esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats
## 1 Introduction
As stated above, monitoring or tracking activities and safety messaging are applications that may be simply enabled by using this type of transmission technology. In addition, we present a real deployment with the aim of monitoring Optimist Class sailing races. The Optimist is an official international class [13] that embraces small sailboats with hull length and weight of 2.3 m and 35 kg, respectively. The Optimist is the biggest youth racing class in the world with an annual world championship. Thus, the proposed system has been tested by monitoring a series of training sessions of Optimist Class sailboats by means of LoRa-enabled communication between the boats and a base station placed in the port. The developed monitoring platform permits tracking not just each boat position but also key environmental and boat parameters. Thus, the main contributions of this work are the following: (i) a deep discussion of LP-WAN and its applicability to maritime scenarios; (ii) an experimental equipment for monitoring both oceanic and boat parameters; and (iii) the validation of a real deployment by which the monitoring of sailboat activities through the Internet is enabled. The rest of the paper is organized as follows. Section 2 reviews relevant work regarding the use of IoT-based communication technologies in maritime scenarios. The main features of LoRa technology are deeply explored in Section 3. Section 4 discusses the main drawbacks of IoT-based communication technologies when applied to coastal scenarios and presents the potential benefits of employing LP-WAN solutions for maritime communications. Section 5 details the test-bench employed for our real deployment. Section 6 presents and discusses the attained results extracted from our validation test. Finally, the paper ends by summarizing the most important findings in Section 7.
## 3 Convective-Tracking Method and Dataset
### Datasets  
As mentioned before, the cooling rate of the IR BTs with spatial resolution of 2 km at the top of cloud cluster observed by H08/AHI, is used to identify the rapidly developing convective storm cluster. After this step, a spatial and temporal matching technique is used to collocate H08/AHI and GPM IMERG data. The GPM IMERG data right after the time when the SWIPE recognizes a convective cloud is used to match the H08/AHI data. For example, if SWIPE recognizes a convective cloud at 07:10 UTC, then the GPM data of 07:30 UTC is used to determine the rain rate of this cloud cluster. A maximum rain rate (from GPM IMERG) from the 10% coldest pixels of potential convective storm cluster is marked as its final rain rate. A temporal linear interpolation technique is also used here to match NWP data (3 h interval) with the collocated H08/AHI and GPM IMERG data. Based on the collocated dataset, all the samples of convective storm systems were tracked and identified from April to October 2016. During this period, a total of 88,351 convective storm events were successfully tracked using the aforementioned method, including 85,102 slight (or none), 2540 medium, and 709 severe convective storm systems. Table 1 lists the numbers of three typical convective storm systems tracked from April to October 2016. Similar to Table 1, Figure 1 shows the spatial distributions of three typical convective storm systems during this period. We find significant geographical and seasonal characteristics of convective storm systems over this area. The most frequent occurrences of convective storm systems are presented in July (14,608), August (16,455), and September (16,994). The monthly proportion, reaching 4.17%, of severe and medium storm systems was the highest in October. In this month, 106 severe and 396 medium convective storm systems were found in all of 12,040 potential convective systems. In Figure 1, we also find that the geographic area of strong convection gradually moves North from April to August. Contrarily, it will move toward south at the beginning of September again [38]. It is well known that the seasonal movement pattern of strong convection is closely associated with the Intertropical Convergence Zone (ITCZ) and monsoon [33].
# Subcritical Water Extraction of Ursolic Acid from _Hedyotis diffusa_
## 3. Results and Discussion
### Fitting the Model  
Table 1 lists UA yields under different conditions, in which BBD was carried out using three independent variables and three levels. The UA yields ranged from 4.29 mg/g in run 15 (100 mesh, 140 \({}^{\circ}\)C, 25 mL/g) to 6.08 mg/g in run 3 (80 mesh, 140 \({}^{\circ}\)C, 30 mL/g). With analysis of variance (ANOVA), the second-order polynomial model was applied to calculate the predicted response as follows:  
\[\begin{split} Y=5.94-0.35X_{1}-0.12X_{2}+0.37X_{3}+0.17X_{1}X_{2}+ 0.068X_{1}X_{3}-0.008235X_{2}X_{3}\\ -0.73X_{1}{}^{2}-0.49X_{2}{}^{2}+0.072X_{3}{}^{2}\end{split} \tag{3}\]  
The negative coefficients for two independent variables (\(X_{1}\), \(X_{2}\)) and one interaction between two variables (\(X_{2}X_{3}\)) indicated an unfavorable effect on UA yield, while positive coefficients (\(X_{3}\), \(X_{1}X_{2}\), and \(X_{1}X_{3}\)) indicated a favorable effect on UA yield. Thus, Equation (3) demonstrated that UA yield was mainly dependent on solvent/solid ratio, followed by particle size and temperature.  
