esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Class 0 (Not Needed) & 0.93 & 0.95 & 0.94 & 40 \\ Class 1 (Needed) & 0.95 & 0.93 & 0.94 & 40 \\ Macro Avg. Accuracy & 0.94 & 0.94 & 0.94 & 80 \\ Weighted Avg. Accuracy & 0.94 & 0.94 & 0.94 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification report summarizing the model’s performance in the prediction task. The \(y\)-axis displays the prediction probabilities generated by the model, ranging from 0 to 1, where higher values indicate a stronger prediction that a space maintainer is necessary. A threshold of 0.5 was applied as follows:  
* Predictions above 0.5 (marked in blue) indicate that the model predicts the need for a space maintainer. * Predictions below 0.5 (marked in red) indicate that the model does not predict the need for a space maintainer. The results in this figure allow for an assessment of how well the model aligns with expert annotations. The clustering of blue points near 1.0 and red points near 0.0 suggests that the model makes confident classifications in most cases. However, a few predictions close to the 0.5 threshold may indicate uncertain cases, which could require further validation. Out of the 86 images tested, the model produced eight errors. Figure 6 illustrates a dental X-ray where the CNN model predicted that there is no need for an SM following the extraction of teeth 75 and 85. This prediction is generated by the trained CNN, which analyzes the features of the X-ray image, such as tooth alignment, spacing, and other structural patterns. For each case, the model evaluates these features and assigns a probability, determining whether an SM is necessary. In this instance, the prediction confidently indicates that the natural spacing and alignment are adequate, and no intervention is required. This demonstrates the model's ability to automate and streamline the decision-making process in pediatric dentistry. Figure 7 illustrates the prediction result generated by the proposed CNN model, highlighting the necessity of a space maintainer following the extraction of tooth 75. The panoramic radiograph displays a clear bounding box around the affected region, emphasizing the location of the missing or soon-to-be-extracted tooth. The model successfully identifies the specific tooth requiring intervention and labels it accordingly, demonstrating its ability to detect and classify cases where space maintainers are essential. This result confirms the model's efficacy in assisting clinicians by providing automated and  
Figure 5: Prediction probabilities by tooth number for space maintainer detection. The dotted line represents the 0.5 prediction probability threshold. accurate assessments, ultimately aiding in timely and effective treatment planning for pediatric patients.
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 2 Numerical Model
### Numerical Method  
In this study, the commercial simulation software COMSOL Multiphysics(r) 6.1 was used, which involves multiphysics fields with strong coupling that are difficult to solve. A transient study was performed to obtain the analysis results of multiple moments. The calculation period of 20 h was chosen based on the actual process.  
To ensure the computational accuracy and smoothness of the large volume, the mesh was refined at the heater and coil, while the rest of the area was coarsened for the mesh division. This accounted for the skin effect, as shown in Figure 2. The grid's independence was tested by expanding it from 41,840 to 273,435 cells. The comparison results indicate that refining the grid with 112,650 additional cells did not produce any significant changes. Therefore, subsequent calculations were performed using 112,650 cells.
# A Dynamic Decision Support System for Selection of Cloud Storage Provider
## 5 Results and Discussion
### Cost-Benefit Analysis  
The AHP technique is widely used for the cost-benefit analysis. The baseline for this calculation is a one-core CPU with 500 GB of storage. Weights of all parameters from the cloud storage providers are calculated as shown in Table 27. The Ranking and the Cost columns are added in order to perform the cost-benefit analysis. The cost is on the basis of CPU cores and repository fixed values.  
