esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Samples, Vineyards and Harvests Characteristics, and Sampling
#### 2.1.2 Vineyard Parcels Characteristics  
Table 1 summarizes the main characteristics of the vineyard parcels under study, such as soil type (defined based on its texture), row orientation, the presence or not of pine trees (which influences vineyards sunlight, shadow, and wind exposures), and altitude (determined with a global positioning system (GPS) apparatus). All these vineyard parcel conditions were defined and selected by [PERSON], Herdeiroos company.  
Although grape quality also depends on agricultural practices [1], in this, the same agricultural conditions were observed: parcels were not irrigated, thinning was not performed, no kinds of mulches were added to the soil, and the bilateral codon trellising system was used in all vineyard parcels.  
Figure 1: Representative scheme of the location of the vineyards of (**a**) São Mateus (SM) and (**b**) Vale de Azar (VA) at [PERSON], Herdeiroos company, in Bairrada Appellation (Portugal), highlighting the varieties (_Vitis vinifera_ L cv. Arinto (AR), Bical (BI), Sauvignon Blanc (SB), Baga (BA), Castelão (CA), and Touriga Nacional (TN)), altitude (ca. 50 to 90 m), and vineyard parcels (SM1 to SM3 and VA1 and VA2) under study.
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
## 5 Results and Analysis  
### Optimization Results of Battery Super-Capacitor Installation Capacity  
Figure 5a shows the relationship among the battery installation capacity, the super-capacitor installation capacity, and the average daily generation cost of the distributed power system. In order to clearly reveal the factors that can affect the installation capacity of the hybrid energy storage system, Figure 5b magnifies the surface of Figure 5a for further analysis.  
As shown in Figure 5a, with the increase of battery and super-capacitor installation capacity, the more electrical energy can be provided by renewable generators to reduce carbon emissions. However, with the increase of renewable generation, the daily energy cost of the distributed power system will increase correspondingly. This is because the power generated by the renewable system has a higher LCOE compared with the diesel generator. Additionally, Figure 5a shows that by increasing the hybrid energy installation capacity, the average daily generation cost of the system increases from 5166.1 U.S. dollars to 5864.9 dollars, which is a 13.5% increase.  
Figure 5b shows the constraints of the carbon emissions, the average daily generation cost, the renewable energy fluctuation, and the LOLP rate when sizing the battery and super-capacitor capacity. \(L_{1}\) shows that the minimum capacity of the super-capacitor is limited by the fluctuation of renewable energy. To smooth the outputs of renewable generation, the capacity of the super-capacitor should be no less than \(L_{1}\). \(L_{2}\) mainly reflects that the battery needs to provide enough spinning reserve to reduce the LOLP rate of the system. \(L_{3}\) and \(L_{4}\) reveal that the total capacity of the hybrid energy storage can be tremendously limited by the maximum allowance of the average daily generation cost  
Figure 5: The relationship among the battery installation capacity, the super-capacitor installation capacity, and the average daily generation cost of the distributed power system: (**a**) an overview; (**b**) in a detailed version.  
and the carbon emissions. Due to the aforementioned limitations, the green area in Figure 5b is the only feasible scope of the hybrid energy storage system capacity. Finally, considering that the installation costs of super-capacitor is far more expensive than that of the battery, it is preferable to choose point P as the best capacity of the hybrid energy storage system.
## 4 Hierarchical Energy Balancing Protocol (HEBP) Design
### Performance Evaluation of Each WS Based on Its Harvesting Capacity
It was also obtained that the total amount of transmitted data by each WS was greater than the case in Figure 12a,b, for which the data size was set at 512 bits. For example, the WS with a higher harvest rate transmits up to 80 kbits during the month instead of 10 kbits transferred when the data size is set at 512 bits. * For Figure 12e,f, a variable data size between 128 and 4096 bits was assumed. It was observed that overall, the total quantity of data transmitted by each of the WSs had increased. However, the WS with the highest harvest rate is not the one that sends the most long-term data. This is explained by the fact that it exhausts its energy reserve at the start of the cycle while the other WSs accumulate enough energy to be then able to transmit large quantities of data. Overall, the results of Figure 12 show that it would be difficult to completely control the industrial process studied here using WS powered by energy harvested from vibrations. Even by minimizing the data size, some nodes will not be able to transmit enough information given their harvesting capacity. This makes it challenging to implement a vibratory energy harvester WSN without an external energy supply to some WSs. We overcame this limitation in the following subsection by proposing the Hierarchical Energy-Balancing Protocol (HEBP).
