esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis
### Satellite Network Traffic Forecasting
#### 5.3.3 Traffic Forecasting Based on the ARIMA-BPNN Hybrid Model  
On the basis of obtaining the forecasted residual \(R^{\prime}(t)\), the forecasting result \(D^{\prime}(t)\) is obtained by integrating the linear sequence \(L^{\prime}(t)\) of traffic obtained from ARIMA(\(p_{i}\), \(d_{i}\), \(q_{i}\)) according to Equation (24). The comparative analysis of the forecasted residuals proved the effectiveness of the IGWO algorithm to improve the BPNN. To verify the effectiveness of the proposed hybrid model on satellite network traffic forecasting, the forecasting results are compared with those from traditional forecasting methods, i.e., ARIMA, LSTM and ARIMA-BPNN. ARIMA-BPNN is a network traffic forecasting model proposed in [13]. The forecasting results of the above four methods are shown in Figure 13; the accuracy metrics and operation time are shown in Figure 14 (see Table 8 for specific values). The inputs of the EMD-ARIMA-BPNN, ARIMA, ARIMA-BPNN and LSTM are the testing dataset of the satellite network traffic.  
Figure 13: Satellite network traffic forecasting results. **(a)** Network traffic forecasted by LSTM. **(b)** Network traffic forecasted by ARIMA. **(c)** Network traffic forecasted by ARIMA-BPNN. **(d)** Network traffic forecasted by the proposed EMD-ARIMA-BPNN.  
Combining Figure 13 and Table 8, it can be seen that the proposed hybrid model (EMD-ARIMA-BPNN) outperforms ARIMA and LSTM significantly on satellite network traffic with LRD characteristics. At the same time, EMD-ARIMA-BPNN also has advantages over ARIMA-BPNN in terms of error metrics and operation time. The forecasted traffic using EMD-ARIMA-BPNN is close to the actual value, which indicates the improved the forecasting accuracy for satellite network traffic.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
###### Abstract  
To improve the accuracy and reliability of orchard spraying robots, an integrated navigation system was developed, consisting of a real-time kinematic positioning-Beldou satellite navigation system (RTK-BDS) receiver, an inertial measurement unit (IMU), a navigation controller, and servo motors. Using the loose coupling combination method, an error Kalman filter algorithm based on the measurement of position and heading angle is implemented to correct the error of the inertial measurement unit in real time. Combining the kinematics model and the pure pursuit model of the spraying robot, a path-tracking control algorithm is proposed. Path planning was conducted according to the terrain characteristics of orchards. Field experiments were carried out on a spraying robot to evaluate the proposed auto-navigation system. The results showed that when the spraying robot was static, the positioning performances of BDS alone and that of the BDS/IMU combined system were similar, the positioning error was less than 1.5 cm, and the heading angle errors were within 0.3\({}^{\circ}\); when the spraying robot moving alone to a straight line at the speed of 0.4 m/s, the position error of the navigation system only using BDS was less than 5.29 cm, the heading angle error was within 3\({}^{\circ}\), while the position error of BDS/IMU integrated navigation system was less than 2.49 cm, and the heading angle error was within 2\({}^{\circ}\). The accuracy of BDS/IMU integrated navigation system is significantly improved. When the orchard spraying robot was moving at the speed of 0.4 m/s, the maximum offset error was lower than 10.77 cm, the average offset error was not higher than 3.55 cm, and the root mean square error (RMSE) of the lateral deviation was 1.19 cm. The results showed that the proposed auto-navigation system could make the spraying robot track the pre-set path smoothly and stably.  
automatic navigation system; orchard spraying robot; BDS/IMU integrated navigation; error Kalman filter; loosely coupled integration; pure pursuit control 2022122128173390/app12168173
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## Materials and Methods
### Sentinel-2 Data and Preprocessing  
Sentinel-2 denotes a group of polar-orbiting observation satellites launched under the European Copernicus program. They are multispectral instruments with a spectral range of 400-2400 nm, including 13 bands ranging from visible and near-infrared to short-wave infrared. The swath width is 290 km, and the spatial resolution is 10 m for B2-B4 and B8, 60 m for B1 and B9, and 20 m for the remaining bands. The repeat cycle of one satellite is ten days, and the two satellites use a co-orbit operation mode, which shortens the repeat cycle to five days.  
The Level-1C (apparent reflectance) product provided by ESA was used in this study. Band B8A was selected as an atmospheric window channel, and band B9 was selected as a water vapor absorption channel. For B8A to have the same spatial resolution as B9, it is necessary to either downsample the spatial resolution of B8A to 60 m or upsample B9 to 20 m. Since upsampling does not increase spatial information and may result in checkerboard artifacts, the Sentinel Application Platform (SNAP) was used to downsample  
Figure 1: Geographic location of the study area. (**a**) The location of the study area in China, (**b**) Sentinel-2A image and RGB color composition using B4, B3, B2.  