The effects of the variables as linear, quadratic and interaction coefficients on the response was tested for significance by analysis of variance (ANOVA), and are shown in Table 2. The statistical significance of the model equations were evaluated by the F-test and \(p\)-value. The high \(F\)-value of 230.22 and small \(p\)-value suggested that the model was significant. The goodness of fit of regression model was carried out by a determination coefficient (\(R^{2}\)) and adjusted determination coefficient (\(R^{2}\)\({}_{\mathrm{adj}}\)). The high values of both coefficients (\(R^{2}\) = 0.9553, \(R^{2}\)\({}_{\mathrm{adj}}\) = 0.9923) were reasonably close to 1, indicating a high degree of correlation between the experimental and predicted values. Moreover, the model's goodness-of-fit and suitability were checked by lack of fit. The \(F\)-value and \(p\)-value of the lack of fit were 5.76 and 0.0619, respectively, which further validated the model. Therefore, the model could be used for the prediction of extracting UA using subcritical water technology.  
Figure 2: Effects of different parameters on extraction yield of unsolic acid ((**A**): particle size; (**B**): extraction temperature; (**C**): extraction time; (**D**): solvent/solid ratio; (**E**): extraction pressure).
## 4 Results
### Qualitative Data Analysis  
#### 4.3.1 Simplicity of Implemented Interactions  
In the second part of the research, participants completed a survey consisting of six questions to assess their satisfaction with the integrated interactions. The design of this survey is described in Section 3.3.  
Figure 6a shows that most participants have no problem reaching objects at the first level. At the second level (Figure 6b), participants at the sublevel without distractors generally found that enlarging the 3D object was easy, as 50.95% of participants gave scores of 4 and 5. The interactions at the sublevel with distractors were rated even better, as 62.26% of participants gave the same scores. At the sublevel with distractors, a decrease in ratings of 1 and 2 can be observed. This pattern could indicate that the experience gained in the first level without distractors helped the participants to develop skills that helped them to solve the tasks in the next sublevel more easily, despite the presence of distracting elements.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Sublevel** & **Accuracy** \\ \hline Horizontal orientation & 98.11 \\ Vertical orientation & 92.45 \\ Distracting background & 62.26 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Percentage of correct answers to questions on the fourth level.  
The participants' subjective ratings of the ease of rotating the 3D object at the third level of the game show a significant difference between the two sublevels (Figure 6c). On the level with direct interactions, most participants (49.06%) thought that rotating the 3D object was very easy. The remaining distribution of ratings shows a balanced number of participants with ratings of 3 and 4, while very few gave a rating of 2, and none gave a rating of 1. For indirect interactions, 83.01% of participants gave a rating of 5, a much smaller number of participants gave ratings of 3 and 4 (16.98%), while no participant gave ratings of 1 and 2. This could indicate that rotating the object, regardless of the type of interaction, was very easy, but that participants generally preferred indirect interaction with the slider.  
The results of the ratings at the fourth level (Figure 6d) show a tendency for positive ratings to increase as progress is made through the sublevels. This result is consistent with previous observations on other types of interactions at the first and second levels. It can also confirm that the learning process had a positive effect on the participants' ability to adapt to the interaction in the virtual space.  
Figure 6: Participants’ ratings for ease of (**a**) 3D object retrieval; (**b**) resizing a 3D object; (**c**) rotating a 3D object; (**d**) pressing 3D button with a virtual finger.
