esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Quick Preliminary Design Model
### Operational Profile and Power Plant Hybridization  
DMFC-AUVQpDM allows the user to define an operational profile in the form of a two-level cyclic power demand. Each stage is defined by a time span; a cruising speed; the utilization factors, \(K_{u}\) of the devices that integrate the payload; and auxiliary equipment and a power margin that covers possible uncertainties. Figure 4 shows two different operational profiles. Profile \(\cancel{A}\) is characterized by a phase with a high power demand and a much longer duration than the low power demand phase. This pattern can be applied to an AUV scanning the seabed with a side-scan sonar in a rectangular scan pattern. Profile \(\cancel{B}\) is the opposite, a power demand with short peaks and long standby periods. It can correspond to a surveillance AUV that takes and transmits data from the environment during short periods and remains in a standby state for the remainder of the time.  
Regular operational profiles, as the one described above, allow the hybridization of fuel cell with an auxiliary energy storage system (AESS). This allows balancing the power delivery in a proper way thereby resulting in higher global efficiencies and more compact power plants. In this case, DMFC-AUVQpDM implements the method proposed by [PERSON] et al. to size the fuel cell and the AESS [26]. As discussed therein, the AESS may consist of a capacitor or a pack of batteries of different types. In this case, only Li-ion batteries are considered for two reasons: First, the power peaks of the  
Figure 3: Framing system. \(l_{s}\), frames span. \(h_{sw}\), web height. \(a_{sw}\), web thickness. \(h_{sf}\), flange thickness. \(a_{sf}\), flange width. \(t_{hk}\) pressure hull wall thickness  
expected operational profiles are not big enough to consider capacitors. Second, this battery type is a proven technology that allows higher energy densities and specific energies than other commercial battery types [27].  
A  
than in profile  
B  
in terms of delivered power. In both cases, the hybridization will be sized in such a way that during low power consumption the fuel cell recharges the batteries and, at the same time, the fuel cell operates near the desired working point. In this paper, this working point is that which is close to the maximum efficiency. For a given power output, this decision results in larger and heavier stacks. However, in this case, the influence on the design of the volume and mass of the reactants and products is higher than those of the fuel cell. Thus, the result is more compact global designs for a given endurance.
## 4 Numerical Simulation Results
### Strength Parameters  
#### 4.3.1 Peak Strength Influenced by RFCA and Confining Stress  
Figure 9 illustrates the peak stress of the numerical specimen, highlighting the influence of RFCA and confining stress. As depicted in Figure 9, the peak strength of the numerical specimen increases with the increase in \(H\) under the same confining stress. For instance, at a confining stress of 0, the peak strength ascends from 1.15 MPa at \(H\) = 0.1 to 1.95 MPa at \(H\) = 0.7, marking a 70.2% increase. Under a confining stress of 0.8 MPa, the peak strength increases from 4.76 MPa at \(H\) = 0.1 to 6.49 MPa at \(H\) = 0.7, indicating a 36.4% increase. This suggests that a higher RFCA results in greater peak strength of the similar material. Moreover, this effect is more pronounced under smaller confining stresses.  
To further quantify the effect of RFCA on the strength of similar materials, the Mohr-Coulomb strength criterion was introduced as follows:  
\[\sigma_{1}=\sigma_{c}+K\sigma_{3} \tag{5}\]  
where \(\sigma_{1}\) signifies the maximum principal stress, \(\sigma_{3}\) represents the minimum principal stress, \(\sigma_{c}\) denotes the uniaxial compressive strength, and \(K\) is the influence coefficient of  
Figure 8: Fitting surfaces for the elastic modulus under different RFCAs and confining stresses.  
