esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Sensitivity Analysis  
The development and growth of PBL depends on the surface heat and moisture fluxes and their upward mixing by the turbulent eddies and subsidence [64; 65]. The advection of cold and moist air by sea breeze can lead to important modifications to the land surface fluxes, and thus have consequences on the boundary layer development. [66]. In other words, the sea breezes move cold and moist air inland, lowering the air temperature, weakening the convection structure, and eventually impeding the growth of PBL. Therefore, in this study, sensitivity analysis was performed on the wind vertical structure of the sea breeze and PBL height at Gangneung, a representative area of Yeongdong for the optimized set of the schemes and the various other sets.  
Figure 7 shows the time variation of the vertical wind profile and PBL height for the experiments of OPTM and EXP1--EXP4 on 15 April 2021. In OPTM, the PBL height started to grow due to solar radiation in the morning and reached the peak at 1100 LST (Figure 7a). After that, the PBL height began to decrease as the sea breeze became stronger. In particular, the PBL height appeared lowest at 0500-0600 UTC (1400-1500 LST), which grew slightly again: this feature is strongly linked to the sea breeze circulation whose intensity was the strongest at 0500-0600 UTC, and then decreased slightly. Both OPTM and EXP1 use Noah MP and show similar features in the PBL height and horizontal wind vector: this is reasonable because LSM calculates the heat and moisture fluxes for land and provides the lower boundary conditions for vertical transport in the PBL. However, in EXP1, the peak PBL height appeared at 0300 UTC (1200 LST), 1 h later than that in OPTM (Figure 7b); this is strongly related to the delay in the sea breeze development--mainly caused by different choices of the physical parameterization schemes other than LSM (see Table 4). In EXP2, the peak of PBL height also appeared at 0300 UTC, similarly to EXP1 (Figure 7c): the only difference between EXP1 and EXP2 is in LSM where the latter employed Noah LSM. Both EXP2 and EXP3 share the same parameterization schemes except for the PBL scheme--YSU for EXP2 and MY] for EXP3 (see Table 4)--which resulted in unreasonable PBL features in EXP3 (Figure 7d). EXP4 is different from EXP3 only in LSM, i.e., 5-Layer scheme in EXP4 vs. Noah LSM in EXP3, but it showed much unreasonable PBL evolution compared to EXP3 (Figure 7e). Overall, the PBL structure and evolution got adjusted to show better features with the optimized set of parametrization schemes.
## 3 Results and Discussion
### Influence of Al Powder Concentration on Color Difference  
Chromaticity refer to the color value when a multi-color light was used as the light source. In the visible light band, color was the main characteristic parameter of its visible light stealth ability. The L' in Table 5 represented the brightness of the waterborne UV-curable coating, indicating the reflection degree of the object to the incident light on its surface. The higher the L', the whiter the object and the higher the brightness. Along with the augmented Al powder concentration, the lightness L' value was gradually augmented and stabilized. As the Al powder concentration was augmented from 10.0% to 30.0%, the lightness L' value of the coating was augmented from 65.8 to 75.2, while when the Al powder concentration was augmented from 30.0% to 40.0%, the lightness L' value of the coating was only augmented from 75.2 to 76.8, with a small change in lightness. The reason was that the gloss was augmented with the augmentation of the sheet metal Al powder concentration, which lead to the augmentation of the luminosity L' value of the coating. A low infrared emissivity coating needs to reduce the brightness value of the coating for the sake of reducing the detection of infrared detection equipment, so the darker coating was better. According to Table 5, the coating with a low Al powder concentration had a lower brightness.  
Figure 3: Influence of Al powder concentration on the coating’s infrared emissivity.
## 5 Analysis of Selected Design Variants of the Considered Structure
### Comprehensive Analysis  
#### 5.6.1 Simulation Comparison for Different Thicknesses under Each Solution  
First, an analysis is conducted on the simulation results of three different side wall thicknesses under various solutions for the box-type substation, as shown in Figure 27a-c.  
An analysis of the simulation results for three different thicknesses of wall panels under various solutions reveals the following:  
1. Deformation Analysis: As the thickness decreases, the maximum deformation generally increases, although the trend is not significant. Notably, Solution V exhibits the highest deformation values in thinner walls, particularly at the 2.0 mm and 1.5 mm thicknesses. In contrast, Solutions II and III demonstrate smaller deformations across most thickness conditions, suggesting that these schemes may offer better structural strength and durability.
