esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 5 Conclusions  
Motivated by the gap in the present literature and with little attention directed towards selecting good sampling times for IM applications, this paper shows a procedure for determining the optimal sampling time for induction machine parameter estimation. The procedure includes determining the poles of the induction machine linearized model by fitting the fifth order complex exponential series (Prony approximation) to the measured line start. The problem of fitting the Prony exponential series to the nonlinear dynamics is solved as an optimization problem that results in the optimal time interval in which the linearization remains accurate. From the computed Prony poles the optimal sampling is determined.  
The computation demands for the procedure include solving multiple rank deficient matrix equations using singular value decomposition in each iteration of the optimization procedure thus the computation can be very demanding. Since the computation demand is closely related to the input data and optimization boundaries it can be reduced to acceptable levels with good selection of initial sampling time and the boundaries to optimization.  
The presented procedure for obtaining optimal sampling time is limited to the line start transient of the induction motor, however different transients in combination with different pole estimation algorithms are encouraged for future research.  
Figure 6: Motor parameters (p.u.) estimated for each interval with (**a**) original sampling time and (**b**) optimal sampling time.  
The results show sampling times of 1.4981 ms for the 4 kW induction motor (computed from the real measurements) and 1.774 ms for the 132 kW simulated motor are the optimal selection. A test on existing parameter identification procedure shows that using the optimal sampling time produces similar results as the original sampling time but with over 10 times less data used.  
Conceptualization, T.B.; methodology, T.B.; software, T.B. and T.V.; validation, T.B., V.J.S.; formal analysis, M.B. and V.J.S; investigation, T.B.; resources, T.B.; data curation, T.V.; writing--original draft preparation, T.B.; writing--review and editing, T.B. and V.J.S.; visualization, T.V.; supervision, M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the Croatian Science Foundation under the project number UIP-05-2017-8572.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
IM Induction machine RMS Root Mean Square
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 4 Results and Discussion  
According to the experimental methods and testing parameters, two sets of test data were obtained using the PDPA experimental system: One set compared the effects of the suppressant solutions on spray atomization, while the other compared their effects on the coupling efficiency between dust and mist particles.
## 3 Results
### Environmental Exposure  
VSM scans were completed in individual cured adhesive resin samples (non-bonded) with concentrations of 5, 10, and 15 vol% MENs before and after environmental exposure, with the results shown in Figure 9. Before exposure, the doped adhesive showed an increasing magnetic moment with increased concentration (3.7 \(\times\) 10\({}^{-5}\) emu for 5 vol% MENs, 10.2 \(\times\) 10\({}^{-5}\) emu for 10.2 vol% MENs, and 15.5 \(\times\) 10\({}^{-5}\) emu for 15 vol% MENs). After exposure, the signal saturated and was consistent over varying MEN concentrations (magnitude approximately 15.0 \(\times\) 10\({}^{-5}\) emu).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \multicolumn{5}{c}{**Peak Tensile Stress (MPa)**} & \multicolumn{2}{c}{**Std. Dev.**} & **Avg. Stress** \\ \cline{2-7}
**Type/Sample \#** & **1** & **2** & **3** & **4** & **5** & **(MPa)** & **(MPa)** \\ \hline Baseline (2R:1H ratio) & 38.09 & 36.61 & 36.10 & 38.44 & 35.00 & 1.28 & 36.85 \\ \hline Doped (5 vol\% MENs) & 38.18 & 36.32 & 42.56 & 40.58 & 43.84 & 2.76 & 40.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum tensile stress in undoped and doped adhesives with 5 vol% MENs.  
Figure 8: In situ images of an ENF specimen of (**A**) the crack tip region prior to loading; (**B**,**C**) the deformed crack tip region.  
As illustrated in Figure 4b, water ingression had the opposite effect on the magnetic moment. This can occur since the polarization of the surface charge of MENs can experience an increase due to the ingression of polar H\({}_{2}\)O molecules. As the concentration of MENs increases, the magnetic moment can result from both the increased amount of magnetic material and the agglomeration of MENs, resulting in a higher percentage of MENs with uncoated free surfaces. After exposure, the saturation point of the magnetic moment was most likely the result of water ingression being a surface-area-dominated phenomenon.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 2 Related Works
### Attention Routing  
The ideas behind CapsNets share similarities with those of attention mechanisms, initially introduced in transformers [20]. Attention between capsules [21] replaces dynamic routing with a convolutional transform and attention routing, thus utilizing fewer parameters. The inclusion of a dual attention mechanism after the convolution layer and primary capsules in [22] enhanced the performance of the CapsNets. The use of a self-attention mechanism in [23] allowed for alternative non-iterative routing, efficiently reducing the number of parameters while maintaining effectiveness.
