esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
## 1 Introduction
To handle those issues, namely salt-and-pepper noise and data missing, in this paper, a joint estimation approach is developed to simultaneously recover the image and missing pattern under the salt-and-pepper noise condition. The primary objective of this work focuses on utilizing a sparse property of signals. To explore the sparse property of images, two representative domains, namely wavelet and framelet domains, are introduced in which the natural images have a sparse representation. Thanks to the impulsive nature of the noise, its sparsity is also observed in the time domain since it contains few nonzero values and lots of zero values. Therefore, the noise suppression becomes a parameter estimation problem, where the noise is actively reduced rather than passively being suppressed. To reveal the sparse property of the missing pattern, a vector that consists of only zeros and ones is introduced to remodel the missing measurements problem in which the element is one if the measurement is received correctly and zero if the corresponding measurement is missed. With the reformulations introduced, a joint optimization framework is readily developed to perform image and missing pattern estimation while suppressing the salt-and-pepper noise. A two-step iterative approach is proposed in order to efficiently solve this optimization problem, where each step contains a convex optimization problem. At every step, the resultant problem is an \(\ell_{1}\)-norm constrained optimization, and two algorithms, called ADMM and APG, are tailored to solve the optimization problems. To demonstrate the performance of the proposed joint estimation approach, experiments in three situations such as salt-and-pepper noise only, data missing only, and the combination of salt-and-pepper noise and data missing are respectively conducted. The main difference compared to the work presented in [1] is that the data missing issue is addressed instead of the deblur problem. The rest of the paper is organized as follows: the formulations of the salt-and-pepper noise and data missing are presented in Section 2. In Section 3, two sparse transform domains, namely wavelet and framelet are briefly introduced. A joint estimation is provided that utilizes the sparse properties of salt-and-pepper noise, data missing and image to perform the noise suppression, missing pattern estimation, and image recovery in Section 4. To efficiently obtain the solution, two solvers based on ADMM and APG are developed. In Section 5, numerical studies for the proposed approach are presented. Finally, this paper concludes with a brief summary in Section 6.
## 4 Results
### Deep Displacement Data
#### 4.3.2 Slow-Motion Stage  
Over the observation period, three SM stages were discerned from the displacement time-series (Figure 12). These periods were characterized by very similar durations, on average from 4 to 5 months, and a similar hydrological regime, suggesting a seasonal coincidence. Indeed, the cumulative rainfall levels were approximately between 410 and 465 mm, with a mean daily rainfall between 3.3 and 3.7 mm (Figure 15 and Table 3).  
As denoted by the linear trends fitted to daily cumulative displacement data, these kinematics stages can be assumed to be characterized by uniform motion over time (Figure 15). The modelled average displacement rates were rather comparable among the different periods and across the various zones of the active landslide; also, all values can be considered statistically significant at the 95% confidence limit (Table 3). The computed displacement rates ranged between 2.2 \(\pm\) 0.1 and 5.2 \(\pm\) 0.1 mm/year (Table 3) and can be assumed as \"extremely slow\" according to [PERSON]'s [63] classification.  
Figure 15: Average cumulative daily displacements recorded by the automatic inclinometer FON2 (**a**), FON3 (**b**), and FON4 (**c**) probes plotted together with daily rainfall during each slow-motion (SM) stage identified. In all panels, the fitted linear trends, the estimated mean rates of displacement in mm/yr, the standard errors of the estimate at 95% confidence, and the main data gaps are indicated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Probe Id**} & **Motion Stage** & **Duration (Days)** & **Data Coverage (\%)** & **Average Displacement Rate (mm/Year)** & **Standard Error at 95\% (mm/Year)** & **Cumulative Rainfall (mm)** \\ \hline \multirow{3}{*}{FON2} & SM1 & 139 & 100 & 2.3 & \(\pm 0.1\) & 466 & 3.4 \\  & SM2 & 135 & 100 & 5.2 & \(\pm 0.1\) & 449 & 3.3 \\  & SM3 & 110 & 66 & 4.1 & \(\pm 0.1\) & 412 & 3.7 \\ \hline \multirow{3}{*}{FON3} & SM1 & 139 & 96 & 3.0 & \(\pm 0.1\) & 466 & 3.4 \\  & SM2 & 135 & 100 & 3.1 & \(\pm 0.1\) & 449 & 3.3 \\  & SM3 & 110 & 56 & 2.7 & \(\pm 0.2\) & 412 & 3.7 \\ \hline \multirow{3}{*}{FON4} & SM1 & 139 & 98 & 3.5 & \(\pm 0.1\) & 466 & 3.4 \\  & SM2 & 135 & 84 & 4.4 & \(\pm 0.2\) & 449 & 3.3 \\ \cline{1-1}  & SM3 & 110 & 88 & 4.0 & \(\pm 0.1\) & 412 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the most important duration, mobility, and hydrological data regarding the slow-motion (SM) stage.