Figure 21 shows the cost-benefit relationship where benefits and costs are plotted on the Y-axis while cloud vendors are on the X-axis. It is clear from the graph that all vendors provide benefits to customers, but some cloud vendors provide almost the same benefits with less cost. The aim of this exercise is to identify those cloud vendors who provide more benefits at less cost. From the graph, we can easily identify Google as the cloud provider with top benefits and less cost. It also shows that iCloud gives fewer benefits, compared to the others, but its cost-benefit ratio is very close owing to its security. As for giving fewer options or features to end users, there will be less chance of exposing sensitive information. A cloud vendor's priority list is generated on the basis of their services (benefits) and the costs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cloud Vendors** & **AHP** & **Proposed System** & **Difference** \\ \hline Sync.com & 0.3204 & 0.3200 & 0.0004 \\ JustCloud & 0.3568 & 0.3560 & 0.0008 \\ Dropbox & 0.4185 & 0.4060 & 0.0125 \\ Tesorit & 0.3545 & 0.3340 & 0.0205 \\ LiveDrive & 0.4173 & 0.4170 & 0.0003 \\ pCloud & 0.0428 & 0.0420 & 0.0008 \\ hubiC & 0.3706 & 0.3712 & 0.0006 \\ Jumpshare & 0.3676 & 0.3670 & 0.0006 \\ iCloudDrive & 0.0767 & 0.0692 & 0.0075 \\ MediaFire & 0.3193 & 0.3190 & 0.0003 \\ OpenDrive & 0.3204 & 0.3201 & 0.0003 \\ SugarSync & 0.3211 & 0.3200 & 0.0011 \\ ADrive & 0.3519 & 0.3441 & 0.0078 \\ Mega & 0.3407 & 0.3400 & 0.0007 \\ Google Drive & 0.6235 & 0.6230 & 0.0003 \\ SafeCopy & 0.3175 & 0.3129 & 0.0046 \\ OneDrive & 0.3376 & 0.3356 & 0.0020 \\ AmazonDrive & 0.4275 & 0.4251 & 0.0024 \\ Atlantic & 0.3567 & 0.3556 & 0.0011 \\ IBM & 0.6023 & 0.6000 & 0.0023 \\ \hline \hline \end{tabular}
\end{table}
Table 26: Ranking Comparison between AHP and the Proposed System.  
Figure 20: Comparison between AHP and the Proposed System.
## 5 Conclusions  
This work introduces HoRFNet, a novel network architecture designed to enhance the classification of BCHI through high-order receptive field modeling. HoRFNet achieves end-to-end integration of multi-branch receptive fields and high-order statistical modeling in a single network architecture. Compared with conventional CNN architectures, HoRFNet effectively overcomes the limitations of fixed receptive fields and the bottleneck of relying solely on first-order feature aggregation. It significantly improves the model's ability to capture complex tissue structures and multi-scale textures. Specifically, HoRFNet replaces the third convolutional layer in the backbone with a multi-branch convolutional structure, which expands the receptive field and enhances multi-scale contextual modeling while keeping the parameter size manageable. In addition, the model incorporates a covariance pooling module to extract high-order statistical features, further improving its capacity to represent nonlinear local relationships in images. Across various magnification levels of the BreakHis dataset, HoRFNet consistently achieves higher image level and patient level classification accuracy than existing mainstream methods, demonstrating strong representational power and generalization ability. More importantly, HoRFNet maintains high accuracy while exhibiting favorable computational efficiency and compactness. Its training time and inference speed are significantly better than those of some lightweight models, indicating strong potential for practical deployment. These advantages suggest that HoRFNet is well positioned to provide efficient and reliable intelligent support for pathological diagnosis in real-world clinical scenarios.  
Conceptualization, J.Z.; methodology, M.Z. and C.H.; validation, M.Z., C.H. and L.C.; writing--original draft preparation, C.H.; writing--review and editing, M.Z. and J.Z.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Applied Basic Research Project of Liaoning Province under Grant 2023 JHz/101300191 and the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023 ZD003.  
The datasets used in this study are available to the public at the following addresses: [[https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/)]([https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/)) for the BreakHis dataset (accessed on 16 January 2025), and [[https://iciar2018-challenge.grand-challenge.org/Dataset/](https://iciar2018-challenge.grand-challenge.org/Dataset/)]([https://iciar2018-challenge.grand-challenge.org/Dataset/](https://iciar2018-challenge.grand-challenge.org/Dataset/)) for the BACH dataset (accessed on 16 January 2025).  
