esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Technical Framework  
This study is divided into two parts. The aim of the first part (precipitation product evaluation) is to evaluate the accuracy of the IMERG series products on daily and monthly scales in the XRRB. Moreover, the IMERG series products over the XRRB are also statistically analyzed in terms of three spatial scales: grid cumulative scale, basin scale, and grid scale. For the grid cumulative scale, the evaluation is performed by accumulating data for all times and all grids into the same sequence. For the basin scale, the average precipitation of each time point in the research region is incorporated, and the evaluation at the basin scale considers the mean precipitation in the basin. In regard to the grid scale, the spatialdistribution characteristics of rainfall events for each product in each grid and indexes are explored. From this, accuracy validation can be conducted using the total data along with evaluations of a single grid, so that a comprehensive comparison between each precipitation product and observed values is achieved [19; 20; 21]. In the second part (runoff simulation evaluation), the SWAT model is used to construct a hydrological model of the XRRB based on two scenarios. In Scenario I (the SWAT model calibrated by rain gauge data), the ER, LR, and FR products are subsequently used to run the model with the same parameter values in the calibration period. In Scenario II (the SWAT model calibrated by the FR), the FR product is also utilized for validation to explore the potential application of satellite data to areas with little information. Both scenarios are modeled on the daily and monthly scales. The four precipitation products are used to drive the hydrological model. The main idea and procedure of this research are illustrated in Figure 2.
## 2 Materials and Methods
### Evaluation Method of ESW and Droplet Deposition  
The WSPs were scanned to 600 dpi digital JPG images after each test in the lab and analyzed by the DepositScan (DS) [29]. The droplet deposition density, uniformity, and the DPR were analyzed further based on the droplet deposition JPG images.  
According to the standard 'Technical specification of quality evaluation for crop protection UAS' (NY/T3213-2018) [30], the first sampling point of droplet quantity not less than 15 droplets per square centimeters (cm\({}^{2}\)) was judged as the boundary of the ESW each line. In this paper, the average bandwidth value analyzed of the three lines was the ESW of each flight for accuracy.  
The droplet deposition uniformity was evaluated with the coefficient of variation (\(CV\), %) of coverage rates [31] on the WSPs calculated from the DS within the ESW. The \(CV\) calculation Equation [18] is as follows.  
\[CV=\frac{S}{\overline{X}}\times 100\% \tag{1}\]  
\[S=\sqrt{\sum_{i=1}^{n}\frac{\left(X_{i}-\overline{X}\right)^{2}}{\left(n-1 \right)}} \tag{2}\]  
where \(S\) is the standard deviation, \(X_{i}\) is the number of droplets per unit zone in the sampling card, \(\overline{X}\) is the average number of droplets per unit zone in the sampling card, and \(n\) is the total number of sampling cards in reach repetition.  
The droplet penetrability into the canopies was expressed by (DPR, %) and calculated by the follow formula.  
\[DPR=\frac{y_{l}}{y_{u}}\times 100\% \tag{3}\]  
where \(y_{l}\), \(y_{u}\) are the coverage rates of the lower layer and the upper WSP of each sampling point within the ESW range, respectively.  
Analysis of variance (ANOVA) was conducted for investigating the significances of FS and FH on the ESW, DPR, and droplet deposition uniformity (DDU), taking the FS and FH as independent variables, and the ESW, DPR, and DDU as dependent variables [8,20].
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation
### Simulation Setup  
In the simulation setup, we consider the specifications of small drones, specifically Parrot AR.Drone [27]. The dimensions of AR.Drone are 51.7 cm \(\times\) 51.7 cm, equipped with an 802.11 radio, and it can fly with its maximal speed of 5 m/s for approximately 12 min.  
In each scenario, we consider the following:  
* N drones flying for 10 min in a confined space with the dimensions 500 m \(\times\) 400 m \(\times\) 30 m.
* All drones have the same protected zone radius R.
* All drones have the same communication range \(\sigma\).
* The drones use IEEE 802.11 protocol to communicate with each other directly.