## 4 Results and Discussion
### Comparative Analysis of Internal Flow Characteristics
This indicates that incoming flow distortion causes an asymmetric distribution of blockage in the impeller passages, with the inlet distortion vortex exacerbating the blockage phenomenon downstream. The blockage factor curve can be divided into three stages based on its growth trend. In Stage A, the blade tip leakage flow at the front of the impeller is squeezed towards the suction surface under the high-speed incoming flow, and the mix  
\[\eta=\frac{Q\times p_{s}}{P} \tag{9}\]2 Comparison of blade passage velocity streamlines: (**a**) original design and (**b**) modified design. flow in the impeller passage weakens, leading to a slow increase in the blockage factor. In Stage B, the leakage at the blade tip in the middle section of the impeller increases, and the leakage flow mixes with the main flow along the suction surface to form a leakage vortex at the blade edge, disturbing the main flow and causing the blockage factor to rise rapidly. In Stage C, the leakage vortex at the rear of the impeller fully develops and interacts with the stall vortex in the passage, causing the blockage factor to reach its maximum value. The average blockage factors for the original impeller passages A and B are 0.29 and 0.21, respectively, while for the modified impeller passages A and B, the average blockage factors are 0.07 and 0.04, respectively. This indicates that the impeller optimization design method based on secondary flow suppression can effectively improve flow stability and mitigate the deterioration of leakage vortices and passage blockage caused by incoming flow distortion.
## 4 Effect of Model Parameters on the Calculation Results
### Effect of Drag Models on the Calculation Results  
At present, there are various models for calculating the drag coefficient of spherical particles in the process of movement. In order to study the effect of different drag forces on the calculation results, different spherical drag models from Equations (7) to (9) were used to calculate the ice crystal trajectories and collection coefficients, respectively, where Equation (7) is named Cd1, Equation (8) is named Cd2, and Equation (9) is named Cd3. The calculation model and conditions were the same as in Section 2.1, and the ice crystal trajectories and collection coefficients obtained for different drag coefficients are shown in Figures 10 and 11.  
It can be seen that the drag coefficients had a certain effect on the ice crystal trajectories. The calculation result of Cd1 was slightly smaller than that of the other two models. This was because the Reynolds number coefficient in the Cd1 model was large, which resulted in a higher drag force on the ice crystal motion and therefore fewer ice crystals being collected. The drag calculated by the other two models was relatively small, and the calculation results of Cd2 and Cd3 were almost identical. However, there was less variation between the various drag models, and the collection coefficient curves were almost the same, in terms of the impact ranges, with small differences in the maximum values. This was due to the fact that, while the drag coefficient affected the ice crystal trajectories and the collection coefficients, the difference in drag was more pronounced at larger differences between the ice crystal velocity and airflow velocity. The area with the largest difference between the ice crystal and airflow velocity was near the leading edge. The velocity difference in this area was large, but the motion distance was short, so the drag action time was short, resulting in a limited influence on the motion trajectories.  
Figure 10: Comparison of the ice crystal trajectories for the different drag models.
## 2 Computational Method  
We conducted first-principles DFT simulations by employing the Vienna ab-initio simulation package (VASP) [54; 55; 56]. Generalized gradient approximation (GGA) is used with the exchange-correlation functional of Perdew-Burke-Ernzerhof (PBE) [57]. The unbounded free electrons in the solid occupy the higher energy levels while the lowest ground-state energy represents the stable structure and the desired solution is the lowest energy structure. Therefore, the energy should cut at some upper bound. While the free electron's kinetic energy is the highest in a solid, the cutoff energy refers in fact to this kinetic energy. The cutoff energy is depending on a system and should be defined high enough to derive accurate results. The plane waves with smaller kinetic energy are included in calculations. We use the plane-wave basis set with an energy cut-off of 500 eV. The VESTA [58] package is used for visualizing the atomic lattice. A dispersion scheme of DFT-D2 [59] is employed to improve the binding energy calculations by accounting for the dispersion corrections. Periodic boundary conditions are imposed in planar directions with a vacuum layer of 16 A along with the sheet thickness. Energy minimized structures are obtained by the conjugate gradient method with a convergence criterion of 10\({}^{-4}\) eV and 0.01 eV/A for the energies and forces. A 3 \(\times\) 3 \(\times\) 1 Monkhorst-Pack [60] k-point mesh size is employed for the finite number of points in the Brillouin zone (BZ). Compared to finer k-point grids, the employed 3 \(\times\) 3 \(\times\) 1 grid yields computationally efficient and accurate results for the adsorption energies. Gradual intercalation simulations were realized by adding metal atoms firstly at the predicted most stable binding sites and then randomly positioning on the surface until the full capacity is reached [28, 61]. To obtain more accurate energy values, we conduct single-point calculations using the tetrahedron method with Blochl corrections and 5 \(\times\) 5 \(\times\) 1 Monkhorst k-point mesh size. Bader charge analysis [62] is then achieved to predict the final charge capacities. Finally, nudged elastic band (NEB) method is employed to simulate the diffusion of a single adatom over P-TpG.
