esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Nonlinear Dynamic Analysis of the RBRS
### Effect of Radical Stiffness on Dynamic Characteristics of RBRS at Variable Speed
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Excitation** & **Rotating Speed** & \\
**Current \(k_{r}\) (N/m)** & **\(\omega\) (rad/s)** & **Main Frequency Feature (m: Positive Integer)** \\ \hline \multirow{6}{*}{\(k_{r}=1\times 10^{8}\)} & 7.0 \(\leq\omega\leq\) 15.4 & \(f_{n}\) \\  & 15.4 \(<\omega\leq\) 39.6 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\), \(5f_{n}\),... \\  & 39.6 \(<\omega\leq\) 52.2 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\); m/\({}_{n}\)/2, m/\({}_{n}\)/3, and m/\({}_{n}\)/4 alternate \\  & 52.2 \(<\omega\leq\) 59.8 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\); \(4f_{n}\),... \\  & 59.8 \(<\omega\leq\) 71 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\); m/\({}_{n}\)/2 and m/\({}_{n}\)/4 alternate \\ \hline \multirow{6}{*}{\(k_{r}=2.6\times 10^{8}\)} & 7.0 \(<\omega\leq\) 15.8 & \(f_{n}\) \\  & 15.8 \(<\omega\leq\) 29.6 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),... \\  & 29.6 \(<\omega\leq\) 32.6 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),..., m/\({}_{n}\)/2 \\  & 32.6 \(<\omega\leq\) 35.8 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),..., m/\({}_{n}\)/2 \\  & 35.8 \(<\omega\leq\) 40.2 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),..., m/\({}_{n}\)/23, and m/\({}_{n}\)/27 alternate \\  & 40.2 \(<\omega\leq\) 47.8 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),..., m/\({}_{n}\)/7 \\  & 47.8 \(<\omega\leq\) 51.6 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),..., m/\({}_{n}\)/2 \\  & 51.6 \(<\omega\leq\) 60.4 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),...,..., \\  & 60.4 \(<\omega\leq\) 66.8 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),..., first m/\({}_{n}\)/2, then m/\({}_{n}\)/3 \\  & 66.8 \(<\omega\leq\) 71 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), m/\({}_{n}\)/7 \\ \hline \multirow{6}{*}{\(k_{r}=4\times 10^{8}\)} & 7.0 \(<\omega\leq\) 15.4 & \(f_{n}\) \\  & 15.4 \(<\omega\leq\) 18.8 & \(f_{n}\) \\  & 18.8 \(<\omega\leq\) 22.2 & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\),...,..., \\  & 22.2 \(<\omega\leq\) 25.6 & \(f_{n}\), \(2f_{
# Mechanical Characterisation and Modelling of Elastomeric Shockpads
###### Abstract  
Third generation artificial turf systems are comprised of a range of polymeric and elastomeric materials that exhibit non-linear and strain rate dependent behaviours under the complex loads applied from players and equipment. An elastomeric shockpad is often included beneath the carpet layer to aid in the absorption of impact forces. The purpose of this study was to characterise the behaviour of two elastomeric shockpads and find a suitable material model to represent them in finite element simulations. To characterise the behaviour of the shockpads an Advanced Artificial Athletic test device was used to gather stress-strain data from different drop heights (15, 35 and 55 mm). The experimental results from both shockpads showed a hyperelastic material response with viscoelasticity. Microfoam material models were found to describe the material behaviour of the shockpads and were calibrated using the 55 mm drop height experimental data. The material model for each shockpad was verified through finite element simulations of the Advanced Artificial Athlete impact from different drop heights (35 and 15 mm). Finite element model accuracy was assessed through the comparison of a series of key variables including shock absorption, energy restitution, vertical deformation and contact time. Both shockpad models produced results with a mean error of less than 10% compared to experimental data.  
shockpad; artificial turf; rubber; finite element analysis; impact +
Footnote †: journal: Applied Mechanics and Mechanics of Solids
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 3 Methodology and Proposed Approach
### Physical Layer  
This layer contains the physical resources to operate in the shared mobility infrastructure as follows:  
* Shared Vehicles: Contains the physical vehicles to be used within the infrastructure.
* Edge Computing Device: used to process the collected data from the vehicle node at the edge.
* GPS Module: Tracks location for localization purposes.
