esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Ku-Band Monopulse Antenna with a Truncated Reflector and an Open-Ended Waveguide Feed
## 3 Reflector Design
The 3-dB beamwidth of the azimuthal sum channel and the azimuth difference channels at 14-17 GHz are obtained as follows: 5.2\({}^{\circ}\), 3.8\({}^{\circ}\) at 14 GHz; 5.3\({}^{\circ}\), 3.6\({}^{\circ}\) at 15 GHz; 5.0\({}^{\circ}\), 3.4\({}^{\circ}\) at 16 GHz; and 4.6\({}^{\circ}\), 3.3\({}^{\circ}\) at 17 GHz, respectively. The sidelobe levels of the \(E\)-plane sum channel are \(-\)13, \(-\)20, \(-\)14.6, and \(-\)12.6 dB at 14, 15, 16, and 17 GHz, respectively. The sidelobe levels of the \(H\)-plane sum channel are \(-\)20, \(-\)18.8, \(-\)19.6, and \(-\)19.9 dB at the same frequencies. The sidelobe levels of the elevation difference channels are \(-\)12.6, \(-\)12, \(-\)10.4, and \(-\)9.3 dB at the same frequencies. The sidelobe levels of the azimuth difference channels are \(-\)13.3, \(-\)14, \(-\)13.7, and \(-\)13.2 dB at the same frequencies. The sum- and difference-channel directivity patterns at wider angles are shown in Figure 12a,b for the \(E\) plane and in Figure 13a,b for the \(H\) plane. Figure 14 shows the sum-channel cross-polarization patterns in the diagonal plane (\(\varphi\) = 45\({}^{\circ}\)). \(E\)- and \(H\)-planes are shown in the range of 14-17 GHz. The maximum directivity of the cross-polarized radiation in the \(E\)- and \(H\)-planes is 0.189, \(-\)1.50, 0.90, and 1.16 dB at 14, 15, 16, and 17 GHz, respectively. Figure 15a,b show the diagonal-plane cross-polarization directivity patterns of the elevation-and azimuth-difference channels, respectively. The maximum cross-polarization directivity of the elevation-difference channel is 1.84, 3.50, 2.50, and 4.72 dB at 14, 15, 16, and 17 GHz, respectively. The maximum cross-polarization directivity of the azimuth-difference channel is 6.42, 6.86, 6.97, and 6.47 dB at 14, 15, 16, and 17 GHz, respectively. Figure 12: (a) \(E\)-plane directivity patterns of the reflector at 14-15 GHz (\(-\)180\({}^{\circ}\) to 180\({}^{\circ}\)). (b) \(E\)-plane directivity patterns of the reflector at 16–17 GHz (\(-\)180\({}^{\circ}\) to 180\({}^{\circ}\)). Figure 13: (a) \(H\)-plane directivity patterns of the reflector at 14–15 GHz (\(-\)180\({}^{\circ}\) to 180\({}^{\circ}\)). (b) \(H\)-plane directivity patterns of the reflector at 16–17 GHz (\(-\)180\({}^{\circ}\) to 180\({}^{\circ}\)). Table 3 shows the comparative analysis between this work and existing results. It can be seen from Table 3 that this work achieved an asymmetrical beam performance in the range of 14-17 GHz with sharp beams in the elevation plane and spread-out azimuth beams, which are useful for applications such as projectile tracking radars, airport surveillance radars, and vehicle tracking radars.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Cooperative Games
#### 3.1.2 Assignment Game Approach
The energy saving process was defined as the amount of energy saved through cooperation and battery life extension referred to the highest battery level of the UEs in a coalition. The utility was non-negative always, because nodes were supposed to act individually if there was no profit to be gained by forming or joining a coalition. Technical strengths:  
* The protocol is extremely energy-efficient and can perform well when several devices are involved. Technical weaknesses:
* The main weakness of the protocol is not considering the downstream links (RAN to UEs) Applications:
* The scheme is especially suitable for internet of things (IoT) devices deployed using WSN paradigm;
* The protocol can be applicable for a scenario where several devices are involved. Possible future improvements:  
* Consideration of downstream links;
* More routing metrics can be introduced in the performance analysis.
