esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Survivability Assessment  
Survivability assessment can be defined as the method by which the penetration performance of a protective material is measured [1]. The key metric here is how effective the material is at dissipating energy throughout its molecular matrix, thereby protecting the material's integrity and providing enhanced protection [2] to the user from both ballistic and non-ballistic threats.  
Because of protective materials' applications, they are subjected to a rigorous qualification process that describes how the materials should be tested and the pass criteria for both ballistic- and-stab resistant materials [3]. This assessment is traditionally conducted by applying tensile load to the material sample until failure occurs [4]. Thereby providing a comprehensive understanding of the material's limitations and the applicability for its intended purpose.  
This alone cannot be relied upon to portray a full perspective of what is happening on impact. To accurately assess a projectile's influence on the human body, the specialist field of wound ballistics is used. Wound ballistics primarily concerns itself with three types of projectiles (handgun bullets, bullets from long-range weapons, and fragmentation), their differences in behaviour within the target, and wound channel formation [5].  
Upon impact with the front face of the simulant, the projectile decelerates, which, as it continues inside the medium, exhibits radial energy, creating the 'Temporary Cavity' [6]. This reaction can also be seen to 'pulse' when the energy exerted inside the body and the entry and exit of air into the wound causes expansion and contraction of the tissue before collapsing because of the human anatomy's internal elasticity [5]. Simulants such as gelatine have been reported to be most desirable for this research because their elasticity is similar to that of the human torso [5], but because of this elasticity, the cavity collapses immediately [7], which, without the use of high-speed video footage, would be rendered useless because of the inability for the human eye to witness a millisecond reaction.  
The permanent cavity is more simplistic; this is the pathway the projectile has created, which can vary in size and routing depending on factors such as tissue density, muscle content, and bone location [5] as well as projectile size, yaw, tumbling, and the angle of entry [8]. The permanent cavity is traditionally clearly identified post impact [9], but the analysis of the temporary cavity can better identify the amount of force generated upon projectile impact and the dissipation of that energy throughout the surrounding tissues/organs.  
It is evident, therefore, that to accurately examine the performance of both current and novel protective materials and projectile performance, a broad range of simulants can be employed to assess their development potential. How can we differentiate between the simulants on offer and identify their strengths and weaknesses?
## 4 Simulation Results
### Step Steer  
Inspired by the ISO 7401:2003 standard, a _Step Input_ test is performed in both directions: left and right turning. In respect to the reference maneuvers, this one is carried out in closed-loop conditions with an initial longitudinal speed of 150 km/h and a wheel steering angle of 90 deg. It is worth noting that this lateral test actually exhibits a ramp steering command. Indeed, the human driver cannot apply an instantaneous angle to the steering wheel [38]. However, the steering rate is high enough to produce a rapid cornering maneuver, letting us investigate the vehicle's fast transient behavior.  
Results of Figure 6 clearly show that both controllers succeed in tracking yaw rate and side-slip angle if compared to Non-controlled and Commercial-controlled scenarios. Controlled state errors are reduced for LQR and SMC solutions thanks to the application of optimal control efforts, visible in Figures 7 and 8.  
It is interesting to highlight that both proposed controllers apply different torque vectoring efforts with respect to the benchmark ESC. In the first stage (at about 1.5 s), all the stability systems perform quite similarly, despite the fact that SMC and LQR solutions show higher control actions. In the steady-state phase of the maneuver, indeed, controllers actuate the wheels on the opposite side with respect to the Commercial-controlled scenario. This is due to the fact that the developed solutions aim at the control of side-slip angle, in addition to yaw rate, which typically is the sole control objective of wide diffused ESC in the market.
