esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Convolutional Model for Digital Color Image Demosaicing
## 5 Conclusions  
In this work, we presented a novel data-driven demoisaicing algorithm that converts a raw image with Bayer pattern in an estimated RGB image. We observed the difference in the requirements for the Luminance estimation and the estimation of the three Chrominance channels and developed two reconstruction models. The two reconstruction models are integrated in a Full Reconstruction Network designed as a parallel composition of the two with a final merging step to recreate the output RGB image. In order to learn the input/output relations between the Bayer patches and the Luminance and Chrominance patches, we created a large dataset of patches extracted from the popular Kodak dataset. Additionally, we described the data selection strategy we followed to capture different populations of image patches with varying chromatic and spatial characteristics as an attempt to reduce model overfitting. We validated our result on a separate set of images and reported various reconstruction quality metrics. For the purpose of estimating the real  
Figure 11: Comparison of our approach with Bilinear, Malvar [24], Menon [25] on noise-free images.  
Figure 12: PSNR comparison joint denoising and demosaicking at different levels of Gaussian noise with standard deviation \(\sigma\). Higher values of PSNR correspond to better reconstruction. Our model is able to cope with relatively high level of noise. In order to provide a fair comparison, we reported the performance scores obtained by a model trained on a noise-free dataset. Further robustness to noise is possible if the training dataset is augmented with samples corrupted by noise.  
applicability of the model and computational complexity, we deployed the proposed Full Reconstruction Network on the low-cost embedded system NVIDIA Jetson Nano.
## 4 Experiments and Results
### Generalization Capability  
Through comprehensive testing on four benchmark datasets, the generalization performance of the HPANet has been rigorously validated, and detailed segmentation metrics comparisons are shown in Table 2. From the comparison results, the HPANet has statistically significant advantages over the benchmark model in five key evaluation indicators. On the CVC-ClinicDB dataset, compared to the Polyp-PVT, the proposed network achieves performance enhancements of 2.66% in mIoU, 2.19% in mDice, 3.31% in recall, 2.26% in precision, and 2.43% in F2-score. For the CVC-300 dataset, improvements extend to 4.71% in mIoU, 3.78% in mDice, 2.09% in recall, 3.31% in precision, and 3.24% in F2-score. In addition, while maintaining dominance in mIoU, mDice, recall, and F2-score across both CVC-ColonDB and ETIS-LaribPolypDB datasets, the network exhibits a marginal 0.18% pre  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mIoU** & **mDice** & **Recall** & **Precision** & **F2** \\ \hline U-Net [3] & 0.6497 & 0.742 & 0.6959 & 0.902 & 0.7094 \\ ResUnet [34] & 0.7637 & 0.8417 & 0.8372 & 0.8875 & 0.8344 \\ PraNet [7] & 0.8155 & 0.8802 & 0.8718 & 0.9239 & 0.8714 \\ CaraNet [35] & 0.8135 & 0.8758 & 0.8978 & 0.8952 & 0.8819 \\ Polyp-PVT [19] & 0.8595 & 0.9144 & 0.929 & 0.9201 & 0.9204 \\ HPANet & **0.8655** & **0.9204** & **0.9323** & **0.927** & **0.9251** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results on the Kvasir dataset. The best results have highlighted in bold.  
cision deficit relative to Polypp-PVT. Specifically, on the CVC-ColonDB dataset, the method delivers gains of 1.31% in mIoU, 0.94% in mDice, 1.55% in recall, and 1.11% in F2-score. For ETIS-LaribPolypDB, corresponding improvements reach 2.08% in mIoU, 1.03% in mDice, 0.22% in recall, and 0.67% in F2-score. Furthermore, the Wilcoxon signed-rank test demonstrated statistically significant improvement achieved by HPANet (\(p<0.05\)).
