esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Analysis
### Performance of Friction Pairs with Different Materials
#### 4.1.2 Surface Temperature of Friction Pairs  
Figure 5 shows the temperature changes of the friction pairs for the 1-A1 and 1-A2 groups during the test. The surface temperature of friction pairs and temperature rise are listed in Table 5 for 1-A1 and 1-A2 groups under 100-300 rpm. The surface temperature rises of the friction pairs in the tests of the two groups showed a trend of first increasing and then decreasing with the increase in the rotational speed. This is because in the process of rising rotational speed, more heat was produced by the relative motion path of the friction pairs per unit time; at the same time, a hydrodynamic pressure effect of the micro-interstitial fluids was increased by rotational speed and the convective heat exchange between the liquid films and the surrounding environment was accelerated. Further comparison of the surface temperature rises of the friction pairs in the 1-A1 and 1-A2 groups showed that the maximum temperature rises in these two groups were 12.3 \({}^{\circ}\)C and 9.9 \({}^{\circ}\)C at 250 rpm, respectively, and the minimum temperature rises in these two groups were 2.6 \({}^{\circ}\)C and 2.4 \({}^{\circ}\)C at 300 rpm, respectively. The significant difference in the maximum temperature rises between 1-A1 and 1-A2 indicated that the temperature rise was closely related to the materials of the friction pairs. SSiC has a higher thermal conductivity and lower expansion coefficient, which could better control the surface temperature rise.  
Figure 4: Real-time friction coefficient of friction pairs with different materials under 100–300 rpm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Load**} & \multicolumn{5}{c}{**200 N**} \\ \hline  & **Rotating Speed** & & **100 rpm** & **150 rpm** & **200 rpm** & **250 rpm** & **300 rmin** \\ \hline \multirow{3}{*}{Friction Coefficient} & Real time & 1-A1 & 0.087–0.1 & 0.078–0.09 & 0.061–0.08 & 0.058–0.079 & 0.056–0.063 \\  & Real time & 1-A2 & 0.146–0.197 & 0.129–0.153 & 0.111–0.154 & 0.094–0.147 & 0.08–0.197 \\  & \multirow{2}{*}{Average} & 1-A1 & 0.091 & 0.085 & 0.074 & 0.071 & 0.061 \\  & & 1-A2 & 0.173 & 0.142 & 0.126 & 0.123 & 0.095 \\  & & 1-A1 & 24–29 \({}^{\circ}\)C & 24.1–31.4 \({}^{\circ}\)C & 24–34 \({}^{\circ}\)C & 23.9–36.2 \({}^{\circ}\)C & 24.4–27.1 \({}^{\circ}\)C \\ \multirow{2}{*}{Surface Temperature} & Real time & 1-A2 & 24.1–27.5 \({}^{\circ}\)C & 24.2–30.7 \({}^{\circ}\)C & 24.1–32.4 \({}^{\circ}\)C & 24.8–34.7 \({}^{\circ}\)C & 23.8–26.3 \({}^{\circ}\)C \\  & \multirow{2}{*}{Rise} & 1-A1 & 5 \({}^{\circ}\)C & 7.3 \({}^{\circ}\)C & 10 \({}^{\circ}\)C & 12.3 \({}^{\circ}\)C & 2.7 \({}^{\circ}\)C \\  & & 1-A2 & 3.4 \({}^{\circ}\)C & 6.5 \({}^{\circ}\)C & 8.3 \({}^{\circ}\)C & 9.9 \({}^{\circ}\)C & 2.5 \({}^{\circ}\)C \\ \
# Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
## 3 Micro Aerial Vehicle Mathematical Model
### Equations of Motion  
In the course of implementing a mathematical dynamics model of an aircraft into MATLAB Dynamical System Toolbox, it is more convenient to present aircraft centre of mass motion equations in a velocity coordinate system \(O\alpha_{x}y_{a}z_{a}\) (Figure 2) [49]. Furthermore, the equations omitted the aircraft yaw (heading) angle \(\Psi\) and the aircraft centre of mass position relative to the system related to the Earth, since these values do not influence the dynamic properties of an aircraft [25]. This enabled the state vector dimension to be reduced to eight components:  
\[\mathbf{x}=[V,\alpha,\beta,P,Q,R,\Theta,\Phi]^{T} \tag{10}\]  
where:  
\(V\) flight velocity,  
\(\alpha\) angle of attack  
\(\beta\) slip angle,  
\(P\) banking angular velocity,  
\(Q\) pitching angular velocity,  
\(R\) yawing angular velocity,  
\(\Theta\) pitch angle,  
\(\phi\) bank angle.  
