esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation and \(H_{2}\) Controller Design
### \(H_{2}\) Control Design for a Swarm of WMRs  
Base on the above tracking error dynamic Equation (5), the optimal \(H_{2}\) control law that analytically satisfies the \(H_{2}\) control performance index in Equation (7) can be expressed as:  
\[u_{i2}^{*}(e,t)=-R_{2}^{-1}\Delta_{i2}(e,t)I_{2}(e,t)e_{i2}(t) \tag{8}\]  
\[\Delta_{i2}(e,t)=\Gamma_{i2}^{-1}VM_{i2}{}^{-1}(r_{i}) \tag{9}\]  
\[V=\left[\begin{array}{c}I_{3\times 3}\\ 0_{3\times 3}\end{array}\right] \tag{10}\]  
\[\Gamma_{i2}=\left[\begin{array}{cc}\psi_{i2}I_{3\times 3}&\phi_{i2}\\ 0_{3\times 3}&I_{3\times 3}\end{array}\right] \tag{11}\]  
where the positive scale \(\psi_{i2}\) and distinct positive matrix \(\phi_{i2}\in\Re^{3\times 3}\) are constants.  
If \(I_{2}(e,t)\) in Equation (8) fulfills the as below nonlinear time-varying Riccati-like Equation (12), then this time-varying Riccati-like equation can solve the nonlinear \(H_{2}\) trajectory tracking problem of a swarm of WMRs in Equation (7).  
\[\dot{J}_{i2}(e,t)+J_{i2}(e,t)W_{i2}(e,t)+W_{i2}^{T}(e,t)J_{i2}(e,t)+Q_{i2}-J_ {i2}(e,t)\Delta_{i}(e,t)R_{i2}^{-1}\Delta_{i}^{T}(e,t)J_{i2}(e,t)=0 \tag{12}\]  
where  
\[\left[\begin{array}{c}\dot{J}_{12}(e,t)+J_{12}(e,t)W_{12}(e,t)+W_{12}^{T}(e,t)J_{12}(e,t)+Q_{12}-J_{12}(e,t)\Delta_{1}(e,t)R_{12}^{-1}\Delta_{1}^{T}(e,t) J_{12}(e,t)\\ \dot{J}_{22}(e,t)+J_{22}(e,t)W_{22}(e,t)+W_{22}^{T}(e,t)J_{22}(e,t)+Q_{2}-J_{22 }(e,t)\Delta_{2}(e,t)R_{2}^{-1}\Delta_{2}^{T}(e,t)J_{22}(e,t)\\ \vdots\\ \vdots\\ \dot{J}_{n2}(e,t)+J_{n2}(e,t)W_{n2}(e,t)+W_{n2}^{T}(e,t)J_{n2}(e,t)+Q_{n}-J_{n2 }(e,t)\Delta_{n}(e,t)R_{n}^{-1}\Delta_{n}^{T}(e,t)J_{n2}(e,t)\end{array}\right] =\left[\begin{array}{c}0\\ 0\\ 0\\ 0\\ 0\\ 0\end{array}\right]\]  
and  
\[W_{i2}(e,t)=\Gamma_{i2}^{-1}\left[\begin{array}{cc}-M_{i2}{}^{-1}(r)G_{i2}( r,\dot{r})&0_{3\times 3}\\ \frac{1}{\psi_{i2}}I_{3\times 3}&-\frac{1}{\psi_{i2}}\phi\end{array}\right] \Gamma_{i2}\]  
Remark: Above the trajectory tracking problem of a swarm of WMRs for the analytic solution, one nonlinear time-varying Riccati-like equation can solve the \(H_{2}\) control performance index Equation (7) proofed in Appendix A.  
From Equations (5) and (12), which are explicitly used to find out a closed-form solution, \(J_{i2}(e,t)\) of this nonlinear time-varying Riccati-like equations and nonlinear differential equations are certainly complicated tasks. In this research, the closed-form solution, \(J_{i2}(e,t)\), will be directly derived from Equation (12), and this is an important contribution to the trajectory tracking design of a swarm of WMRs.
