esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Designs of the Small-Scale Tower from Numerical Experiments
### Comparisons between Empty and One-Tray Tower (Case C and Case D)  
As shown in Table 6, Case D has the same flow conditions (\(L/G=4.9\)) as Case A and C, except only the lowest sieve tray is implemented, as shown in Figure 15a. Figure 15a,b shows the liquid flow structures for Cases C and D. Before the liquid flow reaches the lowest sieve tray, Cases C and D have consistent size of liquid column, as shown in Figures 15a and 16. Moreover, the liquid flow reaches the terminal velocity \(4.2\,\mathrm{m}\mathrm{/}\mathrm{s}\) at y = 0.75 m for both cases, and this stronger inertia causes a more obvious deflection when it impacts the lowest sieve tray for Case D, as highlighted in Figure 16. Although the liquid column becomes smaller after the lowest sieve tray, as discussed previously, the deflection makes the reduction from a wider liquid column. This results in a larger liquid column for Case D than Case C in Figure 16.  
Figure 14: This schematic shows the radius of liquid column of the small-scale tower for Cases A and C.  
Correspondingly, the high SO\({}_{2}\) region near the wall of tower is reduced significantly from Case C to Case D, as shown in Figure 15c at y = 0.615 m. The stronger two-phase mixing near the sieve tray and smaller region of high SO\({}_{2}\) near the wall lead to a higher SO\({}_{2}\) removal efficiency for Case D (61.6%) than Case C (56.9%) in Table 6. In comparison with the four-tray Case A in Figure 13, the one-tray Case D in Figure 15c reduces the high SO\({}_{2}\) region significantly, while it lacks two-phase mixing from the other three trays. The benefit of the first overwhelms the drawback from the latter, which makes Case D with only one tray perform better than Case A (57.1%).
## 3 Visible-Light and Thermal Infrared Confidence Measures  
Our aim is to detect people robustly under dynamic circumstances through automatically choosing between a visible-light and a thermal infrared sensor in a bimodal camera setup. To accomplish this purpose, we introduce a series of so-called confidence measures. The confidence measures are set for each spectrum based on some relevant features of the frames acquired by each kind of camera. The frame features used are the mean illumination and the standard image deviation in the thermal infrared spectrum, while the average grey level value is the main cue in the visible-light spectrum.  
A scheme for how the confidence measures are set up in both spectra is shown in Figure 1. The top of Figure 1 shows a division into three different zones (intervals) separated by two thresholds, \(\zeta_{VL}\) and \(\zeta_{VH}\), which establish the confidence measures in the visible-light spectrum. \(C_{V}\) is the confidence value in the visible spectrum and takes the linguistic values \(HIGH\) and \(LOW\). Notice that a \(MEDIUM\) confidence value is not used in the visible-light spectrum. \(\overline{I_{G}}\) is the average grey level value of the input frame in the visible spectrum.  
Figure 1: Set up of the confidence measures.  
The bottom of Figure 1 offers three possibilities for assigning confidence intervals in the thermal infrared spectrum. Notice that the confidence value in the thermal infrared, \(C_{IR}\), is directly affected by the confidence value established in the visible-light spectrum. Firstly, for \(C_{V}=LOW\) (there is a low reliability on the results obtained by the visible camera), \(C_{IR}\) takes the two different values, \(HIGH\) and \(MEDIUM\). The reason is the following. When the confidence value of the visible-light spectrum is set to \(LOW\), it is clear that the visible-light sensor is almost unable to distinguish humans. This is why the infrared spectrum is always forced with a confidence value above \(LOW\). Indeed, under this circumstance, the infrared sensor always works better than the visible-light one. On the other hand, when \(C_{V}=HIGH\), \(C_{IR}\) takes the three possible values, \(HIGH\), \(MEDIUM\) and \(LOW\). This means that, when the detection in visible-light offers good results, the detection in thermal infrared can be very worse or worse, but sometimes equal or even better.  
Next, the setup of the confidence measures is explained in more detail.
# Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil-Water Separation
## 2 Experimental
### Characterization  
The morphology of magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles was characterized by transmission electron microscope (TEM; F-30, FEI-Tecnai, 300 kV, Hillsboro, USA). Fe\({}_{3}\)O\({}_{4}\) nanoparticle samples for TEM observations were prepared by dropping the ethanol solution of Fe\({}_{3}\)O\({}_{4}\) nanoparticles samples on the copper grids and drying. The morphology of composite sponge samples with a thin layer of gold prior was evaluated by scanning electron microscope (GeminiSEM 300, Germanic Carl Zeiss Co., Ltd, Oberkochen, Germany.). The FT-IR data of magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles, expanded graphite, stearic acid and sponge samples were acquired from fourier transform Infrared Spectrometer within the wave number from 400 to 4000 cm\({}^{-1}\) (NICOLET6700, American Thermo Fisher Co., Ltd, Waltham, USA.). The water contact angles of composite sponge samples were measured with purified water by contact angle measurement at room temperature (Theta Flex, Gothenburg, Sweden.). The weight of samples was obtained from an electronic analytical balance (HC2004, HOCHOICE Co., Ltd, Shanghai, China.).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Fe\({}_{3}\)O\({}_{4}\) Nanoparticles (g)** & **Expanded Graphite (g)** & **Stearic Acid (g)** \\ \hline
1 & 0.05 & 0.01 & 0 \\
2 & 0.05 & 0.01 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specific masses of magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles, expanded graphite, and stearic acid for different sponge samples.  
