esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Aspects and Experimental Designs
### RO Operators  
The signal of phase excess between GPS and LEO is used to derive the bending angle (\(\alpha\)) through Doppler shift under the spherical-symmetry assumption [30]. The refractivity (\(N\)) is related to pressure, temperature, and water vapor pressure [42], and is revised in GSI as  
\[N=k_{1}\left(\frac{P_{d}}{T}\right)Z_{d}^{-1}+k_{2}\left(\frac{P_{w}}{T} \right)Z_{w}^{-1}+k_{3}\left(\frac{P_{w}}{T^{2}}\right)Z_{w}^{-1} \tag{1}\]  
where \(N=(n-1)\times 10^{6}\) and \(n\) is the index of refraction. In (1), \(P_{d}\) and \(P_{w}\) are the pressure of the dry air and water vapor, respectively; \(T\) is the absolute temperature; \(k_{1}\), \(k_{2}\), and \(k_{3}\) are the atmospheric refractivity constants of 77.689 K mb\({}^{-1}\), 71.2952 K mb\({}^{-1}\), and 3.75463 \(\times\) 10\({}^{5}\) K\({}^{2}\) mb\({}^{-1}\), respectively, and \(Z_{d}\) and \(Z_{w}\) are the compressibility factors [22]. Through the Abel transform, the local bending angle is computed by  
\[\alpha(a)=-2a\int_{a}^{\infty}\frac{d\ln n/dx}{\left(x^{2}-a^{2}\right)^{1/2}}dx \tag{2}\]  
where \(a\) is the impact parameter and \(x=nr\) is the refractional radius [26; 30; 43]. In (2), the bending angle is obtained with the local model refractivity using (1), thus called the local bending angle versus the RO bending angle [30]. RO refractivity then is obtained with the RO bending angle through the inverse Abel transform of (2). The observation operator using (1) is called the local refractivity operator for comparison with the RO refractivity. Thus, the local bending angle operator assimilates the RO bending angle that is more upstream of RO refractivity. Through the assimilation with the two operators built in the GSI, the induced increments (changes) for pressure, temperature, and water vapor pressure can be provided.
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
#### Word Dataset
The results obtained when the dataset was trained with the same architectures as the numerical dataset are presented in Table 11.  
In the experiments conducted, the ensemble learning method achieved results that significantly outperformed the performance of individual models. The implementation of the ensemble learning strategy combined the strengths of different deep learning models, producing more consistent and accurate results. This approach balanced the weaknesses of each model, especially in complex datasets where video and natural language data were used together, enhancing the model's generalization ability. The ensemble model achieved significant improvements in both precision and overall accuracy by combining the expertise of different models on spatial, temporal, and sequential data. When the advantages of I3D and C3D models in processing spatial and temporal information were combined with the BiLSTM model's ability to process sequential data, the Ensemble Learning method was observed to provide better results than other approaches. This approach not only enhanced the model's overall success but also allowed for more stable results despite the diversity and complexity of the dataset. The results clearly demonstrate that by integrating the strengths of individual models, Ensemble Learning produces more reliable and high-performance solutions for such complex datasets. The original contribution of this study lies in presenting an ensemble approach that combines the strengths of different deep learning models, specifically increasing generalization ability in video data. In this context, this study contributes to the literature by offering a method that provides higher accuracy and generalization capacity using ensemble learning on a dataset that integrates both spatial and temporal dimensions. The strong performance of the model suggests that the use of Ensemble Learning methods will be beneficial in future studies.
## 5 Conclusions  
In this paper, the ANN method was applied to evaluate the compressive strength and the flexural strength of UHPFRC. Two reliable database, consisting of 166 compressive strength data sets and 102 flexural strength data sets from previous literature, were established, of which, 133 and 80 samples were randomly chosen for training and the remainder for testing to establish the compressive strength ANN model and flexural strength ANN model, respectively. The conclusions were as follows:  
1. The compressive strength ANN model was trained by using the LM algorithm, with twenty neurons in hidden layers, revealing great prediction performance. The predicted values were fairly close to the experimental results for both the training and testing data sets in the proposed model.
2. The flexural strength ANN model was trained by using the LM algorithm, with twenty neurons in hidden layers, revealing great prediction performance. The predicted values were fairly close to the experimental results for both the training and testing data sets in the proposed model.
