esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 4 Simulation and Experiments
### Milling a Free-Form Surface with Bull-Nose Cutter  
In the present study, a bull-nose cutter with the same parameters as above was used in a machining experiment of a free-form surface obtained from the blade model. The CC path was generated according to the iso-parameters, the inclination angle was 30\({}^{\circ}\), and the tilt angle was 0\({}^{\circ}\) at any CC point. The spindle speed was 4000 rpm, and the feed rate per tooth was 0.03 mm/tooth. The workpiece's material was 2 Cr13, and the machined workpiece is shown in Figure 17.  
After machining, the 2D profile of the workpiece was measured by a Mahr XT20. The 2D profile was also measured in three different directions and compared with the simulation results. The point \(P_{S}(x=35.0,y=19.9,z=29.9)\) was selected as the starting point of the 2D profile measurement line in the workpiece coordinate system, and the endpoints were \(P_{2,1}(x=35.0,y=32.0,z=30.6)\) and \(P_{2,2}(x=42.0,y=27.9,z=30.1)\). Figure 18 shows the measurement and simulation results of the free-form surface. The blue line represents the measurement result of the plane surface along the direction, and the red line is for the simulation result. It can be observed that the measurement profiles are consistent with the simulation results along different measurement directions. It can be inferred that the proposed 2D contour can be effectively applied to free-form surfaces.  
Figure 17: The free-form surface being machined.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \
\begin{tabular}{c} **Measurement** \\ **Results Rz (\(\mu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Simulation** \\ **Results Rz (\(\mu\)m)** \\ \end{tabular} & **Error (\%)** \\ \hline \multirow{2}{*}{Plane \#1} & Direction \#1 & 4.3 & 4.5 & 4.7\% \\ \cline{2-5}  & Direction \#2 & 3.7 & 4.4 & 18.9\% \\ \hline \multirow{2}{*}{Plane \#2} & Direction \#1 & 5.4 & 6.0 & 11.1\% \\ \cline{2-5}  & Direction \#2 & 4.8 & 5.3 & 10.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rz values in different directions on plane surfaces.  
The obtained results show that the proposed 2D profile of five-axis CNC machining with a bull-nose cutter can be effectively applied to analyze free-form surface machining. The proposed 2D profile simulation model is expected to provide a reference for optimizing process parameters.
# Quality-Aware Resource Model Discovery
###### Abstract  
Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.  
process mining; quality management; transition system; resource model; process discovery; statistical analyses +
Footnote †: 2021: 11, 5730. [[https://doi.org/10.3390/app1125730](https://doi.org/10.3390/app1125730)]([https://doi.org/10.3390/app1125730](https://doi.org/10.3390/app1125730))
resources and quality. However, the approach for connecting the quality to resource paths, networks, or models was insufficient.  
In this work, we propose a method to discover quality-aware resource models. The resource model explains which resource paths result in high-quality cases (e.g., complete products in manufacturing and high satisfaction of patients in healthcare). The proposed method takes an event log as input and subsequently produces a simplified resource model, annotating the quality data. In more detail, it has five steps, including resource model generation, superior and inferior case identification, integrated model construction, statistical analysis on resources, and model simplification. In addition, to sufficiently perform these steps, we employ case-annotated transition systems and multiple statistical analysis techniques.  
The main contribution of this paper is as follows:  
1. Introducing a quality-aware resource model mining algorithm, which integrates the resource-oriented transition system and quality-based superior and inferior cases (Sections 3.2-3.4);
2. Suggesting a model simplification method based on statistical analyses (Sections 3.5 and 3.6);
3. Providing tooling support for the proposed approach (Section 4).  
In addition, to show the usefulness of the proposed methodology, we provide one of the case studies on a semiconductor manufacturing process at a manufacturing company in Korea.  
The remainder of this paper is organized as follows. The next session discusses related works, and Section 3 explains the proposed methodology. Section 4 discusses the tool implemented in this research. Section 5 presents the application in the case study, and Section 6 concludes with the summary, limitations, and future works.
