esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Financial Volatility Prediction Using Machine Learning in Finance
#### 2.2.3 High-Frequency Data and Hybrid ML Approaches to Financial Volatility Prediction  
The use of high-frequency data and machine learning to forecast realized volatility has been investigated using a panel data approach [30]. This study compares six machine learning methods with traditional linear models and single time-series forecasting. The results show that panel-data-based machine learning (PDML) methods outperform other approaches, with Elastic Net Regularization, Stochastic Gradient Boosting, and NeuralNetworks being the most effective. Key features derived from high-frequency returns are strongly correlated with daily realized volatility.  
A hybrid method combining GARCH with Recurrent Neural Network (RNN) architectures (LSTM, GRU, and mixed models) has been introduced to forecast volatility and implement risk control strategies for a multi-asset portfolio of US and international equities [31]. This study focuses on the Risk Parity method for portfolio allocation, showing positive results despite ignoring asset correlations. It suggests potential improvements by incorporating other risk measures and asset types, exploring alternative neural network algorithms, and comparing Risk Parity with VolTarget strategies. This approach outperforms standard risk/return portfolio structures.  
While attention-augmented LSTM models have improved financial forecasting, prior studies remain limited in their ability to model long-range dependencies, handle multi-scale structures, and mitigate nonstationarity. Additionally, many frameworks fail to capture interactions among key financial indicators and suffer from overfitting due to raw sequence modeling. These gaps motivate the incorporation of signal decomposition techniques like VMD to enhance feature robustness and improve predictive stability.  
As discussed above, forecasting volatility in finance is important both academically and practically. Meanwhile, the LSTM model, which has demonstrated superior performance in time-series forecasting compared to traditional models, is widely used. Therefore, in this study, LSTM is employed to predict the VIX, a representative volatility index. Furthermore, to enhance the performance of LSTM, the Attention mechanism, which has been recently utilized in Transformer models, is incorporated. Additionally, the S&P 500, the underlying asset of the VIX, is used as an input variable, and the VMD technique is applied to extract key signals. Through this approach, a hybrid model that integrates VMD, LSTM, and the Attention mechanism is proposed, and its predictive performance is validated.
# Arsenic (V) Removal by an Adsorbent Material Derived from Acid Mine Drainage Sludge
## 3 Results and Discussion
### Adsorption Isotherm  
Isotherm studies are useful for quantifying adsorption capacity. Generally, Langmuir and Freundlich isotherm models are employed to fit experimental data, and the results are used to describe interactions between adsorbate and adsorbent [28].  
The arsenic sorption plots for the MIRESORBTM and GFH materials were fitted by [PERSON] and Freundlich isotherms (Figure 4). The parameters and correlation coefficients are given in Table 4. The data were fitted more accurately by the Freundlich isotherm than the Langmuir isotherm, indicating multilayer adsorption of arsenic on a heterogeneous surface [29]. This type of adsorption is expected for MIRESORBTM adsorbents, which have several adsorption sites, including iron oxide, iron (oxy)hydroxide, and other metal oxides. The 1/n values for As(V) adsorption on both adsorbents (MIRESORBTM = 0.27 and GFH = 0.23) establish favorable adsorption.  
Figure 3: Arsenic adsorption kinetics. (**a**) MIRESORBTM; (**b**) GFH. The initial arsenic (V) (As(V)) concentration was 30 mg/L, the adsorbent dose was 1 g/L, the pH was 6.0, and the contact times were 1, 3, 6, and 24 h and 2, 3, 5, and 7 d.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & \multicolumn{3}{c}{**Pseudo First-Order**} & \multicolumn{3}{c}{**Pseudo Second-Order**} \\ \cline{2-7}  & \(q_{e}\) **(mg/g)** & \(k_{1}\) **(1/h)** & **R\({}^{2}\)** & \(q_{e}\) **(mg/g)** & \(k_{2}\) **(g/(mg-h))** & **R\({}^{2}\)** \\ \hline MIRESORBTM & 19.76 & 0.227 & 0.990 & 20.88 & 0.015 & 0.958 \\ GFH & 19.58 & 0.076 & 0.977 & 21.74 & 0.005 & 0.986 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kinetic parameters for As(V) adsorption by MIRESORBTM and GFH materials.  
