esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Interior Noise Prediction Based on Elman Neural Network
###### Abstract  
Vehicle interior noise is an important factor affecting ride comfort. To reduce the noise inside the vehicle at the vehicle body design stage, a finite element model of the vehicle body must be established. While taking the first-order global modal of the body-in-white, the maximum sound pressure level of the target point in the vehicle, the body mass, and the side impact conditions into account, the thickness of the body panel as determined via sensitivity analysis is treated as the input variable, and the sample is determined by following the Hamersley experimental design. Specifically, the Elman neural network predicts the noise value in the vehicle, and a vehicle body structure optimization method that comprehensively considers NVH performance and side impact safety is established. The prediction errors of the Elman neural network algorithm were within 3%, which meets the prediction accuracy requirements. To achieve satisfactory restraint performance, the maximum sound pressure level of the target point in the vehicle is reduced by 5.92 dB, and the maximum intrusions of the two points on the B-pillar inner panel are reduced by 31.1 mm and 33.71 mm, respectively. The side impact performance is improved while the noise inside the vehicle is reduced. This study provides a reference method for multidisciplinary research aiming to optimize the design of vehicle body structures.  
finite element model; vehicle interior noise; side collision; Elman neural network +
Footnote †: journal: Applied Sciences
# Experimental Study on the Seismic Performance of Recycled Concrete Hollow Block Masonry Walls
## 4 Test Analysis  
### Hysteresis Behaviour  
The recorded hysteresis curves of all the specimens under cyclic loading are presented in Figure 9. Based on Figure 9a-c, the hysteresis curves of the specimens W1, W2, and W3 are considerably linear in the initial elastic stage. After the walls cracked, the area of the three hysteresis curves slightly increased, and the shapes of the hysteresis curves transformed from the original shuttle to the reversed \"S\" shape, showing a partial pinching effect. The area of the hysteretic curves of W1 and W3 nearly stayed the same due to the flexural effect, and the profile of the two hysteresis curves remained stable until the specimens failed. In addition, the area of the three hysteresis curves showed a marked difference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **W1** & **W2** & **W3** & **W4** & **W5** & **Unit** \\ \hline \(P_{cr}\) & 136.5 & 389.7 & 76.7 & 309.9 & 234.8 & kN \\ \(\Delta_{cr}\) & 2.49 & 13.60 & 4.19 & 0.80 & 0.83 & mm \\ \(P_{u}\) & 201.58 & 399.30 & 108.10 & 457.99 & 431.80 & kN \\ \(\Delta_{u}\) & 8.995 & 14.905 & 20.990 & 6.960 & 2.970 & mm \\ \(P_{cr}/P_{u}\) & 0.677 & 0.975 & 0.709 & 0.676 & 0.543 & — \\ \hline \end{tabular}  
* Key: \(P_{cr}\) is the cracking point; \(\Delta_{cr}\) is the cracking displacement; \(P_{u}\) is the peak load; \(\Delta_{u}\) is the peak displacement.  
\end{table}
Table 4: Characteristic load and displacement.  
Figure 8: Failure mode of specimens: (**a**) W1; (**b**) W2; (**c**) W3; (**d**) W4; (**e**) W5.  
The area of the hysteresis loop of W2 was the best, followed by that of W1, while W3 was the worst. For W1 and W2, the specimen W2 with higher compression stress had a better energy dissipation compared to W1, indicating that the increase in compressive stress can improve the energy dissipation capacity of specimens. However, for W2 and W3, specimen W3, with a higher aspect ratio, had a worse energy dissipation compared with W2, indicating that the increase in aspect ratio can reduce the energy dissipation capacity of specimens.  
As shown in Figure 9d,e, the hysteresis loops of W4 and W5 have a similar linear relationship in the initial elastic stage. During this time, the horizontal drift of the top walls and residual deformation were small after unloading. The area of the hysteresis loops of the specimens was small and nearly overlapped. After the walls cracked and entered into a plastic stage, especially after the maximum load, the hysteresis loops showed partial pinch effect, and the area of hysteresis loops was significantly increased, which indicated that the structural columns had an apparent energy dissipation capacity. Compared with W4, the maximum bearing capacity of W5 was slightly lower than that of W4, which  
Figure 9: Hysteretic curves of five specimens: (**a**) W1; (**b**) W2; (**c**) W3; (**d**) W4; (**e**) W5.  
indicated that the increasing bearing capacity of RAC structural columns was lower than that of ordinary concrete structural columns.
