esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 6 Numerical Results
### 50-Node Scenario
#### 6.1.4 Comparison and Discussion  
In general, the NN-based approach achieves a better performance than the GA-based approach.  
Figure 6 shows the total distance travelled by the AGV with different algorithms (NN-based approach, GWO-based approach, and GA-based approach) to visit the 50 sensor nodes given in Figure 2.  
Figure 5: Achieved path planning solution with GWO in 1000 iterations.  
Figure 6: Achieved path lengths for visiting 100 nodes with NN-based approach, GWO-based approach, and GA-based approach in 1000 iterations.  
From Figure 6, the following observations can be made for the performance of the algorithms for the 50-node scenario. Considering the general trend, the NN-based approach and the GA-based approach perform better than the GWO-based approach. Not only does the NN-based approach give better results than the GA-based approach until the 500 th iteration, but the NN-based approach also solves in a much shorter time (0.098977 s) compared to the GA-based approach's time to reach the solution (3.689545 s). The NN-based approach performs 37.4 times faster than the GA-based approach.  
From Table 1, we can make the following observations. In the first iteration, the GA-based approach and GWO-based approach started with high values of path lengths (14,000-15,000 m), while the NN-based approach achieved a solution of around 6360 m very quickly. At the 100 th iteration, the GA-based approach achieves significantly better results than the GWO-based approach (3000 m, i.e., 32% shorter path than that achieved by the GWO-based approach). At the 300 th iteration, the NN-based approach achieves better results than the GWO- and GA-based approaches. At the 600 th iteration, the GA-based approach achieves better results than the NN-based approach. At iteration 1000, the GA-based approach significantly outperforms the GWO-based approach (2610 m, i.e., 29.5% less than the GWO-based approach), while achieving a shorter path than the NN-based approach; the NN-based approach provides a much faster and practical solution.
# A Survey of the Applications of Text Mining for the Food Domain
## 7 Text Mining in Food Safety Regulation
### Food Public Opinion Monitoring  
In the face of ongoing food security challenges, monitoring public sentiment on food safety is essential for understanding incident impacts and crafting targeted regulatory approaches. This surveillance hinges on vast unstructured data from modern media channels. [PERSON] et al. [136], for example, applied text mining to extract instances of public grievances about food safety from various web forums, enhancing governmental capacity to compile a comprehensive database of food safety concerns and improving regulatory oversight precision.  
Insights gained from monitoring public perspectives on food enable regulators to quickly identify and address focal points of public discourse, proactively mitigate societal distress, and navigate collective sentiment with targeted precision. For example, the IFood-Cloud platform, offering real-time analysis of China's food safety public opinion, aggregates data from over 3100 sources. This platform helps decipher public sentiment, analyze regional disparities, and understand public concerns following food safety incidents [137]. In the aftermath of a food safety crisis, regulators can trigger responsive protocols informed by monitoring outputs to disseminate accurate information and maintain societal equilibrium. [PERSON] et al. [6] showcased the power of big data and machine learning in analyzing public sentiment around food safety in Greater China, particularly during the initial stages of the 2019 NKP outbreak, highlighting the potential of these technologies in enhancing risk communication and decision making.  
In essence, food opinion monitoring is an essential tool for enacting precise and informed supervisory actions. Food safety regulators continue to raise the bar on food safety governance by utilizing techniques such as text classification, sentiment analysis, etc., to enhance effectiveness and enlightenment.
## 2 System Description
### Boost Model  
Figure 2 is the schematic diagram of a boost chopper, where \(u_{pv}\) and \(i_{pv}\) are the output voltage and current of the PV array respectively. The boost converter is composed of switch \(S\), inductance \(L_{pv}\), and capacitance \(C_{dc}\), together with diode \(D_{pv}\).  
**Assumption 1**.: _The inductance \(L_{pv}\) and capacitance \(C_{dc}\) of the converter are large enough that the current \(i_{pv}\) and voltage \(u_{dc}\) keep constant during the switch \(S\) on and off time._  
It is assumed that the conduction duty cycle of \(S\) is \(D_{b}\). Since the energy accumulated by the inductor in one cycle is equal to its released energy, we obtain:  
\[u_{pv}i_{pv}D_{b}=(u_{dc}-u_{pv})i_{pv}(1-D_{b}) \tag{1}\]  
The relation \(u_{pv}=(1-D_{b})u_{dc}\) can be derived from (1).  
