esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Modeling
#### 3.1.2 Finite Element Modeling for Stress Characterization  
In order to get information on the local second-order non-linearity, a study of the behavior and influence of stress is necessary. Again, a stress evaluation across the multilayer WG cross section was made by using a FEM approach. We started this analysis from the previous analysis of Section 3.1.1.  
The interlayer surface was defined with a surface load obtained according to the mass over the layer. A finer, free-triangular grid was employed to mesh the computational model. Deformation, stress, and strain at static equilibrium, in a stationary regime, were calculated assuming the following equations [59]:  
\[\overset{\rightarrow}{F}+\overset{\rightarrow}{\
abla}\cdot\sigma=0. \tag{7}\]  
The total stress (\(\sigma\)) is given by:  
\[\sigma=\sigma_{ad}+\overset{\rightarrow}{\C}:\epsilon_{d},\epsilon_{d}= \epsilon-\epsilon_{inel} \tag{8}\]  
where the additional stress (\(\sigma_{ad}\)) is the sum of initial stress (\(\sigma_{0}\)), external stress (\(\sigma_{ext}\)), and \(q\) stress (\(\sigma_{q}\)):  
\[\sigma_{ad}=\sigma_{0}+\sigma_{ext}+\sigma_{q}. \tag{9}\]  
The total strain contribution (\(\epsilon\)) is established by the elastic (\(\epsilon_{el}\)) and inelastic (\(\epsilon_{inel}\)) strains. Inelastic strain (\(\epsilon_{inel}\)) is given by the sum of initial strain (\(\epsilon_{0}\)), thermal strain (\(\epsilon_{th}\)), hygroscopic strain (\(\epsilon_{hs}\)), plastic strain (\(\epsilon_{pl}\)), and creep strain (\(\epsilon_{cp}\)) [59].  
\[\epsilon_{inel}=\epsilon_{0}+\epsilon_{th}+\epsilon_{hs}+\epsilon_{pl}+ \epsilon_{cr} \tag{10}\]  
Figure 9: TEM mode calculations at different wavelengths for a WG of 9.4 μm width. The figure (**a**) shows the simulated TM mode at 1550 nm and figure (**c**) shows the simulated TE mode at 2640 nm. Figure (**b**,**d**) show the modes at 1550 nm and 2640 nm, respectively, obtained with an InSb camera for near infrared (NIR)/mid infrared (MIR) light.  
\[\varepsilon=\frac{1}{2}\left[\left(\
abla\overrightarrow{u}\right)^{T}+\
abla \overrightarrow{u}\right] \tag{11}\]  
Figure 10 shows the color maps for the stress components \(\sigma_{\rm xx}\) and \(\sigma_{\rm yy}\) obtained from the equations. Several interesting features can be seen from the maps. In order to evaluate the stress-induced behavior, we simulated the stress profile along the x- and y-coordinates. In Figure 10c, it can be easily observed that the \(\sigma_{\rm xx}\) component is not uniform along the y-coordinate; a critical increment is detected for \(\rm{SIN_{y}}\) layers with relaxation behavior in \(\rm{SiO_{2}}\) layers introducing a stress gradient.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Mycotoxins and Food Safety
#### 2.5.6 Zearalenone  
Zearalenone is a mycotoxin produced by certain species of a fungi called _Fusarium_. The members of this genus are commonly found in agricultural products, including grains, particularly maize (corn), wheat, barley, and sorghum [133]. This toxin poses a significant concern in animal feed and human food due to its estrogenic properties, meaning it mimics the effects of the hormone estrogen in living organisms [134]. Exposure to zearalenone can occur through ingestion of contaminated food or feed, leading to various health issues in animals and potentially in humans [135]. In farm animals, especially pigs, zearalenone consumption can result in reproductive problems such as swollen vulvas, vaginal prolapse, and infertility, ultimately affecting livestock production. Moreover, zearalenone contamination in food products intended for human consumption raises concerns about its potential health effects. Although acute toxicity in humans is rare, chronic exposure to zearalenone through contaminated food has been associated with hormonal disruptions and reproductive disorders [136].
