esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Features of \({}^{13}\)C-CP/MAS and \({}^{1}\)H Wide-Line NMR Spectra of PLA  
The \({}^{13}\)C-CP/MAS spectra were stacked before and after the degradation experiment, and the signal shape became sharper as the time of exposure to the decomposition environment increased (Figure S1a). By comparing the \({}^{13}\)C-CP/MAS spectra after the decomposition experiments with those of previous studies [25, 26], it was possible to determine from which functional groups of PLA the sharpened signals originated. These results indicated that the crystal ratio of PLA was enlarged by the degradation experiment, which may be due to the preferential hydrolysis of the amorphous component and the relative increase in the crystalline component in the sample [14]. From this result, we decided to use the NMR spectrum as an explanatory variable. The \({}^{1}\)H wide-line NMR spectra were stacked before and after the degradation experiment (Figure S1b). However, no significant change in signal shape was observed after the decomposition experiment in comparison with that before the decomposition experiment. Therefore, it was considered that by using PCA, it is possible to capture minute changes in the signal shape and extract the characteristics of signals related to decomposition. First, Figure S2 shows the results of PCA for the \({}^{13}\)C-CP/MAS spectra. In the PCA score plot, the difference in crystallinity could be determined by characterizing the difference in sample species. In the PCA loading plot, PC1 extracted a broad signal and PC2 extracted a sharp signal. In the \({}^{13}\)C-CP/MAS spectra of the polymer, the amorphous-derived signal became broad and the crystal-derived signal became sharp [14, 15]; thus, the characteristics of PC1 were classified as amorphous and PC2 was classified as crystalline. Figure S3 shows the results of PCA of the \({}^{1}\)H wide-line NMR spectra. In the PCA score plot, the difference in crystallinity could be determined based on the characterization of the difference in sample species. In addition, in the PCA loading plot, PC1 extracted a broad signal and PC2 extracted a sharp signal. In the \({}^{1}\)H wide-line NMR spectra of the polymer, the amorphous-derived signal was sharp, and the crystal-derived signal was broad [9, 27]; thus, PC1 was classified as crystalline and PC2 was classified as amorphous. The total contribution of PC1 and PC2 was 99.5% for the PCA of \({}^{13}\)C-CP/MAS spectra and 99.9% for the PCA of \({}^{1}\)H wide-line NMR spectra. From this, it was possible to obtain most of the information on the NMR spectra by using only PC1 and PC2. If there are many explanatory variables, the calculation cost becomes enormous when performing machine learning. Therefore, it is possible to construct predictive models that maximize information while suppressing the calculation cost when utilizing a compression technique such as PCA.
## 2 Process and System Description
\[G_{P}(s)=G_{0}(s)e^{-\tau s}=\frac{b}{s^{n}+a_{n-1}s^{n-1}+\cdots a_{1}s+a_{0}}e ^{-\tau s}, \tag{1}\]  
where \(\tau\) is the pure lag time constant, \(G_{0}(s)\) represents the part of the object that does not include the lag link, \(b\), \(a_{0}\), \(a_{1}\cdots a_{n-1}\) are object parameters. For industrial objects with time lag, Equation (1) can be simplified as a first-order inertial link or a second-order oscillating link and described as a first-order time-delay system (FOPTD) or a second-order time-delay system (SOPTD), which is represented by Equations (2) and (3) respectively. \[G_{\text{FOPTD}}(s)=\frac{b}{s+a}e^{-\tau s}, \tag{2}\]  
\[G_{\text{SOPTD}}(s)=\frac{b}{s^{2}+a_{1}s+a_{0}}e^{-\tau s}, \tag{3}\]  
Most of the time, all the poles of the system are stable when \(a>0\), \(a_{0}>0\), \(a_{1}>0\). When \(a>0\) or \(a_{0}=0\) Equations (2) and (3) become pure integrator with a time-delay link and it is intractable for controlled system. Furthermore, the control problem of the system will be more challenging if \(a<0\) or \(a_{0}<0\), \(a_{1}<0\) due to the unstable poles. We choose the controlled objects as shown in Equation (4)  
\[G_{\text{SOPTD}}(s)=\frac{0.01578}{s^{2}+0.276s+0.01578}e^{-3.73s} \tag{4}\]  
When the time delay \(e^{-3.73s}\) is approximately the first order inertial link \(\frac{1}{3.73s+1}\), the controlled object shown in Equation (4) becomes  
Figure 2.0  
Figure 2.1 The basic structure of the flow control system. In order to facilitate the controller design, we transform the representation of the system from frequency domain to time domain for analysis. Considering the disturbance that the system will suffer and the system uncertainty, the state space equation is shown in Equation (5)  
\[\left\{ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=x_{3}\\ \dot{x}_{3}=a(x_{1},x_{2},x_{3})+bu+f(x_{1},x_{2},x_{3},w(t),t)\\ y=x_{1}\end{array} \right., \tag{6}\]  
where \(a(x_{1},x_{2},x_{3})=-0.544x_{3}-0.09x_{2}-0.004x_{1}\), \(b=0.004\), \(f(x_{1},x_{2},x_{3},w(t),t)=w(t)+\Delta a(x)\) represents the total disturbance of the system, where \(w(t)\) represents the disturbance, \(\Delta a(x)\) represents the model uncertainty.