## 4 Simulation Study
### Case 1: Outer Race Fault
The extraction result (seen in Figures 6c and 7c) via single sparsity constraint can not extract the fault feature well, because the low-rankness of the fault feature in OFSC are not fully considered. Moreover, compared with the other two methods, the extraction results are the worst (seen in Figures 6e and 7e). Similar to the noise component in the time-frequency domain, the noise component in the cyclic order-frequency domain also shows the low-rankness property. Thus, the noise component is also contained in the extraction result. To make the fault cyclic order more clear, the EEOSs of the denoised OFSCs obtained by the proposed method are demonstrated in Figures 6b and 7b. It can be seen that the noise near the fault cyclic order is significantly suppressed, which makes the fault feature more obvious. In the EEOSs obtained by the other two methods, the unseparated noise components in OFSC interfere the clear identification of \(\alpha_{\text{BPOO}}\)s, especially in Figures 6f and 7d. From all the above analyses, the proposed algorithm can effectively extract the outer race fault feature from strong background noise-contaminated OFSC with its superior sparsity and low-rankness inducing capability. The proposed method can achieve a better extraction performance than the other two methods in Case 1. Combined with EEOS, the fault cyclic order can be shown more obviously, which makes the determination of the fault type handier. Figure 6: The extraction results of Case 1 under the linearly changing rotation speed condition. The dash lines in red indicate the corresponding BPOO and its multipliers. (**a**) the denoised OFSC obtained by the proposed joint sparsity and low-rankness constraint; (**b**) the EEOS obtained by the proposed joint sparsity and low-rankness constraint; (**c**) the denoised OFSC obtained by single sparsity constraint; (**d**) the EEOS obtained by single sparsity constraint; (**e**) the denoised OFSC obtained by single low-rankness constraint; (**f**) the EEOS obtained by single low-rankness constraint.
## 3 Results and Discussion
### The Effects of Helix Angle on Scaffling Load Capacity
#### 3.3.2 Comparison of Contact Ratio, Specific Sliding, and Load-Carrying Capacity
The patterns of sliding velocity and specific sliding variation at the instantaneous contact point with distance from the pitch circle follow the same trends as previously analyzed cases involving variable modules and pressure angles. Therefore, they will not be reiterated here. Figure 12: The relative sliding velocity and specific sliding of tooth surfaces. (**a**) Relative sliding velocity; (**b**) tooth surface-specific sliding. Figure 11: The tooth contact ratio and bend safety factors for variable modulus. (**a**) Contact ratio; (**b**) tooth bend safety factors. Table 11 presents the tooth surface pressure distribution for five gear sets with varying helix angles. The left column indicates the operating conditions corresponding to peak speed, while the right column indicates the operating conditions corresponding to peak torque. In accordance with the data presented in Table 11, it can be observed that under peak speed operating conditions, there is a notable decrease in tooth surface pressure with an increase in helix angle. This reduction ranges from a maximum of 996.84 MPa to a minimum of 739.5 MPa, which represents a 25.8% reduction. Similarly, under peak torque conditions, the pressure decreases from a maximum of 2588.7 MPa to a minimum of 1737.8 MPa, representing a 32.9% reduction. As the helix angle increases, the load distribution across the tooth surface becomes more uniform. This phenomenon can be attributed to the fact that an increased helix angle results in a greater overlap ratio, which in turn reduces contact stress. This reduction is advantageous for enhancing the gearset load-carrying capacity and contact fatigue life.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Feature Selection for HI  
#### 3.2.1 Filtering with Correlation Analysis  
In general, various features, such as root mean square (RMS), kurtosis, and skewness, can be used for HIs, representing the state of machinery. However, some of them might have a strong correlation, which means that the effect of one feature can be explained through that of another feature. Due to this characteristic, using features with a strong correlation together may cause overfitting and redundant computation for the derivation of HIs. This so-called multicollinearity is an important issue in various machine learning algorithms, which holds back the performance of the algorithm [34]. Thus, as the first step for selecting significant features, we perform correlation analysis to filter features with a strong correlation. This means that we consider only one of them among strongly correlated features.  
For example, if correlation coefficients of features are given as in Figure 4, we can observe that there is a strong correlation between Feature 1 and 2 due to the large value of the correlation coefficient (0.8971). In this case, we can select Feature 1 or Feature 2 as a representative one among those two features. However, we need to consider all possible combinations of features in order to select significant features depending on some performance criteria, such as accuracy.  
Figure 3: Suggested procedure for optimizing design parameters.
