esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries for Designing a Sliding Mode Controller
### SMC Schemes
#### 2.2.1 Higher-Order Sliding Mode Control  
In a higher-order sliding mode (HOSM), the control input drives the sliding variable and its \((r-1)\) initial higher-order derivatives to zero, acting discontinuously on the \(r\)-th time derivative of the sliding function [1; 80]. The parameter \(r\) is called the sliding mode order and is defined as the relative degree of the sliding function with respect to the control variable. The case \(r=1\) corresponds to the well-known first-order SMC; for \(r>1\), it is considered a HOSM [81; 82].  
HOSMC helps face the disadvantages of first-order SMC; therefore, its use reduces chattering, eliminates the restriction of relative degree, reaches the finite-time converge and improves sample time accuracy for discrete implementations [1; 80; 83]. However, implementing HOSMC requires knowing the values of the sliding variable and its derivatives to \(r-1\) order; for this reason, it has been necessary to design accurate and robust differentiators to determine the correct value of derivatives [84; 85].  
One of the most well-known HOSM algorithms is super-twisting, a second-order SMC [1; 86; 87; 88]. When a super-twisting controller (STC) is used for relative degree systems equal to one, the continuous control action reduces the chattering effects [85; 88]. However, some variants of STC, such as the integral SMC (ISMC), allow the STC to be extended to systems of an arbitrary relative degree [89]. The ISMC eliminates the reaching phase and drives nominal controllers, usually designed without considering uncertainties, to improve robustness against matched disturbances [87].  
The design of the SMC of \(r>3\) becomes a more challenging task due to the order of the system [81; 82]. To implement HOSMC, it is necessary to know only the relative degree and derivative boundaries of the system [82; 84]; due to this, the concept of black-box HOSM arises [82; 90; 91].  
Despite the advantages of the HOSMC, it is important to consider that its performance can be affected by unmodeled dynamics and disturbances. Hence, for some systems, a first-order SMC can achieve better results than a HOSMC, benefiting from the simplicity of the former [83].
## 4 Initial Parameters Selection
When the noise level of the dynamic responses is known, we can find the optimum \(\beta_{2}\) by comparing the real residual norm with the computation residual norm, which is shown in Figure 3a. When the real noise level is not available, we calculate the slope of the residual norm \(\|Hf-y\|_{2}^{2}\) with respect to \(\beta_{2}\) and then take the local minimum value as the optimum point of the parameter \(\beta_{2}\), as shown in Figure 3b. In Figure 3, the blue curve represents the slope of the residual norm, while the red curve represents the relative error of the identification load. The black curve gives the real residual norm according to the response noise level. The pink vertical curve represents the selected \(\beta_{2}\) value corresponding to the minimum slope point of the residual norm. The red curve shows that the load identification error is ideal with the selected \(\beta_{2}\) value. It should be pointed out, in this case, that the response noise level is 30 dB (SNR). Based on the above parametric study, we choose the following values for  
Figure 3: (**a**) \(\beta_{2}\) selection with the response noise level known. (**b**) \(\beta_{2}\) selection with the response noise level unknown. \(\alpha_{1},\beta_{1},\alpha_{2},\lambda,\tau,2.1\times 10^{-5},2100,1\times 10^{12}\), respectively, while \(\beta_{2}\) is determined according to the selection method shown in Figure 3.
## 5 Results and Analysis
### Relativity Insights  
The relativity between IMs and DMs was quantitatively assessed using the statistical correlation coefficient \(R^{2}\). Figure 15 provides a visualization of \(R^{2}\) values across different IMs and DMs. The trend in \(R^{2}\) remains substantially consistent irrespective of the specific  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Modulation PGA (g)** & **0.03** & **0.06** & **0.1** & **0.15** & **0.2** & **0.25** & **0.3** & **0.35** & **0.4** & **0.45** \\ \hline PGA & 0.391 & 0.205 & 0.063 & 0.654 & 0.327 & 0.178 & 0.199 & 0.063 & 0.063 & 0.07 \\ Arias intensity & 0.390 & 0.202 & 0.062 & 0.654 & 0.326 & 0.173 & 0.193 & 0.063 & 0.062 & 0.068 \\ \(I_{\text{c}}\) & 0.388 & 0.203 & 0.061 & 0.653 & 0.281 & 0.145 & 0.190 & 0.062 & 0.063 & 0.069 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(p\)-value of different IMs.  