## 6 The Controller Design
### The Inner-Loop Design  
The inner loop was designed based on the dynamic inverse theory. First, given the nonlinear rotational function of the aircraft:  
\[\left\{\begin{aligned} & I_{xx}\dot{p}+(I_{zz}-I_{yy})qr-I_{xx}(pq+ \dot{r})=L\\ & I_{yy}\dot{q}+(I_{xx}-I_{zz})rp+I_{xx}(p^{2}-r^{2})=M\\ & I_{zz}\dot{r}+(I_{yy}-I_{xx})pq+I_{xx}(qr-\dot{p})=N\\ \end{aligned}\right. \tag{32}\]  
where \(I_{xx}\), \(I_{yy}\), \(I_{zz}\) are the inertia moments and \(I_{xx}\) is the inertia product, \(p\), \(q\), \(r\) are the angular rates around the body \(x\)-\(y\)-\(z\)-axis, and \(L\), \(M\), \(N\) are the moments around \(x\)-\(y\)-\(z\)-axis, the angular rate can be expressed as:  
\[\dot{x}=f+Gu \tag{33}\]  
and  
\[x=\left[\begin{array}{c}p\\ q\\ r\end{array}\right]G=\left[\begin{array}{ccc}c_{3}&0&c_{4}\\ 0&c_{7}&0\\ c_{4}&0&c_{9}\end{array}\right],f=\left[\begin{array}{c}c_{1}rq+c_{2}pq+c_{ 3}\hat{L}+c_{4}\hat{N}\\ c_{5}pr-c_{6}p^{2}+c_{6}r^{2}+c_{7}\hat{M}\\ c_{8}pq-c_{2}rq+c_{4}\hat{L}+c_{9}\hat{N}\end{array}\right]u=\left[\begin{array} []{c}\overline{L}\\ \overline{M}\\ \overline{N}\end{array}\right],\]  
where \(\hat{L}\), \(\hat{M}\), \(\hat{N}\) are the moments around the \(x\)-\(y\)-\(z\) axis without control, \(\overline{L}\), \(\overline{M}\), \(\overline{N}\) are the control moments around \(x\)-\(y\)-\(z\) axis, respectively, and \(c_{1}\)-\(c_{9}\) are the inertia coefficients given by Table 4.  
