esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Ablation Study of the Concat Function for SR Reconstruction  
Certain multi-branch methods [7; 9; 21] reconstruct the SR image using multi-prediction, such as SRFBN [21], which generates a prediction in each feedback procedure. However, we argue that previous feedback procedures cannot produce a meaningful prediction as a result of their very shallow HR features. Accordingly, we concatenated the HR features of all feedback procedures to obtain the final SR result. We compared our concat function and the multi-prediction method using the DMFN, as shown in Figure 8. As illustrated in Table 4, the concat function performed better than the multi-prediction method on the DMFN. Therefore, the concat function was shown to be effective and applicable to feedback networks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dual**} & \multirow{2}{*}{**Mutual**} & \multirow{2}{*}{**Feedback**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & \multicolumn{2}{c}{**MRI13**} & \multicolumn{2}{c}{**ADNI100**} & \multicolumn{2}{c}{**OASIS100**} \\ \cline{5-8}  & & & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline ✓ & ✓ & ✓ & & 583 k & 36.72/0.9485 & 30.17/0.8719 & 34.35/0.9478 \\ ✕ & ✓ & ✓ & & 631 k & 36.49/0.9479 & 30.13/0.8716 & 34.24/0.9475 \\ ✓ & ✕ & ✓ & & 687 k & 36.58/0.9480 & 30.14/0.8716 & 34.31/0.9477 \\ ✓ & ✓ & ✕ & & 633 k & 36.53/0.9479 & 30.14/0.8714 & 34.35/0.9477 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ablation studies of dual feedback, mutual feedback, and feedback on DMFN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & \multicolumn{2}{c}{**MRI13**} & \multicolumn{2}{c}{**ADNI100**} & \multicolumn{2}{c}{**OASIS100**} \\ \cline{5-8}  & & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline DMFN with mutual learning & \multirow{2}{*}{\(\times 3\)} & 583 k & 36.72/0.9485 & 30.17/0.8719 & 34.35/0.9478 \\ DMFN with independent-learning & & 574 k & 36.55/0.9479 & 30.13/0.8713 & 34.31/0.9476 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of mutual learning and independent learning on DMFN.  
Figure 7: The comparison of our mutual-learning method and the independent-learning method between the two back-projection units. The black arrows show the mutual learning between two units, which are not used in independent-learning.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 5 Results and Discussion  
The validation of the 3D-LUNA model is conducted using MATLAB R2023b simulations. In these simulations, the airborne unit operates with a 60-degree beam angle and employs a blue light wavelength. Various parameters in the system are susceptible to errors, primarily due to instrumentation inaccuracies and environmental factors. Key aspects such as transmission power, the altitude of the airborne unit, and the depth of the underwater node are all influenced by measurement uncertainties, which ultimately affect light intensity, as described in Equation (1). In practice, these errors can be significantly minimized through proper calibration and the use of high-precision sensors. For the simulations, we assume that errors in measurable parameters follow a Gaussian distribution with a zero mean and a standard deviation of 1%. Additionally, the position of the underwater node is estimated using an inertial navigation system (INS), which introduces its own errors due to spatial and angular displacement. These errors are also modeled as Gaussian with a zero mean and a 1% standard deviation. The underwater environment is simulated as pure seawater with an extinction coefficient of 0.056.  
In our previous work [5], we investigated underwater node localizability while considering the mobility patterns of both airborne and underwater nodes. The key aspect is whether the underwater nodes receive sufficient transmissions for accurate localization. In this study, we focus on the localization error of nodes that do not know their underwater depth and receive an adequate number of transmissions. To evaluate the localization performance of 3D-LUNA, we consider a deployment region of 100 m \(\times\) 100 m \(\times\) 10 m, which is divided into 100 equal-sized cells. Given the unique characteristics of this localization approach, we compare its performance against an alternative VLC-based localization model, which we refer to as OMNI. In the OMNI model, an underwater VLC transmitter is located at the center of each cell. Specifically, each cell is a 10 m cube where the transmitter is positioned at the center, emitting light omni-directionally. The underwater target node,  
Figure 7: Flowchart illustrating the centroid computation process based on VLC transmission reception and shape intersection analysis.  
