esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.  
Keywords:auto-focus; telescope observation; image quality; dynamic adaptive focusing window; Tenengrad function +
Footnote †: journal: Applied Sciences
# Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
## 1 Introduction
This research also aims to show that low-cost GNSS receivers are a possible alternative to geodetic order receivers in applications where centimeter accuracies are required [26; 28; 29; 30]. Likewise, the research aims to prove the potentiality of the GNSS low-cost receivers at high sampling rates and obtained positioning. This paper is organized as follows. In Section 1.1, the functional mathematical model of the PPP method is introduced. Section 2 describes the experimental setup, data collection and processing strategies. The results are discussed in Section 3, and the conclusions are given in Section 4.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 5 Experimental Design and Analysis
### Experimental Evaluation
#### 5.2.4 Comparison with Other Existing Methods  
To verify the performance of the Wi-GC method. Our method is compared with the Ref. [28], CrossGR [45] and WiNum [46] in a conference room environment. The CrossGR method first uses CNN to extract features from CSI gesture data and then uses machine learning to classify gesture-related features. The WiNum method uses discrete wavelet transform to denoise CSI data, the AGS algorithm adaptively segments gestures, and the Gradient Boosting Decision Tree (GBDT) integrates learning algorithm recognizes gestures. The comparison results are shown in Table 1.  
As seen in Table 1, the WiNum method has the worst performance because it does not perform feature extraction on CSI data but feeds the segmented data directly into GBDT for classification. Thus, it ignores the essential features of CSI information and does not perform as well as the other methods. The performance of Ref. [28] and the CrossGR method are comparable and CNN is used for feature extraction. CNN mainly focuses on the local spatial features of the data, not on the time series features. However, our method extracts the critical features of gestures from space and time, so the performance of other methods is inferior to Wi-GC.
## 4 Case Study for Comparison
### Problem Definition  
#### 4.1.1 Data Generation  
The Paris model, which describes crack growth behavior, is used to compare the RUL prediction results from the proposed method with those from a typical NN. This behavior is expressed by the following equation [6]:  
\[\frac{d\alpha}{dN}=C(\Delta K)^{m},\ \ \ \Delta K=\Delta\sigma\sqrt{\pi\alpha} \tag{6}\]  
where \(\alpha\) is the crack size (representing the degradation level), \(N\) is the number of cycles, \(\Delta K\) is the stress intensity factor range related to \(\Delta\sigma\) (the stress/loading condition), and \(m\) and \(C\) are the model parameters to be estimated. Since the information required to compare  
Figure 5: Prediction results in the accelerated growth stage: (**a**) prediction at \(k=27\); (**b**) prediction at \(k=31\); (**c**) predictions from \(k=27\) to \(k=31\); (**d**) RUL prediction.  
the two methods is degradation data, the data points, specifically the crack size \(\alpha_{k}\) after \(N_{k}\) cycles, are generated by integrating and solving Equation (6) for \(\alpha\) as follows:  
\[\alpha_{k}=\left[N_{k}C\Big{(}1-\frac{m}{2}\Big{)}\big{(}\Delta\sigma\sqrt{\pi} \big{)}^{m}+\alpha_{1}^{1-\frac{m}{2}}\right]^{\frac{2}{2-m}} \tag{7}\]  
where \(\alpha_{1}\) is the initial crack size. The following parameter values are used to generate the data points: \(m=3.8,C=1.5\times 10^{-10}\), \(\Delta\sigma=75\) MPa, and \(\alpha_{1}=0.01\) m. These values were selected based on material properties from a representative alloy but are used solely to generate synthetic data for this study. The parameter values themselves are not the focus and need not be considered, as the generated data are intended only for comparing the proposed method with a typical NN.  
