esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Uncertainty Analysis  
There are uncertainties in the spatial resolution of SIF data to track vegetation phenology in Northeastern China, and the coarse spatial resolution of GOSIF data (0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)) used in this paper has some limitations in trackin0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)g phenology. However, it has been demonstrated that the GOSIF dataset has enormous potential for monitoring vegetation phenology and can assess the vegetation growth cycle more precisely than the traditional vegetation indices [44]. At the same time, GPP should be added as a covariate that may make the results more accurate and convincing.  
Figure 13: Interannual variation of vegetation phenological parameters deriving from SIF and NDVI time series in Northeastern China. (**a**) SOS extracted by SIF and NDVI; (**b**) EOS extracted by SIF and NDVI; (**c**) LOS extracted by SIF and NDVI; (**d**) POP extracted by SIF and NDVI.
# Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold
## 3 Point Cloud Denoising Algorithm Based on Geometric Metrics
### K-Means Clustering Algorithm on the Basis of Geometric Metrics  
Since \(\mathfrak{N}_{n}\) and \(\mathbb{R}^{n}\times\mathrm{P}(n)\) are shown to be topologically homeomorphic [19], the geometric structure on \(\mathfrak{N}_{n}\) can be triggered by setting the metrics on \(\mathbb{R}^{n}\times\mathrm{P}(n)\). In addition, we denote the distance on \(\mathfrak{N}_{n}\) as:  
\[d((\mu_{1},\Xi_{1}),(\mu_{2},\Xi_{2}))=\|\mu_{1}-\mu_{2}\|_{F}+d(\Xi_{1},\Xi_ {2}), \tag{20}\]  
and the barycenter of the parameter point cloud \(\widetilde{\mathcal{D}}\) is indicated:  
\[\mathbf{g}(\widetilde{\mathcal{D}})=arg\min_{(\mu,\Xi)}\sum_{i=1}^{\kappa}d^{ 2}((\mu_{i},\Sigma_{i}),(\mu,\Xi))=(\frac{1}{\kappa}\sum_{i=1}^{\kappa}\mu_{i },arg\min_{\Xi}\sum_{i=1}^{\kappa}d^{2}(\Xi_{i},\Xi)), \tag{21}\]  
where the barycenter \(\mathbf{g}(\widetilde{\mathcal{D}})\) represents the geometric center of \(\widetilde{\mathcal{D}}\). The distance and barycenter \(\mathbf{g}(\widetilde{\mathcal{D}})\) rely on the selection of the metric on \(\mathfrak{N}_{n}\). The distance functions in the following algorithm can be induced by FM, AIRM, LEM, KLD, or SKLD, and the geometric means corresponding to each metric can be found in Table 1.  
The local statistical structure of the valid data and that of random noise are very different, so we use the K-means clustering algorithm to divide \(\widetilde{\mathcal{D}}\) into two categories. The algorithm for clustering data and noise is presented below:  
Figure 1 shows the flowchart of Algorithm 1. The efficiency of the algorithm is dependent on selecting the metrics on \(N_{n}\), and it will be shown that the geometric metrics have more advantages than the Euclidean metric in the following text.  
1. Mapping the original point cloud \(\mathcal{D}\) to a parameter point cloud \(\widetilde{\mathcal{D}}\).
2. K-means algorithm: 1. Set the barycenters of the current division as \(\mathbf{g}_{1}^{(i)}\) and \(\mathbf{g}_{2}^{(i)}\), and apply the K-means algorithm based on the distance function (4), (6), (8), (9) or (10) to group the parameter point cloud \(\widetilde{\mathcal{D}}\). 2. Update the barycenters of the current division to \(\mathbf{g}_{1}^{(i+1)}\) and \(\mathbf{g}_{2}^{(i+1)}\) based on the clustering results, where the geometric mean of each category is calculated by Table 1. 3. Set the threshold \(\epsilon_{0}\). When \(d(\mathbf{g}_{1}^{(i)},\mathbf{g}_{1}^{(i+1)})<\epsilon_{0}\) and \(d(\mathbf{g}_{2}^{(i)},\mathbf{g}_{2}^{(i+1)})<\epsilon_{0}\), the current division of \(\widetilde{\mathcal{D}}\) is mapped to two categories of the original point cloud, and then the program ends. Otherwise, run Step (b).
