esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling
### Thermodynamics Model of the SSCs  
Since the fluid flow in the meshing pair gap is partly driven by the differential pressure between the working chamber and the casing cavity, it is necessary to calculate the thermodynamic process of the compressor. The pressure of the casing cavity approximates to the atmospheric pressure. In order to calculate the working chamber pressure and temperature, a thermodynamics model is adopted to simulating the working process of the SSC.  
According to the thermodynamics principle of the variable mass system, the basic equations for simulating the working process of air compressors are given as follows [25]:  
\[\frac{dP(\phi_{\mathrm{gr}})}{d\phi_{\mathrm{gr}}}=\frac{kR_{g}}{V_{c}}\Big{[} \sum\frac{dm_{i}}{d\phi_{\mathrm{gr}}}(T_{i}-T)-\frac{dQ}{d\phi_{\mathrm{gr}} }\Big{]}-\frac{kP}{v}\frac{dv}{d\phi_{\mathrm{gr}}} \tag{5}\]  
where \(P\) is the gas pressure in the working chamber; \(k\) is the specific heat ratio; \(R_{\mathrm{g}}\) is the gas constant; \(V_{\mathrm{c}}\) is the gas volume in the compression chamber; \(m_{\mathrm{i}}\) is the leakage mass of the gas flow into the  
Figure 7: The cross section of the meshing pair.  
chamber; \(Q\) is the heat transfer capacity between the gas and the liquid; \(v\) is the specific volume of the gas; \(T_{i}\) is the temperature of the gas leaking into the working chamber; \(T\) is the temperature of the gas in the working chamber and can be calculated by:  
\[\frac{dT}{d\phi_{\rm gr}}=\frac{kv}{V_{c}}\left[\sum\frac{dm_{i}}{d\phi_{\rm gr }}(T_{i}-T)-\frac{dQ}{d\phi_{\rm gr}}\right]-\frac{k-1}{v}\frac{dv}{d\phi_{\rm gr}} \tag{6}\]  
Several unknown relations are included in the basic equations, such as heat transfer and leakage items. The calculation methods for these items and the whole basic equations are given in references [26; 27; 28; 29; 30; 31; 32]. The basic equations and the items of heat transfer and leakage constitute the thermodynamics model.  
The compression process described by Equations (5) and (6) is neither an adiabatic process nor an isothermal process but a polytropic process. Although a lot of water is injected, the isothermal compression process cannot be achieved because of the high rotation speed and short time for heat transfer. The polytropic process index is much closer to the adiabatic process [25].
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis
### PS-D2D Scheme
#### 3.1.1 Outage Probability of U\({}_{1}\)
The probability of case 1 is given by  
\[\phi_{1}=\Pr\left[\Lambda_{svt1,ps}<\gamma^{\prime}_{p^{\prime}}\gamma^{ \prime}_{p}<\gamma_{p}-\Lambda_{svt1,ps}\right]=F_{\Lambda_{svt1,ps}}(\gamma^{ \prime}_{p})F_{\Lambda_{svt1,ps}}(\gamma_{p}-\gamma^{\prime}_{p}). \tag{30}\]  
Substituting Equations (25) and (26) into Equation (30), we can obtain \(\phi_{1}\). The probability of case 2 is given by  
\[ \begin{split}\phi_{2}&=\Pr\left[\Lambda_{svt1,ps}< \gamma_{p}-\Lambda_{svt1,ps},\gamma^{\prime}_{p}\geq\gamma_{p}-\Lambda_{svt1, ps}\right]\\ &=\Pr\left[\Lambda_{svt1,ps}<\gamma_{p}-\Lambda_{svt1,ps}, \Lambda_{svt1,ps}\geq\gamma_{p}-\gamma^{\prime}_{p}\right]\\ &=\int_{0}^{\gamma_{p}}\int_{\gamma_{p}-\gamma^{\prime}_{p}}^{ \gamma_{p}-y}f_{\Lambda_{svt1,ps}}(x)f_{\Lambda_{svt1,ps}}(y)\mathrm{d}x \mathrm{d}y\\ &=\int_{0}^{\gamma_{p}}F_{\Lambda_{svt1,ps}}(\gamma_{p}-y)f_{ \Lambda_{svt1,ps}}(y)\mathrm{d}y-\int_{0}^{\gamma_{p}}F_{\Lambda_{svt1,ps}}( \gamma_{p}-\gamma^{\prime}_{p})f_{\Lambda_{svt1,ps}}(y)\mathrm{d}y.\end{split}  \tag{31}\]  
Making use of the \(L\)-step staircase approximation approach in reference [23] for the included triangular integral region in Equation (31), we can rewrite the probability of case 2 as  
\[ \begin{split}\phi_{2}&\approx\sum\limits_{i=0}^{L-1} \left\{F_{\Lambda_{svt1,ps}}\left(\frac{i+1}{L}\gamma_{p}\right)-F_{\Lambda_{ svt1,ps}}\left(\frac{i}{L}\gamma_{p}\right)\right\}\\ &\quad\quad\times F_{\Lambda_{svt1,ps}}\left(\frac{L-i}{L}\gamma_{ p}\right)-F_{\Lambda_{svt1,ps}}(\gamma_{p}-\gamma^{\prime}_{p})F_{\Lambda_{svt1, ps}}(\gamma_{p}).\end{split}  \tag{32}\]Substituting Equations (25) and (26) into Equation (32), we can obtain \(\phi_{2}\). Then, substituting Equations (30) and (32) into Equation (29), we can obtain the OP of \(U_{1}\) for the PS-D2D scheme.