The black curve in Figure 6 represents the true degradation growth without measurement error, obtained using Equation (7) and the given parameter values, calculated at every 100 cycles. The final data are generated by adding uniformly distributed random noise to the true growth at two different levels to mimic measurement error in practice. The 31 black dots (generated every 100 cycles from 0 to 3000 cycles) in Figure 6a,b represent examples of the final data, with noise levels of \(1\times 10^{-3}\) m and \(2\times 10^{-3}\) m, respectively. Although the noise level remains constant, the generated data may differ with each run due to the inherent randomness of the uniformly distributed noise, leading to variations in the prediction results. Therefore, a total of 100 data sets (with Figure 6 representing one of them) are generated for each noise level to compare the two methods. Lastly, the degradation threshold is assumed to be 0.05 m, represented by the green horizontal line in the figure.
## 3 Results and Discussion
### The Distillation Experiments of REMES and the Phase Changes of REEs
#### 3.2.3 Further Investigation on the Phase Transformation in the Distillation Process
Considering that the experiments were implemented in the nonstandard state, the Gibbs free energy changes were computed by thermodynamic isothermal equations, which are showcased in Equations (13) and (14). The calculation results are shown in Figure 5:5  
Figure 5:6  
Figure 5:7  
where \(\Delta G_{\rm T}^{0}\) is the standard Gibbs free energy change of the reaction, kJ/(mol); \(T\) is the reaction temperature, K; \(P\) is the partial pressure of the gaseous product in the reaction, Pa; \(n\) is the stoichiometric number of the gas resultant; R is the gas constant, kJ/(mol\(\cdot\)K); and P\({}^{0}\) is the standard pressure, Pa.  
Figure 5:8 The standard Gibbs free energy changes of the reactions between oxide impurities and NdF\({}_{3}\): (**a**) the changes in \(\Delta G_{\rm T}^{0}\) for Equations (2)–(7) with temperature; (**b**) the changes in \(\Delta G_{\rm T}^{0}\) for Equations (8)–(12) with temperature. Figure 5:9 (**a**) SEM image of the rare earth condensate; (**b**,**c**) microdistribution of the rare earth condensate; (**d**) SEM image of the lithium condensate; (**e**,**f**) microdistribution of the Lithium condensate. Because the pressure of the experiments was 0.1 Pa, the partial pressure was also set to 0.1 Pa. In the nonstandard state, NdOF could be generated preferentially by reactions involving CaO and MgO until approximately 1350 K, after which Nd\({}_{2}\)O\({}_{3}\) would appear in the reactions between oxide impurities and NdF\({}_{3}\). However, according to Equation (7), the combination of NdF\({}_{3}\) and Nd\({}_{2}\)O\({}_{3}\) would occur, leading to the reappearance of NdOF again, which agrees with the enhancement of the NdOF diffraction peaks in Figure 6:0  
The calculation results and the experimental outcomes elucidated that oxide impurities could capture the fluorine from rare earth fluorides, transforming the rare earth fluorides into rare earth oxides and rare earth oxyfluorides. This process disturbed the evaporation of rare earth fluorides and resulted in a low recovery efficiency of REEs. Above all, the primary obstacle in vacuum distillation was oxide impurities. Therefore, eliminating oxide impurities was a significant mission for improving the efficiency of the distillation recovery method.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### Evaluation Logic Layer (_EVL_)
#### 4.3.1 Qualitative and Discrete Description of Causal Relationships  
The qualitative discrete description aims to reduce semantic understanding of possible scenarios to a symbolic representation. It seeks to delineate meaningful distinctions in scenarios, even when a detailed understanding of causal relationships is vague. These meaningful distinctions refer to, for instance, behavioral thresholds, characteristic categories of specific PSS elements, etc., that can be defined even if the underlying mechanisms are still unclear. An appropriate distinction of scenario manifestations must reflect the domain knowledge of experts and should, therefore, adopt a suitable representation format (e.g., categories, intervals, binary statuses, or specific numerical values) [67]. These can capture different levels of granularity, e.g., broad categories vs. specific numerical values. This enables the modeling of PSS behavior and causal mechanisms without needing specific descriptions. In Figure 8, scenarios are informally described in a purple thought cloud with bullet-point manifestations for each influence factor (e.g., usage intensity as km driven per day). Figure 8 shows how these vague descriptions are transformed into qualitative discrete values, where node \(F\) (_number of rides_) has both precise and vague forms. Experts can still convey uncertain relationships using coarse formats like binary states or vague categories.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 5 MPPT Algorithm and Simulation of TEG System
### Simulation Analysis  
Matlab/Simulink software is used to simulate and analyze the MPPT algorithm, and the simulation structure diagram of the traditional interference observation algorithm and the improved MPPT algorithm are built in the paper. The simulation structure diagram of traditional algorithm is shown in Figure 23, and the simulation structure diagram of the improved MPPT algorithm is shown in Figure 24.  
