esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Experimental Setup
The AOMs worked in the double-pass configuration to eliminate the movement of light spots. The three beams were then focused into a 3-mm Pr:YSO crystal propagating along the b-axis of the crystal (also along the 3-mm side). The crystal was kept at 3.4 K in a cryostat (Optistat AC-V, Oxford Instruments) to reduce the decoherence effect caused by phonons. The output signals after the cryostat were recorded by an intensified charge-coupled device (iCCD). The EIT effect can be observed and light pulses can be stored in Pr:YSO crystals under appropriate conditions (the details can be found in the following sections).
## 5 Results and Discussions
### Physico-Chemical Parameters  
The pH of water reflects its quality and provides data for geochemical equilibrium and solubility calculations [53]. The pH is a measure of the hydrogen ion concentration, which is indicative of the acidic nature of the water. In January 2020, the hydrogen ion concentration (pH) of the water samples ranged from 7.14 to 8.15, with a median value of 7.41, and in January 2021, it ranged from 7.5 to 8.6, with a median value of 8.12. The map of the spatial distribution of pH shows that elevated pH values affected the northern and central regions of the study area in January 2020, and the southern region to the central and eastern coastal regions in January 2021 (Supplementary Figure S1a,b). The dissolved constituents of water are measured indirectly through the EC of the water. The EC values in this study ranged from 266 \(\mu\)S/cm to 10400 \(\mu\)S/cm in January 2020, with a median of 1156 \(\mu\)S/cm, and from 504 \(\mu\)S/cm to 6011 \(\mu\)S/cm in January 2021, with a median of 1111 \(\mu\)S/cm. In January 2020, the EC values were high in the southern, central, and northern coastal regions (Figure S2a), while in January 2021, they were high in the southern and central coastal regions (Figure S2b). Total dissolved solids (TDS) refers to the total amount of all inorganic and organic matter distributed in a volume of water, including minerals, salts, metals, cations, and anions. Cations such as Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), and K\({}^{+}\), and CO\({}_{3}\)\({}^{2-}\), HCO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\) are the most common constituents of groundwater. In January 2020, the TDS values varied between 186 mg/L and 7280 mg/L, with a median value of 809 mg/L, and in January 2021 they ranged between 834 mg/L and 6857 mg/L, with a median value of 1627 mg/L. The distribution pattern of TDS values was similar to that of EC values, as both parameters were related (Figure S3a,b).
# Gradient Correlation Functions in Digital Image Correlation
###### Abstract  
The performance of seven different correlation functions applied in Digital Image Correlation has been investigated using simulated and experimentally acquired laser speckle patterns. The correlation functions were constructed as combinations of the pure intensity correlation function, the gradient correlation function and the Hessian correlation function, respectively. It was found that the correlation function that was constructed as the product of all three pure correlation functions performed best for the small speckle sizes and large correlation values, respectively. The difference between the different functions disappeared as the speckle size increased and the correlation value dropped. On average, the random error of the combined correlation function was half that of the traditional intensity correlation function within the optimum region.  
image correlation; gradient correlation functions; laser speckles +
Footnote †: journal: Applied Sciences
## 7 Discussion
### Challenges and Limitations  
Despite these advancements, several persistent challenges limit the broader applicability of current models. One of the most pressing issues is generalizability. Many high-performing DL models are developed and validated using patient-specific datasets, which tend to overfit to the unique signal properties of a limited cohort. This restricts the ability of such models to be scaled across diverse populations. Cross-patient variability in EEG patterns, coupled with inconsistencies in acquisition protocols, electrode placement, and sampling frequencies, further undermines reproducibility and external validity [27; 73; 79].  
Another major limitation involves the scarcity of preictal data, which leads to class imbalance and reduced model robustness during training. Although data augmentation techniques, including generative adversarial networks (GANs) [86] and synthetic EEG generation, have been proposed to mitigate this issue [36], their effectiveness is highly dependent on dataset characteristics and model architecture.  
High-resolution models such as 3D CNNs and Transformers also present significant computational challenges. These architectures, while powerful, often require large memory footprints and high processing speeds, limiting their deployment in real-time or embedded systems. Lightweight alternatives, such as LMA-EEGNet and model pruning strategies, offer potential solutions [74], but more work is needed to balance classification accuracy with computational efficiency.  
