esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Model and Materials
### Soil-Tunnel System
#### 4.1.3 Contact Relationships  
As there are lots of different components in the soil-tunnel system, it is of significance to select the correct contact relationships to describe the interaction between them. The surface-to-surface contact in ABAQUS is employed to simulate the interaction between the soil and the segmental lining structures, while the bolt joint is embedded in the segmental linings. Moreover, the hard contact is set for the normal behavior of the surface-to-surface contact, and the penalty friction approach is used with finite sliding formulation in the tangential direction. The friction coefficient between the soil and segments is 0.5, while that between different segments is set to be 0.55.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Prediction Intervals Evaluation
#### 3.6.2 Residual Analyses  
The summary statistics of errors or residuals for the models are shown in Table 9. The table shows model AQRA as having the least standard deviation, which means that its error distribution is the narrowest of all the models, as can also be seen in the box plot of Figure 8. This shows that the best model is AQRA in comparison to the others. The minimum values are all negative and the skewness values are far from being normally distributed. The kurtosis value shows erratic patterns; however, since they are all more than 3, they are termed leptokurtic data. The density plot in Figure 9 shows similar patterns and not much information can be inferred from these plots, except that they are all in between the \(-1\) and \(+1\) regions in the forecast error axis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**PINC\%** & **Model** & **\%PICP** & **\%PINAW** & **\%PICAW** & **\%PNAD** & **ABLL** & **AAUL** \\ \hline
90 & SAverage & 93.1743 & 3.5780 & 7.9661 & 0.0250 & 513 & 524 \\  & Median & 92.3978 & 3.5510 & 7.8617 & 0.0250 & 579 & 576 \\ \hline
95 & SAverage & 97.1500 & 4.4299 & 9.9499 & 0.0370 & 198 & 235 \\  & Median & 96.8012 & 4.2181 & 9.3775 & 0.0330 & 235 & 251 \\ \hline
99 & SAverage & 98.4137 & 7.7858 & 11.2930 & 0.0110 & 198 & 43 \\  & Median & 99.3148 & 7.2292 & 16.7254 & 0.0044 & 52 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Combined prediction interval evaluation.  
Figure 8: Box plots of residuals.  
Figure 9: Density Plots of Residuals.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Min** & **Max** & **Mean** & **Median** & **St.Dev** & **Skewness** & **Kurtosis** \\ \hline BNN & \(-1.2156\) & \(4.8961\) & \(0.0235\) & \(-0.0304\) & \(0.2077\) & \(1.4241\) & \(29.9048\) \\ SGB & \(-1.1015\) & \(8.4607\) & \(-0.0128\) & \(-0.0180\) & \(0.2550\) & \(11.4720\) & \(273.8021\) \\ GAM & \(-1.4350\) & \(6.4689\) & \(0.0028\) & \(0.0045\) & \(0.2468\) & \(4.0097\) & \(72.3618\) \\ AQRA & \(-0.9845\) & \(4.5838\) & \(0.0061\) & \(0.0004\) & \(0.1887\) & \(4.9086\) & \(78.1724\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary statistics for the residuals.
## 3 Maintenance Strategy
### Solution of Opportunistic Maintenance Model  
The steps to solve the opportunistic maintenance model are as follows:  
1. Determine the values of all parameters in the model.
2. Optimize the maintenance plan of the subsystems of the machining center to obtain the optimal maintenance interval \(T_{ik}\) and number of maintenance cycles \(N_{k}\) for the subsystems.
3. Determine and record the time \(t_{n}=\min(t_{n1},t_{n2},\cdots,t_{ns})\) of the \(n\)th shutdown for preventive maintenance or replacement of the machining center. When \(n=1\), \(t_{11}=T_{11},t_{12}=T_{12},\cdots,t_{1s}=T_{1s}\).
