esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Distribution of the Predicted Soil PSFs  
Even though ALR and ILR models were developed on the basis of different covariate sets, they included the same categories of covariates, and the log-ratio models generated similar spatial patterns of soil PSFs (Figures S3 and S4). Higher clay and silt content were mainly distributed in the eastern part in the BQ region, while higher sand content was mainly distributed in the northwestern part. Soil PSFs showed a similar distribution trend at the WQ region, where higher clay and silt and lower sand content distributed in the southeastern part.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**PSF**} & \multirow{2}{*}{**Trans 1**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**Cubist**} \\ \cline{3-8}  & & & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** \\ \hline \multirow{4}{*}{BQ} & clay & \multirow{2}{*}{ALR} & 0.462 & 0.023 & 0.001 & 0.468 & 0.025 & \(-\)0.002 \\  & silt & & 0.474 & 0.093 & \(-\)0.005 & 0.449 & 0.102 & 0.014 \\  & sand & & 0.512 & 0.104 & 0.004 & 0.446 & 0.114 & \(-\)0.011 \\ \cline{2-8}  & clay & \multirow{2}{*}{ILR} & 0.495 & 0.021 & 0.000 & 0.467 & 0.026 & 0.002 \\  & silt & & 0.561 & 0.086 & 0.001 & 0.456 & 0.097 & 0.004 \\  & sand & & 0.564 & 0.100 & \(-\)0.001 & 0.386 & 0.115 & \(-\)0.007 \\ \hline \multirow{4}{*}{WQ} & clay & \multirow{2}{*}{ALR} & 0.405 & 0.047 & \(-\)0.003 & 0.457 & 0.044 & 0.000 \\  & silt & & 0.315 & 0.029 & \(-\)0.003 & 0.382 & 0.030 & 0.000 \\ \cline{1-1}  & sand & & 0.385 & 0.070 & 0.006 & 0.436 & 0.068 & 0.000 \\ \hline \multirow{4}{*}{WQ} & clay & \multirow{2}{*}{ILR} & 0.500 & 0.043 & \(-\)0.004 & 0.467 & 0.044 & \(-\)0.004 \\ \cline{1-1}  & silt & & 0.358 & 0.029 & \(-\)0.002 & 0.435 & 0.029 & \(-\)0.003 \\ \cline{1-1}  & sand & & 0.458 & 0.067 & 0.005 & 0.454 & 0.069 & 0.007 \\ \hline \hline \end{tabular}  
* Trans represents for the two log-ratio transformations, i.e., additive log-ratio (ALR) and isometric log-ratio (ILR) transformation.  
\end{table}
Table 3: Best performance by the combination of log-ratio transformation, covariate selection, and machine learning approach in predicting soil particle size fractions.
# Optimizing Viscoelastic Properties of Rubber Compounds for Ballistic Applications
## 4 Discussion  
Following the assumption that by optimizing material properties, a ballistic impact can trigger the glass transition, rubber should significantly add to the ballistic protection of multilayered armor. In this case, the damping of the rubber is maximized in the area of the glass transition. Using dynamic mechanical analysis and the WLF equation, it is shown that this is the case for the self-developed butyl rubber (IIR). Ballistic tests were applied for further evaluation of the assumption. By observing the behavior of a rubber-coated aluminum plate under a high-velocity impact, it can be seen that this material shows an improved behavior compared to other rubbers with non-optimized glass transition temperatures, like styrene-butadiene rubber (SBR) or butadiene rubber/natural rubber (BR/NR). In consequence, the conclusion can be stated that this butyl rubber is in the glass transition zone during the impact, which affects the behavior of the rubber itself and improves the performance of the total armored laminate.  
To further validate that this phenomenon not only occurs but also increases the protective effectiveness during ballistic impacts, ballistic tests were carried out to determine the energy absorption of multilayered steel-rubber-aluminum armors. Different rubbers (IIR, SBR and BR/NR) are compared to each other and a reference configuration without a rubber interlayer. The configuration with butyl rubber as the interlayer shows better results with an increase compared to the reference configuration of around 8%, while the rubbers with the non-optimized glass transition temperature only reach an increase between 2% and 3%.  
