esa-sceva/satcom-chunk-collection huggingface dataset:
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 4 General Methodology
### Determination of System Feasibility/Viability
#### 4.5.1 Cumulative Distribution Function  
The cumulative distribution function is utilized to express the probability distribution of the stochastic variable, \(x\), which has a real number value. The reason for including the word cumulative, is to signify the integral value of the probability density function. In other words, point probability can be expressed as in the equation below. Since the cumulative distribution function is an increasing function, it is easy to see the probability of it being smaller than or the same as a certain value.  
Figure 16: Independent design variables distribution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Term** & **Coefficient (\(\beta\))** & **Wing Loading** & **Power to Weight** & **MTOW** & **L/D** \\ \hline X4 \(\times\) X5 & 17 & 0.160074 & 0.000151 & 0.533371 & 0.116464 \\ X1 \(\times\) X6 & 18 & \(-0.01231\) & \(-3.18\times 10^{-5  
As shown in Figure 17, the percentiles are indications showing the distribution state of the cumulative distribution function.  
\[\text{P}(\text{X}=\text{b})=\text{F}(\text{b})-\lim_{x\to b^{-}}F\left(x\right) \tag{28}\]  
Wing loading was found to be between 9.40 N/m\({}^{2}\) and 65.5 N/m\({}^{2}\), and the targeted wing loading value is 30 N/m\({}^{2}\) or under. Therefore, the design probability to satisfy wing loading is 20%. Power to weight ratio was found to be from 0.005 hp/kg to 0.022 hp/kg. Since the targeted power to weight ratio is 0.04 hp/kg or lower, the design probability is 100%. Maximum take-off weight was between 38 kg and 291 kg, and the targeted max take-off weight is 200 kg or lower. Thus, the design probability to meet this maximum take-off weight is 92.4%. Lift-to-drag ratio was from 27.72 to 48.38. The target lift-to-drag ratio is 35 or higher, so the design probability to meet this is 75%. The design feasibility of every design objective variable was assessed, and it was found that the design objective variables can be satisfied within the independent design variable ranges set previously.  
Figure 18 provides graphs of the percentages according to the designable ranges of the design objective parameters. The horizontal axis provides the design objective parameter values produced in the Monte Carlo simulation; the vertical axis shows the percentages representing feasibility. For instance, in the case of wing loading, the targeted value is 30 N/m\({}^{2}\); thus, accordingly, the design probability is found to be 20% on the vertical axis. As for the maximum take-off weight, its target value is 200 kg, and, the design probability is 92.4% along the vertical axis. In this manner, it is found that these values are the same as the design probability values analyzed in the percentile table above.  
Figure 17: Percentiles for design objective variables.  
Figure 18: Cumulative probabilities for design objective variables.
## 2 VSWT-PMSG and LC Filter Model
### Permanent Magnet Synchronous Generator Model  
The dynamic model of PMSG in the d-q rotating reference frame can be expressed by the following Equation (5) and Equation (6) [13]:  
\[\frac{d\psi_{sd}}{dt}=-V_{sd}-R_{s}I_{sd}-\omega_{e}\psi_{sq} \tag{5}\]  
\[\frac{d\psi_{sq}}{dt}=-V_{sq}-R_{s}I_{sq}+\omega_{e}\psi_{sd} \tag{6}\]  
with  
\[\psi_{sd}=(L_{sd}+L_{md})I_{sd}+\psi_{m} \tag{7}\]  
\[\psi_{sq}=(L_{sq}+L_{mq})I_{sq} \tag{8}\]  
where \(V_{sd}\) and \(V_{sq}\) are stator voltages, \(R_{s}\) is the stator winding resistance, \(I_{sd}\) and \(I_{sq}\) are stator currents, \(\omega_{e}\) is the generator rotational speed, \(\psi_{sd}\) and \(\psi_{sq}\) are the stator flux linkages, \(L_{sd}\) and \(L_{sq}\) are leakage inductances of the stator winding, \(L_{md}\) and \(L_{mq}\) are magnetizing inductances, and \(\psi_{m}\) is the permanent magnet flux linkage. By substituting Equations (7)  and (8) into Equations (5) and (6), the differential equations of PMSG can be obtained as follows:  
\[L_{d}\,\frac{dI_{sd}}{dt}=-V_{sd}-R_{s}I_{sd}-\omega_{e}L_{q}I_{sq} \tag{9}\]  
\[L_{q}\,\frac{dI_{sq}}{dt}=-V_{sq}-R_{s}I_{sq}+\omega_{e}L_{d}I_{sd}+\omega_{e }\psi_{m} \tag{10}\]  
where \(L_{d}=L_{sd}+L_{md}\) and \(L_{q}=L_{sq}+L_{mq}\).  
