esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Real-Time Monitoring of Power Distribution Systems  
### Power Distribution Systems  
The purpose of PDSs is to meet the customer's energy demand, after receiving the bulk energy from transmission (or sub-transmission) lines ([PERSON], 2011), and comprises: (i) distribution substations, which are fed by one or more transmission (or sub-transmission) lines; (ii) primary distribution lines or feeders (known as the MV networks); (iii) distribution transformers, which connect the MV networks to the LV networks; (iv) secondary distribution circuits (known as the LV networks), and (v) customers' connections.  
The historic passive behavior of PDSs has changed in the last years mainly because of the development of smart grid features, such as massive installation of intermittent generation/load, use of storage devices, and diffusion of electric vehicles. Therefore, PDSs have become highly complex systems which makes it necessary to efficiently monitor their operating conditions.  
The SE is the fundamental tool for the efficient real-time monitoring of electric networks, which has the function of estimating, in real-time, the network state variables (usually the nodal complex voltages) by processing a set of redundant noisy measurements. Through the analysis of these state variables, the operating condition of an electric network can be determined.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
[45] used microseismic monitoring as the primary tool to study the water inrush disaster at the Laohutai coal mine and found some abnormalities in the microseismic parameters prior to the incident, i.e., decreases in the P-wave to S-wave velocity ratio and dominant frequency to abnormal low values, an increase in the S-wave \begin{table}
\begin{tabular}{c c c c} \hline
**Accident Type** & **Deaths** & **Accident Type** & **Deaths** \\ \hline Gas explosion & 23,866 & Electromechanical accident & 1496 \\ Roof accident & 23,769 & Fire & 1092 \\ Haulage accident & 6381 & Blasting accident & 1053 \\ Mine water inrush & 5210 & Others & 4502 \\ \hline \end{tabular}
\end{table} 
Table 1: Statistics for the deaths caused by different coal mine accidents [17]. to P-wave amplitude ratio to abnormal high values, the presence of a high-amplitude and long-period waveform, and a decrease in microseismic events. Within this study, a state-of-the-art review is presented in the scope of applications of the microseismic monitoring technique in studying coal mining-induced disasters. Some fundamental aspects of microseismic monitoring and the quantitative interpretations of microseismicities are first introduced in Sections 2 and 3. Then, the associated achievements of the microseismic monitoring technique in studying mining-induced instabilities are described in Section 4 based on an extended literature review. Finally, the prospects of the microseismic monitoring technique in future studies are depicted in Section 5. This paper aims to familiarize mining managers with the microseismic monitoring technique, which is currently extensively employed in multiple countries, and improve both safety management and mining production during deep coal extraction.
# Ablation of Copper Metal Films by Femtosecond Laser Multipulse Irradiation
## 2 Theoretical Background
### Dynamic Optical Properties  
Using the simple Drude model, we study the evolution of the reflectivity and absorption coefficient of the copper surface during the irradiation by fs laser pulses.  
The dielectric function of copper is [26]:  
\[\varepsilon=\varepsilon_{\infty}-\frac{\omega_{p}^{2}}{\omega(\omega+\frac{i }{\tau_{D}})}=\varepsilon_{\infty}-\frac{\omega_{p}^{2}\tau_{D}^{2}}{1+\omega_ {p}^{2}\tau_{D}^{2}}+i\frac{\omega_{p}^{2}\tau_{D}}{\omega(1+\omega_{p}^{2} \tau_{D}^{2})}=\varepsilon_{1}+i\varepsilon_{2} \tag{7}\]  
where \(\tau_{D}\) is the relaxation time of the electrons in the metal and \(\omega_{p}\) is the plasma frequency. \(\varepsilon_{\infty}=9.428\) is the dielectric constant. \(\tau_{D}\) can be expressed as [15]:  
\[\tau_{D}=(A_{e}T_{e}^{2}+1.41\theta_{e,p})^{-1} \tag{8}\]  
where the expression of the electron-phonon collision rate \(\theta_{e,p}\) can be found in detail in [27].  
