esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 6 Micromegas Readout of TPCs
### Minos  
Another example of the use of Micromegas as a readout for TPCs is the vertex tracker of the MINOS nuclear physics instrument (MagIcal Numbers Of Shell). In nuclear physics, magic numbers are a certain configuration of nucleons on one shell that allows an extraordinary stable nuclear shell. These magic numbers allow forming very heavy and stable nuclei outside the valley of stability. The MINOS instrument is aimed to improve the luminosity of such experiments by coupling a 10 to 15 cm thick liquid hydrogen cryogenic target with a TPC used to localize the vertex of the knock-out reactions inside the target. This localization is required to perform the Doppler corrections on the energy of the emitted \(\gamma\) measured in the spectrometer surrounding the instrument (see Figure 15).  
MINOS uses a compact cylindrical annular Micromegas-based TPC (Figure 16a) that surrounds the liquid hydrogen target. The detector is a 4 cm ring, pixelated, bulk Micromegas with 3604 pads of 4 mm\({}^{2}\) (Figure 16b) and is readout by an AGET based Front-End Electronics [73], deported 80 cm away by 47 pF capacitance micro-coax cables. This instrument achieves a typical vertex reconstruction resolution of 3 to 4 mm [74]. This unique instrument is regularly used for exotic nuclei experiments at RIBF (RIKEN), such as the one dedicated to the study of the neutron-rich 'doubly magic' 78 Ni isotope of nickel [75].  
Figure 14: Encapsulated Resistive Anode Micromegas (ERAM) for the T2K/ND280 High-Angle TPCs.  
Figure 15: The MINOS instrument.
## 1 Introduction
In this study, an ATP model with two degrees of freedom (DoFs) is presented and the pointing error (Figure 1), i.e., the difference between the satellite position and the pointed target, is analyzed in order to verify the possibility of enabling data exchange between ships and LOS satellites. The considered model accounts for ship motions and the coordinates of low-orbit satellites, taking into consideration the acquisition errors associated with ship motions and satellite coordinates during the acquisition phase. The minimum pointing accuracy requested by the optical system is 100 \(\upmu\)rad to reach the satellite's locking area. Achieving a high pointing accuracy for shipborne ATP systems during the initial pointing process is challenging. These challenges arise from factors like random and sharp waggling along with the sea, especially in several sea conditions. The study introduces an innovative methodology to improve the pointing accuracy to higher than 100 \(\upmu\)rad through the application of celestial vault-scanning strategies. The results obtained are used to size the optical system and verify the feasibility of the scanning strategies through the piezoelectric components within the optical system. Figure 1: Pointing error.
# Defect Detection and Error Source Tracing in Laser Marking of Silicon Wafers with Machine Learning
## 1 Introduction
A femtosecond laser was used to manufacture an anti-reflective subwavelength structure (ASS) with ultrahigh transmittance on the surface of infrared window materials directly. [8] proposed a design, manufacturing, and characterization method that can produce an ultrahigh-performance infrared window by a femtosecond laser Bessel beam. The ablation characteristics and intrinsic mechanism of alumina ceramics under the ablation of the long focal condition CW laser were comprehensively revealed through systematic process comparison experiments, high-speed shadow imaging, and theoretical simulation to ensure the design of ceramic-based high-energy laser protection materials [9]. Quality control in high-volume fabs is highly automated. Laser marking stations are typically integrated with wafer handlers and robots (e.g., FOUP loaders and conveyors) to eliminate manual handling [10]. For example, Nikon's system automatically transfers wafers from a pod to a confocal meter without human contact. Many marking tools include built-in visual alignment tools [11]. For example, Pentamaster's PM93 handler uses a granite anti-vibration base and advanced machine vision alignment, such that each wafer is perfectly positioned before marking. After marking, these systems capture a live video feed for the operator and perform an automatic post-mark inspection to verify code accuracy and quality [12]. Artificial intelligence (AI) is playing an increasingly important role. Modern vision systems use deep learning neural networks to improve defect detection and code reading. Keyence and Cognex, for example, offer \"smart\" vision tools that can be trained on large datasets to distinguish true defects from allowable variations [13]. These AI-based inspectors can learn complex patterns (even on transparent or multilayer wafers) and achieve a higher accuracy in picking up scratches, stains, or alignment errors. Cognex's In-Sight D900 uses deep learning to automatically screen many wafers for anomalies (even amid complex layer backgrounds), passing ambiguous cases to a human reviewer [14]. Similarly, AI-based segmentation (e.g., SolVision) has been demonstrated to find very fine scratches and polishing defects on wafer surfaces that rule-based AOI might miss [15]. In summary, the combination of automated handling, high-speed vision sensors, and AI algorithms enables real-time sorting of wafers and rapid feedback on marking quality. These examples demonstrate the industrial trend toward fully automated vision-guided marking lines with feedback. Fabs can enforce marking quality to meet SEMI standards and minimize scrap by combining metrology (confocal or camera), AI software, and robot handling. However, these state-of-the-art technologies do not possess an error source tracing function, which is an improvement of this study.
