esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Malfunction Probability Models for Communication Delays and Bit Errors
### False Trip Probability Model for Inconsistent Delays
#### 3.1.2 False Trip Probability for Inconsistent Delays  
For the current differential protection, the directly measured electric quantities are the currents of both ends, and then the differential current can be calculated. In a normal state, in theory the differential current is equal to zero or an expected small value \(I_{cd}\). However, due to communication delays, the currents at both ends used in the calculation may not correspond to the same time instant, which results in a relatively large differential current in a normal state. According to [24], the practically observed differential current of protection shows a normal distribution. Assuming that the differential current follows the normal distribution \(N\left(0,\,\sigma^{2}\right)\), then the differential current is:  
\[I^{\prime}_{cd}=I_{cd}+\alpha+\beta\sim N(I_{cd},2\sigma^{2}) \tag{8}\]  
where \(2\sigma^{2}\) is the variance; \(I_{cd}\) is the expected small value of the differential current; and \(\alpha\) and \(\beta\) are the errors caused by communication delay. The probability density function of the differential current \(I^{\prime}_{cd}\) is shown in Figure 3.  
Figure 3: The probability density function of the differential current.  
Figure 2: Current vector changes caused by inconsistent delay. (**a**) Vector diagram of currents; (**b**) current differential protection.  
\[P_{f3}(t_{cd})=1-\varphi(\frac{2 kI_{L}-2I_{L}\sin(\frac{t_{cd}\omega_{0}}{4})}{ \sqrt{2\sigma}}) \tag{13}\]  
In China, the first form of braking ratio criterion and the start criterion are jointly used to determine the action of the current differential protection, while in some other countries, the second form of braking ratio criterion and the start criterion are jointly used [25].  
Then the false trip probability of the relay protection with the first form of braking ratio criterion and the start criterion can be obtained:  
\[P_{f}(t_{cd})=\left\{\begin{aligned} P_{f1},\ t_{cd}\geq& \frac{4}{\omega}\arccos(\frac{I_{op}}{2 kI_{L}})\\ P_{f2},\ t_{cd}<&\frac{4}{\omega}\arccos(\frac{I_{op}}{2 kI_{L}})\end{aligned}\right. \tag{14}\]  
where \(P_{f1}\) and \(P_{f2}\) are defined in (11) and (12). Figure 5 gives the false trip probability of relay protection with the first form of braking ratio criterion and start criterion.  
The false trip probability of the relay protection with the second form of braking ratio criterion and the start criterion can be obtained similarly.
## 2 System Modeling
### Outer Voltage Control Loop and Inner Current Control Loop  
Figure 4 represents the outer voltage and inner current control loops, where, \(V_{dc}^{*}\) and \(V_{dc}\) are the reference voltage fed from MPPT controller and measured DC link voltage, respectively. \(Q_{g}^{*}\) is the reference reactive power. \(\vec{i_{dg}}\) and \(\vec{i_{gg}}\) are the reference d- and q-axis current components of grid current, respectively. \(i_{dg}\) and \(i_{qg}\) are the measured d- and q-axis current components of grid current, respectively. \(V_{dg}\) and \(V_{qg}\) are d- and q-axis components of grid voltage.  
\(R_{g}\) and \(L_{g}\) are resistance and the inductance values, respectively, of the filter and transformer. In general, transformer resistances and inductances are neglected in transformerless systems. But if the system size increases, the necessity of a transformer is inevitable for insulation between high-tension and low-tension sides. Hence the internal transformer impedances must be considered for proper values of PI controller tuning. Thus, the equation for \(R_{g}\) and \(L_{g}\) can be written as:  
\[R_{g}=R_{f}+R_{xf(primary)}+R_{xf(secondary)}\text{ and }L_{g}=L_{f}+L_{xf(primary)}+L_{xf( secondary)},\]  
where, \(z_{f}\) refers to the filter component; \(z_{xf(primary)}\) and \(z_{xf(secondary)}\) refer to components associated with the primary and secondary windings of a transformer, respectively. PI/FOPI controllers perform better in dq0 reference frame, hence abc components are converted to dq0 frame using the following relation [13]:  
\[\left[\begin{array}{c}x_{d}\\ x_{q}\end{array}\right]=\frac{2}{3}\left[\begin{array}{cc}cos\theta&cos \left(\theta-\frac{2\pi}{3}\right)&cos\left(\theta-\frac{4\pi}{3}\right)\\ -sin\theta&-sin\left(\theta-\frac{2\pi}{3}\right)&-sin\left(\theta-\frac{4\pi }{3}\right)\end{array}\right]\left[\begin{array}{c}x_{a}\\ x_{b}\\ x_{c}\end{array}\right]. \tag{3}\]  
The reference value for the inner current control loop \(\vec{i_{dg}}\) and \(\vec{i_{qg}}\) can be found using:  
\[\vec{i_{dg}}=\left(KpV+\frac{K_{i}V}{s}\right)\left(V_{dc}^{*}-V_{dc}\right),\] (4a) or \[\vec{i_{dg}}=\left(KpV+\frac{K_{i}V}{s^{Av}}\right)\left(V_{dc}^{*}-V_{dc}\right)\] (4b) and \[\vec{i_{qg}}=-\frac{Q_{g}^{*}}{\frac{3}{2}V_{dg}}. \tag{5}\]  
Figure 4: Outer voltage and inner current control loops.  