## 1 Introduction
[14] combined the depth residual block with the slice convolution to generate a classification score for a particular food category, achieving an accuracy of 90.27% on the Food-101 data set. Based on the residual block concept, [PERSON] et al. [15] added a branch of attention to the last convolutional layer of each block of the network, which aims to focus on finger micro-gestures and reduce noise from the wrist and background. A better recognition effect was achieved on the holographic 3D micro-gesture database (HoMG). Researchers [PERSON] et al. [16] used Visual Geometry Group Network (VGGNet) and ResNet to automatically classify high-resolution remote sensing plant community images. The highest classification accuracy of the ResNet model and the VGGNet model were 91.83% and 89.56%, respectively. The ResNet50 model presented better classification results. [PERSON] et al. [17] designed a layered residual network model to address the problem of large histological images and error-prone analysis, and an accuracy of 0.99 in pathological segmentation tests was achieved. Most importantly, the training based on ResNeXt or ResNet is a weakly supervised learning method, which only need image-level annotations. [PERSON] et al. [18] proposed a model enhancing the weakly supervised deformable part-based models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter, using only image-level annotations. Extensive experimental results proved that the model has competitive final localization performance in weakly supervised object detection. In summary, weakly supervised training can improve the efficiency of target detection and recognition. As a mature deep convolutional neural network, the deep residual network is widely used in various image classification applications, and it has achieved satisfactory recognition effects. Therefore, an integration model combining Squeeze-and-Excitation network (SENet) and ResNext was utilized to improve the efficiency and accuracy of recognizing large volumes of monitoring images. The rest of this paper is organized as follows. Section 1 reviews the related work of automatic wildlife recognition. Section 2 introduces the structure of a multi-branch aggregation transformation residual network and SENet. Section 3 shows the structure of SE-ResNeXt for wildlife recognition. Section 4 evaluates the performance of the SE-ResNeXt on two different datasets. The conclusions are drawn in Section 5.
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 4 Proposed Rapid Estimation Method
### ED Results  
From the \(W(p)\) and \(X(p)\) readings taken by MSSS for each point and from the calculated \(T\), our algorithm identifies those used channels, marked as PU, and those idle channels, marked as TVWS. The outputs of this sub-block are the channel and the number of PU and TVWS detected. This can be seen in Figure 11 for points 1 and 6. Panel (a) shows 10 used channels, which are 35, 38, 40, 41, 42, 43, 44, 45, 47, and 48, and this means 32.25% usage. Panel (b) shows 15 used channels, which are 20, 21, 26,  
Figure 10: Diagram of the rapid estimation method proposed that shows the data flow process: Data collected by the Mobil Spectrum Sensing Station (MSSS) are combined and fed into three sub-blocks, namely, the Energy Detection (ED), the Statistical Analysis (SA), and the ROC Analysis (RA). The output of these blocks is introduced in the Rapid Estimation Method (REM) block to obtain an estimation of the TVWS (E-TVWS).  
31, 32, 38, 39, 40, 41, 42, 43, 44, 45, and 49, and it means 46.87% usage. In this case, point 6 presents 50% more PU presence than point 1.