3. The results that were obtained from the compressive strength ANN model were compared with three analytical models proposed in other studies. The comparison indicated that the analytical models proposed by others may underestimate the compressive strength by approximately 10% on average, whereas the predicted values from the ANN model in this study agree with the experimental values.
4. The results obtained from the flexural strength ANN model were compared with three analytical models that were proposed in other studies. The comparison indicated that the analytical models proposed by others may varied from 0.8429 to 1.1458 on average values, whereas the predicted values from the ANN model in this study agree with the experimental values.
5. The ANN models that were proposed in this study have high applicability and reliability with respect to evaluating the effects of steel fibers on the compressive strength and the flexural strength of UHPFRC.
###### Abstract  
The dependability assessment is a crucial activity for determining the availability, safety and maintainability of a system and establishing the best mitigation measures to prevent serious flaws and process interruptions. One of the most promising methodologies for the analysis of complex systems is Dynamic Reliability (also known as DPRA) with models that define explicitly the interactions between components and variables. Among the mathematical techniques of DPRA, Stochastic Hybrid Automaton (SHA) has been used to model systems characterized by continuous and discrete variables. Recently, a DPRA-oriented SHA modelling formalism, known as Stochastic Hybrid Fault Tree Automaton (SHyFTA), has been formalized together with a software library (SHyFTOO) that simplifies the resolution of complex models. At the state of the art, SHyFTOO allows analyzing the dependability of multistate repairable systems characterized by a reactive maintenance policy. Exploiting the flexibility of SHyFTA, this paper aims to extend the tools' functionalities to other well-known maintenance policies. To achieve this goal, the main features of the preventive, risk-based and condition-based maintenance policies will be analyzed and used to design a software model to integrate into the SHyFTOO. Finally, a case study to test and compare the results of the different maintenance policies will be illustrated.  
Monte Carlo simulation; dynamic fault trees; multistate systems; SHyFTOO; matlab +
Footnote †: journal: Applied Sciences
## 2 Methods and Methodology
### Classifications and Definitions  
#### 2.5.1 Selection of Sources  
The Scopus database was used to obtain 650 papers. Following the first screening, 126 publications were deemed ineligible since they were neither full-length nor able to access certain journals. Thus, after the first screening of titles and abstracts, a total of 524 publications were procured for additional analysis. After excluding irrelevant publications that were unrelated to the search terms (additive manufacturing, metals), 274 papers were discovered. After deleting 167 publications, 99 papers, including experimental data validation with numerical simulations, and 8 papers containing reviews, remained. The 107 papers were kept for scoping review. Of the articles, 15% were considered for scoping review, and the number of papers published followed the trendline shown in Figure 1.  
Figure 1: Source selection process from literature databases.  
2.5.2. Paper Method: Numerical  
A numerical paper is a paper where at least one new or original simulation or application of numerical elaboration of FEM data has been performed by the scholar, which is described in this paper. According to a study of data from 2013 to April 2022, it was retrieved 23 papers used numerical methods. In this instance, 14 papers were verified and compared with the old experimental data, while 9 papers were refined using the old data. According to the study of data, shown in Figure 2, relatively little numerical labor was performed in the early stages. However, numerical analysis is now exhibiting a steady increase.
## 5 Results
### Dimension 1  
The trackers measure information released by the dynamic source following environment Equation (1) without temporal decrease (\(\tau\rightarrow+\infty\)). An attractor is represented by green stars in Figure 6. It is the agent with the highest information measured at time \(t\) within its communication limits.  
The target follows a periodic trajectory. Its speed follows a cosine function with a period \(T=\frac{t_{max}}{4}\), with \(t_{max}\) the simulation time. In Figure 6, we illustrate such a tracking scenario with agents which follow LCPSO Equation (18). The distance between the swarm center of gravity and the source \(D_{100}\) oscillates because the number of attractors and their position change at each time step. However, the swarm is still centred on the target when \(t\geq 1\) s, with en error limited in space (\(D_{100}\leq 1.5\) m with \(r_{max}\) larger than 7 m).  
