esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services +
## 6 Conclusions  
Internet of Things (IoT) is a promising technology that uses self-adaptation and self-organization to support the growth of services in smart cities. Recently, the fog-to-cloud paradigm has started to emerge as a means to facilitate the communication among various IoT sensor nodes, supporting the adaptation of services based on the current context of IoT devices. As the selection of the best fitting services and their configuration is a difficult task, this paper proposes a middleware to facilitate the selection of services in fog-to-cloud platforms to maximize their benefits for the end user. In the work, we employed the MCDA algorithm based on fuzzy similarity and TOPSIS methods to deal with incomplete and imprecise information and introduced a technique for ranking of candidates during the selection process in the face of missing information.  
In the future, we aim at further improving the prototypical implementation with more complex demonstration of the efficiency of the proposed solution. Moreover, we plan to analyze the complexity of the MCDA technique used in this paper compared to the other MCDA methods like the VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) method, and study in which situations the recommended ranking by different techniques diverges and why. Also, we will include the identification of additional criteria which may link further the fog and cloud services, increase the ranking score of MCDA methods, and ensure the sustainability and reliability of services in smart cities.  
The work was supported from ERDF/ESF \"CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence\" (No. CZ.02.1.01/0.0/0.0/16_019/000822).  
The authors declare no conflict of interest.
## 3 Artificial Neural Network Model and Training Results
### The Case of Having Three Scatterers  
In the case of three scatterers, 10,000 data were generated and learned in the same way as in the case of two scatterers, but the results were not good. Thus, 110,000 data were newly created, and the learning was performed again by using 99,500 training data, 500 validation data, and 10,000 test data set. That is, learning was carried out by dividing the ratio of training data and test data by 10:1. As before, every time the artificial neural network is trained five times, the mean square error of the output value from each artificial neural network and the actual value is measured and graphed with each group, as shown in Figure 6a-c. As shown in Figure 6, it can be seen that the mean square error measured using the training data and the validation data are similar when learning is performed in three artificial neural networks.  
Looking at this result alone, it can be seen that there is little error in the third artificial neural network, and the error in the first artificial neural network is relatively large. Through this, it can be deduced that group 1 and group 4 may have a higher mathematical correlation than other groups.  
In order to more objectively check whether the learning was successfully carried out, the mean square error between the output data and the ground truth data were calculated using 10,000 test data that were not used for learning at all. As a result, as shown in Figure 5d, the mean square error for more than 70% of the test data was less than 0.04, the largest error was 0.166, and the smallest error was 0.01.  
In addition, in order to understand the similarity between the ground truth data and the output data through the trained model, as in the previous case, the real-part and the imaginary-part were visualized for randomly generated data, respectively. As shown in Figure 7, it can be seen that the distribution patterns of the two data are similar to each other. That is, in this case as well, it can be predicted that the imaging results obtained by using the two data will be similar.  
Figure 6: Cases with three scatterers, Mean square error (**a–c**) for each group of test data and distribution of mean square error of test data (**d**).
## 3 Numerical Simulation Modeling
### Model Building Phase  
For this study, the influence of adjacent building structures had to be taken into account in the simulation. The east-west (\(X\)-axis) width extended five times the excavation depth from the east and west sides of the launching shaft, and the north-south (\(Y\)-axis) extended five times the excavation depth from the diaphragm wall on the south side of the launching shaft and the steel sheet pile on the north side of the receiving shaft. For the depth of the model soil, the \(Z\)-axis extended to 50 m below the surface, as per the geological drilling report. Therefore, the outer geometric dimensions of the model were \(X_{max}=92.5\) m, \(X_{min}=-92.5\) m, \(Y_{max}=78.5\) m, \(Y_{min}=-78.5\) m, \(Z_{max}=0\) m, and \(Z_{min}=-50\) m, as shown in Figure 7. In the constructed model, the Encasture boundary condition was used at the  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Monitoring Instrument** & **Monitoring Frequency** & **Warning Value** & **Action Value** \\ \hline Strain gauges for strut & & 90\% of the design support load & 125\% of the design support load \\ \hline Tiltmeter & Once a day during excavation and twice a week after the structure is complete & 1/940 & 1/750 \\ \hline Diaphrag steel bar strain gauge & Once a day during excavation and twice a week after the structure is complete & 30 mm & 50 mm \\ \hline Diaphrag steel bar strain gauge & is complete & 0.75\(f_{y}\) & 0.9 \(f_{y}\) \\ \hline Heave indicator & & 30 mm & 50 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Flow chart of the steps for applying PLAXIS 3D CE to simulate the excavation process and analyze the structural stress [38].  
