esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Based on a theoretical model and a naturalistic driving experiment, this paper studied the median opening length, traffic operation characteristics and driving workload in freeway crossover work zones. The following conclusions were drawn:  
1. A theoretical calculation model of the median opening length was established according to the demands of vehicles changing lanes when crossing through the median opening of the freeway. Based on the calculation model, the influencing factors of the median opening length were analyzed, and the calculation values of the median opening length under different speed limits, median widths and cross slopes were proposed.  
2. The characteristics of the vehicle speed and driving workload in different sites of the work zone were obtained based on a naturalistic driving experiment. It was found that the median opening length was positively correlated with the vehicle speed and driving workload. Furthermore, different median opening lengths had different impacts on the operation status of the drivers and traffic: a shorter opening length reduced the vehicle running speed and led to an uncoordinated running speed in the work zone; a longer opening length caused driver tension and led to the vehicle running speed and speed variability being too high.  
3. The theoretical calculation results of the median opening length of the freeway are consistent with the results of the naturalistic driving experiment. The theoretical calculation model is reliable and can be used in the layout of actual work zones. The median opening length is an important influencing factor for the running speed, driving workload and road capacity. In practical application, it should be determined by considering the combined effects of the running speed of different vehicles, the speed limit, the capacity of the work zone and other factors. In this experiment scenario, the opening length of 70 m was reasonable.  
This paper only studied the median opening length of a freeway work zone when the median opening was one lane. Further research and verification are needed to assess the influences of the median opening width, lane width, cross slope, etc., on the driving workload and opening length under the condition of multiple lanes.  
Formal analysis, writing--original draft preparation, S.M.; conceptualization, methodology, supervision, writing--review and editing, J.H.; investigation, R.W.; writing--review and editing, S.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the scientific research project of Fujian Expressway Group Co., Ltd., and the traffic scientific research project of Department of Transport of Shaanxi Province (No. 21-02X).  
Not applicable.  
The data generated in this study are available upon request.  
The authors would like to thank all the participants in the experiments. The authors also would like to gratefully acknowledge the support provided by [PERSON].  
The authors declare no conflict of interest.
## 1 Introduction
For the above challenges, this paper proposes a new DL-based denoising algorithm in the non-subsampled contourlet transform (NSCT) domain to effectively suppress seismic noise, particularly for seismic data with a low signal-to-noise ratio (SNR). This network avoids building models and assumptions because a huge training set is used for training, raw data are employed as inputs, and the desired clean data are the corresponding outputs. The present work has the following contributions:  
1. This paper proposes a new feedback information distillation network (FID-N) that mainly consists of a two-path information distillation (ID) block used in a recurrent manner to form a feedback mechanism, with the advantage of sharing the weights of each iterative block during the procedure, and fully exploits features from seismic signals and effectively restores the noisy seismic signals step by step. 2. Noise suppression is formulated to predict the NSCT coefficients to further remove noise with the FID-N while preserving more details compared with spatial-domain algorithms. 3. Our scheme was verified with synthetic and real seismic data and demonstrated both qualitatively and quantitatively that the denoised data obtained with our method had higher SNR values and richer useful information compared with those obtained with other cutting-edge methods, especially for high-frequency low-SNR data. The rest of this manuscript is structured as follows. Section 2 details our related work. Section 3 details our algorithm. Section 4 presents the experiments and performance evaluation. Section 5 draws conclusions.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.8 Leach-Swdn  
A dynamic method of CH selection is proposed by [PERSON] et al. in [PERSON] et al. (2018), where a sliding window is set up for the election probability of nodes. A random number is generated between 0 to (\(E_{avg}\)/\(E_{max}\)) during the setup state by each node. \(E_{avg}\) is the average energy level of nodes that are never elected as CH and \(E_{max}\) is the initial node energy. In a network of \(k\) clusters and \(N\) sensors, a node is declared CH only if the generated number is less than a threshold \(P(i)\) given as  
\[T(n)=\left\{\begin{array}{ll}\frac{k}{N-k\left(mod\frac{N}{k}\right)}\frac{ E_{max}}{E_{max}}\\ 0&;C(t)=0\end{array}\right. \tag{9}\]  
The non-CH node of the present round sends its remaining energy information to the CH of the last slot, which is then forwarded to the \(BS\) for \(Eavg\) calculation. The simulation result shows improved network metrics such as FND and HND that are 41% and 36% higher than LEACH. However, repeated transfer of residual energy information increases the network load.
