esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modelling of a Gas Network
The change of temperature within the gas due to heat conduction between the pipe and the ground (if it is constructed as an underground pipeline) is sufficiently slow to be neglected [48]. This means, due to the slow flowing gas, it has sufficient time to exchange heat with the ground and its temperature with the ground temperature (for underground pipes) and for aboveground pipes, with the surrounding temperature. Therefore, temperature changes can be neglected and assumed a constant temperature equal to the ground/environment temperature [19; 23; 24]. \[\dfrac{\partial p}{\partial x}=-\dfrac{\rho_{n}}{A}\dfrac{\partial Q}{ \partial t}-\dfrac{f\rho_{n}^{2}ZRTQ|Q|}{2\eta_{l}^{2}DA^{2}p}-\dfrac{gsin( \theta)}{ZRT}p \tag{9}\]  
where \"\(\rho_{n}\)\" and \"\(Q\)\" are the density at standard conditions and flow rate of the NG. \"\(A\)\" and \"\(D\)\" are the cross-sectional area and inner diameter of the pipe, and \"\(p\)\" in Equation (9) is the average pressure between two nodes. \"\(\eta_{l}\)\" is the efficiency of pipe friction factor to convert theoretical friction to actual friction factor (Equation (10)). \[\sqrt{\dfrac{1}{f}}=\eta_{t}\sqrt{\dfrac{1}{f_{t}}} \tag{10}\]  
Another significant and effective term in a pipe network equation is the compressibility factor, \"\(Z\)\". This is a correction factor in the state of gas equations for real gases, which accounts for the deviation from ideal gas behaviour. \"\(Z\)\" depends on pressure and temperature of gas and can be calculated using correlations such as Peng-Robinson, Redlich-Kwong, Soave-RK, the cubic equation, etc. [49]. In this research \"Z\" has been approximated by the PAPAY equation, which is applicable for high-pressure networks [50]. \[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho w)}{\partial x}=0 \tag{3}\]0  
Due to slow changes of the variable in gas network pipelines, the focus of this study is a steady-state model. At steady state, outlet flow equals inlet flow, and the first term of Equation (9) is neglected. The equation then becomes:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho w)}{\partial x}=0 \tag{3}\]1  
By integrating from Equation (12), the final steady-state equation is presented in Equation (13), where pressure drop depends on inertia force, frictional force of the pipe, and different elevations of the pipeline in a gas network. \[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho w)}{\partial x}=0 \tag{3}\]2  
where:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho w)}{\partial x}=0 \tag{3}\]3  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho w)}{\partial x}=0 \tag{3}\]4  
In the next section only the modelling of the Irish gas network with 13 nodes and 14 branches is explained step-by-step using the matrix approach, see \[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho w)}{\partial x}=0 \tag{3}\]5 Due to massive matrices associated with the larger Network 2, details are shown in Appendix D. Boundary conditions and network details can be seen in Table 1 and Table A1.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 6 Conclusions  
In this paper, we propose a comprehensive performance evaluation method for a satellite attitude control system in tracking mode. The method is based on grey-target decision theory, and constructs the grey-target assessment model based on im-proved principal components, which solves the problem of the uncertainty and incompleteness of telemetry data information and achieves the performance evaluation of the attitude control system tracking mode. The feasibility and practicability of the evaluation model are verified by comparing the methods. The main advantages of the method proposed in this paper are as follows: (1) The uncertainty problem existing in the flight status data is described  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Improved Principal Component Grey Target** & **TOPSIS** & **FCE** \\ \hline Maximum–minimum difference & 0.4362 & 0.2965 & 0.4131 \\ Compatibility degree & 0.9238 & 0.9165 & 0.9124 \\ Deviation & 0.0043 & 0.0031 & 0.0055 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of evaluation methods of sun-pointing mode.  
by choosing the generalized grey number method; (2) furthermore, the reasonable weight allocation of the performance evaluation indexes is realized by the improvement in the principal component analysis method, so as to establish the performance evaluation model in tracking mode.  
This study provides a valuable reference for the operational performance evaluation of satellite attitude control systems and provides guidance for the application of further assessment methods to other operating modes of satellite attitude control systems. Future research will continue to improve on this foundation and expand the application of the performance evaluation method in different space orbit environments and to different types of satellites.  