## 4 Discussion of Findings
Table 1 represents a comparison of the hydrate-forming pressures in static conditions stated by [PERSON] and [PERSON] (2008) [20] and dynamic flow conditions generated by the experimental results of this article at different temperatures. The last column of the table shows the ratio of the dynamic hydrate-forming pressure to the static hydrate-forming pressure. It is seen that the pressure ratio drops as the temperature increases. The reason why the pressure ratio drops as the temperature increases is not clear. The reason may be the fact that increased temperature promotes the internal energy of the hydrate-forming gas (CO\({}_{2}\) in this study) molecules in static conditions so that higher pressure is required for the water molecules to capture the gas molecules and trap them in water molecule cages. The mechanism needs more in-depth investigations.
## 6 Results and Discussion
### Comparative Analysis of FOPI-FOPI and PI-PI Controllers
#### 6.2.4 Case 3: Performance Evaluation under Balanced Three-Phase-to-Ground Fault  
This test introduced a three-phase-to-ground fault at the parallel linear load at bus B4, occurring between 0.2 s and 0.25 s. Figure 16 illustrates the performance of both the FOPI-FOPI and PI-PI controllers during this fault condition. In this case, both controllers exhibited competitive performance metrics. As shown in Table 6, the FOPI-FOPI controller achieved a settling time ranging from 0.05 to 0.073 s, demonstrating superior responsiveness compared to the PI-PI controller, which had a settling time of no less than 0.105 s. Despite this advantage, the maximum overshoot was slightly higher for the FOPI-FOPI controller at 18.19%, compared to the PI-PI controller's 18.1%. This indicates that while the FOPI-FOPI controller excels in achieving rapid settling times, it may require further refinement to optimize stability without increasing overshoot. Overall, the results underscore the FOPI-FOPI controller's potential for improved performance in dynamic applications.  
Moreover, Figure 17 demonstrates the significant improvement in voltage regulation for phase (a) when the DSTATCOM, controlled by the FOPI-FOPI controller, is employed. The enhanced stability and quicker response underscore the FOPI-FOPI controller's advantage in maintaining system reliability and performance under fault conditions.
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 4 Experiments  
### Datasets and Experimental Setup  
In this paper, each cloud dataset is divided into seven categories according to the criteria published in World Meteorological Organization (WMO). The first cloud dataset MOC_e is collected in Wuxi, Jiangsu Province, China, and provided by Meteorological Observation Centre, China Meteorological Administration. The cloud images have strong illuminations and no occlusions, and have the resolution of \(2828\times 4288\). There are two cloud datasets, i.e., the CAMS_e and IAP_e, captured in Yangjiang, Guangdong Province, China, but provided by Chinese Academy of Meteorological Sciences, and Institute of Atmospheric Physics, Chinese Academy of Sciences, respectively. Each cloud image in the CAMS_e is \(1392\times 1040\) pixels with weak illuminations and no occlusions. The acquisition device used to collect the IAP_e differs from that of the CAMS_e, and as a result, the cloud images from the IAP_e have higher resolution of \(2272\times 1704\), strong illuminations and occlusions. The total number of the MOC_e is \(2107\), and the CAMS_e's total number is \(2491\). The IAP_e has a large number of \(3533\). The number of each category is listed in Table 2. Samples for each category are shown in Figure 5. It is observed that each cloud dataset is captured from different views and belongs to different feature spaces.  
All images from the three datasets are resized to \(224\times 224\) pixels, and we employ the feature maps of the fourth convolutional layer. We select two parts of the images as the training images, i.e., all of the images from one view and half of images in each category from another view, and the remaining are taken as the test images. We implement experiments 10 times, and we take the average accuracy over these 10 times as the final results.
## 3 Experimental Results
### Results and Discussion
#### 3.2.1 Mapping the Vigor of Vegetation at a Given Instant of Time  
On the one hand, the tool displays, for a chosen date, the NDVI map the colors closer to brown indicate the absence of vegetation while the blue colors indicate the existence of vegetation with great vigor. Figure 9 shows the NDVI maps of the six plants analyzed during the month of May.  