Therefore, the refractive index \(n_{r}\) and extinction coefficient \(n_{i}\) can be written as:  
\[n_{r}=\sqrt{\frac{\varepsilon_{1}+\sqrt{\varepsilon_{1}^{2}+\varepsilon_{2}^ {2}}}{2}} \tag{9}\]  
\[n_{i}=\sqrt{\frac{-\varepsilon_{1}+\sqrt{\varepsilon_{1}^{2}+\varepsilon_{2}^ {2}}}{2}} \tag{10}\]  
The absorption coefficient \(\alpha\) and normal-incidence reflectivity \(R\) can be determined from the Fresnel function [28]. Neglecting the strongly spatial inhomogeneity due to material rarefaction and ionization, we assume these formulas (Equations (11) and (12)) are valid at multipulse laser ablation of high laser fluence with picosecond delay between pulses:  
\[R=\frac{(n_{r}-1)^{2}+n_{i}^{2}}{(n_{r}+1)^{2}+n_{i}^{2}} \tag{11}\]  
\[\alpha=\frac{4\pi}{\lambda}n_{i} \tag{12}\]  
where \(\lambda\) is the laser wavelength. This dynamic TTM was simulated using MATLAB software by the finite differential method.
## 2 Basic Theory of the TPOD Method and the Rotor Model with Coupling Faults
### The Effects of Systematic Parameters
#### 3.2.2 Clearance between Rotor and Stator  
This section discusses the effects of clearance between rotor and stator to the coupling fault. Similarly, Figures 16 and 17 present the bifurcation and amplitude-frequency when the clearance is 0.12 and 0.18, respectively. When the clearance varies, the clearance has little impact on the vibration amplitude and critical speed of the coupling system. Near the frequency of \(2\omega_{c}\), the amplitude of 1/2 sub-harmonic resonance decreases as the clearance decreases, the amplitudes of the systems with different clearance are similar when \(\omega\in[2\omega_{c},3\omega_{c}]\). The amplitude will have a sharp increase as the  
Figure 14: Bifurcation diagrams for coupled fault system. (**a**) \(a=0.6R\). (**b**) \(a=R\).  
Figure 15: Comparison of the amplitude–frequency curves.  
clearance decreases at the frequency of \(3\omega_{c}\), the rub-impact fault characteristic enhanced obviously, which once verifies that the sudden increase of the \(3\omega_{c}\) amplitude of the coupling fault system is primarily caused by the rub-impact fault. At the high frequency (over 1100 rad/s), the amplitude increases a little when the clearance decreases. The topological structures of the bifurcation diagram will be more complex in comparison to Figure 16b.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Signal Processing Methods
#### 3.2.4 Hilbert-Huang Transform  
The Hilbert-Huang transform (HHT) is a time-frequency analysis technique that combines empirical mode decomposition (EMD) with Hilbert transform. The signal is first decomposed into multiple internal mode functions (IMF) by EMD, each of which is a single component function. Then the Hilbert transform is used to calculate the instantaneous frequency of the original signal from the IMF [74]. Unlike the Fourier transform, the expression of the Hilbert transform for an input \(x(t)\) is:  
\[H[x(t)]=y(t)=\frac{1}{\pi}\int\frac{x(\tau)}{t-\tau}\mathrm{d}\tau \tag{11}\]  
Obviously, the Hilbert transform is equal to the convolution of \(x(t)\) with \(1/\pi t\). This method overcame the shortcoming of STFT and WT, which is their time-frequency resolution is closely related to the selection of window size or window function. Based on HHT, [PERSON] et al. calculated the Hilbert energy spectrum of PMSM current to detect the stator short circuit fault [56]. In addition, HHT is suitable for dynamic signal analysis due to its sensitivity to transient frequencies. In the research of [PERSON] et al. [75], HHT was applied to diagnose the demagnetization fault, and the results showed that there were still good results in high-speed and dynamic operating conditions.
