esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Seismic Source Characterization and PGA Distribution Modeling
#### 2.3.1 Statistical Modeling of Source Parameters for the 2017 Pohang Earthquake
Meanwhile, \(M_{0}\) is better modeled by a Burr distribution written as:  
\[f_{2}(x|\alpha,d,k)=\frac{\frac{kd}{\alpha}\big{(}\frac{x}{\alpha}\big{)}^{d -1}}{\left(1+\big{(}\frac{x}{\alpha}\big{)}^{d}\right)^{k+1}} \tag{21}\]  
where \(\alpha=0.485\) is the scale parameter, and \(d=8.971\) and \(k=0.168\) are the shape parameters. Figure 2 shows 10,000 pairs of sampled \(f_{c}\) and \(M_{0}\) from the five copula functions. The sampled pairs in Figure 2b exhibited a Spearman correlation of -0.4566, closely matching the Spearman correlation coefficient of -0.4570 in the observed source parameters. It is confirmed that the sampled data points effectively reflect the dependence structure of the original data. Moreover, the sampling points generated using the Frank copula function preserve the probability distribution of the recorded source parameters as shown in Figure 3. It not only underscores the accuracy of the Frank copula in capturing the underlying data structure but also emphasizes its capability to maintain the integrity of statistical relationships. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Copula Function** & \
\begin{tabular}{c} **Log-Likelihood** \\ **Estimate** \\ \end{tabular} &
\begin{tabular}{c} **Spearman Correlation** \\ **Coefficient** \\ \end{tabular} \\ \hline Clayton & -0.000049 & -0.00489 \\ Frank & 11.88 & -0.457 \\ Gaussian & 11.15 & -0.414 \\ Gumbel & -0.000089 & -0.0138 \\ t & 11.21 & -0.445 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Log-likelihood estimates and Spearman correlation coefficients for various copula functions. Figure 3: Probability density histograms and probability distributions of **(a)** corner frequency, modeled with a lognormal distribution, and **(b)** seismic moment, modeled with a Burr distribution (left panel) The lines represent the histogram and distribution curve fitted to records (solid) and samples (dashed). Figure 2: Sampled corner frequency and seismic moment using five different copula functions: **(a)** Clayton, **(b)** Frank, **(c)** Gaussian, **(d)** Gumbel, and **(e)** t.
# Challenges of PBFT-Inspired Consensus for Blockchain and Enhancements over Neo dBFT
## 5 Promising dBFT Extensions: Towards dBFT 3.0
### dBFT 3.0-WS2: Double Proposals on dBFT  
A known weakness of PBFT, dBFT and other primary-based consensus (spinning, etc.) is that consistent primary failures will always trigger a change view (losing time every round). In the spirit of dBFT 3.0 with multiple proposals, one way to extend single block proposal limitation (single primary) is to simply extend it to _two primaries_. We were hopefully able to design such system in a safe manner, by introducing an extra Pre-Commit Phase. We call this consensus as _dBFT 3.0 with weak synchrony and two primaries_ (dBFT 3.0-WS2). An interesting property of dBFT 3.0-WS2 is that although it may require four phases to achieve consensus, for the great majority of cases (obtained from big set of experiments especially performed to detect such cases), the system could shortcut directly to Commit Phase, leading to three-phase consensus. This happens when the network is synchronous, and the first primary is not faulty.  
The general strategy is quite similar to dBFT 2.0, which we present step-by-step for dBFT 3.0-WS2:  
* Replicas receive past block \(H_{0}\) with timestamp \(t(H_{0})\)
* Primary 0 of next block on view 0, i.e., \(\mathit{Pr.}^{0,0}\), set next timeout to \(t(H_{0})+T\), while Primary 1 of same view 0, i.e., \(\mathit{Pr.}^{1,0}\), sets its to \(t(H_{0})+4\frac{T}{3}\) (extra \(T/D\) seconds, e.g., for a parameter \(D=3\))
* If Primary 0 is well, it will release a good proposal \(H_{1}^{0}\) on time
* If network delays are not huge (good case), non-faulty replicas will receive proposal and respond to it. In this case, Primary 1 does not need to send proposal \(H_{1}^{1}\), but there may be good reasons to do it anyway, as discussed further on.
