esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparative Analyses Results
### Rain-Gauge vs. Radar Observations
#### 3.1.2 OPERA vs. RADOME  
Figure 5 shows the precipitation accumulation estimated by the OPERA product for the April and June cases, and absolute biases in precipitation accumulations between OPERA and RADOME at each rain-gauge station.  
For the April case (Figure 5a), as in the RADOME observations, the precipitations obtained with OPERA were mainly situated over the mountainous areas of the south of France but the extension of the intense precipitation was more important than in RADOME (Figure 2a), especially in the WMC area (Figure 2c). Table 2 shows that the maxima of rain obtained for the different areas of interest with OPERA were higher than RADOME (131.85 mm for WMC, 82.56 mm over SAL, 76.07 mm over the DUR, 199.62 mm over CEV, and 63.41 mm over JUR). The positions of these maxima were less than 53.48 km from the maximum values measured with the RADOME rain gauges for all the areas of interest, except for the WMC area where the OPERA maximum was at 182.19 km (Table 2). Regarding the median values obtained with OPERA for the precipitation accumulation, they were higher than the RADOME ones for WMC, CEV, and JUR, whereas they were lower for SAL and DUR. These trends are also shown in Figure 5c, since an intense underestimation  
Figure 5: As in Figure 4, but for the OPERA product.  
and overestimation are visible, respectively, over the DUR and the JUR areas, especially near the France-Germany border for the latter. Table 3 shows that the mean absolute biases reach \(-9.13\) mm for DUR, \(-3.70\) mm for SAL, \(+6.51\) mm for JUR, and \(+5.15\) mm for CEV. However, the mean absolute bias is only \(+1.09\) mm for WMC.  
For the June case, the precipitation event was localised on the same southwest-northeast axis (Figure 5b) as in the RADOME observations (Figure 2b). However, the area wetted by intense rain amounts (greater than 100 mm) is more extended than in the RADOME observations. The maximum rain amount estimated by OPERA was equal to 293.23 mm over the NMC area (Table 2), which is two times higher than that observed using rain gauges. Moreover, this maximum value was located at a distance of 71.81 km from the RADOME one (Table 2). Figure 5d shows that the rain amounts were generally overestimated by OPERA. Only a few stations randomly sparse over France presented weak underestimations of rain amount in OPERA, as near the France-Spain border or over the northern Alps. The bias at the Chastreix station was \(+14.7\) mm and the average bias of \(+14.13\) mm recorded over the NMC area (Table 3) confirms the global overestimation trend.
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 3 Results and Discussion  
As mentioned earlier, the objective of the proposed control system is to minimize the passenger's MS in an autonomous vehicle by diminishing the lateral acceleration. The lateral acceleration reduction would lead to the mitigation of the passenger's head roll movement towards the lateral acceleration direction, hence minimizing MS. Therefore, in this section, the responses of lateral acceleration, passenger's head roll and MSI are presented and discussed.  
Figure 10 illustrates the lateral acceleration response before and after the inner loop system is applied. The result shows that the lateral acceleration slightly decreased in every peak of cornering. This finding indicates that the corrective wheel angle produced by the proposed fuzzy-PID controller managed to achieve the objective, which is to diminish the lateral acceleration.  
Figure 11 shows the head roll responses of two participants before and after the implementation of the recommended inner loop control system. Due to the reduction of lateral acceleration, the head roll responses are decreasing as well.  
Figure 11: Head roll responses: (**a**) participant 1 and (**b**) participant 2.  
Figure 10: Lateral acceleration response.  
Figure 9: Overview of a participant sitting on the simulator.  
Finally, the MSI index of every participant is calculated. Table 4 tabulates the MSI percentage before and after the proposed inner loop system is included into the whole system. Even though the head roll angle responses among the participants are varied, overall, the MSI for every participant reduced by 3.95% in average. The results indicated that the proposed MS minimization control system consisting of the fuzzy-PID controller succeeded in achieving its objective.  
