esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 4 Experimental Results
### Clinical Applicability  
The findings of this study further highlight the potential of super-resolution techniques to improve diagnostic accuracy. For instance, the proposed CS-SISR method enhances boundary clarity and texture restoration, playing a critical role in melanoma diagnosis. Preliminary evaluations by medical experts suggest that images generated using the proposed method provide more defined lesion boundaries than existing approaches, reducing diagnostic ambiguity.  
However, the impact of super-resolution techniques on diagnostic accuracy can vary depending on the diversity of datasets and clinical contexts. Therefore, additional validation is required to ensure that the proposed method maintains its effectiveness across different medical imaging modalities and patient populations. Future research should focus on verifying the generalizability of the proposed model in diverse clinical scenarios.
This model obtained results very similar to the real ones [33], while the equivalent full cycles model and the cycle counting model obtained lifetime estimations that can be, in some cases (PV-battery systems), two or three times higher than the real battery lifetime. Li-ion batteries ([34; 35; 36]) have a higher cycle life, energy density, and energy efficiency, and lower maintenance compared to lead-acid batteries. The LiFePO4 (LFP) type is the most used in off-grid systems. Li-ion batteries' most significant aging external factors are temperature, charge and discharge rates, and DOD [37]. In simulation and optimization of standalone systems, Li-ion cell level aging models [38] are usually used due to their simplicity. Electrochemical models are usually very complex, even the most simplified ones [37; 39; 40], implying high calculation times [41]. \"Calendar\" aging occurs when a battery is not being used while \"cycle\" aging occurs when the battery is under charge or discharge current [41]. Cycle aging is affected by the total charge (Ah) throughput from the start of battery lifetime, the current, the ambient temperature, and the SOC. Calendar aging main factors are temperature and SOC [38]. A good example of Li-ion aging model text matrix is shown in the work of [PERSON] et al. [42]. The rainflow cycle count model is also used for Li-ion batteries. Arrhenius kinetic-based aging models [38] are the most used cycle aging models for Li-ion batteries. For example, [PERSON] et al. 's [43] model was obtained by performing many accelerated cycling tests to commercial LiFePO4 cells, obtaining a capacity fade model that takes into account the Ah-throughput and temperature for different charge/discharge rates. Li-ion battery calendar aging has been modeled by different researchers [38]. For example, [PERSON] et al. [44] used an expression based on Arrhenius law, considering temperature, time, and SOC. In this work, we compare the battery lifetime estimation of a PV-battery system used to supply electricity to a household located in two different locations with very different average temperatures, considering different models for the degradation of lead-acid or Li-ion batteries. In Section 2, the models of the PV system components and the different battery lifetime models are shown. In Section 3, we show the comparison of the different models applied in the PV-battery systems. Finally, the main findings and conclusions are discussed in Section 4.
# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 4 Results
### Confusion Metrics  
The confusion matrix shows the performance of the ResNet-50 and ResNeXt-50 models on a validation dataset, to show the model's generalizability and ability to correctly classify instances and identify any misclassifications or errors in the predictions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Number of Dense Layers** & **Hidden Units** & **Dropout** & **Optimizer** \\ \hline ResNet-50 & 1 & 288 & 0.3 & Adam \\ \hline ResNeXt-50 & 1 & 384 & 0.3 & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimal hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Range** \\ \hline Number of Dense Layers & [1, 2] \\ \hline Hidden Units & [64, 96, 128, \(\ldots\), 384] \\ \hline Dropout & [0.2, 0.25, 0.3] \\ \hline Optimizer & [“Adam”, “AdamW”] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Hyperparameters for ResNet-50 and ResNeXt-50.  
