esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
1. Compared with the normal temperature environment, although the energy consumption coefficient of electro-osmosis in a low-temperature environment had little change, the current, water displacement, and electro-osmosis coefficient were obviously reduced, and the resistivity and anode potential difference were greatly increased, which indicates that the low temperature environment can reduce the electro-osmosis efficiency of soft clay, and the construction of electro-osmosis reinforcement of soft clay should be carried out at a higher temperature as far as possible.
2. The current, drainage volume, and electro-permeability coefficient increased significantly, while the resistivity, anode potential difference, and electro-osmosis energy consumption coefficient decreased significantly after the anodic FeCl\({}_{3}\) solution was applied in electro-osmosis. The results showed that anodic FeCl\({}_{3}\) solution was beneficial to improving electro-osmosis efficiency and energy saving in the construction of electro-osmosis reinforcement of soft clay, and the effect of anodic FeCl\({}_{3}\) solution was the best when the water content was 35-40%.
3. The pH value of the cathode and anode can be used as one of the indexes to reflect the electro-osmosis effect in the process of electro-osmosis of soft clay. The smaller the pH value of the anode and the larger the pH value of the cathode, the better the electro-osmosis effect.
4. There was a great difference between resistivity and anode potential in electro-osmosis of frozen soft clay. We suggest taking measures to melt soft clay before electro-osmosis reinforcement, and then conducting electro-osmosis, so as to improve the efficiency of the electro-osmosis.  
Figure 19: Current change of frozen soft clay (water content 40%) after electrifying.  
Because the temperature was difficult to control during the test, the low temperature mentioned in this paper refers to the lower temperature above 0 \({}^{\circ}\)C. This experiment did not consider the influence of additive concentration and temperature gradient, and it still requires further improvement in future experiments and research.  
Model test, analysis, translation, editing, and review, P.X.; review, Y.C.; guidance, review, and revision, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Zhejiang Provincial Department of Water Resources, grant number RC2060; the National Science Foundation of China, grant number 51508507; and the Basic Scientific Research Business Fees of Provincial Colleges and Universities, grant number FRF20 QN005.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Seasonality of \(\mathrm{SIF_{d}}\)
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{_VPD_} \\ \hline  & **Amazon** & **Atlantic Forest** & **Caatinga** & **Cerrado** & **Pampa** & **Pantanal** \\ \hline DBF \(\mathrm{SIF_{d}}\) & 0.16 & 0.10 & \(-\)0.57 *** & \(-\)**0.80 *** & 0.59 ** & \(-\)0.29 \\ \hline EBF \(\mathrm{SIF_{d}}\) & 0.01 & 0.60 *** & \(-\)**0.71 *** & \(-\)0.69 ** & 0.47 ** & \(-\)0.23 \\ \hline GRA \(\mathrm{SIF_{d}}\) & \(-\)**0.81 *** & \(-\)0.19 & \(-\)**0.85 *** & \(-\)**0.72 *** & **0.74 *** & \(-\)0.22 \\ \hline SAV \(\mathrm{SIF_{d}}\) & \(-\)0.60 * & 0.03 & \(-\)**0.81 *** & \(-\)**0.79 *** & **0.74 *** & \(-\)0.24 \\ \hline WSAV \(\mathrm{SIF_{d}}\) & \(-\)0.55 * & 0.31 & \(-\)**0.77 *** & \(-\)**0.77 *** & **0.78 *** & \(-\)0.19 \\ \hline \hline \end{tabular}   
The length of season (LOS) for each vegetation class in each biome calculated from TIMESAT showed that the values differed for vegetation classes within biomes (Figure S12). For example, Amazon EBF \(\mathrm{SIF_{d}}\) LOS was the longest at 185 days and Amazon GRA \(\mathrm{SIF_{d}}\) LOS was shortest at 104 days (Figure S12). Pampa WSAV has the longest calculated \(\mathrm{SIF_{d}}\) LOS at 2015 days and Atlantic Forest GRA \(\mathrm{SIF_{d}}\) has the shortest at 99 days (Figure S12). The mean \(\mathrm{SIF_{d}}\) LOS for a biome (the average of the \(\mathrm{SIF_{d}}\) LOS of DBF, EBF, GRA, SAV and WSAV within that biome) resulted in: Pantanal (193 days), Cerrado (168 days), Caatinga (167 days), Pampa (157 days), Amazon (145 days), and Atlantic Forest (135 days) (Figure S13). The only significant difference in mean \(\mathrm{SIF_{d}}\) LOS was between the Pantanal and Pampa (\(p<0.05\)), Pantanal and Atlantic Forest (\(p<0.01\)), and the Pantanal and Amazon (\(p<0.05\)) (Figure S13). A comparison of the \(\mathrm{SIF_{d}}\) LOS of each vegetation class and the total integrated \(\mathrm{SIF_{d}}\) for each vegetation class is shown in Figure 6. Our results show that the length of season only explains 30% of the variation in total integrated \(\mathrm{SIF_{d}}\) (Figure 6; adjusted \(\mathrm{R^{2}}=0.30\), intercept = 0.68, slope = 0.01, \(p<0.01\)). For every increase of one day in the length of season, on average, there is only a small corresponding increase in total integrated \(\mathrm{SIF_{d}}\) of 0.01 \(\mathrm{Wm^{-2}nm^{-1}sr^{-1}}\). Figure 5: Stacked bar plots of vegetation class-specific per season base (lower bars, blue) and seasonal pulse (upper bars, orange) integrated \(\mathrm{SIF_{d}}\) for the (**a**) Amazon (**b**) Atlantic Forest (**c**) Caatinga (**d**) Cerrado (**e**) Pampa and (**f**) Pantanal. Values for integrated \(\mathrm{SIF_{d}}\) are printed on the corresponding bars.
# Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor
## 4 Discussion  
The axle torque, rotational speed, and power transmission efficiency between the measured and simulated results showed similar trends in all gear stages except when the gear shifting occurred. However, the measured values tended to be higher than the simulation results in most sections. The bench test was conducted in harsher conditions. As a result of the t-test, there were no significant differences for the axle torque, rotational speed, and power transmission efficiency between the measured and simulated results. Also, as a result of the linear regression analysis, the R-squared for power transmission efficiency between the measured and simulated results was more than 0.97 in all gear stages. Through this study, a simulation model with the accuracy of an R-squared value of more than 0.97 was developed.  
The previous researches have relied on empirical methods for HMT design and evaluation, which results in significant losses in terms of time and cost [44]. We solved these existing problems using simulation software, developed the simulation model of applicable for a design and evaluation of the HMT, and finally validated the simulation model. The power transmission efficiency of the tractor is influenced by many variables, such as a temperature of the transmission and type of oil [33]. However, since it is difficult to consider all of these conditions, repeated experiments are necessary to improve the power transmission efficiency of the HMT. We can perform simulation analysis repeatedly by setting various conditions and change the specifications of the HMT model. So, it is judged to be useful in the optimization and performance test of the HMT tractor.  
In this study, the concept of the HMT tractor was presented, and the prototype of the HMT was developed. Based on the prototype, a simulation model was developed and verified using the bench test for important indicators of transmission, such as the axle torque, rotational speed, and power transmission efficiency. However, the developed simulation model of HMT is limited to a 50 kW class agricultural tractor. So, it is necessary to review the configuration of the HMT in order to apply it to the tractor of the large horsepower. For mass production of the tractor, qualification test for various performances metrics, such as durability, shift time, and shift shock are required [14; 25]. Therefore, in order to improve the verification of the simulation model of the HMT, we should perform additional empirical tests for other performance indicators of the transmission. In the future study, the reliability of the simulation model will be improved through the analysis of the endurance life of the HMT using agricultural workload data.  
Figure 10: Results of linear regression for the measured and simulated power transmission efficiency based on the gear stages: (**a**) first gear stage; (**b**) second gear stage; (**c**) third gear stage.