## 4 Numerical Examples and Discussion  
### Verification of Moisture Transport Analysis  
In this section, the analysis is performed to validate the numerical model for the moisture analysis and to calibrate the important parameters for the analysis such as the diffusion coefficient of concrete and the moisture loss at the surface of the member (the evaporation rate). [PERSON] et al. [2] performed a parametric moisture analysis and verified the analytical results using experimental data measured at the Yuji River Bridge located in Chongqing, China. They measured the RH inside the top slab of the box girder section with digital sensors with an accuracy of \(\pm 2\%\). The sensors were placed on the top slab of midspan section (Figure 2(b)) at depths of 0, 30, and 100 mm from the top slab surface. The bridge is a cantilever bridge with a free span length of 115 m. The bridge girder has a height of 14.98 m at the fixed end and 4.5 m at the other end. The top slab is 11 m wide. The girder has bottom slab and web thickness of 0.77 m and 0.9 m, respectively, at mid-span. An initial prestressing force of 3375 kN and 4312 kN is applied using 18 and 23 diameter 15.2 tendons at the top and bottom slab, respectively. Modulus of elasticity of the concrete was reported to be 34.5 GPa [2].  
In this study, these measurements were used to calibrate and verify the concrete parameters for the moisture transport analysis. A half-section of the bridge with dimensions identical to the mid-span cantilever section of the Yuji River Bridge was modeled. The boundary condition was assigned such that moisture loss was allowed on all surfaces in contact with air, including the internal side of the box girder. The evaporation rate was calibrated to fit the experimental data. The atmospheric RH was assumed to be 70%. For the sake of simplicity, the diffusion coefficient of the concrete was assumed as constant and was calculated based on the atmospheric RH value.  
Figure 2(a) shows a comparison of the RH changes with time based on analysis and measurement at depths of 30 and 100 mm. The diffusion coefficient of the concrete calculated for the assumed atmospheric RH value and the evaporation rate at the surface with a value of \(3\times 10^{-9}\) m/s gave the best results. The figure shows that the results are in reasonably good agreement with the measured values as reported by [PERSON] et al. [2]. The moisture distribution at the half-section of the bridge at 2000 days is presented in Figure 2(b). It can be seen that the moisture content varies across the section according to the thickness of the members and exposure conditions. Hence, the shrinkage strain varies along the depth from the surface, resulting in additional curvature and deflection.  
Figure 3: Comparison of relative humidity change with time and distribution of relative humidity at the mid-span of cantilever section.
## 3 LADRC Controller Design
### Linear State Error Feedback Controller  
Traditional PID controllers use error integration to eliminate static errors, but the feedback from error integration can make the system prone to oscillation. In contrast, the LESO (Linear Extended State Observer) employs real-time compensation for total disturbances, avoiding the negative effects of integral feedback. The result is shown in Equation (19) [18]:  
\[\left\{\begin{array}{c}e_{1}=\psi_{d}-z_{1}\\ e_{2}=\psi_{d}-z_{2}\\ u_{0}=k_{p}e_{1}+k_{d}e_{2}\end{array}\right. \tag{19}\]  
In the equation, \(\psi_{d}\) is the reference heading angle input, \(u_{0}\) is the error feedback control quantity, and \(k_{p}\), \(k_{d}\) are the controller gains. According to Equation (19), the transfer function of \(u_{0}\) concerning \(r\) can be obtained:  
\[\frac{U_{0}(s)}{R(s)}=\frac{k_{p}+k_{d}s}{s^{2}+k_{d}s+k_{p}} \tag{20}\]  
By setting both poles of the controller on the real axis at \(-\omega_{c}\) (where \(\omega_{c}\) represents the control bandwidth) in the left half of the \(s\)-plane, the controller gain can be determined as [18].  
\[\left\{\begin{array}{c}k_{p}=\omega_{c}^{2}\\ k_{d}=2\omega_{c}\end{array}\right. \tag{21}\]  
Based on \(u_{0}\), an additional compensation term for the total disturbance estimate is added, so the control quantity can be taken as:  
\[\tau_{\psi}=\frac{-z_{3}+u_{0}}{b_{0}} \tag{22}\]  
In Formula (22), \(u_{0}\) is the error feedback control quantity, and \(\tau_{\psi}\) is the actual input of the controlled object.
