esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
We trained the AI models described in Section 2.3 and assessed their performance using accuracy, recall, precision, and F1 score. The results are presented in Table 7. Based on these metrics, the FC-NN model achieved the highest scores across all evaluation criteria, leading us to select it as the best AI algorithm (step 3).  
Given the superior performance of the FC-NN, we utilized it for the classification process illustrated in Figure 8. The confusion matrix for the classification of the full MSL alphabet test dataset is shown in Figure 11, demonstrating robust performance in most cases; here, the blue color tones represent the number of each letter. However, some misclassifications occur with signs that have similar hand configurations, such as \"m\" (89%) and \"n\" (83%), which are relatively lower compared to \"a\" (97%). The primary reason for these misclassifications is the similarity between \"m\" and \"n\", where the difference is limited to the extension of a single finger. A potential solution to this issue is to incorporate a binary classifier specifically designed to distinguish between \"m\" and \"n\".  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline FC-NN & 93.64\% & 92.54\% & 92.67\% & 92.46\% \\ CNN & 80.57\% & 80.26\% & 80.90\% & 80.15\% \\ SVM & 93.14\% & 92.23\% & 92.06\% & 92.25\% \\ Random forest & 91\% & 91\% & 91\% & 91\% \\ Decision tree & 91\% & 91\% & 91\% & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Metric results of AI models.  
Figure 11: Confusion matrix of complete letters test dataset classification.  
An interesting observation is that the classification accuracy for \"Il\" (84%) is higher than for \"I\" (77%). However, when evaluating the model's performance for \"I\" in isolation, its classification accuracy increases to 98%. This discrepancy arises because the final classification decision is based on the confidence value.  
Moreover, signs with distinctive hand configurations, such as \"j\" (98%), exhibit excellent classification results. To provide a comprehensive evaluation, Table 8 presents the average accuracy, recall, precision, and F1 score across all signs, with all metrics exceeding 90%. This demonstrates the overall effectiveness of the classification approach.  
Our work has the advantage of correctly handling the letter \"K\", and for the first time in this research area, we included the dynamic letters \"LL\" and \"RR\". We achieved a precision of 91.25%, as well as high values in other key metrics reported in Table 8, which are not consistently provided in previous studies.  
Notably, the F1 score is a valuable metric for evaluating the performance of classification models, as it balances precision and recall, always ranging between 0 and 1 [23]. A high F1 score generally reflects a well-balanced model, indicating the ability to simultaneously achieve high precision and recall. An F1 score greater than 0.9 is typically regarded as excellent [24]. Our model attained an F1 score of 92.46%, objectively indicating an outstanding classification performance.
# A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements
## 4 Results and Discussion
### Duty-Cycle Effect on Anechoic and Real Environment  
The anechoic-chamber experiment helps to understand vital details behind the performance and errors of GNSS in Android devices. Mitigating a number of error sources (multipath, interference, spurious signals, etc.) by taking the test to an anechoic environment helped zoom in to normally undetectable effects. One such analysed effect in this work was the duty cycle.  
Replicating the detrending of pseudoranges to real-environment measurements shows the limits of quality assessment in such scenarios and, in turn, it justifies analysis in a controlled environment. Figure 16 reports the value of the AccumulatedDeltaRangeState flag (upper panel) and of the detrended pseudorange of GPS PRN 15 (lower panel). Even though, starting from epoch 300, cycle slips were detected, it was hard to make any conclusions about the quality of the pseudorange. This was due to the low quality of measurements under the effects of noise and other error sources, even when a close satellite with clear view from the receiver was selected. On the other hand, in the anechoic environment, it was clearly detected as the AccumulatedDeltaRangeState changed its state only when duty cycle was turned on (Figure 10).  
Figure 16: Effect of cycle slips in a real environment (PRN 11).  
Figure 15: Comparison of the positioning results in controlled (**a**) and real (**b**) environment in the presence and in the absence of bad-quality measurements.
