esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Condition Monitoring Approach for
## 4 Application and Results
### Results and Discussions  
#### 4.2.1 Feature Extraction from Noise Samples  
When using WPT for feature extraction, the decomposition level \(n\) is particularly critical because the recognition accuracy rate is directly related to \(n\). If \(n\) is too small, the energy is mainly concentrated in some certain subbands, resulting in very little difference in the feature distribution of different signals. The signals can not be distinguished effectively by the extracted features, while, if \(n\) is too large, it will cause \"dimension disaster\", which is the feature redundancy and empty subbands, and increases the complexity of the classifier, eventually reducing the recognition accuracy rate. In the test, when \(n\) was less than or equal to two, the recognition rate was less than 70%, while, when \(n\) was greater than or equal to four, some empty bands appeared. It is appropriate to set \(n\) to be three, where the energy features' dimension is eight. Furthermore, from tests, the db5 wavelet function and Shannon entropy were selected for a three-level wavelet packet decomposition of the noise signals. The wavelet packet decomposition tree is shown in Figure 12.  
To provide an example, the subband signals, which were reconstructed by the third-level nodes of the noise sample No. 1-32, are shown in Figure 13. It indicates that eight subbands were decomposed from the original signal.  
Figure 12: Three–level wavelet packet decomposition tree.  
Figure 13: Reconstructed subband signals of noise sample No. 1–32.  
The feature vectors of the first four samples of each health state are provided as examples in Figure 14a-d.  
The mean of feature element values in every frequency band of all samples for every health state are listed in Table 3, and the comparison of the average energies of each frequency band for different health states is shown in Figure 15a-h.  
It can be seen in Figure 15 that the average energy of every frequency band for different health states are significantly different. Therefore, the feature extraction method based on WPT is effective for noise signals of shearing machines.  
Figure 14: Feature vectors of noise signals. (**a**) working state No. 1; (**b**) working state N. 2; (**c**) working state No. 3; (**d**) working state No. 4.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Working State No.**} & \multicolumn{4}{c}{**Mean of Feature Element Values of All Samples in Every Frequency Band**} \\ \cline{2-9}  & **band 1** & **band 2** & **band 3** & **band 4** & **band 5** & **band 6** & **band 7** & **band 8** \\ \hline
1 & 0.7358 & 0.1819 & 0.0107 & 0.0622 & 0.0007 & 0.0021 & 0.0043 & 0.0023 \\
2 & 0.6748 & 0.1537 & 0.0196 & 0.0779 & 0.0544 & 0.0054 & 0.0095 & 0.0046 \\
3 & 0.7828 & 0.1283 & 0.0161 & 0.0506 & 0.0034 & 0.0041 & 0.0088 & 0.0060 \\
4 & 0.6697 & 0.2064 & 0.0199 & 0.086 & 0.001 & 0.0027 & 0.0094 & 0.0049 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean of feature element values of all samples in every frequency band for every health state.
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 2 Method  
### Evaluation Indicator Selection  
The purpose of evaluating the effectiveness of guide signs is to optimize their design and usage, enhancing their guidance capability. Well-designed signs can effectively direct and influence driver behavior, providing a safer and more comfortable traffic environment for highway users. Therefore, conducting a comprehensive evaluation of guide signs is of significant practical importance. The selection of evaluation indicators should follow these principles: (1) Comprehensive reflection: The indicators should fully capture the major impacts across the entire road network. (2) Strong independence: Indicators should be as independent as possible to avoid excessive intercorrelation. (3) Moderate quantity: The number of indicators should remain reasonable, avoiding redundancy, with a preference for quantitative indicators. (4) Practicality and feasibility: The indicators should be operationally feasible to ensure the practicality and effectiveness of the evaluation.  