Fitting by the Langmuir model provides maximum As(V) adsorption capacities (q\({}_{m}\)) for MIRESORBTM and GFH of 50.38 mg/g and 29.07 mg/g, respectively. The q\({}_{m}\) for the MIRESORBTM was higher than that reported in similar previous studies of AMDS, in which the As(V) adsorption capacity of AMDS was approximately 10-70 mg/g and varied with AMDS characteristics and experimental conditions [29; 30; 31]. A composite AMDS with organic binder material showed slightly decreased adsorption capacity. [PERSON] et al. (2015) reported that bead type adsorbents that employed AMDS with alginate had a q\({}_{m}\) for As(V) of 21.8 mg/g [18]. The AMDS coated with polyurethane have shown a q\({}_{m}\) of 7.3 mg/g [15]. MIRESORBTM is an adsorbent prepared by using byproducts from coal mines. Thus, the high adsorption capacity of MIRESORBTM for As(V) is a good indicator of its suitability for removing As(V) from contaminated water.
## 4 Conclusions  
In conclusion, we have theoretically and numerically studied envelope dark (black and gray) solitons in 1D nonlinear acoustic metamaterial composed of a waveguide with a periodic array of side holes, featuring viscothermal and radiation losses. Based on the electro-acoustic analogy and transmission line approach, we derived a nonlinear lattice model. We studied the linear dispersion relation of our system, which was found to be in good agreement with the one obtained by using the transfer matrix method. We used a multiple scale perturbation method to analytically treat the problem, and derived an effective NLS model describing the evolution of the pressure. We predicted the existence of dark solitons and studied analytically their evolution in the presence of losses. We investigated the interplay between dissipation, nonlinearity and dispersion, as described by pertinent characteristic length scales, in the case of a water-filled acoustic metamaterial with a small radius of side holes. The numerical results were found to be in good agreement with the analytical predictions.  
Our results and methodology pave the way for studies on nonlinear phenomena in double-negative acoustic metamaterials, e.g., waveguides periodically loaded with side holes and clamped elastic plates. Our works will also help in designing new nonlinear acoustic metamaterials supporting bright, gap, black and grey solitons. According to the properties of the solitons, i.e., propagation with no distortion of high amplitude waves, our studies could also pave the way to designing new devices in medical applications or to designing non destructive sensors.  
Figure 7: (color online) (**a**) numerical spatial profile of dissipative gray soliton calculated at \(t=0.58\) s with \(\phi=\pi/8\) (blue line). Green dashed lines are the corresponding analytical envelope, Equation (41); (**b**) the corresponding space-time diagram of dissipative gray soliton (blue circles). The slope of the black line shows the analytical velocity of dissipative gray solitons, Equation (42).  
**Author Contributions:** Conceptualization, V.R.-G., G.T., O.R., V.A. and D.J.F.; Methodology, J.Z., V.R.-G., G.T., O.R., V.A. and D.J.F.; Software, J.Z.; Investigation, J.Z., V.R.-G., G.T., O.R., V.A. and D.J.F.; Original Draft Preparation, J.Z.; Writing Review and Editing, J.Z., V.R.-G., G.T., O.R., V.A. and D.J.F.; Supervision, V.R.-G., G.T. and O.R.  
**Funding:** [PERSON] also acknowledges that this work was made possible by NPRP Grant No. (9-329-1-067) from the Qatar National Research Fund (a member of the Qatar Foundation). The findings achieved herein are solely the responsibility of the author [PERSON].  
