esa-sceva/satcom-chunk-collection huggingface dataset:
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 2 Literature Review  
The use of Newton-Raphson (NR) iterations for solving Equation (1) is well established at this time. These iterative methods mainly consist of two steps:  
1. Seek an initial point or segment.
2. Iterate by Newton-Raphson scheme until convergence.  
The robustness and the efficiency of Newton-Raphson scheme depends significantly on the initial guess. Therefore, to assure convergence of the second step, careful selection of initial guess is needed. In addition, if the NURBS entity has only \(\mathscr{G}_{0}\) continuity at some local point, the derivative based Newton-Raphson scheme would fail to converge to such a point.  
To assure robustness of the iterations, a significant focus of the existing literature is on eliminating portions of the curve or surface where the solution cannot lie. [PERSON] and [PERSON] [2] developed a non-iterative, heuristic algorithm where a NURBS surface was decomposed into quadrilaterals and test points were projected onto the closest quadrilateral. [PERSON] and [PERSON] [12] described a search for the initial guess of the footpoint by recursively sub-dividing the Bezier segment associated with a valid control polygon. However, using the control polygon to eliminate segments of the curve may exclude the correct solution [13]. [PERSON] [14] improved [PERSON] and [PERSON]'s method using selective  
Figure 2: Behavioral analysis in the presence of complex free form embedded surface. The spatial point may only influence a local region of the surface with the highlighted control points, which can be identified by point projection.  
sub-division of the NURBS curve (surface) based on distance to the end (corner) point of the entity. [PERSON] et al. [15] pointed out the need for all control points to lie on different sides of a hyperplane in [PERSON]'s algorithm and proposed a circular clipping method with a sufficiency condition for a curve to lie outside an elimination circle. Other iterative methods in the physical space have also been proposed for point projection including the geometric first-order iterative [16; 17; 18] and geometric second-order iterative [19; 20; 21] methods.  
In this paper, a robust and efficient point projection technique for low degree two-dimensional (2D) NURBS curves and three-dimensional (3D) NURBS surfaces is developed. The proposed technique preserves and operates directly on the parametric description of the NURBS curve or surface. Therefore, the technique gives a projected point directly on the curve or surface when query points lie on the parametric curve or surface. In addition, the technique is robust for curves and surfaces with high local curvature or \(\mathcal{G}_{0}\) continuity compared to techniques that rely on derivatives. A detailed comparison of existing methods in the literature relative to the proposed method is listed in Table 1.
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.5 Function Ontology  
The function ontology represents a structure of operations which can be performed by technical equipment. Similar to the equipment module it contains a basic taxonomy. On top of the taxonomic structure it provides relational information of aggregated functions. These relations are modeled through the definition of elementary functions, which cannot be derived further by means of functional decomposition, and composite functions, which in contrast can be expressed as an aggregation of elementary functions. The taxonomic structure as well as the functional decomposition is based on the guideline VDI 2860 \"Assembly and handling\" [36]. Figure 3 depicts a part of the function ontology.  
Figure 2: Part of the equipment ontology.
# Effect of Strain Hardening on Increase in Collapse Pressure during the Manufacture of ERW Pipe
## 4 Conclusions  
The manufacture process for ERW pipes made of API-5L-X70 was simulated via finite element analysis using a solid element. The effect that the manufacture process, particularly the sizing process, exerts on collapse pressure was evaluated. A material model that could account for the Bauschinger effect and strain hardening was adopted for analysis. The main conclusions of this study are as follows.  
* The compressive stress-strain curve in the circumferential direction was modified by plastic deformation during the manufacture of the ERW pipe. The slope of strain hardening was increased following manufacture, and either the Bauschinger effect or strain hardening could be factors depending on the sizing ratio. When the sizing ratio was small, the Bauschinger effect was strong. When the sizing ratio increased, on the other hand, strain hardening appeared immediately after the elastic region resulting in a higher level of compressive yield strength.
