esa-sceva/satcom-chunk-collection huggingface dataset:
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 3 Results  
### Experimental Setup  
#### 3.1.1 Quality Evaluation  
Visual and numerical evaluations were performed according to the Wald protocol [36]. The original MS image was used as the reference image. Correlation coefficient (CC), universal image quality index (UIQI) [37], _erreur relative global adimensionnelle de synthese_ (ERGAS) [38], and spectral angle mapper (SAM) [39] were used for numerical evaluation. These are major evaluation criteria and used in almost all PS-related research [2]. The CC is given by  
\[\text{CC}=\frac{1}{|B|}\times\underset{\begin{subarray}{c}\text{$b \in B$}\end{subarray}}{\text{$\sum$}}CC_{b},\] (19) \[\text{CC}_{b}=\frac{\underset{\begin{subarray}{c}\text{$\sum$} \end{subarray}_{i=1}^{N}\big{(}O_{b}(i)-\overline{O_{b}}\big{)}\times\big{(}  
where \(h\) and \(l\) denote the spatial resolution of the PAN and MS images, respectively. The smaller the ERGAS value, the better the image quality. The SAM is an index for measuring spectral distortion and is given by  
\[\begin{split}\text{SAM}=\frac{1}{N}\sum\limits_{i=1}^{N}SAM(i),\\ \text{SAM}(i)=\text{cos}^{-1}\Bigg{(}\frac{\sum_{k\in B}O_{k}(i) \times\text{PS}_{k}(i)}{\sqrt{\sum_{k\in B}(O_{k}(i))^{2}}\times\sqrt{\sum_{k \in B}(\text{PS}_{k}(i))^{2}}}\Bigg{)}.\end{split} \tag{22}\]  
If the value is closer to 0.0, the spectrum ratio of each band is closer to the reference image.
# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
## 5 Evaluation of MEC Availability
### Case Studies
#### 5.3.1 Availability Analysis  
This section presents the availability analysis. Five scenarios were defined, varying the number of available containers (10, 20, 30, 40, and 50 containers). These scenarios were generated in order to compare the availability of the two models as well as the impact that the number of containers generates in each architecture.  
Figure 16 shows availability and downtime calculated by stationary analysis with the Mercury [45] tool. The availability variation shown in Figure 16 shows that availability drops as new containers are instantiated. In addition, the availability of the extended model tends to fall more slowly than in the base model. We believe that this fact occurs due to the redundancy implemented in the extended model. Even if we add containers, there will be a redundant component to supply the eventual failures in the _slave server 01_.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **MTTF (hours)** & **MTTR (hours)** \\ \hline Hardware & 8760 & 8 \\ Operating System & 2800 & 1 \\ Docker Daemon & 2516 & 0.255 \\ Container & 1258 & 0.238 \\ Load Balancer & 700 & 1 \\ Hardware (Idle) & 17520 & 8 \\ Operating System (Idle) & 5600 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Extended model input values.  
Figure 16: _Cont._Figure 16b shows downtime in hours per year. The extended model downtime tends to be much shorter than the base model. The base model ranges from 60 to 130 h/y. The extended model ranges from 40 to 60 h/y. The extended model's downtime remains below the base model even with five times more containers. The individual values of the base model grow noticeably more, and from the third scenario, they become twice as long as the extended model. After the fourth scenario, the time passes 100 h/y, while the extended model remained below sixty in all scenarios.  
These results are expected since the more components we have in a system, the higher the chances of one of them failing, and the more they fail, the more time it takes to repair. Our metric considers the system working when all containers are up; therefore, higher failure rates will negatively impact the availability. This behavior is observed even in the extended model. However, in the extended model, it happens in a much more subtle way, because there is a redundancy mechanism in the server.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 3 ID-Based Side-Channel Authentication System
### ID-Based Side-Channel Authentication Scheme  
As shown in Figure 3, the pairs of ID and secret key of \(q\) provers, (ID\({}_{0}\), \(sk_{0}\)), (ID\({}_{1}\), \(sk_{1}\)), \(\ldots\), (ID\({}_{q}\), \(sk_{q}\)) are registered in the verifier. The verifier initiates the authentication by sending an ID query to the prover, and then the verifier receives the prover's ID as ID\({}_{i}\). The verifier searches for the corresponding secret key and creates the corresponding leakage profile for the \(n\)-round AES. Then, Pearson's correlation coefficient \(\rho\) is calculated between the measured estimated side-channel information. Finally, the verifier confirms whether the correlation coefficient is larger than the pre-determined threshold \(h\) to decide the authentication result. If the derived \(\rho\) is larger than the pre-determined threshold \(h\), the authentication is successful.  
