esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights into Fuzzy Genetic Algorithms for Optimization Problems
## 4 Experiments and Discussion
### Traveling Salesman Problem  
The traveling salesman problem is another typical benchmark. Given a list of cities and the distances between each pair of cities, the TSP aims to find the shortest possible path such that all cities are visited just once. For a p-city TSP, the distances \(x_{ij}\) between the cities \(c_{i}\) and \(c_{j}\) are collected into a \(p\times p\) matrix, \(\mathbf{X}=\{x_{ij}\}\), known as the cost matrix. The number of its entries represents the number of variables of the problem, of course. The TSP is an NP-hard problem. Here we refer to [11] for a comparison against published results. Following [11], three problems were taken from TSPLIB95 [25]. They are all symmetric TSPs with Euclidean distance, namely Eil51, Eil76, Rat99, i.e., \(p=51\), \(p=76\) and \(p=99\), respectively. As in [11], we fixed a population size of 100, with 200 generations, through 30 runs. The age linguistic variable in [11] has three terms, i.e., young, middle-aged, and old. In GFGA-NNC, we tried both term sets presented in Section 2.2, even though the first term set with five terms provided the best results. Similarly, for the other term sets, even for EFGA-NNC, increasing from 5 to 7, the number of terms did not produce any significant improvement. For both approaches, the pi-shaped MF was adopted. The best results, i.e., the shortest path length by each approach, are shown in Figure 9. Although neither EFGA-NNC nor GFGA-NNC could outperform the baseline, they represent an improvement with respect to LTExGA [11]. The distance from the baseline grows as the problem cardinality increases. In all cases, EFGA-NNC performed better than GFGA-NNC and LTExGA, keeping the shortest distance from the baseline. EFGA-NNC offers a result very close to the baseline, i.e., 477.34 against 426, especially for Eil51, while it is 525.46 by LTExGA and 601.34 by GFGA.  
Let \(r\) denote the ratio between the solution obtained by the proposed approaches and the baseline. Figure 10 shows how \(r\) varies with \(p\) for all the approaches. It can be observed that increasing the problem cardinality does not significantly affect the performance of EFGA-NNC and GFGA-NNC, while LTExGA seems to be influenced by it.  
Figure 8: Average fitness value over the generations for the following: **(a)** The \(g_{1}\) function; **(b)** The \(g_{2}\) function.
## 3 Results and Discussion
### Evaluation of Ray-Tracing Simulation and Photosynthesis Estimation  
In the empty growth chamber without plants, the measured and simulated light intensities corresponded well to the 1:1 line with an R\({}^{2}\) of 0.979 and RMSE of 0.7048 (Figure 4A). When the plants were positioned in the growth chamber, the simulation result also reflected the measured light intensities well, with an R\({}^{2}\) of 0.864 and RMSE of 0.7048, but the accuracy was slightly decreased compared with that of the empty chamber (Figure 4B).  
A decreased accuracy of simulated light intensity by introduction of the plant canopy was found in previous studies that conducted the simulation using a chamber with electrical lights and plant models [23; 28]. From our perspective, the lower R\({}^{2}\) value of light intensities with the plant canopy was attributed to the complexity and denseness of the plants, which can cause some errors between measurement and simulation by touching or misplacement. Because most electrical lights have the feature of direct light, light intensities between shaded and lighted areas are apparently different under light sources. Additionally, small changes in sensor position or angle can induce large differences in measured values.  
At different levels of PPFD and CO\({}_{2}\) concentration, the measured and estimated values of canopy \(P_{n}\) per unit leaf area were well matched, with an R\({}_{2}\) of 0.986. However, at the measurement points of low \(P_{n}\), which were measured and calculated at low PPFDs, the simulation underestimated \(P_{n}\) by approximately 29% (Figure 5).  
In this study, leaf photosynthetic rates measured in the PPFD range of 0 to 1200 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) were used to obtain the parameters of photosynthesis models (e.g., \(V_{cmax},J_{max}\)), whereas the canopy \(P_{n}\) was measured below a PPFD of 300 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). Thus, the model parameters that can be applied for wide PPFD ranges might underestimate the canopy photosynthetic rate at low levels of PPFDs.  
Figure 4: Comparison between measured and simulated light intensities in the empty chamber (**A**) and the chamber with lettuce plants or models (**B**). Light intensities in the empty chamber were measured and simulated at different heights from the floor of 0, 5, and 10 cm. Nine lettuce plants at 21 days after transplanting (DAT) were used.
