esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Frequency Square Kilometer Array Pattern Optimization via Convex Programming
## 3 Materials and Methods  
The proposed design and synthesis framework consists of two main stages: (i) full-wave electromagnetic simulation of the SKALA-4.1 antenna (Figure 4) [22] in its array configuration using FEKO (licensed by Altair Engineering [23]); (ii) post-processing and optimization phase carried out in MATLAB 2024b, aimed at improving the overall station-level radiation characteristics.
# Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks
## 3 Experimental Tests  
This paper prepares specimens based on the GB/T3354-2014 standard, with specific manufacturing conditions detailed in Table 1. Carbon fiber laminates with multiple initial cracks are used as standard tensile test specimens. During the experiment, the prefabricated internal crack length L\({}_{2}\), the angle \(\theta\), and the relative distance S between cracks in the specimens are varied, while the length of the edge crack L\({}_{1}\) is kept constant. The depth of all cracks is set to 1.5 mm, and the relevant experimental setup and results are shown in Figure 6. Furthermore, to more accurately assess the impact of the inter-crack distance S on the performance of carbon fiber laminates under different crack length and angle conditions, this study measures S as the distance between the geometric centers of the internal crack L\({}_{2}\) and the edge crack L\({}_{1}\). This method not only facilitates experimental measurements and numerical simulations but also provides a more conservative estimate by considering the distance between crack centers, thereby more effectively ensuring the safety and reliability of carbon fiber laminates. The test was conducted through an electro-hydraulic servo testing machine model QBS-50 whose maximum static force is \(\pm\)50 KN, with a maximum dynamic amplitude of \(\pm\)50 KN, and an accuracy level of 1. The corresponding test-specific process is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Typical Value or Range** \\ \hline Resin type & Epoxy resin \\ Enhancer type & T700 Carbon fiber \\ Type of knitting & 3K Twill preparation \\ Carbon fiber volume ratio & 60\% \\ Prepreg preparation conditions & Indoor temperature \\ Resin content & 35–40\% \\ Curing temperature & 120 \({}^{\circ}\)C–150 \({}^{\circ}\)C \\ Curing pressure/MPa & 0.5–1.0 \\ Curing time/hours & 2–4 \\ Lamination direction & [0\({}^{\circ}\)/90\({}^{\circ}\)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Carbon fiber laminate manufacturing conditions.
## 2 Theory of Parameter Inversion  
### Prediction Principles of the Probability Integral Method  
The probability integral method, initially developed by prominent scholars, such as [PERSON] and [PERSON], is rooted in the theory of random medium. Over time, this method has seen substantial refinement and enhancement through continuous scholarly contributions, solidifying its position as the predominant approach for predicting mining subsidence.  
The subsidence value at any point within a surface subsidence movement basin induced by mining subsidence can be represented as follows [32; 33]:  
\[W(x,y)=\frac{1}{W_{0}}\times W^{0}(x)\times W^{0}(y) \tag{1}\]  
\[W_{0}=m\times q\times\cos\alpha \tag{2}\]  
\[W^{0}(x)=\frac{W_{0}}{2}\times[erf(\frac{\sqrt{\pi}\times tg\beta}{H}\times x )+1]-\frac{W_{0}}{2}\times[erf(\frac{\sqrt{\pi}\times tg\beta}{H}\times \tag{3}\]  
\[\begin{split} W^{0}(y)=\frac{W_{0}}{2}\times[erf(\frac{\sqrt{\pi }\times tg\beta_{1}}{H_{1}}\times y)+1]-\frac{W_{0}}{2}\times[erf(\frac{\sqrt{ \pi}\times tg\beta_{2}}{H_{2}}\times\\ (y-(D_{1}-S_{3}-S_{4})\times\frac{\sin(\theta+\alpha)}{\sin\theta} ))+1]\end{split} \tag{4}\]  
In the formula provided, \(W_{0}\) represents the maximum subsidence value of the surface when both the strike and the dip reach full mining. \(W^{0}(x)\) signifies the subsidence values on the strike primary section when the dip attains full mining. \(W^{0}(y)\) denotes the subsidence values on the dip primary section when the strike direction reaches full mining. \(m\) stands for the thickness of coal seam mining. \(q\) corresponds to the subsidence coefficient. \(\alpha\) represents the dip angle of the coal seam. \(\theta\) represents the propagation angle of mining influence. \(H\), \(H_{1}\), and \(H_{2}\) denote the average mining depth in the strike direction, the mining depth in the dip downhill direction, and the mining depth in the dip uphill direction, respectively. \(D_{1}\) signifies the dip length of the working face. \(D_{3}\) represents the length of the working face strike. \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) represent the deviation of the inflection point of the left and right boundaries and the deviation of the inflection point in the uphill and downhill directions, respectively. \(tg\beta\), \(tg\beta_{1}\), and \(tg\beta_{2}\) denote the main influence angle tangent value on the strike line, the main influence angle tangent value in the dip uphill direction, and the dip downhill direction, respectively. The spatial mining configuration is illustrated in Figure 1.
# Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)
## 3 Flexible Job Shop Scheduling Problem
### Particle Representation  
The mapping between the particle positions and the \(FJSP\) solutions is referred to as particle representation. In this study, we apply the vector-form coding method as described in [32]:  
A position vector of \(2|O|\) integer elements in each particle \(p\) (\(O\) is the set of operations) where the initial \(|O|\) items, denoted \(X_{p,\rho}\), creates one operation scheduling order. The operation referred to by \(X_{p,\rho}[i]\) takes precedence over the operation referred to by \(X_{p,\rho}[i+1]\). The \(X_{p,m}\) elements in the second \(|O|\) element define an operations-to-machines mapping. An example of \(Xp,o\) and \(Xp,m\) is shown in Figure 1(a). According to \(X_{p,\rho}\), the first operation of \(J_{3}\) is scheduled first, followed by the first and second operations of \(J_{2}\), the second operation of \(J_{3}\), and so on.  
Figure 1(a) shows an example of \(X_{p,\rho}\) and \(X_{p,m}\). \(X_{p,\rho}\) states that the first operation of \(J_{3}\) is the first to be scheduled, followed by the first and second operations of \(J_{2}\), then the second operation of \(J_{3}\), etc. According to \(X_{p,m}\), job \(I_{1}\)'s operations are allocated to machines \(M_{2}\)-\(M_{3}\)-\(M_{3}\), job \(I_{2}\)'s operations are assigned to \(M_{4}\)-\(M_{1}\)-\(M_{4}\), and job \(J_{3}\)'s operations are assigned to \(M_{1}\)-\(M_{2}\)-\(M_{2}\)-\(M_{2}\).  
The final time schedule is shown in Figure 1(b).  
\(X_{p,\rho}(t+1)\) and \(X_{p,m}(t+1)\) are computed as follows (at time (\(t+1\))): According to Equation (7), two temporary vectors are computed, \(\widehat{X_{p,\rho}}(t+1)\) and \(\widehat{X_{p,m}}(t+1)\). Then \(X_{p,\rho}(t+1)\) is set equal to \(X_{p,\rho}(t)\), which is sorted according to \(\widehat{X_{p,\rho}}(t+1)\) in ascending order. The ascending order of \(widehatXp,o(t+1)\) is also followed when sorting \(widehatXp,m(t+1)\). \(\widehat{X_{p,m}}(t+1)\) is also followed when sorting \(\widehat{X_{p,\rho}}(t+1)\). If an element of \(X_{p,m}(t+1)\) is out of range or fractional, then the closest machine number is used to replace it.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Job**} & \multirow{2}{*}{**Operation**} & \multicolumn{4}{c}{**Processing Time**} \\ \cline{3-6}  & & \(M_{1}\) & \(M_{2}\) & \(M_{3}\) & \(M_{4}\) \\ \hline
3 & 4 & 1 & 2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flexible job shop scheduling problem (\(FJSP^{T}\)) instance: 3 jobs, 10 operations and 4 machines.  
Figure 2: Vector form representation and the implicit schedule.
# Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation
## 5 Conclusions  
In this paper, an improved noise reduction method was adopted to process the vibration signal of an SRG, and the vibration characteristics of both the main beam and supporting arm were studied from the perspective of chaos. The following conclusions were drawn:  
1. The FIV values in three directions of five total measuring points on the SRG exhibited chaotic and fractal characteristics. The \(y\)-direction (vertical) correlation dimension of the three measuring points on the supporting arm was greater. Therefore, more attention should be paid to the vertical vibration of the supporting arm when monitoring the gate's vibration.
2. For three opening heights, the maximum Lyapunov exponent of each measuring point ranged from 0.0246 to 0.0681. The lateral direction, \(\lambda_{1}\), values of the three measuring points on the supporting arm were greater than those along the radial and vertical directions of the supporting arm.
3. The chaotic characteristics of the SRG mainly resulted from the fluctuating pressure exerted by the high-speed flow water on the gate.  
