esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of the Integration of the Electric Vehicle on Low-Voltage Rural Networks
## 5 Improvements to the Charging Procedures
### Scenario 2: Charging Process Control Attending to the Maximum Voltage Drop  
To mitigate the problem of voltage drops caused by EVs charging, a car charging control mechanism is proposed to ensure that the minimum voltage at the grid nodes does not drop below the 0.9 p.u. limit [61], while keeping the line load below 100%. For this purpose, an iterative algorithm is proposed (see Figure 4), which, based on monitoring the grid voltage at critical points, checks if the voltage does not drop below a certain safety threshold (0.92 p.u). If it reaches this value, a parameter \(J\) is sent to modify the power setpoint to Mode 3 chargers. The system generates stepwise decreases of 5% in setpoint if the voltage is below the threshold and increments of 5% if the voltage recovers above 0.95 p.u. In Figure 4, \(t\) is the waiting time before the next check. Mode 3 chargers receive the parameter \(J\), which is sent to the BMS to adapt the power to \(P_{set_{\max}}=P\cdot J\) where \(P\) is the initial value of the power.  
Figure 5 shows the effect of applying the control algorithm to the charging process of the EVs. In Figure 5a, it can be observed that the voltage is consistently maintained within the specified range during the whole charging period. Figure 5b illustrates that network overloading is reduced in 48%, approximately.  
Figure 6 shows the resulting charging profiles of the electric vehicles when the charging control operates (Scenario 2). Reducing the maximum charging power during undervoltage inevitably prolongs the vehicle charging duration but ensures that the voltage remains within the acceptable range. It can be seen how the charging profile of the most discharged vehicle demonstrates a constrained power profile, particularly at the initiation of the  
Figure 4: Voltage Control Flowchart.  
Figure 5: Comparison of the results of Scenario 2 regarding Scenario 1.  
charging process when coinciding with other vehicles. As these vehicles progressively complete their charging, the setpoint for the maximum permissible power has been raised. Consequently, the duration of the charging process has been extended by an average of 2 h.
## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Hybrid Flower Pollination Resource Consolidation Algorithm
#### 4.2.2 Local Neighborhood Search Strategy Phase  
Previous neighborhood strategies were static and did not consider the whole search space that we integrated into the FPA. In this research, the LNS is dynamic, and each solution has a knowledge of the current and previous solutions. This reduces the time taken to find a solution. The LNS contains an \(X\) and \(Y\) axis that divides the search space into a fully connected neighborhood. The square contains vectors in which the structure explores to find other solutions instead of the whole population size. The search is carried out in a clockwise movement from the positive Y to the negative X direction within the neighborhood radius in which each neighbor shares their best solution. It is assumed that there exists a differential evolutionary population \(P_{G}=[X_{1,S},X_{2,S},\dots,\ X_{i+1,S}]\), and each \(X_{i,S}(i=1,\ 2,\ 3,\dots,HQ)\) is a parameter vector with D-dimension. Each vector subscript index is randomly divided to ensure the diversity of each neighborhood. Figure 2 illustrates the structure of the LNS strategy of the MOH-FPRC algorithm, where every solution can be compared with each other.  
For each vector \(X_{i,S}\), which defines a neighborhood, and the radius \(k\ (2k+1<HQ)\). The neighborhood consists of vector \(\ X_{i-k,S},X_{i,S},\dots,X_{i+k,S}\). The assumption is that the vectors are in accord with the subscript indices in a loop structure formed within the search space. Hence, \(X_{HQ,S}\) and \(X_{2,S}\) are two direct neighbors of \(\ X_{1,S}\). The neighborhood structure is dynamic and determined by the collection of vector subscripts. Thus, the LNS strategy can be expressed in the Equation (12).  
\[X_{i+1,S}=X_{i,S}+\varnothing\big{(}X_{n-best,S}\big{)}+\vartheta\big{(}X_{p,S }-X_{q,S}\big{)} \tag{12}\]  
where \(X_{n-best,S}\) is the best vector of the \(X_{i,S}\) neighborhood, \(h,q\ \in[i-k,i+k](p\
eq q\
eq i)\), and \(\varnothing,\ \vartheta\) are two scale factors.
