esa-sceva/satcom-chunk-collection huggingface dataset:
# Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation
## 2 Background
### Electric Vehicles
#### 2.3.2 Benefits of Electric Vehicles Environmental Benefits  
Since EVs don't emit tailpipe emissions, they don't contribute to air pollution or greenhouse gas emissions. Even when fossil fuels are needed to generate energy to power the EV, it emits less pollution than a typical gas-powered vehicle [37].  
Lower Operating CostsCompared with regular cars, EVs offer lower running costs. In general, electricity is less expensive than petrol or diesel, and as electric vehicles have fewer moving components, they require less maintenance. Due to electric motors' excellent durability compared with internal combustion engines, they also often have longer lifespans [37].  
Energy IndependenceRenewable energy sources, including solar or wind power, may power EVs. This lessens reliance on fossil fuels and may increase the sustainability of energy use [37].  
EfficiencyCompared with conventional cars, EVs are more efficient. The efficiency of the power plant will also affect the well-to-wheel (WTW) effectiveness. Compared with diesel cars, which vary from 26% to 38%, the overall WTW productivity of petrol vehicles ranges from 12% to 28%. In comparison, the WTW efficiency of EVs powered by natural gas power plants ranges from 14% to 30%, while EVs powered by renewable energy show an overall efficiency of up to 70% [37].  
Smooth and Quiet OperationEVs operate significantly more quietly and smoothly than conventional cars because electric motors generate less vibration and noise. This may result in a more relaxing and pleasurable driving experience [37].  
ConvenienceEVs may be charged at residences or public charging stations, so going to the petrol station is no longer necessary. Additionally, many EVs include capabilities that enable drivers to remotely warm up or cool the cabin, which may be helpful in extremely hot or cold weather [37].  
PerformanceElectric motors can produce instant torque, allowing EVs to accelerate quickly. They could also have a lower center of gravity, making them more maneuverable and stable [37].
## 2 Materials and Methods
### Radiative Transfer Process for Tower-Based SIF Observations
To establish a model for estimating the atmospheric transmittances, a dataset including a range of altitudes, solar/view zenith angles (SZA/VZA), and values of the AOD was simulated using MODTRAN 5. The original full-width at half maximum (FWHM) of MODTRAN simulation is 1 cm\({}^{-1}\) (about 0.06 nm at 760 nm). The detailed input parameters for MODTRAN are listed in Table 1. The sensor height and surface elevation were set as 25 m and 1.5 km to match the case of tower-based SIF observation system at the Daman site, which was used for validation in the study. Figure 1: Relationships between the aerosol optical depth (AOD), radiative transfer path length (RTPL), and the upward/downward atmospheric transmittance for a fixed elevation and atmospheric profile at the 760.6 nm (O\({}_{2}\)-A band) and 757.8 nm (shoulder of the O\({}_{2}\)-A band) based on the simulations made by MODTRAN 5. The spectral resolution is 0.31 nm. The mid-latitude summer atmospheric profile was used and the altitude was set as 1.5 km. Figure 1 shows the relationships between the AOD, RTPL and the upward/downward atmospheric transmittance for a fixed elevation and atmospheric profile based on the simulations made by MODTRAN 5. To match the field observations that we conducted using an Ocean Optics QE Pro spectrometer, a Gaussian function with an FWHM of 0.31 nm was used as the ISRF and was convolved with the MODTRAN simulations. Based on the results shown in Figure 1, look-up tables for estimating the upward and downward atmospheric transmittances from the TOC to the tower-based sensor height at 760.6 nm (O\({}_{2}\)-A band) were established with the AOD\({}_{550}\) and RTPL as inputs. The upward and downward atmospheric transmittances at 757.8 nm (shoulder of the O\({}_{2}\)-A band, where the scattering effect dominates) were also shown in Figure 1b,d for reference.