The authors declare no conflicts of interest.
## 2 Related Work
### Spatial-Temporal Patch Tensor Model  
Generally speaking, given an image sequence \(f_{1},f_{2},\ldots,f_{p}\in R^{m\times n}\) and a cube patch tensor \(F\in R\), the frames can be obtained by stacking them in time order. The tensor of the IR point target image can be expressed as:  
\[D_{\rm T}=B_{\rm T}+N_{\rm T}+T_{\rm T} \tag{2}\]  
where \(D_{T},B_{T},N_{T},T_{\rm T}\in R^{m\times n\times L}\) present the original patch-tensor, background tensor, target-tensor, and noise-tensor. According to the infrared imaging mechanism, the relative motion between the imaging sensor and the target is usually due to small changes at a long distance, such as an early warning system. Therefore, it is generally believed that the backgrounds of different frames change slowly in the whole sequence images, which means that there is a correlation between adjacent sequences [8; 21]. For the reason that images containing infrared points and moving targets are considered to be of low rank, the constructed background tensor can also be considered a low-rank tensor. Compared with the matrix model, constructing a tensor model can not only mine the internal relations between data from more angles in the tensor domain but also further improve the capability of target detection by combining the spatial-temporal information.
# In-Vitro Simulation of the Blood Flow in an Axisymmetric Abdominal Aortic Aneurysm
###### Abstract  
We investigated the blood flow patterns and the hemodynamics associated with an abdominal aortic aneurysm detected in an in vitro measurement campaign performed in a laboratory model of an aneurysm with rigid walls and an axisymmetric shape. Experiments were run in steady flow conditions and by varying the Reynolds number in the range \(410<Re<2650\). High spatial and temporal resolution 2D optical measurements of the velocity field were obtained through a particle tracking technique known as Hybrid Lagrangian Particle Tracking. Conversely to classical Particle Image Velocimetry, both the fluid particle trajectories and the instantaneous and time-averaged velocity fields are provided without constraints on the grid size and very close to the vessel boundary. All the most relevant quantities needed to investigate the flow features were evaluated, and in particular, we focused on the wall shear stress distribution both in the healthy aortic portion and within the aneurysm. Results show that the recirculation zone in correspondence of the cavity moves downstream, and this displacement is found to increase with \(Re\). Very low wall shear stress values are recovered in correspondence of the aneurysm cavity, while a sharp peak occurs in correspondence of the reattachment point. In agreement with the literature data, the peak value is found to decrease with \(Re\) and to be about equal to twice the upstream value.  
blood flow; hemodynamics; image analysis; flow measurements +
Footnote †: journal: Applied Physics Letters
## 3 Regionalized Sensitivity Analysis
### Monte Carlo Filtering and Regionalized Sensitivity Analysis
When \(|\tau|\) is large (i.e., high correlation or high association), DAI tends to be unacceptable. Again, this intuitively makes sense since the component dependence is not taken into consideration in the DAI approach. Figure 2 graphically displays the cumulative distribution functions of each parameter and shows that when the numbers of failure of Components 1 and 2 are few, the results of DAI tends to be significantly inferior to those of DA. Particularly, when there is insufficient evidence of component failures available to the reliability analyst, results obtained by DA are much more accurate. Figure 1: Observed CDF of individual parameters in AA\(-\)DA analysis. As highlighted above in this section, MCF and RSA can be used to identify several key factors that are accountable for producing the output realization of interest which can fall either in a behavioral set or a non-behavioral set. However, it remains complicated to obtain the most appropriate approach for analysis since the relative importance of each input factor is only identified from a univariate analysis perspective mainly due to two reasons. First, the paired comparisons for AA and DAI approaches are carried out separately with the most accurate DA approach. Second, the Kolmogorov-Smirnov tests focus on testing the significance of each individual parameter separately. It is therefore essential to conduct a thorough analysis from a multivariate analysis approach in order to obtain a more complete insight for choosing the most suitable method. Some preliminary guidelines can be derived from the observed distribution functions depicted in Figures 2 and 3. However, the actual range of values of the input factors which cause a great magnitude of error might still be unknown. Hence, to discover possible scenarios leading to a more certain analysis approach, we proceed in a similar way as [23] and propose the classification tree learning. Figure 2: Observed CDF of individual parameters in DAI–DA analysis.