## 1 Introduction
However, the CEL method is different from the ALE method in that the fluid node and Euler mesh remain stationary while the material moves based on the Euler volume fraction. As the fluid mesh does not deform nor does it require remeshing, the computational cost can be reduced, and the fluid element distortion problem can be avoided. As the CEL method is based on an explicit time integration scheme based on operator splitting, its accuracy is relatively low compared with that afforded by conventional CFD simulations; however, it is gaining wide acceptance owing to the simplicity and efficiency of its numerical calculations [28]. There are many previous studies that calculate the sloshing pressure or evaluate the structural behaviors for a given sloshing impact pressure level; however, there are very limited studies available to deal with sloshing pressure and structural assessment simultaneously in single simulation. As it is very difficult to find a relevant method that satisfies analysis accuracy with time efficiency, it is necessary to find the strength and weakness of each method based on studies through various numerical analyses techniques. The CEL technique used in current study is particularly rare for sloshing simulations due to relatively less accuracy than conventional CFD for the fluid domain application; however, it is worthwhile to use the CEL method for structure design purpose. In this study, the plastic deformation of a flat plate for a slamming impact was numerically investigated using the CEL method. The dynamic impact pressure of the flat plates by weight and drop height was investigated under the assumption of viscous and compressible fluids. To evaluate the plastic deformation of the plate, contact between water and the plate was removed after a certain duration after dropping, and then the remaining deformation was measured. Optimized finite element models for the drop simulations were selected via a mesh sensitivity study, and the simulation results were calibrated and compared with the experimental data. Finally, a discussion is presented for a more accurate fluid impact analysis model based on the comparison results with the experimental data.
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 2 Related Works
### Comparison with Existing Methods  
In this section, we compare our proposed method with existing representative studies in the field, focusing on key aspects such as the model architecture, method categories, training strategies, and their respective advantages and disadvantages. To facilitate a clear and detailed analysis, we provide tables summarizing the relevant comparisons. As shown in Table 1, the comparison highlights the key differences across various methods.  
The table provides a comprehensive comparison of various federated learning methods, highlighting the trade-offs between efficiency, personalization, robustness, and computational costs. While methods like FedAvg [29] offer simplicity and efficiency, they struggle in heterogeneous environments. FedProx [14] addresses data heterogeneity through regularization, but with added computational complexity. Personalized methods such as FedPer [20], pFedMe [37], and FedBN [23] offer better adaptation to local data but are more prone to overfitting and incur higher computational costs. PFPS-LWC balances robustness and efficiency, making it a strong choice for environments with data heterogeneity. FedProto [41], focusing on communication efficiency, comes with the downside of slower  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Architecture** & **Categories** & **Strategies** & **Advantages** & **Disadvantages** \\ \hline FedAvg [29] & CNN & FL & Simple aggregation & Simple and efficient & Sensitive to data heterogeneity \\ FedProx [14] & CNN & FL & Regularization & Robust to data heterogeneity & More computational costs \\ FedPer [20] & CNN & pFL & Model personalization & High adaptability & Prone to overfitting \\ FedMe [37] & CNN & pFL & Personalized loss & High personalization & More computational costs \\ FedBN [23] & CNN & pFL & Batch normalization & Fast convergence & More computational costs \\ FedProx [41] & CNN & pFL & Prototype learning & High communication efficiency & Slow training \\ PFPS-LWC & CNN & pFL & Feature alignment and regularization & Efficient and robust & More computational costs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of federated learning and personalized federated learning methods.  
training. Overall, the choice of method depends on the balance between the need for model personalization, computational resources, and the desired robustness to data heterogeneity.
## 3 Results
### Case Study in Slovakia
#### 3.1.4 Capacity Assessment--ETCS L3 Variant  
The following steps are required when using the ELCS L3 control system. In order to calculate the permeability of the specified line section, it is necessary to determine the individual occupancy times with help of calculations. This system is still under development and the procedures for these calculations are not precisely defined in the conditions of ZSR. Therefore, we adapted a methodically correct procedure for obtaining them. Initially, it is desirable to determine the speed and kilometer profile of the examined track section, which is shown in Figure 5. Shown are the maximum speeds (Vmax) in the railway station and between the line sections. The third and fourth rows show the kilometric distances between the railway stations, from the switch and after the cross switch in the Senec, Sladkovicovo and Galanta stations [32; 33].  