It is easy to obtain the inverse matrix of \(G\) as:  
\[G^{-1}=\left[\begin{array}{ccc}I_{xx}&0&-I_{xz}\\ 0&I_{yy}&0\\ -I_{xz}&0&I_{zz}\end{array}\right] \tag{34}\]  
Therefore, the inner-loop controller was designed according to dynamic inverse theory as:  
\[u_{c}=G^{-1}(k(x_{c}-x)-f) \tag{35}\]  
where \(u_{c}=\left[\begin{array}{c}\overline{L}_{c}\\ \overline{M}_{c}\\ \overline{N}_{c}\end{array}\right]\) is the control commands and \(x_{c}=\left[\begin{array}{c}p_{c}\\ q_{c}\\ r_{c}\end{array}\right]\) are the desired angular rates translated from the outer loop.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(c_{1}\) & \(\frac{I_{yy}I_{zz}-I_{zz}^{2}}{I_{zz}I_{zz}I_{zz}I_{zz}I_{zz}I_{zz}I_{zz}I_{zz}I_{zz}I _{
## 2 Materials and Methods
### Antioxidant Assay
#### 2.4.1 1,1-diphenyl-2-picrylhydrazyl DPPH Radical Scavenging Activity Assay  
The method of [PERSON] and [PERSON] [10] was employed to determine DPPH free radical scavenging activity. A solution of 0.135 mM 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) in methanol was briefly prepared. Briefly, 1.0 mL of this solution was mixed with 1.0 mL of the HA oil and standard drugs (BHT and gallic acid) (0.025 to 0.5 mg/mL). The reaction mixture was then vortexed and put in the dark at room temperature for a period of 30 min. The absorbance of the mixture was measured using a spectrophotometer at 517 nm. All tests and analysis were run in triplicates. The plant extract's scavenging ability was then calculated using the following equation:  
DPPH Scavenging activity (%) = [(Abs control \(-\) Abs sample)/(Abs control)] \(\times\) 100  
where Abs control is the absorbance of DPPH + methanol and Abs sample is the absorbance of DPPH radical + sample (sample or standard).  
4.2 2,2\({}^{\prime}\)-Aazino-bis (3-ethylbenzthiaazoline-6-sulfonic acid) (ABTS) Radical Scavenging Assay  
The method described by [PERSON] et al. [11] was adopted for the determination of ABTS scavenging activity. The stock solution of 7 mM ABTS solution and 2.4 mM potassium persulfate solution was prepared. The working solution was prepared by mixing the two stock solutions in equal proportions and then keeping it in the dark room for 12 h. The solution was then diluted by mixing 1 mL ABTS\({}^{+}\) solution with 60 mL of methanol to get an absorbance of 0.708 \(\pm\) 0.001 units at 734 nm with the use of the spectrophotometer. The plant's essential oil (1 mL) and the controls were allowed to react with 1 mL of the ABTS\({}^{+}\) solution and the absorbance was taken at 734 nm after 7 min using a spectrophotometer. The ABTS\({}^{+}\) scavenging capacity of the essential oil was then compared with that of the standards. The percentage inhibition was then calculated as follows:  
\[\text{Inhibition \%}=\text{[(A blank $-$ A sample)/A blank]}\times 100 \tag{1}\]  
where A blank is the absorbance of ABTS radical + methanol used as control and the A sample is the absorbance of ABTS radical + sample extract/standard.  
All the tests were carried out in triplicate. The activity was expressed as 50% inhibitory concentration (IC\({}_{50}\)). The lower the IC\({}_{50}\) value, the higher the antioxidant activity.
# Portable L-Band Radiometer (PoLRa): Design and Characterization
###### Abstract  
A low-mass and low-volume dual-polarization L-band radiometer is introduced that has applications for ground-based remote sensing or unmanned aerial vehicle (UAV)-based mapping. With prominent use aboard the ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, L-band radiometry can be used to retrieve environmental parameters, including soil moisture, sea surface salinity, snow liquid water content, snow density, vegetation optical depth, etc. The design and testing of the air-gapped patch array antenna is introduced and is shown to provide a 3-dB full power beamwidth of 37\({}^{\circ}\). We present the radio-frequency (RF) front end design, which uses direct detection architecture and a square-law power detector. Calibration is performed using two internal references, including a matched resistive source (RS) at ambient temperature and an active cold source (ACS). The radio-frequency (RF) front end does not require temperature stabilization, due to characterization of the ACS noise temperature by sky measurements. The ACS characterization procedure is presented. The noise equivalent delta (\(\Delta\)) temperature (NEAT) of the radiometer is ~0.14 K at 1 s integration time. The total antenna temperature uncertainty ranges from 0.6 to 1.5 K.  
microwave; radiometer; L-band; low-mass; patch array; patch antenna; UAV; soil moisture; passive +
Footnote †: journal: Remote Sensing, 2020, 12, 2780; doi:10.3390/rs12172780
## 4 Mechanical Properties of Geopolymer Concrete
### Concurrent Efficiency of Fly Ash and OPC on Mechanical Properties
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Aim** & **Results** \\ \hline [PERSON] and [PERSON] [37] & The effect of low calcium fly ash as partial replacement on compressive strength, water absorption and porosity & An upward trend with the inclusion of fly ash as 20\%  cement weight was observed at all ages. The increment of fly ash up to 20\% resulted in a reduction in permeation properties, such as water absorption. The increase of more than 20\% FA marginally reduced the strength and permeation properties.