* Ultrasonic Sensor: Measures the distance between the vehicle and nearby objects. It offers non-contact distance measurement functionality from 2 to 400 cm and has a range accuracy that can reach up to 3 mm [29]. The ultrasonic sensor was interfaced with the edge computing device's digital Input/Output ports.
* Force-sensing resistor: Detects if the vehicle faced any impact force caused by another object. The force range detected is 0 to 100 Newtons. This sensor was interfaced with the digital Input/Output ports of the edge computing device.
* Accelerometer and gyroscope sensor: Collects a 6-axis motion tracking. The sensor is interfaced with the edge computing unit using serial communication ports. This work used it to measure the vehicle's x, y, and z acceleration and angular velocities.
* Camera: Records the video footage of the trips while on the move. It is interfaced with the edge computing unit using the Universal Serial Bus (USB) protocol.
* Communication module: Transmits the data to the cloud services through adequate modules connected with the edge device. The communication module choice depends on the appropriate technology specified by the communication layer (discussed in Section 3.2).
## 5 Evaluation: Influence of the Main RPL Parameters on Network Convergence Performance
### Influence of the Redundancy Constant on the Network Convergence Process
The corresponding results are shown in Figure 9.  
The average number of DIO message transmissions increases as the redundancy constant grows up to the node degree. In fact, increasing the redundancy constant within this range of values leads to a lower number of DIO message suppressions. When the redundancy constant is greater than the node degree, varying this RPL parameter does not have a significant effect on the number of DIO messages transmitted, since the number of message suppressions is very low. The number of collisions varies with \(k\) similarly to the number of DIO messages transmitted, since the number of DIO message  
Figure 8: _Cont._  
Figure 9: Average number of DIO messages transmitted (**a**) and collisions (**b**) in three network size scenarios, for different network densities, as a function of the redundancy constant, \(k\). NDeg denotes the average node degree. collisions grows with the number of DIO messages that are actually transmitted. Note that despite the number of DIO message collisions increase with the redundancy constant, the network convergence time decreases with the redundancy constant. This happens because, as the redundancy constant grows, even though a subset of the additional DIO messages transmitted are lost due to collisions, a greater number of DIO messages are received correctly by non-DODAG nodes. Finally, we found that the relative influence of \(k\) on the number of DIO messages transmitted, and to a lower extent, on the number of collisions, increases with both network size and network density. As network size grows, the influence of \(k\) increases due to the multiplicative effect of a greater hop count between the root and any other node. From the results shown in Figures 4-9, it can be concluded that there is a tradeoff between network convergence time (and node join time) and the number of DIO messages sent, which depends on the redundancy constant. In order to achieve a low network convergence time, the redundancy constant should be set to a value equal or close to the average node degree. Greater redundancy constant values might cause an unnecessary increase in the number of DIO messages transmitted and collisions in very dense areas of a network, which however would not yield a network convergence time decrease. The influence of the redundancy constant grows with both the network density and size.
# Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels
###### Abstract  
Aiming at the communication network optimization problem of the Internet of Inland Vessels, this work presented a network model and deployment strategy with shore-based cooperative units as network nodes. Firstly, the system architecture and communication mode of the Internet of Inland Vessels were analyzed. The three-layer model of service, data, and transmission of ship-shore communication was established to calculate the ship-shore communication data volume of the system. Then, considering the comprehensiveness of the signal coverage of the base station, a coverage model of two-layer heterogeneous network communication was established. Furthermore, an optimization model of shore-based cooperative unit deployment was established with power consumption, cost, and data transmission rate as the objectives. The multi-objective optimization model was solved by the genetic algorithm. Finally, the proposed deployment strategy was verified through simulation cases. The simulation results showed that the proposed deployment strategy could reduce the deployment cost of shore-based cooperative units based on meeting the communication demand and deploy regional shore-based cooperative units.  
Internet of Vessels; communication networking; communication demand calculation; base station deployment; inland water transportation Article
## 2 Simultaneous Segmentation and Detection Network (SSADNet)
### Simultaneous Segmentation and Detection Network (SSADNet)  
In this section, we explain the structure and features of the network for the simultaneous segmentation and detection network (SSADNet) proposed in this paper. SSADNet trains the network using a point cloud top view image as input. It has the advantage of generating the results of segmentation and detection at the same time based on inference using the trained model.