Active and reactive powers of PMSG are expressed as follows:  
\[P_{s}=V_{sd}I_{sd}+\ V_{sq}I_{sq} \tag{11}\]  
\[Q_{s}=V_{sq}I_{sd}-\ V_{sd}I_{sq} \tag{12}\]  
and the electrical torque of PMSG can be obtained by Equation (13), where \(p\) is the number of poles.  
\[T_{e}=\frac{1}{2}\,p\big{(}\psi_{m}I_{sq}+(L_{d}-L_{q})I_{sd}I_{sq}\big{)} \tag{13}\]  
Based on the above equations, a dynamic block diagram of PMSG in the d-q rotating reference frame can be derived as shown in Figure 3, where s denotes a Laplace operator.  
Figure 3: Block diagram of a Permanent Magnet Synchronous Generator (PMSG).
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 3 Results and Discussion
### Radiation Performances  
In this subsection, the radiation patterns of MIMO antenna systems were studied. Since the system is symmetrically arranged, for simplicity, only the 3-D radiation patterns of Ant 1 and Ant 3 were analyzed. The simulation results at 4.75 GHz are shown in Figure 16. Obviously, in the XY plane, the radiation patterns of Ant 1 and Ant 3 point to the negative \(y\)-axis direction and the negative \(x\)-axis direction, respectively, showing orthogonal characteristics. The maximum radiation directions do not point to each other, which is beneficial to spatial diversity. The ECC value below 0.049 is also obtained, while the isolation is improved.  
The simulated and measured 3-D radiation patterns of Ant 1 at 4.75 GHz and Ant 3 at 4.8 GHz are depicted in Figure 17, respectively. Obviously, the measured 3-D radiation pattern surfaces of Ant 1 and Ant 3 at the frequency points are not smooth. The reason is that the surface of the welding place is uneven to generate electrostatic effects, as shown in Figure 17 (the area pointed by the red dotted arrow). However, overall, Figure 17 shows the measured 3D radiation patterns agree well with the simulated ones.  
Figure 16: Simulated 3-D radiation patterns at 4.75 GHz.  
Figure 15: Simulated and measured of ECCs between some near antenna-element.
# A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
## 3 Background
### Application-Specific Recovery  
Some researchers presented an application-specific database recovery approach to determine transaction dependencies. They assumed that each application had its own type of dependencies that could be utilized for an enhanced data recovery approach. In [28],the authors proposed an approach that was specific for banking system applications. Although they have a minimized database recovery time, this approach is not generic and cannot be applied to other types of applications.  
Another application-specific approach was presented in [29] for e-government systems. The authors presented an agent-based approach for damage assessment. Agents are assigned to a fixed group of items, and they communicate for more efficient processing. This algorithm is characterized by adding a timestamp to each data item to indicate its version after a write operation. However, each agent must scan the whole log file to perform the damage assessment, which decreases the algorithm efficiency.
## 2 Experimental Setup and Procedure
* During the experiments, the temperature measurements are recorded by means of a data acquisition system every 10 s.
* The arithmetic mean and the highest and the lowest values of the thermocoule measurements are determined as the average, maximum and minimum temperatures, respectively. * Each experiment ends when the voltage is 2 V which is called cut-off voltage. It is known that the lower values of this voltage lead to capacity reduction problems.