## 4 Results  
### Comparison and Verification between Test and Simulation  
Figure 6 shows the temperature change curve of each measuring point in the FSW process. A1 is the temperature measurement point on the advancing side of the workpiece; R1 is the temperature measurement point on the retreating side of the workpiece, which is 11 mm from the center of the weld. It can be seen that, although the simulation result of the temperature of the measuring point is different from the measured result, with the actual result faster than the simulation result in the cooling stage, the overall trend of the curve is similar. Comparing the temperature curves of A1 and R1, it can be found that the temperature on the advancing side is higher than the temperature on the retreating side at the symmetrical position, and the experimental and simulated temperature curves of other measuring points have similar corresponding relationships with A1 and R1. Generally, the simulation result of the temperature measurement point is in good agreement with the measured result.  
During FSW welding, the axial force of the Z axis was tested. Figure 7 shows the comparison between the test results and the simulation results. It can be seen from the curve that the temperature gradually rises and reaches a peak in the first stage of the curve, which is equivalent to the contact between the stirring pin and the workpiece. The temperature increases to soften the base material around the tool pin and promote the pressing down of the tool. In the second stage, the axial force is reduced, the material around the tool pin is gradually softened, and the mechanical interaction between the tool and workpiece allows the FSW tool to continue to be pressed into the workpiece without increasing the external force. In the third stage, when the tool shoulder comes into contact with the extruded material, the axial force begins to increase gradually. After the shoulder is pressed down to a certain amount, the extruded material at the outer ring edge of the shoulder forms a flash, which is also fully reflected in the simulation process. However, the overall axial force output by simulation is slightly lower than the actual test results, but the overall trend is in good agreement. In summary, the comparison of temperature curve and axial force curve shows that the simulation results are accurate, and the FEM is reliable.  
Figure 6: Comparison between simulated and measured temperature.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 3 Methodology
### Overview of the Proposed Scheme
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline \(HG\) & Representing dynamic vehicle clusters using hypergraphs \\ \(V\) & vertices in the hypergraph \\ \(e\) & Hyperedges in the hypergraph. \\ \(\omega\) & weight for hyperedge \\ \(h(v,e)\) & The hyperedge function for nodes \\ H & The total number of hyperedges in the hypergraph \\ \(\delta(e_{k})\) & the degree of a hyperedge \(e_{k}\) \\ \(d(v_{i})\) & the degree of a vertex \(v_{i}\), indicating the number of edges \\  & encompassing the vertex \\ \(S(v_{i})\) & The set of nodes connected to the vehicle node \(v_{i}\) \\ \(L(v_{i})\) & The number of directed edges emanating from node \(v_{i}\) \\ \(Au(v_{i})\) & The authority of vehicle nodes \\ \(Re_{ij}(v_{i},v_{j})\) & The association between vehicle nodes \\ \(R\
u_{i}(V)\) & The relationship between node \(v_{i}\) and the vehicle cluster \(V\) \\ \(R_{C}(i,j,t)\) & The association between adjacent vehicle clusters \\ \(T_{d}V(v_{i})\) & The direct trust value of a vehicle node \\ \(V_{L}\) & The speed limit of the current road segment \\ \(M\) & The standard transmission frequency of sensor nodes \\ \(m_{i}\) & The actual transmission frequency of the vehicle node \(v_{i}\) \\ \(S_{i}\) & The number of scans conducted by the sensor node \(i\) \\ \(T_{d}TF(v_{i})\) & The direct trust value assessed based on the transmission frequency \\ \(\tau_{1}\) and \(\tau_{2}\) & The weighting coefficient \\ \(\delta\) & The fault tolerance coefficient \\ \(Thr\tau_{TF}\) & The threshold of transmission frequency representing the upper limit of scan counts \\ \(RSSI_{receive}(v_{i})\) & The RSS strength of \(v_{i}\) \\ \(D_{RSSI}\) & The distance between vehicle nodes \\ \(D_{norm}Min\) & The minimum distance threshold between nodes \\ \(D_{norm}Max\) & The maximum distance threshold between nodes \\ \(T_{d}(v_{i})\) & The direct trust value of vehicle \(v_{i}\) \\ \(\alpha_{j}\) & The weighting coefficient \\ \(R_{j}\) & Trust assessed through various observational indicators. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations and Description.