Factor analysis is an effective tool for simplifying and reducing the dimensionality of indicator datasets, particularly when numerous indicators overlap or exhibit complex relationships [33]. This approach helps streamline the model structure, ensuring it remains concise and scientifically robust. The general model for factor analysis can be expressed as [33] follows:  
\[\mathrm{X_{i}}=\alpha_{\mathrm{i}}\mathrm{F_{1}}+\alpha_{\mathrm{i}}\mathrm{ 2}\mathrm{F_{1}}+\ldots+\alpha_{\mathrm{i}}\mathrm{F_{b}}+\varepsilon_{ \mathrm{i}}\;\mathrm{i}=1,2,\ldots,n \tag{1}\]  
where \(\mathrm{X_{i}}\) represents the i-th evaluation indicator, with a total of \(n\) indicators; \(\alpha_{\mathrm{i}}\mathrm{b}\) is the weight of the b-th common factor on the i-th evaluation indicator; \(\mathrm{F_{b}}\) represents the m-th common factor in the evaluation indicator system; and \(\varepsilon_{\mathrm{i}}\) is the error term for the i-thevaluation indicator, representing the portion of information not captured by the common factors [34].  
In factor analysis, the Kaiser-Meyer-Olkin (KMO) test quantifies the inter-correlation strength of preliminarily selected indicators, and [PERSON]'s sphericity test assesses compliance with factor analysis assumptions [34]. Following indicator filtration through these diagnostic tests, the factor score matrix is utilized to examine inter-indicator correlations. This methodology facilitates both the systematic optimization and evidence-based construction of highway guide sign evaluation frameworks, thereby establishing a quantifiable foundation for scientifically rigorous assessments of signage efficacy.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology
### Assignment of the Weight Value for Indicators  
Assuming that there are \(m\) sensors in the initial sensor array and \(n\) single indicators are selected to evaluate them, then we can collect the indicator values to construct an evaluation matrix:  
\[X=\begin{bmatrix}x_{11}&x_{12}&\cdots&x_{1n}\\ x_{21}&x_{22}&\cdots&x_{2n}\\ \cdots&\cdots&\ddots&\cdots\\ x_{m1}&x_{m2}&\cdots&x_{mn}\end{bmatrix} \tag{5}\]  
where \(x_{ij}\) is the \(j^{th}\) indicator value of the \(i^{th}\) sensor. Due to the fact that the calculation results of the indicators are different in order of magnitude, it is necessary to perform dimensionless standardization on all indicator values before establishing the evaluation model according to the following equation.  
\[v_{ij}=\frac{x_{ij-min}(\begin{array}{cccc}x_{i,1},&x_{i,2},&\cdots,&x_{i,n }\end{array})}{max(\begin{array}{cccc}x_{i,1},&x_{i,2},&\cdots,&x_{i,n}\end{array} )-min(\begin{array}{cccc}x_{i,1},&x_{i,2},&\cdots,&x_{i,n}\end{array})} \tag{6}\]  
The data obtained by the standardization process are then represented by the matrix \(V\).  
\[V=\begin{bmatrix}v_{11}&v_{12}&\cdots&v_{1n}\\ v_{21}&v_{22}&\cdots&v_{2n}\\ \cdots&\cdots&\ddots&\cdots\\ v_{m1}&v_{m2}&\cdots&v_{mn}\end{bmatrix} \tag{7}\]  
The EWM algorithm uses information entropy to measure the weight value according to the information difference of the evaluation indicator [22]. The smaller the entropy weight of an indicator, the more information the indicator provides, and the greater its impact on the evaluation results. The weight values of the sensitivity, selectivity, correlation, and repeatability can be obtained through the following steps:  
* Calculate the weight value of each sensor of the \(j^{th}\) evaluation indicator: \[p_{ij}=\frac{v_{ij}}{\sum_{i=1}^{m}v_{ij}}\] (8)
* Calculate the entropy value of the \(j^{th}\) evaluation indicator: \[e_{j}=-k\sum_{i=1}^{m}p_{ij}\ln p_{ij}\left(k=\frac{1}{\ln m}\right)\] (9)
* Obtain the total entropy weight of \(j^{th}\) evaluation indicator: \[w_{j}=\frac{1-e_{j}}{m-\sum_{j=1}^{m}e_{j}},j=1,\;2,\cdots,m\] (10)
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 8 Biomedical Applications of PLA-Based Nanofibrous Structures
### Wound Healing  
In view of research, the lack of antibacterial properties of traditional dressings, poor cell respiration at the wound infection, and slow wound healing rate during use would be possible easily. Wound dressing can replace damaged skin to act as a temporal barrier in the wound healing process. The ideal wound dressing should accelerate healing of the wound, prevent infection, and restore skin structure in a short period of time. Wound dressings based on functional PLA nanofiber have been studied by a wide range of scholars. A large specific surface area, a controllable porosity and pore size, good ductility, and good biological properties of PLA fibers are not only useful to cell respiration, but also can prevent bacterial infection of wounds, and can improve cell proliferation and speed up wound healing. Furthermore, due to the good air permeability, they can efficiently absorb wound exudate and keep the wound in an ideal moist