esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Friction and Twisting Angle on the Tensile Strength of Polypropylene Baling Twine
## 1 Introduction
Due to these two opposing effects, there is a level of twisting called optimal twisting, at which the maximum strength of the twine is achieved [39]. [PERSON] et al. [40; 41] explain that not all filaments break simultaneously under tensile load. They also considered the effect of friction and pressure, where maximum tensile strength is achieved at the optimal twist angle. Twisting induces friction between the fibres and thus improves the twine's ability to resist external forces. Twisting also affects the transfer of stress between the fibres within the twine and, therefore, affects the twine's strength and failure mechanism [42; 43]. How does twisting affect the twine's tensile strength and deformation? The twisting process positively affects the friction between the fibres, improving the twine's strength, but only up to a certain level of twisting. Afterwards, the effects of fibre obliquity become substantial, causing a decrease in the twine's strength. PP is often investigated as a base material or part of a composite when the aim is to determine and improve its mechanical characteristics in working conditions. Tests are usually performed on samples of standard shapes using conventional testing devices. Also, many researchers investigate the mechanism of load transfer in the fibres of twines and fabrics based on polymer materials to determine the influencing factors and thus improve the material's characteristics. In contrast to previous studies, in this paper, the conclusions on the behaviour of PP baling twines under tensile load are based on testing both non-prestressed samples and samples prestressed by being twisted before the test. The effects of friction between the fibres and the influence of changes in the twist angle on the material's tensile strength were investigated experimentally.
# Attentional Bias Modification Training in Virtual Reality: Evaluation of User Experience
## 2 Materials and Methods
### Statistical Analysis  
The time measure analysis had a sample size of 58 participants due to missing time data from nine participants and the exclusion of two outliers from the sample. For statistical analysis of time, anxiety, fatigue, and comfort levels, one-way ANOVAs were conducted. Time had four levels (series 1 to 4: S1, S2, S3, and S4), while the rest had five levels (PrS, S1, S2, S3, and S4). The data were not normally distributed across all variables according to the Kolmogorov-Smirnov test. Despite this, the decision was made to proceed with the analyses as ANOVAs demonstrated robustness even in cases of deviation from normality [24].  
A one-sample \(t\)-test was conducted to compare the average SUS score of the participants to the standard benchmark for normal usability, which is typically considered as 68 out of 100 [25], in order to assess the software's usability.  
Pearson correlations were used to explore the relationships among the measures of usability, the participants' attitudes toward technology, their anxiety, fatigue, and comfort levels, and time to complete each ABMT series.  
Furthermore, to explore the potential influence of their technological dependence on their anxiety, a two-way mixed ANOVA was conducted. This analysis included a within-participant factor, anxiety (measured before the first series and after each subsequent series, totaling five levels), and a between-participant factor, dependence, with two levels: low dependence (36 participants) and high dependence (33 participants). The classification criteria for each group were established based on the median value of the sample.  
To investigate the potential impact of wearing glasses or contact lenses on usability, time spent in series, and VAS measures, two-way mixed ANOVAs were conducted for each variable. The within-participant factor had four levels for time spent in each series (S1, S2, S3, and S4) and five levels for VAS assessments (PrS, S1, S2, S3, and S4). The between-participant factor in each ANOVA was visual acuity, categorized into two levels: no visual problems (31 participants, 26 for time) and visual problems (38 participants, 32 for time). For SUS, independent-sample \(t\)-tests were utilized.  
The Greenhouse-Geisser correction was implemented to modify degrees of freedom as needed, and Bonferroni-adjusted pairwise comparisons were executed. All p-values resulting from these comparisons were adjusted using the Bonferroni method. The r method for the \(t\)-test and the partial eta-squared (\(\eta^{2}\)p) method for the ANOVAs were used to calculate effect sizes. The level of statistical significance was established at \(\alpha\) = 0.05.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 4 Results
### Considering the Magnitude of the Earthquake  
It is well known that earthquake magnitude may play an active role in the identification of pre-earthquake perturbations. Therefore, to demonstrate the influence of magnitudes, we divided earthquakes into groups of three--5136 earthquakes with magnitudes between 4.8 and 5.2, 2793 earthquakes with magnitudes between 5.2 and 5.8, and 853 earthquakes with magnitudes between 5.8 and 7.5, and the corresponding datasets were created: DataSet 11, DataSet 12, and DataSet 13 (Table 1).  