###### Abstract  
This study presents a comprehensive numerical investigation of the Laser Powder Bed Fusion (LPBF) process for pure molybdenum, focusing on high-precision modeling and process optimization. The powder spreading behavior is simulated using the Discrete Element Method (DEM), while molten pool dynamics are analyzed through Computational Fluid Dynamics (CFD). Optimization of process parameters is performed using FLOW-3D Release 7 software in conjunction with the HEEDS-SHERPA algorithm. A total of 247 simulations are conducted to assess the effects of four critical parameters: laser power (50-400 W), scanning speed (80-300 mm/s), laser spot diameter (40-100 \(\upmu\)m), and powder layer thickness (50-100 \(\upmu\)m). The optimal parameter set--350 W laser power, 120 mm/s scanning speed, 50 \(\upmu\)m spot diameter, and 50 \(\upmu\)m layer thickness--results in an 80% laser absorption rate, a 60% reduction in micro-porosity, and over a 30% enhancement in both molten pool volume and surface area. Utilizing a fine 10 \(\upmu\)m mesh resolution enables detailed insights into temperature gradients and phase transition behavior. The findings highlight that optimized parameter selection significantly improves the structural integrity of Mo-based components while minimizing manufacturing defects, thus offering valuable guidance for advancing industrial-scale additive manufacturing of refractory metals.  
l academple Editor: [PERSON]  
Received: 7 April 2025  
Revised: 8 May 2025  
Accepted: 9 May 2025  
Published: 14 May 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON] Numerical Optimization of Laser Powder Bed Fusion Process Parameters for High-Precision Manufacturing of Pure Molybdenum _Appl. Sci._**2025**, 15, 5485. [[https://doi.org/10.3390/appl5150485](https://doi.org/10.3390/appl5150485)]([https://doi.org/10.3390/appl5150485](https://doi.org/10.3390/appl5150485))  
Copyright: 6 2025 by the authors.  
Ucosense MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 The Transversal Relation between Ground Motion Polarization and Velocity Anisotropy  
As illustrated in previous sections, directional horizontal motions can occur in fault zones as the effect of the larger compliance transversal to fractures in the fault damage zone. Moreover, in fault zones, the fast S wave direction is controlled by the crack orientation (rather than the regional stress) [69; 89; 90; 91].  
The comparison between horizontal polarization and S wave fast direction was led out, up to now, in three papers [23; 33; 34]. Since these two parameters are estimated in a completely different approach, and considering the different portions of seismograms, it represents a useful test of consistency.  
In the Val d'Agri region, [PERSON]. [91; 92] found an S wave fast direction perpendicular to the current regional extension, as the effect of open and fluid-saturated cracks in fractured carbonate rocks aligned by the active stress field. [PERSON] et al. in [33] recognized a transverse relation between horizontal polarization (red rose diagram) and fast S wave direction (cyan rose diagrams), as shown in Figure 8A. Therefore, open and fluid-saturated cracks affect both (i) the velocity anisotropy of seismic waves (they travel faster parallel to fractures), and (ii) the ground motion horizontal polarization (due to the higher compliancy in the direction orthogonal to the fractures).  
Similar findings were observed in the GF, a newly fractured strike-slip fault system [33]. In Figure 8B, we show the comparison between ground motion polarization (yellow rose diagrams) and fast S-wave orientation (yellow rose diagrams), as assessed by [34; 70].  
Furthermore [23] investigated the seismic wavefield polarization within a transform zone in the Reykjanes Peninsula volcanic rift zone (south Iceland), finding that ground motion polarization has a perpendicular relationship with mapped faults and fractures, as well with fast S wave directions.  
This suggested that wavefield polarization and fast velocity direction are effects of the same cause: an anisotropic medium (fractured rocks) where Vs is larger in the crack-parallel component (causing seismic anisotropy) and compliance is larger perpendicular to the crack strike (causing horizontal ground motion).  
Figure 8: The relationship between shear-wave splitting and ground motion polarization. (**A**) Ground motion polarization in the Val d’Agri region and S wave fast direction exploited by [91; 92]. Redrawn by [33]. (**B**) Redrawn by [34]. Ground motion polarization across the Darfield fault and S wave fast direction exploited by [70].
## 2 Overcoming Sagging and Altitude Loss Problems: Effective Solutions
### Effective Thrust Control Configuration for BWB Aircraft
#### 2.2.1 A Dual-Layer Sleeve Thrust Control Configuration  
A dual-layer sleeve thrust control configuration operates based on its innovative structural design, allowing for precise manipulation of the airflow through the propulsion unit. As illustrated in Figures 3 and 4, this configuration comprises the two following primary elements: the outer sleeve and the inner sleeve, supplemented by a thrust control cascade affixed to the outer sleeve. The outer sleeve envelops the airflow channel, facilitating uninterrupted airflow through the propulsion unit. When an adjustment in thrust is necessitated, the outer sleeve moves backward, revealing the airflow passage on the inner sleeve. This retraction alters the path of the airflow, consequently reducing the  
Figure 1: Sageging problem of BWB aircraft caused by short lever arms.  