## 2 NR Representation of the Probe Voltage on the Spiral
Moreover, \(\chi>1\) is the oversampling factor, and \(\lfloor\cdot\rfloor\) represents the integer part function. Notably, the spiral angle \(\theta^{\prime}\), in contrast to the zenithal angle \(\theta^{\prime}\), can be negative. Concerning the NR representation on the scan spiral, in accordance with [36], the bandwidth \(W_{\xi}\) is equal to \(2/\lambda\) times the arclength of the spiral that wraps \(\Sigma\) from pole to pole, the parameter \(\xi\) is equal to \(2\pi/(\lambda W_{\xi})\) times the curvilinear abscissa of the projecting point lying on the spiral enfolding \(\Sigma\), and the corresponding phase function \(\gamma\) coincides with \(\psi\). The probe voltage at each point \(P(\theta^{\prime},\theta^{\prime})\) on the sphere can then be recovered from an NR number of its samples on the spiral by employing the 2-D OSI expansion [36]:  
\[V\big{(}\eta(\theta)^{\prime},\phi^{\prime}\big{)}=\ \mathrm{e}^{-\overline{\eta} \varphi(\eta)}\sum_{n_{0}-q+1}^{n_{0}+q}\{K(\eta,\eta_{\eta},\overline{\eta}, N,N^{\prime})\sum_{m=n_{0}-p+1}^{m_{0}+p}\overline{V}(\xi_{m})K(\xi(\eta_{ \eta}),\ \xi_{m},\overline{\xi},\ M^{\prime},M^{\prime})\} \tag{13}\]  
More specifically, \(2q\times 2p\) is the number of the closest retained samples, \(\overline{\eta}=q\Delta\eta\), \(\overline{\xi}=p\Delta\xi\) and:  
\[\xi_{m}=m\Delta\xi=2\pi m/(2M^{\prime\prime}+1);\ \eta_{n}=k\phi^{\prime}\ +n \Delta\eta=\eta_{0}+2\pi n/(2N^{\prime\prime}+1) \tag{14}\]  
\[n_{0}=\ \lfloor(\eta-\eta_{0})/\Delta\eta\rfloor;\ m_{0}=\ \lfloor\xi/\Delta\xi\rfloor \tag{15}\]  
\[M^{\prime}=\left\lfloor\chi^{\prime}W_{\xi}\right\rfloor+1;\ M^{\prime\prime}= \left\lfloor\chi M^{\prime}\right\rfloor+1 \tag{16}\]  
\[N=N^{\prime\prime}\ -\ N^{\prime};\ M=M^{\prime\prime}\ -\ M^{\prime} \tag{17}\]  
Moreover:  
\[K(\alpha,\alpha_{t},\overline{\alpha},H,H^{\prime\prime})=D_{H^{\prime\prime }}(\alpha-\alpha_{t})\Omega_{H}[(\alpha-\alpha_{t}),\overline{\alpha}] \tag{18}\]is the OSI function and:  
\[ \begin{split}\Omega_{H}(\alpha,\ \overline{\alpha})=\frac{T_{H}[2\cos^{2}( \alpha/2)/\cos^{2}(\overline{\alpha}/2)-1]}{T_{H}[2/\cos^{2}(\overline{\alpha} /2)-1]}\\ D_{H^{\prime\prime}}(\alpha)=\frac{\sin\left[(2H^{\prime\prime}+1) \alpha/2\right]}{(2H^{\prime\prime}+1)\ \sin(\alpha/2)}\end{split}  \tag{19}\]  
are, respectively, the Tschebyscheff sampling function and the Dirichlet function [31, 36], with \(T_{H}(\cdot)\) being the \(H^{th}\)-degree Tschebyscheff polynomial. It is important to stress that, in order to precisely interpolate the voltage in close proximity of the poles (\(\theta^{\prime}=0\) and \(\theta^{\prime}=\pi\)), a higher value of the enlargement bandwidth factor \(\chi^{\prime}\), related to the representation on the spiral, must be adopted to prevent a significant increase in the band-limitation error in those regions, due to the fact that, nearby the poles, a small variation of the optimal parameter \(\xi\) corresponds to very large changes of the angular parameter \(\phi^{\prime}\) describing the spiral.
