esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Transaction Security Analysis  
#### 4.2.1 Security of Ownership Verification of Notes  
In the BTPP scheme, the transaction parties utilize the Diffie-Hellman key exchange protocol to compute the shared secret keys \(k_{1}\) and \(k_{2}\) based on their respective knowledge. Using this shared secret key, they calculate the stealth address \(T\) for the payment note. However, only the authorized recipient possesses the confirmation key \(t=c+b\), where \(b\) represents the spend private key known only to the recipient, and \(c\) represents the transaction shared secret key known only to the transaction parties. As a result, only the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Note Commitments** & **Nullifier Set** \\ \hline \((h_{1}=Hash(note_{1})\to T_{1})\) & \(nf_{1}=T_{1}\) \\ \((h_{2}=Hash(note_{2})\to T_{2})\) & \(nf_{2}=T_{2}\) \\ \((h_{3}=Hash(note_{3})\to T_{3})\) & \\ \((h_{4}=Hash(note_{4})\to T_{4})\) & \\ \((h_{5}=Hash(note_{5})\to T_{5})\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The world state of the note after the transaction.  
legitimate recipient can prove ownership of the note by computing \(T=t\)-G. Therefore, the BTPP scheme ensures that only the true recipient of the transaction can provide proof of ownership for the note.
## 2 Governing Equations
### Discussion  
There are some significant differences between the solution of Equations (25) and (31). As for the case of an incompressible fluid, the pressure is proportional to the boundary acceleration and it has a logarithmic relationship with the length of the flow field, which implies that the radius of the boundary has a significant effect on the pressure result. In addition, after substituting Equation (31) into Equation (1), it was found that the effect of pressure is equivalent to the effect of added mass. It is remarkable that if the radius of the boundary becomes extremely large (mostly it will be in the CFD solution), the pressure on the boundary surface becomes very large as well. This characteristic can result in unsolvable difficulties in fluid-structure interaction problems using CFD methods.  
As for the case of the compressible fluid case, according to the solution, \(\Delta p=\rho_{0}c\cdot u\), it can be concluded that the pressure is proportional to the velocity of the boundary, and the proportional ratio is the product of the sound speed and the density. In this case, the effect of the pressure is equivalent to the viscous damping, and the damping coefficient is a constant value.  
From the point of view of a physical sense, over time, \(\Delta t\), the amount of fluid affected by the structure is \(\pi[(r_{s}+u_{s}\Delta t)^{2}-r_{s}^{2})]\), whilst, the region it can reach is \(\pi[(r_{s}+c\Delta t)^{2}-r_{s}^{2}]\). Thus when \(\Delta t\to 0\), the air compression ratio is:  
\[\lim_{\Delta t\to 0}\frac{\pi[(r_{s}+u_{s}\Delta t)^{2}-r_{s}^{2})]}{\pi[(r_{s}+c \Delta t)^{2}-r_{s}^{2}]}=\frac{u_{s}}{c} \tag{32}\]  
Plus, the air compression modulus [40] is \(\rho_{0}c^{2}\), and the pressure of compression air can be calculated as follows according to [PERSON]'s law:  
\[p=c^{2}\cdot\frac{u_{s}}{c}=\rho_{o}c\cdot u_{s} \tag{33}\]  
which is identical to Equation (25).
# A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings
## 4 Results and Discussion
### Example of Timespan Prediction  
To apply the HOM to the case of the grinding of roller bearing rings, the _algorithms for causal identification and comparative assessment_ described in Section 3 were followed. The data used in the simulations were collected from the production process of some radial roller bearing rings. The set-up parameters registered are the outer diameter of the ring, \(D_{e}\); the inner diameter of the ring, \(D_{i}\); the width of the ring, \(l\); the weight of the ring, \(g\); the machined surface roughness, \(R_{a}\); the cutting speed, \(v_{a}\); the feed rate, \(f\); the grinding stone rotation speed, \(v_{r}\); and the cutting depth, \(t\). The above data were input data for the simulation tests. The set-up parameters used for the grinding machine tool set-up were recorded during the normal grinding process in a manufacturing workshop. Thus, the required instances database was obtained.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 2 The SR Process for CLLC Resonant Converter
### Above-Resonant State  
Figure 3b shows the critical waveforms for the over-resonant operation of the CLLC resonant converter. The optimal SR signal on and off times are when the resonant inductor current \(i_{Lrl}\) and the magnetizing inductor current \(i_{Lm}\) are twice equal. The key lies in calculating the delayed duty cycle \(D_{SR\_Delay}\) of the rectifier tubes. The half-cycle states of the CLLC resonant converter are as follows.  
