esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 2 Related Work
### General Single-Frame Super-Resolution Methods  
Single-frame super-resolution methods are methods that create a high-resolution image from a single low-resolution image. Most of these methods are general purpose (i.e., they are not specialized to any particular application area, for example, faces).  
In case we do not take into account the interpolation methods (such as bicubic, bilinear, nearest neighbour, etc.), one of the first well-known methods was the Super-Resolution Convolutional Neural Network (SRCNN) [11], which was introduced in 2015. This neural network (NN) architecture was relatively lightweight--it had only three layers, which is a relatively low number when considering the current state-of-the-art architectures. However, it was shown that even such a simple NN outperforms significantly the capabilities of any other interpolation method.  
Another significant progress in the area of super-resolution was based on the so-called generative adversarial networks. Although GANs were introduced in 2014, they showed their applicability in practice for super-resolution only in 2017. One of the well-known projects was Pix2 Pix [12]. It was originally introduced as an image-to-image translation model (the network has the same dimensions of the input and output layers). Especially when compared to interpolation-based methods, Pix2 Pix has also shown a significant improvement [12]. SRGAN [13] is another successful approach based on GANs. Its innovation was to use the residual architecture and improvement regarding the loss function--in particular, it used the perceptual loss function during training instead of the pixel-wise loss, such as Mean Square Error (MSE). Another successful architecture was EDSR, which was introduced in [14] and its main idea was to train high-scale models from pre-trained low-scale models. An interesting innovation was to share parameters across different scales. ESRGAN [15] is inspired by SRGAN. The innovation of ESRGAN is to remove batch normalization layers, the modified perceptual loss function, and transfer learning. The discriminator part of GAN was replaced by a relativistic discriminator [16].  
In 2019, mainly due to using huge computational power and large GPU memory, it was shown that the NNs can generate even relatively complex images with a lot of detail [17]. Although these networks have achieved great success, their big disadvantage is the fact that they do not try to faithfully reconstruct the information that is on the input, but they generate only part of the information. For this reason, they are not suitable for biometrics, face reconstruction, and police investigation, as they tend to generate totally new faces. This is unfortunately a problem of most of the GAN based networks.
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
## 2 Data and Methods
### Modeling Methods
As explained in the HAS principle, the coefficient matrices located at the left end of the Gaussian equation system are presented in Figure 3, and it is evident that both L and J were fixed only at specific points, with the remaining values set to zero. Thus, the iterative process requires computation of the coefficient matrices' inverses just once [29], and the computation time of the HAS method did not significantly increase with the number of iterations. Let H = [L, J]T and K\({}^{n}\) = [M\({}^{n}\), N\({}^{n}\)]T. To ensure that the simulated values at the training stations consistently matched the actual observed values, the least squares problem was formulated with the following equation constraints:  
\[\left\{\begin{array}{l}\min\bigl{|}|HF^{n+1}-K^{n}|\bigr{|}_{2}\\ s.t.\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\where P is the sampling matrix, \(\text{P}(\text{k},(\text{n}-1)\times\lambda+\text{j})=1\), \(\text{Q}(\text{m})=\widetilde{f_{i,j}}\), and the foF2 value at the kth training station is ensured to be derived from an actual observation. For sufficiently large \(\eta\), the HAS method can be transformed for solving the unconstrained least squares problem:  
\[\text{min}\|\left[ \begin{array}{c}H\\ \eta P\end{array} \right]F^{n+1}-\left[ \begin{array}{c}K^{n}\\ \eta\text{Q}\end{array} \right]\|_{2} \tag{14}\]  
In other words, it solves  
\[\left[H^{\text{T}}\;\eta P^{\text{T}}\right]\left[ \begin{array}{c}H\\ \eta P\end{array} \right]F^{n+1}=\left[K^{\text{T}}\;\eta\text{Q}^{\text{T}} \right]\left[ \begin{array}{c}K\\ \eta\text{Q}\end{array} \right] \tag{15}\]  
Subsequently, the issue is ultimately converted into solving a set of linear algebraic equations:  
\[AF^{n+1}=R^{n} \tag{16}\]  
or  
\[F^{n+1}=A^{-1}R^{n} \tag{17}\]  
In summary, the process of applying the HAS method to achieve surface reconstruction in foF2 space is as follows:  