## 3 Developed Drone System for TEM
### Aeromobile Part
#### 3.1.1 Measuring System
The ADC operating mode (and, accordingly, the current switch in the transmitter line) was as follows: positive current pulse, pause, negative pulse, pause--all for 0.125 s, that is, one accumulation contains a signal for 0.5 s. In other words, the operating mode of the current switch provided a measurement frequency of 2 Hz, but in fact the recording was carried out at a frequency of 1 Hz, since half of the time is spent on recording the measured values. For comparison, in the VTEM system, the measurement frequency reached 30 Hz, the high measurement frequency in airborne electrical survey systems allowed it to a large extent to solve the problems of filtering interference, eliminate the effects of the measuring loop tilt, and increase the measurement accuracy due to the possibility of averaging a larger amount of observed data for each survey station. However, the elimination of the noted shortcomings of our meter unit was not a significant problem, but only represents an ordinary technical problem, which nevertheless required some time to create a new version of the equipment. In part, it was already solved by making a new firmware for the ADC unit and the current switch: after analyzing the field data, the maximum duration of the recording time steps was determined, and the pulse frequency was increased. It turned out that by refusing to record later times, it is possible to increase the pulse frequency by at least a hundred times. Thus, in the current version of the ADC and current switch, the pulses frequency already reached 100 Hz, and after the completion of the manufacture of a new version of the equipment with continuous recording, it reached 200 Hz. Taking into account the significantly lower flight speed of the UAV compared to the helicopter, the data density even exceeded the classical manned TEM systems. It is important to note that the decision to start testing with an a priori imperfect meter unit was quite deliberate--if the authors achieved a sufficiently high-quality result for solving geological problems even with such a meter, then its simple improvement would allow us to create a system with very impressive characteristics. The data logger was similar to those used in other versions of the SibGIS UAS complex and is an x86-compatible microcomputer with original software for data collection. High-precision satellite synchronization of the current switch and the meter unit was provided, as well as a similar binding of the measurement results to spatial coordinates. The weight of the recorder, GNSS antenna and battery was 0.5 kg. Thus, the total weight of the entire payload could exceed 7 kg, which is a serious challenge for unmanned light vehicles. In fact, serial UAVs, even of fairly high (by Russian standards) price categories, such as the DJI Matrice 600 PRO or T-Motor M1200, cannot lift such a load.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Methods for Fungal Identification and Characterization in Food Samples  
#### 2.2.1 Microscopic Examination  
Microscopic examination represents one of the earliest and simplest methods for fungal identification in food samples. Direct microscopic observation of fungal structures, such as hyphae, spores, and conidia, provides valuable morphological information that can aid in species identification. Staining techniques, such as lactophenol cotton blue or potassium hydroxide mounts, enhance the visualization of fungal structures under the microscope. While microscopy is rapid and cost-effective, it requires expertise to accurately differentiate fungal species based on morphological characteristics, and it may be limited by the lack of specificity in some cases [79].
# Measurement of Contrast and Spatial Resolution for the Photothermal Imaging Method
## 3 Numerical and Experimental Analysis
### Experimental Apparatus and Setup  
Figure 8 presents the general configuration of the experimental equipment, which consisted mainly of the pump beam, specimen, probe-beam, and data-acquisition configurations. The light source from the pump beam to the probe beam was modulated by a mechanical chopper (SRS 40), passed through a focus lens with a focal length of 1000 mm, and then radiated onto the specimen surface. The intensity of the laser used in this process was 10 W, and a Sprout-G 10-W model with a wavelength of 532 nm, which is a diode-pumped solid-state (DPSS) laser with a Gaussian profile, was used. The radius of the pump beam is determined by the distance from the maximum intensity to 1/e\({}^{2}\) in the Gaussian distribution [19], and that radius is adjusted by the focus lens. The specimen to be measured was installed at the specimen section. The specimen was mounted on the Newport optic mount and then fixed on an \(x\)-\(y\)-axis automatic transport system (\(x\)-axis: M-IMS300 CCHA; \(y\)-axis: M-ILS150 PP) consisting of two automatic transport units. The probe beam section was located near the specimen section. The probe beam was an He-Ne laser (Newport 1125 P) with a wavelength of 632.8 nm, an intensity of 5 mW, and a diameter of 500 \(\upmu\)m. The modulated refraction of the probe-beam that passed through the air layer on the heated specimen was detected by the position sensitive diodes (PSD) sensor (C10443-01). Thereafter, the reference modulation frequency set by the mechanical chopper and the probe-beam response frequency detected by the PSD sensor were collected by the lock-in amplifier (AMETEK 7270 DSP), and the phase difference between the two signals was calculated. Figure 5 presents the arrangement and relative distance between the probe-beam and pump beam.  