**Acknowledgments:** [PERSON] ([PERSON]) acknowledges the warm hospitality at Laboratoire d'Acoustique de l'Universite du Maine (LAUM), Le Mans, where most of his work was carried out.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Results
### Statistical Analysis and Model Assessment
#### 4.2.1 FVC and Major Driving Factors: OLS, GWR, and SGWR
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Local Coefficients**} \\ \cline{2-13}
**Variable** & \multicolumn{3}{c}{**2000**} & \multicolumn{3}{c}{**2005**} & \multicolumn{3}{c}{**2010**} & \multicolumn{3}{c}{**2015**} \\ \cline{2-13}  & **Min** & **Max** & **Median** & **Min** & **Max** & **Median** & **Min** & **Max** & **Median** & **Min** & **Max** & **Median** \\ \hline Intercept & 0.204 & 0.618 & 0.449 & 0.257 & 0.700 & 0.471 & 0.172 & 0.573 & 0.405 & 0.157 & 0.536 & 0.391 \\ Elevation & \(-\)009 & 0.056 & 0.036 & \(-\)001 & 0.066 & 0.045 & \(-\)006 & 0.062 & 0.039 & \(-\)003 & 0.065 & 0.048 \\ Sunshine duration & 0.028 & 0.157 & 0.118 & 0.017 & 0.142 & 0.105 & 0.012 & 0.145 & 0.101 & 0.006 & 0.130 & 0.092 \\ Rainfall & \(-\)006 & 0.051 & 0.030 & \(-\)002 & 0.043 & 0.025 & \(-\)007 & 0.053 & 0.034 & \(-\)003 & 0.046 & 0.029 \\ Urban land & \(-\)0193 & \(-\)0.012 & \(-\)0.107 & \(-\)0.217 & \(-\)0.018 & \(-\)0.116 & \(-\)0.226 & \(-\)0.025 & \(-\)0.132 & \(-\)0.237 & \(-\)0.027 & \(-\)0.145 \\ Population & \(-\)0.102 & \(-\)0.003 & \(-\)0.058 & \(-\)0.109 & \(-\)0.014 & \(-\)0.063 & \(-\)0.115 & \(-\)0.008 & \(-\)0.067 & \(-\)0.120 & \(-\)0.011 & \(-\)0.069 \\ \hline \multicolumn{13}{c}{**Global Coefficients**} \\ \hline
**Variable** & **Estimate** & **Standard Error** & **Estimate** & **Standard Error** & **Estimate** & **Standard Error** & **Estimate** & **Standard Error** \\ \hline GDP per capita & \(-\)0.057 & 0.248 & \(-\)0.064 & 0.215 & \(-\)0.070 & 0.169 & \(-\)0.073 & 0.157 \\ Gross industrial output value & \(-\)0.038 & 0.256 & \(-\)0.041 & 0.277 & \(-\)0.045 & 0.140 & \(-\)0.046 & 0.163 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Local and global estimates from Semi-parametric Geographically Weighted Regression (SGWR) model.
## 5 Discussion and Findings  
### Predictors for Player Performance (Goals and Assists)  
#### 5.1.1 Matches Played  
* **Strong Positive Correlations with Workload Metrics**: It became clear that player performance metrics, particularly goals and assists, exhibit strong positive correlations with workload metrics, including elements like minutes played, matches played, and 90s (minutes played divided by 90). This result is consistent with common sense because more time spent on the field naturally gives players more chances to assist their teammates and score goals.
* **Matches Played (MP) with emphasis**: Within the subgroup of match load measures, matches played (MP) stood out as especially significant. Compared to other measures like minutes played (Min) and 90s (minutes played per match), which had correlations of 0.23, MP had a higher correlation coefficient of 0.34. This suggests that a player's ability to score goals and provide assists is most significantly influenced by the sheer number of games in which they take part, regardless of how much time they spend on the field or whether they are a starter.  