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 2 Dynamic Weighted Protein Network Construction
### Multi-Level Embedding Method Construction
#### 2.3.2 Deep Attributed Network Embedding
```
0: PPI data; Gene expression data; Threshold \(\delta\); GO Slim information
0: Dynamic weighted protein network
1:Step1: Construct adjacency matrix \(A\)
2: Calculate all protein active periods using gene expression data as input to the Equations (1)-(3); construct a subnetwork based on the protein active period and add an edge if \(CS(O_{u},O_{v})\geq\delta\); repeat this step until the end of the calculation;
3:Step2: Construct GO Slim feature matrix \(X\)
4: Traverse the entire protein set and mark the presence of annotation information as 1 to obtain the feature matrix \(X\);
5:Step3: First embedding using VGAE
6: (1) Initialization, input adjacency matrix \(A\) and feature matrix \(X\);
7: (2) Calculate \(\widetilde{A}\), and apply GCN encoder to obtain \(\widetilde{A}ReLU\Big{(}\widetilde{A}XW_{0}\Big{)}W_{1}\);
8: (3) For each node \(i\), calculate the latent representation \(z_{i}\) based on \(\mu_{i}\) and \(\sigma_{i}^{2}\) to obtain the embedding representation \(Z\);
9: (4) Reconstruct the adjacency matrix \(\hat{A}\) through the decoder;
10: (5) Compute the reconstruction loss \(L_{recon}\) and the KL divergence regularization term \(L_{KL}\);
11: (6) Minimize the total loss \(L_{recon}+L_{KL}\), and update the model parameters \(W_{0}\), \(W_{1}\), \(\mu\) and \(\sigma\);
12:Step4: Second embedding using DANE
13: (1) Initialization, input adjacency matrix \(M\) and feature matrix \(Z\);
14: (2) Generate two embedding matrices \(H^{M}\) and \(H^{Z}\) through the auto-encoder, and calculate \(L_{h}\) and \(L_{s}\);
15: (3) Calculate the connection probabilities \(p_{ij}^{M}\) and \(p_{ij}^{Z}\), using the sigmoid function to process the inner product of embedding vectors;
16: (4) Calculate the losses \(L_{f}\) and \(L_{c}\);
17: (5) Minimize the total loss \(L\), update the model parameters;
18:Step5: Calculate the cosine similarity Concatenate the low-dimensional embedding representation, compute the cosine similarity, and finally obtain the weighted adjacency matrix \(C\);
19: Output the dynamic weighted protein network. ```  
**Algorithm 1** Dynamic Weighted Protein Network Construction (DWPNMLE)
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
### Lithium Manganese Oxide (LiMn\({}_{2}\)O\({}_{4}\), LMO)  
LMO is recognized for its high power capacities, rapid charging capabilities, and thermal stability. It is a viable option for applications requiring quick energy delivery and robustness under thermal stress. Despite these benefits, LMO exhibits some limitations, including a lower energy capacity than LiCoO\({}_{2}\) batteries and a reduced lifespan when operated at elevated temperatures. These attributes lead to a relatively shorter cycle life, reducing its suitability for specific marine applications that require long-term durability and higher energy densities to be essential [27].
## 1 Introduction
### Research Gap  
Some potential research gaps in the field of FPGA-based implementations for point multiplication on Binary Edwards curves (BECs) include:  
* Limited focus on optimizing multiple design factors: While many of the studies discussed in Section 1.1 focus on optimizing either area, latency, or throughput, there may be a need for further research that can balance these factors in a more holistic manner. For example, an architecture that achieves high throughput and low latency but also minimizes hardware resources could be useful in many high-speed applications.
* Investigating novel multipliers: Many of the existing implementations use pipelined digit-serial multipliers to optimize throughput, but there may be other novel modular multipliers that could further improve performance.
* Limited evaluation of security and robustness: While the studies discussed in Section 1.1 focus on optimizing speed and resources for BEC architectures, there is limited discussion on the security and robustness of these architectures. Further research could evaluate the effectiveness of these architectures in resisting attacks and ensuring data integrity, which is crucial for many cryptographic applications.
## 2 Materials and Methods
### MODIS data
#### 2.1.3. Clearness Index (CI) Calculation  
CI is usually used to represent the atmospheric transmittance and determine the sky condition. It is defined as the ratio of the downward solar radiation and the extraterrestrial radiation (\(R_{se}\)) [27], as Equation (5) shows:  
\[CI=\frac{R_{s}^{\frac{1}{2}}}{R_{se}} \tag{5}\]  
To obtain the CI at instantaneous (\(Cl_{i}\)) and daily (\(Cl_{d}\)) scales, the corresponding instantaneous \(R_{se}(R_{se,i})\) and daily \(R_{se}(R_{se,d})\) need to be calculated first. \(R_{se,i}\) is calculated by Equation (6) [28]:  
\[R_{se,i}=I_{0}cosz \tag{6}\]  
\[I_{0}=1353\bigg{\{}1+0.034 cos\bigg{[}\frac{2\pi(doy-1)}{365}\bigg{]}\bigg{\}} \tag{6a}\]  
\[cosz=sin\gamma sin\delta+cos\gamma cos\delta cosH \tag{6b}\]  
\[H=\frac{\pi}{12}(t_{noon}-t) \tag{6c}\]  
where \(I_{0}\) is the effective solar constant, \(z\) is the solar zenith angle (unit: rad), \(H\) is the hour angle (unit: rad), \(t_{noon}\) is the local noon time defined as 12:00 hours.  