## 5 Example Analysis and Discussion
### Reliability-Based Optimization of a Welded Beam
Weights of objective functions \(f_{1}\) and \(f_{2}\) are \(\omega_{1}\) and \(\omega_{2}\), respectively, i.e., \(\omega_{1}+\omega_{2}=1\). Suppose that all uncertain design variables follow normal uncertainty distributions due to insufficient sample data. Since the experimental data are imprecise, \(\sigma\), \(\tau\), and \(E\) can be characterized by linear uncertainty distribution combined with uncertain regression analysis [4]. For this design problem, as multiple uncertain variables and constraints are included, there is no solution using the CEP model. Hence, the USGA algorithm is utilized to solve the URBDO model (20) of the welded beam design problem. Parameters in the USGA method are set as follows: \(Pop\_size=30\), \(P_{\text{c}}=0.9\), \(P_{\text{m}}=0.1\), and \(N=40\). To investigate the effectiveness of the URBDO (USGA) method, the RBDO (RIA) method is also employed for this design problem when all the input factors are considered to follow normal or uniform probability distributions. Detailed distribution types and parameters for input factors are listed in Table 5. Design results of objective function values \(f_{1}\) and \(f_{2}\) generated by URBDO and RBDO model under different weights \(\omega_{1}=0.4\), \(\omega_{1}=0.5\), and \(\omega_{1}=0.6\) are displayed in Figure 10. Furthermore, specific optimal results are summarized in Table 6.  
Figure 9: A welded beam structure. According to Figure 10 and Table 6, with an increase in \(\omega_{1}\), the solution result of objective 2 increases, while the solution result of objective 1 decreases. In addition, the value of objective 1 calculated by the URBDO method is higher than the value estimated by the RBDO method. The value of objective 2 calculated by the URBDO method is lower than the value estimated by the RBDO method. This is because the RBDO method neglects the influence of epistemic uncertainties and negatively affects the credibility of optimal results. Hence, when the engineering designers only know the expected value and variance of input factors, it is more reasonable to deal with the input factors as uncertain variables. The presented URBDO model can be applied to help structural designers make the most suitable design when input factors are obtained from the subjective interpretation of insufficient observed data.
# Hyperspectral Imaging Techniques for the Study, Conservation and Management of Rock Art
## 2 Materials and Methods
### Overall Workflow  
Figure 2 shows the overall workflow of the proposed method for the data acquisition, pre-processing, pigment analysis and extraction of the hidden information of rock art. The details of each step are discussed in the following subsections.  
The data acquisition was carried out in 2014 and 2018. Hyperspectral imaging data were acquired with a VNIR Specim V10E sensor and pre-processing steps were applied to transform raw data from radiance into reflectance values and were georeferenced and geometrically corrected.  
Pre-processed hyperspectral images were classified using spectral angle mapper (SAM) and mixture tuned match filtering (MTMF) ([PERSON] et al., 2016) image processing algorithms to generate a pigment cartography. End members for classification were selected from pure samples identification spectral analysis and were sampled by using a field spectroradiometer. In parallel, a process to enhance visualization was used in order to generate false color compositions to ease the pigment interpretation.  
Figure 1: (**a**) View of the 3D model of panel de las manos. (**b**) Orthoimage generated to register all hyperspectral data.
# Wireless Sensor Networks for Building Information Modeling
## 7 Conclusions  
The deployment, technology, topology and cost of robust real-world WSNs installations were discussed in the context of BIM. Pilot-site installations in three European countries were reported in the context of the European collaborative project BIMERR. The WSNs are able to collect and transmit real-time data, as required from the BIMERR renovation toolkits. They consider the different field requirements and options at the pilot sites, and, hence, the presented work can set the lines for similar installations by the construction industry for collecting dynamic data for BIM.  
A use-case example of WSN measured data for BIM is also provided in terms of the application of WSN data for training occupant behavior models in order to increase the accuracy of energy-performance simulations.  
Optimal WSN deployment in smart buildings by using BIM technology, i.e., integration of WSNs as part of BIM tools to exploit building information and automatically generate and visualize the deployment solutions, is an interesting problem that could be considered in future work. Furthermore, ML application to BIM on the full lifecycle of a project in order to learn and provide insights that support decision-making from design to operations is a very interesting open problem.  
Conceptualization, D.K. and G.A.; methodology, D.K., G.T. and G.G.; formal analysis, D.K., G.T., G.G.; writing--original draft preparation, D.K., G.T. and G.G.; writing--review and editing, G.A., G.T. and G.G. All authors have read and agreed to the published version of the manuscript.  
The work in this paper was co-funded by the European Union Horizon 2020 Research and Innovation Programme, under grant agreement no. 820621.  