Additionally, the influence of carbon black content and thus the influence of hardness and damping were investigated. It can be concluded that both material properties play a role in determining the effectiveness of the armor, while hardness shows the bigger influence. An optimum can be reached with intermediate carbon black contents of around 50-65 phr. Linking both series of ballistics, this means that even if the maximum energy absorption is not reached because of using not enough carbon black and thus having a too low hardness, IIR is still is on the same level as a worse-suited compound like BR/BR, even if the latter possesses a higher hardness.  
It is necessary to emphasize that every possible weight reduction is positive and an important input to armor development. According to VPAM [20], to achieve VPAM 10 level, RHA steel with a thickness of 18 mm is necessary to provide protection from small caliber projectiles. With a density of steel of 7.8 g/cm\({}^{3}\) that correlates to an areal density of 140 kg/m\({}^{2}\). Assuming that the surface of an average car is around 8-12 m\({}^{2}\)[23], this would mean that only the ballistic armor of an armored vehicle would weigh around 1100-1700 kg. An 8% decrease of the total weight achieved by using an optimized armor configuration would reduce the total vehicle weight by over 100 kg.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 3 Results and Discussion
### Reactions of Stearic Acid
#### 3.2.2 Influence of the Hierarchical Porosity  
With the goal of assessing the effect of the presence of hierarchical porosity in the zeolitic support, Pd/n-ZSM-5 (32) and Pd/h-ZSM-5 (33) catalysts were tested and compared in the conversion of stearic acid. Figure 6 shows the conversion and selectivities by groups to the different products so obtained over both catalysts. A quite higher activity was exhibited by Pd/h-ZSM-5 (33) with regards to Pd/n-ZSM-5 (32), with values of the stearic acid conversion of 67 and 33%, respectively. Since both materials present similar acid properties, this remarkable difference can be related to the presence of the hierarchical porosity, which increases the accessibility of the zeolite acid sites and enhances the dispersion of the Pd nanoparticles over the zeolitic support.  
Figure 4: Stearic acid conversion and selectivity by groups over the Pd/n-ZSM-5 (32) and n-ZSM-5 (32) samples (T = 325 \({}^{\circ}\)C).  
Figure 5: Selectivity by atom carbon number over the Pd/n-ZSM-5(32) and n-ZSM-5 (32) samples (T = 325 \({}^{\circ}\)C).  
Regarding the product distribution, and under the conditions employed in these experiments, diesel range hydrocarbons with selectivity around 15% were also produced over both catalysts, in addition to the gases and gasoline fractions. Interestingly, the use of the hierarchical zeolitic support also led to a remarkable improvement in the gasoline selectivity (72 over Pd/h-ZSM-5 (33) versus 56% when using the Pd/n-ZSM-5 (32) catalyst). This result can be mainly related to the improved Pd dispersion and enhanced accessibility through its mesopores over the hierarchical ZSM-5 sample, which favors the OCA pathway by increasing the closeness between acid and metallic sites and by enhancing hydrogen transfer reactions.  
Therefore, the addition of a secondary porosity within the mesopore range in the ZSM-5 support had a paramount influence on the behavior of the corresponding catalyst in stearic acid conversion, leading to remarkable enhancements in both catalytic activity and selectivity towards gasoline range hydrocarbons.
## 3 Topological Diversity and Structural Design in Magnetic Gear Systems
### Operating Principles and Theoretical Fundamentals of Magnetic Gear  
This section examines the fundamental operating principles, design parameters, torque transmission losses, and efficiency analyses of magnetic gear systems. Additionally, current research on proposed design approaches and cooling strategies in the literature is discussed, with an emphasis on their impact on system performance. In magnetic gears, torque transfer is achieved through the interaction of opposing magnetic fields generated between the rotors. As the permanent magnets on one rotor rotate, they exert magnetic attraction or repulsion forces on the other rotor, resulting in a magnetic coupling. This mechanism creates a variation in magnetic flux density when one rotor remains stationary and the other rotates, enabling torque transmission.  
The system's maximum torque limit is defined by \(\mathrm{Tmax}\). If this limit is exceeded, magnetic slip occurs between the rotors, causing the intended gear ratio to be compromised. Therefore, ensuring that the operating load remains below this threshold is of critical importance during the design phase [40; 41]. Figure 4 schematically illustrates the torque transmission mechanism in a typical magnetic gear system.  
