esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 5 EA-FSW of Similar Joints
### EA-FSW of Titanium Alloys  
Friction stir welding is a promising welding procedure when joining light metals, especially aluminum alloys. However, when high-melting-point metals such as steel and titanium are to be welded, in many cases, there is not enough heat input during the welding procedure. Thus, applying auxiliary heat sources such as high-density current helps achieve the targeted temperature and plasticity. [PERSON] et al. [34] investigated the electrically assisted friction stir welding of Ti6 Al4V joints. They indicated that the tensile properties of the joint improved when using electrically assisted friction stir welding, with no loss of ductility due to homogeneity of the microstructure and strain, dissolution of the beta phase, and random orientations. They reported that some slip systems are activated just when an electrical current is applied, which is attributed to the higher temperature and deformation strain as a result of the thermal effect of the electrical current. They suggested the application of 200 A as the optimum current density.  
The same titanium alloy was also investigated by [PERSON] et al. [35]. They also reported a smooth change in the microstructure of the joints when electrically current assisted friction stir welding is applied, and random distribution in the stir zone. Improvement in the tensile properties of the joint was also indicated. They reported slightly larger grains in the advanced side of the stir zone compared with the retreating side, which was illustrated by some other authors in welding other alloys, too. They also reported a slightly more robust dynamic recovery in the HAZ, and dynamic recrystallization in the SZ for FSW in comparison with EA-FSW. They suggested the increasing loss rate of elemental Al and larger grains as a reason for the decrease in the hardness. They reported better fatigue performance for the electrically assisted joints with a current intensity of 100 A due to smaller macro zones than conventional friction stir welding. Any increase in the current intensity results in a more homogenous distribution of hardness.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance
### Algorithmic Complexity Analysis
#### 6.1.1 Data Preprocessing  
* Low-Pass Filtering: Applying a Butterworth filter to vibration and acceleration data has a complexity of \(O(n\cdot m)\), where \(n\) is the number of data samples and \(m\) is the number of sensor channels (e.g., \(m=3\) for the x, y, z axes). Since the filter coefficients are constant for a fixed filter order, this operation scales linearly with the number of samples.  
Figure 7: Feature importance comparison for Configurations A, B, and C. The bar plot illustrates the contribution of each feature (Vibration X, Acceleration Y, Vibration Z, Acceleration X, and Acceleration Z) across different configurations.  
* Normalization: Min-Max normalization involves finding the minimum and maximum values for each channel, which is \(O(n)\), followed by a linear transformation of the data, also \(O(n)\). The overall complexity remains \(O(n)\).
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 4 Results
### RQ1: What Is the Level of Awareness of Agri 4.0 Solutions among Farm Enterprises?
This trend is further verified and reinforced by the analysis of the relationship between awareness level and turnover (Figure 5), in which it is possible to see how the level of awareness increases with an increase in the revenue cluster. The boxplot graph is particularly significant because each element (minimum, maximum, inferior quartile, major quartile, mean, and median) of the larger revenue class is greater than each element of the smaller revenue class. The boxplot shows that, on average, companies with a turnover of more than EUR 500,000 are currently using half (48%) of the solutions proposed in the survey.
## 6 Open Challenges, Future Prospects and Research Directions
### Dynamic Real-Time Adjustments  
One primary focus is the development of more sophisticated algorithms capable of dynamically adjusting to real-time channel conditions. These algorithms will leverage advanced signal processing techniques to continuously monitor and respond to changes in the atmospheric environment, such as fog, rain, or turbulence. By doing so, they will ensure that the communication link remains robust and efficient, regardless of external conditions. In this regard, future research will likely focus on the creation of more sophisticated algorithms capable of making dynamic adjustments to transmission parameters in real-time. These algorithms will analyze the present condition of the communication channel, including factors such as AT, alignment errors, and environmental changes, and adjust modulation schemes, power levels, beam steering, and/or other transmission parameters accordingly, all performed in real-time. By doing so, they will maintain optimal communication performance and minimize errors and data loss.