Figure 14: Regression analysis of \(\ln(\theta_{\text{max}})\) and \(\ln(\text{Scale Factor})\).  
DMs and IMs considered. However, the relativity between \(R_{\rm E}\) and each IM is notably weak, as evidenced by the lower \(R^{2}\) values. Conversely, the remaining six types of DM exhibit higher correlation coefficients with each IM. To delve further into IM relativity, Figure 16 displays the variations in \(R^{2}\) values when \(\theta_{\rm max}\) is employed as the DM. It becomes evident that displacement-type IMs exhibit a weak correlation with \(\theta_{\rm max}\) (\(R^{2}<0.7\)), while Arias intensity, EDA, PGA, \(A_{\rm rs}\), SMA, ASI, SMV, \(I_{\rm a}\), and \(I_{\rm c}\) are all highly correlated (\(R^{2}>0.8\)). A comprehensive comparison of \(R^{2}\) values across various IMs for each DM is illustrated in Figure 17. It becomes apparent that Arias intensity, \(A_{\rm rs}\), PGA, ASI, SMA, and \(I_{\rm c}\) all exhibit high levels of correlation with each DM.  
In terms of the relativity between IMs, the analysis focused on PGA due to the large number of IMs included in this study. As Figure 18 demonstrates, acceleration-type, spectral-type, and vector compound-type IMs exhibit a strong relativity with PGA. They are followed by velocity-type and displacement-type IMs. This suggests that if PGA is deemed suitable for the seismic performance analysis of a structure, then other IMs that are highly correlated with PGA will likewise be effective. Furthermore, a scrutiny of the \(R^{2}\) values corresponding to each IM reveals that acceleration-type IMs have a particularly strong relativity with PGA. Similar trends are observed among other types of IMs; namely, the relativity is strong between IMs of the same type but weakens when different types of IMs are compared. If the correlation between IMs is weak, it is recommended to use the concept of relative adequacy of sufficiency as a criterion for judgment.  
Figure 16: The value of \(R^{2}\) under each IM with \(\theta_{\rm max}\) as the DM.  
Figure 15: The value of \(R^{2}\) with different IMs and DMs.
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
## 4 Results
### Computational Performance
#### 4.2.3 Comparison of CPU and GPU Performance  
Figure 11(a) shows an overview of the computation times for the proposed DST (FFTPACK only) and FFT (FFTPACK, MKL, FFTW and cuFFT) methods. The FFTPACK (red) is the slowest and cuFFT (yellow) is the fastest. Execution times for both the MKL (blue) and FFTW (green) libraries are similar, obtaining slightly faster results with FFTW. Overall, the GPU hardware acceleration (with cuFFT) provides a considerable speed-up, making it a good alternative to consider for simulations on plates with large discretization sizes.  
Figure 11(b) compares the execution times of the two test platforms considering both CPU and GPU devices for the most performant FFT method: the 2D odd symmetric algorithm. This comparison shows that the GPU hardware effectively accelerates the computation time, between the fastest CPU (i7-4700K) and the slowest GPU (GTX 960), obtaining up to a 2\(\times\) speed-up for plate sizes larger than \(1024\times 1024\). The performance difference increases as the input plate increases in size. Using more recent GPU hardware (RTX 2060) results show a bigger difference in the achievable compute time speed-up.
# An Improved MbICP Algorithm for Mobile Robot Pose Estimation
## 2 Scan-Matching Algorithm  
### ICP Algorithm  
Robot motion between time steps can be represented by a transformation vector \(T\), which consists of a translation component \(\Delta t=(\Delta x,\Delta y)\) and a rotational component \(\Delta\theta\). Given two successive scansfrom a laser scanner at the beginning and end of motion, the first scan is marked as the reference scan \(S_{ref}\) and the second scan is treated as the object scan \(S_{obj}\) (as shown in Figure 1b).  
