esa-sceva/satcom-chunk-collection huggingface dataset:
# NursingXR: Advancing Nursing Education Through Virtual Reality-Based Training
## 2 Immersive Learning in Nursing Education
### Effectiveness of VR in Nursing Training  
In general, effectiveness and usability issues are of great importance in VR solutions, and particularly in healthcare. Different studies have been conducted to investigate the effectiveness of VR and its utility as an educational or training tool in the nursing domain. For instance, researchers in [5; 7; 10; 11; 13; 32; 33; 34] conducted systematic reviews to evaluate the effectiveness of VR to develop skills and prepare nursing students for real clinical practice. The studies reviewed a large number of articles related to using VR in nursing contexts. The analysis concludes that VR simulations can support nursing students in drug management, intravenous drug infusion, and administer medications safely, among others. Furthermore, the reviewed studies showed that the use of VR in nursing has an improved knowledge effect. In particular, [PERSON] et al. [5] evaluated the integration of VR in nursing, learning, and teaching in the areas of knowledge, skills, satisfaction, confidence, and performance time of different nursing procedures and tasks by conducting a comprehensive evaluation for the literature. The study concludes that VR simulations are effective in reinforcing the knowledge sets of users unlike those in control groups. However, it also mentions that VR provides no significant improvement to skills, satisfaction, confidence, or performance time. Nevertheless, VR can be considered as a complement to other simulation strategies in nursing education to enhance knowledge, and as a way to improve the quality and safety of clinical practice [5; 9]. Another study investigated nursing students' experiences in using VR-based skill learning processes [35]. The study was conducted for students who enrolled in an Adult Nursing and Practice course, and they were asked to use a VR nasogastric tube care skill learning system. The obtained results presented a fast skill learning process and stress-free environment as advantages of the VR application. However, it was noted that instead of replacing conventional skill teaching methods, VR application can be considered a supplementary tool. This was also confirmed by [PERSON] et al., who observed VR's significantly positive impact on clinical training and assessment [14]. Other examples of studies that investigated the effectiveness of VR technology for healthcare and nursing domains include [36; 37; 38].
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 5 Experiments
### Experimental Setup
#### 5.1.4 Baselines  
Our evaluation aims to confirm whether GLO-MIA outperforms the gap attack [21] and achieves performance comparable to that of probability-based MIAs based on prediction probability vectors [9; 10], even if the adversary only uses the prediction labels rather than prediction probability vectors.  
1. **Gap attack**[21]. This is a naive baseline commonly used for label-only MIAs [23; 24]. It determines membership based on whether the target model's prediction label is correct  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Type** & **Graphs** & **Classes** & \
\begin{tabular}{c} **Avg.** \\ **Nodes** \\ \end{tabular} &
\begin{tabular}{c} **Avg.** \\ **Edges** \\ \end{tabular} & **Features** \\ \hline DD & Bioinformatics & 1178 & 2 & 284.32 & 715.66 & 89 \\ PROTEINS\_full & Bioinformatics & 1113 & 2 & 39.06 & 72.82 & 32 \\ ENZYMES & Bioinformatics & 600 & 6 & 32.63 & 62.14 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information of datasets.  
Samples that are correctly predicted are regarded as members, while those incorrectly predicted are considered non-members. The attack accuracy of the gap attack is directly related to the gap between training and testing accuracy of the target model. If the adversary's target data are equally likely to be members or non-members of the training set, the attack accuracy of the gap attack can be calculated as follows [24]:  
\[ACC_{gap}=\frac{1}{2}+\frac{acc_{train}-acc_{test}}{2} \tag{12}\]  
where \(acc_{train}\) and \(acc_{test}\) are the accuracy of the target model on the training dataset and the testing dataset, respectively. If the model overfits its training data, which means it has higher accuracy on the training set, the gap attack will achieve non-trivial performance.  
1. **Probability-based MIAs**. We implement two advanced probability-based MIAs that utilize the prediction probability vectors of the target model in graph classification tasks.  
1. **Cross-entropy-based MIA**[9]. This method assumes that the loss of members should be smaller than that of non-members.
2. **Modified prediction entropy-based MIA**[10]. This method assumes that members are expected to have lower modified prediction entropy than non-members.
