esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation  
In this section, we give detailed descriptions about two key implementations--FRL and network simulator implementations--for evaluations of the proposed system. Furthermore, we explain various experiments and show the evaluation results that the UAV-aided AAN could operate efficiently in densely populated areas where the satellites cannot provide sufficient Internet services, which improves network performances.
## 4 Structural Sensitivity Analysis
### Sensitivity Analysis of the Maximum Deflection to the Boundary Conditions  
The abovementioned analysis was based on the specific boundary conditions which was in consistent with the real bridges. Hence, it is necessary to clarify whether the obtained results are valid for various boundary conditions. For this purpose, three types of boundary conditions were considered to investigate the relationship between the bulk density and maximum variation value of deflection per span: (1) the hinged connection was modelled between the pier labelled N3 and the main beam, while the rigid connection was considered between the other piers and the main beam (labelled boundary condition A); (2) the hinged connection was modelled between the pier labelled N4 and the main beam, while the rigid connection was considered between the other piers and the main beam (labelled boundary condition B); and (3) the hinged connection was modelled between the pier labelled N5 and the main beam, while the rigid connection was considered between the other piers and the main beam (labelled boundary condition C). The results are plotted in Figures 17-19.  
Figure 16: Relationship between the external prestress and the maximum variation value of the deflection per span.  
Figure 15: Variation of the maximum deflection at the cantilever end per span.  
Figure 19: Relationship between the bulk density and maximum variation value of deflection per span corresponding to the boundary condition C.  
Figure 17: Relationship between the bulk density and maximum variation value of deflection per span corresponding to the boundary condition A.  
It can be seen from the above figures that under three different boundary conditions, the maximum deflection change in each span still occurs at the mid-span position, and with the change in bulk density, the maximum deflection change (mid-span position) in each span presents a linear relationship, in which the change is the largest at the fifth span, and the deflection changes by 26% when the bulk density changes by 5%. However, the qualitative conclusions are still the same. Therefore, it is believed that the performed sensitivity analyses are valid for various boundary conditions.
## 6 Governing Software
### Operating Systems
* ROS2 supports real-time capabilities, allowing for the more precise timing of operations, which is crucial for applications such as motor control and other critical robotic functions. * Multiplatform Support:
* ROS1 is primarily developed for Unix-based operating systems, mainly Ubuntu
* ROS2 is more portable and supports a variety of OSs, including Windows, macOS, and various Linux distributions: it is more flexible for different development environments. * ROS1 has limited security options, which can be a concern in commercial applications. Data privacy in agriculture is important! * ROS2 introduces advanced security features such as communication encryption, node authentication, and authorization, improving security in broader and critical scenarios. * Communication Management:
* ROS1 uses a simple TCP/UDP-based communication model, which may not be efficient in complex or large networks. * ROS2 uses DDS for communication management, which is highly scalable and efficient for large-scale distributed systems. * Usability and Documentation:
* ROS1 has a large user base and extensive documentation with many years of development, making it accessible for new users. * ROS2 is still expanding its documentation and examples but is quickly gaining ground due to its modern architecture and new features. These differences make ROS2 the go-to choice for new projects in agriculture that require high performance, enhanced security, and multiplatform support, while ROS1 may still be adequate for less critical applications or for those with existing ROS1 experience and the existing infrastructure.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry  
Accelerometer data are utilized in many applications in various activities, such as vehicle engineering, machinery industry, building and structural monitoring, navigation, handheld products, and image stabilization. The present subchapter reviewed those applications, including the areas of application and the method of data processing, to underline the wide range of applications of accelerometers.
## 4 Numerical Calculation Results and Analysis  
### Modal Results Analysis  
In modal analysis, due to the requirements of the finite element software for the fluid element, it is necessary to set master-slave degrees of freedom for the fluid element. On a fluid-free surface, the degrees of freedom in the normal direction are defined as the primary degree of freedom, and the remaining degrees of freedom are defined as the slave degrees of freedom. In the coupling alignment of the nodes on the fluid-solid interface, only the first degree of freedom can be defined as the master degree of freedom, and the rest are the slave degrees of freedom.  
The sloshing characteristics and mode shapes are important parameters in a dynamic analysis. The determination of these parameters can be very useful in describing the tank's behavior. The vibration modes of the liquid storage tank are classified roughly into convective and impulsive. The convective mode and impulsive mode are the most important vibration modes that have the maximum mass contribution effects. This modal analysis can be used as the starting point of the time history analysis.  