Figure 4 shows the frameworks of the conventional and the ID-based side-channel authentication systems. The major difference between the two authentication schemes is the number of correlation coefficient calculations. In previous work [1], the leakage profile and the correlation were calculated for all registered secret keys. Meanwhile, the calculation of the model and correlation coefficient is performed only once in the proposed authentication scheme as the secret key is identified using the ID sent from prover. Therefore, it is possible to authenticate much faster compared to the conventional scheme.  
Figure 3: Proposed Authentication Method Using ID Query.
## 4 Model Simulation Results
### Case Study 1: Superioities Validation  
In this subsection, two groups of simulations are carried out in MATLAB/Simulink with the simulation time \(10\) s and the sampling interval \(0.002\) s. The prescribed reference (unit: rad) is given by: \(\alpha_{d}(t)=0.3\) when \(t\leq 2\), \(\alpha_{d}(t)=-0.2\) when \(2\leq t\leq 5\), \(\alpha_{d}(t)=0.2\) when \(5\leq t\leq 8\), and \(\alpha_{d}(t)=-0.1\) when \(8\leq t\leq 10\). Then, derivatives of \(\alpha_{d}(t)\) are given by: \(\dot{\alpha}_{d}(t)=\ddot{\alpha_{d}}(t)=\alpha_{d}^{[3]}(t)=\alpha_{d}  
Controller parameters of the three approaches are given based on the performance index that the autopilot system has almost the same response time. In the first group of simulation, the system is affected by constant disturbance \(d(t)=5\). Then, in the second group, time-varying disturbance \(d(t)=5\sin(\pi t)\) is added to the system. The two types of disturbances are only used to show the superiorities of the modified approach, which may have differences with the real scenarios.
# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 4 Results and Discussion
### Relationship of \(L_{s}\) and \(L_{d}\) to Time and Quality  
As per the relationships between \(L_{S}\) and \(L_{D}\) and work time and quality shown in Figure 1, it is assumed that the higher the \(L_{S}\), the shorter the working time and the higher the quality, and the higher the \(L_{D}\), the longer the working time and the lower the quality. The validity of these assumptions is evaluated in this section.  
Figure 6 shows the \(L_{S}\) and \(L_{D}\) test results in relation to working time and quality. In the case of \(L_{S}\) (Figure 6a), as indicated in Figure 1, the higher the operator's skill level, the shorter the working time (\(L_{S}\)-T) and the higher the quality (\(L_{S}\)-Q). However, in the case of \(L_{D}\) (Figure 6b), the higher the difficulty, the lower the quality (\(L_{D}\)-Q), but the working time (\(L_{D}\)-T) does not increase. This suggests that the difficulty of a task should be understood in terms of the technical aspect of performing the task without defects rather than the working time required to complete the task.  
Figure 7 shows the correlation between \(L_{S}\) and \(L_{D}\) in terms of working time (Figure 7a) and quality (Figure 7b). As shown in Figure 7a, as \(L_{S}\) increases, the working time decreases, but as \(L_{D}\) increases there is only a slight change in working time. Figure 7b shows the relationship between \(L_{S}\) and \(L_{D}\) and quality: as \(L_{S}\) increases, the quality increases, but as \(L_{D}\) increases, the quality decreases.  
Figure 5: \(L_{S}\) measurement results of individual operators.
