esa-sceva/satcom-chunk-collection huggingface dataset:
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Security Analysis
#### 4.1.1 Decentralized Approach without an Intermediary  
Compared to the traditional VECNets framework that employs the centralized approach, our proposed model leveraging consortium blockchain enables distributed edge nodes throughout the network. Every edge node (RSUs) has a blockchain replica that maintains a reliable database cooperatively. Therefore, vehicles can securely store and share their data without a central trusted party intermediary in a P2P manner. Furthermore, blockchain, as the model of a decentralized approach, effectively avoids a SPoF as the traditional attack of a centralized system. Thus, the vehicle can safely share its data without worrying that intermediary parties will breach it.
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 4 Discussion  
### Previous Published Work  
Previously, the performances of receivers containing J30020 and J30035 have been compared [11]. These two SiPMs have very similar output pulse widths. However, its larger number of microcells and faster recovery time mean that the maximum count rate of a J30020 is four times the maximum count rate of a J30035. This significant difference was the motivation for a previous comparison of receivers containing these two SiPMs [11]. This comparison showed that, at an irradiance of 100 mW\({}^{-2}\), changing the SiPM increased the data rate from 2.4 Gbps to 3.0 Gbps. This small increase, relative to the increase in the maximum count rate, can now be seen to arise from the rapidly increasing count rate penalty at high data rates.
# Configurational Effects on Strain and Doping at Graphene-Silver Nanowire Interfaces
## 3 Conclusions  
The graphene-AgNW interaction is studied in two different stacking arrangements, where AgNW is placed beneath and above monolayer graphene. The presence of AgNW reduces the work function in graphene by modulation of Fermi energy level and induces n-type doping. This arrangement leads to the formation of local p-n-p junctions for both stacking arrangements. Graphene as a top layer exerts tensile strain up to 0.05% due to the curvature around AgNW. Nevertheless, a lower strain of  
Figure 5: Raman spectroscopy of AgNW/Gr heterostructure. **(a)** Typical Raman spectrum of AgNW-covered monolayer graphene and graphene only region. A significant SERS effect is observed. **(b)** Raman line map (indicated by the purple line) across the AgNW along the axial direction and **(c)** transverse direction. In both cases, the G and 2D peak intensity increases at the AgNW/Gr region (red colour in background) compared to the Gr/ITO region (blue colour in background). **(d)** Strain and doping map of the above regions shows miniature tensile strength (0.01%) at the AgNW region compared to graphene only region. The difference in charge density is negligible.  
0.01% is observed when the graphene is placed between ITO and AgNW. Our results show that the combination of KPFM and Raman spectroscopy can thoroughly inspect the physical and electronic influences from the addition of AgNW to graphene, which provides useful information for devices using hybrid structure of these two 2D materials.
## 4 Simulation and Experimental Results  
### Simulation Results  
The simulations were carried out in MATLAB/Simulink, where the mathematical model of IM drive controlled by the PTC algorithm with switchable delta-star topology both in the control model and machine model was created. The IM model's step size was set to 10 \(\upmu\)s and the control loop's discrete step to 50 \(\upmu\)s. The machine model parameters are given in Table 1 in Appendix B.  
Figures 7 and 8 show the startup of the motor connected to the star and delta, respectively, from 0 to 1500 RPM. The reference stator flux in the delta configuration was set to 1.7 Wb. Following Section 3.1, the stator flux in the star configuration had to be set to 1 Wb; otherwise, the drive would not be able to reach the reference speed. The speed controller torque limit in the star connection was set to one-third of the torque limit in the delta connection, where it represented 125 % of the nominal torque. Due to the flux and torque limitations in the star connection, the startup was faster for a delta winding configuration. However, in both cases, the control model's estimated values coincided well with the actual values because the actual winding topology was respected in the control model.  
Figure 7: Startup of the motor from 0 to 1500 RPM—star-connected winding.  
Figure 9 demonstrates the consequences of not respecting the winding topology in the control algorithm. If the control system is not adjusted to the winding configuration, then the machine operates either in an under-excited or over-excited state, and the estimated torque and flux do not agree with the actual ones.  
