esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Coronary Artery Calcium Score
### Pathophysiological Mechanisms of Calcium Deposition  
When foamy macrophages rich with lipids and vascular smooth muscle cells (VSMCs) start gathering in the coronary arteries, the atherosclerotic process begins. This provokes thickening of the tunica intima which is followed by an increase in free cholesterol, acellular remains and in the final stages of fibroatheroma, an almost total lack of extracellular matrix [48]. The earliest calcified components are made up of membrane-bound matrix vesicles that are actively calcifying, which are produced by apoptotic VSMCs [49]. Then, the coronary arteries calcification becomes an active pathogenic process, with ectopic bone production being the base of the process [50]. Notably, VSMCs usually express proteins that inhibit calcification. However, in the setting of atherosclerosis, the expression of some of these inhibitors (such as matrix Gla protein) is downregulated, contributing to a loss of homeostatic inhibition of calcification. On the other hand, transcription factors such as Msx2, Runx2, Sox9, Cbfa1, and bone morphogenetic proteins (BMPs) significantly increase the expression by VSMCs and macrophages of chondrocyte, osteoblastic, and osteocl-associated proteins facilitating the calcification process [51; 52].  
Only in histologic sections can these microcalcifications be identified, not with conventional imaging methods. When these minute calcium deposits consolidate, bigger calcium granules form that can be seen using common imaging methods such as CCTA. When macrocalcifications further aggregate, they could potentially form a large calcification  
Figure 4: Coronary artery calcium (CAC) score assessment through cardiac computed tomography (CCT) stratified for risk category: (**a**) minimal risk, CAC score <9; (**b**) mild risk, CAC score 46; (**c**) moderate risk, CAC score 119; (**d**) severe risk, CAC score 1768.  
fragment that extends from the necrotic core to the collagen-rich matrix resulting in the formation of calcified plaque that may protrude into the lumen or media [48].  
Inflammation, promoted by apolipoproteins and oxidized phospholipids in the artery wall, and oxidative stress are also essential in the vascular calcification process [53; 54]. Furthermore, it has been estimated that conventional risk factors account for 40% of the variability of coronary calcification [55]. Indeed, CAC is strongly connected with low-density lipoprotein (LDL) cholesterol in young asymptomatic men [56]. Furthermore, glucose has been reported to promote vascular cell calcification, while insulin inhibits the same process [57]. In this regard, an increased coronary calcification has been demonstrated in diabetics, especially those with type II diabetes [58].
# A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model
## 3 Preliminaries
### PSL Model
#### 3.2.1 PID Controller  
The principle of PID [32; 33] is to calculate instantaneous error, i.e., the difference between the true value and the predicted value, and then correct this error according to the proportion (P), integral (I), and derivative (D). Since the update point in the HiDS matrix is discrete, it is compatible with the discrete PID controller. The schematic diagram of the discrete PID controller is shown in Figure 1. The TV and PV in the figure represent true and predicted values, respectively.  
As shown in Figure 1, firstly, it calculates the error between TV and PV, i.e., the instantaneous error, and then inputs it into the three modules, P, I, and D, to accomplish the following calculation (10). Through this formula we can attain the adjusted error, and finally use it to update the predicted value instead of using the instantaneous error.  
\[\bar{E_{t}}=K_{P}E_{t}+K_{I}\sum_{n=0}^{t}E_{n}+K_{D}(E_{t}-E_{t-1}). \tag{10}\]  
The control coefficients for the proportional, integral, and derivative terms are \(K_{P}\), \(K_{I}\), and \(K_{D}\), respectively, and \(E_{t}\) is the instantaneous error in the \(t_{th}\) update point. The PV is reconstructed and returned to the controller based on the adjusted error. This mechanism continues until the termination condition, i.e., the LFA converges, is met.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 3 Proposed Method
### Reference Point Segmentation Network  
After obtaining the meter dial region of the input image, we further segment the pointer and reference points from the meter dial by using a fully convolutional segmentation network. In this reference point segmentation network, we adopt the atrous convolution proposed in [52], which is an efficient and effective way to increase the receptive field of a deep network and also keep the spatial resolution of the resulting feature maps. Due to its fully convolutional framework, the reference point segmentation network is easy to implement and speeds up the inference process on various hardware platforms. This is very important for real-time applications.  