## 4 Numerical Analysis and Discussion
### Parametric Study
#### 4.3.4 Effect of the Incident Angle  
To investigate the effect of the incident angles, seven different incident angles are considered in the study, with the incident angle being from 0-90\({}^{\circ}\), in increments of 15\({}^{\circ}\). As can be seen in Figure 13, the vibration reduction ratios of the maximum displacement with different incident angles are given. With the increase of the incident angle, the reduction ratio in the longitudinal direction increases gradually owing to the decreasing ground motion intensity, and the maximum reduction ratio is 1.5 times that of the minimum reduction ratio. On the contrary, the reduction ratio in the transverse direction decreases with the increase of the incident angle, and the maximum reduction ratio is 3.0 times that of the minimum reduction ratio. Based on the above analysis, the incident angle has a significant influence on the reduction ratio. Therefore, the incident angle cannot be ignored for the analysis of the transmission tower with the PTMD.  
Figure 12: Variation reduction ratios of the maximum displacement with different gap sizes.  
Figure 13: Vibration reduction ratios of the maximum displacement with different incident angles.
## 2 Digital Image Processing for Tracking Target Point Displacement  
This section describes the process of applying the DIP algorithm using a digital image set to track the target point displacement of the specimen. In this study, DIP was used to derive the TP displacement by recognizing the target attached to the specimen and tracking the change in its location from the image acquired at regular time intervals.  
Figure 1 shows the method of measuring the force, displacement, and strain as well as the image acquisition process for the actual experiments conducted in this study. During the three-point bending test, the strain value measured by a strain gauge was collected through a datalogger, and the force applied to the specimen and the deflection value at the center of the specimen were measured using a load tester and stored in the system. The image sets for DIP were acquired at regular intervals using a digital single lens reflex (DSLR) camera, from the time of loading to the time of reaching the target load, and were stored in a separate database.  
Figure 2 illustrates the process of displacement tracking of the target using the DIP proposed in this study, along with a separate pre-process conducted to improve the data processing speed and a DIP tool tracking the displacement of the target. In the pre-process, the region of interest (ROI) was set, in which the displacement of the target on each image included in the image set was tracked. It is possible to remove the pixels containing unnecessary information and reduce the time required to analyze the image by trimming the redundant areas based on the ROI and reducing the image size to be analyzed. In DIP, processes such as target classification, noise removal, and target labeling are performed to measure the displacement of the target using the image whose size was reduced.  
Figure 1: Configuration of the camera image-based measurement and digital image processing experiment.  
Figure 2: Digital image processing (DIP) algorithm for tracking point (TP) displacement tracking.
## 2 Proposed System
### Segmentation  
In the segmentation phase, audio signals are automatically split into syllables isolating each acoustic sound. This procedure is performed applying the method developed by [PERSON] in [30]. It carries out the Short Time Fourier Transform (STFT) to obtain the spectrogram representation of the signal denoted as \(M(f,t)\), where \(f\) is the frequency index and \(t\) represents the time index. Then, the algorithm proceeds as follows:  
1. Find the highest amplitude peak such as \(|M(f_{n},t_{n})|\geq|M(f,t)|\forall(f,t)\) and set the position of the \(n\)th syllable in \(t_{n}\). The amplitude of this point is calculated as Equation (1): \[Y_{n}(0)=20\text{log}_{10}(|M(f_{n},t_{n})|).\] (1)
2. Trace the adjacent peaks between \(t>t_{n}\) and \(t<t_{n}\) until \(Y_{n}(t-t_{n})<Y_{n}(0)-\beta dB\), where \(\beta\) is the stopping criterion. Thus, the starting and ending times of the \(n\)th syllable are defined as \(t_{n}-t_{s}\) and \(t_{n}+t_{e}\).
3. This trajectory is saved as the \(n\)th syllable and deleted from matrix (2): \[M(f_{n},t_{n}-t_{s},\cdots,t_{n}+t_{e})=0.\] (2)
4. Repeat the process from step 1 until the end of the spectrogram.  
In this approach, STFT is computed using a Hamming window of 512 samples with an overlap of 35%, which has been set experimentally. Furthermore, a stopping criterion of \(\beta=20\) dB has been selected. The algorithm is applied in all 102 classes used in this paper. Figure 3 shows the results of the process where the red dashed lines indicate the central points of the syllables detected by the algorithm in the signal.
