esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 3 Analysis of OpenCV Object Tracking Algorithms
### Object Detection Algorithm
#### 3.1.2 Performance Analysis  
Next, we analyze the performance of the object detection algorithm. We measured the latency of processing an image frame by varying the input video size from video graphics array (VGA) to full high definition (FHD). Since the Haar and LBP algorithms exhibit similar properties, we provide the results of the Haar algorithm only in this section. We used a human face training dataset available in OpenCV (_haarcasade_frontalface_alt.xml_). The experimental environment is described as APU in Section 5.1.  
Figure 5(a) shows the frame processing time of object detection with the Haar algorithm with varying image resolution. In the figure, the frame processing time increases as video resolution increases because the data size increases. Figure 5(b) shows the breakdown of the frame processing time. As shown in the figure, the runHaarClassifier kernel accounts for most (60-80%) of the total execution time regardless of the resolution, while the other kernels occupy only 0.5-1.01%.  
The results in Figure 6 show the following observations. First, as the resolution of images increases, the processing time increases rapidly, owing to the increased amount of data to process. Second, the runHaarClassifier kernel constitutes most of the execution time among the kernels at all resolutions. Therefore, to improve the performance of object detection using the Haar algorithm, optimizing the runHaarClassifier kernel is the most reasonable and effective approach.
## 3 Simulation and Analysis
### Pointing Angle  
Due to the pointing angle (\(P\)), sensor azimuth is changed, and the nadir is offset. According to the characteristics of ASTER, the range of \(P\) is \(-24^{\circ}\leq P\leq 24^{\circ}\). When \(P\) is positive, the sensor swings to the left in the flight direction, and when it is negative, the sensor swings to the right in the flight direction. When \(P\) is in the range of [-24\({}^{\circ}\), 24\({}^{\circ}\)] and in steps of 6\({}^{\circ}\), the trend of normalized SG radiance at different SVA is simulated, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Parameter Description** \\ \hline \(IFOV\) & \(2.13\times 10^{-5}\) (\({}^{\circ}\)) & Instantaneous field of view of ASTER \\ \(S\) & 8 (\({}^{\circ}\)) & Scene orientation angle \\ \(h\) & \(7.05\times 10^{5}\) (m) & Satellite height \\ \(m\) & 15 (m) & Image spatial resolution \\ \(P\) & 0 (\({}^{\circ}\)) & Pointing angle \\ \(W\) & 5 (m/s) & Sea surface wind speed \\ \(\phi_{0}\) & 90 (\({}^{\circ}\)) & Sun azimuth angle \\ \(\theta_{0}\) & 20 (\({}^{\circ}\)) & Sun zenith angle \\ \hline \end{tabular}
\end{table}
Table 1: Simulation model parameter settings.  
In the imaging parameters, the sun azimuth is \(90^{\circ}\), which is the position of the sun in the east. According Equations (14), (15), (17) and (18), when \(P\) is \(24^{\circ}\), the sensor azimuth is about \(270^{\circ}\), that is, the sensor takes a remote-sensing image against the sunlight, and thus SG radiance is strong at this time. Conversely, when \(P\) is \(-24^{\circ}\), the sensor takes a remote-sensing image along the sunlight, and thus SG radiance is extremely weak. Considering the SG radiance threshold of 0.004, the range of SVA that can be used for the SSR estimation model was found to increase with higher \(P\). When \(P=24^{\circ}\), the range of SVA that can be used is about \(-35^{\circ}\leq\theta\leq 30^{\circ}\). When \(P\leq-12^{\circ}\), the SG radiance is completely below the threshold, and the range of SVA that can be used is unavailable.  