## 3 Materials and Methods
### MP-IDSSC: Matching and Pose Estimation under Inverse Depth and Structural Semantic Constraints
#### 3.3.3 Error Function Combined with Structural Semantic Constraint  
Because the plumb line contains special semantic information, it can be used as a constraint in 3D registration. For integrating point and plumb line features, the error function model of the image pose solution is:  
\[\delta=\sum_{i=1}^{n}argmin\delta_{pi}+\delta_{l} \tag{16}\]  
where \(\delta_{pi}\) and \(\delta_{l}\) are the point characteristic error and plumb line constraint error, respectively. The point characteristic error, \(\delta_{pi}\), consists of two parts: camera pose error and projection error. First, the camera pose error was calculated, and the projection error was only calculated when the error was less than the threshold. The optimal solution often has the largest number of interior points, and the core method for determining interior points was to calculate the projection error. For each feature point, when the calculated projection error was less than the threshold, it was recorded as an interior point. When the number of interior points was the largest, the pose parameter was optimal, and the corresponding point pair was the matching result. The calculation formula for the projection error is:  
\[\sigma_{p}=\sum_{i=1}^{n}argmin\sum_{k=1}^{m}\|u_{ik}-KpX_{i}\|_{2}^{2}<\varepsilon \tag{17}\]  
where \(\sigma_{p}\) is the projection error, \(u_{ik}\) is the image point, and \(X_{i}\) represents the corresponding 3D point coordinates.  
For the calculation of \(\delta_{l}\), the vertical line segment is mapped to the virtual perspective image by the feature mapping results, and then the vertical line segment in the image is mapped to the 3D model space according to the spatial relationship between the virtual and real images, as shown in Figure 3, to obtain the 3D vertical line segment. The error of the vertical line segment in 3D model space is used as a constraint, and its error is calculated by:  
\[\sigma_{l}=\sum_{i=1}^{m}atan2\bigg{(}\sqrt{dX_{i}^{2}+dY_{i}^{2}},dZ_{i}\bigg{)} \tag{18}\]  
where \(dX_{i},\ dY_{i},dZ_{i}\) are the coordinate difference between the two endpoints of the \(i\)-th 3D space segment in the direction of the three coordinate axes.  
Figure 2: Random sampling distribution.  
In the process of mapping the line segment on the image to the 3D model space, the mapping may fail, for example, by mapping the edge line of a building to the sky background without depth coordinates. To avoid this problem, this study adopted the random drift algorithm. After mapping the image line segment to the inverse depth space, it determines whether the inverse depth value of the two endpoints of the line segment is valid and whether the difference between the two is less than the threshold. If it is greater than the threshold, it indicates that there is a mapping failure, and the line segment must be randomly shifted by \(k\) pixels until a valid mapped line segment is obtained. Since there is often a plane near the edge line, the random offset does not affect the calculation of the final error.
## 5 Results and Discussion
### Compaction Energy Efforts  
As described in compaction theory and image analysis, the temperature may affect not only the natural volumetrics (in terms of density), it also affects the aggregates' behavior during compaction. At lower temperature, the high viscosity of the asphalt binder plays a big role in the resistance on compaction performance. After the compaction temperature reaches a certain point, the compaction becomes easier. That is, the aggregate behavior during compaction is influenced by a given compaction temperature because of the initial viscosity change, as explained in the Mohr-Coulomb theory. However, compaction becomes difficult when the temperature passes a specific viscosity.  