Figure 3 shows the architectural details of the reference point segmentation network used in the second stage of our model. This network employs the widely used VGG16 [53] as its backbone but removes the fully connected layers of VGG16 and only retains the convolutional and max pooling layers. In order to capture the global contextual information of an input image, atrous convolution with a stride ratio \(r=2\) is utilized to enlarge the model's receptive field. A Conv2d layer equipped with \(1\times 1\) convolution is adopted to fuse all the feature maps to output the segmentation result. It is necessary to point out that the segmentation result obtained by the last Conv2d layer is reduced with a downsampling factor of 8x, which is caused by the max pooling layers. Therefore, we apply an upsampler based on the classic bicubic interpolation method to resize the segmentation result to the original image resolution.
# An Overview of Intelligent Transportation Systems in Europe
## 4 Use of ITSs in Europe
### Case Studies of ITS Use  
To improve urban mobility and promote sustainability, ITSs should be deployed in European cities. The strategic plans of many municipalities are in line with the objectives of the European Green Deal and take into account local and global environmental factors.  
As these systems reduce traffic, enable cleaner modes of transport and encourage modal shifts from private vehicles, they are essential for reducing carbon emissions [24].  
ITSs improve the quality of life in cities by improving air quality, reducing noise pollution and making more efficient use of transport infrastructure. They also help support long-term climate goals and digital transformation strategies at the city level.  
Table 1 presents a selection of European cities that have implemented ITSs of various types between 2020 and 2023. The year of implementation, the type of ITS solution chosen, the financial investment allocated (in millions of EUR) and the results achieved after implementation are all presented below. Examples include smart parking systems and traffic management based on artificial intelligence (Berlin) and autonomous bus fleets (Madrid), as well as the integration of bicycles into ITS infrastructure (Amsterdam).  
The results show the benefits of ITSs for urban mobility, which include reductions in travel time, increases in the efficiency of public transport, reductions in CO\({}_{2}\) emissions and improvements in pedestrian safety. This information shows how important the strategic investment in the digitalization of urban transport is and how relevant it is for achieving the objectives of the European Green Deal.  
Each city develops its plans according to the problems arising in the area, but they all have a common goal: the development of environmentally friendly, efficient and accessible transport systems.  
For other cities in Europe seeking smart and sustainable mobility solutions [46], these examples can serve as models.  
The implementation of Intelligent Transport Systems in European cities is a significant step towards sustainable urban mobility and reduced environmental impact. Case studies from several European cities have demonstrated how important it is for residents, corporations and governments to work together for such projects to be successful. As technology advances, European cities will need to change their approaches to fully exploit the potential of ITSs.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wood** & \(I\) & \(w\) & \(t\) & \(s\) & \(D\) & \(K\) \\
**Species** & **(mm)** & **(mm)** & **(mm)** & **(m\({}^{2}\)/100 g)** & **\(\lambda\)** & **(mm)** & **K** \\ \hline \multirow{2}{*}{Poplar} & 86.79 & 11.22 & 0.93 & \multirow{2}{*}{6214.1} & \multirow{2}{*}{121.0} & \multirow{2}{*}{11.2} & \multirow{2}{*}{10.5} \\  & (23.2–119.5) * & (2.46–43.7) & & & & & \\ \hline \multirow{2}{*}{Willow} & 88.45 & 16.91 & 0.91 & \multirow{2}{*}{7840.8} & \multirow{2}{*}{98.1} & \multirow{2}{*}{16.9} & \multirow{2}{*}{12.6} \\  & (10.6–122.9) & (3.89–49.7) & & & & & \\ \hline \multirow{2}{*}{Spruce} & 80.55 & 10.67 & 1.06 & \multirow{2}{*}{6071.5} & \multirow{2}{*}{86.6} & \multirow{2}{*}{10.7} & \multirow{2}{*}{10.2} \\  & (21.4–121.01) & (2.3–40.75) & & & & & \\ \hline \multirow{2}{*}{Pine} & 77.10 & 11.17 & 0.92 & \multirow{2}{*}{6347.8} & \multirow{2}{*}{92.6} & \multirow{2}{*}{11.6} & \multirow{2}{*}{10.4} \\  & (13.2–124.4) & (1.2–52.1) & & & & & \\ \hline \hline \end{tabular}  
* The values in parentheses are the minimum and maximum limits of the confidence interval.  