## 2 Related Work
### Reinforcement Learning Algorithm
#### 2.3.2 Dyna-Q  
Dyna-Q is a combination of the Q-Learning and Q-Planning algorithms. The Q-Learning algorithm changes a policy based on a real experience. The Q-Planning algorithm obtains simulated experience through search control and changes the policy based on a simulated experience [18]. Figure 5 illustrates the structure of the Dyna-Q model.  
The Q-Planning algorithm randomly selects samples only from previously experienced information, and the Dyna-Q model obtains information for simulated experience through search control exactly as it obtains information through modeling learning based on a real experience [18]. The Dyna-Q update-equation is the same as that for Q-Learning (10) [22].  
The Dyna-Q algorithm is as follows (Algorithm 2) [18].  
Figure 5: Dyna-Q model structure [18].  
```
Initialize Q(s, a)  Loop forever: (a) Get s (state) (b) Choose a (action) from s (state) using policy from Q values (c) Take a (action), find r (reward), s' (next state) (d) Calculate Q value and Save it into Q Table (e) Save s (state), a (action), r (reward), s' (next state) in memory (f) Loop for n times  Take s(state), a (action), r (reward), s' (next state)  Calculate Q value and Save it into Q Table (g) Change s' (next state) to s (state)
```  
**Algorithm 2**Dyna-Q.  
Steps (a) to (d) and, (g) are the same as in Q-Learning. Steps (e) and (f) added to the Dyna-Q algorithm store past experiences in memory to generate the next state and a more accurate reward value.
## 2 Related Works
The research incorporated an array of machine learning methodologies, such as Linear Regression, Stochastic Gradient Descent Regression, Multi-layer Perceptron, and Gradient Boosting Regression. The performance of the models was evaluated using MSE, MAE, and \(R^{2}\). In a study by [PERSON] et al. [12], the multi-task learning (MTL) method was proposed to predict hourly air pollution concentrations, incorporating various regularization techniques for optimal model selection. The model leverages historical meteorological and pollutant data from the Department of Meteorology at the University of Utah between 2006 and 2015 to predict pollution levels for the following day. Model effectiveness was assessed using the root mean square error. In a study by [PERSON] et al. [13], two machine learning models, logistic regression and autoregression, were employed to analyze data from an Italian city between 2004 and 2005. While logistic regression classified samples as polluted or not, autoregression predicted \(PM_{2.5}\) levels seven days in advance. The logistic regression model's performance was assessed using mean accuracy (MA) and standard deviation accuracy (SDA), while the autoregression model was evaluated with MAE. [PERSON] et al. [14] introduced airVLC, a web application designed to predict and map air pollution levels in Valencia, Spain. This application harnesses real-time open data sources, capturing metrics like pollution readings, weather conditions, calendar events, and traffic intensities. Analyzing historical data from 2013 to 2015, the team explored multiple regression techniques to pinpoint the most effective pollution prediction method. Additionally, the study assessed various interpolation methods, emphasizing a novel approach that factors in wind direction, enhancing traditional methods like IDW and Kriging. Several machine learning models were employed to predict the primary pollutants in Valencia in real-time, such as Linear Regression, quantile regression with the lasso method, K-nearest neighbors, decision tree regression, and Random Forest. The Root Mean Squared Error was the chosen metric to evaluate each model's efficacy.
## 4 Lipsosomal Microencapsulation Using Conventional and Innovative Methods
### Innovative Methods
#### 4.2.2 Ethanol Injection Method  
The ethanol injection method, first developed by [PERSON] and [PERSON] in 1973, is a widely used technique for producing SUVs in a straightforward and rapid manner. This method was developed to overcome the limitations of the TFH method, which include difficulty in scaling up and the need for sonication to achieve homogeneity. The ethanol injection method involves the injection of an ethanolic lipid solution into an aqueous medium, resulting in the spontaneous formation of single bilayer liposomes without the need for sonication (Figure 8). This method has several advantages: it protects phospholipids from oxidative degradation and prevents potential damage to solute molecules or toxicity associated with sonication probes [70]. Unlike the TFH method, which relies primarily on hazardous organic solvents such as chloroform and methanol, the ethanol injection method uses ethanol, which is safer for workers [71]. Liposomes produced using ethanol injection are typically smaller and more monodisperse, which makes them suitable for applications requiring high transfection efficiency, such as gene delivery [72]. Additionally, this method is more suitable for industrial scale production due to its simplicity, safety, and reproducibility [73; 74].  