## 4 Conclusions  
Through the laser-cleaning test on the surface of corroded 20 steel, the effect of laser energy density on the surface morphology, surface elemental composition, surface roughness, surface microhardness and surface corrosion resistance of corroded 20 steel was investigated, and the effect of laser descaling mechanism and ablation on the surface of 20 steel was analyzed. The conclusions of this study are as follows:  
1. Through this study, at the laser energy density of 1.42-4.26 J/cm\({}^{2}\), the removal of the rust layer on the surface of 20 steel is effective, especially at 4.26 J/cm\({}^{2}\): the surface rust layer is basically removed without causing damage to the sample surface.
2. Infrared nanosecond pulsed laser can effectively remove the rust layer of 20 steel. The removal mechanism of the rust layer is mainly ablation. When the laser energy is higher than 4.26 J/cm\({}^{2}\), the substrate absorbs too much laser energy and melts the substrate.
3. Compared with the traditional cleaning method, when the laser energy density is 4.26 J/cm\({}^{2}\), with a spot overlap rate of 75%, laser cleaning not only does not reduce the corrosion resistance of 20-gauge steel but also improves the corrosion resistance of the original 20-gauge steel 1.218 times.
4. The morphological evolution of the sample surface after laser ablation at high energy density with different spot overlap rates is discussed; the key lies in the different cooling times of the molten material at different laser-scanning speeds at high energies, which leads to different distributions of the molten material flow around. This evolution provides a reference for the subsequent topographic changes of laser-cleaned substrates.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14052058/s1](https://www.mdpi.com/article/10.3390/app14052058/s1)]([https://www.mdpi.com/article/10.3390/app14052058/s1](https://www.mdpi.com/article/10.3390/app14052058/s1)), Figure S1: 20 steel surface SEM map with different laser cleaning parameters.  
Methodology, C.H.; Validation, C.H., C.Y. and J.W.; Formal analysis, C.Y.; Investigation, L.D.; Resources, C.H.; Data curation, C.H. and J.L.; Writing--original draft, C.H.; Writing--review & editing, H.Y.; Supervision, C.L.; Project administration, L.F.; Funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Project Program of Wuhan National Laboratory for Optoelectronics (2022 WNLOKF015), the School-enterprise Cooperation Project of Shenzhen Technology University (HT20220613002), Shenzhen Key Project for Technology Development (JSGG20220831110800002), Shenzhen Science and Technology Innovation Commission Project (29853M-kCJ-2023-002-09), and the Stable Support Project of Shenzhen Higher Education Institutions (SZWD2021008, SZWD2021009), the Key Program of Natural Science Foundation of Shenzhen (Grant No. JCYJ20200109115403807).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Verification of Optimised MES Performance  
The open circuit experiment was carried out at feed pH 7.0 with the potentiostat turned off and for the MES experiment, a cathode potential of \(-\)0.65 V was provided keeping the same feed. The average MPR (Figure 7) during open circuit operation (10 days) was measured to be \(18.4\pm 1.3\) mmol/L(reactor)-day and for the MES mode operation was around \(20.9\pm 0.9\) mmol/L(reactor)-day. This is an increase of approximately 13.6% in MPR due to the supply of electricity. Highest MPR of about 19.1 mmol/L(reactor)-day was obtained during open circuit operation whereas during MES mode, the peak MPR was about 22.1 mmol/L(reactor)-day. Further, COD analysis (Figure 7) during open circuit operation reveals HMY to be approximately 93.5% of COD consumed (607 mL/649.3 mL) whereas for MES mode, HMY was around 107.7% (623.6 mL/578.7 mL). The extra methane produced represents electrochemical CO\({}_{2}\) reduction to methane, which is 98.9% (44.9 mL/45.4 mL) efficiency in supplied electrons to methane conversion. At the same time, the methane concentration (Figure 7) in biogas for open circuit operation is consistently below 90% whereas for MES operation it has been consistently above 90%. High methane percentages in open circuit operation indicate CO\({}_{2}\) solubilisation within the reactor and therefore, require  