# An Overview of Self-Heating Phenomena and Theory Related to Damping and Fatigue of Metals
## 3 The Thermodynamic Framework
### Internal Friction  
Internal friction generally refers to the motion of rubbing between microstructural elements in the material. This may lead not only to heat generation but also to damping and fatigue, causing, depending on the stress amplitude, reversible or irreversible deformation. In metals under cyclic loading at low amplitudes, dislocation segments may bow back and forth between weak pinning points [20; 21; 22; 30]. Even though at the macroscopic level the deformation process is reversible, restricted dislocation glide does occur. Once the stress amplitude exceeds the overall lattice resistance, dislocations can break away from pinning and glide freely along crystallographic planes throughout the lattice, resulting in micro- or macro-plastic deformation. Therefore, before irreversible dislocation motion occurs, there should be no change in the material microstructure and internal energy, so the right side of Equation (9) reduces to (neglecting the thermoelastic and internal state variable-related terms):  
\[d_{1}=\sigma:\dot{\varepsilon}_{p} \tag{16}\]  
where \(d_{1}\) represents the hysteresis energy due to anelastic dislocation motion.  
[PERSON] et al. studied the thermographic behavior of FV520B stainless steel under different stress amplitudes [22], and they observed that the relationship of dissipation energy vs. stress amplitude (\(d_{1}\) vs. \(\sigma\)) exhibits a curve, as shown in Figure 4. They divided the curve into three stages: (i) pure elastic (reversible), (ii) anelastic internal friction, and (iii) intrinsic dissipation, marked by two critical stresses, \(\sigma_{c1}\) = 179 MPa and \(\sigma_{c2}\) = 400 MPa for the material. The first critical stress corresponded to the deviation from the initial elastic line, i.e., the proportional limit on the tensile curve, below which there was no energy dissipation by pure elasticity. The second critical stress divided the stages of linear and nonlinear dissipation, which was found to be close to the fatigue endurance limit obtained from the conventional fatigue testing [22]. Here, the \"bilinear\" relationship of the Risitano method is apparently not sufficient to clearly define the \"intersection\". Between \(\sigma_{c1}\) and \(\sigma_{c2}\), the dissipation energy appears to be a linear function of the applied stress amplitude. [PERSON] et al. argued that internal friction occurs in the stress range (\(\sigma_{c1}<\sigma<\sigma_{c2}\)) as a result of the movement of dislocations, but the process returns the atoms to their equilibrium positions, thereby increasing the kinetic energy and temperature because of releasing the excess energy gained during the movement. In this sense, internal friction does not contribute to fatigue damage.
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
## 4 Experiment and Discussion
### Dissolved Gas Detection Experiment  
The setup described in this paper, as shown in Figure 14, was assessed in a laboratory environment (temperature 25 \({}^{\circ}\)C, pressure 101 kPa).  
To detect dissolved gases in insulating oil, it is necessary to determine the time required for an oil-gas mixture (650 mL) to achieve dynamic equilibrium in an oil-gas separation system. The system operates at a temperature of 50 \(\pm\) 1.3 \({}^{\circ}\)C, a pressure of 28 \(\pm\) 2.7 kPa, and a stirring speed of 72 \(\pm\) 2 r/min.  
As shown in Figure 15, the degassing of the hydrocarbons takes 35 min. Carbon monoxide requires approximately 55 min to reach dynamic equilibrium.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TESTS** & **CO(\(\upmu\)L/L)** & \begin{tabular}{c} **CO\({}_{2}\)** \\ **(\(\upmu\)L/L)** \\ \end{tabular} & \begin{tabular}{c} **CH\({}_{4}\)** \\ **(\(\upmu\)L/L)** \\ \end{tabular} & \begin{tabular}{c} **C\({}_{2}\)H\({}_{6}\)** \\ **(\(\upmu\)L/L)** \\ \end{tabular} & \begin{tabular}{c} **C\({}_{2}\)H\({}_{4}\)** \\ **(\(\upmu\)L/L)** \\ \end{tabular} & \begin{tabular}{c} **C\({}_{2}\)H\({}_{2}\)** \\ **(\(\upmu\)L/L)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Average data of TDLAS \\ Reference data of GC \\ Error value \\ \end{tabular} & 9.21 & 765.26 & 8.46 & 13.28 & 52.48 & 1.09 \\ \
\begin{tabular}{c} Reference data of GC \\ Error value \\ \end{tabular} & 7.54 & 778.73 & 8.41 & 11.32 & 49.32 & 1.00 \\
\begin{tabular}{c} Error value \\ \end{tabular} & 1.67 & 13.47 & 0.05 & 1.96 & 3.16 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data of the TDLAS system and the reference device.  