upon receiving and measuring the light intensity, estimates its location using an inverse square law using underwater attenuation, i.e., using the following:  
\[I=\frac{P}{4\pi w_{d1}^{2}}\,e^{-kx_{d1}} \tag{38}\]  
Though 3D-LUNA modality only deals with directional transmission, it still covers an entire cell since the VLC source is airborne. Omni-directionality is necessary for an underwater light source to cover the entire cell to ensure localizability under the OMNI method (OMNI does not have to handle cross-medium transmission since it is transmitting from underwater). This comparative analysis aims to assess the accuracy and reliability of 3D-LUNA in underwater localization scenarios.
## 4 Conclusions  
The exhausted adsorbent could be efficiently used as a Pd-based photocatalyst in the degradation process of phenol. The presence of the IL on solid support increases the adsorption efficiency of Pd ions from aqueous solutions. In addition, it was found that both the presence of ionic liquid and Pd (II) ions result in an increase in the efficiency of the phenol degradation process. Furthermore, increasing the amount of Pd (II) from the catalyst structure and increasing the S: L ratio has a positive effect on the degradation process. The obtained photocatalyst could be efficiently used in several cycles of degradation processes. A part of the IL from the Florisil surface is degraded during the first process of photodegradation, but at the same time, the Pd ions are more fixed on the solid support. The efficiency of the catalyst decreased only by 5%, and then the efficiency is maintained. These results underlined the efficiency and stability as photocatalyst of the sample F-II-Pd compared with the sample F-IL in the degradation process of phenol from aqueous solutions.  
The results of the present manuscript underline the efficiency of waste resulted from an adsorbent process to be used as a photocatalyst. These results are in the frame of the closed cycle technology responding to the actual need of circular economy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Photocatalyst**} & **Phenol Initial** & **Degradation Degree,** & **Treatment Time,** & **References** \\  & **Concentration, mg/L** & **\%** & **min** & **References** \\ \hline Titanium dioxide-coated on the inner & & & & \\ and outer quartz glass tubes & 30 & 50 & 300 & [37] \\ \hline Titanium dioxide (TiO\({}_{2}\)) Degussa P25 & & & & \\ (80\% anatase, 20\% rutile) & 20 & 83.9 & 360 & [38] \\ \hline ZnO & 25 & 78.2 & 90 & [39] \\ \hline
0.5\% Pd-0.5\% Au/TiO2 & 94.11 & 69 & 5520 & [40] \\ \hline F-IL-Pd & 20 & 78.6 & 180 & Present work \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the photocatalytic efficiency developed by various materials in the degradation process of phenol from aqueous solutions.  
**Author Contributions:** Conceptualization, L.L. and L.C.; methodology, L.L. and A.P.; formal analysis, L.L., B.T. and A.C.; investigation, L.L.; writing--original draft preparation, L.L. and L.C.; supervision, A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** The research infrastructure developed at ICECHIM through the POS-CCE \"AGRI-FLUX\" project, nr. 645/18.03.2014, cod SMIS-CSNR 48695 was partially used to obtain the results presented in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Data Filtering for Short-Term Forecasting of Photovoltaic Power Plants Generation
### Filtration Models Comparative Analysis  
Table 6 shows the comparison of the parameters for assessing the PVPP generation forecast accuracy for three scenarios: without filtering the source data, using an empirical filter, introducing the k-means approach.  
The error assessment criteria analysis shows that the observations filtering using the k-means method has the best performance. The total error value is reduced by almost 2 times compared with the calculation without filtering and more than 1.5 times compared with the calculation using an empirical filter.
# Reliability Assessment of Cyber-Physical Distribution Systems Considering Cyber Disturbances
## 4 Reliability Assessment of Distribution Network CPS
### Reliability Assessment Process  
The reliability assessment model of the distribution network CPS is constructed by combining a fault tree and Petri net, and the reliability assessment is carried out by finding its corresponding correlation matrix from the Petri net model and then finding the minimum cut set. In the selection of reliability indices, the generalized distribution network CPS reliability assessment indices considering cyber disturbances are defined. The flow chart of the reliability assessment for the distribution network CPS proposed in this paper is presented in Figure 11.  
