esa-sceva/satcom-chunk-collection huggingface dataset:
## Preparation and Characterization of
###### Abstract  
Biomedical industries are attempting to utilize natural materials, as they are bio-compatible, non-toxic, and show bioactive properties, like antimicrobial activity. In this study, natural polyelectrolyte complexed chitosan/alginate films (PECs) were prepared via a casting/solvent evaporation technique, and their characteristics and drug release properties were investigated. PEC films made with two different overall polymer contents, 0.4 and 1 w/v%, were loaded with thyme oil nanoemulsion as drug carrier. The structure of the films was studied by FTIR and optical and scanning electron microscopy. Prepared PEC films had good mechanical and water vapor permeability properties. Release of the thyme oil from the pH-sensitive PEC films (TM-PEC) was detected and followed by UV spectroscopy. The results indicated that the drug release rate of TM-PEC films was the fastest when the chitosan content was 1 %w/v, and various mathematical models were analyzed for investigating the drug release mechanism. Antibacterial tests were performed by counting the number of surviving gram-negative and gram-positive bacteria. The in vitro test indicated the limitation _Escherichia coli_ (_E. coli_) and _Staphylococcus aureus_ (_S.aureus_) growth in the presence of TM-PEC films. The MTT test showed more cell viability of the TM-PEC film in comparison with that of the PEC film without TM. Based on the measured physical and antibacterial properties, the chitosan-alginate PEC films loaded with antibacterial essential oils can be considered for biomedical applications, such as wound dressings or controlled release systems.  
Keywords: polyelectrolyte complex; chitosan; alginate; thyme oil nanoemulsion; biomedical applications +
Footnote †: journal: Applied Sci. 2019, 9, 3933; doi:10.3390/app9183933
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 2 Materials and Methods
### Statistical Analysis  
First, the tomato fruit yield characteristics of different pollination treatments were compared with general linear model (GLM) using the software IBM SPSS 20 (Chicago, IL, USA), with 'fruit set', 'fruit weight', 'fruit diameter', and'seed quantity' as the response variables; 'pollination treatment' as the fixed factor; and 'greenhouse' as the random factor. Duncan post-hoc pairwise comparisons were used to test the significant differences of these variables among the levels of pollination treatment. Shapiro-Wilk normality tests were used to test normality and Levene tests were used to test homoscedasticity. Fruit set data did not exhibit equal levels of variance across the different pollination treatments ([PERSON]'s test, \(F_{2,218}=13.092,p<0.001\)), and were, therefore, arcsin transformed to meet the conditions for GLM.  
For the sugar and organic acid data, GLMs followed by Duncan post-hoc method was used to compare the content of fructose, glucose and sucrose in tomato by different pollination treatments. The citric acid and malic acid data both had significantly different variances ([PERSON]'s test, citric acid: \(F_{2,19}=5.583,p=0.012\); malic acid: \(F_{2,19}=7.300,p=0.004\)) and failed to meet conditions for GLM after transformations; then, the non-parametric Kruskal-Wallis one-way ANOVA followed by the Dunn-Bonferroni post-hoc method was used to compare the medians of citric acid and malic acid content in tomato by different pollination treatments.  
Finally, in order to highlight the differences among total VOCs in tomato fruits by different pollination treatments, orthogonal projections to latent structures discriminant analysis (OPLS-DA) was applied. In tomato, there are more than 400 volatile compounds detected [30]. However, not all of these compounds contribute to tomato fruit flavor, let alone consumer liking [41]. Compounds which make important contribution to flavor and consumer liking can be identified by consumer panel tests [42]. The consumer liking related VOCs were selected based on past research [28; 39; 41; 42; 43] and the composition of consumer liking related VOCs in tomato fruits by different pollination treatments was checked by hierarchical cluster analysis (HCA) with the distance metrics based on the Peason correlation, and the normalized data was represented as heatmap. OPLS-DA was conducted by the software SIMCA P+ Version 15 (Sartorius Stedim Data Analytics AB, Umea, Sweden) and HCA was conducted by OriginPro 2022 (Northampton, MA, USA). Before the VOCs data of tomato fruit samples were subjected to OPLS-DA and HCA, the content of each compound was calculated based on the comparison of its FID peak area and the internal standard 2-nonanone and the compounds not detected were assigned a value of 0.