1. Generate a grid covering the modeling range, complete the data on the grid using the foF2 predicted by the IRI model, and then fill in the corresponding grid with the actual foF2 observations at the training station to generate the foF2's initial value surface. 2. Normalize the foF2's initial value surface and compute the surface's first class of fundamental quantities \(B\), \(C\), and \(D\), second class of fundamental quantities \(E\) and \(F\), and the Gaussian system of equations with the coefficients \(G_{11}^{1}\), \(G_{11}^{2}\), \(G_{22}^{1}\), and \(G_{22}^{2}\). 3. For \(\text{n}\geq 0\), solve the iterative equations \(L^{\text{m}+1}=M^{n}\) and \(I\!f^{n+1}=N^{n}\) for the latitudinal and longitudinal directions, respectively. 4. For \(\text{n}\geq 0\), perform appropriate iterations to solve the iterative equation \(AF^{n+1}=R\) and obtain the numerical solution of the surface under the given boundary conditions. Then, apply inverse normalization to derive the predicted foF2 value at the validation station.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) form an important part of industrial application. There has been growing interest in the potential use of WSNs in applications such as environment monitoring, disaster management, health care monitoring, intelligence surveillance and defence reconnaissance. In these applications, the sensor nodes (SNs) are envisaged to be deployed in sizeable numbers in an outlying area, and it is quite difficult to replace these SNs after complete deployment in many scenarios. Therefore, as SNs are predominantly battery powered devices, the energy consumption of the nodes must be properly managed in order to prolong the network lifetime and functionality to a rational time. Different energy-efficient and energy-balanced routing protocols have been proposed in literature over the years. The energy-efficient routing protocols strive to increase the network lifetime by minimizing the energy consumption in each SN. On the other hand, the energy-balanced routing protocols protract the network lifetime by uniformly balancing the energy consumption among the nodes in the network. There have been various survey papers put forward by researchers to review the performance and classify the different energy-efficient routing protocols for WSNs. However, there seems to be no clear survey emphasizing the importance, concepts, and principles of load-balanced energy routing protocols for WSNs. In this paper, we provide a clear picture of both the energy-efficient and energy-balanced routing protocols for WSNs. More importantly, this paper presents an extensive survey of the different state-of-the-art energy-efficient and energy-balanced routing protocols. A taxonomy is introduced in this paper to classify the surveyed energy-efficient and energy-balanced routing protocols based on their proposed mode of communication towards the base station (BS). In addition, we classified these routing protocols based on the solution types or algorithms, and the input decision variables defined in the routing algorithm. The strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols are emphasised. Finally, we suggest possible research directions in order to optimize the energy consumption in sensor networks.  
clustering; energy-balanced; energy-efficient; load-balanced tree; multi-hop; multipath; routing protocols; single-hop; single-path; WSNs +
Footnote †: journal: Sensors  
_Article_  
A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
# GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
## 2 Related Work
### Feature Selection and Genetic Algorithm  
Machine learning uses computers to simulate human learning activities, study how to learn existing knowledge, acquire new knowledge, and improve learning performance constantly. Logic-based classifiers such as Decision Tree, rule learning machine RIPPER, and Perceptron construct classification rules by induction and deduction. The statistics-based classifiers decide the sample categories by constructing probability models, which include Bayesian, KNN, and SVM. Integrated classifiers refer to the combination techniques which integrated different methods, such as Boosting, Bagging, and Stacking. Machine learning is widely used in image recognition, natural language processing, text processing, and security practices.  
When using Machine Learning to process data, the high feature dimension usually leads to more computing consumption and even over-fitting. So feature dimension reduction is usually used in preprocessing. Feature selection is one of the most widely employed methods and it includes filter, wrapper, and embedded model according to the correlation between feature selection and classifiers. The filter is completely independent of classifiers, so it is used widely and flexibly. Typical feature selection algorithms include Fisher, Correlation Coefficient, InfoGain, Chi-square Test, and Mutual Information [22].  
GA (Genetic Algorithm) [23] originated from Darwinian evolution theory; it simulates the natural laws of evolution and searches for high-quality solutions by iterations. GA has good global search ability, and its search spaces and directions can be adjusted adaptively by crossover and mutation. The algorithm begins with a population and selects the best individuals of the population to produce the next generation. The best individuals are determined according to the fitness function defined in advance. Through the crossover and mutation, new individuals in accordance with the fitness function are produced. The population selected by the survival of the fittest is more adaptable to the environment than the original population, and then the high-quality solutions to the problem are determined.  