The models are integrated and packaged as subsystem modules to facilitate the combination of all models into a complete TEG simulation system. The complete TEG simulation system model is shown in Figure 25.  
Figure 24: Simulation structure diagram of improved MPPT algorithm.  
Figure 23: Simulation structure diagram of traditional MPPT algorithm.  
Figure 25: Simulation model diagram of TEG system.  
After setting relevant parameters, the two MPPT algorithms are simulated, and the simulation results are shown in Figure 26. It can be seen from the figure that the time for the improved algorithm to reach the stable maximum power point is about 0.21 s, while the traditional algorithm has a large fluctuation in the early stage and reaches the stable maximum power point at about 1.75 s. Therefore, compared with the traditional interference observation algorithm, the improved MPPT algorithm can reach the stable maximum power point more quickly, and the fluctuation after reaching stable the maximum power point is smaller, which reduces the energy loss and improves the dynamic performance.
# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 3 Experimental Setup and Datasets  
We used six different classifiers, namely the Decision Tree (C4.5), k-Nearest Neighbor (kNN), Logistic Regression (LR), Naive Bayes (NB), random forest (RF), and support vector machine (SVM), to assess the performance of DBT instead of using a single classifier. Considering six different classifiers will also help us to understand whether performance of DBT varies for different classifiers or it is the same. In this study, we used 25 different small datasets with varying levels of IR. All datasets were downloaded from the KEEL dataset repository [42]. Information about the datasets is given in Table 1. More details about the dataset are available at ([[https://sci2s.ugr.es/keel/imbalanced.php](https://sci2s.ugr.es/keel/imbalanced.php)]([https://sci2s.ugr.es/keel/imbalanced.php](https://sci2s.ugr.es/keel/imbalanced.php)) (accessed on 3 November 2021)). The last column in Table 1 is the Imbalance Ratio (IR), which indicates the proportion of the number of majority class instances to the minority class instances.  
We built a total of 1050 (25 datasets times 7 techniques times 6 classifiers) classifiers using open source 'R' software. To build the classification model and assess its performance, the following processes were used: (i) Divide the dataset into training and test sets. The training set contains 80% of the data and the test set contains 20% of the data; (ii) apply DBT on the training dataset; (iii) build the classification model using the balanced training set; (iv) test the performance of the classification model using the test set. The performance of the classifier was measured using the area under ROC curve (AUC value) [43]. To train the classifiers, we used the default hyperparameters settings of the caret package in 'R' tool. No specific hyperparameter tuning was performed, as the objective of this study was not to improve the performance of the classifiers but to assess the performance of DBT. More details about the caret package are discussed by [PERSON] et al. [44].  
We used [PERSON]'s test statistics to compare the performances of different classifiers as they are, based on the average ranked performance of the DBT in the classification task for each dataset [45; 46]. The [PERSON] test statistics helped us to understand whether there was significance difference in the DBT performance of different classifiers [46]. To report the differences in the performance of DBT, we applied a post hoc Nemenyi test [46; 47]. It tells us which DBT differed significantly with respect to its performance in the classification task. We used [PERSON]'s test statistics [48] to test the agreement on rankings of DBT, based on the performance in the classification task, for varying levels of IR in the dataset. [PERSON]'s test was used to assess the performance of the imputation method [49]. If the value of [PERSON]'s 'w' is 1, it means that there is complete agreement over the ranking, and when it is 0, this means there is no agreement over the ranking.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 4 Selected Methods for Risk Analysis  
After providing an overview of the main methods used for risk assessment, Table 2 highlights some capabilities and limitations of those methods.  