In Figure 7, we illustrate the average of \(D_{100}\) during the whole simulation as a function of \(r_{eq}\) and \(r_{com}\) in dimension 1. We have used a Monte-Carlo method with 100 sam  
Figure 5: Example of mobile source tracking in dimension 2 with the LCPSO algorithm (18) when \(t=100\) s. \(N=20\), \(c_{0}=0\), \(c_{2}=2\), \(\tau=1\), \(\beta(p,t)\sim\mathcal{N}(0,0.25)\). (**a**) \(r_{com}=15\) m, \(r_{eq}=7\) m; (**b**) \(r_{com}=15\) m, \(r_{eq}=10\) m  
Figure 6: Example of agents following LCPSO Equation (18) tracking a source following a periodic trajectory with communication constraints in dimension 1. \(N=10\), \(c_{0}=0.8\), \(c_{2}=2\), \(r_{eq}=2.5\) m, \(r_{com}=5\) m, \(\beta(p,t)\sim\mathcal{N}(0,1)\). (**a**) Evolution of tracking as a function of time; (**b**) Evolution of \(D_{100}\) as a function of time.  
ples. We can observe in the simulation results that the target tracking is inefficient when \(r_{eq}\geq\frac{8}{10}r_{com}\) (between \"bad\" and \"correct\" results), but is efficient elsewhere. Further are parameters away from this limitation, better are the results.
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 3 Methodology
### The Ensemble Kalman Filter Scheme  
Data assimilation was designed to create the best analysis of the state of the system and find the optimal model performance, most consistent with observations. EnKF is a sequential data assimilation method and a Monte Carlo based variant of the Kalman filter [15; 17], able to integrate multi-source observations sequentially in time. The standard analysis of EnKF can be expressed as:  
\[\mathrm{A^{a}}\,=\,\mathrm{A}\,+\,\mathrm{P_{e}H^{T}(HP_{e}H^{T}}\,+\,\mathrm{ R_{e})^{-1}(D-HA)}, \tag{4}\]  
where \(\mathrm{A^{a}}\) and \(\mathrm{A}\) are the analysis matrix and forecast matrix of state variable ensembles, respectively. \(\mathrm{P_{e}}\) and \(\mathrm{R_{e}}\) represent the covariance matrixes of state variables and observation variables, respectively. \(\mathrm{H}\) is the observation operator, and \(\mathrm{D}\) is the observation vector. \(\mathrm{D}\)-\(\mathrm{HA}\) denotes the innovation vectors.  
In this study, LAI is considered the state variable that is directly assimilated into Biome-BGC. The model state and external observational data share the same variable. Consequently, the observation operator is a unit matrix, as only the remote sensing data are involved.  
In Biome-BGC, LAI is a key state variable reflecting vegetation growth and development, and is calculated as follows:  
\[\mathrm{LAI}\,=\,\mathrm{C_{leaf}\times SLA}, \tag{5}\]  
where \(\mathrm{C_{leaf}\ (kg\cdot m^{-2}\cdot C)}\) is the carbon state variable and SLA is the special leaf area (\(\mathrm{m^{2}\cdot kg^{-1}\cdot C)}\). Leaf carbon content in current time (\(\mathrm{C_{leaf\cdot t}}\)) is summed by the previous leaf carbon content (\(\mathrm{C_{leaf\cdot t-1}}\)) and the change in leaf carbon content (\(\Delta\mathrm{C_{leaf\cdot t}}\)). Once the LAI observations were assimilated into the model, LAI would convert into the leaf carbon content through Equation (5), with the leaf carbon being crucial for photosynthetic assimilation and allocation.
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Appearance Reconstruction
#### 5.3.3 Attribute Classification  
With the availability of surveillance cameras, the task of object detection and tracking through its visual appearance in a surveillance footage has gained prominence. Furthermore, there are other characteristics of people that are essential to fully understand an observed scene. The task of recognizing the people attributes (age, sex, race, etc.) and the items they hold (backpacks, bags, phone, etc.) is called attribute classification.  
However, occluding the person in question by another person may lead to incorrectly classifying the attributes of the occluder instead of the occludee. Furthermore, the quality of the images from the surveillance cameras is usually low. Therefore, [PERSON] et al. [108] focus on the poor resolution and occlusion challenges in recognizing the attribute of people such as gender, race, clothing, etc., in surveillance systems. The authors propose a model based on DCGAN [109] to improve the quality of images in order to overcome the mentioned problems. The model has three networks, one for attribute classification from the full body images, and the other two networks attempt to enhance the resolution and recover from occlusion. Eliminating the occlusion produces an image without noise and the residual of other subjects that could result in misclassification. However, under severe occlusions, the reconstructed image still contains the remaining of the occluder and the model fails to keep the parts of the image that should stay unmodified.  
