esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach against Sun Glare to Enhance Driver Safety
## Materials and Methods  
The presented system relies on the following components:  
* An eye tracking mechanism;
* A light intensity detection mechanism;
* A sun tracking mechanism;
* A shading mechanism.  
Due to the already accepted solutions on the market for the eye tracking systems that monitor if the driver is going to fall asleep or if he is distracted, the current paper will not focus on solving this problem and will only present some existing solutions. The same applies for light intensity detection. There are systems that, depending on some thresholds, can turn on or off the car's headlights.  
The components of the whole proposed digital sunshade system are briefly presented in a graphical manner in Figure 1a. The data provided by the sun tracking sensor and the camera are used to determine the location of the point on the windshield where the dark spot should be created. The opacity of the spot can be determined based on the information delivered by the light intensity sensor. The flow of the data is also highlighted by arrows. All the used components are small; therefore, they can be placed easily in a car. From a cost of production point of view, the sensors and cameras available on the market are not that expensive, due to the fact that the conventional sun visor can be removed since it no longer has a real purpose. From a hardware point of view, only the sun tracking sensor and the HUD will be presented since these two are the main components of the proposed hardware structure. For the other components, we will not indicate a specific hardware, but we will present the theoretical aspects, principles, and possible implementations in order to make the whole sunshade system work.  
Figure 1b presents a block diagram describing the closed feedback loop system with its main components. The input variables, functional correlations, and how the new position of the center of the dark spot is created are also highlighted. The proposed cyclicity of the system is 1 s, therefore, each second, the sensors will measure the sun's position and light intensity. The provided information, alongside predefined limitations, will serve as input variables for the phase in which the center of the dark spot is computed. Having located the new point, the decision to move the whole darker area on the windshield is taken if a threshold of 1 cm is exceeded. Meaning that if the distance between the previous center and the new one exceeds the defined threshold, new instructions will be generated and provided to the driver, taking into consideration the predefined shape and size of the dark area. From this point on, the instructions will be forwarded to the smart glass and the digital sun visor will be created. Most likely, the center of the area will be the darkest and the transparency will increase towards the limits. The coordinates computed previously will serve as feedback and the whole process will repeat as long as the digital sun visor is needed.
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 3 Example Analysis
### Principal Component Data Analysis  
#### 3.2.1 KMO Inspection and Bartlett Sphericity Test  
Before performing the principal component analysis, it was essential to test the suitability of using this method among various factors. This study conducted the Bartlett sphericity test and KMO inspection, and the inspection results are listed in Table 4.  
With respect to the test results, the fitness of the KMO sampling referred to 0.753, which was greater than 0.600 with a significance of 0.000 and less than 0.05, indicating a correlation between the original indicators. Therefore, the PCA method was suitable for the dimensionality reduction of the original data.
# Weather Radar in Complex Orography
## 1 Introduction
Studies on the precipitation maxima of various time periods, the frequency of thunderstorms and hail, the convective storm paths and the initiation and decay regions of precipitation as a function of the mesoscale flow, all reveal patterns that are clearly related to the terrain [47; 48; 49; 50; 51; 52; 53; 54; 55]. This can be exploited, as has been shown in the quantitative precipitation nowcasting context [56; 57; 58; 59]. Mountain echoes can also be used as a reference signal, for instance for hardware monitoring and the correction of signal attenuation [14].
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis
### Evaluation Metrics  
Average overlap (AO) [34]: Average overlap refers to the mean value of the overlap score, which is computed as the Intersection over Union (IoU) between the prediction box and the ground truth, as specified by Equation (4).  
Success [32]: Success denotes the ratio of frames with an overlap score exceeding a particular threshold (typically 0.5) to the total number of frames. The success plot can be generated by varying the threshold over the range of 0 to 1.  
Precision [32]: Precision is characterized by the center location error (CLE), which is defined as the Euclidean distance between the predicted box center and the ground truth center. The ratio of frames with CLE less than a certain threshold (usually 20 pixels) to the total number of frames represents the precision. The precision plot can be plotted by varying the threshold values.  