* The sizing process improved both the ovality and the compressive yield strength for a \(D/t\) smaller than 19, so that a larger sizing ratio increased the collapse pressure. In particular, when the sizing ratio was larger than 0.4%, improvement in ovality was minor and the increased compressive yield strength increased the collapse pressure.
* When \(D/t\) was greater than or equal to 22, there was no clear correlation between the sizing ratio and the collapse pressure. In particular, elastic buckling governed the collapse for a \(D/t\) greater than 31.
* Considering both elastic and inelastic buckling, minimum sizing ratio of 0.4% could be recommended to improve the collapse pressure.  
Figure 14: Comparison between analysis results and design standards.  
* Comparison between the analysis results and the current design criteria showed a significant margin for smaller values of \(D/t\), in which inelastic buckling governed the collapse pressure. If inelastic material behavior was considered, an increase in collapse pressure of approximately 25% could be obtained with a \(D/t\) of 14.  
Conceptualization, S.-W.H., Y.C.P., and H.-K.K.; methodology, S.-W.H., and Y.C.P.; software, S.-W.H., Y.C.P., and S.-C.K.; validation, S.-W.H, Y.C.P., H.-K.K., and S.-C.K.; formal analysis, S.-W.H.; investigation, S.-W.H. and Y.C.P.; resources, S.-C.K.; writing--original draft preparation, S.-W.H.; writing--review and editing, Y.C.P. and H.-K.K.; supervision, Y.C.P. and H.-K.K.; project administration, H.-K.K. and S.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by grants (Project no. \(2019\times 005\)) from the POSCO.  
The third author was supported by the POSCO Chair Professor Program.  
The authors declare no conflict of interest.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 3 System Overview and Problem Statement
### Challenges and Design Goals
#### 3.2.3 Dynamic Link Establishment  
In a mm-wave backhaul network, only a small number of SBSs is connected to the gateway through fibre, and backhauling relies heavily on mm-wave links. These mm-wave links can be established dynamically, which adds great flexibility to backhaul data transmission. Normally, the line of sight (LOS) path is preferred when performing wireless communication between SBSs. However, due to the high path loss and penetration loss, far-away devices and blocked links may require relays, _i.e._, multi-hop transmissions to accomplish backhauling. In either scenario, both wireless SBSs and wired SBSs can be activated as relays. How to design an appropriate transmission route for each service becomes an important issue. Multiple factors must be taken into consideration in routing process, such as reducing the total latency, optimizing overall data flow, guaranteeing the robustness of network connectivity and providing high quality of service (QoS), _etc._
# Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method
## 2 Presentation of Method
### Analysis of VIV Signal by RDT  
The buffeting of the bridge is a kind of random vibration caused by turbulent flow, and the pulsating wind component in the atmosphere is the main contributing factor. Since pulsating wind is a stationary random excitation with a mean of zero, the response of the structure is also a stationary random response. It is therefore obvious that the buffeting response processed with RDT reduces to a kind of damped free-decaying response. In contrast, vortex-induced vibration is nearly a sinusoidal response with one dominant frequency, and VIV processed with RDT will become a harmonic response with nearly constant amplitude, as shown below.  
The vortex-induced vibration of the bridge is a kind of wind-induced limiting vibration with both forced and self-excited properties. VIV events can be observed at a certain wind speed, and the measured vibration signal is close to the harmonic signal. Therefore, the harmonic response at a certain measurement point can be described as follows,  
\[y(t)=Y\cos(\omega t-\theta)+\eta(t) \tag{8}\]  
where \(Y\) is amplitude of the vibration; \(\omega\) is circle frequency of the vibration, which is close to one of structural modal frequencies; \(\theta\) is phase angle, which is a phase lag between force and structural response; \(\eta(t)\) is Gaussian white noise signal, which is a zero-mean stationary Gaussian process.  