Figure 9: Peak stress of the numerical specimen influenced by different RFCAs and confining stresses.  
the rock on the strength. \(\sigma_{c}\) and \(K\) can be represented by the internal friction angle \(\varphi\) and cohesion \(c\), i.e.,  
\[\left.\begin{array}{l}K=(1+\sin\varphi)/(1-\sin\varphi)\\ \sigma_{c}=2c\cos\varphi/(1-\sin\varphi)\end{array}\right\} \tag{6}\]  
In this analysis, \(c\) and \(\varphi\) in the Mohr-Coulomb strength criterion are fitted according to the results of the curve of the principal maximum stress and principal minimum stress; \(\sigma_{c}\) and \(K\) with each RFCA can be initially fitted by Equation (5). As \(\sigma_{c}\) and \(K\) are solved, the internal friction angle \(\varphi\) and cohesion \(c\) with each RFCA can then be by solved by Equation (6). The fitting results of \(\sigma_{c}\), \(K\), \(c\), and \(\varphi\) are tabulated in Table 5.  
Based on the obtained results in Table 5, the relationship between \(c\) and \(H\), as well as that between \(\varphi\) and \(H\), can be fitted. The fitted linear equations are expressed in Equations (7) and (8), and the fitted results are plotted in Figures 10 and 11.  
\[c=0.322H+0.332 \tag{7}\]  
\[\left.\begin{array}{l}\varphi=-35.858H+42.043,\;H\leq 0.2\\ \varphi=16.325H+31.67,\;H>0.2\end{array}\right\} \tag{8}\]  
Figure 10: Relationship between cohesion and RFCA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Models** & \(H\) & \(\sigma_{c}\) **(MPa)** & \(K\) & \(R^{2}\) & \(c\) **(MPa)** & \(\varphi\) **(\({}^{\circ}\))** \\ \hline A & 0.1 & 1.49 & 4.29 & 0.96 & 0.359 & 38.5 \\ \hline B & 0.2 & 1.53 & 3.67 & 0.96 & 0.400 & 34.9 \\ \hline C & 0.3 & 1.66 & 3.96 & 0.
# Vibration Properties of Dual-Rotor Systems under Base Excitation, Mass Unbalance and Gravity
## 3 Results and Discussions
### Forced Vibration Due to Gravity with Base Rotations  
The influence of gravity on the dual-rotor system are analyzed in this section. For the counter-rotating system, the rotating speeds of the LP and HP rotors were set at \(\Omega_{LP}=1000\) rad/s and \(\Omega_{HP}=-1.2\Omega_{LP}\).  
Gravity was constant and caused only constant displacements along its acting direction when the system was under a fixed base. It became a harmonic force with base angular frequency when the constant base rotations were concerned (see Equations (62) and (69)). The harmonic responses of the dual-rotor system due to gravity and constant base rotations are shown in Figures 11 and 12. Both backward and forward whirling modes could be excited by gravity for the dual-rotor system with pitching base motion, but no resonance was excited with rolling base motion. The whirling frequencies changed with the value of base angular velocities as the additional stiffness and damping matrices were concerned with base angular velocities (see Equation (58), (64) and (65)).  
Figure 11: Responses of the dual-rotor system at disk 1 due to gravity with pitching base motion: (a) \(x_{d1}\); (b) \(z_{d1}\); (c) \(\theta_{d1}\); (d) \(\psi_{d1}\).
## 1 Introduction
Experimental results indicate that this approach significantly improves detection rates and reduces latency, offering a practical and scalable solution for intelligent transportation systems. Overall, this study emphasizes the importance of SDN-based VANET security for both academic and industrial applications and highlights the critical role of AI-based real-time DDoS detection in ensuring the reliability and security of intelligent transportation systems. The introductory section of this study begins with a presentation of the general framework and objectives of the study. This is followed by a detailed literature review. The related studies section examines previous studies and existing methodologies in the field and identifies research gaps. The data collection, analysis, and modeling techniques used in the study are detailed in the materials and methods section. The performance evaluation section presents the results of the study, supported by statistical analyses. Finally, the conclusion section summarizes the main findings of the study. It also discusses the limitations and provides suggestions for future studies.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 4 Research Methods
### Model Accuracy Evaluation  
This study employs three evaluation metrics, namely the coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), and relative root mean squared error (rRMSE), to assess and analyze the constructed model. The coefficient of determination (R\({}^{2}\)) measures the model's explanatory power for the data and indicates the degree of fit between the predicted and actual values [35]. The value of R\({}^{2}\) ranges from 0 to 1, with values closer to 1 indicating better fit and greater variance explained by the model. Conversely, smaller R\({}^{2}\) values indicate poorer model fit. The RMSE measures the magnitude of the error between predicted and actual values, reflecting the average deviation. Smaller RMSE and relative RMSE (rRMSE) values indicate lower prediction error and better predictive performance, while larger values suggest greater prediction error and lower model accuracy [36]. The calculation formulas are as follows.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y_{i}})}^{2}}{\sum\limits_{i=1 }^{n}{(y_{i}-\overline{y})}^{2}} \tag{2}\]\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}^{\wedge}\right)^{2} }{n}} \tag{3}\]  
\[rRMSE=\frac{RMSE}{\overline{y}}\times 100\% \tag{4}\]  
In the formulas, \(y_{i}\) represents the observed AGB, \(\hat{y_{i}}\) represents the predicted AGB, \(\overline{y}\) represents the mean of the observed AGB, and \(n\) represents the number of samples.