## 3 Results
### Machine Learning  
We deployed t-SNE to visualize the distribution of the signals and found that they tend to cluster according to certain age segments but cluster for each subject much more densely (see Figure 11). We also performed a PCA on the same signals to quantify the clustering. Figure 12 visualizes the first three principal components (PCs) of the PCA, which shows that the signals cluster strongly according to age segments. Even though there are some overlapping data points, the overall distribution indicates the data points belonging to the age segment > 19 years cluster more densely than the data points belonging to other age segments. Figure 13 provides an overview of the ratio of explained variance of the first 10 PCs. These analyses indicate that A-scans from similarly aged subjects tend to cluster together in high-dimensional spaces. The evaluation metric of our ML models was the F\({}_{1}\) score, which is defined as the harmonic mean of precision and recall. Table 1 shows a comparison of the performance of different ML methods by comparing the times needed to train and evaluate each model and the average F\({}_{1}\) score for certain subsets of signals that were acquired from transducer #3 with a burst excitation with a length of 3 periods at US center frequency. Figure 10: Plot of integral values of transmission signal changes during motor-based transducer motion (y-axis) in relation to subject bone age (x-axis) using the most distal transducer element. Each circle represents a single individual. Figure 11: Two-dimensional t-SNE visualization showing the interrelation of all A-scans. Color codes for bone age groups: green \(\leq\) 17 years, yellow = 17 to \(<\)19 years, and red \(\geq\) 19 years. Figure 12: Three-dimensional PCA showing the relation of all A-scans to each other. Green dots represent A-scans belonging to a subject younger than 17 years, orange dots represent A-scans belonging to a subject between the age of 17 and 19 years, while red dots represent A-scans belonging to subjects being 19 years or older. Previously, we achieved an average F\({}_{1}\) score of approximately 84% with a ResNet implementation for all subjects [37]. To increase the robustness and improve the times for training and evaluation, we conducted further analyses, including different input data types (raw unprocessed and processed 1D US RF signals) and a variety of different ML models. This resulted in a total average F\({}_{1}\) score of approximately 87% using a CatBoost model with 10,000 iterations, which took ca. 26 h to complete. A CatBoost model training for 1000 iterations performed only slightly worse and achieved a total average F\({}_{1}\) score of ca. 86% in roughly 2 h and 40 min. XGBoost achieved the third-best total average F\({}_{1}\) score of ca. 85% in less than 5 min. The total average F\({}_{1}\) score of all other models was significantly less. We examined several age segments separately to illustrate our model's feasibilities for different scenarios. Notably, the age segments > 18 years and >19 years of bone age
## 4 Results and Discussion
### Analysis of Radionuclides
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country** & **Region** & **No. of Samples** & **\({}^{226}\)Ra** & **\({}^{232}\)Th** & **\({}^{40}\)K** & **Reference** \\ \hline \multirow{8}{*}{Constration} & Variety of Aswan Granite & 29 & 11–89 & 4–97 & 210–1484 & Present study \\  & [PERSON]. Qash Amir & - & 6.4–65.3 ppm & 11.4–43.8 ppm & 3.2–7.5 ppm & [29] \\  & [PERSON]. El Sela & - & 103–2047 & 12.4–101.2 & 831.6–1394.6 & [30] \\  & [PERSON]. Allaqi & 11 & 9–111 & 8–75 & 119–790 & [31] \\  & Abu Ziran & 6 & 19–34 & 11–15 & 216–274 & [31] \\  & Gattar (GII) & 50 & 165–27,851 & 71–274 & 1048–1230 & [32] \\  & Gattar (GV) & 15 & 174–50,378 & 51–902 & 640–1841 & [33] \\  & El-Missikat & 9 & 12.4–534.4 & 56.6–169.8 & 398–1113 & [9] \\  & Wadi El-Gemal & - & 25–59 & 28–759 & 970–1280 & [34] \\  & Ras Baroud & - & 11.5–172.8 & 18.4–103.4 & 87.9–454.7 & [35] \\ \hline \multicolumn{2}{c}{Different countries all over the world} & 167 & 0.2–160 & \(<\)4–253 & \(<\)36–2355 & [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of activity concentrations of the natural radionuclides (\({}^{226}\)Ra, \({}^{232}\)Th and \({}^{40}\)K) (Bq kg\({}^{-1}\)) in granite samples of the present study with those obtained in other published data.
