esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Monitoring calibration bias in reflectivity (\(Z_{\text{H}}\)) in an operational S-band dual-polarization weather radar is the primary requisite for monitoring and prediction (nowcasting) of severe weather and routine weather forecasting using a weather radar network. For this purpose, we combined methods based on self-consistency (SC), ground clutter (GC) monitoring, and intercomparison to monitor the \(Z_{\text{H}}\) in real time by complementing the limitations of each method. The absolute calibration bias can be calculated based on the SC between dual-polarimetric observations. Unfortunately, because SC is valid for rain echoes, it is impossible to monitor reflectivity during the non-precipitation period. GC monitoring is an alternative method for monitoring changes in calibration bias regardless of weather conditions. The statistics of GC \(Z_{\text{H}}\) near radar depend on the changes in radar system status, such as antenna pointing and calibration bias. The change in GC \(Z_{\text{H}}\) relative to the baseline was defined as the relative calibration adjustment (RCA). The calibration bias was estimated from the change in RCA, which was similar to that estimated from the SC. The \(Z_{\text{H}}\) in the overlapping volume of adjacent radars was compared to verify the homogeneity of \(Z_{\text{H}}\) over the radar network after applying the calibration bias estimated from the SC. The mean bias between two radars was approximately 0.0 dB after correcting calibration bias. We can conclude that the combined method makes it possible to use radar measurements, which are immune to calibration bias, and to diagnose malfunctioning radar systems as soon as possible.  
radar reflectivity calibration; relative calibration adjustment; monitoring of ground clutter; self-consistency principle; network-based intercomparison 2021132936. [[https://doi.org/10.3390/rs13152936](https://doi.org/10.3390/rs13152936)]([https://doi.org/10.3390/rs13152936](https://doi.org/10.3390/rs13152936))  
1
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 5 Model Maintenance Algorithm
### The Rules to Maintain The Dimensions  
As we described in Section 3, the size of these three matrices is related to \(N_{i}\) and \(L_{i}\). The column number of \(C\) and row number of \(D\) correspond to the size of \(N_{i}\). The column number of \(D\) and the row and column number of \(U\) also correspond to the size of \(L_{i}\). When an element is added into any of these two sets, one column or row of 0 will be added to the corresponding matrices. If one element is moved out of these sets, the corresponding column or row of matrices will be deleted. Therefore, the principles to maintain these two sets are paramount, and given below:  
* \(v_{i}\) will add an element \(v_{j}\) in set \(N_{i}\): when receiving a data \(d_{k}\) and \(v_{j}\in d_{k}.path\) is not in \(N_{i}\).
* \(v_{j}\) will be removed from \(N_{i}\): when \(v_{i}\) neither has positive connection probability with it nor has any knowledge of it, \(C_{v_{i},v_{j}}\)=0 and \(\sum_{d_{k}\in L_{i}}S_{v_{j},d_{k}}\)=0.
* when generating data \(d_{k}\) based on its detection.
* \(v_{i}\) will delete the element \(d_{k}\) from \(L_{i}\): when \(d_{k}\in dataO\), where \(dataO\) stores data that has been fused or outdated.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 4 Evacuation Scenario Setup  
Itaewon is a famous business district in Seoul, South Korea, and it is surrounded by numerous bars, nightclubs, restaurants, and other entertainment venues, making it a popular choice for young Koreans and tourists to relax and have fun. Because Itaewon's location is close to the subway station, it is a typical place for crowds to gather, as shown in Figure 3.  
The Itaewon stampede occurred on a downhill street near the Hamilton Hotel in front of Metro Exit 1 of Itaewon Station, which has a slope of 7\({}^{\circ}\), a street width of 4 m, and a length of 45 m [29; 30]. The street was flanked on both sides by merchants and hotel facades, creating a blocked situation, as shown in Figure 4.  
Based on on-site video observations of pedestrian orientation, the north side of the street was identified as the entrance and the south side of the street as the exit. At the time of the accident, this narrow street was severely overloaded and was swarming with hundreds of people at one time. This narrow, closed environment was not only an environmental factor in the stampede but also exacerbated the crowd buildup, making rescue more difficult. In order to accurately and thoroughly explore the key issues of slope evacuation, this paper will analyze in detail the specific impacts of slope topography, obstacle layout, and other factors on the evacuation of people in a specific area, centering on the accident site.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Slope (Degrees)**} & \multicolumn{2}{c}{**Density**} & \multirow{2}{*}{\(\mathbf{v_{\text{max}}}\) **(m/s)**} & \multirow{2}{*}{\(\mathbf{v_{\text{ff}}}\)} & \multirow{2}{*}{\(\mathbf{\sigma_{s}}\)} & \multirow{2}{*}{\(\mathbf{\sigma_{p}}\)} & **Actual Speed:** \\  & **(Pedestrians/m2)** & & & & & \(\mathbf{v^{\prime}}\) **(m/s)** \\ \hline
0 & 0.5 & 1.4 & 1 & 0.9 & 1 & 1.26 \\ \(-\)4 & 1.0 & 1.4 & 1.18 & 0.9 & 0.91 & 1.17 \\ \(-\)4 & 1.5 & 1.4 & 1.28 & 0.8 & 0.77 & 0.78 \\ \(-\)10 & 1.0 & 1.4 & 0.75 & 0.9 & 0.91 & 0.74 \\ \(-\)10 & 1.5 & 1.4 & 0.85 & 0.8 & 0.83 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variation in pedestrian speed in different scenarios.  