The transformation T can be estimated by minimizing the distance of the corresponding points between \(S_{ref}\) and \(S_{obj}\) with the ICP algorithm, which is based on a three-step iterative process:  
1. Establish correspondence. For each point in \(S_{obj}\), search for the closest point in \(S_{ref}\) to establish correspondence. A K-D tree is recommended to accelerate the searching process.
2. Calculate the transformation. The rigid transformation \(T\) consisting of rotation matrix \(R\) and translation vector \(t\) is calculated by minimizing the mean square error of the correspondence pairs between \(S_{obj}\) and \(S_{ref}\) (Equation (1)). SVD-based least-squares minimization is commonly used because of its robustness and simplicity [31]. \[T=(R,t)=arg\text{min}\{\sum_{i=1}^{N}\left|m_{j}-Rp_{i}-t\right|^{2}\}\] (1) where \(N\) is the number of points in \(S_{obj}\), \(p_{i}\in S_{obj}\) and \(m_{j}\in S_{ref}\). \(m_{j}\) is the closest point of \(p_{i}\), which is defined as the correspondence.
3. Transform \(S_{obj}\). Apply a rigid transformation to \(S_{obj}\) and update the related parameters.  
These three steps are repeated until convergence or the iteration number exceeds the maximum number of iterations.  
Figure 1: (**a**) Two laser scans that were taken at times \(t_{1}\) and \(t_{2}\); (**b**) object scan \(S_{obj}\) (blue) that was taken at \(t_{2}\) is represented in the coordinate system of the reference scan \(S_{ref}\) (red), and the dotted lines are the correspondences based on the criterion of the closest point; (**c**) \(S_{obj}\) (blue) is brought closer to \(S_{ref}\) (red) as the iterations progress; (**d**) \(S_{obj}\) (blue) closely fits to \(S_{ref}\) (red) at the end of the iteration process.
# Optimizing Nigella Oil Extraction Temperature for Sustainable Production
## 3 Results and Discussion
### Iodine Value (IV)  
The IV provides information about the degree of unsaturation in oil. As the number of double bonds increases, it directly impacts IV [79, 80]. The IV of Nigella oil extracted at different temperatures was observed in the range between 99.2 and 101.8 (Table 5). The lowest value was in the oil extracted at 70 \({}^{\circ}\)C, and the highest value was in the oil extracted at 60 \({}^{\circ}\)C. The previous studies have reported varying IVs ranging from 98 to 128 for Nigella oil from different locations [70, 81, 82, 83].  
Given that no significant effect of extraction temperatures on fatty acid composition, TPC, CUPRAC, IV, and stable levels of IQ between 40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C including the higher FRAP value at 60 \({}^{\circ}\)C were observed in the present study, it suggests that maintaining an extraction temperature of 60 \({}^{\circ}\)C may be optimal. This temperature choice can enhance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Oil Extraction Temperatures**} \\ \cline{2-6}  & **40**\({}^{\circ}\)C & **50**\({}^{\circ}\)C & **60**\({}^{\circ}\)C & **70**\({}^{\circ}\)C & **80**\({}^{\circ}\)**C** \\ \hline IV & 101.2 & 101.0 & 101.8 & 99.2 & 101.0 \\ \hline \end{tabular}
\end{table}
Table 5: IV in Nigella oil extracted at different temperatures.  
resource efficiency, economic benefits, and product quality, thereby contributing to the overall sustainability of the production process.  
