esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Topology Adaptive Differential Protection
### Distributed Current Differential Protection Technology  
The current differential protection function is located in each DTU. Each DTU communicates with adjacent DTUs to perform the current differential protection function.  
The algorithm of the current differential protection is as follows, that is, the differential current and restraint current are calculated as follow:  
\[\left\{\begin{array}{c}I_{d}=\sum\limits_{i=1}^{N}I_{i}\\ I_{r}=\frac{1}{2}|I_{max}-\sum I_{i}|\end{array}\right. \tag{1}\]  
Figure 5: Flow chart of adaptive topology generation.  
In the above formula, \(\sum\limits_{i=1}^{N}{I_{i}}\) is the sum of all sides phase currents; \(I_{max}\) is the largest phase current in all sides; \(\sum{I_{i}}\) is the sum of the phase currents on the other sides (except the maximum phase current side).  
The protection operating characteristic is shown in Figure 6. In the Figure, \(\mathrm{Icd}\) is the setting of the differential current protection.
## 4 Indoor Dynamic Beam Deflection Measurement Experiment and Validation
### DIC Processing Result Comparison and Validation
Table 4 shows that the maximum errors range from 0.037 to 0.047 mm of the five spatial deflection curves, and the maximum average error is 0.012 mm, which is the sub-pixel measurement error. The minimum correlation coefficient \(\rho\) of the five curves is 0.9964, and it is close to 1. From the perspective of the spatial field, the deflection curve obtained by the DFEM-DSMR method almost completely coincides with the DIC calculation results; therefore, the method proposed in this paper exhibits high measurement precision.
# Advanced DSP for Coherent Optical Fiber Communication
## 3 Nonlinearity Compensation
For the PDM WDM system, it can be treated by solving coupled Manakov equations, ignoring the FWM item with high computational requirements, and only compensating the XPM effect [30; 31]. DBP has been experimentally studied in a series of recent works from University College London. [PERSON] et al. have studied the performance improvement of single channel PDM-16 QAM 112 Gbit/s system after nonlinear compensation. It is found that the maximum transmission distance can be increased from 1440 km (EDC only) to 2400 km (DBP) [32]. [PERSON] et al. experimentally studied 112 Gbit/s non-return to zero (NRZ) PDM-QPSK WDM system. It was found that using intrachannel nonlinear compensation, the maximum transmission distance of a single channel system was increased by 46% while that of a WDM system with 100 GHz channel spacing was increased by 23% respectively [33]. [PERSON] et al. studied 10.7 Gbaud PDM-QPSK and PDM-16 QAM systems and reported that the transmission reaches have been extended to 7780 km and 1600 km, respectively [34]. [PERSON] et al. studied a 224 Gbit/s PDM-16 QAM system and proved that the maximum transmission distance can be increased by 18-25% with the DBP implemented [35]. A 112 Gbit/s PDM-QPSK system has been studied and proposed by University College Cork in 2011. The simplification method revealed the correlation effect of adjacent symbols and realized a remarkable reduction of computation cost [36]. Similarly, the use of a correlated backpropagation (CBP) for nonlinearity compensation has been reported in a 112 Gbit/s PDM-QPSK system [37], and the step size of DBP can be increased by 70%. [PERSON] et al. have proposed a low-complexity predistortion method for intrachannel nonlinearity compensation and have experimentally verified this approach in a 43 Gbit/s PDM-QPSK coherent system [38]. Other groups also made impressive contributions to this area, and more details can be found in Refs. [39; 40; 41; 42]. Figure 10: Q factor and step size for XPM and FWM compensation within the 50 GHz and 100 GHz grids. Figure 5:0 Performance results for: (**A**) 12 channels, (**B**) 24 channels and (**C**) 36 channels respectively. Figure 5:1 Step size for the advanced and conventional implementation of the split-step Fourier method (SSFM): (**A**) 12 channels, (**B**) 24 channels and (**C**) 36 channels respectively.
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 4 Control Method Design
### Input Signal and Displacement  
The input signal defines the expected position of the main spool. As shown in Figure 1, the main spool is driven by four HSVs with different duty cycles. The stroke of the main spool is \(-\)7.5-7.5 mm, and the duty cycle input range of the HSV is 0-1. Based on this, the linear relationship between the displacement of the main spool and the duty cycle of the HSV can be obtained, as expressed in Equation (5).  
\[D_{n}(t)=0.067x(t)+0.5 \tag{5}\]  
where \(D_{n}\) (\(t\)) is the duty cycle of HSV. The relationship between the position error and the input position is expressed as follows:  
\[e(t)=x_{in}(t)-x_{t}(t) \tag{6}\]  
where \(e\) (\(t\)) is the error of the spool input position and actual position, \(x_{in}\) (\(t\)) is the input position, and \(x_{t}\) (\(t\)) is the measured actual position.
