esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Research Methods
#### 2.3.3 Hurst Index Analysis  
The Hurst Index, originally proposed by British hydrologist [PERSON], is a quantitative measure used to describe the future trends in long time-series data [32]. It is a commonly employed method for characterizing the persistence of trends in time-series data [33]. The computational principles of the Hurst Index are as follows.  
The NDVI time series \(NDVI_{i}\), \(i\) = 1, 2, 3, 4, \(\ldots\), \(n\), for any positive integer m, define this time series:  
(1) Differential Sequence:  
\[\Delta NDVI_{i}=NDVI_{i}-NDVI_{i-1}; \tag{7}\]  
(2) Mean Sequence:  
\[\overline{\Delta NDVI(m)}=\frac{1}{m}\sum\limits_{i=1}^{n}{\Delta NDVI_{i}} \ (m=1,2,\cdots,n); \tag{8}\](3) Cumulative Difference:  
\[X(t)=\sum_{i=1}^{m}\left(\Delta NDVII_{i}-\overline{\Delta NDVI(m)}\right)\,(1\, \leq\,\mathrm{t}\leq\,\mathrm{m}), \tag{9}\]  
(4) Range:  
\[R(m)=\underset{1\leq m\leq n}{\max}X(t)-\underset{1\leq m\leq n}{\min}X(t)\,\,(m =1,2,\cdots,n), \tag{10}\]  
(5) Standard Deviation:  
\[S(m)=\left[\frac{1}{m}\sum_{i=1}^{m}\left(\Delta NDVI_{i}-\overline{\Delta NDVI (m)}\right)^{2}\right]^{\frac{1}{2}}(m=1,2,\cdots,n). \tag{11}\]  
Regarding the ratio \(R(m)/S(m)\cong\mathrm{R}/\mathrm{S}\), if \(R/S\propto m^{H}\), it indicates the presence of the Hurst phenomenon in the analyzed \(NDVI\) time series [34]. In a double-logarithmic coordinate system, the value of \(H\) is obtained by fitting the equation using the least squares method. \(H\) is referred to as the Hurst Index [35]. The magnitude of \(H\) can be used to assess the persistence of the \(NDVI\) sequence. The calculation mentioned above was applied to the pixel values for each pixel in the study area over a span of 20 years. This process resulted in the Hurst index values for all the pixels in the study area, providing data on the sustainability of the changing trends in the region.  
The Hurst Index can assume three different values: if 0.5 \(<\) H \(<\) 1, it indicates that the time series is a persistent sequence, characterized by long-term correlations; if H = 0.5, it signifies that the NDVI time series is a random sequence, lacking long-term correlations; if 0 \(<\) H \(<\) 0.5, it suggests that the NDVI time series exhibits anti-persistence [36]. The closer the H value is to 0, the stronger its anti-persistence; the closer to 1, the more persistent it is. According to Table 1, the trends in the NDVI and the results of the Hurst Index are combined to analyze and predict the future trends in the NDVI.
## 4 Experimental Results and Discussion  
In this section, the preprocessed data was fed into four deep learning models for performance evaluation and comparison. All training processes for EV charging load forecasting were implemented in a desktop PC with 3.0 GHz Intel i7 and 64 GB of memory, of which the GPU is Geforce Nvidia GTX-1080 Ti and all codes are run by Keras library with Tensorflow [31] backend.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 3 Classical Networks Based on Deep Learning
### Fully Convolutional Networks  
Compared with traditional statistical methods, methods based on CNNs have natural advantages. They can automatically extract semantic features, so they can replace manual feature extraction in original methods. CNNs have end-to-end processing structures He et al. (2016); [PERSON] et al. (2016), which take images as an input and generate pixel-level labels through multiple layers of convolution and deconvolution. The fully convolutional networks (FCNs) proposed by [PERSON] et al. [PERSON] et al. (2017) were the first application of CNN in semantic segmentation. FCNs perform pixel-level classification of images. CNNs [PERSON] et al. (2016) have achieved great success in semantic segmentation tasks He et al. (2017). They use fully connected layers in convolutional layers to obtain fixed-length feature vectors. Meanwhile, FCNs accept images of any size and use deconvolution layers to upsample the feature maps of the last convolutional layer and restore them to the same size as the input image. Subsequently, predictions are generated for each pixel while retaining the spatial information of the original input image. Pixel classification is then performed on the upsampled feature maps. FCN network structure as shown in Figure 1.  