Under different \(P\) values, the distribution of \(\Delta\sigma_{0}^{2}\) at different SVA combinations is shown in Figure 4, and the statistical results of \(\rho_{10}\) and \(\rho_{20}\) are shown in Figure 5. According to Figure 4, when \(P\) is negative, the overall SSR estimation model is ineffective, and at an appropriate value, some optimal combinations of SVA are always available. According to Figure 5, when \(P\leq-12^{\circ}\), both \(\rho_{10}\) and \(\rho_{20}\) approach 0. When \(P\) gradually increases, the values of \(\rho_{10}\) and \(\rho_{20}\) also increase gradually, and the rate of increase gradually weakens with \(\rho_{20}\) being always about 1.5% higher than \(\rho_{10}\). When \(P=24^{\circ}\), \(\rho_{10}\) is about 9.7% and \(\rho_{20}\) is about 11.0%.  
Figure 3: Normalized sun glitter (SG) radiance (\(I\)) simulated by each sensor view angle (SVA) (\(\theta\)) under different pointing angles.  
Figure 5: Statistical results of \(\rho_{10}\) and \(\rho_{20}\) at different pointing angles (\(P\)).
## 2 Materials and Methods
### Heuristic (Approximate) Method for SSTPC2L  
From the topographic map, the point Q is known and two segments A\({}_{11}\)A\({}_{12}\) and A\({}_{21}\)A\({}_{22}\) are determined belonging to the level curves from Figure 1 (A\({}_{11}\)A\({}_{12}\) on curve 610  
Figure 4: Connecting the point Q to the lines d\({}_{1}\) and d\({}_{2}\).  
and A\({}_{21}\)A\({}_{22}\) on curve 620). These two segments are found according to Figure 3. The method consists of drawing the perpendiculars from the given point Q on the two given lines d\({}_{1}\) and, respectively, d\({}_{2}\) (Figure 5). We construct a line passing through Q which is parallel to the line determined by the feet of the two perpendiculars. The obtained line is denoted by d\({}_{3}\), and its intersection with the lines d\({}_{1}\) and d\({}_{2}\) is the searched segment [P\({}_{1}\)P\({}_{2}\)] (approximate solution of SSTPC2L).  
Now, let's present how the points P\({}_{1}\) and P\({}_{2}\) are calculated.  
The equation of the line d\({}_{i}\) (i = 1, 2) starting from the points A\({}_{i1}\) and A\({}_{i2}\) is:  
\[\text{y }=\text{ m}_{i}\text{x}+\text{n}_{i},\text{ where m}_{i}\ =\ \frac{\text{y}_{i2}-\text{y}_{i1}}{\text{x}_{i2}-\text{x}_{i1}}\text{ and n}_{i}\ =\ \text{y}_{i1}-\text{m}_{i}\text{x}_{i1} \tag{3}\]  
The perpendicular from Q on d\({}_{i}\) (i = 1, 2) is:  
\[\text{y }=\ -\frac{1}{\text{m}_{i}}\text{x}+\text{n}^{\prime}_{i},\text{ where n}^{\prime}_{i}\ =\ \text{y}_{\text{Q}}+\frac{1}{\text{m}_{i}}\text{x}_{\text{Q}} \tag{4}\]  
The foot \(\text{Q}_{i}\)\(\left(\text{x}_{i}^{\text{Q}},\text{y}_{i}^{\text{Q}}\right)\) (i = 1, 2) of the perpendicular on d\({}_{i}\) is obtained by solving the system of Equations (3) and (4).  
The slope of the line Q\({}_{1}\)Q\({}_{2}\) is:  
\[\text{m}_{12}\ =\ \frac{\text{y}_{2}^{\text{Q}}-\text{y}_{1}^{\text{Q}}}{ \text{x}_{2}^{\text{Q}}-\text{x}_{1}^{\text{Q}}} \tag{5}\]  
Since m\({}_{12}\) is the slope of Q\({}_{1}\)Q\({}_{2}\), then the parallel through the point Q\(\left(\text{x}_{\text{Q}},\text{y}_{\text{Q}}\right)\) is:  
\[\text{y }=\text{y}_{\text{Q}}+\text{m}_{12}(\text{x}-\text{x}_{\text{Q}}) \tag{6}\]  
Finally, the points P\({}_{i}\) (i = 1, 2) are obtained by solving the system of Equations (3) and (6).  