## 3 Simulation Results of the Proposed Algorithm
By using Equation (12), Figure 7 illustrates how the signal undergoes modulation by both fast and slow signals, which is, of course, caused by the Doppler effect. According to the second term in Equation (12), the noise is modulated by a fast signal due to the estimated Doppler value. This value is very large, as our algorithm was able to accurately approximate the actual Doppler value just from the first iteration. The enlarged portion of the image over a 0.01 ms time interval shows the modulated noise. The median filter used in the proposed algorithm can effectively remove this kind of noise, as illustrated  
In the second iteration, \(B(t)\) becomes the signal that is shown in Figure 8b. The new accumulated value of \(\tilde{f}_{d}\) is 15.014 KHz, which is very close to the actual Doppler value of 15 KHz. In the third iteration, our local minmax detector did not find any local minima or maxima; therefore, it terminated the iterations and fed the estimated Doppler value to the block performing the waveform recovery and computing the target velocity. The novelty of the proposed algorithm is that it converges in just a few iterations. In our case, it only took two iterations to achieve an accurate estimate of the Doppler value. Figure 9, the output of a Matlab workspace, concludes the above description. The estimated Doppler value of the iteration No. 1 = 14,822 Hz, with a difference 177 Hz from actual value  
System does not need another iteration  
>>  
The MF output from the recovered waveform, compared to that of the conventional bi-phased pulse compression (PC) radar system, is shown in Figure 10. The output demon  
Figure 8: (**a**) Signal modulated with the Doppler residue. (**b**) Output of the proposed algorithm. Figure 9: Matlab workspace for iterations of our proposed algorithm. strates the superiority of our algorithm in estimating the exact Doppler value and perfectly recovering the signal compared to the conventional system. We evaluated the robustness of our new module by testing it at very low Doppler values, such as 150 Hz, and comparing it to the conventional PC radar system. The Matlab workspace results, shown in Figure 11, indicate that our module estimated the Doppler value to be 154 Hz, with a difference of just 4 Hz from the actual Doppler frequency. Although the conventional system was able to detect the target with a constant false alarm rate (CFAR), the signal was degraded both in the mainlobe and the near sidelobes, as shown in Figure 12b, compared to the MF output of our module depicted in Figure 12a. Figure 11: Matlab workspace for iterations of our proposed algorithm to detect a 150 Hz Doppler value. Figure 3:0 The MF output (in black) and the associated CFAR (in red) of a 150 Hz Doppler value (**a**) with the proposed algorithm and (**b**) without the proposed algorithm. Figure 3:1 The MF output of a 15 KHz Doppler value (**a**) with the proposed algorithm and (**b**) without the proposed algorithm.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 2 Optical CDMA with MUI Cancellation
### Conventional Shalaby's Scheme  
In Shalaby's scheme, one codeword in each group of MPSCs is reserved from being assigned to any user [20]. Each of these \(q\) codewords is referred to as a reference word to estimate the number of interfering users. The maximum number of users \(N_{\max}\) to which the codewords can be assigned is \(q^{2}-q\). An example of the codeword assignment for [PERSON]'s scheme is also shown in Table 2. In Table 2, \(\mathbf{y}_{i}\) is the reference word in the \(i\)th group for \(i=0,\ 1,\ \cdots,\ q-1\). Because this cancellation scheme is based on on-off keying (OOK) signaling, the \(k\)th user's encoder outputs the codeword \(\mathbf{w}_{k,1}\) when \(I=1\), and it outputs the all-zero sequence when \(I=0\ (k=0,\ 1,\ \cdots,\ q^{2}-q-1)\).  
