esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Reality in Space Technology Education
## 2 Virtual Lab Experience
### Simulation Phases
#### 2.3.4 Students' Mission  
The professor describes the work plan for the mission: assemble the satellite subsystems, integrate all subsystems to build the satellite and finally launch the satellite with the rocket and control it in its orbit.  
Each student avatar is moving to the working bench to connect their system. The horizontal hologram screen appears to give them the main information about their satellite subsystem function and its parts. There are four main satellite subsystems: power subsystems, communication subsystems, onboard computer subsystems and payload subsystems. As shown in Figure 5.  
The student must fix each part in its position in the subsystem module to finish this level and upgrade to the next level.  
The avatars are moving to the integration lab, where a 3D diagram of the remote sensing satellite parts will be displayed in this lab. Players can view and rotate the diagram with their hands. Once the avatars are near the integration bench, they will start integrating the satellite parts in a specific order. The players must try one after the other to find the correct part. If a player attempts to assemble the wrong part, it will not be inserted, and a red light will be displayed to instruct them to try another part. A green light will be displayed when the player puts the right parts together, permitting them to proceed. After the players complete their assembled parts, they will turn to gather at a central assembly point (a worktop) to put all the satellite parts together. A flash screen with the heading \"Go into the control room\" for each player is displayed in Arabic, as shown in Figure 6.  
Figure 4: The hologram shows different types of satellites.  
Figure 5: Assembly of satellite subsystems.  
After the players complete integrating the satellite subsystems, the drone carries the satellite to the launch site, and the players go to the launch observation room (Figure 7).  
The rocket parts are presented as a hologram, and the big screen shows the rocket carrying the satellite and ready for launching into outer space. Finally, the players have to enter the password for the launch.  
After launching, the players have to move to the satellite control station to confirm that the satellite in their orbit is working very well and can send data and receive commands (Figure 8).  
Figure 8: Satellite control station.  
Figure 6: CubeSat after integration.  
Figure 7: The launching observation room and hologram for the rocket.  
The learning outcomes of this stage are:  
1. Understand the satellite manufacturing process.
2. Realize the main function of each satellite subsystem and its essential parts.
3. Recognize the integration procedure and the importance of fixing the module in a specific manner to accomplish the mission correctly.
4. Recognize the method of launching the satellite into the specific orbit using the rocket controlling the satellite in its orbit.
# Experimental Characterization of Two-Phase Aerated Liquid Ethanol and Jet A Spray Flames +
## 4 Conclusions  
An experimental characterization was performed on the two-phase far-field fuel spray produced by a laboratory-grade glass nebulizer in cold-flow and combusting conditions using shadowgraph, Mie scattering, and flame chemiluminescence. Analysis of the spray using ethanol as fuel yielded data on the non-ignited and ignited spray core angles, flame geometry, fuel concentration, liquid droplet atomization characteristics, and levels of CH*, C2*, and CO2*. Comparison of the ethanol spray was made with Jet A spray at multiple equivalence ratios. The results indicated that the Jet A spray atomized from the nebulizer at a rate similar to that of the ethanol spray since both sprays featured similar angles and produced flames beginning \(\sim\)6 mm above the nozzle, and showed the higher energy density (and thus greater combustibility) of the nebulizer's Jet A spray over that of ethanol by the higher overall intensity of chemiluminescence emissions in the Jet A spray flames. Additionally, the Jet A's emission products showed more widespread concentration throughout the flame vs. the ethanol's emission concentrations in the initial combustion region at the bottom of the flame. The results provide an initial characterization of the nozzle's spray cold-flow and combusting spray output for the two fuels. This characterization can act as a benchmark for further analysis of the nozzle performance as well as future work which focuses on fuel studies and diagnostic development.  
Potential future work may involve quantification of the droplet sizes and chemical species presence in the nozzle's spray output using further applications of Mie scattering, chemiluminescence, and laser-induced fluorescence. The nozzle has thus far been applied only in a quiescent environment, and could be analyzed under elevated pressures and in cross-flowing environments. Additionally, the demonstrated successful flow conditions for ethanol and Jet A combustion from the nozzle could be used for designing experimental parameters for comprehensive combustion characterizations using this nebulizer, and comparison of combusting sprays from other liquid fuels of current interest such as aviation biojet propellants. Liquid structures in the dense-spray region near the nozzle exit could also be analyzed via application of SLIPI.  
