esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Scientific Mapping
#### 3.2.1 Application Fields  
Two of the eighteen articles we reviewed are reviews [13; 30]. We excluded them from our analysis since they looked at some of the papers we investigated in this study. Therefore, to avoid counting any research twice, we excluded these two review articles from this quantitative analysis in this section.  
Similar to the method in [13], the application field of an article was defined as the industry and/or technological environment in which the application of virtual reality and digital human modeling was carried out. The observed application fields may be grouped into six categories, as follows: (1) automotive industry; (2) aerospace industry; (3) construction industry; (4) industrial plants; (5) energy industry; (6) field unspecified. Figure 8 shows the distribution of the examined studies in each category.  
There were 4 articles (25%) which focused on the automotive industry [1; 5; 7; 36]. It was expected that a large number of studies on virtual reality and digital human modeling would be undertaken in the automotive market. These results are consistent with those shown by [13]. The automotive industry's relevance may be linked to the fact that virtual technologies have long been employed in this sector, being used in a variety of fields and applications, such as production, training, and maintenance, to name a few [44; 45; 46]. Furthermore, the automotive industry is one of the most mature manufacturing sectors, where cutting-edge technologies are often deployed for the first time [13].  
There were 3 studies (19%) investigating the aerospace industry [8; 16; 37]. The aviation industry is interested in virtual reality and digital human modeling technology for ergonomics assessment for a variety of reasons. Reducing the effect of human errors in production is critical for improving air transportation safety. In general, there is a need to improve aircraft manufacturing performance in order to ensure safe operation at the lowest possible cost [47]. Figure 9 is an example of an aerospace industry study.  
There were 2 articles (12%) which focused the construction industry [42; 43]. Construction tasks are physically stressful, requiring inadequate and prolonged postures, which can cause pain and injuries. Figure 10 is an example of a construction industry study.  
Another 2 studies (12%) were concentrated on industrial plants manufacturing engine [14] and steel pipes [38], and 1 was focused on the energy industry [6].  
Figure 8: Application fields.  
There were 4 articles (25%) which did not specify the field of application [35; 39; 40; 41]. Ref. [47] asserts that it is common for augmented and virtual reality applications developed by a research team in an academic setting to be examined in their own laboratory using prototypes and items, as shown in Figure 11.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### KEM-DEM Algorithms  
The SM9 encryption is a hybrid encryption scheme [25] that is built from an identity-based key encapsulation mechanism (KEM) and a data encapsulation mechanism (DEM). The encryption and decryption schemes are described, as follows.
## 2 Design Issues of Drone Routing for Drone-Based Delivery Systems
### Decision on Customer and Delivery Point  
In a drone-based delivery mission, the drone can either go to single or multiple destinations. For example, the drone needs to pick up the parcel from a certain location or depot and, after delivering the parcel, it needs to decide whether to wait for the next parcel or fly back to the depot. These considerations depend on the current energy of the drone and the delivery route. Thus, it is crucial to design optimal routing decision-making algorithms that would consider these conditions. Hybrid truck-drone techniques have been proven successful in cases where joint considerations of drone scheduling and routing decision-making have been made considering customers widely distributed over a certain region [10; 35].
## 2 Numerical Modeling of the AGF Process including Phase Transition Conditions
### Heat Conduction Governing Equation  
The calculation control equation of the heat transfer process in frozen soil still follows the differential heat conduction equation [18], which can be expressed as:  
\[\rho C_{p}\frac{\partial T}{\partial t}+\
abla\cdot\underset{\sim}{q}=Q \tag{1}\]  
\[\underset{\sim}{q}=-k\
abla T \tag{2}\]  
where \(T\) represents the temperature, \(k\) represents the thermal conductivity, \(\rho\) represents the density, \(C_{p}\) represents the specific heat capacity, and \(Q\) represents the total heat source, here contains the phase transformation latent heat.  
When a material undergoes a phase transition, take solid to liquid as an example, energy is added to the solid, and instead of creating a temperature rise, the energy alters the material's molecular structure. Therefore, in the process of soil freezing, the phase change of water in the soil will slow down the development of the frozen soil curtain.  