bottom, which restricted the displacement and rotation in all directions. The side boundary of the model was set as ruler, which allowed displacement.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 3 Method of This Article
### Contrast Enhancement
#### 3.2.1 Local Contrast Enhancement of Sub-Images  
Most underwater images exhibit a bright foreground and dark background due to the characteristics of the underwater imaging system. Specifically, areas closer to the light source are brighter than areas farther away from the light source [31]. This paper uses Equation (12) to separate the threshold.  
\[h=p_{1}(u_{1}-u)-p_{2}(u_{2}-u) \tag{12}\]  
In Equation (12), \(p_{1}\) and \(p_{2}\) represent the probabilities of foreground and background pixels of the structural layer \(V_{s}\), while \(u_{1}\) and \(u_{2}\) are the foreground and background pixels' average values, respectively. The \(u\) represents the average value of pixels in \(V_{s}\). The objective is to obtain the best threshold where the difference between the foreground and background is maximized. The larger the between-class variance \(h\), the higher the difference between the two parts of the image.  
We select T as the initial threshold to divide the image into two categories and then use Equation (12) as the fitness function to update h. When h is at its maximum, it means that T has been updated to the optimal threshold. Consequently, the input image can be divided into a background sub-image \(V_{s}^{D}\) and a foreground sub-image \(V_{s}^{U}\), as shown below:  
\[V_{s}^{D}=\{V_{s}(x,y)|V_{s}(x,y)\leq T,\;\forall V_{s}(x,y)\in V_{s}\} \tag{13}\]  
\[V_{s}^{U}=\{V_{s}(x,y)|V_{s}(x,y)>T,\;\forall V_{s}(x,y)\in V_{s}\} \tag{14}\]  
where \(V_{s}(x,y)\) represents the pixel value of the red channel at the pixel location \((x,y)\). The probability of sub-images \(V_{s}^{D}\) and \(V_{s}^{U}\) appearing in the sub-histogram of low and high intervals is defined as \(P_{D}(V_{s}^{D})\) and \(P_{D}(V_{s}^{U})\), respectively, and can be expressed as follows:  
\[P_{D}(V_{s}^{D})=H(V_{s}^{D})/N_{D} \tag{15}\]  
\[P_{D}(V_{s}^{U})=H(V_{s}^{U})/N_{U} \tag{16}\]  
The frequency of grayscale occurrences in \(H(V_{s}^{D})\) and \(H(V_{s}^{U})\) are denoted as \(V_{s}^{D}\) and \(V_{s}^{U}\), respectively, where \(N_{D}\) and \(N_{U}\) are the total number of pixels in the background and foreground sub-images, respectively. The CDFs of the background and foreground sub-images are defined as \(C_{D}\big{(}V_{s}^{D}\big{)}\) and \(C_{D}\big{(}V_{s}^{Ul}\big{)}\), respectively, and are expressed as follows:  
\[C_{D}(V_{s}^{D})=\sum_{V_{s}^{U}=0}^{T}P_{D}(V_{s}^{D}) \tag{17}\]  
\[C_{D}(V_{s}^{Ul})=\sum_{V_{s}^{U}=T+1}^{255}P_{D}(V_{s}^{Ul}) \tag{18}\]  
Finally, to solve the low contrast of the image, the background sub-image and foreground sub-image are equalized using Equation (19), where \(V_{1}\) refers to the structural layer \(V_{s}\) after local contrast enhancement.  