## 3 Results and Discussion
### Effect of MC of Barley Grains and Environmental RH on DON Degradation by ACP  
Environmental relative humidity and the moisture content of the substrate are important factors which can influence the decontamination by ACP [8]. The effects of RH of the air and MC of barley grains on the degradation of mycotoxin were evaluated (Table 3). Increasing the MC of the barley grains from 9.5 to 15.76 g water/100 g sample reduced the degradation level from 54.4% to 47.0% by ACP; however, this reduction was not significant (\(p\geq 0.05\)). Generally, the presence of water in the system increases the production of ROS [19], which can contribute to a greater degradation rate. However, due to the drying step after the inoculation of DON, the water contents of the grains were similar to each other before ACP treatment. Although, the reduction in DON degradation rate by increasing the water content was not significant (\(p\geq 0.05\)), the possible changes in the surface of the grains due to the presence of higher water content could change the reaction rate of the reactive species with the toxin and affect the degradation rate. To know the optimum moisture content to achieve highest DON degradation, barley with different water contents should be treated with ACP to draw a conclusion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plasma Time (min)** & **MC (g Water/100 g Sample)** & **DON Reduction (\%)** \\ \hline
0 & \(9.5\pm 0.0\) & 0\({}^{\text{b}}\) \\
6 & \(9.5\pm 0.3\) & 54.4 \(\pm\) 2.2 \({}^{\text{a}}\) \\
6 & \(14.9\pm 0.3\) & 49.8 \(\pm\) 5.9 \({}^{\text{a}}\) \\
6 & \(15.7\pm 0.2\) & 47.0 \(\pm\) 5.9 \({}^{\text{a}}\) \\ \hline \hline \multicolumn{3}{c}{**RH (\%)**} \\ \hline
0 & 12 & 0\({}^{\text{b}}\) \\
6 & 12 & 62.9 \(\pm\) 3.3 \({}^{\text{a}}\) \\
6 & 60 & 65.7 \(\pm\) 3.0 \({}^{\text{a}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of moisture content (MC) of barley and relative humidity (RH) of the surrounding air on deoxynivalenol (DON) degradation by ACP.  
The effect of environmental RH on DON degradation by ACP was assessed. Increasing the RH from 12% to 60% increased the degradation rate of DON, but it was not significant (\(p\geq 0.05\)). A greater RH could be attributed to an increase in the concentration of OH radicals due to the presence of water molecules and lead to greater oxidation [40]. On the other hand, high RH could reduce the surface resistance of the dielectric material, so it would reduce the number of micro-discharges, [41]. A high RH may weaken the plasma due to the loss of the electron energy in electron-molecule collisions. Poorer transmissibility of UV radiation, and protective water film around the mycotoxin, could be the other reasons for reduced ACP efficiency [19]. It is recommended to assess the degradation of DON at different percentages of RH and determine an optimum RH under ACP to achieve the highest decontamination rate.
# AAGA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem
## 2 Problem Definition and the Proposed Solution  
### SDVRP Definition  
Let \(G=(V,E)\) denote an undirected and weighted complete graph representing the network of customers and a depot. The vertex set \(V=\{0,\ldots,n\}\) represents the depot denoted by the vertex \(0\) and \(n\) customers indexed as \(1,\ldots,n\). A non-negative weight \(c_{ij}\) is associated with each edge of the graph \((i,j)\in E\) that stands for the cost of traveling between customers \(i\) and \(j\). Moreover, each customer \(i\in V\) has a positive demand, denoted by \(d_{i}\). A set of \(M\) vehicles is available to serve the customers' demands. Each vehicle has a limited capacity denoted by \(Q\). The objective of the SDVRP is to find a route for each vehicle such that the demands of all customers are satisfied with the minimum total cost. In this paper, we assume that the cost of traveling between customers \(i,j\in V\) is their Euclidean distance denoted by the function \(dist\), i.e., \(c_{ij}=dist(i,j)\).
## 4 Experimental Validation
### Damping System Effectiveness: Dynamic Behaviour of Active Boring Bar When Control Is On
#### 4.2.3 Experimental Setup  
In order to evaluate the effect of the actuation system, the prototype was tested with different dynamic forces applied by means of a shaker (Bruel & Kjaer 4809), controlled by the LMS SCADAS SCM202V acquisition system. The experimental setup is shown in Figure 19b. The active boring bar was fixed by means of a three-jaws chuck and displacement were measured by means of a laser sensor (Keyence LK-H085) and the sensors installed on the boring bar.