The numerical analysis results were verified by experimentally examining the image quality depending on the \(L_{f}/L_{d}\) change. Specifically, experiments were conducted on specimens with \(L_{f}\) value of 0.25 mm, 0.50 mm, and 1.00 mm (with \(L_{f}/L_{d}\) values of 0.362, 0.725, and 1.449, respectively), and the pump-beam radius (\(R_{b}\)) was fixed at 100 \(\upmu\)m. The other parameters were set as shown in Table 3.
## 3 Results
(**a**) Actual values selected for the testing phase; (**b**) RF algorithm-based; (**c**) kNN algorithm-based; (**d**) GB algorithm-based; (**e**) Tree algorithm-based; and (**f**) LR algorithm-based. effects on the dependent variable, variables such as \(t\) and \(Wt\) had relatively lower effects. Descriptive statistics revealed that the estimates obtained from machine learning algorithms were generally quite close to the actual data, and the RF and GB models provided reliable results primarily. Performance measurements and ANOVA tests of machine learning algorithms showed that the estimate models successfully revealed the relationships with the independent variables. RF and GB algorithms performed better than other models with low error rates (MSE, RMSE, and MAE) and high explanatory power (R\({}^{2}\)). However, the LR model showed a more limited performance in evaluating the effects of independent variables despite its high R\({}^{2}\) value. Pareto charts visually presented how the impact of independent variables on the dependent variable changed according to machine learning algorithms, revealing that the models handled complex relationships between variables in different ways. This study results show that machine learning algorithms can powerfully analyze complex processes like wind energy production.
## 4 Results
### Nasa-Tlx  
To determine the perceived task load, the NASA-TLX was performed (Figure 5). This paper will cover the weighted rating and both adjusted ratings of the NASA-TLX subscales relating to performance and frustration. The other subscales were reported by [PERSON] et al. [38]. The performance subscale measures how successfully the participants rate their performance of the task. The perceived performance level was significantly higher (\(p\) = 0.010) in the VR-ET (M = 139.55, SD = 76.31) than in the VR-SET (M = 102.50, SD = 73.11). The frustration level measures how insecure and discouraged (higher score) or secure and content (lower score) the participant felt during the task. The frustration experienced was also significantly higher (\(p\) = 0.038) in the VR-ET (M = 62.50, SD = 115.99) than in the VR-SET (M = 3.86, SD = 12.72). The weighted rating did not show any significant differences. A Pearson correlation coefficient was used to assess the linear relationship between the subjective task load (NASA-TLX weighted rating for the SET and ET) and the usability (TUI scale \"Usability\" for the SET and ET). There was no significant correlation between the variables of the NASA-TLX weighted rating and usability for the SET (r(20) = -0.33, \(p\) = 0.146) and between the variables of the NASA-TLX weighted rating and usability for the ET (r(20) = 0.00, \(p\) = 0.987). Moreover, no significant correlation was found for either type of training when combined with the TUI scale \"Intention to Use\" and the NASA-TLX weighted rating or \"Intention to Use\" and the Frustration subscale of the NASA-TLX.