## 1 Introduction
### Adopting Cognitive Method in WSN  
Recently, cognitive techniques have been used in wireless networks to circumvent the limitations imposed by conventional WSNs. Cognitive radio (CR) is a candidate for the next generation of wireless communications system. The cognitive technique is the process of knowing through perception, planning, reasoning, acting, and continuously updating and upgrading with a history of learning. If cognitive radio can be integrated with wireless sensors, it can overcome the many challenges in current WSNs. CR has the ability to know the unutilized spectrum in a license and unlicensed spectrum band, and utilize the unused spectrum opportunistically. The incumbents or primary users (PU) have the right to use the spectrum anytime, whereas secondary users (SU) can utilize the spectrum only when the PU is not using it.  
Some recent papers in this paradigm, such as references [6; 7; 8; 9; 10; 11], proposed wireless sensor equipped with cognitive radio as one of the promising candidates for improving the efficiency of WSNs. Table 1 lists the capabilities a wireless sensor with a CR needs to have.  
CR allows unlicensed users to access multiple licensed channels opportunistically. This nature of CR gives potential advantages to WSNs by increasing the communication reliabil  
\begin{table}
\begin{tabular}{p{142.3 pt}|p{142.3 pt}} \hline \hline
**Function** & **Action** \\ \hline Cognitive capabilities & \\ \hline Spectrum sensing & Detect unused spaces (white spaces) by the incumbents in the spectrum bands. \\ \hline Spectrum sharing & Use the unused white spaces of incumbents and share the white space information with cognitive users. \\ \hline Prediction & Predict the arrival of incumbents on the channel. \\ \hline Fairness & Distribution of spectrum utilization opportunities fairly among cognitive users. \\ \hline Routing & Route the packet to the destination efficiently considering the network life span, load balancing, shortest route and delay in multi-hop CR-WSNs. \\ \hline Reconfiguration capability & Reconfigure and adjust according to the environment outcomes. \\ \hline Environment sensing & Sensing the environmental factors as in conventional wireless sensors *. \\ \hline Trust and security & Building a trustable environment and secure networks. \\ \hline Power control & Control transmission power considering the legal boundaries and requirements. \\ \multicolumn{2}{p{142.3 pt}}{* application specific.} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prospective capabilities of a wireless sensor with CR.  
energy efficiency. When wireless sensor nodes with cognitive capabilities are introduced to an entire network, it gives exciting new opportunities to researchers and industry to develop algorithms, hardware and software that can overcome the limitations imposed by current wireless sensor design techniques.  
