esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 4 Improved Algorithms
### Periodic Term Detection and Classified Compensation
In order to analyze the relationship between the residual performances and the operation years, the on-orbit operation years of BDS satellites are given in Figure 7. It can be seen that the service years of C01, C03, C04, and C06 all over six years which close to designed life (7.5 years). Similarly, the fitting accuracies of C01, C03 and C04 are worse than that of other GEO satellites (see Figure 6), and the fitting accuracies of C06 are worse than that of other IGSO satellites (see Figure 6). Therefore, the performance of BDS satellite clocks will evidently degrade during increasing years of service. As shown in Figures 3-7, the fitting accuracies with the periodic terms plus the polynomial model are reached within 1 ns, and no significant periodic terms exist in the residuals. In order to enhance the accuracies of ultra-rapid prediction clock offsets, the residuals are further modeled by a BP neural network. As can be shown from Figure 4, there are differences in the significant periodic terms of three types of BDS satellites. The main significant periods of most GEO and IGSO clocks were about 24 h  
Figure 6: The fitting residuals of BDS clock offsets with one periodic term in **(a)** GEO satellite add one period; **(b)** IGSO satellite add one period; and **(c)** MEO satellite add one period, and two periodic terms as **(d)** GEO satellite add two periods; **(e)** IGSO satellite add two periods; and **(f)** MEO satellite add two periods. Figure 7: The service years of BDS satellites on-orbit. and 12 h, while that of MEO clocks were about 12 h and 6 h. Therefore, the corresponding period models in Table 2 were added to the basic prediction model shown in Equation (2). Then, the BDS satellite clock model can be presented as [14; 15; 23]:  
\[y(t)=a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2}+{\sum_{r}}^{l}\Lambda_{r} \sin[2\pi f_{r}(t_{i}-t_{0})+\varphi_{r}]+\Delta_{i}, \tag{5}\]  
where \(l\) is the total number of period errors; \(\Lambda_{r}\), \(f_{r}\) and \(\varphi_{r}\) denote the amplitude, frequency, and phase of the clock offset period. \(r\) is the number of the period.
## 4 Discussion  
We investigated the dependency of the estimated parameters on the concentration of the second species (Figure 1). The CPMs of the first and second species, \(\varepsilon_{1}\) and \(\varepsilon_{2}\), were 5 and 100 kHz, respectively. It was assumed that the first species is known and had been measured by FCS in pure solution in advance, and the second species is its aggregation or molecules labeled by multiple first species. Therefore, the concentration of the second species \(C_{2}\) was lower than that of the first species \(C_{1}\), and \(\varepsilon_{2}>\varepsilon_{1}\) in the simulations. The results demonstrate the effectiveness of the proposed correction method. Figure 1a shows that the apparent \(C_{1,ap}\) can be underestimated by one order even if the \(C_{2}\) is two orders lower than \(C_{1}\) in the simulation condition. Figure 1b,c show that the concentration and CPM of brighter species are likely to be measured without our calculation if the concentration of the brighter species is sufficiently high because, in that case, the contribution of the brighter species in fluorescence intensity becomes much larger than that of the first species. The deviation of the \(\varepsilon_{2}\) is relatively large. This is likely because the error in the estimation of \(\varepsilon_{2}\) is enhanced as the \(\varepsilon_{2}\) was estimated based on the estimated value of \(\varepsilon_{1}\).  
The dependency of the estimated parameters on the given CPM of the second species \(\varepsilon_{2}\) is also shown in Figure 2. It was shown that the error of \(C_{1}\) became larger with the increase in \(\varepsilon_{2}\). Thus, the FCS results can be easily distorted by contamination of the brighter species in the samples.  
In the experiment on the mix of two fluorescent bead solutions with different brightness, the calculation of \(\overline{n}_{1}\) worked well; however, the accuracies of the calculation on \(\overline{n}_{2}\) and \(\varepsilon_{2}\) were lower than those on the \(\overline{n}_{1}\). This was likely caused by the estimation of \(\overline{n}_{2}\) and \(\varepsilon_{2}\) based on the estimated value of \(\varepsilon_{1}\) as shown in Appendix A. To obtain accurate values  
Figure 3: Dependency of the given CPM of the second species. (**a**) Dependency on the measured concentration of the first species \(C_{1}\). (**b**) Dependency on the measured concentration of the second species \(C_{2}\). Only the error bars in positive direction are shown. (**c**) Dependency on the measured CPM. Black broken lines show the expected values. Error bars show the standard errors (\(n=8\)).  