At the \(t_{0}\) moment, the drive signal of the primary-side switch \(S_{2},S_{3}\) becomes zero and \(S_{2},S_{3}\) is immediately turned off. Between \(t_{0}\) and \(t_{1}\), the primary resonant current \(i_{Lrl}\) flows through the body diode \(S_{1},S_{4}\), the secondary-side current \(i_{nat}\) continues to be rectified, and the magnetizing inductor \(i_{Lm}\) is clamped.  
At the \(t_{1}\) moment, the primary-side switch over \(S_{1},S_{4}\) turns off for dead-time, and the primary-side resonant inductor current \(i_{Lrl}\) flows through the drain-source channel \(S_{1},S_{4}\) while the secondary-side current \(i_{rat}\) still flows through \(S_{7},S_{8}\) for rectification.  
At the \(t_{2}\) moment, the primary-side resonant inductor current \(i_{Lrl}\) is equal to the excitation inductor current \(i_{Lm}\). The rectifier tube \(S_{6},S_{7}\) is disconnected and, after \(t_{2}\) moments, the primary-side \(L_{r1},C_{r1}\) forms a resonant network, the secondary-side \(L_{r2},C_{r2}\) forms a resonant network, and the rectifier tube does not participate in the resonance.  
Table 1 shows the state variables and normalization of the CLLC converter.  
\begin{table}
\begin{tabular}{c c c} \hline
**Circuit Specification** & **Symbol** & **Normalized Variable** \\ \hline Resonant frequency & \(f_{r}=1/2\pi\sqrt{L_{r1}C_{r1}}\) & – \\ Resonant angular frequency & \(\omega_{r}=1/\sqrt{L_{r1}C_{r1}}\) & – \\ Inductor ratio & \(k=L_{m}/L_{r1}\) & – \\ Characteristic impedance & \(Z_{1}=\sqrt{L_{r1}/C_{r1}}\) & – \\ Second characteristic impedance & \(Z_{2}=\sqrt{(L_{m}+L_{r1})/C_{r1}}\) & – \\ Input voltage & \(U_{int}\) & \(U_{inN}=1\) \\ Output voltage & \(U_{o}\) & \(U_{oh}=U_{o}/U_{in}\) \\ Output current & \(I_{o}\) & \(I_{oN}=I_{o}Z_{1}/U_{in}\) \\ Resonant capacitor voltage & \(v_{cr1}\delta v_{cr2}\) & \(V_{Cr1N}=V_{Cr1}/U_{in}\) \\  & \(V_{Cr2N}=V_{Cr2}/U_{in}\) \\ Resonant inductor current & \(i_{L1}\delta i_{L2}\) & \(i_{Lr1N}=i_{r1}Z_{1}/U_{in}\) \\  & \(i_{LmN}=i_{L2}Z_{1}/U_{in}\) \\ Magnetizing inductor current & \(i_{Lm}\) & \(i_{LmN}=i_{mL}Z_{1}/U_{in}\) \\ Rectified current & \(i_{rat}\) & \(i_{ratN}=i_{rat}Z_{1}/U_{in}\) \\ Magnetizing inductor voltage & \(L_{m}\) & \(V_{LmN}=V_{Lm}/U_{in}\) \\ Switching frequency & \(f_{r}\) & \(f_{r}=f_{r}/f_{r}\) \\ \hline \end{tabular}
\end{table}
Table 1: Typical fault settings for distribution networks.  
Figure 3: Key waveform of the LLC converter (**a**) in the above-resonance region and (**b**) in the below-resonance region.