Across all five folds of the validation process, the ResNet-50 model consistently demonstrated strong predictive performance in classifying maxillary sinus. Notably, the model achieved high accuracy in identifying \"Moderate\" and \"Severe\" cases, with the best results observed in Folds 1, 2, 4, and 5, where it accurately predicted 25 out of 28 true \"Moderate\" cases and all 17 true \"Severe\" cases. Moreover, the model exhibited remarkable consistency in distinguishing between different severity levels, maintaining minimal misclassifications in both \"Moderate\" and \"Severe\" categories across all folds. Although, the model encountered slight challenges in accurately identifying \"Normal\" cases, particularly in Folds 1 and 2, where it misclassified only 3 and 6 instances out of 57, respectively. The details of the validation confusion matrix of ResNet-50 is shown in Figure 11.  
Similarly, in the validation confusion matrices of Fold 1 to Fold 5 for ResNeXt-50, the model consistently demonstrates strong performance in accurately identifying \"Moderate\" and \"Severe\" cases, with few misclassifications observed. Across all folds, the model correctly predicts the majority of \"Moderate\" cases, ranging from 24 to 25 out of 28 true instances. Similarly, for \"Severe\" cases, the model maintains high accuracy, correctly classifying between 15 and 17 out of 19 true cases in each fold. However, the model encounters challenges in accurately distinguishing \"Normal\" cases, with misclassifications ranging from 0 to 3 instances across the folds. Notably, the model's misclassifications primarily involve \"Normal\" cases being incorrectly labeled as \"Moderate\", indicating a potential overlap in features between these categories. The details of the validation confusion matrix of ResNeXt-50 are shown in Figure 12.  
Figure 11: Confusion matrix of ResNet-50 (validation).
# Hybrid Colloids Made with Polymers
## 5 Polymer-Surfactant Systems  
A comprehensive description of polymer-surfactant systems dates back to [PERSON] and [PERSON] [31]. Thereafter, many researchers focused on these items. According to a huge number of experiments, uptake of surfactant ions onto polymer sites continues up to saturation of the macromolecular chain. Small aggregates, emi-micelles [32], nucleate and grow on the polymer chain until site binding is completed. The process largely modifies the polymer nature, which unrolls and changes its conformation. The resulting complexes do exist in the whole interaction region [33]. The horizontal line in Figure 2 indicates where the above composites dominate. The phase map is a pseudo-binary diagram, and is commonly used in dilute regimes [31].  
Detailed studies place in evidence the presence of two different concentration regimes, with occurrence of physical discontinuities at the _cac_ (critical association concentration) and at the _cmc_* (critical micellar concentration in presence of saturated polymer). Typical examples come from surface tension plots (Figure 3). In terms of efficiency, anionic surfactants are much more effective in binding compared to cationic; nonionic are not used.  
In thermodynamic terms, the binding of surfactant ions on polymers is a multiple equilibrium, with adsorption of amphiphiles onto a chain having several adjacent sites (over 100, usually); very presumably, the process is strongly cooperative. Significant consequences are concomitant to that process; for instance, the kinetics of surfactant exchange to/from the polymer binding sites is nearly constant [34]. This fact is in agreement with the equilibrium hypothesis depicted above. It is immaterial if surfactants associate in small entities; it is the affinity towards the polymer sites that is responsible for the mentioned behavior. Surfactant binding onto proteins occurs nearly in the same way, as observed in _SDS_ uptake on the hydrophobic tasks of albumin [35; 36].  
Figure 2: Pseudo-binary phase map of a polymer-surfactant system in dilute concentration regimes. The red dotted line indicates the critical association concentration threshold, or _cac_, above which interactions among polymer and molecular surfactant start to occur. The blue line, or _cmc_*, indicates free micelles onset when polymer binding sites are surfactant-saturated. The two lines intersect at the _cmc_ of the pure surfactant. The green line indicates a series of complexes, _PSCs_, occurring at fixed polymer content. Polymer in the map is nonionic, the surfactant anionic.  
