esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Brief Introduction tokineros 2
#### 3.4.2 Application to Aviar Basin  
To delineate a basin, we must define the study basin boundary. First, the modeler has to populate the DEM (Digital Elevation Model) to avoid calculation errors. The second step is to define the direction and accumulation grid of flow. Both can be created either from GIS or from KINEROS 2.  
The next step is basin discretization. KINEROS 2 creates flat elements using the calculated basin delineation. Basin discretization is based on a Contribution Area or CSA, which is defined by the KINEROS 2 user. This is the minimum area required to channel the water in a channel. The lower the CSA value, the higher the number of sub-basins. The KINEROS 2 default value for CSA is 2.5%. There are no recommendations about the value to select, although some considerations must be taken into account: small areas create more geometric components, and the CSA value may have to be adjusted to ensure that the results table for planes and streams does not result in null values appearing in the attribute tables [3].
## 2 Methods and Simulations  
Figure 1 depicts the multi-channel, time-wavelength interleaved photonic analog-to-digital converter (PADC) system used for measurements. A multi-wavelength sam  
pling pulse source with equal spacing can be generated by appropriately modulating a continuous-wave laser through cascaded intensity modulators and phase modulators [25]. An erbium-doped fiber amplifier (EDFA) is used to amplify the optical sampling pulse power loaded in the Mach-Zehnder modulator (MZM), ensuring its operation under good conditions. Meanwhile, the RF signals are loaded by the MZM. The sampling optical pulses modulated with RF signals are demultiplexed by a wavelength division multiplexer (WDM) and then converted into parallel N channels of single wavelength optical signals with a pulse frequency of \(f_{s}\), which equals to or is less than the sampling rate of the back-end electronic analog-to-digital converter (EADC), with the benefits of a mature production process and stable performance. Due to the possible delay changes of different wavelength channels after applying the WDM and other optical and electrical elements, the N-channel optical pulses are aligned with the EADC through a tunable delay line (TDL), making sure the EADC collects the peak values of optical pulses within one period, where \(t_{s}=1/f_{s}\). So, the delay between each channel is an integer multiple of \(t_{s}\). In addition, due to the different channel insertion losses caused by the WDM and other components, the optical intensity is relatively compensated through a tunable fiber optic attenuator (TFOA), which also ensures that the optical pulse intensity is located in the linear operating region of the PD. Then, the optical signal is converted into an electrical signal through the PD, amplified by a low-noise amplifier (LNA), keeping it in the optimal working state of the EADC.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 8 Effects of Ohmic Heating on Inactivation Microorganisms in Food
Table 3 represents the effects of ohmic heating on food products for eliminating microorganisms: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Food Matrix** & **Microorganisms** & **Treatment Conditions** & **Inactivation Effects** & **References** \\ \hline \multirow{2}{*}{Orange juice} & \multirow{2}{*}{Bacteria, yeasts, molds} & 50 Hz; 90, 120, 150 \({}^{\circ}\)C; 1.13, & Complete inactivation of bacteria, yeasts, and & [18] \\  & & 0.85, 0.68 s & & molds \\ \hline \multirow{2}{*}{Cloudberry jam} & \multirow{2}{*}{_Bacillus licheniformis_} & \multirow{2}{*}{50 Hz} & D-values lower for OH than & \\  & & & for thermal treatments; & \\  & & & z-values 11.4 and 11.1 \({}^{\circ}\)C & \\ \hline \multirow{4}{*}{Milk} & \multirow{4}{*}{Viable aerobes} & 20 kHz; current varied from 7.3 to 2.0 A (voltage from 70 to 12 V) during the heating and holding phases, respectively & D-values for ohmic samples & \\  & & & (8.64, 6.18, 0.38 min) were & \\  & & & lower than those for & \\  & & & heating and holding phases, respectively & \\ \hline \multirow{2}{*}{Meat} & \multirow{2}{*}{_Listeria innocua_} & 50 Hz, 8.33 V/cm, 95 \({}^{\circ}\)C, 7 min & Bacteria inactivated to non-detectable & [81] \\ \hline \multirow{4}{*}{Tomato soup} & \multirow{2}{*}{_Geobacillus_} & 60 Hz and 10 kHz; 0, 15, 30, & More than 5-log reduction in & \\  & & 90 s for 125 \({}^{\circ}\)C; 0, 5, 10, & _Geobacillus steaothermophilus_ & \\  & & 30 s for 130 \({}^{\circ}\)C & spores & \\ \hline \multirow{4}{*}{Tomato juice} & \multirow{2}{*}{_E. coli_ O157:H7,  Salmonella Typhimurium, Listeria_} & 60 Hz; 90 s; 20 V/cm60 Hz; 180 s; 15 V/cm 60 Hz; 480 s; 5-log reduction in all bacteria & [83] \\  & & 10 V/cm & \\ \cline{1-1}  & & _monocytogenes_ & \\ \hline \multirow{4}{*}{Tomato juice} & \multirow{2}{*}{_Salmonella Typhimuriumium_} & \multirow{2}{*}{25–40 V/cm, 76 \({}^{\circ}\)C, for 30 s} & 5-log reduction in bacteria population & [84] \\ \cline{1-1}  & & & population & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies of ohmic heating effects on inactivation of microorganisms.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 3 An Optimization Problem to Maximize SE
### Optimization Problem  
According to the objective function and constraints, the optimization problem to maximize the SE of OBON is formulated as  
\[\max_{\phi,k_{nc},P_{k_{nc}}}D=\sum_{n_{c}=0}^{N_{c}-1}\left[\log_{2 }\left(1+\frac{P_{1_{nc}}\left|l_{n_{c},k_{nc}}\right|^{2}}{P_{2_{nc}}\left|l_{n_{ c},k_{nc}}\right|^{2}+\sigma^{2}}\right.\right)+\log_{2}\left(1+\frac{P_{2_{nc}} \left|l_{n_{c},k_{nc}}\right|^{2}}{\sigma^{2}}\right)\right], \tag{24}\] \[\text{s.t.}\quad\mathcal{C}1-\mathcal{C}4.\]  
It is found the optimization problem (24) is a non-convex problem and hard to solve, due to the following reasons:  
1. The objective function \(D\) contains non-polynomial elements, leading to the non-convexity of the objective function.
2. Both \(\mathcal{C}3\) and \(\mathcal{C}4\) constraints are non-convex.
## 2 Materials and Methods
### Multiple Regression Analysis
#### 2.2.1 Application of Multiple Regression Analysis  
Figure 3 shows that the dependent variable is profit/type and the independent variables used in this study consisted of seven variables.  
Table 2 shows that the multiple regression is 0.968, which shows that the correlation between the independent and dependent variables is positive, which is usually that the value R\({}^{2}\) range will be 0 \(<\) R\({}^{2}\) \(<\) 1. The coefficient of determination from regression result is 0.937. This means that the combined influence of seven independent variables can explain 93.7% of profit variations.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Regression Statistics** \\ \hline Multiple R & 0.968 \\ R Square & 0.937 \\ Adjust R Square & 0.927 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model summary.  
Figure 3: Dependent variable code and independent variables code of multiple regression analysis in research.  
Table 3 shows the results from the multiple linear regression by displaying the results of the coefficients from each independent variable, where the intercept value is \(-\)384,364.26. Then, it represents the value of Y when the values of all independent variables are zero. Moreover, according to hypothesis testing, this study was assigned a 95% confidence level, which means that the \(p\)-value output of variables will not exceed 0.005. As shown in Table 3, there are two variables with a \(p\)-value \(<\) 0.005, which are land cost and house building cost. They are variables that yield significant value in this multiple linear regression analysis. As a result, land cost has the highest influence on profit or the dependent variable, followed by the house building cost. In Table 3, the multiple regression equation for the profit of each type of house is elaborated. Then, Equation (3) was obtained, and the researchers applied this equation in the next step of research, which is the optimization model.  
\[\text{Profit}/\text{type}=-384,364.26+1.486\times\text{(Land cost/type)}+0.919 \times\text{(House building cost/type)} \tag{3}\]  
2.2 A Graph Comparing the Predicted Profit/Type Values from the Regression Models with the Actual Profit/Type Values  
Figure 4 shows the comparison between actual data and the result of the predicted equation from the multiple linear regression analysis. Moreover, it was found that the prediction equation can predict the result of profit/type quite accurately, with 93.73%, or the coefficient of determination (R2) = 0.9373.