On the basis of the above discussion, the reliability assessment process proposed in this paper is divided into the following four stages:  
Stage I: Establish the reliability model of the distribution network CPS, which can be divided into the following three steps.  
Step 1: Determine the top event of the failure according to the topology and coupling relationship of the system.  
Step 2: Analyze the causes of load failures so that a load fault tree model can be built and simplified.  
Step 3: Perform a qualitative analysis of the fault tree model, and the fault tree model will be transformed into the corresponding Petri net model.  
Stage II: Solve the corresponding correlation matrix in the reliability model, which is divided into the following two steps.  
Step 1: Solve the input and output matrices of the model according to the structure of Petri nets.  
Step 2: Solve the correlation matrix based on the input matrix and output matrix.  
Stage III: Solve the minimum cut set from the correlation matrix. This phase is divided into the following three steps.  
Step 1: Find the top place of the event and the input event.  
Step 2: Determine whether the event is an intermediate place, if not, it is the bottom library house; otherwise, go back to Step 1.  
Step 3: Expand all the bottom places to obtain the cut set and simplify it to the minimum cut set.  
Stage IV: Calculate the system reliability indices. This stage can be divided into the following six steps.  
Step 1: Solve for the probability \(P(T)\) of occurrence of the top event based on the minimum cut set.  
Step 2: Solve for the probability \(p_{i}\) of a load point in the state of cyberattacks.  
Step 3: Calculate the failure rate \(\lambda\) of each load point.  
Step 4: Calculate \(C_{\text{ph}}\), \(C_{\text{in}}\), \(C_{\text{ia}}\), \(U_{\text{ph}}\), \(U_{\text{in}}\), \(U_{\text{ia}}\), \(E_{\text{ph}}\), \(E_{\text{in}}\), and \(E_{\text{ia}}\), based on the failure rate \(\lambda\) of each load point.  
Step 5: Calculate the reliability indices of each system.  
Step 6: Evaluate the system reliability.  
Figure 11: Flow chart of distribution network CPS reliability assessment.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results
### Customized Fine-Tuned Residual Model Structure
#### 4.3.2 Creating the Main Branch of the Network with Five Sections  
* The network begins with an initial section that includes the image input layer and an initial convolution with activation.
* The network has three stages of convolutional layers with different feature sizes (160-by-160, 80-by-80, and 40-by-40) and different kernel sizes (7 \(\times\) 7, 5 \(\times\) 5, and 3 \(\times\) 3) in the first convolution layer. Each stage includes N convolutional units. In this example, N = 2. Each convolutional unit contains two 3-by-3 convolutional layers with activations. The net width parameter shows the network defined as the number of filters in the convolutional layers in the first stage of the network. The first convolutional units in the second and third stages down-sample the spatial dimensions by a factor of two. The number of filters is accumulated by a factor of two each time spatial down-sampling is performed in order to maintain approximately the same amount of computation in each individual convolutional layer throughout the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Output Size** & **14-Layer** \\ \hline convInp & \(160\times 160\) & \(\begin{bmatrix}7\times 7\end{bmatrix}\), \(\begin{bmatrix}5\times 5\end{bmatrix}\), \\ \(\begin{bmatrix}3\times 3\end{bmatrix}\), \(\begin{bmatrix}16\text{~{}stride~{}1}\end{bmatrix}\) \\ \hline S1U1 conv\_x & \(160\times 160\) & \(\begin{bmatrix}3\times 3\text{, }16\\ 3\times 3\text{, }16\end{bmatrix}\times 2\) \\ \hline S2U1 conv\_x & \(80\times 80\) & \(\begin{bmatrix}3\times 3\text{, }32\\ 3\times 3\text{, }32\end{bmatrix}\times 2\) \\ \hline S3U1 conv\_x & \(40\times 40\) & \(\begin{bmatrix}3\times 3\text{, }64\\ 3\times 3\text{, }64\end{bmatrix}\times 2\) \\ \hline globalPool & \(33\times 33\) & Average pooling \\ \hline fcFinal & \(1\times 1\) & \(11\) or \(8\) fully connected, softmax \\ \hline classoutput & Output into \(11\) categories (BW) or \(8\) categories (color) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Residual model’s architecture.  