The main characteristics of the proposed approach are the following: (i) it is able to obtain the optimal solution in a distributed way (_i.e._, the solution is obtained by distributing the optimization problem through the different nodes in the network), (ii) it converges with a significantly lower number of iterations than other state of the art techniques, and (iii) it requires a lower amount of signaling in the network. It is worth noting that the optimization problem is first formulated in a generic way in order not to restrict the solution to a given scenario. Indeed, the proposed algorithm can be easily extrapolated to perform the joint allocation of time and frequency, which is completely aligned with the philosophy of near future systems (e.g., IEEE 802.15.4e). After that, we focus on the beacon enabled mode of IEEE 802.15.4 with a cluster-tree topology and solve the GTS slot allocation problem by adapting the proposed strategy to this scenario. Our method is compared with the first-come-first-served (FCFS) strategy adopted by the standard, showing that our solution provides a significant improvement in terms of fairness performance as the traffic in the network grows. The rest of the paper is distributed as follows. In Section 2, the resource allocation problem is formulated as a NUM problem and the proposed Coupled-Decompositions Method is described. After that, in Section 3, the proposed strategy is adapted to a realistic scenario based on IEEE 802.15.4 and numerical results are provided. Finally, the conclusions of the paper are presented in Section 5.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 4 Results and Evaluations
### Validation of Wi-Fi Hotspot and Internet Connection  
To verify the internet connection provided by the satellite access point, we used a laptop (running Windows 10) to connect to the Wi-Fi hotspot (provided by the satellite access point). We observed that once the laptop was started, it was connected to the Wi-Fi network with the SSID \"satnet\" (as described in Section 4.2), and a private IP address was assigned by the DHCP server in the single-broad computer. After that, we successfully accessed a web page through the Internet.
## 2 Results and Discussion
### Polymerization Rate and Average Number of Radicals per Particle
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**0.05 g/min**} & \multicolumn{2}{c}{**0.15 g/min**} \\ \hline \multirow{2}{*}{\(t_{r}\)} & \multicolumn{2}{c}{\(\tilde{N}\times\mathbf{10^{4}}\)} & \multicolumn{2}{c}{\(\tilde{n}\times\mathbf{10^{3}}\)} \\ \cline{2-6}  & **M** & **MR** & \(t_{r}\) & **M** & **MR** \\ \hline
0.23 & 6.9 & NA & 0.14 & 3.3 & 3.7 \\
0.41 & 6.0 & NA & 0.36 & 1.8 & 2.3 \\
0.58 & 3.4 & NA & 0.53 & 1.7 & NA \\
0.84 & 2.6 & NA & 0.76 & NA & NA \\
1.00 & 2.7 & 2.3 & 1.00 & 1.8 & 1.2 \\
1.18 & 4.4 & 3.8 & 1.51 & 47.7 & 33.8 \\ \hline \end{tabular}
\end{table} 
Table 3: Evolution of \(\tilde{n}\) values along polymerizations. The question now emerging is why \(\tilde{n}\) increases are so high with the increase in the organic-phase dosing rate. The probable cause would be that proposed by [PERSON] [28], who stated that the cross-linked polymer chains would trap the propagating radicals. It is believed that these chains would act as barriers preventing two radicals encounter, thereby retarding the bimolecular termination reactions and consequently, extending the life span of the radicals inside the particles.
