esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 2 Related Work
### Traditional Dehazing Methods  
Traditional methods can be further divided into methods based on image enhancement and methods based on atmospheric scattering models. Methods based on image enhancement restore hazy images by enhancing contrast [5,6] and suppressing low-frequency information [8,9]. [PERSON] et al. [28] combined mixed median filtering with Laplacian to dehaze images and apply it to remote sensing images. [PERSON] et al. [29] combined the phase-consistency feature of remote sensing images with multi-scale retinex theory and used it to dehaze urban remote sensing images.  
The method based on image enhancement is considered to be unstable in many cases because it lacks a foundation in physics. Therefore, the method based on the atmospheric scattering model eventually became the mainstream of traditional dehazing methods. ASM-based methods mainly use prior knowledge to estimate the parameters in Formula (1) and then obtain a haze-free image.  
[PERSON] et al. [12] proposed a dark channel prior (DCP) method through statistics and research on a large number of haze-free images. Studies have shown that in the non-sky area of the image without haze, there are always pixels with very low values close to 0 in the RGB bands. Therefore, the DCP-based dehazing method achieves outstanding dehazing effects and wide applicability. Many research efforts have been dedicated to the improvement of the DCP-based dehazing method. [PERSON] et al. [13] proposed a dehazing method based on the color decay prior. This method obtains the depth of field by modeling the relationship between scene depth and color, obtains the parameters through the supervised learning method to obtain the transmittance, and, finally, restores the hazy image effectively according to Formula (1).  
[PERSON] et. al. [30] proposed a global-based transmittance estimation method, which is different from the previous local-based transmittance estimation. The method estimates the transmittance and restores the image based on the prior knowledge that the color distribution of pixels in a hazy image will generate haze lines. The global-based estimation is more efficient and robust. [PERSON] et al. [31] refined the atmospheric veil through a low-pass filter and redefined the transmittance to reduce color distortion. The experimental results demonstrate well the preservation of ground details and effective dehazing of remote sensing images. [PERSON] et al. [32] proposed a spatial-spectral adaptive dehazing method to effectively remove the haze effect from visible light remote sensing images. This method establishes the relationship between the image gradient and the transmittance between different wavelength bands.
## 4 Results and Discussion
### Scenario Analysis  
#### 4.3.1 Scenario 1: Off-Grid System with Limited Biomass Feedstock  
The total amount of annual food waste from the household sector in Japan is estimated at about 6.46 million tons [48]. Considering Japan population in 2019, which was about 126.2 million, the total kitchen and food waste produced by each inhabitant can be calculated by about 51.2 (kg/year). Take into consideration this assumption that at four dwellers living in each household, the quantity of the annual food waste from one Japanese family is approximated at 204.7 (kg/year), which can be used in the SCWG. The results of this scenario are reported in Table 4. The annual electricity demand in the selected household can be met by a combination of solar electricity generation (39%) and fuel cell electricity generation (61%). The hourly electricity supply-demand in this scenario is represented in Figure 9.  
Since the amount of annual biomass feedstock is limited in this scenario, the required hydrogen for feeding the fuel cell should therefore be provided by the solar electrolyzer. During the cold seasons, the demand for hydrogen increases for the fuel cell, and therefore the SCWG has the pivotal role as the optional function for hydrogen generation, which is basically due to lack of solar electricity generation  
Figure 8: Monthly average solar irradiation in Fukushima.  
during this period. Since the amount of annual kitchen waste generated from one household is limited, additional sources of wet biomass such as sewage sludge from the nearby commercial buildings should therefore be considered in this scenario. Figure 10 shows the monthly average electricity generation from the proposed HRES in this scenario. In the winter season, the amount of the generated electricity from the fuel cell was found to be higher than the other seasons. This was because the demand for electricity in winter-time is very high, and the electricity generated from the PV module was not enough due to insufficient solar irradiation. The monthly hydrogen production from the solar electrolyzer and SCWG is shown in Figure 11.  