Likewise, the RDT is applied to vortex-induced vibration given by Equation (8). By selecting a constant \(A\) to intercept the signal, a series of segments can be obtained. The subsample function is given as follow:  
\[y_{i}(t)=Y\cos[\omega(t+t_{i})-\theta]+\eta(t_{i}) \tag{9}\]Two subsamples of segments are taken as example for illustration, as follows  
\[\begin{array}{ll}&Y\cos[\omega(t+t_{1})-\theta]+Y\cos[\omega(t+t_{2})-\theta] \\ =&Y[\cos\omega t\cos(\omega t_{1}-\theta)-\sin\omega t\sin(\omega t_{1}-\theta) \\ &+\cos\omega t\cos(\omega t_{2}-\theta)-\sin\omega t\sin(\omega t_{2}-\theta)] \\ =&Y[\cos(\omega t_{1}-\theta)+\cos(\omega t_{2}-\theta)]\cos\omega t=2Y\cos( \omega t_{1}-\theta)\cos\omega t\end{array} \tag{10}\]  
Similarly, ensemble average of \(y_{i}(t)\) is given as follows:  
\[y(t)=\frac{1}{N}{\sum_{1}^{N}y_{i}(t)}\approx Y\cos(\omega t_{1}-\theta)\cos \omega t=A\cos\omega t \tag{11}\]  
It can be seen from the above equation that the VIV data processed with RDT becomes a harmonic signal.
## 2 Results
### NPP Spatial Patterns and Temporal Trends  
The spatial pattern of the annual average NPP over Northeast China is shown in Figure 2a. The decreasing gradients from northeast to southwest of the simulated NPP were significant. The annual average NPP was generally higher than 400 gC/m\({}^{2}\cdot\)a in Daxing'anling, Xiaoxing'anling, and the Changbai Mountains, where natural forests (e.g., evergreen forests) predominated. The annual average NPP ranged from 200 to 400 gC/m\({}^{2}\cdot\)a over Songnen, Liaohe, and Sanjiang plains and the agricultural area at the junction of the Hulunbeier Grassland and Daxing'anling, as well as the burned forest area in Daxing'anling. The annual average NPP was usually lower than 200 gC/m\({}^{2}\cdot\)a in Hulunbeier, west Songnen Plain, and the Horqin Sandland, with the land surfaces covered by grass, crop, and sand.  
The relationship between the annual NPP and the time for each pixel was established to calculate the NPP trend (Figure 2b). The results indicated that the NPP experienced a remarkable increase (\(p<0.05\)) at the average rate of 4.48 gC/m\({}^{2}\)-a. The NPP with an increasing tendency accounted for 65% of the total NPP, and mainly occurred in forests over Daxing'anling, Xiaoxing'anling, and Changbai Mountains. The NPP showed a decreasing trend that was mainly concentrated on grass, crop, and disturbed forest areas.  
Figure 1: Validation of the simulated net primary productivity (NPP) from the boreal ecosystem productivity simulator (BEPS) model.  
Figure 2: Spatial and temporal variation characteristics of the vegetation NPP in Northeast China: (**a**) spatial distribution of the average NPP over multiple years and (**b**) the NPP trend.
## 2 Materials and Methods  
### Sampling Design  
To obtain the fine-grained distribution of air pollution in the detection area and to reflect the pollution status of the area to the greatest extent, the sampling points need to cover the entire space area. Considering the cruising range of the UAV, the number of sampling points needs to be limited. Therefore, it is necessary to select the appropriate number of sampling points within the range of a UAV cruise and to distribute the sampling points as evenly as possible in the detection space to improve detection coverage.  
