esa-sceva/satcom-chunk-collection huggingface dataset:
SVM is a powerful tool for problems with a small sample, and has high computational efficiency and good generalization ability. SVM has been applied in the fault diagnosis of rolling bearings [16], wind turbines [17], motor rotors [18], and air compressors [19]. However, SVM is a binary classifier algorithm, which has the disadvantage of classification model building difficulty and low classification efficiency in multi-classification problems. In addition, the optimal classification surface of SVM is determined by the support vector at the edge of the class, and the traditional SVM is particularly sensitive to noise and outliers in the training samples. These problems reduce the diagnostic performance of SVM. Kernel extreme learning machine (KELM) is an improved algorithm proposed by [PERSON] [20] based on the ELM algorithm and a kernel function. KELM not only possesses numerous advantages of the ELM algorithm, but also integrates a kernel function and maps linearly inseparable modes into a high-dimensional feature space to make them linearly separable; therefore, the identification accuracy is improved. KELM does not require its user to determine the number of hidden layer nodes in advance. Compared with ELM, in the network training and learning stages, KELM only requires its user to select the proper kernel parameters and the normalization coefficient to obtain the network output weight via matrix calculations. In reference [21], a multi-kernel extreme learning machine method was proposed and applied to multi-element chaotic time series forecasts to obtain accurate forecasts. In reference [22], a fast single-winner cross validation online KELM method was proposed and successfully applied in chaotic time series forecasts and process recognition for continuously stirred tank reactors. However, the kernel function makes the KELM algorithm sensitive to parameter setting. Therefore, optimal parameter selection is the key to improving the KELM method's state forecast accuracy. For the above problems, a bearing fault diagnosis method based on VMD and an improved KELM algorithm is proposed in this paper. First, the fault signal is decomposed via VMD to obtain the IMF components. Next, the approximate entropy of the IMF component containing major fault information is calculated, and an eigenvector is created from the approximate entropy of each component. Then, the KELM parameters are optimized via the particle swarm optimization (PSO) algorithm to obtain a KELM with optimal parameters. Finally, the method proposed in this paper is applied to bearing fault diagnosis to verify its effectiveness.
## 2 Theory Background  
### Fixed- and Floating-Point Formats  
Multiplier architectures are typically classified into fixed-point and floating-point implementations, each offering distinct trade-offs among precision, dynamic range, and hardware cost. Figure 1 provides a comparison between the fixed-point and floating-point formats used in binary number representations. In the fixed-point format, the position of the decimal point is predetermined. This representation includes a sign bit, an integer part, and a fractional part. While floating-point implementations offer enhanced precision and a wider dynamic range than fixed-point designs, these benefits come with trade-offs in hardware complexity, area, performance, and power efficiency.  
The Institute of Electrical and Electronics Engineers (IEEE) 754 standard [21] formally defines floating-point numbers, detailing the representation of single-precision (32-bit) and double-precision (64-bit) formats. This standard states that a floating-point number consists of a sign bit (\(S\)), an exponent (\(E\)), and a mantissa (\(M\)). In the normalized form, the value represented by a floating-point number is expressed as \(N=(-1)^{S}\times(1+M)\times 2^{(E-bias)}\). The range for \(M\) is [0, 1), and the 1 bit added to \(M\) is known as the implicit bit. For floating-point numbers of any size, the _bias_ is determined using the formula \(2^{k-1}-1\), where \(k\) represents the number of bits in \(E\). Table 1 provides a summary of the specifics for single- and double-precision formats, which are among the most widely used formats today.