\[V_{1}=\begin{cases}T\times C_{D}(V_{s}^{D})&V_{s}^{D}\in[0,T]\\ (T+1)+[255-(T+1)]\times C_{U}(V_{s}^{Ul})&V_{s}^{Ul}\in[T+1,255]\end{cases} \tag{19}\]
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Node**} & \multirow{2}{*}{**Mutual Information (_MI_)**} & **Normalized _MI_** & **Relative _MI_** & **Relative Significance** & \multirow{2}{*}{_p-Value_} \\  & & & & & **Significance** & \\ \hline Cluster prominence & 1.0146 & 64.01\% & 81.33\% & 1.0000 & 0.0000 \\ Cluster shade & 0.9330 & 58.86\% & 74.79\% & 0.9196 & 0.0000 \\ Contrast & 0.5425 & 34.22\% & 43.48\% & 0.5347 & 0.0000 \\ Autocorrelation & 0.1329 & 8.38\% & 10.65\% & 0.1310 & 0.00026 \\ Energy & 0.0895 & 5.64\% & 7.17\% & 0.0882 & 0.006164 \\ Entropy & 0.0519 & 3.72\% & 4.16\% & 0.0512 & 0.0795 \\ Correlation & 0.0394 & 2.48\% & 3.15\% & 0.0388 & 0.176 \\ Correlation2 & 0.0291 & 1.83\% & 2.33\% & 0.0287 & 0.3218 \\ Homogenity1 & 0.0118 & 0.74\% & 0.94\% & 0.0116 & 0.7549 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall analysis of the highly ranked feature “dissimilarity” (target feature) with other extracted features. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Node**} & \multirow{2}{*}{**Binary _MI_} & **Relative Binary** & **Binary Relative** & \multirow{2}{*}{**Maximum Bayes Factor**} \\  & & **Significance** & **Significance** & & \\ \hline Cluster prominence & 0.7847 & 80.19\% & 1.000 & \(\leq\)571,475 (1/3) & 92.64\% & 1.7059 \\ Cluster shade & 0.6640 & 67.86\% & 0.8462 & \(\leq\)163,215 (1/3) & 98.52\% & 1.5657 \\ Contrast & 0.3957 & 40.44\% & 0.5043 & \(\leq\)0.068 (1/3) & 60.29\% & 1.7059 \\ Autocorrelation & 0.0839 & 8.57\% & 0.1069 & \(\leq\)0.021 (1/3) & 88.73\% & 1.1566 \\ Energy & 0.0802 & 8.19\% & 0.1021 & \(\leq\)45,768 (1/3) & 65.96\% & 1.2544 \\ Entropy & 0.0481 & 4.92\% & 0.0614 & \(\leq\)57,145 (1/3) & 73.25\% & 1.1640 \\ Correlation & 0.0279 & 2.85\% & 0.0356 & \(\leq\)0.041 (1/3) & 80.88\% & 1.0910 \\ Correlation2 & 0.0215 & 2.19\% & 0.0274 & \(\leq\)0.058 (1/3) & 87.33\% & 1.0664 \\ Homogenity1 & 0.0112 & 1.14\% & 0.0143 & \(>\)2.201 (1/3) & 9.03\% & 1.1640 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Local analysis with target states dissimilarity \(\leq\) 349, 738.09 (\(\frac{1}{3}\)) 58.62%. Recently, the need has arisen for a comprehensive analysis to compute the associations among the computed features, in order to understand the strength of relationships, associations, the incoming and outgoing forces between parent and child nodes, and the significance of target nodes and target nodes trees; this analysis can be carried out using the robust Bayesian inference approach. This approach will further help us to determine the underlying dynamics and relationships among the extracted nodes, which will help the relevant healthcare professionals to further improve their decision-making capabilities and diagnostic procedures. [PERSON] et al. (2020) extracted the morphological features from prostate cancer data, in order to compute the associations between these features for deeper analysis.
# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 3 Load-Type Identification Method Based on CNN
### PCA Data Dimension  
The specific computational procedures of PCA are as follows: (1) the original data is standardized to ensure zero mean and one variance for each feature, eliminating scale discrepancies; (2) the covariance matrix of the standardized data is computed to capture linear relations among features; (3) eigenvalue decomposition is applied to the covariance matrix to derive eigenvalues and corresponding eigenvectors, with the eigenvectors defining principal component orientations and eigenvalues representing principal component variance; (4) the eigenvalues are sorted, and the top \(k\) eigenvectors are selected as principal components, where \(k\) is the dimension after dimension reduction; (5) the original data is projected onto these selected principal components to yield the dimensionally reduced dataset.  
The PCA results for the selected 8 converter state variables are presented in Table 1. The first four principal components (F1-F4) are selected as the new feature set, achieving a cumulative contribution rate of 94.5%. This dimension reduction process effectively simplifies the feature space while retaining the essential information of the original data. The dimensionally reduced data is then used as input to the CNN.