Taking advantage of the current liberalization in the spectrum utilization rule by FCC and technical advancement in sensor technology, wireless sensors with CR can mitigate the current issue of spectrum inefficiency and increase the network efficiency in a range of terms.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 1 Introduction
For state estimation, the IMM filter estimates the state of the target and the covariance matrix. Nearly constant velocity (NCV) models with two different covariance matrices of the process noise are assumed for the dynamic states of the target [16]. For data association, a gating process excludes measurements outside the validation region of each target. The nearest measurement-to-track association scheme assigns one measurement to the closest track based on the statistical distance of the residual. This nearest neighbor (NN) approach is efficient for the visual tracker because the false measurements cannot appear in the area of a target of interest. It is assumed that the measurement from the target in the next frame is closest to the predicted state of the target. In the experiments, a total of 13 moving pedestrians and one car are captured at a height of 15 m by a drone. Some people are clustered as one target during detection, thus a total of 10 tracks are established by the Kalman (IMM filter with one mode) and the IMM filter with two or three modes. The RMSEs of position and velocity are obtained and compared between the filters, showing their dynamic states are well tracked with good accuracy--the average detection rate is 96.5% and the minimum position and velocity RMSE are obtained at around 0.8 m and 0.59 m/s, respectively, when the two mode-IMM filter is used. The major contributions of this paper lie in the following: (1) we integrate visual detection based on image processing and a target tracking derived from statistical estimation. In the literature, image-based detection and state estimation-based target tracking are often researched individually, but few studies are found that integrate the two parts. It is noted that we instantly get dynamic state estimates, such as position, velocity, and acceleration, in the proposed method; (2) No massive training data is required for target detection and tracking. Thus, this method can speed up the process, with less computational resources. Drones have limited computing power, memory, bandwidth, and battery; thus, a small computational load is required for a drone system; (3) A practical solution is proposed for autonomous stand-alone aerial surveillance. The SUAV can move to any location where CCTV cameras cannot be installed and hover or maintain its position. It is very low cost and can be operated by non-experts. Thus, it is useful for combat missions, counter terrorist operations, or search and rescue in military or commercial use. Figure 1 illustrates fully autonomous stand-alone aerial video surveillance with an SUAV. The SUAV continuously monitors human movement within the field of view of the attached camera at a certain altitude. If any threat is detected, an alert is sent to the authorities. The remains of the paper are organized as follows. Moving pedestrian detection is discussed in Section 2. Multiple target tracking with IMM is presented in Section 3. Section 4 demonstrates experimental results. The conclusion follows in Section 5.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 4 Methodology
### Task Decomposition Strategy Based on Graph Theory and Lagrangian Relaxation
#### 4.1.4 Time Window Constraint  
Each subtask must be executed within its predefined time window:  
\[t_{ij}^{\text{start}}\leq t_{ij}\leq t_{ij}^{\text{end}} \tag{25}\]
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 3 Training of CESDD
### SDD Ensemble Learning  
The effectiveness of sliding-window-styled detection relies on the effectiveness of window-wise classification. Through the scanning process, a great number of window patches are densely sampled from many positions in images of multiple scale levels,  
Figure 3: Training samples consist of vehicles (**a**) and backgrounds (**b**). Vehicle category is divided into three types according to extent of occlusion; background category is divided into two types according to whether vehicle part appears.  
consisting usually of far more background patches than well-bounded vehicle patches. Thus, a window-wise classifier is obliged to achieve high enough classification accuracy as well as good recall rate. Classifier's accuracy and recall rate are factually two views of one essence: modeling quality of target data space.  
In this work, the proposed novel classifier, using dictionary ensemble, exploits this very essence directly. Specifically, training samples are categorized as vehicle and background, and are further divided into subcategories. For vehicle category, dividing criterion is set as vehicle orientation and occlusion level, which influence vehicle appearances severely. For background category, sample contents are not related to useful labels (that is, orientations and occlusion levels) as in the case of vehicle category, so we adopt K-means clustering algorithm to directly produce background training sample clusters as subcategories, each containing samples with similarity. With the constitution of subcategories set, dictionaries could be learned. For each subcategory, with limited variance of appearances, dictionary with adequate representation ability could be learned effectively. Specifically, the proposed SDD dictionary learning method is adopted and described in the following subsection.
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 3 Results and Discussion
### Computational Studies  
#### 3.3.1 Drug-Likeness Properties  
Currently, the discovery of new medicinal substances is associated with the use of in silico methods estimating the similarity of a chemical compound to a drug. Drug-likeness assessments are useful at the initial stage of testing active substances by determining their pharmacokinetic properties and safety of use. [PERSON]'s rule of five (RO5), introduced in the 20 th century, is used to assess compounds absorbed after oral administration. RO5 is based on simple molecular properties, assuming that compounds exhibit poor absorption or permeation when the MW is over 500; the Log P is over 5 or MLOGP is over 4.15; and there are over 10 hydrogen bond acceptors (HAs) and over 5 hydrogen bond donors (HDs). In addition to RO5, other methods of testing the reliability of drugs, proposed by [PERSON] and [PERSON] were introduced [42; 43].  