In the general case of bifurcation analysis of aircraft flight dynamics, the components of the vector of bifurcation parameters \(\mathbf{m}\) may be the control surface deflection angles (aileron, elevator, rudder, thrust) and other parameters such as the position of the center of gravity. In this particular case of bifurcation analysis of stake-wing microdrone flight dynamics, the bifurcation parameters vector has following form:  
\[\mathbf{\mu}=[T,\delta_{e},\delta_{elv}]^{T} \tag{11}\]  
where:  
\(T\) propeller thrust  
\(\delta_{\mathrm{e}}\) angle of symmetrical elevon deflection  
\(\delta_{\mathrm{elv}}\) angle of asymmetrical elevon deflection,  
The general form of the micro-airplane motion equations takes the form of autonomous differential Equation (1). Components of vector \(\mathbf{f}\) have the form [44; 45; 46; 47; 48]:  
Figure 2: Coordinate systems with written micro aerial vehicle motion equations; (**a**) systems related to axes of inertia (so-called “aircraft system”); (**b**) coordinate system related to flow (so-called “velocity system” or “wind axis system of co-ordinates”).  
The linear interpolation of aerodynamic data tables commonly used in simulation studies, due to the lack of derivative continuity, cannot be used in the case of continuation tests, since the continuation software of the AUTO type can misidentify bifurcation points. This is why a different method for interpolating aerodynamic data had to be used. Smooth, differentiable state parameter function were created as a result of interpolating aerodynamic characteristics. The _pchip_ function interpolation in the MATLAB software was used for this purpose, while maintaining linear interpolation and a multi-variative orthogonal function. Block interpolation was also implemented using a 3 rd-order spline function. Owing to the structure of the \"Bee\", which only has elevons, the bifurcation parameters were the eleven deflection angles: \(\delta_{e}\) and \(\delta_{elv}\).
## 5 Discussion
### Limitations and Future Work  
Despite promising results, this study has several limitations. The transducer frequency was fixed at 28 kHz, which may not match the LDR frequency for all specimens, potentially resulting in suboptimal energy transfer and reduced sensitivity in certain configurations. Additionally, the sample set included only 10 specimens, which limits the statistical reliability and generalizability of the findings. The ROI for image analysis was also selected  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample (Type + J)** & **LVT Image** & **PT Image** & **C-scan Image** \\ \hline Thick 20.0 & & & \\ \end{tabular}
\end{table}
Table 4: _Cont._manually, introducing subjectivity that could affect the consistency of defect quantification, particularly in larger or more diverse datasets.  
Future work should address these limitations, by exploring variable or adaptive excitation frequencies, incorporating larger and more diverse sample sets, including different defect types and evaluating advanced image processing techniques to further enhance defect segmentation and quantitative accuracy. Furthermore, the development and implementation of automated ROI selection methods, such as those based on computer vision, would greatly improve the objectivity, efficiency, and repeatability of the analysis process. Additionally, future studies should include a thorough analysis of measurement uncertainty and result repeatability, such as repeated measurements and uncertainty quantification, to strengthen confidence in the robustness and reliability of the detection results. For the next study, it is also important to investigate the effect of the fixture itself, such as potential thermal stress or reflected waves.