## 2 Materials and Methods
### The Proposition of RS-DFV
#### 2.2.3 RS-DFV Extraction
Obviously, RS-DET could effectively overcome the abnormal suspension of DET and was able to complete the DFV extraction of each sample. The procedure of the ATCS is displayed below. Step 1: Extract the DFV in accordance with the instructions of the RS-DET until an abnormal termination appeared, record the current fault sample as an AT fault sample, record the current node as an AT, and record the current heuristic feature as an AT feature (\(x_{AT}\)). Step 2: Find the all child nodes of the ATN in the RS-DET and put them into the node set CN-ATN. Step 3: For each node in CN-ATN, find fault types that were included in it, construct a rough set on \(x_{AT}\) for each fault type, and calculate the roughness of this node based on Figure 8 and Equations (12)-(14). Step 4: Amend the value of \(x_{AT}\) according to the roughness of the child nodes illustrated in Figure 9 and Equations (12) and (13). Step 5: Go back to step 1. Figure 8: The rough set of fault F on the AT feature (\(x_{AT}\)). Figure 7: The RS-DFV extraction tree combining the rough set and DET (RS-DET). \(\bullet\) Feature Correction based on the Rough Set  
The ATCS could overcome the problem of abnormal termination in DFV extraction; hence, it is a most important part in RS-DET. In this paper, a rough set is applied to amend the current heuristic feature. First, the fault sample that caused an abnormal termination is treated as a fault sample in fault type \(F\), and the rough set of \(F\) is obtained through the method illustrated in Figure 8. \(F\). (\(x\)) is the value range of the current heuristic feature for all the training samples in fault type \(F\), and \(RN\)(\(F\), \(xAT\)) is the roughness of fault type \(F\) based on the current heuristic feature. Through Equation (12), the roughness of every fault type based on the current heuristic feature could be acquired. Then, for the child nodes of the ATD, \(RN\)(\(CNh\), \(xAT\)) is the roughness of the \(h\)th child node, and, as illustrated in Equation (13), \(RN\)(\(CNh\), \(xAT\)) is the minimum roughness of all fault types in the \(h\)th child node. Finally, the child node that included the fault type with the minimum roughness is found, the value range (\(X\)) of the current heuristic feature for all the training samples in this child node is obtained, and the value of \(xAT\) toward \(X\) is amended based on Equation (14). Therefore, the ATCS solved the problem of abnormal termination in DFV extraction and made it possible for DFV extraction to be accurately completed. \[RN(F,x_{AT})=\frac{F^{-}(x)-F_{-}(x)}{F_{-}(x)} \tag{12}\]  
\[RN(CN_{h},x_{AT})=min(RN(F_{h1},x_{AT}),RN(F_{h2},x_{AT}),\cdots,RN(F_{h3},x_{ AT})) \tag{13}\]  
\[x_{AT}=\left\{ \begin{array}{c}min(X),x_{AT}<min(X)\\ max(X),x_{AT}>max(X)\end{array} \right. \tag{14}\]
## 4 Topology Optimization of Valve Motion Components Based on Thermal-Fluid-Mechanical Coupling
### Topology Optimization Model  
The main continuum topology optimization methods include solid isotropic material with a penalty (SIMP), evolutionary structure optimization (ESO), and level sets. The most popular method in continuum topology optimization is the isotropic material with the penalty method. Its main advantages are that it is robust, well understood and easy to implement [36].  
The variable density method assumes a mapping relationship between the elastic modulus of the material and its relative density, obtains the relative density values of the cells when the objective function has an optimal solution through optimal iterative calculations, and updates the topological configuration of the structure by adding or deleting cells according to the magnitude of the relative density of each cell. To minimize the existence of intermediate density cells, a material penalty model is artificially introduced.  
The SIMP model assumes that the modulus of elasticity of a material as a function of its relative density can be expressed as:  
\[E(\rho)=E_{\text{min}}+\rho^{p}(E_{0}-E_{\text{min}}),\rho\in[0,1] \tag{7}\]  
where \(\rho\) is the relative density of the cell, \(p\) is the penalty factor, \(E_{0}\) is the modulus of elasticity of the material before being penalized, and Emin is the elastic model of the material out of the hole in the structure, whose value is a very small value approximately equal to 0.  