Conceptualization, Y.Z., L.Y. and Y.C.; methodology, Y.Z.; validation, Y.Z. and L.Y.; formal analysis, Y.Z.; investigation, Y.Z. and L.Y.; data curation, Y.Z., Y.C. and K.Y.; writing--original draft preparation, Y.Z. and L.Y.; writing--review and editing, Y.Z., L.Y. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (No. 2023 YFB3307102) and National Natural Science Foundation Integration Project (No. U22B6001).  
The data used for the example in this paper are the actual in-orbit operational data from a satellite after processing, which is private information and therefore not disclosed here.  
The authors of this paper would like to thank the Fault Detection and Diagnostics (FDD) team at Nanjing University of Aeronautics and Astronautics for their support for this research. We also thank the editors for their rigorous and efficient work and the reviewers for their helpful suggestions.  
The authors declare no conflicts of interest.
## 4 Simulation and Experimental Results
The final output waveforms are shown in Figure 13.  
Seen from Figure 13, the percentage of DC-link voltage variation is 3.49% (1.6 V/45.9 V = 3.49%) under the traditional modulation strategy while the percentage of DC-link voltage variation is 2.20% (1 V/45.4 V = 2.20%) with the improved modulation strategy. Compared with the experimental results, the improved modulation strategy can reduce the DC-link voltage fluctuation greatly under the line frequency, which once again verifies the effectiveness of the proposed modulation strategy. Fourier analysis of \(v_{c}\) under the traditional modulation strategy and the improved modulation strategy are shown in Figure 14a,b, respectively. The THD of the output voltage under the traditional modulation strategy is 1.38%, and correspondingly, the value under the improved modulation strategy is 0.92%, which proves that the proposed modulation strategy can reduce the DC side voltage ripple coefficient under the line frequency, thereby improving the output voltage waveform. When the phase angle \(\varphi\) equals to zero, according to theoretical derivation, the ripple coefficients under the traditional and improved modulation strategies should be equal. The experimental results are shown in Figure 15.  
In Figure 15a,b, the position and spacing of cursors A and B are equal. It can be observed that the voltage fluctuations under the traditional and improved modulation strategies are almost the same (the delay of the controller itself cannot be ignored). The stable operation of the system can be strictly ensured when the ripple coefficient is less than 2%. When the ripple factors under traditional strategy and improved strategy are both about 2%, the required DC-link capacitors are shown in Figure 16.  
Figure 14: FFT Analysis of \(v_{c}\): (**a**) traditional modulation strategy; (**b**) improved modulation strategy. Figure 9:0 Output waveforms: (**a**) traditional modulation strategy; (**b**) improved modulation strategy. It can be observed from Figure 16 that when the voltage ripple is about 2%, the six-switch converter adopting the traditional modulation strategy requires capacitance value of 1800 uF, while the improved one requires capacitance value of 500 uF. The capacitance value is greatly reduced, and long-life film capacitors can be used to replace the large electrolytic capacitors to realize the electrolytic capacitor-less solution of the six-switch converter.
# Cyber Threat Actors for the Factory of the Future
## 1 Introduction  
The managed information security strategy for an organization requires an approach based in risk analysis for efficient resource allocation and to document the due diligence required by law. Multiple common systems have been described for risk analysis. These approaches present the identification of cyber threat actors (CTAs) as a critical step in successfully designing a robust cyber defense for an organization.  
Many information security organizations have defined classifications or lists of types of threat actors, threat agents or malicious actors. However, there is often no consensus on common definitions of the types of attackers, and often the reader must assume the perspective of the organization compiling such a list. Furthermore, organizations may tend to consider just one or only a few sources of information and thus to orientate their actions according to the corresponding scope of the classified threat elements of the respective report. This may result in overlooking a certain emphasis that the majority of security organizations have identified as a risk factor or over-emphasizing a CTA with minor effect in operating a real environment. Thus, there is a tendency to focus on quantitative factors (i.e., the number of occurrences of different threat actors mentioned in the respective reports) rather than qualitative factors (i.e., a competitive analysis).  