* The maximal back-off time is set to 1 s.
* The environment is static, with no obstacles except the other drones and the boundary limits.  
Every scenario is repeated 30 times. Table 1 contains the considered parameters for the simulation environment and Table 2 contains the considered parameters for GM and EGM. Figures 4 and 5 show samples of the paths generated using these parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of drones & 40 \\ Area dimensions & 500 \(\times\) 400 \(\times\) 30 \\ Simulation time & 600 s \\ Back-off time & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for the environment.
# Seismic Comparison of Hybrid Steel-Reinforced Concrete and Conventional Frames
## 3 Results and Discussion
### Five-Story Buildings  
The displayed IDR-X plotlines of the r/c five-story building (Figure 24a) demonstrate that maximum IDR-X values occur at the first story, up to 2.2%. Similarly, at the hybrid steel-r/c building with a fixed-pinned interconnection, the maximum IDR-X values are observed at the first story at 3.4% (Figure 24c). The IDR-X diagram of the steel building shows increased values at the second-story level, even up to 5%, which is the CP level's peak [59] (Figure 24b).  
Figure 23: Base shear ratio “FY ratio” comparison for four-story buildings.  
Figure 22: Base shear ratio “FX ratio” comparison for four-story buildings.  
The IDR-Y of all five-story models presents greater values at the first story (Figure 25), up to 2.7 for the r/c model, 3.9% for the steel one, and 2% for the hybrid one, which are within the permissible limits of the CP level [59]. At both axes, higher IDR values for the steel model are noted compared to the r/c and hybrid ones.  
For the five-story buildings, the IDR plotlines are omitted, as follows, because of violations of the allowed norms [59]: regarding the r/c frames for the earthquakes of Tabas 0\({}^{\circ}\) and Kobe 90\({}^{\circ}\); regarding the steel models under the earthquakes of Landers 90\({}^{\circ}\), San Fernando 90\({}^{\circ}\), Loma Prieta 90\({}^{\circ}\), Chi-Chi 90\({}^{\circ}\), Tabas 0\({}^{\circ}\), Superstition Hills 90\({}^{\circ}\), Northridge 90\({}^{\circ}\), and Imperial Valley 90\({}^{\circ}\); regarding the hybrid models with fixed support for San Fernando 90\({}^{\circ}\) and Tabas 0\({}^{\circ}\); regarding the hybrid models with the fixed-pinned backing for the earthquakes of San Fernando 0\({}^{\circ}\), Loma Prieta 0\({}^{\circ}\), and Imperial Valley 90\({}^{\circ}\). Obviously, the steel models exhibit more structural failures.  
The PFA/PGA-X ratio increases floatingly along the model's elevation, with greater values at the rooftop (Figure 26). The hybrid model top reaches the highest PFA /PGA-X ratio at 4.2 (Figure 26c), while the r/c model top has the lowest at 3.6 (Figure 26a). The steel model (Figure 26b) shows intermediate values. Similarly, the PFA/PGA-Y plots fluctuate and increase along the building height in Figure 27, up to 4.6 for the hybrid model, 3.7 for the concrete one, and 3.2 for the steel model.  
Figure 26: PFA/PGA comparison along the \(X\)-axis for (**a**) r/c, (**b**) steel, and (**c**) hybrid (fixed-pinned connection) five-story buildings.  
Figure 25: IDR comparison along the \(Y\)-axis for (**a**) r/c, (**b**) steel, and (**c**) hybrid (fixed-pinned connection) five-story buildings.  