## 3 Results and Discussion
The conductivity of ionic liquids is a parameter reflecting the current transporting capacity. The conductivity of [EMIm]F as a function of temperature was studied, as shown in \[\rho=1.516-1.22\times 10^{-3}\;T \tag{1}\]0 As can be seen, the conductivity of [EMIm]F increases with the increase in temperature. We fitted the relationship between the conductivity and temperature using a VFT (Vogel-Fulcher-Tammann) equation:  
\[\rho=1.516-1.22\times 10^{-3}\;T \tag{1}\]1  
where \(\sigma_{0}\) (mS-cm\({}^{-1}\)), B (K), and T\({}_{0}\) (K) are constants. The fitting parameters are obtained as follows: \(\sigma_{0}\) = 4417.1 mS-cm\({}^{-1}\), B = 953.17 K, T\({}_{0}\) = 166.65 K.  
The thermal behavior of [EMIm]F was studied via differential scanning calorimetry. The DSC curve of [EMIm]F at a cooling and heating rate of 10 K/min is shown in \[\rho=1.516-1.22\times 10^{-3}\;T \tag{1}\]2 During cooling from 50 to \(-\)150 \({}^{\circ}\)C, we could only observe an exothermic peak at \(-\)71.3 \({}^{\circ}\)C, corresponding to the glass transition. In the heating process from \(-\)150 to 200 \({}^{\circ}\)C, two endothermic peaks were found. One is the glass transition point, which was also observed during the cooling process. The other is assigned to the decomposition temperature around 135 \({}^{\circ}\)C. No melting point on the DSC curve was observed, as is the case for many other ionic liquids. It has been reported that the melting point of many ionic liquids is uncertain because they can undergo considerable supercooling [49; 50; 51]. The temperature of the phase change can differ considerably depending on whether the sample is heated or cooled [52].
## 6 Numerical Results
In general, most of the parametric estimation problems provide some prior information about the localization of the parameter to be estimated. Such a prior information can be used to constrain or project the online estimation to remain in a known convex set. In our case, we need to constrain (project) the parametric estimate \(\hat{b}\) in a convex set to avoid the estimate taking values near to zero. One of the most common operators used in adaptive control systems is the so-called _parameter projection operator_[58]. For our case, we consider the following prior knowledge: the sign of \(b\) and the lower bound \(b_{0}\) of the absolute value of \(b\), i.e., \(|b|\geq b_{0}\). This information can be obtained using the nominal parameter values of the added mass coefficients of the BlueROV2 vehicle structure, reported in [57]. Therefore, the restriction for the parameter estimate can be expressed as \(|\hat{b}|=\hat{b}sgn(b)>b_{0}\). Now,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{**Parameters of the Controller and Adaptive Laws**} \\ \hline \multicolumn{2}{c}{**Heading’s dynamic**} \\ \(a_{\psi_{1}}=-28\) & \(a_{\psi_{2}}=-28\) & \(b_{\psi}(0)=30\,(\text{kgm}^{2})^{-1}\) \\ \(\gamma_{1,\psi}=20\) & \(\delta_{\psi,\psi}=0.5\) & \\ \(\gamma_{2,\psi}=0.025\) & \(\delta_{b,\psi}=0.05\) & \\ \(K_{id,\psi}=[0.2,0.3]\) & \(K_{\psi}=[120,80]\) & \(P_{2,\psi}=\text{diag}([25,15])\) \\ \(W_{0,\psi}=[5,5]\) & \(W_{\psi}(0)=[0,0]\) & \\ \hline \multicolumn{2}{c}{**Depth’s dynamic**} \\ \(a_{z_{1}}=-2\) & \(a_{z_{2}}=-1\) & \(b_{z}(0)=-0.7\) kg\({}^{-1}\) \\ \(\gamma_{1,z}=3\) & \(\delta_{\psi,z}=0.5\) & \\ \(\gamma_{2z}=0.04\) & \(\delta_{b,z}=0.1\) & \\ \(K_{id,z}=[0.2,0.1]\) & \(K_{z}=[4,4]\) & \(P_{2,z}=\text{diag}([27.5,16.5])\) \\ \(W_{0,z}=[1.5,1]\) &following the procedure in [58], we can define the convex set where the parameter estimate must evolve, that is \(S=\{\hat{b}\in\mathbb{R}|b_{0}-\hat{b}sgn(b)\leq 0\}\). Therefore, we can apply the projection operator to the adaptive law:  
\[\hat{b}(t)=\gamma_{2}\Big{(}2\tilde{\xi}^{\top}\mathbf{P_{2}}\mathbf{B}u(\xi)- \delta_{b}\hat{b}(t)\Big{)}=F(\tilde{\xi}) \tag{52}\]  
leading to  
\[\hat{b}(t)=\mathbf{Pr}(F(\tilde{\xi}))= \begin{cases}F(\tilde{\xi}),&\text{ if }|\hat{b}|>b_{0}\\ &\text{ or if }|\hat{b}|=b_{0}\text{ and }\xi^{\top}\mathbf{P_{2}}\mathbf{B}u(\xi) sgn(b)>0,\\ 0,&\text{ otherwise}\end{cases}  \tag{53}\]  
