esa-sceva/satcom-chunk-collection huggingface dataset:
# Microencapsulation of Tomato
###### Abstract  
Microencapsulation by spray-drying is a process used in the stabilization of active compounds from various natural sources, such as tomato by-products, with the purpose to be used as additives in the food industry. The aim of this work was to study the effects of wall material and spray drying conditions on physicochemical properties of microcapsules loaded with lycopene rich extract from tomato pomace. The assays were carried out with ethanolic tomato pomace extract as core material and arabic gum or inulin as wall materials. A central composite rotatable design was used to evaluate the effect of drying air inlet temperature (110-200 \({}^{\circ}\)C) and concentration of arabic gum (5-35 wt %) or inulin (5-25 wt %) on the antioxidant activity, encapsulation efficiency, loading capacity, and drying yield. SEM images showed that the produced particles were in the category of skin-forming structures. The most suitable conditions, within the ranges studied, to obtain lycopene loaded microparticles were a biopolymer concentration of 10 wt % for both materials and an inlet temperature of 200 and 160 \({}^{\circ}\)C for arabic gum and inulin, respectively. Arabic gum and inulin possessed a good performance in the encapsulation of tomato pomace extract by spray drying. It is envisaged that the capsules produced have good potential to be incorporated in foods systems with diverse chemical and physical properties.  
micro encapsulation; lycopene rich extract; arabic gum; inulin; spray drying; antioxidant activity +
Footnote †: journal: Applied Sciences
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 1 Introduction
Extension of sequential composition axioms, ordering axioms, and relevant invariants based on the foundation of Logic of Events Theory. Introduction of the concept of invariants and proposal of a proof method for verifying the security of composite protocols. 2. Abstraction of the security properties of the DTLS-SRTP protocol using Logic of Events. Formal depiction of the interaction processes of its sub-protocols, DTLS and SRTP, and separate proofs for mutual authentication and confidentiality. 3. Proof of the security of the DTLS-SRTP protocol after sequential composition using the proposed composite protocol proof method. The remainder of this paper is organized as follows. Related work is presented in Section 2. Section 3 introduces Logic of Events Theory, elucidating the properties to be satisfied during protocol analysis and the axioms and rules necessary for proving mutual authentication and confidentiality. Section 4 outlines the composite protocol proof method and introduces the DTLS-SRTP protocol. Section 5 provides a detailed exposition of the security proof for the DTLS-SRTP protocol. Finally, Section 6 offers our conclusions and outlines avenues for future work.
###### Abstract  
Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert-Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action.  
wave erosion; landslide; flume test; pore pressure; seismic signal; acoustic signal; time-frequency analysis Article
## 5 Case Study  
### Case Configuration  
As illustrated by the CFD model in Figure 2, this case study is based on the emergency diesel storage tank of a nuclear power plant, focusing on the design and optimization of the diesel conditioning process. The selected diesel pump is model 65 YHC3-25 (The manufacturer is Botou Hengyun Oil Pump Manufacturing Factory, located in Botou City, China), with key specifications listed in Table 1. The rated flow rate of the pump is 25 m\({}^{3}\)/h, and each nozzle of the storage tank has an actual diameter of 800 mm. After comprehensive evaluation, the pump's maximum inlet velocity is set to PM = 0.014 m/s. The key quality parameters of standard diesel are shown in Table 2.  
A tested sample of aged diesel exhibited an oxidation stability value of 3.0, exceeding the acceptable limit of 2.5. To address this, a specific antioxidant was selected. Experimental data indicated that each 1 ppm increase in antioxidant concentration improves the oxidation stability by 0.001 units. To achieve a target oxidation stability of 2.0, a final antioxidant mass fraction of 1000 mg/kg is required [19]. Based on this, the target volume fraction threshold for the antioxidant at all three monitoring points is set to 0.0698%. When all sensors detect concentrations at or above this threshold, the fuel in the tank is considered to meet the conditioning criteria.
