esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Over the last few years, the dealcoholization of wine has piqued the interest of winemakers and researchers. Physical dealcoholization methods are increasingly used in the dealcoholization of wines because they can partially or completely reduce the alcohol content of wines. This study aimed to compare the chemical parameters, volatile composition and sensory quality of white, rose and red wines dealcoholized by two physical dealcoholization methods: reverse osmosis (RO) and vacuum distillation (VD) at 0.7% \(v/v\) ethanol. RO and VD effectively reduced the ethanol concentration in all wines to the required 0.7% \(v/v\), but also significantly affected most chemical parameters. The pH, free sulfur dioxide, total sulfur dioxide, and volatile acidity decreased significantly due to dealcoholization by RO and VD, while reducing sugars and total acidity increased significantly. VD resulted in higher color intensity, which was perceptible in dealcoholized rose and red wines, while RO caused notable color differences in dealcoholized white and red wine fractions. RO were richer in esters (more ethyl esters and isoamyl acetate), higher alcohols, organic acids, terpenics and C\({}_{13}\)-norisoprenoids, and carbonyl compounds, while wines dealcoholized with VD had lower levels of these volatile compounds, which may reflect both the loss of esters into the distillate during evaporation and condensation (in the case of VD) and a shift in the chemical equilibrium responsible for ester formation and hydrolysis after ethanol removal. \(\beta\)-damascenone exhibited the highest OAV in all wines, however, losses equal to 35.54-61.98% in RO dealcoholized fractions and 93.62% to 97.39% in VD dealcoholized fractions were observed compared to the control wines. The predominant aroma series in the original and dealcoholized wines were fruity and floral but were greatly affected by VD. Sensory evaluation and PCA showed that dealcoholization by RO improved the fruity and floral notes (in rose and red wines), color intensity, sweetness, viscosity, and aroma intensity better than dealcoholization by VD, while VD mainly enhanced the color of the dealcoholized wines. Both methods increased the acidity of the respective dealcoholized wines. Nevertheless, RO dealcoholized wines achieved higher acceptance by the panelists than VD dealcoholized wines. Therefore, RO may be a better method for producing dealcoholized (0.7% \(v/v\)) wines with minimal impact on aroma and sensory quality.  
alcohol-free wine; chemical parameters; dealcoholization; membrane; non-alcoholic wine reverse osmosis; sensory characteristics; vacuum distillation; volatile compounds +
Footnote †: journal:
## 3 Proposed Optimization L-Control Strategy  
### Problem Formulation  
This section provides an in-depth description of a PHEV's studied energy management problem using an optimization-based control strategy. Considering the RL-based control strategy limitations, such as the need to have a good knowledge of the system operation and its sensitivity to several parameter fluctuations, the optimization-based EMS methods have more ability to track the optimum solution with its two basic options, i.e., global offline or online optimization (real-time optimization RT) [31].  
Global offline optimization is most often achieved using dynamic programming, which is a time-consuming and thus cost-intensive numerical method or a faster method based on Pontryagin's Minimum Principle (PMP) but with limitations in case of state variable saturation. These algorithms are used to evaluate the system performance, though the outcome is intended as a benchmark for the other real-time concepts and can be adapted to operate in this time scale [32].  
For this purpose, the chosen algorithm for the EMS problem resolution in this work is the so-called \(\lambda\)-control which has been proven to ensure a good distribution of torques minimizing fuel consumption and pollutant emission. The \(\lambda\)-control is thus adapted from [PERSON]'s minimum principle in order to be reusable in real-driving conditions.  
The basic idea is to approximate a one-time variable known as the Lagrange multiplier, on which depends a function H designated as the Hamiltonian, which is defined as a relation of the criterion to be minimized J, generally function of the control variable U, the system state variable \(x(t)\) as well as the multiplier \(\lambda\). This function H has to be minimized by choosing the appropriate control variable U, yielding thus to a near-optimal solution. Appendix B presents more details about [PERSON]'s minimum principle (PMP) [32].  
In this context, the aim of the strategy is:  
* To ensure optimal control achieving the minimum of the criterion chosen (see below).
* To guarantee good PHEV-SoC battery management
* To respect the drive-train components' energy constraints.
* To attain the transition from charge depleting mode (CD) to charge sustaining (CS) if the SoC reaches its minimum discharge limit value in order to keep it around there based on rule-based determined control; (If SoC reaches a predefined threshold, then, a certain selected mode is active).  
To tackle this optimization problem according to the \(\lambda\)-control concept, a set of EMS steps must be identified in terms of the state variable to be controlled, the criteria that should be minimized, the control variable, and the constraints to be respected.
# Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method
## 3 Results
### Electrical Propagation in a 2D Tissue Sheet  
We considered a \(5\times 5\) cm human ventricular tissue sheet, where transmural heterogeneity was included by defining endocardial, midmyocardial, and epicardial regions in a 50:20:30 ratio. The cardiac fiber direction vector \(\mathbf{I}\) was considered parallel to the \(x\)-axis. We used diffusion coefficient \(d_{0}=0.0013\) cm\({}^{2}\)/ms and transversal-to-longitudinal conductivity ratio \(\rho=0.2\). Stimuli with amplitude twice the diastolic threshold, period \(t_{T}=1\) s, and duration \(t_{d}=1\) ms were applied on the left side of the tissue (\(x=0\) cm). The propagation of the action potential (AP) was simulated for a total time \(t_{s}=3\) s.  
We compared the MCM solution with RPI and MKI trial functions against FEM simulation results using bilinear isoparametric elements. We considered regular nodal discretizations and quadrilateral meshes with nodal spacing \(h=\{0.2,\ 0.1,\ 0.05,\ 0.025\}\) cm. The considered support domains in the meshfree approximation had size \(s_{d}=\alpha_{sd}h\), with \(\alpha_{sd}=2.8\). The generated APs at the center of the tissue sheet (\(x=2.5\) cm, \(y=2.5\) cm)in the time interval \(t=[0-3]\) s for the different nodal spacing values are shown in Figure 1. We quantified the differences between MCM and FEM solutions in terms of mean transmembrane potential difference (TPD). Mean TPD between the FEM and the MCM with MKI trial functions was TPD = {3.111, 0.339, 0.401, 0.583} mV while mean TPD between the FEM and the MCM with RPI trial functions was TPD = {3.112, 0.340, 0.400, 0.582} mV for nodal spacing \(h=\{0.2,\ 0.1,\ 0.05,\ 0.025\}\) cm. The efficiency of each simulation was evaluated in terms of execution time in Figure 2a.
### Types of Antenna Systems Used in Underground Mining  
In underground mines, multipath fading is a major factor that degrades communication quality. Multipath fading occurs when a transmitted signal travels through numerous pathways to the receiver; it might interfere with itself, resulting in signal attenuation or cancelation [22]. The multipath fading can be reduced by optimizing the antenna structure and characteristics of the radiated electromagnetic signal to interact constructively with the surrounding environment and generates fewer multipath signals [23]. Different antennas were proposed for different applications in underground mines to reduce multipath fading and enhance the coverage area, as discussed below. Low-profile directional antennas and smart antenna systems such as Butler matrices are employed to improve gain and reduce the impact of multipath fading by focusing signals in desired directions [24]. Furthermore, the use of adaptive beamforming techniques in these antennas has been shown to further minimize the detrimental effects of multipath and enhance communication reliability in underground environments [25].
## 3 Performance Verification and Results  
### Performance Verification  
#### 3.1.1 Head Rotation Validation  
To validate the proposed equation model, we used a motion sensor mounted in a 3D Virtual Reality (3D-VR) Head Mounted Display (HMD) manufactured by FOVE(r). The target was to use the inbuilt inertial motion unit (IMU) to formulate a reference. Similar results would be achieved with an IMU attached to the head. In this case, we designed a simple scene with equidistant objects placed on the left and right sides of the virtual scene and the reference point in the center. The user was to turn their head to view each object sequentially from the center (reference point), holding for at least 1 s at each point in the VR environment. Figure 4(a) shows the raw signal from the neck EMG signal generated when turning the head in a VR environment. Two left turns and two right turns were performed (weak and strong neck turns).  
Figure 4(b) shows the estimation results using the equation and the ML model. The ML model output (yellow line) is factored with twenty-five of the three classes of 1, 0, and \(-1\) for easy visibility. When the signal is weak, as it was in the first two turns (Figure 4(b) between 3-15 s), the equation model reports inconsistent estimates due to the stochasticity of the resulting EMG. This is also the case on a strong neck turn where the initial estimate reached a value of less than \(-100\) before settling down (around \(-40\)). The ML model, on the other hand, is capable of accurately delivering consistent output regardless of signal strength differences.
## 3 Results  
### Optimization of the Electric Fields of the LaGEMPix  
The transfer, drift and induction fields were optimized to maximize the absolute value of the anode current. Initially, only the value of the drift field was modified, keeping the other fields fixed. The current was measured to be almost stable for drift field strengths in the range 0.5-1.25 kV/cm (Figure 4). The measurements were repeated, in turn varying one of the electric fields while keeping the others constant. A drift field of 1 kV/cm was selected. The optimization procedure showed a dependence of the anode current on the applied voltages. The optimal values are as follows: a transfer field between GEM1 and GEM2 of 2.0 kV/cm and a transfer field between GEM2 and GEM3 of 1.75 kV/cm. It was established that the absolute value of the current increases as a function of the induction field. In order to achieve an efficient and safe operation of the detector, the initial value of the induction field was not changed, keeping it at 5 kV/cm.  