Figure 10 depicts the relationship of the energy index and vector magnitudes on the PG 64-28 and the PG 58-28 mixtures as a function of temperature. For both mixtures, the CEI and CFI are highest at low compaction temperatures (90 \({}^{\circ}\)C and 80 \({}^{\circ}\)C). Conversely, the vector magnitudes were lowest. The lowest vector magnitude explains how the aggregates are caught by the asphalt binder due to their high viscosity, requiring high compaction efforts for compaction. Generally, the asphalt binder hardens due to aging, aggregate interlocking, and interactions. As the asphalt binder continues to harden, the aggregates interactions may decrease, and result in the low preferred orientation of the aggregate. For both mixtures at middle compaction temperatures (115 \({}^{\circ}\)C and 110 \({}^{\circ}\)C), the vector magnitudes increase with increasing temperature, and the energy index decreases. So, the viscosity effect helps the ease of compaction. However, the highest compaction temperatures (at 140 \({}^{\circ}\)C for both mixtures) do not give the highest vector magnitudes; rather, energy efforts are constant or higher. What happens in the mixtures is that excessively raising the temperature possibly enhances the aggregate interactions or contacts due to the low viscosity. The possibility of an increase in the aggregate interactions or direct contacts can be seen in the volume change of the effective asphalt (V\({}_{\text{eac}}\)). The V\({}_{\text{eac}}\) can be obtained by subtracting the air void (%) from VMA (%) through volumetric analysis. For the PG64-28 mixture, the V\({}_{\text{eac}}\) were 10.6%, 11.3%, and 10.9% as the compaction temperature increased. For the PG58-28 mixture, the V\({}_{\text{eac}}\) were 10.7%, 11.2%, and 11.1% as the compaction temperature increased. The volumes of the asphalt binder for lubrication decreased rather than increased in temperature. Thus, there are no meaningful changes in the CEI and CFI, although the compaction temperature rises up to 140 \({}^{\circ}\)C for both mixtures. Clearly, the compaction resistance for both mixtures can grow because of an increase in the aggregate interactions, which is not expected from raising the temperature for easier compaction.  
Figure 9: Variations of \(\Delta\): (**a**) PG64-28 mixtures; (**b**) PG58-28 mixtures.
# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 5 Case Study 2
### Experimental Result  
The experimental results are shown in Figure 8 below.  
As shown in Figure 8, the HI model created after setting up the normal data points detected the failure well. Furthermore, the HI value rises before the failure occurs, allowing prediction of the failure in advance. In addition, at the orange arrow in Figure 8, there is a section where the HI rises sharply. These signs, which occur just before the failure, help engineers to detect anomalies and predict equipment failure.  
Originally, the same failure type was expected to have similar thresholds regardless of the equipment. However, there is a difference in the threshold for each fault class. It can be inferred that even with the same failure, the degree of failure is different. That is, it may have different thresholds due to the condition of the equipment the manufacturer of the equipment. This is one of the most difficult parts of predicting failure. The result is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Size of Data (Time Span)** & **Number of Variables** & **Fault Class** & **Name of Equipment** \\ \hline
1 & 113 points (about 4 months) & 20 & N1 & K1 \\
2 & 258 points (about 8 months) & 20 & N3 & K1 \\
3 & 289 points (about 9 months) & 20 & N2 & K2 \\
4 & 134 points (about 4 months) & 20 & N3 & K2 \\
5 & 84 points (about 3 months) & 20 & N1 & K2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of robot arm data.  
Figure 7: The variable plot of robot arm data.  
Figure 8: _Cont_.  
Figure 8: The result of robot arm data. (**a**) Constructed HI model on robot arm No. 1; (**b**) Constructed HI model on robot arm No. 2; (**c**) Constructed HI model on robot arm No. 3; (**d**) Constructed HI model on robot arm No. 4; (**e**) Constructed HI model on robot arm No. 5.  
Figure 9: The result of HI without a model update.  
Figure 9 depicts the results of training and testing the model with the first 50 data from equipment K2. In this case, it can be said that K2's initial trained model continued to be used after failure. Equipment K2 had the first failure of N2 and was restarted after maintenance. However, the HI value remains high after maintenance. In addition, even the HI value decreases at the time of N3 failure.  