Along the X direction, the base shear ratio is shown (Figure 28) to have the lowest value for the r/c building, as 1.7; the greatest for the r/c building, as 3.2; and intermediate for the hybrid buildings with both considered interconnections, as 2.3-2.5. However, in Figure 29, the Y-axis base shear ratio displays the highest value for the hybrid buildings, up to 1.9 and 1.6 for the r/c and steel buildings.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 2 Principles of Power Flow in Power Systems
### MT-HVDC Systems Power Flow
#### 2.2.1 Interface of the Converter Station with the AC Grid  
The PF equations of at the PCC bus of the \(i\)th converter station interfacing with the AC grid is as follows:  
\[P_{gi}=\sum_{j=1}^{n_{A,C}}V_{gi}V_{j}\Big{[}G_{ij}\cos\left(\theta_{gi}- \theta_{j}\right)+B_{ij}\sin\left(\theta_{gi}-\theta_{j}\right)\Big{]} \tag{6}\]  
\[Q_{gi}=\sum_{j=1}^{n_{A,C}}V_{gi}V_{j}\Big{[}G_{ij}\sin\left(\theta_{gi}- \theta_{j}\right)-B_{ij}\cos\left(\theta_{gi}-\theta_{j}\right)\Big{]} \tag{7}\]  
where \(\overline{V}_{gi}=V_{gi}e^{i\theta_{gi}}\).
## 5 Discussion
### Open Challenges
#### 5.3.4 Cost Optimization  
Cost optimization studies of multibeam systems are currently available [61; 62]. However, they are based on fixed systems that provide uniform capacity throughout the service area. On the other hand, it is expected that the implementation of ML techniques can decrease OPEX costs and increase QoS. However, the requirement of reliable data that are usually available from satellite operators needs to be processed in order to extract important features related to the studied problems that can generate high costs. AI Chipset implementation can also significantly increase the system's costs; thus, optimization should be studied to find the compromise and to maintain a low cost of Gbps in orbit while obtaining the benefits of ML-based RRM implementation.
# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
## 2 Materials and Methods  
### Electrocardiogram Signals  
Figure 1 displays a typical example of an ECG signal. It carries information on biological changes in the heartbeat generated by a stimulus from the atrium and the ventricle. The signal presents three significant characteristics: the length given in seconds (or samples), the amplitude in millivolts, and morphology, from which it is possible to identify its waveform.  
Figure 1 shows the main waves of the ECG signal: P wave, QRS complex, and T wave. The QRS complex comprises the Q, R, and S waves [27]. In most cases, cardiac events occur in these waves. For that, much research has focused on complex QRS. For example, QRS complex identification was successfully performed using a sparse representation approach in [28]. In the tests with the system, an AFG1022 Arbitrary Function Generator (TektronixTM, Beaverton, OR, USA) is used to give a reference ECG with a known set of cardiac frequencies, and the signals are then injected into the acquisition interface for real-time processing.  
Figure 1: Typical morphology of normal sinus rhythm seen on ECG.
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 4 Experiments
### Dynamic Experiment  
#### 4.2.1 Experimental Setup  
The dynamic experiment utilizes the UrbanNav dataset provided by Hong Kong Polytechnic University [26]. The specific dataset used, named Odaiba, was collected within the challenging urban canyons of Tokyo. This experiment focuses on single-frequency GPS L1 C/A signals. The GNSS data were acquired at a rate of 10 Hz using Trimble NetR9. The ground truth information was provided by the integration of RTK and INS, offering an RMSE of 5 cm and a frequency of 10 Hz under the multipath/NLoS conditions. The trajectory for the entire experiment is illustrated in Figure 11. It is important to note that the experiment was carried out based on a segment of the entire trajectory featuring strong multipath/NLoS interference.  
The settings for DBSCAN totally inherit the parameters provided in Section 4.1.1.  
Figure 11: Vehicle trajectory for the Odaiba dataset [27].  
Figure 10: CDFs of positioning errors before and after pseudorange multipath/NLoS compensation in horizontal and vertical directions.