Figure 13: Measured results of the TDLAS system: (a) CH\({}_{4}\); (b) C\({}_{2}\)H\({}_{4}\); (c) C\({}_{2}\)H\({}_{6}\); (d) C\({}_{2}\)H\({}_{2}\); (e) CO; (f) CO\({}_{2}\).  
The degassing situation conforms to the trend of the gas separation time [6; 31], and the oil-gas separation system is able to separate the gas dissolved in oil. After determining the degassing time, three standard oil samples were used for testing. The comparison results are shown in Table 4.  
In detection of standard oil samples, the relative error of hydrocarbons at NO.1 oil and NO.2 oil is less than 2.4 \(\upmu\)L/L, carbon monoxide is less than 3.4 \(\upmu\)L/L, and carbon dioxide is less than 10.8%. In addition, the error of carbon monoxide and carbon dioxide in NO.3 oil is less than 4% and 13.4%, respectively, and the error of hydrocarbons does not exceed 3%. The test errors conform to the standards (GB/T7252-2001, DL/T722-2014) provided by the Power Industry Equipment Quality Inspection and Testing Center of the  
Figure 14: Experimental setup.
# Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics
## 2 Materials and Methods
### Data Analysis
#### 2.3.2. Articular Kinematics  
The kinematics of the upper limb was computed in intrinsic articular coordinates. Three articular angles were computed as the _arccosine_ of the scalar product between two vectors, defined as in [PERSON] et al. [30]. The arm vector is defined as the vector having module equal to the length of the arm, direction connecting the shoulder marker to the elbow marker, and orientation from the shoulder to the elbow, while the forearm vector is defined as the vector having a module equal to the length of the forearm, direction connecting the elbow marker to the wrist marker, and orientation from the elbow to the wrist. The arm unit vector is the arm vector divided by its norm; the forearm unit vector is the forearm vector divided by its norm. The shoulder elevation is the angle between the arm unit vector (projected in the sagittal plane for frontal, up and horizontal sectors and in the coronal plane for left and right sectors) and the vertical axis pointing downwards. The shoulder plane of elevation is the angle between the arm unit vector (projected in the horizontal plane) and the x-axis pointing towards the right of the subject. The elbow flexion is the angle between the arm unit vector and the forearm unit vector. Articular angles are shown in Figure 2.
## 3 Results and Discussion
### PLS Regression and Prediction Results of the Data Set  
Partial least square regression model (PLS2) of the mean-centered spectral data with three latent variables (LV) explained 85% of the total variance while LV1 explained 32% after calibration, cross-validation, and outliers' removal. The best fitting to the model showed data at absorbance of A320 (Figure 7).  
PLS2 regression model confirmed PCA findings of two uncorrelated clusters (Figure 5b) and showed the great influence of suspended solids (SS), dissolved solids (TDS and EC), and organic matter (BOD5, COD\({}_{\text{Cr}}\), and DOC) while the influence of pH, N\({}_{\text{Kj}}\), P\({}_{\text{tot}}\) and color was negligible. In Table 2, the limit values of the contamination of the root vegetables washing water and wastewater and absorbance spectra of the UV/Vis predicted by PLS regression model are presented.  
As A320 data have the greatest impact on the model (Figure 5a), it was proposed to use this parameter to monitor water quality in the plants for washing the root vegetables.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Min** & **Max** & **Mean** & **SD** \\ \hline SS, mg L\({}^{-1}\) & 25.0 & 60.0 & 42.5 & 24.7 \\ TDS, mg L\({}^{-1}\) & 2000 & 2000 & 2000 & 0.00 \\ pH & 6.50 & 9.50 & 8.00 & 2.10 \\ EC, \(\mu\)S cm\({}^{-1}\) & 2500 & 2500 & 2500 & 0.00 \\ BODs, mg L\({}^{-1}\) & 20.0 & 46.0 & 33.0 & 18.4 \\ COD, mg L\({}^{-1}\) & 125 & 125 & 125 & 0.00 \\ DOC, mg L\({}^{-1}\) & 105.0 & 115.0 & 110.0 & 7.10 \\ Nkj, mg L\({}^{-1}\) & 2.0 & 30.0 & 16.0 & 19.8 \\ Pot, mg L\({}^{-1}\) & 0.50 & 4.00 & 2.25 & 2.50 \\ Color, dilution times & 3 & 3 & 3 & 0 \\ A210, cm\({}^{-1}\) & 3.997 & 4.009 & 4.003 & 0.008 \\ A225, cm\({}^{-1}\) & 3.886 & 3.902 & 3.894 & 0.011 \\ A240, cm\({}^{-1}\) & 5.193 & 5.235 & 5.214 & 0.030 \\ A254, cm\({}^{-1}\) & 4.042 & 4.047 & 4.045 & 0.004 \\ A320, cm\({}^{-1}\) & 7.387 & 7.406 & 7.397 & 0.013 \\ A660, cm\({}^{-1}\) & 3.946 & 3.937 & 3.942 & 0.006 \\ \hline \end{tabular}
\end{table}
Table 2: Limit values of the root vegetables washing water and wastewater chemical contamination (Table 1) and UV/Vis spectra.  