\end{table} 
Table 1: Systematization of data regarding the average characteristics of the strands. Figure 6: Scoring of the four analyzed strands. strand surface (because only the surface of the strand face was considered active for gluing). Based on this algorithm, we found a calculus relation of the specific surface (Equation (3)). \[S_{\mathrm{s}}\ =\ 0.2\cdot\left(\rho_{10}\cdot t\right)^{-1}\left[\mathrm{m^{2}/10 \,g}\right] \tag{3}\]  
where \(\rho_{10}\) is the density at 10% of the woody species, in g/cm\({}^{3}\); \(t\)-strand thickness, in mm. Using this Equation (3), the graph from Figure 7 was obtained. Even though the four regression curves are very close (because wood densities are appropriated), the power regression coefficient types are different for the analysed species. It can be observed that we have very good surfaces of over 1 m\({}^{2}\)/100 g up to a thickness of 0.5 mm, after which the influence of the thickness on the specific surface is weak and almost constant.
# Optimizing Bladeless Wind Turbines: Morphological Analysis and Lock-In Range Variations
## 3 Methodology
### Test Speed Range  
The study of the VIV phenomenon in laminar and turbulent environment is well known and perhaps sufficiently studied in the available literature [40]. In the present case, the wind tunnel is intended to sufficiently mimic fluid conditions similar to those that such a device might encounter in a standard wind turbine farm. However, as usual, the \"fine-grained\" turbulence content is possibly higher than the airflow that would be encountered in the natural environment.  
The experimental speed range for testing was determined based on the operating conditions and limitations of the wind tunnel. Controlled by a frequency converter governing fan speed, the wind speed was gauged using a hot-wire anemometer, specifically the Almemo 2470 model [41] manufactured by Ahlborn Mess- und Regelungstechnik GmbH from Munich, Germany, positioned within the test chamber.  
The natural frequency of each prototype primarily depends on the mast length, stiffness, and mass distribution it supports. Although the mast remains consistent across the prototypes, variations in the surface or geometry of the top segment cause slight mass discrepancies, which leads to empirically measuring the natural frequency of each prototype. Table 2 presents these frequencies.  
Given that the prototype geometry determines its natural frequency, this frequency is used to estimate the wind speed where VIV occurs, aligning the structure's natural frequency with vortex frequency. The theoretical resonance velocity (V), calculated using Equation (2) [8], stands at 1.46 m/s, considering \(\mathsf{S_{t}}\) = 0.2 (Figure 2), \(f\) = [9.52-10.25] Hz (Table 2), and \(\emptyset\) = 28.5 mm (Figure 4).  
\[f=\mathsf{S_{t}}\times\mathrm{V}/\emptyset \tag{2}\]  
The selection of the test velocity range, grounded in theoretical calculations and starting from the calculated minimum resonance velocity (1.50 m/s) to the maximum experimentally determined velocity (10 m/s), ensures that the frequency range (5.50-10 Hz) aligns perfectly with the operational speeds of the wind turbine. Within this range of velocities, the behaviour of the six prototypes and their operational velocities (Lock-In range) were evaluated. Therefore, the velocities for the tests were fixed according to equivalent frequency drive values (5.50-10 Hz). By considering the common base diameter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Surface/Geometry** & **Convergent** & **Straight** & **Divergent** \\ \hline Smooth & 10.25 Hz & 10.01 Hz & 9.77 Hz \\ Rough & 10.10 Hz & 9.85 Hz & 9.52 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Natural frequency of prototypes.  
of the cylinder (\(\varnothing\)) of 28.50 mm for all prototypes, the corresponding Reynolds number (Re) for each value (Table 3) was computed using Equation (3).  
\[\text{Re}=\text{V}\times\varnothing/\upsilon \tag{3}\]  
where V is the air velocity, and \(\upsilon\) is the kinematic viscosity of air (1.46 \(\times\) 10\({}^{-5}\)).