This study has, therefore, systematically collected relevant literature on CTA from reports and strategy papers of national and expert organizations as well as industries, first to provide an overview and second to identify priorities and potentially ignored or underestimated risks. While industrial espionage is not a new phenomenon and has always been practiced by states and by competitors,the majority of expert literature shies away from discussing this threat actor in cyberspace or appears to neglect them. At least the results of the assessment of the reports and strategy papers provide some indication of this.  
Hence, this study aims to raise awareness of this subject, considering that the economic ecosystem is becoming increasingly interconnected, which is especially true for the factory of the future (FoF). FoF environments are promising great productivity gains and new possibilities for profitable business strategies. However, reality also shows that the implementation of these various conditions required by the FoF environment comes with serious cyber security challenges.  
First, this paper studies the different CTA listings and identifies CTAs from multiple organization types from governmental institutions to cyber security industry experts. Second, the paper groups similar threat actors together to lessen the duplication of actors. Third, threat actors and their capabilities are then mapped to the characteristics of the FoF environment. Finally, the paper discusses the somewhat politely ignored role of competitors as threat actors and the concept of \"hack back\" as a controversially debated defense mechanism.
## 3 Building Energy Consumption Forecasting
### Forecasting Methods
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Author** & **Contribution** & **Limitation** \\ \hline \multirow{4}{*}{[52]} & & A weighted multiple support vector regression (SVR) model that forecasts half-hourly and daily energy utilization for nonresidential building. & The number of indicators can be expanded to decrease the slack between real qualities and anticipated qualities. However, it likewise built the intricacy and preparation time of the model as an outcome. \\ \cline{2-3}  & & An artificial neural network (ANN)-based method for predicting electric energy utilization of buildings or building clusters. & The model was intended to be used only in short-term prediction. \\ \hline \multirow{4}{*}{[54]} & & [PERSON] et al. & A deep learning-based method for building-level load forecasting. & The method excluded the building-level heating demand. \\ \cline{2-3}  & & & In general, the forecasting model presented a noteworthy prognostic capacity but, in the case of electricity consumption for cooling purposes, the model showed lower capacity. \\ \hline \multirow{4}{*}{[55]} & & [PERSON] et al. & A method to estimate the building energy utilization utilizing a recurrent neural network (RNN). & No cross-validation was used because of restrictions in the accessibility of calculation assets. \\ \cline{2-3}  & & & No genuine information was accessible for building occupancy and in this manner, an intermediary variable was utilized to repay the impact of occupancy. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of previous research papers and their contributions to data-driven methods.
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 3 Multi-Template Tracking Algorithm
### Offline Learning  
Suppose that there are \(N\) template patches of the OOI in the database, and each of them is assigned an ID number from 1-\(N\). In each template patch, \(N_{s}\) sample points are selected as the reference intensity pixels denoted by \(\mathbf{X}_{s}^{(j)}=\{\mathbf{p}_{1}^{(j)},\cdots,\mathbf{p}_{N_{s}}^{(j)}\}\), where the symbol \(j\) denotes the ID number of the template, \(\mathbf{X}_{s}^{(j)}\) denotes the sample-point set of the \(j\)-th template and \(\mathbf{p}_{i}^{(j)}\) denotes the image coordinates of the \(i\)-th reference intensity pixel (\(x_{i}\),\(y_{i}\)) in the \(j\)-th sample-point set. We also define an initial parameter vector \(\mathbf{\mu}_{0}^{(j)}\), which stores \(n\) parameters of the \(j\)-th position prediction model \(f_{p}^{(j)}(\mathbf{p},\mathbf{\mu})\) that predicts the new image coordinates of a pixel location \(\mathbf{p}\) of the \(j\)-th template patch w.r.t. a parameter vector \(\mathbf{\mu}\).  