When implementing ETCS L3, we consider a so-called floating section without signals. It does not have a fixed length or an exactly fixed position on the track. For each train, the system recalculates and determines it differently. There are only balises and sensors on the track to check the exact position of the train on the track. The connection with the train is solved through the RBC. The broad track is entirely without signals; in our case only departure signals remain at the stations [34].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Train type** & **Ex** & **R** & **Os** & **Pn** & **Mn** \\ \hline Number of trains & 10 & 10 & 25 & 52 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number and type of trains per day used in the calculations.  
In our opinion, this option is the most progressive of all the proposals. ETCS L3 has not yet been widely adopted. Its development is still ongoing. We assume that all technical train conditions are met in the proposal. We can therefore consider the operation being fully controlled by ETCS L3 [35].
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 3 Proposed Algorithm
### Global Optimal Layer Selection via BayesOpt  
In the proposed algorithm, the optimal number of sharing layer \(n^{*}\) is found through BayesOpt which enables to optimize an objective function, globally. The BayesOpt consists of two major components: (1) Gaussian process (GP) regression, which statistically defines the uncertainty of an objective function, (2) an acquisition function which selects where to sample next [28; 29]. The GP regression provides a Bayesian posterior probability distribution of the proposed objective function, which denotes potential value of \(L(n)\) at an unidentified number of sharing layer \(n\). After the GP regression, the acquisition function measures the value which would be evaluated for next optimization iteration, in other words, it decides the best candidate \(n\) which induces better objective function value than previously computed points. In the following sections, we discuss the components of BayesOpt in detail, first explaining GP regression and describing acquisition functions including probability improvement (PI) and expected improvement (EI).
# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Etiology Prediction  
In the etiology prediction of AIS using radiomics models, Chen Y et al. tried to differentiate between cardioembolic and atherosclerotic etiology in patients with acute ischemic stroke. They included 82 patients, and they obtained RFs from CTA images. They performed a manual segmentation of the embolic region and extracted 116 RFs. In their results, the best AUC was 0.9018 and the best Acc was 0.8929. They concluded that radiomics can effectively predict the subtype of ischemic stroke and help doctors to correctly manage these patients [56]. Jiang J et al. also studied a clot-based model in 403 AIS patients to identify cardioembolic (CE) stroke before MTB. They achieved an AUC of 0.838 for predicting CE stroke. They concluded that clot-based RFs can reliably predict CE strokes [57].  
The current determination of the etiology of AIS is based on a number of clinical, analytical and medical imaging criteria. The management of these patients depends on the etiology of AIS. However, in many cases the etiology determination may be delayed or even undetermined. In this sense, radiomics can provide valuable information to support the diagnosis of patients with cardioembolic or atheothrombotic AIS and thus optimize the management of these patients.
# Integrated Geophysical Methods for Shallow Aquifers Characterization and Modelling
###### Abstract  
Aquifers stock about 31.4% of the freshwater on the Earth, provide about 50% of current potable water supply, constitute the sole water source in many areas, support groundwater-dependent ecosystems, and present more resilience than surface watercourses to the negative effects of climate change and anthropogenic activities. However, groundwater use is growing, and signs of degradation are increasingly reported.  
Groundwater research is increasingly using geophysical prospecting surveys for aquifer characterization in different hydrogeological environments. They have become a part of the holistic strategy to define aquifer geometry and some transient groundwater features, which are necessary data to implement numerical tools for the modelling of groundwater quantity (flow) and quality (salinity, pollution) dynamics. Geophysical prospecting techniques are non-invasive, usually inexpensive to apply, and useful for providing the accurate and fast subsurface information required for detailed groundwater research over multiple observation scales.  
Electrical, electromagnetic, and seismic geophysical techniques are widely used for aquifer characterization. The first two are typically used to deduce aquifer geometry and certain transient groundwater features such as piezometric level, freshwater-saltwater interface, and pore water conductivity, whereas the latter are mostly used to deduce aquifer geometry and certain steady aquifer hydraulic parameters. The integrated use of different techniques reduces the ambiguity of interpretations, especially when the conductive structures and pore-filling fluids (natural and human-induced) are subjected to the temporal dynamics of water content and dissolved ions. Integration can also be referred to using external data (e.g., geotechnical soundings logs, geochemical tracers, physical parameters) to improve and/or validate the geophysical models. Different scientific software platforms with friendly interfaces, robust algorithms for data inversion, and tools for uncertainty analysis are available.  
