esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Setting
#### 5.1.3 Baseline  
Due to the extreme sparsity of AVI trajectories, the spatiotemporal reconstruction of the trajectories is divided into two steps in the model. First, the spatial features of the trajectories are reconstructed, and then the temporal attributes of the trajectories are reconstructed based on the spatial features. Therefore, in the comparative experiments, the method is also divided into two parts: the spatial reconstruction method of the trajectories and the time prediction method of the trajectories.  
The spatial reconstruction method mainly includes the following four methods: RICK, MPR, and HMM for the spatial reconstruction of the trajectories and the spatial trajectory reconstruction method proposed previously, which we named Prob.  
The time prediction method mainly includes the following: history and linear models belong to traditional statistical learning, while the RNN and LSTM are methods that use deep learning.  
Linear [34]: The linear model is a simple statistical learning-based method that uses a linear function to fit the relationship between time and space in the trajectory. For each spatial point, it assumes a linear relationship between time and space and learns this relationship from historical data to predict the time of each spatial point.  
History [35]: History-based highest frequency access is a simple prediction method based on historical data. For any fixed spatial point, it uses the time point with the highest frequency of access to that spatial point as the time at which the spatial point is passed through.  
RNN [36]: The recurrent neural network uses a neural network to model the spatiotemporal relationship in the same trajectory. It inputs the spatial sequence of a trajectory and outputs the time sequence of the trajectory. Using a recurrent neural network to model trajectories can enable the effective extraction of the spatiotemporal relationships between different positions within the trajectory.  
LSTM [37]: The long short-term memory network is a variant of the recurrent neural network. Compared with the recurrent neural network, the advantage of the long short-term memory network is that it can effectively preserve the long-term dependencies in the trajectory sequence, and it performs better when the sequence is longer.
# NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency
## 2 Simple Non-Iterative Clustering
Notice that the seeds hold the priority, thus they still anticipate any neighboring pixels. As shown in Figure 1c, a preliminary label map is acquired after all seeds are popped. In addition, a total of 16 neighboring pixels are pushed and sorted in \(Q\) as candidates. Figure 1d,e demonstrate the jointly repeated assignment and updates step. The neighboring elements 16 th and 14 th inspected by seed D are given as an example, respectively. After inspecting in Figure 1c, 16 th becomes the top-most element in \(Q\). It is then assigned with the label of seed D and treated as the frontier pixel of the cluster \(S_{D}\), as well as absorbed to perform an online update of the centroid value. In Figure 1e, the newly inspected pixels, 17 th, 18 th and 19 th are pushed before popping 14 th elements. In terms of priority by the global minimum distance, 14 th falls behind 16 th but overtops any of the above latest pixels. Consequently, it performs a similar process as 16 th. This kind of repeated procedure is continued until \(Q\) is empty. Figure 1f,g are the state of \(Q\) (note that \"before\") as well as corresponding label map near and in the end, respectively. Eventually, the outlines of superpixels in Figure 1h are determined by the borders of pixel clusters with the same labels. Since all superpixels are generated by seeds expanding that absorb surrounding pixels, SNIC mimics a geodesic distance from cluster centers to their corresponding frontier pixels. Therefore, the connectivity in the \(xOy\) coordinate space is guaranteed, and a post-processing step of split-and-merge is omitted. Figure 1: Dynamic segmentation procedure of simple non-iterative clustering (SNIC). (**a**) Input image; (**b–g**) execution of SNIC; (**h**) segmentation result. Among (**b**) to (**g**), each left column indicates the priority queue \(Q\), in which the upmost element has the highest priority. The number in each pixel grid depicts the global order when it is inspected as a 4-neighbor element. Pixels with filled colors in label maps indicate that they have acquired labels corresponding to their nearest cluster centers. Note that the state of \(Q\) is one step earlier than the corresponding label map with pixel inspection and label assignment.
