esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### FE-SEM Analysis  
The morphology and grain size distribution of the prepared samples are determined using FE-SEM analysis. The results of the samples A01 to A04, and A10 are shown in Figure 5a-e. For all the samples between A01 to A04, larger grain particles (size \(>\) 150 nm) are obtained along with little contribution of grains with comparatively smaller size (size \(<\) 150 nm), cf. Figure 5a-d. However, the results of sample A10 show mostly larger grains (size \(>\) 150 nm), cf. Figure 5e. This suggests that the ZnO impurity detected in our pXRD results of samples A01 to A04 has a smaller grain size when compared to grains that belongs to the Zn\({}_{2}\)TiO\({}_{4}\) host (sample A10). This is also evident when Figure 5a and Figure 5b or Figure 5e are compared, where in the first case, the grains have a comparably lower size distribution (\(\sim\)144 nm) and are dominated by the presence of ZnO in the pXRD pattern. For the four samples, an increase in the grain size is observed with a value of \(\sim\)144 nm for sample A01, \(\sim\)163 nm for sample A02, \(\sim\)217 nm for sample A03, and \(\sim\)253 nm for sample A04.  
This observation is in expected lines as with an increase in the annealing temperature, the crystallinity and grain size are expected to increase. The reason for such a change in the grain size can be attributed to the re-crystallization process. It is also possible that there is a decrease in the deformation and grain boundaries at high temperatures. More interestingly, when the grain size of samples A02 and A10 are compared, both the samples possess a similar grain size of \(\sim\)163 nm, suggesting that the modulation of the concentration of ZnO has no impact on the grain size of Zn\({}_{2}\)TiO\({}_{4}\).  
The purity and elemental composition of the as-prepared samples were further verified by energy dispersive X-ray spectroscopic (EDS) analysis, as shown in Figure 5f,g. The EDS spectrum of the selected image shows the peaks of zinc (K\({}_{\alpha}\) = 8.6 keV), L\({}_{\alpha}\) = 1.0 keV), titanium (K\({}_{\alpha}\) = 4.5 keV, K\({}_{\beta}\) = 4.9 keV), and oxygen (K\({}_{\alpha}\) = 0.5 keV). The EDS semi-quantitative analysis of sample A02 indicates atomic percentages of 30.99% for zinc and 14.98% for titanium, resulting in an atomic ratio of 2.06:1.00 in comparison to the expected value of 2.00:1.00 used during synthesis (see Table 1). Sample A10 shows atomic percentages of 26.25% for zinc and 19.14% for titanium, resulting in an atomic ratio of 1.39:1.00 in  
Figure 4: (**a**) Experimental and fitted pXRD pattern of sample A10, and (**b**) a representative crystal structure of Zn\({}_{2}\)TiO\({}_{4}\).  
comparison to the expected value of 1.40:1.00 (see Table 1). It is also to be mentioned here that the Rietveld refinement suggested that sample A10 is the best sample where no additional signatures of unreacted ZnO are observed.
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 1 Introduction
* Direction-aware detection head: We propose a direction-aware detection head that explicitly models text orientation through a \((\sin\theta,\cos\theta)\) vector representation for accurate orientation modeling. This component addresses a critical gap in existing frameworks by incorporating orientation as a first-class feature rather than a post-processing step. Our approach achieves state-of-the-art direction accuracy of 92.3%with a mean absolute error of only 2.5\({}^{\circ}\), significantly outperforming alternative direction representation methods. * Comprehensive evaluation and analysis: We conduct extensive experiments comparing our approach with both heavy ResNet-50-based models and lightweight alternatives such as MobileNetV3-SSD, EfficientDet-Lite0, YOLOv5s, YOLOv8n, and PP-PicoDet-S. Our model achieves superior detection accuracy (0.715 mAP) while maintaining competitive efficiency (28.6 FPS with 8.3 M parameters), and uniquely provides direction prediction capability that other lightweight models lack. We also construct and analyze TMDLAD, the first large-scale dataset for traditional Mongolian document layout analysis, comprising annotated newspaper pages with eight layout categories. Based on the characteristics of document layouts, we define eight layout element categories. The main body text blocks are labeled as Text, while headlines and section titles are categorized as Title. For non-textual content, we include Figure for images and illustrations, along with their corresponding Figure Caption. Similarly, tabular content is marked as Table with associated Table Caption. The page structure elements are categorized as Header for the top section of each page and Footer for the bottom section. The remainder of this paper is organized as follows: Section 2 reviews related work in traditional Mongolian document layout analysis and lightweight models. Section 3 describes our proposed framework in detail, including the modified backbone, feature enhancement module, and direction-aware detection head. Section 4 presents experimental results and ablation studies. Finally, Section 5 concludes the paper and discusses future research directions.