In addition to occurring when the transmitted torque exceeds the magnetic limit of the system, magnetic slip can also induce undesirable dynamic deviations and resonance risks. Therefore, recent research has increasingly focused on understanding the dynamic stability of CMG systems under transient loading conditions. [PERSON] et al. [42] proposed a fast and analytically tractable simulation approach that models the torque response of CMGs during dynamic load variations. [PERSON] and [PERSON] [43] presented dynamic torque control strategies to manage system behavior during abrupt torque fluctuations. [PERSON] et al. [44] analyzed torque oscillations caused by nonlinear magnetic properties and spatial harmonics, which may lead to deviations from nominal operating conditions. Additionally, [PERSON] and [PERSON] [45] examined the natural frequencies and vibration modes of magnetic planetary gear systems, highlighting potential resonance effects under variable speeds.
## 4 Discussion
### Machine Learning  
Figure 3 demonstrates the positive impact of recursive feature elimination (RFE) on the logistic regression (LR) model, particularly when using smaller optimal feature subset sizes. A notable increase in Letter Accuracy (>2%) was observed for N \(\leq\) 17, attributable to RFE-driven feature reduction.  
However, this effect was not replicated in the random forest (RF) model, where Letter Accuracy remained unchanged following RFE application. The influence of RFE on the support vector machine (SVM) model was minimal across the entire range of optimal feature subset sizes.  
Evaluation results for each model are presented in Table 4 for four selected subset sizes:  
* N = 3--when LR and SVM achieved their highest classification accuracy;
* N = 9--the smallest N after which LR maintained \"stable\" accuracy (-74%);
* N = 11--the smallest N after which SVM maintained \"stable\" accuracy (-74%);
* N = 40--classification without RFE, where RF achieved the highest accuracy.  
\"Stable\" accuracy was defined as classification performance that remained consistent over at least three consecutive values of N. The highest accuracies for letter classification (76.7%) and subject classification (82.5%) were achieved using logistic regression (LR) with only three features, as shown in Table 4. Across all optimal feature subset sizes, recall and precision closely aligned with accuracy, indicating well-balanced model performance and effectiveness.  
To the best of our knowledge, the works cited in [34; 35; 36] represent the most recent and relevant contributions in this domain. Our subject accuracy of 82.5% is comparable to the result reported by a deep learning-based Bidirectional Long Short-Term Memory (BiLSTM) model [34], which detected depression trends with an accuracy of 89.2%. Unlike that approach, our method uses statistical analysis and conventional machine learning, which is less computationally demanding, employs subject-wise leave-one-out (LOO) cross-validation instead of a more fragile train-test split, and requires N times fewer test subjects for the same training set size (where N is the number of letters written per subject).  
[PERSON] et al. [35] investigated timing and ductus-based features, using random forest ensembles (50 trees) to rank their attributes, and achieved a classification accuracy of 72.8%. Their work, along with others, utilized the publicly available EMOTHAW dataset, which included three classes (depression, anxiety, stress) but lacked a control group. Furthermore, EMOTHAW data were collected from individuals aged 21-32, whereas our dataset focuses on subjects aged 11-17.  
[PERSON] et al. [36] examined depression detection using five handwriting and drawing feature categories (pen pressure, timing, ductus, character spacing, and pen inclination) across a set of similarly scaled digital tablet tasks. In contrast to our study, which incorporates both statistical analysis and machine learning for group differentiation in youths, their analysis was conducted on adult participants and relied solely on MANOVA to identify group differences.