As shown in Table 2, Dynamic Fault Tree (DFT) and Monte Carlo simulation (MCs) are the most suitable method for dynamic systems. MCs is considered the ideal solution to model random events with rare probability, which is the case of failure events [78], but this method requires a high computational effort [71]. DFT is the best method to be used for fully dynamic systems with consideration of probability of failure and repair rate [79]. In this work, the authors used DFT to consider the dependencies, sequences, and redundancies of FMs using special dynamic logic gates [73]. Furthermore, it allows the representation of the combination of events and the effects of the order of the failure [79]. In contrast to the static fault tree, DFT covers dynamic and logical gates that represent the relationship between the studied events.  
As in this work, the authors aim to represent the dynamic agent's behavioral and failure modes, so DFT was used to represent the failure propagation between the system's components and perform risk analysis.
# Photocatalytic Activity of TiNbC-Modified TiO2 during Hydrogen Evolution and CO2 Reduction
## 2 Materials and Methods  
### Obtaining of the TiNbC/TiO\({}_{2}\) Heterostructures  
TiNbC MXenes were purchased from the Advanced 2D Materials Co. Ltd. store, Wuzhong District Industrial Park, Suzhou City, Jiangsu Province, China, with claimedmaterial purities of 99% and particle sizes 1-5 \(\upmu\)m. Photocatalysts were prepared from TiO\({}_{2}\) Evonik P25 (anatase 80%, rutile 20%) and MXene with weight contents of 1, 5, and 10% for MXene. Acetone (50 mL) was added to a mixture of TiO\({}_{2}\) and MXene, then the suspension was kept on a water bath at 80 \({}^{\circ}\)C until the acetone completely evaporated (approximately 60 min), the precipitate was dried at 60 \({}^{\circ}\)C for 12 h, and then calcined at 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C for 1 h at a heating rate of 3-4 \({}^{\circ}\)C/min.  
The samples obtained according to the described procedure will be hereinafter referred to as 1% TiNbC/TiO\({}_{2}\), 5% TiNbC/TiO\({}_{2}\), and 10% TiNbC/TiO\({}_{2}\).
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 2 Network RTK Positioning Algorithm
### Ambiguity Resolution of Reference Stations
According to Equation (3), the parameters can be solved by the Kalman filter. The high precision double-difference atmospheric delay can be extracted only when the ambiguity is fixed. In order to improve the fixed rate of ambiguity, the original ambiguity is first converted to the ambiguity of wide lane and narrow lane [23; 24], and then the LAMBDA method is used to fix the ambiguity of wide lane and narrow lane, respectively, in the ambiguity domain. When the ambiguity of wide lane is fixed, the following Equation is used to update the ambiguity of narrow lane:  
\[\left\{ \begin{array}{c}\widetilde{C_{2}}=\widehat{C_{2}}-Q_{\widehat{C_{2} }\widehat{C_{1}}}Q_{\widehat{C_{1}}\widehat{C_{1}}}^{-1}(\widehat{C_{1}}- \widehat{C_{1}})\\ Q_{\widehat{C_{2}}\widehat{C_{2}}}=Q_{\widehat{C_{2}}\widehat{C_{2}}}-Q_{ \widehat{C_{2}}\widehat{C_{1}}}Q_{\widehat{C_{1}}\widehat{C_{2}}}^{-1}Q_{ \widehat{C_{1}}\widehat{C_{2}}}\end{array} \right. \tag{4}\]  
