esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven and Machine-Learning-Based Real-Time Viscosity Measurement Using a Compliant Mechanism
## 3 Numerical Simulations and ML Models
### Hyperparameter Tuning of the Models  
To obtain the best performance of each model in predicting viscosity, we carefully adjusted and tested different settings, or \"hyperparameters,\" for each algorithm. We experimented with multiple setups and found the configurations that yielded the most accurate results with minimal prediction errors.  
1. Multiple Linear Regression (MLR): The coefficients were calculated by minimizing residuals through Ordinary Least Squares (OLS), with grid search confirming the best configuration.
2. Decision Tree (DT) Regression: To prevent the model from becoming too complex and overfitted, we capped the maximum tree depth at 10 after testing depths between 5 and 20. This depth offered a good trade-off between accuracy and generalization. Each split required at least five data points, which ensured enough statistical strength in each split. We tried different criteria for evaluating the splits, including _MSE_, _RMSE_, and _R-squared_. The split strategy was set to \"best\", which allowed the algorithm to focus on maximizing the information gain during each split.
3. Support Vector Regression (SVR): Since the data contained linear patterns, we found that a linear kernel captured these patterns best. The regularization parameter (C) was fine-tuned between values from 0.1 to 100, and a \(C\) value of 10 provided the ideal balance between the model's complexity and accuracy. Epsilon (\(\varepsilon\)), which sets the tolerance for errors, was tuned from 0.001 to 0.1, with 0.01 giving the best performance, allowing some deviation but preserving the generalization.
4. Random Forest Regression: We found that using around 100 trees gave the best results in terms of stability and accuracy after experimenting with a range of 50 to 200. A maximum depth of 15 prevented the model from growing too complex, reducing the risk of overfitting. To avoid overly simple trees, we required a minimum of two samples per leaf node.
## 4 Experimental Setup for Validating an NRS Algorithm and Its Outcomes
### MyNews Dataset  
This dataset contains all articles included in the personalized 'MyNews' area generated for each user and per session by the recommender algorithm. While merely by observing the relative frequencies of news categories in 'MyNews', one can reach early conclusions, e.g., that sports-related news is by far the most frequently observed category (32.3%) followed by some distance by local and international news, this might be deceptive; after all, such behavior could be indicative of the recommender algorithm successfully delivering sport-related content to users who are primarily interested in sports.  
More importantly, a number of observations concerning the algorithm's behavior could be made on the basis of data other than the news category indicating designer choices. First, the algorithm consistently recommended between 28 and 32 articles to each user in each session. Secondly, there were no duplications of news items within any session, as could be expected. More interestingly, observing the times that the various items contained in 'MyNews' and the time a user session started, it became obvious that a number of rules have been set for the recommender system concerning recency: roughly 50% of the recommended items had been published within 3.5 h of the session's start, while 75% had been published within the last ten hours. The maximal time allowable lapsing between the start of a session and the publishing of a news item was 1439 min, which is one minute from 24 h. It then becomes obvious that the system had been designed to only deliver news appearing during the last day. However, this is the only hard rule that can be safely deduced; there appears to be an additional bias toward more recent items, but it is impossible to pinpoint how this operates exactly.
# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
## 2 Improved YOLOv8 Detection Principle  
### Improved Overall Structure of YOLOv8n  
YOLOv8 is a significant upgrade of the YOLO series of real-time object detectors launched by Ultralytics, aimed at enhancing the accuracy and efficiency of object detection. Its structure, as shown in Figure 1, features numerous innovations in architectural design. YOLOv8 employs a C2f backbone, which improves the model's ability to recognize targets in complex environments. Additionally, to enhance detection performance, the decoupled head design allows for the independent processing of object classification and localization. The model also introduces an anchor-free approach, reducing reliance on manual anchor box configuration and ensuring robust generalization capabilities. This version offers five different model options--n, s, m, l, and x. Each model varies in parameter count, computational complexity, and performance, catering to the requirements of different hardware environments and application scenarios. These improvements have enabled YOLOv8 to achieve outstanding results across multiple standard datasets, further advancing the progress of real-time object detection technology.  