In the past few years, FCNs have achieved great success in segmentation [PERSON] et al. (2016); [PERSON] et al. (2017); [PERSON] et al. (2017); [PERSON] et al. (2017). Nearly all semantic segmentation methods adopt the idea of the FCN method, indicating that FCNs are a breakthrough in the field of semantic segmentation. FCNs have also created new opportunities for further improving the architecture of deep semantic segmentation. The main disadvantages of FCNs are their low efficiency in label localization within the feature hierarchy, inability to handle global contextual information, and lack of a multiscale processing mechanism. The majority of subsequent research has attempted to address these issues by proposing various architectures or techniques.  
Figure 1: _Cont._
## 4 Discussion  
In this section, we elaborate on the results section. As shown in Table 2, ORACM can perform the final segmentation based on the three initial contours in two cases: the MAC of both methods. Additionally, LSACM failed only on one of the initial contours and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MiniSeg** & **MobileNet** & **Inf-Net** & **DeepLabv3+** & **EfficientNet** & **EDANet** & **ENet** & **ESPNetv2** & **GLSEPF (ACM)** \\ \hline
**84.95** & 81.19 & 76.50 & 79.58 & 80.25 & 82.86 & 81.26 & 77.84 & 78.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the best active contour method with deep learning methods in terms of the Recall measure.  
Figure 9: Outcomes of all active contour models in terms of the database.  
the remaining methods succeeded only on one of the initial contours. It should be noted that some methods (Figure 9) failed for at least one of the initial contours, which is why we report the best results of each method. In the overall evaluation, it can be seen that ORACM performs best in both quantitative and qualitative evaluations compared to the other methods.  
In the quantitative evaluation, Table 3 shows that ORACM performs significantly better than all other methods in terms of Bfscore, iteration and time and performs as well as the FRAGL method in terms of Dice and Jaccard measures. Therefore, as shown in the table, FRAGL ranks first in terms of the DICE and Jaccard measures. Compared to the other methods, LASCM reaches the highest score in terms of accuracy, followed by ALF and MAC, which rank second and third, respectively. While GLSEPF ranks first in terms of recall measure, ORACM scores second and the other methods do not have promising results in this measure. As can be seen from the low value of time and iteration, ORACM and ALF are also faster than the others. Since the Bfscore measures how well the predicted boundary of an object matches the true boundary, ORACM preserves the boundaries better than other methods. Moreover, Jaccard and Dice similarities of 100% imply that the segmentations in the two images match perfectly. FRAGL and ORACM achieve the best matching images compared to other methods.  
As the qualitative evaluation in Figure 9 shows, RSFOLGE, MAC and C-V methods achieve clean binary images. However, the results of the ORACM, FRAGL and GLSEPF methods are more effective and can efficiently produce higher visual quality from the input image with a dark shadow. In qualitative evaluation, as shown in the figure, ORACM is able to keep the boundaries in the patterns smooth. As shown, ORACM, FRAGL and GLSEPF methods are successful in delineating the area of infection, which serves as the foreground, from the background, but they also fail in maintaining thin strokes. Nevertheless, the methods achieve the best visual quality for the coronal and axial view database samples.  