## 3 Results and Discussion
### Comparison to State of the Art  
Comparing vibrational energy harvesters is a complex task as many different parameters must be considered relative to a specific application area. For a comparison to be relevant, size, weight, considered frequency range, excitation acceleration, displacement range and complexity should be comparable.  
Based on a set of 21 articles (sum of reviewed articles in [28; 64]), the highest relative resonance frequency tunability achieved was 112% [65] (defined as the natural frequency divided by the tuning range), but with only relatively low power (\(\upmu\)W range). The tuning is mechanical and the necessary travel distance to produce the acquired tunability is large, 34 mm in total and 2.8 Hz/mm on average, leading to a bulky tuning system. In [66], a similar tunability is achieved but at magnitudes of larger power (mW range). The required travel distance is again mechanical and still large, 13 mm in total and 1 Hz/mm on average. A similar example can be found in [67].  
[PERSON] et al. [29] apply tuning via electrostatic force, allowing for compact design. Both tunability and power are relatively low. [PERSON] et al. [25] show a theoretically high mechanical tuning sensitivity (420 Hz/mm on average) but with output power still in the \(\upmu\)W range (for low acceleration).
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 4 Tested Material and Experimental Setup  
### Materials and Sample Preparation  
The selected ULE glass was Corning Code 7972 glass. Considering the boundary effect of ultrasonic wave propagation, the experimental samples were prepared in a cylindrical shape with a cross-sectional area that was larger than that of the ultrasonic transducer. To avoid the adverse effects of the scattering attenuation of ultrasound at the interface of the sample, the two end faces of the tested sample should be flat and parallel to each other. As shown in Figure 3, six cylindrical glass samples were cut with an equal thickness (50-0.1 mm) along the radial position of the ULE 7972 boule (No. 82714) using an abrasive water jet; then, these samples were finely ground and polished to achieve a flatness of 0.5\(\lambda\) and parallelism of 20 \(\upmu\)m. The six samples were numbered 1#-6# from the inside to the outside of the boule according to the increasing CTE. The details of the ULE samples are summarized in Table 1.
_Applied_  
_sciences_  
Method for Estimating Optimal Position of S-Band Relay Station through Path Loss Analysis in an Outdoor Environment  
**[PERSON] 1\({}^{\copyright}\)**, **[PERSON] 1**, [PERSON] 2, [PERSON] 3 and [PERSON] 1\({}^{,}\)**\({}^{\copyright}\)  
Footnote 1: footnotemark:  
Received: 16 July 2020; Accepted: 29 August 2020; Published: 2 September 2020  
**Featured Application: This article is related to estimation method to optimize position of a relay station in the S-band.**  
**Abstract: This article investigates a novel estimation method to optimize the position of a relay station of the S-band telemetry system in outdoor environments. To determine an optimal relay station position, complex terrain surface and buildings near the Naro Space Center are included in the EM (Electromagnetic) simulation. The optimal relay station position is then determined using the estimation method by comparing the path losses from the rocket to the main station and some candidate positions of the relay station. To better understand the path loss, the ray analysis is also conducted, and the result demonstrates that once the rocket altitude to be covered by the relay station is determined, it is possible to find the optimum position of the relay station.**  
**Keywords: electromagnetic wave propagation; wireless communications; ray tracing method; relay station**
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 3 Results
### Vertical Excitation Benetah the Sensor  
In the case of vertical excitation beneath the sensor, the study concerns the longitudinal wave since Rayleigh is not directly created from the excitation within the material. The simulation waveforms are shown in Figure 5a. The differences at the waveforms received by different size sensors are almost negligible. It should be noted that the amplitude of the signal received by long sensor (20 mm) is slightly lower than the waveforms of the other two cases. Additionally, the point and medium sensors' waveforms begin slightly sharper than the corresponding of long sensor. This is due to the circular shape of the wave front that affects the wave arrival. More specifically, wave energy arrives vertically to the long sensor only in its center, but part of the energy arrives in slightly different angles at the sensors edges due to spreading. The above agrees with recent literature, stating that when the sensor diameter increases, the displacement distribution along the diameter becomes non-uniform [22]. This is not the case for smaller size sensors at which the whole energy arrives vertically and the wave front shape effect is negligible. The differences are minimized for lower frequencies with the example of 50 kHz in Figure 5b being indicative.  