## 3 Results
### Turning Point Year of NPP
#### 3.4.2 Turning Point Year of NPP at Biome Scale  
At the biome level, the variation of NPP was diverse (Figure 6). All vegetation types had two clearly distinct periods over the study period (\(\Delta\)AIC \(<-2\)), with the exception of the EBF (\(\Delta\)AIC = \(-1.9314\)) which only showed a non-significant decreasing trend of \(-0.0001\) kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.9314) during 2001-2014. Farmland and grassland all increased, with a rate of 0.0011 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.3734) and 0.0016 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) < 0.01), respectively. In addition, the increasing trend before 2004 was larger than that after 2004 for both farmland and grassland. An increasing trend of NPP was found for DNF, ENF, and DBF, but the opposite trend was found for shrub, despite a turning point year occurring in 2010 (all \(\Delta\)AIC \(<-2\) and may also have been caused by the criterion we set up in the calculation) for all above vegetation types. In addition, a decreasing NPP occurred for ENF, DBF and shrub during 2001-2010, while an increasing NPP occurred for DNF during the same time period. After 2010, the trend of these four vegetation types was all positive, despite the existence of different magnitudes.  
Figure 6: Inter-annual variations of Net Primary Productivity (NPP) in China at the biome level (Farmland (**a**), Grassland (**b**), Deciduous needle-leaf forest (**c**), Evergreen needle-leaf forest (**d**), Deciduous broadleaf forest (**e**), Shrub (**f**), and Evergreen broadleaf forest (**g**)). Trends estimated by the least-squares linear regression are shown. The blue and pink lines indicate the linear fit before and after the turning year, respectively. The red line indicates linear fit during the period 2001–2014. TP represents the turning point year.
# CDA-Net for Automatic Prostate Segmentation in MR Images
## 4 Experiments and Discussion
### Experiment Setting and Evaluations
#### 4.2.3 Qualitative Evaluation  
Figure 11 shows the segmentation performance of our algorithm in Test30 of PROMISE12 and ASPS13. It displays that the algorithm we proposed can overcome the low contrast of the image, the serious uneven distribution of the gray gradient inside the gland, and the poor gray contrast between the gland and surrounding tissues in the segmentation results of the central organ slices. In addition, in the slice image segmentation at both ends of the sequence, although some slices still have over- or undersegmentation, the contour extraction results of the algorithm can also match the ground truth to a large extent. All of the above illustrates the promising performance of the algorithm in organ segmentation at both ends of the sequence and the strong generalization ability of the whole algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & & **DSC** & **RVD** & **SA** & **UR** & **OR** \\ \hline \multirow{2}{*}{U-Net-1} & Avg & 0.8494 & 0.1516 & 0.7848 & 0.0087 & 0.0126 \\  & Std & 5.6 & 8.2 & 5.8 & 4.9 & 5.0 \\ \hline \multirow{2}{*}{ResU-Net} & Avg & 0.9046 & 0.0598 & 0.8775 & 0.0065 & 0.0019 \\  & Std & 4.7 & 7.5 & 4.9 & 4.2 & 4.5 \\ \hline \multirow{2}{*}{RAU-Net} & Avg & 0.9206 & 0.0157 & 0.9279 & 0.0037 & 0.0018 \\  & Std & 3.9 & 7.3 & 4.4 & 3.8 & 4.2 \\ \hline \multirow{2}{*}{CDA-Net} & Avg & 0.9265 & 0.0283 & 0.9388 & 0.0021 & 0.0026 \\  & Std & 3.8 & 7.0 & 4.3 & 3.8 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of four algorithms on ASPS13.  