## 2 Materials and Methods
### Incubation Experiment  
One gram each of undisturbed profile soil samples (0-10 cm, 10-30 cm, 30-50 cm, and 50-100 cm) and dry-sieved samples (3-5 mm, 1-2 mm, and 0.25-0.5 mm) along with wet-sieved samples (1-2 mm, 0.25-0.5 mm, and \(<\)0.053 mm) were taken. Then, 0.6 mL of distilled water was added to achieve a moisture content of 60% (\(w/w\)) and placed the samples in 30 mL test tubes with lids for incubation at 25 \({}^{\circ}\)C. The CO\({}_{2}\) concentration was measured as follows: the test tubes were sealed at time 0, 9 mL of air was injected into the tubes, and 9 mL of gas was extracted and injected it into a glass vacuum tube containing 5.8 mL of CO\({}_{2}\)-absorbing solution, serving as the CO\({}_{2}\) concentration at day 0 (d-0). After 1 day, 9 mL of gas from the test tubes was extracted and injected it into a glass vacuum tube containing 5.8 mL of CO\({}_{2}\)-absorbing solution as the CO\({}_{2}\) concentration at day 1 (d-1). Subsequently, the incubation tube cap was opened to achieve gas equilibrium with the indoor environment before sealing it again. The same procedures were repeated on days 2, 3, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60. Gas samples were analyzed using an Agilent 7820A gas chromatograph (Agilent Technologies, Carpinteria, CA, USA). The evolution of carbon dioxide was calculated based on [PERSON] (1999) [26].  
The formula for calculating the SOC mineralization process is  
\[\text{C}_{\text{M}}=\text{Co}\Big{(}1-e^{-\text{kt}}\Big{)} \tag{2}\]  
where C\({}_{\text{M}}\) (mg kg\({}^{-1}\)) is the cumulative amount of carbon dioxide released over different intervals (days); Co is the carbon that can be mineralized without time limitation; and k is the first-order rate constant (d\({}^{-1}\)) [25].
## Security Analysis in Spatial and Frequency Domains
### UACI (Unified Average Changing Intensity)  
UACI is defined in Equation (6). The outcomes of UACI are listed in Table 2. The range of the driving parameter \(\mu\) is from 3.6 to 4.0. The UACI scores of the digital image after shuffling, after dual chaotic watermarking, as well as those of mismatches generated by shuffling and dual chaotic watermarking are listed in four separate rows. Appropriate UACI scores indicate that almost all intensity levels of pixels in the watermarked image are changed, which are no longer distinctive. The low UACI score also covers the broad distribution of intensity changes instead of the special cases exclusively. After dual chaotic watermarking (2 nd row), it shows that generally a bigger driving parameter \(\mu\) corresponds to a smaller UACI score and better security. Still the effect of driving parameter \(\mu\) on UACI and security should be applied to a large quantity of digital images for comparison to make it more convincing.  
\[UACI=\frac{1}{M\times N}{\sum_{i=0}^{M-1}{\sum_{j=0}^{N-1}{\frac{|I(i,j)-J(i,j)|} {255}}}}\times 100\% \tag{6}\]
## 2 Materials and Methods
### Modelling Support
#### 2.3.1 Modelling H Diffusion  
The evolution of the hydrogen concentration from the inside surface of the specimen during the pre-charging step was modelled in a diffusion model using standard quadratic axisymmetric heat transfer nodes (DCAX8). The inside surface of the modelled specimen was held at a concentration of 1.722 \(\times\) 10 \({}^{-11}\) mol/mm\({}^{3}\), which is the expected sub-surface concentration at 293 K and 100 bar according to Sieverts' law [18; 19]. Diffusivities for the BM and WM were taken as 2.25 \(\times\) 10 \({}^{-4}\) mm\({}^{2}\)/s [15] and 2.9 \(\times\) 10 \({}^{-4}\) mm\({}^{2}\)/s [42], respectively. The concentration at the start of the calculation was set to 0 mol/mm\({}^{3}\) throughout the model to reflect the state of the specimen before starting the pre-charging step, after it has just been flushed with nitrogen. At the outer surface, [PERSON] law states the concentration should be 0 because no \(\mathrm{H_{2}}\) is present in the environment. Therefore, the outer surface was given the boundary condition of 0 concentration throughout the simulation. An explicit dynamic analysis was performed that allows for monitoring of the hydrogen concentration at the notch root plane over time. The evolution of the concentration gradient through the wall thickness is shown in Figure 5. The equilibrium state of a steadily decreasing H concentration towards the outside surface is reached after approximately 6 h. It is important to note that this model only shows the concentration profile for a sample without any deformation. The presence of elastic and plastic deformations will change the local equilibrium concentration continuously throughout the test. The deformation model serves as an indication of the stresses that will be present.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gas** & **Smooth BM** & **Notched BM** & **Notched WM** \\ \hline Air & 0 bang & Not tested & Not tested \\ \hline N\({}_{2}\) & 100 bang & 100 bang & 100 bang \\ \hline H\({}_{2}\) & 100 bang & 100 bang & 30, 70, 100 bang \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the different subgroups of base metal (BM) and weld metal (WM) specimens. Test pressures are gauge pressures.
# Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders
## 3 Background and Modeling
### Prediction Model  
The Markov property is a prerequisite for reinforcement learning. In the actual operation process, the operator mainly executes the next actions according to the current state of the loader. Thus, the wheel loader state of the scooping process at the next moment is considered not to be related to the past, but to the current state, which satisfies the Markov property. Therefore, the interaction between the wheel loader bucket and the continuously changing pile can be modeled as a Finite Markov decision process (FMDP) which is expressed by a quadruple \(F(S,A,P,R)\), consisting of the set of possible states \(S\), the set of available actions \(A\), the transition probability \(P\), and the reward \(R\). The state  
Figure 1: V-cycle of wheel loaders.  
Figure 2: The three phases in the scooping process.  
\(s\in S\) includes the velocity, the tilt cylinder pressure, and the lift cylinder pressure. The actions consist of lift, tilt, and throttle commands which are all discrete. The ranges of lift, tilt, and throttle commands are from 0 to 160, 0 to 230, and 0 to 100, respectively. Besides, as the pile's shape and loaded material vary randomly, the change of the pile is considered as a stochastic process, which also satisfies the Markov property. Therefore, the problem of automatic bucket-filling for wheel loaders is considered as a finite Markov decision process (FMDP).  
To achieve indirect RL, a prediction model needs to be constructed to predict the wheel loader state at the next moment according to the current state and action during the scooping phase. In this paper, changes in the wheel loader state are regarded as a series of discrete dynamic stochastic events and described with a Markov chain. The transition probability can be expressed as:  
\[P\big{(}S_{j}|S_{i}\big{)}=\frac{N_{i,j}}{N
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Lower Limit for the Permittivity
#### 3.5.2 Plasma at a Node  
The linear response regime of a set of four resonant modes, which have a minimum electric field strength at the centre of the cavity where the plasma is located, is investigated here. The response of the TM\({}_{110}\) mode for several plasma radii as a function of \(\Delta\varepsilon_{\rm r}\) is shown in Figure 14. The smallest two plasmas geometries do not induce a shift within the range of parameters investigated here which is measurable with the current resolution in the real part of the permittivity.  
In contrast to the response of the cavity with the plasma at an antinode, the maximum permitted frequency shift does depend on the plasma radius for the TM\({}_{110}\) mode. This can be explained by the fact that the electric field strength deviates strongly nearby the heart of the cavity for the TM\({}_{110}\). The maximum permitted \(\frac{\Delta f}{f_{1}}\) for the four resonant modes with a node in the center of the cavity (TM\({}_{110}\), TM\({}_{120}\), TM\({}_{130}\), and TM\({}_{140}\)) and \(r_{\rm p}=10^{-4}\) is approximately \(10^{-5}\)-\(10^{-6}\). The calculations show that for the same plasma radius a larger perturbation by the plasma is allowed when the plasma is located at a node.  
To recapitulate, the maximum permitted change in permittivity for MCRS to deliver trustworthy results depends on the dimensions of the plasma and the position in the mode structure, i.e., the local electric field value. Moreover, the limit that the plasma electron frequency must be much smaller than applied angular frequency is a very conservative and therefore unfit as a limit for the diagnostic method as done often in literature [23; 24; 25; 26].