where \(\hat{b}(0)\) is chosen so that \(\hat{b}(0)sgn(b)>b_{0}\). This modification constrains the parameter estimates inside some known convex bounded sets, which do not include zero. In other words, when the parameter \(\hat{b}\) reaches its lower absolute value \(b_{0}\) and the time derivative \(\hat{b}\) is not zero, the adaption process is stopped. Moreover, by using the properties of the projection operator, the stability analysis developed in this research work holds [58].
# Characterization of Shear Strain on PDMS: Numerical and Experimental Approaches
## 5 Conclusions  
In this work, by the mechanical behavior of PDMS was characterized through an experimental method associating 3D DIC and compared with numerical simulations using four different numerical constitutive models. The main points that we have conclude were:  
* From experimental tests, the hyperelastic behavior of PDMS was verified using a simple shear test. The DIC tests showed that the most significant shear strain occurred in the centre of the PDMS plate. These experimental tests also verified that the PDMS rupture occurred at the bonding interface between the steel and PDMS plates. The rupture happened for different PDMS thicknesses. For 4 mm of PDMS, the specimen broke up at a 154 N of shear force. The specimen with 2 mm of PDMS suffered rupture at a shear force of 80 N. For an equal value of shear force, a higher value of displacement happened for the higher PDMS thickness.
* The numerical simulations were done using four hyperelastic constitutive models (Mooney-Rivlin, Yeoh, Gent and polynomial). All the hyperelastic constitutive models presented similar results despite some critical differences. The main reason to obtain similar results in shear stress was due to the appliance of low displacement values and the constitutive models, for this displacement level, have identical behavior. The values that show a greater difference in shear strain occurred for the Mooney-Rivlin constitutive model when compared with the other constitutive models (see Table 2). The numerical model with 2 mm of PDMS results in higher values of shear strain than with 4 mm. So, for low displacement levels it is possible to use any of the hyperelastic constitutive to simulate the shear test.
* Qualitatively, in the central region of the specimen, the numerical and experimental results have similar behavior, and the values of shear strain are close. Nevertheless, the maximum relative error between the numerical and experimental results is very different for the two thickness values. It is possible to conclude that, for higher values of displacement and thicknesses, the numerical simulation results move further away from experimental values.  
Conceptualization, J.R. and A.S.; methodology, J.R.; software, E.M. and C.B.; validation, A.S., E.M. and C.B.; formal analysis, J.R.; investigation, A.S.; resources, J.R.; data curation, C.B.; writing--original draft preparation, A.S. and E.M.; writing--review and editing, J.R. and A.S.; visualization, A.S.; supervision, J.R.; project administration, J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: CIMO (UIDB/00690/2020).  
The authors declare no conflict of interest.
## 2 An SSVEP-Based BCI with Multi-Domain Feature and Multi-Task Learning
### Multi-Domain Features  
In this study, the SSVEP signals in both the time and frequency domains are used as the multi-domain features. The discrete Fourier transform is used to transform the SSVEP signals from the time domain, \(x\) = {\(x_{0}\), \(x_{1}\), \(\ldots\), \(x_{N}\)}, to the frequency domain, \(X\) = {\(X_{0}\), \(X_{1}\), \(\ldots\), \(X_{N}\)}, and is defined as  
\[X_{k}=\sum_{n=0}^{N-1}x_{n}e^{-\frac{\Omega x}{N}kn} \tag{1}\]  
where \(N\) is the total number of discrete samples in the input signal. \(e^{-\frac{\Omega x}{N}kn}\) represents the basis function of the Fourier transform, capturing the frequency components of \(x_{n}\). Since the discrete Fourier transform can select any analysis window length, the feature lengths for different domains can be made the same. This simplifies the process of extracting embedding features in different domains.
