esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Framework Development  
The framework presented in this paper consists of an integrated workflow which captures patient specific data and fixator parameters, processes the data to predict mechanical behavior of available configurations, and provide feedback to surgeons for preoperative planning. The scope will not include biological parameters and will be an assistive tool for medical practitioners to understand mechanical properties. The framework was developed based on literature and feedback obtained from practising surgeons (Figure 2). Special focus was given to reduce the cost of the solution and complexity, to be suitable for developing regions (Figure 3). As the solution is primarily focused towards developing regions higher priority was given for reducing cost involved. Simpler solution methods which do not require higher computational capacity were identified for solutions where the added accuracy is not required.  
Therefore, the solution was perceived under different scenarios based on fracture complexity and specific use of fixator (i.e., temporal or definitive fixation). Improving stability of the bone and fixator system was considered for temporary fixation while improving healing potential was identified for definitive fixation.  
Figure 2: Integrated workflow of the proposed framework.  
The Muller AO/OTA (AO Foundation/Orthopaedic Trauma Association) classification was used for bone classification [46]. Even though fracture conditions vary largely, the classification provided, enables the workflow to be managed so that all scenarios are addressed.  
According to the British Orthopedic Association providing stability is the main objective during temporal stabilization to facilitate soft tissue recovery in addition to promoting bone healing. They state that spanning ex-fixes are ideal for this purpose because it also provides surgeons access to perform other surgical procedures, especially to facilitate soft tissue healing [24]. The priority during definitive fixation is to promote secondary healing which requires loading and micro-motions in the callus [47], which requires a comprehensive simulation utilizing mechano-regulation (Figure 4).  
Figure 3: Optimized framework for developing regions, reducing complexity in instances where simplification is possible.
## 3 Results
### Single Band Regression
#### 3.2.2 Analysis of the Uncertainty Estimation  
As explained in Section 2.2.1, the regression model is also able to estimate the uncertainty of the predicted value by associating a variance with it. Since this variance is an estimation itself, its meaningfulness needs to be assessed.  
The loss function used to train the model was chosen assuming a Gaussian error model. The histograms in Figures 9 and 10 show that the distributions of the errors are not Gaussian. However, these distributions are mono-modal, which may allow use of the estimated variance as a good proxy for the uncertainty of the estimation. In order to check this hypothesis, we will measure the proportion of pixels having errors higher than a given proportion of the variance.  
In the case of a Gaussian distribution, we have \(P(\mu-1\sigma\leq X\leq\mu+1\sigma)\approx 68.27\%\), \(P(\mu-2\sigma\leq X\leq\mu+2\sigma)\approx 95.45\%\) and \(P(\mu-3\sigma\leq X\leq\mu+3\sigma)\approx 99.73\%\).  
We can therefore compute the proportion of pixels having an absolute error lower than \(\sigma\), \(2\sigma\) and \(3\sigma\) and compare the results to the probability values above.  
Figure 10: Histograms of the errors (true value minus prediction) for the L2A bands.  
Figure 9: Histograms of the errors (true value minus prediction) for the L1C bands.  
Tables 13 and 14 present the above-mentioned proportions of pixels whose errors are within the bounds given by the estimated sigma. We see that, although not identical, the proportions are relatively similar to what one should get in the Gaussian case.  