* The final section of the network includes global average pooling, fully connected, softmax, and classification layers.
# Intrusion Detection Model Based on Improved Transformer
## 3 Materials and Methods
### Data Preprocessing Strategy
#### 3.2.3 Hybrid Sampling
Unlike other over-sampling methods that randomly over-sample minority class samples, Borderline-SMOTE first divides the minority class samples into three categories: Safe, Danger, and Noise, as shown in Figure 3.  
Here, A represents the Safe sample, B represents the Danger sample, and C represents the Noise sample. The steps to determine the type are shown in Algorithm 2. Finally, only the samples labeled as Danger were over-sampled. Figure 3: Three types of samples. After dividing minority class samples into three categories, the Borderline-SMOTE method can easily learn and classify Safe class samples because their neighbors are mostly from the same class. Noise class samples, on the other hand, have neighbors exclusively from the majority class and can be considered as outliers, over-sampling these samples may lead to an increase in noise and negatively affect the model performance. Danger class samples mostly have neighbors from the majority class, causing class overlap and making it difficult for the model to learn. Therefore, Borderline-SMOTE only over-samples Danger class samples to increase the number of minority samples in the overlapping region and improve the model's ability to distinguish minority samples in this area. In intrusion detection datasets, normal samples are often more abundant than abnormal ones, which belong to the minority class. To address the issues of class imbalance and class overlap, a novel under-sampling method based on the characteristics of KNN is proposed in this paper. The method under-samples normal samples while using Borderline-SMOTE to over-sample abnormal samples. This hybrid sampling approach balances the dataset by reducing the number of normal samples and increasing the number of abnormal samples. Moreover, because the KNN under-sampling method only removes samples that belong to overlapping classes, and Borderline-SMOTE only over-samples Danger class samples, the combination of these two methods can effectively address class overlap issues.
review is devoted to the history and progress on radiation-balanced lasers with a special focus on rare-earth-doped lasers. Four main designs of athermal lasers including radiation-balanced bulk and fiber lasers, radiation-balanced disk lasers, and athermal microlasers have been considered.  
[PERSON] et al. [4] describe synthesis and laser cooling of 10%Yb\({}^{3+}\):LiLuF\({}_{4}\) crystals. The 10%Yb\({}^{3+}\):LiLuF\({}_{4}\) (Yb:LLF) crystals have been synthesized through a safe and scalable polyethylene glycol (PEG)-assisted hydrothermal method. The influence of reaction temperature, time, fluoride source, and precursor amount on the shape and size of the Yb:LLF crystals are discussed in the paper. Laser cooling to more than 15 K below room temperature in air and 5 K in deionized water under 1020 nm diode laser excitation have been demonstrated at a laser power of 50 mW.  
New methods for the rapid cooling of solids with increased efficiency have been analyzed and demonstrated experimentally by [PERSON] et al. [5]. The advances offered by optical saturation, dipole-allowed transitions, and quantum interference for improved laser cooling of solids have been comprehensively discussed in this paper.  
[PERSON] et al. [6] review the derivation of the Bloch-Redfield equation for a quantum system coupled to a reservoir, and its extension, using counting fields to calculate heat current. They use the full form of this equation, which makes only weak-coupling and Markovian approximations, to calculate the cooling power for a simple model of laser cooling.  
Two parameters, namely, the external quantum efficiency \(\eta_{ext}\) and the background absorption coefficient \(\alpha_{b}\), are important for assessing the laser cooling grade of the rare-earth-doped materials. A promising method for measuring of these crucial parameters has been presented by [PERSON] et al. [7]. After calibration, the temperature resolution of the thermal camera was better than 0.1 K.  
To conclude, this Special Issue, \"Laser Cooling of Solids: Novel Advances and Applications\", includes research and review papers that present the latest achievements on the subject. The data presented may be of great interest for a better understanding and application of the laser cooling of solids based on anti-Stokes fluorescence.