## 1 Introduction
Layer 1 and 5 are top and bottom covers; layer 3 is the central layer with transitions to planar lines; layers 2 and 4 separate the central layer from the covers. (**a**) 3D view of layers before assembling; (**b**) Cross-section of the ESICL after assembling. be achieved, for instance, by varying the height of a rectangular waveguide. If we vary the width of the rectangular waveguide, the cutoff frequency changes, and so it is not suitable for the traditional synthesis techniques developed for continuous profile filters. Most one-conductor substrate integrated transmission lines (SIW, ESIW, AFSIW, etc.) are H-plane structures, where the width of the rectangular cavity can be changed, but the height is fixed throughout the structure (the height of the substrate). Some efforts have been made to apply the traditional synthesis technique for continuous profile filters in H-plane structures (SIW filter) in Reference [31; 32]. Although good results have been obtained, the synthesis does not provide as accurate results as when the phase constant does not change along the propagation direction. For that reason, an ESICL, with a TEM propagating mode, is an excellent candidate to implement continuous profile filters in substrate integrated waveguide technology (with lower losses than both in planar and classical SIW technologies). Although the ESICL technology is already known, and continuous profile filters have already been studied, no implementation of an ESICL filter with continuous profile has been reported to date, and so the suitability of ESICL for continuous profile filters remains unknown. In this work, a simple continuous profile filter is implemented in an ESICL for the first time. The variation of the filter response with the design parameters is studied. A prototype is successfully manufactured and measured, and a sensitivity analysis is performed in order to test the resilience to manufacturing tolerances of this type of filter in this novel transmission line.
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 3 Limit State Determination
### Divided the Limit State  
According to Figures 6-8, the stress state of the transmission tower from the elastic state to final failure can be divided into the following four limit states [29; 30]:  
(1) When the loading ratio is 0-50%, the stress ratio curves of the members increase linearly. At this stage, RM remains in the elastic stage. However, the bolt slippage occurs for JM when the loading ratio is 25-36%. When the loading ratio reaches 50%, the maximum stress ratio of the compressed diagonal members is 0.65, and the top displacements of the RM and the JM are 0.62 m and 1.13 m, respectively. At this time, the stress state of the transmission tower is identified as the basically undamaged limit state LS1;  
(2) When the loading ratio is between 50% and 68%, the slope of the stress ratio curve for the compressive diagonal members in section A2 changes, and the members start to enter the plastic stage. When the loading ratio is 68%, the maximum stress ratio of the compression diagonal member is 1.15, and the top displacements of RM and JM are 0.83 m and 1.68 m, respectively. At this point, the state of the transmission tower is defined as the limit state LS2, indicating that the tower has suffered minor damage;  
(3) When the loading ratio reaches 98%, the tower top displacements for RM and JM are 1.15 m and 2.2 m, respectively. The transmission tower model is severely deformed. At this time, the state of the transmission tower is defined as the limit state LS3, indicating that the tower has suffered serious damage;  
(4) When the loading ratio is 100%, the tower top displacements of RM and JM reach 1.25 m and 2.35 m, respectively. The tower loses its bearing capacity and is in a collapse state, which is defined as the limit state LS4.  