Conceptualization, Y.W. and Z.Z.; methodology, C.S., M.G., and Y.W.; software, C.S., M.G., and Y.W.; validation, C.S. and M.G.; formal analysis, C.S. and M.G.; investigation, C.S.; resources, Z.Z.; data curation, C.S. and Z.Z.; writing--original draft preparation, C.S., Y.W.; writing--review and editing, Z.Z.; visualization, C.S.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by NSF-2026242 and University of Tennessee. The Jet A fuel was provided by Air Force Research Laboratory.
## 3 Results and Discussion  
### Microstructure and Composition of Fireproof Coatings  
Fireproof coatings generally consist of a binder, flame retardants, and fillers. The binder forms the foundation of the coating and is the main film-forming substance. It can be divided into organic and inorganic film formers. Organic film formers come in various types and are typically flame-resistant synthetic resins, such as phenolic and polyester resins, while inorganic film formers include silicates and phosphates. Flame retardants are the key components that enable fireproof coatings to perform their protective function. Common flame retardants include halogenated, inorganic, and phosphorus-based flame retardants. Fillers can enhance the mechanical and physical properties of fireproof coatings, such as color, acid and alkali resistance, and flame resistance. Common fillers include titanium dioxide and antimony trioxide.  
Scanning electron microscopy (SEM) was used to test the fireproof coating applied to the surface of galvanized steel, revealing the microscopic structure of the coating as shown in Figure 4. The surface of the coating appears rough and porous, with uneven particle sizes and shapes. There are noticeable gaps and voids between the particles, which may facilitate the expansion of the coating at high temperatures, thereby forming an insulating layer that enhances fire resistance.  
Figure 3: Calvanized steel sample coated with fireproof coating.  
The Energy Dispersive Spectroscopy (EDS) test was conducted on the fireproof coating to study its composition, and the results are shown in Table 1. According to Table 1, the main elements of the fireproof coating are C, O, Ti, P, Al, Si, and Ca. The right side of the table indicates that the highest contents are C and O, suggesting that the base material is an organic film-forming substance. The presence of Al, Ca, and Ti indicates that titanium dioxide, calcium carbonate, and aluminum hydroxide are added as fillers in the fireproof coating. The presence of P indicates that the flame retardant used is a phosphorus-based flame retardant.
## 2 Mathematical Modeling of Class-E Series-Tuned RFPAs
### Class-E Load Network Topology with Infinite DC Feed  
The schematic of a Class-E-tuned switched-mode power amplifier with a series-tuned resonator is shown in Figure 1a. The load network encompasses a parallel capacitance (\(C=C_{dev}+C_{ext}\)), which represents the device's inherent capacitance \(C_{dev}\), along with any external capacitance \(C_{ext}\), ensuring the current in and out of the switch-capacitor combination. For simplification, \(C_{dev}\) is neglected in this work.  
The series inductance \(L_{x}\) produces a reactance \(jX\) with a series resonant circuit (\(L_{s}-C_{s}\)) tuned at the design frequency, \(f_{o}\), which is then finally connected to the optimal load resistance, \(R\). The supply voltage is \(V_{DD}\) and \(V_{BB}\) is the biasing voltage. \(i_{s}\) and \(v_{s}\) are the voltage and current in the ideal switch respectively. \(i_{C}\), \(I_{0}\), and \(i_{R}\) are the currents flowing through the capacitor \(C\), RF choke, and load \(R\), respectively. The equivalent circuit is shown in Figure 1b. The current flowing through the load is given by Equation (3).  
\[i_{R}(\omega t)=I_{R}\sin(\omega t+\phi) \tag{3}\]  
where \(\phi\) is the initial phase shift, \(I_{R}\) represents the peak amplitude of the current, and \(\omega=2\pi f\).  