## 5 Conclusions  
This survey deals with \(\beta\)-blockers of the arylaminoethanol type (sotalol, labetalol, bufuralol) and of the aryloxyaminopropanol type (penbutolol, betaxolol, esmolol, propranolol, betaxolol, timolol, carvedilol, nebivolol, and others). It outlines their different pharmacodynamic and pharmacokinetic properties as related to their stereochemical characteristics.  
In both categories of \(\beta\)-blockers higher activity towards \(\beta\)-receptors was observed in their (-)-isomers. These have the absolute configuration (\(R\)) in arylaminoethanols and (\(S\)) in aryloxyaminopropanols. Attempts to apply some \(\beta\)-blockers from the former category as pure enantiomers have not been successful. Thus, (\(R\))-(-)-sotalol had to be removed from clinical practice because of increased mortality. In addition, (\(R\),\(R\))-(-)-labetalol (dilevalol; a non-selective \(\beta\)-blocker/selective \(\alpha\)1-blocker) was discontinued due to the observed hepatotoxicity.  
Aryloxyaminopropanols generally exert higher \(\beta\)-blocking activity in comparison with the former category. Several drugs from this group have found their way into clinical practice as optically pure enantiomers, such as the (\(S\))-(-)-enantiomers of penbutolol (Betapressin(r)), levobunolol (Vistigan(r)), timolol (Dispatim(r)), and atenol (Atpure(r)) used in the therapy of hypertension, as well as (\(S\))-(-)-bunolol (levobunolol; Betagan(r)) and (\(S\))-(-)-betaxolol (levobetaxolol; Betagan(r)) applied in the treatment of glaucoma.  
The distinct pharmacokinetic phases (resorption, distribution, and metabolism) of particular \(\beta\)-blockers have been investigated; nevertheless, in most cases the available experimental data concerning the differences in their activity in dependence on the specific stereochemical configuration of the enantiomers is insufficient. Thus, there is still much research to be done on the \(\beta\)-blockers already established in the clinical practice, which could lead to introduction of optically pure enantiomers of these drugs instead of racemic mixtures, with improved activity and/or toxicity profile (so-called \"chiral switch\").  
Conceptualization, R.C.; investigation, R.C., L.H., M.M., J.V.; visualization, L.H., R.C. and M.M.; writing--original draft preparation, R.C. and L.H.; writing--review & editing, R.C., L.H. and J.V.  
This work has been supported by the Scientific Grants Agency of the Slovak Republic (Project No. 1/0346/16). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Location Methods Comparison and Discussion  
The new binocular location method based on improved YOLOv4 proposed in this paper not only successfully solved the difficult problem of stem node location, but also achieved better results in location accuracy than traditional crop location methods. For example, [PERSON] et al. [25] proposed a binocular vision method for picking point positioning of disturbance lithchi cluster, and the \(H\) component of lithchi image was directly used for stereo matching. This method does not extract and optimize feature points as present in this paper, resulting in the maximum location error of 58 mm in \(Z\) direction. [PERSON] et al. [26] identified the position of beef tomato based on R-CNN and binocular imaging technology. It directly used the SDK of the binocular camera to measure the distance without considering the targeted improvement for the location object, which led to a large number of mismatches. Therefore, the maximum location error of its \(Z\) axis was 18.6 mm, which was not ideal for small crops location. [PERSON] et al. [27] obtained the position information of grape clusters based on the dense disparity calculation method of gray value matching, and finally the maximum location error of \(Z\) axis was 12 mm. Because the gray value might be affected by illumination, the location accuracy based on the matching method of gray value was lower than that of the feature point-based. In the previous finding of [PERSON] et al. [28], which used two CCD color cameras integrated with a window zooming-based algorithm to locate multiple fruits and vegetables, results showed that the maximum \(Z\)-axis location error was 7.5 mm. On the contrary, the maximum \(Z\)-axis location error of the method proposed in this paper was only 2.86 mm, which was far lower than the location methods in the above-mentioned literatures. This fully demonstrates the superiority of the method proposed in this paper. The main reason that this might be applied is because the improved YOLOv4 algorithm can still keep high accuracy with greatly reduced complexity. At the same time, a large number of mismatching points are eliminated by RANSAC algorithm and epipolar constraint, thus further improving the location accuracy.
