esa-sceva/satcom-chunk-collection huggingface dataset:
# State of the Art of Tactile Micro Coordinate Metrology
## 1 Introduction  
Coordinate measuring machines (CMMs) have become versatile and widespread metrology tools to perform complex dimensional measurement tasks in an efficient way. There is an ongoing trend to miniaturization in mechanical and optical production technology, leading to industrial products with generally larger functionality in a smaller volume and with less energy consumption. Small components with often complex geometrical features are found, e.g., in medical products such as ear implants or hearing aids, in gears of micro motors, in small freeform lenses of mobile phones, in injection systems for the automotive industry, or in the telecom sector for fiber optic or next generation radio frequency technology components. Hence, there is a new demand for highly accurate dimensional measurements on micro parts having geometrical features in the meso scale, _i.e._, in the sub-millimeter to micrometer range. Such micro parts are often too complex and too large for optical microscopy techniques and their tiny structures are hardly accessible by means of conventional tactile coordinate measuring machines.  
The major limiting factors of conventional CMMs for the measurement of small object features are the size of the probing element, the contact force as well as the CMM stage accuracy. Whereas modern multi-sensor CMMs with optical probes, mostly based on imaging systems, are well suited for the measurement of small features, only tactile probes have the fundamental properties needed for high precision full 3D capability with well established traceability. The paper discusses the key elements of tactile micro coordinate metrology, _i.e._, the probe head for probing objects with small styli and weak forces, the precision stages with an Abbe error free metrology system, as well as the system calibration and performance verification.  
Several review papers have been published in the field of micro coordinate metrology, in particular on probing systems [1], on aspects of tactile probing [2] and more recently on existing performance verification infrastructure of micro CMMs [3]. The purpose of this paper is to present a review on state of the art machine and probe developments seeking ultimate accuracy for three-dimensional measurements of micro parts, with a special emphasis on those instruments, where comparable qualification and performance assessment results are available. The research conducted on the micro CMM at METAS [4] is presented in more detail, since this machine has been qualified extensively using methods relying as closely as possible on existing written standards.
## 3 Results
### Actual Data
#### 3.1.2 Parameters Contributing to \(\mathrm{Tmax}\)  
The PCA analysis presented above enabled the detection of the relationship among all PK parameters, placing particular interest on \(\mathrm{Tmax}\). The random forest approach was further used to determine the contribution of each PK feature to \(\mathrm{Tmax}\). The latter was accomplished in two ways: first, by including the \(\mathrm{Cmax}/\mathrm{Tmax}\) term in the model alongside the other PK parameters, and second, by substituting the AS for \(\mathrm{Cmax}/\mathrm{Tmax}\). Since RF is a supervised technique and the response variable should be on an ordinal scale, prior to its application, the \(\mathrm{Tmax}\) estimates were transformed into an ordinal scale. A common way to do this was to divide \(\mathrm{Tmax}\) into its quartiles, thus creating four groups with an equal number of elements. The four classes of \(\mathrm{Tmax}\) refer to low, medium, high, and very high values. These quartile values were found to be two, three, and four for the 1 st, 2 nd (i.e., the median), and 3 rd quartile, respectively.  
Figure 4 depicts the application of the RF algorithm either by using \(\mathrm{Cmax}/\mathrm{Tmax}\) (Figure 4a) or AS (Figure 4b). The reason for not including both \(\mathrm{Cmax}/\mathrm{Tmax}\) and AS in the same model is due to their similar natures, and it was attempted to isolate the effect of each one of them on \(\mathrm{Tmax}\). The RF results showed that in both cases, the major contributor to \(\mathrm{Tmax}\) was AS (or \(\mathrm{Cmax}/\mathrm{Tmax}\)), followed by \(\mathrm{AUCp}\), \(\mathrm{AUCt}\), \(\mathrm{Cmax}/\mathrm{AUC}\), \(\mathrm{Cmax}\), and finally, lambda. It is worth noting that \(\mathrm{Cmax}\), which is traditionally used in BE studies as the metric expressing absorption rate, shows a low contribution to \(\mathrm{Tmax}\), even less than that of \(\mathrm{AUCt}\). The \(\mathrm{Cmax}/\mathrm{AUC}\) ratio, which has also been proposed as a metric to express the rate of absorption, also shows a low contribution to \(\mathrm{Tmax}\). Based on the two confusion matrix results, the prediction accuracy is 79.82% for the RF model of Figure 4a and 81.03% for that of Figure 4b. Finally, it should be mentioned that \(\mathrm{AUCinf}\) was not included in the RF analysis since it shows exactly the same properties as \(\mathrm{AUCt}\).  