## 4 Vibration Characteristics for Bearing Clearance Monitoring
### Discussion  
In a WT system, unsteady wind and gusts will lead to high contact stresses on rolling elements and bearing skidding. Investigations show that wear is the main cause of bearing failures. Wear and tear on bearings could result in faults or even fatal breakdowns, causing catastrophic accidents and  
Figure 18: Spectral centroid of low frequency band and high frequency band in the load direction under the rotational speed of (**a,b**) 500 rpm, (**c,d**) 1000 rpm and (**e,f**) 1500 rpm.  
economic losses. Thus, this study, focusing on the bearing clearances caused by wear, provides a vital reference to accurately monitor the health condition of bearing. In practical application, for a WT real-time online monitoring system dynamic model, the investigated vibration characteristics and indicators can be utilized to show the present and predict the future state and will be updated or adjusted through the test data. Thus, the faults can be detected at an early stage, providing sufficient lead time to perform maintenance on WTs and hence reducing accidents and economic losses.  
From all the above analysis, although the dynamic force on each rolling element shows a linear increase with the increase in the clearance and load, it is very difficult to obtain a linear vibration indicator related to bearing clearance. This may be due to the nonlinear effect between the contact force and vibrations. In addition, because the model is established based on ideal assumptions, which inevitably ignored some factors, such as bearing errors and interferences, the responses under different clearance and load conditions will become even more complicated once these factors are taken into consideration. Thus, diagnostic methods and some more advanced approaches are required for nonlinear feature extraction, such as correlation analysis [38], principal component analysis (PCA) [39], time-frequency manifold learning [40; 41] and so on.  
In future research, on the one hand, the authors will take more factors, such as friction, skidding and lubrication, into consideration, making the simulation closer to the real operating state of bearings. On the other hand, nonlinear feature extraction methods will be applied to the existing data to realize online bearing clearance monitoring.
# A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC
## 3 Hierarchical and Multi-Domains Strategy Based on Driving Demands
### Evaluation Functions of Driving Demands
#### 3.2.3 Traffic Rules  
The evaluation function of whether a vehicle has risks of violating a solid lane line, red light, and max speed limit are defined by Equations (8)-(10), respectively:  
\[L_{lane}=vT_{r}+\frac{1}{2}aT_{r}^{2}-DLC \tag{8}\]  
\[L_{red}=vT_{red}+\frac{1}{2}aT_{red}^{2}-D \tag{9}\]  
\[L_{speed}=v+aT_{r}-v_{max} \tag{10}\]  
where \(L_{lane},L_{red},L_{speed}\) are, respectively, evaluation functions of whether the vehicle has risks of violating solid lane line, red light, and max speed limit. \(v,a\) are the speed and acceleration of the SV centroid, respectively. \(DLC\) is the distance from the centroid to the solid lane line along the direction of \(v\)[24]. \(D\) is the distance from the centroid to a red light along the direction of lane. \(v_{max}\) is the max speed limit. \(T_{red}\) is the left time of the red light. \(T_{r}\) is a time constant. The value of \(T_{r}\) is adjusted by experience. Given that time horizon is 2 s, \(T_{r}\) is recommended to be in the vicinity of 2 s.