As previously stated, the six photovoltaic plants have in common that they are surrounded by grass. The maximum vigor of naturally grown grass (not watered) usually occurs between the months of April and May in the northern hemisphere. As a generic criterion, when the NDVI exceeds 0.70 [19], it is considered that the vegetation has sufficiently high vigor. Thus, for this specific application, this will be indicative of the need for maintenance tasks to eliminate it. In all cases (Figure 9), there is a percentage of vegetation that should be removed. It should be noted that, in plant 6, unlike the rest of the solar plants, the area where the solar panels are installed has a bluish-green hue while in the rest of the solar plants it has a reddish hue. This is not due to the presence of vegetation between panels, but to their great separation and the presence of vegetation with a notable vigor in the surroundings.  
According to the alert system implemented, plants 1, 3, 4, and 6 are the ones with the largest area of vegetation to be removed: 54,418 m\({}^{2}\), 4486 m\({}^{2}\), 27,293 m\({}^{2}\), and 5100 m\({}^{2}\), respectively (Figure 10). For these cases, the NDVI value of the areas in need of maintenance have a mean value greater than 0.70; specifically, 0.76, 0.73, 0.79, and 0.81, respectively.
# An IP Multimedia Subsystem Services Proxy Gateway Based on a JAVA Dynamic Module System
## 2 Background
### OSGi (Open Service Gateway Initiative)  
With the capacity of \"integration of software and hardware\", the OSGi (Open Service Gateway Initiative) platform is characteristic of heterogeneous technologies integrated into the same platform and interacting with one another [6]. Currently, the OSGi platform is an open service platform for application programs and remote management of network appliances including mobile phones activated in indoor environments, vehicles or others. In the software framework available in OSGi, the various software services are transformed to different service modules or so-called Bundles that are carried in the OSGi framework through OSGi core services and installed, enabled, upgraded or unloaded remotely without rebooting according to the management architecture of Bundles and the flowchart for the life cycle of a Bundle [6].
## 2 Materials and Methods
### Ground Truth Trajectory
#### 2.5.1 Distance and Angular Errors during a Trajectory  
The determination of the distance and angular errors between the odometry trajectory points \(O(k=1\ldots n)\) and the ground truth trajectory points \(GT(p=1\ldots m)\) require the subsampling of the original odometry trajectory in order to have an odometry trajectory vector with the same length as the ground truth vector. The subsampled odometry trajectory vector \(OSS(p=1\ldots m)=[t(p),[x(p),y(p),\theta(p)]]\) is then obtained by searching for the nearest \(t(k)\) and \(t(p)\) values (see Figure 6b). The distance and angle error vectors are then computed as:  
\[distance_{error}(p)=\sqrt{\left(xgt(p)-x(p)\right)^{2}+\left(ygt(p)-y(p) \right)^{2}} \tag{8}\]  
\[angle_{error}(p)=\sqrt{\left(\theta gt(p)-\theta(p)\right)^{2}} \tag{9}\]
## 4 Design of TLQoS
### Routing Potentials
#### 4.3.1 Hop-Count Potential Field  
The hop-count potential field allows a packet to traverse to the \(BC\) following a shortest path. This field determines the node-depth from the \(BC\). Let \(p\) be the packet at a node \(n_{i}\), and \(HC(n_{i})\) denotes the shortest path length in terms of hop-count from \(n_{i}\) to the \(BC\). Hence, the hop-count potential is defined as  
\[P^{h}_{n_{i}}=HC(n_{i}) \tag{2}\]  
Thus, the hop-count force that acts upon a neighbor \(n_{j}\in NB(n_{i})\) from the node \(n_{i}\) is given by  
\[F_{h}(n_{i},n_{j})=\frac{P^{h}_{n_{i}}-P^{h}_{n_{j}}}{c_{n_{i},n_{j}}} \tag{3}\]  
Figure 3: Neighbor Table Structure.  
Here, \(c_{n_{i},n_{j}}\) denotes the link cost from node \(n_{i}\) to \(n_{j}\). Similar to [20], we consider the distance between two nodes as the link cost metric. The distance can be measured by several techniques such as measuring signal attenuation or received signal strength (RSSI) [21].  