The equivalent heat capacity method is applied to calculate the latent heat of the phase transformation [19], and the following model is used to ensure the continuity of boundary conditions in the finite element calculation process, shown in Figure 1.  
\[C_{p}=\theta_{1}C_{p2}+\theta_{2}C_{p2}+Q_{1-2}\frac{\partial\alpha_{m}}{ \partial T} \tag{3}\]  
\[\alpha_{m}=\frac{1}{2}\frac{\theta_{2}-\theta_{1}}{\theta_{2}+\theta_{1}} \tag{4}\]  
In this equation, \(\Delta T_{1-2}\) represents the phase transition interval, \(T_{\text{pc}}\) represents the transformation temperature, \(\theta_{1}\) and \(\theta_{2}\) represents the ratio of phase 1 and phase 2, and \(Q_{1-2}\) represents the latent heat of phase change, and the proportional coefficient represents the phase change conversion.  
Figure 1: Schematic diagram of latent heat of phase transformation.
# Development and Testing of a Two-UAV Communication Relay System
## 4 Software Development  
The software developed in this project includes mission control software running on the mission control board and Mini GCS running on the GCS laptop. The mission control software is in charge of analyzing the target node as well as the command contents of each message it receives. If the ID of the messages represents other UAV nodes, the software will bypass this message to the corresponding targets. If the ID of the message corresponds to the receiving UAV itself, the software will further resolve the command data in the message and send it to the flight controller. The Mini GCS was developed to better coordinate the multiple UAV system. The condition of each UAV will be displayed on the screen including the flight mode, the GPS position, the attitude etc. The Mini GCS was also used to send commands to each UAV node by the user friendly Graphic User Interface (GUI).
## 7 Experimental Results and Analysis  
To prove the beneficial operation of our proposed model, 10 simulations were conducted. With the experimental scenarios that we carried out, we compared the function of our proposed model with existing AES, RC5, and RSA algorithms. Through these simulations and their results, it can be concluded that our model is more secure and efficient.  
Figures 4-7 show four experimental scenarios that considered the performance of data processing (in megabytes--MB) with the use of the four algorithms (AES, RC5, RSA, and the proposed model) in the measure of time. Each figure shows a different scenario of the amount of data used. Through these scenarios, we observed that by applying our proposed model, we could achieve better/faster data processing in comparison to the existing encryption algorithms AES, RC5, and RSA, as it could process a larger amount of data at the same time. In the figures, the legend is as follows: the red line represents AES, the green line represents RC5, the yellow line represents RSA, and the blue line represents our proposed model.
## 4 Results and Discussion
### Nanofluid Characterization
#### 4.2.3 The Density of the Nanofluids  
Figure 8a shows the measured density of the water and the 0.2, 0.5, 1.0, and 1.5 \(\
icefrac{{v}}{{v}}\)% NFs at test temperatures ranging from 25 to 45 \({}^{\circ}\)C. At 35 \({}^{\circ}\)C, NFs with NP volume concentrations of 0.2 and 1.5 \(\
icefrac{{v}}{{v}}\)% see an increase in density of 0.1% and 2.7% compared to the base fluid (i.e., water), respectively. Figure 8a also shows that \(\rho_{NF}\) decreases with the increase in temperature. From 25 to 45 \({}^{\circ}\)C, the density decreases by 0.3% for the NF at 0.2 \(\
icefrac{{v}}{{v}}\)%, 0.6% for the NFs at 0.5 and 1.0 \(\
icefrac{{v}}{{v}}\)%, and 0.4% for the NF at 1.5 \(\
icefrac{{v}}{{v}}\)%. For comparison, Figure 8b depicts the measured data at 35 \({}^{\circ}\)C and that predicted by the principle of mixture rule [48], defined as:  
\[\rho_{NF}=(1-\phi)\rho_{BF}+\phi\rho_{NP} \tag{10}\]  
where \(\phi\) is the NP volume fraction in the NF, \(\rho_{BF}\) is the density of the base fluid, and \(\rho_{NP}\) is the density of the NPs.  
Equation (10) is a well-known correlation extensively used to predict the density of NFs. Nevertheless, Figure 8b indicates that Equation (10) over-predicts the density of the NFs utilized in this study. The experimental results of \(\rho_{NF}\) are comparable to those reported by [PERSON] et al. [49].  