Figure 1: Schematic preparation of magnetic and hydrophobic composite polyurethane sponge.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq4: What Are the Challenges of Utilizing AI in the Metaverse for Education?
#### 4.4.5 Computational Challenges  
The use of AI in the metaverse can be limited due to the need of high computational processing and advanced hardware, which can be prohibitively expensive and not universally accessible [107]. On the one hand, the related computational demands require a robust infrastructure and significant processing power to handle AI algorithms, in particular, deep learning algorithms, along with real-time interactions of students [106] and 3D simulation content [107]. On the other hand, there is a need for extensive data transfer, which can be difficult to manage and necessitate the use of 5G or 6G networks and edge computing [107]. This may raise concerns about the digital divide, particularly as not all students and educational institutions may have the necessary technological resources.  
Furthermore, the infrastructure layer of the metaverse, which includes AI, is still in its emerging phase. This infrastructure is not yet fully developed, which poses a challenge to the implementation of AI in the metaverse for education [108]. For example, to handle real-time simulations and advanced graphics, such as ray tracing, AI models must process vast amounts of data from IoT devices, digital twins, and geospatial sources to provide immersive visualizations in real time [13]. Balancing computational requirements with the need for interactive and seamless experiences is crucial for education, where AI-based systems must effectively engage learners. Furthermore, developing efficient algorithms and predictive models that reduce latency while ensuring an immersive experience also remains a major challenge.
## 4 Simulation Results and Security Evaluation
### Floating Frequency  
According to [PERSON] et al. [39] and [PERSON] et al. [40], the floating frequency analysis serves as a crucial tool to assess the uniformity of the encryption process  
Figure 16: Data loss and recovery in different degrees.  
Figure 15: Noise (pepper and salt) effect on ciphertext and recovery image in different degrees.  
employed on both the rows and columns of an image. It also evaluates the encryption's ability to generate uniformly distributed random data for all segments of the original image. By applying this robust analysis, any vulnerabilities or weak encryption areas within the cryptogram can be identified. The analysis is conducted by examining windows of 256 elements, arranged based on the image's rows and columns. The primary objective of the analysis is to quantify the number of differing elements within each window. In this study, we propose the concept of \"rows' and columns' floating frequency\" for a 256 \(\times\) 256 gray-scale image. The methodology for calculating this floating frequency is as follows:  
1. Select windows comprising 256 elements for each row and column of the image;
2. Count the number of distinct elements within each window, determining how many different elements are present;
3. Define the \"row floating frequency\" (RFF) and \"column floating frequency\" (CFF) as the frequencies of distinct elements within the corresponding windows;
4. Calculate the mean of both the RFF and CFF values; additionally, generate plots to visually represent the distributions of RFF and CFF across the image.  
Figures 17 and 18 showcase the column and row floating frequency analysis results for both the Lena 256 \(\times\) 256 plain image (P) and its corresponding encrypted image. The CFF values depict the frequencies of distinct elements within the columns of the images. This analysis offers valuable insights into the encryption process's impact on the distribution of elements in the image columns, highlighting any potential deviations from uniformity. A higher percentage of column floating frequency (CFF) in the encrypted image indicates the greater efficiency of the image encryption algorithm in generating a randomized cryptogram at the column scale. Similarly, a higher percentage of row floating frequency (RFF) in the encrypted image signifies the improved efficiency of the image encryption algorithm in producing a randomized cryptogram at the row scale.
# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 3 Results and Discussion
### Performance Evaluation
#### 3.3.4 Overall Performance Analysis  
To find the overall best algorithm, we computed the ranks of all algorithms from three perspectives: average fitness, average accuracy, and average features selected (AFS). Once the ranks were determined, the average rank of each algorithm was computed on all datasets. The overall ranks and average rank of all algorithms on each dataset are presented in Table 6. As the results illustrate, the best rank was attained by EO, which was 1.88. However, the second-best position was shared by SCA and HGSO. It is important to mention that SA was the worst performer on the list.  