* dBFT 3.0 still follows dBFT 2.0 pattern of PrepareRequest and PrepareResponse phases, but, with an additional Pre-Commit phase, which operates as follows:
* _pre-commit_ to Primary 0 is sent when a node has \(f+1\) Preparations (PrepareResponse and the PrepareRequest hashes) to Primary 0 among \(2f+1\) Preparations payloads already received
* _pre-commit_ to Primary 1 is sent when, at least, \(2f+1\) Preparations for Primary 1 are obtained
* The interesting point of Pre-Commit phase is that it can be skipped completely if \(2f+1\) response to Primary 0 are obtained. Then, _commit_ to Primary 0 can be directly sent.
* This strategy allows Primary 0 to have priority over 1, thus resolving consensus very quickly, and directly triggering a Commit message (with signature release that cannot be undone)
* If no commit is issued, and the timer expires, replicas will issue a change view (same as dBFT 2.0)As with dBFT 2.0+, in the next round, some interesting strategies are possible such as trying to stick to previous strong proposals (with next primary re-proposing them). In this sense, allowing newer responses to them and resolve the undecided situations, in which the network does not have enough \(f+1\) Preparations for Primary 0 neither \(2f+1\) to Primary 1. This strategy could be called dBFT 3.0+.
## 2 Problem Formulation  
### System Dynamic  
We note that the ship roll angles may have a large range. As a result, the traditional ship motion mathematical model, which is based on [PERSON]'s theory, is no longer applicable. According to some reasonable simplifications, the ship roll dynamic equation in [1,26] is formulated as follows  
\[\dot{\phi}=p^{\prime}\frac{U}{L} \tag{1}\]  
\[\left(I_{x}^{\prime}+J_{x}^{\prime}\right)\dot{p}^{\prime}-A^{\prime}p^{ \prime}-B^{\prime}\left|p^{\prime}\right|p^{\prime}+W^{\prime}GM^{\prime}\phi= K^{\prime}-2A_{F}^{\prime}C_{L}\alpha_{f}l_{f}^{\prime} \tag{2}\]  
where \(\phi\) is the rolling angle of the ship, \(I_{x}^{\prime}\) is the moments of inertia, \(J_{x}^{\prime}\) is the added inertia moments, \(A^{\prime}\) and \(B^{\prime}\) are the so-called roll motion damping coefficients, \(GM\) is the initial metacentric height, \(\alpha_{f}\) is the control moment generated by the fin stabilizer, \(K\) is the moment caused by the sea wave, \(L\) is the ship length between ship perpendiculars, \(g\) is the gravity acceleration constant, \(\rho\) is the water density, \(U\) is the ship speed, \(A_{f}\) is the fin area, \(l_{f}\) is the acting force arm of the fin stabilizer, and \(C_{L}\) is the slope of lift coefficient. Symbol \({}^{\prime\prime}\) denotes dimensionless variables. According to Equations (1) and (2), the dynamic equation of the rolling angle can be rewritten in the following way,  
\[\dot{p}^{\prime}=a_{1}\phi+a_{2}p^{\prime}+a_{3}p^{\prime}|p^{\prime}|+b\alpha _{f}+F \tag{3}\]  
where \(a_{1}=-W^{\prime}GM^{\prime}/(I_{x}^{\prime}+J_{x}^{\prime})\), \(a_{2}=A^{\prime}/(I_{x}^{\prime}+J_{x}^{\prime})\), \(a_{3}=B^{\prime}/(I_{x}^{\prime}+J_{x}^{\prime})\), \(b=-2A_{F}^{\prime}C_{L}l_{f}^{\prime}/(I_{x}^{\prime}+J_{x}^{\prime})\) and \(F=K^{\prime}/(I_{x}^{\prime}+J_{x}^{\prime})\).  