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 7 Conclusions
The following abbreviations are used in this manuscript:  
asymmetrically clipped optical  
orthogonal frequency-division multiplexing  
adaptive layered asymmetrically clipped optical  
orthogonal frequency-division multiplexing  
bit error rate  
controller area network  
cumulative distribution function  
computerized tomography  
DCM  
discontinuous conduction mode  
direct current-biased optical  
orthogonal frequency-division multiplexing modulation  
deoxyribonucle acid  
ECH7  
electromagnetic field  
EMFT  
electromagnetic field therapy  
EMR  
electromagnetic radiation  
FER  
frame error rate  
FOV  
field of view  
GSM  
graphical information category scale marking  
HACO-OFDM  
hybrid asymmetrically clipped optical  
orthogonal frequency-division multiplexing  
high-brightness light-emitting diodes  
IM/DD-OFDM  
intensity modulation and direct detection  
orthogonal frequency-division multiplexing modulation  
IST  
inter-symbol interference reduction  
LACO-OFDM  
layered asymmetrically clipped optical  
orthogonal frequency-division multiplexing  
LDPC  
low-density parity check  
LEDs  
light-emitting diodes  
LSTM  
long short-term memory networks  
MATLAB  
matrix laboratory  
MIMO  
multiple input, multiple output  
MPs  
mobile phones  
MRI  
magnetic resonance imaging  
M-QAM  
multilevel quadrature amplitude modulation  
MW  
microwave  
OOK  
on-off keying  
PAM  
pulse amplitude modulation  
PAM-DMT  
pulse-amplitude-modulated discrete multitone modulation  
PDF  
probability density function  
PWM  
pulse width modulation  
QAM  
quadrature amplitude modulation  
QCM  
quadrature carrier multiplexing  
RF  
radio frequency  
RLL  
run-length limited  
RNNs  
recurrent neural networks  
SAR  
specific absorption rate  
SDRs  
software-defined radios  
SMP  
spatial multiplexing  
SNR  
signal-to-noise ratio  
STBCs  
space-time block codes  
UART  
microcontroller--universal asynchronous receiver/transmitter  
US  
ultrasound  
USRP  
universal software radio peripheral  
VLC  
visible-light communication
# Seismic Performance of Dam Piers Retrofitted with Reinforced Polymer Cement Mortar
## 4 Results and Discussion
### Strain Distribution of Existing Reinforcing Bars: Comparison of Unreinforced and Reinforced Case  
As shown in Section 4.2, there was no significant difference in displacement between the unreinforced and RPCM-reinforced cases until bending cracks occurred in the elastic region. However, it was confirmed that when the displacement increased, the reinforcing bars of the existing part yielded in the unreinforced case, so there was a difference between the unreinforced and RPCM-reinforced cases. Figures 33 and 34 show the axial strain distribution of the existing reinforcing bars at the peak displacement of each of the four dams under seismic wave A and seismic wave B, respectively. The TYPE-A model showed a tendency for the maximum displacement reduction effect to differ significantly between the two types of seismic waves, and the ratio of existing reinforcing bars that yielded in this type was larger for seismic wave A.  
Next, the reinforcing effects of TYPE-B, TYPE-C and TYPE-D were compared using the strain distribution of the existing reinforcing bars, as shown in Figures 33 and 34. These figures show that the number of existing reinforcing bars that reached yield strain before and after reinforcement decreased in TYPE-B and TYPE-D dams. However, existing reinforcing bars that reached yield strain were not observed in the TYPE-C. Thus, there was no significant difference in displacement between the unreinforced and RPCM-reinforced TYPE-C cases. Therefore, it is concluded that a large reinforcing effect can be obtained in the case of a dam pier having a small amount of existing reinforcing bar and a large mass of attached structures such as switchgear, pier for switchgear and door body. In addition, it was confirmed that when a large number of existing reinforcing bars reach yield strain, as in TYPE-A and TYPE-D shown in Figures 29a and 32a, even partial reinforcement has almost the same effect as 100% reinforcement.  