## 5 Modern Technologies for Ascorbic Acid Biofortification in Tomatoes
### Biofortification through Genome Editing  
The recent advances in genome editing technologies can serve as an alternative rapid transgene-free tool to develop tomatoes with increased AsA levels. The ability to generate specific modifications in the genome due to the targeted design of sequence-specific nucleases is one of the main advantages of genome editing compared to earlier mutational breeding strategies using transgene silencing and random insertion of T-DNAAs [127]. Furthermore, as genome-edited plants do not contain any foreign DNA, they are unlikely to be subjected to regulatory oversight [141]. Regarding AsA content, it has been demonstrated that _GGP_ contains an unusual but highly conserved uORF [143; 26], that is preferentially translated when AsA is highly accumulated, thus reducing GGP protein abundance. Targeting this effective negative feedback loop that enhances AsA levels, the CRISPR/Cas9 genome editing system has been recently employed in _Arabidopsis_, lettuce, and tomato, by disrupting the _cis_-acting _GGP_ uORF [144]. This efficient method of manipulating the translation of mRNA resulted in a 1.5-fold increase in AsA contents, and further improved stress tolerance to MV-induced oxidative stress in lettuce. More recently, AsA-enriched tomato mutants were generated by targeting the uORF of the _SIGGP1_ coding sequence, linking high AsA levels with impaired floral development and pollen fertility, as well as seedless fruits [27]. This intriguing finding that links AsA and GSH with plant reproduction has been previously reported in tomato lines overexpressing _SIGGPI_[126]. Additionally, RNA-seq analysis of these CRISP-mutated lines confirmed that this particular uORF acts as a regulator of AsA synthesis and redox state to enable normal plant/organ development [27].  
Other potential targets for genome editing in tomato include the _Arabidopsis_ regulatory factors _AsA mannose pathway regulator 1 (AMR1)_, _CSN5B_, _CSN8_, _NL33_, that are able to enhance AsA levels when disrupted with T-DNA insertions or downregulated with RNAi [141]. The challenge of using genome editing technologies to enhance the AsA of crop species has phenomenal potential as it can minimize pleiotropic defects due to carbon reallocation in plant growth and fruit development occurring when modifying single genes from the AsA biosynthetic pathways [26].
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 3 Methodology  
The temporal dependence-based LSTM (TD-LSTM) network is mainly divided into two parts. The first part is to analyze the temporal dependence of marine-temperature changes and propose a temporal dependence parameter matrix fusion method. The second part is to use the fusion sequence to train the LSTM neural network to obtain the prediction model.
# Development of Enabling Technologies for Ku-Band Airborne SATCOM Phased-Arrays
## 4 Developed Devices  
The main challenges in the design of the antenna have been: (a) the development of the radiating element, (b) the development of the active module for transmitter/receiver and (c) the design and manufacturing of a digital vector modulator (DVM) able to integrate in a single chip the attenuator and phase shifter features. These three components are described in the following paragraphs.
## 4 Fast Automatic Adjustment Algorithm of Imaging Waveform
### Automatic Judgment of Measurement Mode
#### 4.2.1 Dynamic Centroid Difference Threshold  
Table 1 shows the comparison of centroid shift of imaging waveforms between two adjacent frames, the centroid position shift caused by object motion is obvious, and the state of the measured object can be judged by the centroid difference. When the object moved, the centroid of the imaging waveform shifted at least 0.5312 pixel, therefore, we set the dynamic centroid difference threshold as 0.5 pixel. When the dynamic centroid difference was higher than 0.5 pixel, the sensor entered the dynamic mode. Otherwise, the sensor entered the static mode.
## 4 Simulation and Analysis  
Several simulations were designed to validate the proposed LPSA algorithm for pulse compression in this section. The simulation models were built based on the structure shown in Figure 2. The output pulses from the novel LPSA algorithm were compared to the results obtained by the classical digital pulse compression method in the time domain. The frequency resolution and amplitude accuracy were measured and analyzed. The influence of the nonideal devices and sampling rate on the amplitude is discussed. Finally, the computations are compared between the novel method and the classical method.
## 2 Ultrasonic Cleaner Design Methodology
### Ultrasonic Transducer Driving Circuit  
This research was proposed to design an ultrasonic cleaning machine and its driving electronics model after a comprehensive review of the design, fabrication, and driving techniques for the ultrasonic cleaning machine. We proposed the Class-D Inverter topology [25] to drive the Ultrasonics transducer with high frequency for ultrasonic waves, as shown in Figure 2. The proposed Electronics circuit model is divided into three parts. The  
Figure 1: Block Diagram of Proposed Methodology.  
first part converts the main supply voltage to DC-Link voltage. The second part generates the controllable 40 kHz frequency to drive the piezoelectric transducer for generating the ultrasonic waves.  