4. Determine the maintenance action of subsystem \(k\) at \(t_{n}\). When \(R_{k}(t)>R_{hk}+\Delta R_{k}\), the subsystem does not require preventive maintenance or replacement action at \(t_{n}\); when \(R_{hk}<R_{k}(t)<R_{hk}+\Delta R_{k}\), \(i_{k}<N_{k}\) at the same time and opportunistic maintenance will be performed on subsystem \(k\), where \(i_{k}\) is the maintenance cycle number of subsystem \(k\) at \(t_{n}\). The maintenance cycle \(i_{k}\) is increased by 1 after preventive maintenance; when \(R_{hk}<R_{k}(t)<R_{hk}+\Delta R_{k}\), \(i_{k}=N_{k}\) at the same time and an opportunistic preventive replacement will be performed for the subsystem \(k\). Then, \(i_{k}=1\), and maintenance cycle counting will be restarted.
5. Solve the corresponding time \(t_{n+1}\) of the \((n+1)\)th shutdown for preventive maintenance or replacement of the machining center. \(t_{n+1}=\min(t_{(n+1)1},t_{(n+1)2},\cdots,t_{(n+1)s})\), where \[t_{(n+1)k}=\left\{\begin{array}{ll}t_{nk}+t_{dn}&O(t_{n},k)=0\\ t_{nk}+t_{dn}+T_{i_{k}k}&O(t_{n},k)=1,2\end{array}\right.\] (42) \(O(t_{n},k)=0\) indicates that subsystem \(k\) did not undergo preventive maintenance or replacement at \(t_{n}\), \(O(t_{n},k)=1\) indicates that subsystem \(k\) performed preventive maintenance at \(t_{n}\), and \(O(t_{n},k)=2\) indicates that subsystem \(k\) performed preventive replacement at \(t_{n}\).
6. Repeat Step 3-Step 5 until \(t_{n}>T\).
7. Calculate costs within \([0,\ T]\). Continuously update \(\Delta R\) to minimize costs and record the best \(\Delta R\).  
The solution process of the maintenance model is shown in Figure 3.
# Constitutive Analysis of the Anisotropic Flow Behavior of Commercially Pure Titanium
## 4 Discussion
This work calculated the activation stresses for two slip systems (i.e., prismatic and basal slips) in grains with an SF for prismatic slip less than 0.2. The lowest values were selected from these results, providing a CRSS value of 58 MPa for prismatic slip and 95 MPa for basal slip. Either hardening or softening induced by texture development was quantified by the difference between activation stresses before and after applying a compressive strain of 0.2 (i.e., \(\sigma_{a}|_{e\ =\ 0.2}-\sigma_{a}|_{e\ =\ 0}\)). Consequently, the \(\Delta\sigma_{tx}\) values were calculated to be 27 MPa for RD, \(-10\) MPa for ND, and 5 MPa for TD. Revisiting Figure 4, RD compression gave rise to the most drastic change in SF values after applying a compressive strain of 0.2. The high fraction of lattice orientations favorable for prismatic slip changed into a relatively uniform distribution of SF values due to the activation of [10; 11; 12] extension twinning. Such crystal rotation gave rise to an effective texture hardening during RD compression. On the other hand, ND compression induced less change in SF values, even though the ND specimen showed twin characteristics similar to those of the RD specimen. The average SF value for prismatic slip was slightly increased after the deformation, resulting in a weak softening effect. TD compression also caused the small change in SF distribution from moderate to low values, which accompanied weak texture hardening. The theoretically determined stress components and total stress increment are presented for the investigated compressive directions (Figure 6). The total stress increment was calculated to be 178 MPa for RD, 135 MPa for ND, and 149 MPa for TD. It is of particular note that these data were greatly consistent with the measured stress increments at a compressive strain of 0.2: 180 MPa for RD, 140 MPa for ND, and 151 MPa for TD. This supports the logical validity of the constitutive approach adopted in this work.
## 3 Results and Discussion
### The Developed Model for Determination of Dielectric Parameters of Objects Using UIWB Impulse Radar  
The EM wave propagation model was developed for the case shown in Figure 4: the three-layered medium is made from theoretically tree different dielectrics. Practically, the medium before and after the object under analysis with thickness \(x_{2}\) and relative permittivity \(\varepsilon_{2}\) is air, resulting in \(\varepsilon_{1}=1\) and \(\varepsilon_{3}=1\). Other denominations in the picture are  
* \(E_{0}\)--The unknown magnitude of the wave that comes from the transmitting antenna \(A_{T}\);
* \(E_{1}\), \(E_{2}\)--Waves reflected from the boundaries between front and back of the object measured by the receiving antenna \(A_{R}\);
* \(E_{3}\)--The wave that has passed through object;
* \(\tan\delta_{2}\)--The loss tangent of object;
* \(x_{0}\)--The distance from the antennas to the object.  