Figure 3: Scree plot to select the number of principal components (PC).
## 4 Experiments and Results
### Transformation-Matrices Behavior  
The focus of this last experiment was to determine the existence of some kind of interesting behavior in the _distribution of the matrices that transform the recorded leads into principal (independent) components_. The experimental setup was the following. First, we extracted the transformation matrices for PCA and ICA. Second, we normalized these matrices, in the PCA case using the eigenvectors as they stayed in [\(-\)1,1] interval. For ICA, we perform a normalization similar to the PCA case, but using eigenvectors and eigenvalues, as follows: \(M^{{}^{\prime}}=\frac{\mathbf{M}}{\beta}\), where \(\mathbf{M}^{{}^{\prime}}\) is the new transformation matrix, \(\mathbf{M}\) is the original transformation matrix derived from ICA process, and \(\beta\) is a normalization coefficient computed as \(\beta(i)=\sqrt{\sum_{j}\mathbf{M}_{t,j}^{2}}\).  
In the last part, the transformation matrices were split in eigenvectors and grouped accordingly. Then, each one of this group was aligned by eigenvalues in absolute terms, to prevent sign changes. The alignment was performed inversion when correlation value resulted negative, computed with the maximum non-normalized transformation vector of each group.  
As it can be observed in Figure 21, the distribution of the transformation matrix for the control records, Figure 21a,c is similar to the distribution matrix for the manually fragmented records, Figure 21b,d. This effect shows that PCA transformation is stable and its transformation matrices values appear to be concentrated. Alternatively for ICA, Figure 22 shows the distribution of the transformation matrix for the control records appears to be somehow more spread in Figure 22b,d. Figure 22a,c were also similar to the distribution of the manually fragmented records. As a result it was found that the number of components affected by fragmentation was larger than in the PCA case.  
Figure 21: Distribution of the eigenvector components values for the last principal component of the eight independent lead model. (**a**) Distribution of the eigenvector components for each control patient. (**b**) Distribution of the eigenvector components for each subrogated fragmented patient. (**c**) Aligned distribution of eigenvector components for each control patient. (**d**) Aligned distribution of eigenvector components for each subrogated fragmented patient. The red line shows the value of transformation vector used in the alignment.
# Hydraulic Performance of Wave-Type Flow at a Sill-Controlled Stilling Basin
## 1 Introduction
Previous research has investigated different cases involving wave-type flows, including energy dissipation and hydraulic variables such as sequent depth ratios, maximum wave height, and wave profile. However, the effect of wave-type flows on the ambient pressure head and air entrainment has not been analyzed thoroughly. In this study, we aim to examine the hydraulic characteristics of wave-type flows, with a special focus on the bottom pressure profile along the stilling basin and the concentration of air on the bottom and sidewall. By gathering this data, it will be possible to improve the accuracy of computational fluid dynamics (CFD) models used to predict the hydraulic behavior of wave-type flows. Furthermore, the findings of this research can expand the application of sill-controlled stilling basins by developing new relationships based on the data collected.
## 3 Testing Results
### Deformation and Elastic Modulus
#### 3.1.3 Elastic Modulus  
Figure 4 shows the relation curve between elastic modulus and cycle number of the No. 4 coal specimen in different loading stress intervals. Here, the elastic modulus refers to the secant modulus between the beginning point and some stress point in the cyclic loading phase. When the loading stress interval is between 3.0 and 3.5 MPa in the initial three loading-unloading cycles, the elastic modulus increases significantly with the cycle number; that is, the plastic strengthening effect occurs in coal specimens. However, the elastic modulus is basically stable in the subsequent loading-unloading cycles, indicating that the plastic strengthening effect is no longer obvious. When the loading stress interval is between 3.0 and 4.0 MPa in the initial three loading-unloading cycles, the elastic modulus increases gradually, and the plastic strengthening effect of the coal specimen is very significant. However, in the subsequent loading-unloading cycles, the elastic modulus keeps basically stable, and the plastic strengthening effect of the coal specimen is not obvious. When the loading stress interval is between 3.0 and 4.5 MPa in the initial two loading-unloading cycles, the elastic modulus increases, and the plastic strengthening effect of the coal specimen is very significant. However, in the subsequent loading-unloading cycles, the elastic modulus keeps basically stable, and the plastic strengthening effect is no longer obvious. When the loading stress interval is between 3.0 and 5.0 MPa in the initial two loading-unloading cycles, the elastic modulus increases, and the plastic strengthening effect is very significant. However, in the subsequent loading cycles, the elastic modulus  
Figure 3: Relation curves between axial, circumferential, and volumetric strains and cycle number.  
