esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Obstacles Avoidance Control  
Considering that the formation member will inevitably collide with obstacles in the environment and other members in the motion process, it is necessary to design an effective obstacle-avoidance control law that not only enables the robots to complete the barrier-void task but also ensures the stability of the formation. As a classical obstacle-avoidance control method, the artificial potential field method is widely used in robot's motion control. In the case discussed in this paper, it is not necessary to set up an additional attractive potential field function to generate attractive force because each robot has its own trajectory to track but only to set repulsive potential field to generate repulsive force for obstacles avoidance operation. The classical repulsive field function is defined as  
\[\mathbf{U}_{i,rep}(t)=\left\{\begin{array}{ll}\frac{1}{2}k_{rep}\sum\limits_{k=1 }^{m}\left(\frac{1}{\rho_{i,obs}(t)}-\frac{1}{\rho_{0}}\right)^{2}\!\!,&\text{ if }\rho_{i,obs}(t)\leq\rho_{0}\\ \\ 0\!\!,&\text{ if }\rho_{i,obs}(t)>\rho_{0}\end{array}\right. \tag{23}\]  
where \(k_{rep}\) is a position constant, which is repulsion gain coefficient. \(\rho_{0}>0\) is obstacle-avoidance response distance. \(m\in\mathbb{N}\) is the number of obstacles in domain \(\rho_{0}\). Denote \(\mathbf{\rho}_{i,obs}(t)=\mathbf{p}_{i}{{}^{\prime}}(t)-\mathbf{p}_{k,obs}(t)\), \(\rho_{i,obs}(t)=\left\|\mathbf{\rho}_{i,obs}(t)\right\|\), where \(\mathbf{p}_{k,obs}(t)\) is the position of obstacle \(k\) in the global frame.  
Then, since repulsion force received by the robot is along the negative gradient of the repulsive field function, the obstacle-avoidance control law is expressed as follows:  
\[\mathbf{u}_{i,rep}(t)=-\
abla\mathbf{U}_{i,rep}(t)=\left\{\begin{array}{ll}k_{rep} \sum\limits_{k=1}^{m}\left(\frac{1}{\rho_{i,obs}(t)}-\frac{1}{\rho_{0}} \right)\frac{\rho_{i,obs}(t)}{\rho_{i,obs}(t)^{2}}\!\!,&\text{if }\rho_{i,obs}(t)\leq\rho_{0}\\ \\ 0\!\!,&\text{if }\rho_{i,obs}(t)>\rho_{0}\end{array}\right. \tag{24}\]  
Finally, we combine (18), (20), and (24) to achieve circular formation control with obstacles avoidance, which can obtain the final control input of robot \(i\) as  
\[\mathbf{u}_{i,final}(t)=\mathbf{u}_{i}(t)+\mathbf{u}_{i,rep}(t),\hskip 14.226378 pti=1,2,...,n \tag{25}\]  
By substituting (25) into (5), the linear and angular speeds of all robots in the circular formation can be obtained.