Figure 11: Test results of the algorithm on the Test30 and ASPS13.
## 3 Controller Design
### Disturbance Observer Design Based on Finite-Time Stabilization
 \bigg{]}\Xi \tag{41}\] \[\quad=-\frac{1}{|z_{1,p}|^{\frac{1}{2}}}\Xi^{T}\Pi\Xi\]  
Because \(\Xi^{T}\Xi=\mathbf{\Psi}^{T}\mathbf{\Psi}\), we obtain the following:  
\[\dot{V}_{FDO}\leq-\frac{1}{|z_{1,p}|^{\frac{1}{2}}}\Xi^{T}\Pi\Xi\leq-\frac{ \lambda_{\min}(\mathbf{\Pi})}{|z_{1,p}|^{\frac{1}{2}}}\Psi^{T}\mathbf{\Psi} \leq-\frac{\lambda_{\min}(\mathbf{\Pi})V_{FDO}}{\lambda_{\max}(P)|z_{1,p}|^{ \frac{1}{2}}} \tag{42}\]  
Because \(\big{\|}\mathbf{\Psi}^{T}\mathbf{\Psi}\big{\|}\geq\big{|}z_{1,p}\big{|}^{ \frac{1}{2}}\), \(\frac{V_{FDO}^{\frac{1}{2}}}{\sqrt{\lambda_{\min}(P)}}\geq\big{\|}\mathbf{\Psi}^ {T}\mathbf{\Psi}\big{\|}\), we obtain the folowing:  
\[\frac{1}{|z_{1,p}|^{\frac{1}{2}}}\geq\frac{\sqrt{\lambda_{\min}(\mathbf{\Psi})}}{V _{FDO}^{\frac{1}{2}}} \tag{43}\]  
Substituting Equation (43) into Equation (42) yields the following:  
\[\dot{V}_{FDO}\leq-\frac{\lambda_{\min}(\mathbf{\Pi})\sqrt{\lambda_{\min}( \mathbf{\Psi})}}{\lambda_{\max}(\mathbf{\Psi})}V_{FDO}^{\frac{1}{2}} \tag{44}\]  
From Lemma 5, the observation error of the roll channel will converge to zero at time \(T_{p}\), and \(T_{p}\) satisfies the following relation:  
\[T_{p}\leq\frac{2\lambda_{\max}(\mathbf{P})}{\lambda_{\min}(\mathbf{\Pi})\sqrt{ \lambda_{\min}(\mathbf{P})}}V_{FDO}^{\frac{1}{2}} \tag{45}\]Thus, the observer is effective in estimating the external disturbance, and the proof ends. \(\square\)  
In order to demonstrate the continuity of the observer in this paper, the observer in this paper is compared with the observer in the study [31]. To ensure the uniformity of the comparison conditions, two observers are used to observe the system of Example 1 in the study [31]. The observer designed based on the method of this paper is shown in Equation (46). The observer in the study [31] is shown in Equation (47). The comparison results are shown in Figure 3.  
