esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ride Performance Analysis
### Result Analysis
#### 5.2.1 Effect of the Damping Pipe Diameter  
The damping of hydropneumatic suspension is directly set by the parameters of the pipe installed between the cylinder and the tank. The evaluation function value (EF), bounce acceleration value (BA) and angular acceleration value (AA) for different pipe diameters are obtained and shown in Figure 12, in which the pipe length is 0.5 m and the pneumatic chamber length is 85 mm.  
Figure 12a shows that the EF value sharply decreases with the increase of pipe diameter when traversing both the trapezoidal barrier and step barrier. When the pipe diameter is less than 6 mm, the reduction of pipe diameter will lead to a sharp deterioration of the vehicle ride performance in both conditions.  
Moreover, the worse road condition, this negative influence is more prominent. When the pipe diameter is 1.5 mm, in the case of the trapezoidal barrier, the EF value once reached 21.25 while the step barrier is only 11.34 at the same traversing speed. This phenomenon can also be seen from the red line in Figure 12b,c that both the angular acceleration value and the vehicle bounce acceleration value have severe fluctuations and shocks. Such large impacts are undoubtedly unacceptable for on-board instruments. The oil flow rate results plotted in Figure 13 shows the oil flow rate of 1.5 mm diameter is significantly less than the 6 mm and 12 mm in both road conditions, which means the pipe cannot function well as a damping unit when the diameter is too small.  
When the pipe diameter is larger than 6 mm, further increasing the pipe diameter will slightly increase the EF value, mainly due to the damping force decline caused by the reduction of the flow resistance. Except for the slight difference in peak value, there is not much difference in flow velocity and acceleration between 6 mm and 12 mm throughout the barrier traversing process.  
Overall, it can be seen from the lower EF value in Figure 12 and lower flow rate in Figure 13 that the body stability when the vehicle traversing a step barrier is much  
Figure 12: Performance comparison of different pipe diameters: (**a**) EF value (**b**) Acceleration values of trapezoidal barrier traversing (**c**) Acceleration values of step barrier traversing.  
Figure 13: Flow rate comparison of different pipe diameter: (**a**) Trapezoidal barrier traversing (**b**) Step barrier traversing.  
smoother than traversing a trapezoidal barrier with similar height and an improperly tuned diameter will definitely cause a severe impact on the vehicle body.
## 4 Results and Discussion
### Effect of Nitrogen  
In actual operation of the fuel cell stack, there is more or less nitrogen mixed in the recirculating hydrogen, as mentioned in the previous section. Figure 7a reveals the effect of the nitrogen fraction on the volumetric efficiency under a different rotation speed and pressure difference. With the increase of nitrogen fraction, the volumetric efficiency increased rapidly at first and then increased slowly. From the four different conditions, a reduction of increment is displayed at a lower pressure difference or higher rotation speed, for the same reason as explained for water vapor. In fact, nitrogen plays the same role as water vapor in terms of leakage, with even higher mole mass and larger dynamic viscosity. Although as the proportion of nitrogen increases from 5% to 25% and the proportion of hydrogen decreases from 80% to 60%, the absolute volume flow of hydrogen still increases slightly at a pressure difference of 25 kPa. This result can be attributed to the improvement in volumetric efficiency. Figure 7b represents the effect of nitrogen on the isentropic efficiency, showing similar trends as volumetric efficiency but with smaller increments.  
As can be seen from the Figure 7a,b, taking the condition of pressure difference of 15 kPa and speed of 9000 rpm for example, the volumetric efficiency increased by 10% from 60.5% to 70.4% with an increase of nitrogen fraction from 5% to 25%; however, the isentropic efficiency just increased by 3% from 54.5% to 57.6% with the same change in nitrogen fraction. The reason is likely because the  
Figure 6: Effect of water vapor on the performance: (**a**) volumetric efficiency; (**b**) isentropic efficiency.  
Figure 7: Effect of nitrogen on the performance: (**a**) volumetric efficiency; (**b**) isentropic efficiency.  
volumetric efficiency is mainly affected by the leakage rate, but the isentropic efficiency is influenced both by the leakage and flow resistance. With the increasing of the nitrogen content, the molecular weight and density of the mixture increase, which will reduce the leakage rate and also increase the flow losses. Thus, the increase rate of the isentropic efficiency is smaller than that of the volumetric efficiency, which means that the volumetric efficiency is more sensitive to the change of nitrogen. When the speed is high and the pressure difference is low, the effect of flow resistance is more significant than that of the leakage due to the larger flowrate, while the isentropic efficiency is even less sensitive than the volumetric efficiency, as shown in Figure 7.