Table 2 illustrates the performance of the SafeNet model for the three datasets. As shown in Figure 9, the model had similar performance over the three datasets; for example, the AUC of class 0 and class 1 on all three datasets using the SafeNet method was above 0.86, and the F1 score ranged from 0.697 to 0.812, which suggests that the SafeNet model provides a satisfactory performance in discriminating electromagnetic pre-earthquake perturbations on the datasets with different magnitudes. Moreover, it can be concluded from Table 2 and Figure 9 that the larger the magnitude, the better the classification performance. This is also in line with the reality.  
Figure 8: The ROC curves comparing model performance for (**A**) class 0, (**B**) class 1, and (**C**) class 2 with various spatial window radii of 3\({}^{\circ}\), Dobrovolsky's formula, 5\({}^{\circ}\), 7\({}^{\circ}\), and 10\({}^{\circ}\). (**D**) Bar plots showing the MCC, F1 score, and accuracy of the results.  
Figure 9: The ROC curves comparing model performance for (**A**) class 0, (**B**) class 1, and (**C**) class 2 of earthquakes with magnitudes between 4.8 and 5.2, earthquakes with magnitudes between 5.2 and 5.8, and earthquakes with magnitudes between 5.8 and 7.5. (**D**) Bar graphs displaying the MCC, F1 score, and accuracy.
## 5 Data Processing and Results
### Projection of Future Coordination Degree  
Highly reliable projection of future coordination degree could be very useful for enacting conducive policies to encourage coherent development in technological innovation and financial systems. In this subsection, we report our effort in making such projections. The findings prove that making the correct projection of future coordination degree is very challenging.  
In our investigation, we would like to project for the most recent five years in our dataset, i.e., 2019, 2020, 2021, 2022, and 2023, using the remaining data as the training set for a regression model. The projection accuracy is evaluated using a metric called \(R^{2}\). This metric quantifies the normalized error between the predicted values and the actual values with one caveat, i.e., \(R^{2}\) would be a negative number if the prediction is so bad that it is worse than simply making the prediction using a straight horizontal line. A perfect regression would have a \(R^{2}=1\) (i.e., the regression model explains all the variability in the actual data around the mean), and a regression would have a \(R^{2}=0\) if the model fails to explain any of the variability in the actual data.  
We experiment with two regression models. One is linear regression with polynomial features. The other is gradient boosting regression. Again, we compare the results of the two sets of coordination degrees computed using the dataset \(O\) and the reference dataset \(R\). The regression study is performed with three-fold cross-validation with hyperparameter tuning using grid search. For linear regression, a single hyperparameter, i.e., polynomial degree, with possible values of 1, 2, and 3, is tuned with cross-validation. For GBR, five hyperparameters are tuned with cross-validation:  
* Number of estimators: with possible values of 50, 100, and 200;
* Learning rate: with possible values of 0.01, 0.1, and 0.2;
* Max depth: with possible values of 3, 4, 5;
* Minimum samples split: with possible values of 2, 5, and 10;
* Minimum samples leaf: with possible values of 1, 2, and 4.
## 2 Frequency Factors and Constraints
### Constraints of the ADC Sampling and Pilot-Tone Frequencies with the Beat-Note Frequency  
According to [PERSON]'s theorem, the sampling frequency of the ADC needs to be greater than at least two times the beat-note frequency. That is,  
\[f_{\text{ADC}}>2f_{\text{upper}}. \tag{6}\]  
The USO is used to control the ADC sampling frequency and construct the pilot-tone signal; the divider or synthesizer can realize this process. According to [PERSON] [10], the noise introduced by the dividers is much smaller than that introduced by the synthesizers, and, therefore, the frequency division approach is typically used. In the actual application process, integer frequency division is usually chosen.  