The model is described in detail in [40]. The testing of the model was conducted in order to determine whether it could be used in EM wave propagation simulation for synthetic data generation.
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 4 Improved Genetic Algorithm Stage  
### Improving the Genetic Algorithm Phase  
#### 4.1.1 Chromosome Coding  
Chromosome coding is a technique commonly employed in genetic algorithms for representing individual solutions. It transforms a problem's solution into a format suitable for genetic operations, typically represented as a string or an array. Assuming there are N customers requiring service and that the distribution center can utilize a maximum of K vehicles, with 0 denoting the distribution center, the chromosome length will be N + K - 1. For example, a chromosome depicted as 0 2 4 6 8 1 0 3 5 7 9 0 illustrates the following routes:  
Path 1: 0-2-4-6-8-1; and Path 2: 0-3-5-7-9.
## 2 State of the Art
### Four-Dimensional Modelling
#### 2.2.5 Generative Modelling  
Generative modelling creates a 3D geometry iteratively based on pre-programmed rules [160]. In comparison to traditional modelling, which requires defining every single structure manually, this reduces effort and simplifies the modelling process by predefining the structural elements of objects and enables altering them on a parameter level (e.g., the properties and number of windows of a facade). Various projects use generative modelling for the ease of creating large-scale heritage structures such as complex 4D city models [161; 162; 163; 164]. Another generative modelling technique structures the manual modelling process into single steps and translates them into digital-ontology-based workflows [165]. In our case, the combination of generative modelling rulesets with recognized footprints from historical cadaster data is of high relevance to ease model creation. This is currently under investigation in various projects [166; 157; 167].
## 2 Materials and Methods
### Processing of RGB Color Sensors
#### 2.2.3 Colorimetric Mapping of \(RGB\) Sensor Readouts
\tag{6}\]  
From these tristimulus values, corresponding model chromaticity coordinates \((u^{\prime}_{\text{SS},i},v^{\prime}_{\text{SS},i})\) can be calculated as follows:  
\[u^{\prime}_{\text{SS},i} =\frac{4X_{\text{SS},i}}{X_{\text{SS},i}+15Y_{\text{SS},i}+3Z_{ \text{SS},i}}, \tag{7}\] \[v^{\prime}_{\text{SS},i} =\frac{9Y_{\text{SS},i}}{X_{\text{SS},i}+15Y_{\text{SS},i}+3Z_{ \text{SS},i}}.\]  
The prediction error between model and CIE calculation is then given by  
\[\Delta u^{\prime}v^{\prime}=\frac{1}{n}\sum_{i}^{n}\Delta u^{\prime}v^{\prime} _{i}=\frac{1}{n}\sum_{i}^{n}\sqrt{(u^{\prime}_{\text{CIE},i}-u^{\prime}_{ \text{SS},i})^{2}+(v^{\prime}_{\text{CIE},i}-v^{\prime}_{\text{SS},i})^{2}}, \tag{8}\]  
which is used as the target function to be minimized as part of the optimization depicted in Figure 7. Again, the same interior-point optimization algorithm that was used to determine an optimal set of corrected sensor sensitivities was also applied here in order to find the matrix coefficients of Equation (6) that yield the smallest prediction errors \(\Delta u^{\prime}v^{\prime}\) on the training database of different test light spectra. Instead of using a simple linear \(3\times 3\) matrix, the additional introduction of higher-order nonlinear polynomials in the applied matrix transformation may further reduce the prediction errors. Following the studies of [PERSON] et al. [126] and [PERSON] et al. [127], various polynomials were tested as detailed in Table 1.  
