esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Rangeland Degradation from 1996 to 2011  
The results generated using the LSMA method and remotely sensed derived system of rangeland degradation (Table 2) for the study years are shown in Figure 5, and the individual degraded rangeland statistics are summarized in Table 3.  
Figure 4: A correlation of field recorded bare-land fraction and the estimated data extracted by LSMA (Linear Spectral Mixture Analysis). The linear fitting for the validation is statistically significant at \(p<0.001\).  
Figure 3: Bare-land fractional results for the four years, with light areas on the maps indicating bare-land covered regions.  
In 2011, the total area of rangeland in the study region was 3619.98 km\({}^{2}\), which increased by 17.89% compared with that of 1996. Though the rangeland area in 2001 reduced by 38.88 km\({}^{2}\) since 1996, it extended steadily thereafter. In combining the classification maps from the four years and results of the three levels of degradation (Sl-, M-, and Se-DR) rangeland from 1996 to 2011, the total area for the three levels of degraded rangeland in 1996 was 2922.01 km\({}^{2}\), accounting for 24.76% of the study region. During the 1996-2001 study interval, the degraded rangeland extended by 126.47 km\({}^{2}\) relative to the area observed for 1996, Se-DR and M-DR areas increased 129.20 and 484.85 km\({}^{2}\), an increase of 51.13% and 46.62%, respectively; conversely, the Sl-DR area reduced by 487.58 km\({}^{2}\). However, since 2001, the degraded rangeland area decreased constantly to 2909.97 km\({}^{2}\), resulting in a decrease of 138.51 km\({}^{2}\) as of 2011. This may have an important correlation with the \"Grain for Green\" program and the grazing ban pursued by the Chinese government since 2000. During the period of 2001-2011, both Se-DR and M-DR areas showed a decreasing trend. Additionally, the Sl-DR area increased steadily to 1613.04 km\({}^{2}\) in 2011, resulting in an increase of 471.25 km\({}^{2}\).  
The constant decreasing trend in the total degraded rangeland, Se-DR and M-DR areas, together with the steady increase in Sl-DR area from 2001 to 2011 indicate that the rangeland degradation in the study region reversed during the 2001-2011 period. After the \"Grain for Green\" program and grazing ban policy were implemented since 2000, rangeland vegetation was in a state of rehabilitation, and the rangeland ecosystem has achieved notable improvement.
## 3 Development of New Applications for DIC
### Inspection of the Automated Fiber Placement (AFP) Process  
#### 3.2.1 Synopsis  
Automated fiber placement (AFP) is a composite manufacturing technique in which narrow composite rows consisting of fiber and matrix are pushed against the tool surface. While the head of the AFP machine is laying the composite tows, heat and pressure are applied simultaneously to consolidate the laminate [20]. AFP is a relatively new manufacturing technique, and development of inspection techniques to assure the quality of the composite part during the layup process is the topic of ongoing research and development activities in the aerospace industry. Possible defects needed to be inspected are gaps and overlaps between tows, deviation in fiber orientation and tow location, etc. [21]. Gaps and overlaps are common defects during AFP, and several studies [22; 23; 24] have shown that they reduce structural performance of composite laminates. Figure 20 shows a typical gap between two composite rows. The gaps between tows make resin-rich areas and cause failure initiation points, while overlaps create thickness build-up and cause out-of-plane waviness in adjacent plies and stress concentration. In this study, the possibility of using DIC for inspecting gaps and overlaps was considered, and proof of concept was demonstrated.  
Figure 19: (**a**) Distribution of the lateral deformation along the width and length of the composite tow one minute after layup. (**b**) Variation of lateral deformation along the width of the composite tow one minute after layup [19].  
Figure 20: Gap size defect.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 1 Introduction  
Gaze tracking technology is a potential human-computer interface (HCI) technology. The application of this technology has expanded to the gaze-based remote controlling, human visual attention analysis, psychological analysis, and virtual reality/augmented reality.  
After decades of development, recent gaze tracking devices are mainly based on computer vision [1]. The human visual attention information is obtained by analyzing the human eye images, and the gaze direction or the coordinates of point-of-regard (PoR) are further estimated. However, the gaze estimation method cannot accurately provide the visual information what people really see. This is because that current methods make it impossible for people to directly obtain the complete information of human visual attention characteristics. The gaze estimation method can only rely on the human's description or eye images captured by cameras. Due to the individual differences of human visual attention and the limitation of the gaze estimation methods, accurate estimation of PoR is still a challenging work.  