Interpretability remains a major barrier to clinical adoption. Although temporal features offer a degree of transparency, spectral representations learned by deep models often function as black boxes. Emerging explainable AI methods, including saliency maps, SHAP values, and attention-based visualization tools, have attempted to clarify model reasoning [65]. However, these approaches are still in their early stages and often fall short of providing clinically actionable explanations.
# MiFT: A Moment-Based Local Feature Extraction Algorithm
## 4 Experimental Results
### Keypoint Matching Accuracy
#### 4.1.4 MDGHM Parameter Effects on Performance  
We examined the effects due to MDGHM parameters standard deviation and derivative degree by fixing the MDGHM mask size, using F-score, as shown in Figure 9. The lower plane in each subfigure represents the SIFT F-score, the curve represents the MIFT F-score. Although the performance results exhibit some oscillation, MIFT outperformed SIFT for most parameter settings. In particular, MIFT achieved high F-scores for rotation, viewpoint, and illumination deformations for all parameter settings, with margin > 0.1 between MIFT and SIFT. For scale, blur and JPEG compression deformations, MIFT F-score was higher than SIFT for most parameter cases.  
Thus, the proposed MIFT method always provided superior performance than SIFT for appropriate parameter choices. MIFT exhibited the best efficiency in terms of matching accuracy when derivative order \(\approx\) 3-7 and sigma \(\approx\) 0.3-0.5.
# Facile Preparation of Graphene Oxide-MIL-101(Fe) Composite for the Efficient Capture of Uranium
## 2 Materials and Methods  
### Chemicals  
Graphite (CP grade, \(\leq\)300 mesh) was purchased from Sinopharm Chemical Regent, Beijing, China. Iron(III) chloride hexahydrate (FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, 97 wt%), 2-aminobenzene-1,4-dicarboxylic acid (NH\({}_{2}\)-H\({}_{2}\)BDC, 98 wt%), and \(N\),\(N\)-dimethylformamide (DMF, 99.8 wt%) were provided by Alfa Aesar, Shanghai, China. Polyvinylpyrrolidone (PVP, Mw = 30,000) was provided by Xilong Scientific Co., Ltd., Shanghai, China. Uranyl nitrate was obtained from Sigma Aldrich, Beijing, China. The graphene oxide (GO) was synthesized based on the Hummers method [43].
## 2 Principle of Doppler Reflectometer for Fusion Plasma Science  
Nowadays, Doppler reflectometry (also called Doppler back-scattering: DBS) is a powerful diagnostic tool for plasma turbulence studies, because it can measure the perpendicular velocity of electron density fluctuations \(v_{\perp}\), the radial electric field \(E_{r}\), and the perpendicular wavenumber spectrum \(S(k_{\perp})\) in high-temperature magnetized confinement plasmas with high spatial and temporal resolution.  
The principle of Doppler reflectometry in the toroidal plasma measurements is explained simply as follows. When a probing microwave/millimeter-wave beam is injected into a plasma and approaches a cut-off layer with an oblique angle to the cut-off surface, a back-scattering occurs, caused by the density fluctuations which matches the Bragg condition, i.e., \(\mathbf{k}=-2~{}\mathbf{k}_{\mathrm{i}}\) (where \(\mathbf{k}_{\mathrm{i}}\) is the local wave vector of the launching beam). The power of the back-scattered radiation is proportional to the density fluctuation amplitude, that is, \(P_{s}\propto|\widetilde{n}_{e}|^{2}\). In addition, when the fluctuations move with a velocity \(\mathbf{v}\), the back-scattered signal shows a Doppler frequency shift \(2\pi~{}f_{\mathrm{D}}=\mathbf{v}\cdot\mathbf{k}~{}\approx v_{\perp}k_{\perp}\). Figure 3 shows the example of the frequency spectrum obtained in the LHD plasma experiment. The frequency broadening caused by the density fluctuation and the Doppler shift is clearly observed. In addition, the frequency component of 1.86 GHz is the IF frequency down-converted from the carrier wave. The perpendicular velocity is a composition of the plasma background \(\mathbf{E}\times\mathbf{B}\) velocity \(v_{ExB}\) and the intrinsic phase velocity of the density fluctuations \(v_{ph}\), and this composition is given as \(v_{\perp}=v_{ExB}+v_{ph}\). If \(v_{ph}\) is known or \(v_{ph}\ll v_{ExB}\) (which is usually satisfied at the plasma edge in magnetized confinement toroidal plasmas), the radial electric field \(E_{r}\) can be extracted. The rapid change of \(E_{r}\) and its radial structure is considerable in playing an important role in plasma confinement, such as the H-mode transition. Therefore, simultaneous measurement of multiple spatial points is required.  