According to Table 4, the MSI has a small percentage value due to the short simulation time which was only 10 s. Based on the previous research work, the difference between the driver's and passenger's MSI level escalated as time elapsed [32]. Thus, it is anticipated that the MSI decrement that can be achieved will be increase as the duration increases. To investigate the MSI value for a longer duration, the experiment is continued up to ten laps. Note that the pre-defined course shown in Figure 7 is referred to as single lap.  
Table 5 tabulates the MSI results in a ten laps trajectory. The MSI for every participant was minimized by 11.49% on average after the application of the proposed inner loop system. As hypothesized, the MSI reduction percentages for the ten laps trajectory are higher than for the single lap trajectory. Hence, this finding agrees with the previous study in [32].
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 3 Results
### QoT Execution  
The interaction of LOA and workload had significant effect (F (3, 152) = 5.198, \(p\) = 0.002) on the QoT execution. The QoT execution was higher at the high LOA when the workload was low compared to other LOA-workload combinations, confirming H1. LOA (F (3, 150) = 45.15, \(p\) < 0.001) and workload (F (3, 152) = 18.725, \(p\) < 0.001) were also significant as main effects on the QoT execution. The high LOA produced better QoT execution compared to the low LOA. Best results were obtained for low workload as expected. When the workload is high, the high LOA also produced a better QoT execution compared to the low LOA. Details of the constituent variables in the QoT execution (effectiveness and efficiency) are presented below:
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 2 Materials and Methods
### Materials
#### 2.2.2 Other Data  
The meteorological data were obtained from The National Centers for Environmental Prediction (NCEP) over the same period as the rainfall data--1998 to 2006. The other required data, including that of the Digital Elevation Model (DEM), the MRB land use maps, and the soil, were collected from USGS-HydroSHEDS [37], the MRC [33], and the Food and Agriculture Organization [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data type** & **Products** & **Spatial Resolution** & **Temporal Resolution** & **Sources/References** \\ \hline Meteorological & Temperature, solar radiation, wind speed, relative humidity, potential evapotranspiration & \(0.25^{\circ}\times 0.25^{\circ}\) & Daily 1998–2006 & The National Centers for Environmental Prediction (NCEP) \\ \hline \multirow{6}{*}{Rainfall} & Rain gauge observations & Point (175 rain gauges) & Daily 1998–2006 & The Mekong River Commission (MRC) and the China Meteorological Administration (CMA) \\  & Climate Prediction Center (CPC) & & & \\ \cline{1-1}  & Gauge-based Analysis & \(0.5^{\circ}\times 0.5^{\circ}\) & Daily 1998–2006 & [23] \\ \cline{1-1}  & of Global Daily Precipitation & & & \\ \cline{1-1}  & Global Satellite Mapping of Precipitation (GSMaP) Version 6 & \(0.1^{\circ}\times 0.1^{\circ}\) & Daily 2000 to 2006 & [26] \\ \cline{1-1}  & Climate Prediction Center & & & \\ \cline{1-1}  & Morphing (CMORPH) Version 1.0 & \(0.25^{\circ}\times 0.25^{\circ}\) & Daily 1998–2006 & [25] \\ \cline{1-1}  & Tropical Rainfall Measuring & & & \\ \cline{1-1}  & Mission (TRMM)Version 7, 3B42 & \(0.25^{\circ}\times 0.25^{\circ}\) & Daily 1998–2006 & [24] \\ \hline \multirow{2}{*}{Geography} & Digital Elevation Model (DEM) & \(90\times 90\) m & 2005 & [37] \\ \cline{1-1}  & Land use & \(1\times 1\) km & 2005 & [33] \\ \cline{1-1}  & Soil & \(10\times 10\) km & 2005 & [38] \\ \hline \multirow{2}{*}{Hydrology} & \multirow{2}{*}{Discharge} & Point (6 hydrological gauges) & Daily 1998–2006 & [33] \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information datasets used in this study.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 2 Related Work  
### Introduction  
Over the past two decades there has been an increase in the research and development of autonomous vehicle technologies. An autonomous vehicle is capable of sensing its environment and move with no human input. These vehicles combine a large variety of sensors to perceive their surroundings, such as radars, lidars, ultrasonic sensors, GPS, odometry and Inertial Measurement Unit (IMU). The information provided by these sensors combined with advanced control systems, allow the vehicles to identify appropriate navigation paths, as well as obstacles and relevant signals [1].