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
## 2 Related Work
### Notations
The CLOPE objective function (\(Clopez\))  
CLOPE is an efficient clustering algorithm for large scaled datasets, and the basic idea of its criterion function is simple and straightforward [33; 39]. CLOPE first defined the size and the width of cluster \(C_{l}\):  
\[Size(C_{l})=\sum\limits_{j=1}^{m}\sum\limits_{l=1}^{d_{j}}Occ({{a_{j}}^{(i)}},C_{l}), \tag{10}\]  
\[Width(C_{l})=\sum\limits_{j=1}^{m}\left|D(A_{j}\left|{C_{l}}\right.\right) \right|, \tag{11}\]  
where \(Occ({{a_{j}}^{(i)}}\left|{{C_{l}}}\right. )\) is the number of occurrences of value \({{a_{j}}^{(i)}}\) in cluster \(C_{l}\):  
\[Occ({{a_{j}}^{(i)}},C_{l})=\left|{{C_{l}}}\right.\times p({{a_{j}}^{(i)}}\left| {{C_{l}}}\right.). \tag{12}\]  
Then, the objective is to maximize the following function:  
\[Clopez(P)=\sum\limits_{l=1}^{k}p(C_{l})\frac{Size(C_{l})}{Width(C_{l})}^{r}, \tag{13}\]  
where \(r\) is the parametric power. Moreover, as we can show that \(Size(C_{l})=m\left|{{CI}}\right.\), for the consistency of expression, we rewrite the function as:  
\[Clopez(P)=m\cdot n\cdot\sum\limits_{l=1}^{k}p(C_{l})^{2}[\sum\limits_{j=1}^{m} \left|D(A_{j}\left|{C_{l}}\right.)\right|]^{-r}. \tag{14}\]  
In this paper, we refer to the function using the parameter of \(r\) as \(Clopez_{r}(P)\). 5. The CDCS objective function (\(R\)). The CVIs above only used the intra-cluster information to measure the partition. CDCS use both intra-cluster similarity and inter-cluster similarity, which are [34]:  
\[intra(P)=\frac{1}{m}\sum_{l=1}^{k}p(C_{l})\sum_{j=1}^{m}\left[\underset{i=1}{ \overset{d_{j}}{\max}}\ p(a_{j}^{(i)}\left|C_{l}\right.)\right]^{3}, \tag{15}\]  
\[inter(P)=\frac{1}{n\times(k-1)}\sum_{l=1}^{k-1}\sum_{s=1}^{k}Sim(C_{t},C_{s})^{ \frac{1}{m}}\left|C_{t}\cup C_{s}\right.|, \tag{16}\]  
where \(Sim(C_{t},C_{s})\) is the similarity score between two clusters \(C_{t}\) and \(C_{s}\):  
\[Sim(C_{t},C_{s})=\prod_{j=1}^{m}\left\{\sum_{i=1}^{d_{j}}\min[p(a_{j}^{(i)} \left|c_{t}\right.),p(a_{j}^{(i)}\left|c_{s}\right.)]+\varepsilon\right.\right\}, \tag{17}\]  
and where \(\varepsilon\) is a small value preventing \(Sim(C_{t},C_{s})=0\). The objective of CDCS is to maximize the ratio of \(intra(P)\) to \(inter(P)\). Partitions that have both higher intra-cluster similarity and lower inter-cluster similarity will receive better scores:  
\[Ratio(P)=\frac{intra(P)}{inter(P)}. \tag{18}\]  
We refer to this function as \(R(P)\) in this paper.