The data for 2021/22 and 2022/23 were collected from the official Premier League website. This is to further validate whether the top three performers could maintain their high performance in the succeeding seasons and to check the significance of matches played (MP) as a contributing factor towards goals and assists.  
Referring to Table 7, as forwards, [PERSON] and [PERSON] averaged around 30 goals and assists, with their forward positions allowing numerous scoring opportunities. [PERSON], a midfielder, averaged 20 goals and assists, reflecting his playmaking role. It is worth noting that [PERSON] had an outstanding season in 2020/21, with 30 goals and assists. This outstanding performance highlights his essential role in the team, his influence on team performance, and his ability to positively affect matches. In conclusion, the data emphasize the importance of these top performers' regular field presence (high MP) and their contributions relative to their positions and tactical roles.  
Clubs can use these findings as a guide for evidence-based player management. While using the \"matches played\" knowledge, rotation methods can be improved. For example, the \"85% rule\" [22] could be implemented, requiring important attackers to participate in that percentage of games to strike a balance between exhaustion and sharpness. The association between age and performance supports adjusting training loads; veterans could benefit from fewer, lower-intensity sessions in between games. Real-time substitution decisions might be informed by injury prediction models, and players who pose a danger could be closely observed during games. Ultimately, these resources allow coaches to make data-driven decisions that protect players' well-being while pursuing awards.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 4 Chemical Flocculation
### Inorganic Flocculant  
Iron and aluminum salts form positively charged hydroxides in aqueous media, that destabilize negatively charged algal cells, inducing algae flocculation and subsequent precipitation [71]. Aluminum sulfate (Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)), commercially known as \"alum\", is widely used in algae harvesting processes and wastewater treatments (Table 3). It was reported that 1.2 g/L alum enables a harvesting efficiency of 85.6% for marine alga _Tetraselmis_ sp. of 3 g/L cell density [64]. Despite the high doses required for algae harvesting, the relatively low cost of alum makes it a potential choice for bulk biomass recovery in biofuel production [84]. However, it should be noted that this method is generally not recommended for algal biorefinery processes that utilize whole cells for multiple bio-products including biofuel and biochemicals, since excess aluminum is a well-known carcinogen and also known to cause [PERSON]'s disease [19; 64].
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 3 Theoretical Basis of Numerical Simulation  
### Control Equations  
In this study, the thermal radiation characteristics of hydrogen jet fire under high leakage pressure were analyzed by Fluent 2021 R1 software. The numerical simulations involved four crucial control equations: the component conservation equation, the continuity equation, the momentum conservation equation, and the energy conservation equation [17; 19].  
1. The component conservation equation is: \[\frac{\partial}{\partial t}(\rho\varphi)+\frac{\partial}{\partial x_{j}}(u_{i} \rho\varphi)=\frac{\partial}{\partial x_{j}}\left[\rho\Gamma_{\varphi}\frac{ \partial}{\partial x_{j}}(\varphi)\right]+S_{\varphi}\] (1) where \(\varphi\) is the arbitrary variable; \(\rho\) is the fluid density; \(u_{i}\) is the velocity vector in the \(i\)-direction; \(x_{j}\) is the velocity vector in the \(j\)-direction/m; \(\Gamma_{\varphi}\) is the diffusion coefficient; and \(S_{\varphi}\) is the source term.
2. Continuity equation  
The continuity equation is the fundamental equation describing the law of mass conservation. It ensures mass conservation before and after the reaction, expressed as  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{2}\]  
where \(t\) is the time variable, and \(x_{i}\) is the integration variable of the \(i\)-direction.
3. Momentum conservation equation  
The momentum conservation equation states that the momentum of a closed system remains constant in the absence of external forces. The formula is as follows:  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{j}u_{i}\big{)}=-\frac{\partial p}{\partial x_{j}}+\frac{\partial}{ \partial x_{j}}\tau_{ij}+\rho g_{i} \tag{3}\]  
where \(\tau_{ij}\) is the stress tensor; \(g_{i}\) is the gravitational acceleration component in the \(i\)-direction; and \(p\) is the fluid pressure.