\(P_{n_{i}}^{h}\) is time-invariant, and \(F_{h}(n_{i},n_{j})\) allows a packet to traverse toward the \(BC\) with the least number of hops. Since, \(NB(n_{i})\) includes the nodes which are one-hop way from \(n_{i}\), so the hop-count difference will be 0, \(-1\) or 1. Thus, the hop-count force \(F_{h}(n_{i},n_{j})\) will have either of the following values: 0, \(\frac{-1}{c_{n_{i},n_{j}}}\), \(\frac{1}{c_{n_{i},n_{j}}}\).
## 4 Optimized Analysis  
Based on the results in Section 3, we were able to find the optimum design for the material variations in the layer according to the desired magnitude and distribution of stress for different shaped holes. The variations in SCF shown in Figures 8-12 with exponent \(n\) are discussed further here. The classical definition of SCF for homogenous materials is the ratio of the maximum stress at the circumference of the hole to the applied far-field stress. Recently, considering the realistic physical meaning, a novel definition of SCF for FGMs was proposed by [PERSON] [21; 22] as the ratio of stress in a FGMs plate with and without holes. This novel definition for SCF describes the effect of the geometrical discontinuity (circular hole) in a FGMs plate that is physically meaningful. A detailed introduction and computation can be found in [PERSON] [21; 22]. In this paper, the classical definition for SCF, which is still widely adopted in FGMs [6; 7; 24; 42; 43] is used for further analysis and discussion, i.e., here the SCF is expressed as \(\tau_{63}^{\max}/\tau_{23}^{\infty}\). The variation in SCF with exponent \(n\) for different shaped holes is shown in Figure 13. It can be seen that the position of SCF alters from point A to point B for the five different shaped holes with \(n\) increasing. This is consistent with the conclusion mentioned above, i.e., the position of maximum stress will change from the hole to the interface with the variation of exponent \(n\). Additionally, it can be observed that as exponent \(n\) increases from 0 to 5, the SCF decreases firstly at point A, and then increases at point B. So apparently, the SCF has a minimum value when \(n\) is chosen as a special value. For different shaped holes, the value of exponent \(n\) is different.  
Table 1 summarizes the corresponding relationship between the minimum SCF and exponent \(n\) for five shaped holes. At the same time, the optimized SCF is also compared with the SCF for the case of no FG layer. It is clear that the SCF can be significantly reduced by the optimum design of the material variations in the layer. The most obvious example here is the triangular hole whose SCF decreases by more than 50%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Shape of Hole**} & \multicolumn{2}{c}{**Without FG Layer**} & \multicolumn{2}{c}{**With FG Layer**} & \multicolumn{2}{c}{**The Decreasing Percentage of SCF**} \\ \cline{2-6}  & **Exponent** & **SCF** & **Exponent** & **Minimum SCF** & \\ \hline Circle & \(n=0\) & 2.000 & \(n=0.932\) & 1.716 & 14.2\% \\ \hline Ellipse & \(n=0\) & 3.000 & \(n=1.695\) & 2.255 & 24.8\% \\ Triangle & \(n=0\) & 6.000 & \(n=4.237\) & 2.919 & 51.4\% \\ Square & \(n=0\) & 2.856 & \(n=3.220\) & 1.663 & 41.8\% \\ Rectangle & \(n=0\) & 4.575 & \(n=4.068\) & 2.299 & 49.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of the stress concentration factor (SCF) for two different cases (with and without functionally graded (FG) layer).  
Figure 13: The variation in SCF with exponent \(n\) for different shaped holes.
## 5 Commercially Available Prototypes  
There are several companies that have commercial products available for high voltage electrical current measuring based in optical systems. The company AIRAK developed an optical fiber based current sensor (Figure 18(a)) for monitoring medium voltage electrical distribution lines. The sensor is very lightweight (about 1.25 lbs (0.57 kg)) and small size (about 11\"\(\times\) 7\"\(\times\) 6.25\" (28 cm \(\times\) 18 cm \(\times\) 16 cm)). As for the technical details, the sensor meets the IEEE 36 kV insulation class, has a resolution better than 0.5% f.s., an operating frequency range of 5 Hz-5 kHz, temperature range of 40 \({}^{\circ}\)C to +80 \({}^{\circ}\)C, and has a full scale measurement range of 1 kA, 3 kA or 15 kA. The DISCOS outdoor current sensor from PowerSense, developed for AC measurement in lines of up to 36 kV, has a dynamic range from 5 A to 20 kA with a measuring accuracy of 2%.  