## 4 Discussion of Academic Literature and Council Uses
### Environment  
Diverse environmental tasks are increasingly being tackled by councils through drones. For biosecurity, drones are being used for weed detection and spraying. In addressing climate change, drones help in coastal erosion detection and water quality monitoring, combining thermal imagery within situ observations for comprehensive climate impact assessments. They are pivotal in environmental monitoring in sectors like agriculture, where they aid in rangeland inventory, soil moisture assessment, and crop yield prediction, enhancing sustainability in farming practices. For forest classification and management, drones monitor and manage urban greenery, assess desert vegetation, and enhance forest management with remote sensing and disease detection capabilities. In disaster management, drones facilitate landslide susceptibility analysis, post-fire assessments, and support rescue operations with geospatial surveillance, playing a crucial role in bespoke forest management and disaster mitigation planning. There is no council use of drones in biodiversity.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Major Responsibilities** & **Detailed Aspects** & \
\begin{tabular}{l} **Part 1: Literature Review** \\ **Results** \\ \end{tabular} &
\begin{tabular}{l} **Part 2: NSW Regional** \\ **Council Uses** \\ \end{tabular} \\ \hline Economic & Tourism & & ✓ \\ \hline \multirow{6}{*}{Environment} & Biodiversity & ✓ & \\ \cline{2-4}  & Biosecurity & ✓ & ✓ \\ \cline{2-4}  & Climate Change & ✓ & ✓ \\ \cline{2-4}  & Sustainable Solutions & ✓ & ✓ \\ \cline{2-4}  & Forest Classification and & & ✓ \\ \cline{2-4}  & Management & ✓ & ✓ \\ \cline{2-4}  & Disaster Management & ✓ & ✓ \\ \hline \multirow{3}{*}{Health and Safety} & Emergency Management & ✓ & \\ \cline{2-4}  & Crime Prevention and Safety & ✓ & ✓ \\ \cline{2-4}  & Public health & ✓ & \\ \hline Infrastructure & Roads and Transport & ✓ & ✓ \\ \hline \multirow{3}{*}{Planning} & Building and Construction & ✓ & ✓ \\ \cline{2-4}  & Community Engagement & & ✓ \\ \cline{2-4}  & Heritage & ✓ & \\ \hline \multirow{3}{*}{Social and Community} & Indigenous Affairs & & ✓ \\ \cline{2-4}  & Arts and Culture & ✓ & ✓ \\ \cline{2-4}  & Children and Youth & ✓ & \\ \hline Waste and Recycling & Waste Management & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of findings from two parts.  
From the literature review findings, drones with multispectral sensors provide reliable ecosystem monitoring, aiding in wildlife studies and population censuses with greater accuracy and less invasiveness. This provides future possibilities in council uses.
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 6 Managing Complications
### Valve Disease  
TTE is an essential imaging modality for the assessment and grading of valvular heart disease (VHD) associated with FD. A number of underlying mechanisms have been described as contributory factors predisposing to VHD in FD. In a retrospective study, in a cohort of \(111\) patients with FD, mild aortic, mitral, and tricuspid regurgitation was observed in patients with advanced Fabry cardiomyopathy [68]. Stenosis is uncommon in FD, and it has been hypothesized that direct sphingolipid accumulation in the fibrosis and laminose affects the structural integrity of the aortic valve in such a way as to compromise leaflet closure and promotes regurgitation. With mitral valve disease, a similar mechanism has been suggested but with the additional impact of altered annular motion, chordal retraction and papillary muscle hypertrophy secondary to myocardial changes in the LV (hypertrophy [69], fibrosis [37], and impairment [24]) (see Figure 10). In a similar way, atrial dilatation may alter chamber geometry and lead to functional mitral regurgitation and root dilatation seen in FD may contribute to aortic valve regurgitation [70]. In a recent retrospective cohort study, 100 adults with FD were assessed for valvular disease-burden, finding that VHD is common (10% had moderate to severe disease in at least one valve) and was associated with worsening renal function [71]. This compared with the estimated prevalence in of moderate or greater VHD in the general population of 5.2-6%. The high prevalence observed in this recently published work is in contrast to previous cohort studies [68].