state. At the same time, it is conducive to the full contact of the wound with external oxygen and promotes the timely gas exchange of cells, thereby accelerating wound healing; moreover, the biodegradability of PLA provides better meets the current new requirements for the development of environmentally friendly materials, which is promising in the field of wound dressing research and development in the future [4]. Since the composition of electrospun nanofibers is controllable, loading antibacterial agents in PLA electrospun nanofibers can effectively inhibit wound infection. In addition, PLA nanofiber wound dressings with biologically active substances can be prepared according to the needs of medical treatment, which can better promote the adhesion and proliferation of cells at the wound and accelerate wound recovery. Table 1 shows different studies in which electrospun PLA-based structures have been used as a wound dressing for wound healing applications.
## 2 Materials and Methods
### Mobile Mapping System RIEGL VMX-2 HA  
The VMX-2 HA [13] (Figure 1) mapping system from Riegl is based on two VUX-1 HA [29] laser heads with different rotation axes, capable of measuring up to 475 m, and an INS/GNSS unit [30] supplemented by an odometer (DMI). The system can be equipped with up to nine position cameras, which can be set in the required directions (depending on the object of interest), and a Ladybug 5 + panoramic camera [31], suitable for point cloud colouring. The entire system is on a carrier attached to the car.  
In the product datasheet [13], the manufacturer lists several important parameters of the device. Table 1 shows the cloud densities for a specified distance at various speeds, which may be suitable for many applications but not for this work. As mentioned in the \"MLS Data Acquisition\" Section 2.5, the carrier speed was lower in this test. In the first instance, the speed was 20 km/h, which was chosen for its sufficient accuracy for testing and smoothness of data collection; the same speed is used in [25], where the final accuracy is high. A simple calculation shows that the density of points at 10 m should be 10,250 points/m\({}^{2}\). In the second case, the speed was about 35 km/h, which is the average speed that could be reached in such a densely built-up environment. The calculated point density value is 5860 points/m\({}^{2}\).  
Table 2 lists the accuracy of each sensor from the manufacturer's product datasheet. Accuracies are given for ideal conditions: an uncovered view of the sky, a maximum measured distance with a laser head to a distance up to 30 m, and the system must use a DMI (odometer).
## 1 Introduction  
Silicon-based materials are considered as one of the most potential anode materials for LIB because of their high theoretical capacity of 4200 mAh-g\({}^{-1}\), for Li4.4 Si [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. There are various types of Li-Si intermetallic states. Each of the Li-Si phases has a corresponding capacity, for example, Li1.71 Si (1632 mAh-g\({}^{-1}\)), Li2.33 Si (2224 mAh-g\({}^{-1}\)), Li3.25 Si (3100 mAh-g\({}^{-1}\)). The applicability of particular intermetallic Li-Si states as a LIB anode is determined by the smallest volume change during the lithiation/delithiation process [11]. The phases Li1.71 Si, Li2.33 Si, Li3.25 Si are applicable to both the crystalline and amorphous forms of silicon (monocrystalline and polycrystalline). In amorphous form, silicon can also form other intermetallic states, such as Li3.38 Si [12]. The disadvantage of the silicon anode material is its instability caused by significant change in volume that mayincrease to 300% during the process of lithiation/delithiation. This leads to mechanical destruction, and consequently, to a decrease in capacity, electrical conductivity, and to the formation of SEI--solid electrolyte interphase [13; 14; 15]. This problem can be solved by application of the so-called buffer electroconductive frame that can ensure the stability of the anode material volume. At the same time the buffer frame must have high mechanical strength, low resistivity, scalability, and high porosity [16; 17; 18; 19; 20]. As a material for such buffer frame it can be used a pillared graphene--hybrid carbon material on the base of monolayer graphene covalently bonded to vertically oriented SWCNTs [21; 22; 23; 24; 25; 26; 27; 28]. A particular feature of the pillared graphene atomic structure is high porosity. The pillared graphene has a highly developed surface that allows to fill its pores with atoms of various chemical elements. It is already used for storingatomic gases and lithium ions [29] and serves as the basis for effective gas filter membranes [30; 31]. The theoretical study of pillared graphene as anode material is required for the search of an effective silicon concentration in pores.  