Normalized precision (NP) [33]: Normalized precision is a measure that normalizes the precision by the real target box size to mitigate the adverse effects caused by the target scale. It is computed as follows:  
\[P_{\text{NP}}=\left\|W\left(C^{tr}-C^{gt}\right)\right\|_{2}, \tag{8}\]  
\[W=diag\left(BB_{x}^{gt},BB_{y}^{gt}\right), \tag{9}\]  
where \(C^{tr}\) and \(C^{gt}\) represent the center position of the prediction box and the ground truth, respectively. The function \(diag(\cdot)\) denotes the diagonal matrix. \(BB_{x}^{gt}\) and \(BB_{y}^{gt}\) denote the \(x\) and \(y\) coordinates of the ground truth, respectively. The normalized precision plot is obtained by varying the threshold values from 0 to 0.5.  
Expected average overlap (EAO) [35]: The VOT2020 dataset employs an anchor-based evaluation protocol that consists of accuracy, robustness, and expected average overlap (EAO). EAO is a composite measure that combines accuracy and robustness and is used to rank algorithms in the VOT2020 dataset. The related calculation is presented in [35].
## 4 Results analysis
### Analysis of Vehicular Emissions of BTH Region
#### 4.1.1 Contributions of Different Vehicle Categories to Vehicular Emissions  
Numerous results were obtained by the DRSFO-EES model, and the situation of \(\alpha=0.5\), \(\rho=0.2\), and scenario S2 is used here as an example to illustrate the optimized solutions for the EES. The rapid development of traffic systems has led to large amounts of vehicular emissions such as CO, HC, NO\({}_{x}\), and PM. Figure 3 presents the contributions of the different vehicle categories to vehicular emissions in the BTH region over the planning periods. As indicated in Figure 3a-d, due to the sharp growth of LDVs, the total CO, NO\({}_{x}\), HC, and PM emissions are expected to increase by 23.28%, 17.07%, 17.19%, and 22.22% over the planning periods, respectively. The results indicated that LDVs are the major contributors of CO and HC emissions, to which they contribute 65.46-73.72% and 60.15-69.15%, respectively, over the planning horizon. It is therefore necessary for local governments to limit the number of LDVs through appropriate measures and policies such as improvement of the public transit system and encouragement of the use of EVs.  
Further, HDTs are the major contributor of NO\({}_{x}\) and PM, to which they are expected to contribute 67.07-63.08% and 58.58%-54.98%, respectively, between 2020 and 2030. Limiting the development of HDTs would therefore be effective for reducing vehicular NO\({}_{x}\) and PM emissions. The results also indicated that the traffic sector is set to be one of the major emitters for NO\({}_{x}\) in the BTH region. For example, the NO\({}_{x}\) emission from vehicles will account for 58.65%, 60.13%, and 61.90% of the total NO\({}_{x}\) emission (the total NO\({}_{x}\) emission includes contributions from oil refining, coke processing, heat processing, natural gas-based power, and coal-based power) in 2020, 20  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Vehicular Emissions Standards** & **The Proportion of EVs** & **Power Sources for EVs** \\ \hline S1 & China V & 0\% & — \\ S2 & China V & 1.50\% & 100\% coal-fired power based \\ S3 & China V & 1.50\% & 50\% coal-fired power based, \\ S4 & China V & 1.50\% & 100\% renewable energy based \\ S5 & China VI & 1.50\% & 100\% renewable energy based \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings of scenarios.  
Figure 3: Cont.  
Figure 3: (**a**) CO emissions from traffic system. (**b**) NOX emissions from traffic system. (**c**) HC emissions from traffic system. (**d**) PM emissions from traffic system.
## 4 Flight Load Prediction Case Study
### Determination of Neuron Number **K** of Hidden Layer  
In the traditional ELM, the weight matrix **W** and offset vector **b** are randomly generated, which affects the performance of the ELM and makes it difficult to optimize the neuron number \(K\) in the hidden layer. In this case, the proposed GA-enhanced ELM is adopted to test the prediction performance for different neuron number \(K\), and the results are graphed in Figure 10. Moreover, the activation function is set as a sigmoid function.  