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Managing Scalability
#### 5.3.4 Shaping Peer Networks  
Analyzing degree distributions and connectivity trends between node pairs allows configuring P2P topology for efficient resolvability of transactions [84]. Figure 18 infinite average path length suggests that there are isolated nodes or small disconnected components, highlighting areas for potential improvement in the network's connectivity. This visualization and the calculated metrics demonstrate how analyzing the degree distribution and connectivity patterns in a P2P network can help in configuring the network structure for efficient transaction propagation. Adjustments to the network (like adding or removing edges) would be made to optimize these metrics. The goal is to achieve a network topology that ensures fast and reliable dissemination of transactions, which is crucial for the efficiency of blockchain systems. Nodes represent peers in the P2P network. Edges depict connections between these peers.  
Figure 17: Sharding blockchains.  
**Assumption 17**.:
* _The P2P network is modeled as a graph_ \(G(V,E)\) _where nodes_ \(v_{i}\in V\) _are peers and edges_ \(e_{ij}\) _depict connections._
* _Efficient propagation of transactions requires optimal P2P topology._  
**Application Statement 17**.:
* _Analyzing degree distribution and connectivity patterns in the P2P topology graph allows for configuring the network structure for fast and reliable transaction dissemination._  
Proof.:
* Measure degree distribution \(P(k)\) and avg degrees \(\overline{k}\) of peer nodes in \(G\).
* Evaluate Giant connected component size \(GCC(G)\) and avg path lengths \(l_{avg}\).
* Configure graph via adding/removing edges and nodes to tailor \(P(k)\), \(\overline{k}\), \(GCC(G)\) and \(l_{avg}\) to optimal values.
* This shapes the P2P topology for efficient and resolvable transaction propagation.  
Applying graph metrics facilitates engineering blockchain P2P networks' structure and connectivity patterns for reliable transaction dissemination.  
Overall graph algorithms facilitate systematic tracing of performance issues while graph partitioning enables technical remedies.
# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 1 Introduction
The potential of future GNSS-R-enabled satellite system missions in flood inundation mapping is also explored. The present paper is organized as follows: Section 2 describes the dataset used for model development and comparison, as well as the study area; Section 3 explains the methodology undertaken; Section 4 illustrates the results obtained and discussion therein; and finally, Section 5 provides the conclusion.
## 5 Identification of Fault Arc
### Comparison with Other Identification Methods  
In order to verify the superiority of the proposed model in identifying fault arcs, the same data set collected by the experimental platform was used to train and test different models. The specific comparison models include BP neural network, extreme learning, random forest, and decision tree. In the random forest, the number of trees is 256, and the BP neural network has two hidden layers with three neurons each. The number of iterations is set to 3000, and the learning rate is 0.01.  
Figure 11 shows the AUC curves for the five models: BP neural network with an AUC value of 0.9605, extreme learning with an AUC value of 0.9540, random forest with an AUC value of 0.9726, decision tree with an AUC value of 0.9582, and AHA-XGBoost with an AUC value of 0.9925. The AUC value of AHA-XGBoost is closest to 1, indicating that the fault detection method proposed in this paper can prioritize arc faults.  
Table 3 lists the specific parameters of each model. Among them, compared with other models that have similar numbers of parameters, the accuracy of the designed model AHA-XGBoost is 98.098%, outperforming the other four models. The model proposed in this paper is slightly higher than the other four models in terms of specificity, sensitivity, precision, recall rate, and macro average F1 score. Additionally, this model significantly reduces the number of parameters and computational requirements, which to some extent reflects a simplification of the model complexity.  
Figure 10: Confusion matrix diagram.  
Figure 9: AHA-XGBoost arc fault prediction results.