At present, with the development of sustainable hydropower energy generation, more SRGs need to be operated at small opening heights for the safe and intelligent regulation of reservoirs. Hence, sensitive openings should be avoided according to the chaotic characteristic parameter that is evident at small gate openings.  
Conceptualization, Y.L. (Yangliang Lu) and Y.L. (Yakun Liu); methodology, Y.L. (Yangliang Lu), Y.L. (Yakun Liu), D.Z. and Z.C.; software, Y.L. (Yangliang Lu) and X.F.; validation, Y.L. (Yangliang Lu), Y.L. ([PERSON]) and X.F.; formal analysis, [PERSON] ([PERSON]), Y.L. ([PERSON]), D.Z. and Z.C.; software, Y.L. ([PERSON]) and X.F.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, Y.L. ([PERSON]) and X.F.; funding acquisition, Y.L. ([PERSON]) and D.Z. All authors have read and agreed to the published version of the manuscript. This research was supported financially by the National Natural Science Foundation of China (grant No. 52179060; 51909024).  
**Institutional Review Board Statement:** Not applicable.
## 8 Conclusions  
This study addressed two issues: (1) use sensor data and AI/ML to classify senior citizens with diabetes based on their gait and physical fitness characteristics and (2) develop a personal training program using AI/DL based on 3D skeleton detection. Thus, using IMU sensor data and ML, we could classify the elderly with diabetes based on their gait and physical fitness characteristics and learned how to develop a personal training program using AI/DL, e.g., 3D skeleton detection.  
In fact, this study aimed to prove that abnormalities for senior citizens with diabetes were classified under imposed challenge walking speed conditions; slow (=22% slower than preferred speed), preferred, and fast (=25% faster than preferred speed) walking speeds through AutoML. The dataset for training was constructed with the support of senior citizens in the community by using the IMU. The applied AutoML for classification is an emerging research field within computer science that can help non-experts use machine learning off the shelf. Furthermore, the developed ML-based personal home training system using the single RGB camera showed the high possibility of correcting the exercise posture and speed in real-time. Therefore, the results of this study may be helpful for the self-preservation of senior citizens with diabetes by themselves with a single RGB camera.  
Conceptualization, B.N. and C.Y.; methodology, B.N., M.L., H.P. and H.J.; software, Y.W., S.K., S.A. and H.T.P.N.; validation, Y.W., S.K. and S.A.; formal analysis, Y.W. and H.J.; investigation, H.J.; resources, B.N., M.L., H.P. and C.Y.; writing--original draft preparation, H.J.; writing--review and editing, H.T.P.N., H.J. and B.N.; visualization, S.K. and H.J.; supervision, C.S. and C.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Basic Science Research Program through the National Research Foundation (NRF) of Korea grant funded by the Ministry of Education (No. 2021R11A305521011), and the Design Innovation Program (AHA Platform and Personalized Services for the Elderly Using Universal UX Design, (No. 20012692) funded By the Ministry of Trade, industry & Energy (MOTIE, Republic of Korea).  
**Institutional Review Board Statement:** This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Dong-A University (IRB: 2-104709-AB-N-01-201808-HR-023-02).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The dataset is available for your non-commerical research. Please contact any author. You can download our dataset after you submit the document for your agreement.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### High-Temperature MEMS Performance
This study addresses this gap by presenting a novel approach to analyzing the failure modes of high-temperature thermal MEMSs through a combination of experimental observations and simulation-assisted investigations. The aim is to enhance the understanding of how extreme temperatures affect MEMS reliability, ultimately improving the performance and longevity of these critical systems.
## 4 Simulations and Experimental Results
The proposed D-MPC balances the processing efficiency with the control accuracy. Table 4 presents a comparison of the losses and efficiency of the conventional MPC, multi-vector MPC, and proposed D-MPC under a 5 kW load condition. The experimental results demonstrate that the proposed MPC achieved lower losses at 260 W, demonstrating an improvement of approximately 15% when compared with the conventional MPC (307.5 W). When compared with the multi-vector MPC (344 W), the proposed method reduced the losses by 24%, indicating a significant improvement in the performance. Figure 10.4 Experimental results of three-phase output currents, line-to-line voltage, a-phase voltage, and frequency spectrum of a-phase current in transient-state with step current (8 A to 20 A) and frequency change (60 Hz to 30 Hz) at 0.2 s: (**a**) conventional MPC, (**b**) proposed discontinuous MPC. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Conventional MPC** & **Multi-Vector MPC** & **Proposed MPC** \\ \hline Execution time [\(\upmu\)s] & 25.23 & 19.34 & 20.12 \\ Reduction rate of & - & 0.76 & 0.79 \\ DSP time [\(-\)] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of DSP execution time between conventional MPC, multi-vector MPC, and the proposed discontinuous MPC. In terms of efficiency, the proposed MPC exhibited superior results at 95%, outperforming both the conventional MPC (94%) and multi-vector MPC (93%). These experimental findings demonstrate that discontinuous switching effectively reduces the switching losses by suppressing the switching operations during high-current periods. Furthermore, the results indicate that selecting the optimal zero-voltage vectors based on the offset voltage polarity prevents unnecessary switching, making it an effective methodology for improving both the loss reduction and electric propulsion system efficiency.
