esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustment of Wire Vibrations in Order to Improve Geometric Accuracy and Surface Roughness at WEDM
## 4 Results of Experiments
### Measurement of Wire Tool Electrode Vibrations during Electrical Discharge Machining
After reaching a stable state, the control system restores the original settings of the main technological parameters. Figure 12 shows a diagram of the location and wiring of acoustic emission sensors for measuring wire tool electrode vibrations during WEDM. The amplitude of the oscillation of the wire tool electrode, as well as its critical frequency, are relatively difficult to predict under real conditions with an electroerosion machine. However, a suitable way to identify them is to apply one of the direct or indirect methods. Therefore, the given procedure of identification of vibrations of wire tool electrodes during WEDM was also applied in our proposed solution. Based on the application of the proposed system for adjusting the vibration amplitude of the wire tool electrode during WEDM, a substantial increase in machined surface quality can be achieved, both in terms of the circularity parameter (\(y_{Cmax}\)) and in terms of the cylindricity parameter (\(z_{Cmax}\)). Although some research suggests that workpiece vibrations have a significantly greater effect on the quality of a machined surface, a proposal to locate the AE sensors on the top and the bottom guides of the wire tool electrode was applied. This was mainly due to the simpler application, which can also be used under real conditions of technical practice. However, further research should be focused on a suitable way of monitoring the vibrations of a workpiece, for subsequent adjustment of their amplitudes, in order to minimize geometric deviations of a machined surface. As a follow-up, it will be necessary to develop a complex model that can predict and subsequently modify those main technological and process parameters, which can significantly affect the vibration amplitude of the wire tool electrode during WEDM.
# Multi-Objective Evolutionary Neural Architecture Search with Weight-Sharing Supernet
## 2 Related Works
### Reinforcement-Learning-Based Neural Architecture Search  
[PERSON] et al. [15] first proposed a reinforcement-learning-based NAS algorithm and achieved promising results in both image classification and natural language processing tasks. This study demonstrated the potential and practicability of using NAS methods to automatically design network architectures, which was pioneering in the field of NAS. Following this work, early NAS research [16; 17; 18] mainly used reinforcement learning to design NAS algorithms. Specifically, reinforcement-learning-based NAS methods regard the generation of neural networks as the actions of an agent, and the action space represents the search space in NAS. Then, the network performance on unseen data serves as the reward for this agent. In such a process, the agent interacts with the environment continuously and utilizes a certain learning policy to maximize the reward, which finally completes the search for network architectures. However, most of the reinforcement-learning-based NAS methods typically demand high computational resources. For instance, the NASNet proposed by [PERSON] et al. requires up to 800 GPUs to perform the whole search process. Due to the high computational costs of reinforcement-learning-based NAS, researchers gradually began to prefer more efficient NAS methods, which prompted the proposal of gradient-based NAS.
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
###### Abstract  
High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the \(IEEE\) 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications.  
high impedance faults; power system state estimation; power system protection; power system monitoring; eigenvalue estimation; state space representation; fault detection +
Footnote †: 2021: 11, 12148. [[https://doi.org/10.3390/app112412148](https://doi.org/10.3390/app112412148)]([https://doi.org/10.3390/app112412148](https://doi.org/10.3390/app112412148))
## Appendix E Performance Metrics of the CHT Behavior
### Descriptive Statistics for Study Case 1: Four-Bar Linkage Mechanism  
The descriptive statistics for the four-bar linkage mechanism are presented in Table A16. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CHT** & **Algorithm** & **Mean** & **std** & **Median** & **Minimum** & **Maximum** & **IS** \\ \hline FR & DER1B & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & DER1E & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & DEB1B & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & DEER & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & DECB & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & DECR1B & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & DECR1E & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & GA & 200 & 0 & 200 & 200 & 200 & 0 \\ FR & PSO & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DER1B & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DER1E & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DEB1B & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DECR & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DECB & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DECR1B & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DECR1B & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & DECR1E & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & GA & 200 & 0 & 200 & 200 & 200 & 0 \\ SR & PSO & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DER1B & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DER1E & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DEB1B & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DECR & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DECB & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DECR1B & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & DECR1E & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & GA & 200 & 0 & 200 & 200 & 200 & 0 \\ \(eC\) & PSO & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DER1B & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DER1E & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DEB1B & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DEB1E & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DEER & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DECB & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DECR1B & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & DECR1E & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & GA & 200 & 0 & 200 & 200 & 200 & 0 \\ PF & PSO & 200 & 0 & 200 & 200 & 200 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table A16: EVALS descriptive statistics for study case 1: Four-bar linkage mechanism. IS means the number of infeasible solutions through the executions. Appendix E.5.2. Descriptive Statistics for Study Case 2: Cam-Linkage Mechanism  
The measures of the descriptive statistics for the cam-linkage mechanism are presented in Table A17.