From Figure 10, as the number of neurons \(K\) increases, the prediction accuracy initially decreases, but after 80 neurons, the prediction accuracy reaches a stable level. To reduce the complexity and computational effects, the neuron number \(K\) is suggested to set as 80. However, in order to promise a high prediction accuracy, a large \(K\) such as 100 is also a good choice.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Activation Function** & **Mean Relative Errors** & **Maximum Relative Errors** & **Mean Squared Errors \(\left(\textbf{KN}\cdot\textbf{m}\right)^{2}\)** \\ \hline Sigmoid function & 0.8376\% & 4.8627\% & 2.0044 \\ Sine function & 0.9163\% & 5.0318\% & 2.0247 \\ Hard-limit function & 4.7941\% & 31.943\% & 54.027 \\ Hyperbolic tangent function & 0.9799\% & 5.1406\% & 2.41 \\ Morlet wavelet function & 3.8387\% & 62.962\% & 37.824 \\ \hline \end{tabular}
\end{table}
Table 3: Flight load prediction accuracy via genetic-algorithm-enhanced ELM.  
Figure 9: Optimization process of genetic algorithm.
# Construction of Color Prediction Model for Damaged Korla Pears during Storage Period
## 3 Variation Laws of Colors of the Damaged Korla Fragrant Pears during the Storage Period
### Variation Laws of a* Value  
The a* value expresses the red-green degree. It is red if the a* value is positive, and the color intensity is positively related to the a* value. On the contrary, it is green if the a* value is negative, and the color intensity is negatively related to the a* value. The variation laws of a* with the storage time are depicted in Figure 6. The a* value increased with the increase in storage time. More specifically, the mean a* values of fragment pears with different degrees of damage increased from \(-9.9456\) at 0 d to 2.8608 at 40 d. This meant that the pericarp colors of fragrant pears changed from green to red with the increase in the storage time. The underlying reasons for this effect can be explained as follows. Due to strengthened respiration of the damaged fragrant pears during the storage period, the releasing amount of endogenous ethylene increased to accelerate the degradation of chlorophyll. The chlorophyll content in the pericarp declined with the increase in the storage time [29; 30], thus resulting in the fading green. Anthocyanin began to accumulate during the violent degradation of chlorophyll, making the fruits turn red [31; 32; 33].
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 3 Results
### Model Evaluation Measures
#### 3.2.1 Quantitative Assessment Metrics for Image Reconstruction Models  
In image reconstruction experiments, peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used as image quality metrics to compare the reconstructed images with the ground truth. Among them, PSNR is a comprehensive measure of image quality, while SSIM is a local measure of contrast, brightness, and structural similarity, which can objectively measure the degree of difference between the model reconstructed image and the original image. These model evaluation measures are calculated according to Equations (4)-(6).  
\[PSNR=10\times log_{10}\left(\frac{MAX^{2}}{MSE}\right) \tag{4}\]  
\[MSE=\frac{1}{N}{\sum_{i=0}^{N}}{\left({I_{i}-\hat{I}_{i}}\right)^{2}} \tag{5}\]  
where \(MAX\) is the maximum possible value of the pixel value, \(N\) is the number of pixels, \(I_{i}\) is the ith pixel value of the original image, and \(\hat{I}_{i}\) is the ith pixel value of the reconstructed image.  
\[SSIM(x,y)=\frac{\left({2\mu_{x}\mu_{y}+c_{1}}\right)\left({2\sigma_{xy}+c_{2}} \right)}{\left({\mu_{x}}^{2}+{\mu_{y}}^{2}+c_{1}\right)\left({\sigma_{x}}^{2} +{\sigma_{y}}^{2}+c_{2}\right)} \tag{6}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean values of the original image \(x\) and reconstructed image \(y\), respectively, \({\sigma_{x}}^{2}\) and \({\sigma_{y}}^{2}\) are their variances, \(\sigma_{xy}\) is their covariance, and \(c_{1}\) and \(c_{2}\) are the two constants used for stabilization calculation.
## 4 Travel Speed Reliability
### Analysis of Travel Speed Reliability
#### 4.2.2 Travel Speed Reliability at 10 min Intervals of Morning Peak  
The travel speed reliability and travel speed reliability reduction values of 336 periods of 28 road sections of the Beijing 3 rd Ring Road were analyzed at 10 min intervals during the weekday morning peak based on different travel speed reliability evaluation thresholds, as shown in Figures 12-15. The data showed that the travel speed reliability fluctuated with the delay of departure time, the travel speed reliability of most road sections significantly decreased after the students back to school, and the travel speed reliability reduction value of very few road sections was more than 0.7 in some periods based on different travel speed reliability evaluation thresholds.  