\begin{table}
\begin{tabular}{c c c} \hline
**MODIS Product** & **Spatial Resolution** & **Parameters Used** \\ \hline MOD/MYD02 & 1 km & 1 km\_RefSB, 1 km\_Emissive \\ \hline \multirow{4}{*}{MOD/MYD03} & \multirow{4}{*}{1 km} & SolarZenith (SZA), \\  & & SolarAzimuth (SAA), \\ \cline{1-1}  & & SensorZenith (VZA), \\ \cline{1-1}  & & SensorAzimuth (VAA), Height \\ \hline MOD/MYD35 & 1 km & Cloud Mask \\ \hline \end{tabular}
\end{table}
Table 2: MODIS data used in the study.  
\(R_{se\_d}\) could be calculated by Equation (7):  
\[R_{se\_d}=\frac{1440G_{sc}d_{r}}{\pi}(\omega_{s}sin\gamma sin\delta+cos\delta cos \omega_{s}) \tag{7}\]  
\[d_{r}=1+0.033 cos\left(\frac{2\pi doy}{365}\right) \tag{7a}\]  
\[\omega_{s}=arccos(-tan\gamma tan\delta) \tag{7b}\]  
where \(G_{sc}\) is the solar constant (0.0820 M) m\({}^{-2}\cdot\)min\({}^{-1}\)), \(d_{r}\) is the inverse relative distance from the Earth to the Sun, and \(\omega_{s}\) is the sunset hour angle (unit: rad). If the sky was clear, then the values of \(Cl_{i}\) and \(Cl_{d}\) should be both larger than 0.7.
## 1 Introduction
The research question for this investigation is RQ1: \"How can an ensemble transfer learning system, combined with  
Figure 1: Different samples of brain tumors: meningioma, glioma, and pituitary. data augmentation techniques, precisely and effectively identify and classify various brain tumor diseases?\". The objective of this study was to create the composite ensemble model known as SETL_BMRI, which combines a data augmentation strategy with a deep stack ensemble technique to lower generalization errors and surge the accuracy of brain tumor classification. The model employed two well-known pre-trained CNN models (AlexNet and VGG19), which were concatenated using an augmentation technique. The experimental results demonstrated that the proposed technique yields more accurate and likely outcomes compared to single models and existing state-of-the-art methodologies. The main contributions of our study are outlined below to highlight the significance of our work:  
* The proposed unique stack ensemble transfer learning technique called SETL_BMRI uses CNN-based models like VGG19 and AlexNet to identify and classify tumors in an imbalance dataset of MRI brain tumor images, and furthermore, to reduce generalization errors and provide exact predictions for brain tumor classification. * This work addresses the challenge of inadequate datasets, by incorporated data augmentation method to expands the size of a dataset. * In this research work, a stack ensemble transfer learning model was used that incorporated data augmentation techniques to achieve high performance and accuracy. * The performance of the recommended model was evaluated using the Kaggle dataset [16], which is open to the public. The model obtained a significant overall classification accuracy of 98.70% against several other baseline models. The performance parameters included accuracy, re-call, precision, and F1-Score. * In the end, the suggested model produced good results, showing a low misclassification rate, and outperforming the functionality of existing techniques. The following outline is included in the paper. In Section 2, the literature review is covered. Section 3 presents the methodology. Section 4 presents the implementation details, Section 5 presents the results, and Section 6 covers the discussion and limitations. The study's conclusion is presented in Section 7.
## 4 Conclusions  
The present study compared and discussed the chloride-binding capability, synthesized Cl-bearing phases, and pore characteristics between alkali-activated fly ash and GGBF systems when seawater is used as the mixing water. Overall, the alkali-activated GGBFS system showed a significantly better Cl-binding capacity than the alkali-activated fly ash system at 28 days although it demonstrated a slightly lower performance at 7 days. More detailed conclusions are given as follows:  
1. The main Cl-bearing phases were zeolites (i.e., Cl-chabazite and Cl-sodalite) in the FSW sample, while they were LDH phases (i.e., Cl-hydrotalcite and Cl-hydrocalumite) in the SSW sample.
2. The bound chloride content of the SSW sample was 91% at 28 days, whereas that of the FSW sample was 69%. Therefore, at 28 days, the alkali-activated GGBFS system is more advantageous for Cl-binding than the alkali-activated fly ash system.  