# Automated Image-Based User Interface Color Theme Generation
## 2 Related Work  
Much research in recent years has focused on creating models and tools for generating color themes or palettes. [PERSON] et al. [1] have presented a tool for creating discriminable and aesthetically-preferred color palettes for information visualization. The tool uses an iterative, semi-random sampling procedure in the CIELAB color space to generate palettes of a given size and allows the user to customize the palette creation by changing the number of colors, defining which hues to sample from, and providing an existing palette to build upon. Even though the tool considers discriminability or color diversity, it does not consider other usability aspects, such as prominence and contrast.  
[PERSON] et al. [2] presented a method for identifying key colors from the image or mood board using a clustering algorithm similar to k-means. However, in this method, the initial centroids are fixed based on a predefined pattern, and each iteration includes merging based on a predefined color difference threshold. Although the method focuses on finding diverse colors, it does not take into account other usability aspects and aesthetics of colors--color harmony or preference.  
A lot of research has been concentrated on creating data-driven models utilizing machine learning techniques. [PERSON] et al. [3] used 327,381 human ratings of 22,376 color themes to train a model with the LASSO algorithm. The proposed model used more than 300 features to describe the color themes. Similarly, [PERSON] et al. collected 1,600 color themes from 160 participants and extracted 70 theme features. [PERSON] model was recently extended by [PERSON] et al. [4] to support the creation of color themes with different numbers of colors. [PERSON] et al. [5] also developed an interactive tool for creating color palettes for infographics that use a recommendation engine based on deep learning to characterize color design practices from data.  
Only a limited number of studies focus on the automatic generation of color themes for user interfaces. One of these studies, conducted by [PERSON] et al. [6], introduced a framework for automatically coloring web pages. The framework implements probabilistic models based on data to ensure appropriate visual contrasts, compatibility of multiple colors, and semantic associations.  
Table 1 compares the features of the models and methods mentioned in this section with the model presented in this article.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 2 Related Work
### Underwater Image Enhancement
Shallow-UWNet [21] is a lightweight CNN network containing only 10 convolutional layers with efficient training and testing speeds and low memory consumption. UWCNN-SD [9] is a two-stage underwater enhancement network that contains a preliminary enhancement network and an improved network. [PERSON] et al. [22] proposed a SCNet underwater image enhancement network based on spatial and channel dimension normalization. SIBM [23] incorporates knowledge of semantic, gradient, and pixel domains to hierarchically enhance underwater images. By consideringunderwater images in extreme scenarios, ReX-Net [7] leverages the complementary information of reflectance and utilizes attention mechanisms to enhance channel and spatial information. In order to enhance color recovery accuracy, many approaches are used to design different modules, network structures, and strategies. For instance, UGIF-Net [24] proposes a multicolor space-guided color estimation module to adaptively perceive crucial color information during underwater image enhancement. IACC [6] improves color correction by introducing unified luminance features under mixed lighting. MFEF [25] employs white balance and contrast-limited adaptive histogram equalization to introduce multiview features, which provide high-quality and rich features for enhancement. Since it is difficult to obtain the corresponding clear images for underwater images, the GAN network-based approach can effectively solve the problem of difficult dataset acquisition by using the adversarial learning of generators and discriminators, which does not require pairs of training data and can effectively learn the features of underwater images and clear images. By using CycleGAN, UGAN [26] transforms images from one domain into another without the need for paired training data. WaterGAN [27] uses RGB-D images to simulate underwater images for color correction, and a color correction network is utilized to effectively recover color bias. As a multiscale feature fusion network, MLFcGAN [8] effectively removes the color bias of underwater images. Funie-GAN [28] is a fast, real-time enhancement method that greatly improves the color of images. Recently, HCLR-Net [29] utilizes nonpaired data by introducing a local patch perturbation strategy, developing a more robust sample distribution. Most existing works explore multiscale information for underwater image enhancement. However, multiscale information is straightforwardly utilized or combined with simple strategies. The information lost during the downsampling and upsampling processes is not utilized effectively. Our method proposes a novel back-projection feature fusion mechanism for multiscale features and introduces denoising blocks in the feature fusion mechanism, which can handle underwater images with complex degradation.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 3 The Components of HY-2A Capf