2. Stress Analysis: In all solutions, as the thickness of the side walls decreases, the maximum stress values increase. This indicates that thinner materials endure higher stresses under the same loading conditions, increasing the risk of material failure. Notably, Solution V exhibits the highest stress values across all thicknesses, especially at 2.0 mm and 1.5 mm, suggesting that this scenario places the most demanding stress requirements on the materials. In contrast, Solutions II and III generally show lower stress levels across most thicknesses, potentially indicating more robust design choices.
## 2 Behavioral Genetics
### Quantitative Genetics
#### 2.1.2 Twin Studies: Statistical Methods and Models  
Twin studies primarily employ two statistical approaches, univariate and multivariate models, depending on the specific research objectives. The univariate model is implemented to estimate the relative contributions of genetic and environmental factors to the variance of a single phenotype, whereas the multivariate model is applied when analyzing the interrelationships between two or more phenotypes [20]. In the univariate model, the analysis involves computing and comparing intraclass correlations between MZ and DZ twins. In contrast, multivariate models primarily focus on cross-twin/cross-trait correlations, which assess the relationships between two or more phenotypes observed in each twin within a pair [20]. Structural Equation Models (SEM) are used to assess the influence of genetic and environmental factors on phenotypic variance and covariance [23]. In these models, the phenotypes under investigation are treated as observed variables, while A (additive genetic factors), C (shared environmental factors), and E (non-shared environmental factors) function as latent variables. Three primary models are commonly employed for parameter estimation: the Cholesky model, the Independent Pathway model, and the Common Pathway model. The Cholesky model [Figure A1, Appendix A] proposes that correlations among traits arise from both genetic and environmental influences [18; 20]. When analyzing n variables, the Cholesky decomposition incorporates n distinct genetic and environmental factors: the first factor influences all traits, the second factor affects all traits except the first, the third factor impacts all traits except the first two, and so on [18; 20]. The Independent Pathway model [Figure A2, Appendix A] posits that common genetic and environmental latent factors directly influence all traits under investigation. These shared factors contribute to the associations among traits, while a set of distinct latent variables accounts for the unique variability of each individual trait [24]. Finally, the Common Pathway model [Figure A3, Appendix A] assumes that genetic and environmental influences contribute to a single underlying latent variable, which directly impacts the observed traits. Additionally, similar to the Independent Pathway model, this framework includes trait-specific latent factors that account for the unique variance components not shared across traits [24]. In statistical analyses, model selection is based on comparisons using chi-square tests, following the principle of parsimony. Under this principle, models with fewer latent variables are chosen over more complex ones as long as they do not lead to a substantial decline in data fit [24].
## 2 Materials and Methods
### Experimental Techniques
#### 2.3.4 Thermal Properties  
Differential scanning calorimetry (DSC) tests were conducted through a Mettler DSC30 machine under a nitrogen flow of 100 mL/min in a temperature range from \(-100\) to 250 \({}^{\circ}\)C at a heating/cooling rate of 10 \({}^{\circ}\)C/min. All the specimens, with a weight of approximately 10 mg, were subjected first to a heating scan, a cooling scan, and a second heating scan. In this way, the thermal transitions of both the blend constituents were detected, in particular the glass transition temperature (\(T_{g}\)) of the PCL, the melting temperature and enthalpy (\(T_{m}\), \(\Delta H_{m}\)) of both the PCL and PA6, and the crystallization temperature and enthalpy (\(T_{c}\), \(\Delta H_{c}\)) of both the PCL and PA6. Equation (1) shows how the degree of crystallinity (\(\chi\)) of the PA6 and PCL phases in the blends was evaluated.  
\[\chi=\frac{\Delta H_{m}-\Delta H_{cc}}{\Delta H_{m}^{*}\;\omega}\cdot 100 \tag{1}\]  
where \(\Delta H_{cc}\) is the enthalpy of cold crystallization of the PA6 (or PCL), \(\Delta H_{m}^{*}\) is the enthalpy of melting of fully crystalline PA6 (or PCL), equal to 230 J/g [33] and 139.5 J/g [34], respectively, and \(\omega\) is the weight fraction of PA6 (or PCL) in the blends.  