Table 3 shows the tower top displacement at different limit states of the TTLS under three different lateral loads. In this paper, the average value of tower top displacement is selected as the limit state index of the tower-line system. The difference in the tower top displacement between the JM and the RM is significant. Therefore, to accurately assess the seismic fragility of TTLSs, it is not feasible to simply divide the limit states of tower-line systems considering the bolt slippage effect using the limit performance index of the RM.  
Figure 8: The failure members of the tower: (**a**) RM; (**b**) JM.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 4 Results and Discussion  
### Electrical Energy Forecasting  
One-year wind power and load historical data, which are shown in Figure 4, of the hybrid system are used to predict the output power of the diesel generation.  
In this paper, a sodium-sulfur battery is used as the energy storage unit and the slowest battery charge/discharge rate is 0.5 C. Three types of forecasting methods are studied and conducted to calculate the time series of the outputs of the generations. The total capacity of the ESSs is determined herein. The performances of the predicting methods are compared, and the results are depicted in Table 1.  
It can be seen from Table 1 that, with the help of the NARX-BPNN method, the total capacity of the ESS is reduced from 1465.68 MWh to 54.4 MWh with a minimum variance of 0.00019. Compared with the other prediction model, not only the maximum deviation but also the mean deviation of the dynamic prediction method achieves the lowest, which is nearly 4 times less than that of the persistence model. Figure 5 presents the performance of the NARX-BPNN method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Network Type** & **Capacity (MWh)** & **Maximum Deviation** & **Mean Deviation** & **Variance** \\ \hline No prediction & - & 1465.68 & 100\% & 21.34\% & 0.00686 \\ Persistence model & - & 226 & 52.09\% & 8.58\% & 0.009 \\  & Standard BP & 217.48 & 50.13\% & 5.28\% & 0.00695 \\  & Variable gradient BP & 213.56 & 49.23\% & 5.30\% & 0.00634 \\ Static prediction & BFGS & 205.84 & 47.45\% & 5.13\% & 0.00562 \\  & Conjugate gradient BP & 209.57 & 48.31\% & 5.02\% & 0.00533 \\  & LM & 201.07 & 46.35\% & 4.72\% & 0.00484 \\ Dynamic prediction & NAR BP & 56.52 & 13.03\% & 0.86\% & 0.00022 \\  & NARX BP & 54.4 & 12.54\% & 0.89\% & 0.00019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy storage system (ESS) capacity based on different forecasting methods.  
Figure 4: One-year data of wind generation and load.
## 3 Data and Methodology
### Resampling Strategy and Correlation Analysis  
In statistical learning, an experiment with the purpose of comparing and ranking several algorithms in terms of a certain performance measure is referred to as a benchmark experiment. The resampling strategy is usually utilized to generate point estimations for the performance, which allows for an observation on the performance distribution of an algorithm on resampled subsets [75]. In this work, resampling was used as a pretreatment method for evaluating the correlation between candidate algorithms (Figure 6).  
To be specific, when an ensemble model was simulated and constructed in the training stage, the training data could be randomly divided into ten folds to test single base learners. Each fold data was used to produce a prediction by a base learner, and then its prediction accuracy on this fold was recorded. This processing was repeated with \(\mathrm{n}\) rounds. In other words, \(\mathrm{n}\times 10\) different subsets with the same size were randomly resampled from the initial training data, and thus \(\mathrm{n}\times 10\) accuracy values were obtained for each base learner. In this study, we set \(\mathrm{n}\) as 2, 3, and 4 to produce 20 resamples, 30 resamples, and 40 resamples, respectively. Recalling the processing of resampling, for each resampled subset, four base learners produced prediction in parallel. As resampling proceeded, a series of point estimates of prediction accuracy was derived. Thus, the correlation between base  
Figure 6: The flowchart of the resampling strategy and correlation evaluation. ACC: accuracy.  
learners could be measured according to the variation degree of those prediction accuracy pairs over the change of subset.  