The optimal solution in this scenario was obtained at the discounted annual cost of 244,550 JPY/year. The LCOE was estimated at 55.92 JPY/kWh in this scenario. The size of the solar electrolyzer increased extremely in order to provide a sufficient amount of hydrogen for the fuel cell. Figure 12 shows the PSO process through the swarm's motion in different iterations from 0 to 20 in this scenario. It can be seen that particles (PV, fuel cell, and SCWG) flew from random initialization toward the particle best and global best so that all the particles converged to one point, which is called global best. The particles met each other after 20 iterations within an objective domain from 0 to 5.
###### Abstract  
In this research, a comparative analysis was carried out to characterize the content of phenolics and biological activities of the whole plant of _Papaver rhoas_ L. (_P. rhoeas_) from different geographical regions of Morocco, as well as to determine the synergistic antimicrobial and antioxidant effects of all parts of _P. rhoas_. The determination of total polyphenol content (TPC), total flavonoid content (TFC), and total anthocyanin content (TA) in extracts of whole plants of _P. rhoas_ from three different geographical regions: Taoutane (P1E), Fex (P2E), and Seftrou (P3E) were estimated by the Folin-Ciocale reaction, the aluminum trichloride method and the differential pH absorption technique, respectively. Two tests were used to evaluate the antioxidant power of our samples: the DPPH test and the TAC test. Using two methods, disk diffusion and microdilution, antimicrobial activity was studied against four pathogenic bacteria and one yeast. The results of TPC, TFC, and TA show that the P3E sample is the richest in polyphenols, flavonoids, and anthocyanins, with values 37.33 \(\pm\) 1.307 mg GAE/g, 4.72 \(\pm\) 0.346 QE/g, and 1.77 \(\pm\) 0.026 CGE/g, respectively. In addition, P3E showed the best antioxidant activity with an IC50 = 0.27 \(\pm\) 0.001 mg/mL and TAC = 9.99 \(\pm\) 0.768 mg AAE/g, respectively. The results of antimicrobial activity showed significant activity on almost all the tested strains. The lowest MIC was recorded for P3E against _E. coli_ ATCC 25922 and _E. coli_ CIP 53126 strains at 0.78 and 0.78 mg/mL, respectively. These results show that the geographical region can influence the plant's phytochemistry and then these biological activities.  
Papaver rhoas L.; geographical region; polyphenols; flavonoids; anthocyanins; biological activities
## 4 Simulation Results and Discussion
### Performance Evaluation of JS-MFO for Benchmark Functions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Algorithm**} & \multicolumn{3}{c}{**Index**} & \multicolumn{3}{c}{**Unimodal Functions**} & \multicolumn{3}{c}{**Multimodal Functions**} \\ \cline{3-10}  & & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) & \(f_{5}\) & \(f_{6}\) & \(f_{7}\) & \(f_{8}\) \\ \hline \multirow{3}{*}{JS} & Mean & \(2.00\times 10^{-18}\) & \(2.03\times 10^{-19}\) & \(0.7100\) & \(2.67\times 10^{-10}\) & \(3.49\times 10^{-10}\) & \(12.585\) & \(8.68\times 10^{-5}\) & \(0.0098\) \\  & SD & \(2.84\times 10^{-18}\) & \(2.68\times 10^{-19}\) & \(0.8835\) & \(2.65\times 10^{-10}\) & \(1.49\times 10^{-10}\) & \(9.0885\) & \(1.94\times 10^{-4}\) & \(0.0368\) \\  & Best & \(9.14\times 10^{-20}\) & \(1.57\times 10^{-20}\) & \(0.0179\) & \(3.97\times 10^{-11}\) & \(1.50\times 10^{-10}\) & \(3.0574\) & \(1.36\times 10^{-6}\) & \(1.57\times 10^{-5}\) \\  & Time (s) & \(1.2488\) & \(1.2332\) & \(1.0741\) & \(1.3384\) & \(1.4780\) & \(1.4203\) & \(2.4955\) & \(2.7288\) \\ \hline \multirow{3}{*}{MFO} & Mean & \(2.67\times 10^{3}\) & \(383.527\) & \(1.88\times 10^{4}\) & \(32.0963\) & \(11.1412\) & \(139.56\) & \(4.5098\) & \(1.36\times 10^{7}\) \\  & SD & \(5.20\times 10^{3}\) & \(505.098\) & \(3.64\times 10^{4}\) & \(20.3943\) & \(8.7387\) & \(27.52\) & \(2.8850\) & \(7.48\times 10^{7}\) \\  & Best & \(0.3104\) & \(0.0524\) & \(178.5437\) & \(0.1066\) & \(0.1323\) & \(77.67\) & \(0.4706\) & \(0.2722\) \\  & Time (s) & \(0.7218\) & \(0.7127\) & \(0.8212\) & \(0.8202\) & \(1.2363\) & \(0.9198\) & \(2.0499\) & \(2.2922\) \\ \hline \multirow{3}{*}{JS-MFO} & Mean & \(6.53\times 10^{-83}\) & \(4.94\times 10^{-80}\) & \(0.0520\) & \(2.59\times 10^{-39}\) & \(3.13\times 10^{-15}\) & \(3.89\times 10^{-13}\) & \(5.08\times 10^{-29}\) & \(2.41\times 10^{-25}\) \\  & SD & \(3.43\times 10^{-82}\) & \(1.79\times 10^{-39}\) & \(0.1296\) & \(1.40\times 10^{
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## Appendix A Symbols and Acronyms  