# An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier-Stokes Equations
## 2 Mathematical Model  
In a bounded convex polygonal region \(\Omega\) in \(\mathbb{R}^{n}\) (\(n=2\) or \(3\)) with Lipschitz continuous boundary \(\partial\Omega\), the well-known unsteady incompressible NS equations [25] are formulated by  
\[\begin{cases}\dfrac{\partial\mathbf{u}}{\partial t}+(\mathbf{u}\cdot\
abla) \mathbf{u}-\
u\Delta\mathbf{u}+\
abla p=\mathbf{f},&\text{in}\;\Omega\times(0,T],\\ \
abla\cdot\mathbf{u}=0,&\text{in}\;\Omega\times(0,T],\end{cases} \tag{1}\]  
with boundary conditions  
\[\mathbf{u}=\mathbf{g}\;\text{on}\;\partial\Omega,\]  
and initial conditions  
\[\mathbf{u}|_{t=0}=\mathbf{u}_{0},p|_{t=0}=p_{0},\]  
where \(\mathbf{u}\) is the velocity field, \(p\) is the pressure, \(\
u\) is the kinematic viscosity, \(\mathbf{f}\) is the volume force, \(T\) is the total time for the calculation, and \(\mathbf{g}\) is the distribution function of velocity on the boundary. \(\mathbf{u}_{0}\) and \(p_{0}\) are the distribution function of velocity and pressure in the flow field at the initial time. Since non-homogeneous problems for Dirichlet boundaries can be transformed into homogeneous problems for the solution, the case of zero boundary conditions will be considered henceforth [26; 27].  
Let the velocity test function space and pressure test function space be  
\[V=\Big{\{}\mathbf{v}\in H^{1}(\Omega)\;|\;\mathbf{v}|_{\partial\Omega}= \mathbf{0}\Big{\}},Q=\Big{\{}\mathbf{q}\in L^{2}(\Omega)\;|\;\mathbf{q}|_{ \partial\Omega}=\mathbf{0}\Big{\}}.\]  
Following a standard derivation, the weak form of (1) is obtained as  
\[\begin{cases}\int_{\Omega}\dfrac{\partial\mathbf{u}}{\partial t}\cdot\mathbf{v }\,d\Omega+\int_{\Omega}(\mathbf{u}\cdot\
abla)\mathbf{u}\cdot\mathbf{v}\,d \Omega+\
u\int_{\Omega}\
abla\mathbf{u}\cdot\
abla\mathbf{v}\,d\Omega-\int_{ \Omega}p\cdot\
abla\mathbf{v}\,d\Omega=\int_{\Omega}\mathbf{f}\cdot\mathbf{v} \,d\Omega,\\ \int_{\Omega}\mathbf{q}\cdot(\
abla\cdot\mathbf{u})\,d\Omega=0.&\end{cases} \tag{2}\]  
To solve this problem in a mixed framework, the mixed finite element space \((V_{h},Q_{h})\) must satisfy the following inf-sup (Ladyzhenskaya-Babuska-Brezzi (LBB)) condition  
\[\inf_{\mathbf{q}\in\mathbb{Q}_{h}}\sup_{\mathbf{v}\in V_{h}}\dfrac{b(\mathbf{v },\mathbf{q})}{\|\mathbf{v}\|_{V_{h}}\|\mathbf{q}\|_{Q_{h}}}\geq\beta_{h}, \tag{3}\]  
to ensure the compatibility of the velocity-pressure spaces and to prevent spurious oscillations or non-uniqueness of the pressure solution, where \(b(\mathbf{v},\mathbf{q})=\int_{\Omega}\mathbf{q}\cdot\
abla\mathbf{v}\,d\Omega\) and \(\beta_{h}\) is a constant independent of the mesh size \(h\).