## 5 Application Case
### Analysis of Work by Overcoming Wind Resistance and Wind Environment Influence
Figure 8: The relative speed between the approaching wind and the athlete’s movement (\(U\)) and the relative speed between the static air and the athlete’s movement (\(-V\)) in training case 1. Figure 9: (**a**) The situation of overcoming aerodynamic drag to do positive work and negative work in each stage. (**b**) Work to overcome aerodynamic drag in the downwind and upwind conditions of each stage. For the athlete sliding a lap in training case 1, the work to overcome the aerodynamic drag was 3581.92 J under downwind conditions, and the work to overcome the aerodynamic drag was 7747.06 J under upwind conditions, accounting for 31.62% and 68.38% of the total work to overcome the aerodynamic drag, respectively. In the initial stage \(S_{5\text{P}_{1}}\), the work to overcome the aerodynamic drag was 2570.47 J, and \(r_{w}\) was 19.62%. As shown in Figure 10, the average speed of the athlete in the uphill stage \(S_{1}\) is 3.43 m/s. The relative speed between the approaching wind and the athlete's movement is relatively small, and the work to overcome the aerodynamic drag is the lowest, the work is 907.24 J and the \(r_{w}\) is 4.29%. The downhill stage \(S_{2}\) is mainly affected by the downwind conditions, and the \(r_{w}\) is \(-23.5\%\). The field wind plays a major role in assisting the athlete, the athlete overcomes the aerodynamic drag to do some of the negative work, and the athlete can slide relatively easily in this stage. Because of the fluctuation of the terrain in stage \(S_{3}\), the athlete's sport posture is converted into a two-step one support, the drag area is enlarged, and the work to overcome the aerodynamic drag does the most work, at 3317.17 J, and the \(r_{w}\) is 22.96%. The athlete's average speed in stage \(S_{4}\) is relatively fast and the sliding distance is longer, so the work to overcome the aerodynamic drag is more than that in the other stages, and the \(r_{w}\) is 2.27%. The athlete is less affected by the field wind in stage \(S_{5\text{P}_{3}}\) and the \(r_{w}\) is 0.09%.
## 2 Theoretical Background
### Response Function Method
When considering many approximations \(f_{\rm a}(b)\), the selection of the final response function in the initial stage of RFM development was made arbitrarily [30; 66], while now it is the subject of additional optimization [57; 65; 67; 68] related to curve fitting error measures (see Section 2.3): correlation maximization, minimization of the root-mean-square error (RMSE) or the residues variance. In addition to or interchangeably with the criteria calculated between the discrete data and the approximation, analogous magnitudes calculated in-between the LSM and the WLSM solutions are used [28; 42]--the weights change in calculations does not affect how the structure behaves in reality. If the selected criteria indicate different approximations, it is assumed that the one with the smaller coefficients number is chosen [64; 67], which reduces the risk of the Runge effect [69]--deterioration of the quality of polynomial interpolation, especially visible at the ends of the intervals, despite increasing their degree--and is consistent with the principle of using the simplest possible theory called Occam's razor. It is widely used, among others, to choose between statistical models with a different number of parameters: in the AIC [70], BIC [71], SABIC [72] or RMSEA criteria [73].
## 2 Theoretical Background
### Parametric Lifetime Models  
Different parametric lifetime models exist because the different types of data and failure mechanisms occurring over time can be better described graphically by different distributions [17]. For failure data, a particular distribution is assumed in parametric lifetime models for non-repairable components [19].  
In parametric lifetime models, the alpha, beta, and gamma parameters are used to characterize the shape of a PDF. Generally, the alpha parameter is the scale parameter that controls the spread of the distribution, while the beta parameter is the shape parameter, which controls the skewness of the distribution [17]. The gamma parameter is an additional shape parameter that allows more flexibility in fitting the data [17]. The selection of a specific parametric model depends on the availability of data, the convenience of analysis of the model parameters, and the capability of the model to accurately describe the lifetime data [17]. Brief details of some of the most common distributions are given below.