keeps basically stable, and the plastic strengthening effect is no longer obvious. When the loading stress interval is between 3.0 to 5.5 MPa, although only two loading cycles are carried out, it can also be concluded that the plastic strengthening effect of the coal specimen is very significant after one loading-unloading cycle. The above results show that within the same loading-unloading stress interval, the elastic modulus first increases and then keeps basically stable as the loading-unloading cycle number increases; in addition, the higher the maximum stress of a loading-unloading cycle, the more significant the plastic strengthening effect produced by this cycle.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 4 Experiments
### Comparative Experiment  
In order to measure its performance comprehensively, the proposed algorithm was compared with (1) Hidden [13] and DwiMark [17] robust watermarking algorithms based on deep learning, also trained in a two-stage separated manner for fair comparison,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **LHFA Block** & **Avg PSNR/dB** \\ \hline
1 & ✓ & 60.18 \\
2 & ✗ & 58.43 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation of the effectiveness of the LHFA block in image reconstruction quality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack Type** & **Parameter** & **BER/bit** \\ \hline rotation & \(\theta=5\) & 0.000785 \\ rotation & \(\theta=15\) & 0.002837 \\ rotation & \(\theta=30\) & 0.001154 \\ rotation & \(\theta=45\) & 0.001025 \\ cropping & q = 0.125 & 0.000004 \\ cropping & q = 0.25 & 0.000004 \\ cropping & q = 0.5 & 0.000004 \\ cropping & q = 0.7 & 0.000004 \\ scaling & r = 0.5 & 0.000003 \\ scaling & r = 0.75 & 0.000004 \\ scaling & r = 1.25 & 0.000003 \\ scaling & r = 1.5 & 0.000004 \\ scaling & r = 2 & 0.000004 \\ \hline \hline \end{tabular}
\end{table}
Table 4: BER values under geometric attacks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack1** & **Attack2** & **Attack3** & **BER/bit** \\ \hline MF K = 7 & GN v = 0.03 & scaling r = 0.5 & 0.0003 \\ GF K = 5 & rotation \(\theta=45\) & JPEG Q = 30 & 0.0632 \\ GF K = 3 & cropping q = 0.125 & JPEG Q = 90 & 0.0011 \\ SPN \(p=0.02\) & cropping q = 0.25 & JPEG Q = 70 & 0.0756 \\ MF K = 5 & GN v = 0.01 & JPEG Q = 30 & 0.0504 \\ SPN \(p=0.02\) & rotation \(\theta=30\) & JPEG Q = 30 & 0.0876 \\ \hline \hline \end{tabular}
\end{table}
Table 5: BER values under multiple attacks.  
and (2) three traditional robust lossless watermarking algorithms [10; 11; 12] with good performance. Table 7 shows the comparison results of the algorithms regarding the visual quality of watermarked images, and the proposed algorithm achieved the highest visual quality of the reconstructed images. Table 8 shows the comparison results of the algorithms in terms of robustness. The proposed algorithm exhibited the best robustness against Gaussian noise and scaling attacks, with a corresponding BER of 0 for watermark extraction. For rotation attacks, the proposed algorithm achieved a BER of 0.002 at a rotation angle of 15\({}^{\circ}\). Moreover, the proposed algorithm also performed well against other common attacks on medical images, with a BER of 0 for multiple attacks, demonstrating its robustness against most attacks.