To perform the verification of the model, the numerical results are compared with results of a theoretical formula and standard formula. The basic frequency of the liquid sloshing and the basic frequency of the coupling vibration of the tank were obtained by modal analysis. The theoretical formulas for the basic frequency \(f_{\rm w}\) of the liquid convective oscillation and the basic frequency \(f_{\rm c}\) of the coupling vibration of the tank and liquid are given in [17]. In China, the code of the \"vertical cylindrical steel welded tank design\" (GB50341-2014) [18] also provides the formulas for \(f_{\rm w}\) and \(f_{\rm c}\). The results comparison of the numerical calculation, theoretical formulas, and recommended formulas in the code are listed in Table 1.  
The fundamental frequencies of the liquid sloshing and the liquid-structure coupling vibration calculated in this paper are 0.228 Hz and 7.046 Hz, respectively. The calculated values are highly similar to those obtained using the proposed method, with the maximum error of 5.56% only. Thus, the analytical model established in this paper is validated. Rayleigh damping is used for the subsequent seismic behavior of the liquid-structure system. The damping ratio is 2% for impulsive mass, and the damping ratio is 0.5% for convective mass (damping for water).
## 2 Materials and Methods
### Device Fabrication and Characterization  
The OLED devices were fabricated on glass substrates precoated with a 150 nm thick indium tin oxide (ITO) layer with a sheet resistance of \(\sim\)10 \(\Omega\) per square. The ITO glass was cleaned with ultrasonication in deionized (DI) water, detergent, acetone, methanol and DI water for 10 min each in sequence. Then, the ITO substrate was dried with N\({}_{2}\) gas flow and treated with UV-ozone for 15 min at 100 \({}^{\circ}\)C. After the cleaning procedure, the ITO substrate was loaded into a high-vacuum chamber at a base pressure of \(<\)1.0 \(\times\) 10\({}^{-7}\) Torr. The 4,4\({}^{\prime}\)-bis[\(N\)-(1-naphthyl)-\(N\)-phenylamino]biphenyl (NPB), tris(8-hydroxyquinolinato)aluminum (Alq\({}_{3}\)), and DPAVBi were purchased from Luminescence Technology Corp. and they were used without further purification. The organic materials and LiF were sequentially deposited onto the substrate with thermal evaporation using Knudsen cells. The NPB and Alq\({}_{3}\) were deposited with a rate of 0.8 A\(\cdot\)s\({}^{-1}\) while each host material and DPAVBi were codeposited with a rate of 0.2 A\(\cdot\)s\({}^{-1}\) and 0.016 A\(\cdot\)s\({}^{-1}\). After that, the sample was transferred to another high-vacuum chamber without breaking vacuum, and then Al was deposited using a BN boat at a rate of \(\sim\)1 A\(\cdot\)s\({}^{-1}\). The total thicknesses and deposition rates were monitored with a quartz crystal microbalance. The device area was 0.04 cm\({}^{2}\). The current density-voltage-luminance (\(J\)-\(V\)-\(L\)) characteristics of the OLEDs were measured using two Keithley 2400 source measure units and a Si photodiode. The electroluminescence (EL) spectra were measured using a PR650 spectroradiometer (StellarNet Inc., Tampa, FL, USA). Assuming Lambertian emission, the external quantum efficiency (EQE) was calculated from the measured \(J\)-\(V\)-\(L\) characteristics and EL spectrum.
# Lineal Energy of Proton in Silicon by a Microdosimetry Simulation
## 1 Introduction
tried to use a simplified Monte Carlo method, which allow only one filial of secondary particles to compute their corresponding ranges. They then used the range to calculate LET and predict SEE [8]. However, the secondary particles generated under proton irradiation are usually quite unstable and will decay or have further interactions with silicon, which may generate light ions with lower energy and higher stopping powers. All these ions can affect the SEE. An example of such can be found by [PERSON] et al. who found that carbon ions can fragment into several secondary particles during transportation [27]. Recently, there is an increasing number of Monte Carlo based SEE evaluation toolkits. Intel developed the Intel Radiation Tool (IRT) based on Geant4 [28]. [PERSON] and colleagues in Vanderbilt University has also developed another Geant4 based Monte Carlo Radiative Energy Deposition (MRED) Code [29]. In Geant4 Space Users' Workshop and Nuclear & Space Radiation Effects Conference (NSREC), using Monte Carlo technique to predict radiation effect in semiconductor is a routine discussion. However, within the Monte Carlo framework, there are hundreds of parameters that could affect the results, and there is no conclusion on which setting is more accurate. Besides, the setting should be different for different purpose. In this work, a Geant4 [16] based Monte Carlo simulation is performed to simulate the effect of proton beams on silicon. Different sensitive volume thicknesses are compared. The lineal energy is also analysed in detail for each particle crossing the sensitivity volume, and the physical models for intra-nuclear interaction in Geant4 are also compared.