## 3 Materials and Methods
### Physico-Chemical Analysis  
For the analysis of pH, electrical conductivity, and hydroxymethylfurfural content, samples were prepared by a 1:5 dilution with ultrapure distilled water from a Millipore Milli-Q lab (Merck KGaA, Darmstadt, Germany) water system. Honey samples were thoroughly mixed to ensure homogeneity, after which a 5 g aliquot was taken and dissolved in 25 mL of water. For the pH determination, the honey solution was measured using a PHM 210 Standard pH Meter (MeterLab, Radiometer Copenhagen, Laan van Westenenk 50, Apeldoorn, The Netherlands), which was previously calibrated with standard pH 4 and pH 7 solutions. The same diluted solution was used for the electrical conductivity (EC) measurement using a conductometer (Conductimeter GLP 31 CRISON, Barcelona, Spain) calibrated with appropriate standard solutions. The results were expressed in mS/cm. The maximum EC for honey is 0.8 mS/cm according to Italian law (Directive 2001/110/EC), while for honeydew, multifocal/mixed, and chestnut honey, the EC values must be greater than 0.8 mS/cm.  
Hydroxymethylfurfural (HMF) was quantified following the HPLC method, which had been previously described in other studies with a few adjustments in accordance with Italian legislation guidelines [29; 30], 5 g of each sample honey was diluted with ultrapure distilled water (1:5) and mixed. Then, within 12 h, samples were filtered on a 0.45 \(\upmu\)m syringe filter, and 20 \(\upmu\)L were injected into the HPLC system (Azura, Knauer, Berlin, Germany) coupled to a UV detector (Analytical UV Flow Cell Detector UVD 2.1S, Knauer, Berlin, Germany). The chromatographic column was Eurospher II 100-5 C18 150 \(\times\) 4 mm, and the analysis conditions were: isocratic mobile phase, water-methanol 90:10 \(v/v\); flow rate 0.6 mL/min; column temperature 30 \({}^{\circ}\)C. The detector wavelength was fixed at 285 nm, the identification of HMF was carried out by comparing the retention time of standard solution, and the quantification was performed using a calibration curve specific for each molecule (Figure 1). The calibration curve for HMF was made with five solutions at different concentrations (0.0005, 0.005, 0.01, 0.05, and 0.1 mg/mL. According to the law, the results were expressed in mg/kg, and the legal limit for HMF in commercial honey is 40 mg/kg.  
Figure 1: Calibration curve of HMF.  
The water content of the honey samples was determined with a handheld refractometer (HHTEC) with automatic temperature compensation. The samples were measured as-is, and the results are expressed as moisture content percent. The legal threshold for selling honey is 20%, but in competitions for premium/quality honey, the limit is usually lowered to 18%.  
The brix degrees of the honey samples were measured with the same refractometer for moisture content measurement. Brix degrees represent the percentage of sugar content in honey by weight, with 1 Brix degree equivalent to 1 g of sucrose in 100 g of solution [31].
## 3 Results and Discussion
### Heavy Metal immobilization Percentage
Given the structural resemblances between sword bean protein and soybean protein, it is reasonable to assume that the metal-binding capabilities of proteins in sword bean urease may present a similar trend in this study. Figure 6 shows the variation in the immobilization percentage of each heavy metal when Ca\({}^{2+}\) is added. In GT-ZnO.25/0.5, GT-Ni0.25/0.5, and GT-Cr0.25/0.5, it is evident that heavy metal immobilization percentage increased with higher concentrations of Ca\({}^{2+}\). The highest heavy metal immobilization percentages were achieved with 0.5 M Ca\({}^{2+}\), followed by the groups with 0.25 M Ca\({}^{2+}\), while the groups without Ca\({}^{2+}\) showed the lowest immobilization percentage. This indicates that Ca\({}^{2+}\) plays a role in heavy metal immobilization. In GT-ZnO.25/0.5, -Ni0.25/0.5, and -Cr0.25/0.5, the final immobilization percentage of Zn\({}^{2+}\), Ni\({}^{2+}\), and Cr(VI) was up to 99.9%/99.9%, 43.24%/64.2%, and 18.66%/24.4%, respectively. This presents an increase of 74%, 32.54%/53.5%, and 9.86%/15.6% compared to GT-Zn0, GT-Ni0, and GT-Cr0. However, in GT-multiple/0.25/0.5, the highest immobilization percentage of each heavy metal was achieved in GT-multiple0.25, followed by GT-multiple0.5, and then GT-multiple0. This may be due to the greater amount of calcium carbonate generated in GT-multiple0.25 compared to GT-multiple0.5. Calcium carbonate has been confirmed as a metal adsorbent through physical adsorption or co-precipitation (42; 43; 44). The more calcium carbonate generated, the higher the potential for heavy metal absorption. Therefore, calcium carbonate adsorption may be considered one of the immobilization mechanisms in EICP. Additionally, in GT-ZnO.25/0.5, GT-Ni0.25/0.5, GT-Cr0.25/0.5 and GT-multiple0.25/0.5, the EICP method was most effective in