Figure 33: Strain distribution of existing reinforcing bars at peak displacement in wave A: (**a**) Type-A; (**b**) Type-B; (**c**) Type-C; (**d**) Type-D.
## 3 Results and Discussion
### Chemicals Screening
#### 3.1.3 Static Adsorption Results  
The calibration curve for static adsorption calculation was constructed by analyzing standalone surfactant solutions at different concentrations (50 ppm, 100 ppm, 150 ppm, and 200 ppm) for their degree of adsorption using a UV-Vis spectrophotometer. Figure 8 shows the developed calibration curve along with the equation of the best fit line.  
Figure 7: IFT of 2S/2A solutions.  
The absorbance of each alkali-surfactant or NP-surfactant sample was measured by UV-Vis Spectrophotometer and was converted to surfactant adsorption using the calibration curve. The standalone surfactant static adsorption was in the range of 14.1-45.7 mg/g of rock at different mixing times. The general trend was that with increasing aging/mixing time, the static adsorption of surfactants increased in all cases. Since the typical contact times in the reservoir are higher, the results at 72 h mixing time were considered more reliable for screening.  
Na\({}_{2}\)CO\({}_{3}\) at a concentration of 0.02 wt% reduced surfactant adsorption by 21% after 72 h of mixing time with crushed Field A rock. However, a reduction of only 5% was observed by 0.02 wt% NaOH. Figure 9 shows a comparison of both alkalis in reducing surfactant adsorption after 72 h of aging time. It can be seen that Na\({}_{2}\)CO\({}_{3}\) caused 16% more reduction in surfactant adsorption compared to NaOH at the same concentration of 0.02 wt%.  
In the case of silica NPs, a concentration of 0.05 wt% reduced surfactant adsorption by 4%. Figure 10 shows the effect of NPs on surfactant adsorption at 72 h. The poor performance of NPs in controlling surfactant adsorption may be due to low compatibility between silica NPs and the surfactant reducing the stability of the solution and causing more surfactant to adsorb on the rock.  
Figure 8: Surfactant calibration curve at a fixed 2S/2A ratio of 30/70.  
Figure 9: Comparison of the effect of alkali on surfactant adsorption after 72 h of aging time.  
Figure 11 shows the comparison between both alkalis and NPs in controlling surfactant static adsorption after 72 h of aging. It can be seen from these results that Na2 CO3 worked best in minimizing surfactant adsorption at an optimum concentration of 0.02 wt%. The reason for this phenomenon is that the alkaline solution renders the rock surface more negatively charged, causing it to repel the negatively charged surfactant molecules. For silica NPs, 0.05 wt% concentration worked better than 0.1 wt%, mainly because of increased instability of the solution at a higher concentration of NPs. Based on the results of static adsorption study, 0.02 wt% Na2 CO3 and 0.05 wt% SiO2 were selected for surfactant dynamic adsorption tests.
## 2 Materials and Methods
### Data and Methodology
#### 2.2.1 Remote Sensing Data and Pre-Processing
For the LC product, we selected the International Geosphere-Biosphere Program (IGBP) layer for this study. According to the IGBP layer, the permanent wetlands (PW) pixels (LC code 11) were identified. The corresponding LAI and GPP values located in the identified PW pixels were extracted with the strict consideration of the QC. The mean and standard deviation of the LAI and GPP at 8-day intervals were computed for further analysis.