Polymer-surfactant systems are efficient viscosity modulators and detergents. Blends of the two components are combined in several pharmaceuticals (see the leaflets in most pharmaceutical boxes), or in napppies. The related formulations, usually prepared in nonaqueous gel form, adsorb significant amounts of urine; the volume of the final gels may increase more than ten times compared to the original conditions.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 4 Neural Network (NN) Based Elasticity for Fracture Problems
### Neural Network Architecture  
In this part, a feed-forward neural network with a similar architecture as that in Section 3 is applied. As a loss function, the mean-squared error (MSE) is considered. Here, also the Levenberg-Marquardt algorithm is chosen as the training optimizer. Since the stress \(\mathbf{\sigma}\) and strain \(\varepsilon\) values are on different scales of magnitude, a normalization of the data set is required before training. Thus, the data set is scaled to a comparable range, in which the input and target data are initially scaled to be in the range \([-1,1]\). The training data are collected from the analytical model, similar to the method used in NN-based elasticity; see Section 3. The material parameters used for the creation of the training data set are given in Table 4.  
In the following, the performance of the proposed machine learning based model will be examined for the phase-field fracture simulations. Herein, the elasticity is predicted by the neural network. The formulation of the ML-based model substituting the above formulations defines a function in the following format:  
\[\widetilde{\sigma}_{NN}(\varepsilon,d,\mathbf{W},\mathbf{b})=FFNN(\varepsilon,\mathbf{W}, \mathbf{b}), \tag{22}\]  
where \(\mathbf{W}\) is the weight matrix and \(\mathbf{b}\) is the bias of the neural network.
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 8 Conclusions  
In this paper, we propose a scenario where an MEC server is equipped on the LEO satellite to form an edge computing LEO satellite, where user-generated computing tasks can be computed locally or offloaded to the MEC server for execution. This paper considers the multi-user multi-task scenario, and we define the utility function of the system as the weighted sum of system energy consumption and delay, and define the optimization problem as the problem of minimizing the utility function of the system. We propose an offloading algorithm based on deep learning to obtain near-optimal offloading decisions and allocate bandwidth to each user through an adaptive optimal bandwidth allocation algorithm to obtain the optimal system utility function values. The simulation results show that the algorithm proposed in this paper can obtain the optimal system utility function values with a low average time cost, and can achieve a good convergence effect. The satellite-ground collaborative network has a large amount of dynamic and time-varying resources, which are difficult to optimize. The future work is to use deep reinforcement  
Figure 8: Average time cost for multiple experiments.  
learning with a good global search capability, solve the computation offloading problem in the multi-user, multi-satellite, and multi-task scenario in the satellite-ground collaborative network, and solve the bandwidth allocation problem in the complex scenario.  
Conceptualization, J.W. and L.Z.; methodology, M.J. and J.W.; validation, J.W., formal analysis, J.W.; investigation, J.W.; resources, J.W. and L.Z.; data curation, J.W.; writing--original draft preparation, J.W.; writing--review and editing, M.J. and J.W.; supervision, M.J. and Q.G.; project administration, M.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China No. 62231012, the National Key Research and Development Program of China under Grant 2021 YFB2900500, the Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001, and the Fundamental Research Funds for the Central Universities under Grant FRFCU 9803503821.  
Data Availability Statement: Data are described within the article.  
The writers would like to thank the editor and anonymous reviewers for their helpful comments for improving the quality of this paper.  
The authors declare no conflict of interest.
