esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 1 Introduction  
With the rapid development of industries such as mining, metallurgy, electroplating, and battery manufacture, many heavy metal ions are released into natural waters [1]. These heavy metal ions cannot be biodegradable and can enter and accumulate in the human body through the food chain and drinking water, resulting in a series of irreversible physiological diseases, such as kidney damage, nervous-system disorders, and bone necrosis. Therefore, developing new adsorbent materials for removal of heavy metal ions from wastewater is of great significance [2; 3].  
Sodium alginate is a byproduct of the extraction of iodine and mannitol from kelp or brown algae (_Sargassum_ sp.) and is a natural polysaccharide. Sodium alginate is often used as a food thickener and an antidote for heavy metal poisoning. In our previous research, we have used vacuum-freeze-drying technology and chemical-grafting technology to prepare a series of calcium alginate and modified calcium alginate aerogels [4; 5; 6; 7]. These synthesized aerogels provide good selectivity and adsorption capacity for Pb\({}^{2+}\) ions. The maximum adsorption capacity of the chitosan-modified calcium alginate aerogel for Pb\({}^{2+}\) ion is up to 468.5 mg/g, which is higher than most of the reported Pb\({}^{2+}\)-adsorbents [5].  
Disodium ethylenediaminetetraacetate dihydrate (EDTA) is a good complexing agent with six coordinating atoms (four O and two N atoms), which can coordinate with all metal ions at a 1:1 molar ratio. The content of coordinated metal ions can also be determined by EDTA through complexometric titration [8]. The present study aimed to further improve the adsorption performance and practicability of modified calcium alginate aerogels, which can adsorb various heavy metal ions for treating wastewater containing different types of metal ions. Ethylenediamine was used as a linkerto connect alginate and EDTA, and then the resulting complex was cross-linked with calcium ions in solution. After vacuum-freeze-drying, a novel Alg-EDTA aerogel adsorbent was obtained and used to remove heavy metal ions in wastewater. The composition of the prepared Alg-EDTA adsorbent, as well as its adsorption performance for heavy metal ions and recyclability, were also studied.
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 3 Developments in Innovative Detection Methods
### Improvements and Developments in Real-Time and Online Analysis Techniques
#### 3.4.2 Surface Plasmon Resonance
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Techniques** & \begin{tabular}{c} LOD \\ (UFC/mL) \\ \end{tabular} & \begin{tabular}{c} **Analysis** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Needed for** \\ **Sampling** \\ \end{tabular} & \begin{tabular}{c} **Real-Time** \\ **Analysis** \\ \end{tabular} & **Specificity** & & **Advantage** & **Disadvantage** & **Cost** \\ \hline \multirow{3}{*}{**Blood culture**} & \multirow{3}{*}{10-100} & \multirow{3}{*}{20 h} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} & \multirow{3}{*}{Not specific} & - & Easy-to-use & - & Long analysis time & \multirow{3}{*}{+} \\  & & & & & - & Automated & - & Non-specific, & \\  & & & & & & - & Good detection & non-qualitative & \\ \hline \multirow{3}{*}{\begin{tabular}{c} **Raman** \\ **spectroscopy** \\ \end{tabular} } & \multirow{3}{*}{1} & \multirow{3}{*}{10 min} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} & \multirow{3}{*}{Database} & - & Quick analysis & - & Software learning & \multirow{3}{*}{++} \\  & & & & - & Very sensitive & - & Microorganism & \\  & & & & & - & Very specific & & database & \\ \hline \multirow{3}{*}{**Paper Sensor**} & \multirow{3}{*}{100} & \multirow{3}{*}{1–8 h} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} & \multirow{3}{*}{Design} & - & Easy-to-use & \multirow{3}{*}{-} & \multirow{3}{*}{Long analysis time} & \multirow{3}{*}{+} \\  & & & & & Ligand & - & Forudability & - & Long analysis time & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} **Microfluatics** \\ **and DNAzyme** \\ \end{tabular} } & \multirow{3}{*}{1} & \multirow{3}{*}{90 min} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} & \multirow{3}{*}{Design} & - & Quick analysis & - & Low volume & \multirow{3}{*}{++} \\  & & & & DNAzyme & - & Universal analysis & & sampling & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **Bio-conjugated** \\ **nanoparticles** \\ \end{tabular} } & \multirow{3}{*}{1–10} & \multirow{3}{*}{20 min} & \multirow{3}{*}{No} & \multirow{3}{*}{Yes} & \multirow{3}{*}{Design} & - & No need for & - & Wide range of & \multirow{3}{*}{++} \\  & & & & & sampling & & antibodies & \\  & & & & & & Immediate & - & Non-qualitative & \\  & & & & & visibility if & - & Free particles in & \\  & & & & & contamination & & solution & \\ \hline \hline \multirow{3}{*}{**SPRi**} & \multirow{3}{*}{10} & \multirow{3}{*}{15 h} & \multirow{3}{*}{No} & \multirow{3}{*}{Yes} & \multirow{3}{*}{Design} & - & Real-time, & \multirow{3}{*}{-} & \multirow{3}{*}{Real-time, & \multirow{3}{*}{-} \\  & & & & qualitative, and & & & & \\  & & & & quantitative & & & & \\ \cline{1-1}  & & & & & quantitative & & & \\ \cline{1-1}  & & & & & Without circulating & & & \\ \cline{1-1}  & & & & ligand & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Synthesis of different analysis techniques (AMP: antimicrobial peptide; SPRi: Surface Plasmon Resonance imaging) (Cost scale: from less expensive (+) to more expensive (++)).