Figure 7: (**a**) Experimental dynamic viscosity of TiO\({}_{2}\)–W NFs as a function of temperature and NP volume concentration. (**b**) Comparison of the measured dynamic viscosity at 35 \({}^{\circ}\)C and that predicted by the classical models of dynamic viscosity.
###### Abstract  
This article analyzes the current status of the lighting quality at the _Escuela Tecnica Superior de Ingenieria Aeronautica y del Espacio_ (ETSIAE), the aerospace engineering faculty at the _Universidad Politecnica de Madrid_ (UPM), and evaluates possible improvement actions based on the use of DIALux(r) lighting simulation software together with measured data. The results show rather low levels of measured illuminance on classroom desks and blackboards in one of the buildings comprising the faculty. The improvements proposed (a new coat of paint on the walls and replacement of luminaires) were simulated in four individual classrooms representing all rooms in two of the ETSIAE buildings (where the lower illuminance levels were measured). In order to study these improvements, the current situation of the four selected classrooms was simulated using DIALux(r) and fine-tuning attenuation of the luminaires to take into account their wear and tear. The correlation between the DIALux(r) simulation and the test results was analyzed with quite good results. The results clearly reveal a need to fully replace the classroom lighting systems in ETSIAE building A (the oldest building, dating back to 1955). According to the results from the selected classrooms, the average lighting over the desks can be greatly improved by using LED technology in order to meet UNE 12464-1 standard (that is, 500 lx, from an initial situation with much lower illuminance values: 129 lx to 295 lx). This article represents an innovative way to perform lighting improvement projects as real measured lighting data is used as initial input for the lighting simulations.  
Keywords:DIALux; simulation; lighting quality; teaching institution; classroom lighting +
Footnote †: journal: Applied Sciences
# Factors Affecting Trueness of Intraoral Scans: An Update
## 2 Materials and Methods  
This literature review adopted a systematic design to identify factors affecting IOS trueness. The PUBMED and EMBASE databases were searched for articles published in the last two years (from February 2020 to February 2022), restricted to the English language, based on the following search strategy using keywords developed for PUBMED and used in both databases: \"(IOS OR intraoral scanning) AND (trueness OR accuracy)\". Reference lists of all potential articles were also screened. In addition, the OpenGrey database was screened for relevant unpublished studies or papers not identified by electronic searching [17].  
The present inclusion criteria considered original articles but not systematic reviews or case reports for the analysis. To be considered eligible for inclusion, studies should have performed statistical comparisons addressing the impact of variables affecting IOS trueness. Studies on implant digital impressions using scan bodies and studies that did not assess 3D mesh trueness in relation to a reference scan/measure or that solely performed 2D linear measurements were excluded from the analysis. The review text structure followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [18].  
Two reviewers with expertise in digital dentistry screened the titles and abstracts as well as the full texts of the studies identified. The following data were extracted and recorded: year of publication, type of study, comparisons performed, statistical findings and identified factors that were affecting IOS trueness. A quality assessment protocol was developed and conducted exclusively by the authors who are experts in research about intraoral scanning. Following this protocol, articles had to clearly explain the method of 3D mesh superimposition, describe the type of reference scanner used for control values and provide statistical data on 3D discrepancy measurements (i.e., trueness volumetric assessment) following best fit algorithm analyses. In addition, quantitative data of IOS trueness and precision (reproducibility) were also collected and compared among the studies. Risk of bias of individual studies was assessed by the same reviewers using an adapted scale for in vitro and in vivo studies described by a previous study [13]. Briefly, this adapted scale consisted of 12 items that should be adequately reported in non-randomized studies. Each item receives a score varying from 0 to 2, with 0 indicating that the item was not reported, 1 indicating that the content of the item was reported but inadequately and 2 indicating that it was adequately and sufficiently reported. Scores higher than 20 for in vitro studies and 24 for in vivo studies were considered high risk. Detection of high risk of bias was considered an exclusion criterion. Discrepancies between the two reviewers were resolved by means of discussion and consensus.