Thermogravimetric analysis (TGA) was performed through a Mettler TGS0 IR thermobalance (Mettler Toledo Inc., Columbus, OH, USA), testing samples of approximately 10 mg at 10 \({}^{\circ}\)C/min from 35 \({}^{\circ}\)C up to 700 \({}^{\circ}\)C, under a nitrogen flow of 100 mL/min. This test allowed the calculation of the temperature associated to a mass loss of 1% (\(T_{1\%}\)) and of 5% (\(T_{5\%}\)) and of the temperature associated to the maximum mass loss rate (\(T_{D}\)) of both PCL and PA6 phases, considered as the peak of the mass loss derivative (DTG) curves.
## 3 Results and Discussion  
PEF applies millisecond- or even microsecond-long pulses of various voltages depending on the purpose of its application [31]. It was found that the higher increase of mass transfer rate was achieved by applying a PEF of 0.7-3.0 kV cm\({}^{-1}\) and energy input of 1.0-20.0 kJ kg\({}^{-1}\). During this study, we attempted to develop a PEF-assisted SOLL polyphenol extraction method under the scope of a green and sustainable standalone process. Moderate electric field intensity of 1 kV cm\({}^{-1}\) and short pulses of 10 and 100 us in a total processing time of 30 min were applied, while mixtures of EtOH-H\({}_{2}\)O in five different ratios were tested as extraction solvents. In particular, the gradual addition of ethanol to water (25% step gradient) was evaluated to determine whether the PEF effect could offset the increased recovery usually achieved using polar organic solvents in relation to water. EtOH and other polar organic solvents possess a good solubility and, thus, extractability for bioactive phytochemicals, but they must be removed after the end of the treatment, increasing the cost of the whole process. A possible reduction in the need for EtOH when using PEF can result in financial and environmental benefits. To the best of our knowledge, reported studies of SOLL extraction using PEF technology and aqueous organic solvents do not exist in the literature.
# New Isolated Autochthonous Strains of _S. cerevisiae_ for
## 2 Materials and Methods  
### Isolation and Genetic Identification of Yeast Strains Isolated from the Natural Environment  
In the first stage of the research, isolation of microorganisms from three grape varieties, that is, Seyval blanc, Regent, and Solaris was carried out. The samples were collected from grapevines (leaves, fruit, or soil) of the varieties in selected vineyards (Swietokrzyskie and Lubuskie vovodeships). Yeast isolation was carried out on YPG medium with chloramphenicol (composition: yeast extract 10.0 g/L, peptone 20.0 g/L, glucose 20.0 g/L, chloramphenicol0.1 g/L, agar 20.0 g/L; pH 5.1 \(\pm\) 0.1). Pure yeast cultures were stored on YGC slants (YPG with chloramphenicol) at refrigerated temperatures. In order to determine the fermentation potential, a tentative process was carried out on the apple juice from concentrate. In each variant, 300 mL of juice inoculated with a 48-h cultivation of the strain refreshed on liquid YGC medium was fermented in an amount of 15% of the set volume. Fermentation was carried out at the temperature of 28 \({}^{\circ}\)C for 72 h. Then, the content of ethyl alcohol, total sugars, pH, and total acidity in each sample were determined.  
In the case of newly isolated strains that could carry out alcoholic fermentation, their species affiliation was determined. The most active yeasts were identified using molecular biology methods. Genomic DNA was isolated using the Plant/Fungi Isolation Kit (Norgen Biotech) according to the manufacturer's instructions. The D1/D2 fragment of the 26S rDNA gene was amplified using dedicated primers for the amplification of yeast genetic material, that is, the primers NLL (5\({}^{\prime}\)-GCATATCAATAAGCGGAGAAAAG-3\({}^{\prime}\)) and NLL (5\({}^{\prime}\)-GGTCCGGTTTCAAGACGG-3\({}^{\prime}\)). DNA amplification was performed using DreamTaq Green PCR Master Mix polymerase (Thermo Scientific) in a peqSTAR2X Gradient thermal cycler (VWR). PCR reactions were performed in a volume of 25 \(\mu\)L. The assay began at 95 \({}^{\circ}\)C for 5 min and ran for 30 amplification cycles at 95 \({}^{\circ}\)C for 1 min, 52 \({}^{\circ}\)C for 45 s and an extension step took place at 72 \({}^{\circ}\)C for 1 min; final extension at 72 \({}^{\circ}\)C for 7 min, holding at 4 \({}^{\circ}\)C [38] (with our modification). DNA sequencing was carried out in an external laboratory (Genomed SA). The final sequence of the analyzed DNA fragment was assembled using the Serial Cloner program. The comparison of the test sequence with the sequences deposited in the international GenBank sequence database was performed using the BLAST program. The sequences were deposited in the GenBank sequence database.