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
## 3 Results
### Comparison of the Two EoL Scenarios
product system under study are reduced. According to [42], for CFF, only a maximum of 80% of the benefits can be allocated to the product system under study, whereas in the avoided burden scenario, the product system receives 100% of the benefits. In this study, the CFF scenario reduces the total benefits by 19, 17, and 22% for the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively. This outcome is in line with [42]. The avoided GWP impacts resulting from recycling are illustrated in Figure 6. It can be seen that steel recycling (dark blue bars) has a significant impact on conventional vehicles, accounting for approximately 50% of the total benefits for DIE-ICEV and 40% for CNG-ICEV. Aluminum recycling (light blue bars) accounts for approximately 15% of the total benefits for DIE-ICEV and 20% for CNG-ICEV. The recycling of the polymers accounts for less than 10% for both DIE-ICEV and CNG-ICEV, while the recycling of platinum and palladium in the DIE-ICEV configuration (approximately 20% of the total benefits, red bars) and the recycling of platinum and rhodium in the CNG-ICEV configuration (approximately 30% of the total benefits, red bars) significantly reduce the impacts. The share of benefits due to steel recycling in the BEV configuration is comparable to those of the conventional configurations (approximately 40% of the total benefits). A significant reduction in the GWP impact is due to the avoided impact on the production of virgin materials involving the Li-ion batteries (approximately 30% of the total benefits). The avoided impact of primary polymer production accounts for approximately 10% of the total BEV benefits, while approximately 8% is the share of benefits due to electronic equipment recycling. Less significant are the avoided impacts due to aluminum and copper recycling.
## Materials and Methods
### Adsorption of Ni\({}^{2+}\) by nZVI@ATP  
#### 2.3.1 Screening of nZVI@ATP Adsorbents with Different Ratios of Fe and ATP  
ATP, nZVI, and nZVI@ATP samples with different iron-to-ATP ratios were added to centrifuge tubes loaded with 30 mL of 200 mg/L Ni\({}^{2+}\) solution at 1 g/L. The solution pH was adjusted to 5.0 using 0.1-mol/L HCl and 0.1-mol/L NaOH before adding the Ni\({}^{2+}\) solution. The resulting solution was placed in a constant temperature water bath, shaken at 298 K and 220 r/min for 12 h, and passed through a 0.45 um filter membrane. The Ni\({}^{2+}\) residual concentration after the aforementioned reaction was detected using a flame atomic absorption spectrophotometer (FAAS) to determine the composite with the best adsorption performance for Ni\({}^{2+}\). Subsequent experiments were conducted based on the determination of the optimal iron-to-ATP ratio in combination with the actual situation.
## 3 Results
### Keywords Co-Occurrence (Figure 3)  
The keyword co-occurrence showed that the network was loose during the first slice (Figure 3). There were only few keywords, but these were still present in the second slice. This showed a relatively straight forward dynamic since the beginning of bioprinting. The innovative concept was set with, among others keywords, \"cells\" and \"deposition\". At this time, probably none of the research teams could handle all the scientific aspects of bioprinting. The number of keywords grew then dramatically between 2011 and 2015 and the network became denser than the previous  
Figure 2: The science mapping of Web of ScienceTM (WoS) categories for assessing bioprinting evolution in time slices, (**a**): 2006–2010; (**b**): 2011–2015. The WoS categories were grouped into “macro-disciplines”: Matter Sciences (yellow), Engineering (blue), Biology (green), and Medicine (red). The size of each node is proportionate to its degree and the thickness of the links represents the tie strength.  