## 1 Introduction
We provide environmental inconsistency through video collection at different positions and angles of the court. Changes in subjects and views make it possible to evaluate various 3D-based action analysis methods accurately across subjects and views. Our dataset can be widely used in the field of basketball players' action recognition and motion analysis. It can help researchers who study basketball action recognition based on computer vision and deep learning move forward without the limitations of time and conditions. Our dataset also makes the application of data-driven methods such as deep learning technology possible. Another contribution was inspired by the application of graph convolution neural networks in the field of skeleton-based human action recognition [6]. We propose a feature-enhanced LSTM-DGCN model based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods for basketball action recognition. This model combines a feature extraction technique and the data-driven deep learning method. Compared with the current mainstream skeleton-based motion recognition methods, which use joint coordinates directly, we use the feature extraction module to filter the effective basketball motion features and calculate the corresponding joint distance, joint angle, and other eigenvalues before inputting the end-to-end model. In so doing, we achieve targeted and efficient motion recognition. According to small numbers of samples and parameters, we can achieve an enhanced classification effect. In summary, the main contributions of this study are as follows: (1) defining action classification and introducing the NPU RGB+D dataset with RGB image data and Depth data captured in Northwestern Polytechnical University for basketball action recognition; (2) proposing the feature-enhanced LSTM-DGCN algorithm, which is used to classify and identify basketball players' actions; and (3) conducting an experiment with several state-of-the-art action recognition methods to evaluate and set the benchmarks on the NPU RGB+D dataset. The rest of this paper is organized as follows. Section 2 explores the current activity analysis datasets and action recognition methods. Section 3 defines action classification and introduces the proposed dataset, its structure, and evaluation criteria for basketball players. Section 4 presents the feature-enhanced LSTM-DGCN method. Section 5 shows the experimental evaluations of state-of-the-art action recognition methods alongside the proposed feature-enhanced LSTM-DGCN method on our benchmark. Section 6 discusses our method, and Section 7 concludes the study.
## 4 Implementation and Results
### Selection of Feature Extraction Methods and Classification Algorithms  
The experiments to be carried out were based on the results of a literature review where a total of 25 studies were analyzed, addressing the following questions:  
* Q1. What methods are used for feature extraction in the time, frequency, and time-frequency domains for emotion recognition?
* Q2. Which classification algorithms yield better results in emotion classification?
## 5 Simulations and Performance Analyses
### Comparison with the Exiting Methods
Additionally, SRA provides  
Figure 8: Impacts of ICM-MP fluctuation on target detection performance of the proposed algorithm and the existing methods. (**a**) \(\sigma_{2,v}=0\); (**b**) \(\sigma_{2,p}=0.8\); (**c**) \(\sigma_{2,p}=1.6\). Figure 9: Impacts of MDR on SINR loss performance of the proposed algorithm and the existing methods. (**a**) MDR = \(-22\) dB; (**b**) MDR = \(-17\) dB. substandard performance, and the performance degradation is due to the off-grid issue and the ICM problem. Conversely, the proposed joint optimization approach provides superior SINR loss performance in these two MDR scenarios. Then, the proposed algorithms, SRA and LSA, are compared quantitatively. Table 4 represents the SINR loss values at the target Doppler frequency considering different MDR values, i.e., MDR = \(-\)15 dB, \(-\)19 dB, \(-\)23 dB, \(-\)27 dB, and \(-\)31 dB, respectively. It is found from Table 4 that the proposed approach provides enhanced SINR loss performance compared to SRA and LSA when the MDR value changes. Therefore, it is concluded that regardless of the MDR values, the proposed algorithm can resolve the off-grid issue and is robust to the ICM-MP fluctuation. Finally, we evaluate the computational complexities of different algorithms. The computational complexity of the proposed algorithm is discussed in Section 4.4, and the computational complexity of SRA and LSA has been analyzed in references [10; 11]. The simulation results are presented in Figure 10. The curves indicate that the proposed algorithm has lower computational complexity than SRA. This is because the inverse operation of the high-dimensional matrix is computationally intensive for SRA. In addition, the proposed algorithm has mildly higher complexities than LSA due to the dictionary updating and the added calibration step. However, the proposed algorithm outperforms LSA in MP clutter suppression, which was presented in the previous experiments. Therefore, it is concluded that the proposed algorithm has satisfactory suppression performance and moderate complexity in the presence of ICM-MP errors.
