region of Magallanes. 2. Resilience Metrics for Wind Turbines To assess resilience to extreme events, metrics including the following were used:
* Average stop time: The average time the turbine is inactive due to weather events. Typical values are 2-10 days/year (depending on the region). Figure 5: Average monthly wind speed profile at different heights registered in the Northeast region of Magallanes. * Availability factor: Percentage of operating time of the annual total. Typical values are 95-98% (under normal conditions). * Energy loss from extreme events: Percentage of generation lost due to storms/ice. Typical values are 5-20% in high-risk areas. * Repair cost per event: Average cost of post-storm maintenance. Typical values are USD 10,000-500,000 (according to damage). Finally, Figure 5 shows that wind speeds vary between 5 m/s and 13 m/s throughout the year, with minimum values recorded in June, while maximum values are observed in November. The curve shows an upward trend from November to January, with decreases from May to July. Validation of wind energy projections is essential to ensure model reliability and efficiency in wind farm planning. Given the high variability and uncertainty associated with wind resources (wind speed, turbulence, geographic, and climatic factors), advanced statistical methods are used to quantify the risks and improve the accuracy of estimates. A detailed justification of the Monte Carlo methods used is presented below:  
* Uncertainty in input parameters: Wind energy generation depends on random variables such as wind speed, air density, and turbine availability. Monte Carlo simulation allows these uncertainties to be modeled using probability distributions (Weibull, Rayleigh, Normal, etc.). * Evaluation of the probabilistic scenarios: Through thousands of iterations, multiple possible scenarios are generated, allowing the estimation of an expected value and confidence intervals (e.g., P10, P50, P90) for energy production. * Financial risk analysis: This method incorporates economic factors (investment costs, electricity rates) to help assess the risk that a project will not meet expected returns.
## 3 Joint Stiffness Modeling and Identification
Equation (19) can be expressed as  
\[\Gamma_{q}=sc\big{(}K_{q\theta}\Theta_{q}\big{)}, \tag{20}\]  
where \(\Gamma_{q}\) is the torque vector, \(K_{q\theta}\) and \(\Theta_{q}\) are dual matrices, respectively. \[K_{q\theta}=\left[ \begin{array}{ccc}\hat{K}_{1}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&\hat{K}_{n}\end{array} \right] \tag{21}\]  
\[\Theta_{q}=\left[\Delta\hat{\theta}_{1},\cdots,\Delta\hat{\theta}_{n}\right] \tag{22}\]  
Combining Equations (18) and (20), we obtain the following relationship based on linear matrix algebra  
\[D=JK^{-1}J^{T}FT^{T}, \tag{23}\]  
where \(K\) is the joint stiffness matrix represented in dual quaternion space. The relationship between external force \(F\) and the Cartesian infinitesimal displacement \(\Delta X\) based on dual quaternion algebra is written as  
\[F=sc\big{(}\hat{K}_{X}\bullet\Delta\hat{X}\big{)}, \tag{24}\]  
where \(\hat{K}_{X}\) is the diagonal Cartesian stiffness matrix, and \(\Delta\hat{X}\) is the displacement of the EE. By using the matrix and Taylor's expansion, Equation (24) can be rewritten in the following form  
\[dF=K_{q}\Delta X_{q}, \tag{25}\]  
where \(K_{q}\) is the modified joint stiffness matrix, and \(\Delta X_{q}\) is the displacement of the EE; both of them are represented in dual quaternion space. By differentiating the transpose of Equation (18), the following relationship is obtained  
\[d\tau_{q}=dJ^{T}FT^{T}+J^{T}dFT^{T}. \tag{26}\]  
Using Equation (25), Equation (26) can be rearranged as  
\[Kd\theta=\sum_{\text{i = 0}}^{\text{n}}\frac{\partial J}{\partial q_{\text{i}}} dq_{\text{i}}FT^{T}+J^{T}K_{q}JD_{q}T^{T} \tag{27}\]  
where \(D_{q}\) is the modified dual quaternion. Equation (27) can be rewritten as  
\[{J_{k}}^{T}K_{d}J_{k}=K-K_{c}, \tag{28}\]where \(J_{k}\) is the modified Jacobian matrix, \(K_{c}\) is the complementary stiffness matrix, \(K_{d}\) is the joint stiffness matrix. The complete stiffness matrix of the industrial robot can be written as  
\[K_{d}={J_{k}}^{-T}(K-K_{c}){J_{k}}^{-T-1}. \tag{29}\]  
The following relationship is obtained:  
\[dq={K_{d}}^{-1}, \tag{30}\]  
where \(dq\) is the deflection dual quaternion. Equation (30) can be expressed in the following form  
\[y=Ax, \tag{31}\]  
where  
\[A=\left[ \begin{array}{cccc}{J_{11}}\sum\limits_{i=1}^{8}{J_{i1}F_{i}}&\cdots &{J_{18}}\sum\limits_{i=1}^{8}{J_{i1}F_{i}}\\ \vdots&\ddots&\vdots\\ {J_{81}}\sum\limits_{i=1}^{8}{J_{i1}F_{i}}&\cdots&{J_{88}}\sum\limits_{i=1}^{8 }{J_{8}F_{i}}\\ \end{array} \right], \tag{32}\]  
\[y=dq, \tag{33}\]  
\[x=D. \tag{34}\]  
Equation (31) can be solved by the least-squares algorithm and the solution is expressed by  
\[x={{{(A^{T}A)}^{-1}}}{A^{T}}y. \tag{35}\]  
Dual quaternion algebra is used to calculate \(x\) and \(y\) in the convenient way, and then the stiffness identification result can be obtained by solving Equation (35). After identification, the joint stiffness parameters are used in the next compensation procedure that improves the performance of industrial robots.