## 4 Experimental Design and Setup
### Datasets
#### 4.1.4 Data Preprocessing  
Normalization  
Normalization is a common technique employed when the values of numeric data have very different scales. It is also essential for the application of some machine learning models that use scale-sensitive distance metrics, such as KNN, which uses Euclidean distance to identify the nearest neighbors. In this study, the dataset is linearly rescaled using min-max normalization to normalize all numeric values in our datasets to the interval [0, 1][32]. Equation (1) defines min-max normalization, where min and max are the minimum and maximum values in the datasets, respectively, and value is the value being normalized:  
\[\text{Min -- Max Normalization }=\frac{\text{value -- min}}{\text{max -- min}} \tag{1}\]  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Analysis Technique** & \multicolumn{3}{c}{**Description of Metrics Removed**} & \multicolumn{3}{c}{**Datasets**} & \\ \hline  & & **anti4** & **junit** & **MapDB** & **mcMMO** & **mt** & **oryx** & **titian** \\ \hline  & & \multicolumn{3}{c}{**Number of metrics removed**} & & & & & \\ \hline
**Descriptive statistics** & Remove metrics with zero values & 13 & 15 & 14 & 17 & 9 & 15 & 12 \\ \hline
**Spearman correlation** & Remove metrics with strong correlation with others & 35 & 32 & 31 & 37 & 33 & 32 & 38 \\ \hline  & & & \multicolumn{3}{c}{**Number of metrics remained**} & & & \\ \cline{3-8}  & & 54 & 55 & 57 & 48 & 60 & 55 & 52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of metrics removed in each data analysis technique.  
Feature Selection  
Having identified and addressed any multicollinearity problems, FS is performed in the second and fourth scenarios as an additional step to improve the quality of the dataset as it still contains numerous attributes.  
In this study, we use the ensemble method to determine the best features using two types of filter-based feature ranking techniques, namely, relief and Pearson's correlation coefficient. We chose these types because relief is the most frequently used in the selected primary studies proposed in [7] and the Pearson's correlation coefficient is the second most popular. First, we apply these techniques on the datasets that assign a score to each feature; then we calculate the average of the two techniques and select the ten highest-scoring features (there is no clear evidence in the literature on a suitable number of features to select [11], so the number of features chosen was determined by previous studies either by identifying the number of features [33] or employing a cut-off value [34]). We apply these techniques using WEKA with the parameters set to the default values.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 1 Introduction  
With the development of the current technology and the extending of applied range of wireless sensor networks(WSNs), wireless sensor networks have gained widely attention for applying in natural disasters and some dangerous environment [1], such as earthquake stricken area and old-growth forest fire. Dangerous environment exits huge security risk for the rescuers if they need enter the primary scene, Fortunately, casting a mount of sensors to the monitoring area can not only solve the security risk problem but also make up WSNs networks to obtain the instant message of disaster area. However, the random deployment sensors may lead to the coverage holes and WSNs is the data-centered network. Thus, it is a worthy research to achieve efficient coverage.  
Currently, numerous researchers have focused on the coverage problem and the classic papers including [2; 3; 4; 5; 6]. The main methods of increasing coverage include figuring out the coverage holes, healing the holes, and using adjustable radii to enlarge coverage. While the cost of enhancing the coverage is expensive, for instance, the former method requires all of the sensors have locomotion capabilities and the latter needs the sensors have several different power levers.  
In this paper, we study the area coverage of hybrid sensor networks [7]. At first, we use static sensors to estimate the size of coverage hole and the number of assisted mobile sensor in the random distribution sensor network. Then deploying the assisted mobile nodes to heal the coverage holes. We assume that the initial network deployed a certain number of static nodes in the sensing field stochastically. The static nodes construct DT, then estimating the coverage holes in each triangle, and finding out the optimal positions of assisted nodes in every triangle. At last, the static sensors conductthe mobile sensors move to the optimal location to heal the coverage holes. Compared with the existing methods, our approach mainly shows several advantages as follows.  
* Our calculation method of coverage is simpler than other works, the simulation of coverage ratio in Section 6 shows that our approach is better than previous works.
* We provide an auxiliary nodes deployment strategy that is applied to heal the coverage hole.