With the initial on-state condition, the DC can be given as \(I_{0}=-I_{R}\sin(\phi)\), and the switch current is given by Equation (4).  
\[i_{s}(\omega t)=\begin{cases}I_{R}[\sin(\omega t+\phi)-\sin(\phi)]&0\leq \omega t\leq\pi\\ 0&\pi<\omega t\leq 2\pi\end{cases} \tag{4}\]  
With the switch being off, the current through the capacitor is given by Equation (5).  
\[i_{c}(\omega t)=I_{0}+I_{R}\sin(\omega t+\phi) \tag{5}\]  
Figure 1: A Class-E amplifier with infinite DC feed (**a**) schematic and (**b**) its equivalent circuit, where the arrows show the direction of current flow.  
and the switch voltage is given by Equation (6), which can then be expanded to Equation (7).  
\[v_{s}(\omega t)=\frac{1}{\omega C}\int_{\pi}^{\omega t}i_{c}(\omega t)d(\omega t) \tag{6}\]  
\[v_{s}(\omega t)=\begin{cases}0&0\leq\omega t\leq\pi\\ -\frac{I_{B}}{\omega C}[\cos(\omega t+\phi)+\cos(\phi)-\omega t-\pi\sin(\phi )]&\pi<\omega t\leq 2\pi\end{cases} \tag{7}\]  
The primary objective of the problem is to find the unknown parameters \(I_{R}\) and \(\phi\). Applying the ZVS condition given by Equation (1), the unknown values can be calculated as \(\phi=57.5180\) and \(I_{R,max}=2.8621I_{0}\). Finally, the optimal load resistance \(R\) and the values of \(L_{x}\) and \(C\) can be calculated using Equations (8)-(10).  
\[R=0.5768\frac{(V_{DD})^{2}}{P_{out}} \tag{8}\]  
\[L_{x}=\frac{1.1525R}{\omega_{0}} \tag{9}\]  
\[C=\frac{0.1836}{R\omega_{0}} \tag{10}\]
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 4 The Experiments of Liver Tumor Segmentation
### Loss Function  
In this research, convolutional neural networks utilize weighted cross-entropy (WCE) loss functions. This function assigns penalties to every class-based, object-based, and detection problem based on their individual median frequencies [31].  
\[WCE=-\frac{1}{n}\sum_{i=1}^{n}W_{c,i}[T_{i}\text{log}P_{i}+(1-T_{i})\text{log}(1 -P_{i})] \tag{2}\]  
In this equation, the sum covers the images for training, denoted by \(n\), where \(P_{i}\) represents the segmentation class, \(T_{i}\) corresponds to the detectable object target or real segmentation label, and \(W_{c,i}\) represents the weight used for class.
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 5 Defect Recognition Algorithm Based on Acceleration of FPGA  
To date, hardware acceleration of neural networks has been extensively studied. Due to the high degree of parallelism of the network's operation, the current hardware acceleration means are mainly based on the CPUs (Central Processing Unit) and FPGAs. The CPU implementation is characterized by high versatility and relatively simple development effort, but high-power consumption. In contrast, FPGAs offer high performance, low power consumption and high flexibility. What's more, it could be arranged into different dedicated neural network configurations according to the application environment. Therefore, we use a FPGA to realize the BP neural network and achieve faster run times and lower power consumption than conventional CPUs [26]. Figure 11 shows the flow chart of a BP neural network algorithm based on the FPGA. It may be seen from this figure that the algorithm requires a neural element accumulation multiplication module, an excitation function module of the Sigmoid function, an error calculation module, a weight update module and a RAM (Random Access Memory) module. Among them, the RAM module is implemented using an IP (Intellectual Property) core, and the Sigmoid function module is realized by the piecewise nonlinear function approximation. Other modules may be implemented using simple addition, subtraction and multiplication.  
We then use the same training set to test the running speed and power consumption of the neural network algorithm using the FPGA and CPU implementations. Table 5 shows the results. We see that compared to the CPU, the calculation speed of the FPGA is increased by about 30 times, while its power consumption is 1/10 of the CPU's.  
Figure 11: Flow chart of the back propagation (BP) algorithm based on the FPGA. RAM: Random Access Memory.
# Aerodynamic Sensitivity Analysis for a Wind Turbine Airfoil in an Air-Particle Two-Phase Flow
## 7 Conclusions  
To investigate the aerodynamic sensitivity to particles for a wind turbine airfoil, the incompressible RANS equations have been coupled with a DPM model, and the air-particle flows have been simulated for the S809 airfoil of the Phase VI blade, the NH6 MW25 airfoil of a 6 MW wind turbine and the NACA0012 airfoil. According to the numerical results and analyses, the following conclusions can be drawn.  
Figure 19: Angles of attack of Phase VI blade sections at different tip speed ratios.  
Figure 18: Pressure coefficient distributions over two airfoils at \(Re=1\times 10^{6}\) and \(m=0\%\): (**a**) NH6 MW25 airfoil; (**b**) S809 airfoil.  
