esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description the PV System Based on the NEC Boost Converter  
The circuital description of the proposed PV system, based on the NEC boost converter, is presented in Figure 2. Such a circuit models the second stage of the microinverter using the voltage source \(v_{b}\), which is an acceptable model taking into account that the grid-connected inverter has a control loop to ensure a stable average voltage in the DC-link. However, the sinusoidal power injection into the grid produces a sinusoidal voltage in \(v_{b}\) at the double of the grid frequency, which is added to the average value of \(v_{b}\) as an additional perturbation. The circuit also shows the measurement of the PV current and voltage because both are needed to perform the MPPT action; in this work, it is considered the P&O algorithm, but any other MPPT algorithm can be adopted.  
The input capacitor \(C_{pv}\) is added to the NEC boost converter proposed in [19], which is used to set the operation voltage to the PV source. Moreover, the converter has two inductors, \(L_{1}\) and \(L_{2}\), which are used to impose continuous current to both the input capacitor and output port, i.e., to the grid-connected inverter. Such a characteristic has two main purposes:  
* Ensure a small voltage ripple to the PV voltage to reduce deviations from the MPP, thus ensuring a high efficiency of the MPPT algorithm.
* Introduce small current ripples to the DC-link of the second stage, which ensures a correct operation of the grid-connected inverter.  
The NEC boost converter also has an internal capacitor \(C_{cb}\), which is needed to ensure the continuous current condition at the output port as it will be explained afterwards. Finally, the converter has a MOSFET and a diode, which are complementarily activated.  
Figure 2: Circuital description of the PV system based on the NEC boost converter.  
The controller of the proposed PV system is designed so as to ensure a correct tracking of the reference \(v_{r}\) defined by the P&O algorithm, thus ensuring that the PV voltage \(v_{pv}\) is always at the optimal value. Such a controller also requires the measurement of both inductor currents \(i_{1}\) and \(i_{2}\); the measurement of the output voltage \(v_{b}\); and the values of the PV voltage and current \(v_{pv}\) and \(i_{pv}\), respectively, which are also needed to process the P&O algorithm.
# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 2 Methodology
### Database Compilation and Transfer Learning  
Many image quality assessment databases are available online, such as TID2013 [35] or LIVE In the Wild [36], for research purposes. In this study, we selected the recently published KonIQ-10k [33] database to train and test our system, because it is the largest available database containing digital images with authentic distortions. Furthermore, we present a parameter study on KonIQ-10k [33] to find the best design choices. Our best proposal is compared against the state-of-the-art on KonIQ-10k [33] and also on other publicly available databases.  
KonIQ-10k [33] consists of 10,073 digital images with the corresponding MOS values. To ensure the fairness of the experimental setup, we selected randomly 6073 images (\(\sim\)60% for training, 2000 images (\(\sim\)20%) for validation, and 2000 images (\(\sim\)20%) for testing purposes. First, the base CNN was fine-tuned on target database KonIQ-10k [33] using the above-mentioned training and the validation subsets. To this end, regularly the base CNN's last 1000-way softmax layer was removed and replaced by a five-way one in previous methods [18], because the training and validation subsets were reorganized into five classes with respect to the MOS values: Class \(A\) for excellent image quality (\(5.0>MOS\geq 4.2\)), Class \(B\) for good image quality (\(4.2>MOS\geq 3.4\)), Class \(C\) for fair image quality (\(3.4>MOS\geq 2.6\)), Class \(D\) for poor image quality (\(2.6>MOS\geq 1.8\)), and Class \(E\) for very poor image quality (\(1.8>MOS\geq 1.0\)). Subsequently, the base CNN was further trained to classify the images into quality categories. Since the MOS distribution in KonIQ-10k [33] is strongly imbalanced (see Figure 3), there would be very few images in the class for excellent images. That is why we took a regression-based approach instead of classification-based approach for fine-tuning. Namely, we removed the base CNN's last 1000-way softmax layer and we replaced it by a regression layer containing only one neuron. Since GoogLeNet [31] and Inception-V3 [32] accept images with input size of \(224\times 224\) and \(299\times 299\), respectively, _twenty_\(224\times 224\)-sized or \(299\times 299\)-sized patches were cropped randomly from each training and validation images. Furthermore, these patches inherit the perceptual quality score of their source images and the fine-tuning is carried out on these patches. Specifically, we trained the base CNN further for regression to predict the images patches MOS values which are inherited from their source images. During fine-tuning, Adam optimizer [43] was used; the initial learning rate was set to 0.0001 and divided by 10 when the validation error stopped improving. Further, the batch size was set to 28 and the momentum was 0.9 during fine-tuning.
# Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load
## 3 Problem Formulation
### Objective Function and Problem Constraints  
The optimal controller parameters and optimal power-sharing coefficients are designed based on time domain simulation. The problem constraints are the parameter bounds. Therefore, the design problem can be formulated as the following optimization problem  
Figure 4: Common reference frame (\(DQ\)).  
Figure 5: Phase locked loop (\(PLL\)).  
Figure 6: Active load controllers.  
\[\text{Minimize }J \tag{27}\] \[\text{Subject to }\{K^{\text{min}}\leq K\leq K^{\text{max}},\,m_{p}^{ \text{min}}\leq m_{p}\leq m_{p}^{\text{max}},\,\text{and}\,n_{q}^{\text{min}} \leq n_{q}\leq n_{q}^{\text{max}}\}\] (28) \[\text{where }J=\int_{t=0}^{t=t_{\text{sim}}}\biggl{[}\Bigl{(}P_{m}-P _{ref}\Bigr{)}^{2}+(V_{dc}-V_{dc}^{*})^{2}\biggr{]}\,t\,dt \tag{29}\]  
where \(J\) is a weighted objective function, \(K=[k_{pe},\,k_{iv},\,k_{pc},\,k_{ic},\,k_{pv\_AL},\,k_{v\_AL},\,k_{Pc\_AL},\,k_{ ic\_AL},\,k_{kp}PLL]^{T}\) are the controller parameters constrained as \(K^{\text{min}}\leq K\leq K^{\text{max}}\), \(m_{p}\) and \(n_{q}\) are the power-sharing parameters, \(t\) is minimum settling time, \(P_{ref}\) is the reference active power of the DG and \(V_{dc}^{*}\) is DC reference voltage.  
In the optimization procedure, the goal is to minimize \(J\) to make sure that DG inject the required active power and make sure that the dc voltage of the active load is close to the reference dc voltage. PSO starts randomly assuming their particles (controller parameters in this work). Using these parameters, the cost function \(J\) is calculated. This cost function depends mainly on the variations of \(P_{ref}\) and \(V_{dc}^{*}\) as it is mentioned in Equation (29). This step will be repeated and after each step, the cost function will be compared with the minimum calculated cost function until it arrives to the lowest cost function. The corresponding parameters to this lowest cost function will be selected to be the optimal controller parameters.
###### Abstract  
This study aimed to formulate and characterize experimental dental adhesives charged with different concentrations of nanofillers. Different concentrations (0, 7.5 wt%, and 15 wt%) of nanosized silica (50 nm) were added to the bond of a two-bottle experimental etch-and-rinse adhesive system (EA0, EA7.5, and EA15). The following physiomechanical properties were evaluated: degree of conversion (DC%), ultimate tensile strength (UTS), flexural strength (FS), static modulus of elasticity (SME), dynamic modulus of elasticity (DME), and glass transition temperature (Tg). Marginal integrity (%MG) was evaluated in standardized class I cavities hybridized with the EAs and restored using two dental composites (CON-conventional and OBF-bulk-fill): EA0 CON, EA7.5 CON, EA15 CON, EA0 OBF, EA7.5 OBF, and EA15 OBF. Gap formation was measured in the occlusal and mesial tooth-restoration interfaces using a 3D laser confocal microscope. Microensile bond strength (uTBS) was evaluated using dentin-composite beams (1 \(\times\) 1 mm) obtained from restorations. Data were submitted to ANOVA and [PERSON]'s test (\(\alpha\) = 0.05). For DC% and Tg, EA15 \(<\) EA0 = EA7.5 (\(p\) < 0.05). For UTS, EA0 \(<\) EA7.5 \(<\) EA15. For FS, SME, and DME, EA0 \(<\) EA7.5 = EA15 (\(p\) < 0.05). For the gap formation analysis, there were statistical differences only for the conventional composite (EA0 CON \(>\) EA7.5 CON = EA15 CON). The lowest values (\(p\) < 0.05) of uTBS were observed for the groups restored with EAs without inorganic content. In conclusion, charging dental adhesives with nanofillers may be a suitable strategy for improving their properties as well as their interaction with dental substrates.  