## 2 Materials and Methods
### Laboratory Analysis  
Blood samples were first drawn 24 h after hospital admission. Blood sampling in heart-failure patients and the control group was conducted without defined time windows. Standard clinical laboratory parameters were measured in the respective Department of Clinical Chemistry of University Hospital Jena and University Clinic Halle (Saale). Analysis of serum concentrations of novel cardiovascular biomarkers was conducted at the Department for Cardiology of University Hospital Salzburg. The respective biomarkers sST2, GDF-15, suPAR and H-FABP were quantified using commercially available ELISA kits (DuoSet ELISA, DY523B, DY957, DY807 and DY1678, R&D Systems, Minneapolis, MN, USA). All ELISA tests were performed according to the instructions provided by the manufacturer. Serum samples and standard proteins were applied to multiwell plates (Nunc Maxisorp plates, VWR International, Vienna, Austria). Respective plates were coated with the correspondent capture antibody and subsequently incubated for two hours. In the next step, the multiwell plates were washed with washing buffer (Tween 20, Sigma Aldrich, St. Louis, MO, USA) and phosphate-buffered saline solution. After washing, a biotin-labeled antibody was added to each well, and the plate was incubated for another two hours. Following incubation, the ELISA plates were washed again. Then, a streptavidin-labeled horseradish peroxidase solution was added. After adding tetramethylbenzidine (TMB; Sigma Aldrich, USA) a color reaction was achieved. The optical density was measured at 450 nm using an ELISA plate reader (iMark Microplate Absorbance Reader, Bio-Rad Laboratories, Vienna, Austria). Intra-assay precision and inter-assay precision were recorded as follows: ST2: 4.4-5.6%, 5.4-7.1%; GDF-15: 1.8-2.8%, 5.1-5.9%; suPAR: 2.1-7.5%, 4.7-6%; and H-FABP: 0.3-4.7%, 1.3-17.4%, respectively.
# Networked Roadside Perception Units for Autonomous Driving
## 3 Design and Implementation of the RSPU System
### Implementation of AutoC2X
#### 3.2.3 Coordinate Transformation  
Autoware utilizes the self-centered local coordinate system to maintain the coordinates while OpenC2X makes use of the geodetic reference system involving longitude and latitude. This is what makes it necessary to perform a coordination transformation. The planned Autoware extension initially changes the local coordinate system in the Universal Transverse Mercator, or UTM, coordinate system in accordance with the 3D map. The detected object's location in the UTM coordinate system (\(\binom{X_{obj}}{Y_{obj}}\)) is expressed as  
\[\left(\begin{array}{c}X_{obj}\\ Y_{obj}\end{array}\right) =\left(\begin{array}{c}X_{ego}\\ Y_{ego}\end{array}\right)+R(\alpha)\left(\begin{array}{c}x_{obj}\\ y_{obj}\end{array}\right) \tag{1}\] \[=\left(\begin{array}{c}X_{ego}\\ Y_{ego}\end{array}\right)+\left(\begin{array}{cc}\cos\alpha&-\sin\alpha\\ \sin\alpha&\cos\alpha\end{array}\right)\left(\begin{array}{c}x_{obj}\\ y_{obj}\end{array}\right)\] \[=\left(\begin{array}{c}X_{ego}+x_{obj}\cos\alpha-y_{obj}\sin \alpha\\ Y_{ego}+x_{obj}\sin\alpha+y_{obj}\cos\alpha\end{array}\right),\]  
where (\(\binom{X_{ego}}{Y_{ego}}\)) denotes the ego-vehicle's location in the UTM coordinate mechanism published in /ndt_pose; on the other hand, \(R(\alpha)\) denotes the ego-vehicle's rotation concerning the UTM coordinate system, published in /tf; and (\(\binom{X_{obj}}{y_{obj}}\)) signifies the detected objects' relative portion from the ego-vehicle its heading is in the X-axis, published in the topic of /detection/objects. Thereafter, the extension tuns the UTM coordinate system into the geodetic reference system 1980 (GSR80) by using PROJ library [39].  
In contrast, when the extension gets a \(CPM_{CAM}\) within the GSR80 form from the OpenC2X, the extension turns the coordinates into the local coordinate system for getting it handled in the Autoware.