The following Algorithm 2 (denoted A1 SSTPC2L) is obtained to solve the problem SSTPC2L:  
Figure 5: Approximate method of finding points P\({}_{1}\) and P\({}_{2}\).
## 3 Results and Discussion
### Composite Design by Combining 3D-Printed Pieces with Electrospinning PLA-Based Fibers
Figure 7: Storage modulus (E\({}^{\prime}\)) (**I**) and tan \(\delta\) (**II**) measured in DMA from a temperature scan for single square (a), and single ellipsoidal (b) 3D-printed pieces. The E\({}^{\prime}\) and tan \(\delta\) of two 3D-printed pieces with ellipsoidal (c) and square (d) pores bonded forming a double composite are also shown. The E\({}^{\prime}\) at room temperature (25 \({}^{\circ}\)C) was measured from DMA for the composites and were compared between single, double composite and 3D-print/electrospun composite (Figure 8). Single ellipsoidal 3D-printed pieces present a E\({}^{\prime}\) of 3400 \(\pm\) 1200 MPa, meanwhile, the single pieces with a square pore shape present a E\({}^{\prime}\) of 4100 \(\pm\) 2000 MPa. The coupling of two 3D-printed pieces (composite) produces a material with low E\({}^{\prime}\), 1400 \(\pm\) 490 MPa for an ellipsoidal composite, and 730 \(\pm\) 40 MPa for square composite, when compared with single pieces. Small changes are observed with tan \(\delta\) (ellipsoidal single 2.1 \(\pm\) 0.1; square single 1.8 \(\pm\) 0.1; ellipsoidal composite 2.2 \(\pm\) 0.1; square composite 1.9 \(\pm\) 0.1). Similar results in E\({}^{\prime}\) have been observed in 3D-printed PLA blends with a different thickness [53]. Interestingly, the obtained E\({}^{\prime}\) for 3D-printed PLA pieces, \(>\)10\({}^{3}\) MPa, are similar to values obtained by the DMA characterization of the viscoelastic behavior of human cortical bone [18]. E\({}^{\prime}\) also was measured for 3D-printed/electrospun composites, and the obtained values are low in comparison with single 3D-printed pieces. However, an increase in E\({}^{\prime}\) is observed in square composites containing PLA/PEG/5.0 HA, when this sample is compared with composite containing PLA10 fibers. Fibers used to produce the 3D-printed/electrospun composite reinforce the mechanical properties of the composites; thus, tuning the mechanical properties of composites is done by tuning the composition of electrospun fibers.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 3 VL-OCC System Architecture and Applications  
In the VLC structure LEDs are deployed for the dual purposes of lighting and communication. OCC is one of the promising technologies of OWC, whereby the light intensity of LED or OLED is modulated to transmit the data, but the receiver is a camera instead of photodiodes, unlike traditional VLC systems, and; thus, requires almost no additional implementation cost and hardware modifications [42; 43; 44; 45].
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 3 Materials and Methods
### GNN-Based Optimization
#### 3.2.2 Surrogate Model  
The task of the surrogate model is to predict the nodal displacements of a truss using the trained GNN. To accomplish this, an Encode-Process-Decode architecture is proposed, as described in Section 3.3.2[47]. To ensure the robustness and reliability of the model, the five-fold cross-validation technique is applied to evaluate the errors for both the training and validation datasets and to establish a suitable architecture. The network is trained using Adam optimizer [48] with the Mean Square Error (MSE) as the loss function, as given  
Figure 1: Flowchart illustrating the GNN-based truss size optimization.  
by Equation (2). In addition, the Root Mean Square Error (RMSE), as given by Equation (3), is employed as the accuracy metric for assessing the network performance.  
\[\text{Loss}=\text{MSE}=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}, \tag{2}\]  
\[\text{Metric}=\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{ 2}}, \tag{3}\]  
where \(m\) is the number of training samples, and \(\hat{y}_{i}\) and \(y_{i}\) are the predicted and target outputs, respectively.  
Moreover, an initial learning rate of 0.01 is utilized, which exponentially decays at a rate of 0.5 over 500 steps. This enables the network to attain convergence and enhance the accuracy of the predictions.  
