esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prediction of the Pool Shape
### Fuel Discharge Experiment Considering Inclination Effects
#### 3.1.2 Experimental Results
The length, breadth, and area of the spilled pool are tabulated for the dimensionless times of 0.50 and 1.00. As emphasised above, higher discharge rates resulted in a larger pool length, breadth, and area. Moreover, the pool length and area were larger at greater inclination angles. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Exp. No. **} & \multirow{2}{*}{ \begin{tabular}{c} **Inclination** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Discharge Rate** \\ **(mL/min)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Length** \\ **(mm)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Breadth** \\ **(mm)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Area** \\ **(10\({}^{3}\) mm\({}^{2}\))** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Length** \\ **(mm)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Breadth** \\ **(mm)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Area** \\ **(10\({}^{3}\) mm\({}^{2}\))** \\ \end{tabular}  } \\ \hline
1-1 & 1 & 200 & 303 & 193 & 50.1 & 513 & 252 & 104 \\ \hline
1-2 & 1 & 300 & 279 & 167 & 37.5 & 552 & 258 & 119 \\ \hline
1-3 & 1 & 400 & 363 & 274 & 86.4 & 691 & 339 & 191 \\ \hline
2-1 & 2 & 200 & 431 & 143 & 52.8 & 801 & 195 & 116 \\ \hline
2-2 & 2 & 300 & 538 & 21 The spreading behaviour of JP-5 was experimentally characterised through fuel discharge experiments on an inclined steel plate. The experimental parameters were determined considering the limitations of the testing laboratory capacity. The breadth of the steel plate limited the range of the fuel discharge rate that could be examined. However, even within this limited test condition, the pool length, breadth, and area exhibited clear trends under different discharge conditions. In future work, experiments must be conducted  
Figure 7: Experimentally measured pool dimensions (\(t_{m}/t_{d}=1.00\)): (**a**) length with respect to inclination angle; (**b**) length with respect to discharge rate; (**c**) breadth with respect to inclination angle; (**d**) breadth with respect to discharge rate; (**e**) area with respect to inclination angle; (**f**) area with respect to discharge rate. using additional substrate inclinations and fuel discharge rates to improve the applicability of the proposed formulae. Furthermore, the effects of the material properties of various flammable liquids, such as the liquid viscosity, density, and surface tension, as well as the friction of the metal plate, under various substrate conditions must be explored. The pool length, breadth, and area can be predicted through the substitution of the inclination angle, spill rate, and duration of spillage accidents into the empirical formulae, rendering them valuable and reliable inputs for fire simulation. Therefore, the experimental results are expected to be useful in the early fire safety design stages.
## 3 Results and Discussion
Figure 4: The measured temperature rise of the bovine liver tissue was exposed continuously for 5 s at 10 W, 40 W, and 125 W.  
Furthermore, viscous heating was removed from the numerical simulation model, and the temperature elevation of the bovine liver is given in Figure 5. The temperature rise curves were approximately linear (without the infrared glass). The difference between the predicted temperature and the measured temperature reached the maximum within 5 s, and then gradually decreased. The modified predicted temperature elevation after sonication was 13.1 \({}^{\circ}\)C (power = 4 W), 19.8 \({}^{\circ}\)C (power = 6 W), and 25.4 \({}^{\circ}\)C (power = 8 W). The width of the acoustic focal area (\(-\)3 dB acoustic field) of the transducer used in this study is 2.95 mm. Figure 6 shows the temperature elevation at 1.5 mm away from the focus in the direction of the focal plane. It can be seen in Figure 6 that the simulated results considering viscous heating were close to the measured results, and the maximum relative error was 6.7%. After removing the viscous heating from the numerical simulation model, the predicted temperature of the bovine liver at 4 W, 6 W, and 8 W reached 40 \({}^{\circ}\)C, 45.8 \({}^{\circ}\)C, and 49 \({}^{\circ}\)C, respectively, at the end of sonication, which was 3-4 \({}^{\circ}\)C lower than the temperature at focus. These studies were based on an ex vivo bovine liver without considering blood perfusion. However, blood can take away part of the heat and reduce the efficacy of FUS. Figure 5: Temperature elevations at focus (**a**) 4 W, (**b**) 6 W, and (**c**) 8 W. The yellow solid lines present the measured temperature (measurement). The blue solid lines present the simulated temperature considering viscous heating (simulation + vis). The red solid lines present the predicted temperature (without glass and without VH) in the pure bovine liver (prediction). Figure 6: Temperature elevations at 1.5 mm away from the focus (**a**) 4 W, (**b**) 6 W, and (**c**) 8 W. The yellow solid lines present the measured temperature (measurement). The blue solid lines present the simulated temperature considering viscous heating (simulation + vis). The red solid lines present the predicted temperature (without glass and without VH) in the pure bovine liver (prediction). This study further quantified the effect of blood perfusion on the efficacy of FUS. The temperature at focus after sonication (40 s) and the temperature at 1.5 mm from the focus (Figure 7) at different power were calculated by numerical simulation, whether or not blood perfusion was considered in both cases. As shown by the best-fitting line, the relationship between temperature and power was approximately linear in the MIFU range.