## 4 Simulation Verification and Result Analysis
### Conciseness of Saddle Point Approximation  
The average mutual information has no closed-form expression, so it is usually calculated by the Monte Carlo method. The more sample points, the more accurate the  
Figure 4: Comparison on MI calculated by Monte Carlo and the lower bound of saddle point approximation method under different input types and correction parameters (\(\rho_{\rm T}=\rho_{R}=0.5\)) over doubly correlated Rayleigh fading channel model.  
Figure 3: Comparison on normalized MI calculated by Monte Carlo and saddle point approximation according to upper and lower bounds of \(\alpha_{m,k}\) by (14) under different input types, correction parameters (\(\rho_{\rm T}=\rho_{R}=\rho=0.5\)) over doubly correlated Rayleigh fading channel model (\(\rm N_{T}=N_{R}=2\)).  
calculation result is. We denoted the sample points as N\({}_{\text{W}}\). In order to obtain a relatively accurate value of mutual information, N\({}_{\text{W}}\) was at least \(10^{4}\). Tables 1 and 2 compare the computational complexity of the Monte Carlo method and the saddle point approximation method according to the number of operations and CPU time under the condition that N\({}_{\text{W}}\) was \(10^{4}\). The codes of mutual information calculation based on the Monte Carlo method and the saddle point approximation method were executed on an Intel Core i5-5200U 2.20 GHz processor. The results showed that the computational complexity of the proposed saddle point approximation method was much lower than that of the traditional Monte Carlo method. For example, as shown in Table 2, when N\({}_{\text{T}}\) and N\({}_{\text{R}}\) were equal to 2 and the input type of the two transmitting antennas was 16 QAM, the CPU time of saddle point approximation methods by (15) was several orders of magnitude less than that of Monte Carlo method.
# Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses
## 4 Results
### PCA Results  
Several studies proposed that because of the high dimensionality of InSAR time series, it is difficult to study separate crustal movements that may occur simultaneously. For example, seasonal movements and long-term movements occur in the study area, as shown in Figure 12a. However, a conclusive estimation of different trends needs further analysis, such as PCA. PCA may fail for large SBAS results. Hence, we resampled only 10,000 points randomly in different parts of the study area. The results show that the first three principal components (PCs) explain a major part of the SBAS results: PC1 (~76%), PC2 (~18%), and PC3 (~2%) together explain about 96% of the entire variance (Figure 12b). However, some information might be hidden in other PCs.  
Each PC corresponds to a specific score value that describes the variation shape. Figure 12c shows PC1, PC2, and PC3 loading values from 2015 to 2020. We can consider PC1 as a long-term component of ground subsidence, which is the largest portion of the variance, while PC2 and PC3 are seasonal changes with several troughs and peaks. The behavior of PC2 and PC3 is complex despite their similar pattern, indicating that seasonal change may have occurred differently between 2015 and 2020. Figure 12d shows a correlation circle of the results, which is a projection of the SBAS variables in the PC space. If two variables are far from the center of the circle and close to each other, then it can be interpreted that they are positively correlated (near +1), and if two variables are perpendicular to each other, they are not correlated (near 0). If two variables are located in opposite directions of the center, they are negatively correlated (near -1). Comparing PC1 and PC2 in Figure 12d, the positive correlation of the results between 2018 and 2020 is higher, probably because the number of obtained images in this period is considerably higher than in 2015-2017.  
Figure 11: Scatter plot of SBAS mean displacement and mean coherence values at locations of 10 piezometric wells.
## 4 Results and Discussion
### Flow Regime Identification
#### 4.1.1 Pressure Fluctuations  
Pressure fluctuation signals at a position located at 81D from the inlet are presented in Figure 8. All three types of flow regimes show different pressure fluctuation patterns and magnitude. Stratified flow shows negligible pressure fluctuation due to low superficial velocities and pressure amplitude increases with an increase in mixture superficial velocities. Slug flow shows considerable fluctuations compared to other flows and the magnitude of fluctuations increases with an increase in superficial velocities. Three stages of pressure variations can be observed in pressure signals when a slug is passing inside a pipe, which agrees with observations made by [41]. First, there is a sudden rise in pressure caused by acceleration of elongated bubble. The second stage is a steady drop in pressure due to the steadily falling liquid film, and the last stage is a constant lower pressure because of the tail of the slug. In annular flow, the magnitude of pressure is higher due to high velocities, but the fluctuation is considerably smaller compared to the overall pressure magnitude. Fast Fourier transform and probability density function were used to determine recognizable parameters for flow regime identification in the next section.