immobilizing Zn\({}^{2+}\), followed by Ni\({}^{2+}\), with Cr(VI) having the lowest immobilization percentage. This phenomenon may be attributed to the different adsorption abilities of calcium carbonate for various heavy metal ions. Zn\({}^{2+}\) can be efficiently immobilized due to the notable adsorption capabilities of calcium carbonate (45). Ni\({}^{2+}\) can be partially removed through adsorption or co-precipitation with calcium carbonate (46; 47). However, the adsorption capacity for Cr(VI) onto calcium carbonate decreases as pH increases, reaching its peak at pH = 2 (48). Therefore, the lowest adsorption capacity of Cr(VI) was observed. The subsequent XRD microscopic analysis (see Section 3.5) also confirmed that no CaCrO\({}_{4}\) was detected, indicating that Cr(VI) immobilization was not by forming CaCrO\({}_{4}\) precipitates. Additionally, the immobilization percentage of each heavy metal in GT-multiple was lower than that in GT-Zn, -Ni, and -Cr. This decrease is possibly due to the reduced urease activity in GT-multiple, which in turn influenced urea hydrolysis, pH, and calcium carbonate formation, thereby limiting heavy metal immobilization.
###### Abstract  
This study focuses on the fabrication of plasma-polymerized polyethylene glycol (pp-PEG) with porous morphology in a pulsed dielectric barrier discharge (DBD) plasma under atmospheric pressure. The signal frequency that modulates the plasma discharge was found to have a major influence on the pp-PEG film morphology. The recorded discharge current-voltage characteristic allowed us to establish a homogeneous regime of the DBD plasma operated in helium gas flow upon the frequency range 2-10 kHz. The as-prepared pp-PEG films were characterized by the Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and liquid-phase chromatography (HPLC) techniques. The performed analysis revealed that as the discharge frequency increases, the morphology of the obtained films becomes porous due to the plasma-induced stronger monomer fragmentation. To gain knowledge about the plasma species and the interaction processes that impact the film morphology, optical emission spectroscopy (OES) and intensified charge-coupled device (ICCD) fast imaging technique were applied. The determined vibrational (T\({}_{\text{vib}}\)) and rotational (T\({}_{\text{rot}}\)) temperatures exhibit a decrease with the introduction of monomer vapors into the discharge gap. For instance, T\({}_{\text{rot}}\) drops from approximately 475 K to 350 K, and T\({}_{\text{vib}}\) falls from 2850 K to 2650 K for a monomer vapor injection rate of 16 \(\mu\)L/min. This was attributed to the energy losses of the plasma-generated particles, as the inelastic collisions augment with the injection of a monomer. Concurrently with the change in temperature, the discharge current varies significantly for the investigated frequency range and exhibits a drop at high frequencies. This discharge current drop was explained by an enhancement of the recombination rate of charged particles and seems to confirm the prevalence of a plasma-induced monomer fragmentation process at high frequencies.  
atmospheric-pressure plasma polymerization; dielectric barrier discharge; porous polymer-like films +
Footnote †: journal: Applied Sciences
# Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
## 3 The Augmented Digital Control Room
### The Digital Control Room  
In another project in collaboration with the company Next Vision GmbH and the HSW University of Applied Sciences, we have introduced the digital control room [20]. The digital control room aims at supporting the monitoring of a shop floor, predictive maintenance of the equipment, and optimizing the production. It is based on the digital boardroom and extends it by integrating the Open Platform Communication Unified Architecture (OPC UA) protocol, which is a widely recognized standard for interoperability and data exchange [22]. The OPC UA integration communicates with an OPC UA server through a publisher/subscriber pattern, cf. Figure 4. Several machines can be connected to one OPC UA server. Through this protocol, we have introduced a real time data source, which we additionally use to store the manufacturing data in a special NoSQL time-series database [20].  
Figure 4: The Digital Control Room: Integrating the OPC UA Protocol.  
Figure 3: Multitouch and Multiuser Interaction.  
In this way, operational as well as manufacturing data can be integrated in the digital control room and holistic analyses can be performed. In particular, real-time or near real-time data can be visualized and interacted with through annotation dashboards. For this, we have designed special history functionality for the user interface, which allows users to scroll in a time series of data and annotate it with respect to the time dimension. Human-created semantic annotations are captured as an additional data source.