We define \(\chi=[x_{1},x_{2}]^{T}=[\phi,p^{\prime}]^{T}\) as the state vector, \(y=\phi=x_{1}\) as the output and \(u=\alpha_{f}\) as the control input. Considering the unknown part in the modeling process and the uncertainty caused by the external sea wave, the dynamic Equation (3) can be written as  
\[\dot{x}_{1}=\frac{U}{L}x_{2} \tag{4}\]  
\[\dot{x}_{2}=a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{2}|x_{2}|+bu+F \tag{5}\]  
\[y=x_{1}. \tag{6}\]  
Denoting \(f(x)=a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{2}|x_{2}|\), \(c=U/L\). The system dynamic equation can be simplified as follows  
\[\dot{x}_{1}=cx_{2} \tag{7}\]\[\dot{x}_{2}=f(x)+bu+F. \tag{8}\]
# Feed Rate Variation Strategy for Semi-Conical Shell Workpiece in Ball Head End Milling Process
## 3 Design of Feed Rate Variation Strategy
### Design of Continuous Feed Rate Variation  
In the design of CFV, the selection of the feed rate is based on the vibration response amplitude of the workpiece and restrained by the cutting force in the whole machining process. The selection of the user-defined vibration amplitude is based on the demand of the machining quality, and the simulation results from Figure 12. Considering the end milling process in this paper is finish milling, and the maximum of the simulation displacement is 13.5 \(\upmu\)m in Figure 12, the user-defined vibration amplitude is set as 10 \(\upmu\)m. The selection of the user-defined cutting force threshold is based on the simulation results from Figure 5. The resultant force is ranging from 150 N to 300 N, and the radial cutting force is ranging from 50 N to 100 N. As discussed before, the radial cutting force is the main factor considering the special geometry of the semi-conical workpiece. The cutting force threshold is then set as 100 N according to the distribution of the radial cutting force. Based on the chosen user-defined vibration amplitude and cutting force threshold, the variable feed rate, radial cutting force and vibration response amplitude of contours from different heights are shown in Figure 13.  
The results of CFV for contours from different heights are shown in Figure 13 with black lines. It can be observed that both cutting force and vibration displacement have an influence on the design of the feed rate. At the beginning of each contour, the feed rate is mainly restrained by the vibration response because the workpiece radius of this position is large, and the dynamic stiffness is weak, as shown in Figure 7. The length of this period is nearly a quarter of the total length of each contour. As the cutter moving along the contour tool path, it will experience a period where the vibration response is small, and the cutting force is stable. This is the period where a higher feed rate can be used. At the middle of the contour, the cutting force becomes the main factor, and the optimal feed rates decrease to a low level. Due to the symmetry of the workpiece and contour tool path, the results of CFV on the second half is similar to the first half, while the results are distributed symmetrically.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 5 Conclusions and Future Work
Appendix A The Derivation and Definition of \(\boldsymbol{P_{\text{cross}}}\), \(\boldsymbol{P_{\text{dot}}}\), \(\boldsymbol{B(n)}\), \(\boldsymbol{C_{1}(n)}\), \(\boldsymbol{C_{2}(n)}\)  
The coherent integration in contiguous epoch, \(n\) and \(n-1\), is redefined as:  
\[I_{\text{P}}(n)=A\cdot R(\delta\tau_{n})\sin\text{c}\big{(}\delta f_{n}T_{ \text{coh}}\big{)}\cos(\overline{\delta\varphi}_{n})+n_{\text{IP}} \tag{10}\]  