During an atmospheric environment detection mission, the study area A (space of interest) is decomposed using the hexagonal prism unit to generate points of interest covering sampling, and then the sampling path is mapped. As shown in Figure 1, the basic decomposition unit is a regular hexagonal prism with a height of \(\sqrt{3}L\); its base is a regular hexagon with a side length of \(L\) and the six side edges are perpendicular to the base. The entire study area is uniformly decomposed by the basic unit of the hexagonal prism. In the vertical direction, the space of interest is divided into subspace regions of interest with the same height. The basic unit whose centroid is in the study area is called the sub-region of interest, and these centroids are the sampling locations used to collect data samples. For a given study area and \(L\), the number \(N\) of decomposed subspace regions of interest, the set \(M\) of sub-region of interest in a single subspace and their number \(\mid M\mid\) can be obtained. When the UAV collects environmental data, the navigation path requires access to each sub-region of interest. Suppose the planned path is \(P\) and the path length is \(\mid P\mid\), as shown in Formula (1).  
\[P=\{s_{1},s_{2}\ldots s_{n}\}\quad s_{n}\in M,n=1,2\ldots N\mid M\mid \tag{1}\]  
where \(s_{n}\) represents the sampling location (waypoint) visited in path \(P\).  
Figure 1: \(Cont.\)
# Experimental Investigation of Vertical Density Profile of Medium Density Fiberboard in Hot Press
## 3 Results and Discussions  
Several tests were performed on MDF board by varying the hot press parameters, i.e., temperature, pressure, cycle time, and moisture content. Temperature was varied from 200 to 220 \({}^{\circ}\)C, pressure from 145 to 155 bar, cycle time from 260 to 275 s, and moisture content from 8 to 10%. The experimental results are shown in Table 2.  
Figure 4: Internal bonding specimen.  
Figure 5: X-ray graph apparatus.  
From the experimental data presented in Table 2, the input values of pressure, temperature, cycle time, and moisture content are determined based on the output of bending and internal bonding. According to the experimental data, the maximum value of bending is 32 N/mm\({}^{2}\), corresponding to the maximum internal bonding of 0.64 N/mm\({}^{2}\). These are achieved by applying a pressure of 155 bar with a temperature of 220 \({}^{\circ}\)C, cycle time of 265 s, and moisture content of 8%.  
Figure 6 demonstrates the effect of variations in process parameters on bending and internal bonding through a 3D graph of experimental data.  
Figure 7 shows the percentage representation of mediumdensity fiberboard parameters. Some of the data are selected from the experimental data in Table 3 for further analysis. To obtain the peak and center densities of the specimen, vertical density profiles are generated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S.No.**} & **Temperature** & **Pressure** & **Cycle Time** & **Moisture Content** & **Bending** & **Internal Bonding** \\  & \({}^{\circ}\)**C** & **Bar** & **S** & **\%** & **N/mm\({}^{2}\)** & **N/mm\({}^{2}\)** \\ \hline
1 & 195 & 145 & 260 & 8.00 & 12 & 0.25 \\
2 & 200 & 145 & 260 & 8.00 & 24 & 0.56 \\
3 & 200 & 153 & 268 & 8.80 & 16 & 0.40 \\
4 & 202 & 152 & 265 & 9.50 & 17 & 0.38 \\
5 & 205 & 155 & 270 & 10.00 & 22 & 0.46 \\
6 & 206 & 154 & 266 & 9.75 & 19 & 0.41 \\
7 & 207 & 147 & 260 & 8.70 & 18 & 0.39 \\
8 & 210 & 150 & 265 & 8.50 & 28 & 0.55 \\
9 & 211 & 148 & 263 & 8.20 & 15 & 0.44 \\
10 & 213 & 150 & 270 & 9.20 & 14 & 0.42 \\
11 & 215 & 155 & 275 & 9.00 & 26 & 0.58 \\
12 & 218 & 145 & 275 & 10.00 & 21 & 0.43 \\
13 & 220 & 155 & 265 & 8.00 & 32 & 0.64 \\
14 & 225 & 155 & 272 & 8.00 & 20 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental data results.  
Figure 6: Three-dimensional graphical representation of experiments.