in alkalinity' or the change in amount of dissolved carbon dioxide within the reactor compared to the feed W\({}_{\text{a}}\). It can be observed that during open circuit operation the increase in alkalinity was more than the increase during the MES mode of operation. A similar trend can be seen with effluent pH (Figure 8) which also decreased as the experiment progressed towards electrochemical operation. These trends denote a decrease in CO\({}_{2}\) solubility within the reactor. Combined with high methane percentages in produced biogas, these results indicate electrochemical CO\({}_{2}\) conversion to methane. However, during the MES operation, the average current production remained very low at approximately 2 mA. Further attempts are being made to divert more microorganisms towards electrochemical methane production and increase current density. More importantly, new strategies must be developed to extract more energy-rich compounds from the carbon dioxide fed or produced within the reactor. This could mainly be done by controlling the pH within the reactor and keeping it less than 8.0 while maintaining the feed pH at 7.0. It will also be interesting to look at the effects of decreasing HRT on MES performance, as increased flow rate could control pH effectively.
## 2 Theoretical Background
### RBF Mesh Morphing  
Mesh morphing is a well-established computational technique in finite element analysis (FEA), enabling the modification of an existing computational mesh to accommodate changes in geometry. Unlike traditional parametric CAD-driven approaches, which require remeshing for each new geometry, mesh morphing directly manipulates the mesh itself. This technique offers two significant advantages: (i) enhanced control over mesh quality and element distortion, and (ii) reduced computational cost, particularly when exploring design alternatives through DoE-based optimization.  
The primary goal of mesh morphing is to preserve both mesh quality and connectivity while adapting the geometry to new configurations. This aspect is particularly critical when building ROMs, as maintaining node numbering and connectivity across all variants is essential for consistent model reduction.  
In this study, RBF-based morphing is employed. RBFs interpolate a scalar function, defined at discrete source points, across the entire computational domain. The displacement field \(s(\mathbf{x})\) is defined as:  
\[s(\mathbf{x})=\sum_{i=1}^{N}\gamma_{i}\phi(\|\mathbf{x}-\mathbf{x}_{s_{i}}\|)+h (\mathbf{x}) \tag{1}\]  
where \(N\) denotes the number of source points, \(\gamma_{i}\) are the interpolation weights, \(\phi\) represents the selected radial basis function, \(\mathbf{x}\) is the position vector of a generic mesh point, \(\mathbf{x}_{s_{i}}\) identifies the coordinates of the \(i\)-th source point, and \(h(\mathbf{x})\) is a polynomial correction term whose order depends on the specific basis function.
## 4 Experimental Results and Discussion
### Future Work  
The primary future work involves conducting real-world case studies, as the defects observed in our experiments were common surface defects artificially created, and other more complex defects were not considered. We need to further validate the robustness of the proposed method. Additionally, one of our upcoming publications will focus on deep learning approaches, which, thanks to the powerful feature extraction capabilities of neural networks, generally outperform traditional methods. Our goal is to use defects identified through the current method as labels to train the neural network, rather than relying on manually created datasets, which is a time-consuming process. We also plan to develop a new automated solution for input parameter initialization, as different types of magnetic tiles require different input parameters. Notably, our method exhibits lower robustness to stains on the magnetic tile surface, due to the inherent color of the tiles. Although stains are visible to the naked eye, their reflectivity is nearly identical to the tile surface, causing them to be absent from the collected data. Since stains typically only affect aesthetics and have negligible impact on product performance, we ignored them in this study, but we may explore the use of newly developed laser scanners to address this limitation. Finally, we will focus on the challenge of handling large datasets, as overly dense point cloud data have been shown to be unnecessary, imposing a significant computational burden. Several downsampling methods have been proposed to reduce redundant data while preserving accuracy, though there is still substantial room for improvement.
## 4 The Principle of the Balanced Cross-Layer Fuzzy Logic Routing Algorithm
### The Calculation of the Utility of Each Node
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