Let \(\mathbf{I}_{ref}^{(j)}\) denote the intensity image of the \(j\)-th template patch and \(f_{m}^{(j)}(\mathbf{X},\mathbf{I})\Big{|}_{\mathbf{\mu}}\) a motion variation model that predicts a motion variation vector \(\delta\mathbf{\mu}\) w.r.t. the intensity differences between two consecutive images at some pixel locations \(\mathbf{X}\) evaluated under a given parameter vector \(\mathbf{\mu}\). In the offline learning process, the goal is to find the motion variation model for each given template patch. To achieve this goal, a large number of random motion transformations \(\delta\mathbf{\mu}_{k}^{(j)}\) with \(k=1\)-\(N_{t}\) and \(N_{t}>>N_{s}\) is generated a priori to form a reference motion variation matrix \(\mathbf{Y}^{(j)}=[\delta\mathbf{\mu}_{1}^{(j)},\delta\mathbf{\mu}_{2}^{(j)}, \cdots,\delta\mathbf{\mu}_{N_{t}}^{(j)}]\in\Re^{n\times N_{t}}\). Next, a corresponding intensity variation matrix \(\mathbf{H}^{(j)}=[\delta\mathbf{\mu}_{1}^{(j)},\delta\mathbf{\mu}_{2}^{(j)}, \cdots,\delta\mathbf{\mu}_{N_{t}}^{(j)}]\in\Re^{N_{s}\times N_{t}}\) is computed based on the initial parameter vector \(\mathbf{\mu}_{0}^{(j)}\) and the random transformations \(\delta\mathbf{\mu}_{k}^{(j)}\) such that:  
\[\delta\mathbf{\mu}_{k}^{(j)}(\mathbf{X}_{s}^{(j)},\mathbf{I}_{ref}^{(j)}) \Big{|}_{\mathbf{\mu}_{0}^{(j)}+\delta\mathbf{\mu}_{k}^{(j)}}=\mathbf{I}_{ref} ^{(j)}(f_{p}^{(j)}(\mathbf{X}_{s}^{(j)},\mathbf{\mu}_{0}^which is computationally expensive because it requires inverting an \(N_{s}\)-by-\(N_{s}\) matrix \(\mathbf{H}^{(j)}{\left(\mathbf{H}^{(j)}\right)}^{T}\). Therefore, the closed-form solution (4) is unsuitable to be used in the online learning of the template tracking module.
## 3 Application of Pavement Inspection Indicators to Maintenance Decision-Making
### Grading Analysis of Inspection Indicators
#### 3.2.2 Analysis of Inspection Data  
* Inspection Range  
This study selects the \"Provincial Highway No. 2 from 4K+000 to 8K+000 and from 23K+000 to 27K+000\", totaling 400 pavement units, for inspection based on the method in Section 2.2, including the crack rate, rutting depth, and IRI. The data are then subjected to K-means clustering analysis for grading the inspection indicators.  
1. Provincial Highway No. 2 from 4K+000 to 8K+000, inner, middle, and outer lanes in both directions, totaling 240 pavement units.
2. Provincial Highway No. 2 from 23K+000 to 27K+000, inner and outer lanes in both directions, totaling 160 pavement units.
* Analysis Results of Inspection Indicators  
This study evaluates the inspection results using the K-means clustering analysis method to analyze data for the crack rate, rutting depth, and IRI. Data from 400 pavement units on Provincial Highway No. 2 from 23K+000 to 27K+000 and from 4K+000 to 8K+000 are analyzed using K-means clustering, dividing each indicator into three clusters. The specific analysis process and results are as follows:  
1. Data Collection and Organization:  
Collect the crack rate, rutting depth, and IRI data from the mentioned sections, reflecting the condition and quality of the pavement.
2. K-means Clustering Analysis:  
Use K-means clustering to divide the data points into three clusters, each representing a group of pavement units with similar characteristics. The analysis results are shown in Tables 11-13.  
3. Cluster Grading Definition:  
* Define Cluster 1 as \"Good\", indicating sections in good condition with no immediate maintenance required.
* Define Cluster 2 as \"Continuous Monitoring\", indicating sections that need monitoring to promptly detect pavement issues.
* Define Cluster 3 as \"Requires Maintenance\", indicating sections in poor condition that need maintenance work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clustering Analysis Results** & **Cluster 1** & **Cluster 2** & **Cluster 3** \\ \hline Maintenance Grading Definition & Good & Continuous Monitoring & Requires Maintenance \\ \hline Crack Rate (\%) & 0–4.5 & 4.6–12.5 & 12.8–29.5 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Crack rate clustering analysis results.