## 2 Theoretical Model for Active Polarization Control System
### Mathematical Model of Polarization Controller
5. accumulate the square gradient, as follows: \[r_{i+1}=\beta r_{i}+(1-\beta)g^{2},\] (8) where i is the iteration number, the attenuation coefficient \(\beta\) is a hyperparameter, which is set to 0.9, and the initial accumulation of square gradient r\({}_{0}=0\); 6. update the control signals as follows: \[u_{i+1}=u_{i}+\frac{\eta}{\sqrt{r_{i+1}+\varepsilon}}\cdot\delta\cdot\delta u_{i},\] (9) where \(\eta\) is global learning rate, and the hyperparameter \(\varepsilon\) is a small constant, which is 0.01 in our simulation; 7. iterate the steps (2)-(6) descripted above until the RMS-Prop controller is turned off.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 3 Materials and Methods
### Location Estimation
#### 3.4.1 Scene Recognition  
Scene recognition is done using the trained CNN classifier. The image from the user phone is captured and sent to the server for scene recognition. The captured image is pre-processed before scene recognition can be done. Preprocessing is an important task to improve the accuracy of an approach and various preprocessing techniques have been used in research [65] with hidden Markov model, support vector machines, etc. For example, research [66; 67; 68] uses silhouette preprocessing before generating features to produce body skeleton models. The produced models are then used for human tracking [69]. Similarly [70; 71] have used depth silhouette from depth camera images for motion tracking. Preprocessing in our model involves changing the size of the image and color normalization. The size of the image is changed to 200 \(\times\) 256 while the color is normalized for color range of [0 256]. We need only one image to identify the scene. It will save the phone battery which is substantially consumed while capturing images. During the training process, the images are captured at points as shown in Figure 3. The trained classifier presents the probability distribution for the scene images used during the training phase. We take the higher probability scene and use it for search space narrowing.  
The recognized scene during the scene recognition phase is used to load the magnetic database of a specific floor. Since each scene is labeled with respect to its floor as well as a specific place, so, it can be used to identify which floor the user is at. Then the recognized scene is utilized to narrow down the search space in the magnetic database of that floor. The magnetic data can be very similar at different places on a floor which causes higher localization errors. Scene recognition helps in narrowing down the search space which will reduce the localization error.
# Poly(vinyl alcohol)/Gelatin Scaffolds Allow Regeneration of Nasal Tissues
## 2 Materials and Methods
### Porosity and Pore Features of PVA/G Sponge Surfaces  
Micro Computer Tomography (MicroCT) is a non-disruptive method recently used to measure 3D porosity and pore interconnectivity of scaffolds. A parallelepiped scaffold was scanned with a microCT imaging system (SkyScan 1172; SkyScan, Aartselaar, Belgium). Data were acquired and analyzed as previously reported [37; 38]. Briefly, the raw micrographs were reconstructed using a Feldkamp algorithm. Then, grayscale thresholds of 45 (lower) and 255 (upper) were selected to have a realistic correspondence between grayscale tomograms and their binarized representations. Representative 3D reconstructions of the samples were generated to show a 3D model of the scaffold. Quantitative data were calculated considering a cylindrical volume of interest (VOI) within the boundaries of the scaffold. The total VOI selected in this study was 1.373 cm\({}^{3}\). Scaffold porosity was obtained using Equation (1):  
\[\%\;\text{Porosity}=\left(1-\frac{\text{V}_{\text{bin obj}}}{\text{VOI}} \right)\cdot 100\% \tag{1}\]  
in which \(\text{V}_{\text{bin obj}}\) is the binarized object volume within the total VOI. Thereafter, the scaffold interconnectivity was calculated as the void volumetric fraction accessible from the outside through pore openings of a predefined size [37; 38]. In this study, a cut-off size of 4 voxels, equal to 51.2 um, was defined as dimensionally compatible with MSC migration. The interconnected volume was estimated via a \"shrink-wrap\" process, as reported in the CT Analyzer instructions. The percentage of inaccessible volume can be finally determined in Equation (2), as:  
\[\%\;\text{Inaccessible Volume}=\left(\frac{\text{VOI}_{\text{POST}}-\text{V}_{ \text{bin obj,POST}}}{\text{VOI}_{\text{PRE}}-\text{V}_{\text{bin obj,PRE}}} \right)\cdot 100 \tag{2}\]  
Here, the terms POST and PRE refer to after and before the shrink-wrap process, respectively. Finally, interconnectivity percent can be calculated as the complement to the inaccessible volume percent.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 3 Code Generators
### Proposed Universal Code Generator
signals, while configuring bits 20-30 of R0 and R1 generates B1I or B2I signals. In mode 1001, two sets of L1 OCd and L1 OCp are generated by configuring bits 0-43 of Register R0 and bits 0-31 of R1. In mode 1010, the structure of L2 OCp, L3 OCd, and L3 OCp code generators is completely identical, generating different signal spread spectrum codes by configuring bits 0-41 in R0 and bits 0-20 in R1. In mode 1011, configuring bits 0-13 of R0 and R1 generates E5b-I components, while configuring bits 14-27 of R0 and R1 generates E5b-Q components. In mode 1100, configuring bits 0-39 in Register R0 and bits 0-19 in R1 generates a spread spectrum code for both L6D/E channels.