The nominal value of DC link voltage can be found using:  
\[V_{dc}^{*}=\frac{\sqrt{6}V_{a}}{m_{a}}, \tag{6}\]  
where \(V_{a}\) is grid phase voltage and \(m_{a}\) is the modulation index.  
Considering the cross-decoupling terms, the inner loop equations can be written as:  
\[V_{di}=\left(K_{P}I+\frac{K_{i}I}{s}\right)\!\left(\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!
###### Abstract  
The vibration waves generated by pressure fluctuations can substantially impair and jeopardize the structural integrity of roadway anchorage within adjacent rock formations, thereby presenting a significant risk to the safety and operational efficiency of mining activities. In order to address this issue and elucidate the response characteristics of roadway-anchored surrounding rock subjected to P-wave and S-wave influences, this study employs a roadway that is experiencing actual impact instability within a mine situated in Xinjiang as the engineering context. The synchrosqueezing wavelet transform, enhanced by a Butterworth filter, is utilized to isolate and filter seismic wave data, thereby facilitating the extraction of time-frequency signals corresponding to both P-waves and S-waves. Subsequently, a dynamic numerical model is developed to simulate the propagation of these vibration waves. An analysis of the dynamic behavior and response characteristics of P-waves and S-waves is performed, focusing on their interaction with roadway anchoring within the surrounding rock at various stages of propagation. The results indicate that weak rock and plastic zones can absorb vibrational waves, with S-waves exhibiting a stronger absorption effect than P-waves. S-waves contribute to increased stress and displacement in the surrounding rock, leading to the accumulation of elastic energy and an expansion of the plastic zone. The rapid fluctuations in the axial force of bolts along the roadway, caused by S-waves, can result in instability within the roadway. The research findings possess considerable reference value and practical applicability for the design of anti-scour support systems in roadways.  
pressure bumping; vibration wave propagation; anchored surrounding rock; rock bolting support; FLAC\({}^{\text{3D}}\) numerical simulation +
Footnote †: journal: Applied sciences
## 3 Typical Weight Models  
As noted earlier, the SNR can be an effective tool for evaluating signal quality. In addition to the SNR, the satellite elevation angle was considered an index by which to weight GPS measurements. In this section, we review typical weight models based on the SNR and elevation angles and show how the new weight model was devised.
###### Abstract  
Surface movement and deformation induced by underground coal mining causes slopes to collapse. Global Navigation Satellite System (GNSS) real-time monitoring can provide early warnings and prevent disasters. A stability analysis of high-steep slopes was conducted in a long wall mine in China, and a GNSS real-time monitoring system was established. The moving velocity and displacement at the monitoring points were an integrated response to the influencing factors of mining, topography, and rainfall. Underground mining provided a continuous external driving force for slope movement, the steep terrain provided sufficient slip conditions in the slope direction, and rainfall had an acceleration effect on slope movement. The non-uniform deformation, displacement field, and time series images of the slope body revealed that ground failure was concentrated in the area of non-uniform deformation. The non-uniform deformation was concentrated ahead of the working face, the speed of deformation behind the working face was reduced, the instability of the slope body was increased, and the movement of the top of the slope was larger than at the foot. The high-steep slope stability in the mine was influenced by the starting deformation (low stability), iso-accelerated deformation (increased stability), deformation deceleration (reduced stability), and deformation remaining unchanged (improved stability).  