Figure 3: The full scenario of Itaewon’s geographic location.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
###### Abstract  
With the continuous development of intelligent transportation systems (ITSs), traffic flow prediction methods have become the cornerstone of this technology. This paper comprehensively reviews the traffic flow prediction methods used in ITSs and divides them into three categories: statistics-based, machine learning-based, and deep learning-based methods. Although statistics-based methods have lower data requirements and machine learning methods have faster calculation speeds, this paper concludes that deep learning methods have the best overall effect after a comprehensive analysis of the principles, advantages, limitations, and practical applications of each method. Deep learning methods can overcome many limitations that traditional statistical methods and machine learning methods cannot surpass, such as the ability to model complex nonlinear relationships. Experimental results show that hybrid neural networks are significantly superior to traditional methods in terms of their prediction accuracy and generalization abilities. By combining multiple models and techniques, hybrid neural networks can improve the accuracy of traffic flow prediction under different conditions. Although deep learning methods have achieved remarkable success in short-term prediction, challenges still exist, such as the generalization of models in different traffic scenarios and the difficulty of long-term traffic flow prediction. Finally, this paper discusses future research directions and anticipates the future development of ITS technology.  
Keywords:intelligent transportation systems; statistical analysis theory; machine learning; deep learning
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 5 Evaluation of UWB Data
### Visibility Analysis  
To analyze more in deep the UWB's high material penetration property, an anchor visibility analysis is performed for all five test and reported in Figure 5, where the number of visible anchors is plotted in function of the time. In this case, the visibility consists in both LOS and NLOS received signals and, therefore, is representative of the number of ranges (also biased) used in the position estimation. Thanks to the time reference it is possible to identify the portion of the plot which correspond to the indoor portion of the path represented in red. As supposed, the majority of contemporary ranges acquisition is concentrated in this area with a variation of visible satellites between 4 and 8. Moreover, there are also other areas which present a high number of signals received simultaneously, once again to indicate the penetrative properties of the UWB signal.  
Figure 4: The horizontal positioning estimation obtained by the UWB only in real time.  
Figure 5: _Cont._Nevertheless, the visibility plot shows the lowest number of acquired ranges in the first figure section, which correspond to the outdoor environment. This consideration is important as it provides the first motivation for relying on GNSS positioning in open-sky conditions.
# Landslide Prediction with Model Switching +
## 4 Accurate Early Warning System Design
### Learning-Based Re-Training Flow  
Figure 8 shows the flow of learning-based re-training. The determination of ret-raining is based on the error estimation. The error estimation process calculates the average error of all (\(FS_{actual},FS_{predict}\)) pairs in an error-estimation window (EEW). For each period of the EEW, this procedure compares the average error, \(AVGE\), of two error estimation windows, \(EEW_{now}\) and \(EEW_{pre}\), and the accumulated error, \(ACCE\), to check the two conditions for re-training. Equations (17) and (18) give the two conditions under which the prediction model needs to be re-trained, where \(C_{IE}\) is the coefficient of interval error used to specify the short-term tolerable error range, which is equal to the size of the prediction horizon in our work. If the difference of \(AVGE\) between two continuous \(EEW\) is too large, the prediction model will be re-trained, due to the high variability of the input pattern that the original prediction model could not predict. Here, \(C_{AE}\) is the coefficient of accumulated error used to specify the long-term  
Figure 6: Flow of the switching strategy.  
tolerable error range (here, long-term means since epoch). If the \(ACCE\), compared to average error of the prediction model, \(AVGE_{Model}\), is too large, the prediction model will also be re-trained, as the prediction results are becoming inaccurate. This further implies that the environment is changing with time, and that adaptation is needed. \(AVGE_{now}\) and \(ACCE_{now}\) are calculated using Equations (19) and (20), respectively.  
\[AVGE_{now}>C_{IE}\times AVGE_{prev}, \tag{17}\]  
\[ACCE_{now}>C_{AE}\times AVGE_{Model}, \tag{18}\]  
\[AVGE_{now}=\frac{\sum\left|FS_{actual}-FS_{predict}\right|}{SIZE_{EEW}}, \tag{19}\]  
\[ACCE_{now} =ACCE_{prev}+\Delta AVGE\] \[=ACCE_{prev}+\left(AVGE_{now}-AVGE_{prev}\right). \tag{20}\]  
**Prediction Horizon tuning flow**  
Figure 7: Overall Flowchart of Prediction Model Analysis.
