esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 2 Materials and Methods
### Classification Model
#### 2.4.3 Model Validation  
Several metrics have been used to evaluate the performance of the built support vector machine models. It has been validated via the F1 score, and receiver operating characteristic (ROC) and area under the ROC curve (AUC) metrics. These metrics are calculated based on a confusion matrix [32]. It is a matrix of the predicted label values based on the true label. Figure 4a explains the confusion matrix of the binary classification. Here, precision is the probability that the result of the true label is positive when the result of the prediction label is positive. Precision does not guarantee the model's reliability when the prediction results are negative. On the other hand, recall is the probability that the result of the predictive label is positive when the result of the true label is positive. Here, the recall does not provide information on predictions made when the true label is negative. In this study, a multi-classification model was used. Figure 4b describes the confusion matrix of multi-class classification. When evaluating multiple categories, metrics are calculated in two ways. One of those is macro-averaging. Using the example in Figure 4b, the precision and recall are calculated independently for each class as follows. The precision for classes 1, 2, and 3 is (2/7, 3/4, and 4/5), and the recall for classes 1, 2, and 3 is (5/5, 3/6, and 4/5), respectively. The precision and recall using macro-averaging are the sum average of each class value calculated independently, where the values are 0.61 and 0.77, respectively. Micro-averaging, on the other hand, is a method for calculating metrics using the overall positive and negative figures by developing a confusion matrix for each class and summing up the developed confusion matrix. Figure 5 describes developing a confusion matrix for each class using micro-averaging using the figures in Figure 4b. The precision and recall using micro-averaging are 0.75.  
Figure 4: Illustration of the confusion matrix. (**a**) Binary classification and (**b**) Multi-class classification.  
Figure 5: Illustration of the confusion matrix for micro-averaging.  
The F1 score is an evaluation metric considering the recall and precision, calculated as Equation (28).  
\[\text{F1 score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{28}\]  
The ROC curve is an evaluation index built by combining the true positive rate (TPR) and the false positive rate (FPR). Here, the TPR is the same indicator as the recall. The FPR is calculated as \(1-\text{specificity}\). Specificity is the probability that the prediction result is negative when the true label is negative. As opposed to the recall, this indicator does not provide information on predictions made when the true label is positive. The AUC with the TPR as the y-axis and the FPR as the x-axis is shown in Equation (29).  
\[\text{AUC}=\int_{0}^{1}TPR\Big{(}FPR^{-1}(x)\Big{)}dx \tag{29}\]  
Python 3.7 and libraries were used to build and validate the model [33; 34; 35; 36].
# Multimode Guided Wave Detection for Various Composite Damage Types
In this paper, a new methodology is presented to detect and separate various composite damage types based on multiple pure-mode guided waves generated by an adjustable ABT pair. The idea is that various wave modes interact differently with various types of composite damage. First, the tuning angles of various wave modes were obtained based on [PERSON]'s law applied to the guided-wave dispersion curves of composite plates. Then, pitch-catch experiments using an adjustable ABT pair were conducted to validate the excitations of pure S0 mode and SH0 mode in a 2-mm quasi-isotropic carbon fiber-reinforced polymer (CFRP) composite plate. Although an ABT excitation consists of pressure waves, the generation of SH0 was possible due to the anisotropy of the quasi-isotropic composite layup [28]. Next, experiments were conducted on the 2-mm quasi-isotropic composite plate to detect and separate the delamination simulated by a Teflon insert and the actual impact damage due to low-velocity impact. Finally, the proposed technique was applied to a 3-mm quasi-isotropic CFRP composite plate using multimode guided wave detection to distinguish between delamination and impact damage. The experimental results demonstrate that the proposed method has a good capability to detect and separate various damage types in composite structures.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 4 Results and Discussion
### Discussion and Major Findings  
This discussion reviews the application of multi-objective optimization techniques for decarbonizing isolated electrical grids, with a focus on the case of El Hierro. While this case study provides a detailed methodology, the approach is broadly applicable to other systems.  
The global decarbonization process faces substantial challenges, primarily due to the continued reliance on fossil fuels for energy production. Transitioning to renewable energy sources, primarily through wind and solar power, introduces challenges related to reliability and stability. This is because these sources are intermittent and non-dispatchable, challenges that are further compounded in islands and other isolated regions, which must be self-independent due to their lack of connection to larger power grids. For these regions to transition to renewables, effective energy storage solutions are essential to ensure a continued, reliable and stable energy supply, particularly during periods of low renewable generation.  