The head-mounted gaze tracking device is a potential human-computer interface system in free space. However, the accurate estimation of the 3D PoR on classical methods still needs the assistanceof scene information and inertial sensors [2]. This research also focuses on the 3D PoR estimation method based on a head-mounted gaze tracking system.  
In this paper, we propose a novel gaze tracking system based on a head-mounted device and stereo vision. The method uses two pairs of stereo cameras to capture eye region images and extract necessary eye features, and directly solves the spatial coordinates of these features in real-time. In addition, a pair of scene cameras are set to capture scene images, and the stereo vision system formed by the scene cameras can solve the spatial coordinates of matching points in the scene. The whole gaze estimation model is simpler than the classical geometry-based model and composed of three parts.  
(1) A simplified geometry-based gaze estimation model is proposed, which takes the pupil center normal as the initial value of the eye's optical axis.  
(2) A corresponding personal calibration process for calibrating the parameters of the eye model. The personal calibration uses the spatial coordinates of the target point solved by the scene cameras as the calibration reference, and uses common eye features to estimate the position of the eyeball center and the angle between the optical axis and the visual axis.  
(3) The spatial coordinate of the PoR is estimated as the middle point of the nearest point of the two visual axes.  
This paper also gives several experiments to evaluate the proposed gaze tracking system. The experimental results show that the method can accurately estimate the spatial coordinates of the PoR.  
This paper is organized as follows: We start by summarizing related work in Section 2. In Section 3, the main method is explained. Section 4 presents experiment and discussion, and Section 5, the conclusion.
## 1 Introduction
proposed a reliability assessment method for existing reinforced concrete bridges with incomplete test data based on fuzzy theory [42]. [PERSON] et al. used the triangular membership function to characterize the fuzzy randomness of the interface stiffness of a composite beam bridge, and analyzed the dynamic response of the composite beam under a moving load [43]. [PERSON] et al. determined the most appropriate superstructure form in the bridge design stage based on the fuzzy comprehensive evaluation method [44]. [PERSON] et al. proposed a bridge reliability assessment method using interval-defined fuzzy criteria to deal with parameter uncertainties [45]. The application of fuzzy theory in bridge reliability assessment may not be sufficiently mature, and its application in hinge joint damage has not been found so far based on consulting relative literature. A large number of systematic studies have been carried out on the structural reliability assessment of hinged slab bridges under the condition of hinge joint damage by the authors of this paper. A modified hinged plate method was proposed to calculate the transverse distribution of hinge joints after damage based on the traditional hinged plate method [46], and a reliability assessment method of the bridge system considering different failure criteria was proposed based on multiple Copula functions [47; 48]. As the stress state of hinge joints is very complex, it is difficult for engineers and technicians to determine the damage degree of hinge joints without carrying out load tests, and they often give fuzzy information about the damage degree. The failure criterion of the existing system reliability assessment considers that the failure rate of the bridge structure due to each plate failure is equal, but the different slab failure contributions to the structure system failure are different because the slab space position is different. Therefore, how to effectively deal with the fuzzy information of the hinge joint damage degree and consider the different contributions of member failure to structural system failure are very important for the reliability evaluation of existing hinged slab bridge systems. Member Reliability Evaluation Method of Considering Fuzzy Detection Information of Hinge Joints Damage
## 5 Sensitivity Analysis with Respect to Measurement Errors  
### Sensitivity of the LoS Angle with Respect to Range Measurement Errors  
As shown in the previous section, the LoS angles are computed from three measurements: range, \(R\), and the phase difference between the main and secondary antenna placed on x- and y-axis, \(\Delta\varphi_{x}\) and \(\Delta\varphi_{y}\), respectively. The baseline distance, \(d\), and the wavelength, \(\lambda\), are considered parameters for the sensitivity analysis.  
By definition, the sensitivity of the LoS angle \(\alpha\) (or its counterpart on the y-axis, \(\beta\)) with respect to the range \(R\) is as follows:  
\[S_{R}^{\alpha}=\frac{R}{\alpha}\frac{d\alpha}{dR}=\frac{R}{\alpha}\arccos^{ \prime}\bigg{(}\frac{\lambda\Delta\varphi}{d}-\frac{\lambda^{2}\Delta\varphi^{ 2}}{2 dR}+\frac{d}{2R}\bigg{)}. \tag{12}\]  
The impact of the range measurement error, \(\varepsilon_{R}\), on the LoS angle error, \(\varepsilon_{\alpha}\), is as follows:  
\[\frac{\varepsilon_{x}}{\alpha}S_{R}^{\alpha}=\frac{\varepsilon_{R}}{R}. \tag{13}\]  
As seen in the Equation (13), a range measurement error will inherently result in an error in the LoS angle. Sources of ranging errors are twofold: one can be the misplacement of the antenna (on the face of a satellite many instruments and other antennas must be mounted, so in the case of antenna placement constraints, the antenna may be placed closer as indicated in an initial design); second is the resolution of the fine ranging. In Figure 7 the LoS angle relative error with respect to the range is depicted for the next situations: the baseline is \(d=0.95\) m and \(1\) m, respectively, and the absolute measurement error \(\varepsilon_{R}\) is \(1\) cm and \(10\) cm, respectively. A shorter baseline will result in smaller LoS angle errors for small ranges; for large ranges the LoS angle errors are negligible.