A graphical comparison of the torque, flux, and current ripple during an online winding changeover is depicted in Figure 10. The reference speed, flux, and load torque were set to 1000 RPM, 1.35 Wb, and 20 Nm, respectively. The figure clearly shows that the ripple of the IM quantities was significantly reduced in the star connection. A quantitative  
Figure 8: Startup of the motor 0 to 1500 RPM—delta-connected winding.  
Figure 9: Online change of the winding topology—motor/predictive torque control (PTC): delta/delta (0.2–0.3 s), star/delta (0.3–0.4 s), star/star (0.4–0.5 s), delta/star (0.5–0.65 s).  
comparison of the current ripple in a star and delta configuration in terms of THD will be presented in the next section, which is dedicated to experimental results.
## 4 Conclusions  
In this work, physical and chemical properties of SiN\({}_{\mathrm{x}}\) as a gate spacer were evaluated. The process environment was implemented in a remote plasma ALD system designed to overcome the limitations of the high thermal budget of thermal ALD and to minimize the thin film damage that can occur in direct plasma ALD [19]. The atomic concentration and chemical binding state were confirmed by deposition of SiN\({}_{\mathrm{x}}\) with CSN-2 and N\({}_{\mathrm{2}}\) plasma. The fundamental properties of step coverage and wet etch rate were analyzed. The H\({}_{\mathrm{2}}\) plasma step was introduced before the N\({}_{\mathrm{2}}\) plasma step, which is a three-step process resulting in a reduction of growth rate. Many studies using NH\({}_{\mathrm{3}}\) plasma as a reactant have been performed. In our research, the H\({}_{\mathrm{2}}\) plasma and N\({}_{\mathrm{2}}\) plasma were used as reactants, respectively, instead of NH\({}_{\mathrm{3}}\) plasma. We compared the effect of the two-step process (only N\({}_{\mathrm{2}}\) plasma) with the three-step process at low temperature (\(\leq\)400 \({}^{\circ}\)C) [22]. According to the results of the study for the narrow pattern, the longer H\({}_{\mathrm{2}}\) plasma exposure time increased the side step coverage from 81% to 105%, and bottom step coverage increased from 90% to 110%. In addition, the wet etch rate was reduced from 43.2 to 32.4 A/min. Based on the results of this study, it was confirmed that the step coverage and the wet etching characteristics of the gate spacer thin film can be appropriately controlled by the optimization process.  
Data curation, H.C. ([PERSON]), H.P., C.J., S.S., H.Y., Y.K., J.-W.K., K.K., Y.C., and S.P.; Original draft preparation, H.C. ([PERSON]); writing--review and editing, N.L.; Supervision, H.J.  
This work was supported by the Nano Material Technology Development Program (2014M3A7B4049367) through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (MSIT), Korea. This work is supported by the Samsung Electronics' University R&D program [Study of high step coverage ONO thin film for CTF device].  
The authors declare no conflict of interest.  
Figure 8: Cross sectional TEM images for step coverage of the RPALD SiN\({}_{\mathrm{x}}\) film as a function of H\({}_{\mathrm{2}}\) plasma exposure time in a wide pattern for (**a**) 10 s, (**b**) 20 s, and (**c**) 30 sec and in a narrow pattern for (**d**) 10 s, (**e**) 20 s, and (**f**) 30 s. All the samples were deposited at 350 \({}^{\circ}\)C.