Finally, the comparison with deep learning methods shows that the active contour methods are competitive with deep learning methods, although the results show that deep learning methods are better overall.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 2 Related Work
### DASH-Based Bitrate Adaptation  
HTTP adaptive streaming, standardized as DASH, is the main scheme for video delivery over the Internet today. HTTP-based video delivery allows content providers to utilize existing content delivery networks at a low cost without additional modifications. In DASH, video is pre-stored on a server as multiple segments, each segment containing a portion of the video. A video segment is encoded to have various qualities for the purpose of performing quality adaptation according to the time-varying network conditions. Figure 1 shows the structure of DASH-based HTTP adaptive streaming. The client requests and downloads the media presentation description (MPD) file for the video to be transmitted to the server when video streaming begins. The MPD file describes information, such as the bitrate, size, playback length, and request address of an encoded segment in eXtensible Markup Language (XML) format. After that, the client requests the video segment through the ABR algorithm. This algorithm uses the information described in the MPD file and contextual information such as measured available bandwidth and current buffer level to determine the quality of the next segment. Because DASH-based HTTP adaptive streaming selects the quality adaptively to suit the network situation, it provides a seamless streaming service to users and high QoE [8]. To provide a seamless streaming and high QoE to users, an ABR algorithm should be designed that takes into account the various contextual factors that affect the user's QoE. ABR algorithms can be broadly classified into model-based algorithms and learning-based algorithms. These can be further classified according to their contexts of use.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 3 Results and Discussion  
### Error Analysis of the Three-Dimensional Wind Field Measurements  
The wind profile Lidar is often interfered with by various noises during the detection process, which affects the spectral data. The environmental pollution sources of the spectral data include precipitation pollution, ground object clutter, and electromagnetic interference. Determining an accurate wind profile, especially at higher heights, is a challenging task due to these factors. Compared with clear-air turbulence, precipitation particles are strong  
Figure 5: The method of calculating the CNR of the range gate division of the Lidar.  
scattering targets; so, the power spectrum within the range gate exhibits double peaks or even multiple peaks [50]. Cluttering, i.e., echoes, is produced by the presence of nearby stationary and slow-moving terrestrial targets such as trees and buildings. It presents as symmetrically placed low-frequency Doppler components around a frequency of 0 Hz [51; 52]. Under the influence of radio signals in the environment surrounding the radar, narrow and sharp salt and pepper noise caused by the electromagnetic interference will appear in the power spectrum, which has a certain influence on the wind measurements. Due to the nature of the coherent detection, the main noise in the coherent Lidar system is proportional to the local oscillator power, such as the relative intensity noise; while the noise sources independent of the local oscillator power can be ignored [53]. In actual studies, it has been demonstrated that the maximum height that the radar system can detect is about 2.5 km after noise reduction processing; so, it can only extract 50 echo signals around the distance gate.  
Figure 6 shows the power spectrum graph corresponding to the fourth range gate after Gaussian fitting. It can be seen from the graph that there are differences in the amplitude and frequency corresponding to the highest peak of the echo signal of the beam in different directions. The amplitude of the echo signal indicates the strength of the echo signal. In a certain amplitude range, it has no effect on the extraction of the Doppler frequency shift. After coherent beat demodulation, the difference between the corresponding frequencies of the highest peak of the echo signal directly reflects the magnitude of the Doppler frequency shift corresponding to the beam in the different directions, and this difference ultimately affects the magnitude and direction of the radial wind speed of the beam in the different directions.
# Study of the Phenolic Compound Profile of _Arbutus unedo_ L.
## 3 Results and Discussion
### Analysis of Phenolic Compounds by HPLC-TQ-MS/MS
#### 3.3.2 Profile of Phenolic Compounds at Different Ripening Stages  
The results obtained for the 21 target PC analyzed in the _A. unedo_ L. fruits at different ripening stages are shown in Table 4. As it can be seen, several families of phenolic compounds were identified and quantified in the strawberry tree berry extracts, including phenolic acids, different kinds of flavonoids (flavonols, flavanols, flavanones, and flavones) and, for the first time in these fruits, stilbenes such as _trans_-resveratrol and _trans_-piceid. Different hydroxycinnamic and hydroxybenzoic acids were detected. Gallic acid and 4-hydroxybenzoic acid were quantified in the three ripening stages, while other acids, such as syringe and chlorogenic, could only be quantified in the ripe stage of the berries. Gallic acid was the