The symbols and acronyms used in this paper are summarized below.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
6G & Sixth Generation \\ \hline AS & Average Shadowing \\ \hline BER & Bit Error Rate \\ \hline BS & Base Station \\ \hline CSCG & Circularly Symmetric Complex Gaussian \\ \hline FDMA & Frequency Division Multiple Access \\ \hline FHS & Frequent Heavy Shadowing \\ \hline GEO & Geostationary Earth Orbit \\ \hline ILS & Infrequent Light Shadowing \\ \hline IP & Internet Protocol \\ \hline LEO & Low Earth Orbit \\ \hline LoS & Line of Sight \\ \hline Mbps & Megabits per Second \\ \hline MEO & Medium Earth Orbit \\ \hline NOMA & Non-Orthogonal Multiple Access \\ \hline NTN & Non-Terrestrial Network \\ \hline \hline \end{tabular}
\end{table}
Table 10: A list of symbols and acronyms.
## 1 Introduction
Experimentally verify the calculated results by making measurements at different horizontal and vertical positions of the receiving antenna inside the closet. Two measurement scenarios are examined: with and without the RF-DC converter. 4. Asses the sensitivity of the cavity-based WPT system's PTE to variations in the operating frequency by performing frequency sweeps for several Rx antenna horizontal and vertical positions. This paper is organized as follows: Section 2 outlines a cavity-based WPT system involving two Yagi-like antennas inside a carbon steel closet, presents the Ansys HFSS model of the WPT system constructed to compute the system's PTE, and provides a detailed description of the decomposition method. Section 3 describes the WPT system's optimization process and presents the optimized model results. Section 4 details the PTE measurement procedure and compares the simulated and experimentally obtained ones for different positions of the receiving antenna. Section 5 concludes the paper by summarizing the outcomes of this study.
## 6 Quantitative Evaluation
### Cost of Operation  
The quantitative evaluation of the SSFXG model provides insights into its cost-efficiency and scalability for blockchain-based identity management and network service provisioning, as illustrated in Figure 7. The analysis examined the breakdown of gas fees for blockchain transactions, computational overhead for executing smart contracts, and the overall impact of a competitive bidding mechanism. Gas fees were observed to increase proportionally with the number of subscribers and the complexity of smart contracts, ranging from \(2.4\times 10^{18}\) Wei to \(2.4\times 10^{19}\) Wei as the subscriber count scaled up to 1000. This linear cost progression is due to the deterministic nature of blockchain operations, where transaction costs are tied to the number and complexity of operations performed. Each additional subscriber introduces a similar computational and storage overhead, resulting in a predictable increase in cost. Such behavior confirms that the system scales in a controlled and consistent manner, making it viable for handling large-scale deployments with transparency in resource utilization.  
Similarly, computational overhead exhibited a linear growth pattern with an increase in the number of requests, indicating consistent efficiency in handling smart contract operations as demand rises. The implementation of a competitive bidding mechanism resulted in significant cost savings, reducing the total service provisioning costs by approximately 20%. For instance, the estimated cost for 1000 subscribers without bidding was \(3.0\times 10^{19}\) Wei, while the cost decreased to \(2.4\times 10^{19}\) Wei with bidding.  