# Sand Removal Mechanism of a High-Speed Roller Bit with Helical Sealing
## 5 Discussion
To obtain the helical sealing structure with good sand removal performance, the influence rule of main helical sealing parameters on the sand removal performance is studied through feasibility analyses. Additionally, optimum parameters are determined and provide a theoretical basis for subsequent practical application. Experiments and simulation reveal that the sand removal performance is inversely proportional to the gap (where on account of the bit processing coordination, the gap cannot be small but should be as small as possible in the feasible range), proportional to the screw pitch, and inversely proportional to the inclination of the helical surface. The simulation and experimental results of the sealing gap and helical inclination are consistent. The sand removal performance is best for a sealing gap of 0.5 mm and inclination of 23\({}^{\circ}\). There is a slight difference between the simulation and experimental results of the screw pitch, which may be due to errors in the measurement, equipment, and operation. According to the theoretical analyses, the pumping performance is proportional to the screw pitch. Simulation results show that the difference between the pressure and residence time is small for pitches of 2 mm and 2.4 mm. A pitch of 2.4 mm is selected in this paper. The above experimental research does not apply the drilling pressure to the bit, which is conducted on sealing equipment independently developed by us. After the successful experiment, a simulation of the uniaxial testing machine is carried out, with the aim of simulating the bottom hole load and mud condition and the helical sealing performance at work and at rest. Results show that the helical sealing structure can effectively prevent the external medium from entering the bearing system (as shown in Figure 14). In this study, a new type of \"spiral seal\" structure is proposed, which has a screw pumping action under high-speed rotation through a spiral seal ring to discharge the debris particles out of the sealed cavity, thereby prolonging the service life of the roller bit. The proposed new structure breaks the current idea of passive sealing and converts passive sealing into active sealing, which marks the birth of a new sealing concept, which can bring huge economic and social benefits. Conceptualization, Y.Z. ; methodology, Y.Z. ; software, B.T. ; validation, B.T. and Y.H. ; investigation, Y.Z. ; resources, Y.Z. ; writing--original draft preparation, Y.H. and B.T. ; writing--review and editing, Y.Z. and B.T. ; visualization, Y.H. ; supervision, Y.Z. **Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51604238. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study.
## 3 Computational Modeling
### Dimensions of the Numerical Wave Tank and OWC  
The dimensions of the numerical wave tank (see Figure 3) depend directly on the wave characteristics adopted in the analysis. The depth of the water (\(h\)) and the wave height (\(H\)) (see Figure 2) must be taken into account to determine the numerical wave tank height. This care is taken so that water does not occupy the space outside the computational domain. Thus, it is possible to define that the tank's minimum height is given by \(h+3\)\(H\), as described in [PERSON] et al. (2018). The wave considered in this work follows a real scale ([PERSON] et al., 2018).  
The depth of wave propagation is the same as the wave tank. For the length of the tank, it is necessary to consider the wavelength (\(\lambda\)). The tank's dimensions and the waves' characteristics are the same as presented in [PERSON] et al. (2018) and can be seen in Table 2.  
The dimensions of the converter device are also defined and can be seen in Figure 4, in detail, highlighting the height of the hydropneumatic chambers (\(H_{1}\), \(H_{2}\), \(H_{3}\), \(H_{4}\), \(H_{5}\), \(H_{6}\)), the submersion depth of the device (\(H_{depth}\)), the heights of the turbine ducts (\(H_{7}\), \(H_{8}\), \(H_{9}\), \(H_{10}\), \(H_{11}\)), the length of the ducts (\(l_{1}\), \(l_{2}\), \(l_{3}\), \(l_{4}\), \(l_{5}\)), the length of the chambers (\(L_{1}\), \(L_{2}\), \(L_{3}\), \(L_{4}\), \(L_{5}\)), and the thickness of the columns that divide the devices (\(e_{1}\), \(e_{2}\), \(e_{3}\), \(e_{4}\), \(e_{5}\), \(e_{6}\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Values** \\ \hline Wave period (\(T\)) & 7.5 s \\ \hline Wavelength (\(\lambda\)) & 65.4 m \\ \hline Wave height (\(H\)) & 1 m \\ \hline Depth (\(h\)) & 10 m \\ \hline Tank length (\(L_{T}\)) & 327 m \\ \hline Tank height (\(H_{T}\)) & 14 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Problem characteristics and wave climate.  