## 5 Energy Efficiency of the MHCP-MHCP Network  
In this paper, the energy efficiency is defined as the ratio of total downlink channel capacity to total power consumption in the MHCP-MHCP network, that is  
\[\eta_{EE}=\frac{C_{total}}{P_{total}} \tag{10}\]  
where \(C_{total}\) is the total downlink channel capacity, \(P_{total}\) is the total power consumed by all the BSs in the MHCP-MHCP network. Firstly, we characterize the total downlink channel capacity, then analyze the total power consumption of all BSs and model the network EE.
## 2 Materials and Methods
### Statistical Analysis  
Means, standard deviations, and 95% confidence intervals were calculated for all of the variables investigated. The Shapiro-Wilk test was used to evaluate the distribution of the angular parameters assessed. Student's paired sample \(t\) tests and Wilcoxon tests were used to assess differences between the two reference systems (Sagittal FS1'Sagittal CBCT vs Sagittal FS2'Sagittal CBCT; Coronal FS1'Coronal CBCT vs Coronal FS2'Coronal CBCT). For each coordinate (x-transverse; y-sagittal; z-vertical) of each point identified on the FS, the Dahlberg error was calculated and a Bland-Altman plot with relative related limits of agreement (LoA = 1.96 \(\times\) SD) was constructed. To assess the reliability of the analysis, intra-class correlation (ICC), Student's paired sample \(t\) tests, and Wilcoxon tests were used to compare the data (Sagittal FS1'Sagittal CBCT, Sagittal FS2'Sagittal CBCT, Coronal FS1'Coronal CBCT, Coronal FS2'Coronal CBCT, Sagittal FS1'Sagittal FS2) at different time points (T0 vs T1). All of the analyses were performed with commercial software (SPSS version 22.0, SPSS IBM, New York, NY, USA). The Dahlberg values were judged as good if they were smaller than 0.7 mm; ICC above 0.9 was judged as very good [29]. The level of significance was set as \(p<0.05\).
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Generation of Collision Advoidance Course  
#### 3.3.1 Collision Point and Collision-Free Circle Zone  
Collision avoidance trajectories are generated by collision points and collision avoidance circle zones in this investigation. A collision point is generated using real-time information of two encountered ships. Geometrically translating DCPA in the heading direction of the controlled USV until it intersects with the target ship's path, this intersection point is defined as the collision point(\(x_{n}^{cp},y_{n}^{cp}\)), as shown in Figure 8.  
By using the collision point in Figure 8, a collision circle zone and a collision avoidance waypoint can then be illustrated, as shown in Figure 9.  
Figure 8: Generation of collision point.  
Figure 9: Geometric relationship of the collision waypoint and collision circle zone.  
The collision circle zone, which is located in front of the controlled USV, is constructed using two parameters, \(\xi\) and \(b\). \(\xi\) represents the parallel offset from the collision point to the center of the collision circle zone, which is perpendicular to the controlled USV's heading. If \(\xi>0\), the center of the collision circle zone is on the left side of the controlled USV, otherwise it is on the right side.  
\(\xi\) can be expressed as:  
\[\xi=\zeta\times L_{S} \tag{11}\]  
where \(\zeta\) can be obtained from the output of the collision circle zone generator, and \(L_{S}\) is determined from Equation (9).  
\(b\), which represents the radius of the collision circle zone, can be expressed as:  
\[b=p\times L_{S} \tag{12}\]  
where \(p\) can be obtained from the output of the collision circle zone generator.  
The collision circle zone generator is built up using the fuzzy interference system. Input variables are assigned as the relative bearing (RB) and the historical data from experienced captains [18]. The inferred results of \(\zeta\) and \(p\) are shown as Figures 10 and 11, respectively.  
The collision avoidance waypoint is the point in the collision circle zone that intersects with the line segment that connects the center of the collision circle zone and the collision point; this line segment can be expressed as a linear equation. The angle between the original heading route and the new collision avoidance route is inferred as \(\zeta\), which means that the controlled USV can avoid the collision by turning a degree of \(\zeta\).