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 2 WAMS-Based Power System Modeling
### Power System Dynamic Modelling  
The system frequency response (SFR) model has been commonly used to represent the frequency dynamics of a power system [35; 36]. The model can make use of re-heated turbines or non-reheated turbines. An SFR model block diagram of a non-reheated power system is shown in Figure 1. Generally, the model is commonly used in load frequency control (LFC) [37; 38]. The dynamics of area \(i\) considering a multi-area power system is described by the differential Equations (4)-(8). This model can be used for practical power systems modelling and still giving technically sound results.  
\[\Delta\dot{f}_{i}=\frac{1}{2H_{i}}\Delta P_{mi}-\frac{1}{2H_{i}}\Delta P_{Li}- \frac{D}{2H_{i}}\Delta f_{i}-\frac{1}{2H_{i}}\Delta P_{tie,i} \tag{4}\]  
\[\Delta\dot{P}_{mi}=\frac{1}{T_{ti}}\Delta P_{gi}-\frac{1}{T_{ti}}\Delta P_{mi} \tag{5}\]  
\[\Delta\dot{P}_{gi}=\frac{1}{T_{gi}}\Delta P_{ci}-\frac{1}{R_{i}T_{gi}}\Delta f _{i}-\frac{1}{T_{gi}}\Delta P_{gi} \tag{6}\]  
\[\Delta P_{tie,i}=2\pi\sum_{j=1,j\
eq i}^{N}T_{ij}\left(\Delta f_{i}-\Delta f_{ j}\right) \tag{7}\]  
\[\Delta P_{tie,i}=\sum_{i=1}^{m}\left(\Delta P_{tie,actual_{j}}-\Delta P_{tie, scheduled_{j}}\right) \tag{8}\]  
where \(H_{i}\) is the inertia, \(D\) is the damping coefficient, \(T_{ti}\) is the turbine time constant, \(T_{gi}\) is the governor time constant, \(T_{ij}\) is the tie line coefficient and \(R_{i}\) is the droop. In addition, \(\Delta f_{i}\) is the frequency deviation, \(\Delta P_{mi}\) is the mechanical power deviation, \(\Delta P_{gi}\) is the governor deviation, \(\Delta P_{tie,i}\) is the tie line power deviation and \(\Delta P_{ci}\) is the control input.  
The overall power system dynamics can be expressed in state space form as follows:  
\[\dot{x}_{i}=A_{i}x_{i}\left(t\right)+B_{i}u_{i}\left(t\right)+F_{i}\Delta P_{i} ;\ i=1,2,3\cdots n \tag{9}\]  
where \(A_{i}\), \(B_{i}\), \(F_{i}\), \(\Delta P_{i}\), \(x_{i}\) and \(u_{i}\) are the system matrix, input matrix, disturbance matrix, disturbance, state vector and the input respectively.  
Figure 1: System frequency response (SFR) model with non-reheat turbines.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 1 Introduction  
The driving safety of road and off-road vehicles is the key feature that allows for safe and reliable transportation of passengers and different types of loads. Driving safety is a broad concept; here, it mainly refers to road holding and vehicle handling varying with respect to different configurations of vehicle suspension.  
Road holding describes the ability of the vehicle to track the desired road trajectory and it can be influenced by different factors. Each tire and vehicle exhibits its optimized window of tire inflation, or tire deflection, which has a similar, but opposite, effect. Studies on inflation of tire were discussed in [1; 2], where the typical deformation of underinflated tire was presented. Further analysis of non-uniform contact stress distribution for over and underinflated tire was presented in [3].  