## 3 Fusion of FL and Medical Domain
### Challenges of FL in Medical Domain
The recent COVID-19 pandemic posed quite different data-related issues in the framework of COVID-19-oriented FL. For instance, data governance was one of the biggest challenges amid the pandemic, which hindered the application of AI and FL, owing to data misuse and privacy concerns. In some countries, the work on data governance, particularly personal data handling and privacy laws started after the pandemic [25]. Furthermore, the urgency of the situation and mutation leads to higher concept and data drifts in the FL models, and therefore, the performance of most models was fragile. Cross-silo FL, which involves multiple hospitals, is a widely used architecture in the medical domain. However, the data disparity among these hospitals may need a higher number of iterations to accomplish the desired accuracy on a global server [26]. This increased computation time poses challenges in maintaining responsiveness and efficiency in medical applications. The aggressive use of digital technologies in the pandemic era led to an explosion in clinical (and general) data, and therefore, it was very challenging to apply FL to extract useful knowledge. Furthermore, the existence of less diverse data of one entity (1:M) across multiple hospitals posed a serious challenge to generalizability gain in FL. The diverse medical data such as laboratory measurements, clinical data, omics, etc., and its integration with existing knowledge bases posed a serious challenge in terms of data truthfulness/fairness. The limited availability of medical data concerning the pandemic and the lack of methods to evaluate the fairness/truthfulness, also known as the non-IID nature of data, hindered the FL-based model development. This was already a major obstacle in the general medical domain and biomedical research, but a pandemic of a poorly understood novel disease that overburdened hospitals' capacity has divupled the importance of this problem [27]. Also, the incomplete and redundant data that are heavily biased towards fewer aspects (e.g., mostly negative test data) of the pandemic or certain populations led to poor convergence of the FL-based model or imbalanced learning issues. To curb the spread of the pandemic, both clinical and non-clinical (location, mobility, etc.) data were supposed to be processed with FL models. However, it was very challenging to simultaneously process both these kinds of data with FL to either restrain the spread of the virus or determine clinical complexities in patients. The solution to the above challenges is urgent and vital to unlocking the hidden potentials of FL in the medical domain.
# CLensRimVision: A Novel Computer Vision Algorithm for Detecting Rim Defects in Contact Lenses
## 4 Methodology
### Image Preprocessing  
We employ image processing to refine and enhance the input image, making edges sharper and more readily detectable by the circle algorithm. This enhancement utilizes two primary functions in image processing: firstly, the HSV color space, and secondly, the morphology method.  
A color space is a multidimensional system by which colors can be represented, usually within three or more dimensions. Each dimension corresponds to a distinct color component. Recognizing the differences between various color spaces is pivotal in image processing, as the chosen color space can profoundly influence tasks such as segmentation, representation, and image manipulation.  
Morphological image processing focuses on the shape or morphology of features within an image. Rooted in set theory, this method provides tools for extracting components of an image that are instrumental in describing and representing region shapes, like boundaries, skeletons, and the convex hull. The primary applications of morphological processing include noise reduction and sealing the contour of the circular edge of contact lenses using two core operations: dilation and erosion.  
Thus, our image preprocessing involves using the HSV color space to filter and pinpoint the section within our desired color range as in Figure 5, which, in this context, is the rim of a contact lens. Subsequently, morphological image processing is employed to minimize noise and seal the circular rim of the contact lens. Following this preprocessing step, the algorithm identifies the image contours and progresses to the subsequent phase: circle detection. The final image then undergoes the image processing polar coordinate transformation.  