Figure 7: PLS regression model overview plot: (**a**)—correlation loadings between chemical properties (x variables) and UV/Vis spectra (y variables); (**b**)—the importance of the chemical properties (x variables) on the model, (**c**)—model calibration, (**d**)—model validation for A320.
## 1 Introduction
HD-ZIP I proteins are structurally the simplest, containing only the HD (homeodomain) and LZ (leucine zipper) domains located centrally within the protein [34; 36; 37], whereas HD-ZIP proteins from subfamilies II-IV possess more complex structures [33]. HD-ZIP I proteins typically bind to the CAAT(A/T)ATTG motif, though various sequence variants exist. The binding affinity is highest for the canonical CAAT(A/T)ATTG site [33; 34; 38] but may differ depending on sequence composition and complementarity at the ends [35]. HD-ZIP II and HD-ZIP III proteins share similar binding sites, but the central base in these sites might prefer (C/G). HD-ZIP IV proteins have distinctly different binding sites from the other subfamilies, with a specific binding site of TAAATG(C/T)A [34]. Although the diosgenin biosynthesis pathway in _D. zingiherensis_ has been extensively studied [1], with considerable depth in understanding the synthesis mechanisms, current research suggests that HD-ZIP transcription factors may be involved in the biosynthesis of diosgenin [11; 39]. Additionally, HD-ZIP transcription factors are capable of responding to various abiotic stresses and promoting the production of secondary metabolites [27; 28; 29; 30; 31; 32; 34; 33; 32; 34; 40; 41; 42]. However, the role of HD-ZIP transcription factors in _D. zingiherensis_ has not been reported, and their function in diosgenin synthesis remains unclear. This study identifies a gene involved in diosgenin biosynthesis, clarifies its regulatory function, and elucidates the underlying mechanism, aiming to uncover the specific transcriptional regulation of diosgenin biosynthesis in _D. zingiberensis_. Understanding these regulatory elements will not only enhance diosgenin yield but also contribute to the broader understanding of secondary metabolite biosynthesis in plants.
# Spanish Poplar Biomass as a Precursor for Nanocellulose Extraction
## 1 Introduction
Moreover, nanocellulose has a well-proven and promising role in the developing construction, energy, and electronics industries [28]. Nanocellulose can be extracted through one of many routes. The first one is mechanical extraction, where mechanical work is exerted through disc grinder, ball milling, sonicator, or homogenizer to disintegrate the cellulosic hierarchy, yielding cellulose nanofibrils (CNF), which exhibit a very high aspect ratio and moderate crystallinity [29]; long fibrils entanglement yields highly viscous suspensions even at relatively low concentrations (lower than 1 wt%) [30]. The second one is biological extraction; bacterial preparation is performed in which bacteria assemble glucose monomers in long cellulose chains, yielding bacterial nanocellulose (BNC), which carries both advantages of high crystallinity and a very high aspect ratio [31]. BNC is an extracellular product of bacterial microorganisms. The most reportedly productive organism is Gluconacetobacter Xylinum. Unlike plant-sourced nanocellulose, which usually requires pretreatment to remove hemicellulose and lignin before hydrolysis, BC is synthesized (constructed) as pure cellulose. BC nanofibers generally size 20-100 nm in diameter and micrometers in lengths [32]. The third route is utilizing an ionic liquid to extract highly crystalline nanocellulose [33], as the major advantage of ionic liquids is their recyclability. The fourth route is chemical extraction in which an acid (majorly sulphuric acid) attacks amorphous regions of the cellulosic structure leaving CNC, which exhibits a moderate aspect ratio and very high crystallinity [29; 34], generally 5-20 nm \(\times\) 100-500 nm dimensions and sulfuric acid sourced sulfate group, of negative charge are grafted on CNC, which performs a preventive action against aggregation and flocculation in water suspension due to electrostatic repulsion (-ve charge) between particles. In this article, we will attempt to provide those willing to proceed in the nanocellulose field a preliminary scientific background to build upon in their research. The main objective of this study was to investigate nanocellulose extraction, chemically from Andalusian poplar wastes. MCC underwent the same hydrolysis process as a control experiment; we also sought to understand the effect of hydrolysis time on the produced CNC.