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
## 4 Conclusions and Future Work  
### Conclusions  
Through the actual survey data and the simulation analysis, the following conclusions were reached in this study: (1) at the whole intersection level, the proposed algorithm has less delay than the COP algorithm, and the average vehicle delay is reduced by 17.95%; (2) At the intersection phase level, compared with the COP algorithm, the proposed algorithm can reduce the vehicle delay in each phase; (3) Smaller rolling time steps can bring better performance.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 4 Computational Results  
In this section, we present the computational results obtained to validate the proposed model and algorithms. The real-world test scenarios and parameters in the experimental analysis are set in Section 4.1. Then, we report the performance of the CGBH algorithm in Section 4.2, followed by the scheduling scheme obtained in Section 4.3. In Section 4.4, we provide management insights regarding the impact of MB battery capacity and dispatch cost. All computational tests are performed on a computer with an Intel Core i7-8700 CPU processor, 8 GB RAM, and 3.2 GHz. The algorithm is coded in Python and relies on GUROBI 9.5.1 to solve the mathematical problem formulation.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 3 Modelling of the MFPG System  
The modeling of the MFPG system is a multi-physics problem since it couples a linear motor with an internal combustion engine [54]. Therefore, a dynamic model of the MFPG prototype is proposed here, consisting of a heat release model, gas exchange model and electromagnetic force model, etc. Special attention is drawn to the modeling of the mass leakage through the cylinder-piston gap and the heat transfer through the cylinder wall.
###### Abstract  
Recently, a hypothesis providing a detailed equation for the Plantower CF_1 algorithm for PM\({}_{2.5}\) has been published. The hypothesis was originally validated using eight independent Plantower sensors in four PurpleAir PA-II monitors providing PM\({}_{2.5}\) estimates from a single site in 2020. If true, the hypothesis makes important predictions regarding PM\({}_{2.5}\) measurements using CF_1. Therefore, we test the hypothesis using 18 Plantower sensors from four datasets from two sites in later years (2021-2023). The four general models from these datasets agreed to within 10% with the original model. A competing algorithm known as \"pm2.5 alt\" has been published and is freely available on the PurpleAir API site. The accuracy, precision, and limit of detection for the two algorithms are compared. The CF_1 algorithm overestimates PM\({}_{2.5}\) by about 60-70% compared to two calibrated PurpleAir monitors using the pm2.5 alt algorithm. A requirement that the two sensors in a single monitor agree to within 20% was met by 85-99% of the data using the pm2.5 alt algorithm, but by only 22-74% of the data using the CF_1 algorithm. The limit of detection (LOD) of the CF_1 algorithm was about 10 times the LOD of the pm2.5 alt algorithm, resulting in 71% of the CF_1 data falling below the LOD, compared to 1 % for the pm2.5 alt algorithm.  
algorithm; low-cost monitors; calibration; precision; accuracy; PM\({}_{2.5}\); Plantower; PurpleAir; pm2.5 alt; CF\({}_{-1}\)
## 3 Results
### Effect of CCA Extract on Metabolic Variables in T2 DM Rat Model
Additionally, the HOMA-\(\beta\) of the DMC group significantly reduced when compared with the NDC group (\(p<0.001\)). It implied that these T2 DM rats not only exhibited insulin resistance but also represented impaired \(\beta\)-cell function. However, the HOMA-\(\beta\) of the DML and DMH groups had a trend to improve when compared to the DMC group, and the significant improvement of the HOMA-\(\beta\) was found only in the DMM group. All of these findings confirmed that the antihyperglycemic effect of CCA extract supplementation at the doses of 200 and 400 mg/kg BW was due to the amelioration of insulin resistance without affecting the pancreatic \(\beta\)-cell function in the T2 DM rats. The current study also examined the hypolipidemic effect of the CCA extract on T2 DM rats, which are shown in Table 3. In normal rats, supplementation with the CCA extract did not change plasma triglycerides and total cholesterol levels. In DMC rats, plasma triglyceride and total cholesterol levels were significantly higher than those of NDC rats (\(p<0.001\)). It is remarkable that CCA extracts administered to