## 3 Results and Discussion
### Interpretation of Spectroscopic Analysis
After the different fermentation processes, the highest concentration of flavonoids and total phenolic contents (18.05 mg CE 100 mL\({}^{-1}\), 40.15 mg GAE 100 mL\({}^{-1}\), respectively) was observed in the alcoholic beer by using classical fermentation (ABC), with a higher free radical scavenging capacity than the non-alcoholic beer by using the thermal process (NABT). The lowest concentration of flavonoids and total phenolic contents (10.01 mg CE 100 mL\({}^{-1}\), 35.1 mg GAE 100 mL\({}^{-1}\), respectively) was detected in the non-alcoholic beer by using thermal process (NABT) Figure 4b. The FTIR spectra analysis of the barley seeds and beer are demonstrated in Figure 5, which displays the -OH functions associating phenol and alcohol from 3200 to 3400 cm\({}^{-1}\), C-H cyclohexane bonds from 2850 to 2925 cm\({}^{-1}\), C=O bonds of amides and aromatic ketones, N-H bonds of primary amines from 1550 to 1650 cm\({}^{-1}\), C=O carboxylic acid from 1400 to 1450 cm\({}^{-1}\), C-O bonds of esters between 1300 and 1450 cm\({}^{-1}\), C-O bonds of primary and secondary alcohols between 1040 and 1090 cm\({}^{-1}\), C-O ethers from 1000 to 1090 cm\({}^{-1}\), Ar-C bonds of aromatics between 850 and 890 cm\({}^{-1}\), and finally, monosubstituted aromatics from 700 to 800 cm\({}^{-1}\). Figure 4: Total flavonoid, phenolic contents (**a**), and percentage inhibition of free radical scavenging activity (**b**) of the analyzed samples. **HW**: Hopped Wort; **BSE**: Barley Seed Extract; **ABC:** Alcoholic Beer using Classical fermentation; **BHT**: Butylated hydroxytoluene; **NABT**: Non-Alcoholic Beer using Thermal process.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 2 Cut-In Scenario Classification and Cut-In Possibility Quantification
### Potential Cut-In Vehicle Screening  
In the cut-in scenario, a nearby vehicle performs a lane change operation from the original lane to in front of the host vehicle, which is considered as the cut-in behavior of the  
Figure 2: The FSM of the host vehicle.  
nearby vehicle. If all vehicles around the host vehicle are used for cut-in recognition, the calculations of the ACC system will be increased. Therefore, it makes sense that vehicles in the two adjacent lanes are considered as potential cut-in vehicles.  
Using the width of a city lane as a reference and the center of the vehicle directly in front of the host as the base point, the maximum lateral distance and minimum lateral distance of the area where the next vehicle exists is determined, as shown in Figure 3.  
\(W_{max}\) and \(W_{min}\) can be written as:  
\[W_{max}=2H-\frac{1}{2}W_{ego} \tag{1}\]  
\[W_{min}=\frac{1}{2}W_{ego} \tag{2}\]  
where \(H\) is the width of the lane and \(W_{ego}\) is the width of the host vehicle.  
By calculating the lateral distance of surrounding vehicles relative to the host vehicle, the vehicle of which the lateral distance meets the interval \([W_{min},W_{max}]\) is regarded as the adjacent lane vehicle. As shown in Figure 4, nearby vehicles A and B are in the forward direction of the host vehicle, and the azimuth angles of the nearby vehicles are \(\alpha\) and \(\beta\), respectively. \(d_{relA}\) and \(d_{relB}\) are the relative distances between the nearby vehicles and the host vehicle. Therefore, the relative lateral distances \(d_{relA}\) and \(d_{relB}\) between the nearby vehicles and the host vehicle are expressed as follows.  
\[d_{latA}=d_{relA}\sin\alpha \tag{3}\]  
\[d_{latB}=d_{relB}\sin\beta \tag{4}\]  
When the lateral distances, \(d_{relA}\) and \(d_{relB}\), of nearby vehicles A and B meet the interval \([W_{min},W_{max}]\), the nearby vehicles are both regarded as adjacent lane vehicles.  
In the cut-in scenario, in addition to the relative lateral distance of the surrounding vehicles, the relative longitudinal distance of the next vehicle also needs to be considered.  
Figure 4: Calculation of the relative lateral distances of the nearby vehicles.  
Figure 3: Determination of the lateral distance range.  