Additionally, there has been a growing demand for Nigella oil in the international market driven by the needs of various industries such as pharmaceuticals, nutraceuticalals, flavoring, cosmetics, and culinary industries [84; 85; 86]. For instance, according to the Black Seed Oil Market Research: Size, Trends, And Forecast 2033, assessed on 15 July 2024 [87], the global oil market for Nigella was valued at USD 20.6 million in 2023 and is expected to reach USD 42.5 million by 2033, with a compound annual growth rate (CAGR) of 7.7% from 2024 to 2033. Given the substantial quantity of Nigella seeds required to meet this demand, it is crucial to adopt an optimized extraction method that ensures both high oil yield and quality. This method not only enhances the efficiency of the extraction process but also guarantees that the final product meets the highest quality standards. By implementing these optimized extraction methods, producers can strike a balance between high production volumes and the integrity and potency of Nigella oil, thus securing a stable position in the competitive global market. At the same time, the large quantity of produced byproducts can also be utilized for different purposes, such as animal feeds, instead of being wasted [88; 89]. This is not possible with solvent extraction methods due to the use of soap stock acids in chemical refining, bleaching earth, metals, and pigments, catalysts used in filters for the hardening process, as well as and mucilage from degumming and deodorizer distillate [90].
## 3 Stamping Simulation and Property Mapping
### Validation and Quality Control  
The validation of the stamping simulation was performed using the Gom Argus system by Zeiss Spa, Oberkochen, Germany, which combines optical measurement with dedicated software for accurate strain analysis. This process began with the preparation of the sheet-metal, where a pattern of equidistant points was engraved using laser technology to serve as markers for orientation during the analysis, as shown in Figure 5a. After stamping the part under the same conditions as those of the simulation, the surface was cleaned to ensure accurate data acquisition, as shown in Figure 5b. The Gom Argus system captured images from multiple angles, constructing a 3D representation of the part's surface in real time, highlighting any areas requiring further imaging for completeness. The acquired data were then compared to the simulation results in a post-processing phase. As shown in Figure 6, the comparison between the scanned and simulated strain distributions revealed that while the overall correlation was within \(\pm\)0.5%, significant localized differences were observed, particularly in high-strain regions. These discrepancies are evident in the red zones of the scanned model, which indicate higher strain concentrations compared to the simulation results. The primary reason for this deviation is that the simulation model assumes idealized boundary conditions and material behavior, while the physical stamping process introduces localized effects such as strain localization, friction variations, and microstructural anisotropies, which are difficult to fully replicate in a numerical model.  
Figure 5: Sheet-metal: (**a**) raw sheet with engraved equidistant points and (**b**) stamped part.  
Figure 6: Comparison between (**a**) scanned and (**b**) simulated strains.  
Additionally, the simulation does not account for secondary deformations or slight misalignments that may occur during the physical stamping process. The optical strain measurement system captures real-world irregularities, whereas the FEM model relies on idealized constraints that may lead to a slightly more uniform strain distribution. While these localized discrepancies are present, the overall accuracy remains within acceptable engineering limits, and the simulation results are still considered representative for structural performance assessments. Future refinements may include more detailed modeling of friction effects, anisotropic hardening, and secondary deformation mechanisms to further reduce these discrepancies.  
This validation confirms the reliability of the stamping simulation for predicting the material behavior of the component, ensuring sufficient alignment with real-world conditions to optimize the design and manufacturing processes.
## 1 Introduction
Furthermore, the recent literature has highlighted the growing role of wearable IMU-based systems combined with AI techniques in enabling reliable motion tracking and remote assessment of motor function, addressing many of the limitations of traditional lab-based systems [23,24,25]. Concerning spine kinematics, the effectiveness and reliability of IMUs were evaluated by different studies, exploring their ability to provide accurate and consistent measurements when compared to gold standard systems. For instance, [PERSON] et al. [26] assessed and confirmed the accuracy of IMUs in measuring cervical spine motion; this finding was further reinforced by [PERSON] et al. [27], who reported a strong agreement between IMU measurements and marker-based MoCap data during walking, with thoracic trunk ROM values consistently ranging from of 2.4\({}^{\circ}\) to 2.6\({}^{\circ}\). In addition, the effectiveness of IMUs in estimating spine kinematics was assessed in relation to marker-based MoCap across different movement planes. For instance, [PERSON] and colleagues [22] suggested that IMUs can capture trunk posture with reasonable reliability. Similarly, [PERSON] et al. [28] reported that IMUs could estimate trunk ROM with moderate to excellent agreement under various conditions, such as standing and locomotion. This capability may be particularly beneficial for assessing populations with pathological conditions such as low back pain, where reliable measurement of trunk movements is essential for effective treatment planning [29]. In general, the literature underscores the potential of IMUs as a promising alternative to traditional motion capture technologies, providing a more accessible and portable solution for quantifying trunk overall kinematics and, more specifically, the ROMs. However, several challenges remain open. Factors such as the inherent differences in the measurements realized by different devices highlight the need for further research aimed at thoroughly evaluating the clinimetric properties of IMUs, such as reliability and validity of their use in different clinical scenarios. In this context, the main objective of our research is threefold, including providing a systematic comparison between IMUs and marker-based MoCap systems for trunk ROM analysis in healthy individuals, identifying movement-specific differences in accuracy and agreement between the two approaches, and highlighting the clinical applicability of IMUs as a portable and cost-effective tool. Therefore, we hypothesized that a wearable system based on a single IMU was able to provide reliable information concerning trunk range of motion in different planes. The novelty of this project lies in the potential of further bridging the gap between controlled laboratory-based human motion analysis and practical user-friendly solutions reliable and suitable for real-world applications.