The block diagrams illustrated in Figure 2a,b are also applied to the encoder and decoder of [PERSON]'s scheme, respectively. For [PERSON]'s scheme, \(\mathbf{w}_{k,0}\) in the encoder is an all-zero sequence with a length of \(q^{2}\). At the \(k\)th user's decoder, Correlator 1 computes \(\Gamma_{1}\), which is the correlation between the received sequence \(\mathbf{r}\) and the assigned codeword \(\mathbf{w}_{k,1}\). Correlator 0 computes \(\Gamma_{0}\), which is the correlation between \(\mathbf{r}\) and \(\mathbf{y}_{i}\) where \(\mathbf{y}_{i}\) is the reference word in the same group with \(\mathbf{w}_{k,1}\). \(\Gamma_{1}\) and \(\Gamma_{0}\) are represented as follows:  
\[\Gamma_{1}=\Gamma(\mathbf{r},\mathbf{w}_{k,1}), \tag{6}\]  
\[\Gamma_{0}=\Gamma(\mathbf{r},\mathbf{y}_{i}), \tag{7}\]  
where \(i=\lfloor k/(q-1)\rfloor\). Then, the decoder calculates the difference \(\Gamma_{1}-\Gamma_{0}\). If the difference is greater than or equal to the threshold, the decision circuit outputs \(I=1\). Otherwise, it outputs \(\hat{I}=0\). The ideal threshold is \(q/2\) when we suppose that the light intensity of each user's positive chip is 1. [PERSON]'s scheme cancels MUI completely at the decoder, if the receiver can estimate the received light intensity of a single positive chip correctly. Because \(\Delta(1)=q\) and \(\Delta(0)=0\) for an ideal link, the decision distance of [PERSON]'s scheme is \(q\).
# Performance Analysis of a PEMFC-Based Grid-Connected Distributed Generation System
## 2 DG Case Studies at World Universities  
One of the best examples of a DG system on a university campus is at the University of California, San Diego (UCSD). The system has an installed capacity of 42 MW and meets 92% of the annual electricity demands of UCSD. An upgraded 22 kW prototype of a concentrated PV system is installed on the UCSD campus, including energy purchase agreements. This green university campus can be assumed to be an illustration of a \"lab to market\" model. UCSD has been further aiming to increase the utilization of PV production by integrating an energy storage system [10; 11]. Another example of a DG system is located at the Illinois Institute of Technology (IIT). This system includes 4 MW gas turbine, wind turbines, and PV modules [12]. A DG case study at Princeton University consists of a 15 MW gas turbine and a 4.5 MW PV plant [13].  
The DG system installed at Westlakes Campus of Central Lancashire University in England consists of a 5 kW wind turbine, a 20 kW PV system, a 21.6 kW heat pump, and a 6 kW solar thermal power system. For increasing renewable-based generation, enhancingefficiency, and reducing the energy costs of the Lindow Building of the University of Central Lancashire at its Westklakes Campus, a microgrid system is operated [14]. At the campus of Genoa University in Italy, PV modules, a geothermal heat pump, thermal solar collectors, and a wind turbine on the rooftop of the faculty building are used for power generation. The total energy consumption and production of the centers are monitored in real time to be able to analyze the beneficial effects [15].  
In addition to these universities, Hangzhou Dianzi University in China, the University of Nottingham in the UK, Chiang Mai Rajabhat University in Thailand, Technical University of Denmark, Berkeley Lab and New York University in the US have small and medium-scale capacity DG systems [16; 17; 18; 19; 20].