For higher voltage systems, companies like NxtPhase (now Alstom Grid) and ABB present similar solutions to measure electrical current on electrical potentials up to 800 kV.  
The sensor NXCT from NxtPhase (Figure 18(b)), has a dynamic range from 1 A to 4 kA with performances that exceeds the IEC Class 0.2S and IEEE Class 0.3 accuracy (0.15%) for metering applications. The sensor is also able to measure up to 63 kArms for short-time current (about 1 s) and accurate waveform reproduction up to 6 kHz. Different sensor weights, between 108 lbs (49 kg) for 72.5 kV and 210 lbs (95 kg) for 800 kV are supplied. The sensor can operate in a temperature range between 40 \({}^{\circ}\)C to +55 \({}^{\circ}\)C.  
As for the sensor MOCT from ABB (Figure 18(c)), it also has a dynamic range from 1 A to 4 kA with accuracy performances that exceed IEC Class 0.2S and ANSI Class 0.15 s for metering applications. The system provides accurate waveform reproduction up to 100 kA. Different sensor weights, between 110 lbs (50 kg) for 72.5 kV and 410 lbs (186 kg) for 800 kV are also supplied.  
An optical current sensor from SIEMENS is also presented in Figure 18(d), although technical data are not available.
# Analysis and Modeling of Mueller-Muller Clock and Data Recovery Circuits
## 5 Results  
The simulations in Figure 10 are obtained using a 56 Gb/s quarter-rate Mueller-Muller clock and data recovery circuit on 28 nm CMOS, with the structure shown in Figure 6.  
We use a 56 GHz clock with period jitter to generate a PRBS31 code as the input data. Varying the input data jitters, the number of phases of the PI and sub-resolution of the phase integrator measure the error jitters between the recovered clock and the input data. The results are shown in Figure 10, where black dashed, green solid, and the red dashed lines correspond to (24), (26) and (27), respectively. The blue line with circles represents the error jitter of a CDR operating on 56 Gbps of serial data. Figure 10a-c respectively show the relationship between the results and the following factors: input data jitter, the number of phases of the PI, and sub-resolution of the phase integrator. it can be seen from Figure 10a that the error jitter caused by the input jitter is the dominant factor at this point, and when \(\sigma_{\mathrm{DAT}}\) increases, the total error jitter increases as well.  
According to Figure 10b, the total error jitter follows the quantization noise of PI when \(N_{\mathrm{PI}}\) is low, and when \(N_{\mathrm{PI}}\) is high, \(\sigma_{\mathrm{ER,tot}}\) varies in accordance with \(\sigma_{\mathrm{ER,in}}\). In fact, the MM-CDR bandwidth is large at low \(N_{\mathrm{PI}}\), and (21) is high-pass, thus the input jitter has little effect on the total error jitter, and the quantization noise of PI accounts for the dominant factor. This has been validated by the simulation results in Figure 10b.  