## 3 Nano-Elicitation of Flavonoids
### ZnONPs as a Novel Elicitor of Flavonoids
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plant Species** & **Type of In Vitro Culture** & **Growth Media** & **NP Size (nm)** & \begin{tabular}{c} **Effective** \\ **Concentration** \\ **of NPs** \\ \end{tabular} & **Effect on Flavonoid Production** & **References** \\ \hline _G. glabra_ & Seedlings & \begin{tabular}{c} Hoagland’s \\ solution \\ \end{tabular} & - & 1 \(\mu\)M & \
\begin{tabular}{c} Both CuO and ZnO enhanced \\ the production of tannins, \\ anthocyanins, phenolics, \\ flavonoids and glycyrrhizin. \\ \end{tabular} & [65] \\ _E. purpurera_ & Callus & MS media & - & 75 mg/L & Flavonoid production enhanced. & [67] \\ _L. usitatissimum_ & Cell suspension & MS media & 35 & 1–500 \(\mu\)g/L & 123.83 mg/L highest TFC & [68] \\ _Stevia rebaudiana_ & Shoot & MS media & 34 & 1.0 mg/L & Increase in TFC. & [69] \\ _L. ladeburii_ & Shoot & MS media & - & 20–100 mg/L & Anthocyanin, phenolics and \\ _S. tuberosum_ & Plant & Potting soil & - & 300, 500 ppm &
\begin{tabular}{c} flavonoid production increased. \\ Increase in anthocyanin and \\ flavonoid content. \\ \end{tabular} & [71] \\ _S. rebaudiana_ & Plant & Nutrient solution & - & 75 mg/L & 87.8\% flavonoid production. & [72] \\ _S. rebaudiana_ & Root culture & MS media & ZnO 20–30 & - & TFC level enhanced when \\  & & & & & 1 with 2 mg/L ZrONPs & [73] \\  & & & & & and 20 mg/L CuONPs. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Zinc oxide NPs (ZnONPs) as an elicitor of flavonoids. ZnONPs enhanced anthocyanin and flavonoid contents in potato plants at an effective concentration of 300-500 ppm; however, an increase in enzymatic and non-enzymatic antioxidants led to oxidative stress and toxicity to potato plants [71]. There was 87.8% flavonoid production in _S. rebaudiana_ plants when stressed with plant-synthesized ZnONPs [72]. The physiological and metabolic responses of candy leaf were elicited as a defense against applied ZnONPs and CuONPs [73].
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 3 Transmitter Design
### Transmitter Model
For the analog precoder, we address two cases of the availability of the CSI knowledge at the transmitter side. First, we proposed a random precoder where each UT has no access to CSI. Then, on the basis of [23], we suppose that the UT has access to a partial CSI, that os, only the average AoD of each channel cluster.
###### Abstract
In [18], the authors review several applications in the biomedical field, such as modeling tissue deformation, and report significant gains in simulation time compared to FE methods. In [19], the authors show that brain injury can be predicted using DL tools and report significant time gains compared to FE simulations. Another field where these ideas are applied is in the civil engineering field to predict the behavior of complex structures, such as a dam in a seismic scenario [20] or the structural level strength of composite laminates [21]. However, in vehicle safety, we are only aware of the use of DL techniques to evaluate the crash response of a mechanical bump absorber [22], but to the best of our knowledge, we are the first ones to propose the use of DL to predict the crash severity on a test dummy. Our results show that the significant savings in time and computational resources make our proposed procedure a potential game changer in the passive safety design process, as DL can be used to approximate crash curves in order to accelerate the design process compared with relying solely on FE methods. Hence, the main contributions of this paper are as follows:  
* We propose several architectures based on DL to evaluate the severity of a crash, taking as input the parameters of the restraint system. Namely, we propose using feed-forward NNs (FFNNs) and bayesian NNs (BNNs). * We also discuss the important topic of uncertainty by making use of BNNs, which not only provide a predicted output but also a measure of the uncertainty of that output that can be used to identify situations where minimal changes do have a strong impact on the crash severity and focus the design in such situations. * In order to assess the performance of the proposed models, we use real data from FE simulations in the BMW Passive Safety Department. Due to the computational load required for each of these simulations, we only have 140 crash test sequences to validate our DL models, which, however, show very promising results, both in terms of good prediction capability and a significant reduction in computational load. FE models require 36 CPUs and more than 45 h of processing time, but DL models train in minutes using a GPU. * In order to facilitate the replicability and foster future research, we publish the data from the FE simulations used in this paper along with the code needed to replicate our results, so that future research can use our code and/or data as a baseline. The rest of the paper goes as follows: in Section 2 we present the input and output data used in this work; in Section 3 we explain the theoretical foundations of the algorithms used; and then in Section 4 we present the most relevant results from our research; and finally, in Section 5 we draw the conclusions that stem from this work.