Figure 10: Relation between disc opening angle and molten salt inlet velocity.  
Simplify Equation (7) and get the following equation:  
\[E(\rho)=E_{0}\rho^{p},\rho\in[0,1] \tag{8}\]  
The main role of the penalty factor \(p\) is to penalize the intermediate densities so that they better converge to 0 or 1 and eventually redistribute the material.  
The goal of topology optimization is to redistribute the material and provide maximum stiffness for various mass constraints. In this study, based on the SIMP model, the optimization objective is to minimize the structural compliance under the given load and boundary conditions, the constraints are the masses of the valve motion components, and the established mathematical model for topology optimization is shown in Equation (9).  
\[\left\{\begin{array}{l}\min C(\rho)=U^{T}KU\\ \text{s.t.}\ K(\rho)U(\rho)=F\\ \sum\limits_{c=1}^{n}V_{c}\rho_{c}-V\leq 0\\ 0\text{<}\rho_{\min}\leq\rho_{c}\leq 1\\ \sigma_{\max}\leq[\sigma]\end{array}\right. \tag{9}\]  
where \(C\) is the structural compliance; \(\rho\) is the discrete unit density, \(\rho=[\rho_{1},\rho_{2},\dots,\rho_{n}]^{T}\); \(K(\rho)\) is the stiffness matrix of the overall structure; \(U(\rho)\) and \(F\) are the displacements and loads of the discrete model, respectively; \(V_{c}\) is the volume of the discrete unit; \(V\) is the target volume of the discrete unit; \(\rho_{\min}\) is the lower limit set to prevent the singularity of the overall stiffness matrix due to 0 density; \(\sigma_{\max}\) is the maximum stress of the model structure and \([\sigma]\) is the allowable stress of the model structure.
## 3 Methodology and Solution
### General Solution
If we ignore the two boundaries of power spectrum in Figure 3b, after downsampling, the power spectrum is more similar to an interference on top of flat noise. This flattened shape allows the system to benefit from the designed detector for the GNSS signal. Figure 3a,b try to explain improvement in focusing and resolution by the help of downsampling. As an example, three CWIs with different powers are utilized as interference. Based on this comparison, after downsampling by the right factor (\(M=4\)), the area of interest is zoomed-in and that increases the resolution of the detector. Considering that the detected center frequency of the CWI is downsampled equal to \(f_{\text{interference}}^{down\ sampled}\), then the center frequency of interference can be easily expressed as:  
\[f_{\text{interference}}=\frac{1}{M}\cdot f_{\text{interference}}^{down\ sampled}. \tag{1}\]  
If a detector transfers a window of time domain samples to the frequency domain by FFT with size \(N_{FFT}\), the resolution between two frequency samples is equal to the \(\frac{1}{N_{FFT}}\). Based on Equation (1), after downsampling, the resolution will improve to \(\frac{1}{M\times N_{FFT}}\). Downsampling can improve the detector performance in both resolution and capability in the weak signal detection; however, it increases the required samples number for correct detection. Consider that the detector needs \(N\) samples to reach convergence or to fulfill its duty, after downsampling by a factor M, the input of the detector now requires \(N\times M\) samples. The time of detection is now multiplied directly by \(M\). Thus, the direct drawback of downsampling is that the convergence time to the correct estimation is multiplied by \(M\) factor. Figure 3: (a) Original power spectrum of a modulated signal (b) power spectrum of the signal after downsampling.
# Bird Cherry (_Prunus padus_) Fruit Extracts Inhib Lipid
## 3 Results
### HPLC Results  
The chromatograms obtained for the investigated extracts and sirup are presented in Figures 3 and S2. The four main peaks were clearly observed in nonaqueous extracts. The corresponding UV-Vis spectra of compounds **1-4** are presented in Figure S3. ESI-MS spectra were recorded for isolated compounds. The ESI-MS spectrum of compound **1** (r.t. 12.0 min) in positive mode (Supplementary Figure S4) shows the three main ions at \(m/z\)  
Figure 2: Fluorescence spectra registered for \(\lambda_{\rm ex}\) = 260, 280, and 310 nm and total fluorescence maps of investigated extracts and sirup.  