EnergyPLAN software, a recognized tool for simulating energy system performance, was employed to model various energy scenarios. The tool enables hourly simulations of energy system operations, providing detailed insights into energy balance, reliability, and the economic impact of different renewable energy configurations. This capability allows for the evaluation of multiple energy source combinations, assessing their ability to meet demand, ensure stability, and minimize costs in a sustainable manner.  
To optimize the system, a multi-objective optimization method was implemented, aiming to minimize system costs, GHG emissions, and excess generation. This approach incorporates key constraints, such as generation technology capacities, storage limits, demand coverage levels, and land use restrictions, to ensure the system's feasibility and efficiency. The optimization was carried out using the EPLANopt tool, an extension of EnergyPLAN that provides enhanced optimization functionalities. EPLANopt allows for the determination of the optimal system configuration by balancing performance, costs, emissions, and excess generation while respecting technical and operational constraints. This multi-objective approach offers a robust framework for designing economically viable and sustainable energy systems.
## 3 Engineering Context and Site Condition
### Slope Rock Mass Structure Testing Research  
The stability of slopes is fundamentally governed by their structural configuration. To enable accurate modeling and effective analysis of slope stability, it is essential to determine the slope's structure and strength parameters prior to analysis, facilitating predictions of failure patterns and development trends. Geo-radar technology can be utilized for slope monitoring, offering precise detection of weak inclusions and cracks while providing foundational data for 3D modeling. Rock acoustic testing, particularly ultrasonic testing, is extensively applied to evaluate geotechnical parameters, identify faults, detect weak inclusions, and characterize loose zones. This method assesses rock properties by measuring the propagation velocity and attenuation coefficient of sound waves in the rock mass. The principle underlying this technique is that sound waves traversing cracks or interlayers experience an extended propagation path, resulting in reduced wave velocity compared to intact rock, thereby revealing structural changes within the rock mass caused by fracturing. Combined, these techniques serve as critical tools for comprehensive slope stability assessments.
# Evaluation of the Variability of Vibration Measurement Results in Rolling Bearing Quality Control
###### Abstract  
The final result of rolling bearing quality control is influenced by many factors that are not always taken into account. If non-conforming products leave the factory, then the reliability of equipment containing rolling bearings is significantly weakened. The paper presents an analysis of the reasons for the variability of vibration measurement results in rolling bearing quality control. The paper describes the organisation of rolling bearing acceptance inspection on the production line and its place in the rolling bearing production process. The study evaluates the influence of the measuring device and the location of the sensor on the result of the rolling bearing vibration monitoring process. The problem of repeatability and reproducibility of rolling bearing vibration measurement was also investigated. On the basis of the obtained results, conclusions were drawn regarding the improvement of the quality control process of rolling bearings in production plants.  
rolling bearings; quality control; industrial measurements; vibration Article
# Spectrum Demand Forecasting for IoT Services
## 5 Study Case Implementation: Colombia
### Spectrum Forecasting: Share of Devices per Services and Technology  
Having defined the scenarios, the following step is finding the share of each technology in each scenario's peak case. This is again based on all the statistics collected for the market study. Notice that for some scenarios (2, 3, and 10), a cellular access link is not considered in the following 10 years. This is mainly due to the difficulty for cellular networks to reach the short-range communications market.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 4 Development of FPS with Xilinx ZYNQ for Minimizing a Latency
### Modified Mitigation Node  
The modified mitigation node is a 2U-high 19-inch rack module. The interior of the module consists of one PCB with _FPGA_, one power supply, cables, and chassis. The following are the changes in the modified mitigation node.  
1. SFP+ port on the main board (9 EA to 10 EA)
2. _MPSoC_ to ZYNQ XC7Z100
3. Ethernet PHY (Marvell to TI)
4. Dip-type connector for booting device selection
5. Wet contact driver in the daughter board
6. Change in the SMA library
7. Front SFP+ port (8 EA to 9 EA)  
Table 4 presents the specification of the modified mitigation node.  
Figure 6 shows the configuration diagram of the modified mitigation node.  