## 2 Background
These data collected from each site are used by structural engineers to make the modal analysis and study the structural properties. Focusing on this type of WSN application, we propose a family of CDSAs. Our CDSAs are now studied above the MAC layer and we assume that the contention-based MAC protocol exists underneath. The CDSAs can also be applied to other applications based on contention channel, because our CDSAs are based on DTMC and the scheduling probability parameters are all from the statistical value. From the articles we have researched, our CDSAs are uniquely different from all scheduling approaches used by other papers. On the one hand, we set up working state model based on DTMC to schedule nodes' sleep and data transmission, on the other hand, we also construct the behavior of the child nodes, the sleep behavior of the parent node and the dynamics of communication channel to effectively schedule nodes' state. The behavior of the child nodes is inspired by the analytical model of the next-hops in the paper [22], but our behavior model of the child-nodes is based on the CSMA/CA protocol and is implemented through statistical methods. Moreover, the behavior of neighboring nodes and state information of communication channel are new in this paper and are learned from another Markov process model. From a higher view above the MAC layer, the transmission scheduling of CDSAs can adjust the transmission rate of nodes, which is somewhat similar to the rate control transport algorithm in [23, 24] and the application data unit download scheduling in paper [25]. But the approaches in the above are centralized and base station is required to make a decision. Our CDSAs are distributed and can be applied to each node. Our basic purpose is to let nodes collaboratively learn the scheduling probability parameters for the working state scheduling. We also discuss the adaptability and practicability of the proposed two CDSAs in this paper. We evaluate the CDSAs with the underlying CSMA/CA protocol and discuss some network performances of the CDSAs. The results show that these two approaches can reduce energy consumption effectively. Moreover, the O-CDSA is practicable to a lightweight workload application and T-CDSA can also be applied to heavyweight workload application.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 4 Simulation Results
### Comparison of the Average Energy Mean Square Deviation of Nodes  
After 10 rounds, we calculate the average energy mean square deviation of the remaining nodes to analyze the energy distribution in the whole network. We can obtain the optimum number of CHs depending on the analysis of the average energy mean square deviation of nodes to transmit information and increase the performance of the entire network. Figure 9 shows that the slope of the LEACH is larger than the proposed protocol before the degree of energy dispersion increases to the maximum. However, the volatility of the proposed IEE-LEACH is less than 0.1. This finding implies that the performance of the proposed IEE-LEACH protocol is better than other protocols in terms of the energy consumption. This improvement is because the proposed IEE-LEACH protocol extends the lifetime of the network by balancing the node energy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol Name** & **Improved Details** \\ \hline LEACH & NULL \\ IEE-LEACH & Threshold setting considering the energy adjustment parameter, and closer nodes to the BS do not participate in cluster formation. \\ IEE-LEACH-A & Threshold setting in the same way as LEACH, but closer nodes to the BS do not participate in cluster formation. \\ IEE-LEACH-B & Threshold setting considering the energy adjustment parameter, but the cluster formation is the same as that of LEACH. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the improvement schemes of the proposed routing protocols.  
Figure 8: Comparison of data transmission.  
Figure 9: Comparison of average energy mean square deviation of nodes every 10 rounds.
## 3 Results and Discussion
### Comparison of the Sensitivity to Parameter Initialization  
In this section, we give experimental results which prove low sensitivity of the proposed neuronal architecture to the setting of its parameters, unlike other classification methods. The parameters considered for each method in this section are \(\eta\) for LVQ1 and MLP, \(\zeta\) for SVM and \(m\), \(\delta\) and \(\zeta\) for MCLNN.  
What is compared here is the process of fine-tuning the algorithmic parameters, or hyper parameters, of different methods. The experimental results also show that the process of hyper-parameter fine-tuning is much easier for the proposed method, for which optimal results are obtained for much larger intervals of values for these parameters.  
For each parameter, we examine the length of the interval of values for which the studied method provides the best error rate at least once. The bigger the length of this interval, the lower the sensitivity of the studied method to the chosen parameter. To normalise this measure for all parameters, we use the mathematical expression:  
\[l=\frac{\hat{z}_{max}-\hat{z}_{min}}{z_{max}-z_{min}}\times 100\% \tag{15}\]  
where \(\hat{z}_{max}\) and \(\hat{z}_{min}\) are, respectively, the maximal and minimal values of a chosen parameter, which give the best error rate; \(z_{max}\) and \(z_{min}\) are borders of the interval of used values in all simulations. These values are given in Table 6.  
The results generated by LVQ1 and MLP algorithms depend on the variation of the learning rate in a relatively strong way. In fact, for about two-thirds of the cases, LVQ1 and MLP give a better result for values in very small intervals. For the proposed architecture and SVM, it is clear that both of them give the best result for very different values of their parameters (Table 9).
## 1 Introduction  
Engineers are daily involved in predictions over the outputs of natural phenomena and their interaction with the behavior of structures [1]. A good prediction is a number that represents the best estimation for the expected outputs and a range that accounts for the uncertainty of the estimation. When natural phenomena become complex, predictions usually rely excessively on the engineer experience and common sense. A systematic approach to the uncertainties surrounding the predictions is many times missed.  