After a suitable matrix transform has been found by means of optimization, a verification of the appropriateness of the transform for new light spectra that were not part of the training set is still pending. Thus, a second set of test light sources that differed from the initial training set was available for the purpose of validation. Basically, Equations (3)-(8) were again used to evaluate the final model prediction error for this new set of test light sources as obtained when applying the optimized matrix transform. A comparison of the performance for different matrix sizes and arrangements of nonlinear polynomials is given in Section 3.1. Finally, it should be noted that the accuracy and predictive performance of this matrix optimization strategy strongly depends on the suitability of the training database with regard to the color sensor's intended application. Depending on whether the sensor is used for evaluating artificial or daylight light sources, different representative datasets should be considered for performing the optimization in order to derive case-specific matrices that exhibit superior performance compared to using \"averaged\" matrices only.
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 2 Development of the Dynamic Monitoring System for Asphalt Concrete
### _Composition of Digital Monitoring System_
#### 2.2.4 Quality Control System for Asphalt Concrete Pavement Paving  
The quality control system for asphalt concrete pavement paving is composed of screed real-time tracking and positioning, integrated controller, 5G wireless communication network and temperature acquisition systems. Based on the BDS system and carrier phase splitting real-time kinematic (RTK) technique [34; 35; 36; 37], coordinate information data transmission is carried out by installing reference stations and mobile stations on the construction site and construction machines and tools, respectively, so as to obtain the paving track, paving speed and paving time. In addition, multi-point infrared thermal imaging equipment is installed on the paver to acquire the overall pavement temperature of the asphalt mixture to monitor the paving temperature and pavement uniformity [38; 39]. At the same time, the system display terminal LED screen enables operators to obtain visual paving information and thereby helps guide construction operations. The digitized asphalt pavement paver is shown in Figure 4.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Software Solutions  
This component is of vital importance for the systems as it determines how to handle obtained information. Either only for storage and registration, to build a prediction system to improve the allocation and reservation of parking spaces, or an online system with which the end user can interact. Without software components, Smart Parking systems would be limited to perform very primitive tasks.  
Software solutions are the central core for processing information collected by sensors and disseminate results to end-users of other systems. The purpose of every solution relies on the needs defined by stakeholders [13; 29]. Software solutions should not act only as a repository because its information help to reduce time, fuel, and money. Its architecture has to be robust enough to deal with greats amount of information and provide services to a large scale of users. To achieve this, they could be deployed over cloud infrastructures either private or public. These solutions combined with a good mobile application are an important work tool because they allow users to perform certain tasks such as reserving parking spaces, or finding the nearest parking lot at a specific time of day. Software solutions should provide real-time information, as it is key for drivers to make decisions.  
Data that resides in software solutions represent a useful tool mainly for governments that are working to improve urban development and mobility. Data could show points of high concentration and suggest alternatives for users that are nearby. This information could be used to predict parking availability in places with no sensors or poor communication coverage. From a commercial perspective this information is a gold box as service points could be established near sites with high vehicular congestion. Also, construction companies could benefit from this information to determine where to build more parking buildings or to increase the capacity of current ones.  
Software solutions are essential and must handle information to benefit cities and users. In the future, they should contain all these features: information management, analysis and prediction, and e-parking services.
## 3 Case Study
### Discussion
#### 3.2.1 Analysis of the Construction Sequence
However, according to the real-time data from the Chengdu Rail Transit Company (Chengdu, China) (November and December 2020) ([[https://www.chengdurail.com/index.html](https://www.chengdurail.com/index.html)]([https://www.chengdurail.com/index.html](https://www.chengdurail.com/index.html)) (accessed on 3 February 2021)), the passenger flow and passenger transport intensity of Line 7 were actually higher than those of other lines (Figure 6). This shows that the construction sequence should be advanced. The calculation result of the model is more reasonable than the original network construction schedule. For Line 4, the model calculation ranks it 7 th (see Table 8), and the values of its indicators are mediocre, as can be seen in Figure 5d. Passenger flow, traffic location, and urban development location are all at a disadvantage. The reason for giving priority to the construction of Line 4 in that it is a main line running east to west through Chengdu. For Line 10, the model calculation ranks it 11 th (see Table 8), the value of five indicators (\(C_{1}\), \(C_{3}\), \(C_{4}\), \(C_{6}\), and \(C_{7}\)) are the lowest of any line, as shown in Figure 5g. Meanwhile, the real-time passenger flow data (Figure 6) also proved that the construction of Line 10 was premature, and the passenger flow did not meet the expected levels. The network accessibility contribution degree (\(C_{6}\)) of Line 10 is also the lowest of any line. Not all lines have a positive contribution to the network accessibility of the whole network, the contribution degree of Lines 4 and 10 are negative. In fact, their existence decreases the network efficiency. The main reasons for this are that the radiation distance to the periphery of the network is too large, and that the topological distance between the stations on these two lines and other stations in the network is large. The connection and transfer stations between these lines and other lines are insufficient, and their use has little influence on the shortest path between other lines. This finding indicates  
Figure 6: The real-time passenger flow (**a**) and passenger transport intensity (**b**) of the Chengdu rail transit network from 1 November to 30 December 2020.  