## 4 Data Acquisition and Analysis
### The Transfer Function  
As mentioned in Section 2, a remote reference station was implemented to mitigate noise effects in our data. Based on the transfer curves (Figure 3), most observation stations exhibit similar characteristics. At short periods, the apparent resistivity is around tens of Ohm-m, increasing to approximately 200 Ohm-m at a period of about 1 s. At longer periods, the apparent resistivity decreases to below 10 Ohm-m. This decrease is accompanied by an increase in phase values, suggesting the presence of a low-resistivity layer at greater depths. The transfer function results indicate that the subsurface below the study area consists of a low-resistivity near-surface layer, which overlies a higher-resistivity layer, followed by another low-resistivity layer at greater depth. However, high-frequency (short-period) data appear to be affected by scattering due to noise. For example, stations F1 and F5 show values that are out of range. Additionally, stations N2 and F2 exhibit significant scattering in the dead-band frequency range, from approximately 1 s to around 10 s.
## 4 Radiometric Processing Workflow
### Scene Reflectance Correction
#### 4.4.3 Shadow Correction  
Shadows are caused by 3D objects within the scene and created by clouds. The approaches for treating shadowed areas include de-shadowing and separating the analysis of the shadowed and sun-illuminated areas. [PERSON] et al. [174] categorized shadow detection methods into six classes: histogram thresholding, invariant color models, object segmentation, geometrical methods,physics-based methods, and unsupervised and supervised machine learning methods. The geometric method requires the object 3D model and the information of the solar elevation and direction to calculate the positions of shadows. Due to various uncertainties, the accuracy of the geometric method is not sufficient in most cases, especially with high resolution images [175]. Therefore, image-based methods are needed. [PERSON] et al. [174] used simulated data to obtain accurate reference shadow masks. In these experiments, histogram thresholding on RGB and NIR channels performed the best, followed by physics-based methods. De-shadowing based on physical radiation modeling relies on all of the areas in shadows being illuminated by diffuse irradiance only; the shadow correction provided good results for hyperspectral airborne images and satellite images [176] and the ADS high-resolution photogrammetric multispectral scanner [175]. To the authors' knowledge, no such studies exist for high-resolution UAV approaches; thus, further studies are needed in this field.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 3 Proposed Algorithm
### Data Encryption Process
#### 3.2.2 Salting  
Salting is random data in which additional bits are added to the existing text, providing security to data. In this paper, eight random bits are taken for salting, out of which four bits are used at the start and four bits at the end. In salting, when the four bits are added to the start, the starting four bits and the next four bits are merged, and when the eight-bit value is converted to decimal, the salted data are completely different from the plain text. When any decryption mechanism is implemented on salted values, the original value can never be obtained, which can be considered a complete data security technique.
## 2 Key Theories and Technical Points of Ground Source Heat Pump
### Types of Heat Exchangers  
Heat exchangers are of two types: DE-GSHP and a secondary loop ground-coupled source heat pump (SL-GSHP) system. An SL-GSHP comprises a heat pump and a subsystem for ground heat exchangers, a water pump, and a water-refrigerant exchanger. It exhibits a high heating capacity and is generally used for large-scale buildings. In a DE-GSHP, a buried refrigerant loop exchanges heat with the surrounding soil. Therefore, the heating capacity of DE-GSHP systems is relatively low, but a high thermal extraction and/or rejection from or to the subsurface or the aquatic environment can be achieved. Additionally, a DE-GSHP can reduce the cost of application by eliminating the secondary loop from the ground. In ref. [20], the DE-GSHP and SL-GSHP systems were compared based on their economics and technologies, and it was found that the DE-GSHP system exhibits a higher efficiency than the SL-GSHP system by 23.8%, so it is more economic than the SL-GSHP system.  
Frost damage to embankments mainly occurs in cold and extremely cold regions. Groundwater, the local microclimate, and other factors disperse frost damage. The transition section of a road bridge and road culvert is also highly vulnerable to frost damage.  
Figure 2: Schematic of the refrigerant circuit of the GSHP and a pressure–enthalpy graph of the GSHP. (**a**) The refrigerant circuit of the GSHP. (**b**) Pressure–enthalpy diagram. 1–2: Compression, 2–b: gas desuperheating, b–3: condensation, 3–4: throttling, and 4–1: evaporation.  