###### Abstract  
To investigate the impact of airfoil angle of attack on the output performance of a valveless piezoelectric pump with airfoil baffles, this study conducted comprehensive performance tests and full-flow field simulations of piezoelectric pumps across a range of angles. At a driving voltage of 100 V and with a Clark Y airfoil set at an angle of 0\({}^{\circ}\), the piezoelectric pump reached a peak output flow rate of 200.7 mL/min. An increase in the angle of attack corresponded to a decline in both the maximum output flow rate and the maximum back pressure of the pump. Flow field simulation results demonstrated that an increased airfoil angle of attack led to a gradual increase in entropy production within the piezoelectric pump. Turbulent dissipation and wall entropy production were found to be more pronounced compared to viscous entropy production. High turbulent dissipation was primarily observed at the pump chamber inlet, the trailing edges of the airfoils in both the inlet and outlet pipes, and the outlet bend. As the angle of attack increased, the complexity of the vortex core structures within the flow field escalated as well. Regions with significant wall entropy production were notably concentrated at the outlet bend.  
piezoelectric actuator; micropump; airfoil; vortex; entropy generation +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Selection of Variables of the Study  
After selecting the sample size, the next step involved choosing the variables we intended to analyze in our survey. These variables are consistent across all selected municipalities in accordance with key international agreements and may be updated based on regional particularities and/or new technological and environmental developments.  
The variables to be analyzed include the following:  
* Cost and inconvenience of implementation.
* Evaluate the cost of the service in operation and its operability due to inclement weather (snow, rain, etc.).
* Quality of the selective residue based on characterizations.
* CO\({}_{2}\) and greenhouse gas emissions.
* Circular economy.
* Impact of collection systems on mobility and transit in cities (Transit Oriented Development, TOD). Reduction in heavy traffic.
* Noise levels produced.
* Quality of service, as well as inconvenience due to implementation and operation.
* Quality of life, security, and citizen service.
* Odor emissions, leachates released, public roads, and environmental cleanliness.
* Effect on employment.
* Information and monitoring of waste in real time (weighing, user, habit, etc.) with the possibility of implementing Pay as you throw.
* Integration of collection systems in SMART CITIES. Sensorization and digitalization in the operation of systems.
* 24/7/365 system availability.
* Resilience of collection systems to strikes.
* Citizen acceptance.
* Vandalism.
* Sustainable development and its basic pillars (environmental, social, and economic).
* Occupation of public roads.
* Human contact with waste fractions.  
In terms of ethical considerations, data collection had to prioritize safeguarding the privacy and anonymity of the participants. Data collection was performed with the informed consent of the participants, ensuring their confidentiality and anonymity. The informed consent provided comprehensive details on the procedures, as well as the significance and aims of the research concerning the confidentiality of gathered information and its potential applications.
## 3 Dataset and Methodology
### Statistical Analysis  
The aim of this section is to use a statistical approach to detect and evaluate the spatial autocorrelation of the elevations of the rock glaciers, considering the global geographic domain and separation by basins. As a first step to analyze the influence of the spatial factor on the elevations of rock glaciers, the variance analysis technique (ANOVA) was applied. This technique supports the equality of the means of elevations in the set of basins, under the assumptions of normality and homoscedasticity.  
Spatial autocorrelation is the distinctive assumption of spatial statistics and is defined as the tendency of the variable to take increasingly similar values in closer and closer places, separated by a distance (h). This property is measured and parameterized with the semi-variogram function, \(\gamma(h)=\frac{1}{2}E\Big{\{}[V(s+h)-V(s)]^{2}\Big{\}}\): where, E = Expected value; V = Variable (elevation); s = Site (X,Y); and h = Distance between two sites (s).  
Spatial autocorrelation is validated by the increasing behavior of the semi-variogram function \(\gamma(h)\), from a minimum observed distance to a limit range (h = \(\phi\)). This function also shows directions of greater or lesser influence (anisotropy) based on a discriminated calculation in the directions \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\).  
One limitation in calculating the semi-variogram is that it requires the variable to be stationary, i.e., its average does not vary or does not depend on the site, \(E[V(s+h)]=E[V(s)]=\mu\).  