The aliasing signal generated by the ADC sampling of the pilot-tone signal interferes with the beat-note frequency measurement. For example, when the sampling frequency of the ADC is 82 MHz and the frequency of the pilot-tone signal is 80 MHz, the frequency of the aliased signal generated by the ADC in the under-sampled pilot-tone signal will be 2 MHz. Therefore, the frequency of the aliased signal must not overlap the beat-note frequency range. This constraint can be expressed by the following equation:  
\[|f_{\text{ADC}}-f_{\text{PT}}|<f_{\text{lower}}. \tag{7}\]  
Therefore, setting the pilot-tone signal frequency\(f_{\text{PT}}\) and ADC sampling frequency \(f_{\text{ADC}}\) imposes the following constraints:  
\[\left\{\begin{array}{l}n_{1}*f_{\text{ADC}}=f_{\text{USO}}\\ n_{2}*f_{\text{PT}}=f_{\text{USO}}\\ f_{\text{ADC}}>2f_{\text{upper}}\\ |f_{\text{ADC}}-f_{\text{PT}}|<f_{\text{lower}}\\ f_{\text{USO}}<5\,\text{GHz}\\ f_{\text{PT}}<98\,\text{MHz}\end{array}\right.\quad n_{1},n_{2}\;\text{are integers}, \tag{8}\]  
where the value of 5 GHz is the artificial upper bound set for \(f_{\text{USO}}\) to constrain it to finite values. Based on [PERSON]'s [15] study, the upper bound of \(f_{\text{PT}}\) is set to 98 MHz to avoid poor performance of the pilot tone correction.
# An RF-MEMS-Based Digitally Tunable SIW Filter in X-Band for Communication Satellite Applications
## 2 Tunable Resonator Topology
The unloaded quality factor of this resonator varied between 440 (state NWSE) and 500 (state U) (Xpost = 1.9 mm), and was significantly better than that of standard planar resonators, e.g., stub resonators. Consequently, there are two ways to control frequency shift, depending on the position of the tuning post and the number of connected wire bondings. The first parameter (post location on the cavity) enabled the definition of the frequency range of the tuning, whereas the second (number of wire bondings) allowed the control of the magnitude of frequency shift. However, the variation in magnitude was limited by the tuning range as defined by the position of the post.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 3 Methodology
### The ESP-NOW Protocol  
ESP-NOW is a proprietary, low-power communications protocol that enables fast and direct communication between ESP32 devices [20]. The protocol can form large networks with minimal energy consumption, which is critical for battery-powered sensor nodes. ESP-NOW's peer-to-peer communications model allows for data to be passed across multiple nodes, ensuring that even remote sensors can transmit data reliably to the central server [21]. The system uniquely combines ESP-NOW with MQTT by using ESP-NOW for local multi-hop communication and MQTT for centralized monitoring, bridging the gap between low-level node communication and high-level server integration.
Applications in smart cities, healthcare, Industry 4.0, and satellite systems are emphasized, while foundational theory is only discussed as necessary to contextualize recent advances. The main contributions of this paper are as follows:  
* A detailed analysis of recent RF energy-harvesting techniques including rectenna designs, impedance matching networks, and power management circuits. * A comprehensive review of applications in IoT, smart cities, biomedical devices, industry, and satellite communications. * The identification of current challenges and research gaps in RF energy harvesting. * The exploration of future research directions emphasizing AI-driven energy optimization and emerging 6G networks. * An integrative perspective that uniquely combines AI/ML-based approaches and simultaneous wireless information and power transfer (SWIPT) techniques with practical implementation considerations. The remainder of this paper is structured as follows. Section 2 covers the literature on the fundamental principles and components of RFEH systems. Section 3 discusses various RFEH techniques, including rectenna design and impedance matching methods. Section 4 reviews recent technological advancements and applications in different fields. Section 5 highlights challenges and limitations. Section 6 presents future opportunities in RFEH. Section 7 concludes the article with key takeaways and recommendations for future research. By addressing these critical aspects, this article aims to advance research into RFEH and facilitate the development of more efficient wireless power solutions for next-generation networks and IoT ecosystems.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 2 Problem Statement
#### Our Contribution and Paper Organization  
His paper makes the following contributions:  
* We provide an optimized scheme for multicast group establishment based on summing smaller receiver sets into larger ones.