The modified mitigation node consists of a main board for signal processing and data storage and a daughter board for outputting signals to the _FPS_ devices. The main board is equipped with ZYNQ for PetaLinux, memory, buffer, and sensor, where PetaLinux is ported. The front interface consists of nine SFP+ transceiver modules, Ethernet transceivers, SD cards, and USB to JTAG port. The rear panel is composed of a connector for outputting data to the daughter board. The daughter board then converts output signal that is received from the main board to wet signal and transmits the signal to an external device through an SMA connector.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Requirement** & **Implementation Detail** \\ \hline \multirow{8}{*}{Configuration} & \multirow{8}{*}{Chassis} & 19-inch 2U-height rack \\  & & Front panel: SFP+ port 9 ea, BJ-45 1 ea, USB to JTAG 1 ea, DSUB-9 1 ea, \\  & & SD card 1 ea, LED and character LCD for display status \\  & & Rear panel: SMA 24 ea for output, 220 V AC power \\  & & Cooling system: fan for cooling \\ \hline \multirow{8}{*}{Integrace} & \multirow{8}{*}{Digital board} & SoC: Xilinx ZYNQ XC7Z100-2 FFG900I \\  & & Memory: DDR 1 GB SPI Flash 16 MB \\  & & Temperature sensor \\  & & Transceiver, buffer, etc. \\ \cline{2-3}  & & SMPs and cable & Power and signal connection flame-retardant cable \\ \hline \multirow{8}{*}{Interface} & SFP+ (9) & _EVG_ 3.25 Gbps speed \\ \cline{2-3}  & Ethernet (1) & 1 Gbps \\ \cline{1-1} \cline{2-3}  & USB (1) & USB to JTAG \\ \cline{1-1} \cline{2-3}  & UART to JTAG (1) & 115,200 bps UART \\ \cline{1-1} \cline{2-3}  & Output (24) & Wet contact (SMA) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hardware specification of the modified mitigation node.
## 4 Discussion
It is concluded from Table 2, that the higher values of \(c,\mathrm{Pr}\) and \(Rd\) enhance the heat transfer rate, while the augmented value of \(Nb\) and \(Nt\) decrease the mass transfer rate. Table 2 presents the numerical values of the mass transfer rate for the different embedded parameters; \(c,Le,Nb\) and \(Nt\). Here we observed that the higher values of \(c,Nb\) and \(Nt\) enhance the mass transfer rate, while the augmented value of \(Le\) reduces the heat transfer rate. Tables 1-3 display the validation of the current results with the previous published results. Both the current and previous results are compared and an excellent outcome is found. This verifies the validity of the current results along with the accurateness of the analytical method (HAM) we used in this research work. Figure 1: Physical representation of the problem. Figure 2: Cont. Figure 5: Effect of the thermal radiation parameter \(Rd\) on \(\theta(\eta)\), when \(Bi_{1}\ =\ 0.1,\ Nb\ =\ 0.4,\ Nt\ =\ 0.5,\)\(\Pr\ =\ 0.6,Le\ =\ 0.7,c\ =\ 0.8.\)  
Figure 6: Effect of Prandtl number \(\Pr\) on \(\theta(\eta)\), when \(Bi_{1}=0.1\), \(Nb=0.4\), \(Nt=0.5\), \(Rd=0.6\), \(Le=0.7\), \(c=0.8\). Figure 8: **(a,b)** Effect of the Brownian motion parameter on temperature and concentration profiles. **(a)** Effect of \(Nb\) on \(\theta(\eta)\), when \(Bi_{1}\ =\ 0.1,Rd\ =\ 0.4,Nt\ =\ 0.5,\Pr\ =\ 0.6,Le\ =\ 0.7,c\ =\ 0.8\). **(b)** Effect of \(Nb\) on \(\phi(\eta)\), when \(Bi_{1}\ =\ 0.1,Bi_{2}\ =\ 0.2,Nt\ =\ 0.4,Le\ =\ 0.7,c\ =\ 0.8\). Figure 9: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\boldsymbol{c}\) & _We_ & \(\gamma\) & \(\boldsymbol{C_{f}\text{ \bf for }f\text{ (0)}}\) & \(\boldsymbol{C_{f}\text{ \bf for }f\text{ (0)}}\) & \(\boldsymbol{C_{f}\text{ \bf for }g\text{ (0)}}\) & \(\boldsymbol{C_{f}\text{ \bf for }g\text{ (0)}}\) \\  & & & **Previous Results**[43] & **Present Results** & **Previous Results**[43] & **Present Results** \\ \hline
1.0 & 0.1 & 0.5 & \(-1.489532\) & \(-1.23942\) & \(-0.695215\) & \(-2.04702\) \\
1.1 & & & \(-1.564022\) & \(-1.25192\) & \(-1.564022\) & \(-2.23996\) \\