1. In an attached flow, the particle-induced aerodynamic performance degradation is relatively slight and is caused by the boundary layer momentum loss. In the case of flow separation, a significant degradation is induced by a more severe separation due to particles.
2. For a wind turbine airfoil in an air-particle flow, the lift decrement, drag increment and the upstream-moving distance of separation point vary with the increasing angle of attack from increasing to decreasing and to increasing again. A most sensitive angle of attack to particles is found in the light stall region, and it is about 3\({}^{\circ}\) higher than the angle of attack for the maximum lift-to-drag ratio for the S809 airfoil. Hence, the aerodynamic performance of a wind turbine is very susceptible to particles considering its operating state, environments as well as pitch control.
3. For the S809 airfoil, the most sensitive angle of attack is not affected by the particle concentration within the considered range of \(m\leq 2\%\).
4. Unlike a wind turbine airfoil, there is not a most sensitive angle of attack to particles for the NACA0012 airfoil due to the lack of light stall.  
Conceptualization, [PERSON], [PERSON] and Z.L.; data curation, [PERSON]; methodology, T.G., J.J. and Z.L.; project administration, [PERSON]; software, T.G. and J.J.; validation, T.G., J.J., Z.L., D.Z. and T.W.; writing--original draft, T.G. and J.J.; writing--review & editing, [PERSON], D.Z. and T.W.  
This research was funded by the National Natural Science Foundation of China, grant number 51566011 and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
The authors declare no conflict of interest.
# Conceptual Design of BCI for Mobile Robot Control
## 4 Discussion
The offline tests of the proposed algorithms for SSVEP characteristic parameters detection confirmed that the accuracy of the parameters classification is influenced by the length of the analyzed signal, the number of multiples taken for analysis, and the number of measuring electrodes. The offline tests allowed the selection of control algorithms based on statistical methods (LASSO) to be more efficient in detecting characteristic parameters of SSVEP [10] in comparison with methods using spectral analysis (PSDA). A virtual environment was created that fully imitated the brain-computer interface, implementing feedback based on the control system model and the mobile robot's kinematic model. This environment allowed free offline testing by \"injecting\" previously registered data, and then used it to conduct final online tests. This approach significantly reduced the time needed to develop theresulting system. The conducted online tests confirmed the SSVE potentials fitness to control a mobile robot. The presented conclusions also confirm the effectiveness of the electroencephalographic signals application as control signals in the brain-computer interfaces.
## 3 Imaging in the Follow-Up of Treated HCC
### TACE-Associated Necrosis  
As previously mentioned, due to its complex therapeutic mechanisms, TACE may induce a mix of coagulative and liquefactive necrosis. Coagulative necrosis shows a typical T2-WI hypointensity. However, T2-WI focal hyperintensities may be seen when hemorrhage, inflammation, or liquefactive necrosis are associated [146]. Furthermore, after TACE, HCC nodules commonly show variable unenhanced T1-WI intensity, a combined effect of areas of hyperintense hemorrhage and hypointense necrosis [147]. The lipiodol used in cTACE also appears hyperintense on T1-WI in the days following the procedure and increases the contrast-to-noise ratio (CNR); however, it appears that T2-WI is not affected by lipiodol [148]. The extent of lipiodol uptake within the tumor nodule is considered to correlate with therapeutic success [149]. However, this is easily assessed through CT due to the composition of iodine. MRI is not an equally reliable instrument in this regard, although gradient echo images can provide good approximation [150]. Therefore, volumetric techniques have been developed for the prediction of cTACE-induced necrosis extension using MRI contrast enhancement and CT lipiodol volume measurements [151].  
DWI also plays an important role in the post-therapeutic assessment of cTACE. Highly vascularized lesions will show prominent restricted diffusion with lower values on the ADC map. This will likely correlate with better results of the TACE [152]. However, necrotic tumors will likely show higher values on the ADC map, which may lead to a poorer prognosis and correlate with higher aggressiveness [153; 154]. Regarding DEB-TACE, DWI proved to be useful in predicting treatment response, as increases in ADC after the procedure correlate with longer survival [155].