## 3 Materials and Methods  
### Plant Material  
Strawberry fruits (_Fragaria \(\times\) ananassa_) Albion variety, Festival variety, and Frontera variety were collected at seven stages of maturation from an experimental field located in the Jacona municipality of Michoacan, Mexico (19.961566\({}^{\circ}\) N, 102.317995\({}^{\circ}\) W). All of the strawberry varieties were cultivated in a macrotunnel under the same fertilization, irrigation and agronomic management conditions. Harvest and classification of fruits was in accordance with [PERSON], [PERSON] and [PERSON] [14] and [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [16]. Seven maturation stages based on the number of days after anthesis (DAA) and the percentage of red color on the surface of each of the harvested fruits were defined, leading to the following criteria for each stage: stage 0: 20-25 DAA, 0% red; stage 1: 26-30 DAA, 15% red; stage 2: 30-35 DAA, 20% red; stage 3: 36-38 DAA, 30% red; stage 4: 39-40 DAA, 60% red; stage 5: 41-42 DAA, 80% red; and stage 6: 43-44 DAA and 100% red. The stages of maturation were combined with the variety to name each maturation stage-variety combination examined: Festival variety: FE0, FE1, FE2, FE3, FE4, FE5, FE6; Frontera variety: FR0, FR1, FR2, FR3, FR4, FR5, and FR6; Albion variety: AL0, AL1, AL2, AL3, AL4, AL5, and AL6. After harvesting, fruits were transported immediately to the laboratory, maintaining a temperature of \(4\pm 1\,^{\circ}\)C, and they were stored in refrigeration for no more than 24 h before analysis. Prior to each analysis, it was verified that the fruits were free of physical or biological damage.  
Figure 3: Metabolic pathways for the synthesis of the main VOCs detected in the Variable importance in projection (VIP) graph for fruits of the three strawberry varieties during the seven stages of maturation. The nine most important metabolites are classified into three chemical families; Compounds with white triangles facing upwards represent the highest total concentrations for the variety indicated on the labels, on the other hand, compounds with a black triangle facing downwards represent a lower total concentration of the metabolite for the variety indicated on the labels.
# Comparative Study on Vibration Characteristics of Biaxial Carbon/Glass Hybrid Wind Turbine Blades
## 4 Numerical Simulation  
### Wind Turbine Parameter Design  
This article selects the NACA0012 airfoil, with a power of 10 kW and a rated wind speed of 10 m/s, for the design of wind turbine blades.  
Determine the blade angle of attack based on the blade lift-drag ratio coefficient, assuming that the blade working environment is between 6 and 14 m/s wind speed, and the fluid Reynolds number range in the flow field is 8.65 \(\times\) 10\({}^{5}\)-2.16 \(\times\) 10\({}^{6}\). The lift-drag ratio curve of the NACA0012 airfoil varies with angle of attack as shown in Figure 7:  
From the figure, the following can be seen:  
When the Reynolds number is Re = 8.65 \(\times\) 10\({}^{5}\), when the angle of attack is greater than 7\({}^{\circ}\), the lift-drag ratio curve shows a downward trend. The maximum lift-to-drag ratio is 72.3, and the angle of attack is 7\({}^{\circ}\). At an attack angle of 8\({}^{\circ}\), the decrease in lift-drag ratio is not significant.  
When the Reynolds number is Re = 2.16 \(\times\) 10\({}^{6}\), when the angle of attack is greater than 9\({}^{\circ}\), the lift-drag ratio curve shows a downward trend. The maximum lift-to-drag ratio is 92.1, and the angle of attack is 9\({}^{\circ}\). At an attack angle of 8\({}^{\circ}\), the rise in lift-drag ratio is not significant.  
Take the median Reynolds number of 1.5 \(\times\) 10\({}^{6}\) as a reference. The change curves of lift coefficient and drag coefficient obtainedare shown in Figure 8, and the detailed data are shown in Table 3.  
Figure 8: Curve of lift-drag coefficient changing with angle of attack.  
Figure 7: NACA0012 airfoil lift–drag ratio curve.  
From the table, it can be seen that the lift-drag ratio is the highest at an angle of attack of 8\({}^{\circ}\), with a maximum value of \(C_{L}/C_{D}\) = 84.229, a lift coefficient of 0.924, and a drag coefficient of 0.01097. In summary, the attack angle of wind turbine blades should be selected as 8\({}^{\circ}\) in the current environment.  
The rated speed n of a 10 kW small wind turbine is 160 rad/min, and the diameter of the wind turbine is calculated to be 10 m, resulting in a tip-speed ratio of approximately 7.  
Using MATLAB, perform iterative calculations on \(a\) and \(b\), assigning the initial values of \(a\) and \(b\) to 0. With the above formula, the modified chord length \(c\) and torsion angle \(\theta\) can be calculated. The blade length is 4.8 m, and it is divided into 10 segments for design. The calculation results are shown in Table 4.