Figure 3: Example of Doppler shift frequency spectrum obtained by reflectometer in LHD. Here, \(f_{\mathrm{comb}}=1.86\) GHz is IF frequency down-converted from carrier wave and \(f_{\mathrm{D}}\) is estimated Doppler shift frequency.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 2 Data and Methods
### Data Analysis Methods
#### 2.3.2 Partial Correlation Analysis  
The relationship between WUE and temperature, precipitation, and EVI was explored by second-order partial correlation. The correlation between one independent variable and ecosystem WUE, as the dependent variable, was analyzed separately by controlling for other variables. This method has been used in prior studies to overcome the shortcomings of simple correlation analysis [25; 26; 27].  
\[r_{xy,z1z2}=\frac{r_{xy,z2}-r_{xz1,z2}\ast r_{yz1,z2}}{\sqrt{1-r_{xz1,z2}^{2} \sqrt{1-r_{yz1,z2}^{2}}}} \tag{6}\]  
\(r_{xy,z2}\), \(r_{xz1,z2}\), and \(r_{yz1,z2}\) are calculated from Equation (7) as follows:  
\[r_{xy,z}=\frac{r_{xy}-r_{xz}r_{yz}}{\sqrt{1-r_{xz}^{2}\sqrt{1-r_{yz}^{2}}}} \tag{7}\]\(r_{xy}\) is the correlation coefficient, which is calculated from Equation (8):  
\[r_{xy}=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{ \sqrt{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}}}\sqrt{\sum\limits_{i=1}^{ n}{(y_{i}-\overline{y})^{2}}}} \tag{8}\]  
\(r_{xy,z1z2}\) is the partial correlation coefficient of variables \(x\) and \(y\) after excluding the effect of the control variables \(z_{1}\) and \(z_{2}\).  
The significance of the partial correlation coefficient was determined with the \(t\)-test:  
\[t=\frac{r\sqrt{n-q-1}}{\sqrt{1-r^{2}}} \tag{9}\]  
where \(r\) is the partial correlation coefficient, \(n\) is the number of samples, and \(q\) is the number of degrees of freedom. A probability (\(p\)) value \(\leq\) 0.05 was considered statistically significant.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 2 Measurement Geometry under LEO Augmentation
### Satellite Numbers
This indicates that the mis-modelled multipath effects in kinematic and complicated environments are likely to be categorized as measurement noise, which is usually done in actual GNSS-based positioning for simplicity, but actually incorrect. In such cases, low-elevation LEO signals might have large noise that needs to be properly down-weighted, but not mis-modeled biases that need to be strongly down-weighted or screened out in extreme cases. All these imply that in urban canyons or other environments with a limited sky view, a lower elevation mask can be set for LEO satellites.
## 4 Discussion
### Impacts of Killing Methods on Storage Trail (Lipid Oxidation Content)  
The sample characteristics and environmental conditions during storage were the major factors which influenced the level of lipid oxidation [34]. The occurrence of free radicals and tissue damage led to lipid oxidation from the reaction between lipids and enzymes [37; 38]. Medium-chain saturated fatty acids, called lauric acids, were a major component of the fatty acid profiles of BSFs, which have a low ratio of polyunsaturated fatty acids compared with vegetable oils [5; 6]. However, saturated fatty acids were the main component of BSFs. The high proportion of lipids was observed in BSFs depending on their diets [5; 6]. Therefore, the characteristics of BSFs carry the risk of presenting lipid oxidation. Moreover, inappropriate storage conditions of high oxygen, temperature, water activity and optimal pH for metabolism can accelerate lipid oxidation [34].  
The freezing method was only able to decrease the metabolic enzyme activity but did not terminate it; therefore, lipid oxidation was still occurring [9; 10]. Moreover, the metabolomics approach confirmed that several enzymatic methods were activated during killing by freezing because the adaptation to survive in a cold environment was established as the insect was still alive during this slow killing procedure [9]. In addition, cell membranes were damaged by ice crystals obtained from the slow freezing procedures, which led to the risk of higher lipid oxidation [8; 10]. On the one hand, the freezing method deteriorated the lipid quality of yellow mealworm (_Tenebrio molitor_: TM) [39].  