To drive the piezoelectric transducer in the ultrasonic cleaner, the square wave voltage signal generated by the class-D inverter works with an LC resonant circuit to generate a sine wave voltage signal [26]. The piezoelectric disc static capacitance (C\({}_{\text{o}}\)) works in resonance with the inductor (L\({}_{\text{s}}\)) as a low-pass filter to generate a sine wave signal. The values in Table 1 were used for the equivalent model of the proposed scheme.  
\[Fs=\frac{1}{2\pi\sqrt{LC}} \tag{1}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Electrical Components**} & \multicolumn{2}{c}{**Operating Values**} \\ \hline Input Dc-Link Voltage & V\({}_{\text{in}}\) & 170 V \\ Maximum Output Power of Circuit & P\({}_{\text{m}}\) & 50 W \\ Maximum Current & I\({}_{\text{m}}\) & 100 mA \\ Maximum Output Voltage & V\({}_{\text{o}}\) & 840 V \\ Magnetizing Inductor & L\({}_{\text{m}}\) & 1.0 mH \\ Switching Frequency & F\({}_{\text{s}}\) & 40 KHz (range) \\ MOSFET Model & M1 and M2 & 6N80 \\ Piezo Capacitance & C\({}_{\text{o}}\) & 8.35 nF \\ PZT Resistance & R\({}_{\text{p}}\) & 310 \(\Omega\) \\ PZT Disc Diameter & mm & 50 mm \\ PZT Disc Thickness & mm & 2.5 mm \\ PZT Material & - & PZT-4A \\ PZT Electrode Printing Type & & Wrap Around Feedback \\ PZT Disc Power & watt & 35 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical Properties of D Class Inverter.  
Figure 2: Proposed System Block Diagram.  
Table 1 shows the electronic parameters for the UCT driving machine, and the piezo disc parameters. The proposed circuit was designed for a maximum power output of 50 W and a maximum current of 100 mA. The magnetizing inductor is the core element to adjust the transducer input voltage and current capacity (to get 35 W of power). As the piezoelectric disc is made of Hard piezoelectric ceramic (PZT-4A), the driving voltage can be set to a maximum value of about 750 V, which corresponds to 300 V/mm and is in a safe range to not cause de-poling and excess heating.
## 4 Results and Discussion
### Effect of Flow Distribution on the WSM
#### 4.2.4 von Mises Stress on the Rake  
The von Mises stresses on the FSI boundaries of the rake were computed for the select rake pins for vertical and posterior positions of the rake. The stresses for the select pins at 0.2 m/s are shown in Figure 18, where 1 represents the displacement of the FSI boundary on the rake pin. The legend in the graph shows the selected rake pins of the rake movement mechanism. R1, R6, R12, and R18 represent the 1 st, 6 th, 12 th, and 18 th rake pins of the rake movement mechanism, respectively. The initial point of the FSI boundary on the rake is set at the tip of the pin. Furthermore, 1 represents the total length of the rake pin. The two peaks in von Mises stress in each plot correspond to the stress concentrator (corner), wherein the rake teeth are attached to the rake head. When comparing the von Mises stress on the rake with the 2D analysis conducted by [PERSON] et al. [7], their von Mises graphs exhibit rounded peaks, whereas the present study, which is conducted in 3D, shows pointed peaks. This can be attributed to the fact that the present study accounts for the full geometric complexity of the structure and thus allows for a more accurate representation of the stress distribution, particularly in regions where stress gradients are significant.  
The von Mises is significantly higher in the posterior position than that in the vertical position. This finding is due to the flow behavior in the gap between the main screen and the sub-screen; that is, as the fluid passes this gap, the velocity of the fluid is increased right before the flow hits the rake, as shown in Figure 10a. However, the fluid interaction with the rake decelerated in the case of a vertical rake due to the passage in the sub-screen, which is located directly in front of the rake. The high peaks are for the posterior positions at 35.0 and 33.0 MPa, which are lower than the material yield stress of the structural steel (250 MPa). Thus, the sub-screen and rake models remain safe under the applied loading conditions.