Actually, \(L_{pv}\) and \(C_{dc}\) of the converter are finite, and \(u_{dc}\) may decrease somewhat. However, when \(L_{pv}\) and \(C_{dc}\) is large enough, the error can be ignored.  
If \(0\leq D_{b}\leq 1\), then \(u_{dc}>u_{pv}\), proving the characteristic of the voltage boosting in the boost chopper. Moreover, \(u_{pv}\) and \(D_{b}\) vary inversely when \(u_{dc}\) keeps constant. A suitable PV voltage can be obtained by adjusting \(D_{b}\) such that the PV inverter can work at the stage of the maximum power point.  
Figure 1: Single-phase grid-connected PV inverter model.
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
## 5 Conclusions  
In this article, a new metric, combined reliability with stopping tree, was defined to effectively identify corrected code bits in the BPC decoder. A rule for the correction operation defined by a threshold is designed to reduce the number of additional decoding attempts, especially in low to medium SNR regions. A higher-order version is designed based on the nested correction set to better decrease the gap with the performance of the CA-SCL decoder. The simulation results illustrate that the proposed higher-order MBPC decoding algorithm can effectively improve BLER performance with lower average latency in medium to high SNR regions and ensure better applicability than existing BPF decoders. Compared with the CA-SCL decoder, the proposed MBPC-2 decoder achieves around a 0.2 dB performance gain with \(L=4\) in all simulated SNR regions and competes with the CA-SCL with \(L=8\) when \(\text{SNR}\leq 2.2\) dB. Further research directions involve latency reduction in low SNR regions and BLER performance improvement in high SNR regions. For the latency overhead caused by massive increases in the iterations at low SNR regions, the modified refresh operation enables the decoder to be implemented in a parallel version to reduce the latency for further research, in which \(T_{1}\) decoders with the same structure can be executed independently with different initializations. In addition, the method for identifying the corrected locations by deep learning is also an attractive direction of study.  
Conceptualization, M.Z.; methodology, M.Z. and Z.L.; validation, Z.L.; investigation, M.Z.; writing--original, M.Z.; writing--review and editing, L.X. and X.L. All authors have read and agreed to the published version of the manuscript  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Decoder** & 1.5 & 1.75 & 2.0 & 2.25 & 2.5 & 2.75 & 3.0 \\ \hline MBPC-1 & 3080 & 946 &  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) (61372072), by the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) (B08038), and by the Fundamental Research Funds for the Central Universities.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### The Evolution Law of the Bursting Liability of Coal with Different JIAs
#### 3.2.2 Relationship between JIA and Coal Bursting Liability Index  
The experimental results of the bursting liability index of coal with different JIAs are presented in Table 4, and the change trend is illustrated in Figure 5. It can be observed that only when the JIA was 15\({}^{\circ}\) the \(Rc\) of coal less than 14 MPa, indicating weak bursting liability. With an increase in JIA, the \(Rc\) of coal decreased first and then increased (Figure 5a). The \(DT\) decreased with an increase in JIA (Figure 5b). The JIA has a significant impact on the \(DT\) of coal. When the JIA was small, multiple stress drops occurred during coal failure. When the JIA ranged from 0\({}^{\circ}\) to 45\({}^{\circ}\), the \(DT\) of coal exceeded 500 ms, indicating no bursting liability. However, when the JIA was at angles of 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\), the \(DT\) was less than 50 ms, indicating a strong bursting liability. The \(K_{E}\) showed a gradually increasing trend (Figure 5c). The coal with a 0\({}^{\circ}\) joint dip had no bursting liability, while the coal with 15\({}^{\circ}\) joint dip exhibited weak bursting liability. The \(W_{ST}\) gradually increased with an increase in JIA (Figure 5d). When the JIA was 0\({}^{\circ}\), 15\({}^{\circ}\), and 30\({}^{\circ}\), the \(W_{ST}\) was less than 3, indicating no bursting liability. When the JIA was 45\({}^{\circ}\), the \(W_{ST}\) was less than 100, indicating a weak bursting liability. When the JIA was 75\({}^{\circ}\), the \(W_{ST}\) had a large dispersion, which led to a \(W_{ST}\) of 75\({}^{\circ}\); the average impact energy velocity index was greater than 90\({}^{\circ}\), but the inclined coal with 75\({}^{\circ}\) and 90\({}^{\circ}\) joint inclination was strongly inclined to impact.  