# Aerothermoelastic Analysis of Conical Shell in Supersonic Flow
## 2 Dynamic Model
### Constrained Mode  
According to [PERSON]'s first approximation theory, the strain field on an arbitrary point of a conical shell is described as follows:  
\[\left\{ \begin{array}{c}\varepsilon_{x}\\ \varepsilon_{\theta}\\ \varepsilon_{x\theta}\end{array} \right\}=\left\{ \begin{array}{c}\varepsilon _{x,0}\\ \varepsilon_{\theta,0}\\ \varepsilon_{x\theta,0}\end{array} \right\}+z\left\{ \begin{array}{c}k_{x,0} \\ k_{\theta,0}\\ k_{x\theta,0}\end{array} \right\}, \tag{5}\]  
Figure 1: Schematic diagram of conical shell in supersonic flow. where \(\left\{\varepsilon_{x,0}\quad\varepsilon_{\theta,0}\quad\varepsilon_{x\theta,0} \right\}^{\mathsf{T}}\) and \(\left\{k_{x,0}\quad k_{\theta,0}\quad k_{x\theta,0}\right\}^{\mathsf{T}}\) are the mid-surface strains and surface curvature changes of the conical, respectively. They are stated as below:  
\[ \begin{array}{l}\varepsilon_{x,0}=\frac{\partial u}{\partial x}\\ \varepsilon_{\theta,0}=\frac{\partial u}{r(x)}\frac{\partial v}{\partial\theta }+\frac{\sin x}{r(x)}u+\frac{\cos x}{r(x)}w\\ \varepsilon_{x\theta,0}=\frac{\partial v}{\partial x}+\frac{1}{r(x)}\frac{ \partial u}{\partial\theta}-\frac{\sin x}{r(x)}\end{array}  \tag{6}\] \[k_{x,0}=-\frac{\partial^{2}v}{\partial x^{2}}\frac{\partial v}{ \partial x}-\frac{1}{r^{2}(x)}\frac{\partial^{2}v}{\partial\theta^{2}}+\frac {\cos x}{r(x)}\frac{\partial v}{\partial\theta}\] \[k_{x\theta,0}=2\Big{(}\frac{\sin x}{r^{2}(x)}\frac{\partial v}{ \partial\theta}-\frac{1}{r(x)}\frac{\partial^{2}w}{\partial\alpha\theta}+ \frac{\cos x}{r(x)}\frac{\partial v}{\partial x}-\frac{\sin 2x}{2r^{2}(x)}v\Big{)},\]  
where \(r(x)=r+x\sin\alpha\). Based on [PERSON]'s law, the components of stress in terms of strains of a functionally graded conical shell when thermal strain is considered can be expressed as  
\[\left\{ \begin{array}{l}\sigma_{x}\\ \sigma_{\theta}\\ \sigma_{x\theta}\end{array} \right\}=\left\{ \begin{array}{l}Q_{11}\quad Q_{12 }\quad 0\\ Q_{21}\quad Q_{22}\quad 0\\ 0\quad 0\quad Q_{66}\end{array} \right\}\left\{\left\{ \begin{array}{l} \varepsilon_{x}\\ \varepsilon_{\theta}\\ \varepsilon_{x\theta}\end{array} \right\}-\left\{ \begin{array}{l}\beta_{x} \\ \beta_{\theta}\\ \beta_{x\theta}\end{array} \right\}\Delta T(z)\right\}, \tag{7}\]  
where \(\Delta T(z)=T-T_{0}\), \(\beta_{x}=\beta_{\theta}=\beta(z,T)\), \(\beta_{x\theta}=0\), \(T_{0}=300\) K.\(\beta(z,T)\) is the thermal expansion coefficient.