The network was successfully implemented on a desktop computer equipped with a Corei7-6700 HQ CPU running at 2.60 GHz and boasting 12 GB of RAM. The implementation was performed by utilizing the Spektral library, which is an open-source Python library designed for constructing graph neural networks via TensorFlow and Keras interfaces. This versatile library features an extensive array of cutting-edge techniques for deep learning on graphs, thus simplifying the GNN implementation process by offering essential building blocks [49].
## 6 Discussion
Studies have investigated the standard of care in patients with or without EGFR T790M mutation who had progressed following prior EGFR tyrosine kinase inhibitor (TKI) therapy. The investigators found that pretreatment BMI, specifically a BMI of less than 18.5 kg/m2, was a significant prognostic factor for poor progression-free survival and overall survival in EGFR TKI treatment [86, 87, 88, 89]. This study is the first to use ELISA tests to analyze these proteins in serum for neuroendocrine tumor patients. Although two proteins showed no significant differences, the remaining ones had higher or comparable concentrations. These results can still offer valuable insights into protein expression profiles, despite the lack of statistical significance. Our results also show no correlation between groups in terms of sex, hypertension, and smoking. Moreover, there is no correlation of these parameters with IGF-1R, ERK2, or EGFR. However, there is a correlation between groups in terms of diabetes (\(p<0.05\)). In the study group, the chance of developing diabetes was more than five times higher than in the control group. The results are consistent with the latest research that shows the impact of diabetes in patients with pancreatic NETs [90, 91]. Diabetes and pancreatic neuroendocrine tumors (pNETs) are closely related entities. Diabetes has been identified as a risk factor for the development and aggressiveness of pNETs. Conversely, diabetes resulting from pNETs is often undiagnosed or misclassified as type 2 diabetes when it is actually type 3 diabetes. The strong correlation between diabetes and the likelihood of developing NETs, particularly pancreatic NETs, aligns with current research and emphasizes the importance of metabolic health in cancer prognosis and management [89, 90]. Overall, our results contribute to the growing body of knowledge on the molecular underpinnings of NETs and support the continued exploration of targeted therapies, including mTOR and EGFR inhibitors, to improve patient outcomes. Future research should focus on validating these findings in larger cohorts and exploring the potential of combined pathway blockades to enhance therapeutic efficacy in NETs. Although two of the proteins did not show statistical significance, we believe their inclusion still contributes valuable insights to the field. However, further research is needed to validate these findings and to investigate additional proteins that could strengthen and support the conclusions of this manuscript.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 2 Fast Charging Techniques for Electric Vehicles
### Tesla Supercharges  
Tesla Superchargers are exclusively designed for Tesla vehicles and offer fast DC charging capabilities. These chargers are unique to Tesla and cannot be used with other electric vehicle brands [25]. Superchargers possess the ability to generate power outputs reaching a maximum of 250 kW, possibly adding up to 200 miles of range in just 15 min [72]. However, the charging rate experienced in practice can fluctuate due to various factors, including the battery's current charge level, battery temperature, and the capacity of the charging infrastructure [73]. Control strategies implemented in Tesla Superchargers include constant current and constant voltage modes, temperature monitoring, and battery management techniques. Tesla Superchargers also use advanced communication and vehicle-specific protocols to optimize the charging process and ensure compatibility with and safety for Tesla vehicles. The charging power levels of Superchargers have undergone advancements, resulting in newer versions that offer higher power outputs compared to their earlier counterparts. The charging time for Tesla vehicles utilizing Superchargers can also differ based on factors such as the specific model and battery size [74]. Superchargers can charge a Tesla vehicle to approximately 80% of its battery capacity in optimal conditions in about 20 min. However, it is essential to note that the charging rate may slow as the battery approaches total capacity to protect its health and longevity [75]. Tesla has been continuously expanding its Supercharger network, adding new charging stations and increasing the number of charging stalls at existing locations [76]. This expansion aims to provide Tesla owners with convenient access to charging infrastructure, reduce range anxiety, and promote long-distance travel with electric vehicles. Tesla Superchargers' epitomize convenience, offering an exceedingly user-friendly experience seamlessly integrated into the Tesla ecosystem. Safety is paramount, and there are rigorous safety protocols in place to ensure the well-being of users. Built to withstand diverse weather conditions, these chargers' boast exceptional durability. However, access to the Tesla supercharger network is not universally available, necessitating reliance on Tesla's infrastructure. Compatibility is exclusively reserved for Tesla vehicles, thus limiting accessibility to owners of such vehicles. While the rapid charging provided by Tesla Superchargers may contribute to slightly faster battery degradation compared to slower charging methods, their extensive network, ongoing expansion, and potential for future upgrades underscore their scalability and upgradability. Despite their high efficiency, some energy loss occurs during the conversion process that is inherent to these chargers.