Similarly, [PERSON] et al. [110] tackle the occlusion issue by implementing a combination of UNet and GAN architecture. The model requires as input the occluded person image and its corresponding attributes. The generator takes the input and restores the image. The output is then forwarded to three networks: ResNet-101 [77], VGG-16 [111], and the discriminator to calculate the loss. The loss is backpropagated to update the weights of the generator. The goal of the model is to obtain a result image of a person that (a) is not occluded, (b) is similar at the pixel level to a person shape, and (c) contains the similar visual features as the original image. The results show that the model can detect and remove occlusion without any additional information. However, the model fails to fully recover the pixels around the boundary of the body parts. The authors constraint the input images by not having occlusion of more than six-sevenths of the image height.
## 4 Proposed HTPR Method
### Feature Supplementation Based on Item Clustering
For an item \(e\) (\(e\in N_{u}\)) that has not been rated, if \(x\) is an arbitrary item of the same cluster that \(e\) belongs to, then \(e,x\in E_{i},i\in[1,k_{1}]\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Item**Feature & **Attribute** & **Attribute** & \(\cdots\) & **Attribute** & **Tag 1** & **Tag 2** & \(\cdots\) & **Tag q** \\ \hline Item 1 & 1 & 1 & \(\cdots\) & 1 & 0.123 & 0 & \(\cdots\) & 0 \\ Item 2 & 1 & 0 & \(\cdots\) & 0 & 0 & 0 & \(\cdots\) & 0.248 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ Item m & 0 & 0 & \(\cdots\) & 0 & 0.387 If \(e\) is not a new item (rated by other users), then the similarity of item \(e\) and item \(x\) can be calculated by Pearson correlation,  
\[sim(e,x)=\frac{\sum\limits_{u\in U_{ex}}{(r_{u,e}-\overline{r_{e}})\cdot(r_{u,x}- \overline{r_{x}})}}{\sqrt{\sum\limits_{u\in U_{ex}}{(r_{u,e}-\overline{r_{e}})^ {2}}\cdot\sqrt{\sum\limits_{u\in U_{ex}}{(r_{u,x}-\overline{r_{x}})^{2}}}}} \tag{5}\]  
where \(U_{ex}\) is the set of all users that provide ratings for the item \(e\) and the item \(x\); \(\overline{r_{e}}\) and \(\overline{r_{x}}\) are respectively the average ratings of all users for the item \(e\) and the item \(x\); \(r_{u,e}\) and \(r_{u,x}\) respectively denote ratings of the user \(u\) for the item \(e\) and the item \(x\). If \(e\) is a new item (not rated by any user), then the similarity of the item \(e\) and the item \(x\) can be calculated based on the item feature matrix (according to Equation (4)),  
\[sim^{\prime}(e,x)=\sum\limits_{k=1}^{l}{\frac{\lambda_{k}}{1+d(s_{e,k},s_{x,k })}}=\sum\limits_{k=1}^{l}{\frac{\lambda_{k}}{1+\left|s_{e,k}-s_{x,k}\right|}}. \tag{6}\]  
According to Equations (5)-(6), the set of nearest neighbors \(K_{1}NN(e)\) was obtained which was comprised of the most similar \(k_{1}\) items with the item \(e\) (not rated) in the same cluster. The nearest neighbor \(k_{1}\) items were used to predict rating, as shown in the formula below:  
\[\mathrm{P}_{u,e}=\left\{ \begin{array}{ll}\frac{\sum_{f\in K_{1}NN(e)}sim^{ \prime}(e,f)\times r_{uf}}{\sum_{f\in K_{1}NN(e)}sim(e,f)}&\text{if $e$ is a new item}\\ \frac{\sum_{f\in K_{1}NN(e)}sim(e,f)\times r_{uf}}{\sum_{f\in K_{1}NN(e)}sim(e,f)}&\text{otherwise}\end{array} \right. \tag{7}\]  
A null value in the user-item rating matrix means that one user has not rated the item. The null values in the user-item rating matrix will be replaced by the predicted rating as above. For the user-item rating matrix after feature supplementation, i.e., \(FR=\left(R_{i,j}\right)_{n\times m}\), the ratings of the user \(u\) for the item \(e\) can be represented as follows:  
\[R_{u,e}=\left\{ \begin{array}{ll}r_{u,e},\ if\text{ user $u$ had rated item $e$}\\ P_{u,e},\ otherwise\end{array} \right.. \tag{8}\]