## 3 Results and Discussion
### Alginate-Glycerol Films

Table 2: Assignments of Raman modes of 5% alginate, glycerol, starch, 5% alginate–glycerol, 5% alginate–starch and 5% alginate–starch–glycerol films at room temperature; \(\delta\) = bending, \(\
u\) = stretching; \(\tau\) = twisting, s = symmetric; as = asymmetric; T = trans; G = gauche; \({}^{*}\) = fitted value []. The study of the two area ratios \(\tau\) and \(\mu\) defined above seems relevant to describe these phenomena. In Figure 5, we present the evolution of these ratios according to the mass proportion of alginate in the water. From this figure, it is noted that between alginate and alginate-glycerol films the area ratio \(\tau\) seems to have the same evolution according to mass proportion of alginate in the water but with lower values: a reduction with a factor of four for the small percentages (1%, 2% and 3%), then by a factor neighbor of two for 4% and 5%. These results are consistent with what we expected: a decrease in the vibrational contribution of hydrogen bonds. For area ratio \(\mu\), we remark that the ratio \(\mu\) of alginate-glycerol films is very slightly lower for the 1%, 2% and 3% mass proportion of alginate in the water compared to alginate films. It is only for a rate above 3% that a significant drop is observed: for 5% alginate this area ratio goes from about 5.0 to 2.5, i.e., a division by 2. If the vibrational contribution of the _gauche_ C-C stretching mode is divided by 2, it means that the intermolecular mobility within the alginate-glycerol-water mixture is multiplied by 2. However, it is the role of glycerol to increase this intermolecular mobility.
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 5 Conclusions  
From the above theoretical analysis and experimental results, it can be seen that the double LT method increases the number of times to intercept the eavesdropper's continued decoding due to the twice LT coding, effectively improves the channel security capacity, and improves the effect of anti-eavesdropping. Since this method performs secondary LT encoding on a smaller number of degree-1 symbols in the LT code, it reduces the number of encoded symbols required to be sent by the source for decoding and the complexity of coding compared to other secondary LT coding schemes. When the main channel is better or slightly worse than the eavesdropping channel conditions, due to the increase in the original symbols with the source, this effectively increases the number of symbols for the secondary LT coding degree-1. The number of symbols decoded by the eavesdropper is reduced, which improves the untranslated rate of the eavesdropper, and at the same time reduces the number of encoded symbols that need to be sent from the source for decoding, so the use of the long codes of the double LT code has a better effect in anti-eavesdropping.  
Conceptualization, L.W.; Methodology, L.W.; Software, L.W.; Validation, L.W.; Formal analysis, L.W.; Investigation, L.W.; Resources, L.W.; Data curation, L.W.; Writing--original draft, L.W., F.N. and J.J.; Writing--review & editing, L.W., J.J. and L.Y.; Visualization, L.W. and F.N.; Supervision, F.N.; Project administration, F.N. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Liaoning Provincial Education Department Fund (IJKMZ2020965, IJKZ0624).  
Not applicable.  
Author [PERSON] was employed by the Liaoning University of Technology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results
### Quantum Dot-Based Sensors  
#### 3.4.1 Synthesis Approaches and Sensor Integration  
Quantum dots are nanoscale semiconductor particles that exhibit quantum confinement effects, resulting in discrete energy levels and tunable optical properties [60]. Several synthesis approaches and sensor integration strategies have been developed for quantum dot-based sensors.  
One common method for quantum dot synthesis is colloidal synthesis [61]. In this approach, precursor materials, such as metal chalcogenides or semiconductor nanoparticles, are dissolved in a solvent, and appropriate capping ligands are added. Through controlled chemical reactions, the precursor materials nucleate and grow into quantum dots. The size and composition of the quantum dots can be precisely controlled, allowing for tailoring of their optical properties.  
Other techniques, such as epitaxial growth, molecular beam epitaxy, and electrochemical synthesis, have also been employed for quantum dot fabrication. These methods offer additional control over quantum dot properties, including size, shape, and composition. Epitaxial growth techniques enable the growth of quantum dots directly on substrates with high crystalline quality, facilitating their integration into sensor devices [62].  
To integrate quantum dots into sensors for bridge maintenance, various strategies can be employed. Quantum dots can be dispersed or embedded within a host matrix, such as polymers or thin films, to form a sensing layer. They can also be functionalized with specific ligands or receptors to enable selective sensing of target analytes. Integration strategies also involve coupling quantum dots with appropriate optical or electrical readout systems for signal detection and analysis.