## 3 Results and Discussion
### Films Characterization
#### 3.1.3 Mechanical Properties  
The mechanical properties of the developed films were evaluated since the film integrity is a crucial requirement in food packaging applications. When compared to neat LDPE, no significant differences (\(p<0.05\)) in elongation at break and tensile strength were observed with PA incorporation at 5 wt % and PB at 5 and 13 wt % (Table 2). However, the addition of PA and PB caused a decreased in Young's modulus values. This effect could be explained as a plasticizing effect of PA and PB resulting in an increase in the ductility of the polymer. These results are in accordance with the ones obtained by other authors in LDPE films containing a volatile liquid aromatic compound such as carvacrol where a marked decrease in elastic modulus was noticed due to the plasticizing effect of carvacrol [35]. In this sense, it is important to highlight that the physical state of the PA, i.e., a liquid, promotes a better intimation of the additive into the polymer chains permitting the enhancement of the internal movement and consequently improving its ductile properties as reported previously [36]. In relation to the addition of different amounts of PB to polyethylene, it has been reported that an increase in elasticity is observed as the amount of PB increases, acting the PB in a similar way to that of a plasticizer [37].
## 4 Experimental Results
### Forecasting Sales Based on Economic Indicators  
After the most significant economic indicators have been identified with respect to the three retail firms, they are treated as the predictors for forecasting sales revenues. To justify the validity of these economic indicators, MARS, SVR, and DNN are compared in sales forecasting. As we know, MARS, SVR, and DNN originate from statistics, quadratic programming, and deep learning. Deep learning algorithms like RNN, GRU, and LSTM are not considered in this research, because they require lots of data samples to optimize their network topologies and the associated hyperparameters. Specifically, the training set (Table 4) is from 2005/Q1 to 2019/Q4 and the test set is from 2020/Q1 to 2021/Q4 (Table 5). For all the three firms, it is interesting to observe that SVR exhibits the best performance in the training set, while MARS exhibits the best performance in the test set. Generally, the performances of Costco and Kroger are worse than Walmart, and their MAPEs are slightly greater than 10%. In data science, overfitting means good training performance but poor testing performance. The differences between the training set and the test set are limited, and these results guarantee no overfitting is found in this research.  
Figure 5: Quarterly sales revenues for the three US firms (in millions SUSD).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Walmart** & **Costco** & **Kroger** \\ \hline Consumer price index (CCI) & & & \\ Consumer confidence index (CPI) & * & * & * \\ Personal consumption expenditure (PCE) & * & & * \\ Gross domestic product (GDP) & * & * & \\ Dow Jones Transportation (DJT) & & * & * \\ Producer price index (PPI) & * & & \\ Purchase manager index (PMI) & & & \\ Non-manufacturing purchase index (NMI) & & & \\ Import price index (IMPI) & & & \\ Export price index (EXPI) & & & \\ Oil price & * & & \\ US dollar index (USDX) & & & \\ Regular wage & * & * & * \\ Non-farming population (NFP) & & & \\ Unemployment rate & & & \\ Interest rate & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identified economic indicators for the three US retailers using Lasso. * means significant indicators.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 3 Challenges of Subsurface Defect Characterization by Thermography
### Dataset Augmentation  
Dataset augmentation is a crucial technique in machine learning, particularly for thermography, where data diversity and volume significantly influence model performance. Several methods have been developed to augment datasets, each with its own advantages and limitations.  