## 4 Thermal and Structural Analysis and Design Improvements of the BC Region
### The Second Update (v2) of the BC Region Design
#### 4.2.1 The FEM Model  
Starting from the results of the analysis already presented and following the same approach already used for the TC region design analyses [4], a BC region layout has been set up by increasing the SPs thickness of the elementary cell housing the BC plate, as reported in Figure 19. Then, a 3D FEM model was set up for the thermal analysis and a spatial grid composed of \(\sim\)3.8 M nodes connected into \(\sim\)1.7 M tetrahedral and hexahedral elements has been built-up, whereas, as to the structural analysis, since the PbLi, the DWTs and the cooling water flowing within the cooling channels and the DWTs have not been directly modelled, a mesh of \(\sim\)2.5 M nodes connected into \(\sim\)650 k tetrahedral and hexahedral elements has been set-up.  
The same set of loads and boundary conditions already seen in Section 4.1.1 has been implemented in the up-to-date model with the aim of performing its complete thermo-mechanical analysis under NO and OP steady-state loading scenarios.
## 3 Proposed Architecture: Multi-Model IDS Based on Traffic Load
### Benchmark Dataset  
This section provides an overview of the datasets used in this study. It is recommended that researchers utilize the CIC-MalMem-2022, CIC-IDS-2018, and CIC-IDS-2017 datasets for intrusion detection and malware analysis purposes. The fact that they are based on real-world attacks, are labeled, and are publicly accessible has made them popular tools for developing and evaluating new security solutions. Additionally, there are U2R attacks, such as SQL injections and buffer overflows, and 2L attacks, such as remote login attacks and Trojan horse attacks [56; 57; 58]. The datasets used in this study contain various types of threats. CIC-MalMem-2022 contains several types of attacks, including U2R attacks, R2L attacks, probe attacks, malware attacks, and botnet attacks [56], while the CIC-IDS-2018 dataset contains probe attacks, such as port scanning, ping sweeps, and network scans. It also contains U2R attacks, such as buffer overflows and SQL injection attacks. Additionally, it contains 2L attacks, such as remote login attacks and Trojan horse attacks [57]. Also, the CIC-IDS-2017 dataset consists of probe attacks, such as port scanning, ping sweeps, and network scans. Additionally, the dataset consists of U2R attacks, such as buffer overflows and SQL injection attacks, and 2L attacks, such as remote login attacks and Trojan horse attacks [58]. In Table 5, the datasets and attack classes are described in detail. A diagram of the model's architecture is shown in Figure 2. The process begins with selecting a dataset, followed by data preparation and feature selection based on the dataset. After the data have been split into training and test sets, 80% of them are used for training and 20% for testing. Finally, the ML model is fitted to the training set and then to the test set in order to determine whether the data are benign or malicious.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Network Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{**Centralized**} & • • Easy to manage & • • & Single point of failure \\  & • Efficient resource allocation & • & Reduced resilience \\  & • Strong security & • & Reduced user autonomy \\  & • Adaptability to failure & • & Increased complexity \\  & • Reduced censorship & • & Difficult consensus mechanisms \\  & • Enhanced user autonomy & • & Limited scalability \\ \hline \hline \end{tabular}
\end{table}
Table 4: Advantages and disadvantages of centralized and decentralized network architectures.
## 2 Materials and Methods
### Validation
#### 2.4.1 Comparison with Real Sentinel-2 SCE Maps  
Spatial validity was carried out by comparing the predicted SCE maps with an independent dataset of high-quality SCE maps from S2 with low cloud cover, i.e., between 10 and 15%, which were excluded from RF training and which achieved an overall accuracy of 96% [67]. A total of 44 images met these requirements and were, thus, used for the validation. Cloudy pixels and other gaps in the real S2 scenes, such as no data due to partial acquisitions, were excluded from the computation. The accuracy of the products was investigated both overall and in the different seasons, as well as throughout the different topographies.