period. Interestingly, the term \"bioprinting\" became one of the most used keywords and defined the identity of a field. A new bioprinting topic emerged with the rise of \"hydrogel\" issues and the need for a perfect bio-ink. Indeed, the hydrogel is required to fulfill a lot of expectations, from the matter sciences, engineering, biological and medical point of views. In this context, the extracellular matrix components of connective tissue, such as \"collagen\" and \"gelatin\", were often used despite their limitations [27]. As expected, the \"in vitro\" keyword remained one of the most relevant, as a necessary step to prove a tissue engineering concept. The frequent iteration of \"cells\" rather than \"tissue\" or \"organs\" may be seen as marker of immaturity when considering the organ transplantation. Interestingly, \"stem cells\" and \"differentiation\" seemed to indicate the emergence of a new pathway towards regenerative medicine, such as bony transplants [9]. However, engineering in vitro complex systems with enough \"vascularization\" and \"angiogenesis\" for cell survival and proliferation is still a challenge. \"Cartilage\" was the most cited tissue, probably thank to its structural simplicity and its limited needs in cells, proteins and vascularization.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 2 Materials and Methods  
### Experiment Location and Research Material  
Field trials were conducted in Yongxingtun village, Shanyang District, Jiaozuo City, Henan Province, China, during two cropping seasons in 2016-2017 and 2017-2018. Figure 1 showed the specific location of the study area. The experimental area was 110 m in length from east to west and 38 m in length from north to south, with each plot measuring \(6\text{ m}\times 8\text{ m}\). There were 48 plots, 16 treatments, and three replications. The orthogonal experiment was conducted with different amounts of nitrogen fertilizer, different moisture contents, and different crop varieties (Table A1). Four levels of N fertilizer were set: 0, 0.0195, 0.0390, and 0.0585 kg/m\({}^{2}\), respectively; three levels of water irrigation were set: rainfed (W1, no irrigation), normal water (W2, irrigation water 192 mm), and twice normal water (W3, irrigation water 384 mm); there are two crop varieties: Jing 9843 (J9843) and Zhong Mai 175 (ZM175). The experimental design scheme was described in Figure A1.  
Figure 2 showed the general framework of this paper, which consists of four main parts. (1) Data acquisition: simultaneous acquisition of winter wheat canopy hyperspectral data and LAI data at the jointing, flagging, anthesis, and grain filling stages, and acquiring the GY data during the winter wheat harvest period; (2) Data processing: pre-processing and statistical analysis of measured data using ViewSpecPro, SPSS, Origin and Excel, and construction of various vegetation indices; (3) Data analysis: to explore the relationship between GY and LAI and vegetation indices, respectively, through correlation analysis; (4) Model framework construction: first, 15 vegetation indices and LAI coupled vegetation indices were used as input features for the ENET model to construct individual growth stages GY prediction models. Subsequently, based on the Stacking technology, the GY prediction results of the different growth stages were stacked to construct the GY prediction model that integrates multiple growth stages. Finally, the accuracy of each model was evaluated to screen the optimal model for winter wheat GY prediction.  
Figure 1: Geographical location of study area.
## 2 Methodology
### Numerical Method and Validation
The SST turbulence model with automatic wall function treatment was used because of its highly accurate predictions of flow separation. In this model, the Reynolds stress terms in the momentum equations are treated using the shear-stress transport turbulence model, in which the k-w model is used in the near-wall region and the k-e model is implemented for the far field region [33]. The inlet pressure and temperature were entered as 101,325 Pa and 288.15 K, respectively. The total mass flow rate of the compressor was entered as 0.789 kg/s. As boundary conditions, a non-slip wall was chosen for the blade and hub, and a counter-rotating wall was chosen for the shroud. The advection scheme was chosen as the high-resolution, first-order turbulence number option in the solver controls. The convergence criterion was set to RMS \(10^{-5}\). Table 2 gives detailed boundary conditions and methodology. After the necessary settings are made, the CFX visual for a single blade of our radial compressor is shown in Figure 5. \begin{table}
\begin{tabular}{l l} \hline
**Solver** & **ANSYS CFX** \\ \hline Turbulence model & Shear–stress transport \\ Convergence criterion & Total residue \(<10^{-5}\) \\ Inlet total pressure & 101,325 Pa \\ Inlet total temperature & 288.15 K \\ Solid wall & Nonslip and adiabatic boundary conditions \\ Periodic surface & Periodic boundary condition \\ \hline \end{tabular}
\end{table} 
Table 2: Details of the CFD numerical methodology and boundary conditions. Various analyses were performed to obtain mesh independence. These analyses were examined according to the change in total pressure ratio and the results are shown in Figure 6. In the graph below, it is seen that after the number of cells reaches 289,045, the output values of the problem reach a constant value and become independent of the number of mesh. Considering this graph, the network structure containing 289,045 cells is considered sufficient. Many active and passive flow control ideas such as adjustable inlet guides, air jet injection, and vortex generation have been tried to increase the operating range, total pressure ratio, and efficiency of radial compressors. However, due to some deficiencies in these methods, they are far from applicability. Therefore, the tandem blade design, which is a more innovative idea, has attracted the attention of researchers.