Figure 5: Variation Law of coal bursting liability with different JIAs. (**a**) Uniaxial compressive strength, (**b**) Dynamic failure time, (**c**) Impact energy index, (**d**) Impact energy velocity index.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Angle/\({}^{\
u}\)} & \multicolumn{4}{c}{_Rc/MPa_} & \multicolumn{4}{c}{_K\({}_{E}\) Index_} & \multicolumn{4}{c}{_DT/ms_} & \multicolumn{4}{c}{_Average Impact Energy Velocity Index_} \\ \cline{2-13}  & **0-1**  
The results of different bursting liability indices varied slightly. With an increase in JIA, the overall impact propensity of coal presented a gradually increasing trend. Based on the findings in Section 3.2, it may be concluded that the existence of joints changes the bursting liability of coal. With an increase in JIA, the elastic strain energy accumulates before the main failure of coal, and the dissipated energy released by the coal failure increases gradually. Consequently, the coal gradually evolves from no bursting liability to strong bursting liability.
## 4 Results and Discussion
### Relationship between the Subjective Evaluation Value and Physiological Indices
#### 4.2.3 Calculation of the Evaluation Index Using Multiple Regression Analysis
In Figure 11a,b, the value of the comfort sensation drops to \(-3\) (very uncomfortable) at 30 min, and at the same time, the value of \(Yc\) drops and becomes a negative number. Thereafter, the value of the comfort sensation vote remains at an uncomfortable state (\(-3\): Very uncomfortable, \(-2\): Uncomfortable), and similarly, the value of \(Yc\) remains at a negative value. These results suggest that \(Yd\) and \(Yc\) can be used to evaluate the drowsiness level and thermal comfort of occupants continuously. Figures 10 and 11 show, respectively, changes in the normalized \(Yd\) (a) and drowsiness level (b), (subject C) and changes in \(Yc\) (a) and the comfort sensation vote (b) (subject B). Figure 11: Changes in \(Yc\) (**a**) and the value of the comfort sensation vote (**b**): Time series of the value of the comfort sensation vote, (subject B). Figure 10: Changes in the normalized \(Yd\) (**a**) and drowsiness level (**b**), (subject C): (a) Time series of normalized \(Yd\), (**b**) Time series of the value of the drowsiness level. (d) Review of the Utility and Validity of the Evaluation Index  
To review the utility and validity of the evaluation index, as an example, we set the threshold value in relation to \(Yd\) and the drowsiness level. When the threshold value of \(Yd\) is sets as 2.3, as shown in Figure 12, \(Yd\) can be used as the classifier, which can classify the drowsiness level between level 0, 1, 2, or more with an 86.7% accuracy. This result suggests that there is a significant difference in the physiological reaction between the drowsiness level of 1 (slightly drowsy) and 2 (moderately drowsy), and is a similar trend to the results of a previous study about the relation between the arousal levels and physiological indices of a driver [21], though the task of the subjects is different from that of the previous study (arithmetic task vs driving). This result also suggests that \(Yd\) is a valid indicator of the drowsiness level of occupants to classify between the drowsiness level of 1 (slightly drowsy) and 2 (moderately drowsy). Figure 12 shows the setting of the threshold value in relation to \(Yd\).
## 3 Implementation and Results
The classification accuracy of the SVM model using stratified five-fold cross-validation is shown in Figure 3.1 It demonstrates fold-3's superior accuracy. The stratified 5-fold cross-validation was supported by this model, which had a mean accuracy of 85.22% and an SD of 0.0839. Kernel = rbf, Gamma = 0.1, and C = 3 were chosen as the optimal parameters following Hyperparameter tuning using stratified 5-fold cross-validation. The classification accuracy of the SVM model using stratified ten-fold cross-validation is shown in Figure 3.2 It demonstrates that the accuracy of fold-6 is the highest. This model was then discovered to support stratified 10-fold cross-validation with a mean accuracy of 85.14% and an SD of 0.0940. Using stratified 10-fold cross-validation, the optimal Hyperparameter values were kernel = rbf, gamma = 0.1, and C = 8. Figure 3.3 The classification accuracies of the SVM model using the stratified five-fold cross-validation. Figure 3.4 The classification accuracies of the RF model using the stratified ten-fold cross-validation. The classification accuracy of the KNN model using stratified five-fold cross-validation is shown in Figure 3.5 It demonstrates that folds 3 and 5 have the most remarkable accuracy. This model supported the stratified 5-fold cross-validation with a mean accuracy of 85.22% and an SD of 0.0740. After Hyperparameter tuning using stratified 5-fold cross-validation, the optimal parameters were determined at n_Neighbor = 9 and p = 2. The classification accuracy of the KNN model using stratified five-fold cross-validation is shown in Figure 3.6 It demonstrates that the highest accuracy folds are 2, 4, 5, 6, 7, 9, and 10. The KNN model at the time was discovered to have a mean accuracy of 84.03% and an SD of 0.0535 that supported the 10-fold stratified cross-validation. The best parameters were determined to be n_Neighbor = 11 and p = 2, given Hyperparameter tuning by 10-fold stratified cross-validation. Figure 3.7 The classification accuracies of the KNN model using the stratified five-fold cross-validation. Figure 3.8 The classification accuracies of the SVM model using the ten-fold stratified cross-validation.