diabetic rats significantly reduced plasma triglyceride levels (\(p<0.001\)). Plasma triglyceride levels also reduced significantly in DMM rats. In DML, DMH, and DMM rats, total cholesterol levels in plasma were reduced markedly (\(p<0.001\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Groups**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{2-6}  & **BW (g)** & **VF (g)** & **VF/BW** & **Food Intake (g)** & **Calorie Intake (Kcal)** \\ \hline NDC & \(538.13\pm 13.95\) & \(40.49\pm 4.39\) & \(7.08\pm 0.57\) & \(24.06\pm 0.68\) & \(91.58\pm 4.88\) \\ NDH & \(547.50\pm 16.48\) & \(38.31\pm 2.90\) & \(6.41\pm 0.31\) & \(25.07\pm 0.38\) & \(93.77\pm 1.42\) \\ DMC & \(491.25\pm 15.52\) *** & \(38.85\pm 5.48\) & \(6.94\pm 0.69\) & \(18.41\pm 0.47\) & \(86.18\pm 2.21\) \\ DML & \(487.50\pm 16.93\) *** & \(34.76\pm 4.57\) & \(7.17\pm 0.43\) & \(19.88\pm 1.40\) & \(93.02\pm 6.57\) \\ DMH & \(484.38\pm 16.97\) *** & \(27.80\pm 3.33\) *** ** & \(5.20\pm 0.50\)*** ** & \(21.21\pm 1.04\) & \(99.25\pm 4.86\)*** \\ DMM & \(499.29\pm 20.94\)*** & \(38.19\pm 8.53\) & \(7.84\pm 0.94\) & \(21.30\pm 2.32\) & \(99.68\pm 10.87\)*** \\ \hline \end{tabular}  
* Results are presented as mean \(\pm\) SEM (n = 7–8 for each group);
* \(p<0.05\), *
* \(p<0.01\), **
* \(p<0.001\) vs. NDC; *
* \(p<0.01\), **
* \(p<0.001\) vs. DMC.  
\end{table} 
Table 2: Effect of CCA extract on body weight, visceral fat, visceral fat/body weight (VF/BW) ratio and food intake in rats at end of experiment.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
###### Abstract
Thus, improving the ant colony algorithm with the security factor of the VAE model helps the satellite network establish secure routing paths. The remainder of this paper is organized as follows. In the Section 2, the relevant research work on satellite network secure routing is introduced. In the Section 3, the structure of the secure intelligent model is analyzed and applied to the ant colony algorithm satellite network routing. In the Section 4, we carry out a simulation. The Section 5 is the summary and prospect of the article.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 3 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Protocol** & **Category** & **Advantages** & **Limitations** \\ \hline  & & \(-\)Exhances the network’s life cycle by utilizing the TDMA schedule. & \(-\)Assuming that CH nodes are not uniformly deployed. \\ LEACH & & \(-\)Balancing energy usage. & Random election of CH. \\  & & \(-\)The number of packets for communication is decreased. & \(-\)The number of nodes is not distributed evenly in each cluster \\  & & \(-\)Reduces nodes’ energy discharge. & \(-\)Uses the single hop. \\ \hline LEACH-B & & \(-\)Consistent Count of CHs. & \(-\)Consistent \\ LEACH-B & & \(-\)Considers between energy after the initial turn & \(-\)Growth in overhead cost. \\  & & \(-\)Improves the longevity of the network. & \\ \hline  & & \(-\)optimum no. of clusters. & \(-\)Needs location information of nodes. \\ LEACH-C & & \(-\)High no. of turns in a small network zone & \(-\)Centralization brings in an overhead on the Base station. \\ LEACH-C & & \(-\)The residual energy is the key for CH selection. & \(-\)The single hop transmission adds an additional distribution of CHs. \\ LEACH-M & Mobility & \(-\)CHs as well as non-CH node as high mobility. & \(-\)Supplementary overhead. \\ LEACH-I & Residual energy & \(-\)One CH grounded on the left-over energy and node’s location. & \(-\)CH combines received data to save costs of further data transfer, but nodes that receiving different data it is not practical. \\ \hline LEACH-YH & & \(-\)CHs selection on the fundamental of the leftover energy. & \(-\)Supplementary treatment for VH node. \\  & & \(-\)Substitutes CH by VH node when the main CH attains a threshold. & \(-\)Drives data from CHs to Bs in a single hop. \\ \hline LEACH-T & Energy efficiency & \(-\)Determines the number of time slots in TDMA based on the number cluster members. & \(-\)CHs are selected randomly. \\  & & \(-\)Bids an optimum count of clusters. & \(-\)Enhanced the network’s life cycle and the CHs count is fixed & \(-\)Grounded on a random timer CHs selection is carried out. \\ \hline Improved-LEACH & & \(-\)Enhancing network performance through optimal CT selection. & \(-\)Complexity of calculation \\ \hline EADCR & Distance & \(-\)Improve the network lifespan & \(-\)CH selection is a complex process \\ \hline BN LEACH & & \(-\)Execute better than other & \(-\)High complexity \\ \hline \end{tabular}
\end{table} 