Figure 4: (**a**) Waveforms received by different size sensors for the case of surface source and excitation frequency of 50 kHz; (**b**) FFT functions of waveforms received by different size sensors (exc. frequency at 50 kHz and surface source). The corresponding waveforms are depicted in Figure 4a.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 5 Experiments
### Experimental Results and Analysis
#### 5.2.4 Simulation of Node Residual Energy and Routing Overhead  
In this section, the average residual energies of the nodes are compared between AODV, AODV-UU, and AODV-EOCW protocols. The experimental settings are the same as in Table 7 with the exception that the nodes' initial energy is set to 5 J to make all nodes alive during the simulation. The simulation time was set to 100 s, all nodes were moved with a speed of 5 m/s, and the number of nodes was 40.  
Figure 12 illustrates the residual energy level between protocols. As we can see from the figure, the average residual energy levels decrease as the simulation goes on. In the simulation, AODV-EOCW outperforms AODV and AODV-UU unexpectedly. This is because AODV-EOCW can select a route path effectively and thus broadcast fewer packets for route discovery.  
Figure 13 shows that AODV-EOCW outperforms the other two protocols in the routing overhead. As Figure 13 shows, the routing overhead of AODV-EOCW is slightly better than the other, but the three protocols remain in the same level, which is because AODV-EOCW is based on the AODV protocol; although the RREQ and RREP packets are modified to  
Figure 11: Comparison of survival rates.  
Figure 12: Comparison of residual energy.  
carry more information, the AODV-EOCW protocol broadcasts fewer packets than the other two for route discovery because it selects a better route than AODV and AODV-UU.
## 3 Mathematical Model for Optimal Trajectory Planning
### Constraint Function Design
#### 3.3.6 Collision Avoidance (CA)  
When multiple UAVs are used for a complex surveillance mission, the paths should be carefully designed for UAVs to remove the collisions among them, which is considered one of the most important tasks for feasible path planning. For separated trajectories, a minimum distance should be kept between UAVs to avoid collisions. Therefore, collision avoidance constraint can be described as,  
\[CA=0,CA=\,\sum_{i=1}^{N_{w}^{p}}\sum_{j=1}^{N_{w}^{p}}CA_{i} \tag{24}\]  
where,  
\[CA_{i}=\left\{\begin{array}{c}P,\,if\,d_{ij}^{mm}<d_{min}\\ 0,\,\text{otherwise}\end{array}\right.\]  
where \(d_{min}\) is the minimum safe distance to avoid the collision, \(d_{ij}^{uv}\) is the Cartesian distance between the \(i\)th waypoint of \(p\)th UAV trajectory and the \(j\)th waypoint of \(q\)th UAV trajectory.
## Appendix 1: Derivation of the Isochronous Condition (General Case)--Equation 16
### Calculation of \(A\)  
Approximate expressions as a function of \(E\) are now to be used. Elementary mathematics show that to the first order of the electric field \(E\):  
\[\begin{split}\sin\theta_{4}&\approx\sin\theta_{40}+ n_{0}\frac{\sin\beta}{\cos\theta_{20}}\varepsilon E\\ &\cos\theta_{4}\approx\cos\theta_{40}-n_{0}\frac{\sin\beta\sin \theta_{40}}{\cos\theta_{20}\cos\theta_{40}}\varepsilon E\\ &\sin\theta_{2}\approx\sin\theta_{20}\left(1-\varepsilon E \right)\\ &\cos\theta_{2}\approx\cos\theta_{20}\left(1+\varepsilon E\tan^{ 2}\theta_{20}\right)\end{split} \tag{14}\]  
where we used the following definitions:  
\[\begin{split}& n=n_{0}\left(1+\varepsilon E\right)\\ &\text{with }\varepsilon=-\frac{n_{0}^{2}r}{2}\end{split} \tag{15}\]  
Parameter \(A\) can be calculated to the first order of \(E\) using the set of Equation 10:\[A=b\big{(}\sin\theta_{40}-\sin\theta_{4}\big{)}+a\big{(}\cos \theta_{40}-\cos\theta_{4}\big{)} \tag{122}\] \[\approx-bn_{0}\frac{\sin\beta}{\cos\theta_{20}}\varepsilon E+an_{0} \frac{\sin\beta\sin\theta_{40}}{\cos\theta_{20}\cos\theta_{40}}\varepsilon E\] \[=\frac{n_{0}\sin\beta}{\cos\theta_{20}}\big{(}an_{0}\tan\theta_{ 40}-b\big{)}\varepsilon E\]  