The drug-likeliness properties of triterpenoids **6-10** based on their chemical structure and physicochemical properties were evaluated using [PERSON]'s rule of five and [PERSON]'s and [PERSON]'s rules (Table 3).  
Molecular descriptors (MW--molecular weight; HAs--hydrogen bond acceptors; HDs--hydrogen bond donors; RBs--rotational bonds; TPSA--topological polar surface; and MR--molar refractivity) for derivatives **2-3** and **5-10** were obtained using the Swiss-sADME web server [35] (Table S3, Supplementary Materials).  
The tested triterpenoids **6-10** violate two [PERSON] criteria regarding the permissible value of molar mass (MW \(>\) 500) and lipophilicity (MLOGP \(>\) 4.15). These compounds violate all criteria formulated by [PERSON]. The molecular weight of the compounds ranges from 631.03 to 731.10 g/mol (MW \(>\) 480), and the lipophilicity parameter value ranges from 10.26 to 10.83 (WLOGP \(>\) 5.6). The calculated molar refractivity for compounds **6-10** ranges from 187.95 to 208.59 (MR \(>\) 130), and the number of atoms is higher than 70 [35]. The rule developed by [PERSON] takes into account two parameters such as the number of rotatable bonds (RB \(<\) 10) and the topological polar surface of the molecule (TPSA \(<\) 140 A\({}^{2}\)) [35]. [PERSON]'s rule is met by compounds **6** and **10** (RB = 9; TPSA = 97.10 A\({}^{2}\)). The remaining derivatives 7-9 do not meet this rule, exceeding the number of rotational bonds (RB is 11-14).
## 2 Materials and Methods
### Statistical Analysis  
We used the mean difference (MD) as the effect size measure to assess the effect of exercise on several quantitative outcomes. We calculated the MD by subtracting the mean change in the exercise group from the mean change in the control group. Homogeneity among studies was computed using the Cochran's Q statistic and I2 statistic. The I2 provides an estimate of proportion of the variance in the pooled effect size that is attributable to between-studies heterogeneity (low heterogeneity [0% to 25%], moderate [26% to 75%], and high [76% to 100%]). \(p<0.1\) for the Cochran's Q-test was considered as a significant heterogeneity among the studies [21]. Due to the significant heterogeneity between the studies, the pooled MD for each outcome was estimated using the random-effects model and the DerSimonian-Laird estimator for \(\tau^{2}\)[22]. The analysis included calculating the mean effect size with its 95% CI and we considered a statistically significant effect when \(p<0.05\). We used a correlation value of 0.5 to calculate the variance of each study group when the studies did not report the change standard deviation (SD) or the correlations between pre-intervention and post-intervention measures. We evaluate the sensitivity of the meta-analytic results for distinct correlation values for pre-intervention and postintervention values (0.25, 0.5, 0.75). We conduct a sensitivity analysis to the sequential omission of every study in turn, to reflect the influence of the data from individual studies on the pooled MDs and evaluate the stability of the results. We also analyzed the impact of excluding trials that involved a small number of exercise sessions. The statistical analyses were performed using software R version 3.6.10 2019 (R Foundation for Statistical Computing, Vienna, Austria), in RStudio environment version 1.2.1578% 2009-2019 R Studio, Inc. Narrative synthesis was carried out for outcomes where meta-analysis was not possible.
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 2 Materials and Methods
### Detection of Feature Points with SIFT  
Harris corner detection method is robust and rotation invariant. However, it is scale varying. Fast algorithm has rotation invariance and scale invariance, and has better execution time. But when there is noise, its performance is poor. SURF corner detection speed is better than SIFT, but its stability and number of feature points are not as good as SIFT. SIFT algorithm has rotation invariance and scale invariance, and is more effective in the case of noise [7]. By comparing the advantages and disadvantages of each algorithm and considering the underwater noise environment, the SIFT algorithm is utilized to carry out feature transformation.