Both under and overdeflected tires result in accelerated tire wear and the deterioration of tire adhesion to road surface. Furthermore, not only static, but also dynamic changes in tire deflection should be taken into account [4]. The latter can be significantly increased by a wheel hop effect, which is related to the resonance of vehicle wheel vibration.  
Vehicle handling, the second driving-safety-related feature, is commonly evaluated for steady-state or transient response of the vehicle, as shown in [5]. The steady-state response was analysed for a vehicle moving along a circle with increasing speed. Transient tests can be closed-loop, e.g., severe double line change or open-loop, e.g., J-turn, Fishhook. Further, vehicle handling can be analysed with respect to the chance of vehicle rollover.  
An indicator of a possible rollover that was evaluated based on measurement data of roll angle and additional information about vehicle kinetic energy was presented in [6]. Other researchers proposed the roll rate as quantity that is related to the likelihood of rollover [4]. Consequently, pitch angle can also be used for the assessment of ride performance and steering stability as it was stated in [7]. Further, the approach of assessing vehicle handling on the pitch and roll rates was also applied in [8], presenting an analysis of whole body vibration for an off-road vehicle with magnetorheological (MR) dampers.
## 2 Materials and Methods
### Kinetics Study  
A kinetic experiment was conducted to check the impact of contact time on the adsorption of the tested heavy metals by the employed adsorbents. A similar protocol was followed as in the adsorption experiment, except for using variable contact time. The best performing three adsorbents were added to 5 g of contaminated soil at a 2% \(w/w\) application rate in a polypropylene tube with 25 mL DI water. The pH was adjusted at 7 using 0.1 M solutions of HCl and NaOH. The tubes were placed in a reciprocal shaker, and samples were taken at regular time intervals of 0.5, 1, 4, 8, 16, and 24 h. Collected samples were analyzed by ICP-OES to observe Pb and Cd concentrations in solution. All treatments were performed in triplicate, and a control treatment was added with soil only and no added adsorbent. Additionally, the experimental data were further analyzed by employing first-order and pseudo-second-order kinetic models, as described in Equations (3) and (4) [37].  
\[\text{First-order model}:\qquad\text{Inq}_{t}=\text{Inq}_{o}-\text{k}_{1}t \tag{3}\]  
\[\text{Pseudo-second-order model}:\qquad\frac{t}{\text{q}_{t}}=\frac{1}{\text{ k}_{2}\text{q}_{e}}+\frac{1}{\text{q}}t \tag{4}\]  
where \(\text{q}_{t}\) is the adsorbed quantity of Pb and Cd (mg kg\({}^{-1}\)) by the sorbents at time t (min), and \(\text{q}_{o}\) is the adsorbed quantity of Pb and Cd at t = 0. k\({}_{1}\) and k\({}_{2}\) represent rate constants of the first order and second order (mg kg\({}^{-1}\) min\({}^{-1}\)), respectively.
## 2 Materials and Methods
### Characterization Techniques  
Surface morphology of the synthetized nanoparticles was characterized with a field emission gun scanning electron microscope (GeminiSEM, ZEISS, Oberkochen, Germany) at an accelerating voltage of 20 kV. The HR-SEM was coupled with energy dispersive X-ray spectroscopy (EDX).  
The crystal structure of the synthesized nanoparticles was studied using a D8 X-ray diffractometer (Bruker AXS GmbH, Karlsruhe, Germany) with filtered CuK\(\alpha\) radiation. All samples were evaluated over the diffraction angles (20) range of 20-80\({}^{\circ}\).  
The particle size analysis was carried out by dynamic light scattering (DLS). Powders were suspended (0.01 g/100 mL) in milli-Q water and sonicated for 30 s at 25\({}^{\circ}\)C \({}^{\circ}\)C.  
The specimen's surfaces were characterized by Fourier Transform Infrared Spectroscopy (FTIR), before and after the coat application. Spectra--in the range of wavenumber from 400 to 4000 cm\({}^{-1}\) during 64 scans--were recorded on a Fourier-transform infrared spectrometer (Nicolet 6700, Thermo Scientific, Massachusetts, USA) using an ATR diamond crystal.  
Once tested in the raceway, coated substrates were examined using an optical microscope (Olympus DP72, Tokyo, Japan) at 20\(\times\) magnification, in order to assess the adhesion of microalgae. All coated surfaces were examined by digital image processing making use of ImageJ software.  
Surface roughness was characterized with a Perthometer (Perthometer, S 6 P, Mahr GmbH, Gottingen, Germany). The measurements were carried out by scanning the surface of the sample with a very sharp needle, in order to define its profile. For the roughness assessment, the specimen was divided in three sections.