Figure 6: Pore size distribution curves of the FSW and SSW samples at 7 and 28 days: (**a**) cumulative distribution of the FSW sample, (**b**) log differential distribution of the FSW sample, (**c**) cumulative distribution of the SSW sample, and (**d**) log differential distribution of the SSW sample.  
3. The FSW sample contained zeolite X, zeolite Y, Cl-chabazite, and Cl-sodalite as crystalline zeolite phases. Zeolite X and Y, which do not contribute to the chemical Cl-binding, comprised a larger portion of the XRD pattern than Cl-chabazite and Cl-hydrocalumite.
4. The SSW sample contained C-S-H(I), Cl-hydrocalumite, and Cl-hydrotalcite. Its Cl-binding mechanisms are similar to hydrated portland cement systems. C-S-H(I) is responsible for physical binding and Cl-LDHs contribute to chemical binding.
5. Between the two Cl-LDHs in the SSW sample, the XRD peak of only Cl-hydrotalcite grew significantly from 7 to 28 days, and the bound chloride content of the SSW sample considerably increased from 42% to 91% over that time. Therefore, after 7 days, Cl-hydrotalcite is an important phase for chemical Cl-binding in the alkali-activated GGBFS system.
6. The SSW sample showed both pore-size refinement and porosity reduction from 7 to 28 days. In contrast, the FSW sample only showed pore-size refinement over that time.  
This research was supported by Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2017R1D1A1B03034700) and also supported by the Project of Conversion by the Past R&D Results through the Ministry of Trade, Industry and Energy (MOTIE) (N0002150, 2016).  
[PERSON] designed and performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON], [PERSON], and [PERSON] discussed the experiment. [PERSON] wrote the manuscript, and [PERSON] and [PERSON] reviewed this study.  
The authors declare no conflict of interest.
## 1 Introduction  
### General Overview  
Water-in-oil emulsions are a common occurrence in petroleum production and processing systems [1, 2]. These emulsions form when water becomes finely dispersed in the continuous oil phase, typically stabilized by naturally occurring surface-active compounds such as asphaltenes or externally introduced emulsifiers [3, 4]. In the petroleum industry, such emulsions can significantly affect flow assurance and the overall process of separation, which in turn affects the recovery of the reservoir process [5, 6, 7]. While in some cases emulsions pose operational challenges, their controlled formation and stability can also be harnessed for enhancing oil recovery and drilling fluid formulation [8, 9].  
A particularly interesting subclass of emulsions is the high internal phase emulsion (HIPE), defined by a dispersed phase volume exceeding 70% [10; 11; 12; 13]. In the context of water-in-oil systems, HIPEs exhibit unique structural and rheological properties, including high viscosity and elastic behavior, which make them valuable for in situ permeability modification during enhanced oil recovery [14; 15; 16]. By selectively blocking high-permeability zones, stable W/O HIPEs can redirect flow into under-swept areas of the reservoir. This improves sweep efficiency; however, the formation of stable HIPEs is inherently difficult as increased water content raises the risk of phase inversion and droplet coalescence, especially in the absence of well-designed emulsifiers.  
In recent years, simulation techniques have become indispensable tools across multiple disciplines that enable researchers to predict and optimize complex multi-phase behaviors at different scales [17; 18; 19; 20; 21; 22]. Within this context, dissipative particle dynamics (DPD) has emerged as a powerful mesoscale simulation method for modeling soft matter systems and capturing hydrodynamic behavior in emulsions and polymers [23; 24]. Compared to atomistic simulations, DPD offers significant computational advantages for simulating larger systems over longer timescales. It has been successfully used to explore surfactant aggregation and interfacial film dynamics in various emulsion systems [25; 26]. The ability of DPD to bridge microscopic interaction rules with macroscopic behavior makes it particularly suited for studying the formation and stabilization of HIPEs.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 4 Results and Discussion
### Tea Plantation Mapping Based on Multi-Temporal Sentinel-1 Data
These land types often exhibit significantly high or low backscatter coefficients in SAR images, combined with mixed pixels and other factors, resulting in significant noise in the tea plantation pixels in SAR images, thereby hindering the accurate identification of tea plantations. Figure 14: Tea classification results based on time-series Sentinel-2 dataset. (**a**) Classification results based on the UNet model. (**b**) Classification results based on the SE-UNet model. (**c**) Classification results based on the SWIN-UNet model. (**d**) Local classification result of SWIN-UNet model.