The travel speed reliability of 24 road sections was larger than 0.8 in 195 periods (about 58% share) and the travel speed reliability reduction value of 21 road sections was larger than 0.1 in 155 periods (more than 46% share) based on the Threshold-1, as shown in Figure 12. The travel speed reliability reduction value of 23 road sections was larger than 0.2 in 138 periods (more than 58% share) based on the Threshold-2, as shown in Figure 13.  
Figure 12: Travel speed reliability of the Beijing 3 rd Ring Road based on the travel speed reliability evaluation Threshold-1: (**a**) Inner Ring; (**b**) Outer Ring.  
The travel speed reliability reduction value of 22 road sections was larger than 0.3 in 161 periods (about 48% share), and the maximum travel speed reliability reduction value was larger than 0.85 (Section 11-10) based on the Threshold-3, as shown in Figure 14.  
Figure 14: Travel speed reliability of the Beijing 3 rd Ring Road based on the travel speed reliability evaluation Threshold-3: (**a**) Inner Ring; (**b**) Outer Ring.  
Figure 13: Travel speed reliability of the Beijing 3 rd Ring Road based on the travel speed reliability evaluation Threshold-2: (**a**) Inner Ring; (**b**) Outer Ring.  
Before students back to school in August, the travel speed reliability of the road sections was larger than 0.6 based on the Threshold-4, as shown in Figure 15. After students back to school in September, the travel speed reliability of the road sections significantly decreased, the travel speed reliability reduction value of 27 road sections was larger than 0.1 in 240 periods (more than 71% share), the travel speed reliability reduction value of 17 road sections was larger than 0.5 in 81 periods (more than 14% share), and the maximum travel speed reliability reduction value was larger than 0.85 (Section 9-8) based on the Threshold-4, as shown in Figure 15. The data showed that the travel speed reliability reduction value of 11 road sections was lower than 0 in 42 periods (12.5% share) based on the Threshold-4.
## 1 Introduction
Using GOSAT's SIF [30] showed that midday fluorescence explained the water stress over the Amazonian forests during a dry season in 2010. [PERSON] et al. [31] used GOME-2's SIF to study the impact of the 2010 Russian drought, and they highlighted the drought-related SIF decrease for croplands and grasslands. [PERSON] et al. [32] studied the drought onset mechanisms and its impact on agriculture using GOME-2 SIF during the drought of 2011 in Texas and that of 2012 in the central Great Plains, United States. They concluded that SIF is sensitive to both structural and physiological variation in vegetation during drought and considered it an appropriate tool for drought monitoring. [PERSON] et al. [33] developed a GOME-2 based SIF product to show that spatial SIF response was in good agreement with meteorological (temperature, soil moisture, evapotranspiration, and terrestrial water storage) anomalies during the 2015-2016 El Nino Amazon drought. Recently, [PERSON] et al. [34] constructed a global gridded SIF dataset (CSIF) using a neural network trained by OCO-2 and MODIS data, which they also employed for drought monitoring. Their preliminary result showed that the CSIF well captured the spatial extent of droughts during the 2015 European heatwave. Here, we present the impact of the 2018 European hotter drought on the terrestrial ecosystem as perceived through OCO-2 SIF data. The novelty of this study is highlighted through the detection of spatial and temporal patterns of OCO-2 SIF responses to drought and heat across different vegetation types. We capture and analyze the spring-summer seasonal variation and anomaly in chlorophyll fluorescence across different vegetation types and tracked it with vapor pressure deficit (VPD) and temperature anomalies. Furthermore, we compare SIF responses of different vegetation types under drought and non-drought conditions in Europe to explore the ability of OCO-2 SIF for drought impact characterization. We also discuss and compare our results with recent MODIS-based NDVI drought impact study. Finally, we discuss the drought-induced changes in satellite-based SIF measurements and perspectives of OCO remote sensing satellite products for future studies.