## 3 Results
### Automatic Bowel Sound Detection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**MFCC**} \\
**Threshold of SNR (dB)** & \multicolumn{5}{c}{**Before Soda Intake**} \\ \cline{2-6}  & **Sen (\%)** & **Spe (\%)** & **PPV (\%)** & **NPV (\%)** & **Acc (\%)** \\ \hline
2 & 78.8 \(\pm\) 4.7 & 83.9 \(\pm\) 2.1 & 28.8 \(\pm\) 2.3 & 98.0 \(\pm\) 0.4 & 83.5 \(\pm\) 1.8 \\
1 & 79.7 \(\pm\) 4.9 & 83.3 \(\pm\) 2.1 & 17.1 \(\pm\) 1.4 & 99.0 \(\pm\) 0.2 & 83.2 \(\pm\) 1.9 \\
0.5 & 78.9 \(\pm\) 4.1 & 83.2 \(\pm\) 2.0 & 13.5 \(\pm\) 1.4 & 99.2 \(\pm\) 0.2 & 83.1 \(\pm\) 1.9 \\
0 & 78.0 \(\pm\) 4.8 & 81.1 \(\pm\) 2.0 & 10.2 \(\pm\) 1.0 & 99.3 \(\pm\) 0.2 & 81.0 \(\pm\) 1.9 \\ \hline \multicolumn{6}{c}{**MFCC**} \\
**Threshold of SNR (dB)** & \multicolumn{5}{c}{**After Soda Intake**} \\ \cline{2-6}  & **Sen (\%)** & **Spe (\%)** & **PPV (\%)** & **NPV (\%)** & **Acc (\%)** \\ \hline
2 & 77.6 \(\pm\) 2.6 & 79.9 \(\pm\) 1.6 & 48.2 \(\pm\) 1.6 & 93.7 \(\pm\) 0.6 & 79.4 \(\pm\) 1.0 \\
1 & 81.9 \(\pm\) 2.4 & 83.8 \(\pm\) 1.1 & 32.2 \(\pm\) 1.3 & 98.0 \(\pm\) 0.2 & 83.6 \(\pm\) 0.9 \\
0.5 & 82.4 \(\pm\) 2.4 & 84.7 \(\pm\) 1.2 & 26.0 \(\pm\) 1.3 & 98.7 \(\pm\) 0.2 & 84.6 \(\pm\) 1.0 \\
0 & 82.6 \(\pm\) 2.8 & 83.6 \(\pm\) 1.4 & 21.4 \(\pm\) 1.2 & 98.9 \(\pm\) 0.2 & 83.6 \(\pm\) 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of automatic BS extraction using an ANN-based approach based on MFCC (using performance evaluation through random sampling). Sne: sensitivity; Spe: specificity; PPV: positive predictive value; NPV: negative predictive value; Acc: Accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**PNCC**} \\
**Threshold of SNR (dB)** & \multicolumn{5}{c}{**Before Soda Intake**} \\ \cline{2-6}  & **Sen (\%)** & **Spe (\%)** & **PPV (\%)** & **NPV (\%)** & **Acc (\%)** \\ \hline
2 & 79.4 \(\pm\) 4.5 & 85.3 \(\pm\) 2.0 & 30.9 \(\pm\) 2.6 & 98.1 \(\pm\) 0.4 & 84.9 \(\pm\) 1.7 \\
1 & 81.9 \(\pm\) 3.8 & 86.6 \(\pm\) 1.7 & 20.9 \(\pm\) 1.8 & 99.1 \(\pm\) 0.2 & 86.4 \(\pm\) 1.6 \\
0.5 & 82.2 \(\pm\) 4.0 & 87.4 \(\pm\) 1.4 & 17.8 \(\pm\) 1.4 & 99.3 \(\pm\) 0.1 & 87.3 \(\pm\) 1.3 \\
0 & 85.6 \(\pm\) 4.3 & 87.8 \(\pm\) 1.6 & 16.3 \(\pm\) 1.6 & 99.6 \(\pm\) 0.1 & 87.8 \(\pm\) 1.5 \\ \hline \multicolumn{6}{c}{**PNCC**} \\
**Threshold of SNR (dB)** & \multicolumn{5}{c}{**After Soda Intake**} \\ \cline{2-6}  & **Sen (\%)** & **Spe (\%)** & **PPV (\%)** & **NPV (\%)** & **Acc (\%)** \\ \hline
2 & 80.6 \(\pm\) 2.3 & 83.5 \(\pm\) 1.6 & 54.1 \(\pm\) 2.1 & 94.7 \(\pm\) 0.6 & 82.9 \(\pm\) 1.1 \\
1 & 84.4 \(\pm\) 1.9 & 87.6 \(\pm\) 1.1 & 38.9 \(\pm\) 1.9 & 98.4 \(\pm\) 0.2 & 87.3 \(\pm\) 0.9 \\
0.5 & 87.1 \(\pm\) 1.8 & 88.2 \(\pm\) 0.9 & 32.4 \(\pm\) 1.4 & 99.1 \(\pm\) 0.1 & 88.1 \(\pm\) 0.8 \\