287, 449, and 595, which correspond to the cyanidin chromophore itself, and glucoside and trutinoside of cyanidin, respectively. The compounds with retention times of 17.5 min (marked 3) and 19.0 min (marked 4) are glycosides of quercetin, as, in both cases, ESI-MS spectra contain the same ion signals at \(m/z\) 303 in positive mode. In the case of compound **3**, it is difficult to distinguish between glucoside or galactose of quercetin as the obtained \(m/z\) signal is 463 (negative mode) (Supplementary Figure S5). The ESI-MS spectrum of compound **4** contains a signal at \(m/z\) 433 (negative mode), which can be arabinoside of quercetin (Supplementary Figure S6). In the case of compound **2** with r.t. 13.8, its UV-Vis spectrum and retention time were identical to the chlorogenic acid standard sample (Supplementary Figures S2 and S3).  
Therefore, the HPLC chromatographic method was used to quantify and specify the presence of phenolic and flavonoid compounds in the investigated extracts, which was supported by UV-Vis and ESI-MS spectra of isolated compounds (Table 1). The obtained results show the highest amount of cyanidin in methanol extract; however, in acetone and ethanol extracts, it was also detected. The chlorogenic acid and quercetin glycosides were clearly visible in sirup and all extracts except water extract in which no presence of any of compounds **1-4** were detected.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Peak Mark** & **Retention Time [min]** & **Found Mass [\(m/z\)]** & **Compound** \\ \hline  & & 287 [M]\({}^{+}\) & cyanidin \\ \cline{3-4}
**1** & 12.0 & 449 [M]\({}^{+}\) & 3-glucoside of cyanidin \\ \cline{3-4}  & & 595 [M]\({}^{+}\) & 593 [M\(-\)H]\({}^{-}\) & 3-rutinoside of cyanidin \\ \hline
**2** & 13.8 & - & chlorogenic acid \\ \hline
**3** & 17.5 & 303 [M+H]\({}^{+}\) & quercetin \\ \cline{3-4}  & & 463 [M\(-\)H]\({}^{-}\) & quercetin glycoside \\ \hline  & & 303 [M+H]\({}^{+}\) & quercetin \\ \cline{3-4}
**4** & 19.0 & 433 [M\(-\)H]\({}^{-}\) & quercetin glycoside \\ \cline{3-4}  & & 457 [M+Na]\({}^{+}\) & \\ \hline \end{tabular}
\end{table}
Table 1: The list of compounds found in nonaqueous extracts with possible identification.  
Figure 3: HPLC elution profiles (330 nm) of extracts and sirup. The proposed identification of main components (marks with numbers) was collected in Table 1.
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 5 Experimental Results  
To further evaluate the effectiveness and feasibility of the proposed method, a comparative evaluation is conducted in relation to other notable methods, which mainly include the Laplace pyramid algorithm (LP) [11], low-pass pyramid fusion algorithm (RP) [12], DWT [13], double-tree complex wavelet transform algorithm (DTCWT) [14], curve wave transform algorithm (CVT) [15], and nonsubsampled contourlet transform algorithm (NST) [16].  
A widely used dataset for image fusion is selected in the experiments, which consists of 20 pairs of color and 10 pairs of gray multi-focus images, 16 pairs of medical, and 8 pairs of infrared and visible multi-mode images, namely 2 categories and 4 sub-categories. All images are 520 \(\times\) 520 in size. For images of the same type, all the quantitative indicators are averaged to reduce random errors and improve credibility. All experiments are simulated on MATLAB R2020a, the computer processor is Intel (R) Core (TM) i5-8250U [EMAIL_ADDRESS] GHz, and the installed memory (RAM) is 8.00 GB.