## 4 Derivation of Performance Indices and Stability Conditions  
To evaluate the performance of the tracking filter, steady state errors for the reduction of measurement noise and the tracking of accelerating targets are used [11; 12]. These indices are more effective in evaluating steady state tracking accuracy than the error covariance matrix in the Kalman filter equations, as discussed by [PERSON] (see Section 9.8 of [12]). Moreover, a comprehensive performance index for measurement-error smoothing and tracking of an accelerating target based on these indices is presented in [4]. This comprehensive index is used for our proposed gain design strategy. Consequently, this section derives these performance indices of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter. Stability conditions are also derived for practical filter designs.
## 4 Performance Analysis
### Energy Efficiency  
Before defining the energy efficiency metric for the cooperative protocols, we consider the simple case of single hop S&W ARQ communication between the source and the destination. The total energy consumed in the network for a single attempt of data packet delivery by source (that results in an _ACK_ or _NACK_ response from the destination) is given by:  
\[E_{{}_{ARQ}}=E_{{}_{ARQ}}^{s}+E_{{}_{ARQ}}^{D}+E_{{}_{ARQ}}^{CN} \tag{2}\]  
where \(E_{{}_{ARQ}}^{S}\), \(E_{{}_{ARQ}}^{D}\) and \(E_{{}_{ARQ}}^{CN}\) are the energies consumed at the sender, the destination and the neighbour nodes (_i.e._, cooperative nodes for the cooperative protocols) respectively, and are further expressed as:  
\[E_{{}_{ARQ}}^{S}=E_{{}_{Tx}}^{DATA}+E_{{}_{Rx}}^{ACK/NACK}+E_{{}_{Ltle}}^{S} \tag{3a}\]  
\[E_{{}_{ARQ}}^{D}=E_{{}_{Rx}}^{DATA}+E_{{}_{Tx}}^{ACK/NACK}+E_{{}_{Ltle}}^{D} \tag{3b}\]  
\[E_{{}_{ARQ}}^{CN}=\sum_{i=1}^{nCN}E_{{}_{Ltle}}^{i} \tag{3c}\]  
where, for any network packet \"_Pac_\", _Pac_\(\epsilon\)\(\{\)_DATA_, _FEC_, _ACK_, _NACK_, _C_\({}_{Req}\), _C_\({}_{Avl}\)\(\}\) and length \(l_{Pac}\), \(E_{Tx}^{Pac}\) and \(E_{Rx}^{Pac}\) are the energies consumed in transmitting and receiving that packet, respectively. \(E_{Ltle}\) is the energy consumed by a node when operating in the idle mode. \(nCN\) denotes the number of _CNodes_ that are present in the neighborhood of the source and destination (_i.e._, in the cooperative region). For S&W ARQ the _CNodes_, obviously, do not interfere, and hence only their idle mode energy consumption is considered. The above mentioned energy consumptions can be further expressed as:  
\[E_{Tx}^{Pac}=P_{Tx}I_{Pac}T_{t} \tag{4a}\] \[E_{Rx}^{Pac}=P_{Rx}I_{Pac}T_{t}\] (4b) \[E_{dle}=P_{dle}t_{dle} \tag{4c}\]  
where, \(P_{Tx}\), \(P_{Rx}\) and \(P_{dle}\) are the transmission power, reception power and idle mode operating power, respectively. \(t_{Idle}\) is the total amount of time for which a node operates in idle mode, during the entire process.  
Using the same methodology as S&W ARQ, the energy consumption for both CARQ and C-HARQ are obtained next; where, we present the energy consumption model, employed during the computer simulation of the cooperative protocols, by expressing the energy efficiency equations for a very simple example case that considers a major assumption. It is to be noted that the computer simulations are fully fair, without any of the assumptions; whereas the example case is considered, in the following part, only to give an insight into the energy consumption model.