Generative Adversarial Networks (GANs) are powerful tools for generating new, synthetic data that mimic real data. GANs consist of two neural networks, a generator and a discriminator, that are trained simultaneously. The generator creates synthetic data, while the discriminator evaluates its authenticity. This adversarial process helps produce high-quality synthetic data that can be used to augment datasets, improving model robustness and performance, especially when dealing with small sample sizes [45; 46]. However, training GANs can be complex and computationally intensive, and they require a significant amount of data to begin with [47].  
U-Net is another advanced technique particularly useful for image segmentation tasks. Originally developed for biomedical image segmentation, U-Net has been adapted for various applications, including thermography. It works by capturing the context of an image through a contracting path and then enabling precise localization through an expansive path. U-Net can be used for data augmentation by generating segmented images from raw input, thereby increasing the diversity and volume of the training dataset [47].  
Traditional methods such as image rotation, scaling, flipping, and cropping are also widely used to augment datasets. These techniques are relatively simple to implement and computationally inexpensive. They work by applying various transformations to the original images, thereby creating new training samples. While these methods can significantly increase the dataset size and help prevent overfitting, they do not add new information to the dataset as GANs do [48; 49].  
Table 1 shows the summary of challenges researchers face in subsurface defect characterization by machine learning algorithms with thermography and the state-of-the-art solutions they proposed to these challenges.
## 4 Performance Analysis and Discussion
### Control Overhead  
The RRM inter-cluster routing is obtained by exchanging the relationships between clusters. Discovering inter-cluster routes can reduce the overhead of constructing and maintaining Inter-cluster routing. Conversely, the on-demand routing protocols, _i.e._, AODV and DSR, adopt flooding for route discovery, so provide no previous routes to guide the packets to the destination. These methods not only have higher route discovery latency than RRM, but also have a higher overhead, depending on the range of flooding. The flooding of routing overhead rises with the size of network. Figure 17 indicates that the slope of curve increases more slowly in the proposed RRM mechanism than in the other mechanisms, revealing that RRM has the most stable control overhead, regardless of the number of sensor nodes. This is because RRM limits RREQ flooding within a cluster.  
Figure 16: Average reliability of delivery.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 4 Hydrogen Utilization
### Generating Electricity  
Hydrogen currently represents the best non-fossil fuel for some heavy vehicles that require large energy storage and fast recharge rates, such as long-haul trucks, buses, hybrid trains designed to operate on both electrified and non-electrified train tracks, and even for a common car, since refilling the hydrogen tank takes a few minutes and gives around 600 km of range [28]. This application is denominated as Power to Mobility (P2M).  
For grid applications, the fast response time of some fuel cells makes them adequate as dispatchable power plants for peak demand or for frequency control. This application is denominated as Power to Power (P2P). Furthermore, some solid oxide systems can operate with high efficiencies in both electrolyzer and fuel cell modes; however, it should be noted this technology has not reached the commercial level yet [12; 29].  
The main fuel cell technologies are Polymer Electrolyte Membrane, also known as Proton Exchange Membrane (PEMFC), Alkaline (AFC), Phosphoric Acid (PAFC), Molten Carbonate (MCFC), and Solid Oxide (SOFC) [30]. The first two are considered low-temperature fuel cells, and the remaining are high-temperature fuel cells. As far as the efficiencies are concerned, they range from 40% (PAFC) to 60% (PEM, AFC, SOFC), with MCFC in between (50%).  
Both AFC and PEMFC have quick start-up times; however, PEMFC presents greater power density, so it is the primary choice to equip hydrogen-based vehicles. Due to this emerging market, intensive research and development of PEMFC is being performed by car and bus manufacturers; consequently, cost reductions and increased durability are expected in the coming years.  
Most stationary installations of fuel cells are at high operating temperature [31], with one example being the 50 MW Daesan Hydrogen-Fuel-Cell Power Generation that started operating in South Korea in 2020. The plant is composed of 114 PAFC fuel cells and will produce around 400,000 MWh of energy annually [32; 33].  
In the past few years, PAFC and MCFC have presented the highest growth rate, though this is expected to change due to several companies offering PEMFC in the order of >1 MW [31], some of them stackable modules.