# Polarimetry for Photonic Integrated Circuits
## 3 Results
### PIC Characterization
#### 3.2.1 Integrated Polarization Rotator  
The results obtained from integrated polarization rotators as in [16; 17] are shown in Figure 7. The devices are designed to have a retardance of \(0.5\) (\(180^{\circ}\)) and a fast axis of \(45^{\circ}\).  
Figure 6: Validation of the calibrated setup accuracy by setting \(\overrightarrow{s}_{in}\) and measuring \(\overrightarrow{s}_{out}\) in the back-to-back case (no DUT). We express the error in terms of the angular error \(\Delta\xi\) between the two SOPs, see illustration **top left**. Ideally \(\overrightarrow{s}_{in}=\overrightarrow{s}_{out}\), i.e., \(\Delta\xi=0\). The angular error over the entire Poincaré sphere at \(1.55\)\(\upmu\)m is shown in the **top right**. Plots for all wavelengths for zero longitude \(\chi_{in}\) (zero latitude \(\psi_{in}\)) on the Poincaré sphere are shown on the **bottom left (bottom right)**. The average angular error is \(1.5^{\circ}\) for all wavelengths and over the entire Poincaré sphere.
# Dynamic Multi-Attribute Decision-Making Method for Risk-Based Ship Design
## 3 GRA Model for RCO Ranking
### Model Definition  
Assuming that there are n RCOs forming an option set \(O=(O_{1},O_{2},\ldots,O_{i},\ldots,O_{n})\), each RCO has m attribute indices forming an attribute set \(V=\big{(}V_{1},V_{2},\ldots,V_{I},\ldots,V_{m}\big{)}\). Thus, \(x_{ij}\) is defined as the experiment value of attribute \(V_{i}\) in option \(O_{i}\). The information sequence \(X_{i}=(x_{i1},x_{i2},\ldots,x_{im})\) indicates m attribute experimental values in option \(O_{i}\).  
For the ideal option \(O_{0}\), \(X_{0}=(x_{01},x_{02},\ldots,x_{0m})\) indicates the ideal sequence. \(x_{0j}\) can be determined depending on the types of attribute indices \(V_{i}\) as follows:  
\[x_{0j}=\begin{cases}\max\big{(}x_{1j},x_{2j},\cdots,x_{nj}\big{)},V_{j}\in LBA \\ \min\big{(}x_{1j},x_{2j},\cdots,x_{nj}\big{)},V_{j}\in SBA\end{cases} \tag{1}\]  
where _LBA_ indicates 'the larger the better attribute' indices, such as \(\triangle\)PLL, \(\triangle\)PLO, and \(\triangle\)PLP, whereas _SBA_ indicates 'the smaller the better attribute' indices, such as GCAF, NCAF, and CATS.
## 3 Numerical Simulation Model
### Calculation Process  
The calculation process of the model is mainly divided into three parts: geostress balance, supporting and soil excavation [31]. Soil excavation and anchor cable supporting is carried out step by step. The specific calculation process is described as follows:  
Step 1: Assign the corresponding material parameters along the depth to the soil, and run the geostatic step.  
Step 2: Clear the displacement field in step 1, assign the corresponding element parameters to the cast-in-place pile and activate the contact surface between pile and soil.  
Step 3: Excavate soil at the depth of 0-3 m, assign the element parameters to the 1 st layer of waist beam, and activate the 1 st layer of anchor cable element at the same time.  
Step 4-Step 11: Excavate soil at the depth of 3-19 m and excavate 2 m downward each step. Assign the element parameters to the 2 nd to 9 th layers of the waist beam, and activate the anchor cable element in the 2 nd to 9 th layers.  
Step 12-Step 15: Excavate soil at the depth of 19-28 m, and excavate 3 m downward each step. Assign the element parameters to the 10 th-13 th layers of the waist beam, and activate the anchor cable elements of the 10 th-13 th layers.  
Step 16: Excavate soil at the depth of 28-34 m and complete the simulation of excavation and supporting steps.  
The excavation steps and depth of the foundation pit are consistent with the actual construction on-site.