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 2 Phase Calibration Method
### Apply Phase Calibration
\[T(\omega,n_{\text{tx}},n_{\text{rx}})=T_{\text{tx}}^{-1}(\omega,n_{\text{tx}}) \ T_{\text{meas}}(\omega,n_{\text{tx}},\ n_{\text{rx}})\ T_{\text{rx}}^{-1}( \omega,n_{\text{rx}}) \tag{12}\]  
The following equation is the S-parameter between phase centers calculated from the S-parameter between calibration planes:  
\[S(\omega,n_{\text{tx}},n_{\text{rx}})= \begin{bmatrix}S_{11}(\omega,\ n_{\text {tx}})\cdot e^{-2i\phi^{\prime}_{\text{rx}}(\omega,n_{\text{rx}})}&S_{12}( \omega,n_{\text{tx}},n_{\text{rx}})\cdot e^{-j(\phi^{\prime}_{\text{rx}}( \omega,n_{\text{rx}})+\phi^{\prime}_{\text{rx}}(\omega,n_{\text{rx}}))}\\ S_{21}(\omega,n_{\text{tx}},n_{\text{rx}})\cdot e^{-j(\phi^{\prime}_{\text{rx}}( \omega,n_{\text{rx}})+\phi^{\prime}_{\text{rx}}(\omega,n_{\text{rx}}))}&S_{22}( \omega,\ n_{\text{rx}})\cdot e^{-2j\phi^{\prime}_{\text{rx}}(\omega,n_{ \text{rx}})}\end{bmatrix}  \tag{13}\]
## 2 Algorithm
### Two-Dimentional MDPO Algorithm Using a Pre-Known User Altitude  
It is known that when a user altitude \(z_{R}\) is known by other means, \(\Delta z\) in Equation (12) becomes zero and can be eliminated [16]. Accordingly, the z-spatial distribution \(\frac{\partial\Delta\
abla r}{\partial z}\) can be removed from \(G\) in Equations (17)-(21):  
\[R=G\Delta X+w \tag{33}\]  
\[R=\left[\begin{array}{cc}R(t_{k})&\cdots&R(t_{k+N-1})\\ \end{array}\right]^{T} \tag{34}\]  
\[\Delta X=\left[\Delta x,\Delta y\right] \tag{35}\]  
\[w=\left[\begin{array}{cc}-\Delta\
abla\omega(t_{k})&\cdots&-\Delta\
abla \omega(t_{k+N-1})\\ \end{array}\right]^{T} \tag{36}\]  
\[G=\left[\begin{array}{ccc}\frac{\partial\Delta\
abla r(t_{k})}{\partial x}& \frac{\partial\Delta\
abla r(t_{k})}{\partial y}\\ \vdots&\vdots\\ \frac{\partial\Delta\
abla r(t_{k+N-1})}{\partial x}&\frac{\partial\Delta \
abla r(t_{k+N-1})}{\partial y}\\ \end{array}\right] \tag{37}\]  
We call this method two-dimensional (2D) MDPO. This helps the MDPO algorithm achieve a smaller GDOP value in the same or a shorter observation period and, as a result, provide a better user position accuracy compared with three-dimensional estimation. Therefore, in this study, only the 2D MDPO algorithm is used hereafter. In 2D MDPO, the number of multi-epoch observations can be reduced to as low as 2 (N = 2).  
In a lunar navigation problem, rover altitude \(z_{R}\) can be pre-estimated using a lunar digital elevation model (DEM) [17; 18]. As shown in Equation (38), the DEM is a function of longitude and latitude, which are not known at the start. The estimation of sequences proceeds in the following sequence: First, \(\mathbf{X}_{R}^{0}(t_{k})\) is estimated using the rover position before its relocation, i.e., \(\mathbf{X}_{R}(t_{k-1})=(x_{R}(t_{k-1}),\;y_{R}(t_{k-1}),\;z_{R}(t_{k-1}))\). Then, a new estimated rover position, i.e., \(\mathbf{X}_{R}^{1}(t_{k})\), is estimated as \(({x_{R}}^{1}(t_{k}),{y_{R}}^{1}(t_{k}),z_{R}(t_{k-1}))\) by Equation (23). Note that \(z_{R}\) is not updated at this moment. After that, the altitude of the rover is updated to \({z_{R}}^{1}(t_{k})\) using \({x_{R}}^{1}(t_{k})\) and \({y_{R}}^{1}(t_{k})\) by Equation (38). The calculation continues until the number of iterations reaches the designed value, i.e., \(n\).  