This requirement does not imply a restriction since, in the case of non-stationary variables, the structural model consisting of the sum of the global spatial tendency \(\mu(s)\) and a stationary residual variable \(\delta(s)\) is considered:  
\[V(s)=\mu(s)+\delta(s)\]  
With \(\mu(s)=E[V(s)]\) as the global trend in the entire geographic domain, which is parameterized in a polynomial function of degree 0, 1, or 2 in the site coordinates, s = (x,y), representing a plane with and without a slope or a curved surface, and \(\delta(s)\) as the stationary residual variable with zero mean and spatial autocorrelation measured by \(\gamma_{\delta}(h)\). This approach was used to conduct the structural analysis.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 2 Materials and Methods
### Generate Training Dataset
#### 2.1.1 Generate Velocity Models
The range in this study is \(V_{min}=0.15\ Km/s\), \(V_{\max}=0.3\ Km/s\), which is more consistent with the velocity characteristics of the near surface. Step 3: Generate random S-wave velocity for the remaining layers. We define the probability \(\alpha=0.8\) for the velocity increasing linearly with depth and the probability \(\alpha=0.1\) for other two cases. Then, we randomly generate a random number \(x\) between 0 and 1. Next, if \(x\leq 0.8\), the following formula is used to calculate \(V_{s,m+1}\):  
\[V_{s,m+1}=V_{s,m}+\lambda_{m+1,m}\cdot V_{s,m} \tag{2}\]  
In Equation (2), \(\lambda_{m+1,m}\) is the random floating number between \((\lambda_{\min},\lambda_{\max})\), which determines the increasing or decreasing rate. In this study, we defined \(\lambda_{\min}=0.01\) and \(\lambda_{\max}=0.35\). If \(0.8<x\leq 0.9\), \(V_{s,m+1}\) is taken as the random number between \((V_{s,m}+\lambda_{\max}\cdot V_{s,m},min(V_{s,m}+0.3,1.0))\). If \(0.9<x\leq 1.0\), \(V_{s,m+1}\) is taken as the random number between \((max(V_{s,m}-0.3,0.1),\lambda_{\min}\cdot V_{s,m})\). Step 4: Calculate the compressional wave velocity (\(V_{p}\)) and density (\(\rho\)). Although their influence on the Rayleigh wave velocity is much smaller than that of \(V_{s}\), accurate values are also important for calculating dispersion data. Much previous research provided the empirical relationships among \(V_{p}\), \(V_{s}\) and \(\rho\)[27; 28], which apply to different lithology and depth ranges. In our study, we use the approximate relationship given by [PERSON] et al. to calculate the \(V_{p}\). \[V_{p}=V_{s}/\left[0.5684\times(Z/0.2)^{0.163}\right] \tag{3}\]  
Here, \(Z\) is the depth (Km) and \(Z=\sum_{i=1}^{m}h_{i}\);\(h_{i}(i=1,2,\cdots,m)\) is the thickness of each layer. Equation (3) is suitable for sedimentary strata when the depth is less than 0.2 Km. For the density \(\rho\), we use the empirical relationship given by [PERSON] et al. [29]. \[ \begin{array}{ll}\rho&=1.6612V_{p}-0.4721V_{p}^{2}\\ &+0.0671V_{p}^{3}-0.0043V_{p}^{4}+0.000106V_{p}^{5}\end{array}  \tag{4}\]  
Here, the unit of \(\rho\) is \(g/cm^{3}\). The above method can generate a complex geological model with any layer number, but in our study, we randomly generated a 20-layer model and then interpolated it into a 100-layer smooth velocity model with a spacing of 0.5 m. The method of using the smooth velocity model is conducive to describing complex geological structures and is closer to the velocity variation characteristics of the near surface.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
###### Abstract  
For system identification problems associated with long-length impulse responses, the recently developed decomposition-based technique that relies on a third-order tensor (TOT) framework represents a reliable choice. It is based on a combination of three shorter filters, which merge their estimates in tandem with the Kronecker product. In this way, the global impulse response is modeled in a more efficient manner, with a significantly reduced parameter space (i.e., fewer coefficients). In this paper, we further develop a Kalman filter based on the TOT decomposition method. As compared to the recently designed recursive least-squares (RLS) counterpart, the proposed Kalman filter achieves superior performance in terms of the main criteria (e.g., tracking and accuracy). In addition, it significantly outperforms the conventional Kalman filter, while also having a lower computational complexity. Simulation results obtained in the context of echo cancellation support the theoretical framework and the related advantages.  