###### Abstract
in [38], hybrid of WOA with Pattern Search Algorithm (PS) presented by authors in [39], hybrid of PSO and Artificial Physics Optimization (HPSOAPO) presented by [PERSON] et al. in [40], hybrid of PSO, and Shuffle Frog Leaping algorithm (HPSO-SFLA) presented by [PERSON] et al. in [41]. Other techniques that have been used with different multi-objective functions for OPF solutions are fuzzy logic-based techniques. A fuzzy logic-based approach has been used for the contingency constrained OPF problem formulation and solution in a decomposed form [42]. The authors in [43] used fuzzy based membership functions to form fuzzy optimal power problem formulations with an artificial bee colony (ABC) algorithm. In [44], the authors proposed an OPF solution using fuzzy logic with integrated GA and PSO. A hybrid of fuzzy based PSO techniques has also been used in [45], while considering the uncertainties in load demand and wind speed. The uncertainties in load demand and wind speed have been forecasted using fuzzy set notation. Generally, there is not a single algorithm that can be considered better than others to solve all of the objectives of OPF problems, due to the inconsistent nature of OPF solutions. Thus, the performance of an algorithm or technique depends upon the nature and space of the problem that needs to be considered. Therefore, there must exist a space for new algorithms which can efficiently solve various objectives of OPF solutions. The objective of this paper is to accomplish a renewable integrated energy system with the use of WT and SPV sources to fulfill the energy requirements in the best possible way with a reduction in emissions for a sustainable environment. In this paper, utilization of the HPSO-GWO algorithms for OPF solutions incorporated with uncertain RES is presented. The results achieved with the suggested hybrid PSO-GWO algorithm (here after will be called HPSO-GWO) will be compared with PSO, GWO, success history based adaptive DE combined with superiority of feasible technique (SHADE-SF) [8], and with those results of GOA and Barnacles Mating Optimizer (BMO) reported in the literature [46]. The rest of the sections in this paper are set out as follows: Section 2 provides OPF problem mathematical formulations. Section 3 formulates frequency distributions of stochastic SPV and WT. Section 4 presents details of selected metaheuristic algorithms. Section 5 provides a detail discussion on the simulation results of each case. Section 6 provides a conclusion and future work.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 5 Experiments and Results
### Datasets  
For our experiments, we use two datasets. The first one is the OTT dataset [34]. This dataset consists of 1600 reviews of 20 hotels located in Chicago. It has 800 truthful reviews collected from multiple platforms such as TripAdvisor, Expedia, Hotels.com, Orbitz, Priceline, and Yelp. It also consists of 800 fake reviews from Amazon Mechanical Turk. The data were created through crowd-sourcing, where the participants were asked to write fake reviews based on given instructions.  
The second dataset was published in 2022 by [PERSON] et al. [35]. In their research paper, they developed a dataset to study the detection of fake reviews for online products. This dataset consists of a total of 40,000 reviews. It has 20,000 truthful reviews sourced from Amazon. The 20,000 fake reviews were computer-generated using GPT-2.  
We experimented with different subsets of the datasets, see Table 1. Sample sizes were chosen based on the experiments of previous studies for comparative analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Total Reviews** & **True Reviews (\%)** & **Fake Reviews (\%)** \\ \hline OTT Dataset & 1600 & 50\% & 50\% \\ Salminen Dataset & 40,000 & 50\% & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets.  
The objective was to test the model's effectiveness in detecting both human-generated and computer-generated fake reviews.
## 3 Results
### Working Memory Capacity  
The working memory capacity measure was divided into the verbal working memory capacity and spatial working memory capacity; the descriptive results and analysis of variance are shown in Figure 4. The training group showed an improvement in both their verbal and spatial working memory capacity [verbal: M\({}_{1}\) = 5.70, SD\({}_{1}\) = 1.16, M\({}_{2}\) = 7.00, SD\({}_{2}\) = 1.05; spatial: M\({}_{1}\) = 5.20, SD\({}_{1}\) = 1.31; M\({}_{2}\) = 6.90, SD\({}_{2}\) = 0.99]; the control group did not show significant changes in their verbal and spatial working memory capacity [verbal: M\({}_{1}\) = 5.55, SD\({}_{1}\) = 1.04, M\({}_{2}\) = 5.64, SD\({}_{2}\) = 0.92; spatial: M\({}_{1}\) = 5.46, SD\({}_{1}\) = 1.04, M\({}_{2}\) = 5.36, SD\({}_{2}\) = 0.81]. Here, M is the mean, SD is the standard deviation, and subscripts 1 and 2 denote the pre-training and post-training values, respectively.  