Figure 13: Greek pilot site’s trained thermal comfort bounds and thermostat model.
## 4 Clinical Indications
### Striae Distensae  
Striae distensae (stretch marks) are visible linear scars which develop in areas where the dermis has been damaged due to excessive stretching [25]. Anatomically, these lesions predominantly appear on the buttocks, lower back, thighs, calves, breasts/chest, abdomen, upper arms, and knees [26]. Striae are twice as prevalent in females as compared to males, mainly among individuals aged 5-50 years [25]. The clinical presentation depends on their maturation stage. In the early stage, they manifest as erythematosus, raised, linear lesions, which may be accompanied by mild pruritus. These are commonly referred to as the red striae. With further maturation, they undergo atrophy and depigmentation, eventually evolving into hypopignented, atrophic scars known as the white striae [27]. The pathogenesis of striae distensae remains to be elucidated, although the prevailing theories suggest that it is associated with the alterations in the components of the extracellular matrix: fibrillin, elastin, and collagen. These structures ensure that the skin possesses tensile strength and elasticity. Elevated concentrations of glycosaminoglycans and a significantly reduced number of vertical fibrillin fibers beneath the dermo-epidermal junction and within the elastic dermis are found in patients with striae as compared to individuals with normal skin. The orientation of the elastin and the fibrillin fibers in the deeper dermis is disrupted. In the early stages of striae formation, the elastic fiber network is markedly disorganized, and the newly synthesized fibrils, rich in tropoelastin, are thin and structurally disorganized. Furthermore, as the collagen fibers become significantly separated during this period, the newly disorganized collagen fibrils are not able to repair the damaged collagen fibers due to excessive stretching of the skin, ultimately leading to the formation of the atrophic striae [28]. Typically, striae distensae develop during puberty, pregnancy, periods of rapid weight fluctuations (both loss and gain), and may arise as a consequence of various systemic diseases or pharmacotherapy [29]. Striae constitute a common aesthetic concern which significantly affects the quality of patient life, especially young women [30]. Currently, a whole range of treatment modalities may be used to reduce their visibility, with laser therapy proving to be highly effective [31]. The eCO\({}_{2}\) laser (10 600 nm) and the Er:YAG (erbium-doped yttrium aluminum garnet) laser (2940 nm) are among the commonly em
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 2 Materials and Methods
### VF Prediction  
To distinguish VF from nVF, nine TDA features, two box-count features, and seven HRV features were fed into 5 ML classifiers, respectively, including decision tree (DT), random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). VF was predicted when the output was 1, while nVF was predicted when the output was 0. Accuracy was used as the performance indicator:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{10}\]  
where TP is true positive, TN is true negative, FP is false positive, and FN is false negative.
## 4 Simulation and Experiment of Suspension System
### PEMS Platform Suspension Experiment
Under the three control methods, the air gap changes of each suspension control unit are shown in Figure 12.  
It can be seen from Figure 12 that under the same step interference and under the control of PID, the peak value of platform air gap fluctuation is 0.58 mm and the time to return to the original steady state is 1.08 s. Under the control of LADRC, the system cannot return to the original steady state in a limited time until the interference disappears, and the peak value of the platform air gap fluctuation is 0.44 mm. Under the control of Fuzzy-LADRC, the system can return to the original steady state in a limited time. The peak value of platform air gap fluctuation is 0.48 mm, which is about 17.24% lower than PID, and the time to return to the original steady state is 0.58 s, which is about 46.29% faster than PID. It shows that among the three controllers, Fuzzy-LADRC has the best anti-interference performance and can quickly return to the original stable state when disturbed. (3) Robustness experiment  
Given that the suspension air gap of the PEMS platform is 18 mm and on the basis of stable suspension of the platform, an object weighing 2 kg is made to fall freely at 0.5 m above the \"X\" point of the external interference experiment mark of the platform, and the heavy object will be removed after 4 s. Under the three control methods, the according air gap changes of each suspension control unit are shown in Figure 13.  