The ethanol injection method is widely used in the pharmaceutical, cosmetic, and food industries for its efficacy in encapsulating both hydrophilic and lipophilic drugs, thereby enhancing their stability, bioavailability, and controlled release profiles [75]. In pharmaceutical applications, this method has been particularly effective in encapsulating quercetin, a potent antioxidant flavonoid. By injecting an ethanolic solution of lipids and quercetin into an aqueous phase, researchers have consistently produced quercetin-loaded liposomes with a uniform size of approximately 120 nm and high encapsulation efficiencies ranging from 70% to 75% [51].  
Moreover, the ethanol injection method plays a crucial role in the preparation of silymarin-loaded liposomes aimed at enhancing antitumor efficacy [76]. This method was optimized using response surface methodology (RSM) to achieve high entrapment efficiency (EE%) of silymarin. The optimized formulation involved 7.8 mg/mL of lecithin, with a silymarin-to-lecithin mass ratio of 1:26 and a lecithin-to-cholesterol ratio of 10:1. The resulting liposomes showed a remarkable entrapment efficiency of 96.58% \(\pm\) 3.06%, with a particle size of 290.3 \(\pm\) 10.5 nm and a zeta potential of +22.98 \(\pm\) 1.73 mV. Sustained release of silymarin was observed in vitro, primarily driven by a diffusion mechanism. Furthermore, cytotoxicity studies on A549 lung cancer cells indicated that the silymarin-loaded liposomes exhibited a stronger inhibitory effect compared to free silymarin, highlighting their potential for intravenous cancer therapy.  
Figure 8: Liposome preparation via ethanol injection method.
## 3 Methodology  
### Input Data and Assumption  
The evaluation used Engineering Equation Solver (EES) software for analysis and modeling. The software calculates properties of thermodynamics, including temperature, exergy, pressure, and entropy. The fundamental parameters for entry of the proposed hybrid system are presented in Table 1. The assumptions used in the simulation are as follows:  
* Negligible pressure drops, heat loss, and friction effects are assumed in heat exchangers and the pipe network.
* The difference between potential and kinetic energy is zero.
* The air compressor, pumps, and turbines are assumed to operate adiabatically.
* All processes within the cycle operate in an equilibrium state.
* Air is assumed to behave as an ideal gas.  
Figure 1: Gas turbine schematic diagram.  
Figure 2: Diagram of the suggested hybrid system.
## 3 Machinability of AM Ti6 Al4V Alloy
### Chip Morphology  
High cutting speeds lead to more frequent chip segments, especially in turning titanium alloy [3]. Scanning electron microscopy (SEM) is an optical investigation methodology that produces high-resolution visual images such as those of chip morphology. The chip morphology helps to analyze the cutting forces and the energy consumption during machining by calculating the chip compression ratio, which can be obtained by dividing uncut chip thickness (\(t_{1}\)) by cut (deformed) chip thickness (\(t_{2}\)) and chip shear angle (\(\phi\)), as shown in Figure 5. Different methods used by some researchers [3,66,67] for describing the chip morphology were reported, where the researcher considered a minimum chip thickness and maximum chip thickness to calculate the degree of segmentation (\(G\)), as shown in the equation below. The results show that chip formation depends on cutting speed and uncut chip thickness.  
\[G=\frac{t_{2 max}-t_{2 min}}{t_{2 max}} \tag{1}\]  
Another interesting report described the chip morphology using an undeformed chip cross-sectional area of the line (\(A\), \(B\)) and its characteristic length of (\(L\)), as shown in Figure 6. The results show that the slipping angle (\(\theta\)) of the segmented chip was \(55^{\circ}\), higher than that of a continuous chip, at \(38^{\circ}\). The characteristic length was observed to be increased with feed rate, but independent of cutting velocity [66].  
Figure 5: Segmented chip and parameters according to the maximum and minimum chip thickness [67].  
Figure 6: Chip morphology according to the slipping angle and characteristic length [66].  