## 4 Hybrid Fiber Composition
### Hybrid Combinations of Other Fiber Types
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Hybrid Fibers**} & \multicolumn{8}{c}{**Properties (Effectiveness \%)**} & **Ref.** \\ \hline
**Part A** & \multicolumn{4}{c}{**Part B**} & \multicolumn{1}{c}{} \\ \cline{2-10}
**Type** & **1\({}_{\text{f}}\) (mm)** & **d\({}_{\text{f}}\) (mm)** & **V\({}_{\text{f}}\) (\%)** & **Type** & **1\({}_{\text{f}}\) (mm)** & **d\({}_{\text{f}}\) (mm)** & **V\({}_{\text{f}}\) (\%)** & \\ \hline crimpedsteel 25 & 0.7 & 1.5 & carbon & 7 & 0.010 & 1.0 & \begin{tabular}{c} compressive strength = 133.5 MPa \\ (\(-\)8.6\%)/flexural strength = 21.1 MPa (\(+\)22.7\%) \\ \end{tabular} & [84] \\ \hline straight steel & 15 & 0.6 & 1.0 & carbon & 20-30 & 0.018 & 1.0 & \begin{tabular}{c} compressive strength = 175.0 MPa \\ (\(+\)34.9\%)/flexural strength = 19.3 MPa \\ (\(+\)34.9\%)splitting tensile strength = 16.4 MPa \\ (\(+\)32.3\%)elastic modulus = 48.3 GPa (\(+\)5.7\%) \\ \end{tabular} & [85] \\ \hline carbon & 20-30 & 0.018 & 1.0 & glass & 6-12 & 0.012 & 1.0 & \
\begin{tabular}{c} compressive strength = 173.0 MPa \\ (\(+\)13.2\%)/flexural strength = 17.5 MPa \\ (\(+\)23.4\%)splitting tensile strength = 14.3 MPa \\ (\(+\)15.3\%)elastic modulus = 47.8 GPa (\(+\)4.6\%) \\ \end{tabular} & [85] \\ \hline basalt & 12 & 0.013 & polypropylene & 12 & 0.013 &
\begin{tabular}{c} compressive strength = 114.0 MPa \\ (\(-\)13.1\%)/flexural strength = 10.0 MPa \\ (\(+\)7.5\%)splitting tensile strength = 6.8 MPa \\ (\(+\)19.3\%)/complexwise strength = 115.7 MPa \\ (\(-\)11.8\%)/flexural strength = 9.4 MPa \\ (\(+\)1.1\%)splitting tensile strength = 8.1 MPa \\ (\(+\)42.1\%)/compressive strength = 116.9 MPa \\ (\(-\)10.9\%)/flexural strength = 9.3 MPa \\ (\(+\)0.5\%)splitting tensile strength = 8.2 MPa \\ (\(+\)43.9\%)/compressive strength = 107.5 MPa \\ (\(-\)18.1\%)/flexural strength = 11.5 MPa \\ (\(+\)23.7\%)splitting tensile strength = 7.8 MPa \\ (\(+\)36.8\%)/compressive strength = 11.6 MPa \\ (\(-\)14.9\%)/flexural strength = 9.7 MPa \\ (\(+\)43.3\%)splitting tensile strength = 8.3 MPa \\ (\(+\)45.6\%)/compressive strength = 117.0 MPa \\ (\(+\)0.8\%)/flexural strength = 9.4 MPa \\ (\(+\)1.1\%)splitting tensile strength = 8.7 MPa \\ (\(+\)52.6\%) \\ \end{tabular} & [155] \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Static properties of UHPC reinforced with different combinations of hybrid fibers. improvement was due to the shape of the steel fibers, which improved their anchorage in the matrix.
## 3 Different Aspects of Ransomware Studies
### Zero-Day Ransomware  
Zero-day ransomware are those variants which are not discovered previously. No information about them is available till the date they reported first. These ransomwares are detected by the detection system which are well designed to detect the unknown ransomware variants also. Different studies in the literature address the zero-day detection. Some of these are mentioned below.  
A system was proposed in [14] which claimed ransomware detection with no data lose. A windows desktop screen was monitored as a parameter to detect ransomware attack. This technique of finding a change in the desktop was the base of detection the previously unknown ransomwares.  
Another mobile ransomware prevention technique was proposed in [55] for the android platform. This method was able to detect modified patterns of ransomware. To perform the ransomware detection, this technique monitored the file events by observing the CPU and I/O usage.  
A ransomware detection system for the fog layer was presented in [20], that can detect the zero-day ransomware attacks. This study utilized the two deep learning technologies LSTM ad CNN to detect and distinguish the ransomware from good ware. However, this system starts working after the 10 s of launch of an application. This study considered few ransomware families to train the model.  