# Applications of Magnesium and Its Alloys: A Review
## 2 Production Techniques  
There are two basic methods of producing magnesium--(a) the electrolysis of fused anhydrous magnesium chloride and (b) metallothermic reduction of magnesium oxide by ferrosilicon [14]. A typical production process using the electrolytic and thermal methods is illustrated in Figure 1.  
Electolysis is a two-step approach. First, it involves the hydrometallurgical preparation of the feedstock (dehydrated magnesium chloride), followed by feeding it directly through electrolytic cells [5]. [PERSON] first discovered the core principles of producing magnesium metal with this method in 1833 [6].  
In 1852, [PERSON] improved on [PERSON]'s process to achieve permanent separation of chlorine and magnesium, and that tweak in the process kickstarted the commercialization of magnesium [8]. Preventing the recombination of chlorine and magnesium was critical to [PERSON]'s success as anhydrous magnesium chloride is hygroscopic, which can lead to the formation of undesirable oxides and oxychlorides during direct dehydration [14]. Presently, it is still a technological challenge to produce anhydrous magnesium chloride with minimal or, ideally, no oxychlorides [16]. [PERSON]'s electrolytic process is illustrated in Figure 1.  
The second method of producing magnesium metal is with heat. Unlike electrolysis, intense heat in the thermal reduction process eliminates the need for an elaborate feedstock preparation [8]. The Pidgeon and Magnetherm processes are the main thermal routes. They are also batched processes [14] that use the same basic chemistry [5]. In the Pidgeon  
Figure 1: Process flowchart of primary magnesium production via a thermal and electrolytic process. Reproduced with permission from [PERSON], et al., LCA of magnesium production; published by Elsevier, 2008 [15].  
process, dolomite and ferrosilicon are formed into briquettes with an externally heated retort to attain magnesium vapors. However, the Magnetherm process calicines a mixture of dolomite, ferrosilicon, and alumina to achieve the same by-product. The magnesium vapors obtained from both routes are then cooled and condensed separately before being extracted [5]. A typical thermal route is also illustrated in Figure 1. When the thermal reduction process was first introduced in the 1920s [16], it was touted to be the eventual replacement of electrolysis production. Today, the thermal routes, especially the Pidgeon process, are the most widely used due to their ability to produce high purity magnesium and the plethora of its raw material, dolomite [17].
## 3 Comparison of Finite Element Simulation of Almen Test and Almen Test
### FE Simulation of Almen Test
#### 3.1.1 Boundary Conditions  
Figure 2 shows the initial mesh configuration and boundary conditions of the Almen strip when the modified strip holder [27] is employed, with which the boundary conditions can be greatly simplified. Traction free boundary condition is given to the surface of the Almen strip where shot balls are impacted, so is the opposite side of the Almen strip. U1 and U2 denote the displacement toward x-direction and y-direction, respectively. UR1, UR2, and UR3 stand for the rotation of the Almen strip on its x-direction and y-direction, and z-direction.  
Enlarged meshes at a part are shown in Figure 2b. Since elastic-plastic deformation during shot peening remains in the thin layer of the Almen strip where shots are bombarding, the dense mesh is assigned in the thin layer zone and the coarse mesh is made in the middle zone of the Almen strip. Transition mesh is built between two zones.  
During shot peening to avoid the stresses in the Almen strip oscillating around a mean value, the coefficient of material damping \(\xi=0.5\) is chosen [30]. The friction force of the relative motion of the shots and Almen strip surface is assumed to obey [PERSON]'s law. [PERSON] and [PERSON] [31; 32] reported that the effect of the Coulomb friction coefficient on the residual stress and plastic strain is insignificant if it is in the range of 0.1-0.5. Coulomb friction coefficient \(\mu=0.2\) is used in this study. The temperature variation raised by impacting is neglected.