## 2 Problem Modeling  
### WSSN Architecture  
The architecture chosen for the WSSN is a single-tier clustered-based architecture [1] where IP-based cameras (sensors) are wirelessly connected to a central clusterhead, which is also in charge of processing the multimedia data (audio, video and possibly other scalar sensor data) received from the cameras. To cope with the high bandwidth requirements of multimedia transmission and the described architecture, we have resorted to IEEE 802.11n (Wi-Fi) technology working in the 2.4-GHz band. In this architecture, the clusterhead will be a device connected to (or integrated in) the Wi-Fi access point. As this architecture uses existing Wi-Fi networks, we have considered that, in addition to the cameras, there are other wireless devices in the Wi-Fi network, which are connected to the Internet through their associated access point.  
Figure 1 shows a graphical view of the architecture. Although there are other technologies for sensor networks, like ZigBee or IEEE 802.15.4, these technologies have a clear shortcoming that impedes their use in WSSNs, as their transmission data rate is limited to 250 kbps, which makes real-time video transmission almost impossible [20] if we want to meet the quality standards needed for surveillance. Wi-Fi, on the contrary, provides the necessary bandwidth to transmit high-quality video in sensor networks [21, 22, 23].  
Obviously, the main drawback of using Wi-Fi technology instead of ZigBee is that it is a less energy-efficient solution. For example, in [22], the authors propose a video sensor using Wi-Fi and estimate that Wi-Fi networking accounts for about a third of the total energy consumed by cameras. However, there are already studies that deal with this issue. In [24, 25], the power consumption of video sensors using Wi-Fi is analyzed, while in [20], a dual radio proposal is discussed, where sensors use ZigBee when they transmit small amounts of data and Wi-Fi when they must transmit large amounts of data to the home router in order to reach a remote node connected to the Internet. In [26], the authors describe an energy-harvesting technique to use Wi-Fi for sensor networks.
## 6 Comparison Algorithms
### Dalg  
DALG aims to learn a mapping that transforms a high-dimensional Lie group (LG) into a more discriminative, low-dimensional one. \(\mathbb{X}=\{X_{1},\cdots,X_{n}\}\subseteq\text{Sym}_{++}^{D}\) and \(\mathbb{Y}=\{Y_{1},\cdots,Y_{n}\}\subseteq\text{Sym}_{++}^{d}\) (\(d<D\)) are points on two LGs. \(\overline{\mathbb{X}}=\{\overline{X}_{1},\cdots,\overline{X}_{n}\}\subseteq T_ {I_{D}}\big{(}\text{Sym}_{++}^{D}\big{)}\) and \(\overline{\mathbb{Y}}=\{\overline{Y}_{1},\cdots,\overline{Y}_{n}\}\subseteq T _{I_{d}}\big{(}\text{Sym}_{++}^{d}\big{)}\) are their corresponding Lie algebras in the unit tangent space mapped by logarithmic mapping. Then, DALG defines the transformation \(DF(X):T_{I_{D}}\big{(}\text{Sym}_{++}^{D}\big{)}\to T_{I_{d}}\big{(}\text{Sym} _{++}^{d}\big{)}\) between unit tangent spaces as \(DF(X):\overline{Y}_{i}=W^{T}X_{i}W,\ i=1,\cdots,n\) with matrix \(W\in R^{D\times d}\). On the basis of the exponential and logarithmic mappings between the LGs and their unit tangent space, transformation \(F:\text{Sym}_{++}^{D}\to\text{Sym}_{++}^{d}\) is given by  
\[F:Y_{i}=exp\Big{(}W^{T}log(X_{i})W\Big{)}. \tag{45}\]  
To maximize the similarity between points belonging to the same class and minimizing similarity between points from different classes in low-dimensional LG, optimized \(W\) is as follows:  
\[\underset{W\in R^{D\times d}}{\text{min}}\ d_{1}=\sum_{i,j}\delta_{LEM}^{2} \big{(}Y_{i},Y_{j}\big{)}g_{w}\big{(}Y_{i},Y_{j}\big{)}, \tag{46}\]  
\[\underset{W\in R^{D\times d}}{\text{max}}\ d_{2}=\sum_{i,j}\delta_{LEM}^{2} \big{(}Y_{i},Y_{j}\big{)}g_{b}\big{(}Y_{i},Y_{j}\big{)}, \tag{47}\]  
where \(g_{w}(\cdot,\cdot)\) and \(g_{b}(\cdot,\cdot)\) are shown in Equations (41) and (42). Combining both constraints, the overall objective function is  
\[\underset{W\in R^{D\times d}}{\text{min}}\ (d_{1}-d_{2}). \tag{48}\]