of \(\overline{n}_{2}\) and \(\varepsilon_{2}\), the measurement should be performed carefully and accurately. Small differences in particle brightness owing to hardware conditions, such as laser power fluctuation and/or changing fluorescence collection efficiency by changing the light path during the measurement can deviate the corrected values.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
###### Abstract
Moreover, this approach permits us to run a fair comparison among different experimentation frameworks which run FRR. Thus, we seek to perform the necessary implementations so that ns-3 can support FRR, in order to develop simulations in ns-3 DCE that use FRR code. While FRR implements a set of network protocols, the scope of this work is to support the implementation provided for BGP. For the implementation of the simulations, we will focus on the fat tree CLOS topology, which is widely used in massive data centers. The aim is to study the behavior of the BGP protocol in this context. The main contribution of our work consists of a simulation platform to test and analyze routing protocols in the context of MSDCs. This platform provides the ability to simulate the FRR suite and in particular the MSDC routing algorithms. To achieve this, our work includes: (i) an extension of DCE to support the FRR suite, (ii) a _FRRHelper_ class, which facilitates the instantiation and usage of FRR in a simulation script, (iii) an extension of the fat-tree topology generator _VFTGen_[18] to produce ns-3 simulation scripts, (iv) a comparison and validation between emulation and simulation-based approaches for BGP in data center, and (v) a comparison and validation between the experimental results and a theoretical analysis of BGP behavior over selected scenarios. The remainder of this paper is organized as follows: Section 2 provides the background and presents the Sibyl framework as related work. Section 3 describes the process of porting FRR to ns-3, using the DCE module. Section 4 exposes the validation of the port and the experimental results generated. Consequently, a basic functional evaluation is described. Secondly, a comparison against the Sibyl framework is carried out. Finally, a validation against a theoretical analysis of BGP behavior over two selected scenarios is performed. In Section 5, a performance analysis is exposed. It evaluates the scalability, memory usage and execution times for different network sizes. Additionally, two features to improve the performance of the port are described; and a comparison between the execution times in the simulated and emulated environment is exhibited. Finally, in Section 6, we discuss the most relevant aspects and conclusions of this work.
## 3 Experimental Results
Figure 2: Experimental setup and results of the laboratory tests: (**a**) Experimental setup of the laboratory test; (**b**) MZM output intensity of signal modulation. The black and red solid lines represent the output intensities with and without the proposed control method, respectively; (**c**) Direct-current (DC) bias voltage to compensate for the bias drift; (**d**) MZM output intensities of the diagnostic modulations. The abbreviations are defined as follows: distributed feedback laser (DFB laser); photodiode (PD); and microcontroller unit (MCU).
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 6 Conclusions  
This study investigated an ERT as a means to enhance energy system reliability. Compared to a conventional dynamic energy pricing on time only, the ERT is designed to provide a price in terms of reliability range as well as time. Using the two-way information exchange of price from the utility and prosumer situation and reliability band selection, both the utility and the prosumer are expected to have opportunities to maximize their benefits. The proposed mechanism can motivate prosumers to participate in the system reliability improvement by incentivizing or penalizing them depending on the contribution. A numerical case study using BPA data validates the effectiveness of the proposed ERT and shows that both the reliability of the utility and prosumer revenue is enhanced compared to a conventional energy-based dynamic pricing mechanism. The result shows that the prosumers' revenue enhancement is 6.6% when the EES capacity applied is 7% of installed wind generation. It is also shown that the revenue enhancement and reliability improvement are higher under the proposed ERT than under the conventional pricing case by applying two band ERT prices. In addition, the advantages and disadvantages of the proposed ERT are discussed. This suggests guidance in adopting the proposed ERT as a transaction mechanism to enhance the energy system's reliability. The study can be extended for appropriate or optimal price-band model design using the proposed ERT.  
S.-Y.S. conceptualized and designed the structure of the manuscript. E.O. performed data analysis and investigation. [PERSON] wrote the original draft, and S.-Y.S. reviewed and edited the manuscript. All the authors have read and approved the final manuscript.  