## 5 Case Studies
### Daily Field Profile of Solar Irradiation and Temperature in Hong Kong  
For the purpose of testing the specific practicability of TRL in practical application, the temperature and solar irradiation measured in Hong Kong was used to simulate the PV system for MPPT (See Figures 10 and 11). The metrical data are mainly selected from four representative days of four different seasons in 2016, in which the interval of data is set to 10 min. Note that the randomness and intermittence of solar energy and renewable energy system (RES) [34; 35; 36; 37] is a very common issue usually resulting from uncertain atmospheric conditions.  
Figure 10: Daily profile of solar irradiation and temperature in Hong Kong. (**a**) Irradiation; (**b**) Temperature.  
Figure 9: PV system responses of seven methods obtained on the gradual change in both solar irradiation and temperature. (**a**) Voltage; (**b**) Power.  
Figures 12 and 13 demonstrate the output power of seven algorithms for MPPT in different seasons. It can be well illustrated that, compared with INC, in the PV system, all the meta-heuristic algorithms can obtain more output power, where the output energy of TRL reaches 115.52% of that of INC in the spring. That aside, one can derive that although the performances of all meta-heuristic algorithms are comparatively small during the whole simulation period, TRL can still outperform other algorithms, which means that it can always give out the most power in any season.  
Figure 11: The detailed geographical position of the measuring device for solar irradiation and temperature.  
Figure 12: PV system responses obtained on a typical day in Hong Kong. (**a**) Spring; (**b**) summer; (**c**) autumn; (**d**) winter.
## 4 Discussion  
### Variability of Crude Protein and Fibre (NDF) Reference Data  
In this study CP and NDF variability fit within the ranges of variation of these parameters published in other works. This variability is a function of the diversity of plant species that constitute pastures in the Mediterranean region and the different growth rates, which in turn are dependent on temperature and precipitation in each field, as well as on the spatial variability of soil fertility [12]. Also, [PERSON] et al. [28] state that pasture spatial and temporal diversity is related to the diversity of species, the interactions with grazing animals, natural environmental conditions and management production systems. [PERSON] et al. [37], carried out a study throughout the pasture vegetative cycle (autumn, winter and spring) and obtained CP values between 10.4% and 24.3% and NDF between 29.4% and 60.2%. [PERSON] et al. [43], in permanent pastures in Germany, obtained CP values between 5% and 22.6%, from May to October. Similar values (CP between 6.1% and 25.6%) were obtained by [PERSON] et al. [44] in a study carried out during the vegetative cycle in mixed pastures. Also, [PERSON] et al. [45] in a study carried out on permanent pastures with heterogeneous floristic composition in Chile, obtained CP values between 17.8% and 29.0% and NDF values between 37.9% and 50.3%. This variability, which in this study is magnified by the inclusion of samples from various experimental fields, with different climatic, soil and management conditions, is important and necessary to validate and increase the robustness of calibration models [24]. Also, [PERSON] et al. [33] state that sample variability is essential, both to develop NIRS calibration models and to assess their applicability in future predictions of the nutritive value of a pasture. In this sense, [PERSON] et al. [11] report that there is considerable spatial and temporal variation in pasture production and quality, which is still not well understood, especially in terms of nutrient concentration of different types of pasture.  
Figure 5: Plot of the calibration and internal validation phases between reference and predict values of fibre (NDF, **a**) and crude protein (CP, **b**).