B8A to the same spatial resolution as B9, and the apparent reflectance of the two bands was converted to radiance.
## 2 Vibration Signal Processing Method Based on VMD
### IPSO-VMD Algorithm Design
With this elite guidance mechanism, the ELPSO algorithm is able to balance the need for global and local searches more efficiently, in line with the need for efficient and reliable optimization methods in complex optimization problems. For the selection of fitness function, a new fitness function \(fitness\) can be constructed based on envelope entropy \(Ep\)[32] and envelope spectrum sparsity \(S\)[33]:  
\[fitness=Ep/S \tag{8}\]  
It can be observed that the sparser the signal, the smaller the \(Ep\), and the larger the \(S\), the smaller is the value calculated by the fitness function \(fitness\). To determine parameter combination \([K,\alpha]\) in VMD, after VMD decomposition of different parameter combinations for the original signal, the \(fitness\) value of each component under different parameter combinations is calculated, respectively, and the global minimum \(fitness\) value is the optimization object. The minimum \(fitness\) value corresponds to the optimal parameter combination \([K,\alpha]\), which is expressed as follows:  
\[[\hat{K},\hat{\alpha}]=\underset{(K,\alpha)}{\text{arg min}}\{fitness(i)\} \tag{9}\]  
Based on the adaptive inertia weight and compression factor method, the original particle swarm optimization algorithm is improved in the early stage, and IPSO is obtained, while \(fitness=Ep/S\) is determined as the fitness function. Comparison experiments are designed to verify the improvement strategies for the several particle swarm algorithms above, and the best improvement strategy is selected as the improvement strategy for the particle swarm algorithm in the final diagnostic architecture, and the final iterative graph results are as follows:Where IPSO stands for PSO based on adaptive inertia weight and compression factor method, PSO stands for unimproved PSO, EL-PSO stands for improved PSO based on elite learning strategy, and FDR-PSO stands for PSO based on fitness distance ratio optimization. According to Figure 1 it can be observed that the IPSO gives the best results and has the fastest convergence rate. When VMD decomposition is carried out on the original vibration signal, the IPSO algorithm is used to carry out adaptive optimization on the two parameters \(K\) and \(\alpha\) in VMD to obtain the best combination of parameters, while the corresponding VMD decomposition is carried out based on the best combination of parameters. The design idea of the IPSO-VMD algorithm shown in Figure 2 can be constructed. Figure 1: Comparison of PSO algorithms. Figure 2: IPSO-VMD algorithm design diagram.
Consequently, there is a pressing need for an educational assistant that (i) bases its answers in authoritative Spanish-language sources and (ii) aligns explanations with the cognitive demands of parallel computing courses. Therefore, in this study, we present NOVA, an intelligent virtual educational assistant developed for the Parallel Computing course at the Universidad Politecnica Salesiana. The design of NOVA was guided by the pedagogical and technical challenges outlined earlier: the complexity of parallel computing concepts, the limitations of generic LLMs in providing contextually aligned explanations, and the additional barrier posed by the predominance of English-language technical resources for Spanish-speaking students. To address these challenges, NOVA adopts a Retrieval-Augmented Generation (RAG) architecture, which allows the system to ground its responses in a curated corpus of course-specific materials, mitigating factual inaccuracies and ensuring conceptual alignment with curricular objectives. The modular architecture, composed of five distinct components: relational database for interaction logging, vector database for semantic retrieval, FastAPI backend for orchestration, Next.js frontend for user interaction, and automation server for workflow management, was specifically chosen to enable scalability, maintainability, and clear separation of responsibilities. This design supports real-time retrieval of relevant content, integration of user feedback, and delivery of culturally and linguistically appropriate instructional resources, making it particularly suitable for the demands of specialized technical education. The main contributions of this paper include:  
* Development and deployment of a RAG-based educational assistant optimized for Spanish-speaking students. * Integration of domain-specific, parallel computing instructional materials into the retrieval framework. * Empirical evaluation of the system's effectiveness, measuring both the accuracy of responses and alignment with curricular objectives. * Insights into the strengths and limitations of the RAG approach in technical educational settings. The remainder of this article is structured as follows. Section 2 reviews related work in AI-based educational systems and parallel computing instruction. Section 3 outlines the design and implementation of the proposed assistant, detailing its system architecture and content pipeline. Section 4 presents the experimental setup and evaluation methodology. Section 5 discusses the results in terms of learning outcomes and system usability. Finally, Section 6 concludes with implications, limitations, and directions for future research.