## 2 Structure of the SDD and Buffer Compliance Strategy
### Buffer Compliance Strategy  
In the third stage, the end-effector of space robot collides with the target satellite, and the impact energy will be quickly buffered and consumed by SDD when it is transmitted to the motor rotors so as to protect the joints. In the fourth stage, due to the impact effect, the instantaneous impact torque will be generated when the motors are turned on. If the instantaneous impact torque exceeds the limit of joint and the motor still turns on, the joint will be damaged. Therefore, it is necessary to set a torque threshold according to the ultimate torque value that the joints can withstand. When the instantaneous impact torque is detected to exceed the set threshold, the motor will be turned off. At this time, the spring will provide elasticity to mitigate the impact torque on joints, and the damper will quickly dissipate energy to suppress the flexible vibration. However, if only set one threshold is turned on, the motors will be frequently switched (which easily causes motor damage). The buffer compliance strategy proposed in this paper set a threshold in order to turn on and off at the same time. When the impact torques of the joints exceed the turn off  
Figure 1: Structure of the SDD. (**a**) 3D structure of SDD, (**b**) Modeling of the SDD.  
Figure 2: Connection mode of the SDD.  
torque threshold, the motor is turned off; when the impact torque is reduced to the turn on threshold by SDD, the motor is turned on again.
## 4 Structural Optimization
### Optimization of Constant Output Stiffness  
Under load conditions, constant stiffness in the direction of motion is one of the essential characteristics required for the displacement reducer. To achieve good load-bearing capacity, the primary motion stiffness should remain constant or increase within the range of motion. Therefore, in this section, constant stiffness along the Y-axis is achieved by optimizing both the geometric and beam parameters.  
The output displacement is divided into n segments. The maximum stiffness value of each segment is selected, and the difference between this value and the stiffness of the other segments is calculated. The smaller the difference, the closer the stiffness values between the segments, demonstrating that the goal of optimizing the constant stiffness range has been achieved, while also maximizing the overall stiffness. The displacement interval is divided into n segments, with each segment having a length  
\[u_{j}=\frac{ju_{\text{max}}}{n},\quad j=0,1,2,\ldots,n, \tag{12}\]  
where \(u_{j}\) represents the displacement in each segment and \(u_{\text{out}}\) is the total displacement. According to [PERSON]'s difference formula, the output stiffness in the y-direction is as defined below:  
\[K_{\text{out}}=\frac{f_{j}-f_{j-1}}{u_{j}-u_{j-1}},\quad j=0,1,2,\ldots,n, \tag{13}\]  
and \(f_{i}\) represents the output force in each segment. The objective function can be defined as  
\[\text{Min}\;\rho\big{(}a_{x},\ldots,h_{x},h_{y},w_{\text{ad}}, \ldots,w_{\text{ef}}\big{)} =\sqrt{\frac{1}{n}\sum_{j=1}^{n}\left(K_{\text{out}}-\frac{1}{n} \sum_{k=1}^{n}K_{\text{out}}\right)^{2}}\] \[j =0,1,2,\ldots,n, \tag{14}\]  
Set the expression above as the objective function, with the constraints given in Tables 2 and 3.  
After 1000 generations of iteration, the fitness values are shown in Figure 11a. The results tend to converge after about 650 iterations, demonstrating the feasibility of the optimization algorithm. Figure 11b shows the structure with a constant stiffness in the Y-axis direction after optimization. The range of stiffness changed from 0.6-0.72 N/\(\upmu\)m to 0.83-0.85 N/\(\upmu\)m. It can be observed that some of the beam lengths have significantly increased, which helps to improve the load-bearing capacity of the mechanism. However, excessively thick beams may cause stress concentration, which could negatively affect the displacement reduction ratio of the structure. Figure 11c shows the stiffness characteristics of the displacement reducer before and after optimization. It can be seen that the stiffness has significantly increased after optimization and remains nearly constant from 0 to 2000 \(\upmu\)m, which greatly enhances the overall load-bearing capacity of the structure.  