## 4 Experiment and Analysis
### Discussion  
Based on the results presented in Section 4.4, it can be seen that the predictions of Batch A are significantly better than the predictions of Batch B. For one thing, the data of Batch A and Batch B were collected by two groups of researchers, and there might be some non-standard operations when collecting Batch B--for example, the hitting directions and the hitting speeds when using the rebound device. Further, the re-trained deep learning network was used for image classification, and there were only 10 clusters in the training sample. By enlarging the size of training data, the prediction of Batch B can be improved. For Batch A, the KNN algorithm made the best predictions, meaning that the fitting accuracy of KNN was the highest. For Batch B, the SVM achieved the best results, meaning that it had the strongest generalization ability in this regression.
## 3 Uncertainty Propagation Approach of Constellation Phase
### Semi-Analytical-Based Monte Carlo Simulation Design  
Without loss of generality, we assume that the initial state quantities of each co-planar satellite in the satellite orbit coordinate system follow a three-dimensional Gaussian distribution with zero mean \(X_{0}\sim N(0,\Sigma)\), and the components are independent of each other. Then, the joint Gaussian PDF is  
\[p(x)=\frac{1}{(2\pi)^{3/2}|\Sigma|^{1/2}}\exp\left[-\frac{1}{2}x^{\mathrm{T}} \Sigma^{-1}x\right], \tag{14}\]  
where the covariance matrix of the initial position velocity errors \(\Sigma_{r}\) and \(\Sigma_{\sigma}\) are  
\[\begin{cases}\Sigma_{r}=\mathrm{diag}(\sigma_{x}^{2},\sigma_{y}^{2},\sigma_{ 2}^{2}),\\ \Sigma_{\sigma}=\mathrm{diag}(\sigma_{v_{x}}^{2},\sigma_{v_{y}}^{2},\sigma_{v_ {z}}^{2}),\end{cases} \tag{15}\]  
where diag represents a diagonal matrix with the entries on the diagonal.  
To further simplify the problem, Monte Carlo simulations are performed in two hypothetical cases: Case I, where both two satellites have only initial position errors, and the position standard deviation is \(\sigma_{x}=\sigma_{y}=\sigma_{z}=\sigma_{r}\); Case II, where both two satellites have only velocity errors, and the velocity error is noted as \(\sigma_{v_{x}}=\sigma_{v_{y}}=\sigma_{v_{z}}=\sigma_{v}\).  
Figure 1: Evolution of Phase Deviation of two co-planar Satellites.  
A number of \(N\) samples are generated, and the samples are then propagated over time individually, and the a posteriori uncertainty distribution is reconstructed from the integrated samples. Multiple sets of position (velocity) standard deviations \(\sigma_{r}\) (\(\sigma_{r}\)) with orbital propagation time \(t\) are taken, and multiple Monte Carlo simulations are performed to count the first two order moments of the relative phase distribution, which are the mean and standard deviation. Combining hypothesis testing and surface fitting, an approximate mapping between the initial covariance and the terminal covariance can be obtained.  