## 3 Results and Discussion
### Coverage Characteristic Analysis
Therefore, when selecting the configuration of the platform, the specific requirements of the observation mission need to be taken into consideration. We conducted calculations on the annual accumulated observable time of MS-BiSAR for Earth observation during a lunar nodal regression cycle (approximately 18.6 years) in the GEO configuration. The calculations were based on different latitude locations below the satellite's ascending node longitude, and the results are presented in Table 1. It shows that there are differences in the annual accumulated observable time at different latitude positions due to the periodic changes in the lunar orbit and axis. By calculating the coefficient of variation (CV) for the annual accumulated observable time at different locations, we observe that low-latitude regions have minor annual variations in accumulated observable time. As latitude increases, the annual variability progressively increases, particularly for high-latitude areas where the CV can exceed 40%. Since the operational cycle of MS-BiSAR exceeds the lunar precession period, when designing the receiving satellite orbit for observation tasks in high-latitude regions it is necessary to consider the coverage capability over the entire lunar nodal regression cycle. Figure 10: The global accumulated observable time of MS-BiSAR (different configuration) over 2022. It should be noted that this study is based on certain assumptions, which may result in slight errors when compared to actual situations. For example, we assumed that the Earth and Moon were ideal spheres, while in reality their irregularities could also impact their coverage characteristics. Additionally, as mentioned earlier, this paper assumes that the lunar-based observatory is located at the center of the near side of the Moon; however, some studies have suggested that the observatory's location can affect the Earth observation coverage, which needs further analysis. Furthermore, although the ephemeris, EOP, and orbit propagators used in this research have been endorsed by experts in the field, these models still contain errors that contribute to the overall error in this research. Additionally, model and algorithm errors might occur during reference system transformations, with up to six transformations in this particular paper, resulting in a further magnification of their impact on simulation results. Moreover, the assessment of observable area in this section is solely based on observed geometry. Therefore, factors such as the bistatic electromagnetic scattering characteristics of the target, SNR, the angle between the range direction and azimuth direction, and imaging resolution need to be considered for a more comprehensive analysis. Overall, while we have made efforts to minimize any potential errors, it is important to acknowledge the limitations resulting from the assumptions we made. Further investigation and verification are needed to verify our results.
## 3 RTSC Problem Within DRBPS and Its Complexities  
This study focuses on the most typical and complex case of DRBPS at a four-leg intersection with protected left turns and permitted right turns [2]. As shown in Figure 3, it uses two rings and two barriers to organize eight phases in a signal cycle. The barriers separate the phases on two streets. Between the barriers, any phase in ring 1 can be served together with any phase in ring 2. This flexibility enables phases in different rings to adjust their timing to meet imbalanced demand, making DRBPS the most commonly used structure in real-world settings.  
DRBPS involves some signal timing constraints that are fundamental in terms of safety in engineering practice [2; 34; 48]. They include (1) a fixed phase sequence, (2) simultaneous ending of lagging phases, (3) minimum and maximum green times, and (4) yellow change and red clearance intervals. The first two constraints are the most overlooked in previous studies, yet they are central to this study. We elaborate on both as follows.  
1. Fixed phase sequence. The sequence is namely the order of phases in each ring. It should be pre-determined and remain fixed during certain times of the day. Otherwise, the signals for all movements will turn green or red arbitrarily and unpredictably. This can confuse or even surprise drivers, and increase the risks of red-light-running and rear-end collision.
2. Simultaneous ending of two concurrent lagging phases. Given that the two lagging phases precede the same barrier, this constraint ensures both rings cross this barrier together. Otherwise, conflicting vehicle movements will occur, leading to an increased risk of angled collisions. This is because each lagging phase conflicts with both phases that follow the barrier on the intersecting street.  
The RTSC problem within DRBPS involves how to cyclically determine the green time of each phase in real-time, subject to the signal timing constraints above. In this study, efforts will be made to define an action and develop a training algorithm to solve this problem. Below, we describe the complexities of the problem facing us.  
We need to enable the agent to control two concurrent phases in the dual ring, such as phases (1 and 5). They run in parallel but their green times overlap and can differ. They may start green successively and have different minimum greens. The agent should be capable of (1) making fully independent decisions on the green times for concurrent leading phases to accommodate varying demands, and (2) making partially independent decisions on the green times for concurrent lagging phases to balance their demands and end them together at a trade-off moment.