\[z_{R}^{i}(t_{k})=z_{R\;\text{\emph{DEM}}}\Big{(}x_{R}^{i}(t_{k}),y_{R}^{i}(t_ {k})\Big{)} \tag{38}\]Here, \(z_{R\ DEM}\) is a lunar DEM model that is a function of latitude and longitude. Note that, according to Equation (38), when \(z_{R}\) changes along with \(x_{R}\) and \(y_{R}\), errors in the X-Y position induces errors in the Z position, which ultimately induces errors in estimated \(x_{R}\) and \(y_{R}\) according to Equation (12), and as a result, Total UPE deteriorates stochastically. In our research, we do not apply the case in which the rover altitude changes too rapidly, such as the rover dropping off the cliff or roving on steep slopes. In that case, Total UPE will not deteriorate too significantly, which was confirmed by the simulations in the following section.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 3 Main Synthesis Methods of Magnetic and Maghemite Nanoparticles
### Synthesis by Co-Precipitation
[56] showed that the presence or absence of oxygen in the reaction medium affects the size of the nanoparticles obtained. In fact, the particles obtained during a synthesis under nitrogen are relatively smaller than those obtained during a synthesis under air. In conclusion, the synthesis of iron oxide nanoparticles by co-precipitation is a synthesis method allowing to quickly obtain a large quantity of nanoparticles. It is simple in its implementation, but its parameters are complex. It presents the potential to produce nanoparticles in aqueous solution and without toxic solvent. By adjustment of several parameters of synthesis, it is possible to obtain nanoparticles of desired composition and size. However, if the monodispersity of samples can be improved by adjusting the synthesis parameters, the latter remains less good than for other more complex synthesis complex (the thermal decomposition for example). In addition, the synthesized nanoparticles have a strong tendency toward aggregation in aqueous solution without further treatment. This trend will considerably limit the possible applications, especially in the biomedical field. These two main defects that are the polydispersity and the aggregation of nanoparticles obtained could be limited by the stabilization of nanoparticles in aqueous solution. The functionalization of iron oxide nanoparticles by adding a ligand during the synthesis or after it in order to stabilize them in aqueous solution will be the subject of the end of this review.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 3 Materials and Methods
### BP-ANNs  
BP-ANNs is one of the most popular neural network learning algorithms based on the error backpropagation computing for adjusting net connection weights [84]. The backpropagation algorithm is based on Delta learning rule in which the weight adjustment is performed through mean square error of the output response to the sample input. The set of these sample patterns is repeatedly presented to the network until the error value is minimized. Many researchers use it to cope with the nonlinear problem and a trained backpropagation network is able to detect and classify an input template. It has been used successfully for many applications, e.g., image pattern recognition, medical diagnosis, stock market prediction, and automatic controls.  
As is well known, three-layers BP ANNs (Figure 4) is able to approximate any continuous functions [85]. In consequence, this means that one hidden layer-based BP-ANNs can create a new mapping from \(N\)-dimensional to M-dimensional. Convergence rate of BP-ANNs increases with the number of hidden nodes. It is worth noting that the increase of number of hidden layers will not necessarily yield to an increase of performance [86]. In this work, based PCA methods [69], the first three principle components of features from AP were used to train the BP-ANNs, namely the number of neurons in the input layer was chosen to be three. One neuron node in output layer was used to classify AP signal.  
Therefore, in this paper, considering the convergence rate and fitting accuracy, we compared classification performance using one hidden layer with a different number of hidden nodes respectively. Meanwhile, the Log-sigmoid transfer function is used as the activation functions for both the hidden and output layers, i.e., \(f(x)=\frac{1}{1+e^{-x}}\), \(0<\) f(x) \(<1\).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Classification** & **Feature Parameters** \\ \hline Time-domain features (seven items) & Amplitude, Duration, Rising duration, Decline duration, Rising slope, Decline slope, Area \\ \hline Statistics characteristics (five items) & Skewness, Kurtosis, Hjorth: Hjorth activity, Hjorth mobility, Hjorth complexity \\ \hline Frequency-domain features (two items) & Average power, Energy \\ \hline Nonlinear features (five items) & Permutation entropy, Sample entropy, Correlation dimension (CD), LLE, Hurst exponent \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features of AP.