[PERSON]'s correlation analysis is considered as an effective method to estimate the linear correlative degree between variables [76]. Therefore, the correlation between those base learners was measured using [PERSON]'s correlation coefficient (PCC) in this study. The PCC between two base learners \(L_{i}\) and \(L_{j}\) is defined as below:  
\[PCC\big{(}L_{i},L_{j}\big{)}=\frac{cov\big{(}ACC_{i},ACC_{j}\big{)}}{\sqrt{var (ACC_{i})\times var\big{(}ACC_{j}\big{)}}} \tag{10}\]  
where \(cov\big{(}ACC_{i},ACC_{j}\big{)}\) is the covariance, and \(ACC_{i}\) and \(ACC_{j}\) are respectively the predictive accuracy sets of \(L_{i}\) and \(L_{i}\). \(var(ACC_{i})\), and \(var\big{(}ACC_{j}\big{)}\) are respectively the variance of \(ACC_{i}\) and \(ACC_{j}\).
## 2 Improved Homotopy Analysis Method  
### Formulations  
The proposed homotopy analysis method (IHAM) is essentially different from the HAM in view of construction methods.  
HAM constructs a high-order nonlinear iterative homotopy differential equation using selected linear differential equation and the original non-linear differential equation, whereas IHAM formulates a high-order nonlinear iterative homotopy differential equation with the selected linear differential equation, the selected simple non-linear differential equation, and the original non-linear differential equation. Table 1 lists the formulation steps of IHAM and HAM for comparison.  
To approximate the exact solution, differences also rise from their respective approaching paths. HAM approximates the exact solution of the original non-linear differential equation by iterating with linear differential equation. Whereas IHAM uses linear differential equation and simple non-linear differential equation to iterate in approximating the exact solution to the original non-linear differential equation. Thus, the convergence rate of IHAM is much higher than that of HAM. IHAM not only accelerates the convergence rate, but also enlarges the convergence range in solving strong nonlinear problems.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**HAM** & **IHAM** \\ \hline \multirow{5}{*}{Original \(\mathcal{N}(\xi)=0\) and Auxiliary \(\mathcal{N}_{0}(\xi)=0\)} & \multirow{5}{*}{\(\exists\) New \(\overline{\mathcal{N}}(\xi;q)=0\) and Auxiliary \(\mathcal{L}(\xi)=0\)} \\  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}  
* \(\mathcal{N}\)—nonlinear operator, \(\mathcal{L}\)—linear operator, \(\mathcal{N}\)—auxiliary nonlinear operator, \(\overline{\mathcal{N}}\)—new nonlinear operator, \(\mathcal{H}\)—homotopy operator, \(\xi\)—independent variable, \(q\)—embedded parameter.  
\end{table}
Table 1: Improved homotopy analysis method vs. homotopy analysis method.  
In this study, a boundary value problem is considered for second order nonlinear differential equations, as can be seen in the following form  
\[\mathcal{N}[w(\xi)]=0:w^{\prime\prime}(\xi)+f[w(\xi)]=0,\xi\in[0,1] \tag{3}\]  
Here \(f(w)\) is a derivable function with the initial values of \(f_{w}(0)=c_{0}\), \(f_{w}^{\prime}(0)=c_{1}\), and \(\frac{1}{2!}f_{w}^{\prime\prime}(0)=c_{2}\). In compliance with the boundary conditions, we have  
\[w(0)=0,\ w^{\prime}(1)=0 \tag{4}\]  
It is worth noting that the prerequisite of any small or large perturbation parameters in Equation (3) is dismissed. Thus, the proposed approach is general.  
Linearizing Equation (4) yields  
\[\mathcal{L}[w(\xi)]=0:w^{\prime\prime}(\xi)+c_{0}+c_{1}w(\xi)=0,\xi\in[0,1]: \tag{5}\]  
with the concomitant boundary conditions  
\[w(0)=0,w^{\prime}(1)=0 \tag{6}\]  
The auxiliary nonlinear operator is chosen as  
\[\mathcal{N}_{0}[w(\xi)]=0:w^{\prime\prime}(\xi)+c_{0}+c_{1}w(\xi)+c_{2}\varepsilon w ^{2}(\xi)=0,\xi\in[0,1]: \tag{7}\]  
wherein \(\delta\in[0,1]\).