## 6 Construction and Principals of Smart Home System for New Elders
### Construction of Smart Home System
#### 6.1.4 Application Layer  
This layer consists of the thirty smart home subsystems which have been discussed and analyzed in the previous content of this article, which contains various home devices, systems, and service platforms, including home appliances system, daily life service system, nursing system, health detection system, disease prevention system, telemedicine system, training rehabilitation system, emergency response system, temperature and humidity control system, lighting control system, door and window control system, environment monitoring system, energy-saving system, smart home theater system, digital game system, smart party system, smart knowledge system, smart education system, smart interest assistance system, family connectivity system, friendship connectivity system, couple connectivity system, pet connectivity system, artificial intelligence connectivity system, grandchildren babysitting system, peers supporting system, uncompleted wishes fulfillment system, re-employment and society contribution system, reality accomplishment system and virtual accomplishment system. These systems realize the intelligent management and service of a smart home through intelligent algorithm analysis and effective decision-making based on the dynamic perception data of the elderly's home life, environment changes, and operating status of different devices, systems, and platforms. It is worth to mention that because the physical state and specific needs of different elderly people are different, the smart home can carry out modular development and provide personalized module customization services by combining different subsystems.
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 4 Results and Discussion  
The prediction performance of the proposed RUL estimation algorithm is evaluated here. It is also compared with other widely used methods, vanilla LSTM and one-dimensional CNN.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 1 Introduction
### Community-Based Adaptation to Climate Change  
CBA is an approach to address the impacts of climate change through the active involvement of the concerned communities in identifying and implementing adaptation strategies [10]. CBA recognises that communities are the first to experience the effects of climate change and possess valuable knowledge and experiences about themselves, their environment and vulnerabilities [11]. By involving communities into the adaptation process, CBA seeks to enhance resilience and sustainability in the face of changing climatic conditions. One key aspect of CBA is the recognition of local knowledge and traditional practises as valuable sources of information for developing effective strategies [2]. Local communities often have a thorough understanding of their ecosystems, weather patterns, and natural resources, allowing them to identify early signs of environmental changes and develop context-specific adaptation measures. To integrate this understanding through a participatory approach not only empowers communities but also contributes to the effectiveness and long-term sustainability of adaptation efforts. Adaptation initiatives under the CBA framework are context-specific and acknowledge the unique characteristics of each community, including their socio-economic conditions, cultural contexts, and environmental vulnerabilities. Sustainable and community-based solutions are integral to CBA, with a focus on building the capacity of communities to manage and respond to the impacts of climate change in a way that enhances their resilience and fosters long-term sustainability. The concept of CBA has gained prominence in international discussions on climate change, specially within the United Nations Framework Convention on Climate Change (UNFCCC). Article 7 of the Paris Agreement explicitly recognises the importance of enhancing adaptive capacity and fostering resilience, emphasising the need for adaptation actions to be locally driven and gender sensitive. CBA initiatives have been implemented across various regions, with successful examples demonstrating the positive outcomes of community-based adaptation efforts. For instance, projects have focused on building the capacity of communities to manage water resources, develop resilient agricultural pracises, and establish early warning systems for extreme weather events [7; 10; 11; 12].