We repeated the drift field intensity scan with the final configuration. Figure 4 shows a comparison of the anode current before and after the optimization of the fields. For a drift field of 1 kV/cm, the maximum value of the current is 273 nA (absolute value), 180 nA higher than the absolute value measured with the initial settings of the fields.  
The observed behavior of the drift and transfer field scans is in agreement with the results published by [PERSON] et al. (2018). The difference in the values of the two transfer fields can be explained by the asymmetry of the transfer gaps (1 mm and 2 mm). By applying the optimized configuration to the drift and transfer fields, we measured a 180 nA higher signal compared to the approach with non-optimized fields, representing an increase of the detectable signal by around 150%.
## 4 Analysis of Position-Holding Strategy of Constellation Stations
Before the relative control strategy is controlled, the reference track should be optimized, and the reference track parameters should be updated to reduce the control frequency, which is of high complexity and low reliability. The absolute phase control method is simple and mature, with high reliability. Relative phase control is more suitable for constellations with fewer satellites. Absolute phase control is recommended for large constellations. Table 1 shows a detailed comparison of the control strategies.
## 2 Authentication in the Smart Grid (SG) Network
### Authentication Types in the Smart Grid (SG) Network
#### 2.1.3 User-to-Network/Device Authentication  
Users play an important role when initially setting up the devices and the network. It is important as the malicious devices and forged connections can reveal the private information of users. This can be achieved by setting up a user password to each device and the network so that users have control over these entities. [PERSON] _et al._[19] proposed an authentication scheme for a mobile RFID (radio frequency identification)-based SG network, but the scheme generates memory overhead and incurs additional cost.
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
## 1 Introduction
Thus, the model analyzes the energy impact of each type of energy for optimization of the algorithm in various protocols of the literature. These protocols and the energy model are implemented in an event simulator programmed in C++. The proposed energy model is implemented in the simulator for each routing protocol to observe the impact of the performance parameters and how they influence energy consumption in each protocol. This proposal resembles the MPH routing protocol [10], which is a hybrid that combines proactive and reactive protocols and energy conservation properties at the same time. It is used in this work as a protocol with hierarchical topology for optimizing the sending of information, maintaining a smaller number of routes, and monitoring them to determine whether they remain valid or have become obsolete [23]. Figure 1 shows a conceptual scheme for a hierarchical topology with a sink node or information collector. This scheme is widely used in WSN for low power consumption [24]. The problem described here is that energy is not distributed equally throughout the topology and there are different energy levels called crowns [25]. Energy increases in the nodes that form information bottlenecks. Therefore, in this work we classify the types of energy to establish compensations in the busiest nodes of the network. This scheme overviews the energy consumption of a many-to-one network. The coordinator or sink node is the sole destination of the network nodes (there may be more than one coordinator node), which makes it a single failure point. The nodes send traffic to the coordinator node. As the nodes are closer to the coordinator, they have to forward traffic from other network nodes. Therefore, with this scheme, we want to represent an ideal scenario of energy behavior of the nodes from the coordinator node to the nodes farthest from it. Being the darkest color the higher energy consumption nodes and the lightest, the lower energy consumption ones.
## 4 Discussion
### Discussion of Aerosol Contamination Protection Using Sentinel Data  
The usefulness of Sentinel 5p data was analyzed on the mesoscale and synoptic scales. The case study of Saharan dust movement over the Mediterranean to the European continent confirms the possibility of using Sentinel 5p data on a synoptic scale. This case study was chosen because the aerosol origin was well known, and it was possible to confirm that highly positive AI values indicate the presence of a high concentration ofdust in the atmosphere. The desert dust belongs to the group of aerosols characterized as PM\({}_{10}\) particles.  
The case study on the mesoscale includes faults in the power network in one part of Croatia. When the entire area was observed as a whole, there was no significant difference in the correlations between the overall dataset and the dataset when only dates with detected outages were isolated. When the research was more local (e.g., the surroundings of one air-quality station), results showed a better correlation for the situations with faults appearance. In further modeling, it should be kept in mind that the COVID-19 pandemic took place during the period of analyzed data. In that period, anthropogenic aerosol was decreased, and a smaller number of faults in the observed segment of the power network was recorded.  
Additionally, a comparison of the surface measurements of PM\({}_{10}\) particle concentrations with AI and LH data showed that those data may serve as a tool in a surface air-quality measurement data control to confirm the high concentration of PM\({}_{10}\) particles or cast doubt on something else. Also, it may help to determine the prevalence of aerosol contamination in the area of interest where there is a lack of surface air-quality stations.