## 4 The 3D Point Cloud Reconstruction  
We can obtain global real-time pose estimation through visual-IMU fusion. In order to form a 3D point cloud map, we adopt projection, densification, and loop closure correction to combine laser data with pose information.
[33] compared the simulated net primary productivities (NPP) of the grassland by a sahelian transpiration, evaporation, and productivity (STEP) model that coupled different indices (NDVI, soil adjusted vegetation index [SAVI], global environmental monitoring index [GEMI], and a simple ratio of near-infrared to visible reflectance [SR]) from the NOAA/AVHRR images, and found that the SAVI and NDVI were adequate with minimized atmospheric effects. [PERSON] et al. [34] applied the particle swarm optimizer (PSO) algorithm to estimate the NPP for the grasslands with different vegetation types using the CASA model and MODIS time-series NDVI, and found that the set in the CASA model should have been different for various vegetation types. These models were employed for estimating the GPP/NPP of the forests and grasslands, but they were not suitable for the GPP estimation of the crop type [35]. As these models are determined by a minimum impact factor or by a coupled effect of the influencing factors, the accuracy of these models limits their extension in agriculture. [PERSON] et al. [36] coupled a simple yield estimate algorithm (SAFY) and green area index from the Formosat-2 NDVI time series to estimate the maize and sunflower biomass over large areas, and the results showed the relative error of <28% for the crop biomass estimation. However, the SAFY model requires 14 input parameters, which describe the complicated crop growth process. During the crop growth period, the GPP or yield production is sensitive to water shortages, temperature and fertilizer status, and light condition [37; 38; 39]. Crop growth is a combined result of the external environmental factors. Even with the same serious N deficit, a higher wheat yield was obtained with more water. However, the integration of the remote sensing data, LUE theory and information on physiological response to fertilizer deficit, water drought, and temperature was less studied for the winter wheat production estimation. Thus, this study aimed to develop a GPP estimation model (Agriculture Crop Photosynthesis Model [ACPM]) based on the environmental stress factors from the satellite observations for the winter wheat aboveground biomass (DAM) and yield estimation. The model must be economical, timely, simple, robust, and easily operational.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 4 Fault Diagnosis Process for Parallel-Axis Gearboxes Based on WOA-VMD and t-SNE  
### Optimizing VMD with IDBO  
The IDBO is utilized to optimize the number of modes (K) and penalty factor (\(\alpha\)) in variational mode decomposition (VMD). The value of K significantly affects the effectiveness of the original data decomposition. A K value that is too large may lead to excessive decomposition, generating some ineffective intrinsic mode functions (IMFs), while a K value that is too small may result in insufficient decomposition of the original signal. The penalty factor \(\alpha\), when too large, can cause loss of frequency band signals, and, conversely, can introduce information redundancy. Therefore, an optimal combination of [K, \(\alpha\)] is essential. In this study, the IDBO algorithm is employed to optimize the parameters of VMD. The optimization process aims to minimize the envelope entropy, which serves as the fitness function. Envelope entropy reflects the sparsity characteristics of the original signal. When there is more noise in the IMF and less feature information, the envelope entropy value is larger, and vice versa. The optimization process is as follows:  
1. Set the IDBO population size and the number of iterations, and define a suitable range for [K, \(\alpha\)] values for VMD decomposition. Ensure that the range is not too narrow to avoid losing essential feature information in the modal components.
2. Use VMD to decompose the vibration signal from the gearbox, resulting in several intrinsic mode functions (IMFs).
3. Calculate the fitness function value for each set of [K, \(\alpha\)] values and continually update the iterations to find the best fitness function value.
4. Determine if the iteration is completed, i.e., whether the maximum number of iterations has been reached. When the maximum number of iterations is reached, terminate the iteration and save the optimal parameters [K, \(\alpha\)].
## 3 Results and Discussion
### Topic 1: Health Status and Needs
#### 3.2.1 FIFO Work Arrangements  
One of the most widely researched health and safety concerns within the resource sector is the array of consequences relating to FIFO work arrangements. A recent systematic review [2] found that FIFO arrangements in the oil and gas industry contribute to poor physical health as well as higher levels of psychological distress, depression, and anxiety.