Before training, no statistical difference was found in the memory capacity of the training group and the control group in the pre-test in the independent-samples \(t\)-test [verbal: T(19) = 0.323, \(p\) = 0.750; spatial: T(19) = \(-\)0.332, \(p\) = 0.743]. A 2 (pre-test, post-test) \(\times\) 2 (training group, control group) repeated-measures ANOVA was conducted on the working memory capacity of the two groups, with a non-significant main effect of the grouping [verbal: F(1, 19) = 3.490, \(p\) = 0.077, \(\eta^{2}\) = 0.155; spatial: F(1, 19) = 2.615, \(p\) = 0.122, \(\eta^{2}\) = 0.121], a significant main effect of the time of measurement [verbal: F(1, 19) = 11.320, \(p\) = 0.009 \(<\) 0.01, \(\eta^{2}\) = 0.373; spatial: F(1, 19) = 12.265, \(p\) = 0.002 \(<\) 0.01, \(\eta^{2}\) = 0.392] and a significant interaction between the grouping and time of measurement [verbal: F(1, 19) = 8.554, \(p\) = 0.003 \(<\) 0.01, \(\eta^{2}\) = 0.310; spatial: F(1, 19) = 15.194, \(p\) < 0.001, \(\eta^{2}\) = 0.444]. Further simple effects analyses showed that there was a significant increase in the verbal and spatial working memory capacity in the training group compared to the pre-test period [verbal: F(1, 19) = 18.878, \(p\) \(\leq\) 0.001, \(\eta^{2}\) = 0.498; spatial: F(1, 19) = 26.136, \(p\)\(\leq\) 0.001,\(\eta^{2}\) = 0.579], and there was no significant difference between the pre-test and post-test in the control group [verbal: F(1, 19) = 0.102, \(p\) = 0.753, \(\eta^{2}\) = 0.005; Spatial: F(1, 19) = 0.82, \(p\) = 0.777, \(\eta^{2}\) = 0.004]. Bidimensional N-back working memory training can effectively improve the verbal working memory and spatial working memory capacity.
## 5 Conclusions  
In this study, a novel CSRO-MHDT method was developed for the optimal choice of routes for data transmission in a UWSN. The primary intention of the CSRO-MHDT technique is to reduce energy dissipation and enhance the lifetime of the UWSN. Primarily, the CSRO-MHDT technique involves WCA for effective choice of \(CH\)s. In addition, the CSRO-MHDT technique derived a fitness function and effectively selected a set of routes in a UWSN. For assessing the outcomes of the CSRO-MHDT technique, a wide-ranging experimental examination was carried out and the results were assessed under several aspects. The extensive comparative analysis highlighted the superior performance of the CSRO-MHDT technique over recent state-of-the-art approaches. Therefore, the CSRO-MHDT method can be used in application for optimal data transmission in UWSNs. In the future, delay-aware data aggregation schemes can be designed to improve the efficiency of UWSNs.  
Conceptualization, N.S. and D.A.; organization, N.S. and D.A.; validation, N.S. and Y.A.; formal analysis, M.R. and N.S.; investigation, D.A. and N.S.; resources, N.S. and M.R.; data curation, D.A., S.A. and M.R.; writing--original draft preparation, D.A.; writing--review and editing, N.S. and Y.A.; visualization, O.I.K. and S.A.; supervision, N.S. and Y.A.; project administration, N.S. and Y.A.; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript.  
This Research is funded by Umm Al-Qura University, 22 UQU4281768 DSR03.  
Not applicable.  
The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4281768 DSR03).  
The Authors declare no conflict of interest.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### BExo Impact on PPE in Different Tasks
#### 3.3.2 Carrying a Box  
Looking at the effect of the BExo on PPE in the task where the participants carried the box (Figure 7), the lower back exhibited the highest PPE score without the exoskeleton, at approximately 55, which was substantially reduced to around 25 with the exoskeleton, demonstrating the BExo effectiveness in alleviating lower back strain. Similarly, the shoulders showed a high PPE score of about 45 without the exoskeleton, which decreased to approximately 20 with its use, while the neck showed a reduction from around 35 to 15, highlighting the exoskeleton's benefits for these body parts. The upper back had a PPE score of about 30 without the exoskeleton, reduced to around 10 with it, and the arms showed a decrease from about 25 to 10. This highlights that the body parts experiencing the highest exertion without exoskeleton support benefited the most from its use. This evidence validates the exoskeleton's role in significantly reducing physical exertion, particularly for the lower back, shoulders, and neck, during carrying tasks, emphasizing its potential as a valuable ergonomic tool in high-exertion activities.