The chip compression ratio (\(r\)) was found to be an essential factor in estimating energy consumption efficiency, cutting forces, chip formation, and shear angle during machining [3]. The larger value of r results in a smaller shear plane angle and leads to larger strain and energy consumption, as shown in Figure 7.  
\[r=\frac{h_{1}}{h_{2}} \tag{2}\]
# Dynamics and Control of Typical Orbits around Saturn
## 1 Introduction
\(\Omega\) is the right ascension of the ascending node. \(\lambda\) is the latitude of body-fixed coordinate system. \(\mu\) is the Saturn's gravitational constant. \(\varphi\) is the longitude of body-fixed coordinate system. \(\omega\) is the argument of perigee. \(\omega_{s}\) is the rotational angular velocity of Saturn. In this paper, we mainly use the aspheric gravitational field zonal harmonic coefficients to design different typical orbits. Some basic parameters of Saturn are listed in Table 1[1; 7; 30]. Similar to Jupiter, the magnitude of \(J_{2}\) of Saturn is not predominant among the zonal harmonic coefficients [28; 29]. Therefore, we choose the dynamic model involved \(J_{2}\) and \(J_{4}\) terms. The gravitational potential function could be approximated as [31]:  
\[U(r)=\frac{\mu}{r}[1-\frac{J_{2}}{2}(3\sin^{2}\varphi-1)(\frac{R}{r})^{2}-\frac {J_{4}}{8}(35\sin^{4}\varphi-30\sin^{2}\varphi+3)(\frac{R}{r})^{4}].\]
## 3 Results
### SMCI Model Adaptations
#### 3.2.1 Pre-Processing  
When creating the adapted SMCI model, the DEAP data were used for training and testing; however, the RESP signal was removed. The Gazepoint system used for the data collection of the participants with SMCIs did not collect the respiration data. The average baseline GSR was also subtracted from all the values to normalize the data and keep them within the same range as the DEAP GSR data.  
The data were cleaned using the neurokit process function. This cleaning function was conducted on the pre-processed DEAP data in addition to the BDAT data. The additional cleaning phase decreased the training accuracy by less than one percent for each of the pleasure, arousal, and dominance values from the original DEAP model.  
Since the stimuli in the BDAT dataset were only shown for six seconds, an adaptation to the DEAP model was required to decrease the duration of the training trials in the model. However, as the duration decreased, the number of unique outputs for validation also decreased. For the DEAP data, neurokit could not process the training data that were less than 11 s due to the limited number of peaks. Thus, the SMCI model was trained on trials of 12 s from the DEAP dataset, while the BDAT data test set for persons with SMCIs was 6 s (neurokit was able to process original data from the SMCI participants of over 6 s).
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 3 Single Cell Patterning
### Results and Discussions
#### 3.3.2 HL-60 Cell Seeding Results
If the seeding time is extremely long, the cells that have not been caught may be attached on the surface of the substrate with nonselective adhesion, thus resulting in difficulties in the subsequent cleaning process. The experiments were conducted with the seeding times of 10, 20, 30, 40, and 50 min, and the pattern with a large feature size was employed as the observing area to reflect the antigen-antibody reactions. It was indicated that almost all the full patterns were covered with the seeding time of 30 min. Therefore, we chose 30 min as the seeding time in the following experiments. (4) The influence of geometric parameters on single-cell capture rate. For most cases, we aim to develop single-cell patterning chips. When the patterning islands become larger, more cells tend to be immobilized on one island. Meanwhile, if the island is extremely small, only a few cells could be captured on the island. Therefore, the relationship between the number of cells captured and the diameters of the patterns was investigated. According to the analyses above, the seeding concentration was still 5 \(\times\) 10\({}^{6}\) cells/mL. Figure 10 shows the ratios of the patterning islands capturing no cells, only one cell, two, or more than two cells. It is indicated that when the  
Figure 9: Patterning results of CD15 antibodies and HL-60 cells. diameter of the islands is less than 10 \(\upmu\)m, the streptavidin on each island was insufficient to capture the cell. Meanwhile, when the islands are larger than 30 \(\upmu\)m, two or more cells appear on most islands. The islands with diameters between 15-25 \(\upmu\)m are the optimal ones to capture only one cell, and are favorable for forming single-cell patterning chips. CD45 was used in this experiment.
## 2 System Model  
In this section, the system model for the LEO-SMC-ICN-IoT is described. The model consists of the LEO-SMC-ICN-IoT scenario, LEO satellite mega-constellation network, and mobility calculation model.