# Components of the Fracture Response of Alkali-Activated Slag Composites with Steel Microfibers
## Materials and Methods  
### Mixtures  
The first alkali-activated slag composite was a reference version without fibers. Granulated blast furnace slag provided by Kotouk, s.r.o. (CZ) was chosen as a binder. The specific surface and mean grain size of the slag were 383 m\({}^{2}\)/kg and 15.5 \(\upmu\)m, respectively. Solid sodium silicate (Susil MP 2.0) fabricated by Vodni sklo, a.s. (CZ) was used to achieve the alkali activation of the slag. The alkaline activator has a molar SiO\({}_{2}\)/Na\({}_{2}\)O ratio equal to 2.0, and a SiO\({}_{2}\) content of 52.4%. The fine-grained AAS composites were produced using quartz sand with a maximum grain size of 2.5 mm. Brass coated steel microfibers with an average length of 6 mm and a diameter of 0.175 mm supplied by KrampeHarex CZ s.r.o. were used as reinforcement (see Figure 1). The added steel microfibers amounted to 5, 10, 15 and 20% of the weight of the slag.  
The AAS mixtures used for casting the prismatic specimens were prepared according to the following previously optimized procedure. At first, solid alkaline activator was suspended in water in which it dissolved partially. Then, the slag and quartz sand were added to the activator and the mixture was stirred in the planetary mixer for about 5 min to provide a fresh slurry. The aggregate to slag ratio was equal to 3.0. Finally, steel microfibers were added to the mixture and further mixed for another 3 min so as to disperse them properly. The AAS mixture composition is presented in Table 1. The designation of individual mixtures is based on their steel microfiber content: SF00, SF05, SF10, SF15, and SF20. Mixture SF00 is a reference mix without steel microfibers.  
The prepared fresh mixtures were cast into prismatic moulds of 40 \(\times\) 40 \(\times\) 160 mm in size. Three specimens were made from each mixture. After 24 h the hardened specimens were immersed in a water bath at 20 \({}^{\circ}\)C for further 27 days. Before the fracture tests were performed, all specimens were pulled out of the water and allowed to dry spontaneously under ambient conditions for 24 h.  
Figure 1: The used steel microfibers with an average length of 6 mm and a diameter of 0.175 mm.
# Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials
## 3 Results and Discussion
### Experimental Works Results
Figure 10: PSO optimisation method on the thick plate part for shrinkage and tensile strength. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Responses**} & \multicolumn{1}{c}{**Specimens**} & \multicolumn{1}{c}{**Mould**} & \multicolumn{1}{c}{**Melt**} & \multicolumn{1}{c}{**Packing**} & \multicolumn{1}{c}{**Cooling**} & \multicolumn{1}{c}{**Optimal**} \\  & & **Temperature** (°C) & **Temperature** (°C) & **Pressure (MPa)** & **Time (s)** & **Values** \\ \hline \multirow{8}{*}{RSM} & R0 & 59.20 & 175.41 & 15.85 & 22.21 & 1.85\% \\  & Shrinkage in & R30 & 42.41 & 160 & 18.56 & 19.42 & 2.45\% \\  & R40 & 41.42 & 160 & 18.51 & 19.42 & 2.65\% \\  & R50 & 46.53 & 170.42 & 16.49 & 19.48 & 2.80\% \\ \cline{2-8}  & R0 & 40.35 & 179.77 & 18.48 & 22.65 & 1.92\% \\  & Shrinkage in & R30 & 48.03 & 178.92 & 12.82 & 24.43 & 2.01\% \\  & R40 & 49.36 & 180 & 12.72 & 25.23 & 2.05\% \\  & R50 & 46.92 & 177.15 & 13.96 & 25.27 & 2.08\% \\ \cline{2-8}  & & R0 & 49.95 & 