## 4 Experiments
### Baseline Methods  
We aimed to explore and evaluate the effectiveness of different clustering algorithms in performing age-clustering tasks with EEG signals. We compared the total of ten clustering algorithms, covering methods reliant on a single information source (either node attributes or graph structure) and those integrating both types of information.  
Methods based on the single information source:  
* K-means: A foundational method for many clustering approaches [31; 32].
* Spectral clustering: Utilizes eigenvalues for dimensionality reduction followed by clustering.
* GraphEncoder [33]: Trains stacked sparse autoencoders to obtain representations.
* DeepWalk [34]: A structure-based representation learning method.
* DNGR [35]: Uses stacked denoising autoencoders, encoding each vertex into a low-dimensional vector representation. Methods based on graph structure and attributes:
* GAE: A graph-based autoencoder model for learning low-dimensional representations of nodes in a graph.
* VGAE [15]: A variational version of GAE, introducing variational inference to learn probabilistic distributions of nodes.
* ARVGE [36]: A graph-embedding method that integrates variational autoencoders and adversarial training, learning low-dimensional robust representations of graph nodes by introducing adversarial regularization.
* AGC [21]: An adaptive graph-clustering method that integrates structural and content information for node clustering.  
Note that some of the above-mentioned methods, including GraphEncoder, DeepWalk, DNGR, GAE, VGAE, and ARVGE, are actually representation-learning methods; our approach is first to utilize these methods to get representations from graph data, followed by applying the representation to spectral clustering.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
###### Abstract  
Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.  
multi-robot; collaborative navigation; deep reinforcement learning +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In conclusion, the research findings indicate that microwave flow pasteurization (MFP) may be a highly suitable and promising technique for preserving high-quality fruit juices while retaining essential nutrients. The innovative MFP method showed significant results over a 12-month storage period, with no presence of _Listeria monocytogenes_ or _Salmonella_ spp. bacteria being detected. Moreover, the inactivation of polyphenol oxidases (PPO) and minimal peroxidase (POD) activity in apple-peach juice demonstrate the effectiveness of MFP in maintaining the physicochemical and sensory quality of the juices.  
Furthermore, the gradual decrease in total polyphenol content (TPC) and other bioactive compounds during storage was slower with MFP than traditional pasteurization (TP), indicating better retention of essential nutrients. Additionally, higher concentrations of anthocyanins and carotenoids were noted for up to 4 and 6 months of storage in apple-peach and apple-chokeberry juices, respectively, when using MFP.  
These findings suggest that MFP has the potential to offer a more sustainable and effective preservation method for fruit juices, contributing to better nutritional value and overall quality. However, the recommended storage time for MFP-treated juices should not exceed four months. Further research and development in this area can help optimize MFP techniques for commercial-scale applications and improve the shelf life of high-quality fruit juices.
## 4 Results
### Optimal K Value Selection  
Generally speaking, the number of topics in topic modeling needs to be manually specified based on technical indicators, theoretical conditions, and practical problems. The BERTopic algorithm has a built-in HDBSCAN clustering algorithm that can automatically determine the number of topics based on density. However, BoW-LDA and TF-IDF-NMF need to be determined manually. We set the range of K and find the inflection point of the Topic Coherence score under different numbers of topics to determine the optimal number of topics. As shown in Figure 6, the results showed that the optimal number of topics for BoW-LDA is 7, while the optimal number of topics for TF-IDF-NMF is 5. The optimal number of topics for the BERTopic model is confirmed to be 4  
Figure 5: The clustering-dimensionality reduction algorithm is used to classify students based on their performance. 5 (**a**) shows the shortcomings of the elbow method for classifying students into the best category, and 5 (**b**) shows the distribution of students under two-dimensional PCA.  