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
## 3 Virtual Hill Concept and Open Path Indicator, _New e\({}_{b}\)_
### Problems with Previous \(\mathbf{e}_{b}\)  
\[\mathbf{F}_{total}=\mathbf{F}_{rep}+\mathbf{F}_{ext}=\left\{\begin{array}{ ccc}k_{e1}\mathbf{e}_{t}+\left(k_{r}\Big{(}\frac{1}{\rho}-\frac{1}{\rho_{0}} \Big{)}\frac{1}{\rho^{2}}-2k_{e2}\rho\ \Big{)}\mathbf{e}_{n},&\rho\leq\rho_{0}\\ k_{e1}\mathbf{e}_{t}-2k_{e2}\rho\mathbf{e}_{n},&\rho>\rho_{0}\end{array}\right. \tag{15}\]  
From the moment the robot is trapped in the LM to the moment of its escape, the artificial force applied to the robot is \(\mathbf{F}_{rep}+\mathbf{F}_{ext}\). In Equation (10), the first term \(k_{e1}\mathbf{e}_{t}\) of \(\mathbf{F}_{ext}\) acts for a robot to move along the obstacle. The second term \(-2k_{e2}\rho\mathbf{e}_{n}\) acts in the opposite direction to the repulsive force so that the robot does not move too far away from the obstacle. The extra force operates to move the robot along the outline of the obstacle until the robot escapes the LM without being too far away from the obstacle. In the previous virtual hill algorithm, \(\mathbf{e}_{b}\) was determined by the location relationship, and the form of the surrounding obstacle was not considered. Therefore, depending on the form of the map, the robot sometimes set an inefficient path. Figure 4 shows the moment when the robot is trapped in the LM, and \(\mathbf{P}_{to}\) is the position of the closest obstacle at the moment when it is trapped in the LM. According to Equation (14) used in the previous algorithm, \(\mathbf{e}_{b}\) has the \(-\mathbf{K}\) direction. In the LM section, since \(\mathbf{e}_{t}\) indicating the driving direction is \(\mathbf{e}_{n}\times\mathbf{e}_{b}\), the robot moves in the negative direction (clockwise). Eventually, it goes around the blocked path, as shown in Figure 5 (Point E is the moment it is determined that the robot has escaped the LM). Depending on which direction is selected at the moment when the robot is trapped in the LM, it may take quite a long path, or a dead path may be selected. Determining \(\mathbf{e}_{b}\) by considering only the relative positions, as in Equation (14), may complicate the movement path, as in Figure 5. Therefore, a more effective path planning algorithm that considers the form of obstacles is required.
## 2 Analytical Representation
### Signal Modelling  
In this module, we will establish the signal model for \(L\) narrowband uncorrelated unknown sources. Figure 1 shows each parameter of CSA for physical location and estimated as:  
\[I_{p}=\{0,Qd,Pd,2 Qd,2 Pd,...,(Q-1)Pd,(M-1)Pd\} \tag{4}\]  
The DOA of these \(L\) uncorrelated signals are summarized in vector \(\Phi\)  
\[\Phi=[\Phi_{1},\Phi_{2},\Phi_{3},...,\Phi_{L}] \tag{5}\]  
Accordingly, the received signal of L uncorrelated sources of coprime array is specified in Equation (6).  
\[x(t)=\sum_{l=0}^{L}a_{l}(\Phi)s_{l}(t)+n(t) \tag{6}\]  
Figure 1: Generalized coprime antenna array structure.  
where  
\[a_{l}(\Phi)=[1,e^{\pi\mathcal{Q}\sin\Phi_{1}},e^{\pi P\sin\Phi_{2}},e^{\pi 2 Q\sin\Phi_{3}},e^{\pi 2P\sin\Phi_{4}},...,e^{\pi(Q-1)Pd\sin\Phi_{L-1}},e^{\pi(P-1) \mathcal{Q}d\sin\Phi_{L}}] \tag{7}\]  
\[s_{l}(t)=[s_{1}(t),s_{2}(t),...,s_{L}(t)]^{T} \tag{8}\]  
In Equation (6), \(n(t)\) is the complex iid and AWGN with variance \(\sigma I^{2}\) and zero mean.  