* We connect the summing strategy for group establishment to a combinatorics problem of covering designs and conclude that improvements to the above scheme are unlikely.
* We introduce a novel scheme for multicast group establishment based on onion encryption that minimizes the communication overhead.
* We introduce a framework of _separating families_ for studying sender authentication schemes in tiny networks under the assumption of a highly constrained communication channel.
* We provide an efficient sender authentication scheme that fits the above framework to enable tweaking the security level and making different trade-offs based on assumptions about the adversary's behavior.
* We extend the separating families framework to the setting of two corrupt parties, showing that the problem of sender authentication becomes significantly more difficult.
* We propose security policies that complement the proposed sender authentication scheme, increasing the overall security of the system.  
Section 3 describes schemes for group establishment, with Section 3.1.1 introducing an optimized scheme for summing receiver sets, Section 3.1.2 studying potential improvements over this scheme, and Section 3.2 focusing on the onion encryption approach. Section 4 concerns sender authentication. Section 4.1 describes the TESLA protocol and Section 4.2 explains why it is inapplicable to the type of networks we are interested in. Section 4.3 introduces the notion of separating families, while Section 4.4 describes a way to view the separating families framework through the lens of graph theory. Section 4.5 studies the optimal separating families for sizes for group sizes up to 20. In Section 4.6, we provide a practical solution to the sender authentication problem based on the separating families framework. We analyze the security of our construction in Section 4.7. Section 4.8 initiates the study of _2-separating families_ in the context of protecting sender authenticity in the presence of _two_ corrupt nodes. In Section 4.9, we propose techniques and policies aimed at further increasing the security of networks in a low-bandwidth environment. Section 5 summarizes related work, and we conclude the paper in Section 6.
## 2 Numerical Model and Adopted FE Techniques
### FE Model and Boundary Conditions  
Elastic-plastic large deflection FEM analysis with the FE code ABAQUS, which is well suited to simulating the collapse behavior of both global and local structural systems, is adopted in this study. The four-node thin shell element S4R is employed to discrete the stiffened plate FE model, which has six degrees of freedom at each node and is not very sensitive to distortion [26].  
1/2 + 1/2 span models with transverse frame in the middle and symmetric boundary conditions at both ends are created for the numerical simulations, which is the same setting as that used by [PERSON] and [PERSON] in the ISSC'2000 VI.2 benchmark calculations [11]. The symmetric boundary conditions for FE model are depicted in Figure 2 and described in Table 2 in detail.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Size 1** & **Size 2** & **Size 3** \\ \hline flat-bar (\(h\times t_{w}\)), mm & \(150\times 17\) & \(250\times 19\) & \(350\times 35\) \\ angle-bar (\(h\times t_{w}/b_{f}\times t_{f}\)), mm & \(150\times 9/90\times 12\) & \(250\times 10/90\times 15\) & \(400\times 12/100\times 17\) \\ tee-bar (\(h\times t_{w}/b_{f}\times t_{f}\)), mm & \(138\times 9/90\times 12\) & \(235\times 10/90\times 15\) & \(383\times 12/100\times 17\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of stiffeners.  
Figure 2: 1/2 + 1/2 span stiffened plate model.  
In the ISSC'2000 VI.2 benchmark calculations, the employed mesh principle was 10 elements for the plate, 6 elements for the web and at least 1 element for the flange [11]. To avoid the errors induced by the adopted mesh size, a stricter mesh principle, with average mesh size of 20 mm \(\times\) 20 mm, is adopted in the present study. According to this mesh principle, there are 40 elements used for the plate, of which 4 are for the region of the residual tensile stress state (the width of this region is determined by Equation (4) in the following section), at least 7 are for the web of stiffened plate and 4 are for the flange. The FE model of a typical stiffened plate is shown in Figure 2.