Figure 6: System scalability analysis.
## 2 Proposed Paint-CUT Model
### Loss Function
#### 2.3.4 Perceptual Loss  
Chinese landscape painting focuses on the use of ink techniques and has its unique style characteristics. Other than the above CUT loss functions, perceptual loss is introduced to solve the problem of poor style transfer, retain the content of the landscape photo, and generate a similar style to the target landscape painting. Landscape photos contain rich information about scenery and the target landscape paintings contain specific style features. The main idea of perceptual loss is to quantify the similarity between the generated image and the target image by calculating the feature differences between them. Specifically, the layers of the pre-trained convolutional neural network are used to map the generated paintings and landscape photos and target paintings to the feature space, respectively. These features have better semantic information and, then, the similarity of the style and content is measured by comparing the difference between the feature maps. The perceptual loss is added so that the generated landscape paintings have rich details and realistic style characteristics. The pre-trained VGG16 network is used to extract the feature maps of landscape photos, target landscape paintings, and generated landscape paintings. And then, the style loss \(\ell_{style}\) and content loss \(\ell_{content}\) are calculated, respectively. The formulas are defined as follows:  
\[\ell_{style}=\sum_{j}\frac{1}{C_{j}H_{j}W_{j}}\left\|\phi_{j}(y)-\phi_{j}(\hat{ y})\right\|_{2}^{2} \tag{5}\]  
\[\ell_{content}=\sum_{j}\frac{1}{C_{j}H_{j}W_{j}}\left\|\phi_{j}(x)-\phi_{j}( \hat{y})\right\|_{2}^{2} \tag{6}\]  
where \(\phi_{j}\) denotes the output feature map of the \(j\) layer in the VGG16 network; \(C_{j}\), \(H_{j}\), and \(W_{j}\) are the number of channels, height, and width of the feature map, respectively; \(x\) is the input landscape photo; \(y\) is the target landscape painting; and \(\hat{y}\) is the generated landscape painting.  
The perceptual loss is calculated by a weighted summation of style loss and content loss, which is defined as follows:  
\[L_{per}(G,X,Y)=\alpha\ell_{style}+\beta\ell_{content} \tag{7}\]  
where hyperparameter \(\alpha\) is a weight that controls the style loss and hyperparameter \(\beta\) is a weight that controls the content loss. Better results are obtained when \(\alpha\) and \(\beta\) are set to 0.6 and 0.4, respectively.
## 2 Control Strategies
### Sliding Mode Lateral Stability Control  
This lateral stability solution is based on the Sliding Mode methodology. The objective is the direct control of vehicle side-slip angle \(\beta\) and yaw rate \(r\) by the application of corrective steering and TV efforts. The SMC-based ESP algorithm, whose simplified block diagram is visible in Figure 2, is developed on three different layers, depending on the task the sub-controller must accomplish:* **High-level layer**: which defines the control objective according to driver intention and vehicle states, for example, longitudinal speed, side-slip angle, yaw rate;
* **Intermediate-level layer**: consisting of a first-order Sliding Mode Controller;
* **Low-level layer**: devoted to the generation of the control references for the actuators, i.e., SBW, BBW, and traction motors.  
In-depth information about each controller layer can be found in the following chapters. The proposed solution respects fundamental features to exhibit portability and flexibility properties: scalability, abstraction, parametrization, modularity, and RT capability. This allows the controller to be implemented for different vehicle architectures, for example, Internal Combustion Engine (ICE), EV, FWD, and 4 WD.
## 3 Materials, Methods, and Findings
### Support Vector Machines (SVMs)  
The SVM algorithm is used to classify non-linear datasets. The radial basis function (RBF) was used as the kernel function and optimized the class separation with a hyperparameter of C = 1.0. The model provides effective classification in high-dimensional datasets and forms decision boundaries based on support vectors. Its performance was evaluated using accuracy and recall metrics. Figure 4 shows the complexity matrix of the Navie Bayes algorithm.  
Figure 4: SVM confusion matrix.  
Figure 3: Navie Bayes confusion matrix.  