The work of [PERSON] was supported by the National Research Foundation of Korea through the Korean Government (Ministry of Science and ICT) under Grant 2017R1E1A1A03070136. The work of S.-Y.S. was supported by the Korea Institute of Energy Technology Evaluation and Planning and the Ministry of Trade, Industry and Energy of the South Korea under Grant 20168530050080.  
The authors declare no conflict of interest.
## 5 Conclusions  
We proposed a novel shaping and machining method based on SSEDC for herringbone gears that can realize high-precision control of spatial symmetry error in a one-time forming and machining process. First, a herringbone gear spatial symmetry error measurement and evaluation model was initially established based on spatial projection and LSM. Then, a herringbone gear spatial symmetry error compensation model was further established. Finally, an online detection and compensation machining method for herringbone gear spatial symmetry errors was first created.  
Based on the established spatial symmetry error measurement, evolution and compensation mathematical model, a general fixture and an on-line spatial symmetry error detection device were designed for automatic detection and control of spatial symmetry errors. Additionally, to improve machining efficiency in the detection and compensation process, three-dimensional spatial symmetry errors were reasonably reduced to one-dimensional displacement deviations. Further, the specific processes of practical engineering were proposed and applied.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Machining Method** & **Machining Accuracy (mm)** & **Efficiency (h)** & **Batch Machining Feature** \\ \hline Traditional method & 0.1–0.2 & 2.5 h/piece & Machining with single piece repeat \\ \hline Novel method & 0.02 & 0.8 h/piece & Continuous machining \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of herringbone gear machining accuracy and efficiency with different machining methods.  
Figure 15: Diagram of herringbone gear machining and detection with SSEDC. (**a**) Machining process. (**b**) Accuracy measurement. (**c**) Measurement results.  
Numerous machining cases were performed based on the proposed SSEDC method and extended to other, different sizes of herringbone gears. The machining cases indicated that the proposed machining method could consistently maintain symmetry within 0.02 mm. Thus, the proposed SSEDC method for solving the out-of-tolerance problem of spatial symmetry error shows reliability and stability.  
Conceptualization, Z.L.; Methodology, H.Z.; Validation, H.Z.; Investigation, Z.L.; Writing--original draft, H.Z.; Writing--review and editing, Z.L.; Project administration, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Youth Fund project of the National Natural Science Foundation of China (No. 52109157).  
Data Availability Statement: Data sharing not applicable.  
The authors thank the reviewers and editors for useful comments and suggestions that helped to improve the paper.  
The authors declare no conflict of interest.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 1 Introduction
### Methodology Overview  
In this article, we propose a watermarking method for AM. The proposed approach is composed of the following steps. At first, the input geometric model is converted into a distance field. At the second step, the watermark is inserted into a region of interest (ROI) by using self-organizing mapping (SOM). Finally, the watermarked model is converted into a G-code program by using a specialized slicer, and thus the watermark is implicitly encoded into the G-code program. If the G-code program is executed by a 3D printer to manufacture an object, the printed part will contain the watermark too.  
Compared with conventional watermarking methods, our algorithm possesses the following advantages. First, it protects not only digital and physical models but also G-code programs. Second, it can embed watermarks into both polygonal and volumetric models. Third, our method is capable of inserting watermarks inside the interiors or on the surfaces of complex objects. Fourth, the watermark can appear in various forms, for example, signature strings, randomly distributed cavities, embossed bumps, and engraved textures.  
Various verification methods are also developed in this work to authenticate digital and analog contents. If the target is a G-code program, we emulate it by using a simulator to generate a volume model at first. Then, the result is rendered to search for a trace of watermark. If a watermark is found, we extract it and compare it with the recorded watermark to verify the G-code program. When dealing with a geometric model, we first render the content to confirm the existence of a watermark. Then, this watermark is retrieved from the model and compared with the recorded one to evaluate the genuineness of the geometric model. If the target is a physical part, we illuminate the object by using light rays to uncover the watermark. Then, the revealed watermark is compared with the recorded watermark to authenticate the physical part.  
The rest of this article is organized as follows. Section 2 describes the embedding and detecting procedures. The test results are given in Section 3. Discussion and analysis of this research are presented in Section 4. Comparisons with others' methods and future work are also included in Section 4. This article ends with a conclusion in Section 5.