\begin{table}
\begin{tabular}{c c r r r r r r} \hline \hline
**Dataset** & \multicolumn{1}{c}{**SA**} & \multicolumn{1}{c}{**GSA**} & \multicolumn{1}{c}{**SCA**} & \multicolumn{1}{c}{**ASO**} & \multicolumn{1}{c}{**HGSO**} & \multicolumn{1}{c}{**EO**} \\ \hline DS1 & AFS & 5.4 & **3.6** & 4.8 & 4.2 & 3.8 & 4.8 \\  & Std. & 2.07364 & 0.54772 & **0.44721** & 0.83666 & 0.83666 & 0.83666 \\ \hline DS2 & AFS & 9.6 & 10.2 & **6** & 11.8 & 9.4 & 8.4 \\  & Std. & 1.81659 & 2.16795 & 1.41421 & 1.30384 & 3.91152 & **1.14018** \\ \hline DS3 & AFS & 4.4 & 4.4 & 4 & 4.8 & **3.4** & 4.2 \\  & Std. & 1.67332 & **0.54772** & 1.41421 & 1.09545 & 0.89443 & 0.83666 \\ \hline DS4 & AFS & 11.6 & 10 & **3.4** & 10.6 & 3.6 & 5 \\  & Std. & 4.92950 & 2.91548 & 0.89443 & 3.43511 & 1.14018 & **0.70711** \\ \hline DS5 & AFS & 1986.6 & 1891.8 & 18 & 1682.6 & 77.2 & **3.4** \\  & Std. & 37.35371 & 73.07667 & 28.53945 & 107.37225 & 60.95654 & **0.54772** \\ \hline DS6 & AFS & 25.8 & 23.8 & 8 & 17.8 & **7.8** & 11.4 \\  & Std. & 5.84808 & 4.14729 & **2.91548** & 3.11448 & 5.01996 & 4.15933 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean selected feature subsets of all algorithms and their standard deviations.  
Figure 4: Convergence curves of all algorithms on all selected datasets i.e., (**a**) Breast cancer (DS1) dataset, (**b**) German (DS2) dataset, (**c**) Heart (DS3) dataset, (**d**) Ionosphere (DS4) dataset, (**e**) Ovarian cancer (DS5) dataset, (**f**) Sonar (DS6) dataset.  
Figure 5: Average computational time and standard deviation of all algorithms on all selected datasets.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### Testing Techniques  
Various techniques, such as static and dynamic testing, to name a few, can be used to develop testing software. Static testing identifies static errors pre-compilation by analysing the source code. This facilitates the analysis of the code in detail and fully covering the application source code without launching the application itself. Unfortunately, due to the nature of parallel applications, it is more difficult to analyse statically as programmes behave unpredictably when they are actually running [29; 30]. Nevertheless, static analysis facilitates the identification of run-time defects within the source code, as well as both potential and existing issues, such as race conditions and deadlocks. Dynamic testing involves analysing a system while it is running to detect errors that may arise during run-time. However, such programmes can be affected by the execution environment, which can cause the application to slow down. Dynamic analysis facilitates the flexible monitoring and detection of each thread in parallel applications. However, it is difficult to test parallel code in its entirety as it is not possible to verify whether the errors have actually been corrected after they were corrected the first time around.  
The type and behaviour of an error determines how and which techniques to use as both static and dynamic techniques cannot detect all errors. As such, this present study proposes a hybrid technique that operates parallelly to identify both run-time errors and examine threads and was tested on a variety of situations and applications.
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 6 Simulation Results  
### Dataset and Evaluation Metrics  
We conducted our experiments on three datasets: Yale, Rochester and Credit defaulter, which were constructed in [1]. Yale and Rochester are social network datasets, which are collected from Yale University and Rochester University. These two datasets describe user attributes and their relationships with each other. The Yale contains 8578 nodes, 405,450 edges, while the Rochester contains 4563 nodes, 167,653 edges. The Credit defaulter is an ethical dataset of defaulter, which contains 30,000 individuals with 14 spending and payment patterns. These three datasets offer a large scale and complexity, enabling the model to be evaluated in social networks of different scales and structures, and being able to test both universality and robustness. And all of them are collected from the real world, providing an opportunity for the effectiveness evaluation of the model in practical applications. Additionally, characteristics of these datasets are summarized in Table 1 and the dataset visualization is displayed in Figure 2.  
The evaluation tasks in this study are divided into two main components: utility tasks and privacy tasks. The utility performance of the models is quantified through link prediction, with the Area Under the ROC Curve (AUC) and Average Precision (AP) serving as key metrics--higher values indicate stronger performance. For node classification, accuracy (ACC) and Macro-F1 are employed to assess the models' resilience against inference attacks. Specifically, the goal is to maintain high inference accuracy for utility attributes while achieving low accuracy for privacy attributes.
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 7 Conclusions  
An in-depth analysis of the evolution of SCE along with Ra in three different machining regions (conventional, transitional, and high-speed machining) has been performed in this research. The following general conclusions can be drawn from the analysis of these three machining regions:  
* All cutting parameters have been found to be statistically significant for SCE in the three studied machining regions.