In Figure 10c, we can find that although the contribution of the input jitter to the total error jitter increases as \(N_{\mathrm{SR}}\) increases, it is still too small compared to the quantization noise of PI, and the fluctuation of \(\sigma_{\mathrm{ER,in}}\) does not cause the growth of the total error jitter. When \(\sigma_{\mathrm{ER,in}}\) is the dominant factor, the total error jitter approaches the error jitter caused by the input jitter, so the estimate using (23) is reasonable.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 3 Experimental Results and Discussion
### Subjective and Objective Evaluation of the Synthesized View
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Sequence**} & \multicolumn{8}{c}{**PSNR**} & \multicolumn{8}{c}{**PSIMc**} & \multicolumn{8}{c}{**PSIM**} \\ \cline{2-13}  & [16] & [18] & [19] & [26] & [27] & **Proposed** & [16] & [18] & [19] & [26] & [27] & **Proposed** \\ \hline BA3 & 31.27 & 31.52 & 31.61 & 31.98 & 32.21 & 32.96 & 0.8577 & 0.8584 & 0.8588 & 0.8607 & 0.8611 & 0.8615 \\ BA5 & 30.77 & 30.80 & 30.86 & 31.32 & 31.26 & 31.41 & 0.8402 & 0.8413 & 0.8439 & 0.8436 & 0.8443 \\ BR3 & 31.05 & 31.12 & 31.15 & 31.26 & 31.24 & 31.52 & 0.7968 & 0.7977 & 0.7979 & 0.7982 & 0.7981 & 0.7992 \\ BR4 & 31.75 & 31.81 & 31.80 & 32.52 & 32.30 & 32.74 & 0.8162 & 0.8179 & 0.8183 & 0.8209 & 0.8204 & 0.8219 \\ DA5 & 37.61 & 37.68 & 37.72 & 37.99 & 38.12 & 38.33 & 0.9403 & 0.9424 & 0.9437 & 0.9455 & 0.9495 & 0.9465 \\ SH5 & 39.12 & 39.43 & 39.38 & 39.54 & 39.69 & 39.81 & 0.9386 & 0.9412 & 0.9404 & 0.9431 & 0.9438 & 0.9451 \\ KE3 & 33.19 & 33.64 & 33.72 & 33.88 & 34.06 & 34.12 & 0.8743 & 0.8762 & 0.8766 & 0.8778 & 0.8787 & 0.8799 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) comparison results for MVD sequences. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Sequence**} & \multicolumn{8}{c}{**PSNR**} & \multicolumn{8}{c}{**PSIMc**} & \multicolumn{8}{c}{**PSIM**} \\ \cline{2-13}  & [16] & [18] & [19] & [26] & [27] & **Proposed** & [16] & [18] & [19] & [26] & [27] & **Proposed** \\ \hline BA3 & 0.9539 & 0.9543 & 0.9558 & 0.9560 & 0.9566 & 0.9573 & 0.9916 & 0.9918 & 0.9919 & 0.9922 & 0.9923 & 0.9926 \\ BA5 & 0.9491 & 0.9493 & 0.9505 & 0.9523 & 0.9510 & 0.9536 & 0.9982 & 0.9894 & 0.9895 & 0.9898 & 0.9989 & 0.99 \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{6}{c}{**FSIMc**} & \multicolumn{6}{c}{**VSI**} \\ \cline{2-13}  & [16] & [18] & [19] & [26] & [27] & **Proposed** & [16] & [18] & [19] & [26] & [27] & **Proposed** \\ \hline Baby1 & 0.9601 & 0.9613 & 0.9616 & 0.9627 & 0.9635 & 0.9642 & 0.9910 & 0.9915 & 0.9914 & 0.9921 & 0.9927 & 0.9929 \\ Books & 0.9563 & 0.9584 & 0.9579 & 0.9580 & 0.9592 & 0.9612 & 0.9872 & 0.9875 & 0.9874 & 0.9881 & 0.9884 & 0.9889 \\ Art & 0.9619 & 0.9642 & 0.9645 & 0.9658 & 0.9661 & 0.9665 & 0.9871 & 0.9881 & 0.9892 & 0.9891 & 0.9898 & 0.9905 \\ Aloe & 0.9538 & 0.9558 & 0.9574 & 0.9641 & 0.9637 & 0.9659 & 0.9875 & 0.9880 & 0.9879 & 0.9897 & 0.9893 & 0.9902 \\ Reindeer & 0.9707 & 0.9717 & 0.9701 & 0.9724 & 0.9719 & 0.9725 & 0.9938 & 0.9941 & 0.9936 & 0.9943 & 0.9940 & 0.9943 \\ Lampshade1 & 0.9676 & 0.9688 & 0.9706 & 0.9763 & 0.9758 & 0.9772 & 0.9940 & 0.9969 & 0.9986 & 0.9975 & 0.9987 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: FSIMc and VSI comparison results for still-image data sets.