# Blockchain-Based E-Voting Mechanisms: A Survey and a Proposal +
## 4 Background
### Borda Count and Condorcet Method  
The Borda count and the Condorcet method [45; 46] are two of the most widely studied and used counting methodologies. The Borda count method is a voting system that is used to rank a list of candidates based on the preferences of voters. This positional voting method bases where the position of a candidate on a voter's ballot contributes to their final score. In the Borda count method, voters first rank all candidates in order of preference. For example, in a race with three candidates, A, B, and C, a voter might rank them B \(>\) A \(>\) C, C \(>\) B \(>\) A, etc. In this system, voters must rank all candidates. After the ranking portion is completed, points are assigned to each position on a voter's ballot. The most commonly used method is to give the bottom-ranked candidate 0 points, the next rank up 1 point, and so on until the top-ranked candidate. Once the voting process concludes and all ballots are evaluated, the points for each candidate are tallied. The candidate with the highest aggregate point total across all ballots is declared the winner. This method aims to identify a consensus candidate who garners broad support across the electorate, rather than a majority candidate.  
The Condorcet method is a voting system which elects the candidate that would win by a majority of votes in a series of hypothetical head-to-head match-ups against each other candidate. In this method, voters rank all candidates in order of preference. The essential step in this method lies in pairwise comparisons, where each candidate is pitted against  
Figure 1: Structure of a block. The left side represents a regular block, while the right side represents a smart marker block.  
Figure 2: Linear storage structure of Borda–Condorcet chain.  
every other candidate in head-to-head contests in turn. In each head-to-head contest, the candidate who is preferred by the majority of voters is declared the winner of that pairing. For each match-up, a candidate is awarded a point if they are ranked higher than the opponent on the voter's ballot; this candidate is considered the winner of that match-up. The candidate with the most points at the end of the head-to-head match-ups is the final winner of the method. However, the Condorcet method in some cases can result in a situation where there may be no clear majority winner. To resolve this voting paradox, or _Condorcet cycle_[47], various methods can be employed. The method can either be finalized by using the ranked pairs method [48], where the most decisive pairwise contest is locked first and then it continues ignoring any pairings that would create a cycle. Or, it could be finalized using the [PERSON] method [49], which identifies the strongest path between each pair of candidates and then selects the candidate who has the strongest paths against all the others.
# P System with Fractional Reduction
## 5 Experimental Results
### Parameter Experiments
#### 5.2.4 Summary of Parameter Experiments  
Based on the comparison results of the above experiments, we can draw a specific conclusion: for the vast majority of data sets, when the parameter \(\xi\), value is 8, the steps required for the UPS experiment are the least, while the larger the parameter \(\xi\), value, the more steps required for the UPS experiment. When the \(\xi\), value exceeds a certain range, the steps required for the experiment do not change.  
Based on the properties and functions of parameter \(\xi\), the smaller the value of parameter \(\xi\), the longer it takes for the system to transition from more phase derogation to division. That is to say, when conducting experiments on the same set of data, the larger the proportion of execution time that is reduced, the higher the efficiency of the system. From the perspective of limit, when \(\xi\) gets smaller and smaller until it approaches zero, it means that only when the difference between the larger value and the smaller value is less than zero can it be converted to the Euclidean algorithm. For the positive integer fractional reduction membrane system designed in this article without considering symbols, there is no case where the difference is less than zero. Therefore, in the combination of the two methods, only the first part of the more phase derogation method is used, and only the rules in membrane M\({}_{1}\) and class A membranes are executed. There is no need to use class B membranes for rolling and dividing to obtain the results. However, when \(\xi\), becomes larger and larger and the difference between the numerator and denominator is even larger, only membrane A\({}_{1}\) of type A films normally executes the subtraction rule. After judging the difference and the size of \(\xi\), directly use type B films to use the Euclidean algorithm method. At this time, the initial value of membrane B\({}_{1}\) is the initial value of the numerator and denominator, which means that in this case, only the second part of the Euclidean algorithm is performed.  
Through the above analysis on the limit, we have a new guess: when comparing the more phase derogation method, the Euclidean algorithm method, and the combination of the two methods, is the number of time slices for the more phase derogation the least?