###### Abstract  
The rhizosphere microbiome is a major determinant of plant health. Plant-beneficial or plant growth-promoting rhizobacteria (PGPR) influence plant growth, plant development and adaptive responses, such as induced resistance/priming. These new eco-friendly choices have highlighted volatile organic compounds (biogenic VOCs) as a potentially inexpensive, effective and efficient substitute for the use of agrochemicals. Secreted bacterial VOCs are low molecular weight lipophilic compounds with a low boiling point and high vapor pressures. As such, they can act as short- or long-distance signals in the rhizosphere, affecting competing microorganisms and impacting plant health. In this study, secreted VOCs from four PGPR strains (_Pseudomonas koreensis_ (N19), _Ps. fluorescens_ (N04), _Lysithinacillus phareicus_ (T19) and _Paenibacillus alvei_ (T22)) were profiled by solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC-MS) combined with a multivariate data analysis. Metabolomic profiling with chemometric analyses revealed novel data on the composition of the secreted VOC blends of the four PGPR strains. Of the 121 annotated metabolites, most are known as bioactives which are able to affect metabolism in plant hosts. These VOCs belong to the following classes: alcohols, aldehydes, ketones, alkanes, alkenes, acids, amines, salicylic acid derivatives, pyrazines, furrans, sulfides and terpenoids. The results further demonstrated the presence of species-specific and strain-specific VOCs, characterized by either the absence or presence of specific VOCs in the different strains. These molecules could be further investigated as biomarkers for the classification of an organism as a PGPR and selection for agricultural use.  
metabolomics profiling; multivariate data analysis (MVDA); plant growth promoting rhizobacteria (PGPR); solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC-MS); volatile organic compounds (VOCs) +
Footnote †: journal: Metabolites
## 4 Energy Management Strategies
### Energy Management Protocols
#### 4.3.1 Energy-Efficient Routing Protocols  
* **Objective:** Optimize the path for data transmission to minimize energy consumption.
* **Examples:** Energy-efficient variants of routing protocols like LEACH (Low-Energy Adaptive Clustering Hierarchy) and TEEN (Threshold-sensitive Energy-Efficient Network) [77, 78].
## 4 Literature Review
### Electromyogram (EMG)  
The EMG technique records muscles' electrical activity during contraction and relaxation during sleep, which makes EMG a significant signal for classifying sleep stages [63]. Figure 4 presents a sample of time series EMG data for the five sleep stages, illustrating that EMG activity reaches its peak during the W stage. As we progress from stage N1 to stage N3, EMG activity gradually decreases as the muscles begin to relax. In the REM stage, the EMG activity is at its lowest point as the muscles are inactive and relaxed.  
Few studies have explored the use of combining EEG and EMG signals to enhance the accuracy of sleep stage classification. [PERSON] et al. [64] used a combination of one channel of EEG (F3-M2) and one channel of EMG signals as input. They extracted statistical and FFT features from the raw data to pass to RF and MLP classifiers to classify five sleep stages, achieving accuracies of 88.65% and 66.70%, respectively. [PERSON] et al. [65] proposed a machine learning model that used one channel of EMG and one channel of EEG signals (C3-A2) as input, and applied a wavelet transform (WT) to extract features from the signals. The deep neural network (DNN) model they developed achieved a 98% accuracy in classifying five sleep stages.  
[PERSON] et al. [66] used a temporal decomposition method to extract features from one channel of EEG signal (Fpz-Cz) and one channel of EMG signal, and used SVM as a classifier to classify five sleep stages, achieving an accuracy of 93.8%. [PERSON] et al. [67] selected multichannel EEG signals (Fpz-Cz and Pz-Cz) and one channel of EMG signal as input, and passed them through a deep learning model named as SSNet model containing  
Figure 3: Samples of EEG patterns in five sleep stages from the Sleep-edfx dataset [36].  
two deep learning architectures. The first architecture contained five 1D-CNN layers, and the second architecture contained two LSTM layers. The features extracted from the two architectures were combined and passed to a fully connected layer to classify three sleep stages, achieving an accuracy of 95.46%.