The maximum stiffness is 0.85 \(\mathrm{N/\upmu m}\), which is an improvement of 15.29% compared to the pre-optimization result of 0.6 \(\mathrm{N/\upmu m}\). The range of variation decreased to 0.17% of its previous value.
## 5 Discussion
### Interpretation of Earthquake-Induced Anomalies
#### 5.3.1 Major Ions and Isotopic Perspectives on Earthquake Anomalies
Ion and isotope fluctuations during the 38-23 days pre-seismic interval further indicate asynchronous activation of fractures at different depths. Initial deep-fracture fluid upwelling (synchronous increase in Na\({}^{+}\), \({\rm SO_{4}}^{2-}\), \(\delta^{18}\)O) followed by shallow cold water mixing (\(\delta^{18}\)O, ion decline) reflects a competitive mechanism, highlighting spatiotemporal heterogeneity in multi-scale rupture propagation. Comparable hydrogeochemical anomalies have also been observed across tectonically diverse regions. Prior to the 2023 Mw7.7 and 7.6 Kahramanmaras earthquakes in Turkiye, [PERSON] et al. (2024) documented sustained increases in Cl\({}^{-}\) and \({\rm SO_{4}}^{2-}\) and a pronounced \(\delta\)D decline in Ayran spring waters six months before the events [1]. In the Matsushiro earthquake swarm in Japan, [PERSON] and [PERSON] (2024) observed increasing trends in major ions (Na, K, Al, Cl, Br, B, \({\rm SiO_{4}}\)) and isotopic signatures (\(\delta\)D, \(\delta^{18}\)O, \(\delta^{13}\)C) in fault-zone wells [3]. Similarly, in northern Iceland, variations in major cations and isotopes were reported four to six months before two \(M>5\) earthquakes in 2012 and 2013 [25]. These cross-regional cases reinforce the interpretation that pre-seismic stress promotes vertical migration of deep, ion-rich fluids and alters hydrochemical baselines, validating the universality of this mechanism. Compared to the Atushi \(M_{5}\)5.4 earthquake, the hydrogeochemical responses of the Wushi \(M_{5}\)7.1 earthquake was notably more sustained and intense. The S13 monitoring site, located along the MDF where the Wushi \(M_{5}\)7.1 earthquake nucleated, exhibited hydrogeochemical anomalies lasting from 38 days before to 24 days after the earthquake, with significantly higher intensity and duration than the Atushi \(M_{5}\)5.4 earthquake, confirming the observation that \"monitoring sites within the same tectonic belt are more sensitive to the main shock\".
## 3 PIESAT-01 Multiple Satellites Interferometric Imaging
### Synchronous Data Processing
#### 3.1.2 Bistatic SAR Phase Synchronization  
Phase error arises from clock timing error, frequency cumulative error, phase noise, and initial phase difference. The initial phase difference is the main cause of the time-invariance phase error, while the frequency cumulative error and phase noise are the main causes of the time-varying phase error. Time-invariance phase error results in phase error between different transmitter-receiver combinations, leading to defocusing of the tomographic dimension, while time-varying phase error results in azimuthal phase error and, consequently, azimuthal defocusing [29]. Therefore, it is necessary to estimate and compensate for the phase error in distributed SAR systems.  
Similar to time synchronization, the phase of the direct wave signal peak after distance pulse compression extraction can be used to obtain the phase of the bi-directional direct wave. Subsequently, the phase error between the two receivers can be estimated as follows:  
\[\Delta\hat{\phi}_{ij}=\frac{\varphi_{B,ij}\big{(}t_{i};t_{0}\big{)}-\varphi_{ B,ij}\big{(}t_{j};t_{0}\big{)}}{2} \tag{3}\]  
The estimated bistatic phase error can be compensated. Upon bistatic phase synchronization, the phase of the signal transmitted from radar \(i\) and received by radar \(j\) is as follows:  
\[\varphi_{C,ij}\big{(}t_{j};t_{0}\big{)}\approx-2\pi f_{0}\tau_{ij} \tag{4}\]From this, it can be inferred that the time-varying phase error has been compensated, implying that if the involved time errors are not significant, the bistatic 2D imaging can be effectively focused.