1.2 & & & \(-1.682498\) & \(-1.26417\) & \(-2.647314\) & \(-2.38904\) \\  & 0.2 & & \(-1.731420\) & \(-1.06979\) & \(-1.892121\) & \(-1.22441\) \\  & 0.3 & & \(-1.544321\) & \(-0.83279\) & \(-1.744322\) & \(-0.42052\) \\  & 0.4 & & \(-1.324151\) & \(-0.38181\) & \(-1.212141\) & \(0.35420\) \\  & 0.6 & \(-1.829575\) & \(-1.28570\) & \(-1.829575\) & \(-1.82788\) \\  & 0.7 & \(-2.083805\) & \(-1.32900\) & \(-2.083805\) & \(-1.60457\) \\  & 0.8 & \(-2.310564\) & \(-1.36945\) & \(-2.310564\) & \(-1.38981\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical values of surface drag force for different embedded parameters.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Factors for HSN Latency  
#### 5.6.1 Simulation  
This section analyzes the factors that influence the latency of the HSN, where the number of nodes in the backbone network is four and the number of nodes in each access network is six. The transmission direction of each node is random, with transmission sizes ranging from 64 Kb to 64 Mb. The small cycle period is set to 1 ms, 20 ms, 30 ms, and 40 ms, respectively. The number of wireless connections ranges from 1 to 4 times the number of access network nodes.
## 3 Experimental Results
### Comparison with Other Methods
[10] report a performance of 94.48% and 84.04% in terms of recall and precision, respectively. Our proposed fusion method achieved 100% recall and 82.35% precision. Specifically, it correctly detected 14 out of 17 traffic signs. The proposed method outperformed [32] in term of precision rate and achieved the highest recall rate among the compared methods.
## 2 Materials and Methods
### Load, Boundary Conditions, and Mesh  
After importing the STEP files of models into the ABAQUS 6.14 software (Dassault Systemes, Velizy-Villacoublay, France), a mesh convergence was carried out by using a technique called \"h-refinement\". This involved refining the mesh in zones of interest by increasing the number of elements from a coarse mesh to a finer mesh until the results converged. The convergence was evaluated by monitoring the changes in the maximum von Mises stress with respect to the number of elements. The convergence was met when the relative error in the maximum von Mises stress between two consecutive mesh refinements was below a specified tolerance value. This tolerance value was set to 2% in this study, as it provided a good balance between accuracy and computational efficiency. After mesh convergence, we achieved a total number of 208,685 (C3D10) elements. In our study, the cortical bone was represented by 10,834 (C3D10) tetrahedral elements, and the cancellous bone was meshed using 13,891 (C3D10) tetrahedral elements. The crown was modeled with 2056 (C3D10) tetrahedral elements, and the dental implant required a total of 181,904 (C3D10) tetrahedral elements. To ensure that the mesh had a high quality and met the necessary criteria for accurate and reliable results, the \"Mesh Verify\" feature in ABAQUS was used to evaluate the element distortion, aspect ratios, and skewness. The bone-implant interfaces were meshed with smaller element sizes (0.1 mm) and other regions with coarse element sizes (varying between 0.5 and 1 mm). A static load of 200 N was applied to the occlusal surface of the crown. This chewing force of 200 N was reported on the first premolar and first molars [41; 42]. The bone's side surfaces were fixed (zero degrees-of-freedom, (U1 = U2 = U3 = URI = UR2 = UR3 = 0)) in all directions as shown in Figure 1c. Two scenarios of osseointegration were investigated: full osseointegration, where the full implant and bone interfaces were assumed to be perfectly bonded (100% osseointegration), and this was achieved by using a tied contact as shown in Figure 1c (in green color). For partially osseointegrated cases, a frictionless contact was assigned between the upper part of the implant (upper half) and the bone interface to allow the relative movement between the implant and surrounding bone as shown in Figure 1c (in red color). The lower part of the implant and the bone interface was tied perfectly to mimic the osseointegration in the lower part (lower half) as shown in Figure 1c (in green color).