\[Q_{\text{P}}(n)=A\cdot R(\delta\tau_{n})\sin\text{c}\big{(}\delta f_{n}T_{ \text{coh}}\big{)}\sin(\overline{\delta\varphi}_{n})+n_{\text{QP}} \tag{11}\]  
\[I_{\text{P}}(n-1)=A\cdot R(\delta\tau_{n-1})\sin\text{c}\big{(}\delta f_{n-1 }T_{\text{coh}}\big{)}\cos(\overline{\delta\varphi}_{n-1})+n_{\text{IP}} \tag{12}\]  
\[Q_{\text{P}}(n-1)=A\cdot R(\delta\tau_{n-1})\sin\text{c}\big{(}\delta f_{n-1 }T_{\text{coh}}\big{)}\sin(\overline{\delta\varphi}_{n-1})+n_{\text{QP}} \tag{13}\]  
After that, the \(P_{\text{cross}}\) and \(P_{\text{dot}}\) contained in ATAN2 discriminator algorithm are defined as Equations (10) and (11). \[P_{\text{cross}}(n)=I_{\text{P}}(n-1)Q_{\text{P}}(n)+I_{\text{P}}(n)Q_{\text{ P}}(n-1) \tag{14}\]  
\[P_{\text{dot}}(n)=I_{\text{P}}(n-1)I_{\text{P}}(n)+Q_{\text{P}}(n-1)Q_{\text{ P}}(n) \tag{15}\]  
Inserting Equations (10)-(13) into Equations (14) and (15), \(P_{\text{cross}}\) and \(P_{\text{dot}}\) can be expressed as:  
\[P_{\text{cross}}(n)=B(n)B(n-1)\sin(\delta\varphi_{n}-\delta\varphi_{n-1})+C_{1 }(n) \tag{16}\]  
\[P_{\text{dot}}(n)=B(n)B(n-1)\cos(\delta\varphi_{n}-\delta\varphi_{n-1})+C_{2 }(n) \tag{17}\]  
where  
\[B(n)=A\cdot R(\delta\tau_{n})\sin\text{c}(\delta f_{n}T_{\text{coh}}) \tag{18}\]  
\[I_{\text{P}}(n)=A\cdot R(\delta\tau_{n})\sin\text{c}\big{(}\delta f_{n}T_{ \text{coh}}\big{)}\cos(\overline{\delta\varphi}_{n})+n_{\text{IP}} \tag{10}\]0  
\[I_{\text{P}}(n)=A\cdot R(\delta\tau_{n})\sin\text{c}\big{(}\delta f_{n}T_{ \text{coh}}\big{)}\cos(\overline{\delta\varphi}_{n})+n_{\text{IP}} \tag{10}\]1  
\[I_{\text{P}}(n)=A\cdot R(\delta\tau_{n})\sin\text{c}\big{(}\delta f_{n}T_{ \text{coh}}\big{)}\cos(\overline{\delta\varphi}_{n})+n_{\text{IP}} \tag{10}\]2  
\[I_{\text{P}}(n)=A\cdot R(\delta\tau_{n})\sin\text{c}\big{(}\delta f_{n}T_{ \text{coh}}\big{)}\cos(\overline{\delta\varphi}_{n})+n_{\text{IP}} \tag{10}\]3  
It should be noted here that the noise terms, \(n_{\text{IP}}\) and \(n_{\text{QP}}\), are dependent and of the same distribution when we derive Equations (19) and (22).
###### Abstract  
The decrease in overall inertia in power systems due to the shift from synchronous generator production to renewable energy sources (RESs) presents a significant challenge. This transition affects the system's stable frequency response, making it highly sensitive to imbalances between production and consumption, particularly during large disturbances. To address this issue, this paper introduces a novel approach using Multivariate Empirical Mode Decomposition (MEMD) for the accurate estimation of power system inertia. This approach involves applying MEMD, a complex signal processing technique, to power system frequency signals. The study utilizes PMU (Phasor Measurement Unit) data and simulated disturbances in the IEEE 39 bus test system to conduct this analysis. MEMD offers substantial advantages in analyzing multivariate data and frequency signals during disturbances, providing accurate estimations of system inertia. This approach enhances the understanding of power system dynamics in the context of renewable energy integration. However, the complexity of this methodology and the requirement for precise data collection are challenges that need to be addressed. The results from this approach show high accuracy in estimating the rate of change of frequency (RoCoF) and system inertia, with minimal deviation from actual values. The findings highlight the significant impact of renewable energy integration on system inertia and emphasize the necessity of accurate inertia estimation in modern power systems.  