## 2 Methods
### Size Effect Relations and Convergence to the Rve Limit  
We denote the mean and standard deviation of a property \(f\) by \(\tilde{f}\) and \(D_{f}\), respectively. The coefficient of variation is defined as \(c_{f}=D_{f}/\tilde{f}\). For a variation-based analysis, the RVE is determined as the SVE size beyond which the variation (standard deviation \(D_{f}\) or coefficient of variation \(c_{f}\)) falls below a user-specified tolerance \(\epsilon\). When the variation in a field is below a limit, the field can be considered homogeneous. The standard deviation is used for non-dimensional fields, whereas the coefficient of variation is used for dimensional fields. Examples of the first group are the anisotropy indices and volume fraction and examples of the second group are the bulk modulus and yield strength. If \(f\) is ergodic and stationary, an empirical relation is proposed to approximate the decay of variation in \(f\) as in [16],  
\[D_{f}(L_{\text{SVE}}) =\frac{b_{f}}{L_{\text{SVE}}^{a_{f}}} \tag{5a}\] \[D_{f}(\Delta^{D_{f}}) =\epsilon\rightarrow\Delta^{D_{f}}=\left(\frac{b_{f}}{\epsilon} \right)^{1/\alpha_{f}}\] (5b) \[c_{f}(L_{\text{SVE}}) =\frac{b_{f}}{L_{\text{SVE}}^{a_{f}}}\] (6a) \[c_{f}(\Delta^{cf}) =\epsilon\rightarrow\Delta^{cf}=\left(\frac{b_{f}}{\epsilon} \right)^{1/\alpha_{f}} \tag{6b}\]  
where \(b_{f}\) and \(\alpha_{f}\) are the power scaling factor and convergence rate, respectively. Equations (5) and (6) are used for non-dimensional and dimensional quantities, respectively. The RVE size is denoted by \(\Delta\). The linear regression of the logarithm of variation quantity \(D_{f}(L_{\text{SVE}})\) versus the logarithm of \(c_{f}(L_{\text{SVE}})\) provides the parameters \(b_{f}\) and \(\alpha_{f}\), hence the RVE size. A similar power relations in the form \(\tilde{f}(L_{\text{SVE}})=\tilde{f}_{\infty}+b_{\tilde{f}}/L_{\text{SVE}}^{a_ {f}}\) can be used to determine how the mean value of a homogenized field tends to its terminal value \(\tilde{f}_{\infty}\) (if it exists) when \(L_{\text{SVE}}\) tends to infinity.  
While we do not analyze the corresponding mean-based RVE size [13,15] for all properties, we need to use it to determine anisotropy indices. For the materials considered, the anisotropy indices tend to be zero for \(L_{\text{SVE}}\rightarrow\infty\) as the materials are macroscopically isotropic. Thus, by setting \(\tilde{f}_{\infty}=0\), the mean-based RVE size for anisotropy indices is obtained as \(\tilde{f}(L_{\text{SVE}})=\epsilon\Rightarrow\Delta^{f}=\left(b_{\tilde{f}}/ \epsilon\right)^{1/\alpha_{f}}\) for \(f=A_{\text{RO}}\), \(A_{Y}\), and \(A_{H}\). By setting the isotropic limit as the maximum of mean-based and variation-based RVEs\(\Delta^{f}:=\max(\Delta^{f},\Delta^{Df})\), we ensure that both the mean and variation in the anisotropy index are smaller than the tolerance \(\epsilon\), thus indirectly stipulating that the overall anisotropy index of a material is close to zero for SVEs larger than \(\Delta^{f}\). Similar to [13; 15], we use this approach to define the isotropy size limit of the apparent properties.
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 2 Materials and Methods  
### Beam Steerers for Space Applications  
Several LC structures can be used as beam steerers. Those intended to be applied to space environments need to account for resilience under ionizing radiation conditions. This usually excludes any LC device in which the active area includes electronics, such as the LCoS or the ubiquitous active-matrix thin-film transistor (AM-TFT). In both cases, LCoS and AM-TFT, electronics cannot be adequately shielded from ionizing radiation because the beam steerer must have visibility to outer space.  
Passive-driven transmissive systems present a good alternative. The reliability of LC materials under ionizing radiation has already been tested [2]. In passive addressing mode, driving electronics are outside the steering cell, allowing the drivers to be adequately shielded.  
The operation principle of this passive-addressed BS is based on phase diffraction gratings. Diffraction gratings are made of a set of linear parallel elements with a pitch comparable to the light wavelength [25]. Diffractive gratings are manufactured using different methods, as recently reviewed in [26].  
The device presented in this work is driven digitally (Figure 2). Analog tunable beam steerers can be achieved using high resistivity electrodes [27], albeit with a limited scanning range. The transmissive device is a blaze-grating, i.e., a saw-tooth refractive index profile where every tooth is discretized in a number of steps by setting the effective refractive index of the liquid crystal, pixel by pixel [28; 29]. Thus, as the light passes through the diffractive structure, it is deviated by an angle.  
The diffraction efficiency is the relationship between the diffracted power and the input power. The larger the number of pixels per tooth, the better approximation to the continuous wedge profile, and the higher the diffraction efficiency, at a cost of reduced deviation angle. Fringing fields and crosstalk have been reported to cause a significant loss in efficiency [30; 31].  
Figure 1: The combination of two 1D LC cells generating a 2D deviation in the incident angle.