## 3 Results and Discussion
### Profile Surface Texture Analysis
#### 3.2.2 Percentage Deviation from the Reference Value  
Figure 6 is obtained by comparing the roughness results in Tables 3 and 4 with the roughness results obtained using standard \(\lambda_{\text{c}}\) and \(\text{l}_{\text{n}}\). The percentage deviation of the results obtained for cases 1 to 7 was calculated by comparing the results in Table 3 (\(\lambda_{\text{c}}\) and \(\text{l}_{\text{n}}\) selected from ISO 4288) and Table 4 (\(\lambda_{\text{c}}\) and \(\text{l}_{\text{n}}\) selected from the investigative framework). From Figure 6, it can be concluded that the smallest percentage deviation in roughness results was obtained for case 6 (X-axis) (\(\lambda_{\text{c}}\)/\(\text{l}_{\text{n}}\): 2.5/4). Therefore, this answers questions 1 and 2 in Section 1 for an AM component, \(\text{l}_{\text{n}}\) can be compromised, whereas \(\lambda_{\text{c}}\) should not be changed. In case 7 (Figure 6b,d), the percentage deviation is substantially larger for \(Rz\) (DMLS, EDM) than \(Ra\) and \(Rq\) because the surface is filled with smaller sized irregularities with smaller wavelengths than the \(\lambda_{\text{c}}\) value of 8 mm. Applying a \(\lambda_{\text{c}}\) of 8 mm on a surface with smaller sized irregularities will include all the short and long wavelengths in the roughness profile itself rather than separating it as roughness and waviness errors. Therefore, for an accurate roughness measurement of AM components, the \(\lambda_{\text{c}}\) should be selected based on the size of asperities present on the surface, and \(\text{l}_{\text{n}}\) can be compromised to the next smallest length available.
# How to Isolate Non-Public Networks in B5G: A Review
## 7 Conclusions  
In this article, isolation schemes for spectrum isolation, RAN isolation and CN isolation were discussed for seven deployments of B5G NPNs. For spectrum isolation, the indices of isolation performance and requirements were defined and calculated for NPNs quantitatively. Based on the indices, spectrum resources can be assigned among NPNs to meet isolation requirements. For RAN isolation, the customized configuration of the RAN can be achieved by setting the parameters of the RAN protocol properly. Then, RAN software can be run on the accelerated computing resource, which is virtualized using the scheduling scheme of the computing tasks. For CN isolation, with a newly defined identifier and CAG, an NPN can recognize its subscribers, and the subscribers can also recognize their NPN. CN software runs on general computing resources, which can be virtualized with virtualization technologies, such as KVM. When deploying NPNs, the spectrum and computing resources can be shared among the NPNs if the QoS and the isolation requirements of all NPNs can be met. Otherwise, more resources should be added or else the system will only be able to serve fewer NPNs.  
**Author Contributions:** Conceptualization, Q.S. and Y.Z.; methodology, Q.S., N.H. and L.T.; formal analysis, Q.S. and Y.Z.; investigation, Q.S. and N.H.; resources, L.T.; writing--original draft preparation, Q.S. and N.H.; writing--review and editing, Q.S., Y.Z., J.Z. and X.G.; visualization, Q.S. and N.H.; supervision, Q.S.; project administration, Q.S.; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript.  
This paper was funded by national key research and development program grant number 2021 YFB2900504.  
The authors declare no conflict of interest.
## 3 Results
### Experiment II  
In Figures 7 and 8, the results of Experiment II.1 are presented to evaluate how an electrically conductive material (aluminum) influences the frequency-dependent contrast behavior of copper within the same phantom. The reconstructed image in Figure 7 illustrates the normalized conductivity intensity distribution within the phantom, captured at a resolution of 256 \(\times\) 256 pixels.  