## 3 Materials and Methods
### Gradient Boosting
Gradient Boosting Model and Methodology in This Paper  
Gradient Boosting is an ensemble learning technique that sequentially trains weak learners, correcting the errors of previous models to build a strong learner. In this study, Scikit-learn's gradient boosting classifier was used for implementation. The model was configured with estimators = 100, learning rate = 0.1, and random state = 42, optimized for solving the multi-class classification problem. During training, the dataset was randomly resampled in each epoch to evaluate the model's performance on both training and validation data, enhancing generalization. This random resampling approach helps prevent overfitting and ensures performance assessment across diverse data distributions. At each epoch, the model undergoes a fitting process using training data and generates both predictions and class probability values for the validation set. The model's performance is monitored using multiple evaluation metrics, including F1 score, accuracy, log loss, and AUC (area under the curve). The F1 score is computed using a macro-averaging approach to ensure balanced performance across classes, while accuracy quantifies overall predictive performance. Log loss measures the difference between predicted probability distributions and actual labels, and AUC is calculated using the one vs. rest (OvR) method to assess the model's discrimination ability. The model's weights were saved at each epoch based on log loss, preserving the best-performing version. The final gradient boosting model was selected based on the lowest validation loss. After training, the model was fine-tuned using the entire training dataset, ensuring reproducibility and real-world applicability, and the final model was stored as a file. Gradient boosting allows performance optimization by tuning key hyperparameters, such as the learning rate, number of trees, and maximum depth. The configurations used in this study were well suited for the multi-class classification task. Performance evaluations confirmed that gradient boosting effectively learns complex data patterns and delivers reliable results across different data distributions and problem types.
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 2 Related Work
### Feature Extraction
#### 2.1.1 The Active Appearance Model (AAM)  
Initially proposed by [PERSON] et al. [16], AAM is a statistical model for representing shape and appearance variations of the face. The shape representations, obtained from key landmark points on the face, result from the AMM algorithm conducting a series of Procrustean and Principal ComponentAnalyses (PCA). Due to its success, various extensions of the AAM have been proposed for different system objectives. [PERSON] et al. [11], extended AAM to obtain person-specific features wherein the authors successfully extracted craniofacial growth-related representations for the child and adult faces.  
Since AAM relies on landmark points, it is intuitive that the number of points should impact the performance of the proposed system. Although the selection of the number has yet to be reviewed comprehensively, available literature suggests the use of different numbers for different proposals; for example, 48 points were considered in [20], 17 points in [38], 79 points in [39], etc. However, the majority utilized 68 points [10; 40; 41]. We observe that the choice depends on the objectives of the proposal. In this proposal, we utilized the 68 facial landmark points for the following reasons:  
1. They are extensively utilized for age estimation and allied systems, attesting to their ability to represent face aging features.
2. Some age estimation datasets are already annotated with the 68 facial landmark points.  
Based on these advances, we utilized the 68 landmark points of the AMM to represent facial features similarly to the proposal by [PERSON] et al. [14], wherein the features were represented as terms that described the shape and appearance of the face.
## 4 Test Results and Insights
### Attractiveness  
To assess the attractiveness and overall user experience of both systems, we utilized the AttrakDiff survey. Figure 12a shows that the two systems are both in the desirable sphere with one variation: the Stratos is perceived as more self-oriented, while the trackpad is task-oriented. The Stratos has a higher hedonic quality than the trackpad; the pragmatic quality (PQ) and the attractiveness (ATT) are equal, whereas in PQ, the Stratos is more direct, and in ATT it is more pleasant, as seen in Figure 12b. In the hedonic stimulation quality (HQ-S), the Stratos is higher, with a gap of almost one point compared to the tablet. The Stratos also turns out to be more challenging than the trackpad, although this figure is only somewhat relevant since it is particularly close to zero. The only note of interest in the hedonic identification quality (HQ-I) is how the Stratos are perceived as more professional and premium.  
Figure 11: Results of the Raw NASA-TLX.  
The minimal discrepancy observed between the two interfaces can likely be attributed to the novelty effect that users encountered while interacting with the trackpad. Despite this interface being commonplace now, it was perceived as a fresh and innovative method of interaction within the car, as reported by users during the post-test interview. Indeed, when asked what interface the users have used at least once inside the vehicle, only one said he used the trackpad. The other participants have yet to use this technology inside a car.