## 4 Discussion
### Shortcomings and Uncertainties  
In this study, we used data from only 99 solar radiation stations, as meteorological stations in the Tibetan Plateau are scarce. The sparse distribution of climate data limited the detail and accuracy of the relationship between climatic factors and plant productivity. Therefore, the results are likely to be subject to some ambiguity.  
Vegetation NPP is influenced by a variety of factors. In this study, only three climatic factors and LOS were considered. Although these factors have explained a total of 56% of the variation of NPP (Figure 8), other climate-related factors, such as sunshine duration, soil temperature, precipitation characteristics (e.g., effective precipitation, precipitation intensity), CO\({}_{2}\) concentrations, N enrichment and deposition (e.g., policy and planning changes), should be considered in future studies [67]. Moreover, other phonological metrics (e.g., Start of Season, End of Season), species competition, and disturbances such as anthropogenic activities (irrigation, fertilization [68; 69], harvest, land use/land cover change), wildfires, plant diseases, pests, floods, and droughts as well as the time-lag effect of the above-mentioned variables can vary by region [50], and should also be considered in future studies.  
The results presented here indicate the complexity of vegetation NPP dynamics and the response or reaction strategy of vegetation to climate change during the study period. However, the trend for NPP before and after turning points were mostly non-significant despite all turning points being significant (except evergreen broadleaf forest) based on \(\Delta\)AIC. This may be caused by a short time period, or a non-linear variation on NPP during the study period. Therefore, the conclusions mentioned above should be made with more caution, and more effort should be made to explore the NPP trends (e.g., non-linear trend) in the future to clarify the direction of dynamic NPP trends [70]. In addition, it is difficult to estimate future NPP dynamics using mathematical models (e.g., linear regression analysis)due to the nonlinear characteristics of NPP dynamics in a long time series, the uncertainty about future climate change, and the time-lag effect of NPP responses to climate change. To date, the Hurst exponent, which has been widely used in hydrology, climatology, economics, geology, and geochemistry, could be considered to predict the NPP variation. The results presented here (Figure S4) indicate that NPP in China will continue to increase based on the Hurst exponent. Unfortunately, this exponent characterizes trends based only on past and present environmental conditions, without considering future environmental change, especially in developing areas, or giving a time period for dynamic NPP trends. These limitations restrict our ability to predict and anticipate future NPP variation, the carbon cycle and its relationship with and response to climate change and human activity, and hence more effort must be made to predict future NPP variation.
## 6 Simulation Results  
In this section, the simulation results are presented. The simulation parameters are shown in Table 1. Each PV panel received different solar irradiance, i.e., PV1: 900 W/m\({}^{2}\), PV2: 1000 W/m\({}^{2}\), and PV3: 800 W/m\({}^{2}\). This allowed us to verify the operation of the topology and the MPPT algorithm, with different duty cycles for the individual flyback modules.  
Figure 14 shows four waveforms. Figure 14a shows the control signals of the main and snubber switches. As can be noted, to protect the devices, there was a dead time between the gating signals of the main and the snubber switches. Active snubbers allowed the drain-source voltages of the main switches not to exceed the allowed ratings and to reduce the \(dv/dt\) (this is further shown in the experimental results). Figure 14b shows the currents in the main switches and in the output diode. Slightly different peak values can be appreciated in the three switches currents due to the different solar irradiances. Figure 14c,d shows the voltages and currents of the solar modules, respectively, where again, different magnitudes can be observed. The performance of the individual MPPT implemented for each PV panel was then verified, considering different irradiance conditions.  