GA is widely used in image processing [24], control systems [25], and network information security [26]. [PERSON] et al. [27] proposed an improved chaotic GA to solve the problems of slow convergence and local optimization of BP neural networks. [PERSON] et al. [28] presented a hybrid nested genetic fuzzy algorithm (HNGFA) to distinguish most intrusion categories from normal traffic. [PERSON] and [PERSON] [29] constructed a permission feature subset by GA, and it achieved good detection results with SVM in Android malware detection.
# Antimicrobial Activity of Human C-Type Lectin Domain Family 3 Member A (CLEC3A)
###### Abstract  
C-type lectins (CTLs) are a group of proteins that play a crucial role in immunological functions. With the rise of antibiotic-resistant bacteria, CTLs have emerged as a potential alternative to traditional antibiotics and antimicrobial peptides (AMPs), the latter exhibiting limited application due to their low biostability. In this study, we used viable count assays to investigate the antimicrobial activity of the human C-type Lectin Domain Family 3 Member A (CLEC3A) and its two protein domains, CLEC3A Ex23 and CLEC3A Ex3, against gram-positive and gram-negative bacteria. Additionally, using immunoblot analysis, we assessed the biostability of CLEC3A and its protein domains in bacterial supernatant and murine serum. Our findings demonstrate that CLEC3A, CLEC3A Ex23, and CLEC3A Ex3 possess antimicrobial activity against gram-positive _Staphylococcus aureus_ and gram-negative _Pseudomonas aeruginosa_. CLEC3A is more effective against _P. aeruginosa_ than the well-investigated antimicrobial peptide LL-37. Furthermore, CLEC3A and its domains have low sensitivity to bacterial and serum proteases, making them more advantageous for systemic application than most AMPs. In conclusion, our research has demonstrated that CLEC3A is not only a precursor of AMPs but also an antimicrobial protein itself, with favorable characteristics for therapeutic applications.  
C 2023, 14, 184. [[https://doi.org/10.3390/app14010184](https://doi.org/10.3390/app14010184)]([https://doi.org/10.3390/app14010184](https://doi.org/10.3390/app14010184))  
1][PERSON]; [PERSON]; [PERSON]; [PERSON] Antimicrobial Activity of Human C-Type Lectin Domain 3 Member A (CLEC3A). _Appl.  
CTLs of invertebrates [8; 9; 10; 11; 12; 13], RegIII\(\alpha\) is an important example of a bacteria-killing, human CTL. However, RegIII\(\alpha\) is limited in its ability to kill gram-negative bacteria such as _Escherichia coli_ (_E. coli_) [7]. Cartilage-specific human C-type lectin Domain Family 3 Member A (CLEC3A) shows a comparable structure to human RegIII\(\alpha\). It consists of 197 amino acids and has a molecular weight of 23 kDa. The full-length protein is encoded by three exons. The first exon encodes a signal peptide and a cationic protein segment, exon 2 encodes an alpha-helical oligomerization domain, and exon 3 a CRD [14] (Figure 1).  
Two peptides derived from CLEC3A, Ex1, and Ex12, have been shown to exhibit antimicrobial activity [15]. The structural similarities between CLEC3A and human RegIII\(\alpha\) raise the question of whether CLEC3A, the CRD of CLEC3A (CLEC3A Ex3) or the oligomerization domain in combination with the CRD (CLEC3A Ex23) could also exert antimicrobial activities. This study examines the potential of CLEC3A and its protein domains to act as an antimicrobial agent. Additionally, we assess the biostability of CLEC3A and its protein domains in bacterial supernatant and murine serum to determine their potential use for therapeutic applications.
## 5 Vibration Velocity Prediction of Long Hole Blasting
### Peak Particle Velocity Prediction Using Random Forest Model  
The training process for the RF aligned with the methodology used for SVR. Employing the PSO algorithm (main parameters listed in Table 2), the training set data were iteratively optimized to determine the number of decision trees (\(k=23\)) and the random variable number for splitting nodes (\(m=3\)) in the RF model. Utilizing these optimized parameters, an RF model was constructed to predict the test set data, and the results were compared with the measured values. The MAE, MSE, and \(R^{2}\) between the final prediction values of the peak particle vibration velocity for the testing set and the measured values were 0.1024, 0.0274, and 0.8048, respectively (Table 5). Comparing the results with those of the fitting equation based on the USBM formula, the MAE increased by 7.99%, the MSE decreased by 0.52%, and the \(R^{2}\) increased by 2.17% (Table 4).  
The optimized RF model, characterized by a decision tree count (\(k\)) of 23 and a random variable number (\(m\)) of 3 for splitting nodes, was employed. Figures 10 and 11 depict the prediction curve for the mass peak vibration velocity in the test set compared with the measured values. The correlation curve between the predicted and measured values is also presented in Figure 11, demonstrating an \(R^{2}\) of 0.8048.  