## 4 Results and Discussion
### Buffer Measure
#### 4.3.2 Influence of Buffer Structures on the Sudden Change in Aerodynamic Load of HST  
Figure 15 illustrates the variation in the aerodynamic load coefficients as the HST enters the wind barrier area through buffer structures with different lengths. Tables 7 and 8 present the decrease rate in the amplitude of changes in the aerodynamic load coefficients and the decrease rate in the amplitude of the rate of changes in the aerodynamic load coefficients, respectively. It can be concluded from Figure 15, as well as Tables 7 and 8, that buffer structures effectively mitigate the abrupt change in the aerodynamic load of the HST, with effectiveness correlated with the length of buffer structures.  
Compared to scenarios without a buffer structure, a buffer structure of 45 m leads to a 6.93% reduction in the amplitude of the change and a 31.41% reduction in the amplitude of the rate of change in the lateral force coefficient of the head car. When the length of the buffer structure is increased to 90 m, the amplitude of the change and the amplitude of the rate of the change in the lateral force coefficient of the head car decrease by 10.04% and 55.59%, respectively. However, when the length of the buffer structure is further increased to 135 m, the corresponding indicators decrease by 10.83% and 56.97%, respectively. These results indicate that a buffer structure with a length of 45 m has limited effectiveness in mitigating abrupt aerodynamic load changes. However, a buffer structure of a 90 m length exhibits substantial improvement in mitigating such an abrupt change. Beyond this length, the enhancement in the buffering effect becomes negligible. Therefore, a buffer structure measuring 90 m in length is considered the most suitable.  
Figure 15: Influence of buffer structure length on the aerodynamic load coefficients: (**a\({}_{1}\)**-**a\({}_{3}\)**) head car, (**b\({}_{1}\)**-**b\({}_{3}\)**) middle car, and (**c\({}_{1}\)**-**c\({}_{3}\)**) tail car.
## 2 Materials and Methods
### Validation of the Model  
The merit of a QSPR model is related to the ability to correctly predict the properties of an external set of VOCs and SVOCs, commonly not considered during the calibration of the model. Consequently, the dataset should be split into a training set and a test set in such a way as to guarantee a structure-property representation of the test set molecules in the space defined by the volatile and semi-volatile compounds of the training set. To this end, the Balanced Subsets Method (BSM) [22] was used for the partition of 102 compounds in the dataset. The essence of the BSM is to create groups based on \(k\)-means cluster analysis in order to identify similar volatile and semi-volatile compounds according to the descriptors(structure) and the retention index (property). During the genetic algorithm-supervised selection, the training set was used to calibrate the MLR model, while the test set was used to measure the predictive ability of the QSPR model. The computational model was further validated by the leave-one-out and leave-many-out cross-validation approaches. The former procedure excludes one compound from the training set at a time, while the latter randomly excludes a user-defined percentage of the molecules at each iteration. In the leave-many-out cross-validation, we applied the Venetian blinds approach, Monte Carlo random sub-sampling validation and the Bootstrap method [23].