\[n(t)=[n_{1}(t),n_{2}(t),...n_{P+Q-1}(t)] \tag{9}\]  
Taking the expectation of the output signals the computation of covariance matrix can be performed in terms of coprime array framework, which are defined as  
\[R_{xx}=E[x(t)x^{H}(t)] \tag{10}\]  
where \(R_{xx}\) is the vectorization of the covariance matrix and can be further expanded as  
\[R_{xx}=AR_{ss}A^{H}+\sigma^{2}I \tag{11}\]  
The covariance matrix amplitude of the output signal is denoted by \(R_{ss}\) and written as  
\[R_{ss}=E[s(t)s^{H}(t)] \tag{12}\]  
With the help of \(R_{xx}\) the virtual uniform linear array can be computed which is described as  
\[z=vec(Rxx)=vec(ARssA^{H}+\sigma^{2}I)=B\sigma^{2}+\sigma^{2}I \tag{13}\]  
where \(B\) is the Kronecker product and the elements in B are expressed in the form of following equation  
\[e^{iK(q_{i}-q_{j})\sin\Phi_{L},i,j=1,2,...,P+Q-1}. \tag{14}\]  
In Matlab, the _vec_ command is used for vectorization. After performing vectorization of \(Rxx\), this matrix will become a column vector having the dimension \(z^{(P+Q-1)^{2}\times 1}\). Furthermore, this \(z\) vector will be sorted out in accordance with the exponent term of the received signal and presented as  
\[I_{c}=\{(Phot-Qmd)\cup(Qmd-Phot)\},\textit{ where }0<m<P-1\textit{ and }0<n<Q-1 \tag{15}\]  
The locations of these virtual ULA elements are presented as  
\[I_{v}=\{(q_{i}-q_{j})\ i,j=1,2,...,P+Q-1\} \tag{16}\]  
Our prime concern is the continuous part of vULA of CSA in this research work. The continuous part of vULA causes an enhancement in the degree of freedom. Moreover, this DOF is directly proportional to the length of continuous vULA.
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 3 Proposed Method
### Roberta Layer  
In the coding layer, in addition to the text that needed to be processed originally, we also inputted the entity pairs as external features and performed feature fusion in the tokenizer stage to improve the effectiveness of the final model. As illustrated in Figure 2, text information was first inputted into Tokenizer Dictionaries, which obtained token vectors based on dictionary-matching methods.  
The token embeddings obtained above only represent the characteristic value of a single word at the word level. In addition, other features also have a significant impact on the entity relationships to be extracted, such as text sorting and inter-line order. Therefore, as illustrated in Figure 3, in feature processing it is necessary to integrate features such as position and text line order with the characteristics of the text itself.  
Figure 1: The overall framework of the RBF model.  
The original text is represented by x = {x1, x2,..., xn}. After feature extraction, the calculation methods for the three features were as follows, where W represents the maximum number of characters. In this paper, the threshold of W was set to 128. If each line of text was more than 128 it would be truncated, and if it was less than 128 it would be supplemented with 0. Vk represents the dimension of a character vector.  
\[X_{Token}=R_{1}^{w\times V_{k}} \tag{1}\]  
\[X_{Position}=R_{2}^{w\times V_{k}} \tag{2}\]  
\[X_{Segment}=R_{3}^{w\times V_{k}} \tag{3}\]  
The input of the transformer layer is E = XToken + XPosition + XSegment, where CLS (classification) and SEP (separator) fields are separators specified by [PERSON].  
After embedding inputs into the Roberta model, the Roberta model continuously extracts sentence features through the multi-head attention mechanism method. Firstly, it multiplies the random initialization matrix M with the fusion feature vector of the above input to obtain vectors Q, vectors K, and vectors V, and then updates and optimizes the values of these three vectors as the training process proceeds. The attention mechanism function represents a mapping from Q to a series of K, and then to V, The expression formula is as follows, and dk represents the dimension of Q.  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{4}\]  
To prevent the effect of an iteration from being bad, the model performs residual connection and layer normalization after multi-head attention calculation and converts the input vector into data with a mean value of 0 and a variance of 1. The normalization  
Figure 3: Input Embeddings Fusion.  
Figure 2: Token Embedding.  
formula is as follows, where E[x] is the mean value of the sample and \(\sigma^{2}\)[x] is the variance of the sample:  
\[\mathrm{y}=\frac{\mathrm{x_{i}-E[x]}}{\sqrt{\sigma^{2}+\epsilon}}*\mathrm{r}+\beta \tag{5}\]  
The whole process needs to be iterated many times without a segment optimization effect. The structure of the network in a single iteration is illustrated in Figure 4.