Figure 10: The relation between PPVs predicted by the PSO-RF model and measured PPVs for testing datasets.  
Figure 9: Predicted PPVs by the PSO-SVR model versus measured PPVs.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**The Number of** & **The Random Variable Number for** & **MAE** & **MSE** & \(R^{2}\) \\
**Decision Trees, \(k\)** & **Splitting Nodes, \(m\)** & **QME** & **MSE** & \(R^{2}\) \\ \hline
23 & 3 & 0.1024 & 0.0274 & 0.8048 \\ \hline \end{tabular}
\end{table}
Table 5: Key indicators in predicting PPVs by the PSO-RF model.
# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
## 3 Materials and Methods
### Evaluation  
#### 3.4.1 Confusion Matrix  
Confusion matrix is a method for evaluating the performance of a classification model in making predictions [47]. The confusion matrix generates scores for accuracy, precision, recall, and F1 score. This score is used to assess the model's performance. Predicted and actual classifications are shown in a \(n\times n\) confusion matrix, where \(n\) is the number of classes [48]. There were three classes employed in this study: normal, COVID-19, and pneumonia. The form of the confusion matrix for the three classes can be seen in Table 2.  
In the three-class confusion matrix, there is a slight difference in calculating true positive, true negative, false positive, and false negative. In the context of class A in Table 2, the calculations for those values are as follows:  
\[True\ Positive=T_{AA} \tag{1}\]  
\[True\ Negative=T_{BB}+T_{CC}+F_{BC}+F_{CB} \tag{2}\]  
\[False\ Positive=F_{AB}+F_{AC} \tag{3}\]  
\[False\ Negative=F_{BA}+F_{CA} \tag{4}\]  
From the confusion matrix, we can calculate the evaluation metric as follows:  
\[\text{precision}=\frac{TP}{TP+FP} \tag{5}\]  
\[\text{recall}=\frac{TP}{TP+FN} \tag{6}\]  
\[\text{F1 score}=2\times\frac{\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}}=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{7}\]  
In this case, we did not use accuracy because, when dealing with imbalanced data, where one class has significantly fewer instances than the others, accuracy can be misleading. In such cases, the F1 score is a more suitable evaluation metric as it takes into account both precision and recall. The F1 score is particularly useful in cases of unbalanced data. This is because the F1 score considers both false positives and false negatives, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{3}{c}{**Predict**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline A & T\({}_{\text{AA}}\) & F\({}_{\text{BA}}\) & F\({}_{\text{CA}}\) \\ \hline B & F\({}_{\text{AB}}\) & T\({}_{\text{BB}}\) & F\({}_{\text{CB}}\) \\ \hline C & F\({}_{\text{AC}}\) & F\({}_{\text{BC}}\) & T\({}_{\text{CC}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for three classes.  
are crucial in such cases to identify the model's performance in correctly predicting the minority class [49].
# Evaluation of the Nutritional Quality and Shelf Life of
## 3 Results
### Humidity and Water Activity Analyses  
The results of the humidity and water activity analyses in these periods of time are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **S1** & **S2** & **S3** & **Legislation** \\ \hline Lipids (\%) & 30.69 \({}^{\text{a}}\)\(\pm\) 0.00 & 33.85 \({}^{\text{a}}\)\(\pm\) 0.08 & 29.72 \({}^{\text{a}}\)\(\pm\) 0.03 & Max. 35.00 \\ \hline Protein (\%) & 35.05 \({}^{\text{a}}\)\(\pm\) 1.66 & 33.19\({}^{\text{a}}\)\(\pm\) 1.89 & 31.14 \({}^{\text{a}}\)\(\pm\) 1.94 & min. 20.00 \\ \hline Total carbohydrates (\%) & 9.62 \({}^{\text{a}}\) & 11.59 \({}^{\text{a}}\) & 8.06 \({}^{\text{a}}\) & Max. 4.00 \\ \hline Ash (\%) & 2.98 \({}^{\text{a}}\)\(\pm\) 0.00 & 2.65 \({}^{\text{a}}\)\(\pm\) 0.00 & 2.64 \({}^{\text{a}}\)\(\pm\) 0.00 & - \\ \hline Caloric Value (Kcal) & 454.89 & 483.76 & 427.27 & - \\ \hline \multirow{3}{*}{Colorimetry} & L = 41.26 \(\pm\) 0.53 & L = 35.62 \(\pm\) 0.83 & L = 49.97 \(\pm\) 0.57 & \\  & a\({}^{\text{a}}\) = 12.71 \(\pm\) 0.95 & a\({}^{\text{a}}\)\(\pm\) 10.82 \(\pm\) 0.94 & a\({}^{\text{a}}\)\(\pm\) 9.04 \(\pm\) 0.27 & - \\ \cline{1-1}  & b\({}^{\text{a}}\) = 10.98 \(\pm\) 0.66 & b\({}^{\text{a}}\) = 7.88 \(\pm\) 0.71 & b\({}^{\text{a}}\)\(\pm\) 11.53 \(\pm\) 0.46 & \\ \hline Texture (KgF) & 6.53 \({}^{\text{a}}\)\(\pm\) 1.63 & 4.25 \({}^{\text{a}}\)\(\pm\) 1.41 & 9.45 \({}^{\text{a}}\)\(\pm\) 1.42 & - \\ \hline \multirow{3}{*}{Acidity (g/mL)} & 9.42 \({}^{\text{a}}\)\(\pm\) 0.01 & 14.14 \({}^{\text{a}}\)\(\pm\) 0.00 & 7.48 \({}^{\text{a}}\)\(\pm\) 0.01 & - \\ \cline{1-1}  & & & & \\ \hline \end{tabular}
\end{table}
Table 4: Physical and Physicochemical Composition of Salami.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **Aw-Day 0** & \
\begin{tabular}{c} **Humidity** \\ **(\%)-Day 0** \\ \end{tabular} & **Aw-Day 84** &
\begin{tabular}{c} **Humidity** \\ **(\%)-Day 84** \\ \end{tabular} \\ \hline S1 & 0.95 \({}^{\text{a}}\)\(\pm\) 0.00 & 40.90 \({}^{\text{a}}\)\(\pm\) 0.03 & 0.93 \({}^{\text{a}}\)\(\pm\) 0.00 & 43.91 \({}^{\text{a}}\)\(\pm\) 0.01 \\ \hline S2 & 0.94 \({}^{\text{b}}\)\(\pm\) 0.00 & 41.89 \({}^{\text{a}}\)\(\pm\) 0.04 & 0.90 \({}^{\text{b}}\)\(\pm\) 0.00 & 32.46 \({}^{\text{b}}\)\(\pm\) 0.03 \\ \hline S3 & 0.96 \({}^{\text{a}}\)\(\pm\) 0.00 & 44.56 \({}^{\text{a}}\)\(\pm\) 0.04 & 0.92 \({}^{\text{a}}\)\(\pm\) 0.00 & 38.66 \({}^{\text{c}}\)\(\pm\) 0.02 \\ \hline Legislation & Max. 0.92 & Max. 40.00 & Max. 0.92 & Max. 40.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Moisture analysis and water activity performed on samples of fermented salami sausages.  
In evaluating the results of the humidity level on day 0, it was observed that none of the results stayed up to the minimal standards required by legislation [19].
# The Electromagnetic Shielding of Optoelectronic Devices by Mesh Structures
## 3 Samples and Measurement Methods
Figure 2: Sketches of the experimental samples 1 (**a**) and 2 (**b**), and macrophotonography of the samples (**c**). The radiation attenuation in the millimeter and centimeter spectral ranges was measured on several setups. An original equipment was assembled for measurements at a fixed frequency of 99 GHz (3030 \(\upmu\)m). The source of continuous wave radiation was a GF-10/99/20 generator(DOK LTD, Moscow, Russia), and the detector was a Golay cell (TYDEX LLC, Saint Petersburg, Russia [15]). Measurements were carried out in a free space. A horn antenna was used for the radiation channeling, and diaphragms and microwave absorbers were used to extinguish the parasitic radiation and re-reflections. The radiation power of the generator was 22 mW, and the dynamic range of the equipment was 50 dB. The attenuation in the mesh structure on the glass was measured in the centimeter range in a microwave waveguide transmission line by a scalar network analyzer. The radiation was emitted by a N5183A generator (Keysight Technologies, Santa Rosa, CA, USA) using an amplifier with a maximum power of 15 W and measured by a calibration receiver--N9030A spectrum analyzer (Keysight Technologies, Santa Rosa, CA, USA). The dynamic range of the equipment is 90 dB. Samples with diameters of 5 cm and 8 cm were used to measure the attenuation in the frequency ranges of 8-12 GHz (3.75-2.5 cm) and 2.2-4 GHz (13.6-7.5 cm), respectively.