# Implementation of Digital Twin for Engine Block Manufacturing Processes
## 3 Materials and Methods
### Characteristics of Production Line  
The process flow for aluminum-alloy engine block manufacturing includes melting, holding casting, fettling, heat treatment, and machining. However, the subject of the present study is only the machining part of the production line. Once the engine block is removed from the casting process a number of different machining processes need to be performed. The engine block machining line consists of a robotic arm, two horizontal machining centers, conveyor system, and buffer system (see Figure 1).  
After loading of the serial number of the casting by the line scan camera fixed on the input conveyor, the robotic arm is used to transport the workpiece from the conveyor to the empty buffer. The robot's decision-making algorithm is shown in Figure 2.  
Figure 1: Machining cell layout.  
Figure 2: Robot’s decision-making algorithm.  
Engine block computer numeric control (CNC) machining includes processes such as milling, face milling, drilling, boring, tapping, threading, etc. Two clamping fixtures are positioned in the machining area of the CNC machine by a rotary table. Machining is followed by measurement and piercing of excess workpiece parts. After a trouble-free execution of all operations, the engine block is placed on the OK output conveyor. In case of an error, the workpiece is defined as defective. Therefore, manual processing is required and workpiece is placed on the Not OK conveyor.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
###### Abstract  
In this paper, novel energy-aware and reliable routing protocols are proposed. The aim is to maximize the lifespan of wireless sensor networks (WSNs) subject to predefined reliability constraints by using multi-hop routing schemes, in which the source node forwards the packet to the base station (BS) via other nodes as relays. In the first proposed protocol, energy efficiency is achieved by maximizing the minimum residual energy of the path subject to fulfilling a predefined reliability constraints. The second protocol is an optimized version of the first one with respect to lifespan and complexity. The optimal path is the one in which the residual energy distribution of the nodes along the path is as close to uniform as possible and the packet arrives at the base station with a given success probability. To measure the uniformity of the residual energy distribution, we use an entropy like measure. The information about the current energy state of the network is maintained by using a look-up-table from which the optimal routes are computed on the BS. The BS broadcasts the updated optimal paths to each node after each round of packet transmission.  
WSN; residual enerrgy; routing protocols; entropy of energy; energy efficiency +
Footnote †: journal: Sensor and Actuator Networks
# Kinetics of Hyaluronidazoleae Inhibition by Rice (_Oryza sativa_ L.) Protein Hydrolysate
## 3 Results
### Hyaluronidase Inhibition Ability of Rice Protein Hydrolysate  
We used tannic acid as a positive control. As Figure 3a illustrates, hyaluronidase activity inhibition increased as the added amount of tannic acid was increased, with the half maximal inhibitory concentration (IC50) being 0.14 mg/mL--close to the IC50 of 71.1 mM (0.121 mg/mL) reported by [PERSON] et al. [21]. Figure 3b illustrates the inhibition of hyaluronidase activity using the rice protein hydrolysate solution as an inhibitor. The rice protein hydrolysate inhibition of hyaluronidase activity was dose dependent. Moreover, the line of linear regression had a slope and \(y\) intercept of 4.0073 and 13.788, respectively. Accordingly, we determined the IC50 of our rice protein hydrolysate solution to be 7.61 mg/mL. [PERSON] et al. [22] also concluded that bioactive peptides from milk fermented using specific strains of _Lactobacillus plantarum_ could inhibit hyaluronidase.  
Figure 3: Hyaluronidase activity inhibition by different dilutions of (**a**) tannic acid (mg/mL) and (**b**) rice protein hydrolysate (mg/mL).  
In general, plant-derived peptides, with low molecular weights and various biological functions, are regarded as healthy and safe. These peptides are absorbed easily and therefore are bioavailable in human body. Rice has nutritional, hypoallergenic, and health-promoting characteristics, all of which are retained by rice starch byproduct-derived peptides [23]. Although the rice protein hydrolysate does not have inhibitory activity higher than that of tannic acid, it is generally a natural ingredient safely usable in cosmetics [24].