## 2 Methodology
### Tunnel Cross Section Extraction
#### 2.3.3 Determination of Cross-Section Points
We assume the circle center \(\bar{o}=(\bar{o}_{x},\bar{o}_{y},\bar{o}_{z})\) with radius \(\bar{r}\) on the plane \(\alpha\). The equation of the circle can be expressed below:  
\[x^{2}+y^{2}+z^{2} =2x\bar{o}_{x}+2y\bar{o}_{y}+2z\bar{o}_{z}+\bar{r}^{2}-\bar{o}_{x} ^{2}-\bar{o}_{y}^{2}-\bar{o}_{z}^{2},\] \[s.t.\;\;a\bar{o}_{x}+b\bar{o}_{y}+c\bar{o}_{z}+d=0, \tag{7}\]  
where \(\{a,b,c\}\) is the normal vector of plane \(\alpha\) and \(d\) is the constant term of normal plane. In this equation, the terms \(x^{2}+y^{2}+z^{2}\), \(2x\), \(2y\) and \(2z\) are regarded as observation values, and meanwhile the variables \(\bar{o}_{x}\), \(\bar{o}_{y}\), \(\bar{o}_{z}\) and \(\bar{r}^{2}-\bar{o}_{x}^{2}-\bar{o}_{y}^{2}-\bar{o}_{z}^{2}\) are regarded as estimated values. We assume that the extracted cross section has \(m\) observation points. Equation (7) can be written as the matrix form:  
\[Y+e =(B+E_{B})A \tag{8}\] \[s.t.\;\;KA =K_{0}\]  
where, \(Y= \begin{bmatrix}x_{1}^{2}+y_{1}^{2}+z_{1}^{2}\\ x_{2}^{2}+y_{2}^{2}+z_{2}^{2}\\ \vdots\\ x_{m}^{2}+y_{m}^{2}+z_{m}^{2}\end{bmatrix} \), \(B= \begin{bmatrix}2x_{1}&2y_{1}&2z_{1}&1\\ 2x_{2}&2y_{2}&2z_{2}&1\\ \vdots&\vdots&\vdots&\vdots\\ 2x_{m}&2y_{m}&2z_{m}&1\end{bmatrix} \), \(A= \begin{bmatrix}\bar{o}_{x}\\ \bar{o}_{y}\\ \bar{o}_{z}\\ \bar{r}^{2}-\bar{o}_{x}^{2}-\bar{o}_{y}^{2}-\bar{o}_{z}^{2}\end{bmatrix} \), \(K=[a,b,c,0]\)  
and \(K_{0}=-d\). Parameter \(e\) and \(E_{B}\) are the error matrices of \(Y\) and \(B\). Based on Equation (8), two new matrices \(Y_{\epsilon}=\left[Y^{T},K_{0}\right]^{T}\) and \(B_{\epsilon}=\left[B^{T},K^{T}\right]^{T}\) can be generated. Then, we use the SVD technique [23] mentioned in Section 2.2.3 to decompose an augmented matrix \([B_{\epsilon},Y_{\epsilon}]\) to estimate the parameters of the fitted circle defined in matrix \(A\). Figure 8: Comparisons of tunnel cross-sectional points before and after refinement. (**a**) the raw tunnel point clouds; (**b,c**) are results of tunnel cross-sectional points before and after refinement.
# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Summary  
Section 3.1 introduced studies involving prediction of traffic flow, traffic accidents, and emissions using artificial neural networks and machine learning algorithms such as KNN, linear regression, and random forest. RNNs and LSTM are suitable for prediction of time series data. Fast R-CNN and Faster R-CNN, which improve the computational bottleneck of existing CNNs, are also commonly used.  
Some of the key gaps identified in the reviewed literature include:  
* Data standardization issues;
* Privacy of users;
* Sparse data and dense data issues;
* How to use emission prediction results.  
In the reviewed papers, Apache Kafka and Apache Spark streaming were primarily used for real-time data analysis [41; 42; 54; 58]. Apache Spark streaming has slower query processing times compared to Apache Kafka, but it is more compatible, so it is important for researchers to decide which model is most appropriate for their research. The incremental problem of big data has always been a concern. Multiple authors [39; 40; 47; 55] have improved the incremental problem of big data by using a big data framework.
## Appendix A  
Results of the convergence time analysis presented in tabular form.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Solution** & **GNSS** & **N** & **E** & **U** & **2D** & **D3** \\ \hline \multirow{3}{*}{BRDM} & G & 2.24 & 3.17 & 2.55 & 3.35 & 3.13 \\  & E & 2.34 & 3.51 & 2.80 & 3.92 & 3.66 \\  & GE & 1.82 & 2.86 & 2.25 & 3.10 & 2.91 \\ \hline \multirow{3}{*}{HAS} & G & 2.07 & 2.82 & 2.59 & 3.10 & 2.89 \\  & E & 1.14 & 2.66 & 1.47 & 2.94 & 2.25 \\  & GE & 1.20 & 2.61 & 1.39 & 2.91 & 2.16 \\ \hline \multirow{3}{*}{CNES} & G & 0.61 & 1.50 & 1.37 & 1.58 & 2.17 \\  & E & 0.88 & 1.83 & 1.85 & 2.09 & 2.87 \\ \cline{1-1}  & GE & 0.52 & 0.98 & 1.00 & 1.06 & 1.45 \\ \hline \multirow{3}{*}{CODE} & G & 0.54 & 1.14 & 1.06 & 1.21 & 1.52 \\  & E & 0.50 & 1.02 & 1.08 & 1.15 & 1.58 \\ \cline{1-1}  & GE & 0.40 & 0.69 & 0.72 & 0.76 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mean convergence time of all stations for all analyzed solutions. Values in minutes.