## 2 Materials and Methods  
Under the condition of 50 W power, CoFeVB was deposited on a Si(100) structure by the sputtering method, and the thickness of CoFeVB was 10-40 nm. The deposited temperature (T\({}_{\text{s}}\)) of CoFeVB was maintained at RT. The power density was 1.09 W/cm\({}^{2}\) and the deposition rate was 1.2 nm/min. The sputtering chamber was square, and the distance between target and substrate was about 15 cm. The substrate faced the target. The target composition of the CoFeVB alloy was 40 at% Co, 40 at% Fe, 10 at% V, and 10 at% B. The typical base chamber pressure was \(1\times 10^{-7}\) Torr, and the Ar working chamber pressure was \(2\times 10^{-3}\) Torr. The structure of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films was detected by X-ray diffraction (XRD) (Philips, Amsterdam, The Netherlands) of CuK\({}_{\alpha 1}\) (Philips X'pert). The field emission scanning electron microscopy (FESEM) (JEOL, Tokyo, Japan) equipped with energy dispersive spectrometer (EDX) spectrometer was used for surface morphology and determination of the elemental composition of films. In addition, the in-plane low-frequency alternate-current magnetic susceptibility (\(\chi_{\text{ac}}\)) of Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) was studied using a \(\chi_{\text{ac}}\) analyzer. The model, manufacturer, and country of manufacture of the \(\chi_{\text{ac}}\) analyzer were XacQuan, MagQu Co. Ltd. New Tapei City, and Taiwan, respectively. First, the \(\chi_{\text{ac}}\) analyzer with an external magnetic field was used to calibrate the reference standard sample. Then, the tested sample was inserted into the \(\chi_{\text{ac}}\) analyzer. The driving frequency was between 10 and 25,000 Hz. The \(\chi_{\text{ac}}\) was determined by magnetization measurement. All tested samples had the same shape and size to eliminate demagnetization factors. The \(\chi_{\text{ac}}\) valve is an arbitrary unit (a.u.), because the alternating current result corresponds to the reference standard sample and is a comparative value. The \(\chi_{\text{ac}}\) analyzer measured the relationship between susceptibility and frequency. The optimal resonance frequency (f\({}_{\text{res}}\)) is measured by the \(\chi_{\text{ac}}\) analyzer, which represents the frequency of the maximum \(\chi_{\text{ac}}\). The surface roughness and morphology of CoFeVB films were studied by atomic force microscopy (AFM) (NT-MDT, Moscow, Russia). The in-plane hysteresis loop of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) films was obtained by alternating gradient magnetometer (AGM) (PMC, Ohio, USA). Finally, using deionized (DI) water and glycerol as the experimental liquids, the surface energy of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films was calculated by measuring the contact angle [24; 25; 26], which is defined as the surface excess free energy of a specific crystal surface area [27].
## 5 Results
### Training Results  
Table 3 shows the model training results. We tested six different types of models with or without weather information. The training results indicate that the GCN with meteorological information performed better in the MSE of UB, and the GCN in the MSE of LB. When we compared two models, models with meteorological information and models without meteorological information, models without meteorological information performed better. On the other hand, the GCN performed worst in the dataset without meteorological information. This is mainly because the model did not recognize the difference between upper and lower bounds in this case. Figure 4 visualizes the model prediction results. Figure 4a,b represent the first week of 2022 with the upper and lower bounds, respectively. Green lines, representing the proposed model, perform similarly to red and yellow lines, representing the MLP and GAN.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{\
\begin{tabular}{c} **Dropout** \\ **Probability** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Meteorological** \\ **Information** \\ \end{tabular} } & \multirow{2}{*}{**MSE-UB**} & \multirow{2}{*}{**MAPE-UB**} & \multirow{2}{*}{**MSE-LB**} & \multirow{2}{*}{**MAPE-LB**} \\ \hline
1 & MLP & - & O & 18,690.69 & 0.228 & 14,901.66 & 0.264 \\
2 & MLP & 0.3 & O & 16,731.72 & 0.236 & 20,039.50 & 0.266 \\
3 & GCN & 0.3 & O & 16,001.57  
Heatmaps of differences between observed and predicted results over 60 days are expressed in Figure 5. Our proposed model performed the best over 60 days, except for a period from days 28 to 34. These dates contained three consecutive holidays (Lunar New Year) in South Korea. Note that morning peaks of the period accounted for 80.4% of the predicted MSE.  
Figure 4: Model prediction results.  
Figure 5: Heatmaps of differences between observed and GCN-predicted results on upper bound (**a**) and lower bound (**b**) starting on 3 January 2022.