## 2 Results
Pd/OF2 and Pd/OF3, which have the highest weight-swelling ratio in toluene (see Section 4.2) were chosen for hydrogenation of MNY (Figure 9, Table 1, #5-6) and DHIP (Figure 10, Table 1, #7-8). In contrast to MBY, in the case of alkynols C\({}_{10}\) (MNY) and C\({}_{20}\) (DHIP), the catalytic activity related to the palladium content was noticeably higher for the mesoporous sample (Pd/OF2) in comparison with the microporous one (Pd/OF3). The difference in activity between Pd/OF2 and Pd/OF3 is mostly noticeable in the hydrogenation of MNY. In order to verify if pore sizes influence the observed hydrogenation rates, unrestricted DFT calculations were carried out for MBY, MNY, and DHIP at the BP level of theory using triple-zeta basis sets (see Figure S1 in the Supporting Materials). It was found (see Figures S1-S3) that MBY molecules have a collision size (\(5.91\times 5.93\times 7.15\) A) that is much smaller than the pore diameters. The collision size of MNY is 5.95 \(\times\) 6.23 \(\times\) 14.16 A. The collision size of DHIP is 6.09 \(\times\) 8.30 \(\times\) 21.99 A. Obviously, for Pd/PS and Pd/OF3 catalysts, MNY and DHIP will undergo strong steric hindrances in micropores (below 2 nm) and in small mesopores (2-6 nm), which will cause internal diffusion limitations. At the same time, for the sample Pd/OF3 there is  
Figure 10: Kinetic curves of IP accumulation (**a**) and dependence of the selectivity, in respect to IP on DHIP conversion of (**b**). Figure 9: Kinetic curves of 3-methyl-1-nonen-3-ol (MNE) accumulation (**a**) and dependence of the selectivity, with respect to MNE on MNY conversion of (**b**). almost no difference in the hydrogenation rates of MBY and MNY (Table 1, #4, and #6). However, in the case of DHIP (Table 1, #8), in spite of the obvious steric hindrances, the hydrogenation rate is 1.8 times higher in comparison with MNY (Table 1 and #6). The higher hydrogenation rate is likely due to the positive inductive effect of the alkyl chain to the triple bond, which makes it more reactive. Relatively lower selectivity, with respect to olefinic alcohol, in the case of DHIP (see Table 1 and #8), in comparison with other alkynols with a shorter alkane chain (Table 1, #6, and #4), can be due to the higher hydrophobicity of DHIP (calculated dipole moment (\(\upmu\)) of DHIP is 1.05 D, \(\upmu\)(MNY) = 1.22 D, \(\upmu\)(MBY) = 1.24 D), which results in its higher affinity to the polymeric HPS support. It can be proposed that the mesoporous structure along with the small sizes of Pd NPs (about 2 nm) are beneficial for the hydrogenating substrates with short alkane chains. For high-molecular weight alkynols, such as DHIP, the existence of mesopores (sample Pd/OF2) results in another effect--the blockage of pores, presumably by hydrogenation products. As a result, an incomplete conversion of DHIP (90%) and a fast decrease in selectivity, with respect to IP at high conversions, were found while using Pd/OF2 (Figure 9b, Table 1, and #7).