It is important to understand that the value of \(\sigma\) is provided by the regression algorithm as a prediction. These results show that this prediction of \(\sigma\) is indeed a good proxy for the probability of the reflectance estimation being in the predicted interval. Therefore, the estimation of \(\sigma\) can be a threshold and used as a validity mask for the estimations.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Models and Methods for Sound Transmission Loss Calculation
#### 4.1.5 Wave Finite Element Method  
Finite element methods have been used to model the vibroacoustic emission of sandwich and composite DPS [32; 104]. An improved method--the wave finite element method (WFEM) described by [PERSON] and [PERSON] [105], [PERSON] et al. [106] and [PERSON] et al. [107], is a numerical model reduction method which permits the modelling of each cell element rather than modelling the complete structure. The method entails the combination of both the finite element and the periodicity of the structure with the advantage of low computational cost. In recent times, this model was adopted by [PERSON] et al. [108; 109] to predict the STL of multi-layered panels with fluid layers. In their approach, small segments of the solid layers were initially discretized to evaluate their stiffness and mass matrices. Different conditions were applied to determine their spectral dynamic stiffness matrices and the acoustic response of the panels during excitation. The effect of finite size on the STL was investigated and the authors arrived at the conclusion that the WFEM was able to predict the STL very fast with low computational cost. [PERSON] et al. [110] also adopted this model recently to model the STL of sandwich structures particularly in the low and medium frequency range. The authors used the DLM and the WSFE model with experimental results to verify and validate, respectively their predicted model. It was evident from their results that while the cell angle of the hexagonal core had no influence on the STL, increased thickness of the panels increased the STL and a lowering of the stiffness-weight ratio in the mid frequency regime.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 2 Electronic Band Structure
### Turbulent Multilayer Graphene  
Turbostatic multilayer graphene layers are rotationally disordered and, thus, electrically decoupled. Epitaxial graphene formed on SiC(000\(\bar{1}\)) by thermal decomposition on both (0001) and SiC(000\(\bar{1}\)) by solid-phase epitaxy and on 3C-SiC/Si via the alloy mediated graphitization is reported to be turbostatic [21; 46; 47]. The rotational disorder in EG/SiC(000\(\bar{1}\)) was shown by low-energy electron diffraction (LEED) investigations in which an azimuthal diffraction pattern was found, and the strong intensity modulation in the bands denoted the rotational orientation [10; 12; 48]. In the case of EG/3C-SiC, the presence of turbostratic in-plane modes in the Raman spectroscopy indicated the rotational disorder [21]. With rotational disorder, the linear dispersion is recovered in the vicinity of  
Figure 3: Band structure of BLG on SiC(0001) from ARPES and theoretical (solid lines). Reprinted with permission from Reference [34]. Copyright (2006) American Association for the Advancement of Science.  
the \(K\)-points. The ab initio calculations show that the dispersion is nearly identical to that of an isolated MLG linear dispersion (see Figure 1), and predicts \(\mathbf{v_{F}}\) to be the same as that of MLG [12; 22; 31].  
[PERSON] et al. measured the linear dispersion using ARPES, as shown in Figure 4[35]. In the ARPES data, the linear dispersions of adjacent decoupled layers can be readily seen as well as the n-type conductivity, where \(\mathbf{E_{\mathrm{F}}}\) is as low as \(\sim\)14 meV, and the carrier density is \(\sim\)10\({}^{11}\) cm\({}^{-2}\). Furthermore, the \(\mathbf{v_{\mathrm{F}}}\) of MLG is close to the \(\mathbf{v_{\mathrm{F}}}\) of MEG formed by thermal decomposition of the SiC(000\(\overline{\mathrm{l}}\)) obtained from infra-red measurements (1.02 \((\pm 0.01)\times 10^{6}\) ms\({}^{-1}\)) [49] and scanning tunneling spectroscopy (1.07 \((\pm 0.01)\times 10^{6}\) ms\({}^{-1}\)) [50].  
In the next sections, we will discuss the electronic and transport properties of EG on SiC and 3C-SiC substrates with respect to the substrate, ambient, and metal contact interactions. Section 3 will discuss the different measurement methods used for the charge carrier transport study.