The model made 93 correct predictions in the \"no verbal violence\" class and incorrectly labelled 9 instances as \"verbal violence exists\" (false positive). In the \"verbal violence present\" class, the model made 104 correct predictions and incorrectly labelled only 4 instances as \"no verbal violence\" (false negative). These results show that the success of the model in detecting verbal violence is quite high and that the false negative rate (3.7%) is extremely low. However, the low number of false positives (8.82%) indicates that the rate at which the model generates false alarms in non-verbal violence situations is also acceptable. This performance indicates that the SVM can discriminate between classes in a balanced way.
## 6 Results and Analysis
### Statistical Analysis of Generated Graphs  
We applied the graph construction technique on the captions from the validation set of the MSCOCO captioning database. The results revealed that the proposed approach can generate about 5.16 \(\pm\) 1.00 entities, 2.06 \(\pm\) 0.24 actions, 2.49 \(\pm\) 0.87 attributes, and 5.73 \(\pm\) 1.55 relations on average. The average length of the captions was 11.85 \(\pm\) 2.04. The detailed breakdown for captions of different lengths is provided in Table 3.  
Upon further analysis, as shown in Figure 13, the number of graph elements increased monotonically along with the number of words in the caption. The number of entities was highly proportional followed by attributes and then actions. This is logical, as most of the actions in an image are shared by multiple entities.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 2 Materials and Methods
### Proposed Segmentation System  
The scheme of our speech/singing segmentation system is shown in Figure 2. In the first step, a GMM-HMM VAD is applied to the Mel Frequency Cepstral Coefficient (MFCC) features to locate voice segments. Then, the pitch contour is extracted and a smoothing process is applied on it to remove vibrato effects. Using the smoothed contour, we detect note areas using our algorithm described in Section 2.2.2 and we calculate the voicing and note percentages in each segment. Finally, a statistical classifier is used to assign the correct class to these percentage parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Singer** & **Host** & **Singers Who Host** & **Hosts Who Sing** & **Total** \\ \hline Female & 33 & 43 & 1 & 9 & 86 \\ Male & 140 & 528 & 13 & 28 & 709 \\ \hline Total & 173 & 571 & 14 & 37 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of speakers and singers in the Bertsolaritza database.  
Figure 1: Distribution of singing and speech segment durations. (**a**) Bertsolaritza database; (**b**) NUS database.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method
### CSBlock
This strengthened the proposed spatial attention structure and enabled efficient feature extraction. \[SA(x)=H_{1}(x)\times x. \tag{9}\]  
Equation (9) is the multiplication between the input feature map and the spatial attention feature map to generate the latter. Here, \(H_{1}(x)\) represents a convolution operation that results in a single-channel output, and \(x\) represents the input feature map. \(H_{1}(x)\) is used to calculate the interpixel correlation within the input feature map. The previously performed channel attention ensures that the emphasized feature map with enhanced high-frequency components is preserved, while suppressing low-frequency or unnecessary features. From this emphasized feature map, Equation (10) calculates the interpixel correlation through spatial attention on regions with significant differences in neighboring pixel values. Equation (10) emphasizes the critical parts of the input feature map and extracts features by  
Figure 11: Spatial attention structure. Figure 12: Integrated architecture of the CSBlock with channel and spatial attention. considering the correlation between pixels. This further enhances the emphasis on the spatial features, and the spatial attention structure enables effective feature extraction. Figure 10:0  
Equation (10) represents the equation for the CSBlock, which incorporates channel and spatial attention. Here, SA represents the spatial attention process from Equation (9), and CA denotes the channel attention obtained from Equation (8). Additionally, a residual scale is used to ensure stable learning. The CSBlock is the result of sequentially applying channel and spatial attention to the input feature map, denoted as \(x\). The CSBlock refines the essential parts and spatial features of the input feature map through the SA and CA processes, and the resulting feature map is multiplied by the residual scale. This design addresses the instability and performance degradation that can occur in deep networks. The gradient values were constrained using a residual scale, allowing for stable learning. Equation (10) is the key formula for the CSBlock, which combines channel and spatial attention. It captures the important features of the input feature map and implements strategies for stable learning to address performance degradation issues effectively.