* Cutting feed is the most significant cutting parameter affecting SCE (having a CR% > 60%) in all the three machining regions.  
Figure 15: Effect of cutting speed-depth of cut on SCE in three machining regions.  
* Cutting speed and depth of cut have an almost similar effect and CR% in the conventional and HSM region.
* SCE has been observed to follow a parabolic trend in the HSM region, signifying the presence of an optimum cutting speed at which SCE is minimum.
* The chip morphology study revealed no plausible cause for an increase in SCE above the cutting speed of 2000 m/min.
* Ra has been observed to be highly affected by feed in all three machining regions.
* SCE and Ra are two competing responses, and the settings of cutting parameters required to achieve a minimum SCE and Ra are almost opposite and contrary in nature. These conflicting requirements of SCE and Ra build the case for the multi-objective optimization of cutting parameters.  
A comprehensive sustainability analysis of other alloys (especially difficult-to-cut super alloys) is envisioned in future research. Furthermore, full factorial-based experiments in the three cutting speed regions (CSM, TSM, and HSM), an analysis of machining parameters and their interactions, and the subsequent development of regression-based models of SCE and Ra are also other potential areas of future research. Such analyses are expected to provide valuable insight regarding SCE consumption at varied cutting parameters and speed regions.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11199032/s1](https://www.mdpi.com/article/10.3390/app11199032/s1)]([https://www.mdpi.com/article/10.3390/app11199032/s1](https://www.mdpi.com/article/10.3390/app11199032/s1)), Supplementary data.  
S.S.W.: Conceptualization, Methodology, Formal Analysis, Investigation, and Writing--Original Draft; T.Z.: Conceptualization, Methodology, Resources, and Supervision; H.E. and F.G.: Methodology, Formal Analysis, Investigation, and Writing--Original Draft; R.A.L. and F.G.: Methodology and Formal Analysis; S.B.: Project Administration, Reviewing, and Funding Acquisition; U.G.: Writing--Review and Editing and Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available from Supplementary Materials.  
The authors declare that there is no conflict of interest in the subject matter or materials discussed in this manuscript.  
List of Abbreviations
## 1 Introduction  
The impact the temperature has on the remodeling and healing processes of the bone during implant site preparation has been extensively described in the literature [1]. The shape of the drill, its design, fatigue, cooling technique, cooling material, the force applied during drilling and the rotation speed have been considered crucial factors [1]. Different temperature measuring tools have been used to analyze temperature changes including: real time infrared thermography [2], type T thermocouples [3], type K thermocouples [4], thermographic digital camera [5], digital thermometer [6], type K thermocouples and digital thermometer [7].  
[PERSON] and [PERSON] [8] reported that the temperature generated by the drill while preparing the implant site should not exceed 47 \({}^{\circ}\)C for more than 1 min, as such overheating causes irreversible osteonecrosis. Other reported factors influencing the overall success of osseointegration are implant biocompatibility, design, surface, condition of the host bed, surgical technique, and loading [9].  
[PERSON] et al. [10] suggested that the number of drills used during implant bed preparation has no significant influence on the bone temperature increase following either conventional or simplified drilling procedure. What is more, the drilling technique (continuous versus intermittent) did not cause rise in the temperature and subsequent bone overheating (i.e., above 47 \({}^{\circ}\)C) [11]. Nevertheless, there are some techniques which utilize bone collected from the drill as a graft material and while applying them it is recommended not to use cooling water and a decreased drilling speed [6].  
Polyurethane (PUR) foam blocks have been commonly used in experimental studies replacing cadaver or animal bone specimens [4; 7; 12; 13; 14; 15; 16; 17]. According to [PERSON] [18] and [PERSON] [19] polyurethane foam is a good alternative for human cancellous bone as it displays similar mechanical properties and may be used as a medium for implant testing. [PERSON] et al. [17] conducted experimental studies to evaluate its mechanical properties (temperature, strain and density) and reported that it is not only suitable for mechanical investigations but also for investigations involving surgical instruments that generate heat. Moreover, this type of foam meets the ASTM F-1839-08 \"Standard Specification for Rigid Polyurethane Foam for Use as a Standard Material for Testing Orthopaedic Devices and Instruments\" which makes it an ideal material for comparative testing of bone screws and other medical devices and instruments.  
The aim of the present in vitro study was to measure and verify the maximum temperature of the drills used for placing crestal and lateral basal implants, with different drill shapes. We hypothesized that disc cutters for lateral basal implants, regardless of their design, would not exceed the temperature of 47 \({}^{\circ}\)C and could be safely used while preparing the implant bed site. Therefore we aimed to evaluate disc cutters safety protocol in terms of maintaining non-hazardous bone temperature levels.