###### Abstract  
This study aimed to find out the incidence of impacted third molars and associated pathologies in people who live in the North Cyprus population. [PERSON] State Hospital is the only hospital that has an oral and maxillofacial surgery department among the state hospitals in North Cyprus. Patients who were referred to this department during a one-year period due to the complaints regarding their third molar were included in our study. This retrospective study involved 550 patients aged 16 to 65 years (1752 third molars). Chi-square tests were done for bilateral comparison between age, gender, and third molar (_p_ < 0.05). Among the groups included in the study, the highest number of third molars originated from the 20-29 age group (_n_ = 1050). Among all 1752 third molars, 716 (40%) of them erupted, while 1036 (60%) were impacted molar teeth, with significant differences between genders (_p_ > 0.05). The most often impacted position in the mandible was the mesioangular type (42%) and in the maxilla was the vertical type (62%). Partially and completely impacted mandibular third molars showed a significant difference between the left and right sides (_p_ < 0.05). A huge proportion of third molars are impacted in the North Cyprus population. The degree of impaction of wisdom teeth and the problems they cause should be well evaluated, and the surgical approach should be considered according to the baseline of this data.  
m 2022 2022 2022 2022 2022 2022 2022 2022 2022 202  
Although impacted third molars are mostly asymptomatic, they can sometimes cause complications. Some of these complications can include pain, infection, cyst and tumor formation, root resorption in permanent teeth, fracture in the jaw, crowding in the mandibular anterior teeth, and bone loss in adjacent teeth [5, 7, 8, 9, 10, 11, 12, 13, 14].  
Surgical removal of impacted third molars is the most common treatment method in oral surgery. However, various complications may occur during and after surgical extraction. The most common complications are pain, edema, trismus, bleeding, infection, alveolar nerve injury, displacement of the extracted tooth into neighbor spaces, and mandible fractures [15, 16].  
Many methods are used in the classification of impacted teeth. These classifications are made according to the level of impaction, the angle of the third molar, and its relationship with the anterior border of the mandibular ramus [8]. According to the Pell and Gregory classification system, third molars can be classified by their depth or level according to the occlusal surface (OS) of the adjacent second molar tooth [9].  
There have been previous studies in Caucasian [3, 10, 11, 13, 17, 18], Arab [19, 20], African [21, 22], and Chinese [5, 12] populations regarding the impaction of mandibular third molars. However, no study has been done on the North Cyprus population. Hence, it was considered worthwhile to assess the prevalence of impacted third molars using a panoramic radiograph with the associated pathologies according to the age, gender, and type of impaction in a sample of patients living in North Cyprus.
## 3 Results and Discussion  
### Rheometric Analysis  
As shown in Table 2, the following results were obtained during the rheometric tests: minimum torque (\(M_{L}\)), maximum torque (\(M_{H}\)), torque variation (\(\Delta M\)), scorch time (\(t_{S1}\)), and optimal curing time (\(t_{g0}\)). Based on the results in Table 2, and taking into account the statistical error, the presence of fillers does not significantly affect the rheometric parameters compared with the reference sample (neat VNR).  
The \(M_{L}\) is defined as the shear strength observed at the beginning of vulcanization. This value indicates the consistency of the mixture before starting, which is crucial for the formation of the crosslinks between the polymer chains. In the VNR/MWCNT nanocomposite containing 4 phr (parts per hundred rubber) of MWCNTs, compared to those with 0, 1, 2, and 3 phr of MWCNTs, there was a noticeable trend of increasing \(M_{L}\) values. This increase can be attributed to the resistance of the NR chains to movement as the amount of filler in the composite rises. \(M_{H}\) is strongly linked to the formation of crosslinks, while \(\Delta M\) reflects both the formation of crosslinks and the presence of fillers in the composites. It is clear that both parameters increase during the vulcanization process. Moreover, analyzing the torque variation indicates that the MWCNTs enhanced the interaction between the filler and the polymer matrix. There was, however, a trend towards reducing the \(t_{S1}\), which denotes the time elapsed from the beginning of heating to the point at which vulcanization begins to be detectable, as well as the \(t_{g0}\) value, which refers to the time it will take for the VNR or VNR-based composites to reach 90% of their total cure state or crosslinks. As can be seen from the results, the addition of MWCNTs to the VNR matrix had little effect on the \(t_{g0}\) v value; i.e., the dispersion of MWCNTs into VNR matrix did not affect the vulcanization process.