\[ \begin{cases}\hat{x}=-x+\hat{d}+a_{1}|x-\hat{x}|^{\frac{1}{2}}\text{tanh}(k(x- \hat{x}))\\ \qquad\hat{d}=a_{2}|x-\hat{x}|^{\frac{1}{2}}\text{tanh}(k(x-\hat{x}))\end{cases}  \tag{46}\]  
\[ \begin{cases}\begin{array}{l}\dot{x}_{1}=-x_{1}+\hat{x}_{2}+Lc_{1}(x_{1}- \hat{x}_{1})^{r_{2}},\\ \dot{x}_{2}=\hat{x}_{3}+L^{2}c_{2}(x_{1}-\hat{x}_{1})^{r_{3}},\\ \dot{x}_{3}=\hat{x}_{4}+L^{3}c_{3}(x_{1}-\hat{x}_{1})^{r_{4}},\\ \dot{x}_{4}=-\text{Sat}_{B}(\hat{x}_{3})+L^{4}c_{4}(x_{1}-\hat{x}_{1})^{r_{5}} \end{array}\end{cases}  \tag{47}\]  
From the results, it can be seen that, at the beginning, the outputs of both observers are overshooting because the initial value of the observer output is zero by default, and the observer designed in this paper has a faster response speed. At the peaks and troughs, the observed curves of the study [31] show chattering, while the observer designed in this paper does not show the chattering phenomenon. **Remark 2**. : _In the comparison process, \(a_{1}=5\), \(a_{2}=20\), and \(k=200\), the parameters of Equation (47) are set as in the study [31]._
## 2 Materials and Methods
### Cytotoxicity and Adhesion
#### 2.3.2 Cytotoxicity Testing--Direct Contact  
Balb/3T3 cells were trypsinized using 0.25% Trypine-EDTA (Sigma-Aldrich), resuspended in culture medium and plated in a plate with 6 wells (TPP, Trasadingen, Switzerland) at 1.5 \(\times\) 10\({}^{5}\) cells per well. After 24 h, the tested material was placed on the wells. After 24 h of incubation with the material, the cell morphology was assessed under the disc, in its vicinity and in the rest of the well (CKX53 inverted phase-contrast microscope, Olympus, Tokyo, Japan). The control in the research was a culture carried out in a full medium and in standard conditions that had no contact with the tested material. The cytotoxicity of the analyzed materials was assessed according to the criteria describing changes in cell morphology presented in the EN ISO 10993-5 standard (Biological evaluation of medical devices--Part 5: Tests for in vitro cytotoxicity) [36; 37; 38]. The degree of cytotoxicity and changes in the cells' morphology were evaluated on a scale of 0-4 [6] as follows: 0--no toxicity, when no changes are visible under or near the tested material; 1--weak degree of reactivity, when individual cells have degenerated under the sample; 2--moderate degree of reactivity, when the zone of changed cells is limited to the surface under the sample; 3--medium degree of reactivity, when the zone of changed cells is limited to 1 cm around the sample; 4--strong degree of reactivity, where the zone of changed cells exceeds the 1 cm limit around the sample. According to the above standard, a cytotoxic material has a grade of at least 2 [6].
## 4 Power Loss Model of the DAB Converter
### Power Switch Losses  
In this paper, the power switch is considered the ideal switch. Therefore, the power losses of the intrinsic diode can be ignored. Generally, power switch losses are divided into switching and conduction losses [17]. Switching loss, which is duty-cycle dependent, occurs during the turning on and turning off time of the power switch. Steady-state losses caused by a power switch can be calculated as follows:  
The turn-on loss can be described as:  
\[P_{SW(on)}=\frac{V_{DS}\times I_{P}\times T_{r}\times f_{sw}}{6} \tag{8}\]  
where \(V_{DS}\), \(I_{P}\), \(T_{r}\) refer to the drain voltage, peak current, and rising time of the power switch, respectively.  
The turn-off loss can be described as:  
\[P_{SW(off)}=\frac{V_{DS}\times I_{P}\times T_{f}\times f_{sw}}{6} \tag{9}\]  
where \(T_{f}\) refers to the falling time of the power switch.  
The switching losses can be expressed as:  
\[P_{SW\_loss}=P_{SW(on)}+P_{SW(off)} \tag{10}\]  
The gate drive loss can be calculated by using Formula (11).  
\[P_{Gate}=\frac{(C_{iss}+C_{rss})\times V_{Gs}{}^{2}}{2\times T} \tag{11}\]  
where \(C_{isss}\) and \(C_{rss}\) refer to the input capacitance and reverse transfer capacitance of the power switch, respectively. \(V_{GS}\) is the gate-source voltage of the switch, and \(T\) is the switching period.  
The conduction loss of the power switch can be calculated by using Formula (12).  
\[P_{Cond}={I_{rms}}^{2}\times R_{DS(on)} \tag{12}\]where \(I_{rms}\) refers to the RMS drain current, and \(R_{DS(m)}\) is the conduction resistance.  