# Ten Years of TerraSAR-X Operations
## 10 Conclusions  
A decade of successful TerraSAR-X operation has proven that the basic design of the overall system, consisting of space and ground segment, is both robust and flexible enough to meet changing requirements and to fulfill the desires of the scientific and commercial community during such a long period of time. Also, thanks to the great commitment of the ground segment team, all of the modifications could be made without any particular disruption or interruption of operations. The interaction of these factors has enabled ensuring the successful use of the mission by scientists and commercial users for more than ten years. This will continue to be the case since radar performance and calibration of the individual satellites is still well within specification, and no indication of any degradation is noticeable, a fact that is remarkably reflected in the consistently excellent quality of the SAR products. From today's point of view, the satellites and the ground segment are operating without restrictions and the irretrievable resources, specifically the propellant and the battery capacity, allow for operating both satellites for another five years.  
[PERSON] assumes the role of the Mission Manager performing administrative and organizational tasks, he monitors the system changes and wrote Sections 1 and 10. [PERSON] is the Ground Segment Systems Engineer. She coordinates and tests the technical system changes and wrote Sections 2, 4, and 9. [PERSON] is in charge of the SAR product portfolio and user product quality, he wrote Section 3. [PERSON] managed the commissioning phase and conceived experimental new SAR modes, he wrote Sections 5.2-5.5. [PERSON] is in charge of all flight dynamics and orbit control aspects, he wrote Section 7. [PERSON] is the manager of the Mission Operations Segment and in charge of the satellite operations, she wrote Section 6. [PERSON] is the manager of the System Engineering and Calibration Segment and in charge of the performance monitoring, [PERSON] is in charge of the overall SAR performance and  
Figure 18: Map visualization of Stripmap left-looking data takes over Antarctica.  
image quality, they wrote Section 8. [PERSON] developed the Mission Planning System in order to generate executable acquisition timelines, he contributed to Sections 2, 4, and 6. [PERSON] manages the Neustrelitz ground station and supervises the SAR operations, he contributed NRT aspects in Section 2. [PERSON] is in charge of the SAR image processing, respectively the TerraSAR-X Multi-mode SAR Processor (TMSP) and contributed to Section 3. [PERSON] is in charge of the commanding and monitoring of the radar instrument, he implements command sequences for even the weirdest new SAR modes and wrote Section 5.1.  
**Acknowledgments:** The authors would like to thank the entire TerraSAR-X/TanDEM-X team, which ensures the smooth running of the mission and enables the scientific and commercial exploitation of the results.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 4 Future Prospects  
Nowadays, smartphone devices are more pervasive, user-accepted and powerful than ever. A large proportion of people carry their smartphones with them all the time and thus the idea of simple and continuous connectivity is not inaccessible anymore [97]. Mobile health (termed as mHealth) technologies have also experienced a slight change in direction from wearing and/or implanting body sensors to carrying a powerful wireless device with multifunctional capabilities such as a smartphone [98]. Healthcare providers may soon be able to monitor and measure vital signs without the need of on-body and/or implanted sensors (non-contact vital sign monitoring) [99]. For example, researchers from Rice University have been developing a non-contact video-camera system that can precisely monitor and measure temperature, pulse and breathing rate from changes in a patient's skin color [99]. Smartphones can also be independently used for sleep monitoring. For instance, iSleep [100] takes advantage of the built-in microphone to detect the unconscious actions during sleep such as body movement, coughing and snoring, which are closely associated with the perceived quality of sleep people receive. Although, a more complete review of non-contact vital sign monitoring systems is not in the scope of this article, a number of examples of such systems are described in the literature [101; 102; 103]. The rest of this section is categorized into three parts. Part A summarizes a number of major advantages of smartphone-based healthcare applications. Part B considers some challenges of such solutions and finally part C explains the most areas of mHealth research that are expected to grow in the near future.
## 4 Inter-Satellite Link Angle Measurement Simulation Experiment  
From the above analysis, it can be concluded that the beam walk affects the results of the DWS measurements, leading to unnecessary nonlinear errors. Meanwhile, the design employing dual-lens conjugate imaging effectively suppresses the beam walk. To validate the feasibility of this approach in practical usage, an inter-satellite link pointing control system was constructed. Angle measurement data were obtained both with and without the addition of the imaging system, followed by thorough analysis.