Figure 5: The workflow of image processing and circle detection steps.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 1 Introduction
### The State of the Art
#### 1.2.2 Lamb Mode  
[PERSON] et al. [33] studied the interaction of a basic symmetric Lamb wave mode \(S_{0}\) with laminations in eight layers of glass-fiber-reinforced polymer (GFRP) laminated composite by numerical simulation and experimental verification. The results showed that the sensitivity of \(S_{0}\) mode reflection depended on the thickness direction and layering direction of the composite laminate. Among all laminates tested, the \(S_{0}\) mode was highly sensitive to DL23 and DL34 (Delamination is considered between the layers 2&3 and 3&4 locations, which are abbreviated as DL12 and DL34, respectively. The thickness is 0.42-1.26 mm). [PERSON] et al. [34] experimentally studied the transmission characteristics of Lamb wave \(S_{0}\) and \(A_{0}\) modes at the contact edges of two plates. The results showed that the transmission coefficient of the \(S_{0}\) mode increased monotonically with increasing contact pressure, but the transmission coefficient of the \(A_{0}\) mode had a non-monotonic relationship with contact pressure and local minimum frequency. [PERSON] et al. [35] calculated the reflection and propagation coefficients of the selected guided wave modes using simulation and compared the results with experimental data. The results showed that defect characterization was possible if a suitable criterion for modal selection was established. [PERSON] et al. [36] used two zeroth-order Lamb wave modes to detect various types of invisible impact damage in composite materials. Because the digital shearography and ultrasonic C-scan technology could identify 5- and 10-J impact damage and the impact damage of 3-J could not be detected, the results of the Lamb wave detection were confirmed by comparison.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 2 Cooperative HSTN
### HSTN Relay Network
#### 2.2.1 Single Relay Node
Considering the Rice and Nakagami-m fading distributions for the satellite and terrestrial channels, respectively, the authors of [73] evaluated the ergodic channel capacity of a DF-based single-frequency HSTN when the satellite-destination link is not available due to heavy fading. For a DF-based HSTN where no LOS link is present due to masking, and the satellite and terrestrial links follow shadowed Rician and Rayleigh fading, respectively, the authors of [74] obtained analytical channel capacity for optimal power and rate adaptation and truncated channel inversion adaptive transmission protocols with improved performance at low and medium SNR ranges. To improve reliability and efficiency, a hybrid automatic repeat request (HARQ) in an AF-based HSTN was studied in [75], where delay-limited throughput for both CSI-assisted and fixed-gain relaying protocols were theoretically analyzed with respect to both chase-combining and incremental-redundancy schemes.
# The Effect of High-Pressure Treatment and Skimming on Caprine Milk Proteins
## 3 Results and Discussion
HP-treatment (\(>\)100 MPa) increased casein solubility [9,14,25,26] and induced changes in the size of casein micelles which are influenced by process parameters [5,12]. The disintegration of casein micelles and an increase in the solubility of different fractions (e.g., \(\beta\)-casein) is determined by the number of phosphosenyl residues, and it is influenced by the dissociation of colloidal calcium phosphate and the weakening of hydrophobic interactions. Subject to the applied pressure, HP-treatment decreases the size of casein micelles (200 MPa), induces differences in the size of casein micelles due to their fragmentation and the re-aggregation of subunits, promotes hydrophobic interactions that lead to the formation of micelles larger than the native micelles (250 MPa), and decreases the size of casein micelles (\(\geq\)300 MPa), in particular under exposure to lower temperature [9,12,14]. Figure 3: The effect of high-pressure treatment on the total color difference \(\Delta\)E in whole milk (open circle) and skim milk (open square). Vertical bars denote 0.95 confidence intervals. Significance: pressure effect at \(p<0.05\); skimming effect at \(p<0.05\); interaction effect of pressure and skimming at \(p<0.05\). Pressure-induced changes in the composition and processing suitability of caprine milk proteins exert a significant influence on the properties of dairy products. Changes in protein structure, including pressure-induced denaturation of whey proteins, and the formation of whey protein and casein aggregates, influence the functional attributes of proteins, such as the ability to stabilize emulsions, bind water and form gels. Proteins determine the properties of both raw milk and dairy products; therefore, pressure-induced changes in protein structure are an important consideration in industrial processes. These changes are particularly important in the production of emulsions, ripened cheese, cottage cheese and fermented milk products (such as yogurt), where the chemical, functional and nutritional properties of proteins play a pivotal role [1; 2; 3; 4; 5].
###### Abstract  
The reliability of Very Large Scale Integration (VLSI) circuits has become increasingly susceptible to transient faults induced by environmental noise with the scaling of technology. Some commonly used fault tolerance strategies require statistical methods to accurately estimate the fault rate in different parts of the logic circuit, and Monte Carlo (MC) simulation is often applied to complete this task. However, the MC method suffers from impractical computation costs due to the size of the circuits. Furthermore, circuit aging effects, such as negative bias temperature instability (NBTI), will change the characteristics of the circuit during its lifetime, leading to a change in the circuit's noise margin. This change will increase the complexity of transient fault rate estimation tasks. In this paper, an NBTI-aware statistical analysis method based on probability voltage transfer characteristics is proposed for combinational logic circuit. This method can acquire accurate fault rates using a discrete probability density function approximation process, thus resolving the computation cost problem of the MC method. The proposed method can also consider aging effects and analyze statistical changes in the fault rates. Experimental results demonstrate that, compared to the MC simulation, our method can achieve computation times that are two orders of magnitude shorter while maintaining an error rate less than 9%.  