Hence, the active artificial heating apparatus should exhibit a decentralized heat source, large heat output depth, variable heating capacity, and compact size for quick deployment. The development and operation of SL-GSHP systems are complicated for embankment heating. In addition, for common single-track embankments in seasonally deep-frozen soil regions, the maximum instantaneous and average heating load along the longitudinal direction of the embankment are 200 W/m and 20 W/m. It is worth noting that these values are significantly lower than the typical heating loads in residential buildings [21]. Therefore, the DE-GSHP system is comparatively more suitable for embankment heating.
# Nickel-Based Structured Catalysts for Indirect Internal Reforming of Methane
## 3 Results and Discussion
### Catalytic Activity
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**X\({}_{\text{CH4}}\) (\%)**} & \multicolumn{3}{c}{**X\({}_{\text{CO2}}\) (\%)**} & \multicolumn{3}{c}{**H\({}_{\text{2}}\)/CO**} \\ \cline{2-10}  & **700 \({}^{\circ}\)C** & **750 \({}^{\circ}\)C** & **800 \({}^{\circ}\)C** & **700 \({}^{\circ}\)C** & **750 \({}^{\circ}\)C** & **800 \({}^{\circ}\)C** & **700 \({}^{\circ}\)C** & **750 \({}^{\circ}\)C** & **800 \({}^{\circ}\)C** \\ \hline Run 1 & 55.2 & 73.1 & 87.4 & 70.0 & 84.4 & 93.7 & 0.78 & 0.87 & 0.94 \\ Run 2 & 52.0 & 68.9 & 82.1 & 67.2 & 81.6 & 90.7 & 0.76 & 0.84 & 0.91 \\ Run 3 & 59.1 & 76.7 & 88.7 & 72.7 & 86.7 & 94.3 & 0.81 & 0.89 & 0.95 \\ Run 4 & 63.4 & 79.4 & 90.2 & 75.7 & 88.1 & 94.5 & 0.84 & 0.91 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reactants’ conversions and H\({}_{2}\)/CO molar ratios at 700 and 800 \({}^{\circ}\)C of four catalytic runs. Figure 8: Arrhenius plots of CH\({}_{4}\) (closed symbol) and CO\({}_{2}\) (open symbol) consumption rate in F1. Figure 10 shows SEM micrographs cross-section, as a result of FIB milling, of F3\({}_{\text{inlet}}\) after DRM reaction. The SEM cross-section analysis allowed us to investigate the presence of carbon in the inner part of the catalytic layer and verify the origin of carbon formation. The analysis was focused on the cracks of the coating catalyst (Figure 10a), where the formation of carbon was mostly evident. Figure 10b shows the presence of carbon nanotubes through the whole thickness of the coating layer and Figure 10c displays the presence of carbon filaments even at the interface between the oxide layer and the alloy. The reducing atmosphere necessary for the activation of the catalytic system, as evidenced by the TPR analysis and during the DRM, where a high concentration of H2/CO is present, favors the formation of Ni particles on the surface of the foam, although it cannot be excluded that some of them may be attacked by the internal Ni-SmDC layer, which strongly interacts with the foam. Despite the considerable formation of carbon nanotubes, the catalytic activity did not decrease, probably because they did not completely cover the catalytically active Ni particles. However, they damaged the overall structure, preventing a possible reactivation of the structured catalyst. This observation was in agreement with other authors [14], who have studied Al2O3-coated on Ni-foam. During the DRM they showed carbon residues originating in the interface between Ni particles strongly interacting with the Al2O3 layers, but not on the bare Ni-foam. Figure 10: Cross-section SEM micrographs of FIB milled F3\({}_{\text{inlet}}\) structured catalyst after DRM reaction at low (**a**), medium (**b**), and high magnification (**c**). Figure 9: SEM micrographs of the three portions of F3 catalyst: F3\({}_{\text{inlet}}\), F3\({}_{\text{medium}}\), and F3\({}_{\text{outlet}}\), after DRM.
## 4 Results and Analysis
### Results and Analysis of Surveys
#### 4.1.3 Users' Previous Emotions and Their Effects in Surveys  
Considering initial emotions, happiness and surprise make up the vast majority in the two surveys. For the cases when users encounter a single interaction error, the effects of previous emotions, i.e., RQ3, were also obtained by [PERSON]'s exact test, and the results are shown in Table 3. It shows significant differences (\(p<0.05\)), and the difference between the count and the expected count is relatively large (\(\chi^{2}=44.149\)) for previous emotions. The [PERSON]'s V coefficient is 0.248, which means the magnitude of the effect size is small. Furthermore, when the previous emotion is happiness, there exists a statistical difference between the count and the expected count for the cases where the user emotion is surprise, and when the previous emotion is surprise, there is no statistical difference, but the largest adjusted residual of 1.9 is shown in the cases where the user emotion is anger.  