## 3 Results and Discussion
### Optimization of SPE Procedures  
We chose a group of acidic pharmaceuticals ranging from strongly acid to weak ones as target analytes (NAP, KEP, DIF, MEF, DIC, and NIF), and their molecular structures, log P, and pKa values are listed in Table 2. To obtain an ideal result in the real sample experiment, the optimized SPE protocol is necessary for the theoretical conditions, which can be applied for the real sample. The parameters that need to be evaluated involve the pH of the sample solution, the volume of sample solution (breakthrough volume), and the volume of elution solvent. All of the experiments for the SPE optimization except for the optimization of breakthrough volume were performed by loading 0.5 \(\upmu\)g of NAP and 5 \(\upmu\)g of KEP, DIF, MEF, DIC, and NIF in 10 mL of ultrapure water. One set of three samples was tested in parallel. Except for the condition being evaluated, all the others remained the same.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{S_{BET}}\) & **Pore Volume** & **Pore Size** \\  & **(m\({}^{2}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(nm)** \\ \hline Poly(DVB-co-DEAEMA) & 575 & 0.94 & 10–30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural properties of the sorbent.  
Figure 4: FTIR spectra of poly(DEAEMA-co-DVB) microspheres.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 1 Introduction
### Research Background and Motivation
Traditional monitoring of vital signs for these chronic illnesses is commonly performed in a hospital bed using specialized medical equipment under the supervision of a doctor, making it difficult to diagnose complications when the patient is mobile [7]. PlaMoS provides the important advantage of monitoring and transmitting data in real time to the doctor. It also transmits real-time patient body temperature, heart-rate (register the ECG during normal activity), blood oxygen, respiration rate, galvanic resistance and fall data, all while permitting the patient a high degree of mobility. This technology is a step forward as it represents an important diagnostic tool for patients suspected of suffering from chronic health issues or require continuous monitoring to diagnose or confirm a health issues related to cardiovascular disease. The novelty of the PlaMoS platform is that integrates health sensors to monitor parameters associated with chronic respiratory illnesses, a wireless sensor network, a 128-bit advanced encryption standard (AES) security level, a patient localization algorithm and software apps for iOS, Android, and Windows mobile devices.
## 3 Experiment and Results
### Experimental Results and Analysis
: _A general survey of the forecasting results for wind speed and wind power in the first season showed that the proposed combined model hds an optimal wind speed forecasting capacity. MAPE values at one day ahead were 6.51%, 6.41%, 7.00%, and 6.16% for Sites 1 to 4, respectively. Meanwhile, for wind power forecasting, the proposed combined model had the lowest forecasting error among all involved models. Moreover, for the forecasting results of wind speed and wind power, the MAPE value of wind power was twice that of wind speed with similar goodness-of-fit values._  
5.2 Experiment II: The Forecasting Performance of Combined Models Optimized by Different Optimization Algorithms  
This experiment was designed to compare combined models optimized by different optimization algorithms, including MOGWO, MODA, MOMVO, and MODE. This experiment aimed to assess wind speed and wind energy forecasting in the second season. The evaluation metrics obtained for each model (MAE, RMSE, STDAPE, DA, U1, U2, MAPE and R\({}^{2}\)) are shown in Table 5 and Figure 3.  
For Site 1, the combined model optimized by MODA for wind speed forecasting and the combined model optimized by MODA for wind power forecasting produced the most accurate forecasting results in the most efficient manner. The MAPE values of the optimal combined model were 8.965% and 19.506%, respectively. In comparison, the forecasting performances of the combined models optimized by four algorithms showed no significant differences; for example, the RMSE values of the four combined models were 0.4647, 0.4649, 0.4644, and 0.4648 for wind speed forecasting. The wind power forecasting results for each model are listed in Table 5.