# Machine Failure Prediction Using Survival Analysis
## 3 Background  
### Survival Analysis  
#### 3.1.1 Kaplan-Meier and Nelson-Aalen Methods  
The Kaplan-Meier estimator is a non-parametric statistical method used to calculate the survival function. The survival function indicates the probability that the subject participating in the study will survive beyond a certain period [12]. In the case of this study, the event is the time until the machine ceases to function. The probability of survival for each time point is calculated using the following formula:  
\[S=\frac{NSL-NSD}{NSL} \tag{1}\]  
where \(NSL\) is the number of machines that function at the start and \(NSD\) is the number of machines that stop functioning.  
The Nelson-Aalen estimator is used for the same purpose as the Kaplan-Meier estimator, to summarize and display data. Unlike Kaplan-Meier, the Nelson-Aalen estimator uses the hazard function instead of survival [12]. The hazard function focuses on the rate of occurrence of the event we want to observe at time t and is defined by the formula:  
\[\widehat{\text{H}(\text{t})}=\sum\
olimits_{\text{t}_{\text{i}}\leq t}\frac{ \text{d}_{\text{i}}}{\text{n}_{\text{i}}} \tag{2}\]  
where \(\text{d}_{\text{i}}\) is the number of events at time t\({}_{\text{i}}\) and n\({}_{\text{i}}\) is the number of machines at time t\({}_{\text{i}}\).  
Both the Kaplan-Meier and Nelson-Aalen estimators are commonly used in survival analysis to estimate the survival function and hazard function, respectively. These methods can provide valuable insights into the time-to-event data and can be used to compare survival between different groups or treatments.
## 4 Deep Learning Methods for HDR Reconstruction
### Diffusion Models for HDR Reconstruction  
Diffusion Models [28] constitute a class of probabilistic models employing a parameterized Markov chain to produce data samples matching the training set distribution after a finite time. DMs define a diffusion process in which Gaussian noise is gradually added to data until the original signal is destroyed. The model then learns to reverse that diffusion process, enabling the generation of samples resembling the training data.  
Formally, the initial stage, called the forward diffusion process, takes a data sample \(x_{0}\) and maps it through a series of intermediate latent variables \(x_{1},x_{2},\ldots,x_{T}\)[164]. In this process, Gaussian noise \(e\) is added step by step in each stage \(t\) following a variance schedule \(\{\beta_{t}\in(0,1)\}_{t=1}^{T}\). The forward diffusion process is given by Equation (8).  
\[q(x_{t}\mid x_{t-1})=\mathcal{N}(x_{t};\sqrt{1-\beta_{t}}\cdot x_{t-1},\beta_{ t}\cdot I) \tag{8}\]  
This process results in a sequence of latent variables that become increasingly noisy until the original information is nearly completely lost in \(X_{T}\).  
The second stage, called the reverse diffusion process, involves learning a model that reverses this diffusion. The goal is to model the conditional probability of retrieving \(X_{T-1}\) from \(X_{T}\) by using Gaussian conditional distributions. Each transition in the reverse process is parameterized by a neural network estimating the mean \(\mu_{\theta}(x_{t},t)\) and variance \(\beta_{t}\)[165]. The reverse diffusion process is defined by Equation (9).  
\[p_{\theta}(x_{t-1}\mid x_{t})=\mathcal{N}(x_{t-1};\mu_{\theta}(x_{t},t),\beta_ {t}\cdot I) \tag{9}\]  
By iteratively applying these transitions, the model can reconstruct the original sample \(x_{0}\) from noisy \(x_{T}\). Throughout this process, the model learns to generate new samples consistent with the training data distribution [166].  
DMs demonstrate substantial potential for HDR image reconstruction, especially for their ability to learn from noisy data and generate coherent samples. By handling uncertainties and variations in data, DMs stand out in scenarios where multiple LDR images are combined to produce a single HDR image. The iterative structure of DMs allows for continuous adjustment during reconstruction, refining pixel estimates and reducing unwanted artifacts (ghost or luminous artifacts). However, this same iterative feature, when used for large models to estimate entire images, becomes inefficient and impairs real-time usage.  
This method can be implemented as single-frame, multi-frame, or image enhancement.