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
###### Abstract
Through convex relaxation of the original problem's upper bound, the optimized projection matrices are derived, enabling discriminative feature selection through embedded sparsity constraints. The primary contributions of this work are summarized as follows:  
1. Multiple view-specific basic partitions are integrated into a unified consensus matrix, which guides the feature selection process by preserving comprehensive pairwise constraints across diverse views. 2. A hybrid regularization strategy incorporating the \(L_{2,1}\)-norm and the Frobenius norm is introduced into the feature selection objective function, which not only promotes feature sparsity but also effectively prevents overfitting, thereby improving the stability of the model. 3. The proposed CGMvFS framework is extensively evaluated on multiple multi-view datasets, demonstrating superior performance in unsupervised feature selection and robustness compared to existing methods. This paper is structured systematically as follows. Section 2 conducts a critical survey of current methodologies and theoretical foundations in multi-view unsupervised feature selection. Section 3 provides detailed exposition of our innovatively designed cross-view feature selection mechanism operating in unsupervised contexts. Section 4 delivers empirical validation through standardized datasets, rigorously validating the efficacy of our approach with comparative performance metrics. Finally, Section 5 concludes the paper and discusses future research directions.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 5 Simulation and Experimental Results
### Real-World Experiment I  
After the navigation architecture was successfully configured, not only was a precise local area network constructed, but a rigorous network configuration for the robot was also implemented. Particular attention was given to ensuring that both the host and the mobile robot operate within the same network segment, prioritizing communication reliability and efficiency. With this robust foundation, the phase of experimental verification commenced. The size of the experimental environment was 4.2 \(\times\) 5.2 m. A 281 \(\times\) 306 \(\times\) 141 mm TurtleBot3 (Waffle Pi) robot was selected for algorithm experiments. The safety distance \(l\) = 0.25 m was chosen for the experiment, and the maximum allowable linear and angular velocities of the robot were 0.26 m/s and 1.82 rad/s, respectively, and the peak parameters \(v_{\text{max}}\) = 0.23 m/s and \(w_{\text{max}}\) = 1.5 rad/s were set for this experiment.  
This experiment was conducted to validate the scenario simulated in Experiment 1: avoiding unknown obstacles in a known map. A physical experimental environment was set up, where black objects represent known obstacles in the known environment, and brown obstacles represent the unknown obstacles. The experimental site contained four black rectangular obstacles of 20 \(\times\) 20 \(\times\) 30 cm, two black rectangular obstacles of 26 \(\times\) 8 \(\times\) 16 cm, one black rectangular obstacle of 30 \(\times\) 13 \(\times\) 40 cm, one black rectangular obstacle of 35 \(\times\) 15 \(\times\) 30 cm, and two brown obstacles of 20 \(\times\) 35 \(\times\) 30 cm. Additionally, the robot's navigation process was captured in a sequence of 80 frames, documenting the real robot's trajectory.  
Figures 14 and 15 show that DBR-RRT algorithm can avoid obstacles with stability and relatively high speed in environments with unknown obstacles. It exhibits fewer sudden changes in speed and maintains stability even in situations requiring turns, while also planning a smooth path.
## 2 Requirements from Autonomy and Remote Control in Smart Farms  
Unmanned tractors with different capabilities and different ways to operate impose various technical requirements on the underlying information technology and communication infrastructure. Similarly, smart farm environments set their own restrictions and demands. This section explores these requirements, particularly from the perspective of network quality in Section 2.1 and cybersecurity in Section 2.2.  
The autonomous and remotely controlled operations of farm machinery are actually the end points of a spectrum of operation modes. The International Society of Automotive Engineers (SAE) has defined the autonomy levels of vehicles ranging from no driving automation (Level 0) to full driving automation (Level 5) [31], as listed below:  
* Level 0--No Driving Automation
* Level 1--Driver Assistance
* Level 2--Partial Driving Automation (\"hands off\")
* Level 3--Conditional Driving Automation (\"eyes off\")
* Level 4--High Driving Automation (\"mind off\")
* Level 5--Full Driving Automation  
A range of assisting technologies enables intermediate modes, where some of the functions are explicitly controlled by an operator, while others can be independently decided by the machinery. Such assisting technologies include global navigation satellite system positioning, video cameras, and proximity sensors. The tendency is from remote control to fully autonomous operation, as this will lessen the operator workload and hence improve efficiency. However, legislation and regulations set the boundaries for the operation. Currently, legislation in several countries, such as Finland, does not permit unmanned vehicles to operate outside closed environments. Before fully unmanned autonomous or remotely controlled tractors may become more common, the development of legislation is needed [32] to clarify liabilities.
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 3 Problem Statement and Mathematical Model
### Objective Function Linearization  
In model **[P1]**, the objective function is non-linear. To transform the model into a linear programming and then solve it via CPLEX or other commercial solvers, we linearize the objective function in this subsection.  