# Using Tablets to Teach Basic Literacy Skills to Kindergarten Students: A Case Study
## 5 Discussion
### Discussion of the Findings Related to H3a-c  
The results related to H3a-c offer further insights into why learning outcomes were better when tablets were utilized. Indeed, these results provide evidence supporting the hypothesis that tablets significantly enhance kindergarten students' engagement, enjoyment, and motivation in the context of letter identification and letter sound recognition, as compared to conventional materials. The use of tablets demonstrated a notable superiority in engaging students, which is in alignment with the existing literature (e.g., [29; 33; 34; 40]), underscoring that their interactive and tactile nature is quite effective in fostering active participation [33], and in enhancing emergent literacy skills [35]. The very large effect size observed for engagement indicates that tablets are particularly proficient in capturing and maintaining young learners' attention, a critical component for effective learning in early childhood education. The use of AR technology is also likely to have contributed to the increased engagement of students, as it captures their attention [7], rendering the learning process more engaging, interactive, and effective [54].  
Moreover, the study found that tablets compared to conventional materials, made learning a more enjoyable experience. This finding corroborates previous studies that highlighted the potential of digital tools to not only facilitate learning but also make it an enjoyable process [12; 34]. The large effect in enjoyment suggests that tablets provide an engaging alternative to conventional methods, offering a playful and immersive way for children to practice foundational literacy skills. This aligns with the notion that enjoyable learning contexts can lead to improved educational outcomes by increasing students' willingness to participate and persist in educational activities [12].  
Motivation also significantly increased among students using tablets. The very large effect sizes for motivation further substantiate the role of tablets as effective tools for fostering a love of learning. This is consistent with past research which emphasized the role of tablets and educational applications in motivating children to engage more deeply with learning materials, particularly in literacy (e.g., [34; 40]). Moreover, the use of AR may also have played a role, as it is associated with increased motivation due to its interactive and immersive nature [57].
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 5 Experimental Results
### Comparison with SOTA Methods
#### 5.2.2 Chinese Results  
Quantitative Results: Table 2 highlights the performance of Patch-Font against FUNIT, MX-Font, and VQ-Font for Chinese printed fonts, evaluated on both SCs and UCs.  
For printed SCs, Patch-Font achieves the highest Acc (C) at 98.70%, surpassing VQ-Font (95.20%) and MX-Font (88.10%). Its Acc (S) of 80.20% outperforms VQ-Font (74.45%) and MX-Font (65.30%). Patch-Font records the lowest LPIPS (0.108) and FID (72.5), along with the highest SSIM (0.789), reflecting its superior ability to replicate stylistic and structural details.  
For printed UCs, Patch-Font maintains its dominance with Acc (C) of 97.40% and Acc (S) of 77.80%. Its LPIPS score of 0.130 and FID of 89.7 are significantly better than those of the baselines, while its SSIM of 0.760 confirms its robust structural preservation for unseen characters.  
**Qualitative Results:** Figure 4 visually compares font generation results for Chinese printed fonts. Red boxes highlight common failure cases in baseline methods. FUNIT struggles with structural preservation, causing breakage in characters like '\(\mathbb{R}\)l'. MX-Font introduces noise and inconsistencies in style transfer for characters like '\(\mathbb{R}\)l'. VQ-Font performs better but fails to maintain local stylistic details in complex characters like '\(\mathbb{R}\)l'. However, Patch-Font accurately preserves global and local style characteristics, generating outputs closely matching the ground truth in terms of stroke thickness, curvature, and structural details.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (C)** \(\uparrow\) & **Acc (S)** \(\uparrow\) & **LPIPS** \(\downarrow\) & **SSIM** \(\uparrow\) & **FID**\(\downarrow\) \\ \hline \multirow{4}{*}{Printed SCs} & FUNIT & 82.50 & 46.75 & 0.142 & 0.673 & 120.5 \\  & MX-Font & 88.10 & 65.30 & 0.150 & 0.726 & 86.3 \\  & VQ-Font & 95.20 & 74.45 & 0.119 & 0.752 & 79.0 \\  & Patch-Font & **98.70** & **80.20** & **0.108** & **0.789** & **72.5** \\ \hline \multirow{4}{*}{Printed UCs} & FUNIT & 76.00 & 30.20 & 0.165 & 0.470 & 134.7 \\  & MX-Font & 86.90 & 60.50 & 0.168 & 0.668 & 96.5 \\ \cline{1-1}  & VQ-Font & 92.30 & 58.10 & 0.140 & 0.701 & 90.2 \\ \cline{1-1}  & Patch-Font & **97.40** & **77.80** & **0.130** & **0.760** & **89.7** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison on Chinese printed datasets. All the methods are evaluated on both seen and unseen characters for unseen fonts during training.  