Table 2: Advantages and weaknesses of various LEACH protocols.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## 1 Introduction
However, it also offers a totally new possibility: since image reconstruction is performed after receiving the raw channel data, the data sampling grid is not fixed and focusing can be performed at each specific location. Redefining the data sampling grid directly influences the sampling of the pattern matching function. Hence, it allows to choose the available samples and their spacing throughout the peak of the pattern matching function, which we will refer to as the appearance or shape of the function throughout this paper. The possibility to influence its appearance allows to optimize the match between the peak of the function and the interpolation method. This will in theory enable more accurate subsample displacement estimation [34]. More work has been published recently on the combination of ultrafast imaging and ST, for the assessment of blood velocity [28; 29; 38] and vessel wall strain [39; 40; 41; 42]. Here, subsample resolution displacement estimates were obtained by performing 1D [28; 29; 38; 43] or 2D [42] parabolic interpolation of the pattern matching function. No clear reasoning was provided concerning the match between this interpolation function and the sampling, or shape, of the pattern matching function, which is a direct result of the spacing in the ultrasound sampling grid. In this work, we propose a new concept that combines the choice of the beamforming grid with the choice of the subsample estimator. By doing so, the available data and interpolator can be matched. For this, the sampling grid for ultrafast data was based on the dimensions of the point-spread-function (PSF) of the imaging system. When ST is performed, the pattern matching function is hereby captured in a standardized way, with equal amount of information, i.e., sample points, in the axial and lateral direction throughout the peak. The use of a 2D cubic subsample interpolator, which matches with the shape of the peak, allows for a joint estimate of subsample displacement in both directions. We hypothesize that this will increase the accuracy of subsample displacement estimation and thereby will enlarge the range of detectable displacements and velocities. The concept of this technique and first simulation results on parabolic blood flow were presented in a conference proceeding [44]. The present paper fully describes the reasoning and theory behind the method and presents in vitro rotating disk experiments and carotid artery vessel wall simulations to further study the performance of the method for both vector velocity imaging and strain estimation in the carotid artery. Both wall and flow dynamics were studied, showing a wide range of inter-frame displacements and displacement gradients, to investigate the applicability of the proposed method in a broad field.
## 3 Proposed Joint Decoding (JD) Framework for Video
The TV minimization makes use of piece-wise smooth features of the signals to provide a better solution within the possible space rather than finding the sparse solution within the transformation domain \(\Psi\). The basic TV minimization function is given in Equation (4):  
\[\mathrm{TV(F)}=\sum\
olimits_{i,j}\left|\mathrm{F_{i+1,j}-F_{i,j}}\right|+ \left|\mathrm{F_{i,j+1}-F_{i,j}}\right| \tag{3}\]  
\[\mathrm{F}=\mathrm{argmin_{F}}\left|\mathrm{y-\Theta F}\right|_{\ell_{2}}+ \lambda\ \mathrm{TV(F)} \tag{4}\]  
However, the CS reconstruction using TV minimization as in Equation (4) is exposed to extra computational problems that limit its use for CS, i.e., it is harder to computationally access and explain certain properties of TV minimization (non-differentiable, non-linear) than \(\ell_{1}\) minimization. To counter such issues, a scheme [24] referred as TV-AL3 is presented that uses the conventional augmented Lagrangian (AL) with variable splitting and alternating direction method. The approach decreases the computational burden and provides the same output as standard TV. After obtaining the complete GoP, the proposed joint decoding (JD) method is implemented to utilize the inter and intra-view correlations among the frames. As shown in Figure 1, the first frames of the current and next GoP's aid as the keyframes \(\mathrm{F_{K}}\) (reference frames) for the JD to produce side information of frames (prediction, residual) that will help in enhancing the quality of (J-1) non-key frames \(\mathrm{F_{NK}}\) of the current GoP.