## 4 Conclusions  
In this study, the effect of UAPR coating on the performance of Ag nanowire TEs was investigated. UV-curable UAPR was coated onto the Ag nanowire TEs, and the resulting effect on the optical performance, ambient stability, and resistance to external wiping damage of the TEs was investigated. The UAPR coating was found to improve the optical transmittance of the Ag nanowire TEs because its refractive index was between that of air and the PET or PEN substrate. Given the importance of the optical transmittance of Ag nanowire TEs, a system including a UV-curable UAPR coating is expected to exhibit significant potential for use in industrial applications utilizing Ag nanowires. In addition, the UAPR coating successfully protected the Ag nanowires from corrosion when placed in contact with ambient air for 180 h at 70 \({}^{\circ}\)C, with no significant change in the optical transmittance or sheet resistance, while a significant degradation of optical transmittance and sheet resistance was observed for bare Ag nanowire TEs. Furthermore, mechanical wiping tests revealed that UAPR-coated Ag nanowire TEs experienced no significant change in their optical or electrical performance with mechanical wiping, while bare Ag nanowire electrodes exhibited significantly lower performance. The wiping tests thus confirmed that the UAPR coating was also effective in protecting the Ag nanowires from external wiping damage. In conclusion, the proposed UAPR coating represents a promising strategy for the electronics industry because it confers Ag nanowires a higher optical transmittance, ambient stability, and resistance to external wiping damage, all of which are highly desirable features for Ag nanowire-based TEs.  
Data curation, H.H., Y.S. and B.H.; Formal analysis, H.H., X.Q. and B.H.; Funding acquisition, B.H.; Investigation, H.H., Y.S. and P.M.; Project administration, S.L. and B.H.; Resources, S.L. and B.H.; Supervision, S.L. and B.H.; Writing--original draft, H.H., Y.S., P.M. and B.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2019K1A3A1A25000230 and NRF-2018R1C1B5043900). P.M. acknowledges support from the Ministry of Foreign Affairs and International Cooperation of Italy (MAECI) through the \"Development of a cost-effective wearable metal nanowire-based chip sensor for optical monitoring of metabolites in sweat\" DESWEAT Project (n\({}^{\circ}\) KR19 GR08) funded within the framework of the Executive Programme of Scientific and Technological Cooperation between the Italian Republic and the Korean Republic 2019-2021. This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Government of Korea (NRF-2018R1D1A1B07043938). The authors declare no conflict of interest.
## 2 Finite Element Modeling
### Parametric Finite Element Study
#### 2.6.1 Parametric Study: Results of Major Axis  
The influence of various reinforcement spacings for the uprights under major axis loading has been discussed in this section. The normalized moment-displacement curves of the numerical models for each thickness are indicated in Figures 11-14. As it is observed from the figures, using more reinforcement to partially close the section leads to improving the specimen's flexural capacity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Non-reinforced model} & \multicolumn{2}{c}{Evaluation criteria} \\ \cline{2-3}  & Std \({}^{*}\) & 0.1242 \\ \cline{2-3}  & Pearson (r) & 0.9989 \\ \cline{2-3}  & \(\text{R}^{2}\) & 0.9978 \\ \hline \multirow{3}{*}{200 mm reinforced model} & Evaluation criteria & \\ \cline{2-3}  & Std & 0.1357 \\ \cline{2-3}  & Pearson (r) & 0.9957 \\ \cline{2-3}  & \(\text{R}^{2}\) & 0.9914 \\ \hline \multirow{3}{*}{300 mm reinforced model} & Evaluation criteria & \\ \cline{2-3}  & Std & 0.1224 \\ \cline{2-3}  & Pearson (r) & 0.999 \\ \cline{2-3}  & \(\text{R}^{2}\) & 0.9981 \\ \hline \hline \end{tabular} \({}^{*}\)Std = standard deviation.  
\end{table}
Table 4: FEM vs. experimental results accuracy details in terms of evaluation criteria.  
Figure 11: Normalized moment-deflection curves for 1800 mm models about major axis.  
Figure 10: Designation of models.  
Figure 12: Normalized moment-deflection curves for 2400 mm models about major axis.  
Figure 13: Normalized moment-deflection curves for 3000 mm models about major axis.  
Figure 15 displays the normalized ultimate moment of the sections with various thicknesses at different reinforcement spacings. It is deduced that the shorter reinforcement spacing increases the ultimate bending capacity of the sections.  
Figure 14: Normalized moment-deflection curves for 3600 mm models about major axis.  
Figure 15: The normalized ultimate moment of 1800, 2400, 3000, and 3600 mm models about major axis with respect to reinforcement spacing.  