The reduction in lipid oxidation on the first day after oven drying and grinding was observed with the vacuum procedure because lipid oxidation was delayed by the lack of oxygen [34]. Blanching was considered as the best procedure to conserve the fat integrity of BSFs based on the results in this study and others [7; 8] because heat treatment can destroy the enzymes which reduce the lipid oxidation process. On the other hand, blending was suggested as a way to conserve fat quality, whereas blanching caused severe deterioration of tissues, leading to high lipid oxidation [10]. According to this information and the results of this study, the lower proportion of lipids in blending methods compared with others [10] could be the cause of these different outcomes. However, further analysis could confirm this hypothesis.
## 2 FRC Under Chloride Ingress
### Behaviour of FRC Under Tensile Stress  
The beneficial effects of fibres on post-cracking concrete performance are well established after decades of research. Once the concrete's tensile strength is exceeded, further crack opening is influenced not only by the concrete's tensile strength, as in plain concrete (PC), but also by the bond between fibres and the matrix (pull-out) and the fibres' own tensile strength [27]. Depending on the characteristics of the constituent materials, such as fibre volume fraction (\(V_{\ell}\)), bond strength or fibre orientation, FRC elements might exhibit strain-softening (where the deformations localise in one crack) or strain-hardening (where multiple cracking occurs before the peak load is reached) behaviour [28]. Relative to PC, FRC elements are found to be more ductile, with narrower and more closely spaced cracks [29; 30; 31], implying that the ingress of chlorides through FRC might be slower than in the case of PC.  
On this account, extensive research has been conducted to investigate the impact of various fibres on the behaviour of fibre-reinforced concrete (FRC) under chloride ingress. Steel (S) fibres have been studied the most, with macro-synthetic (MS) fibres, such as polyethylene (PE), polypropylene (PP) and polyvinyl alcohol (PVA), also receiving significant attention. Furthermore, the performance of hybrid-fibre-reinforced concrete (HyFRC), which combines multiple fibre types (Hy), was also investigated. In this study, the following naming convention is used: XFRC refers to instances where rebar is not used, with \"X\" representing the previously defined fibre material and \"FRC\" denoting fibre-reinforcedconcrete; in instances where both rebar and fibres are used, the prefix RR is added, i.e., RR-XFRC.
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 4 The Finite Element Models
### Damaged FE Models  
Considering the two typologies of damage described in Section 3.1, the occurrence of damage was simulated by reducing the stiffness in specific elements of the FE model. This is a classic model choice [53], frequently applied also in geometrically complex models [54]. The location of the damaged area is described in Table 2 and graphically portrayed in Figure 4.  
In Table 2, \(z_{\text{c}}\) and \(\theta_{\text{c}}\) represent the cylindrical coordinates of the centroid for the damaged area (the damage extends symmetrically from it along both \(z\) and \(\theta\)). C indicates the pipeline circumference (3.14 m). Damages #1 and #3 occupy an area of 0.0314 m\({}^{2}\), encompassing about 5% of the whole circumference. These are intended to represent localized damages, which are inherently more difficult to defect. For the same damage length along the \(z\)-axis, damage #2 covers 100% C, resulting in a much larger surface (0.628 m\({}^{2}\)). This second typology can be seen as both a weakened or degraded girth weld or a loose bolted joint.  
Several combinations of damage intensity levels have been considered. From hereinafter, these will be indicated by their percentage of reduction with respect to the pristine Young's modulus. For instance, 80%/20%/0% will indicate an 80% reduction for damage #1, 20% for damage #2, and no damage at \(\theta_{\text{c}}\) = 0\({}^{\circ}\) and \(z_{\text{c}}\) = 12 m. Damage growth was emulated as well, increasing step-by-step the damage intensity in the selected areas. This procedure mimics the natural progression of damage, even if at a discrete pace.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage ID \#** & \(z_{\text{c}}\) **[m]** & \(\theta_{\text{c}}\) **[\({}^{\circ}\)]** & **size (\(z\times\theta\)) [m] \(\times\) [\({}^{\circ}\)]** \\ \hline Damage \#1 & 8 & 90\({}^{\circ}\) & 0.20 \(\times\) 20\({}^{\circ}\) \\ Damage \#2 & 10 & 0\(\div\) 360\({}^{\circ}\) (whole circumference) & 0.20 \(\times\) 360\({}^{\circ}\) \\ Damage \#3 & 12 & 0\({}^{\circ}\) & 0.20 \(\times\) 20\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The location and extension of the damaged areas.  