A decoy-based ransomware detection system was proposed in [56]. This system observed the deployed honey files to detect the crypto-ransomware. Whenever a ransomware reaches the honeypots, the detection is triggered. However, this detection system raised high false alarms.
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 1 Introduction
Quantitative Analysis of Decision Variables: To address the issues of lack of quantitative analysis mechanisms and poor scalability in existing decision variable grouping methods, this paper introduces a quantitative analysis method for decision variables. The proposed algorithm analyzes the convergence or divergence trends of decision variables and their impact on objectives, constructing a quantitative analysis mechanism based on the inherent characteristics of decision variables. 2. Dual-population Cooperative Evolution Algorithm (DVA-TPCEA): For the LaMoOP problem in multi-objective optimization, this paper combines decision variable analysis methods with a dual-population cooperative evolution mechanism. Specifically, based on decision variable analysis methods, a directional search mechanism for high-dimensional decision spaces is provided. A dual-population cooperative evolution algorithm is employed, where convergence and diversity populations are optimized independently and in parallel, aiming to achieve scalable optimization for both the scale of decision variables and the number of objective variables. 3. To validate the effectiveness of the proposed algorithm, DVA-TPCEA was first compared with several popular LaMOEAs, demonstrating its efficacy across various test problems. Additionally, to assess the algorithm's performance in practical applications, a large-scale container resource scheduling model based on the DVA-TPCEA algorithm was constructed, and the effectiveness of this strategy was verified through a simulation environment. The remainder of this paper is organized as follows. Section 2 presents the current research status and challenges related to this study. Section 3 provides a detailed exposition of the algorithm framework and specific design of the proposed algorithm, DVA-TPCEA. Section 4 compares the performance results of DVA-TPCEA with some existing algorithms and analyzes the reasons for these outcomes. Finally, Section 5 summarizes the work presented in this paper.
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## Appendix C  
Given a system function,  
\[y=f(x)=f(x_{1},\cdots x_{i},\cdots x_{n}) \tag{10}\]  
Supposing the joint PDF of the n-dimensional input random variables is \(p_{X}(x)\), then the expectation of the output \(y\) is:  
\[Q=E(y)=\int f(x)p_{X}(x)dx \tag{11}\]  
By the Monte Carlo simulation, one can estimate Equation (11) based on the samples. Supposing totally \(N\) samples are generated:  
\[\left\{x^{1},\cdots x^{i},\cdots x^{N}\right\} \tag{12}\]  
Then the expectation can be estimated by the following equation.  
\[\hat{Q}=E(y)=\frac{1}{N}\sum_{i=1}^{N}f\left(x^{i}\right) \tag{13}\]  
Based on the mathematical definition [29], \(\hat{Q}\) is an unbiased estimator of the original \(Q\). Given the confidence level \((1-\alpha)\), then based on the central limit theorem:  
\[\left|\hat{Q}-Q\right|\leq\frac{\sigma}{\sqrt{N}}z_{1-\alpha/2} \tag{14}\]  
where \(z_{1-\alpha/2}\) is the quantile of the standard normal distribution, if \((1-\alpha)=0.95\), then \(z_{1-\alpha/2}=1.96\). \(\sigma\) is the standard variance of the function \(f(x)\).  
Set the quantile as a constant c, then the sample size for the Monte Carlo simulation is:  
\[N\geq\left\{\frac{\sigma}{\left|\hat{Q}-Q\right|}\dot{z}_{1-\alpha/2}\right\} ^{2}=\left\{\frac{\sigma c}{\varepsilon}\right\}^{2} \tag{15}\]  
where,  
\[c=z_{1-\alpha/2},\quad\sigma=V[f(x)],\quad\varepsilon=\left|\hat{Q}-Q\right| \tag{16}\]Based on Equation (A12), the sample size needed in Monte Carlo simulation is determined by the confidence level, the estimation error, and the variance of the system function. The sample size is not determined by the variable dimension.
## 3 Results
### Pre-Operation Testing  
Unit testing and integrated testing were conducted two times each. Twenty-two items out of a total of 79 in the first unit test were evaluated as inadequate, and all these items were supplemented and corrected in the second unit test. In the first integrated test, 81 items from seven categories (registration, PHR function, ED record, management, etc.) were accessed, and 18 items were evaluated as inappropriate. All items passed in the second integrated test.