Figure 2: Schematic of BWB aircraft with distributed electric BLI propulsors.  
volume of air passing through the primary propulsion unit, thereby modulating the thrust. Additionally, the thrust control cascade, attached to the top end of the outer sleeve, moves in tandem with it, effectively obstructing a portion of the airflow, thereby fine-tuning the quantity of air passing through. The movement of the outer sleeve causes the connecting rod to pull the blocker doors towards the inner shaft, positioning the thrust control plate in a fully closed state, as shown in Figure 3b. The airflow is expelled through the channel in the cascade.
# A Compact Numerical Scheme for the Heat Transfer of Mixed Convection Flow in Quantum Calculus
## 6 Conclusions  
A second- and fourth-order numerical approximation for the second order \(q\)-derivative has been proposed for solving PDEs in Quantum Calculus. These numerical approximations have been further utilized for proposing a numerical scheme. The numerical approximations have been developed by using the \(q\)-Taylor series. Two different numerical schemes have been applied for handling time derivative terms for the mathematical model of flow over stretching sheets. The comparison showed that Crank-Nicolson performed better than the Runge-Kutta method in two out of the three cases.  
The application of the proposed numerical scheme can be further considered to solve different types of partial differential equations in applied science and engineering. In addition to the present applications [42; 43; 44; 45; 46], it is feasible to suggest other applications for the current methods following the conclusion of this study. In addition, the method suggested is easy to use and can be used to solve a wider range of partial differential equations in Quantum Calculus, both in practice and in theory.  
Conceptualization, Y.N.; Funding acquisition, K.A.; Investigation, Software, Formal Analysis, Writing--review and editing, Methodology, and Writing--original draft, Y.N.; Methodology, M.S.A.; Project administration, K.A.; Resources, K.A.; Supervision, M.S.A.; Visualization, K.A.; Writing--review and editing, M.S.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  
The manuscript included all required data and implementing information.  
The authors wish to express their gratitude to Prince Sultan University for facilitating the publication of this article through the Theoretical and Applied Sciences Lab.  
The authors declare no conflict of interest.  
Figure 14: Contour plot for temperature profile of [PERSON] second problem in Quantum Calculus by the proposed scheme for spatial direction and Crank–Nicolson scheme for time direction using \(N_{y}=50,\ N_{t}=100,\ \lambda=0.01,\ \beta=0.01,\ Pr=3,\ q=0.8,\ L=24,\ u(t,0)=cos(t),\ \theta(t,0)=sin(t)\).
## 4 Verification of Energy-Based Impact Formula  
### Scabbing Depth Assessment  
As mentioned in the Introduction, there is no formula that predicts the scabbing depth, which is a failure on the rear face of the concrete panel. In this section, the newly proposed scabbing depth formula is verified using the experimental results. However, there is little experimental data on scabbing depth, as the penetration depth and penetration were the main objectives in the previous impact studies. According to the experimental results obtained by [PERSON] [28] and [PERSON] [29], 81 specimens were measured for scabbing depth, and the ratios of tested-to-predicted scabbing depth are shown in Figure 7.  
The minimum and maximum ratios of the tested-to-predicted scabbing depth were 0.74 and 5.92, respectively. Of the 81 specimens, 52 were predicted at a ratio of 2 or less, with an average of 1.16. On the other hand, 29 specimens were predicted with a ratio of 2 or more, with an average of 3.14 (Table 1). The accuracy was lower than that of the penetration depth prediction formula. Similar penetration depths were observed in the impact experiments conducted under similar conditions. However, the scabbing depths were not similar even under the same conditions. Figure 8 shows the tested scabbing depth-to-tested penetration depth ratios, most of which are scattered below 2.5. However, for some specimens, the ratios are observed to be above 3, with a maximum value of about 5. In other words, it is quite difficult to predict the scabbing depth. As the reflected energy may be amplified depending on the reflection angle and the properties of materials in the concrete matrix, it is not easy to predict the reflected energy accurately. Despite these difficulties, the scabbing depth prediction formula presented in this paper seems to be predicted quite well.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ratio Range** & **Number of Specimens** & **Average Ratio** \\ \hline Under 1 & 19 & 0.85 \\ Under 1.5 & 43 & 1.05 \\ Under 2 & 52 & 1.16 \\ Over 2 & 29 & 3.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of specimens with scabbing failure.  