Figure 12: Suspension experiment results when the platform is under internal interference. **(a)** Air gap of each suspension unit under PID control; **(b)** Air gap of each suspension unit under LADRC control; **(c)** Air gap of each suspension unit under Fuzzy-LADRC control. (4) Adaptability experiment  
Considering the irregularity of the joints between the tracks of the maglev train, which is likely to lead to the fluctuation of the suspension gap when the train is running, it is necessary to study the effect of the suspension control method on maintaining the suspension state when the platform is moving. Under the three control methods, the air gap changes of each suspension control unit are shown in Figure 14.  
It can be seen from Figure 14 that when the platform reciprocates without control, the platform air gap fluctuates in the range of 16.86\(\sim\)17.86 mm. Under the control of PID, the platform air gap fluctuates between 17.61 and 18.39 mm. Under the control of LADRC, the platform air gap fluctuates between 17.67 and 18.37 mm. Under the control of Fuzzy-LADRC, the platform air gap fluctuates in the range of 17.79-18.19 mm, which is about 60% lower than that without control, about 48.72% lower than that with PID and about 42.86% lower than that with LADRC. It shows that among the three controllers, Fuzzy-LADRC has better adaptability when the platform is moving and can maintain the given suspension state well.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
###### Abstract  
Satellite remote sensing is developing towards the micro-satellite cluster, which brings new challenges to mission assignment and planning for the cluster. A multi-agent system (MAS) is used, but the time delay caused by communication and computation is rarely considered. To solve the problem, a neural-network-based multi-granularity negotiation method under decentralized architecture is proposed. Firstly, we divided negotiation into three levels of granularity, and they work in different modes. Secondly, a neural network was trained to help the satellite select the best level in real-time. Through experiments, we compared the satellites working in three different levels of granularity, in which a multi-granularity decision was used. As a result of our experiments, a lower cost-effectiveness ratio was obtained, which proved that the multi-granularity negotiation method proposed in this paper is practical.  
satellite cluster; mission assignment and planning; decentralized; multi-granularity negotiation 2020
## 3 Data and Methods
### Vertical Deformation Time-Series Retrieval  
After SBAS processing, the LOS displacement time-series are geocoded to a common spatial grid of high-coherent points. For our analysis, let \(\mathbf{d}\left(P\right)=\left[d_{1}\left(P\right),d_{2}\left(P\right),\ldots, d_{Q}\left(P\right)\right]\) be the (geocoded) LOS deformation time-series, which is computed in correspondence with the generic pixel \(P\). Note that the LOS displacement time-series are only known relative to one reference acquisition time, which corresponds to the first SAR acquisition of the available dataset. Accordingly, following [28], we assume \(d_{1}\left(P\right)=0\). Moreover, we also note that the presented method implies a pixel-by-pixel temporal analysis; accordingly, from now on, the dependence of the deformation time-series on pixel \(P\) will be not directly mentioned.  
LOS deformation values can be expressed as [55; 56; 57]:  
\[\mathbf{d}=\mathrm{sin}\theta\mathrm{cos}\varphi\cdot\mathbf{e}+\mathrm{sin} \theta\mathrm{sin}\varphi\cdot\mathbf{n}+\mathrm{cos}\theta\cdot\mathbf{h} \tag{1}\]  
where \(\mathbf{e}=\left[e_{1},e_{2},\ldots,e_{Q}\right]\), \(\mathbf{n}=\left[n_{1},n_{2},\ldots,n_{Q}\right]\), and \(\mathbf{h}=\left[h_{1},h_{2},\ldots,h_{Q}\right]\) are the 3D cumulative deformation components in the east-west, north-south, and vertical directions, respectively. Moreover, \(\theta\) is the radar side-looking angle, and \(\varphi\) is the corresponding satellite-heading angle. Note that SAR satellite orbits are near polar, circular, and sun-synchronous; accordingly, the sensitivity of the SAR measurements to north-south deformation components (see Equation (1)) is rather limited (i.e., \(\left|\mathrm{sin}\varphi\right|\ll 1\)). Furthermore, if we assume the negligibility of the east-west deformation components, with respect to the vertical ones, the time-series of the vertical deformation components can be derived from the LOS measurements (see Equation (1)) as [19]:  
\[\mathbf{h}\cong\frac{\mathbf{d}}{\mathrm{cos}\theta} \tag{2}\]  
where \(h_{1}\equiv 0\), since the first image of the considered SAR dataset is assumed to be the temporal reference [28].