### Radio Detection AND Ranging (RADAR) Theory
#### 1.2.1 Pulse RADAR  
The pulse RADAR periodically emits short pulses of EM waves. The period between two consecutive pulses is related to the RADAR range. The period needs to be bigger than the time it takes for the waves to be emitted, reflected and received [32; 33].  
Ultra-WideBand (UWB) RADAR is a type of pulse RADAR that uses pulses of EME for a very brief period. To achieve that with good quality, expensive equipment is needed to send the pulses with high accuracy; high fidelity receiver electronics are also required to measure the reflection.
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Influence of Stiffness Excitation
#### 3.2.2 Analysis of Supporting Stiffness  
The elastic deformation of the supporting bearing affects the meshing force, which, in turn, affects system stability. In this subsection, the influence of the supporting stiffness on the dynamic response is analyzed. Let the load torque \(T_{c}\) = 5.4 Nm, the rotational speed be 1500 rpm, the gear transmission error \(e_{0}\) = 0.1 mm, and the damping coefficient \(\xi\) = 0.1. The bifurcation diagrams of the system, with respect to the supporting stiffness \(K_{\mathrm{dry}}\), are shown in Figure 21.  
Figure 19: \(K_{1}\) = 0.33; (**a**) phase diagram; (**b**) Poincaré map; (**c**) time-domain diagram.  
Figure 20: \(K_{1}\) = 0.45; (**a**) phase diagram; (**b**) Poincaré map; (**c**) time-domain diagram.  
As shown in Figure 21, with an increase in \(K_{\text{hxy}}\), the system motion gradually evolves from an unstable motion to a stable motion, and the maximum value of displacement along the LOA exhibits a downward trend. Compared with Figure 15, the influence of the dimensionless supporting stiffness on the displacement along the LOA is more complicated, characterized by the adding periodic bifurcation and the chaotic band. For example, when the dimensionless supporting stiffness increases from 10.07 to 13.44, the system motion turns from a quasiperiod-tripling motion into a three-band chaotic motion and then becomes a stable period-tripling motion. As shown in Figures 22b and 23b, it can be seen from the mapping aggregation state of the points that when \(K_{\text{hxy}}\) is 3.3 and 10.3, respectively, the motion state of the system is in the quasiperiod-doubling motion and the quasi period-tripling motion. Similarly, as shown in Figures 24 and 25, when \(K_{\text{hxy}}\) = 11, the mapping points in the Poincare map are irregular, indicating that the system is in a chaotic motion state, while the system falls into a stable period-tripling motion because of the three isolated points shown in the Poincare map when \(K_{\text{hxy}}\) = 14. Although system stability is complicated, the amplitude of the system along the LOA decreases significantly with an  
In order to further explore the effect of bearing supporting stiffness, the bifurcation diagrams of the displacement of the supporting bearings in the \(X\)-axis and \(Y\)-axis were obtained, as shown in Figure 26. It can be seen that the displacement of the supporting bearings, whether in the \(X\)-axis or the \(Y\)-axis, decreases with increasing supporting stiffness. When the supporting stiffness is large enough, the displacement of the supporting bearings is close to zero. If ignoring friction, the \(X\)-axis force subjected to the gear mainly comes from the dynamic center distance. Therefore, the displacement of the supporting bearings in the \(X\)-axis is very small, as shown in Figure 26a.  
In conclusion, a dimensionless supporting stiffness plays an important role in the dynamic responses of the gear system. Generally, a large supporting stiffness will improve system stability and decrease vibration response.
###### Abstract
In the work [21], the authors have analyzed the effect of UV, VIS and NIR laser wavelengths on the machining performance of wood. The effect of laser properties on the surface characteristics of different types of woods was studied in the works [22; 23; 24; 25]. The authors of the work [22] studied the surface properties of beech wood after CO\({}_{2}\) laser engraving. In the previous work [23] the authors studied impact of radiation forms on beech wood color changes. The effect of CO\({}_{2}\) laser parameters on color changes in hardwoods and in limewood was published in the works [24; 25]. Artificial neural networks have found very broad usage in material science such as wood science, polymers science, metals science but also optimization of technologies for materials processing. Many authors have used this very useful method for predicting wood materials properties. Authors of the work [26] have predicted thermal conductivity of wood using artificial neural networks. Surface roughness of wood in machining process was modelled in [27] and ANN was applied for minimalizing surface roughness and power consumption in abrasive machining of wood in the work [28]. ANN was also applied for prediction of optimum power consumption in wood machining in the work [29]. Neural networks have also been widely used for optimization of materials technologies. The authors in the work [30] modelled formaldehyde emissions during a particleboard manufacturing process and published the effect of the manufacturing technology on the modulus of rupture (MOR) and the modulus of elasticity (MOE) vs. pressing conditions in the work [31]. Other authors have dealt with prediction of adhesive bonding strength and bonding quality vs. pressing conditions using ANN or multiple linear regression models [32; 33; 34]. Artificial neural networks have also been used for prediction of color changes in wood which are largely affected by radiation from CO\({}_{2}\) or other types of lasers. The authors in the work [35] predicted the color changes of heat-treated wood during artificial weathering and during heat treatment by natural atmospheric conditions in the work [36]. The authors of the work [37] used ANN for determination of CNC processing parameters for the best wood surface quality. In the article [38], they determined the surface properties of MDF and optimized CNC processing parameters for this type of wood composite. In comparison to MLP and RBF networks, artificial neural networks offer other possibilities for prediction of manufacturing industry demands using a deep learning approach. This method was described in detail in [39]. The goal of the article was to the predict values of cutting parameters at non-measured values of laser power \(P\). These results can be successfully used for complex characterization of \(P\), the AR effect on cut parameters for all applicable values of laser power for Picea abies. The results of ANN can be used for prediction of cut parameters at any laser power values between 100 and 500 W for all possible values of AR.
