esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Discussion
**(c)**: **Discussion of the computational complexity of the proposed method:** When evaluating the computational complexity of our proposed dual-branch learning network, we recognize that, while the network significantly enhances image quality through the use of TAB, MMFIM, SMFFM, these advanced feature extraction techniques--such as attention mechanisms, convolution operations, and residual connections--also introduce notable computational overhead. Our tests show that, on a system equipped with an NVIDIA 3090 Ti GPU and 24 GB of memory, processing 96 brain images with a resolution of 256 \(\times\) 256 pixels take approximately 18 s, averaging 0.2 s per MR image. While this performance is sufficient for many clinical applications, there is still room for improvement in scenarios requiring real-time feedback. To address these concerns, we plan to explore techniques like Linformer [56], which approximates the attention mechanism to reduce computational complexity, or model pruning to reduce model size. Our ultimate goal is to develop a lightweight model that can meet the real-time processing demands of clinical applications. Figure 12: Ablation study of the key components in the proposed method using 1D random undersampling pattern with acceleration factor 8\(\times\) on fastMRI brain datasets.
# Regeneration Performance of Activated Carbon for Desulfurization
## Results and Discussion
### AC Regeneration  
The SO\({}_{2}\) breakthrough curves after ten adsorption desorption cycles are illustrated in Figure 7. Under suitable adsorption conditions (4 g AC, 1.65 L/min flow gas, and 5% O\({}_{2}\)), the average desulfurization rate of fresh AC over the first ten minutes is about 96%, and the highest desulfurization rate of all the samples achieves more than 90%. The desulfurization efficiency decreased gradually after multiple regenerations. First, one of the reasons maybe that the micropores became disrupted after thermal treatment. This problem can be minimized in mesoporous carbon materials, such as porous carbon nanofibers, owing to their thermal and mechanical stability [30]. Second, the surface of AC produced some sulfates or oxygen groups in the regeneration [31; 32], a part of the micropores of AC was blocked after this situation. In addition, a certain amount of carbon loss may occur during the process of thermal treatment. Despite this, the highest desulfurization efficiency of 95.4% was still achieved at the tenth cycle.  
Figure 6: Effect of change in oxygen content on SO\({}_{2}\) adsorption behavior (operated at 60 \({}^{\circ}\)C, 4 g sample, 2000 ppm SO\({}_{2}\), N\({}_{2}\) to balance, and 1 L/min total flow rate).  
As shown in Figure 8, the sulfur capacity of fresh AC is 38.88 mg/g. After the first heating regeneration at 400 \({}^{\circ}\)C, the decrease in sulfur capacity is most obvious. Though the SO\({}_{2}\) capacity of AC decreases continuously with multiple cycles, the regenerated AC after 10 cycles showed a sulfur capacity of 23.44 mg/g for SO\({}_{2}\) adsorption, which only decreased by 15.44 mg/g compared to that of fresh AC, while after the seventh time, the rate of sulfur capacity decrease became slower. [PERSON] et al. [15] showed that the adsorption capacity of CuFe\({}_{2}\)O\({}_{4}\)/AC magnetic adsorbent is high, but the adsorption capacity of CuFe\({}_{2}\)O\({}_{4}\)/AC decreases significantly after regeneration. According to [PERSON] et al. [33] biochar made from cow manure, sewage sludge, and rice husks showed an SO\({}_{2}\) adsorption capacity of up to 64 mg g\({}^{-1}\), but its specific surface area was too low (the maximum was 42 m\({}^{2}\) g\({}^{-1}\)). In comparison, the adsorption capacity of commercial AC in the regeneration process did not significantly decrease. The reason is that in the process of sulfuric acid etching, the pore widths in the range of microporous to mesoporous increased, which improved the capacity for storing sulfuric acid, and new micropores were produced in the process of sulfuric acid etching. Those conditions are good for the recovery of the porous volume and adsorption capacity of AC after the cycles of regeneration.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Datasets and Preprocessing
#### 2.2.3 Climate Data  
In this work, we mainly used three kinds of meteorological data, including air temperature (T), dew-point temperature (Td), and photosynthetically active radiation (PAR), which were all downloaded from the GEE platform. The daily PAR from 2000-2019, with a resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), was derived from the BESS (Breathing Earth System Simulator) product. This product's data exhibits a robust linear relationship with in situ measurements (R\({}^{2}\) = 0.94, and the relative bias = 1.7% for PAR) [40]. The air temperature and dew-point temperature, with a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), were derived from ERA-5. The VPD was calculated using the T and Td according to [PERSON] et al. [41]. To keep the same resolution as the NDVI, the climatic data were resampled to 1 km, and a 16-day average was calculated over the same time frame as the NDVI.