## 2 Data Collection  
### Scintillation Detection  
In the present study, the S4 parameter was used for detecting ionospheric amplitude scintillation. The types of ionospheric irregularities are derived from the \(C/N_{0}\) parameter obtained from GNSS receiver observations [24]. The \(C/N_{0}\) parameter is obtained from S1 and S2 provided within the GNSS observation file. The signal-to-noise ratio (\(S/N_{0}\)) is estimated by \(C/N_{0}\) using Equation (1), and then using the detrending method mentioned in Equation (2). The \(S/N_{0}\) detrended values (\(SI_{detrended}^{S/N_{0}}\)) are calculated as follows [25; 26]:  
\[S/N_{0} = 10^{0.1(C/N_{0})} \tag{1}\] \[SI_{detrended}^{S/N_{0}} = \frac{\left(S/N_{0}\right)_{k}}{\sum\limits_{i=1}^{60\times f_{s} }\frac{\left(S/N_{0}\right)_{k-1}}{60\times f_{s}}} \tag{2}\]  
where \(f_{s}\) represents the sampling interval, and the \(SI\) represents the detrended signal intensity measured by \(S/N_{0}\) values. According to Equations (1) and (2), the parameter is obtained from the following equation:  
\[\text{S4} = \sqrt{\frac{<\left(SI_{detrended}^{S/N_{0}}\right)^{2}>\ -<SI_{ detrended}^{S/N_{0}}>^{2}}{<SI_{detrended}^{S/N_{0}}>^{2}}} \tag{3}\]The S4 is equal to the standard deviation of the SI in each epoch of interest, which is normalized with the average \(SI\) at the specified interval and thus has no unit. These two parameters demonstrate the intensity of the signal-to-noise ratio of the wave transmitted on both frequencies L1 and L2 from the satellite [27; 28]. The parameter S4 is used for periods ranging from a few seconds to a few hours, based on the type required by the model. Within our study, the average values over a period of five minutes were used to calculate the S4 observations [29].
## 3 Results and Discussion  
### Oscillation of Two T-Section Prisms in Tandem  
The oscillation characteristics curves were considered separately for each of the five spacing ratios between the two T-section prisms. The amplitude ratio \(A^{*}=A/D\) and the frequency ratio \(f^{*}=f_{osc}/f_{n,air}\) along with the hard galloping (HG) and soft galloping (SG) are presented and discussed in this section. The amplitude (\(A\)) of the T-section prism was calculated by averaging the absolute values of the highest values of positive and negative peaks in 60 s of continuous oscillation; \(D\) is the projection width of the prism in the direction of incoming flow.  
In this section, two typical working conditions were selected to illustrate the FIM responses of UTP (\(Ist\)) and DTP (\(2 nd\)), comparing the FIM responses of UTP and DTP with different spacing ratios to the single T-section prism (STP).
## 6 Numerical Results and Discussion
Figure 10: Phase-induced intensity noise against received power when the number of simultaneous users K = 250 and data rate \(R_{b}=1.25\) Gbps. Furthermore, the parameters of BER and SNR are considered as the most conclusive factors of merit for evaluating the performance of the optical system; however, another parameter is taken into consideration in order to reinforce the results obtained. The Error Vector Magnitude (EVM) is taken into account in recent years in order to measure the quality of the signals transmitted in optical communication systems; it compares the received data with the original data. Furthermore, the lower the EVM, the better the signal quality. In our work, the \(EVM\) is estimated as a function of \(SNR\), as indicated in the equation below [30,31]. \[EVM\left(\%\right)=\left(\sqrt{\frac{1}{SNR}}\right)\times 100 \tag{39}\]  
As shown in Figure 13, the variation of \(EVM\) as a function of the number of users was studied, where the results are obtained at a data rate of 1.25 Gbps and an effective source power of \(-10\) dBm. It is clear that our 3D-VWZCC code has a very low EVM value compared to the other codes. At acceptable EVM (EVM = 10%), our code supports a large number of users more than 300 users, whereas 3D-PD, 3D-PD/MD, 3D-DCS/MD, 2D-VWZCC, and 1D-VWZCC codes support 125, 142, 180, 185, and 74 users. As a result, we explain this superiority by the ZCC property of our code, which perfectly eliminates the multiple access interference (MAI) and reduces the effect of PIN noise. Figure 3:0 SNR versus number of simultaneous users when effective source power \(\mathrm{P_{sr}}=-10\) dBm and data rate \(\mathrm{R_{b}}=1.25\) Gbps. Figure 14 shows the variation of EVM as a function of the data rate where the number of users and the effective source power are fixed at 250 and \(-\)10 dBm, respectively. It is clear that the proposed code has a low percentage of EVM despite the high data rate compared to the 3D-PD, 3D-PD/MD, 3D-DCS/MD, 2D-VWZCC, and 1D-VWZCC codes. It accommodates a data rate above 2.5 Gbps with its allowable EVM value (10%), while 3D-PD, 3D-PD/MD, 3D-DCS/MD, 2D-VWZCC, and 1D-VWZCC codes are accommodating 0.52 Gbps, 0.59 Gbps, 0.75 Gbps, 0.71 Gbps, and 0.15 Gbps, respectively. We explain these results by the fact that our code shows a low error rate at the receiver level, which results in a low EVM value due to low PIN noise and short code length compared to other codes. Figure 3:1 Q factor versus number of simultaneous users when effective source power \(\mathrm{P_{sr}}=-10\) dBm and data rate \(\mathrm{R_{b}}=1.25\) Gbps. Figure 3:2 EVM versus number of simultaneous users when effective source power \(\mathrm{P_{sr}}=-10\) dBm and data rate \(\mathrm{R_{b}}=1.25\) Gbps.