## 5 Results and Discussion
(**a**) Line voltages of converter with d-q control and STHILSC, (**b**) line voltages of converter with d-q control and STHILSC, (**c**) line currents of converter with d-q control and STHILSC, (**d**) line currents of converter with d-q control and STHILSC, (**e**) STHILSC implemented upper arm phase-a SM-1 capacitor voltage, (**f**) STHILSC implemented upper arm phase-a SM-1 capacitor voltage, (**g**) STHILSC implemented lower arm phase-a SM-1 capacitor voltage, (**h**) STHILSC implemented lower arm phase-a SM-1 capacitor voltage, (**i**) primary and secondary phase-a transformer currents when STHILSC is implemented, (**j**) primary and secondary phase-a transformer currents when STHILSC is implemented. Here, the proposed converter's performance is analyzed with sin third harmonic injection for both levels--shifted and phase-shifted PWM schemes--when the converter has been short-circuited from t = 1 s to t = 1.2 s at 50 Hz. Figure 15a,b shows the line voltages with d-q control and STHILSC and STHIPSC respectively. Figure 15c,d shows the line currents with d-q control and STHILSC and STHIPSC respectively. During short-circuited operation, Figure 15e-h shows the capacitor voltages of the upper arm and lower arm phase-a SM-1 with STHILSC modulation scheme. The primary and secondary phase-a transformer currents with STHILSC and STHIPSC schemes are shown in Figure 15i,j respectively. Figure 14: Converter during short circuited condition. (**a**) Line voltages of converter with d-q control and SPWMLSC, (**b**) line voltages of converter with d-q control and SPWMLSC, (**c**) line currents of converter with d-q control and SPWMLSC, (**d**) line currents of converter with d-q control and SPWMLSC, (**e**) SPWMLSC implemented upper arm phase-a SM-1 capacitor voltage, (**f**) SPWMPSC implemented upper arm phase-a SM-1 capacitor voltage, (**g**) SPWMLSC implemented lower arm phase-a SM-1 capacitor voltage, (**h**) SPWMPSC implemented lower arm phase-a SM-1 capacitor voltage, (**i**) primary and secondary phase-a transformer currents when SPWMLSC is implemented.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Effect of External Excitation Parameters of a Particle Damper
#### 3.5.2 Amplitude Displacement  
In order to investigate the effect of the displacement amplitude of the particle damper on the total energy dissipation of the particle system, different displacement amplitudes (2 mm, 4 mm, 6 mm, 8 mm, and 10 mm) were applied to the damper in the x-direction.  
As shown in Figure 16, as the displacement amplitude of the damper structure increased, the total energy dissipated by the particle system became larger and larger. This was due to the fact that the displacement amplitude of the damper structure changed the motion of the particles inside the container. The intensity of the motion of the particles in the damper cavity increased with the amplitude of the displacement of the structure. The momentum exchange between the particles and the damper system increased, and the total energy dissipated by the particle system increased. For the same displacement amplitude, the total energy dissipated by the particle system increased with the filling rate. The maximum value of the total energy dissipated by the particle system was found at an amplitude displacement of 10 mm and a particle filling rate of 60%.
## 3 Results and Discussion
### Degree of Cell Rupture  
Figure 4a-c show the degree of cell rupture of samples with three different yeast suspension concentrations at each agitation cycle. We observed a high number of broken cells with released intracellular content, such as cytoplasm and organelles, as described by [PERSON] et al. (2016) in a previous study [29]. [PERSON] et al. (1991) introduced for the first time the concept of effective disruption volume. This concept implies that disruption occurs only in a specific region were beads collide [13]. Therefore, the black arrows indicate the broken cells with irregular cell walls caused by the collision of yeast in the effective disruption region between glass beads, while the red arrows show dead cells stained inside with methylene blue. These stained cells still hold their intracellular content because they are undamaged by glass beads and cannot be dissolved in the continuous phase [30].  
Figure 3: Optimal values of the factors and disruption efficiency.  