Kalman filter; system identification; echo cancellation; third-order tensors; tensor decomposition; low-rank approximation
## 6 AOG Downtime Reduction and Implementation Results
### Survey Observations and Results
The pie chart (see Figure 9) shows that Improved Data Sharing ranked as the top priority (30%), stressing the need for better communication across departments. Closer Collaboration with Suppliers followed at 26.7%, emphasizing the importance of reliable partnerships. Advanced Forecasting Tools accounted for 23.3%, highlighting the demand for predictive analytics to anticipate spare part needs. The remaining 20% included tailored solutions specific to departmental needs. Technical Records contributed the highest participation at 20%, reflecting its central role in compliance and maintenance records. MCC and Supply Chain each accounted for 16.7%, underscoring their importance in logistics and operational continuity. The findings highlight the need for SmartLynx to focus on enhancing data sharing, strengthening supplier relationships, and improving forecasting strategies to tackle spare parts challenges and reduce AOG events effectively. Figure 8: Evaluation of spare parts inventory system effectiveness by departments and spare parts delays caused by flight cancelation. Figure 9: Key tools for optimizing spare part procurement. As a continuation of the previous analysis on spare part procurement tools, another histogram (Figure 10) was addressed to evaluate the accuracy of predictive maintenance in spare parts, rated by respondents on a Likert scale from 1 (Very Inaccurate) to 4 (Very Accurate). The survey results reveal that 40% of responses fall within the 2.0-2.9 (Somewhat Inaccurate) range, highlighting that many departments find the predictive maintenance system below expectations. Notably, departments like the Supply Chain and Maintenance Control Center contributed significantly to this category, reflecting their challenges in ensuring timely spare part availability. The next most common rating, 3.0-3.9 (Accurate), garnered 36.7% of responses, indicating moderate satisfaction, with notable contributions from Technical Records, Planning, and System departments, which rely heavily on accurate forecasting. The highest rating, 4.0-4.5 (Very Accurate), accounted for 13.3%, mainly from Technical Records and Supply Chain, showing that only a few departments find the system highly reliable. Conversely, 10% of responses were in the 1.0-1.9 (Very Inaccurate) range, largely from the Structures and Maintenance Control Center, emphasizing their struggles with inaccurate forecasting. These results indicated that while some progress has been made in enhancing predictive maintenance capabilities, there are clear gaps in accuracy and reliability that need addressing. For SmartLynx, this reflects an ongoing need to refine predictive tools and processes, ensuring spare parts are anticipated and available more effectively, resulting in avoiding frequent AOG occurrences.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Proactive Approach to Road Safety  
The proactive approach focuses on identifying and mitigating road safety risks before accidents occur. Unlike reactive methods that rely on historical crash data, proactive methods assess the intrinsic safety of roads by analyzing geometric features, traffic flow, and speed. This enables the estimation of potential fatalities, serious injuries, and the associated social costs, supporting the design of targeted interventions to improve safety. For this matter, the iRAP framework has been adopted.  
The International Road Assessment Program (iRAP) combines five tools (i.e., crash risk mapping, star ratings, FSI, SRIP, and performance tracking) to enhance road safety globally, particularly for VRUs.  
The assessment of road characteristics is based on \"coding\", which involves analyzing georeferenced images sampled every 100 m along the road segment. These images, combined with traffic flow data and field measurements, form the foundation for safety evaluations. The steps of iRAP methodology are presented in Figure 3.
###### Abstract  
Turbulence and attenuation are signal degrading factors that can severely hinder free-space and underwater OAM optical pattern demultiplexing. A variety of state-of-the-art convolutional neural network architectures are explored to identify which, if any, provide optimal performance under these non-ideal environmental conditions. Hyperparameter searches are performed on the architectures to ensure that near-ideal settings are used for training. Architectures are compared in various scenarios and the best performing, with their settings, are provided. We show that from the current state-of-the-art architectures, DenseNet outperforms all others when memory is not a constraint. When memory footprint is a factor, ShuffleNet is shown to performed the best.  
convolutional neural networks; orbital angular momentum; underwater communications 2020 ac  
Several data sets under varying environmental conditions are used for this effort. In free-space, three sets of data are collected at different turbulence levels. In water, four sets of data are collected at various attenuation levels. All tests are performed on specific combinations of these data sets.  
Contributions of this paper include a comparison of recent, state-of-the-art CNN architecture in both turbulent free-space and attenuated underwater OAM communications. Baseline performance, inter-set performance, and parameter count are analyzed. At the end of the analysis, the best performing architectures, along with their parameters, are provided.