###### Abstract  
In rotating machinery components, it is difficult to extract incipient weak fault features directly from vibration signals. A fault feature extraction method based on refined multiscale symbolic dispersion entropy (RMSDE) is proposed. This method mainly combines symbolic coding and dynamic complexity evaluation to achieve multi-dimensional feature extraction. By constructing multiple sets of simulation signals to evaluate the correlation performance of the proposed method, the superior performance in complexity estimation, anti-noise interference and computational efficiency is mainly demonstrated. When RMSDE is combined with a self-organizing fuzzy logic classifier (SOF), the results show that the proposed RMSDE-SOF method can achieve high-precision fault diagnosis of rotating machinery. The effectiveness and robustness of the proposed method in fault feature extraction and diagnosis are verified by using the rolling bearing dataset and gear dataset, which contain multiple fault types, and the comprehensive performance of the proposed method is better than that of similar feature extraction methods. The final results show that the proposed method is superior to the other methods in rotating machinery fault diagnosis.  
RMSDE; SOF; rotating machinery; fault diagnosis Article  
A Refined Multiscale Symbolic  
Detecting Weak Faults in Rotating Machinery  
M.J.S., 2020  
137576
## 5 Potential Challenges
### Implementation of More Than Three-User Pairing in Real-Time  
Significant attention has been paid to NOMA's intriguing ability to serve many users inside a single resource block. However, it is clear from several real-world studies and applications that the three- or more-user pairing approaches and the two-user pairing idea are only taken into account in a small number of studies [19]. The fundamental limitation on the number of users multiplexing in a NOMA user pair is most likely caused by the need to retain user channel gains at the BS to carry out SIC at receivers. Furthermore, due to the increased need for interconnected devices, particularly for the Internet of Things, machine-to-machine, and massive machine-kind interactions, these vastly diverse networks must be maintained by developing esoteric multiple-user pairing techniques and methodologies. Therefore, it is necessary to create approaches that may cluster numerous users inside a given resource block to fully and effectively profit from NOMA, improve mass coverage, and fulfill future network requirements.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work  
### GP and Strongly Typed GP  
As an evolutionary algorithm, GP models the solutions to various application problems as individuals within a population. In this process, each individual is assigned a fitness score, which is determined based on a predefined fitness function. The population then undergoes several generations of evolution, thereby improving the fitness scores. At the end of the algorithm, the individual with the highest fitness score is selected as the best solution to the problem. In the application of image classification, Strongly Typed Genetic Programming (STGP) [17], based on tree representation, is widely used. This is because it allows for the specification of the input and output types of internal nodes as well as the output types of leaf nodes, enforcing constraints on the construction of the program tree, ensuring that only nodes with matching types can be connected. For example, in some operations in image processing (such as image filtering or feature extraction), the required input is of a specific type of image, and the output will also be of a specific type of image or feature value [12]. STGP is used to ensure that the generated program strictly follows the preset steps and requirements when processing images and extracting features.