Figure 6: Optimal cluster K selection for BoW-LDA and TF-IDF-NMF algorithms in different groups.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## Design of the Improved MPPT Control Method
### Estimation of Equivalent Aerodynamic Torque  
As shown in Equation (10), the observable states, \(\omega_{\tau}^{\prime}\) and \(\omega_{\mathbb{g}}\), can be used to estimate the equivalent aerodynamic torque \(T_{\text{m}}^{\prime}\) using the UKF-based estimator with clipping and uncorrelated conversion. The discretized state-space model is necessary for estimating variables. Under assumption 9, the discretized form of Equation (10) is:  
\[\left\{\begin{array}{l}\mathbf{x}(k+1)=\mathbf{G}\mathbf{x}(k)+\mathbf{H}\mathbf{u}(k)+\mathbf{\xi} (k)\\ \mathbf{y}(k+1)=\mathbf{C}\mathbf{x}(k)+\mathbf{\mu}(k)\end{array}\right. \tag{32}\]  
Figure 5: Control diagram of MPPT considering dynamics. In part 1, \(\upsilon\), \(\omega_{\tau}\), \(\omega_{\mathbb{g}}\), \(\mathbf{\mu}\), \(T_{\text{e}}\), and \(\beta\) represent the input wind speed, rotor speed, generator speed, measurement noise vectors, the electromagnetic torque, blade-pitch angle. In part 2, \(\hat{\omega_{\tau}^{\prime}}\), \(\hat{T_{\text{m}}^{\prime}}\), \(\hat{\upsilon}\) and \(\hat{\lambda}\) represent the estimated equivalent rotor speed, the estimated aerodynamic torque, the estimated wind speed, and the estimated TSR. In part 3, \(k_{\text{opt}}\omega_{\mathbb{g}}^{2}\) represents the optimal steady torque. \(k_{\text{p}}\) and \(k_{\text{i}}\) are the proportional gain and integral gain in the feed-forward path and are regulated by estimated generator speed. In part 4, \(L_{\text{s}}/n_{\text{p}}L_{\text{s}}\psi_{\text{s}}\) and \(n_{\text{p}}L_{\text{s}}\psi_{\text{s}}/L_{\text{s}}\) are the transfer coefficient between \(T_{\text{e}}\) and \(i_{\text{dr}}\). \(\hat{T_{\text{dr}}}\) and \(i_{\text{qr}}\) represent the setting values of d-axis and q-axis currents in the rotor side. \(u_{\text{dr}}\) and \(u_{\text{qr}}\) represent the d-axis and q-axis voltages on the rotor side. “PI” represents the Proportional-Integral controller. DIFG dynamics is corresponding to Equation (6). In part 5, “\(\beta\)-\(\lambda\) look-up table” is the interpolation scheme for attaining the setting value of the blade-pitch angle.  
where \(\mathbf{\xi}(k)\) and \(\mathbf{\mu}(k)\) represent the process noise vectors and measurement noise vectors, respectively; and the matrix \(\mathbf{G}\) and \(\mathbf{H}\) are:  
\[\mathbf{G}=\mathrm{e}^{\mathbf{A}T_{\mathrm{s}}}\quad\mathbf{H}=\int_{0}^{T_{\mathrm{s}}} \mathrm{e}^{\mathbf{A}t}\mathrm{d}t\mathbf{B} \tag{33}\]  
where \(T_{\mathrm{s}}\) is the sampling step. The estimated values of the equivalent aerodynamic torque, the equivalent rotor speed, and the generator speed are written as \(\hat{T}_{\mathrm{m}}^{\prime}\), \(\hat{\alpha}_{\mathrm{r}}^{\prime}\), and \(\hat{\omega}_{\mathrm{g}}\), respectively.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 5 Comparison and Analysis of Experimental Results
### Effects of the Initial Solution
#### 5.2.2 Cost Distance  
The results of each initial solution algorithm are presented in Table 3, with a comparison shown in Figure 5. Figure 5 clearly demonstrates that the INNC algorithm outperforms both the random generation method and the random traversal insertion method in terms of the number of vehicles used, transport distance, and total cost. By analyzing Table 3 and Figure 5, it is evident that the INNC algorithm achieves a 42.86% reduction in the transfer rate compared with the random generation method and an 11.11% reduction compared with the random traversal insertion method. Furthermore, the INNC algorithm shows a decrease in total transport distance by 34.87% compared with the random generation method and 15.03% compared with the random traversal insertion method, while also exhibiting a significantly faster running time. These results indicate that initializing the population with the INNC algorithm leads to significantly improved computational outcomes compared with the random generation and random traversal insertion methods. The INNC algorithm is shown to generate high-quality initial solutions, bringing it closer to the optimal solution and enhancing the algorithm's ability to search the solution space effectively, ultimately increasing the likelihood of finding a lower-cost optimal solution, speeding up convergence, and saving computational resources. In practical applications, the reduction of cost distance can, firstly, reduce the distribution cost, which will cause the logistics company to save costs and improve efficiency whilst operating; secondly, it can improve the distribution efficiency, which will cause the goods to be delivered to the destination faster, improving customer satisfaction; thirdly, it can reduce environmental pollution by reducing transport distance, so that the fuel and emissions of the corresponding reduction's impact on the environment is conducive to sustainable development.  
Figure 4: Optimization process. (**a**) Random generation method; (**b**) Random traversal insertion method; (**c**) INNC.  
Figure 5: Comparison of results.