# Tunable Effect of the Calcination of the Silanol Groups of KIT-6 and SBA-15 Mesoporous Materials
## 3 Results and Discussion
### Effect of the Calcination Temperature on the Textural Properties of the MNS
Since SBA-15 has hexagonal cells and KIT-6 has cubic cells, the calculation of the network parameters \(\mathrm{a_{0}}\) is different, and consequently, the calculation of the pore wall thickness (\(W_{\mathrm{t}}\)) will also be performed by two different methods. For the SBA-15 samples, the formula adopted [13] is \(W_{\mathrm{t}}~{}=~{}\mathrm{a_{0}}~{}-~{}\mathrm{D_{P}}\), and for the KIT-6 samples, the thickness is determined by the expression [66]\(W_{\mathrm{t}}~{}=~{}\frac{\mathrm{a_{0}}}{2}~{}-~{}\mathrm{D_{P}}\). The pore diameter (\(\mathrm{D_{P}}\)) was determined with the adsorption branch by the BJH (Barrett-Joyner-Halenda) method, and the mesoporous correction was done in accordance with to the VBS (Villarroel-Barrera-Sapag) method. This is an improved macroscopic method that modifies the Kelvin equation used in the BJH method and considers the most accepted capillary evaporation and condensation mechanisms for mesoporous materials [76]. Figure 4: N\({}_{2}\) sorption isotherm and pore distribution of SBA-15 calcined under different conditions. The results show that, for SBA-15, the mesoporous volume and pore diameter increased as the heat treatment conditions became more drastic, ultimately reaching their maximum values upon calcination at 500 \({}^{\circ}\)C for 5 h, showing that, under these conditions, dehydroxylation becomes more intense. The literature reports that calcining at temperatures above 600 \({}^{\circ}\)C greatly affects the textural properties of the MNS [77]. For KIT-6, the maximum mesopore volume was obtained under the calcination conditions of 500 \({}^{\circ}\)C for 5 h. When the heat exposure time was doubled, a contraction of the mesoporous volume was observed, evidencing the greater tendency to dehydroxylate the material at higher temperatures. Structure shortening under the most extreme conditions was also observed in the XRD analysis. This behavior is attributed to the structure of the interconnected mesopore channels, forming cubic symmetry. This can hinder heat flow within the material, and, as the results of the elemental analysis show, the carbon content in the most extreme methodology is higher in KIT-6 than in SBA-15, showing a greater difficulty for heat flow in those materials. Figure 5: N\({}_{2}\) sorption isotherm and pore distribution of KIT-6 calcined under different conditions.
## 3 Midas GTS Simulation Process and Results
### Constitutive Model  
In the construction model, the plate elements represent tunnel segments, underground diaphragm walls, and shield shells of the shield machine. In contrast, beam elements are used for the concrete support, crown beam, waist beam, and steel struts. The soil constitutive model employs the modified Mohr-Coulomb model, an improved constitutive model based on Mohr-Coulomb, which is suitable for various types of foundations, particularly materials with frictional properties such as sand. It simulates the combined behavior of nonlinear elastic and elastoplastic models, incorporating a power-rate relationship. The shear yield surface of the modified Mohr-Coulomb aligns with that of the traditional constitutive model. However, the compressive yield surface adopts an elliptical hat constitutive model. Unlike traditional fully elastic and semi-plastic stress models, the elastic yield point plane in the modified Mohr-Coulomb model is not static within the space of high principal stress but expands in response to the plastic strain of the material [48]. The modified Mohr-Coulomb model has replaced the hyperbolic model [49], reintroducing plastic stress theory and establishing an upper yield limit [50]. For this study, the reference compression modulus (\(E_{\text{ood}}^{\text{ref}}\)) at 100 kPa, based on the investigation report, was utilized. The reference secant stiffness (\(E_{50}^{\text{ref}}\)) [51] and the reference unloading and reloading moduli (\(E_{\text{ur}}^{\text{ref}}\)) were derived from the literature [51; 52].  