179.96 & 12.46 & 24.08 & 9.86 MPa \\  & Tensile & R30 & 57.36 & 167.43 & 18.5 & 23.84 & 9.81 MPa \\  & strength & R40 & 51.67 & 177.31 & 14.39 & 19.49 & 9.66 MPa \\  & R50 & 59.56 & 167.79 & 16.18 & 23.96 & 9.57 MPa \\ \hline \multirow{8}{*}{PSO} & R0 & 60.00 & 174.16 & 16.08 & 23.62 & 1.71\% \\  & R30 & 40.48 & 160.25 & 18.25 & 19.72 & 2.56\% \\ \cline{1-1}  & R40 & 41.46 & 160.35 & 18.55 & 19.66 & 2.75\% \\ \cline{1-1}  & R50 & 40.09 & 170.58 & 17.88 & 19.48 & 2.75\% \\ \cline{1-1} \cline{2-8}  & R0 & 40.09 & 179.91 & 18.41 & 22.67 & 1.92\% \\ \cline{1-1}  & Shrinkage in & R30 & 48.7 & 179.88 & 18.54 & 24.26 & 1.87\% \\ \cline{1-1}  & \(y\) direction & R40 & 48.77 & 179.62 & 12.28 & 26.50 & 2.01\% \\ \cline{1-1}  & R50 & 47 & 179.96 & 12.33 & 26.48 & 1.97\% \\ \hline \multirow{8}{*}{Tensile strength} & R0 & 52.46 & 179.8 & 18.52 & 22.01 & 9.97 MPa \\ \cline{1-1}  & R30 & 59.92 & 165.05 & 18.51 & 25.34 & 9.71 MPa \\ \cline{1-1}  & R40 & 49.95 & 179.88 & 12.78 & 19.43 & 9.71 MPa \\ \cline{1-1}  & R50 & 59.99 & 167.92 & 12.38 & 26.47 & 9.62 MPa \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimal shrinkage and tensile strength values. 3.03% respectively, as compared to R0 (RSM), while tensile strength for R30, R40 and R50 specimens was decreased using PSO method by 2.68%, 2.68% and 3.64% respectively. What is interesting in Figures 9 and 10 is that the closest recycled material ratio performance to the virgin is produced using R30 specimen. On top of that, the optimal shrinkage and tensile value generated by RSM and PSO did not exceed the maximum tensile strength value and maximum shrinkage rate. Table 8 also shows the optimal processing parameters produced by both methods which are applicable to mould the thick plate part.
## 3 Results and Discussion
### Ferric Reducing Power Activity  
The data indicated clearly that the thiomethyl parent (**1**) had the superiority as the ferric reducing power agent (Table 3). In a similar manner to the results of DPPH assay, compound **1** recorded the highest ferric reducing power activity of 3567 \(\pm\) 14.84 \(\mu\)mol.  
Figure 2: DPPH radical scavenging activity of **1–14**. Data are means \(\pm\) standard deviation of triplicate experiments.  
Trolox/100 g, while phenoxy and methylsulfonyl parents (**2** and **3**) showed lower ferric reducing capacity of 953 \(\pm\) 12.50 and 882 \(\pm\) 7.09 \(\upmu\)mol Trolox/100 g, respectively. Substitution on the structure of **1** led to a large decrease in the reducing activity. In particular, **8** showed the lowest activity of 315 \(\pm\) 7.37\(\upmu\)mol Trolox/100 g, while **6** and **7** exhibited reducing capacity of 681 \(\pm\) 9.07 and 385 \(\pm\) 6.56 \(\upmu\)mol Trolox/100 g at the same concentration. However, compounds **5** and **9** displayed good activity (1482 \(\pm\) 8.19 and 1474 \(\pm\) 11.06 \(\upmu\)mol Trolox/100 g) in comparison to **1**. In case of parent **2**, the substitution on its structure with 2-hydroxy-5-methoxy (**11**) resulted in a clear increase in the reducing ability to reach about three-folds (2093 \(\pm\) 6.08 \(\upmu\)mol Trolox/100 g), while with 2-hydroxy-5-methyl and 2-hydroxy-5-nitro groups (**10** and **12**) delivered 542 \(\pm\) 10.60 and 564 \(\pm\) 12.86 \(\upmu\)mol Trolox/100 g. Generally, compounds **1**, **5**, **9**, and **2** appeared more active than the other investigated compounds.