This work is devoted to a quantum-chemical study of the lithiation/delithiation process in films of pillared graphene filled with silicon clusters in order to evaluate the effectiveness of its application as a buffer frame of LIB anode material.
## 3 Results and Discussion
### Depth of Water Penetration under Pressure  
For the two HPC mixtures with the lowest internal frost resistance, as described in Section 3.3, the depth of water penetration under pressure was determined on two specimens. Based on the results presented in Table 9, it can be concluded that the designed HPCs were characterized by high resistance to water penetration under pressure and met the requirement for penetration depth not exceeding 30 mm after 56 days of curing. The HPC mixture with 30% fly ash content (C100_SFA30_SF3) showed slightly greater resistance to water penetration than C100_SFA15_SF3 concrete, which is consistent with the results obtained in the frost resistance tests and indicates the important role of SCMs (FA) in creating a dense concrete structure. No leakage was observed in any of the samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{2}{c}{**Avg. Specim Mass [kg]**} & \multirow{2}{*}{**Average Mass Loss [\%]**} & \multicolumn{2}{c}{**Avg. Compressive Strength**} & \multirow{2}{*}{**Avg. Compressive Strength Retained**} \\ \cline{2-2} \cline{5-6}  & **Before F-T Test** & & & & **Reference** & **F-T treated** & **[\%]** \\ \hline C55_SFA15_SF3 & 2.491 & 2.488 & 0.12 & 81.45 & 81.15 & 100 \\ C100_SFA15_SF3 & 2.498 & 2.494 & 0.19 & 98.60 & 93.93 & 95 \\ C100_SFA30_SF3 & 2.494 & 2.490 & 0.16 & 93.04 & 89.16 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Internal frost resistance of low-clinker HPC.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{2}{c}{**Depth of Penetration**} \\ \multicolumn{2}{c}{**of Water under Pressure [mm]**} \\ \hline C100\_SFA15\_SF3 & 10 \\ \hline C100\_SFA30\_SF3 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Depth of penetration of water under pressure for HPC with 100% CEM III/B as a main binder.
## 2 Materials and Methods
### Statistical Model
#### 2.3.1 The Equivalent Stress  
Significant shear stresses arise in the open-hole tensile testing of woven composite materials, due to the stress intensification induced by the presence of the notch. Moreover, differently from homogeneous materials, the local stress field is altered from the heterogeneous material distribution. This results in further local stress concentration. To appropriately consider these two factors in the numerical model, an equivalent stress was introduced for the orthotropic material, which represents the single tow.  
This equivalent stress, whose formulation follows the Tsai-Hill failure criterion, has four quadratic terms, considering longitudinal (fiber direction) tensile stress, transverse tensile stress, their interaction, and shear stress. For the \(i\)-th element, it is defined as follows:  
\[\sigma_{eq,i}^{2}=\varphi_{1}^{2}\,\sigma_{1,i}^{2}+\varphi_{2}^{2}\,\sigma_{2,i}^{2}+\varphi_{3}^{2}\,\sigma_{1,i}\,\sigma_{2,i}+\varphi_{4}^{2}\,\sigma_{1 2,i}^{2}, \tag{1}\]  
where \(\sigma_{1,\,i}\), \(\sigma_{2,\,i}\), and \(\sigma_{12,\,i}\) are, respectively, the fiber-direction tensile, transverse tensile, and shear stress in the \(i\)-th element; and \(\varphi_{1}\), \(\varphi_{2}\), \(\varphi_{3}\), and \(\varphi_{4}\) are parameters related to the material strength, to be estimated from experimental results.  