Only the cut-in behavior of vehicles on both sides that are closest to the host vehicle in the longitudinal direction affect the driving state of the host vehicle. Therefore, the vehicles on both sides are selected as potential cut-in vehicles that are closest to the host vehicle in the longitudinal direction. As shown in Figure 5, H is the host vehicle. Assume that there are four target vehicles P(1), P(2), P (3), and P(4) in front of host vehicle H. P(1) is the follow target of host vehicle H. Since P(2) and P(3) are in the left and right lanes with the smallest relative longitudinal distance to the host vehicle, P(2) and P(3) are regarded as potential cut-in vehicles.
## 2 Related Works
### Satellite Images
#### 2.1.2 Transfer Learning with Satellite Images  
In order to train a model with more information about the path from transmitter to receiver location, [PERSON] et al. [16] merged the satellite images of four locations along the path from the transmitter to receiver location into single images and extracted features from the images using a ResNet50V2. The satellite images were also from various environments, as in [23]. The extracted features were merged with eight other features and used for training and testing various regression algorithms. As ResNet50V2 takes a long period of time to train and requires large memory space, a customized CNN with lower complexity should have been used for the purpose.  
[PERSON] et al. [26] used VGG-16 and ResNet-50 to predict a path loss exponent and shadowing for 1.8 km\({}^{2}\) coverage areas using satellite images. Separate models were trained for the path loss exponent and shadowing values resulting from a 3D ray Tracing simulation at a frequency of 900 MHz, a transmitting antenna height of 300 m, and a receiving antenna height of 1.5 m. VGG-16 and ResNet-50 had the same accuracy in path loss exponent prediction, but ResNet-50 was better in terms of shadowing prediction by 1%. A modification of the two CNNs by including a parallel, fully connected layer to learn a histogram of the difference between path loss values and a reference path loss improved their performance by 2%. The process of learning two parameters from a single CNN is called multi-tasking. The prediction of the path loss exponent and shadowing has drawbacks. Both parameters were determined based on curve fitting/least squares regression, and these values contain errors and are not actual values. Training the CNN has its own error as well, thereby compounding the error of the curve fitting/least squares regression. In addition, separate models have to be developed for other frequency bands (for example, 1800 and 2600 MHz) for the same environment. [PERSON] et al. trained a VGG-16 to predict the histogram of path loss. Three models were developed at different single frequency values (900 MHz and 3.5 G Hz) and antenna heights (80 and 300 m) [7]. The prediction of the path loss histogram does not give path loss values location-wise; rather, it gives the ranges of path loss values for a large area. Such information cannot be used in the optimization of cell radius and number of cells during design. A single model should have been developed for the various frequencies and antenna heights. This will save the time needed to train several models and memory allocation if the model is to be implemented in software.
# Chalcogenide Thin Films--Holographic Media for Augmented Reality Devices
## 3 Materials and Methods  
In this study, we investigated three groups of the ChG samples. All of them were provided by Kurnakov Institute of General and Inorganic Chemistry of the RAS (Moscow, Russia). The components of chalcogenide materials (99.999% purity) were sealed in evacuated (\(5\times 10^{-3}\) Pa) quartz ampoules. Then, they were heated step by step to a temperature above melting point in a rocking furnace to ensure homogeneity of the melt. The melts were cooled and quenched to the room temperature using the switched-off furnace mode. Then, the ChGs thin films were prepared by thermal evaporation of the synthesized powders in vacuum chamber using Knudsen effusion cell. Residual pressure in the chamber was \(10^{-4}\) Pa. The temperature of glass substrates during evaporation did not exceed 50 \({}^{\circ}\)C. Scanning electron microscope with energy dispersive X-ray (EDXR) spectrometer was used for the microanalysis of fabricated thin films. The thicknesses of ChG films were controlled by the atomic-force microscope. Experimental results for the samples are divided into several following sections. In Section 4.1, four samples of As\({}_{2}\)S\({}_{3}\) compositions were tested for a holographic recording of volume and surface relief gratings. These are the most frequently used ChGs [16; 20]. In Section 4.2, volume and relief holograms were recorded in optimized ChG compositions. Several As-S and Ge-S samples were made and used for the investigation. All samples were in a form of 0.7-1-\(\upmu\)m thickness films evaporated to glass substrates.  