Figure 7: Ratio of tested-to-predicted scabbing depth.
## 2 Measurement Method and Systems
### Microsphere Measurement System  
Figure 1 has illustrated the developed measurement system, which comprises a micro stage controller, two CCD cameras, a signal generator, a processing circuit, and a measurement structure. The measurement structure is composed of three micro stages with nano-drive precision in two directions. Figure 8 presents the photograph of the measurement system.  
Among the three micro stages produced by Aerotech Inc, Pittsburgh, PA, USA. The stages in the X and Y directions are QNP-50-250L and QNP-60-500L, respectively. The strokes of the stages in the X and Y directions are 250 and 500 \(\upmu\)m, respectively. During the measurement, the micro stages are set to work in a closed-loop mode, and the closed-loop control of the stages is conducted with their built-in piezoelectric actuators and capacitance sensors. The corresponding driving resolutions of the stages under the closed loop in the X and Y directions are 0.5 and 0.9 nm, respectively. The pitch and yaw errors of the stages in X and Y directions are 1.2 and 2.5 arc sec, respectively. The position errors of the stages are tiny, the linearity of the stages in X and Y direction are 0.01 and 0.007 percent of the motion displacement, and the bidirectional repeatability errors of the stages in X and Y direction are 1 and 3 nm, respectively. The displacement range of the stage in the Y direction ensures that the microsphere with a diameter of less than 500 \(\upmu\)m can be fully measured. The micro stages in X direction are installed  
Figure 8: Photography of the measurement system.  
Figure 7: The clamping fixture of the microsphere.  
on two separate 3D macro platforms, which have strokes of \(\pm\)6.5 mm in the X and Y directions and 10 mm in the Z direction.
## 4 Discussion and Example  
### Parameters' Influence on Planing Surface  
The influence of changing the shape coefficients \(\lambda_{k1}\) and \(\lambda_{k2}\) on the bow curve can be observed in Figure 18. \(\lambda_{k1}\) determines the relative position of point \(\mathbf{P}_{k2}\) in the direction parallel to the chord line, mainly affecting the distribution of the convex positions of the curve. When the value of \(\lambda_{k1}\) increases, the convex position moves forward, and when the value decreases, the convex position moves backward. On the other hand, \(\lambda_{k1}\) determines the perpendicular distance between point \(\mathbf{P}_{k2}\) and the chord line, influencing the overall curvature of the curve. The larger the value of \(\lambda_{k1}\), the greater the overall bending degree of the curve will be. When the value of \(\lambda_{k2}\) is \(-1\), the curve degenerates into a straight line.  
The transverse shape of the planing surface is primarily influenced by \(\gamma_{c}\), \(\gamma_{k}\), \(k_{s}\), and \(k_{m}\). When \(\gamma_{c}\) and \(\gamma_{k}\) take a value of 0, the corresponding cross-sectional shape represents a  
Figure 18: The bow curved lift section influenced by \(\lambda_{k1}\) and \(\lambda_{k2}\).  
flat-bottomed planing surface, as presented in Figure 19. When both \(\gamma_{c}\) and \(\gamma_{k}\) are positive, the planing surface becomes a concave curve, as illustrated in Figure 20. In this case, \(k_{s}\) affects the overall curvature, whereas \(k_{m}\) affects the symmetry of the curve. The same principles apply to the convex surface. When \(\gamma_{c}\) and \(\gamma_{k}\) have opposite signs, the station curve forms a waveform curve, as illustrated in Figure 21. The effects of \(k_{s}\) and \(k_{m}\) on the curve can be observed in this figure. As can be seen, \(k_{m}\) influences the overall curvature of the curve, while \(k_{s}\) primarily affects the symmetry of the curve. Therefore, through the shape coefficients, various transverse bending shapes of the planing surface can be designed and modified.  
Three sets of planing surface styles generated by the longitudinal functions of deadrise angles are presented in Figure 22. Figure 22a demonstrates a planing hull with a small deadrise angle, whereas Figure 22b exhibits a substantial variation in the deadrise angle with increasing vessel length. According to Figure 22c, the planing hull demonstrates a larger deadrise angle and a sharper bow. These results demonstrate that the modification of the deadrise angle function could effectively design and modify the distribution of deadrise angles for the planing hull.  