In the following, the limit value of the equivalent stress of the material is expressed statistically, modeling the scale effect that describes the variation of the material strength with the critical volume (i.e., the volume subjected to critical stress).
## 2 Background
### Feature Selection (FS)  
Feature selection, as a data preprocessing technique, has been shown to be effective and efficient in preparing high-dimensional data for ML problems. The objectives of the selection of features include the development of simpler and more comprehensible models, the improvement of ML efficiency, and the preparation of clean and understandable data. The recent proliferation of large data has posed some major challenges and opportunities for feature selection algorithms [9]. The most common feature selection techniques are as follows: The filter approach, where the typical features are ranked via specific criteria. Features are then identified with the highest ratings then used as inputs for the wrapping or classification process [8; 10]. On the other hand, the definition of the wrapper method requires the use of learning strategies to choose the optimum function subset to be used in the classification process. Usually, the wrapper method uses nature-inspired computational algorithms (NICs) to direct the search process by choosing the optimum feature subsets. The third approach is hybrid, which uses both filter and wrapper approaches. Based on [11], feature selection is a difficult task due to the need for searching over a large space, which is impossible in some applications that have large features and small samples. This problem can be solved using NIC algorithms that are able to search globally and can be utilized to solve the feature selection problem.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 4 Constructing the Multispectral Datasets  
In this section, we provide details of constructing the labelled source dataset \(\mathcal{D}^{s}\) and labelled target dataset \(\mathcal{D}^{t}\).
## 2 Chitooligosaccharides Production
Chitosanases (EC 3.2.1.132) belong to the families GH5, GH7, GH8, GH46, GH75 and GH80 [41] and are commonly found in soil microbes (both bacteria and fungi) [20; 42]. According to their cleavage sites, chitosanases can be classified into two main categories: endo-chitosanases, which are employed for COS production; and exo-chitosanases, which cleave glucosamine residues from the nonreducing end of the substrate. Considering their specificity, three subclasses of chitosanases can be distinguished: (1) chitosanases comprised in class I can carry out the hydrolysis of both GlcNAc-GlcNAcN and GlcN-GlcNAcN bonds; (2) class II chitosanases exclusively cleave GlcN-GlcNAcN linkages; (3) chitosanases belonging to class III can split both GlcN-GlcNAcAc and GlcN-GlcNAcN linkages [43]. Although the yields reported for the hydrolysis of chitosan by chitosanases are notable, the development of more efficient strains that produce these enzymes constitutes a challenge [3; 44]. Besides chitinases and chitosanases, other nonspecific enzymes have shown the ability to recognize glycosidic bonds of chitin and/or chitosan and hydrolyze them [45], although the activity can be one or two orders of magnitude lower than the obtained with specific enzymes [17]. Among these enzymes, most studies have been carried out with other glycosidic enzymes, mostly glycosidases (cellulases, lysozyme, pectinases, amylases, etc. ), but also with proteases and lipases [31; 33]. However, most of these enzyme preparations are not purified and derive from organisms that could produce chitosanolytic enzymes. Thus, it cannot be discarded that in the case of enzymes of very different nature (e.g., proteases, lipases, amylases, etc. ), the presence of a contaminant enzyme is, in fact, responsible for the activity [46]. Nevertheless, commercial preparations have several advantages, including availability, lower cost, and the approval for employment in food processes. Although some nonspecific enzymes give rise to a limited degradation of chitosan, the synergistic action of these enzymes with specific chitosanases can favor COS preparation with a narrow size distribution [3]. More recently, lytic chitin monooxygenes (LPMOs, EC 1.14.99.53) are being investigated as auxiliary enzymes for COS synthesis, as they can break down \(\beta\)(1\(\rightarrow\)4) bonds in crystalline chitin by oxidation, thus facilitating the attack by chitinases [47]. In addition, a few chitosanases and GH18 chitinases have shown a certain degree of transglycosylation activity, which means that they are capable of transferring the released oligosaccharideside residue to a suitable acceptor generating new glycosidic linkages and forming specific COS [48; 49]. Several factors can influence the outcome of a polysaccharide degradation reaction. Considering COS preparation, both the selection of enzyme and the choice of substrate exert a significant influence on the obtained products.