## 5 Barriers
### IoT Implementation Barriers
#### 5.1.3 Application Layer Security Threats  
The application layer handles and offers end-users numerous solutions in a simple approach. Smart homes, smart grids, smart cities, and other applications are part of this layer. The application layer has distinct security flaws, such as data theft and privacy risks, unlike the different layers. Below is a list of the various security vulnerabilities at the IoT application layer [129]. (a) Data Theft: Several IoT solutions, such as public healthcare monitoring, intelligent transportation systems, and smart metering, manage critical and sensitive data. Data in transit is more vulnerable to attack than data stored on IoT devices because there is so much data flowing between them. (b) Data Integrity: Maintaining data integrity in IoT networks is a significant concern. Data trust is a fundamental component of large-scale interoperation, and the rapid progress of IoT applications has resulted in segregated data collection and interchange. Because IoT service facilities are so common, Quality of Service (QoS) has become an essential factor to consider when choosing a service. (c) Injection of Insecure Code: Insecure programming/firmware is one of the drawbacks of the IoT. In general, software/firmware updates should be completed in a secure manner. Languages such as JSON (JavaScript Object Notation), XML (Extensible Markup Language), SQLi (Structured Query Language injection), and XSS (Cross-Site Scripting) should be used with caution. (d) Sniffing Attack: The attacker uses the sniffer program to monitor network traffic in the IoT. An attacker can access personal information by monitoring network traffic if proper security measures are not applied. (e) DoS Attack on CoAP: In the IoT, the Constraint Application Protocol (CoAP) is the illegitimate application layer protocol for resource-limited devices. In the event of a DoS attack, CoAP, on the other hand, does not give any security measures for safe communications. Instead, CoAP recommends using the DTLS protocol.
## Materials and Methods
### Measurements  
A cylindrical tube of 10 cm in diameter and 30 cm in length was used as the ingrowth core. The cylindrical tube was made of plastic mesh, with mesh openings of approximately 15 mm\({}^{2}\), which allowed roots, soil animals and microorganisms to enter. This cylinder was filled with soil from the study site but all roots including dead root and decomposing organic matter were removed by washing the soil with tap water, similar to the method used by [PERSON] et al. [9]. In February 2013 (T0), 24 ingrowth cores were buried in each plot (72 cores in total). Six cores were collected in each plot at four time points: June 2013 (T1), September 2013 (T2), March 2014 (T3) and March 2015 (T4). These time points corresponded to sample intervals of \(\sim\)3, 6, 12 and 24 months.  
On each sampling date, six ingrowth cores from each plot were collected by cutting off the surrounding roots and washing them with tap water. The cores were stored at 4 \({}^{\circ}\)C until processing. The sampled cores were sieved through a 0.5 mm-mesh to collect all root samples, including broken segments. These samples were washed clean of attached soil particles. All roots on the sieve were collected and separated into woody roots and understory roots based on color and texture. Then, fine roots (\(<\)2.0 mm in diameter) were divided into live and dead roots based on color and resilience. Live roots have an intact stele and cortex with white color and are slightly elastic. Dead roots often have fragmented bark and are soft and brittle. Living fine root biomass and fine root necromass outside the cores were also sampled at three locations in each plot using core samplers with diameters of 10 cm and to a depth of 30 cm in March 2013, following the same method used for ingrowth cores. Sampled root were dried at 50 \({}^{\circ}\)C for 48 h.  
Root litter bag experiments were conducted to determine the decomposition rate of roots. Fine roots were sampled in each study plot and dried at 50-60 \({}^{\circ}\)C over 2 days. Dried fine roots totaling 5 g in weight were placed in mesh bags and added to the ingrowth cores. Root litter bags of 5 \(\times\) 5 cm size were made from a casting net of synthetic fabric with 6 mm mesh size. In total, 12 root litter bags for each plot were set in February 2013 and three root litter bags for each plot were collected at each sampling time point. Fine root litter was gently washed by water, dried and weighed.  
Air temperature was monitored on the crane at 75 m above the ground using a thermohyrograph (HMP35A, Vaisala, Helsinki) and the data were logged using a programmable data logger (CR10X, Campbell Scientific, Logan, UT, USA). Rainfall was monitored using a tipping bucket rain gauge (RS102, Ogasawara Keiki, Tokyo, Japan) with a data logger (HOBO event, Onset Computer, Bourne, MA, USA) at the top of the crane (Figure 1).