## 3 Results and Discussion
### Heat Transfer Coefficient Analysis
The HTC improvement achieved in this study in comparison to the plain surface was up to 118.7% for the Staggered surface, 119.3% for the Small Lattice, and 206% for the Large Lattice, compared to the Sintered Plain surface. Comparative Analysis of HTC with Literature  
In comparing the HTC vs. heat flux graph (Figure 12) with other literature data, at the lower heat flux range (generally below \(\sim\)750 kW/m\({}^{2}\)), all the porous surface structures, including Large Lattice, Small Lattice, and Staggered, demonstrate comparable or superior HTC values compared to much of the included literature data in this range. Starting generally at similar HTC values to the literature data at low heat fluxes, they then rise more steeply and stand out significantly, indicating better heat transfer performance. At the medium heat flux range (\(\sim\)1000 kW/m\({}^{2}\)), the Large Lattice, Small Lattice, and Staggered surfaces are comparable to literature values. While some literature data ([PERSON] et al. [29] and [PERSON] et al. [52]) indicate higher HTC values, the Large Lattice and Small Lattice decrease gradually with an increase in heat flux, while maintaining relatively high HTC values. The Large Lattice slope decreases more while the Small Lattice flattens out. The Staggered surface also exhibits strong performance, maintaining a relatively high HTC and achieving a higher heat flux than the data points of [PERSON] et al. [53], [PERSON] et al. [54], and [PERSON] et al. [55]. Its HTC shows a stable, slightly increasing trend at its highest heat fluxes compared to the initial rise. Generally, the Large Lattice and Small Lattice surfaces demonstrate a superior ability to maintain high HTC at extremely high heat fluxes where many conventional surfaces or even some enhanced surfaces might have already experienced a boiling crisis. The Staggered surface also shows competitive performance, particularly in extending the range of high HTC compared to the Sintered Plain surface. This highlights the effectiveness of 3D-printed porous structured surfaces in enhancing boiling heat transfer performance.
## 3 Results and Discussions
### Volatile Compounds in Melon Wines  
The volatile compounds identified in these melon wines (Table 5) were classified as esters (34), alcohols (17), unsaturated aliphatics (3), aldehydes (5), organic acids (3), and miscellaneous (5). As expected, during the fermentation process, in both melon wines, the concentration of total volatile compounds increased. These increases went from 2145 ug/L, measured in the must, to 12,788 ug/L for the wine from the Jimbee melon, and from 1769 obtained in the must to 20,504 ug/L in the wine from Okashi. As expected, the alcoholic fermentation produced CO\({}_{2}\), ethanol, acetic acid, and other volatile compounds described below. The Jimbee must presented mainly ester compounds such as butyl acetate, ethyl acetate, isobutyl acetate, methyl acetate, and fatty alcohols as 1-octanol, 1-hexanol, and isomers of nonen-1-ol isomer. These same volatiles have been reported in orange honeydews and cantaloupe fresh melon [34; 35]. At the end of the alcoholic fermentation, the increase in volatile content changed the aroma profile. The relative proportions of alcohol increased from 18% to 56%, mainly caused by the presence of ethanol, 3-methyl-1-butanol, 2,4-bis(1,1-dimethylethyl)-phenol, and phenylethyl alcohol produced by _S. cerevisiae_[5; 36]. The content in the total ester volatile group also increased during fermentation, from 1021 to 4325 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{L}\), decreasing the initial ester compounds to medium-chain fatty acid (MCFA) ethyl esters such as ethyl octanoate, ethyl decanoate, ethyl laurate, and ethyl hexanoate. However, other esters such as ethyl acetate and 2-methylbutyl acetate showed increases produced by _S. cerevisiae_[5] and which are commonly present in white and red wines [18; 37].
## 3 Simulation  
### Physical Model and Governing Equations  
As shown in Figure 2b, a physical model used for numerical simulation is a rectangular cross section flow path in which the sealing sheets are embedded. The flow path length \(L\), the width \(W\), and the heights \(h_{2}\) and \(h_{1}\) are 1300 mm, 400 mm, 150 mm, and 120 mm, respectively. The height of the flow path \(H\) can be adjusted to change the gap size.  
The following simplifications and assumptions are made in the numerical simulation calculation.  
* Both the spacing of the adjacent sealing sheets and the gap size between sector plate and sealing sheet top end keep constants at a position of short length along the radial direction.
* The fluid is an incompressible Newtonian fluid.
* The fluid flow is in a turbulent state.  
Continuity, energy, momentum, and gas state equations to describe the leakage process are as follows,  
\[\text{div}(\rho U)=0 \tag{3}\]  
\[\text{div}(\rho UT)=-\rho\text{div}U+\text{div}(\lambda\text{grad}U)+\Phi \tag{4}\]  
\[\text{div}(UUI)=\text{div}(v\text{grad}U)-\frac{1}{\rho}\frac{\partial p}{ \partial x_{i}} \tag{5}\]  
\[p=\frac{RT}{V_{m}-b}-\frac{a}{T^{0.5}V_{m}(V_{m}+b)} \tag{6}\]  
where \(\
u\), \(\lambda\), and \(\Phi\) are the kinematic viscosity, the thermal conductivity, and the dissipation function, respectively; \(a\) and \(b\) are van der Waals constants. Other main symbols can be seen in the Appendix A.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Description** & \(x/\)**mm** & \(\Delta p_{\text{tot}}/\)**kPa** & \(Z\) & \(d/\)**mm** & \(a/^{\circ}\) \\ \hline Value range & 15–25 & 3–7 & 1–3 & 150–450 & \(-\)50–50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conditions for experiments.  