Keywords:dental adhesives; inorganic filler; mechanical properties; marginal gaps; bond strength +
Footnote †: journal: Applied Sciences
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
###### Abstract
[43] developed a ViT-based classification model for chest X-ray images, which outperformed previous methods. [PERSON] et al. [44] proposed a Transformer-based hyperspectral image classification method that uses CNN to extract spatial features while using a densely connected Transformer to capture sequence spectral relationships. Transformer has proved to be very successful in some fields, but it has almost no reported applications for GCI classification, which is, in fact, a complex problem, with some images containing large cloud areas and others containing only a small portion. Thus, the models for GCI classification should have the ability to extract both global features and local features. Spurred by the above reasons, a novel Transformer-based GCI classification method is proposed in this paper, which first sends the images to a CNN model to extract low-level features and generate the local feature sequences of images, then uses the Transformer to learn the relationship between the low-level feature sequences. It is able to capture both the local features and global features of images, which improves the discrimination of the images by the model. To the best of our knowledge, this is the first time that Transformer has been introduced to the field of GCI classification. The results of experiments on three GCI datasets show that the classification performance of the method exceeds the available methods. The main contributions of this paper are summarized as follows:  
(1) We apply Transformer to GCI classification task and propose a Transformer-based classification method that combines the advantages of Transformer and CNN to extract both local and global features of images, maximizing their complementary advantages for GCI classification. (2) We optimize the loss function to enhance supervised feature learning by supplementing the cross-entropy loss with the center loss. (3) An experimental evaluation is performed on three datasets (ASGC, CCSN, and GCD), and the results show that the proposed method in this paper has better classification accuracy. The rest of the paper is structured as follows. Section 2 details the components and overall structure of the research method. Section 3 reports different GCI datasets and the experimental setup used in this paper. The experimental results, as well as the discussion, are presented in Section 4. Section 5 provides the conclusion of this study.
## 4 Discussion  
The results obtained by applying the Genetic Algorithms were verified by experimentation. The TWB tubular structure was built according to the specifications resulting from applying the above method. Thus, the new tubular structure presents the dimensional characteristics shown in Table 16.  
The dynamic loading process was performed respecting the following characteristics: the height between the impact surface of the sample and the gravitational hammer L = 2100 mm  
Figure 13(a) represents the load history for the TWB tubular structure in both simulation and experimental crash tests. Good agreement and almost full conformance can be observed between the maximum force of the estimated and experimental results for both tubular structures. The impact load applied to the TWB tubular structure, and the load-displacement graph, are illustrated here for comparison.  
It is observed from Figure 13 that the results of the estimation are lower than the experimental results and the difference between them is about 3% at the peak of the diagram. However, in some parts of this diagram, the difference is more than 55% or without considerable differences.  
Based on the data presented above, the values obtained for the new TWB tubular structure are very close to those obtained from the optimization process using the method of Genetic Algorithms. The resulted data have an error rate of less than 3%, thus considering that this method can be used successfully in the qualitative and quantitative estimation of dynamically loaded TWB tubular structures.
## 3 Results and Discussion
### Regression Models  
Absorption coefficient values in the range of 1.0-2.5 THz in 0.25 THz increments were selected for regression analysis to be close to the 1.014-2.471 THz range, for which each glycol contamination level was significantly different from each of the others. The linear regression provided a tentative relationship of absorption coefficient to glycol contamination based on this study. The coefficient of determination (R\({}^{2}\)) values for the seven models started at 0.79 at 1.0 THz, increased to a peak of 0.84 at 1.75 THz, and dropped to 0.77 at 2.5 THz. Slope values ranged from a high of 2613 ppm \(\times\) cm at 1.0 THz, and decreased with each frequency increase to a low of 746 ppm \(\times\) cm at 2.5 THz. X-intercepts increased with frequency from 0.68 cm\({}^{-1}\) at 1.0 THz to 1.92 cm\({}^{-1}\) at 2.5 THz. The linear model at 1.75 THz was the best model based on R\({}^{2}\) (Figure 4). Although the range of absorption coefficient values for some contamination levels was wider than the others, none of the absorption coefficient ranges among the glycol contamination levels overlapped, and the absorption coefficient means of each contamination level were significantly different from all other contamination levels.