For the case of repeated interaction errors, the example of three times of CF was used, and the abbreviations of user emotions were recorded by the number of times, as Emotion 1, Emotion 2, and Emotion 3, respectively. The percentages of users' emotion types are shown in Figure 5. The percentage of happiness gradually decreases from the initial emotion to Emotion 3, and the percentages of anger and disgust clearly increase. The analyzed results by [PERSON]'s exact test are shown in Table 4. The [PERSON]'s V coefficients shown in Table 4 indicate the magnitude of the effect size is medium, and it increases with the number of times of CF. This reveals that previous emotions show effects on emotions after users encounter interaction errors.
# Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission
###### Abstract  
One of the important directions for improving modern Wide Area Networks is efficient and secure packet routing. Efficient routing is often based on using the shortest paths, while ensuring security involves preventing the possibility of packet interception. The work is devoted to improving the security of data transmission in IP networks. A new approach is proposed to minimize the number of distrustful nodes on the path of IP packet transmission. By a distrustful node, we mean a node that works correctly in terms of hardware and software and fully implements its data transport functions, but from the point of view of its organizational subordination, we are not sure that the node will not violate security rules to prevent unauthorized access and interception of data. A distrustful node can be either a transit or an end node. To implement this approach, we modified [PERSON]'s shortest path tree construction algorithm. The modified algorithm ensures that we obtain a path that will pass only through trustful nodes, if such a path exists. If there is no such path, the path will have the minimum possible number of distrustful intermediate nodes. The number of intermediate nodes in the path was used as a metric to obtain the shortest path trees. Routing tables of routers, built on the basis of trees obtained using a modified algorithm, provide increased security of data transmission, minimizing the use of distrustful nodes.  
IP networks; OSPF; packet routing; packet routing security; routing tables Article
## 2 Problem Statement  
Consider a MIMO nonlinear system given as:  
\[\left\{\begin{array}{rl}\xi_{i}^{(\gamma_{i})}&=f_{i}(\xi,\eta,w)+\sum \limits_{q=1}^{p}g_{i,q}(t)u_{q},\\ y_{i}&=\xi_{i},\;i\in\{1,2,..,p\}\end{array}\right. \tag{1}\]  
The above system belongs to a class of a partial exact feedback linearizable system if the relative degree \(\gamma\) is less than the order of the MIMO nonlinear system \(n\), where the control input \(u\) is defined as \(u=\big{(}u_{1}(t),u_{2}(t),\ldots u_{p}(t)\big{)}^{T}\in\mathbb{R}^{p}\), the measured output \(y\) for the MIMO system is defined as \(y=\big{(}y_{1}(t),\ldots,y_{p}(t)\big{)}^{T}\in\mathbb{R}^{p}\), \(w=\big{(}w_{1}(t),\ldots,w_{p}(t)\big{)}^{T}\in\mathbb{R}^{p}\) is the exogenous disturbance, the state vector of Equation (1) is denoted as \(\xi=\big{(}\xi^{1}(t),\xi^{2}(t),\ldots,\xi^{p}(t)\big{)}^{T}\in\mathbb{R}^{ \gamma}\), is the state vector of the system, \(\gamma=\gamma_{1}+\gamma_{2}+\ldots+\gamma_{p}\) is the total relative degree, \(f_{i}\in C\big{(}\mathbb{R}^{\;\gamma\times(n-\gamma)\times p},\mathbb{R} \big{)}\), \(i\in\{1,2,..,p\}\) is the unknown system function, which includes system uncertainties, exogenous disturbances, and any unwanted dynamics, and \(g_{i,j}\in C(\mathbb{R},\mathbb{R})\) is the input gain function for \(i,q\in\{1,2,\ldots,p\}\). The internal dynamics of the plant (Equation (1)) is described as \(\dot{\eta}=f_{0}(\xi,\;\eta,w)\), where \(f_{0}\in C\big{(}\mathbb{R}^{\;\gamma\times(n-\gamma)\times p},\mathbb{R}^{( n-\gamma)}\big{)}\) is the unknown internal dynamics function.  