The increased ultimate moment percentage utilizing various reinforcement spacing for the profiles from 1800 mm to 3600 mm length with respect to specimens without reinforcement is presented in Figure 16. In general, the reinforcement technique influences the ultimate capacity of the open sections in a range of about 5% to 40%. As it is seen, reinforcement addition from 300 mm spacing to 50 mm spacing can improve the frame's strength by a significant amount. It is also found that reducing reinforcement spacing (up to 50 mm) can considerably increase the ultimate strength of the upright section under flexural loading.
## 4 Materials and Methods
### CRISPR-Mediated Knockout  
Lentiviral single guide (sg) RNA expression vector-targeting CSNK1D (#GSGH11838-24652777) and the corresponding non-targeting control (#GSG11811) were purchased from Dharmacon (Horizon Discovery, Waterbeach, United Kingdom). For expression of the Cas9 protein, the expression vector was used (Addgene plasmid#59262; [[http://n2t.net/addgene-52962](http://n2t.net/addgene-52962)]([http://n2t.net/addgene-52962](http://n2t.net/addgene-52962)) (accessed on 1 December 2017), RRID:Addgene_52962) [80]. Cells were first transduced with the lentiCast9-Blast vector, selected for basiccidin resistance, and, afterwards, were lentivirally transduced with the CSNK1D-targeting sgRNA construct conferring puromycin resistance. Knockout efficacy was determined by target-locus sequencing of genomic DNA isolated using the DNA blood mini kit (Qiagen, Hiliden, Germany). Knockout efficacy was calculated using TIDE ([[https://tide.deskgen.com](https://tide.deskgen.com)]([https://tide.deskgen.com](https://tide.deskgen.com)) (accessed on 1 December 2017)) [81].  
A GLII sgRNA-targeting sequence was designed using GPP sgRNA designer ([[https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)]([https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)) (accessed on 1 December 2017)), and a sgRNA-targeting eGFP was used as control. The following sgRNA nucleotide sequences were used for CRISPR-mediated knockout: GLII: 5'-AACTGCGACTCCC-3', non-targeting control: 5'-GAGCTGGACGGGCAGCTA-3'. sgRNA oligos were cloned into the lentiCRISPRv2 vector (Addgene plasmid#52961; [[http://n2t.net/addgene:52961](http://n2t.net/addgene:52961)]([http://n2t.net/addgene:52961](http://n2t.net/addgene:52961)) (accessed on 1 December 2017); RRID:Addgene_52961) [80]. Cells were transduced with either the GLI1-targeting or the control construct, which were then selected for puromycin resistance, as described in [79]. GLII knockout efficacy was calculated using TIDE ([[https://tide.deskgen.com](https://tide.deskgen.com)]([https://tide.deskgen.com](https://tide.deskgen.com)) (accessed on 1 December 2017)) [81]. To isolate GLI1 knockout cells, sgGLI1-transduced cells were seeded at a low density in a 96-well plate and cultured in A673-conditioned media cleared by filtration through 0.45-\(\upmu\)m filters. Clonal colonies were expanded for further experiments.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Algae
#### 2.2.2 Microalgae  
Microscopic marine organisms known as microalgae (\(\sim\)20 \(\upmu\)m) are located in both littoral and benthic environments and exist in the ocean as phytoplankton, which are considered the earliest and most organized members of the plant kingdom. These phytoplankton populations often encompass various types, including diatoms, green algae, blue-green algae, dinoflagellates, and yellow-brown flagellates. In particular, dinoflagellates, green and yellow-brown flagellates, and _Bacilariophyceae_ (diatoms) also share many of the same photosynthetic pigments that are similar to those found in terrestrial plants. Consequently, they are rich sources of bioactive compounds and biochemicals such as pigments like carotenoids and chlorophyls, PUFAs, polysaccharides, antioxidants, sterols, and vitamins. For instance, diatoms produce essential PUFAs such as DHA, EPA, and other omega-3 fatty acids, as well as substances like fucoxanthin and chlorophyll [81; 82]. Nonetheless, environmental conditions such as abiotic stresses and salinity could affect the pigment and antioxidant components of microalgal species (e.g., _Tetraselmis itetruthele_) [83]. However, a significant number of these bioactives found in microalgae are not suitable for human consumption due to the potential production of potent neurotoxins or hepatotoxins [51]. Consequently, ensuring their safety is crucial before considering their use for human consumption.