In this study, we streamlined the soil layer by merging strata with similar properties, resulting in a simplified representation of all soil layers as horizontal layers. This approach prevents the creation of excessively small layering during grid division in the numerical  
Figure 4: Diagram illustrating the finite element model of an existing tunnel undergoing various disturbances.  
simulation, thereby enhancing mesh quality and ensuring better convergence of the calculations. The soil material attributes is detailed in the following Table 2. Table 3 displays the structural material attributes.
# Sentiment Analysis Based Requirement Evolution Prediction
## 4 Evaluation and Experimental Results
### Experimental Settings  
LSTM-Based RNN:  
We used a fixed learning rate of 0.01 and changed the batch size depending on the sentence length. We ran Stochastic Gradient Descent (SGD) for 30 epochs. We set the hidden layer sizes (h) as 50, 100, 150.  
Pre-Trained Word Vectors:  
We used the publicly-available word embedding Google News word2 vec ([http://word2 vec.googlecode.com/svn/trunk/](http://word2 vec.googlecode.com/svn/trunk/)). [PERSON] et al. proposed two log linear models for computing word embedding from large corpora efficiently: (i) a bag-of-words model, CBOW, which predicts the current word based on the context words, and (ii) a skip-gram model that predicts surrounding words given the current word. They released their pre-trained 300-dimensional word embedding (vocabulary size of 3 M) trained by the skip-gram model on the part of the Google News dataset containing about 100 billion words [24].  
Hierarchical Topic Model:  
The HLDA model was set up to discover hierarchical feature requirements with positive and negative sentiment from users' review sentences. The Dirichlet hyperparameter of the HLDA was set as \(\alpha\) of 25.0, and the prior was set as \(\gamma\) of 0.01.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 8 Regulated Structural Alterations in LPS
### Regulation of Lipid A Modifications
McA sRNA could as well regulate the LpxT synthesis at a post-transcriptional level, since a base-paring region between the seed sequence in the _micA_ sRNA and the _lpxT_ mRNA has been predicted [64]. Another sRNA GcvB also represses _phoP_ mRNA translation by base-pairing [65]. The lipid A part can also undergo non-stoichiometric modification by PagP-dependent palmitoylation due to the incorporation of a palmitate chain linked to the hydroxyl group of 3-hydroxymystic acid at C-2 position of reducing-end [66]. Transcription of the _pagP_ gene is positively regulated by the PhoP/Q TCS and via RcsB in biofilm environment or increase in osmolarity that requires the GadE auxiliary regulator independently of the Rcs phosphorelay cascade [56]. The PagP enzyme is usually inactive in the OM and is post-transcriptionally activated when the OM permeability is breached and this modification can dampen recognition by host immune responses. Another PhoP/Q-regulated modification is PagL-dependent deacylation. The _pagL_ gene encodes a lipid A 3-O-deacylase and this deacylation of lipid A modifies its ability to induce immune response [67]. The PagL-dependent modification also occurs post-translationally after LPS is incorporated in the OM [13]. It is noteworthy that in _Pseudomonas aeruginosa_ the expression of _pagL_ is positively regulated by sRNA (Sr006) [68]. Regulation of another deacylase LpxR with a 3\({}^{\prime}\)-O-deacylase activity of relevance to pathogenicity is of interest and this modification also occurs in the OM [69; 70]. The expression of LpxR is subjected to negative regulation by MicF _trans_-acting base-pairing RNA. The base-pairing of MicF within the coding sequence of the _lpxR_ mRNA decreases its stability by promoting its degradation by RNase E [71]. Thus, variety of mechanisms exist to modify lipid A part of LPS which involve TCSs, the RpoE sigma factor and sRNAs and this regulation can occur at different steps of LPS biosynthesis transcriptionally or post-transcriptionally.
# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 2 Related Work
### Application and Data of Fine-Grained Classification  
In recent years, an increasing number of researchers have been dedicated to creating various challenging datasets aimed at enriching databases and enhancing the practicality of fine-grained tasks. These efforts have accelerated the real-world applications of fine-grained classification. For example, iNat2017 [31] is used in the research of flora and fauna identification in wild environments and provides an important technical foundation in the field of biology. The fashion clothing dataset DeepFashion [32] can be applied to visual tasks such as clothing detection and clothing recognition, while it can provide a large amount of fashion materials for clothing designers. The daily sale merchandise dataset RPC [33] provides data support to realize visual Automatic Check-Out (ACO) service for retail scenes. The Stanford Cars [34] dataset is helpful for fast and accurate fine-grained recognition of high-speed vehicles.  
In contrast to the mentioned datasets, this paper has collected images of famous towers from the whole of China in Figure 1, thereby enabling the extremely similar task of recognizing these Chinese ancient tower structures. This dataset can be used for the identification of tourist attractions and urban landmarks.  
Figure 1: Different views of the three towers. (**a–c**) Hangzhou Leifeng Tower; (**d–f**) ancient towers of Fogong Temple of Shanxi Yingxian.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Algorithmic Tuning  
To estimate the data patterns and make prediction computations, we facilitated ML development by utilizing a variety of python modules from the'scikit-learn' library [38]. For the development of KNN, we used the 'KneighborsClassifier' class and its methods from the'sklearn.neighbors' module. The number of neighbors was set to five. It was the best choice after running five trials, arbitrarily setting different k values (1, 3, 5, 7 and 9) and the default values for five-fold cross validation using the 'KFold' class from the'sklearn.model_selection' module for dataset training and validation [40]. In addition, we implemented SVM with the help of the 'SVC' class and its methods from the'sklearn.svm' module using a linear kernel for the decision boundary. The use of linear kernel provides better results for stock market forecasting problems in comparison with the other types of kernel, especially when the number of data points is not so high [41]. For the application of LR, we utilized the 'LogisticRegression' class and its methods from the'sklearn.linear_model' module, while the NB algorithm was applied with the help of the 'MinMaxScaler' class and its methods from the'sklearn.preprocessing' module. Furthermore, we employed the DT algorithm with the 'tree.DecisionTreeClassifier' class and its methods from the'sklearn.tree' module and the RF algorithm with the 'Random  
Figure 4: Sentiment analysis for StockTwits (**left** piechart) and Twitter (**right** piechart) datasets using TextBlob.  
Figure 3: Sentiment analysis for StockTwits (**left** piechart) and Twitter (**right** piechart) datasets using VADER.  
ForestClassifier' class and its methods from the'sklearn.ensemble' module. The last one is the MLP algorithm. The class used was the 'MLPClassifier', along with its methods from the'sklearn.neural_network' module. The number of hidden layers was set to two and the number of neurons was 40 and 40, respectively [42]. These parameters were tested to be the optimal after performing an exhaustive grid search hyperparameter tuning with the 'GridSearchCV' class and its methods from the'sklearn.model_selection' module.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Fusion and Cascading Architectures
#### 5.3.1 Vision Fusion
The second part of the network consists of finding the translational and rotational components of the transformation matrix \(T\in SE(2)\). Since no information about the altitude is present in either the spinning radar scan or the overhead image, no  
Figure 8: 4 DRVO-Net [124] includes three main components. The first is a multi-scale extraction block for radar and visual data with an adaptive 4D radar–camera fusion module (A-RCFM) fusion block at each scale. Image features and 4D point features are extracted from the vision and 4D radar data respectively. The second is an initial pose estimation module that takes the two 4D point clouds at the coarsest scale and their features to produce a confidence score, embedding features and pose estimate. The final component is a pose refinement module that takes the confidences, embedding features, and predicted poses of a prior scale to progressively improve the estimates at each scale. Reproduced with permission from [124]. information about the height transformation can be inferred. The network splits the search for the translation and rotation component by determining the angle of rotation through the biggest correlation in the Fourier domain between the original radar data and rotated versions of the artificial scan. Finally, linear and convolutional layers are used to determine the translational offset. This approach requires an approximately correct ego-pose in order to obtain a satellite image.