Figure 1: Structure diagram of YOLOv8.  
The industrial requirements for defect detection are extremely stringent, and YOLOv8n offers a good balance between real-time performance and accuracy. As the smallest version in the YOLO series, YOLOv8n features a compact design that allows it to operate in resource-constrained environments, making it suitable for embedded systems and mobile devices. By adopting an optimized network architecture, it reduces computational load while maintaining good detection performance. This enables YOLOv8n to achieve high responsiveness when processing real-time data, allowing for timely identification and localization of surface defects. Therefore, this paper selects the smallest version of YOLOv8n as the basis for further improvement, proposing the YOLOv8-BSPB model. In this model, the SPPF pooling layer in the network structure is replaced with SCRD, the BAM attention mechanism is introduced into the C2f backbone, and the PSA mechanism is applied after the pooling layer. Additionally, the BiFPN is incorporated at the neck's connection layer to enhance model performance, and the loss function is replaced with the WIoU loss function to improve classification performance. The overall structure of the improved model is shown in Figure 2.
# An FCM-GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
## 3 Methodology
### Performance Indicators  
In order to evaluate the effectiveness of the model, the _MSE_, mean absolute error (_MAE_), and mean absolute percentage error (_MAPE_) were adopted as the indicators to evaluate the performance of the approaches, in which the predicted data \(\hat{o}_{i}\) are the predicted least feeding panel and the original data \(o_{i}\) are the least feeding panel. The _MSE_, _MAE_, and _MAPE_ can be described as \(MSE=\sum\limits_{i=1}^{N}\left(\hat{o}_{i}-o_{i}\right)^{2}/N\), \(MAE=\sum\limits_{i=1}^{N}\left|\hat{o}_{i}-o_{i}\right|/N\), and \(MAPE=\frac{1}{N}\sum\limits_{i=1}^{N}\left|\frac{\hat{o}_{i}-o_{i}}{o_{i}} \right|\times 100\), respectively, where \(N\) is the number of samples.  
The indicators surplus rate (_Surpr_) and supplemental feeding rate (_Supfr_) in the PCB template workshop were also considered. The predicted surplus rate (_Surpr_Pd_) and predicted supplemental feeding rate _Supfr_Pd_ can be computed with Equations (10) and (11) in [2], respectively. The final performance is evaluated by the _MSE_, _MAE_, _MAPE_, _Supfr_Pd_, and _Surpr_Pd_.
## 2 Material and Methods
### Study Areas and Datasets  
In order to investigate the three DL algorithms' abilities to model new COVID-19 infections, four countries from different geographic locations, climates, and cultures were selected as case studies. Saudi Arabia, Egypt, India, and Italy were considered. Table 2 below depicts the geographic location, population, and climate information of the case studies considered.  
The dataset (number of confirmed infected cases) used in this study for the four countries was collected from [[https://github.com/owid/covid-19-data/tree/master/public/data](https://github.com/owid/covid-19-data/tree/master/public/data)]([https://github.com/owid/covid-19-data/tree/master/public/data](https://github.com/owid/covid-19-data/tree/master/public/data)) (accessed on 5 February 2022). The dataset covers the period from 1 April 2020 to 4 February 2022.  
Thus, the 675 observations collected were divided into 80% for the models' development and 20% for the models testing and calibration. Each sequence of 14 observations was used to predict the next-day number of infections. Additionally, the time period was selected such that it covers almost all epidemic peaks as well as the discovery of different variants of the virus. Moreover, more than one year of vaccination campaigns were also covered.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Country** & **Geographic Location** & **Climate** & **Population * \\ \hline Saudi Arabia & 25\({}^{\circ}\)007 N 45\({}^{\circ}\)007 E & Desert & 35,836,864 \\ Egypt & 30\({}^{\circ}\)207 N 31\({}^{\circ}\)137 E & Dry and hot & 105,975,920 \\ India & 8\({}^{\circ}\)4\({}^{\prime}\)-37\({}^{\circ}\)6\({}^{\prime}\) N 68\({}^{\circ}\)7\({}^{\prime}\)-7\({}^{\circ}\)25\({}^{\prime}\) E 97\({}^{\circ}\)25\({}^{\prime}\) east & Hot and humid & 1,405,606,396 \\ Italy & 41\({}^{\circ}\)87\({}^{\prime}\) N 12\({}^{\circ}\)56\({}^{\prime}\) E & Mediterranean & 60,294,088 \\ \hline \hline \end{tabular}  
* [[https://www.worldometers.info/coronavirus/](https://www.worldometers.info/coronavirus/)]([https://www.worldometers.info/coronavirus/](https://www.worldometers.info/coronavirus/)) (accessed on 20 June 2022).  