rate of change of frequency at the center of inertia (Col RoCoF); multivariate empirical mode decomposition (MEMD); power system inertia; renewable energy sources (RES); converter-interfaced generation (CIG) +
Footnote †: journal: Applied Sciences  
Article  
Estimation of Power System Inertia with the Integration of Converter-Interfaced Generation via MEMD during a Large Disturbance
## 2 Related Works
### Transfer Learning  
Transfer learning [38] refers to the transfer of knowledge learned in a large number of adjacent data domains to solve similar problems in new data domains faster and more  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Techniques** & **Outcomes** & **Limitations** \\ \hline LSTM-GAN [35] (2019) & LSTM, GAN & Higher accuracy rate compared to traditional methods & Relationships between multidimensional variables are ignored \\ \hline SAE-LSTM [31] (2019) & Stack AE, LSTM & Higher detection rate in multiple features sequence & Unbalanced data have not been considered \\ \hline VAE-LSTM [32] (2020) & VAE, LSTM & Higher recall rate and F1 score compared to standard methods & The relationship between multivariate has not been considered \\ \hline DAGAN [34] & Dual AE, GAN & High AUC is maintained even with a small quantity of training data & Unbalanced sample distribution has not been considered \\ \hline ART-GAN [33] (2021) & Data augmentation, GAN & Using data augmentation to solve data imbalances & Data augmentation only considers a single sensor \\ \hline Improved LSTM [36](2022) & LSTM & Time series data anomaly detection for diverse distributions & Unable to detect anomalies for small quantity of data \\ \hline LSTM-FS [37] (2022) & LSTM, FS, Transfer learning & Using transfer learning to reduce the differences between data distributions & Power status has a large impact on monitoring data \\ \hline MTAD-GAN (proposed) & LSTM, GAN, Digital twin, Transfer learning & Using digital twin to solve problem of data imbalances and small quantity & Performance can be improved with exploring relationships of time series and adjusting the network structure for higher anomaly detection rate \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative Analysis of the proposed deep learning scheme with related research.  
effectively. [PERSON] et al. [39] applies transfer learning to anomaly detection, transferring labeled examples from the source domain to the target domain with no available labels. [PERSON] et al. [40] uses transfer representation learning for anomaly detection using convolutional neural networks. The MSTLN [41] aggregates and transmits diagnostic knowledge from multiple source machines by combining multiple locally distributed adaptive subnets (PDA-Subnets) and a multi-source diagnostic knowledge fusion module. Based on a combination of long and short-term memory, fuzzy synthesis and feature-based approaches to transfer learning, [PERSON] et al. [37] use three feature-based transfer learning methods to reduce the differences between WTG data distributions. In the actual operation of the oil and gas station, due to the lack of abnormal samples, it is difficult to obtain a relatively balanced datasets to train a classifier with generalization.