The total power losses of the power switch can be expressed as Formula (13).  
\[P_{Loss(SW)}=P_{SW\_loss}+P_{Gate}+P_{Cond} \tag{13}\]
# Current Knowledge on the Fungal Degradation Abilities
## 4 Enzymatic Action
Fungal proteolytic ability characterization in cultural heritage scenarios has been helpful to study the biodeteriorative contribution of isolates retrieved from air, funeral clothes and accessories, graphic documents, materials present in libraries and museums, frescoes, textiles, human remains, mummies, mural paintings, cinematographic films, wax seals, paper, parchment, wooden organs and pipes [28; 38; 83; 84; 78; 85; 86; 87; 88; 89; 93; 95; 96; 97; 98; 99; 100]. Proteolytic ability screening can be mainly conducted using Gelatin agar (R2A-Gel), Casein agar (CN), Milk Nutrient agar (MilkNA) and media containing rabbit glue [78; 94; 96]. After a period of incubation, positive proteolytic ability can be detected by flooding of agar plates with 10% tannin solution and the visualization of the formed hydrolysis zones [101]. Over one hundred and thirty species have been found to be able of promoting protein attack (Table 8). As similarly verified in other enzymatic activities, _Aspergillus_ and _Penicillium_ species also dominate this biodeteriorative profile. Nonetheless, a significant number of species from genera _Alternaria_, _Cidosporium_ and _Talaromyces_ displaying these characteristics can also be confirmed. Detection of these fungal species on proteinaceous materials will putatively result in their accentuated biodeterioration.
# Cm-Level Photonic-Crystal-Like Subwavelength Waveguide Platform with High Integration Density
## 1 Introduction
### A Square Lattice of Circular Air Holes  
Unlike the triangular lattice structures, the square lattice structures can appear as an integer column. Figure 7 shows the structures of the PhC-like subwavelength waveguides with the square-latticed air holes, while Figure 8 shows their spectra.  
Figure 6: The structures of two closely spaced waveguides and their propagating fields: (**a**) the two waveguides with 0.5 column air holes; and (**b**) the two waveguides with 1 column. Lengths of waveguides are 100 \(\upmu\)m at the wavelength of 1.55 \(\upmu\)m. The dotted lines represent the boundaries between metal and silicon.  
2.1 The Propagation Loss and Integration Density Versus Column Numbers of Square Lattice Air-Holes Waveguide  
Figure 9 shows dependences of the propagation loss and integration density on column number of air holes in the square-lattice hybrid waveguide. It reveals that the trends of the propagation loss and integration density of the hybrid waveguide with square-lattice are similar to those of the waveguide with triangular lattice. Similarly, the transmittances of the waveguides are close to 1 around 1.45-1.57 \(\upmu\)m.  
The comparison between Figures 4 and 9 does not show a pronounced difference on the trend. Only there is the difference of waveguide width between the structure (1.59 \(\upmu\)m) with 1.5 columns of air holes and the structure (1.14 \(\upmu\)m) with 1 column of air holes, which brings different integration densities of 629/mm for the former in Figure 4 and 877/mm for the latter in Figure 9.
# The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
## 1 Introduction
The results of the study showed that there is an inverse relationship between the level of interphase adhesion and the damping properties of the composites, meaning that lower adhesion leads to higher energy dissipation. This is advantageous for applications where high damping properties are required.