# Eye Movement Parameters in Children with Reading Difficulties
## 2 Method  
### Participants  
Altogether, 379 children (6-13 years old; 183 boys and 196 girls) from four basic education schools of Latvia were voluntarily involved in the study. Since the number of participants who were 6 years old (n = 6) and 13 years old (n = 1) was low, only the data from 7-12-year-old children were involved in further data analysis, leading to 371 children (7-179 boys and 192 girls) as the final number of participants. The demographic statistics of the participant data are represented in Table 1. All participants underwent general visual function evaluation, including visual acuity at near (65 cm) and eye dominance evaluation (Dolman test). Only the participants with the uncorrected binocular and monocular visual acuity of at least 0.6 decimal units (with or without contact lens correction) were involved in the eye tracking study. None of the children reported any previous eye diseases or other functional disorders that could affect eye movement parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Demographics** & **Reading Difficulties (RD)** & **Control Group (CG)** \\ \hline \multirow{3}{*}{Saccadic eye movements} & Gender \({}^{*}\) & M n = 49 & M n = 119 \\  & & F n = 41 & F n = 137 \\  & Age & 9.71 \(\pm\) 1.65 & 9.16 \(\pm\) 1.61 \\ \hline \multirow{3}{*}{Smooth pursuit eye movements} & Gender \({}^{*}\) & M n = 48 & M n = 120 \\  & & F n = 44 & F n = 136 \\  & Age & 9.74 \(\pm\) 1.68 & 9.17 \(\pm\) 1.62 \\ \hline \multirow{3}{*}{Fixation stability} & Gender \({}^{*}\) & M n = 42 & M n = 98 \\  & & F n = 32 & F n = 106 \\  & Age & 9.39 \(\pm\) 1.76 & 8.90 \(\pm\) 1.62 \\ \hline \hline \end{tabular} \({}^{*}\)M—male, F—female.  
\end{table}
Table 1: Demographic statistics of the two study groups—children with reading difficulties (RD) and the control group (CG).
## 3 Results and Discussion
### Optimization of Experimental Conditions
#### 3.4.3 The Effect of ST-AP Concentration on the Peak Current  
Figure 6 shows the relationship between the ST-AP concentration and the peak current of \(\alpha\)-NP. It can be seen that the current value increases significantly when the ST-AP concentration increases from 0 to 0.7 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and then reaches a stable value. Therefore, 0.7 \(\mathrm{\SIUnitSymbolMicro g}\)/mL is used as the optimal concentration of ST-AP in the following experiments.
## 4 Discussion
### Strategies to Improve Gluten-Free Dough Rheology and Bread Characteristics  
#### 4.1.1 Careful Selection of the Base Flour  
A key issue is to select the most suitable replacement for wheat flour. The substitute must have adequate nutritional content, dough rheology, technological characteristics, and taste.  
Several authors have highlighted the potential of rice flour, due to its neutral taste, white color, low sodium content, easily digested carbohydrates, hypoallergenic properties [3], and low level of prolamins [15]. However, its starch granules can significantly influence dough rheology and bread texture, especially when present in large amounts (>10%) [14; 15; 17]. It is important, therefore, to carefully select the type of flour as a function of the desired final product, as different flours have different impacts on quality [14; 15; 17]. This observation is supported by [PERSON] et al. (2018) [21], who highlighted significant differences in amylose content, damaged starch, water absorption, starch granule dimensions, dough rheological properties, and loaf volume. Many authors have found it to be particularly suitable when blended with other sources of starch (such as potato or corn), and in combination with a suitable hydrocolloid [13; 14; 15; 17].  
Another interesting alternative is corn. It is important to highlight that corn and legumes are already widely-used in pasta production. Gluten-free doughs and bread formulations based on cornstarch have been improved with binding agents such as xanthan gum, guar gum, and locust bean gum, which substitute the gluten network [15]. Their addition has been found to significantly increase loaf volume [14; 15; 17]. Ground corn is already used in products such as corn meal, corn starch, masa farina and corn flour. Corn flour is used in the manufacture of food products such as tortillas, corn pasta, pancake mixes, corn chips, and cereal, as well as in home cooking for stews, soups, and casseroles [15].  
Teff is another promising substitute. Teff contains 11% protein, 80% complex carbohydrates, and 3% fat [15]. It is an excellent source of essential amino acids, especially lysine, which is most often deficient in grain [15]. Furthermore, it is an excellent source of fiber and iron, and contains far more calcium, potassium, and other essential minerals than other grains [14; 15; 17]. It is widely used in products such as fermented flat bread (injera), and is mixed with sorghum and pear millet to produce another flat bread (kisra) in Sudan and Eritrea [15]. Teff is an adjunct in homemade beverages and beer, and is used to make waffles, chocolate cake, muffins, biscuits, cookies, soups, stews, and puddings [15].  