Figure 15 shows five waveforms. The switches voltages are shown in Figure 15a-c and it can be seen that their magnitudes did not exceed 200 V. In Figure 15d, the output voltage is depicted, which had an average value of 320 V. Finally, Figure 15e shows the power in the PV panels (green) and the output power of the proposed converter (blue).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Parameter** \\ \hline PV panels power & 340 [W\({}_{\text{P}}\)] \\ Input capacitors, \(C_{in_{1}}=C_{in_{2}}=C_{in_{3}}\) & 100 [\(\mu\)F] \\ Output capacitors, \(C_{out}\) & 470 [\(\mu\)F] \\ Snubber capacitors, \(C_{snb_{1}}=C_{snb_{2}}=C_{snb_{3}}\) & 0.1[\(\mu\)F] \\ Magnetizing Inductance, \(L_{\text{m}}\) & 17 [\(\mu\)H] \\ Flyback transformer run ratio, \(N_{P_{s}}/N_{S}\) & 1 \\ Leakage inductances, \(L_{l_{1}}=L_{l_{2}}=L_{l_{3}}\) & 0.5 [\(\mu\)H] \\ Load (resistive) & 100 [\(\Omega\)] \\ Number of primary windings, \(N_{P}\) & 3 \\ Switching frequency, \(f_{s}\) & 10 [kHz] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 14: Simulated waveforms: (**a**) Main and snubber switches gate control signals. (**b**) Main switches drain currents and output diode current. (**c**) PV panels’ voltage. (**d**) PV panels’ current.  
In Figure 16, the operation of the MPPT can be observed. The power available in the solar panels is shown in green, and the converter output power in purple. Step changes in the solar irradiance were applied. The converter output power closely followed the input available power, validating the correct operation of the MPPT algorithm.
## 7 Conclusions  
We successfully investigated, numerically, a nonlinear boundary value problem (BVP) that models the natural convection flow and heat transfer to a non-Newtonian pseudoplastic fluid in a long thin channel by recasting the BVP as an initial boundary value problem (IBVP). Our numerical algorithms are based on efficient and convergent semi-implicit time-space finite difference methods. The fluid temperature and velocities develop in time, from the zero initial conditions inside the channel until steady states are reached. At a steady state, the solutions both increase or decrease with decreasing (respectively increasing) Prandtl and/or Reynolds numbers. Changes in those flow parameters that are directly linked to the fluid velocity, such as the Grashof and viscosity parameters, are shown to have a significant effect on the fluid velocity but not on the fluid temperature.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 27: Transverse velocity dependence on Re at \(t=5\).
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design
### System Software Architecture Design
#### 2.2.1 Multi-Channel Synchronous Design
When the variance is larger and the distribution is wider, the probability of a large delay between channels is larger, and the reference clock jitter range is also larger. A large clock jitter makes it difficult to meet synchronization constraints (2) and (3), and in the DBF, generates a large sub flap and beam-pointing jitter. Therefore, in a digital array signal processing system with high sampling rates, the variance in the delay distribution between channels directly reflects the synchronization and performance of the system.
## 2 Materials and Methods
### Spatial Explanatory Variables  
Past studies in Malawi show that land use and land cover changes are impacted by myriad social and biophysical drivers/causes. These include population density, infrastructure such as roads, and biophysical features such as soil properties, slope, and location near riverbanks [5; 10; 22; 47]. The static or dynamic properties of driver variables have the potential to either constrain or enable certain land-cover changes [26; 36; 48]. Assumptions were made based on past studies that project future land-cover changes using existing drivers that change over time. For example, distance from major roads or rivers and population density are well known to drive urban and/or agricultural expansion and other land-cover changes. Driver variables such as slope, elevation and temperature have generally stable characteristics and static properties and are thus considered physical constraints to land-cover change [29; 39; 49]. These drivers were used as explanatory variables to estimate the expected quantity of change in land cover (see Table 1).  
Soil data were extracted from the iSDA Soil texture and properties 30 m Africa layer and subset for Dedza and Ntcheu district boundaries ([[https://www.isda-africa.com/isdasoil/](https://www.isda-africa.com/isdasoil/)]([https://www.isda-africa.com/isdasoil/](https://www.isda-africa.com/isdasoil/)) (accessed on 15 April 2021)). Elevation and Slope were derived from the 30 m Shuttle Radar topography Mission (SRTM) dataset using the Google Earth Engine. Population estimates were extracted from the gridded population of the world version 4 (GPWv4) data set that contains global human population distributions from 2005 to 2014 at 1 km spatial resolution using the Google Earth Engine. This layer was subsequently resampled to 30 m resolution using ArcGIS software, assuming even distribution of people within each parent 1-km pixel to align spatial resolution with the other driver variables. Vector datasets for roads and rivers were derived from the Malawi Spatial Data Platform ([[http://www.masdap.mw/](http://www.masdap.mw/)]([http://www.masdap.mw/](http://www.masdap.mw/)) (accessed on 1 April 2021)), and buffer zones were calculated at 30 m and 50 m of roads and rivers, respectively, using ArcGIS software. These buffer sizes were chosen to exclude restricted buffer zones based on the local Malawi department of roads and surveys because areas for land use are predominantly outside these zones based on a recent study [39] (see Table 1).