### The Routine Customized Processing Subsystem  
The routine customized processing subsystem is a web application deployed in an intranet environment and can be accessed by staff of the SOA. With the pre-defined processing workflows, the subsystem can access the HY-2A level-2 data and automatically generate the daily, weekly, monthly, seasonal and annual statistics products of marine dynamic environments. Because these products are applied for the regular business, the batch processing programs of the workflows are relatively stable. Once a staff defines a batch processing program, it can be validated and deployed into the framework by a system manager. After users define the statistic period and set the area of interest with the Quartz Job Scheduling Library [39], the subsystem can automatically start tens-of-thousands of jobs and can schedule the jobs in the future when the more recent data is ready. Each job corresponds to a pre-defined batch processing workflow. The backend of the subsystem can execute the batch processing workflows on time. It accesses the archived data by a web service from the existing data archive and distribution system. Once data are within the statistic period, the subsystem will download the data directly from the data repository and start the processing workflows in parallel. As soon as the products are successfully generated, they will be archived to the existing data archive and distribution system. Users can download them directly. The detailed flow chart is shown in Figure 2.
## 4 Method
### Evaluation Metrics  
In this part, we present some evaluation metrics commonly used in histopathological classification. The analysis of these metrics is sufficient, although we have a multiclass problem [21].  
We can define True Positive (TP) if the prediction is correct and refers to the predicted label. True Negative (TN) indicates the number of times the model correctly classified a negative sample as negative. True is defined by the correct match between the predicted label and the actual label. False represents a mismatch between the predicted and actual labels. False Positive (FP) means that the model incorrectly classifies a negative sample as positive, and False Negative (FN) means that a positive sample is classified as negative. In a multi-class classification, _Positive_ and _Negative_ refer to the individual label classes.  
* The Confusion Matrix shows the classification performance of the model in validating the data sets.
* Accuracy represents the proportion of the true number of classified samples among all samples. \[Accuracy=\frac{TP+TN}{Total}\] (1) where _Total_ represents the sum of TP, TN, FP and FN.
* Precision measures how high the proportion of samples classified as positive is that are actually positive. \[Precision=\frac{TP}{TP+FP}\] (2)
* Recall indicates how many positive samples are classified as positive. \[Recall=\frac{TP}{TP+FN}\] (3)
* F1-Score is the harmonic average of precision and recall. \[F_{1 Score}=\frac{2}{\frac{1}{Precision}+\frac{1}{Recall}}\] (4)
* Receiver Operating Characteristic Curve (ROC curve) is a commonly used evaluation metric to assess the quality of a classifier. The classifier can also be evaluated by the area under the ROC curve, called Area Under Curve (AUC). It represents the rate of true positives versus the rate of false positives. The higher the value, the greater the discriminatory capacity of the model. AUC is defined by the rate of true positives (TPR) and the rate of false positives (FPR). The TPR and FPR are given by \(TPR=TP/(TP+FN)\) and \(FPR=FP/(FP+TN)\). Thus, AUC can be computed by \[AUC=\int_{x=0}^{1}TPR(FPR^{-1}(x))dx=P(X_{1}>X_{0})\] (5) where X1 is the score for a positive instance and X0 is the score for a negative instance.
* Loss Function defines a method for evaluating how well the learning algorithm models the data set in terms of prediction. Categorical cross-entropy is a loss function used in classification tasks with multiple classes since it can consider a sample as belonging to one category with probability 1 and to other categories with probability 0. The loss for N classes, of which 9 classes are used in our work, is calculated as follows: \[Loss=-\sum_{i=1}^{N}lt_{i}log(W_{i})\] (6) The loss is calculated as a categorical cross-entropy between the ground true label \(lt_{i}\) and \(W_{i}\) labels (Softmax is then applied to these labels, producing the values of the \(W_{i}\) predictions).