GNSS monitoring system; long wall mining; dynamic deformation; high-steep slope; stability +
Footnote †: journal: Applied Sciences
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.19. Self-Organizing Network (SON)  
The 5G networks are intended to establish a heterogeneous and dense network with a variety of access modalities to give a seamless user experience. The expected density makes designing the network layout of 5G networks difficult. As a result of the dynamic nature of the process, SON strives to automate network configuration, resulting in easier deployment and operation as well as increased performance. This technology is regarded as a critical enabler of these goals and a critical facilitator of new UCs, such as autonomous driving and tactile Internet, which necessitate a fundamental shift in network management with significantly greater automation and dynamic predictive resource allocation [25].
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
## 3 Results
### Performance of the Three-Way Alignment on Benchmark Datasets  
We performed a quick evaluation of the performance of the three-way alignment approach by using the BAliBASE [42] benchmark datasets of protein sequences. We selected 60 highly diverged reference alignments, including (1) the first 20 sets in in RV11 (BB110001-BB11020), (2) 20 randomly chosen sets in RV30, and (3) 20 arbitrarily chosen sets from RV12 (BB12002-BB12006, BB12009, BB12010, BB12012-BB12024). These MSAs were corroborated with other information, such as protein structure, and may be considered the best approximation of the true alignment. From each of these 60 sets of protein sequences, we generated two additional alignments, one from MAFFT with the accurate L-INS-i option and the other from the three-way alignment approach. These three MSAs are referred to as BAliBase, L-INS-i, and three-way. From each alignment, a PhyML tree is built with the default LG model and the simultaneous optimization of tree topology, branch lengths, and rates. The three resulting trees were also designated BAliBase, L-INS-i, and three-way, respectively. The BAliBase tree was taken as the best approximation of the true tree. The RFd value was calculated between the BAliBase tree and the L-INS-i tree and between the BAliBase tree and the thee-way tree. The results are similar to those with simulated sequences. The mean RFd is 3.03333 between the BAliBase and L-INS-i trees and 2.66667 between the BAliBase and three-way trees. The two are marginally significant based on a one-tailed paired-sample test (t = 1.6638, DF = 59, one-tailed \(p\) = 0.0507).
## 5 Empirical Test and Result Analysis
### Influencing Mechanism Analysis
#### 5.4.3 Increase Information Transparency
\begin{table}
\begin{tabular}{c c c c c} \hline  & **(1)** & **(2)** & **(3)** & **(4)** \\ \hline  & _Analyst_ & **ESG** & _Analyst_ & **ESG** \\ \hline _Lndigital_ & 0.0895 *** & 0.0089 *** & 0.0757 *** & 0.0106 *** \\ \hline  & (0.0103) & (0.0024) & (0.008) & (0.0022) \\ \hline _Analyst_ & & 0.0399 *** & & 0.0076 *** \\ \hline  & & (0.0024) & & (0.0029) \\ \hline  & & & 0.5750 *** & 0.0704 *** \\ \hline  & & & (0.0103) & (0.0033) \\ \hline lev & & \(-\)0.6250 *** & \(-\)0.0618 *** \\ \hline  & & (0.0640) & (0.0184) \\ \hline growth & & \(-\)0.0183 * & \(-\)0.0031 \\ \hline  & & (0.0096) & (0.0027) \\ \hline roa & & 4.6500 *** & \(-\)0.0936 * \\ \hline  & & (0.1810) & (0.0535) \\ \hline cashflow & & 0.7840 *** & 0.1110 *** \\ \hline  & & (0.1500) & (0.0430) \\ \hline mb & & \(-\)0.8300 *** & 0.0122 \\ \hline  & & (0.0264) & (0.0079) \\ \hline fsh1 & & \(-\)0.505 *** & 0.0336 * \\ \hline  & & (0.0619) & (0.0177) \\ \hline dlzb & & \(-\)0.8570 *** & 0.0531 \\ \hline  & & (0.1640) & (0.0470) \\ \hline big4 & & \(-\)0.1170 *** & 0.1470 *** \\ \hline  & & (0.0312) & (0.0089) \\ \hline lange & & \(-\)0.3910 *** & 0.0913 *** \\ \hline  & & (0.0286) & (0.0082) \\ \hline \(\_\)cons & 2.1470 *** & 2.7140 *** & \(-\)9.6330 *** & 1.0190 *** \\ \hline  & (0.1020) & (0.0250) & (0.2530) & (0.0774) \\ \hline Industry & control & control & control & control \\ \hline Year & control & control & control & control \\ \hline \(N\) & 9767 & 9767 & 9767 & 9767 \\ \hline \(R^{2}\) & 0.097 & 0.173 & 0.466 & 0.301 \\ \hline adj. \(R^{2}\) & 0.092 & 0.169 & 0.462 & 0.297 \\ \hline \end{tabular}
\end{table} 
Table 11: The mediating role of information transparency (* \(p\) < 0.1, *** \(p\) < 0.01).