## 1 Introduction
[18] proposed a new method called Protrugram, which calculates the kurtosis of the envelope spectrum amplitudes of demodulated signals instead of the kurtosis of the filtered temporal signals when the signal to noise ratio is low. Based on the sparsity measurement, [PERSON] et al. [19] proposed Sparsogram, which can detect the high resonant frequency to extract bearing fault feature and then apply the genetic algorithm and Morlet wavelet to enhance the detection results of Sparsogram. [PERSON] et al. [20] identified the optimal frequency band under the interferences of the motor and industrial field based on the singular value negentropy. These methods have achieved good results in fault diagnosis and feature frequency selection. Although these frequency band selection methods can reduce noise and have a good effect in feature frequency selection and fault diagnosis, they also ignore some effective information out of the selected frequency band. Meanwhile, as the transient impact caused by bearing fault can spread within a wide frequency band [16], the above methods cannot retain all the information caused by bearing fault. Although these frequency band selection methods can improve the recognition results of the LZ indicator to some extent, there could be some deviations in fault severity recognition based on the LZ indicator. Aiming to resolve this problem, in this paper, a fault severity recognition method of bearing based on the full frequency band of vibration signal is proposed. According to the average coding length of Huffman coding (ACLHC), the original signal is processed to reduce the noise and highlight the impact components of the signal. The signals processed by ACLHC not only reduce the influence of noise, but also contain all the fault information. Then, the ACLHC of the signal is further encoded by symbolic aggregate approximation (SAX) to reflect the modulation information of bearing vibration signal. Finally, the LZ indicator is obtained to reflect the fault severity. After the above processing and calculation, the bearing fault severity under different working conditions can be effectively recognized. The rest of this paper is organized as follows. Section 2 introduces the basic theory of ACLHC and symbolic aggregate approximation Lempel-Ziv indicator (SAX-LZ). In Section 3, the new recognition method of bearing fault severity is proposed and the characteristic of the ACLHC is discussed. In Section 4, the effectiveness of the proposed method is validated by the bearing datasets under different working conditions. Section 5 concludes this paper.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 3 Results and Discussions
### Characterization of Ground Granulated Blast Furnace Slag (GGBFS) and Alkali-Activated Slag (AAS)
#### 3.1.3 Scanning Electron Microscope Analysis  
Figure 2 presents SEM images of GGBFS and AAS mortars with a Na\({}_{2}\)O concentration of 8%. As shown in Figure 2a, GGBFS particles undergo a remarkable alteration due to the alkaline activation process. The granulate GGBFS particles reacted with the alkaline solution to form AAS, which appeared to have a dense structure (Figure 2b). According to previous studies, the dominant hydration product of AAS is seen to be calcium silicate hydrate xCaO-ySiO\({}_{2}\cdot\)zH\({}_{2}\)O (C-S-H) with a lower CaO/SiO\({}_{2}\) ratio compared with that of Portland cement [28]. The EDS analysis results demonstrated the formation of C-S-H in AAS due to the dominant presence of Si element with a low content of Ca element on the surface of specimens.  
Figure 1: (**a**) Mechanical strengths of alkali-activated slag (AAS) mortar with four different Na\({}_{2}\)O concentrations and (**b**) XRD patterns of ground granulated blast furnace slag (GGBFS) and AAS. A—aluminum sulfate (Al\({}_{2}\)(SO\({}_{4}\))), G—gypsum (CaSO\({}_{4}\cdot\)2H\({}_{2}\)O), T—tobermorite (Ca\({}_{5}\)Si\({}_{6}\)O\({}_{16}\)(OH)\({}_{2}\)), F—fukalite (Ca\({}_{4}\)Si\({}_{2}\)O\({}_{6}\)(CO\({}_{3}\))(OH)\({}_{2}\)), CSH—calcium silicate hydrate (1.5 CaO-SiO\({}_{2}\cdot\)xH\({}_{2}\)O).