###### Abstract  
The vibration characteristics of bolted structures are crucially affected by the pre-tightening torque. An approach for identifying the pre-tightening torque dependent stiffness parameters of bolted joints is proposed in this paper. Firstly, the interface of the bolted joint is characterized by the thin layer element with the isotropic material property, and the parameter value of the property is assigned relative to the distance from the center of the bolt; the influence of the bolt is ignored. Secondly, the model updating method is adopted to identify the parameters of thin layer elements using experimental data, and modal data under different values of pre-tightening torque in the range of 2 N-m-22 N-m are obtained; the torque wrench is used to determine the pre-tightening torque in the modal test. Finally, after identifying the material parameters using partial experimental data on pre-tightening torque range, the empirical equation of the interface parameters with the pre-tightening torque parameter is obtained by curve fitting and the rest of the experimental data are used to verify the accuracy of the fitted empirical equations. It is concluded that this method can obtain all the parameters of the equivalent thin layer elements within a certain range of pre-tightening torque, which can provide a reference for the empirical modeling of bolted structures, improve modeling efficiency and reflect the characteristic performance of real structural dynamics.  
b odted joints; parameter identification; thin-layer element; pre-tightening torque; contact interface +
Footnote †: journal: Applied Sciences
# Effect of Furnace Structure on Burden Distribution and Gas Flow in Sinter Vertical Cooling Furnace
## 4 Results and Discussion
### Gas Pressure Distribution of Sinter Layer  
Figure 9 shows the pressure distribution cloud diagram of cooling gas in the existing furnace and Venturi furnace. For the existing furnace type, the pressure drop of the cooling gas was 4140 Pa, while for the Venturi furnace type, it was 6410 Pa, with an increase of 54.83%. This was explained by the fact that first, the stacking shape of the sinter bed was changed after the Venturi furnace type was adopted. The existence of the variable-diameter section changed the height/diameter ratio of the furnace type, which was equivalent to extending the height of the sinter bed. Second, the overall cooling gas velocity in the Venturi furnace type increased. According to the Ergun formula, the greater the gas velocity through the packed bed, the greater the resistance loss. The above factors led to a significant increase in the gas pressure drop. At present, the \"short-circuit\" phenomenon of cooling gas in the burden of a vertical cooling furnace in Meishan Steel is serious, resulting in a small pressure drop in the furnace and a low utilization rate of cold air. Increasing the air volume cannot improve the cooling effect of sinter, but it will seriously reduce the exhaust temperature. Therefore, the increase in the cooling gas pressure drop caused by the Venturi furnace type was acceptable. Moreover, the increase in the pressure drop meant that the power of cooling gas entering the burden was increased, which enabled it to penetrate more sinter layers with poor permeability to achieve a uniform distribution of gas velocity in the variable-diameter section. This not only inhibited the development of high-speed gas channels but also improved the gas-solid heat transfer efficiency. Of course, the gas pressure drop should not be increased too much to avoid increasing the cost due to excessive fan power and affecting the smooth operation of the furnace. Therefore, it was necessary to further design the structure of the Venturi furnace to take into account the improvement in the gas velocity distribution and the increase in the gas pressure drop.  
Figure 9: Gas pressure distribution. **(a)** Existing furnace type. **(b)** Venturi furnace type.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
###### Abstract  
With the development of image processing technology, the copyright protection of digital images has become an urgent problem to be solved. As an effective method, the robust digital watermarking technique emerges at a historic moment. Currently, most robust watermarking schemes are performed in the transform domains, such as the discrete cosine transform (DCT) and singular value decomposition (SVD). Compared with spatial domain watermarking schemes, these methods have achieved good performance, such as better robustness and higher security. However, the computational complexity increases with the use of forward and reverse transforms. In this paper, we analyze the SVD-based watermarking scheme and its impact on the spatial domain. Based on this analysis and the mathematical characteristics of SVD, we present a robust image watermarking scheme where a binary watermark is embedded into the largest singular value of each image block in the spatial domain. Several experiments are conducted to verify the performance of the proposed watermarking scheme. The experimental results show that compared with the existing SVD domain watermarking schemes, our proposed method has maintained good robustness against various attacks. Moreover, it avoids the false positive problem existing in traditional SVD-based watermarking schemes and has lower computational complexity.  
robust image watermarking; singular value decomposition (SVD); spatial domain; Arnold transform +
Footnote †: journal:  
_Future Internet_**2017**, 9, 45; doi:10.3390/f9030045