## 4 Experiment and Result Analysis
### Comparison with No Time Coordination and Deterrence Maneuver  
To verify the effect of the proposed method on reducing the risk of mission execution and improving the deterrence of strike capability, the spatial density of tasks represented by Equation (2) is taken as the scout risk; the strike capability availability of tasks represented by Equation (5) is taken as the capability coverage. In each simulation scenario, the average risk at the beginning of each scout task and the average strike capability coverage for scouting tasks during the whole simulation process are counted. The comparison results with the algorithms without time coordination or deterrence maneuver are shown in Table 7, and each result is the statistics of the mean and standard deviation of 10 simulation scenarios.  
The statistics of the results show that time coordination can reduce the scout risk by about 23%, and deterrence maneuver can improve the strike capability coverage by about 30%, which verifies the effectiveness of time coordination and maneuver deterrence.
## 3 Results and Discussion  
### Model Fitting  
In the present study, optimization experiments based on RSM were performed to maximize the solubilization of hydroxyl cinnamic acids in honey samples using ultrasound irradiation. A BBD was applied to investigate the effects of ethanol concentration (A), temperature (B), solvent-to-sample ratio (C), and sonication time (D) on caffeic acid (Y1), chlorogenic acid (Y2), and ferulic acid (Y3) recoveries. Table 2 presents the experimental data for all measured responses, expressed as chromatography determined peak areas, obtained under different combinations of sample preparation parameters.  
The yields of caffeic acid (Y1), chlorogenic acid (Y2), and ferulic acid (Y3) were correlated in terms of coded values of the process variables by the following second-order polynomial equations obtained using regression analysis.  
Y1 = +17,079.72 - 2465.00 A + 820.12 B - 12,046.86 C - 727.82 D + 1909.50 AB - 2682.46 BD - 1875.96 B\({}^{2}\) + 5934.19 C\({}^{2}\) - 2547.33 D\({}^{2}\)  
Y2 = +19,191.67 - 1210.78 A + 1104.90 B - 11,724.47 C - 1880.17 BC - 1449.74 B\({}^{2}\) + 5269.16 C\({}^{2}\)  
Y3 = +28,853.87 + 2569.99 B - 20,152.97 C - 4824.67 BC + 9539.10 C\({}^{2}\)  
The significance and goodness of fit of the quadratic models developed were investigated by analysis of variance (ANOVA) and descriptive statistics, as presented in Table 3. The polynomial models provided a good fit to the data, with the respective coefficients of determination (R\({}^{2}\)) being higher than 0.94. At the same time, the reasonable agreement between predicted and adjusted coefficients of determination, with the difference between R\({}^{2}\)pred and R\({}^{2}\)adj being lower than 0.2, demonstrates the good agreement between the experimental and predicted values. Figure 1 demonstrates the close agreement between the experimental results and those calculated from the model values. Additionally, ANOVA demonstrated significant model F- (>89.46) and \(p\)-values (<0.0001) and non-significant lack of fit terms (\(p\) > 0.05), which validate the models' adequacy to describe the experimental data. As for coefficients of variation (CV), they were in all cases lower than 10%, demonstrating good precision and reproducibility of the models. Desirable signal-to-noise ratios were also obtained for all models developed, as demonstrated by the adequate precision values (>4). In general, the results verify the accuracy of the polynomial models in reflecting the relationship between process variables and measured responses, as well as predicting the optimal extraction conditions.