## 4 Data Collection  
Considering the limitation of selecting random and large-scale samplings as well as simulating the diversity in traffic scenarios of driving simulators, a questionnaire survey was conducted. The questionnaire consisted of three parts: socio-demographics, psychological characteristics when facing CAVs, and a behavioral choice survey when driving on an identical road to CAVs. To clarify the scope of the experiments and help the respondents understand CAVs, a brief description of CAVs was presented before conducting the survey. In the language description of the questionnaire, a brief introduction to the performance of CAVs was provided: CAVs equipped with advanced on-board sensors, controllers, and actuators, and integrated with network communication technology. The technical system of CAVs consists of sensing, decision making, control, communication positioning, and a data platform, enabling the precise understanding of traffic conditions (such as surrounding vehicles' movement information, road conditions, etc.) and making rapid driving decisions, achieving autonomous driving under controllable operating conditions. Compared to conventional vehicles, CAVs have a shorter response time and can maintain a smaller following distance for stable operation. Data were collected via a professional web-based survey tool \"Wen Juan Xing\". The questionnaire was distributed to groups who were aged older than 18 and had driver's licenses, and spread by the social media platform \"WeChat\". After, the data were cleaned; the validated responses of 339 respondents were obtained.
## 4 Our Proposed MSE Algorithm
### Route Set-up Phase
#### 4.1.2 Cluster Formation  
In this section, we construct a routing tree based on cluster heads set which have been elected and the communication procedures are illustrated in Figure 4.  
During the broadcasting phase, each cluster head broadcasts an advertisement message (ADV_Msg) and its ID, location and type to sensors within its range using carrier-sense multiple access mechanism. Each normal cluster head will record the ID and location of an advanced cluster head with the strongest received signal strength (RSS).  
During the decision phase, each non-cluster head node determines its cluster in this round by choosing the appropriate cluster head which has the strongest RSS of the ADV_Msg. After such a node has  
Figure 4: Flowchart of our proposed MSE method.  
decided which cluster it belongs to, a join message (J_Msg) will be send to the corresponding cluster head with its ID and the chosen cluster head's ID. After the cluster head receives all the J_Msg, it sets up a TDMA schedule and transmits this schedule to the sensor nodes in its cluster. By using a TDMA schedule, collisions during messages transmission can be effectively avoided, and sensor nodes can be turned off if not on duty. This can effectively reduce the energy consumption for sensor nodes and prolong the lifetime of network.  
Assume a normal node's distance to the mobile trajectory and to the nearest advanced node, which has a distance of \(d_{m}\) to the trajectory, are \(d_{n}\) and \(d_{a}\) respectively. As is shown in Figure 5, if the vertical distance from a normal cluster head to the mobile sink trajectory is longer than the distance between itself to its nearest advanced cluster head, it will calculate the vertical distance from the advanced cluster head to the trajectory. Finally, only when \(d_{m}^{\,2}>d_{a}^{\,2}+d_{n}^{\,2}\), the normal cluster head will transmit its packet to the advanced cluster head. The packet will be fused and forwarded along with the data gathered in the cluster. As the distance will be calculated in each round, our algorithm's complexity equals to \(O\left(n^{2}\right)\).
## 4 Discussion
#### Strengths and Limitations  
This study is the first examining the relationship between meeting the 24 h movement guidelines and HRQoL during the COVID-19 pandemic. Accordingly, we provide insights into the importance of promoting healthy behaviours among children and adolescents that could increase or preserve HRQoL--especially during the COVID-19 pandemic. Moreover, this study contains a large sample of pre-schoolers, children, and adolescents from Spain and Brazil, with an analysis on the meeting of these 24 h movement guidelines and HRQoL. However, this study did not capture the type of housing (e.g., with a backyard or apartments), rural/urban environments, gaming, or parenting styles, which could influence the level of movement behaviours. Additionally, the questionnaire did not ask for the physical activity or sports background or habits that participants were previously engaged in; therefore, it does not allow for a comparison of the situation during the COVID-19 lockdown with the period before. Besides this, BMI was based on self-reported data, which could represent inaccurate information.
###### Abstract  
An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia.  
landside detection; LiDAR; recurrent neural networks (RNN); multi-layer perceptron neural networks (MLP-NN); GIS; remote sensing +
Footnote †: journal: Applied Soft 2017  
1