## 4 Maneuvering Policy Generation Algorithm Designing in 1-vs-1 WVR Air Combat Based on MADDPG  
### Maneuvering Strategy Generation Algorithm Outline  
The algorithm framework is composed of the air combat environment model, MADDPG, reward shaping, and target position predication modules. Specifically, we introduced the future position of the target UCAV in the MADDPG algorithm. It should be noted that, unlike the traditional MADDPG algorithm, when using the air combat strategy generated by this algorithm, the Q-network after training is still required in order to predict the target's future position. Moreover, we Introduce potential-based reward shaping method to improve the efficiency of maneuver strategy generation algorithm of UCAV. Figure 3 shows the overall framework of the maneuvering strategy generation algorithm. In Figure 3, the upper left part denotes the air combat environment model, and its input are the actions of both the UCAVs, and the output are the next motion states \(s\) and the reward values of the UCAVs. The upper right part denotes the MADDPG algorithm module, which contains two actors and two critics. The actor's input is the state vector \(\mathcal{S}\) described in Section 3.3, and the output is the action vector \(\mathbf{a}\) described in Section 3.2. Two neural networks are used in the actor, one as the target network and the other as the online network, in order to improve the stability of the learning process. The structures of the two networks are the same, but the update methods are different. The online network uses the deterministic policy gradient for updating, and the target network copies the parameters of the online network through soft updates. The critic's inputs are the state vector \(\mathcal{S}\) and two action vectors \(a_{1},a_{2}\), and the output is the \(Q(s,a_{1},a_{2})\) value. Similar to the actor, there are two neural networks in the critic, one for the target network and the other for the online network. The update method is also similar to that used in the case of the actor. However, the difference lies in that the input during updating is sampled from the prioritized replay memory. The inputs of the reward shaping module are the state vector \(\mathcal{S}\) and the reward value \(r\). The shaping reward is a designed reward that is received from the air combat environment. The specific method of designing the shaping reward is explained in Section 4.2. The input to the target prediction module are the state vector \(\mathcal{S}_{base}\), \(-\alpha\
abla_{a_{1}}Q^{\mathbf{\mu_{2}}}(s,a_{1},a_{2})\) and \(-\alpha\
abla_{a_{2}}Q^{\mathbf{\mu_{1}}}(s,a_{1},a_{2})\), and the output is the vector of predicted target position \(\mathcal{S}_{predict}\). The method for target position prediction is explained in Section 4.3. Algorithm 2 explains the MADDPG training process for 1-vs.-1 WVR air combat engagement. Figure 3: The overall framework of the maneuvering strategy generation algorithm. WVR: Within Visual Range.
## 6 Analysis  
In this chapter, we analyze the performance of the proposed model based on the experiments in the previous chapter. Table 3 shows the highest accuracy, F1 score, epoch value and training data for each model observed with the data size fixed at 20 k and increasing the number of epochs. First, the accuracy of the proposed model was the highest at 0.9141, and the average accuracy was also the highest at 0.9026. Next, the accuracy was high in the order of hybrid, LSTM, CNN, and MLP. The epoch value for obtaining the optimal accuracy of each model was 10 for cnn, 14 for LSTM, 5 for MLP, and 15 epochs for hybrid 5, which took the longest learning time. The F1 score also showed the best performance of the proposed model at 0.9018, followed by hybrid, CNN, LSTM, and MLP. The proposed model for F1 score also showed the best performance at 0.9018.  
Figure 6: Model performance as data volume increases: (**a**) Accuracy; (**b**) F1 Score.  
Next, Table 4 shows the results of the experiment conducted while fixing the number of epochs and increasing the data size from 5 k to 15 k. When the data size was 13 k, the proposed model had the highest accuracy at 0.902, followed by the hybrid model at 0.879, CNN model showed the same accuracy as 0.871, LSMT model 0.825, and MLP model 0.726. In terms of average accuracy, the proposed model was the highest at 0.874, followed by CNN, hybrid, LSTM, and MLP. The F1 score also showed the highest performance at 0.901 when the data size was 13 k.  
The model proposed in this paper has demonstrated improved performance compared to the existing models, and the accuracy increases as the size of the data increases and the number of training increases. This approach addresses the data-loss and long-term dependency problems which affects the existing models especially when the data size becomes high. The only disadvantage of the current model is that it requires more training data and training time than the existing baselines. Even with this limitation, it can be effective in classification that requires a lot of training data, such as sentiment analysis, which is a representative field in text classification in recent years, and text classification for specialized fields (which require more inferences). In addition, in the case of MLP used in the experiment, the F1 score was not optimized, and it can be confirmed that the accuracy is significantly lower than that of other models. This is because the data set is a sentiment classification representing positives and negatives. It is judged that the learning has not been completed because the rules for classification are not clear. It is evident that the existing MLP for multi-classification is not suitable for sentiment analysis, which is a topic in recent text classification.