## 3 Modelling, Analysis Tools and Experimental Support for Stability Analysis
### Nonlinear Analytic State Model
#### 3.1.2 State Model Validation  
In order to validate the previous state model, we have considered a circuit solver (Saber) that is seen here as a reference. In that circuit simulation, the synchronous machines (generator and motor) are modelled from proprietary models from Safran Tech including [PERSON]'s model. The relevance of such models has been proved in several previous studies. We consider here a single-motor association in which the topology is equivalent to the state model (11). The Saber circuit solver is then compared with the nonlinear state model (11) simulated on Matlab Simulink in the same operating conditions: the generator speed being controlled at constant speed, the powertrain has to face load torque steps successively at 5, 10 and 15 s. Figure 5 emphasizes the quasi-perfect accordance of both time simulations. Note that these simulations were obtained with a high bandwidth (3 Hz) for the gas turbine speed controller in order to ensure the system stability, this latter issue being under study in the next section. Note also that in the Figure 5, the initial conditions are set differently in Saber and in Matlab, just to satisfy the constraints specific to each solver: the initial transients are not to be considered for the analysis.  
The previous simulation also emphasizes the capability of this system to face load torque variation on the propeller side. Indeed, in Figure 5, load torque variations are provoked at times 5 s, 10 s and 15 s and their consequences are visible on the displayed electric variables (\(I_{d,\theta}\) and \(\delta\)) which remain stable despite the propeller torque transients. More generally, it was verified in our study that the system is capable to face transients on the propeller torque while the powertrain is in stable operation.
# A Case Study of a Large Unstable Mass Stabilization:
## 3 Results
### Field Investigations and Satellite Surveys
Topographical works developed during the previous investigation [15] detected the existence of three main landslides in the area under study ( landslides, 1, 2 and 3 in Figure 8). This information was confirmed by the DinSAR data [21; 22]. One of those landslides ( landslide 3) was not affected by any anthropogenic activity (i.e., was not the consequence of the parking excavation) and was an extremely slow movement [46; 47] with an estimated average velocity [22] of approximately 16 mm/year and affected by seasonal rainfalls. The second landslide (landslide 1) was activated by the parking excavation and corresponds to the slope movement under study. Its movement rate, estimated by the auxiliary DGPS campaign of approximately 0.1 m/year, meant it was not possible to compute its velocity with the DinSAR technique [22]. The third landslide (landslide 2) was located next to the previous one, was also activated by the parking excavation and its average velocity was estimated [22] at approximately 25 mm/year. The ALOS PALSAR differential interferometry [24] was also applied to this last landslide, obtaining similar results. Figure 7: Inclinometers records: (**a**) Inclinometer placed in borehole B-5, reprinted with permission from ref. [15], Copyright Year 2005, Copyright Owner’s [PERSON]. [PERSON] (old geotechnical investigations [15], conducted in 2005); (**b**) Inclinometer placed in borehole B-9 (new geotechnical investigations, conducted in 2016). DGPS measurements [14], conducted after performing the stabilization work on the slope (i.e., landslide 2 according to Figure 8), showed that maximum displacement rates were around 1 mm/day in the summer while rates increased up to 2 mm/day in the fall. Those measurements also identified that the landslide's most active part was an area located below the main scrap. GB-SAR technique discovered that the landslide displacements were fairly linear in time, although slight differences were observed due to daily rainfall variations. Generally, an acceleration of the landslide occurs when rainfall events take place. In fact, hydrogeological results obtained in the previous study [15] and the new geotechnical investigation indicated that surface materials were permeable and the hillside drains with abundant subterranean water flow. Continuous flow springs near the slope toe were identified, with that flow occurring through both the permeable material and the failure surface. The water table was found approximately on the failure surface, suffering oscillations with precipitations. Thus, the relationship between the landslide evolution rate and rainfall reported by the use of the GB-SAR technique [14] was confirmed due to the easy infiltration of water in the sliding mass, thanks to the high drainage capacity of the colluvial material located on the top of it.