## 3 Methods
### Data Collection  
In this study, we are trying to verify whether the steering law can be applied to any tracking direction by head movement. The steering law models the task of manipulating a cursor with a fixed width so that it does not go out of the path (steering task). For a given path C, let ds be the length of a small section and \(W(s)\) the width of the pathway, where \(s\) denotes curvilinear abscissa of the pathway. That is, \(W(s)\) means the spatial margin at a position \(s\) in the pathway. When the cursor is moved along the pathway, \(MT\) and the Index of difficulty (\(ID\)) can be expressed by Equations (1) and (2)  
\[MT=a+b\times ID \tag{1}\]  
\[ID=\int_{\text{C}}ds/W(s) \tag{2}\]  
where a and b are the constants dependent on the accuracy of the track which the surgeon intends to specify, the device used in the task and the individual who performs the task.  
Equation (1) shows that \(ID\) and \(MT\) have a proportional relationship, and a regression line can be obtained from the plot of \(MT\) when a steering task is performed. The reciprocal of the slope of this regression line is known as the Index of Performance (\(IP\)) for the device that performed the steering task [21; 23] and is shown in Equation (3).  
\[IP=1/b \tag{3}\]  
In order to examine whether the path-following performance by head movement can be modeled by steering law, two independent variables were included: \(ID\) and tracking directions (\(\theta\)). \(ID\)s were determined by the track's total lengths (\(A\)) and widths (\(W\)). A had two levels (20 cm, 50 cm), and \(W\) had two levels (2 cm, 4 cm). Since the track used in the experiment was a straight line, Equation (2) can be simplified as Equation (4) to express \(ID\) with two parameters, \(A\) and \(W\), as four different levels composed of a combination of two variables, \(A/W\), i.e., 5.0, 10.0, 12.5, and 25.0.  
\[ID=A/W \tag{4}\]  
\(\theta\) had 24 levels from 0\({}^{\circ}\) to 345\({}^{\circ}\) with an increment range of 15\({}^{\circ}\). Thus, the data were acquired under 2592 conditions including nine experimental participants, three trial numbers, four different \(ID\)s, and 24 different \(\theta\)s. Each participant performed a total of 108 trials in randomized order.  
Dependent variables included elapsed time when the cursor is moved from the start to the goal: _MT_ [sec], and the number of out of bounds during trials: number of errors. MT data were subjected to logarithmic transformation to provide normality.  
_IPs_, the number of errors, and the scores obtained from the questionnaire were evaluated for each tracking direction. Error rates for each tracking direction were calculated by the number of errors divided by the number of all trials, including invalid trials. The linear regression model was applied to represent the relationship between _ID_ and _MT_ for each tracking direction to evaluate whether the model could be applied regardless of the tracking direction. A repeated-measures ANOVA was performed for _MT_ and error rates. All statistical analyses were performed using the software package R-4.0.3.
## 4 Conclusions  
The use of elicitors through foliar applications to _Vitis vinifera_ L. cv. Tempranillo grapevines affected grape volatile composition. Methyl jasmonate (MeJ) treatment increased the concentration in the grapes of total terpenoids, and total C6 compounds in 2019 and 2020, and the total C13 norisoprenoids in 2019; while decreased the concentration of total benzenoid compounds in 2019, total carbonyl compounds in 2020, and total alcohols in both seasons. In addition, ACP-MeJ increased the amount in the grapes of total terpenoids, and total benzenoid compounds in 2020; whereas decreased the content of total terpenoids, total C13 norisoprenoids, total benzenoid compounds, total alcohols, total carbonyl compounds, and C6 compounds in 2019. These results are not completely conclusive since this is the first time that foliar application of ACP-MeJ has been performed in _Vitis vinifera_ L. cv. Tempranillo grapevines to evaluate the effect on grape aroma. Nevertheless, the results suggest that MeJ is still a better option than ACP-MeJ in order to enhance the grape volatile composition, but considering that the applied dose in the ACP-MeJ treatment was 10 times lower than that applied in the MeJ conventional treatment, it can be said that nanotechnology has given very positive results in order to improve the grape aromatic quality.  
Figure 4: Discriminant analysis of volatile compounds content (ug/L) in grapes from control, methyl jasmonate (MeJ) and nanoparticles doped with this elicitor (ACP-MeJ) treatments, in (**a**) 2019, (**b**) 2020, and (**c**) 2019 & 2020 seasons, carried out with the treatment as factor; and (**d**) carried out with the sample as factor.  