###### Abstract  
Trajectory similarity search (TSS) is a common operation for spatiotemporal data analysis. However, the existing TSS methods are mainly focused on GPS trajectories produced by moving objects such as vehicles. Further, these corresponding optimization strategies cannot be directly applied in the metro scenario because the metro passenger trajectory is totally different from the GPS trajectory. To fill this gap, we systematically analyze the unique spatiotemporal characteristics of metro passenger trajectories and propose a similarity search solution named PMMTss for the metro system. The core idea of this solution has two key points: first, we design a multi-layer index based on the spatiotemporal feature of metro trajectories, and all points of a trajectory are stored in this index. Second, we design a parallel multi-way merging-based trajectory similar search method, in which the candidate trajectory segments are merged and filtered. We evaluate this solution on a large dataset (Shenzhen Metro data for 3 consecutive months, 6.976 million trajectories with 260 million records). When lengths of input trajectories are 16, 32, and 64, respectively, the corresponding search times are 0.004 s, 0.016 s, and 0.036 s, respectively. Compared to the baseline PPJion+, the query times are reduced by 99.7%, 98.8%, and 97.6%, respectively.  
metro system; trajectory similarity; multi-way merging; smart card +
Footnote †: journal: _Applied_  
1
Footnote 1: email: {[[https://doi.org/10.3390/app13137988](https://doi.org/10.3390/app13137988)]([https://doi.org/10.3390/app13137988](https://doi.org/10.3390/app13137988))}
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 2 Materials and Methods
### Preprocessing Data  
Data pre-processing was employed to enhance data processing performance and minimize errors, ensuring high-quality data for the prediction process. The first step in the data pre-processing procedure was data cleaning, which involved filtering the data to improve its quality.  
In addition, the second procedure involved splitting the data into training and testing sets. Training data were used to train models, while test data were employed to evaluate the model architecture selection with optimal parameters. The study used a total of 286 data points for the lunar calendar (ranges from 0 to 1137) and 273 data points for the Gregorian calendar (ranges from 0 to 11,374). These data points were categorized into estimated durations of 3, 6, 12, 18, and 24 months.  
The third phase involved normalizing the data using a scaling or mapping technique. Min-max normalization was employed to normalize the data. This process generated values ranging from 0 to 1. The equations used for data normalization are as follows:  
\[\widetilde{x}_{norm,\,i}=\frac{\widetilde{x}_{i}-\widetilde{x}_{min}}{x_{max}- \widetilde{x}_{min}}\ ;\,i=1,2,3,\ldots,\,t \tag{7}\]  
and data denormalization is  
\[\widetilde{x}=\widetilde{x}^{\prime}\Big{(}\widetilde{x}_{max}-\widetilde{x}_ {min}\Big{)}+\widetilde{x}_{min} \tag{8}\]  
where \(x_{norm,i}\) was the normalized value of the rainfall data, \(x_{max}\) is the maximum value of the entire rainfall data, and \(x_{min}\) was the threshold of the entire rainfall data.
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 4 Experimental Design
### Dataset 2: Case Packer  
In this case, a subgroup of an automatic industrial machine is considered. In particular, the case packer group is responsible for putting the blisters into the case and sticking its edges. The incorrect execution of the sticking phase may generate unacceptable final products. For this reason, it is necessary to recognize the malfunction as soon as possible. To this aim, the current (mA) absorbed by the main actuator of the system is collected with a sampling frequency of 500 Hz under different conditions. In total, one normal condition and four fault conditions were simulated, i.e., without load, with overload, with a regular load but without withdrawal of the die-cuts, and the emptying of the last die-cuts. The dataset collected in the nominal condition and in the first three improper conditions include 100 working cycles. The dataset corresponding to the last fault condition includes only 47 working cycles. Each cycle is made of 75 data samples and lasts 0.15 s. Therefore, 15 s of data have been collected for each condition, except for fault 4, for which the monitoring is 7.05 s. Figure 2 shows the trend of the current signal during one cycle for each condition.  
Figure 1: Raw signals generated by the system during the three nominal conditions and the fault condition.