## 3 Simulation and Experimental Results
### The Reflectance Curve of the Edge Filter
In this case, we also conducted simulations on the reflectance curve of the edge filter, with various random errors on each layer of the stack. Figure 5 shows the simulation results, and it is evident that the random errors of the thickness not only affect the position, but the steepness of the curve. Meanwhile, the magnitude of the random error aids in the enhancement of the fluctuation range of the curve, which in turn decreases the stability and reliability of the laser beam-combining system. Hence, random errors in the thickness for such a filter should be limited to within \(0.3\%\), so as to obtain a high combination efficiency and a stable output power level. Figure 4: (**a**) The reflectance curves of the edge filter under different surface roughness values. The simulation results of the reflectance curve of the edge filter with smooth surface, obtained with: (**b**) TFCalc; (**c**) uncorrelated surface roughness model. (**d**) The reflectance curve of the edge filter versus the thickness error. Based on the Fresnel equation and Snell's law, we extended the uncorrelated surface roughness model to calculate the filter's reflectivity under the circumstances of a small incident angle. Consider a multilayer coating with rough surfaces, as above (\(\sigma\) = 0.85 nm), with the thickness error not taken into account. The simulation results (the solid lines) and the experimental results (the dotted line) of the relationship between the reflectance and incident angle for the two cases of polarization are shown in Figures 6a and 6b, respectively. As can be clearly seen in the Figure 6, the reflectance curve of the edge filter only moves toward short wavelengths, and the steepness of the rising edge is irrelevant to the incident angle. The simulations are consistent with the experiments results, verifying the accuracy and validity of the model.
# Mn(II) Complexes of Enlarged Scorpiand-Type Azamacrocycles as Mimetics of MnSOD Enzyme
## 3 Results
### Electrochemical Behaviour  
For a manganese complex to act as an SOD mimetic, it is necessary that the metal be able to cycle between the Mn\({}^{2+}\) and Mn\({}^{3+}\) oxidation states. Moreover, the redox potential should be between the one-electron reduction potential for oxygen (\(-\)160 mV vs. NHE) and the one-electron reduction potential of superoxide radical (890 mV vs. NHE) [8]. To study and quantify this behaviour in our systems, cyclic voltammetry studies were carried out using L1/Mn\({}^{2+}\) as the reference. A reversible oxidation process by one electron was observed, as can be seen by the anodic (A1) and cathodic (C1) peaks that appear around +0.5 V vs. Ag/AgCl in Figure 4, in which the cyclic voltamperograms of [Mn(L1)]\({}^{2+}\) is represented. In this case, the relationship between the current of the cathodic peak and the current of the anodic peak is close to unity; however, it decreases with increasing scanning speed, which indicates that the initial oxidation process A1 is followed by a partial dissociation of the electrochemically generated Mn\({}^{3+}\). This hypothesis would explain the appearance of an additional cathodic signal near \(-\)0.5 V (C2), which could be attributed to the reduction of the Mn\({}^{3+}\) aqueous complex from the proposed dissociation process.  
In the complex studied, the formal potential for the Mn\({}^{3+}\)/Mn\({}^{2+}\) pair was +736 mV (vs. SHE), which is within the required range for SOD activity. It is, however, greater than the ideal potential for oxidation and reduction of superoxide (+360 mV), being rather close to the upper limit of +890 mV. Nevertheless, this could be an advantage for enzyme mimetics since, as it has been described in the literature, higher potentials may be necessary to achieve greater selectivity for superoxide, avoiding the oxidation of Mn\({}^{2+}\) by other oxidizing species present in the biological medium, such as nitric oxide, peroxynitrite, hydrogen peroxide, hypochlorite, or even oxygen [31]. Moreover, the value of the formal potential of L1 is within the same range as other similar compounds built with a scorpionally-like polyaminic scaffold. For example, the six compounds reported in reference [15] had potentials in the 550-850 mV range.
## 3 Results and Discussion
### Optimal Green Solvent Design for Antioxidant Extraction from Lemon Balm  
The above AC results suggested that the capability of DESs to extract antioxidant compounds differed depending on the type of DES mixture, which was probably because DESs with various HBAs and HBDs can form intermolecular hydrogen bonds of different strengths with bioactive compounds, resulting in additional extraction capabilities. Therefore, ChCl:U was selected as the optimal new DES for recovering antioxidants from the richest source, such as LB. A BBD was employed to investigate the effects of the ChCl:U molar ratio, the water content (WC) in this DES, and extraction time (t) on the antioxidant potential analyzed by three analytical assays, creating a total of 15 experiments under different conditions. The experimental and predicted CUPRAC, ABTS, and DPPH values of the obtained LB extracts are listed in Table 4.  