\end{table}
Table 2: Information about the case studies considered.  
Figure 2: Basic structure of an LSTM.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 2 Recent Progress in Development and Applications of Soft Actuators
### Dielectric Electroactive Polymers (DEAPs)
#### 2.2.1 Recent Trends in the Modification of DEAPs
Fabrication of such thin multi-layer DEAPs allows for a decrease in the operating voltage to about 500 V and exceeds the DEAP lifetime to 2 million actuation cycles. Another example of thin DEAP actuator fabrication is investigations presented by [PERSON] et al. [6]. The authors present DEAPs with a 600 nm thick silicon membrane covered by 200 nm thick electrodes composed of poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS). The application of such a thin dielectric membrane allows DEAPs to operate at a voltage of about 70 V. That low voltage increases the safety and decreases the complexity of the electric circuit required to drive the DEAP. DEAP thickness reduction is an important direction of current research in this field. Another direction of studies, which in the near future may become more and more important, is the use of dielectric membranes other than silicone or VHB tape. [PERSON] et al. present the DEAP fiber actuator with a dielectric elastomer of poly(styrene-b-butyl acrylate-b-styrene) (SBAS) (elastomer thickness of about 24 \(\upmu\)m) with isotropic and anisotropic structure. The DEAP fiber was obtained by rolling the rectangular DEAP with low thickness. Fabricated anisotropic SBAS film exhibits high linear actuation strain and works output energy density. [PERSON] et al. present the application of thermoplastic polyurethane-styrene ethylene butylene styrene (SEBS/TPU) blend as a dielectric membrane for DEAP preparation [30]. The obtained DEAP presents improved maximum actuation strain and electrical breakdown strength Z. [PERSON] et al. [64] present the DEAP actuator with dielectric elastomer obtained with thiol-ene reaction of mercaptopropyl silicone with 1-vinylimidazole and later crosslinked with 1,12-dibromododecane. As a result, imidazole-grafted silicone has been developed and applied as a dielectric layer. Synthesized polymers have much higher relative permittivity in comparison with standard silicon membranes. The presented papers show that the application of new elastomers as a dielectric membrane in DEAPs can improve their properties, and this is a promising direction for future research.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.2 Ablation Tests
This also demonstrates that using the dynamic fusion mechanism on the encoder end and the knowledge extraction paradigm on the decoder end is an optimal fusion strategy. Figure 15: Loss function curves on training set and verification set.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 1 Introduction
### Main Contributions  
The main contributions of this paper are illustrated in Figure 2 and summarized as follows:  
* We propose a new biometric authentication scheme with post-quantum security that conveniently adapts the Classic McEliece algorithm to a non-device-centric architecture with honest-but-curious servers.
* Taking advantage of a homomorphic property of Classic McEliece, the proposal is suitable for privacy-preserving non-device-centric architectures where the communication, storage and comparison of biometric features are performed in the protected domain.
* The recognition performance is greater than or equal to the authentication obtained with unprotected features and provides irreversibility, unlinkability, revocability and resistance to FAR and similarity-based attacks.  
* The proposal provides protected biometric data with much smaller size than other solutions with similar security properties, which reduces storage and communication overheads.
* A practical implementation of the proposal in a facial authentication system based on an Android App (for smartphones that act as client devices), Python code (for computers that act as computation and authentication servers) and MySQL (for database servers) demonstrates that authentication is performed in real time.  