Figure 3: Boundary conditions and dimensions of the computational domain composed of the wave tank and the OWC device with five coupled chambers.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 3 Related Work-Coordination Analysis
### Coordinated VNE  
Other VNE approaches have improved the performance of their solution by applying coordination mechanisms between the two embedding phases, in contrast to their uncoordinated counterparts. Coordinated VNE can be solved either in two separate, but coordinated, stages or in only one.
###### Abstract  
This study investigates the feasibility of identifying the axial position of circumferential defects in laminated pipeline structures based on damage indices. Wavelet packet decomposition is combined with damage indices, and the effects of dual defects with the same circumferential position but different axial positions, as well as dual defects with different circumferential and axial positions, on damage indices are separately studied. Our aim was to determine the potential to use damage indices to identify the axial position of circumferential defects in laminated pipeline structures. ABAQUS finite element analysis software was used to establish models of laminated pipeline structures with single defects and dual defects (with the same circumferential position but different axial positions, and with different circumferential and axial positions). The laminated pipeline structure was composed of a steel pipe (structural layer), a rigid polyurethane foam (insulation layer), and a high-density polyethylene (anticorrosion layer). The received sensing signals were averaged, and subjected to 5-level wavelet packet decomposition, to calculate the damage index values, which were then organized into a damage index matrix. Based on the trend of changes in the damage index matrix, the effects of variations in the number and circumferential position of the defects on the identification of the axial position of the damage were analyzed. The results indicate that the trend in damage index changes is influenced by the number of defects, and the increase in the circumferential distance between the second and the piezoelectric element sensor. This study found that when \(1.7\lambda\leq PD\leq 3.4\lambda\), \(I_{\text{double defect 90}^{*}}<I_{\text{single defect }}<I_{\text{double defect 0}^{*}}\); when \(3.7\lambda\leq PD\leq 4\lambda\), \(I_{\text{double defect 90}^{*}}<0.3<I_{\text{double defect 90}^{*}}<I_{\text{single defect}}\). This article demonstrates that the identification of the axial position of damage in laminated pipeline structures can be achieved using the damage index values in the damage index matrix. Additionally, this damage identification method overcomes the limitation of the wavelet packet's inability to identify dual defects with relatively small relative axial distances. This provides new ideas and methods for finite element analysis in identifying the axial position of damage in laminated pipeline structures.  
layered pipeline structures; dual defects; wavelet packet decomposition; damage index value; relative axial position 2023
## 3 Results
### Exhaustive Descriptions of Included Studies
#### Group 2: Regression
As a limitation it was mentioned that only one city was considered as a case study, and also the time period was short. _Forecasting daily ambient air pollution based on least squares support vector machines [49]_: aims to perform air quality prediction using LSSVM. The data used in this study were collected from 2003 to 2006. To evaluate the method, it was compared to MLP by applying relative error measure. The data were taken from 2003 to 2006 years. The results confirmed the advantages of the proposed method. _Online prediction model based on support vector machine [50]_: is concentrated on the prediction of air quality in the city of Hong Kong using an online SVM, which was compared to conventional SVM. The dataset consisted of hourly measurement of air quality data (CO, NO, NO\({}_{2}\), SO\({}_{2}\), NO\({}_{\rm X}\), O\({}_{3}\), RSP), and meteorological data (indoor and outdoor temperature, solar radiation, wind direction, wind speed). To evaluate the methods, the following metrics were taken, including MAE, RMSE and WIA. The results showed the superiority of the online SVM. _Air pollutant parameter forecasting using support vector machines [51]_: [PERSON] et al. studied air quality prediction by applying SVM. They compared SVM to Radial Basis Function (RBF). The data used in this research contained hourly measurements of air quality of the city of Hong Kong during the year of 1999. Taking into consideration the effects of RSP on the case study, the authors selected this pollutant to evaluate the proposed method. The data of June and December were taken. In case of analysing data during December, meteorological data were ignored, while during June the data were included. As an evaluation metric, MAE was used. The results showed that SVM is better in the term of generalization performance, and it provides higher accuracy.