# Modeling of Water Losses in Hydraulic Tunnels under Pressure Based on Stepwise Regression Method
## 2 Theoretical Background
### MLR and Stepwise Regression  
The general form of the MLR model can be written as follows (3):  
\[y_{i}=\beta_{0}+\beta_{1}\cdot x_{1i}+\beta_{2}\cdot x_{2i}+\ldots+\beta_{j} \cdot x_{ji}+\ldots+\varepsilon_{i}, \tag{3}\]  
where \(y_{i}\) (\(i=1,2,\ldots,n\)) is the \(i\)th observation for the response variable \(y\), \(x_{ji}\) (\(j=1,2,\ldots,k\)) are the values of \(j\)th predictor variable in \(i\)th observation, \(\varepsilon_{i}\) are the values of the independent, stochastic, and normally distributed random variable which fulfils the assumption of homoscedasity, n is the number of observations in the dataset, and k is the number of predictor variables. Coefficients \(\beta_{0}\ldots,\beta_{k}\) are the unknown parameters of the model, which are usually estimated by using the least squares method. As mentioned in the introduction section, one way of choosing the number and type of predictors in Equation (3) is the stepwise regression method.  
The stepwise regression method is a statistical technique designed with the idea of reducing the complexity of the regression model while maintaining satisfactory accuracy. The stepwise method is essentially based on semi-partial correlation, which is expressed through the semi-partial correlation coefficient. The square of the semi-partial correlation coefficient, \(sr^{2}\), for a specific single variable, indicates by how much the value of the coefficient of determination, \(R^{2}\), will be reduced if this single variable is removed from the regression equation. In the other words, let \(\chi\) be the set of all independent variables, expressed through matrix \(\mathbf{X}\), and \(\psi_{k}\) be the set of all independent variables \(\mathbf{X}\), excluding \(x_{k}\). Then the squared semi-partial correlation coefficient is expressed as follows:  
\[sr_{k}^{2}=R_{\chi}^{2}-R_{\psi_{k}}^{2}, \tag{4}\]  
The form of expressing semi-partial correlation coefficients may vary, and one of the most common forms is:  
\[sr_{k}=\frac{t_{k}\cdot\sqrt{1-R_{\chi}^{2}}}{\sqrt{residualf}}, \tag{5}\]  
where \(t_{k}\) is the Student's t-statistic value for the kth regressor in the MLR model, \(residualf=n-k-1\) is the number of degrees of freedom for the sum of residuals, nis the number of observations, and \(k\) is the number of regressors. Stepwise regression is explained in detail in [19; 24].
## 4 Performance Results
### Performance Indicator
#### 4.1.6 AUC-ROC Curve  
The Receiver Operation Characteristic (ROC) curve is a ratio graph showing True Positive Rate (\(TPR\)) versus False Positive Rate (\(FPR\)). The graph is represented by \(FPR\) on the X-axis and \(TPR\) on the y-axis, and the formula for obtaining each value is as follows.  
\[TPR=\frac{TP}{TP+FN}=Recall=Sensitivity \tag{13}\]  
\[FPR=\frac{FP}{FP+TN}=1-Specificity \tag{14}\]  
TPR is the percentage of correct answers that the model actually gave, and FRP is the percentage of correct answers that were not actually correct. The closer TPR is to 1 and the closer \(FPR\) is to 0, the better the model. The AUC score is the area of the area under the graph drawn by the ROC curve. As the model shows good performance, \(TPR\) approaches 1 and \(FPR\) approaches 0, so the graph is drawn in the upper left corner. Then, the area of the base of the AUC score also approaches 1, which is an indicator of good performance.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 3 Materials and Methods
### Markov-Based Next Destination Prediction
 \tag{11}\]  
Then \(\lambda\) is the number of times per unit time interval that a node selects location \(k\) as its destination. A time-continuous Markov chain can be used to describe the entire transfer procedure when considering two nodes in a network. We can symbolize the state \((u_{0},v_{0})\) in the Markov chain when node \(N_{a}\) is at position \(u_{0}\) and node \(N_{b}\) is at position \(v_{0}\). After a period of time, nodes \(N_{a}\) and \(N_{b}\) are shifted to arbitrary positions. If \(N_{a}\) moves to \(u_{k}\), \(N_{b}\) moves to \(v_{l}\), the current state is \((u_{k},v_{l})\) and the transfer rate of this process is \(q_{ij}(N_{a})\) and \(q_{ij}(N_{b})\). In specific, the Markov chain enters the absorbing state \(A(u_{k},v_{k})\) if \(N_{a}\) and \(N_{b}\) are moved to the same location. The state transfer process of the node is shown in Figure 1.  