## 3 Types of Scanning Patterns
### Raster Scanning
#### 3.2.2 Mean Acquisition Time for Raster Scan  
The raster scan pattern begins at the top-left corner of the uncertainty area and alternates direction in each row, moving from left to right or right to left, with a vertical step down between rows. If the uncertainty region spans from \(-u\) to \(+u\) in both the vertical and horizontal directions in the rectangular coordinate system, and scanning is performed with a step size of \(n\), with the starting point of the raster scan at \((-u,u)\), the cumulative length \(L_{\text{r}}\) from the scan start point up to any point \((x,y)\) can be expressed as follows:  
\[L_{\text{r}}(x,y)=N_{\text{rows}}\times 2u+N_{\text{rows}}\times n+L_{\text{ row}}(x), \tag{25}\]  
where \(N_{\text{rows}}=\left\lfloor\frac{u-y}{n}\right\rfloor\) is the number of full rows traversed from the starting point to reach the row containing \(y\), and \(L_{\text{row}}(x)\) is the distance within the current row up to \(x\), determined by the direction of movement.  
For an even row (left to right), the horizontal distance is given by  
\[L_{\text{even\_row}}(x)=x+u. \tag{26}\]  
For an odd row (right to left), the horizontal distance is given by  
\[L_{\text{odd\_row}}(x)=2u-(x+u). \tag{27}\]  
\(N_{\text{rows}}\times 2u\) gives the total horizontal distance covered by the fully completed rows, while \(N_{\text{rows}}\times n\) accounts for the cumulative vertical steps taken after each completed row. \(L_{\text{row}}(x)\) calculates the horizontal distance from the start of the current row up to the specified \(x\) value, based on the row's direction.  
The scan time \(T_{\text{r}}\) function can be expressed as  
\[T_{\text{r}}(x,y)=\frac{L_{\text{r}}(x,y)}{n}\times\Delta t, \tag{28}\]  
where \(\Delta t\) is the dwell time at each scan spot, and \(n\) is the step size.  
To calculate the mean acquisition time to scan the uncertainty area, the total scan time needs to be weighted by the joint PDF of the receiver satellite's position in both the vertical and horizontal directions. The mean acquisition time is given by  
Figure 2: Raster scan path covering the uncertainty area. (**a**) Raster scan path with an overlap factor = 0. (**b**) Raster scan path with an overlap factor = 0.2.  
\[E[T_{r}]=\iint_{\theta_{\text{area}}}T_{r}(x,y)\,f(x,y)\,dx\,dy. \tag{29}\]  
This expression represents the expected time to scan the uncertainty area using a raster scan, taking into account the joint pdf of the receiver satellite position. The value can be computed numerically.
# MINC-NRL: An Information-Based Approach for Community Detection
## 5 Experiments
### Comparison Results with the Other Algorithms
#### 5.4.2 Overlapping Community Detection  
The following Tables 6-9 show the CN, \(Q_{ov}\), AC, and \(EQ\) values, respectively, of the results of MINC-NRL algorithm on the data sets, compared with overlapping community detection algorithms ASLPAw [42], DEMON [17], and Ego-splitting [43]. Note that smaller values are better when evaluated by AC.  