that the route or transfer station setting of these lines is not based on the network, but more on urban development requirements and other factors. In contrast, Line 7 increases network efficiency. In summary, it is calculated by the double-level model that the construction sequence is basically consistent with the actual sequence. Where there are differences, Line 7 should have been the first line constructed, while Lines 4 and 10 should have been placed later in the schedule. The analysis shows that the results calculated by the model are better than the original construction sequence plan.
## 4 Results and Discussion  
Multiple experiments on simulated and real multiplex networks have been conducted to investigate the effectiveness and robustness of the proposed **Mx-CRTSA** compared to other community detection methods, including **PM**[11], **RMSC**[14], **SCML**[15], **CentroidCoreg**[16], **CSNMF**[20], **CPNMF**[20], **CSNMTF**[20], **DiMMA**[19] and 2**CMV**[18]. A comparison between the objective functions of the different methods considered in the experiments is illustrated in Table 2. The performance of the different methods is quantified using accuracy (ACC), normalized mutual information (NMI), adjusted Rand index (AR) and F-score [30,31]. All the adopted quality metrics are normalized in the range of \([0,1]\), where higher values indicate more accurate clustering results. The experiments were carried out using MATLAB 2020a on a PC (Intel(R) Core(TM) i7 processor and 16 GB of RAM). For reproducibility purposes, the implemented MATLAB code of the proposed **Mx-CRTSA** approach is publicly available in our GitHub repository [[https://github.com/wardat99/Mx-CRTSA](https://github.com/wardat99/Mx-CRTSA)]([https://github.com/wardat99/Mx-CRTSA](https://github.com/wardat99/Mx-CRTSA)) accessed on 8 February 2023.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 2 Materials and Methods
### Climate Data  
As outlined, the two MODIS NPP estimates, MODIS GLOB and MODIS EURO, differ only in the daily climate data input: MODIS GLOB employs the global NCEP2 climate data set [23] and MODIS  
Figure 1: Our study region separated into four regions, countries with forest inventory data for estimating terrestrial National Forest Inventory (NFI) Net Primary Production (NPP) are marked with dots.  
EURO uses European downscaled climate data [25]. We provide here a brief overview of the two climate data sets.  
The NCEP2 data set (NCEP-DOE Reanalysis 2) is a reanalyzed global daily climate data set with a spatial resolution of 1.875\({}^{\circ}\)\(\times\) 1.875\({}^{\circ}\). This corresponds to approx. 220 km at the equator at latitude 0\({}^{\circ}\) (approx. 136 \(\times\) 220 km at latitude 50\({}^{\circ}\)). To compensate the coarse spatial resolution, for MODIS GLOB the climate data for the 1 km MODIS pixels was deduced with an bilateral interpolation method based on the neighboring NCEP2 pixels [9].  
The downscaled climate data used for MODIS EURO provide daily climate data on a \(0.0083^{\circ}\times 0.0083^{\circ}\) resolution (approx. 1 \(\times\) 1 km at the equator and approx. 0.6 \(\times\) 1 km at 50\({}^{\circ}\) latitude) [25]. This data set was developed out of the E-OBS gridded climate data set (0.25\({}^{\circ}\) resolution, using data from 7852 climate stations) [24] in conjunction with the WorldClim data set [26].