4. Energy conservation equation  
The energy conservation equation is expressed as  
\[\frac{\partial}{\partial t}(\rho e)+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{j}e+u_{j}p\big{)}=\frac{\partial}{\partial x_{i}}\left\{k\frac{ \partial T}{\partial x_{j}}+u_{i}\tau_{ij}\right\}+S_{\rm h} \tag{4}\]where \(e\) is the specific energy of the fluid, and \(k\) is the heat flux.
## 1 Introduction
(3) Low-quality images were generated from a high-resolution concrete crack dataset to address the issues of limited detail, unclear texture structure, and restricted high-precision segmentation in the detected crack regions of metro tunnel linings, from which the Real-ESRGAN image super-resolution model was trained. This process transferred high-quality, high-resolution concrete crack features to the detection results of metro tunnel lining cracks, enhancing the spatial resolution of the detected crack image regions by four times. Additionally, the texture representation and detailed information of the cracks were enhanced, providing technical support for the high-precision and accurate extraction of apparent cracks in metro tunnel linings. The primary objective of this research was to develop a comprehensive algorithm for detecting apparent defects in metro tunnel linings and enhancing defect feature extraction. The specific objectives of this study were as follows: (i) To thoroughly analyze the spatial characteristics of defect occurrence and integrate the coordinate attention (CA) module and Bottleneck Transformer 3 into the backbone of YOLOv8, thereby improving detection accuracy in defect-prone areas. (ii) To incorporate a high-resolution detection layer into the head of YOLOv8, thus enhancing the model's sensitivity to small-scale defects and improving detection accuracy. (iii) To validate the effectiveness of the proposed model in detecting defects within metro tunnel scenarios through ablation studies and comparative experiments. (iv) To construct a low-quality concrete crack dataset and develop a crack enhancement algorithm by combining transfer learning with the Real-ESRGAN image super resolution model. (v) To evaluate the efficacy of the proposed enhancement algorithm for high-precision crack extraction using both subjective and objective assessments. Figure 1: The prone areas of apparent tunnel lining defects.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Promising Molecular Techniques: ddPCR  
Droplet digital PCR (ddPCR) is a new technology based on water-oil emulsion droplets which provides accurate DNA quantification [158]. ddPCR shows a higher quantitative range in comparison to qPCR for the identification of clinical _Candida_ spp. in blood samples, providing an early diagnosis as well as a prognostic value for candidemia [159]. ddPCR has yet to be used with oral samples.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 3 Results
### Electron Motion and Corresponding Radiation Properties 2
Therefore, the electrons undergo an almost-unobservable falling edge phase after the rising edge of the laser field; i.e., they fail to exhibit the reduced radiated power that they should have exhibited. This is manifested by the smooth motion of the electrons after contact with the laser field, and the radiated power of the electrons remains red after they are out of contact with the laser field; i.e., this not much different from their own maximum radiated power. At pulse lengths \(L=4\lambda_{0},6\lambda_{0}\), the contact time of the laser field with the electrons becomes longer. As a result, the deceleration process, which was originally unobservable, i.e., the part of the radiated power that becomes smaller, also becomes visible. When the magnetic field is small, the power radiated by the electrons in the back half is still not far from the maximum power radiated. The reason for the appearance of this phenomenon will be explained in Section 3.3. At pulse lengths \(L=20\lambda_{0}\), we find a new phenomenon: a clear phenomenon of double peaks in the image; i.e., the radiated power decreases and then rises, and the rise is located at the position where the radial radius of the electron trajectory is transformed from decreasing to smooth. It is worth noting that the relative magnitude of the radiated power of the electrons in the final spiral part and at the maximum radiation varies for different magnetic field strengths. The radiated power of the electrons is very close when the magnetic field is small and differs by one or two orders of magnitude when the field is large. The reasons for this phenomenon will also be explained later.