Combined Summary: Across both Korean and Chinese datasets, Patch-Font consistently outperforms baseline methods in all metrics, demonstrating superior performance in both quantitative and qualitative evaluations. Its patch-based attention mechanism and multitask encoding strategy enable it to capture fine-grained stylistic details while preserving structural integrity. These results highlight Patch-Font's ability to generate high-quality fonts across diverse scripts, ensuring both authenticity and readability.
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 2 Related Work
The first stage of the network uses the Gaussian distribution for coarse features, while the second stage refines the local details based on the results obtained in the previous stage, which are effective for synthetical and real-world images. [PERSON] et al. [27] designed a two-stage hierarchical artificial neural network. The first stage was responsible for capturing the main colors from the fixed feature map in an unsupervised manner, while the second stage controlled the number of color clusters for segmentation quality improvement. For single-image super-resolution, [PERSON] et al. [30] proposed a two-step restoration approach to predict missing region features when object features are severely missing. This method significantly improves texture blurring and feature discontinuity issues. [PERSON] et al. [31] utilized a two-phase approach to restore images corrupted by blur and impulse noise by identifying outlier candidates in the first stage and employing variational methods in the second stage. [PERSON] et al. [32] expanded the three-stage network based on CNN by incorporating wavelet transform for image denoising. Although two-stage algorithms are widely applicable to image processing tasks, most existing methods neglect the interaction between the two stages, which results in the limited performance of the models. In this paper, our proposed two-stage hybrid network (TTH-Net) integrates the strengths of both transformer and CNN, which also achieves state-of-the-art performance in the leaf vein segmentation task. The proposed TTH-Net utilizes deep learning methods to significantly improve the level of automation in vein segmentation while achieving high accuracy. Additionally, different from other two-stage methods, we extensively interact with the features generated from both stages and guide the representation of long-range features via auxiliary loss functions.
# Fire Behavior of Wood-Glass and Jute-Glass Hybrid Laminates Manufactured by Vacuum Infusion
However, wood is a material considered to be a thermal insulator, and, especially in the case of structural applications, it is extremely important to assess its fire behavior, which is not found in the literature in the studies of laminates produced by the VIP. Most of the studies focused on studying the behavior of laminates in relation to fire concentrate on exploring laminates produced with commonly used natural mats or fabrics (e.g., jute, cotton, bamboo, ramie, etc.). For example, [PERSON] et al. [15] evaluated the influence of fire on hybrid laminates of polypropylene, bamboo fabrics, and glass (BGPP). The BGPP laminates were manufactured by replacing around 30% of the glass fiber with bamboo fabric, using the compression molding technique. The results of the calorimeter cone tests revealed a superior performance of the hybrid composite, BGPP, compared to the glass-propylene composites, with a significant 19% reduction in the heat release rate and smoke release, as well as a 67% higher ignition time compared to the glass-propylene composite [15]. In addition, [PERSON] et al. [16] studied the fire exposure behavior of hybrid epoxy and flax cotton laminates (EP/FF) by adding proportions of 1-10 wt% of micrometric expanded vermiculite (VMT). The addition of VMT quantities of 1 and 2 wt% enabled a 60% reduction in the heat release rate (HRR) and a 20% reduction in the total heat release rate, compared to EP/FF [16]. Despite the growing research on the fire behavior of laminates, it is important to highlight the scarcity of studies on laminates manufactured by the VIP that mainly use wood, precisely because wood-based laminates manufactured by the VIP are an innovation in laminates in the literature. The VIP involves the use of vacuum pressure to drive resin into a dry fiber lay-up, ensuring complete saturation and optimal distribution of the resin throughout the composite structure. This process has been attracting great attention for its ability to provide a balance of performance, cost efficiency, and environmental sustainability compared to other composite manufacturing methods such as hand lay-up, RTM, and filament winding [17; 18]. Its advantages in terms of uniform resin distribution, reduced void content, and environmental benefits make it a preferred choice for producing high-quality composite laminates across various industries [19]. This research aims to address the existing research gap by exploring the fire behavior of wood-glass and jute-glass hybrid laminates, utilizing a cone calorimeter, with a special emphasis on evaluating the effect of natural material-based components.