## 4 Results and Discussion
It empowers families, educators, and society to create a more inclusive and effective education system, leading to better outcomes for individuals and communities. Over the past few years, various tools and methods have been developed to achieve relatively high detectability of dyslexia. These include studies of brain activity associated with cognitive processes during specific tasks using highly specialized equipment, such as fMRI imaging. Analysis of these images with convolutional networks has achieved an accuracy of 72.73% (13; 19). Combining fMRI with DTI can provide extended DICOM data, which, when analyzed using PCA, serve as a source of information for neural classifiers, achieving an accuracy of 94.87% (18). Figure 13: Confusion matrix obtained after the completed learning process. Cells colored with pink indicate the fields of wrong answers given by the network, while green cells indicate the fields of correct answers. The gray color indicates the cells containing the percentages of correct and incorrect network responses for each class, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Estimate** & **SE** & **tStat** & \(\mathbf{p}\)**Value** \\ \hline (Intercept) & 0.03301 & 0.052743 & 0.62586 & 0.53488 \\ \(\times 1\) & 0.99709 & 0.014096 & 70.734 & \(1.7749\times 10^{-44}\) \\ \hline Number of observations: 43, Error degrees of freedom: 41. Root Mean Squared Error: 0.154. R-squared: 0.992, Adjusted R-Squared: 0.992. F-statistic vs. constant model: \(5\times 10^{3}\), \(p\)-value = \(1.77\times 10^{-4}\). & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of error types. In contrast, other systems [13] (with an accuracy of around 94.73%) are convenient and easy to use, as they require only modules for acquiring graphical task results. This is an important feature that facilitates their application in school settings without the need for an expert's involvement. On the other hand, the age and academic skills of the children being tested may necessitate differentiating the test tasks used for dyslexia detection. Methods for diagnosing dyslexia that utilize reading process analysis often employ SVM binary classifiers [28], which leverage the properties of fixation and saccade observations, achieving an accuracy of 80.18%. Higher accuracy is achieved by KNN classifiers [30], tested on a relatively large group of children, reaching an accuracy of 95%. Some reading task analysis methods are tailored to specific languages, such as Serbian [32], where analysis of the geometry of observation trajectories achieves an accuracy of 87%. In methods aimed at individuals with reading and writing skills, both lexicographic and semantic tasks can be used. Efforts to increase dyslexia detection accuracy also focus on acquiring a large number of coordinates representing the state of the perception and scene recognition process. In such cases, the task of extracting relevant information is left to CNNs, achieving an accuracy of 95.6% [31].
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 3 Results
### The Performance of Purposed Method
#### 3.1.2 F1-Score  
Precision is the ratio of the predicted true positive observationsto the total number of predicted positive outcomes, given asfollows:  
\[\text{Precision }=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
Recall is the ratio between the predicted true positiveobservations to the total number of actual positive values, givenas follows:  
\[\text{Recall }=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
F1-score calculation formula is as follows:  
\[\text{F1-score }=2\frac{\text{Recall }\times\text{Precision}}{\text{Recall }+\text{Precision}}\]  
The F1-score of BCOA-KELM is shown in Table 5, which reflects the diagnostic ability of the model.The recall and precision of faults 1, 4, 6, 7, 14 almost achieve 100%, demonstrating a great true positive rate and false positive values. Moreover, the table size trends for the F1-score are almost identical to those for the FDR, which could indicate that the method is not severely overfitted for arbitrary faults. Finally, Figure 6 shows the recall and precision of WCForest, which indicates that the proposed method offers a good performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{2-4}  & & **Positive (P)** & **Negative (N)** \\ \hline Real & True (T) & TP & FN \\  & Flase (F) & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion Matrix.  
Figure 5: FDR and FPR of BCOA-KELM.
## 4 Results
### Review Papers and Hybrid MCDA  
According to [PERSON] and [PERSON] [110] and [PERSON] et al. [56], the recent literature shows a notable shift towards the use of hybrid methods; because of the potential of this approach to make decisions in more realistic circumstances, it can respond to complexity effectively. This is particularly evident in the two review papers targeting hybrid MCDA, while 18.75% (15 out of 80) of the review papers report the widespread application of hybrid MCDA in their studied publications. Furthermore, either a medium or limited level of implementation of hybrid MCDA appears in 8 and 38 review papers, respectively. However, in the other 17 review papers, there is no mention of a hybrid approach at all.  
Focusing on the two papers specifically examining hybrid MCDA, the recent literature underscores the increasing use of hybrid methods due to their capacity to tackle complex, multidimensional decision making in more realistic and adaptable ways. [PERSON] et al. [56] highlight the versatility of hybrid MCDA for sustainability-related decisions, whereby the combination of diverse MCDA techniques facilitates a comprehensive evaluation of multiple criteria, including economic, environmental, and social factors. This adaptability enhances the models' effectiveness in addressing the complexities inherent to real-world contexts. Similarly, [PERSON] et al. [115] discuss the application of hybrid MCDA in forest management, where it is used to integrate multiple ecosystem services. The combination of methods such as AHP and TOPSIS within a hybrid MCDA framework allows for the assessment of ecological and economic trade-offs in forest planning in a nuanced manner, thereby facilitating the development of more balanced and sustainable management strategies.