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 5 Conclusions  
In this study, an improved density peaks clustering algorithm based on divergence distance and the tissue--like P system is proposed, termed TP-DSDPC. In the proposed algorithm, we introduce the divergence distance to relieve the impact of similarity measurements and the chain reaction. The main clustering process of TP-DSDPC is compatible with DPC. First of all, it calculates local density and updates relative distance by divergence distance. Then, it automatically selects the clustering centers by the score value from high to low. Lastly, the remaining points are clustered by the relative distance. Furthermore, the whole flow is implemented in the structure of the tissue--like P system and the efficiency of the algorithm is immensely promoted by the parallelism of the P system in theory. The proposed algorithm performs better on synthetic and real-world datasets, according to the experimental results.  
In the years ahead, the proposed method will be expanded further so that it can address more clustering and optimization issues. Additionally, to improve the effectiveness and efficiency of TP-DSDPC, certain optimized methods should also be considered.  
Conceptualization, F.G. and X.L.; methodology, F.G. and X.L.; software, F.G.; validation, F.G.; Formal analysis, F.G.; Writing--original draft preparation, F.G.; writing--review and editing, F.G. and X.L.; supervision, F.G.; project administration, X.L.; Funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
Figure 16: The ARI of the six algorithms on six real-world datasets.  
**Funding:** This research was partially funded by the National Natural Science Foundation of China (Nos. 62172262, 61876101, 61802234, and 61806114), China Postdoctoral Science Foundation Funded Project (2017M612339, 2018M642695), Natural Science Foundation of the Shandong Provincial (ZR2019 QF007), China Post-doctoral Special Funding Project (2019T120607) and Youth Fund for Humanities and Social Sciences, Ministry of Education (19Y)CZH244).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Contact the authors for the full datasets.  
**Conflicts of Interest:** The authors of this paper declare no conflict of interest.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 3 Communication  
To validate the feasibility of combining acoustic and millimeter wave communication, a series of communication experiments were conducted using the TARF communication system.
###### Abstract  
Fuel flowrate control system and fuel thermal management are very important for aeroengine and the overall aircraft, and it has been researched for several decades. This survey paper makes a comprehensive and systematic overview on the exiting fuel flowrate regulation methods, thermal load of fuel metering units, fuel-based thermal management, and the fuel tank's thermal management topology network with drain and recirculation. This paper firstly reviews the mechanism, technical advantages, and technical challenges of the fuel metering unit with flowrate control valve and constant pressure difference valve compensator, flowrate control valve and variable displacement pump-based pressure difference compensator, and motor-based flowrate regulation. Then, the technical characteristics of above fuel flowrate control methods related to thermal management are discussed and compared. Meanwhile, the behaviors of recirculated fuel flow within single tank system and dual tank system are explored. Thirdly, the paper discusses the future directions of fuel flowrate control and thermal management. The survey is significant to the fuel flowrate control and fuel thermal management of the aircraft.  
fuel flowrate control; metering control; motor-based flowrate control; heat sink; thermal management system +
Footnote †: journal: Applied sciences  
1. Metering-based flowrate control: The constant pressure difference compensated valve (CPDCV) is used to maintain the pressure difference across metering valve constant, and the discharge flowrate is regulated by fuel metering valve [6]. Although its response speed is fast, the application of FCS with metering-based flowrate control is limited due to the high power loss.
2. Pump controlled flowrate control: In this method, the pressure difference can be kept constant through CPDCV and variable displacement pump, so as to reduce the recirculation and throttling loss of excess fuel [7]. Nevertheless, its application is mainly limited to gas turbines because of the high cost and nonlinear characteristics [8].
3. Motor-based flowrate control: This method replaces hydraulic regulating mechanism with electro actuators [9]; hence, it can simplify structure and improves efficiency [10]. However, the application in electric engine system is limited due to the low control accuracy in the small flowrate range [11].