**Author Contributions:** Conceptualization, T.G.-C., J.M.D.-L. and E.P.P.-A.; methodology, T.G.-C., J.M.D.-L., E.P.P.-A. and G.B.R.-R.; formal analysis, S.M.-S.R., I.S.d.U. and T.G.-C.; investigation, B.P.-T., [PERSON], S.M.-S.R. and I.S.d.U.; data curation, S.M.-S.R.; writing--original draft preparation, S.M.-S.R. and [PERSON]; writing--review and editing, all authors; supervision, [PERSON], [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been carried out thanks to funding from the Ministerio de Ciencia, Innovacion y Universidades (FEDER/MCIU/AEI, Spain) through the Projects RTI2018-096549-B-I00 and RTI-2018-095794-A-C22. [PERSON] thanks Gobierno de La Rioja for her predoctoral contract. [PERSON] and [PERSON] acknowledge the Ministerio de Ciencia, Innovacion y Universidades for their Ramon y Cajal and Juan de la Cierva-Incorporacion contracts, respectively. [PERSON] would like to thank to Junta de Andalucia for her postdoctoral contract (PAIDI 2020, DOC_01383).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Analysis of Acceleration Response Results
### Acceleration Response at the Top of the Piers
Particularly for pier 2, the peak responses to the El Centro and Lanzhou waves were 15 times and 8 times higher, respectively, in the Y-direction than in the X-direction. The difference between the Y- and X-direction responses at the top of pier 2 was particularly significant compared to that of pier 1. Pier 1 was to the left of the straight main beam 1 with a variable section, while pier 2 was between the main beam 1 and branching beam 2, so it was shared by the two straight beams. The complexity of the overall structure made the response of pier 2 worthy of attention, and the degree of influence of the multidirectional excitation on the Y-direction response could not be ignored. Figure 7 shows the X- and Y-direction acceleration responses at the tops of piers 2 and 4 to the bidirectional (X + Y) Imperial Valley seismic waves. Although the X- and Y-direction input waves were the same, the X- and Y-direction acceleration responses at the tops of piers 2 and 4 clearly had very different waveforms. Overall, the peak acceleration of the input wave was greater in the X-direction than in the Y-direction, but the response was much greater in the Y-direction than in the X-direction. The peak Y-direction acceleration was 13 times greater than the peak X-direction acceleration at the top of pier 2, and 7 times greater at the top of pier 4. The peak acceleration response was greater at the top of pier 4 than at the top of pier 2 because pier 4 supported the straight beam 2 and irregular beam 3. Thus, it was subjected to greater forces than pier 2, which only supported straight beams. Figure 5: Acceleration response at the top of Pier 1. (**a**) X-direction; (**b**) Y-direction. Figure 6: Acceleration response at the top of Pier 2. (**a**) X-direction; (**b**) Y-direction. Table 5 presents the X- and Y-direction acceleration responses at the tops of the piers subjected to bidirectional (X + Y) and tridirectional (X + Y + Z) El Centro and Imperial Valley waves. The responses, at the tops of piers 1-3, were greater with the multidirectional El Centro wave than with the bidirectional wave. In contrast, the responses of the pier tops were less with the multidirectional Imperial Valley wave than with the bidirectional wave. This indicated that the multidirectional excitation did not necessarily increase the structural response of the structure. Under multidirectional excitation, the responses at the tops of piers 4-6 exceeded those of the other three piers, which supported straight beams. The response was the greatest at the top of pier 6. The above analysis showed that the response of the pier tops to multidirectional excitation was always greater in the Y-direction than in the X-direction. Thus, additional attention should be given to the structural performance of piers in the cross-bridge direction (i.e., tangential to the main beam) in designing irregular bridges.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### Elbow Estimation
#### 4.3.3 Elbow Distance Estimation  
The elbow distance is directly derived from the tangent estimation. As shown in Figure 9, the tangent estimation identifies the location of the tangent point \(T_{E}\). Moreover, the distance from the elbow starting point \(S_{E}\) to tangent point \(T_{E}\) can be computed regarding \(R_{E}\) and \(\gamma\) as follows:  
\[\overline{S_{E}T_{E}}=R_{E}\tan\gamma \tag{4}\]  
Given the point \(T_{E}\) and the computed distance \(\overline{S_{E}T_{E}}\), we can derive the location of \(S_{E}\) and the distance \(\Delta x_{E}\). From this distance \(\Delta x_{E}\) together with the elbow direction \(\theta_{E}\) and elbow radius \(R_{E}\), the origin \(O_{E}\) is the point on the elbow plane and is \(R_{E}\) distance away from \(S_{E}\) along the \(\gamma_{E}\)-axis.