## 3 Results and Discussion
### Statistical Validation  
As a simple assessment of the statistical quality of the prime number sequences obtained, the distribution of 10 M samples was plotted for increasing lengths in each of the four cases considered. To illustrate these results, the 32-bit prime output is represented for the CS and QS cases, respectively, in Figure 6a,b. The distributions seen for all other lengths evaluated can be found in Figure A1 from Appendix A. As can be seen, the distribution significantly improves after applying the binning algorithm and uniformizing the input noise. In fact, both the raw CS and QS distributions appear to be clustered around certain values in the domain of possible outcomes, presenting prominent peaks in the number of counts. Meanwhile, the uniformized sources approach the desired uniform distribution among all possible outcomes. As seen in Figure A1, this behavior remains remarkably consistent across all prime lengths, with the outcome distribution following the same recognizable pattern. Given this, the raw noise sources appear to not be suitable for safe prime number generation and will be excluded from the following analysis. Additionally, as is exemplified in Figure 6c, we see an additional improvement of the prime distribution when the QS is considered.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Prime Length** & **QS** & **Uniformized CS** & **Uniformized QS** & **Uniformized QS** \\
**(Bit)** & **(CS)** & **(CS)** & **(CS)** & **(Uniformized CS)** \\ \hline
32 & 890.7 & 837.1 & 8749.0 & 844.3 \\
64 & 53.7 & 33.4 & 54.0 & 15.5 \\ \hline \end{tabular}
\end{table}
Table 1: Difference (expressed in %) in the diversity of prime numbers found, when compared with the CS and uniformized CS cases, for the QS, uniformized CS, and uniformized QS schemes. In each case, all the prime numbers found in the entire 1 GB dataset were considered. Data from [34].  
Figure 6: _Cont._
# Complex Processing of Saponite Waste from a Diamond-Mining Enterprise
## 3 Material Balance Calculation  
According to the company report for 2017 [7], 2,640,000 carats of diamonds were produced. The amount of ore processed at the processing plant in the same year is 3,259,000 tonnes.  
To calculate the ratio, it is necessary to convert the data obtained into kilograms. We calculate the mass of all extracted diamonds based on a ratio of 1 carat = 0.0002 kg:  
Weight of all diamonds (m\({}_{\text{Diamonds}}\)) = 2,640,000 carats \(\times\) 0.0002 kg/carat = 528 kg.  
We also convert the mass of ore processed from the material (10\({}^{6}\) kg of ore using material from the mine) at the processing plant:  
\[\text{Ore mass (m}_{\text{Ore}}\)) = 3,259,000 t = 3259 \(\times\) 10\({}^{6}\) kg  
Calculating the ratio of the mass of all the diamonds obtained to the mass of the processed ore, we find that the mass of all diamonds achieved from the ore mass equals the following:  
\[528\text{ kg}/(3259\times 10^{6}\text{ kg})\approx 1.62\times 10^{-7}\]  
Thus, 1 kg of ore delivered to the processing plant contains 1.62 \(\times\) 10\({}^{-7}\) kg of diamonds.  
The approximate ratio of the mass of jewelry-quality diamonds to the mass of processed ore is also a point of interest. Gem-quality diamonds make up 10% of the total amount of diamonds extracted at the processing plant [7]. Thus, one can calculate this ratio by taking 10% of the previous number:  
Weight of gem-quality diamonds per ore mass (m\({}_{\text{Gem-quality}}\)) = 1.62 \(\times\) 10\({}^{-7}\)\(\times\) 0.1 = 1.62 \(\times\) 10\({}^{-8}\) kg  
We find that 1 kg of ore received at the processing plant contains 1.62 \(\times\) 10\({}^{-8}\) kg of diamonds of jewelry quality.  
The value of 528 kg of diamonds is negligible in comparison with 3259 \(\times\) 10\({}^{6}\) kg of extracted ore. Thus, after the processing process, the tailings dump receives almost the same mass of waste as the mass of ore delivered to the processing plant: on average, it is about 3250 million tonnes of waste per year.
###### Abstract  
The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow monitoring through a synthetic aperture radar (SAR) signal processing technique that has the capability to perform water flow elevation estimation. This paper proposes the application of a new method for the design of a robust procedure to track over the time double-bounce reflections from bridges crossing rivers to measure the gap space existing between the river surface and bridges. Specifically, the difference in position between the single and double bounce is suitably measured over the time. Simulated and satellite temporal series of SAR data from COSMO-SkyMed data are compared to the ground measurements recorded for three gauges sites over the Po and Tiber Rivers, Italy. The obtained performance indices confirm the effectiveness of the method in the estimation of water level also in narrow or ungauged rivers.  
synthetic aperture radar (SAR); rivers water-flow elevation estimation; pixel-tracking; phase unwrapping 2019 11 2574 doi:10.3390/rs11212574