VLSI circuit; transient fault; aging effects; voltage transfer characteristics; discrete probability density function approximation +
Footnote †: journal:  
Article
## 2 Materials and Methods
### Correlation between the Variables and Soiling Losses  
In this study, it was fundamental to understand the relationships between the variables in order to study their dependencies. For this purpose, the Pearson correlation coefficient was used, which is widely used, especially in studies modeling atmospheric and/or meteorological variables. With the idea of estimating the result of a model in a simple and abbreviated way, this coefficient allowed us to know the parameters that most influence the output variable [29]. The coefficient values range from \(-1\) to \(1\), with the correlation being positive when it is greater than zero and negative when it is less. If the values coincide with the previous figures, the correlation is perfect, being nonlinear when the null value is obtained.  
In this section, the correlation was studied between the meteorological and installation-specific variables and losses caused by soiling. Knowing this is of fundamental importance. Having measurements that are perfectly standardized and calibrated means that any possible differences between the panels (with or without maintenance) were the result of a soiling layer (or film) on the unmaintained panel--this will be the loss that occurs due to soiling on the photovoltaic panel. Figure 2 shows the dispersion diagrams of each variable against the instantaneous soiling losses, while Table 2 shows the correlations between the instantaneous soiling losses and the different variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tpanel (\({}^{\circ}\)C)** & **Isc (A)** & **Iglo (Wm\({}^{-2}\))** & **RH (\%)** & **Tamb (\({}^{\circ}\)C)** & **P (hPa)** & \(\boldsymbol{\alpha}\) (\({}^{\circ}\))** \\ \hline
0.17 & 0.03 & 0.06 & \(-0.07\) & 0.47 & \(-0.20\) & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficient of variables against instantaneous soiling losses.  
Figure 2: Scatter plots of variables against instantaneous soiling losses. Tpanel: module temperature; Iglo: global irradiation; RH: relative humidity; Tamb: ambient temperature; P: atmospheric pressure; \(\alpha\): solar altitude.  
No single variable was highlighted as having a strong correlation with soiling losses because it is complex to model them. The variables with the highest correlation were ambient temperature, pressure, and panel temperature, but none stood out; indeed, they indicated rather moderate relationships. The rest of the values were very close to zero, so there would be no correlation with soiling losses in any of these cases. Relative humidity and pressure had negative values, unlike the others, which were positively correlated. The graphs support these parameters, as only the ambient temperature shows some correlation with the losses. Therefore, there was a circumstance in which there was no linearity between the input and output variables that would be obtained in a future model.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.3 Classification Model  
Both the accuracy and the increase in the number of parameters to be learned are influenced by the architecture depth. Moreover, the number of parameters has a direct relationship with the computational cost. Therefore, the deeper the architecture is, a larger number of varied samples to build robust models and prevent over-fitting is needed. To tackle this issue, this work proposes enhanced DL architectures for crop classification based on patch analysis. The studied DL architectures are described in Table 2. Considered architectures are intended to achieve high accuracy at low-depth while being able to deeply and meaningfully learn features from stacked multi-spectral images. Particularly, the DL architectures considered in this work are 2D-CNN type. Additionally, an RNN architecture was also evaluated. They were compared to select the most suitable DL model for crop classification. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Crop** & **Soybean** & **Corn** & **Cotton** & **Sorghum** & **NCC** & **Pasture** & **Eucalyptus** & **Grass** & **Soil** & **Cerrado** \\ \hline
**Sequence 1** & 0 & 576 & 0 & 800 & 832 & 0 & 0 & 900 & 0 & 0 \\
**Sequence 2** & - & 0 & 0 & 0 & 0 & 0 & 0 & 900 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Amount of synthetic patches created per selected classes (soybean, corn, cotton, sorghum, non-commercial crops (NCC), pasture, eucalyptus, grass, soil, and Cerrado) by using horizontal and vertical translations. 2D-CNN Model  
A 2D-CNN is an architecture composed of a sequence of convolutional and pooling layers mostly used to learn features from images [48; 49]. This kind of architecture often ends with fully-connected layers to predict a single class label or a set of class probabilities [50; 51; 52]. Convolutional layers apply filters over all pixels of the input image to obtain a set of high-level abstract features; pooling layers reduce the features number--controlling the over-fitting; and fully-connected layers reshape the output into a vector with a size equal to the number of classes [53]. For classification purposes, a 2D-CNN commonly applies two activation functions: softmax for the output layer and rectified linear unit (ReLu) for the rest of the layers. Softmax aims at scaling the outputs between zero and one, providing a probability of belonging of coverage to a specific class. ReLu is a linear function that will output the input directly if it is positive. Otherwise, it will output zero [54; 55]. Furthermore, 2D-CNN 1 is composed of three successive convolutional layers followed by a max-pooling layer. For this sequence, the filter size (\(fs\)) was chosen among \(fs=[2\times 2,4\times 4,8\times 8]\) being the first value that allowed to achieve the highest performance. Figure 4 depicts a graphical explanation of the architecture 2D-CNN 1.