## 2 Data and Methodology
### Data
#### 2.2.2 Synthetic Aperture Radar Data  
The Sentinel-1 (S1) data are obtained from the \"COPERNICUS/S1_GRD\" remote sensing image collection on Google Earth Engine (GEE). Within this study, the Sentinel-1 radar image is directly accessed via the GEE platform, having undergone official preprocessing procedures, including updating orbital metadata, removal of GRD boundary noise, elimination of thermal noise, radiometric calibration, and ortho-correction. The data mode employed for this study is the IW mode, which is well-suited for remote sensing studies of land surfaces, particularly with VV + VH polarization.
## 4 Proposed Model  
The above-mentioned problems caution the ability of OM, in its current state, to be applied to OSNs. The main shortage is in the sedulity to use the same opinion elements while being applied to OSNs and their environmental uncertainty. The proposed model aims to improve the effectiveness of applying OM to Twitter. To solve the mentioned problems, we should readapt the opinion elements to accommodate the application domain properties. Accordingly, we need to introduce new elements to qualify the OM process on OSNs that are specifically related to opinion holders' hallmarks and social reactions to their actions. Other than that, we must solve common uncertainty problems that can improve the opinion classification process. The proposed model mainly focuses on all the previously mentioned problems while holding the following contributions:  
* The proposed model suggests adding a new element (assume: \(w_{kl}\)) to the five elements of opinion, describing the importance of opinion holders and their reactions to their opinions. This element takes into consideration the impact of the opinion holder and his reaction to his opinion on the assigned opinion orientation. Importance weighting should be applied to opinion holders so that they are provided weights based on their authority over the audience. As such, perspectivism can be practically represented in the main OM process;
* The users' weighting method is proposed in this model, which depends on applying SNA and ANN. A comparative analysis of three famous SNA tools must be conducted to adopt the most applicable one. ANN is applied to rank users based on their centrality measure produced by the SNA tool as well as the reaction-based features of users' texts (i.e., likes, shares, etc.). Weights are provided based on users' ranks; the top-ranked is the most weighted and vice versa. ANN was chosen for being efficient in dealing with the complicated behavior of humans that could not be mathematically represented;
* The innovative adoption of NL is to integrate the new elements with the other traditional elements of opinion. Moreover, NL adoption hybrids the process of OM, providing more accurate polarity scores through performing the OM combining lexicon with machine learning (ML) (i.e., TextBlob and NL). Finally, NL was proven to effectively deal with uncertainty especially opinion ambiguity and dynamicity. Figure 2 presents the proposed model and its components. The phases of the proposed model are described as follows:  
Figure 2: The proposed NL-based OM model.
# University Student Dropout Prediction Using Pretrained Language Models
## 2 Related Work
### Pretrained Language Models Based on Transformer  
Recently, transformer-based [26] language models pretrained with large datasets [27; 28; 29; 30] have achieved promising results in handling various NLP tasks. In particular, BERT [27], which utilizes the encoder part of the transformer, has demonstrated promising performance in various classification tasks, such as sentiment classification, question answering, and NLI. BERT adopts two pretraining objectives (i.e., masked language modeling (MLM) and next sentence prediction (NSP)) on a large-scale unlabeled text, before fine-tuning the model for a specific downstream task. MLM is an important component in the pretraining of the BERT model. It randomly masks 15% of the tokens from the input sentences and thereafter trains the model to predict the original vocabulary ID of the masked tokens based only on their context (i.e., the remaining tokens). BERT exhibits outstanding performance in most NLP tasks by fine-tuning the model with minimal task-specific architectural modifications (e.g., connecting a fully connected layer to the BERT representation). Subsequent studies, such as RoBERTa [30], have improved the performance by removing NSP and modifying the static MLM to a dynamic MLM. In parallel, SimCSE [31] employs a supervised method that enhances pretrained models by incorporating annotated pairs from NLI datasets into the contrastive learning framework. Transformer-based PLMs also work in multimodal tasks [32; 33; 34; 35] and tasks built on other types of data formats (e.g., tabular data) [15; 36] as well as in NLP. Inspired by these studies, we propose a framework for predicting student dropout by feeding both structured and unstructured data into a model.