###### Abstract  
Accurate characterization of spatial patterns and temporal variations in dryland vegetation is of great importance for improving our understanding of terrestrial ecosystem functioning under changing climates. Here, we explored the spatiotemporal variability of dryland vegetation phenology using satellite-observed Solar-Induced chlorophyll Fluorescence (SIF) and the Enhanced Vegetation Index (EVI) along the North Australian Tropical Transect (NATT). Substantial impacts of extreme drought and intense wetness on the phenology and productivity of dryland vegetation are observed by both SIF and EVI, especially in the arid/semiarid interior of Australia without detectable seasonality in the dry year of 2018-2019. The greenness-based vegetation index (EVI) can more accurately capture the seasonal and interannual variation in vegetation production than SIF (EVI \(r^{2}\): 0.47-0.86, SIF \(r^{2}\): 0.47-0.78). However, during the brown-down periods, the rate of decline in EVI is evidently slower than that in SIF and in situ measurement of gross primary productivity (GPP), due partially to the advanced seasonality of absorbed photosynthetically active radiation. Over 70% of the variability of EVI (except for Hummock grasslands) and 40% of the variability of SIF (except for shrundlands) can be explained by the water-related drivers (rainfall and soil moisture). By contrast, air temperature contributed to 25-40% of the variability of the effective fluorescence yield (SIF\({}_{\text{yield}}\)) across all biomes. In spite of high retrieval noises and variable accuracy in phenological metrics (MAE: 8-60 days), spaceborne SIF observations, offsetting the drawbacks of greenness-based phenology products with a potentially lagged end of the season, have the promising capability of mapping and characterizing the spatiotemporal dynamics of dryland vegetation phenology.  
SIF; EVI; phenology; NATT +
Footnote †: journal: Remote Sensing
## 5 Open Problems and Future Opportunities for UAV Wireless Networks  
In the previous sections, we have presented studies on UAV deployment and trajectory for UAV use cases in wireless communication networks including channel characteristics, and summarized the main lessons learned from this survey. Despite extensive studies on UAV deployment and trajectory in wireless communication networks, there are still many open problems to be investigated and future opportunities to make solutions. Most of the research introduced in this survey proposes the optimal UAV deployment or trajectory algorithm to achieve their target performance; however, it requires a computational complexity to derive the optimal result, thereby limiting their practical utilization in a real world. Recently, the machine learning (ML) framework is integrated into UAV wireless networks and expected to provide solutions for channel modeling, throughput enhancement, and complexity reduction along with UAV deployment and trajectory. Open problems, future opportunities, and applications of ML techniques for solutions are presented in the next section.
# Automatically Expanding User-Management System for Massive Users in the Cloud Platform
## 3 Load Balancing for Massive Concurrent Users
### Load-Balancing Algorithms in Nginx  
Compared with conventional load-balancing tools, Nginx has a better load-balancing efficiency and a lower cost. Nginx uses event-driven architecture, which is a lightweight, scalable, and high-performance HTTP server. It is also very convenient to deploy and manage in our cloud environment. Nginx is an HTTP and reverse proxy server that has the characteristics of load balancing, accelerated reverse proxying with caching, and autoindexing. Some classical load-balancing algorithms have been included in Nginx. DNS refers to the Domain Name System, which is a directory that connects names to the user registration Internet addresses, as shown in Figure 6.  
Figure 6: The flows of Nginx load balancing.  
Figure 6 shows that the Nginx master allocates the user registration requests to other workers. There are some load-balancing algorithms used in Nginx to ensure the Nginx workers running at an equalized level. Typically, the following three load-balancing algorithms can be applied to the large-scale cloud platform.  