Finally, pseudo-cereals such as amaranth and buckwheat are another interesting alternative, as their protein structure and aminoacidic composition make them a very valuable addition to food [14; 15; 17]. Combined with hydrocolloids and emulsifiers, they have been found to produce high-quality gluten-free doughs (with increased water absorption), and bread with increased volume, lower staling, and a better nutritional profile [15].
## 2 Materials and Methods
### Optimization Problem of DPE
Instead, these effects are assumed to be mitigated through differential GNSS corrections or pre-calibrated error models. This is acceptable since our focus is on evaluating the robustness and efficiency of the proposed DPE algorithm, not detailed signal error modeling. Second, although GNSS systems provide carrier-phase observations, this study does not utilize phase-based positioning. The proposed method operates on complex baseband correlation outputs, where no carrier-phase ambiguity resolution is required. The polarity ambiguity introduced by modulation is resolved by detecting the telemetry preamble. Thereby, the time-frequency parameterization model of the received signals in (2) can be represented by the parameter vector \(\mathbf{\gamma}\) in DPE:  
\[\mathbf{y}=\mathbf{S}(\mathbf{\gamma})\mathbf{a}+\mathbf{n}. \tag{5}\]  
The fundamental concept of DPE involves the simultaneous processing of observations from all visible satellites. Differing from traditional 2 SP, it directly correlates PVT estimateswith the received signal in the navigation domain. Note that the tilde symbol \(\bar{\cdot}\) is used to denote the candidate's parameters. By calculating \(\tau_{i}(\hat{\tau})\) and \(f_{d_{i}}(\hat{\tau})\) for each satellite, a signal replica for each candidate PVT is generated and correlated with the received signal. The correlation values of all candidate points are then combined into a correlogram, where the result with the highest correlation value yields the MLE of the PVT. The corresponding optimization problem is given by  
\[\hat{\gamma}=\arg\max_{\hat{\tau}}\left\|y^{\mathrm{H}}\mathbf{S}(\hat{\gamma })\right\|^{2}. \tag{6}\]  
DPE usually employs a global search strategy to determine the candidate corresponding to the maximum correlation value. By synthesizing information from all satellite signals simultaneously, DPE shows improved robustness compared with traditional methods, thus enabling the receiver to maintain high positioning stability in complex environments.
## 5 Deep Structurural Features and Analysis
### Analysis of Structure Characteristics  
Combining the regional geological and deep structural analysis (Figure 8) of the Huailai area revealed that in the upper crust, the shallow low-resistance C2 body shown on the electrical profile appears as a horizontal layered reflector in the seismic reflection. This corresponds to the sedimentary cover and strata of the basin, and its thickness gradually increases from the edge to the center of the basin; the average thickness is about 2 km [13]. The high-resistance R1 and R2 bodies under the Huailai Basin and Xuanhua Basin have no obvious reflection interface and appear as strong reflectors in the seismic reflection, showing characteristics of uniformity and stability; this is presumed to be the reflection of the Archean gneiss of the crystalline basement of the basin, with a thickness of about 25 km [12]. On the horizontal scale, along the Huailai-Xuanhua direction, the thickness of  
Figure 8: Deep geoelectric structure (**a**) and seismic reflection imaging (**b**) in the Huailai area (seismic reflection imaging modified according to [29]).  
the upper crust tends to increase [35], which corresponds with the high resistance R1 and R2 bodies in the electrical structure.  
In the lower crust, the electrical profile shows the presence of the high conductor, C1. At the same position in the reflected seismic profile, there is also a strong energy bright spot reflection anomaly, which extends downward through the Moho plane. Due to the depth limitation of the reflected seismic date, the bright spot reflection is cut off to a depth of about 55 km (Figure 8b). The result of the MT profile shows that the C1 anomaly has a deeper root, which penetrates the lithospheric mantle and connects with the asthenosphere. A seismic bright spot reflector in the lower crust is usually interpreted as magma, water, or a combination of both [36]. The MT result showed that the C1 extends to 100 km, which is obviously unsuitable for interpretation as only water. In addition, early studies indicated that there may be an upwelling of mantle-derived material in this area [3; 4; 12; 37]. It can be inferred that the formation of the high-conductivity C1 anomaly is closely related to the magma or upwelling of deep mantle-derived material.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 2 Mathematical Preliminaries: SVS Indices
### Transient Voltage Severity Index (Tvsi)  
There is another index similar to \(TVI\) which is used in [17; 18] as the voltage index for dynamic VAR planing called the transient voltage severity index (\(VSRI\)). This index is calculated using (17).  
\[TVSI(t_{f})=\frac{\sum\limits_{i=1}^{n}\sum\limits_{t=t_{cl}}^{t_{f}}TVDI_{i, t}}{n\times(t_{f}-t_{cl})} \tag{17}\]  
Figure 1: \(TVI\) concept.  