This can be rewritten as:  
\[A=-n_{0}x_{30}\varepsilon E \tag{123}\]  
It is easy to show that \(x_{30}=BC\) (See Figure 5), the light pass in prism 2 without electric field applied, has the following form:  
\[x_{30}=\frac{\sin\beta}{\cos\theta_{20}}\big{(}b-a\times\tan\theta_{40}\big{)} \tag{124}\]
## 3 Uncertainty Modeling Techniques
### Info-Gap Decision Theory (IGDT)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ApplicationType** & \multicolumn{2}{c}{**Probabilistic**} & \multirow{2}{*}{**Hybrid**} & \multirow{2}{*}{**Analytical**} & \multirow{2}{*}{**RO**} & \multirow{2}{*}{**IGDT**} \\ \cline{2-2} \cline{5-6} \cline{7-8} \multicolumn{1}{c}{} & **Monte Carlo** & & & & & & \\ \hline Reliability evaluation & [63; 64; 65] & & [38; 44; 66] & & [67] & & [60] \\ \hline Renewable energy & & [68] & [29] & [40; 69] & [50] & [32; 70] & [53; 54; 56] & [58; 59] \\ (operation and planning) & & & & & & & & \\ \hline EV & [71; 72] & & [43] & & [73] & & [59] \\ \hline Energy storage & [25] & [30] & & & & & & \\ \hline DG units & [74] & & & & [34; 75] & & \\ \hline Load flow/optimal power & & [76] & & [36] & & [77] & & \\ flow & & & & & & & \\ \hline Generation/ transmission/ distribution planning, operation, and control & & [24; 78] & [27] & [37; 79] & [46; 47; 48; 80] & [51; 52; 55] & [58; 61] \\ \hline State estimation & [81] & & [82; 83] & & [33; 84] & & \\ \hline Electricity market & [85; 86] & & [79] & [50] & & [62] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications of uncertainty modeling techniques in the power system studies.
## 2 Materials and Methods
### Coating Characterization  
For microstructural investigation, the specimens were first sectioned using an alumina blade, and the transverse sections were cold mounted using a low viscosity epoxy resin. The mounted specimens were polished using a semi-automatic polisher/grinder (PowerPro 5000, Buehler, Lake Bluff, IL, USA) system. The polished cross-sections were examined under a Scanning Electron Microscope (SEM) (TM3000, HITACHI-High Technologies Corporation, Tokyo, Japan). Gold sputtering (2-3 atomic layers of Au) was done on the samples prior to SEM analysis to ensure electrical conduction between the ceramic coating and the SEM holder. For porosity (pores and cracks) quantification using image analysis (Image) software, Open source, National Institute of Health, Bethesda, MD, USA), over 20 micrographs at \(\times\)1000 magnification were analyzed. To allow minimum plausible noise, only pores \(\geq\)1 \(\upmu\)m in any dimension (2D) were taken into consideration. The constitution of crystalline phases present in the starting powders as well as in the coating were investigated by X-ray Diffraction (XRD) (PANalytical X'Pert PRO, Malvern Panalytical B.V., Almelo, EA, The Netherlands) with CuK\({}_{\alpha}\) radiation. The powder sample used was derived by drying a small quantity of the mixed suspension while the coating specimen was analyzed in an as-sprayed condition. The data was collected using Data Collector 5.1a and analysis performed with HighScore PLUS 4.1 software (PANalytical X'Pert PRO, Malvern Panalytical B.V., Almelo, The Netherlands). Rietveld refinement was performed on the XRD data to determine the phase quantities present. Electron Dispersive Spectroscopy (EDS) analysis of the coating was performed by using the INCA x-act EDS module (Oxford Instruments, Abingdon, UK) embedded into a Field Emission Scanning Electron Microscope from Zeiss, SIGMA series (Jena, Germany). The EDS spectra were analyzed with AZtec software (Oxford Instruments, Abingdon, UK).