## 5 Conclusions
The differences and errors in measuring precipitation amounts at multi-temporal scales demonstrate the importance of performance evaluations and allow other investigators to select an appropriate product/s according to their specific requirements. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14194773/s1](https://www.mdpi.com/article/10.3390/rs14194773/s1)]([https://www.mdpi.com/article/10.3390/rs14194773/s1](https://www.mdpi.com/article/10.3390/rs14194773/s1)), Figure S1: Annual average precipitation in JRB measured from five GPPs with reference gauges; Figure S2: (a) Temporal precipitation distribution estimates derived from five GPPs and reference gauges at monthly scale. (b) Seasonal precipitation distribution derived from five GPPs and reference gauges in JRB during the entire study period; Table S1: Geographical information of in-situ gauging stations used in the current study; Table S2: The statistical metrics for performance evaluation of GPPs in contrast to in-situ gauges data during the entire study period 1981-2009 in five SBs at daily and winter (season) scales; Table S3: The statistical metrics for performance evaluation of GPPs in contrast to in-situ gauges data during the entire study period 1981-2009 in five SBs together with pre-monsoon and monsoon seasons. **Author Contributions:** Conceptualization, S.A. and Y.C. ; Methodology and data curation, S.A., P.M.K. and A.G.; Software and Visualization, S.A., P.M.K. and J.Q. ; Formal Analysis and writing of the original draft preparation, S.A.; Reviewed and editing previous draft, M.A., R.M., Z.A. and A.T.; Supervision and Funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This study is supported by the National Natural Science Foundation of China (Grant No. 52161145102). **Data Availability Statement:** The data used and analyzed in this study is included in this article. All relevant processed data are accessible from authors upon request. **Acknowledgments:** Authors would like to thank the Water and Power Development Authority (WAPDA) and Pakistan Meteorological Department (PMD) for providing in-situ gauges data of Jhelum River Basin. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussions  
### Thermodynamic Model Validation  
For the present model, the following considerations were taken into account which delimit the scope of the results and govern the phenomenology of the process:  
* All the equipment in the cycle are thermally insulated;
* To ensure that the RORC studied works under steady state conditions, it is assumed that the coupling flow in the oil cycle compensates for the temperature changes in the combustion gases;
* Pressure changes in the heat exchangers are considered as a function of the geometry and hydraulic characteristics of the fluid, whereas in the pipes they are not considered as pressure changes.  
To validate the RORC model studied in this document, the results of two geothermal applications were taken as reference [38; 50]. The parameters of both investigations are shown in Table 3.  
A comparison of the results obtained for the model proposed in this research with the referenced results are shown in Table 4. For the three thermodynamic models, there is a good fit of the parameters calculated in this work and those published previously [38; 50], and in the case of thermal efficiency an absolute deviation of 0.68% and 0.67%, respectively, was obtained. For the exergetic efficiency, the absolute deviation was 0.27% in both cases, and therefore this model can be used for the evaluation of this configuration operating as a heat recovery system.
## 3 Results
The red line is theoretical profile, the blue curve is estimated mean profile, the dashed black curves are the relative standard deviation. Figure 8 presents statistics for the case with a 90 degrees beam-to-flow angle and 0 degrees rotation angle for all 11 planes along the vessel. The RSD varies between 1.09% and 1.41% for \(v_{x}\), between 0.89% and 0.99% for \(v_{y}\), between 0.39% and 0.54% for \(v_{z}\) components of the flow. The RB for \(v_{x}\) varies between 0.67% and 2.72%, between \(-1.04\%\) and \(-0.53\%\) for \(v_{y}\) and for \(v_{z}\) component is between \(-7.23\%\) and 6.75%. The \(v_{z}\) component was estimated using the full Hann apodized aperture. This effectively introduces an angle between the axis of the array and the line used to estimate the \(v_{z}\) component, which causes a bias in the estimates. Figure 9 shows the RSD and the RB for 60 degrees beam-to-flow angle and 45 degrees rotation angle for all 11 cross planes along the vessel. The RSD varies between 4.61% and 4.95% for \(v_{x}\), between 4.81% and 6.29% for \(v_{y}\), between 0.88% and 1.06% for \(v_{z}\) components of the flow. The RB for \(v_{x}\) varies between \(-13.52\%\) and \(-9.04\%\), between \(-9.09\%\) and 3.08% for \(v_{y}\) and for \(v_{z}\) component is between 1.76% and 14.65%. On both Figures 8 and 9 the RB varies for the \(v_{z}\) component with position in the imaged volume. Figure 7: RSD and RB for simulations with (90, 75, 60) degrees beam-to-flow anlges and 45 degrees rotation angle. The dashed lines show the relative standard deviation, the solid lines show the relative bias. The red color is \(v_{x}\), the black is \(v_{y}\) and the blue is \(v_{z}\) components of the flow.