As can be seen in Figure 6, the antioxidant properties of the LB extracts prepared by the UAE technique with the ChCl-based DES differ significantly from each other (Table 4, Duncan test). This variability can be explained by the influence of extraction conditions (ChCl:U, WC, and t) as well as analytical parameters of the proposed assays, which would affect the total level of antioxidants. The highest antioxidant properties (CUPRAC = 493.7 \(\upmu\)mol TE/g, ABTS = 273.5 \(\upmu\)mol TE/g, and DPPH = 209.5 \(\upmu\)mol TE/g) had LB extract treated for 15 min by ChCl:U solvent synthesized in the 1:1 ratio with 40% WC. In contrast, the same molar ratio (1:1) of ChCl-based DES components containing the lowest concentration  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Independent Variables**} & \multicolumn{6}{c}{**Dependent Variables**} \\ \cline{2-10} \multirow{2}{*}{**Exp. **} & \multicolumn{3}{c}{**ChCl:U**} & \multirow{2}{*}{**WC**} & \multirow{2}{*}{**t**} & \multicolumn{2}{c}{**CUPRAC \({}^{*}\)**} & \multicolumn{2}{c}{**ABTS \({}^{*}\)**} & \multicolumn{2}{c}{**DPPH \({}^{*}\)**} \\  & & & & **(\(\upmu\)mol TE/g)** & & \multicolumn{2}{c}{**(\(\upmu\)mol TE/g)**} & \multicolumn{2}{c}{**(\(\upmu\)mol TE/g)**} \\ \cline{5-10}  & \multicolumn{1}{c}{**(mol/mol)**} & & \multicolumn{1}{c}{**(\(\upmu\)mol TE/g)**} & & **Exp. \(\pm\) SD** & **Pred. ** & **Exp. \(\pm\) SD** & **Pred.
# Analysis of the Use of Similarity Coefficients in Manufacturing Cell Formation Processes
###### Abstract
Based on the criterion of whether, in an optimized process, part \(i\in[\)set of parts\(]\) requires or does not require machine \(j\in[\)set of machines\(]\), the parameters \(a\), \(b\), \(c\), and \(d\) are defined in terms of the relevance between machines and parts. The main purpose of this study is to analyze the use of similarity coefficients in the cell formation process. The technical contribution of this study is a detailed guide to applying similarity analysis supported by a similarity coefficient in the cell formation process. The technical contribution includes a method to verify the outcomes of the process and choose the best options. The application consists of separating machines in cells, verifying if there are exceptionalities that could be amended, and evaluating the options to support the final decision. In particular, exceptional cases are continuous improvement opportunities that practitioners should not neglect when applying the suggested method. A bibliometric search was conducted using convenient keywords in the Google Scholar search engine to identify the incidences of twenty types of similarity coefficients. The most cited coefficient was tested in standard and non-standard application cases, and the results were compared to support a conclusion. The literature presents several cases of the use of similarity coefficients for the cell formation process in manufacturing [8]. Among many others, in engine manufacturing, the authors used hierarchical clustering to allocate machines to cells [9]. In an aluminum industry plant, the authors used non-hierarchical clustering, specifically the PFA (product flow analysis) method [10]. Finally, in the auto parts industry, the authors employed non-hierarchical clustering, specifically the ALC (average linkage clustering) technique, to allocate machines to cells [11]. The use of similarity coefficients is also observed in totally disparate activities outside the scope of manufacturing, such as co-citation management in bibliometric studies [12] or risk management in regions subject to river flooding [13], which conveys the capacity of generalization in this study. The rest of the article is structured as a review of MCM and GT, methodology and results, discussion, and conclusion.