phenolic acid with the highest concentration quantified (Figure S2). It was observed that its concentration increased as the degree of ripening of the strawberry tree fruits increased, appreciating statistical significance between the different ripening stages of the berries (1.4, 2.1 and 4.7 mg gallic acid/100 g of DW for unripe, intermediate, and ripe berries, respectively) (Table 4). In the edible parts of plants, the amount of hydroxybenzoic acids is usually low, while hydroxycinnamic acids are more abundant. Nevertheless, high concentrations of hydroxybenzoic acids can also be detected in certain red fruits, as it is the case of gallic acid in strawberry tree fruits [32]. Hydroxycinnamic acids are essential for the synthesis of other very important compounds, such as stilbenes [32]. P-comamic acid, one of the main hydroxycinnamic acids, was identified in all the ripening stages of the berries and was quantified in the unripe fruits. Many of the hydroxycinnamic acids are found as esters of quinic acid [15]. This is the case for chlorogenic acid, which was identified in all the ripening stages, and 0.676 mg chlorogenic acid/100 g of DW was quantified in the ripe strawberry tree fruits. This may explain why caffeic acid was not detected in any of the samples analyzed, since chlorogenic acid is an ester formed between caffeic and quinic acids [33]. The occurrence of flavonoids in fruits, as it happens in the majority of PC, is highly influenced by environmental conditions and other factors. All the flavonoids that were analyzed (quercetin, quercetin-3
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 2 Novel Hybrid Data-Fusion System
### Eigen-Level Damage Detection Model  
A proposed three-stage eigen-level data fusion model for damage detection is shown in Figure 3, which illustrates the architecture of this fusion model. As mentioned above, processing the initial data is necessary. Just as explained in Section 2.2, some relevant preprocessing techniques are used for de-noising. The key stage is the second stage, namely, to extract correlation dimensions (CD), then fuse them extracted from multiple sensors. In the last stage, the fused feature groups are to be input into a PNN classifier then make a damage detection decision. Since feature-level data fusion fully considers the importance of individual features and multi-sensor mutual information features, it is convinced beyond doubt that the eigen-level damage detection model can obtain more accurate identification results than any individual feature can.
## 2 Associations of Whole Grain-Specific Polyphenols with Cardiovascular and Metabolic Diseases
### Avenanthramides (Avns)  
Avenanthramides are \(N\)-cinnamoyl derivatives of anthranilic acid, a group of phenolic alkaloids [30]. They exist in various plant materials, but they are widely known as phytochemicals in whole grain oats.  
It has been reported to prevent vascular diseases in various preclinical studies. In LDLr/- mice fed a high-fat diet (HFD), oat bran with Avns reduced total cholesterol (TC) and atheroma lesions in the aortic valve. These effects were more pronounced in the high-Avns diet group than in the low-Avns diet group [17]. The mechanism, for the protective effects of Avns against vascular diseases, has been investigated using vascular endothelial and smooth muscle cells (SMCs). Avns inhibit the proliferation of SMCs by increasing the levels of nitric oxide (NO), cyclic guanosine monophosphate (cGMP), upregulating endothelial nitric oxide synthase (eNOS), and protein kinase B (Akt) in human umbilical vein endothelial cells (HUVECs), human aortic endothelial cells (HAECs), and SMCs [37; 38]. [PERSON] et al. [39] reported that Avns induce G1 phase arrest of SMC proliferation in rat embryonic aortic SMCs by suppressing the phosphorylation of retinoblastoma protein (pRb), involved in the cell cycle. The phosphorylation of pRb and cell cycle arrest are induced by the inhibition of cyclin D1 expression and upregulation of the p53-p21 cip pathway. Since vascular inflammation is one of the factors that induces cardiovascular diseases, including atherosclerosis, its effects have also been explored in various in vitro models. In IL-18- or TNF-\(\alpha\)-stimulated endothelial cells or SMCs, the secretion of inflammatory mediators is increased by mitogen-activated protein kinase (MAPK) signaling, and cell migration and proliferation are increased by matrix metalloproteinase (MMP) activity. Avns decrease the secretion of pro-inflammatory cytokines and inhibit the migration and proliferation of SMCs by downregulating MAPK signaling and suppressing MMP protein expression and NF-kB activation [40; 41; 42].
## 5 Case Study and Discussion
### Performance Metrics in Terms of the Evaluation Index  
Several evaluation indices are applied for the accuracy of SPVG. In this work, we measured the accuracy of the difference between the measured value and the predicted output using metrics such as the root mean squared error (RMSE in %), mean absolute percentage error (MAPE in %), and Pearson's correlation coefficient (R\({}^{2}\)).  
The RMSE is the most common metric used to measure the accuracy of SPVG for continuous variables and can be defined as follows:  
\[\text{RMSE}\ =\ \sqrt{\frac{1}{\text{N}}\cdot\sum\
olimits_{i\ =\ 1}^{\text{N}}{(\text{P}_{i}-\hat{\text{P}_{i}})}^{2}} \ast 100\ (\%) \tag{11}\]  
where P\({}_{i}\) is the actual value, \(\hat{\text{P}_{i}}\) is the forecast value, and N represents the size of the test dataset for the i-th day in the test dataset.  