## 3 Results and Discussion
### E-Field Distribution and EM Energy Density Analysis
The interfaces between Fe\({}_{3}\)O\({}_{4}\) and adsorbed functional groups, air, and PVDF also promotes interfacial polarization giving rise to improved dielectric relaxation and enhanced EM wave absorption. As a result, of the superior MAP displayed by the M40 sample, it can be inferred that the optimal loading of C-Fe\({}_{3}\)O\({}_{4}\) in the PVDF matrix is 40 wt.%. Figure 13: 3D plot EM energy density of Ku-band simulated rectangular waveguide of samples at 3.0 mm thickness at a frequency of (**a**) 12.4 GHz, (**b**) 12.4 GHz, (**c**) 17.56 GHz and (**d**) 14.96 GHz. The magnified view of EM energy density of region filled with samples at frequency of (**a’**) 12.4 GHz, (**b’**) 12.4 GHz, (**c’**) 17.56 GHz and (**d’**) 14.96 GHz.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Creation of the Look-Up Tables (LUTs)  
Radiative transfer equation resolving is required for both the AOD retrieval and atmospheric correction; however, it is a very time-consuming procedure and it is impossible to do this on-line for  
Figure 3: The AOD variations in multiple years based on the ground measurements from four different AERONET sites around the world (**a**) Fowlers_Gap, (**b**) Mezaira, (**c**) Tamanrasset_INM, and (**d**) Dalanzadgad.  
remote-sensing imagery. Therefore, the look-up table (LUT) method [22; 23; 24] which calculates different atmospheric quantities off-line by predefined parameters' settings is employed and each output from radiative transfer model, such as Second Simulation of a Satellite Signal in the Solar Spectrum (6S) [25], is listed regularly in a table. Subsequently, the output for each pixel of an imagery can be retrieved by searching the table using the parameters' information from the imagery. The 6S is used in this study for building up the LUT. The settings of the 6S's parameters are listed in Table 3.
# Kinetic Modeling and Analysis of Fuel Element Pneumatic Lifting System in Pebble Bed Reactors
## 2 Kinetic Modeling of Fuel-Element Pneumatic Lifting System  
### Modeling the Motion of a Fuel Element in a Typical Bend  
The force characteristics of the fuel element in a typical bend are that its pneumatic force \(F_{\text{d}}\), lateral force \(F_{\text{L}}\), torque \(M_{\text{o}}\), and gravity \(F_{\text{g}}\) are acting in the plane where the axis of the bend is located. As the gravity \(F_{\text{g}}\) is along the plumb direction, the typical bend is along the plumb plane, that is, the axis plane of the bend is vertical, so the typical bend is also called the vertical bend. The force and motion of the fuel element in the vertical bend are shown in Figure 3. In the piping design of the fuel-element pneumatic lifting system, the shortest possible pipe length should be used to achieve the highest possible lifting drop, so a large number of such typical bends are used.  
Figure 2: Atypical-bend diagram.  
Equations (1)-(3) show the kinetic model of the fuel element in a typical bend based on the basic equations of kinetics.  
\[m\frac{\mathrm{d}v_{z}}{\mathrm{d}t}+m\frac{v_{y}v_{z}}{R}=F_{\mathrm{d}}-F_{g}\sin\theta \tag{1}\]  
\[m\frac{\mathrm{d}v_{y}}{\mathrm{d}t}-m\frac{v_{z}v_{z}}{R}=F_{\mathrm{L}}+F_{g}\cos\theta \tag{2}\]  
\[(\frac{1}{10}mD_{\mathrm{s}}{}^{2})\frac{\mathrm{d}w}{\mathrm{d}t}=M_{\mathrm{ o}} \tag{3}\]  
where \(m\) and \(D_{\mathrm{s}}\) are the mass and the diameters of the fuel element, respectively. \(v_{z}\), \(v_{y}\), and \(w\) are the axial, lateral velocities, and angular velocity of the fuel element, respectively. \(F_{\mathrm{d}}\), \(F_{\mathrm{L}}\), and \(M_{\mathrm{o}}\) are the tangential force, normal force, and rotational moment, respectively. \(R\) is the radius of the pipe, and \(\theta\) is the tilt angle, which is the angle between the gravity force \(F_{\mathrm{g}}\) and \(F_{\mathrm{L}}\). \(t\) is time.