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 4 Optimization Problem of the Proposed System  
### The Formulation of the Objective Function  
The aim of this paper is to minimize the combined objectives, the total running cost, and the emission of GHGs for the proposed CCHP system while satisfying a variety of realistic constraints of the electric, heating, and cooling systems. This is achieved by optimal planning of both the generation and consumption of end users simultaneously. The interaction between electric, heating, and cooling systems allows energy to flow between these systems to drive the proposed CCHP system operation economically and environmentally friendly. Therefore, the formulation of the objective function takes into consideration the interaction between these systems. One of the effective approachesto convert the multi-objective optimization problem into a single objective function is the monetized emission of GHGs and its incorporation with operation costs in a single objective optimization approach [37; 38]. In this study, this approach has been utilized to formulate the optimization problem of the proposed CCHP system, where the equation is 12. Additionally, considering the constraints of Equation 59 enables more control over the emission of GHGs in the area of the system. The objective function is formulated as follows:  
\[\begin{array}{c}F=min\Sigma_{t=1}^{T}\{\zeta_{MT}^{+}+C_{IC}^{+}+C_{DE}^{+}+C _{SB}^{+}+C_{U}^{+}+SU_{MT}^{+}+SU_{FC}^{+}+SU_{DE}^{+}+SO_{MT}^{+}+SD_{FC}^{+} +C_{CB}^{+}+C_{U-L}^{+}\\ +C_{am-MT}^{+}+C_{am-FC}^{+}+C_{am-DE}^{+}+C_{am-CB}^{+}+C_{am-LC}^{+}+C_{am-CA}^{ +}+C_{am-EH}^{+}\\ +(1-a),C_{-sh},P_{-sh}^{t}\}\end{array} \tag{33}\]  
where \(P_{L-sh}^{t}\) is the shedding load during the high load hour which is not supplied and \(C_{L-sh}\) is the penalty for uncovered loads (EUR/kWh).
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 3 Results and Discussion
### Effect of Microwave-Treated Acorn Flour Addition on Technological Properties of Muffins
[75] examined the effects of microwave treatment on corn and rice flour properties, reporting an increase in amylose content and gelatinization temperature alongside a decrease in swelling power and solubility. [PERSON] et al. [76], in their assessment of the effect of microwave treatment on rice flour, identified not only the changes already observed by other authors [64; 65] but also modifications in the morphological structure of the flour particles and the ratio of crystalline-to-amorphous regions. The effect of microwave heating on the functional properties of flour, including pasting properties (primarily a significant reduction in viscosity), was also observed in chestnut flour in the studies by [PERSON] et al. [48]. However, no studies are currently available that directly address the behavior and changes in acorn starch during microwave treatment, which represents a potential area for future research.
## 3 Experimental Results and Analysis
### Datasets  
The Waterloo Streaming QoE Database-III dataset, published by University of Waterloo in 2018 [23], is one of the largest datasets containing 450 videos. Coding bitrates recommended by Netflix [25] and Apple [26] are used to encode the source video sequences of 20 different types of content in the dataset. The videos are encoded at 235-7000 Kbps, obtaining 11 coding bitrate levels, and then the video bitstreams are stored on the server. The client selects six representative ABR algorithms based on the DASH standard, simulates under 13 representative network conditions, and uses the ITU-R Absolute Category Rating (ACR) scale for subjective testing and scoring.  
The LIVE-NFLX-II [24] subjective video QoE dataset is one of the most comprehensive datasets available [27]. The dataset consists of 15 source videos and 420 distorted videos. Seven mobile network traces and four client adaptation algorithms are adopted to generate the simulation data. Furthermore, the dataset includes both continuous and retrospective MOS. Due to using a dynamic optimizer to obtain the encoding bitrate, the uniform video quality cannot be achieved. To facilitate further processing, when the statistical video quality level switch, it is unified with six quality levels.