Soil mechanics is one of those scientific fields where approaches to uncertainty rely massively on experience. This situation makes that uncertainty of the predictions falls behind the current developments more often used in other fields of structural design.  
During the last decades, structural reliability methods have been developed. This is not least due to the fundamental works on probabilistic approach methods performed within the Joint Committee on Structural Safety (JCSS). The recent reinforcements in providing such a basis for design are condensed in the JCSS Probabilistic Model Code (PMC), which includes, among others, the basic reports on soil mechanic properties [2, 3].  
Statistical methods have been successfully applied to different geotechnical problems. It is worth noting the works on slope stability and underground excavation [4]. Indeed, today many slope stability programs include probabilistic approaches to the ultimate limit state.  
In line with the ongoing joint efforts to explore alternatives to include uncertainties coming from the ground condition, we present a methodology affordable in practical applications to predict deformations.  
In this paper, a probabilistic method to deal with the outputs of geotechnical site investigations is proposed. Then, they are used to feed an analytical model that predicts the settlement of a rigid inclusion below a slab foundation. Since inputs data are a statistical variable, the settlement prediction will be.  
In this way, not only a settlement model for this specific problem is validated, but also a systematic method to deal with a soil investigation and the uncertainty involved.
# The Shear Behavior of the Curved Interface in Polyurethane-Concrete Composite Structures
## 3 Results and Discussion
### Comparison of Shear Behavior in Curved and Planar Composite Specimens
#### 3.1.3 Shear Moduli of Curved and Planar Specimens with Varying Polyurethane Densities  
The shear modulus (the slope during the linear growth phase) variations in curved and planar composite specimens with respect to polyurethane density are illustrated in Figure 8. The shear modulus of curved specimens is slightly lower than that of planar specimens, and the difference between the shear moduli of curved and planar specimens becomes more pronounced with increasing polyurethane density. Furthermore, the shear modulus trends of curved and planar specimens exhibit significantly different patterns. As the density of polyurethane increases, the rate of shear modulus growth decreases with increasing polyurethane density for curved specimens, whereas it increases for planar specimens.
## 5 Comparison of Forecasting Results by Different Forecasting Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**LSR**} & \multirow{2}{*}{**GM (1,1)**} & \multicolumn{2}{c}{**FOA-GM**} & \multicolumn{2}{c}{**MFO-GM**} & \multirow{2}{*}{**Rolling-LSR**} & \multicolumn{2}{c}{**Rolling-GM**} & \multicolumn{2}{c}{**Rolling-FOA-**} & \multicolumn{2}{c}{**Rolling-MFO-**} \\  & & & **(1,1)** & & **(1,1)** & & **(1,1)** & & **GM (1,1)** & **GM (1,1)** \\ \hline
2010 & 13.61\% & \(-\)8.57\% & \(-\)7.68\% & \(-\)0.62\% & 13.61\% & \(-\)8.57\% & \(-\)7.68\% & \(-\)0.62\% \\
2011 & 23.04\% & \(-\)7.17\% & \(-\)6.28\% & \(-\)1.32\% & 13.35\% & 0.97\% & 0.77\% & \(-\)0.91\% \\
2012 & 22.58\% & \(-\)18.59\% & \(-\)17.61\% & \(-\)14.39\% & 9.98\% & \(-\)11.97\% & \(-\)8.72\% & \(-\)8.55\% \\
2013 & 22.77\% & \(-\)38.46\% & \(-\)37.31\% & \(-\)36.25\% & \(-\)12.88\% & \(-\)14.30\% & \(-\)16.27\% & \(-\)11.97\% \\