###### Abstract  
Background: This study aimed to evaluate the asymmetry of optic nerve head parameters measured by confocal scanning laser ophthalmoscopy (CSLO) in myopic anisometropic eyes. Methods: A total of 36 eyes of 18 healthy myopic anisometropic subjects, defined as cases in which the difference in spherical equivalent (SE) between both eyes is \(\geq\)1.5D, were recruited. The optic nerve heads were measured using the Heidelberg retina tomograph II (Heidelberg Engineering, GmBH, Heidelberg, Germany). Differences in optic nerve head parameters between more myopic eyes and fellow eyes were evaluated using the paired-sample \(t\)-test. Pearson correlation and multiple linear regression analysis were used to evaluate factors associated with cup/disc ratio (CDR). Results: The cup/disc area ratio (mean difference 0.07 \(\pm\) 0.11, \(P\) = 0.027), horizontal (mean difference 0.10 \(\pm\) 0.17, \(P\) = 0.033), and vertical CDR (mean difference 0.13 \(\pm\) 0.18, \(P\) = 0.008) were significantly smaller in more myopic eye. Larger disc area was independently and significantly associated with larger cup/disc area ratio (\(\beta\) = 0.561, \(P\) = 0.001) and vertical CDR (\(\beta\) = 0.499, \(P\) = 0.03). Conclusion: The CDR, horizontal, and vertical CDR were significantly smaller in the more myopic eyes in myopic anisometropic subjects. Further studies with larger samples are needed to confirm the asymmetry of the optic nerve head in myopic anisometropic eyes.  
optic nerve head parameter; asymmetry; myopia; anisometropia Article
# New Flame Retardant Systems Based on Expanded Graphite for Rigid Polyurethane Foams
## 4 Summary  
Based on the analysis of the presented results--including both the synthesis and physicochemical tests of RPUFs--it can be concluded that the synergistic flame retardant system based on expanded graphite with the addition of silica and ionic liquid had a positive effect on the properties of the porous  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**T\%**} & \multirow{2}{*}{**T\({}_{10\%}\)**} & \multirow{2}{*}{**T\({}_{50\%}\)**} & \multirow{2}{*}{**T\({}_{70\%}\)**} & **Char** \\  & & & & & **Residue** \\  & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & **(\%)** \\ \hline PU–0 & 202 & 241 & 399 & 580 & 23.4 \\ PU–EG & 213 & 250 & 467 & 592 & 28.3 \\ PU–EG+SiO\({}_{2}\) & 219 & 257 & 480 & 598 & 29.4 \\ PU–EG+SiO\({}_{2}\)+IL & 209 & 247 & 420 & 591 & 27.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the thermogravimetric analysis of PU foams.  
Figure 8: TGA graph plotted for PU foams.  
Figure 9: DTG graph plotted for PU foams.  
composite. It was observed that the introduction of fillers has a meaningful impact on the structure and further mechanical and thermal properties of RPUFs. Although the addition of graphite expanded alone or in the presence of silica and ionic liquid can interfere with the foam structure and increase apparent density, this can also improve its strength properties. As a result of the introduction of the synergistic system, it was noted that the compressive strength improved by approximately 60% for PU-EG+SiO\({}_{2}\) samples compared to the reference sample. Higher three-point bending strength, impact strength, and higher hardness were also observed. The modified RPUFs were characterized by similar values of thermal conductivity and better thermal properties. The RPUFs modification has, above all, a positive effect on the fire resistance of composites. The results showed that the fire resistance of PU foams containing EG and SiO\({}_{2}\) is significantly improved, namely the incorporation of expanded graphite and silica significantly reduced the peak heat release rate (pHRR) by about 70% in relation to the reference sample. Based on SEM images, it can be seen that the introduced graphite can form an expansion carbonizing layer during combustion, which can significantly reduce the spread of fire in the foam.  