* We figure out the bound of assisted nodes number which is related to the ratio of \(\frac{R_{c}}{R_{s}}\).  
The rest of the paper is organized as follows. In Section 2, we summarize the related work. In Section 3, we make some preparations and give out solution to the problem. Then, we propose the theoretical framework of the problem and show the analysis about the solution in Section 4. In Section 5, we elaborate our coverage algorithm. Performance evaluation and analysis are presented in Section 6. Conclusions and future work are given in the last part Section 7.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Regression  
In the earlier sections, the noise figure was assessed based on four input parameters. This analysis delves into how the noise figure changes with each parameter individually, providing a rationale for employing a polynomial structure. Subsequently, the suitable degree for each of the four polynomials will be determined. The coefficients will be calculated using the GLS algorithm. Each model is built by integrating datasets from various devices of the same brand, type, and gain range.
## 3 Results and Discussion  
### Selection of the SVMs Parameters  
The two parameters in the RBF kernel, C and \(\gamma\), were analyzed, in order to identify the optimal configuration for each implemented SVM classifier. To this end, a method of \"searching the grid\" on the C and \(\gamma\), using leave-one-specimen-out was implemented [59].  
For each binary classifier, various pairs of the C and \(\gamma\) values were tried and the one with the best accuracy was taken. A practical method to identify good parameters makes use of the exponentially growing sequences.  
Figure 8: Flow chart of the iterative method used for the optimization of each developed binary classification. Two preprocessing functions are extracted from among those present in Table 1, the flow of operations envisaged by the system is performed (preprocessing, segmentation, features extraction, linear discriminant analysis (LDA), Support Vector Machines (SVM) best parameters, classification, and accuracy calculation). If the obtained accuracy result is the best, the configuration is stored (in terms of preprocessing functions used, selected features and SVM parameters obtained). In any case, the process is cycled for all configurations of the two functions that were obtainable, starting from Table 1.  
In this work, the analyzed values for the C and \(\gamma\) were:  
\[\text{C=}2^{-5},2^{-4},\ldots,2^{10}\,\gamma\text{=}2^{-10},2^{-9},\ldots,2^{2}\]  
For each OAA classifier implemented, the analyzed grid had sizes equal to \(16\times 13\), for a total of 208 grid-points.  
With regard to the intensity analysis and the relative SVM classifier used, the analysis of the parameters were carried out to achieve the following values:  
\[\text{C=64\ }\gamma\text{=}0.25\]  
Table 3 reports the best values of the parameters C and \(\gamma\), related to the classifiers implemented for the identification of fluorescence patterns and obtained for each class pattern.
## 1 Introduction
Alternatively, LST can be used to estimate the evaporative fraction, which is then used to derive ET [16]. On the other hand, conductance-based ET models typically employ the Penman-Monteith (P-M) equation, utilizing shortwave remote sensing data [17]. A crucial aspect of these models is the canopy conductance to water vapor, which is influenced by factors such as canopy structure and leaf stomatal conductance. Canopy structural parameters are determined using shortwave remote sensing data, and stomatal conductance can be estimated through various methods. By linking the carbon and water cycles, the stomatal conductance can be consistently derived from the plant's photosynthesis rate [18]. Within the temperature-based category, three subclasses can be identified: one-source, two-sources, and contexture-based models. One-source models integrate soil and vegetation components into a unified component to estimate ET, while two-source models separately consider soil and vegetation inputs. Contexture-based models were designed to leverage the information derived from LST across areas with varying vegetation fractions (\(\psi_{\text{v}}\)) through a vegetation index such as the Normalized Difference Vegetation Index (NDVI). Recent contributions in the field of ET estimation have included comprehensive reviews that consolidate and analyze existing knowledge and methods. These reviews provide valuable insights into the strengths, limitations, and advancements in the field,helping to guide future research and application. Notable contributions include the works of [5; 9; 16; 17; 19]. Future Earth Observation missions show the technological advancement that is destined to come in the near future that will lead to more accurate results in the field of remote sensing related to ET estimation. The paper starts by discussing the surface energy balance as a cornerstone in understanding variables related to ET and RS, focusing on components such as the sensible heat flux, ground heat flux, and latent