Holographic recording and the recording kinetics measurements were made either by standard two-beams recording scheme (Sections 4.1, 4.2.1 and 4.2.2) or using Lloyd interferometer recording setup (Section 4.2.2, volume holograms) by DPSS and gas laser sources. Diffraction efficiency signal was controlled by laser diode irradiation of the sample during the recording and measurement of the diffracted signal. Features of the recording setups are shown below in corresponding figures.  
Figure 4: The dependence of maximum diffraction efficiency (**a**) and uniformity (**b**) of two-exposure HOE on ChG layer thickness for different average refractive indices and refractive index modulations. Probing beam is \(s\)-polarized. Results for 3-\(\upmu\)m thickness are not shown because they were unsatisfying.
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
## 3 Conclusions  
A mathematical model was developed to explain the reason behind sterilization in the combinatorial treatment order. The average porosity of the biofilm was estimated to be about 80% during the plasma treatment time, suggesting that the application of plasma is disrupting the biofilm and ultimately making it porous. This is allowing CHX to penetrate deeper inside the porous biofilm and kill all bacterial cells and sterilize the biofilm. This might be the reason behind complete sterilization in this treatment method. It should be noted, however, that more work will be needed to further validate the calculation and conclusion using various other treatment times.  
Conceptualization, T.T.G., R.F., and H.A.; Methodology, T.T.G., S.B.K., R.F., and H.A.; Investigation, T.T.G., S.B.K., R.F., and H.A.; Formal analysis, T.T.G., R.F., and H.A.; Writing-original draft, T.T.G.; Writing-review and editing, T.T.G., S.B.K., R.F., and H.A.; Resources, H.A.  
This research received no external funding.  
The authors declare no conflict of interest.
# Bond Graph-Based Approach to Modeling Variable-Speed Gearboxes with Multi-Type Clutches
## 5 The One-Way Clutch Model  
A one-way clutch is a mechanical clutch that is mainly composed of an inner ring, a circular array wedge, an outer ring, and a spring. Its structural characteristics prevent it from rotating in one direction, whereas the resistance when rotating in the other direction is negligible [27; 28]. To simplify the modeling process, the one-way clutch in the present study was modeled as a nonlinear spring with discontinuous stiffness. The inner ring was simplified as an inertial part without elastic damping, the outer ring was simplified as a grounding part without inertia and elasticity, and the circular array wedge was simplified as a nonlinear elastic damping part without inertia. Accordingly, zero stiffness was assumed in the disengaged state, while a finite linear stiffness was assumed in the engaged state, provided there was relative speed or displacement between the inner and outer parts of the one-way clutch, and provided there was rotational damping. The relative rotation speed [29] or the relative angular displacement [30] between the outer and inner rings could be used for judging the working state. Here, the mathematical model of the one-way clutch was an angular displacement difference model, and the expressions for the torque \(\tau_{o}\) of the one-way clutch were as follows:  
\[\tau_{o}=\left\{\begin{array}{ll}K_{o}(\theta_{ir}-\theta_{or})+C_{o}\Big{(} \theta_{ir}-\dot{\theta}_{or}\Big{)}&\theta_{ir}>\theta_{or}\\ 0&\theta_{ir}\leq\theta_{or}\end{array}\right. \tag{20}\]  
where \(\theta_{ir}\) is the torsion angle of the inner ring, \(\theta_{or}\) is the torsion angle of the outer ring, and \(K_{o}\) and \(C_{o}\) are the stiffness and damping, respectively. When the one-way clutch was engaged, the wedges were in close contact with the inner and outer rings. The clearance angle of the clutch during engagement owing to the inertia and edge deformation was ignored. The block diagram of the angular displacement difference model of the one-way clutch is shown in Figure 8.  
Figure 7: Block diagram of the Karnopp model for hydraulic friction clutches.  
In the model, \(\omega_{ir}\) and \(\omega_{or}\) are, respectively, regarded as the rotational speed of the parts affixed to the inner ring and the outer ring, respectively, which could be obtained from the gear train model, and \(\tau_{o}\) was the torque transmitted by the one-way clutch to the gear train.