In Figure 8A, the profile line of the normalized electrical conductivity intensities is highlighted along the 128 th pixel row. Copper occupies pixels 1 to 32, while aluminum spans pixels 224 to 256. Peaks in the profile line occur at the 18 th and 239 th pixel numbers, indicating a deviation in the center of the objects by the second and first pixel, respectively. This observation confirms that both copper and aluminum exhibit significant contrast in their profile lines within the frequency range of 2-40 kHz.  
In Figure 8B, the normalized contrast values of the images are illustrated. It is evident that the frequency range between 15 kHz and 35 kHz yields higher image contrast with a threshold of 0.95. This conclusion is supported by the analysis of two key parameters: sharpness, illustrated in Figure 8C, and CNR, depicted in Figure 8D. Both parameters indicate that the frequency range spanning from 18 to 36 kHz is the most effective to achieve a high contrast in copper EIT imaging, maintaining a threshold of 0.95. This particular range is especially significant for understanding the influence of the proximity of another conductive material, such as aluminum, on the contrast behavior of copper in EIT imaging.  
Figure 7: Analysis of Experiment II.1 with a normalized conductivity reconstruction image with a resolution of 256 \(\times\) 256 pixels and a scale of 10 mm/16 pixels.  
Figure 8: Graph of the analytical indices in Experiment II.1: (**A**) the profile line at the 128 th pixel row. The red-dashed square represents the position of the object; (**B**) the normalized contrast values; (**C**) the normalized sharpness values; (**D**) the normalized CNR values corresponding to the surveyed frequency range.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 4 Global TTL Coupling
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Main Sources** & **Degrees of Freedom** & **TTL Coupling Coefficients** & **Origin of TTL Noise** \\ \hline \multirow{4}{*}{Optical components} & \multirow{2}{*}{Yaw} & \(<2\) μm/rad@QPD1 & \multirow{2}{*}{The imperfect imaging of the lens systems and the impact of the mismatch between the interfering beams.} \\  & & \(<2\) μm/rad@QPD2 & \\  & & \(<1.5\) μm/rad@QPD1 & \\  & pitch & \(<3.5\) μm/rad@QPD2 & \\ \multirow{4}{*}{Piston effect (lateral)} & \multirow{2}{*}{Yaw} & \(<3\) μm/rad & \multirow{2}{*}{Additional LPS variation due to the offset between the pivot and the reflective point of the FSM.} \\  & & & \\ \multirow{4}{*}{Piston effect (longitudinal)} & \multirow{2}{*}{Yaw} & \(<1\) μm/rad & \multirow{2}{*}{The longitudinal offset should be kept under 200 μm and the lateral offset should be kept under 800 nm} \\  & & \(<3\) μm/rad & \\ \multirow{4}{*}{Piston effect (longitudinal)} & \multirow{2}{*}{Pitch} & \(<4\) μm/rad & \multirow{2}{*}{Misalignment of L2 in \(x\)-axis, offset is set to be 1 μm.} \\  & & \(<2.5\) μm/rad & \\ \multirow{4}{*}{misalignment \(L1_{y}\)} & \multirow{2}{*}{Yaw} & \(<4\) μm/rad & \multirow{2}{*}{Misalignment of L1 in \(y\)-axis, offset is set to be 1 μm.} \\  & & \(<4\) μm/rad & \\ \multirow{4}{*}{misalignment \(L2_{z}\)} & \multirow{2}{*}{pitch} & \(<2.5\) μm/rad & \multirow{2}{*}{Misalignment of L2 in \(z\)-axis, offset is set to be 1 μm.} \\  & & \(<2.5\) μm/rad & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the main sources of the local TTL coupling in yaw and pitch degrees of freedom, respectively. Estimates on the TTL coupling variation is given.
## 4 Experiments
### Experimental Results  
To assess the robustness and effectiveness of the method proposed in this paper, we conducted comparisons with state-of-the-art networks that are commonly used for medical image segmentation. To ensure the fairness and validity of the experimental results, we employed three publicly available datasets for validation: the BUSI dataset, the Kvasir-SEG dataset, and the ISIC 2018 dataset.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 3 Results  
### Inter-Unit Consistency  
The results of ICC analysis showed _slight_ reliability for the total sprint distance and _fair_ reliability for peak speed (Table 1). These results indicate that the 8 tested GNSS units are not sufficiently consistent among themselves.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 4 Recurrent Neural Network Models  
This section briefly describes the fundamentals of LSTM, Bi-LSTM, and GRU approaches. After that, the proposed model descriptions are given.