Comprehensively, it can be inferred that maintenance does not simply return the equipment to its original state. Even the same equipment may be in a different state from the normal state after maintenance. This implies that separate models are required for each equipment. Therefore, after the maintenance of the equipment, the model should be newly re-trained using the normal state data obtained after the new operation, rather than using the initial trained model. Then the failure can then be properly predicted as shown in Figure 9.
## 2 Experimental Set-Up and Results
### First Layer of Inner Metal Coating by Electroless-Plating  
Before being electroless-plated, three FBGs were pretreated in sensitizing solution (SnCl\({}_{2}\cdot\)2H\({}_{2}\)O, 10 g/L; HCl, 40 mL/L) for about ten minutes and then in nucleating solution (PdCl\({}_{2}\), 0.5 g/L; HCl, 5 mL/L) for about fifteen minutes. After the pretreatment, the first layer metal coating was deposited on the FBGs by electroless-plating as shown in Figure 1. We chose copper as the first layer of inner metal coating for one FBG and nickel for the other two FBGs. These first layer metal coatings cover the entire length of Bragg grating. The electroless copper plating solution is mainly listed as the following: CuSO\({}_{4}\cdot\)5H\({}_{2}\)O, 10 g/L; NaK\({}_{4}\)H\({}_{4}\)O\({}_{6}\cdot\)4H\({}_{2}\)O, 40 g/L; NaOH, 8 g/L; Na\({}_{2}\)CO\({}_{3}\), 2 g/L; NiCl\({}_{2}\cdot\)6H\({}_{2}\)O, 1 g/L; and HCHO (37%), 20 mL/L. The electroless nickel plating solution is mainly listed as the following: NiSO\({}_{4}\cdot\)6H\({}_{2}\)O, 25 g/L; NaH\({}_{2}\)PO\({}_{2}\cdot\)H\({}_{2}\)O, 20 g/L; H\({}_{3}\)BO\({}_{3}\), 20 g/L; and C\({}_{3}\)H\({}_{6}\)O\({}_{2}\), 20 mL/L.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 3 Modeling Approaches and Frameworks in the Field of Industrial IoT
### MDE-to-IoT Challenges
#### 3.2.2 Code Generation for Heterogeneous Platforms  
MDE automation enables the management of heterogeneous technologies in IoT by automatically transforming models and generating code for specific technological platforms. In this section, we summarize and evaluate the approaches' code generation capabilities. Papyrrus for IoT adds automatic code generation support to two solutions: Prismtech's Vortex [52] and MicroEJ's operating system [53].  
The Eclipse 4 diac IDE can generate C++ code that can be used in the 4 diac FORTE development process. The 4 diac FORTE is a portable implementation of an IEC 61499 runtime environment designed for small embedded control devices. Unlike Papyrus, 4 diac FORTE supports a diverse range of operating systems, boards, and PLCs [54].  
The device information models in Eclipse Vorto are based on the Eclipse Modeling Framework (EMF) [55], which allows code generators to generate code in any language, such as XML, LUA, Java, or C/C++. The transformation process in FRASAD takes the model as input and generates the equivalent code for the selected target platform automatically (operating system and hardware). There is no information available about the target platforms that FRASAD supports.  
ThingML is made up of an abstract framework and a collection of ready-made code generators for three languages (C/C++, Java, and JavaScript), as well as several libraries and open platforms (e.g., Arduino or Raspberry Pi). ThingML components in an IoT application are distributed across multiple heterogeneous platforms, each with its own code generator. ThingML is distinguished by its support for specific code generator plug-ins that enable the generation of communication and message exchange between components on different platforms.  
IoTA-MD does not support code generation; the authors only mention the development of transformation programs (model-to-text) as future work. Through internal model-to-model and model-to-text transformations, AutoIoT can generate ready-to-use IoT server-side applications. The above-mentioned MagicDraw-based approach [41] supports generating a JSON description from the model, which can then be transferred into the Arrowhead Management Tool.