###### Abstract
[PERSON] and [PERSON] [33] extended traditional BWM to uncertain situations and, for multi-criteria decision-making problems, proposed the HFBWM using hesitant fuzzy multiplicative preference relations. The integration of hesitant fuzzy language with MCDM has become a significant improvement direction in addressing multi-objective decision-making problems in QFD. Based on the above analysis, this paper attempts to propose an improved QFD method in the context of hesitant fuzzy language. The main contributions are summarized as follows. First, the use of HFLTSs can effectively reflect various hesitant and uncertain linguistic expressions of QFD team members, avoiding information loss. This enables QFD team members to use more flexible and rich language to express their subjective judgments more realistically. Second, we propose an improved BWM that incorporates hesitant fuzzy language. In contrast to traditional BWM, which constructs comparison vectors based on precise values, the expert evaluation information expressed by hesitant fuzzy semantic sets is ultimately represented in the form of interval numbers. This representation enhances comparison consistency, resulting in more reliable weights. Third, we introduce an improved TOPSIS method that integrates hesitant fuzzy language. Compared to the calculation based solely on one-dimensional numerical values, the algorithm combining binary semantic information better reflects the real decision opinions of experts. This leads to the more accurate and trustworthy prioritization of ECs, facilitating correct decision-making regarding engineering characteristic planning goals. The rest of this paper is structured as follows. In Section 2, we provide an introduction to the preparatory knowledge of HFLTS theory and binary semantics. Section 3 presents the QFD framework proposed using the HFLTS-BWM and HFLTS-TOPSIS methods. In Section 4, we illustrate the practical application of the proposed methods through a specific case and conduct a comparative analysis with other relevant QFD approaches. Finally, Section 5 summarizes some conclusions and provides suggestions for future research.
## 3 Results and Discussion
### Case Study A: Soil Profile Interpretation  
In the quest to interpret the soil profiles, we inferred from the results that the soil profiles for each tower consisted of two, three and sometimes four layers with different values of soil resistivity. As indicated in Figure 8, the Tower T42 soil profile had two layers. The surface layer, referred to as air, had infinite resistivity and thickness. The first layer of the soil had a resistivity of 2240.443 \(\Omega\cdot\)m and a thickness of approximately 5.3335 m, while in case of the second layer, it was 842.7448 \(\Omega\cdot\)m and an infinite thickness.  
Figure 9 shows the soil structure for Tower T48, which was composed of three layers with different soil resistivity. Results indicated that the highest soil resistivity was recorded at the third layer with 2520.846 \(\Omega\cdot\)m and an infinite thickness. The first and second layer showed 2066.665 \(\Omega\cdot\)m and 535.7491 \(\Omega\cdot\)m and the thickness of 0.7022 m and 9.4521 m, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Radius of the borehole (m) & 0.12 \\ Radius of the electrode (m) & 0.003302 \\ EEC thickness (m) & 0.116698 \\ \hline \end{tabular}
\end{table}
Table 4: Details about the EEC required for modelling in CDEGS.  
Figure 8: Soil profile interpretation for the Tower T42.  
Figure 9: Soil profile interpretation for the Tower T48.  
Figure 10 presents the four-layer soil profile for the Tower T50. Result indicated that the soil resistivity of the first layer was 1129.640 \(\Omega\cdot\)m and its thickness was 0.8880 m. The second and third layer showed the values of soil resistivity of 610.0205 \(\Omega\cdot\)m and 487.6068 \(\Omega\cdot\)m, having the thicknesses of 0.2014 m and 1.3932 m, respectively. The fourth layer, denoted as the bottom layer, had a resistivity of 873.3768 \(\Omega\cdot\)m and an infinite thickness.