**Theorem 1**.: _For problem \(P_{m}||SI\), minimizing \(SI\) is equivalent to minimizing_  
\[\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{J}}\sum_{j^{\prime}\in\mathcal{J} \setminus\{j\}}p_{j}\cdot p_{j^{\prime}}\cdot y_{ijj^{\prime}} \tag{7}\]  
Proof.: [PERSON] and [PERSON] [27] have proved that minimizing \(SI^{2}\) is equivalent to minimizing  
\[\sum_{i\in\mathcal{M}}\big{(}\sum_{j\in\mathcal{J}}p_{j}^{2}\cdot y_{ijj}+2 \cdot\sum_{j\in\mathcal{J}}\sum_{j\in\mathcal{J}\setminus\{j\}}p_{j}\cdot p_{ j^{\prime}}\cdot y_{ijj^{\prime}}\big{)} \tag{8}\]  
Furthermore, since \(\sum_{i\in\mathcal{M}}y_{ijj}=\sum_{i\in\mathcal{M}}x_{ij}=1\), \(\sum_{i\in\mathcal{M}}(\sum_{j\in\mathcal{J}}p_{j}^{2}\cdot y_{ijj})\) is a constant. Therefore, minimizing \(SI\) is equivalent to minimizing Equation (8). The proof is completed.  
According to Theorem 1, mathematical model **[P1]** can be reformulated as below  
\[\textbf{[P2]}:\min f=\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{J}}\sum_{j^{ \prime}\in\mathcal{J}\setminus\{j\}}p_{j}\cdot p_{j^{\prime}}\cdot y_{ijj^{ \prime}}\]  
subject to Constraints (2)-(6) and the following Constraints (9)-(11).  
\[y_{ijj^{\prime}}\geq x_{ij}+x_{ij^{\prime}}-1,\quad\;i\in \mathcal{M},j\in\mathcal{J},j^{\prime}\in\mathcal{J}\setminus\{j\} \tag{9}\] \[y_{ijj^{\prime}}\leq x_{ij},\quad\;i\in\mathcal{M},j\in\mathcal{ J},j^{\prime}\in\mathcal{J}\setminus\{j\}\] (10) \[y_{ijj^{\prime}}\in\{0,1\},\quad\;i\in\mathcal{M},j\in\mathcal{ J},j^{\prime}\in\mathcal{J}\setminus\{j\} \tag{11}\]  
Constraints (9) and (10) give the processing sequence of jobs on each machine. Constraint (11) gives the range of the variables. Since **[P2]** is a linear programming model, we can directly adopt some commercial solver such as CPLEX to solve it.
###### Abstract  
Recently, various application fields utilizing Wi-Fi fingerprint data have been under research. However, fingerprint data collected from a specific location does not include relevant information, such as continuity. Therefore, most indoor positioning technologies predict the user's location based on location signals collected at specific points within the indoor space, without taking into account the user's movements. Hence, there is a need for technology that improves the accuracy of indoor positioning while moving. This paper proposes a technique to generate movement path data by applying the concepts of \"BB\" and \"Grid Cell\" from computer vision to Wi-Fi fingerprint data. This approach represents data points as bounding boxes (BBs), then establishes grid cells and clusters of these BBs to generate adjacency information. Subsequently, movement path data are created based on this information. We utilized the movement path information generated from the dataset as training data for machine learning and introduced an enhanced indoor positioning technology. First, the experiments in this paper assessed the performance of the proposed technology based on the number of paths in the LSTM model. Second, the performance of clustering methods was compared through experiments. Finally, we evaluated the performance of various machine learning models. The experimental results confirmed a maximum accuracy of 94.48% when determining the location based on route information. Clustering performance improved accuracy by up to 3%. In comparative experiments with machine learning models, accuracy improved by up to 2.8%.  
Wi-Fi fingerprint; calculate movement path; IoU (Intersection over Union); LSTM (Long Short-Term Memory); bounding box (BBs); grid cell +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells. _Appl. Sci._**2023**, 13, 10647. [[https://doi.org/10.3390/app131910647](https://doi.org/10.3390/app131910647)]([https://doi.org/10.3390/app131910647](https://doi.org/10.3390/app131910647))