## 2 Results and Discussion
### Antioxidant Activity of Hydrolyzed Gelatin Peptides  
In the literature, it is widespread that low-molecular-weight peptides offer greater biological activities whereas high-molecular-weight peptides show greater techno-functional properties [40]. The structure of gelatin hydrolyses is made up of repetitive Gly-Pro-Ala sequences that are considered responsible for their antioxidant activity [41].  
The antioxidant activity was investigated via FRAP assay and the results were expressed as ferrous equivalents (mM) using a calibration curve built with the ferrous ammonium sulphate standard solutions supplied in the kit. The utilized test measures the reducing power capacity of a compound to convert ferric iron (III) to ferrous iron (II) using a redox colorimetric reaction [42]. Different concentrations of HGPs, ranging from 25 to 200 mg/mL, were considered, corresponding to the results of 0.21 to 1.16 mM ferrous equivalents. The results can be considered higher than the one obtained, at the same conditions of temperature, by [PERSON] et al. [43] for collagen peptides of bovine bone (0.025-0.04 mM ferrous equivalents).  
The test was also evaluated for G, obtaining the same result at 25 mg/mL, but at higher concentrations, solubility issues occurred.
###### Abstract  
Introduction: Mental fatigue is a key factor in soccer that can affect physical performance. Mental fatigue protocols based on the Stroop test used in soccer involve extensive time, ranging from 10 to 30 min. Offering a protocol with a shorter duration that is more applicable in ecological situations seems to be an exciting challenge. Objective: To analyze the effects of a mental fatigue induction protocol (2 min Stroop test in its incongruent mode) on decision-making, response time, and technical execution accuracy in soccer players. Methods: A total of 18 semi-elite soccer male players, 25.83 (4.71) years from a Spanish soccer team, participated in this cross-sectional study. The 2 min incongruent Stroop test was used like mental fatigue protocol, followed by a specific analytical soccer task. The Wilcoxon signed-rank and Chi-squared tests were used to analyze the statistical differences. Results: A significant increase in participants' response time after the fatigue protocol (\(p<0.001\); r = 0.878) was observed. Regarding the variables of correct decision (\(p\) = 0.457; r = 0.031) and technical execution accuracy (\(p\) = 1; r \(<\) 0.001), no significant differences were found. Additionally, no differences were observed between the first and second attempts of the 2 min incongruent Stroop test, in terms of execution accuracy, errors, or total words. Conclusions: The 2 min Stroop incongruent task may induce some degree of mental fatigue, leading to slower response time during certain technical tasks in semi-elite soccer players. However, this Stroop task was insufficient to promote significant changes in the accuracy of technical execution and the effectiveness of decision-making.  
mental fatigue; mental effort; small-sided games; training intervention +
Footnote †: journal: Applied sciences
# Identifying the Initial Corrosion Fatigue Failure Based on Dropping Electrochemical Potential
## 1 Introduction  
In the context of carbon capture and storage (CCS) and future geothermal energy extraction applications, such as those involving injection pipes, delivery pumps, and filtration components, engineering materials face new challenges. These materials must be able to withstand both mechanical stress and exposure to highly corrosive environments. Mechanical stress, especially cyclic loading, combined with factors like temperature and chloride concentration, has a significant impact on corrosion processes [1]. In high-alloy steels, corrosion fatigue becomes more severe in the presence of low chromium content [2], chlorides [3], hydrogen sulfide (H\({}_{2}\)S) [4], and carbon dioxide (CO\({}_{2}\)) [5]. Increased temperature, mechanical loading, and lower pH levels further reduce the endurance limit, worsening corrosion fatigue (CF) in these steels [6].  