## 3 Results and Discussion  
### Single-Factor Experiments  
#### 3.1.1 Effect of Extraction Solvent on the Yield of Essential Oil  
Obviously, for MA-SDE, the nature of the extraction solvent could have a significant impact on the yield of the essential oil, but not impact on the yield of the polysaccharides. MA-SDE with different extraction solvent, including hexane, dichloromethane, petroleum ether (30-60 \({}^{\circ}\)C), diethyl ether, and chloroform was investigated. Effect of the extraction solvents on the yield of the essential oil obtained from the powdered leaves of _T. chinensis_ var. _mairei_ was seen in Figure 2. It is clear from the results shown in Figure 2 that the use of hexane, petroleum ether, diethyl ether, or chloroform as the extraction solvent resulted in a low yield of the essential oil. In contrast, the use of dichloromethane as the extraction solvent gave much higher yields. The results are consistent with the reference [31]. Based on these results, dichloromethane was identified as the optimum solvent for the extraction of the essential oil from _T. chinensis_ var. _mairei_ leaves and used in all of the following experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Runs**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{3}{c}{**Yields (\%)**} \\ \cline{2-6}  & \(X_{1}\) **(W) a** & \(X_{2}\) **(min) b** & \(X_{3}\) **(mL/g) c** & **Polysaccharides** & **Essential Oil** \\ \hline
10 & 0 (500) & 0 (15) & 0 (20) & 6.15 & 0.27 \\
11 & \(-\)1 (400) & 1 (20) & \(-\)1 (15) & 5.54 & 0.23 \\
12 & 0 (500) & 0 (15) & 0 (20) & 6.33 & 0.26 \\
13 & \(-\)1.68 (331.82) & 0 (15) & 0 (20) & 5.49 & 0.20 \\
14 & 0 (500) & 0 (15) & 0 (20) & 6.12 & 0.27 \\
15 & 1.68 (668.18) & 0 (15) & 0 (20) & 6.27 & 0.20 \\
16 & 0 (500) & 0 (15) & \(-\)1.68 (11.59) & 5.41 & 0.22 \\
17 & 0 (500) & \(-\)1.68 (6.59) & 0 (20) & 5.45 & 0.13 \\
18 & 0 (500) & 0 (15) & 1.68 (28.41) & 5.94 & 0.27 \\
19 & 0 (500) & 0 (15) & 0 (20) & 6.29 & 0.27 \\
20 & 0 (500) & 1.68 (23.41) & 0 (20) & 5.91 & 0.24 \\ \hline \hline \end{tabular}  
* \(X_{1}\) is expressed as the irradiation power (W); b\(X_{2}\) is expressed as the extraction time (min); c\(X_{3}\) is expressed as the liquid/solid ratio (mL/g).  
\end{table}
Table 1: _Cont._  
Figure 2: Effect of different extraction solvents on yields of essential oil. Error bars indicate standard deviation (\(n=3\)).
## 4 Discussion and Outlook  
### Challenges and Findings  
The objective of the implementation of the standardized industrial Ethernet-based I/O have been achieved. The standardized I/O can operate in the testing environment of flight controls. However, the objectives of this research were purely technical. The impact of obsolescence is economically studied in the literature and norms/standards. The economic efficiency of the presented approach remains to be quantified. The technical modifications to the existing test benches were complex and far-reaching. Especially the integration of the EtherCAT master was complex.  
The use of the industrial Ethernet/EtherCAT in test benches only makes sense if a sufficient number of suppliers support these standards. If they do not, a failure of even the standardized I/O will mean the obsolescence of the test bench.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods
### Signal Processing System  
Signal processing system is also import for measurement repeatability. As seem in Figure 13, the feature of the system has both manual and automatic signal processing functions in the soft layer of the system. Every unit realizes its corresponding function. Among them, signal shaping is the basic function causing the triggering signal for synchronous sampling. Its working principle is shown in Figure 14. The white light interference fringes generate a group of pulse outputs. The rising edge of first pulse is used to start signal sampling, and the falling edge of the last pulse is used to stop signal sampling. The signal processing methods are also critical to obtain good measurement repeatability. The signal processing methods include signal smooth filtering, the positioning strategy, bidirectional measurement, and averaging the multiple measurement results, etc.  
Figure 12: The signal of the combination of laser interference and white light interference.  
Figure 13: Block diagram of signal processing system.