The paper is structured as follows. Section 2 includes a review of the proposals in the literature for post-quantum-secure biometric schemes. Section 3 presents our proposal of a secure biometric scheme and a security analysis. Section 4 presents a practical realization of the proposed scheme for facial recognition using FaceNet [18]. Finally, Section 5 concludes the work.
## 5 CGE-SSA Multi-Objective Optimization
### Improved Sparrow Algorithm  
#### 5.2.1 Circular Sinusoidal Chaotic Mapping Strategy for Population Initialization  
The traditional SSA adopts random generation for the initialized sparrow population, which can then easily fall into the local optimum. Sinusoidal chaotic mapping can solve this drawback of the algorithm, in addition to its slow convergence speed [32]. In this study, we combined circular chaotic mapping with sinusoidal chaotic mapping, thus making the initialized sparrow distribution more uniform, as well as improving the convergence speed and computational accuracy of the algorithm. Circular sinusoidal chaotic mapping uses weight coefficients to combine circular and sinusoidal mapping to form a new function, as shown in Equation (6). The strategy is first used to generate random values from \(X(i)\), then Equation (6) is used to generate chaotic values. Finally, the chaotic values are mapped to individual sparrows in order to achieve sparrow population initialization.  
\[X(i+1)=\text{mod}(X(i)+b-\frac{a}{2\pi}\sin(2\pi X(i))+\omega\cdot c\cdot\sin( \pi X(i)),1) \tag{6}\]  
where mod denotes the residual function; \(X(i)\) is a random sequence, \(X(i)\in[lb,\,ub]\); \(ub\) and \(lb\) are the upper and lower bounds of the search space, respectively; \(X(i+1)\) is the chaos value, \(X(i+1)\in[lb,ub]\); \(a,\,b\), and \(c\) are the control parameters, taken as 0.16, 0.556, and 0.24, respectively, after testing and comparing; and \(\omega\) is the weight coefficient, which takes the value of 0.2 after testing and comparing.  
In order to verify the superiority of the circular sinusoidal chaotic mapping strategy for initializing the population, the population number was set to 200, and the sparrow population was initialized in the two-dimensional space. The random distribution and circular sinusoidal chaotic mapping population distribution are shown in Figure 8. In the figure, it can be seen that the population initialized with the circular chaotic mapping strategy has a more uniform distribution, and there is no positional repetition, which is conducive to preventing the algorithm from falling into a local optimum during the optimization process.
## 3 Simulation Analysis of High-Energy Pulse EMAT
### Influence of Lift-Off Distance on High-Energy Pulse EMAT Transduction Mechanism  
The simulations of the high energy pulse EMAT, and the conventional EMAT, were used for comparison to show the difference in amplitude and the two simulations used the same simulation settings. In the conventional EMAT simulation model, the residual magnetic flux density of the magnet is set to 1.2 T and the lift-off distance is 0 mm. At the distance \(h=0\) mm, the Lamb signals of the high-energy pulse EMAT and the conventional EMAT at point \(P\) is shown in Figure 5. The group velocity of S0 mode is faster than that of A0 mode, and S0 mode reaches \(P\) point first. The vibration  
Figure 4: Magnetic flux density in steel plate. (**a**) Horizontal component of magnetic flux density; (**b**) vertical component of magnetic flux density.  
amplitude of A0 mode is much larger than that of S0 mode. The time-domain signal amplitude of A0 mode in high-energy pulse EMAT is 3.2 times that of conventional EMAT. The S0 mode signal amplitude of the high-energy pulse EAMT is 2.9 times that of the conventional EMAT.  
To facilitate the study of the attenuation of the particle displacements of S0 and A0 modes, the particle displacements of the S0 and A0 modes were extracted and analyzed. The change curve of displacement amplitude with lift-off distance is obtained, as shown in Figure 6.  
The ultrasonic amplitude generally decreases with an exponential function as the lift-off distance increases, in non-ferromagnetic materials, such as aluminum plates. However, in ferromagnetic materials such as steel plates, the A0 and S0 modes in the Lamb waves excited by high-energy pulse  
Figure 5: The received signals of high-energy pulse EMAT and conventional EMAT at the lift-off distance \(h=0\) mm in the simulation.  