## 3 Method
### Triplet Attention Mechanism
The calculation process of the compound pooling layer is shown in the following Figure 8. In this layer, the input tensor is processed through both max-pooling and average-pooling operations along the channel dimension, and the resulting features are combined through concatenation. Similarly, in the second branch, the input \(X\) is first rotated 90 degrees counterclockwise along the W axis to obtain a rotated tensor \(X_{W}\ \in\ \mathrm{R}^{\mathrm{H}\times\mathrm{C}\times\mathrm{W}}\). Then, \(X_{W}\) is input into the attention branch to generate the weighted feature maps. Finally, the weighted maps are rotated 90 degrees clockwise along the W axis. The calculation process of this branch can be represented by the following equation:  
\[X_{W+}=\mathrm{R}^{\mathrm{W}+}(X_{W}\cdot\sigma(\mathrm{ConvBN}(\mathrm{C} \text{-pool}(X_{W})))) \tag{4}\]  
where, \(\mathrm{R}^{\mathrm{W}+}\) represents a clockwise rotation of 90 degrees along the W axis. In the last branch, rotation is not carried out on the input tensor \(X\), but directly weighting the feature parameters in the same channel and generating weighted feature maps is performed. The calculation process is shown in Equation (5). \[X_{\mathrm{C}}=X\cdot\sigma(\mathrm{ConvBN}(\mathrm{C}\text{-pool}(X))) \tag{5}\]  
After the calculation of each branch, as shown in Equation (6), the triplet attention module would aggregate the refined tensors generated by each branch using simple averaging, so as to realize the fusion of channel attention and spatial attention information. \[y=\frac{1}{3}(X_{H+}+X_{W+}+X_{C}) \tag{6}\]
## 3 Related Theories and Technologies
### Paillier Homomorphic Encryption  
Homomorphic encryption is an encryption technology that allows computations to be performed on encrypted data and to obtain encrypted results, which, when decrypted, are consistent with the results obtained by performing the same computations directly on the original data. This means that homomorphic encryption enables data to be processed and analyzed without revealing any content. It is an important technology for protecting online privacy, allowing cloud computing services to perform complex data processing tasks on users' encrypted data without accessing the actual data.  
Paillier homomorphic encryption is a public-key cryptosystem that specifically supports homomorphic addition operations on encrypted data. The applications of the Paillier encryption scheme are extensive, and it can be used to protect the privacy and security of data. For example, in distributed computing, the Paillier encryption scheme can be used to encrypt data and transmit it to various nodes for processing, ensuring the security and privacy of the data. Furthermore, the Paillier encryption scheme can also be used to implement homomorphic secret sharing, private-set intersection, and other application scenarios. Overall, the Paillier encryption scheme is an efficient homomorphic encryption scheme with a wide range of application prospects. This paper uses the Paillier cryptosystem in its final scheme. The homomorphic properties utilized in this paper are as follows:  
1. **Additive Homomorphism**: If \(c_{1}=\mathrm{Enc}(m_{1})\) and \(c_{2}=\mathrm{Enc}(m_{2})\), then \(\mathrm{Dec}(c_{1}\cdot c_{2}\mod n^{2})=m_{1}+m_{2}\). This allows for performing addition operations on ciphertexts without needing to decrypt them first.
2. **Scalar Multiplication Homomorphism**: If \(c=\mathrm{Enc}(m)\), then \(\mathrm{Dec}(c^{k}\mod n^{2})=k\cdot m\). This means that it is possible to perform multiplication operations between a ciphertext and a plaintext scalar without decryption.  
This paper will utilize homomorphic encryption technology to construct the third protocol of this paper: Unbalanced PSI-CA Protocol Based on Dual-Cloud Assistance.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Head Selection Phase
#### 5.1.3 Base Station-Assisted Clustering Optimization  
In BS-assisted clustering schemes, sensor nodes depend on the high processing power and inexhaustible energy resources of the BS by shifting the burden of the CH selection and cluster formation phases to the BS. Therefore, the end-user can control CH placement in the BS based on the network characteristics and the application type. However, these schemes require that the BS be periodically updated with relevant information by the SNs [55].
## 3 Analytical Model
### Experiment on the Retaining Wall Effect  
To evaluate the influence of a retaining wall on the near-field blast of an explosion inside the ammunition storage magazine, experiments for explosions inside the ammunition storage magazine without a retaining wall and for explosions with a retaining wall were created.  