heat flux. It then progresses to explore temperature-based ET models, including single-source and two-source surface energy balance and contexte-based ET models, detailing their advantages, drawbacks, and the evolution of research in this area. Subsequently, it delves into the intercomparison of these models through the related studies that were performed during the last fifteen years. Following, emerging earth observation missions and advancements in the field are discussed. By contributing to the consolidation of core theories and tracing the evolution of temperature-based evapotranspiration (ET) estimation methods, this study delves into the concept of surface energy balance. It underscores the significance of validation studies and enhancements conducted by researchers. Through a comparative analysis of the strengths and limitations of these methods, this effort aims to provide a solid foundation for future progress in the field.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 4 Experiments
### Ablation Study  
In order to explore the effect of the multi-layer feature fusion, ablation comparison experiments were conducted. Table 1 shows that the use of multi-layer feature fusion is better than just using a single feature, and the effect is better when using the three-layer feature fusion of CONV3, CONV4, and CONV5 than when using the two-layer feature fusion of CONV4 and CONV5, indicating that the features extracted from the different stages of the network are not the same, and fusing multi-layer features is beneficial to improving the tracking accuracy. The correlation method based on pixel matching of the template features also shows an improvement compared to the channel-by-channel correlation method, with an improvement of 0.8% on the UAV123 dataset. The addition of the attention module to the network further improves the effect of the network, and the use of both spatial and channel attention models enables the network to achieve the best effect, with a final accuracy of 65.5% on the UAV123 dataset.  
In order to analyze the effect of fusing multi-layer features, we tested the model on three datasets. As shown in Table 2, the use of three feature maps from different convolution layers leads to the best results on all three datasets, which shows that the use of multi-layer feature fusion is beneficial to improving the accuracy.  
Another ablation experiment was conducted to explore the attention mechanism and pixel-level correlation. As shown in Table 3, the baseline uses three convolution layers with pixel-level correlation, while MCA and GSA are the multi-scale feature aggregated channel attention block and the global-to-local-information-fused spatial attention block. Every addition improves the accuracy. In the end, all modules are used, achieving the best performance with an AUC of 65.5% and a precision rate of 85.2%.
# Evaluation of ISO 31010 Techniques for Supply Chain Risk Management in Automotive Suppliers
## 5 Discussion
The prioritization model presented in this study can guide decision-makers in efficiently allocating resources to SCRM initiatives, ensuring that the most appropriate tools are applied at each stage of the process. From a theoretical perspective, this study contributes to the literature by structuring the prioritization of risk management tools in the supply chain, with an emphasis on suppliers. The combination of Delphi and AHP methods enabled a systematic selection process, allowing the choice of tools to be based on technical criteria and not only on the individual perception of experts. Despite these contributions, this study presents some limitations that should be addressed. The main one is the lack of empirical validation of the proposed prioritization model. Although the Delphi and AHP methods provided a structured approach for the analysis and ranking of risk management tools, future studies could carry out case studies or simulations to evaluate the practical effectiveness of the selected tools in real supply chains. In addition, the focus on the automotive industry may restrict the generalization of the findings to other sectors with different risk profiles and operational dynamics. Finally, in the experts' view, it appears that FMEA is the most suitable tool for performing SCRM in automotive industry suppliers. This result is not surprising, as FMEA has been applied by researchers and professionals in risk management in several areas [92]. It is a tool widely used in the industry and its main purpose is to provide information for decision making in risk management [93], working to identify critical components whose failures can cause physical and financial damage, thus making production systems safer and more reliable [94].
## 3 Results and Discussion  
### Scaffold 1  
The FmlH-parent reference molecule complex is shown in Figure 2A. Two scaffold growing points were selected on the parent reference molecule to replace the central scaffold (Scaffold 1) of the molecule (Figure 2B). Both in silico ligand-based and in silico receptor-based scaffold hopping were performed on Scaffold 1.  