## 3 Construction of Time Series Based on the UTC Scale
### The Method of Constructing the TS1_1, TS2_1, and TS3_1 Time Series Prepared with the 1-Day Interval  
Predicting UTC(k) by means of the NN and the time series TS1_5 and TS2_5 requires the necessary number of n elements of the time series, covering too long a period of time. This time period is determined by the five-day time interval between the \(xb(t)\) deviations determined by the BIPM for each UTC(k). Given the \(xb(t)\) values determined with a one-day interval, the prepared time series would further contain the necessary number of n elements, but would include a five times shorter time interval in relation to the TS1_5  
Figure 4: Sample set of the phase time \(x(t)\) and the trend \(x_{r}(t)\) for a one-month period for UTC(PL).  
and TS2_5. Therefore, it has been proposed to use the PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) interpolation function, available in the MATLAB tool for the \(xb(t)\) data set. The mathematical model determined on its basis makes it possible to extend the set of \(xb(t)\) deviations by calculating their values for each day of the analyzed period of time. Based on the simulation work presented in [21], the Hermite interpolation turned out to be several times better than the linear interpolation and the cubic spline functions.  
The properties of the interpolating function also show that the obtained values of \(xb(t)\) in the interpolation nodes falling on MJD days that end with digits 4 and 9 are exactly equal to the values of \(xb(t)\) determined by BIPM (Figure 5). This is important because the prediction values for these days are the basis for correcting UTC(k).  
The values of \(x(t)\) elements for the proposed new time series TS1_1 and TS2_1 are determined with a 1-day interval according to the described rule for the TS1_5 and TS2_5 (Section 3.1). The advantage of such preparation of the TS1_1 and TS2_1 time series is also that in the training process of the NN, all changes in the gait of the clock occurring between MJD days that end with digits 4 and 9 (Figure 5), which affect the prediction result, are taken into account.  
In certain situations, e.g., failure, it may be necessary to replace the atomic clock implementing the UTC(k) national scale with another clock. Then there may be a lack of time series elements to correctly perform the UTC(k) deviations prediction. This fact resulted in the creation of another time series (TS3_1), containing elements with the 1-day interval. The values of these elements are only the \(xb(t)\) deviation values, published by BIPM, interpolated by the PCHIP function.
# Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea
## 4 Discussions
If the source alone is considered--the GNSS array distribution; the source forward model, which includes geometry, the elastic model, slip distribution; inversion algorithm itself--there is only a short list of issues requiring testing and optimization. It is the same for tsunami simulation and decision-making; each of them also have dozens of options. Authors believe that decisions about better methods and better practices should be taken on an individual basis, in harmonization within particular TEWS and its individual components.
## 2 Risk Modeling Techniques
### Qualitative System Analysis  
Qualitative system analysis is an approach for examining and comprehending complex systems, focusing on their structure, behavior, and interactions without the use of numerical data or mathematical models.  
Fault Tree Analysis (FTA) and Markov chains are widely used qualitative system analysis methods in risk modeling, particularly for assessing the reliability and safety of power systems.  
FTA is a top-down approach for predicting the probability or frequency of system failures by mapping out various failure modes in a tree-like structure [15]. It helps uncover vulnerabilities by highlighting logical relationships between component failures and system-level risks. The FTA is a deductive method used to analyze potential causes of system failures by systematically breaking down the system into its components [16].  
The method of obtaining cut sets is a standard top-down approach for obtaining minimal cut sets in FTA. The process involves the following:  
1. Start with the top event and break it down using AND/OR gates until basic events are reached.
2. Construct a table where each row represents a cut set, and columns represent basic events. Begin by placing the top event in the first row.