where \(\widehat{C_{1}}=N_{1}-N_{2}\) represents the ambiguity of wide lane (WL), \(C_{2}=N_{1}\) is the ambiguity of narrow lane (NL), and Q refers to the corresponding covariance matrix. Compared with directly solving the narrow lane float ambiguity \(\widehat{C_{2}}\), the narrow lane float ambiguity \(\widehat{C_{2}}\) updated by using the wide lane fixed ambiguity \(\widehat{C_{1}}\) has higher accuracy and is easier to fix. In the third part of this paper, the specific processing strategy of each parameter is given. After the narrow lane ambiguity parameters of L1 and L2 frequency signals are fixed, the high-precision double-difference tropospheric delay and double-difference ionospheric delay on the oblique path of each fixed satellite can be directly extracted according to Equations (5) and (6) [25], where \(\varphi\) represents phase observations expressed by weeks:  
\[\Delta\
abla T_{rb}^{pq}=\frac{f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}(\lambda_{1} \Delta\
abla\rho_{rb,1}^{pq}-\lambda_{1}\Delta\
abla N_{rb,1}^{pq})-\frac{f_{2 }^{2}}{f_{1}^{2}-f_{2}^{2}}(\lambda_{2}\Delta\
abla\rho_{rb,2}^{pq}-\lambda_{2 }\Delta\
abla N_{rb,2}^{pq})-\rho_{rb}^{pq} \tag{5}\]  
\[\Delta\
abla I_{rb}^{pq}=\frac{f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}[(\lambda_{1} \Delta\
abla\varphi_{rb,1}^{pq}-\lambda_{2}\Delta\
abla\varphi_{rb,2}^{pq})-( \lambda_{1}\Delta\
abla N_{rb,1}^{pq}-\lambda_{2}\Delta\
abla N_{rb,2}^{pq})] \tag{6}\]
## 4 Experiments
### Blind Observation State Classification Experiment  
We used the K-means++ algorithm to cluster the PO data. The results are shown in Figure 11, from which we obtained the coordinates of the center point of the gesture cluster c1 when the pedestrian was not observing the road condition (i.e., looking forward) and the coordinates of the center point of the gesture clusters c2 and c3 when the pedestrian was observing the left and right sides within the blind area of the visual field, respectively. We defined the pose of each pedestrian using a triplet of angles (i.e., \(\alpha\), \(\beta\), \(\gamma\)), where \(\alpha\) was positive when the pedestrian's head turned to the left, \(\beta\) was positive when the shoulder turned to the left, and \(\gamma\) was positive when the body leaned forward.  
In addition, we constructed a test set from the pedestrian pose data collected under other view-obscuring scenarios to verify the classification effect of the obtained clustering centroids. The results showed that the above-mentioned clustering centroids provided a good classification benchmark for the pedestrian blindness observation degree states. Some of the experimental classification results are shown in Figure 12.  
\begin{table}
\begin{tabular}{c l l l l l} \hline
**Variable** & & **Acc.** & **Rec.** & **F1.** & **Pre.** \\ \hline \multirow{3}{*}{Distance [8]} & Min & 0.653 & 0.631 & 0.501 & 0.619 \\  & Max & 0.706 & 0.757 & 0.667 & 0.774 \\  & Ave. & 0.684 & 0.701 & 0.558 & 0.677 \\ \hline \multirow{3}{*}{Distance, Angle [5]} & Min & 0.943 & 0.963 & 0.845 & 0.916 \\  & Max & 0.970 & 0.987 & 0.941 & 0.970 \\  & Ave. & 0.957 & 0.976 & 0.897 & 0.945 \\ \hline \multirow{3}{*}{Ours} & Min & 0.980 & 0.986 & 0.973 & 0.948 \\  & Max & 0.986 & 0.993 & 0.980 & 0.961 \\ \cline{1-1}  & Ave. & **0.984** & **0.991** & **0.976** & **0.954** \\ \hline \end{tabular}
\end{table}
Table 4: Comparison results of input parameters for pedestrian interference recognition. Recognition Accuracy (Acc.), Recall (Rec.), F1-score (F1.), Precision (Pre.). Min, Max, and Average (Ave.) results.  
Figure 11: Classification results of pedestrian blindness observation degree characteristics.