Numerous existing X-ray structures of FmlH-ligand complexes show the inhibitor to be in the binding site investigated in this work (PDB IDs: 6 MAQ, 6 MAP, 6 MAW, 6 ARM, 6 AOW, 6 MOY, 6 ARN, 5 LNE, 6 ARO) [14; 17; 18]. Additionally, existing mutagenesis evidence for a highly homologous type 1 pilus, FimH, further supports our choice of binding site [18; 48; 49; 50]. Several existing X-ray structures of this highly homologous type 1 pilus, FimH, with the ligand bound in the same binding site that we have studied, further validate our site (PDB IDs: 4 BUQ, 3 MCY, 5 AUC, 5 JCQ, 4X5P, 4 XSQ, 4 ACV, 5L4V, 4X5R, 6G2S, 5L4T, 5L4U) [24; 51; 52; 53; 54]. We performed a SiteMap analysis using Maestro to identify all the possible binding sites of FmlH. SiteMap conducts a search to determine which regions of the protein are suitable for interaction with a binding partner and then generates contour maps to produce hydrophobic and hydrophilic maps. Finally, it conducts an evaluation step where the program assesses each identified potential binding site by calculating a variety of proteins and provides a SiteScore at the end of the calculation. The various calculated properties that are accounted for in the SiteScore are the number of site points, size of the binding site, exposure and enclosure (indicates solvent accessibility), contact (indicates how strongly the site point interacts with the surrounding receptor using van der Waals interactions), as well as the site's donor and acceptor properties [55; 56; 57; 58]. SiteScore assesses a site's propensity for ligand binding and is used to rank all the possible binding sites with respect to their relevance. SiteMap is a widely used method to identify ligand binding sites and protein-protein interfaces with high accuracy [56; 57; 58]. For FmlH, two sites were identified (Figure S2). The top scored site corresponds to the small molecule docking site that we have investigated in this work. The second site is close to the protein-protein interface at which FmlH is known to interact with the neighboring FmlH pili domain (FmlH\({}_{\text{P}}\)) to form the F9/Fml pili [18; 59; 60].
## 7 IoMT Healthcare Services and Applications
### Robotics Medical Applications  
Robotics-based smart healthcare applications significantly improve the accuracy of diagnosis and treatment. They enhance patient monitoring and enable faster response times to medical emergencies. Additionally, they automate tedious and repetitive tasks, allowing healthcare workers to focus on more complex issues. A humanoid robot reduces physical interactions between healthcare workers and infected individuals [71]. Among the many tasks robots assist with are food preparation, medication administration, and cleaning and disinfection of medical facilities [72]. This reduces the time and labor required for these tasks, improving efficiency and reducing the risk of human error. Although robotics-based smart healthcare applications offer great potential, there are also some risks. Moreover, robots perform tasks deemed too dangerous for humans, such as cleaning hazardous areas [73]. Another risk is excessive reliance on technology at the expense of human interaction, potentially compromising the quality of patient care.
## 1 Introduction
### Contribution of the Proposed Model  
This research designed an approach for cancer classification by proposing the SPWO-based Deep maxout network. The nodes are allowed to capture the medical information of patients in the IoT framework, and the collected data are routed toward the sink node using a hybrid optimization algorithm. The proposed hybrid algorithm, named SPWO, was devised by inheriting the features of the Student Psychology based Optimization (SPBO) with Whale Optimization Algorithm (WOA). The process of routing was effectively made by concluding fitness factors, namely energy, delay, link quality, distance, and trust. The features were selected from input data and based on their features; classification was performed using a deep learning framework. The key contribution of this research is illustrated below:  
* Proposed SPWO-based Deep maxout network: A productive classification framework was designed in an IoT network for classifying cancer disease with medical data. The proposed classifier automatically learns the features and enables accurate classification results based on their fitness values.