It should be noted that since the relative phase is a directional statistic, and \(0^{\circ}\) and \(360^{\circ}\) are identical angles, so that for example \(180^{\circ}\) is not a sensible mean of \(1^{\circ}\) and \(359^{\circ}\). To avoid this error, the directional mean \(E(\theta)\) and the directional standard deviation \(\sigma(\theta)\) of the directional quantity \(\theta\) should be calculated by Equation (16) [28]  
\[\begin{cases}E(\theta)=\tan^{-1}(\bar{S}/\bar{C}),\\ \sigma(\theta)=\sqrt{-2\ln\bar{R}},\end{cases} \tag{16}\]  
where the center of mass \((\bar{C},\bar{S})\) of the directional statistic is  
\[\bar{C}=\frac{1}{N}\sum_{j=1}^{N}\cos\theta_{i},\,\bar{S}=\frac{1}{N}\sum_{j= 1}^{N}\sin\theta_{i}, \tag{17}\]  
and the mean resultant length is  
\[\bar{R}=(\bar{C}^{2}+\bar{S}^{2})^{1/2}. \tag{18}\]
# Three-Phase PV CHB Inverter for a Distributed Power Generation System
## 2 System Topology and Control Strategy  
The three-phase topology of the multilevel CHB inverter analyzed in this paper is shown in Figure 1. As can be seen, it is made by \(N\) series connected cells for each phase leg. Each cell is a H-bridge inverter powered by a photovoltaic generator, where the PV generator can be either a single solar panel or a PV subsystem. The output waveform for each leg is the sum of the output voltages (\(v_{Hi,p}\), with \(i\) = 1,..., \(N\), and \(p\) = 1, 2, 3) of each H-bridge. An inductive filter \(L\) is exploited to filter the undesired harmonic components in the grid current.  
The control strategy we propose has a twofold main objective: (i) adjusting the input voltage of each cell to track the MPP of the corresponding PV generator; (ii) driving only one cell at time in PWM mode in order to reduce switching losses.  
The core of the control strategy is a specially-suited sorting algorithm that establishes which cell must work in PWM mode while the others are kept in a fixed state, the overall constraints being the sum of the input voltages (which must exceed the utility grid peak voltage) and the phase relation among the three legs.  
The control algorithm compares the errors between the MPPT reference values and the voltages actually measured across each PV generator, with the aim of driving only one cell in PWM mode, while the others can be either connected or bypassed, according to the detailed analysis reported below.  
Figure 1: Three-phase 2\(N\) + 1-level grid-tied photovoltaic (PV) cascaded H-bridge (CHB) inverter.
## 4 Performance Analysis of Islanding Detection Methods
### Suitability Based on the Type of DGs  
The type of DGs that are connected to the main grid can be either inverter-based or rotating-based. For grid-connected PV systems, inverter based, while for grid-connected wind systems, rotating-based. Some islanding detection methods are suitable for inverter-based systems and are not suitable for rotating-based systems. For example, frequency shift methods are effective in the inverter-based system and are not in rotating-based systems.  
Figure 3: Islanding Detection Criteria.  
Methods that are based on terminal voltage change are proper for rotating-based systems but are not good for inverter-based systems.
## 4 Discussion of Results
### Fibre Reinforcement Efficiency in Flexure
#### 4.5.2 Contribution for Flexural Toughness  
The effect of the fibres on the ductility of mortars was evaluated by quantifying the flexural toughness ratios; see Figure 22. Different procedures were suggested by the standards for the evaluation of the toughness in flexure [65; 67; 68; 69]. In this study, a ratio between the flexural toughness from fibre-reinforced mortar and plain mortar was used, allowing comparison between the values obtained from different sources.  
Figure 22 shows that two groups of FRMs can be identified: CEM-FRMs are presented in Figure 22a and NHL-FRMs in Figure 22b. The FRMs of the first group, for the same fibre fraction, always showed a lower toughness ratio than the FRCs belonging to the NHL-FRM group, independently of the type of fibre. This indicates in general a lower  
Figure 22: Efficiency of fibre reinforcement on the flexural toughness ratio: (**a**) FRM with Portland cement and Geopolymeric binders (10 SF means 10% replacement of CEM by silica fume) [28; 30; 32; 34]; (**b**) FRM with NHL binders (20 Zeo means 20% replacement of NHL by zeolite and 20 Tuff stands for 20%replacement of NHL by tuff) [36; 37; 38; 40].  
ductivity of the CEM-FRMs when compared to NHL-FRMs. The NHL-FRM developed in this study reached a flexural toughness nine times higher when 1.00% of fibres were added. Nonetheless, for CEM-FRMs the same fibre fractions led to increased factors of around three. In contrast, NHL-FRMs in general show very high flexural toughness ratios [36; 38], showing higher efficiency for the same fibre fractions, as shown in Figure 22a,b. Figure 23 represents the flexural strength vs. flexural toughness ratio of FRMs, where it is possible to identify that the mortar reinforced with glass fibres developed by [PERSON] et al. [36] presented low flexural strength. The mortar developed by [PERSON] et al. [38] presented a high flexural strength and flexural toughness ratio. However, when considering the mechanical properties set as target for the overlay application, 10 \(<f_{c}<\) 15 MPa and 5 \(<f_{i,\#}<\) 7 MPa, these NHL-FRMs clearly underperformed considering the compressive strength; see Figure 1.