## Materials and Methods
### Block-Wise Local SVD Clutter Filtering for H-Doppler  
To provide a better image of the blood flow in deep tissue, the block-wise local SVD (BWL-SVD) filtering method was introduced, which can improve the small vessel imaging quality and clutter rejection by decomposing the full dataset into multiple blocks and performing SVD filtering on each block, respectively, as shown in Figure 3a. For the BWL-SVD filtering approach, the size and overlap percentage of the blocks determine the quality of the image. According to Reference [15], the block-wise SVD clutter filtering does not offer benefits until a block size of 50 and provides similar results for block sizes from 80 to 150, until the background ramp-shaped noise and the near field tissue signal become prominent again at a block size of 200. Increasing the overlap percentage can improve the smoothness of the power Doppler image but reduce the computational efficiency. Overall, an 80% overlap was suggested to offer a good tradeoff between the computational cost and the image quality [15]. In this case, a block size of 128 (Height) \(\times\) 24 (Width) and overlap of 120 (Height) \(\times\) 20 (Width) are used in our experiment to obtain high-quality power Doppler images.  
For H-Doppler flow imaging, the R and B channel data in each block are separately applied with the subtraction operation following local SVD clutter filtering, as shown in Figure 3b. The final blood flow signal \(S^{f}(x,z)\) for each pixel (\(x\),\(z\)) is given by:  
\[S^{f}(x,z)=\frac{1}{N}\sum_{n=1}^{N}\left(\frac{S^{f}_{n}}{\sum_{\text{low- cutoff}}\lambda}\right) \tag{19}\]  
where \(S^{f}_{n}\) is the clutter-filtered signal from block \(n\), \(N\) is the total number of overlapped blocks containing the target pixel (\(x\),\(z\)) and \(\lambda\) are the remaining singular values corresponding to the blood signal. Notice that same experimental setup is used for both UCDI and the H-Doppler methods in this paper for comparison purposes.  
Figure 2: Spectral content estimation of all singular vectors. To determine the low-order singular value cutoff threshold, a tissue motion frequency threshold needs to be set manually (i.e., 19 Hz). Then, the singular vector Doppler frequency curve is utilized to search for the corresponding horizontal coordinate (singular vector orders) as the low-order singular cutoff value for differentiation of the tissue and blood signals (i.e., 26, in this case).
## 2 Materials and Methods
### Tests in CO  
To evaluate the capacity and efficiency of the NiSb\({}_{2}\)O\({}_{6}\) powders calcined at 900 \({}^{\circ}\)C for detecting air-CO concentrations, experiments were carried out in static and dynamic atmospheres at different temperatures (100-300 \({}^{\circ}\)C). In both cases, pellets were made with 0.4 g of powder using a hydraulic press (Simplex-Italy Equip-25 Ton) compressing at 10 tons for 20 min. The dimensions of the pellets were 12 mm in diameter and 0.5 mm thick. Then, two colloidal silver paint ohmic contacts (Alfa Aesar 99%) were placed on the pellets for having good contact with the system's electrodes (see Figure 2). Subsequently, the pellets were taken to the measurement chamber.  
Figure 1: Synthesis process of NiSb\({}_{2}\)O\({}_{6}\) powders in air.  