3. Recursively expand each event in the table: * AND gate: Add inputs to new columns within the same row. * OR gate: Add inputs to new rows.
4. Apply Boolean algebra to eliminate redundant rows and events, isolating minimal cut sets.
5. Review and analyze the minimal cut sets to identify critical failure combinations.  
This method systematically identifies the smallest combinations of basic events that could lead to the top event, facilitating reliability improvements in complex systems.  
On the other hand, the Markov chain approach as a reliability quantification technique can be applied for analyzing how the state of a system may change over time. While time is continuous, the process must satisfy the Markov property. The set of all possible states is referred to as the state space, denoted by \(X\). Let the possible states in \(X\) be numbered as \(0,1,2,\ldots,r\), where the system has \(r+1\) different states. Where applicable,assume that state 0 represents the \"best\" state and state \(r\) represents the \"worst\" state of the system. Let \(X(t)\) represent the state of the system at time \(t\). The state \(X(t)\) is a random variable, and \(P(X(t)=i)=P_{i}(t)\) is the probability of the system being in state \(i\) at time \(t\), for \(i=0,1,2,\ldots,r\). The state \(X(t)\) changes over time, making this a stochastic process, specifically referred to as a continuous time Markov process. The combination of Markov chain and Monte Carlo simulation makes a strong method, referred to as MCMC, for modeling the uncertainties in power systems.
## 4 Comparison of Simulation and Experimental Results
#### Pressure Dynamics  
Figure 8 shows the pressure profile over one drive shaft revolution (following 388 operating cycles) of the eight-piston axial piston pump. The pressure profile exhibits a characteristic rise, a period of near-constant pressure, and a subsequent decline. This is followed by the suction phase, marking the start of the next pumping cycle.  
The diagram shows colored lines that represent the results of the experimental test (red), and the other colors represent the results of the simulation of the mathematical model for different combinations of the pump's operating and structural parameters. The diagram shows the different peaks corresponding to the maximum pressure during discharge and the valleys where the pressure drops during suction. Observing the sharpness or gradual nature of these peaks can provide insight into pump performance characteristics and fluid handling efficiency.  
Given the transducer's 100 kHz bandwidth, it is capable of capturing fast dynamic pressure. This allows for a more detailed understanding of pressure transients that may  
Figure 8: Diagram of the pressure after 388 operating cycles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ordinal Number** & **An Example** & \
\begin{tabular}{c} **Pressure in the Cylinder,** \\ **p\({}_{\text{c}}\), Bar** \\ \end{tabular} &
\begin{tabular}{c} **Number of Revolutions of the Pump Drive** \\ **Shaft, \(n\), min\({}^{-1}\)** \\ \end{tabular} \\ \hline
1. & E 03 & 180 & 1000 \\ \hline
2. & E 04 & 50 & 800 \\ \hline
3. & E 05 & 160 & 800 \\ \hline
4. & E 06 & 180 & 800 \\ \hline
5. & E 07 & 200 & 800 \\ \hline
6. & E 08 & 200 & 1000 \\ \hline
7. & E 09 & 200 & 875.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operating modes and parameters used in pump experiments.  
occur during rapid changes in pump operation. An accuracy class of 0.1 indicates high reliability in the pressure reading. It was important to ensure that the transducer was properly calibrated to maintain this accuracy, especially given the high-pressure range.  
This diagram provides valuable insight into the operating performance of a reciprocating pump, showing how pressure varies over the cycle, which can help diagnose performance problems and optimize design parameters. The pressure rise and fall lines in Figures 9 and 10 indicate pulsations, which can cause vibrations in the pump housing.  
Figure 11 indicates the occurrence of high-pressure pulsations as a result of the number of pump pistons (in this case, eight pistons).  
Figure 10: Diagram of the pressure fall after 388 operating cycles.  
Figure 9: Diagram of the pressure rise after 388 operating cycles.
## 2 Data and Methods
### Methodology
#### 2.3.4 GEP Method
In this research, to avoid overfitting, different strategies such as population diversity, selection of larger datasets, and formulation of precise fitness function were considered. It is worth mentioning that to avoid overfitting, the ensemble methods and regularization techniques such as penalizing complex programs or using techniques like Occam's razor [64] can also be implemented [65]. The ensemble methods can improve generalization performance and reduce the impact of overfitting [66].