## 3 Results
### Classification Results
#### 3.3.3 Combination of HRV and Game Performance  
By combining the HRV and game performance features, the total of features sums up to 53 features. TPOT chose the Decision Tree Classifier, and was able to achieve the accuracy of 81.20%. The sensitivity and specificity were 87.50% and 75%, respectively, as shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Recall Rate**} \\ \cline{3-3} \cline{5-6}  & & **MCI** & **NC** & & \\ \hline \multirow{2}{*}{**Actual**} & **MCI** & 16 & 8 & 24 & 66.67\% \\ \cline{2-6}  & **NC** & 7 & 17 & 24 & 70.83\% \\ \hline \multirow{2}{*}{**Total**} & **Total** & 23 & 25 & 48 & \\ \hline \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Rate**} & \multirow{2}{*}{69.57\%} & \multirow{2}{*}{68\%} & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy**: \\ 68.75\% \\ \end{tabular} } \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular} MCI—Mild Cognitive Impairment; NC—Normal Cognition.  
\end{table}
Table 7: Confusion Matrix with Precision and Recall rates using HRV only.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Recall Rate**} \\ \cline{3-3} \cline{5-6}  & & **MCI** & **NC** & & \\ \hline \multirow{2}{*}{**Actual**} & **MCI** & 19 & 5 & 24 & 79.17\% \\ \cline{2-6}  & **NC** & 3 & 21 & 24 & 87.50\% \\ \hline \multirow{2}{*}{**Total**} & **Total** & 22 & 26 & 48 & \\ \hline \multirow{2}{*}{**Precision**} & \multirow{2}{*}{86.36\%} & \multirow{2}{*}{80.77\%} & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy**: \\ 83.33\% \\ \end{tabular} } \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular} MCI—Mild Cognitive Impairment; NC—Normal Cognition.  
\end{table}
Table 8: Confusion Matrix with Precision and Recall rates using game performance only.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Recall Rate**} \\ \cline{3-3} \cline{5-6}  & & **MCI** & **NC** & & \\ \hline \multirow{2}{*}{**Actual**} & **MCI** & 21 & 3 & 24 & 87.50\% \\ \cline{2-6}  & **NC** & 6 & 18 & 24 & 75\% \\ \hline \multirow{2}{*}{**Total**} & **Total** & 27 & 21 & 48 & \\ \hline \multirow{2}{*}{**Precision**} & \multirow{2}{*}{77.78\%} & \multirow{2}{*}{85.71\%} & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy**: \\ 81.20\% \\ \end{tabular} } \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular} MCI—Mild Cognitive Impairment; NC—Normal Cognition.  
\end{table}
Table 9: Confusion Matrix with Precision and Recall rates using both HRV and game performance.
# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
## 3 Experimental Design and Results Analysis
### Simulation Experiment of Wind Turbine Blade Ice Accretion Volume Estimation  
To verify the feasibility and accuracy of stereo vision technology in measuring the volume of ice accretion on wind turbine blades, a simulated icing experiment was designed and conducted, which mainly included two parts: ice mass measurement and stereo vision image acquisition and processing.  
Firstly, a specific model of a wind turbine blade made of composite material with a smooth surface was selected. The blade had a length of 0.820 m, a maximum chord length of 0.120 m, and a minimum chord length of 0.032 m. Before the experiment, the blade was carefully cleaned and dried to ensure its surface was free of any residues. An electronic balance with a precision of 0.1 g was used to measure and record the initial mass of the blade, \(m_{\text{before ice}}\).  
The icing experiment was carried out in a refrigerator, with the temperature set to \(-15\)\({}^{\circ}\)C. The prepared blade model was placed in a fixed position inside the refrigerator to ensure uniform icing conditions. The blade was immersed in water, with the amount of water carefully controlled to cover the entire blade surface without causing runoff. The environmental parameters were maintained for more than 4 h, allowing the water to freeze and form a sufficiently thick ice layer on the blade surface.  