## 4 Discussion
### The Role of Ankle Joint Reflex Control  
Based on the results shown in Section 3.4, the muscle activation and the ankle joint torque are relatively linear with each other. Calf muscles work in an isometric-like manner. Substituting Equation (2) into Equation (13), a linearized muscle reflex force controller can be yielded as follows:  
\[\tau=\sum_{i}c_{i}(a_{0}^{i}+p_{a}^{i}\theta(t-t_{0})+d_{a}^{i}\dot{\theta}(t- t_{0})) \tag{14}\]  
where \(cp_{a}\) and \(cd_{a}\) are in Nm/rad and Nm/rad/s, and have equal units of stiffness and damping, respectively. Based on the model denoted by Equation (14), it can be concluded that muscle reflex  
Figure 13: Boxplot of prediction results with and without acceleration. Column (**A**) to (**E**) represents the results of subject A to E, respectively. The \(p\) values of the first row are 0.041, 0.601, 0.922, 0.153 and 0.790, respectively. The \(p\) values of the second row are 0.164, 0.638, 0.958, 0.160 and 0.872, respectively.  
seems to try to control the MTUs of the ankle joint to achieve a spring-damper-like behavior. A limited disturbance will eventually vanish in such a system.  
There are two components in the concept of ankle joint impedance, that is, intrinsic and reflex components [44]. The intrinsic component arises from the musculoskeletal system tissues' mechanical properties and the muscle fiber's tonic contraction. The reflex component originates from muscle activation due to sensory responses to stretch. As reported in [45], the reflex component accounts for 10% of the required impedance to remaining upright in a quiet standing posture (compared with _mgh_). However, our experimental results show that the reflex component accounts for more than 10% of the required impedance to complete the push-recovery motion, which indicates that the reflex component plays a more important role in maintaining balance in a dynamic situation than in a static situation.  
The contribution of the damper component, namely, the component evoked by velocity, is significant. The ratio between fiber length and velocity contributions is depicted in Figure 14. The velocity component accounts for more than 60% in the first 300 to 500 ms in subjects C, D and E. For the other two subjects, the velocity parts account for 20 to 80% in the first 500 ms. According to [9], angular velocity is assumed to be a factor that effects reflex gains under the circumstances of upright standing ankle strategy. In the early phase of push-recovery, the velocity part leads to increasingly rapid muscle activation and assists in inhibiting the trend of leaning forward. According to the results obtained in this study, the damper component is of great significance in the forepart of push-recovery.
## 5 Conclusions  
In this paper, we have studied the joint problem of user association and power allocation using P-DQN in the downlink of a two-tier HetNet without knowledge of the environment transition probability. The wireless network has been formulated as a parameterized action Markov decision process with a hybrid (discrete-continuous) action space. The P-DQN has been adopted as a model-free framework to avoid quantization noise resulting from rounding the continuous power space into discrete levels. With the consideration of realistic scenarios, we have designed the reward function as the energy efficiency with QoS constraint per user as well as backhaul capacity constraint. We have introduced a penalty mechanism when the constraints are violated. We have also utilized the cluster size constraint for intra-cluster interference mitigation. In simulations, the proposed P-DQN has been verified to outperform other traditional methods in terms of overall energy efficiency while satisfying QoS requirements and backhaul constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% over the traditional DQN, both with the cluster size constraint. Meanwhile, the proposed P-DQN may still suffer from the curse of dimensionality when dealing with problems with sizable action spaces. It will be worthwhile to investigate advanced DRL techniques (such as the DDPG technique or the multi-agent RL), in future work, capable of handling the problems of joint user association and power allocation, which typically have large action spaces in scenarios of practical interest.  
Conceptualization, F.-T.C.; Investigation, C.-K.H. and F.-T.C.; Methodology, C.-K.H. and F.-T.C.; Resources, F.-T.C.; Writing--original draft, C.-K.H. and K.-L.C.; Software, C.-K.H. and K.-L.C.; Funding acquisition, F.-T.C.; Writing--review and editing, F.-T.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology (MOST) in Taiwan under grant number MOST 109-2221-E-009-101.  
Not applicable.  
The authors declare no conflict of interest.