Figure 6: The relation between the logarithm of S0 mode and A0 mode amplitudes and lift-off distance in the simulation.  
EMAT do not decay exponentially, in the small lift-off distance. When the lift-off distance \(h\) is more than 2 mm, the S0 and A0 modes decay exponentially with the lift-off distance. So the displacement \(\log\ln(P)\) of the S0 and A0 modes vary approximately linearly with the lift-off distance \(h\), as is shown in Figure 6. Due to the attenuation rate of the Lorentz force \(\mathbf{F_{L}}\) determined by \(\mathbf{B}\) and \(\mathbf{J_{e}}\) is higher than the magnetostrictive force \(\mathbf{f_{M}}\) decided only by \(\mathbf{B}\). In this case, the energy conversion mechanism is mainly dominated by the magnetostrictive force \(\mathbf{f_{M}}\) in the steel plate. In the steel plate, the high-energy pulsed electromagnetic ultrasonic energy conversion mechanism is composed of the Lorentz force and magnetostrictive force, in the lift-off distance \(h\) less than 2 mm. In this lift-off distance section, the Lamb wave A0 and S0 modes do not decay with an exponential function as the lift-off distance increases.
# Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method
## 1 Introduction
Meanwhile, in this study, we explored the potential application of seismic redatuming for target-oriented imaging, with proper incorporation of the overburden effect to image the targeted area or the region below complex overburden. We developed an optimal workflow for target-oriented imaging through the combination of seismic redatuming with Kirchhoff migration. The objective was to establish an efficient migration process while ensuring a satisfactory level of accuracy. We applied the proposed workflow to both synthetic and field seismic datasets, evaluating its advantages and limitations, while also discussing potential future research.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 7 Conclusions and Future Work  
This paper highlights the promising potential of our FakeDetectionGCN model. This model uses aspect-based sentiment analysis and graph convolutional networks to detect fake reviews effectively. It handles both human-generated and computer-generated fake reviews well.  
FakeDetectionGCN performs well when dealing with fake reviews created by advanced algorithms or manually written by individuals. This ability is key. It demonstrates the model can reliably distinguish genuine content from fake content across various data sources. By handling both types of fake reviews, our GCN model proves to be a strong tool against digital misinformation.  
Despite its effectiveness, the model has some drawbacks. It requires long training times and involves significant computational complexity. These factors are challenging when resources or time are limited. The limitations are especially problematic in situations where quick processing is needed.  
To improve the model's performance and efficiency, several areas can be explored:  
* Word embeddings: Using different word embeddings could lead to better accuracy. Advanced embeddings like ELMo, BERT, or RoBERTa may capture context more effectively than traditional embeddings.
* Hyper-parameter tuning: Optimizing the hyper-parameters can reduce computational demands and enhance learning efficiency. Techniques like grid search or Bayesian optimization can find the best parameter settings.
* Diverse datasets: Testing the model on a wider variety of datasets, especially larger and more complex ones, is crucial. This will validate the model's applicability across contexts and improve its ability to handle real-world scenarios.
* Reducing training time: Decreasing the long training time and complexity is essential. More efficient training algorithms or hardware acceleration like GPUs could help. Simplifying the model architecture without affecting performance is another option.  
In conclusion, while FakeDetectionGCN is promising for detecting fake reviews, there are opportunities to improve efficiency and scalability. Addressing these areas can make the model more practical for wider use.  
Conceptualization, P.P. ([PERSON]) and K.P.; methodology, P.P. ([PERSON]); software, P.P. ([PERSON]); validation, P.P. ([PERSON]), P.P. ([PERSON]), and K.P.; formal analysis, K.P.; investigation, P.P. ([PERSON]); resources, P.P. ([PERSON]); data curation, P.P. ([PERSON]); writing--original draft preparation, K.P.; writing--review and editing, K.P.; visualization, P.P. ([PERSON]); supervision, K.P.; project administration, P.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 13: AUC scores of different models on OTT dataset (100%) [35].  
* This research received no external funding.
* Not applicable.
* Data available in a publicly accessible repository.
* The authors declare no conflicts of interest.