(1) Without a retaining wall  
This study placed 30 g of spherical C4 explosive at the ammunition storage magazine portal and 30 cm away from the portal inside the ammunition storage magazine to implement the experiment. The explosive center was located 15 cm above the ground. Two pencil sensors were located 15 cm above the ground and arranged longitudinally and transversely 60 cm away from the magazine's portal to measure the overpressure generated without a retaining wall outside the ammunition storage magazine. The experimental configuration is shown in Figure 4.  
(2) With a retaining wall  
The same mass of C4 explosive and ignition position as the previous experiment were used to perform the explosion experiment using a retaining wall. A steel retaining wall was located 30 cm away from the portal. The retaining wall size was \(30\times 30\) cm and had a thickness of 2 cm. The pencil sensors were arranged in the same manner as that for the experiment without a retaining wall, as shown in Figure 5.
# Locally Scaled and Stochastic Volatility Metropolis-Hastings Algorithms
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Heart dataset**} \\ \hline  & \(m\)ESS & \(t\) (in sec) & \(m\)ESS/\(t\) & **NLL (train)** & **NLL (test)** \\ \hline NUTS & **5656** & 25 & **223.34** & **132** & **56** \\ MALA & 848 & 18 & 47.14 & **132** & **56** \\ MH & 0.29 & **9** & 0.04 & 298 & 67 \\ LSMH & 97 & 41 & 2.38 & 352 & 82 \\ SVMH & 114 & 9 & 12.01 & **132** & **56** \\ \hline  & \multicolumn{5}{c}{**Australian credit dataset**} \\ \hline NUTS & **5272** & 33 & **159.40** & 248 & **70** \\ MALA & 787 & 15 & 51.43 & 248 & **70** \\ MH & 0 & **7** & 0.00 & 750 & 112 \\ LSMH & 109 & 37 & 2.89 & 407 & 88 \\ SVMH & 115 & 9 & 12.35 & **247** & **70** \\ \hline  & \multicolumn{5}{c}{**Fraud dataset**} \\ \hline NUTS & **4449** & 921 & 4.84 & **919** & 144 \\ MALA & 110 & 16 & 6.69 & 921 & **143** \\ MH & 0 & **8** & 0.00 & 993 & 150 \\ LSMH & 98 & 38 & 2.54 & 983 & 146 \\ SVMH & 96 & 10 & **9.88** & **919** & **143** \\ \hline  & \multicolumn{5}{c}{**German credit dataset**} \\ \hline NUTS & **7493** & 31 & **239.57** & **510** & **134** \\ MALA & 654 & 16 & 38.65 & **510** & **134** \\ MH & 0.0 & **8** & 0.00 & 1 662 & 267 \\ LSMH & 110 & 62 & 1.76 & 745 & 174 \\ SVMH & 107 & 10 & 10.58 & **510** & **134** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results averaged over thirty runs of each method for the logistic regression datasets. Each row corresponds to the results for a specific method for each performance metric. NLL is the negative log-likelihood.
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
## 2 Related Work
### Research Gaps and Positioning of Current Work  
Our review identifies several key gaps addressed by this research. Firstly, there is a lack of investigation into hybrid models that explicitly leverage statistical time series de composition techniques like STL as a pre-filter to improve the performance and efficiency of DL-based IDS classifiers, particularly focusing on the critical FNR/FPR balance. Secondly, many existing IoT IDS studies, despite using advanced techniques, often lack a rigorous analysis of FNR and FPR trade-offs, reporting primarily aggregate metrics like accuracy, which can be misleading, especially on imbalanced datasets [9; 29]. Thirdly, efficiently processing large-scale, realistic IoT datasets like BoT-IoT [40] while maintaining low detection latency remains a challenge.  
This paper aims to address these gaps by proposing and evaluating a novel hybrid framework explicitly integrating STL pre-filtering with DL classification (LSTM, RNN, and DNN). Our primary goal is to demonstrate that this synergistic approach can achieve high detection performance while significantly reducing both FNR and FPR compared to standalone DL models and existing literature benchmarks. We provide a detailed analysis of the framework's performance on the BoT-IoT dataset, directly addressing the FNR/FPR trade-off and computational considerations, thus contributing a robust and practical solution for enhancing behavioral intrusion detection in demanding IoT environments.