2014 & 24.97\% & \(-\)52.28\% & \(-\)51.03\% & \(-\)52.89\% & \(-\)12.28\% & \(-\)11.95\% & 8.06\% & \(-\)9.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage Error (_PE_) comparison of eight forecasting models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**LSR**} & \multirow{2}{*}{**GM (1,1)**} & \multicolumn{2}{c}{**FOA-GM**} & \multicolumn{2}{c}{**MFO-GM**} & \multirow{2}{*}{**Rolling-LSR**} & \multicolumn{2}{c}{**Rolling-GM**} & \multicolumn{2}{c}{**Rolling-FOA-**} & \multicolumn{2}{c}{**Rolling-MFO-**} \\  & & & **(1,1)** & & **(1,1)** & & **GM (1,1)** & & **GM (1,1)** \\ \hline _MAPE_ & 21.39\% & 25.01\% & 23.98\% & 21.09\% & 12.42\% & 9.55\% & 8.30\% & 6.29\% \\ _RMSE_ & 441.16 & 662.34 & 643.2 & 642.52 & 235.98 & 217.18 & 195.6 & 164.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Mean Absolute Percentage Error (_MAPE_) and Root Mean Square Error (_RMSE_) for different forecasting models. Above all, the superiority of Rolling-MFO-GM (1,1) can be safely concluded. The proposed Rolling-MFO-GM (1,1), in which the parameters of GM (1,1) are optimized by employing the new intelligent optimization algorithm MFO and the introduction of rolling mechanism, is of great practical significance. Meanwhile, it can also be seen that MFO-GM (1,1) performs better than LSR, GM (1,1) and FOA-GM (1,1), and Rolling-MFO-GM (1,1) is better than Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1) in terms of annual electricity consumption forecasting. These findings indicate that the utilization of MFO to optimize the parameters of GM (1,1) can greatly enhance the precision of annual electricity consumption forecasting. Additionally, as the rolling mechanism uses recent data as input sequence of forecasting model, which can consider the latest change trends of policy, economy and society as well as the characteristics of forecasting subject, the models with rolling mechanism can perform better than those without rolling mechanism in terms of annual electricity consumption forecasting.
# Editorial for Special Issue \"Remote Sensing of Precipitation: Part II\"
## 1 Introduction  
The ongoing and intensive consideration by the scientific community of the many facets of precipitation science constitutes a broad recognition of the significance of this indispensable component of the hydrologic cycle. The interest in the state-of-the-art scientific investigations of precipitation is maintained and even rejuvenated by current developments in the field, embracing the availability of new precipitation databases, technological improvements and methodological advances.  
This Special Issue comprises a collection of papers embracing a wide range of aspects of precipitation science. This volume hosts 25 papers devoted to remote sensing applications in precipitation, which include studies on satellite precipitation retrievals together with their corresponding methodologies, weather radar precipitation estimations, the understanding of cloud and precipitation microphysical properties, precipitation downscaling, droplet size distribution, the performance of precipitation forecasts by numerical weather prediction models, precipitation retrievals from global navigation satellite systems, spatiotemporal precipitation distribution and its statistical characteristics, rain gauge- and satellite-based precipitation products and their comparisons and spatiotemporal distributions of precipitation modeling.  
The next section summarizes the individual articles hosted in this Special Issue in alphabetical order based on the first author's name.
## 1 Introduction
### Assessment of Individual Ecosystem Functions
#### Climate Regulation at the Local Level: Evapotranspiration  
Vegetation can significantly cool the environment through shading [44; 45] and evapotranspiration [46; 47; 48]. A large number of studies used evapotranspiration as an indicator for the cooling effect of vegetation [22; 49; 50; 51]. The cooling effect of vegetation in cities can be assessed by measuring and comparing the temperature in vegetated and non-vegetated areas, using land surface temperature [3; 52], air temperature [53; 54], or bioclimatic indices to capture human perception of temperature comfort [55]. These methods are more appropriate for the microscale or local scale. For larger areas, airborne remote sensing methods using satellite imagery [56; 57] or aircraft/airship thermal scans of surface temperature [58] are more suitable. However, data processing and interpretation can be challenging. Therefore, a habitat-based approach may be easier to apply in some cases, such as urban planning or assessment of ESs [59]. Examples of habitat-based assessment of cooling function exist in Berlin (Biotope Area Factor [60]) and Malmo (Green Space Factor [61]).