As illustrated in the above tables, the algorithm correctly figured out the number of communities on almost all networks. Although it does not get the best result on every data sets, but the average accuracy is the best compared with the other algorithms.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Karate** & **Dolphins** & **Football** & **Polbooks** & **LFR500** & **LFR2000** & **LFR10000\_b** \\ \hline MINC-NRL & **2** & **2** & **11** & **3** & **3** & **4** & **87** \\ ASLPAw & **2** & 3 & 6 & 5 & 1 & 2 & **87** \\ DEMON & **2** & 4 & 8 & 5 & 48 & 344 & 492 \\ Ego-splitting & 3 & 4 & 5 & **3** & 7 & 228 & 43,193 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CN of MINC-NRL compared with other algorithms.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & **Karate** & **Dolphins** & **Football** & **Polbooks** & **LFR500** & **LFR2000** & **LFR10000\_a** & **Avg.** \\ \hline MINC-NRL & **0.162** & **0.170** & **0.176** & **0.164** & **0.133** & **0.140** & **0.173** & **0.160** \\ ASLPAw & 0.158 & 0.165 & 0.173 & 0.162 & - & 0.135 & **0.173** & 0.138 \\ DEMON & 0.139 & 0.084 & 0.073 & 0.086 & 0.008 & 0.004 & 0.037 & 0.062 \\ Ego-splitting & 0.137 & 0.145 & 0.166 & 0.121 & 0.0641 & 0.041 & - & 0.112 \\ \hline \hline \end{tabular}
\end{table}
Table 8: \(EQ\) of MINC-NRL compared with other algorithms.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & **Karate** & **Dolphins** & **Football** & **Polbooks** & **LFR500** & **LFR2000** & **LFR10000\_b** & **Avg.** \\ \hline MINC-NRL & **0.100** & **1.000** & **0.924** & **0.589** & 0.978 & **1.000** & **0.850** & **0.906** \\ GN & 0.580 & 0.554 & 0.879 & 0.558 & - & - & - & 0.643 \\ FN & 0.914 & 0.606 & 0.762 & 0.534 & 0.969 & 0.635 & - & 0.706 \\ Louvain & 0.837 & 0.753 & 0.885 & 0.554 & 0.972 & 0.755 & 0.741 & 0.785 \\ EdMot & 0.587 & 0.511 & 0.851 & 0.504 & **0.980** & 0.823 & 0.799 & 0.722 \\ \hline \hline \end{tabular}
\end{table}
Table 5: NMI of MINC-NRL compared with other algorithms.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & **Karate** & **Dolphins** & **Football** & **Polbooks** & **LFR500** & **LFR2000** & **LFR10000\_a** & **Avg.** \\ \hline MINC-NRL & **0.100** & **1.000** & **0.924** & **0.589** & 0.978 & **1.000** & **0.850** & **0.906** \\ GN & 0.580 & 0.554 & 0.879 & 0.558 & - & - & - & 0.643 \\ FN & 0.914 & 0.606 & 0.762 & 0.534 & 0.969 & 0.635 & - & 0.706 \\ Louvain & 0.837 & 0.753 & 0.885 & 0.554 & 0.972 & 0.755 & 0.741 & 0.785 \\ EdMot & 0.587 & 0.511 & 0.851 & 0.504 & **0.980** & 0.823 & 0.799 & 0.722 \\ \hline \hline \end{tabular}
\end{table}
Table 5: NMI of MINC-NRL compared with other algorithms.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 11 Estimated Computational Complexity of MMT-WP  
The estimated computational complexity of the several components of MMT-WP is shown in Table 5. Table 2 shows the mode decision results of each sequence. Based on the mode decision, the estimated computational complexity of MMT with pre- and post-weighting and pitch filter is shown in Table 6. When the sequence is 100% voiced, the computational complexity for MMT-WP is the worst case. Based on the per component estimated computational complexities in Table 5, the worst computational complexity for MMT-WP including Mode decision, tree coder, the G.727 decoder, pre- and post-weighting, and pitch predictor is 4.83 WMOPS. Since \"we were away\" is a fully voiced sentence with no silence, the computational complexity for this sentence is the worst case. The probability of silence for sequences af1s01m af1s02, af1s03, am1s01, am1s02, and am1s03 is about 58%. Since the computational complexity for silence is 0.08916 WMOPS, the computational complexity of these sequences is less than half of the worst computational complexity for \"we were away\" in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **MMT-WP** & **Average Bitrate** \\ \hline lathe & 3.938 & 14.55 \\ we were away & 4.012 & 24.12 \\ af1s01 & 3.838 & 9.06 \\ af1s02 & 3.784 & 9.72 \\ af1s03 & 3.768 & 11.73 \\ am1s01 & 3.638 & 7.90 \\ am1s02 & 3.825 & 8.35 \\ am1s03 & 3.681 & 9.06 \\ \hline Average & 3.811 & 11.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PESQ of the MMT for narrowband sequences using 3 bits/sample for Voiced and Onset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **MMT-WP** & **Average Bitrate** \\ \hline lathe & 3.424 & 10.74 \\ we were away & 3.284 & 16.40 \\ af1s01 & 3.435 & 7.21 \\ af1s02 & 3.319 & 7.73 \\ af1s03 & 3.476 & 9.51 \\ am1s01 & 3.416 & 6.22 \\ am1s02 & 3.523 & 6.76 \\ am1s03 & 3.396 & 7.39 \\ \hline Average & 3.409 & 9.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PESQ of the MMT for narrowband sequences using 2 bits/sample for Voiced and Onset.