Figure 4: The location of the three damaged areas.
# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 5 Conclusions  
Aiming at the problem that the existing neural network accelerator cannot directly execute the LSTM layers, the quantization method integrating LSTM layers is proposed. One of the innovations is that the LSTM layer is split into multiple regular layers supported by the neural network accelerator. The other is that the quantization calibration set takes into account the sequential characteristics of the LSTM layer. The proposed method for LSTM layers differs by only 0.803% in terms of accuracy between the deployment model  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **PC(CPU)** & **PCGPU** & **NPU** \\ \hline AlexNet Alone & / & / & 25.7 ms \\ \hline AlexNet-LSTM & 42.6 ms (1.6 times) & 34.2 ms (1.3 times) & 26.4 ms \\ \hline LSTM Alone & / & / & **0.7 ms** \\ \hline Vgg16 Alone & / & / & 102.4 ms \\ \hline Vgg16-LSTM & 194.6 ms (1.87 times) & 39.7 ms (0.38 times) & 103.8 ms \\ \hline LSTM Alone & / & / & **1.4 ms** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison experiment of running speed.  
on the NPU and the benchmark model on the PC. Execution speed on neural network accelerators is up to 1.87 times faster than on CPUs and 1.3 times faster than on GPUs.  
The method not only meets the accuracy and speed requirements of complex sequential forecasting tasks but also quickly and efficiently implements the support of the neural network accelerator for the LSTM layer on the embedded platform. The quantized LSTM model can be widely used in various sequential data-prediction tasks. The quantization parameter generation methods can be migrated to other RNN models. The operators' disassembly operation of LSTM has strong application potential in terms of the quantization aspects of novel operators for future neural network accelerators. It can be widely deployed on various embedded computing platforms with low power consumption and limited resources. What needs to be accomplished in the future is improving the quantization accuracy of LSTM models with mixed-bit-width configurations and deploying them on hardware platforms that support mixed-bit-width architectures.  
Conceptualization, Y.W. and Z.M.; Methodology, Y.W.; Software, Y.W. and Z.Y.; Validation, Z.M. and Z.Y.; Formal analysis, Y.W.; Investigation, Y.W. and Z.Y.; Resources, Z.M.; Data curation, Y.W. and Z.Y.; Writing--original draft, Y.W.; Writing--review & editing, Z.M.; Visualization, Y.W.; Project administration, Z.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Method
### Optimization Algorithm  
An intelligent search algorithm is essential for the direct numerical optimization of airfoil design. This algorithm operates iteratively and utilizes the inputs of the shape parameterization method to define the airfoil shape, and then it uses the flow solver to calculate the aerodynamic coefficients. Its efficiency is assessed according to its ability to provide a global optimal solution with reasonable computational resources. Therefore, the choice of such an algorithm influences the solution convergence and its feasibility.  
Figure 4: Grid around the airfoil used in Reynolds-averaged Navier–Stokes (RANS) simulations.  
Researchers have implemented various algorithms, such as the Gradient-Based, Adjoint-Based and Evolutionary algorithms with the aim to investigate and solve various optimization problems [74]. These algorithms have been considered to solve different aerodynamic optimization problems based on their advantages and disadvantages. The hybrid optimizer based on the Particle Swarm Optimization (PSO) algorithm was used in this paper for the UAS-S45 optimization, and it was further combined with the Pattern Search algorithm in order to enhance the solution convergence and its refinement.  
Evolutionary algorithms have attracted the attention of shape designers due to their characteristics, such as their suitability for modelling discontinuous shape functions, obtaining global optimal solutions, ease of parallel computing, etc. During morphing airfoil optimization, gradient-based algorithms converge fast, but they only cover a small area out of a large airfoil search space, and thus a local minimum solution instead of global minimum solution is found. It is also difficult to find the gradient of the non-linear flow fields for gradient-based optimizers.  
Therefore, a PSO algorithm is used, based on a simplified social behavior closely related to the swarming theory, where the solutions are represented by a set of particles that heuristically navigate through a design space [79]. The efficiency of PSO algorithms over genetic algorithms is due to their independence from parameters, such as crossovers and mutations; instead, the solution is updated by sharing its information amongst its populations of particles.