From Equation (10), the probability that a node chooses to move to any location in time \(t\) is \(p_{ij}^{nk}(t)\), where the probability of going to location k once is expressed as \(f_{1}^{k}\). \[f_{1}^{k}=\{p_{ij}^{nk}=\frac{(\lambda t)^{j-i}}{(j-i)!}e^{-\lambda t}|j=i+1\} \tag{12}\]  
The probability of traveling m times to a particular place \(k\) is written as Equation (13). \[f_{m}^{k}=\{p_{ij}^{nk}=\frac{(\lambda t)^{j-i}}{(j-i)!}e^{-\lambda t}|j=i+m\} \tag{13}\]  
Therefore, the probability of moving to location \(k\) is \(f^{k}=f_{1}^{k}+f_{2}^{k}+\ldots+f_{m}^{k}=1-f_{0}^{k}\). It can be considered that two nodes will meet if node \(N_{b}\) arrives before node \(N_{a}\) leaves location \(k\). To remove the exponential restriction of the traditional Markov process, matrix \(P\) can be used to record the node's choice of the next location when it is at a different location, and the elements \(p_{uk}\) in matrix \(P\) denote the probability that the next location is \(k\) when the node is at location u. The linked table \(w\) records the dwell time at each location, and \(w_{k}(t)\) denotes the probability that the node's dwell time at location \(k\) is greater than or equal to \(t\). In conclusion, the probability that a node will be active within the kth location in the upcoming period t is indicated by \(p^{k}\). \[p^{k}=\left(p_{uk}+f^{k}\right)\cdot w_{k}(t)/2 \tag{14}\]  
Therefore, the probability that nodes \(N_{a}\) and \(N_{b}\) meet is:  
\[fc(N_{a},N_{b})=p_{N_{a}}^{1}\cdot p_{N_{b}}^{1}+p_{N_{a}}^{2}\cdot p_{N_{b}}^ {2}+\ldots+p_{N_{a}}^{m}\cdot p_{N_{b}}^{m}=\sum_{i=1}^{m}p_{N_{a}}^{i}\cdot p _{N_{b}}^{i} \tag{15}\]
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 5 Results and Discussion
### Whirl Orbit Ellipticity Analysis  
For the orbit decomposition method, the influence of orbit ellipticity is studied. The ellipticity \(\lambda\) is defined as:  
\[\lambda=\frac{a-b}{a} \tag{19}\]  
The values for \(\lambda\) range from 0 to 1. Figure 16 shows the three typical whirl orbits. When major semiaxis \(a\) is equal to the minor semiaxis \(b\), the value of \(\lambda\) is 0. The whirl orbit is a circle, as shown in Figure 16a. In this case, the present method cannot be used to identify the frequency-dependent rotordynamic coefficients according to Equation (6). When the minor semiaxis \(b\) is 0, the value of \(\lambda\) is 1. The whirl orbit is a straight line, as  
Figure 14: Comparison of rotordynamic coefficients.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Computational Method** & **Frequency Number** & \
\begin{tabular}{c} **Solution Number** \\ **(Each Frequency)** \\ \end{tabular} &
\begin{tabular}{c} **Time for One** \\ **Computation (h)** \\ \end{tabular} & **Total Time (h)** \\ \hline Transient CFD method & 5 & 1 & 45 & 225 \\ Orbit decomposition method & 5 & 2 & 0.8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of computational time for two methods.  
shown in Figure 16c. The present method is still effective to solve the frequency-dependent rotordynamic coefficients according to Equation (6).  
Figure 16: Three typical whirl orbits.  
Figure 15: Variations in rotordynamic coefficients with whirl amplitude.  
In this analysis, the major semiaxis \(a\) is set to be 0.1 \(C_{r}\), and three ellipticities are calculated by changing the minor semiaxis \(b\), corresponding to a \(\lambda\) value of 0.1, 0.5, and 1. The whirl frequency is 150 Hz. Figure 17 shows the variations in rotordynamic coefficients with ellipticity \(\lambda\). It can be found that the rotordynamic coefficients are not sensitive to the orbit ellipticity.