\(TVDI\) is the transient voltage deviation index and calculated by  
\[TVDI_{i,t}=\left\{\begin{array}{cc}\frac{\left|y_{i}(t)-v_{i,0}\right|}{v_{i,0} }&\frac{\left|y_{i}(t)-v_{i,0}\right|}{v_{i,0}}>\mu\\ 0&o.w\end{array}\right. \tag{18}\]  
The \(\mu\) parameter is maximum acceptable voltage deviation and usually is equal to 20%.
## 3 Results and Discussion
### Trivariate Histogram Analysis of the Three Modalities  
The logical extension of the uni- and bivariate histogram analysis of registered slices is the generation of a trivariate histogram. This 3D histogram also allowed for a meaningful application of k-means clustering, see Figure 11. Recoloring of the histology slice gave rise to an unexpected insight into the components of the jaw tissues: the clustering discriminates mature bone (blue-colored), newly formed bone (red-colored), connective tissue with fat (green-colored) and soft tissues with many nuclei (black). The clear separation between green-black and red-blue regions in the trivariate histogram corresponds to the evident delineation between soft and hard tissues. We observe from the angle of the green-black division on the SR\(\upmu\)CT-histology axis of the trivariate histogram that discrimination of connective tissue and fat from soft tissues with many nuclei is mostly based on the H&E-stained histology. Even the hard tissues can be divided into mature bone and newly formed bone, mainly because of the degree of calcification detected by hard X-ray imaging. The further discrimination of the soft tissue components led to the hypothesis that combining  
Figure 10: Corresponding data for the common area from the selected slices of two of the three modalities can be displayed as 2D histograms. Analysis of these bivariate histograms applying k-means clustering enabled segmentation. The segmented anatomical features within the slices are colored to elucidate the structural similarities between the pair-wise combination of the three selected modalities, see images in the third column.  
the three modalities could be a way of discriminating between different types of collagen. This hypothesis could be tested in a future study with suitable staining [34; 35].
# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
## 4 GANA-VDC System Architecture
### Virtual Network Constraints  
VDC allocation aims to map VN to the underlying fat tree physical network. We define a VN topology to not extend beyond three layers, as, in general, the majority of the VDC applications rely on an architecture formed in three layers: access, core, and aggregation. We use XML description to describe the GANA model, in which the depth must be \(\leq\)3. Moreover, we define additional constraints such that each VS is connected by a number of VMs given by the number of ports existing on the physical switch. In defining these constraints, we must agree with the number of real layers the underlying physical network (fat-tree type) can manage.
## 3 Methodology  
### The Proposed Model  
We propose an end-to-end multi-task learning model integrating glyph restoration, text detection, and recognition into a unified framework. The model is designed to improve the robustness and overall accuracy of text extraction from historical book images and stone inscription images, particularly in challenging scenarios with low image quality, complex background noise, and unclear character structures. By leveraging multi-task learning on shared features, the model effectively captures both the structural characteristics of characters and contextual semantics, which enhances the stability and generalization of text extraction. As shown in Figure 2, unlike traditional multi-stage approaches, our method employs a single shared backbone network for hierarchical feature extraction from the input image. Task-specific branches are then used to perform image-level glyph denoising and region-level text detection and recognition. This architecture simplifies the overall pipeline, reduces deployment and maintenance complexity, and improves cross-task learning efficiency through feature sharing. The network structure and parameters of each module in Figure 2 correspond to those summarized in Table 1.  
Specifically, the model consists of three main components: a shared feature extraction network, a glyph restoration branch, and a text detection and recognition branch. The shared backbone network is based on a ResNet-like multi-layer residual structure, which extracts bottom-up multi-scale hierarchical features. These features serve as common inputs to the task-specific branches, providing rich and stable representations. The glyph restoration branch combines shallow visual details with deep semantic features. It performs image-level reconstruction and denoising of character regions through upsampling and feature fusion, generating a full-image character segmentation map. This output enhances the readability of degraded text and supports text extraction. The text detection and recognition branch focuses on extracting high-level semantic information related to textual content from the shared features. It integrates both detection and recognition sub-modules to predict the spatial locations and class labels of characters. By sharing backbone features and jointly optimizing the three tasks during training, the model improves text extraction performance in complex scenes while maintaining overall computational efficiency. Figure 2: Architecture of the proposed model. Among them, the glyph restoration branch adopts a typical encoder-decoder architecture. The encoder is implemented by the shared backbone network, which extracts hierarchical visual features from the input image. The decoder progressively upsamples the feature maps to restore the original spatial resolution. During the encoding stage,