Footnote †: journal: 2022, 12, 2271. [[https://doi.org/10.3390/app12052271](https://doi.org/10.3390/app12052271)]([https://doi.org/10.3390/app12052271](https://doi.org/10.3390/app12052271))
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 5 Performance Evaluation
### Performance Metric Modeling  
The experimental performance metric model used to conduct the evaluation of the impact of the reviewed access control model focuses on three critical parameters: (1) the response time of the access control model to establish an authorized E2E secure session; (2) the energy cost of this model for the protected CDS running on finite battery resources; and (3) the model's impact on the local storage on the CDS and memory footprint.  
Summarizing the acceptance criteria, the response time is required to be below accepted standard values if the proposal is to be useful, and the energy consumption, local storage, and memory footprint, due to the nature of the CDSs and their resource constraints, cannot exceed rational and proportional limits in the CDS if the proposal is to be feasible and scalable.
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Classification Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline MEL & DenseNet121 & 0.98 & 0.66 & 0.7 & 0.68 \\ NV & DenseNet121 & 0.99 & 0.85 & 0.9 & 0.87 \\ BCC & DenseNet121 & 0.97 & 0.88 & 0.8 & 0.83 \\ AKIEC & DenseNet121 & 1 & 0.82 & 0.78 & 0.8 \\ BKL & DenseNet121 & 0.97 & 0.9 & 0.8 & 0.85 \\ DF & DenseNet121 & 0.96 & 0.96 & 0.98 & 0.97 \\ VASC & DenseNet121 & 1 & 1 & 0.86 & 0.93 \\ AVERAGE & DenseNet121 & **0.9814** & **0.8671** & **0.8314** & **0.8471** \\ MEL & EfficientNet\_b3 & 0.98 & 0.7 & 0.71 & 0.71 \\ NV & EfficientNet\_b3 & 0.99 & 0.89 & 0.9 & 0.89 \\ BCC & EfficientNet\_b3 & 0.97 & 0.88 & 0.8 & 0.84 \\ AKIEC & EfficientNet\_b3 & 1 & 0.8 & 0.87 & 0.83 \\ BKL & EfficientNet\_b3 & 0.96 & 0.86 & 0.8 & 0.86 \\ DF & EfficientNet\_b3 & 0.95 & 0.95 & 0.98 & 0.97 \\ VASC & EfficientNet\_b3 & 1 & 0.96 & 0.86 & 0.91 \\ AVERAGE & EfficientNet\_b3 & **0.9786** & **0.8629** & **0.8457** & **0.8586** \\ MEL & GoogleNet & 0.98 & 0.78 & 0.64 & 0.7 \\ NV & GoogleNet & 0.98 & 0.8 & 0.88 & 0.84 \\ BCC & GoogleNet & 0.96 & 0.86 & 0.75 & 0.8 \\ AKIEC & GoogleNet & 1 & 0.82 & 0.78 & 0.8 \\ BKL & GoogleNet & 0.96 & 0.84 & 0.79 & 0.81 \\ DF & GoogleNet & 0.95 & 0.94 & 0.98 & 0.96 \\ VASC & GoogleNet & 1 & 1 & 0.83 & 0.91 \\ AVERAGE & GoogleNet & **0.9757** & **0.8629** & **0.8071** & **0.8314** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of model performance of HAM datasets. Tables 5-9 clearly illustrate the performance of various models across different dataset classification tasks. According to the data, the DenseNet121 model achieves the highest accuracy of 98.14% in the HAM dataset, and the greatest precision of 97.5% in the chest-X-ray dataset. The ResNet50 model attains the highest recall and F1 Score, each at 97%, also within the chest X-ray dataset. The data further indicates that the type of dataset significantly affects classification outcomes; for instance, while models generally excel in the chest X-ray dataset with baseline performances around 95%, performance in the breast cancer dataset typically falls below 80%. The DR dataset notably scores lower in all metrics except accuracy, likely due to data imbalance. Additionally, DenseNet121 consistently outperforms other models across all datasets, especially in the HAM dataset. In contrast, the ResNet50 model maintains stable precision across various tasks, with rates ranging from 67.2% to 97%. The lowest accuracies recorded are in the breast cancer dataset, with GoogleNet and EfficientNet_b3 models achieving only 81.64%; however, accuracies in other datasets range between 90% and 99%. Overall, these results affirm that our chosen models satisfy our classification requirements and exhibit stable performance across diverse datasets.