0 & 87.0 \(\pm\) 2.1 & 88.3 \(\pm\) 0.9 & 28.7 \(\pm\) 1.4 & 99.2 \(\pm\) 0.1 & 88.2 \(\pm\) 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of automatic BS extraction using an ANN-based approach based on PNCC (using performance evaluation through random sampling). Figure 3: Comparison of accuracies of ANN-based approaches based on MFCC and PNCC, respectively.
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 3 Fatigue Life Assessment for Wind Turbine Blades
### Sampling of Probable Wind Speed  
To assess the accurate fatigue life of the wind turbine blade, it is ideal to use the fatigue load spectrum that corresponds to the entire design life. It is, however, an inefficient approach that requires excessive cost and time to secure long-term observation data in the time domain and conduct numerical analysis based on them. Therefore, the design of various engineered structures adopts a method for predicting long-term responses by combining stochastic approaches based on short-term data. In this study, the fatigue life of the wind turbine blade was predicted using 60 min wind speed data. The probability density distribution is mainly used to generate likely wind speed data. To secure similar wind speed distribution based on actual observation data, the long-term observation data of offshore wind farms in Europe were used (Weibull distribution, shape parameter \(\alpha=2.0\), scale parameter \(\beta=15.0\)[21]). Equation (6) represents the Weibull distribution. The 60 min wind speed data, which are randomly extracted using it, are shown in Figure 4. The tip-speed ratio presented in Figure 4 is calculated based on the experiment results of [PERSON] et al. [40].  
\[f(x)=\frac{\alpha}{\beta}\left(\frac{x}{\beta}\right)^{\alpha-1}\exp\left[- \left(\frac{x}{\beta}\right)^{\alpha}\right] \tag{6}\]
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 1 Introduction
The scoring results are used to assist in the selection of high-quality templates and promote the updating of templates. 3. The proposed algorithm is evaluated on four benchmark datasets, including TrackingNet, GOT-10K, LaSOT, and VOT2020. Both quantitative experiments and qualitative analysis validate the effectiveness of the proposed method in this work. The rest of the paper is organized as follows. In Section 2, we review related work in the field of object-tracking algorithms. Section 3 describes the proposed algorithm in detail, including the motion direction-guided loss function and the tracking result scoring module based on the attention mechanism. Section 4 presents the experimental setup and results, as well as an ablation study to analyze the effectiveness of the proposed algorithm. Section 5 provides an additional analysis of our approach. Finally, we conclude the paper in Section 6.