In static atmospheres, changes in NiSb\({}_{2}\)O\({}_{6}\)'s electrical resistance were recorded as a function of gas concentration (1, 50, 100, 200, and 300 ppm of CO) and operating temperature (100, 200, and 300 \({}^{\circ}\)C). The sensitivity magnitude in the static tests was estimated using the equation \(S=(G_{\rm G}-G_{\rm O})/G_{\rm O}\)[19], where \(G_{\rm G}\) and \(G_{\rm O}\) are the electrical conductances in CO and air, respectively. The electrical conductance (1/electrical resistance) was considered according to reference [20]. For the tests in dynamic atmospheres at a constant temperature of 200 \({}^{\circ}\)C, an adaptation was made to the measurement system by placing a metal box (volume = 19 cm\({}^{3}\)) inside of it. The box had two perforations: one for the CO inlet and the other one for the test gas outlet and for introducing the electrodes in contact with the pellets' surface (see in Figure 2). Subsequently, the gas was extracted using a pump (with a capacity of 10\({}^{-3}\) Torr) incorporated in the measurement chamber. The detection tests consisted of supplying two different flows (300 and 400 cm\({}^{3}\)/min) of extra dry air (21% O\({}_{2}\) + 79% N\({}_{2}\)) as stabilizing gas for the pellets' surface. The CO flows were 100, 150, and 200 cm\({}^{3}\)/min. The partial pressure of the CO flows in static and dynamic atmospheres was monitored with a Leybold TM20 electronic detector. Changes in the test gas electrical resistance were recorded with a Keithley 2001 multimeter coupled to a data acquisition and control system using the LabView 8.6 software (National Instruments). Brooks Instruments mass flow regulators with capacities of 2600 cm\({}^{3}\)/min (GF100 CXXC-SH452.6L) and 10 cm\({}^{3}\)/min (GF100 CXXC-SH40010C) were used for the CO flows in the dynamic tests.
# Latency Analysis of Blockchain-Based SSI Applications +
## 4 Evaluation Results
### Four-Node Measurements in a Private Cloud
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**TX**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Arrival Rate (TXs/s)**}} & \multicolumn{1}{c}{\multirow{2}{*}{**50**}} & \multicolumn{1}{c}{\multirow{2}{*}{**100**}} & \multicolumn{1}{c}{\multirow{2}{*}{**150**}} & \multicolumn{1}{c}{\multirow{2}{*}{**200**}} & \multicolumn{1}{c}{\multirow{2}{*}{**250**}} & \multicolumn{1}{c}{\multirow{2}{*}{**300**}} & \multicolumn{1}{c}{\multirow{2}{*}{**350**}} & \multicolumn{1}{c}{\multirow{2}{*}{**400**}} & \multicolumn{1}{c}{\multirow{2}{*}{**450**}} & \multicolumn{1}{c}{\multirow{2}{*}{**500**}} \\ \cline{2-2} \cline{6-11}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline
**Min (W)** & 0.1004 & 1.6858 & 1.4348 & 2.3036 & 4.7245 & 5.1294 & 4.5748 & 5.8767 & 6.3978 & 6.7831 & 7.1836 \\ \hline
**Max (W)** & 2.0020 & 2.2518 & 3.3587 & 4.8211 & 5.0167 & 5.4596 & 5.9855 & 6.3402 & 6.8323 & 8.5989 & 7.7109 \\ \hline
**Avg (W)** & 1.7919 & 1.8107 & 1.7563 & 3.9873 & 4.8259 & 5.2884 & 5.6173 & 6.1577 & 6.6395 & 7.1475 & 7.4985 \\ \hline
**TX** & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline
**Type** & **1** & _50_ & _100_ & _150_ & _200_ & _250_ & _300_ & _350_ & _400_ & _450_ & _500_ \\ \hline
**Avg (W)** & 0.0143 & 0.1694 & 0.2172 & 0.3529 & 0.6244 & 0.5323 & 0.7636 & 0.8521 & 0.9900 & 1.3708 & 1.3517 \\ \hline
**Max (R)** & 0.0263 & 0.2425 & 0.4751 & 0.7329 & 0.9451 & 1.1434 & 1.3563 & 1.5009 & 1.7062 & 2.0700 & 2.3891 \\ \hline
**Avg (R)** & 0.0214 & 0.1526 & 0.2926 & 0.4009 & 0.5749 & 0.7272 & 0.8616 & 0.9998 & 1.1941 & 1.3387 & 1.4752 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Latency measures, in seconds, for read (R) and write (W) TXs in a local Docker environment where network size = 8.