# Efficiency Optimization of the Main Operating Points of an EV Traction Motor
## 4 Conclusions  
This study optimizes the efficiency of an EV IPMSM motor featuring hairpin windings by selecting the main operating points based on a driving cycle. The selection process employed MATLAB 2024 to analyze the density of operating points, enabling the identification of effective optimal design points for an EV drive motor while considering the loss values that fluctuate between low- and high-speed operating regions, particularly the increasing AC copper loss at higher operating speeds. To enhance the validity of the operating point selection, EV simulations considered all driving cycles of the MCT method used for driving range measurements in Korea. The design problem was formulated by considering the critical motor parameters, specifically, the torque ripple and the THD of the back-EMF. 11 metamodels were used to determine the optimal metamodel, which varied according to the distribution of experimental points based on the design problem, output, and constraints. The RMSE test was used to select the metamodel with the optimal predictive value for the objective and constraint functions, and the optimal design was determined using four optimization algorithms. The optimal point combined with the MGA algorithm was verified to validate the optimal design via FEA. The efficiency of the selected optimal model improved by 0.83% at MOP1, 0.47% at MOP2, and 0.09% at MOP3, representing a total improvement of 1.4% compared with the initial model. Additionally, the improved efficiency contributed to a 1.13% reduction in the THD of the back-EMF, indicating smoother flux flow. This research confirms that a highly accurate metamodel and valid selection of the main operating points enable an efficient optimal design for an EV drive motor.  
Analysis, optimal design, and writing, G.-h.L.; writing, writing-review, and editing, Y.-m.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government (MSIT) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R11A3060525).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.  
Figure 11: Magnetic flux lines (**a**) initial model (**b**) optimal model.
## 4 Linearized Dynamics and the Proposed Control Law
### Proposed Affine State Feedback Control Law  
The main goal of this research is to precisely control the position of the knee-joint exoskeleton robotic system. To achieve this, we will employ a state-feedback control law designed based on the robot's equation of motion (5).  
Therefore, an affine state-feedback control law can be adopted as follows:  
\[u=u_{d}+\Delta u \tag{25}\]  
The definition of the term \(\Delta u\) is given below:  
\[\Delta u=\left\{\begin{array}{ccc}\mathcal{K}\mathcal{X}&\text{if}&\| \mathcal{X}\|^{2}>\epsilon^{2}\\ 0&\text{if}&\|\mathcal{X}\|^{2}\leq\epsilon^{2}\end{array}\right. \tag{26}\]  
where \(\mathcal{K}\in\mathbb{R}^{2}\) is the gain matrix to be determined, and the small constant \(\epsilon\) will ultimately be either fixed or minimized through the LMI approach.  
Note that the restriction \(\|\mathcal{X}\|^{2}>\epsilon^{2}\) on the proposed controller \(\Delta u\) in (26) becomes:  
\[\mathcal{Z}(\mathcal{X})=\left[\begin{array}{cc}\mathcal{X}\\ 1\end{array}\right]^{\text{T}}\left[\begin{array}{cc}\mathcal{I}&\mathcal{O }\\ \star&-\epsilon^{2}\end{array}\right]\left[\begin{array}{cc}\mathcal{X}\\ 1\end{array}\right]>0 \tag{27}\]  
The equation of motion (22) under the proposed controller (25) with (26) becomes:  
\[\mathcal{\hat{X}}=(\mathcal{A}+\mathcal{B}\mathcal{K})\mathcal{X}+\mathcal{D} _{1}\Phi_{1}(\mathcal{X})+\mathcal{D}_{2}\Phi_{2}(\mathcal{X})+\mathcal{B} \Delta_{t}\qquad\text{where}\qquad\mathcal{Z}(\mathcal{X})>0 \tag{28}\]  
**Remark 1**.: _In this study, we adopted the LMI approach to determine the feedback gain for the proposed control law (25), providing distinct advantages over both linear controllers, such as LQR, and nonlinear controllers, like PD with gravity compensation. While LQR is limited to linear systems, our method leverages the nonlinear dynamics of the knee exoskeleton to enhance performance. Unlike PD controllers, which are computationally intensive and constrained by gain structure requirements, the LMI technique allows for flexible and unrestricted gain matrix design. Furthermore, it incorporates additional conditions to minimize the feedback gain \(\mathcal{K}\), resulting in a straightforward, efficient, and robust controller suitable for real-world rehabilitation applications._