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 7 Conclusions and Future Work  
In this paper, we constructed the multimodal data by integrating the numerical data, the dataset in our previous study [2] showing the best performance among others, and the satellite image with aerosol information. We proposed several multimodal deep learning models by combining LSTM-based models and CNN-based models to learn the multimodal data. We also optimized the hyperparameters set of the models using katib to achieve optimal performance.  
As a result, our multimodal deep learning models outperformed a single-modal deep learning model using only numerical data. In particular, Multimodal3, a model combining the deep network VGG19 among CNN-based models, performed the best. Regarding the image data, we prepared two types of image data: original images and cropped images. Regarding the minimum Test RMSE, the cropped image case performed better. However, regarding the average Test RMSE, the original image performed better.  
Figure 14: Average Test RMSE of all experimental results.  
In future work, we will explore the optimal multimodal deep learning model by applying various models in addition to those presented in the paper.  
Conceptualization, K.-K.K. and E.-S.J.; methodology, K.-K.-K. and E.-S.J.; software, K.-K.K.; validation, K.-K.K. and E.-S.J.; formal analysis, K.-K.K. and E.-S.J.; investigation, K.-K.K.; resources, K.-K.K. and E.-S.J.; data curation, K.-K.K.; writing--original draft preparation, K.-K.K.; writing--review and editing, E.-S.J.; visualization, K.-K.K.; supervision, E.-S.J.; project administration, E.-S.J.; funding acquisition, E.-S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI-2021-01310.  
Not applicable.  
AirKorea data is available in [[https://www.airkorea.or.kr/](https://www.airkorea.or.kr/)]([https://www.airkorea.or.kr/](https://www.airkorea.or.kr/)) (accessed on 12 October 2022), and KMA data is available in [[https://data.kma.go.kr/](https://data.kma.go.kr/)]([https://data.kma.go.kr/](https://data.kma.go.kr/)) (accessed on 12 October 2022).  
The authors declare no conflict of interest.
## 4 Example Analysis
### Reliability Evaluation of the Distribution Network Considering the Impact of Terminal Faults
The reliability index of medium-voltage systems in the three scenarios is shown in Table 6. The scenarios 4-6 are shown in Figures 9-11. The system reliability indices in the three scenarios are shown in Table 6. By comparing Scenario 4 and Scenario 5, it can be seen that configuring different types of automation switches at the same position has different impacts on the reliability of the system. Due to the automatic power restoration function of the section switch of the TUs with tele-control function, the reliability of Scenario 4 is higher than that of Scenario 5. By comparing Scenario 4 and Scenario 6, it can be seen that even for the same type of terminal, different configuration positions will have different impacts on the reliability of the system. Therefore, it is necessary to determine the optimal access plan when configuring terminals. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **SAIDI (h/Household\_Year)** & **ASAI (\%)** \\ \hline Scene 4 & 3.71 & 99.958 \\ \hline Scene 5 & 5.10 & 99.942 \\ \hline Scene 6 & 4.05 & 99.954 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation results of the system reliability index. Figure 9: IEEE RBTS BUS-2 distribution system with the section switch of the TUs without tele-control function configuration scenario 4.
## 3 Results
### Keywords Occurrence Analysis (RQ-2 and RQ-3)
#### 3.2.1 The Most Occurring Keywords  
We have created the most occurring keywords map, which is presented in Figure 3. For its development, we have restricted the minimum number of keywords' occurrences to 17 (it corresponds to 20% of all found keywords by Pareto distribution). VOSviewer has identified 48 keywords that meet the threshold. The 10 most occurring keywords are fuzzy ontology (139), knowledge (139), information (136), data (96), framework (86), concept (85), domain (75), semantic web (72), reasoning (71), and user (66).  