A number of small fragments of the broken cell wall and cell debris can be observed behind the whole yeast cells. Similar results were presented by [PERSON] et al. (2014) for _S. cerevisiae_, disrupted cells with no intracellular components being observed [14]. The efficiency of the bead mill lysis method is indicated by the degree of cell fragmentation that contributes to the leaching of cytosol components. Another cell disruption technique using a horizontal bead mill highlighted that a 5% (\(w/v\)) yeast suspension under a rotational speed of 2500 rpm resulted in asymmetrical deformation of the spherical cell shape [30]. After methylene blue staining, microscopic analysis at 0, 1.5, and 4.5 min revealed that a high number of cell walls and cells with intracellular content released were present in the suspension with the longest duration of milling. This is in agreement with the results obtained and suggests that an increased duration of vortexing cycles has a major influence on cell rupture and implicitly on the elimination of intracellular content.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 3 Analytical Model
### Acquisition Failure Probability Calculation  
In the actual acquisition process, acquisition failures can be categorized into two cases [24]. The first case occurs when the orientation of the receiving SC relative to the scanning SC is outside the pre-estimated UC, preventing the scanning SC from sweeping over the receiving SC. The second case occurs when, even though the receiving spacecraft is within the UC, the scanning beam either does not sweep over it or fails to accumulate enough energy due to factors such as jitter and acquisition parameters like scanning speed and track width.  
Figure 4: PSD of platform jitter. The red curve represents the theoretical PSD, while the blue curve shows the PSD of the simulated data generated from the theoretical model The method for generating simulation data will be introduced in Section 5.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 2 Quasi-Static Modeling of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrication
### Elastohydrodynamic Lubrication Model and Equations for Cylindrical Rollers  
Under fully lubricated conditions, the lubricant film separates the rolling elements from the raceways, leading to pressure distributions distinct from Hertzian contact pressure. The Reynolds equation, governing the flow of lubricant between two surfaces, serves as the foundation for hydrodynamic and elastohydrodynamic lubrication theories.  
Assuming isothermal Newtonian fluid conditions, the Reynolds equation for finite line contact elastohydrodynamic lubrication between cylindrical rollers and raceways is expressed as follows [13]:  
\[\frac{\mathrm{d}}{\mathrm{dx}}\left(\frac{\mathrm{ph}^{3}}{\mathrm{\eta}} \frac{\mathrm{dp}}{\mathrm{dx}}\right)=12\mathrm{U_{s}}\frac{\mathrm{d}( \mathrm{ph})}{\mathrm{dx}} \tag{1}\]  
where \(\mathrm{\rho}\) is the lubricant density (\(\mathrm{kg/m^{3}}\)); \(\mathrm{p}\) is the film pressure (Pa); \(\mathrm{\eta}\) is the dynamic viscosity (Pa-s); \(\mathrm{u_{s}}\) is the entrainment velocity (\(\mathrm{m/s}\)); and \(\mathrm{h}\) is the film thickness (\(\mathrm{m}\)).  
The film thickness equation is given by the following:  
\[\mathrm{h(x)}=\mathrm{h_{o}}+\frac{\mathrm{x}^{2}}{2\mathrm{R_{x}}}+\mathrm{ v(x)} \tag{2}\]  
In this formula, \(\mathrm{v(x)}\) can be expressed as follows:  
\[\mathrm{v(x)}=-\frac{2}{\mathrm{\pi E}}\mathrm{\int_{x_{o}}^{x_{e}}\mathrm{p (s)ln\,(s-x)^{2}d_{s}+c}} \tag{3}\]  
Figure 1: Schematic diagram of the roller bearing structure.  
where E is the comprehensive modulus of elasticity (MPa) of the contact surface; R\({}_{\rm x}\) is the combined radius of curvature in the direction of the roll; h\({}_{\rm 0}\) is the central membrane thickness; and x\({}_{\rm o}\)andx\({}_{\rm e}\) are the solution boundaries specified in this study.  
The Roelands viscosity formula is as follows:  
\[\eta=\eta_{\rm 0}{\rm exp}\big{\{}\big{(}\ln\eta_{\rm 0}{+}9.67\big{)}\big{[} {-}1+\big{(}1+{\rm p}/\rho_{\rm o}\big{)}^{\rm Z}\big{]}\big{\}} \tag{4}\]  
The Dowson-Higginson formula was adopted:  
\[\rho=\rho_{\rm o}\bigg{(}1+\frac{0.6{\rm p}}{1+1.7{\rm p}}\bigg{)} \tag{5}\]  
Under the given load conditions, the pressure distribution p within the roller-raceway contact zone must satisfy the load equilibrium constraint:  
\[{\rm w}-\int_{\rm{x}_{\rm 0}}^{\rm x_{\rm o}}{\rm p(x)dx}=0 \tag{6}\]  
where w is the applied load.