Consider the state vector of the \(i\)-th subsystem, denoted as \(\xi^{i}(t)=\Big{(}\xi_{i}(t),\ldots,\xi_{i}^{(\gamma_{i}-1)}(t)\Big{)}^{T}\in \mathbb{R}^{\gamma_{i}}\), \(i\in\{1,2,\ldots,p\}\). Let the coefficient \(b_{i,q}\) be approximate of \(g_{i,q}\) in the system with a \(\pm 50\%\) zone [61,62], which then means Equation (1) can be described as,  
\[\left\{\begin{array}{rl}\xi_{i}^{(\gamma_{i})}=f_{i}(\xi,\;\eta,w)+\sum \limits_{q=1}^{p}\Big{(}g_{i,q}(t)-b_{i,q}\Big{)}u_{q}+\sum\limits_{q=1}^{p}b_ {i,q}u_{q}\\ y_{i}=\xi_{i},\quad i\in\{1,2,..,p\}\end{array}\right. \tag{2}\]  
The generalized disturbance \(F_{i}\), \(i\in\{1,2,\ldots,p\}\) of the nonlinear MIMO system in Equation (1) is expressed as:  
\[F_{i}=f_{i}(\xi,\;\eta,w)+\sum\limits_{q=1}^{p}\Big{(}g_{i,q}(t)-b_{i,q} \Big{)}u_{q},\;i\in\{1,2,..,p\} \tag{3}\]  
Figure 2 illustrates the system given in Equation (2), considering the generalized disturbance in Equation (3).  
It is necessary to design a controller for the MIMO system of Equation (1), which provides the following:  
1. Disassociation of the state couplings.
2. Disassociation of the input couplings.
3. Rejects the effect of the generalized disturbance, \(F_{i}\), \(i\in\{1,2,..,p\}\), on the outputs.
4. Maintaining an acceptable performance during both transient and steady-state responses.
## 3 Results
### Effects of Initial Estimates on the Prediction Accuracy of NNG and ALASSO
Comparison of Prediction Accuracy of NNG and ALASSO with the Models That Generated the Initial Estimates  
We investigated whether NNG and ALASSO, using optimal tuning parameters from CV, could yield better prediction accuracy than the models that generated the initial estimates: OLS, Ridge (CV), and Lasso (CV). Specifically, we compared three sets of models: (i) NNG (L, CV), ALASSO (L, CV), and Lasso (CV); (ii) NNG (R, CV), ALASSO (R, CV), and Ridge (CV); and (iii) NNG (O, CV), ALASSO (O, CV), and OLS. The first two sets showed similar prediction patterns (Figure 2 and ), while NNG (O, CV) and ALASSO (O, CV) consistently outperformed OLS models in the third set (see Figure A3 in Appendix A). Therefore, we focus on the first set. Figure 2 compares the predictive performance of NNG (L, CV), ALASSO (L, CV), and Lasso (CV) across various sample sizes and SNRs, in low- (upper panel) and high-correlation (lower panel) settings. NNG and ALASSO outperformed lasso only in scenarios requiring minimal shrinkage, such as those with large sample sizes, low correlation, and high SNR (top-right panel). However, in scenarios where severe shrinkage is often required, such as small sample sizes with low SNR (top-left panel), or high-correlation settings (lower panel), lasso outperformed both NNG and ALASSO. Overall, NNG and ALASSO produced simpler models than those used to generate their initial estimates (see Figure A4 in Appendix A). While they consistently outperformed OLS models in terms of prediction, they only outperformed lasso and ridge models in scenarios that required minimal shrinkage. When model simplicity is the primary goal, NNG and ALASSO may be preferred. Figure 2: RTE as a function of SNR for beta-type A. The **upper panel** shows low-correlation settings (C2), and the **lower panel** shows high-correlation settings (C3). **Left** and **right panels** display results for small (n = 100), and large (n = 400) sample sizes, respectively. A good prediction model should have an RTE close to 1. One standard error bars were added but are not visible due to the small standard errors.