The MAPE is also known as the mean absolute percentage deviation [26]. This index calculates the average error ratio in terms of a measure of prediction accuracy using the following formula:  
\[\text{MAPE}\ =\ \ \frac{1}{\text{N}}\cdot\sum\limits_{i\ =\ 1}^{\text{N}} \left|\frac{\text{P}_{i}-\hat{\text{P}_{i}}}{\text{P}_{i}}\right| \tag{12}\]  
Pearson's correlation coefficient (R\({}^{2}\)) is the test statistic that measures the statistical relationship or association between two continuous variables [28]. If x\({}_{1}\), x\({}_{2},\ldots,\)x\({}_{n}\) are the numbers of one set and  
Figure 6: 3D view of four types of input data of adjacent days in Yeonseong-gun, South Korea (1 January 2015 to 31 October 2018).  
\(y_{1},\ y_{2},\ \dots,y_{n}\) are the numbers of another set, the coefficient of correlation between the two sets is as follows [50; 51]:  
\[\text{R}^{2}\ =\ \ \frac{\sum_{1}^{n}x_{i}\cdot y_{i}}{\sqrt{\sum_{1}^{n}x_{i}^{2} \cdot}\sqrt{\sum_{1}^{n}y_{j}^{2}}} \tag{13}\]  
This quantity must lie between \(-1\) and \(1\). The Pearson correlation coefficient is calculated to measure the correlation between solar PV and three different input data for training. It identifies the most relevant parameters affecting the solar PV output power, as presented in Figure 7. Three input data were calculated: solar irradiation (0.92), device surface temperature (0.62), and ambient temperature (0.34), as depicted in Figure 7. All cases indicate a positive correlation, which has a positive effect on SPVG, and solar irradiation has the highest correlation compared to the other two cases. The device surface and ambient temperatures have a similar pattern distribution, whereas the device surface temperature is 0.28 better than the ambient temperature.
## 2 Materials and Methods
### Method
#### 2.2.4. Specifications of the Product  
Once the metric tolerances and proportions are defined, we need to indicate and establish them. The metric tolerances and the corresponding proportions are the specifications for the product to be controlled in this hypothesis test. Both parameters are linked through the base model. The base model can be an observed distribution function or a parametric model. In this explanation, the well-known Gaussian model is used for simplicity. In addition, an example with two tolerances is developed, but the reasoning is similar for applications with more tolerances. Table 6 presents, for the 1D, 2D and 3D cases, some usual errors in cartography and their corresponding expansion formulae [34] for the two given probabilities (50% and 90%) when considering a Gaussian model.  
Consider a planimetric product and suppose that positional errors in X and Y follow a normal distribution with parameters \(\mu=0\) and \(\sigma=2\) m. Now, consider that it is required to control the distribution function of errors in two points given by two tolerances \(T_{1}\) and \(T_{2}\), which means that it is required to ensure that the distribution of observed errors meets at least two conditions. For instance, the following two conditions are specified:  
[At least 50% of the errors are less than \(T_{1}\)]  
and  
[At least 90% of the errors are less than \(T_{2}\)]  
Because in this example, the error data follow a bivariate normal distribution, \(T_{1}\) is equal to the value of the circular probable error (\(T_{1}~{}=~{}1.1774\times\sigma=2.3548\) m) which has 50% probability (see Table 6) and \(T_{2}\) is equal to the value of the circular map accuracy standard (\(T_{2}~{}=~{}2.1460\times\sigma~{}=~{}4.2920\) m), which has 90% probability (see Table 6). In this way, the proportions are \(\pi_{1}^{0}=0.5,~{}\pi_{2}^{0}=0.9-0.5=0.4,~{}\pi_{3}^{0}=1-0.9=0.1\). For the case of an observed distribution function, the proportions are the values corresponding to the given tolerances (see the above figures). Of course, the tolerances can be established to determine the corresponding proportions or vice versa (establish the proportions and determine the corresponding tolerances). It is important to note here that this method establishes a gradation of error levels. Moreover, due to the multinomial distribution, the last level must have a probability; however, it may be small. This last level is sufficient to limit the number of outliers in the error data.