Studies show a strong connection between the mechanical and corrosion properties of different steel types and their surface conditions resulting from machining processes [7, 8, 9, 10, 11]. In general, reducing surface depth enhances corrosion resistance in carbon steel [8], austenitic stainless steel after shot peening [11], and ferritic stainless steel when the surface roughness R\({}_{\text{a}}\) exceeds 0.5 \(\upmu\)m [10]. Notably, internal pipeline corrosion is more influenced by relative humidity than by initial surface roughness, with lowering humidity being more effective than altering the surface condition [9]. Additionally, smoother surfaces and internal compressive surface stresses are linked to better fatigue performance in air, even incorrosive environments. This correlation extends to improved corrosion resistance, which in turn improves endurance limits and enhances corrosion fatigue behavior [12; 13; 14; 15].
## 3 Results of Data Training and Discussion
### Neural Network Modeling
#### 3.3.1 Accuracy  
The accuracy metric plays a pivotal role in evaluating the model's overall performance. It is determined using Equation (1).  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
where:  
TPs (true positives) = 282;  
TNs (True negatives) = 342;  
FPs (False positives) = 16;  
FNs (False negatives) = 20.  
In our case:  
\[accuracy=\frac{282+342}{282+342+16+20}\approx 0.945 \tag{2}\]
## 3 Results and Discussion
### Burning Effect Width  
BE width for both the entry and exit holes can be seen in Figure 8. The BE width seems to increase nearly linearly in all cases, which indicates that the processing temperatures increase with the increase in pulse length. At the shortest pulse lengths, the BE width seems to start decreasing more rapidly. According to the heat temperature map published by the British Stainless Steel Association, the yellow to dark brown color seen in the samples indicate temperatures in the range of 57-673 K (30-400 \({}^{\circ}\)C) [36]. This agrees well with the previous findings published by the authors [34] where the processing temperature in engraving was measured with thermocouples.  
There are significant differences between the samples, even after accounting for the darker color and rough surface of the PBF sheets that made it more difficult to estimate where the color began  
Figure 8: Measured BE widths for the entry and exit holes.  
Figure 7: Material removal rates for holes laser drilled in PBF and cold rolled sheets.  
to change. For the entry hole, there is on average almost a 0.25 mm difference in the width of the BE, which indicates higher temperatures on the surface of the cold-rolled sheet. It should be noted, however, that the surface of the PBF sheet is rough and consists of ball-like formations. The roughness of the surface can be seen from the magnified images taken of the surface of the PBF sheet and from the hole profile micrographs. The PBF sheets clearly have a much larger effective surface area. The increase in surface area should help dissipate heat faster, thus resulting in a smaller BE on the PBF sheet.
## 2 Localization Solution
### Adapting the Model for Ceiling-Mounted Beacons  
Mathematical Adaptation  
To incorporate this ceiling-to-ground configuration and variable robot heights, the equation must be modified as follows:  
\[\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}+(H-h_{r}-z_{i})^{2}}-\sqrt{(x-x_{j})^{2}+(y- y_{j})^{2}+(H-h_{r}-z_{j})^{2}}=\Delta t_{ij}\cdot v \tag{3}\]  
where  
* \((x,y,0)\) represents the robot's position on the ground plane;
* \((x_{i},y_{i},z_{i})\) and \((x_{j},y_{j},z_{j})\) are the coordinates of two ceiling-mounted beacons;
* \(H\) is the ceiling height;
* \(h_{r}\) is the height of the specific robot;
* \(\Delta t_{ij}\) is the measured time difference between signal arrivals from beacons \(i\) and \(j\);
* \(v\) is the speed of sound in the medium.  
This equation ensures that the calculated position accurately reflects the robot's true location on the ground, accounting for its specific height and the ceiling-mounted beacon configuration.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 3 Results  
In this section, the results of experiment 1 are presented, which demonstrate the most accurate FM. Subsequently, two simulated errors of the most accurate FM are obtained on the basis of three widely used analyses: EC4 Dvar, FNL, and ERA5.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 2 Method Principle  
Currently, we are facing massive amounts of high-dimensional streaming data, which can be difficult to process efficiently due to limited computing power and memory. However, distributed streaming computing methods can enable high-speed data processing. In terms of water quality detection, using clustering algorithms to classify water quality indices into different categories can improve rapid analysis of water variation situations. The density-based DBSCAN algorithm can achieve water quality clustering and detect abnormal points of numerical mutation, enabling water management departments to rapidly understand the situation of water quality variation.