Conceptualization, A.S.; Methodology, A.S.; Software, A.S.; Validation, A.S., S.C. and P.K.; Formal Analysis, A.S.; Investigation, A.S., S.C. and P.K.; Resources, A.S; Data Curation, A.S; Writing-Original Draft Preparation, A.S; Writing-Review & Editing, K.S.; Visualization, A.S.; Supervision, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Conclusions and Future Developments  
In this paper, an optimized alternative approach to estimate muscle force patterns of an athlete during cycling activity using NNs has been presented. The NNs were trained using the results obtained with a validated method that uses a deterministic optimization algorithm. The validation of the estimation obtained by NNs was accomplished analyzing the Bland-Altman plot, used to evaluate agreement between the two different methods. The results have been compared showing a good agreement between the two methods and the neural network was able to display smoother signals, as compared to the deterministic optimization. A further result can be appreciated in the significantly reduced computational costs provided by the use of NNs. Indeed, whereas the optimization algorithm requires an iterative evaluation of several non-linear functions, the NN can be evaluated directly in much less time. The considerable reduction in computational time (as shown in section 3, about two orders of magnitude as compared to the previous method) opens the possibility of implementing this approach in embedded environment using low level language. Moreover, the sampling frequency adopted in this work (_i.e._ 1000 Samples/s) was higher than necessary and used only in accord with the previous work [15]. In a future real time application the sampling frequency necessary to acquire forces and angles data can be reduced down to 30 Samples/s. This value is compatible with the lower computational cost found for the NNs approach, as shown in the results. Of course, the use of this approach is, as of now, subject specific: since the Neural Network requires to be trained on a specific dataset, the original deterministic algorithm must run at least once. However, this operation can be performed off-line and just one time. Once the neural networks are trained, the prediction can be performed at full speed with the method proposed in this work. On the Neural Network specific performance, even if the results for the prediction were satisfactory, an increase in the variance of the anthropometric characteristics subjects could be desired. Indeed, the generalization capabilities of the network are inherently tied to the variance of the training pattern, and to ensure the usability of this neural system for a variety of participants, a larger population should be used. The enlargement of the sample size, however, can yield a more complex optimization problem, which could require parallel architectures and advanced algorithms to be solved [46; 47; 48; 49].
###### Abstract
Service life calculation, specifically residual service life, is based on these measurements and analytical calculations using a layered elastic half-space model [15]. The layered elastic half-space model defines stress and deformation in the pavement structure by considering the strength characteristics of the pavement layers and underlying sub-layers. The crucial parameter for calculating the pavement's structural life is the fatigue characteristics of asphalt surface materials. Due to variations in asphalt type, aggregate, additives, and their ratios, the fatigue properties must be determined experimentally. The dynamic modulus (absolute value of complex modulus) method [16] is presented and recommended for this task, as concluded in previous research efforts [17; 18; 19]. From a pavement life perspective, economic assessment is required in the planning phase, the pre-design and design phases, and the in-operation phase as part of the decision-making processes of the pavement management system. In all cases, life cycle analysis (LCA), life cycle cost analysis (LCCA), and cost-benefit analysis (CBA) of the pavement are considered. A cost-benefit analysis is a systematic and quantitative approach used to assess the economic efficiency of a project by comparing the life cycle costs of a road in its current and desired condition. In the context of pavement management, a cost-benefit analysis helps the decision-making process to evaluate and compare different project alternatives and options and to prioritize projects based on their expected economic returns; this approach is called prioritization. More advanced optimization methods are described in [20; 21]. Both the herein described and derived polynomial mechanistic-empirical pavement deterioration model and an older exponential empirical pavement deterioration model were used in the LCA and the LCCA to calculate road user costs for both \"do nothing\" and \"do something\" scenarios for both PPMs. The user costs form the socio-economic context of the construction; these costs are not directly translated into the budget of the public management organization, the state budget, or the profit and loss account of the private entity. Nevertheless, in transport construction, they constitute the majority of construction costs. These are primarily costs to users of the road network. These costs are higher when using a road of a lower technical level, i.e., when driving on a damaged carriageway. Here, the impact of both PPMs can be observed as both evaluated PPMs produce different road user costs during the pavement life. The comparison of the LCCA for both scenarios and both PPMs produces the CBA results, i.e., economic efficiency indicators to assess the impact on road administrator's decision making in pavement management operations.