After the icing process, the blade was quickly removed to prevent the ice layer from melting due to an increase in environmental temperature. The total mass of the blade after icing, \(m_{\text{after ice}}\), was measured again using the electronic balance. The mass of the ice layer was calculated from the difference between the two measurements:  
\[m_{\text{ice}}=m_{\text{after ice}}-m_{\text{before ice}} \tag{27}\]  
Based on the density of ice \(\rho_{\text{ice}}=0.92\,\text{g}/\text{cm}^{3}\), the volume of the ice layer \(V_{\text{ice (mass method)}}\) was calculated:  
\[V_{\text{ice (mass method)}}=\frac{m_{\text{ice}}}{\rho_{\text{ice}}} \tag{28}\]  
At the same time, the blade was photographed before and after icing using a stereo camera. The volume of the ice layer was then calculated, obtaining \(V_{\text{ice (vision method)}}\).  
Finally, the volumes of the ice layer obtained by the two methods were compared and analyzed. The relative error of the stereo vision measurement result with respect to the mass method was calculated:  
\[\delta=\left|\frac{V_{\text{ice (vision method)}}-V_{\text{ice (mass method)}}}{V_{\text{ice (mass method)}}}\right|\times 100\% \tag{29}\]  
The experimental results are shown in
# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 2 Materials and Methods
### Explainability  
Ensemble classifiers are widely utilized in classification tasks for the well-recognized virtue to improve performance metrics in terms of accuracy. However, when dealing with high stake predictive models such as those in healthcare applications, there are major concerns also related to the explanation of decision-making and the avoidance of erroneous ones. In our effort to construct models that can decipher the uncertainty of real-world problems, we have created black box mechanisms that produce accurate results but are not transparent and trustworthy [46]. For experts to embrace AI in the healthcare domain, the provided predictions should be retraceable and reliable. In this framework, efforts of computer vision researchers are directed towards the discovery of methods that can highlight the relationships and interactions between the visual patterns included in an input image and the final prediction. Unveiling these connections are of crucial importance [47] since humans demand that health threatening decisions are thoroughly justified.  
Especially in the domain of computer vision and deep learning XAI (Explainable Artificial Intelligence), attempts to extract localization information of important visual patterns for decision-making have been widely witnessed. One way to achieve this goal is the construction of class activation maps [48]. Class activation mapping is a method which indicates the discriminative regions of an image that influenced the predictive model in reaching its final decision. Initially, it was mandatory that the predictive model should follow a certain architecture for the technique to provide plausible results, meaning that the output of the convolutional layers should be directed to a global average pooling layer and then directly to SoftMax activation function. This architecture, as discussed earlier, demands retraining of the predictive model and sacrifices complexity (added by the insertion of fully connected layers) for explainability. A generalization of this method (Grad-CAM) is proposed in [29]. In the same paper, the combination of Grad-CAMs with the guided-back propagation technique is proposed to provide a fine-grained pixel to pixel visualizations. This approach fits better to the visual characteristics of digital pathology images, where the patterns correspond to small cellular structures as opposed to larger structures. By computing the gradients for the score of each class with respect to the feature maps from the last convolutional layer and performing global average pooling on them, the importance weights for each feature map are obtained. In this fashion, the architecture of the predictive model remains intact.
## 1 Introduction
Hence, information within 1 km of the ship cannot be yielded, and, accordingly, cannot contribute to the early response stage of the HNS spill in detail. Previously, our group conducted a two-dimensional CFD simulation for predicting the near-field propagation behavior in the vicinity of the ship, within a few hundred meters [12], and presented the spatial HNS distribution over time. Therefore, the current study aims to examine HNS propagation characteristics by conducting three-dimensional and transient CFD simulations with the key parameters of current velocity, HNS density, and crack location. Based on the Kriging model and the CFD results for 18 test cases, a new metamodel is suggested for predicting the near-field propagation of HNS spilled from a ship. Moreover, we newly define the key parameters, such as the propagation velocity in a streamwise/transverse direction and the averaged HNS mass fraction at sea level, to aid the analysis of the change in HNS concentration over time, in a quantitative manner.