Figure 21: The waveform station curve of the planing surface.  
Figure 19: The planing surface as \(\gamma_{c}\) and \(\gamma_{k}\) equal 0.  
Figure 20: The concave station curve of the planing surface.
# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 1 Introduction
(2) The experimental results show that the MSIS method can not only improve the efficiency of unordered image stitching but also that of ordered image stitching compared to other unordered image stitching methods. The other sections in this paper are organized as follows. In Section 2, related work on image registration and image fusion is briefly introduced. In Section 3, we detail the image registration method used in this study and present the registration results. In Section 4, the image stitching method based on the merge-sorting model and the image fusion method used in this study are introduced in detail. The experimental results and a comparison of this method with other methods are presented in Section 5. Finally, Section 6 provides the conclusion of this study.
# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Article Title** & **Author/Year** & **Journal** & **Total Citations** & **Norm. Citations** & **Disease** & **Type of Images/ Dataset** & **Methodology** & **Metrics/ Observation** \\ \hline  & & & & & MR- & & & \\  & & & & & DWI— & & & \\  & & & & & 242 & & & \\   Automatic & & & & & private & & \\ Segmentation of & & IEEE & & & & subjects: & & DSC: 79.13\% \\ Acute Ischemic & & Transac- & & & training & & Lesionwise \\ Stroke from & [PERSON]. & & & & (90), & precision: \\ DWI Using 3-D & et al. (2018) & on & 47 & 2.18 & Ischemic & testing & 3D-CNN & 92.67\% \\ Fully & & Medical & & & (90),validation & & Lesionwise F1 \\ Convolutional & & Imaging & & & (62) & & score: 89.25\% \\ DenseNets ([PERSON] et al., 2018) & & & & & Additional & & \\  & & & & & dataset: & & \\  & & & & & ISLES & & \\  & & & & & 2015-SSIS & & & \\ \hline ISLES 2016 and & & & & & & & & It is a \\
2017— & & & & & & MR-DWI- & & comparison of \\ Benchmarking & & & & & & PWISLES & RF-CNN & tools developed in Challenge \\ Ischemic Stroke & et al. (2018) & & in Neuro- & 45 & 2.09 & Ischemic & stroke & 2016–2017 & \\  & & & & & & 2016–2017. & \\  & & & & & & & 2016–2017. & \\  & & & & & & & \\ \hline Prediction & & & & & & MRI— & & \\ Tissue Outcome & & & & & & 222 & & AUC = \\ and Assessment & & & & & & stroke & & cases & 0.88 \(\pm\) 0.12 \\ of Treatment & & [PERSON] & & & & & MRI— & \\ Effect in Acute & et al. (2018) & & & & & & 222 & \\ Ischemic Stroke & et al. (2018) & & & & & private & CNN deep & AUC = \\ Using Deep & & & & & & cases & & 0.88 \(\pm\) 0.12 \\ Learning [10] & & & & & & (patients) & & \\ \hline  & & & & & MRI— & & \\  & & & & & & multimodality & & \\ Enhancing & & & & & & BRATS & & \\ Interpretability & & & & & 2013: & & \\ of & & & & & training & & Boltzmann \\ Automatically & & & & & Brain & leader- & Machine & BRATS 2013 \\ Extracted & & & & lesions: & board & & for & Dice score: 0.81 \\ Machine & & & & & & & unsupervised & SPES \\ Learning & [PERSON] & & & & & brain & feature & Dice score: \\ Features: & et al. (2018) & & & & & & \\ Applications to a & & & & & ischemic & (10) & learning, & 0.75 \(\pm\) 0.14 \\ RBM-Random & & & & & stroke & SPES & and a & ASSD: \\ Forest System & & & & & from & random & 2.43 \(\pm\) 1.93 \\ on Brain Lesion & & & & & MICCAI— & & for \\ Segmentation & & & & & ISLES: 30 & classifier & \\
[10] & & & & & training, & & & \\  & & & & & & 20 & \\  & & & & & challenge & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Machine Learning/Deep Learning Methods in the Diagnosis of Ischemic Stroke and Demyelinating Disease  
In the following subsections, we discuss how artificial intelligence (AI) through ML and DL methods is used in the development of algorithms for brain disease diagnosis and their relation to the central theme of this review.