As can be seen from Figure 3, those keywords were divided into three clusters. According to [59], clustering in VOSviewer is based on minimizing distances between keywords, i.e., the most related keywords fall into one cluster. Consequently, the five closest keywords in those three clusters are the following: (1) information, domain, user, query, and term; (2) fuzzy ontology, knowledge, concept, semantic web, and reasoning; and (3) data, framework, development, context, and rule. Preliminary conclusions can be drawn that the first cluster represents the user's needs, the second cluster represents knowledge representation through fuzzy ontology for reasoning about concepts, and the third cluster represents data framework development according to context and rules, i.e., IS development.  
In Table 2, a part of the keywords co-occurrence matrix is presented (for the whole matrix, please see GitHub ([[https://github.com/Jolantux13/Onto_Fuzzy_IS](https://github.com/Jolantux13/Onto_Fuzzy_IS)]([https://github.com/Jolantux13/Onto_Fuzzy_IS](https://github.com/Jolantux13/Onto_Fuzzy_IS)) (accessed on 25 February 2021)). The columns and the rows of the matrix present keywords, and the cells are the co-occurrence strength of two keywords. Moreover, the intensity of the green color indicates that keywords tend to appear together. The more intense the green, the stronger the co-occurrence.  
Figure 2: Chronological distribution of ontology- and fuzzy-based IS research.  
The most co-occurring keywords are the following: fuzzy ontology and knowledge (46), knowledge and information (42), fuzzy ontology and information (38), information and user (38), concept and information (37), and domain and knowledge (37).
_Applied_  
_sciences_  
_Article_  
**High Voltage Graphene Nanowall Trench MOS**  
**Barrier Schottky Diode Characterization for High Temperature Applications**  
**[PERSON] *, [PERSON] *, [PERSON], [PERSON], [PERSON] and**  
**[PERSON]**  
MIMOS Berhad, Taman Teknologi Malaysia, Bukit Jalil, 57000 Kuala Lumpur, Malaysia;  
[PERSON]L_ADDRESS] (M.R.M.H.); [PERSON]SS] (M.H.O.); [PERSON]ESS] (M.A.S.M.H.);  
[PERSON]L_ADDRESS] (M.I.S.)  
* Correspondence: [PERSON]IL_ADDRESS] (R.M.S.); [PERSON]DDRESS] (S.K.W.S.); Tel.: +60-3-89955621 (R.M.S.); +60-3-89955637 (S.K.W.S.)  
Received: 28 December 2018; Accepted: 11 February 2019; Published: 17 April 2019  
**Featured Application: This study highlights about graphene nanowall (GNW) application in trench metal-oxide-semiconductor (MOS) barrier Schottky (TMBS) diode for improvement of reversed leakage current at high operating temperature. This novel application will enable designer to develop products with higher energy efficiency.**  
**Abstract:** Graphene's superior electronic and thermal properties have gained extensive attention from research and industrial sectors to study and develop the material for various applications such as in sensors and diodes. In this paper, the characteristics and performance of carbon-based nanostructure applied on a Trench Metal Oxide Semiconductor MOS barrier Schottky (TMBS) diode were investigated for high temperature application. The structure used for this study was silicon substrate with a trench and filled trench with gate oxide and polysilicon gate. A graphene nanowall (GNW) or carbon nanowall (CNW), as a barrier layer, was grown using the plasma enhanced chemical vapor deposition (PECVD) method. The TMBS device was then tested to determine the leakage current at 60 V under various temperature settings and compared against a conventional metal-based TMBS device using TiSi\({}_{2}\) as a Schottky barrier layer. Current-voltage (I-V) measurement data were analyzed to obtain the Schottky barrier height, ideality factor, and series resistance (R\({}_{8}\)) values. From I-V measurement, leakage current measured at 60 V and at 423 K of the GNW-TMBS and TiSi\({}_{2}\)-TMBS diodes were 0.0685 mA and above 10 mA, respectively, indicating that the GNW-TMBS diode has high operating temperature advantages. The Schottky barrier height, ideality factor, and series resistance based on _dV/dln(I)_ vs. \(J\) for the GNW were calculated to be 0.703 eV, 1.64, and 35 ohm respectively.  
**Keywords:** graphene nanowall; carbon nanowall; trench Schottky diode; leakage current; Schottky barrier