Because the pressure of leaking air is higher than that of atmospheric pressure, the ideal gas state equation is not proper anymore in calculation the gas pressure. Equation (6) is also called the \(R\)-\(K\) equation, which is generally used to predict the pressure of non-ideal gas and the prediction accuracy is better for non-polar gas. As the air contains a lot non-polar gas N\({}_{2}\) and O\({}_{2}\), almost 4 to 1 in volume ratio, we applied the \(R\)-\(K\) equation to calculate the air pressure. In the computation, the quadratic upstream interpolation for convective kinetic scheme is used to interpolate the convective terms. The semi-implicit method for pressure-linked equations consistent algorithm is used for the pressure-velocity decoupling.
## 4 Result and Analysis
### Non-TL Approach
This misclassification is expected, because the healthy cases at 41k ft (Target) would deviate from the healthy cases at 28k ft (Source), as shown in Figure 8, and the deviation is high enough so that a healthy case at 41k ft is more similar to a case at 28k ft with minor RI blockage. With the knowledge that the k-NN classifier used in this work calculates the distance between cases by Euclidian distance [32], the healthy case in the target domain naturally shares a much smaller Euclidian distance with the RI blockage case in the source domain than the healthy case in the source domain, which results in the misclassification. Figure 6: k-NN predictive accuracy against input dimension. The misclassification situation shown in Figure 8 can also be physically understood. Because the ambient air temperature at 41k ft in the target domain is lower than that at 28k ft in the source domain, a smaller mass flow rate of the cold ram air stream is required to be mixed with hot bleed air. Consequently, a healthy case at 41k ft would be similar to a minor RI blockage case at 28k ft. Similar to the misclassification example demonstrated above, other cases with the same true label can also show major discrepancy under different operating conditions, also called a domain shift. This causes misclassification when classifiers trained in one certain operating condition are applied to a different operating condition, because the behaviour of domain shift is influenced simultaneously by complicated factors including the ambient conditions, engine bleed conditions, and target conditions in ECS operation.
# A Convex Combination Approach for Artificial Neural Network of Interval Data
## 3 The Proposed Method Artificial Neural Network with Convex Combination (ANN-CC)
Thus, twelve ANN specifications were used to describe and forecast the excess return of PTT, SCC, and CPALL stock returns under the CAPM context. To evaluate the forecasting performance of the twelve ANN-class specifications presented in this section, the forecasting process was handled as follows: Each dataset was split into 80% for training and 20% for testing. Thus, both in-sample and out-of-sample forecasts were conducted in this comparison. The performance evaluation of the interval-valued data forecasting models ANN-CC under CAPM was accomplished through MAE, MSE, and RMSE. Table 4 shows the in-sample and out-of-sample estimation results for different ANN-CC specifications. Focusing on the MAE, MSE, and RMSE, the result indicated that the interval-valued prediction results were a bit sensitive to activation functions. These activation functions were compared, and we found that the exponential activation function enabled more accurate forecasting in most cases, as the MAE, MSE, and RMSE of this activation were lower than of other activation functions. When the number of hidden nodes considered in this comparison was compared, we observed that the higher the number of hidden nodes, the lower MAE, MSE, and RMSE in some cases. Finally, we considered the weight of interval-valued data, which was calculated by the convex combination method. The results show that most of the weight parameters for interval excess stock return and interval excess market return were not equal to 0.5. Therefore, it can be said that the assumption of the center method in the neural network forecasting of [PERSON] et al. [25] may be inappropriate for practical problems. This result confirms the reliability of the convex combination method in ANN-CC forecasting. Figure 4: Interval-valued data plots for Stock Exchange of Thailand (SET) and stocks. The red dot presents the midpoint data plot for SET and stocks.