* Round Robin load balancing: This is a simple way to distribute client requests across a group of servers via the Nginx master node. A client request is forwarded to each Nginx worker node in turn and submitted to each user registration node. The algorithm instructs the load balancer to go back to the top of the list and repeats again. Round robin is the most widely deployed load-balancing algorithm. Using this method, client requests are routed to available servers on a cyclical basis. Round-robin load balancing works best when servers have roughly identical computing capabilities and storage capacity.
* Weighted Load Balancing: The weighted round robin load-balancing algorithm allows the Nginx master node to assign weights to each Nginx worker node based on criteria like the traffic-handling capacity. Nodes with higher weights receive a higher proportion of client requests.
* IP HASH Load Balancing: The IP Hash policy uses an incoming request's source IP address as a hashing key to route non-sticky traffic to the same user registration server. The load balancer routes requests from the same client to the same backend server as long as that server is available.  
Among these load-balancing algorithms, the weighted load-balancing algorithm is more flexible and scalable for the cloud platform. All the Nginx worker nodes can be given different weights depending on their server specifications to realize load balancing. However, the unchanged weights for worker nodes were proved limited for load balancing. So, we chose the weighted load-balancing algorithm for optimization by adjusting the weights of worker nodes.
## 2 Materials and Methods
### Correlation between CVS and Pump Parameters  
A block diagram of the LVAD biotechnical system is shown in Figure 2. It represents the following parameters: \(P_{1}\) and \(P_{2}\) are pressures in the left ventricle and aorta, respectively; \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\) are the blood flow rates through the aortic valve, pump, and aorta, respectively; \(H\) represents the pressure head across the pump; \(I\) is the electric current measured in the motor windings; \(A\) is the external work of the left ventricle; and \(\omega\) is the rotation speed of the pump set to continuous-flow mode.  
Among the described parameters, it is necessary to highlight the following equations:  
\[Q_{3}=Q_{1}\ +\ Q_{2}; \tag{5}\]  
\[H=P_{2}-P_{1}. \tag{6}\]  
Control issues of the LVAD presented in Figure 2 include: (1) The discrepancy between the parameters that need to be controlled ensuring circulatory support (i.e., \(P_{1}\), \(P_{2}\), \(Q_{1}\), \(Q_{3}\)); (2) the parameters generated by the continuous-flow rotary pump (\(H\), \(Q_{2}\)); and (3) the parameters that can be tracked and changed in real-time during LVAD support (\(I\), \(\omega\)). Itwas previously shown that there are ways to find correlations between different parameters using: (1) A ratio between the maximum flow acceleration and flow pulsatility at baseline pump speed as an alternative to a regression coefficient between the maximum flow acceleration and flow pulsatility at different pump speeds [25]; (2) the external work of the ventricle expressed by the area inside the P-V diagrams which correlates with the area inside dynamic H-Q curves [26]; or (3) energetic characteristics of LV-LVAD interaction [27]. Our hypothesis is that changes in the work (\(A\)) of the left ventricle affect the recorded parameter of the electric current (\(I\)). In particular, we assume that myocardial function may be evaluated by changing \(I\).  
In order to investigate the correlation between cardiac function and pulsatility index of the pump electric current, a comparison of cardiac output and cardiac power output characteristics vs. \(PI\) was performed. The obtained data was processed using univariate linear and nonlinear regression analysis.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 4 Results  
### Coherent Component Reflectivity  
The measurements collected by SMAP-R and processed into a coherent component reflectivity, following Equations (10)-(16), are presented in Figure 1, and compared to the total power waveform reflectivity. The total power waveform and the coherent component have the same sensitivity to terrain features but present an average difference of \(-9.2\) dB and an unbiased root mean square difference (ubRMSD) of \(3.4\) dB. Some specific regions present more noticeable differences between both components. High latitude areas in the winter period, where the soil is mostly covered by snow or ice, present stronger coherent reflections.  
Densely vegetated areas such as Boreal forests, or the Congo and the Amazon rain-forests present a lower difference between the coherent and the incoherent component, in accordance with simulations from [56], with both components severely affected by the attenuation of the vegetation volume scattering [23]. Other regions present a consistent bias with variations of \(\pm 1\) dB, but with a consistent bias of \(--9\) dB, indicating that the coherent component is an order of magnitude smaller than the total power waveform (\(-10\%\)), hence indicating that from the total power received in Equation (1) most of it corresponds to the incoherent component, and some of it corresponds to the coherent one. It should be noted that these initial findings suggest that the incoherent component, when integrated over \(30\) ms, dominates the reflection. Furthermore, as other studies have previously concluded over specific geographical areas [39; 57], our initial findings suggest that the GO model would be better suited for modeling the topography effect of GNSS-R over land.