esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Estimation of Time Indices  
The timings of the estimated aortic and pulmonary component are extracted from the envelope peak position. It is operated in the following. Firstly, get the upper envelope of the absolute aortic component using the local maxima with the help of cubic spline interpolation. The way to calculate the upper envelope is like extracting the upper envelope in empirical mode decomposition (EMD) [17].  
\[en_{a}(t)=\text{upper\_envelope}(|\hat{a}(t)|) \tag{8}\]  
Then, the local peaks' amplitude and associated peaks' location of the powered envelope are searched  
\[[\mathbf{A}^{a},\mathbf{T}^{a}]=\text{findpeaks}([en_{a}(t)]^{k}) \tag{9}\]  
where \(k\) is empirically set as 8. The function of this power operation is to make the dominant peaks protruding. \(\mathbf{A}^{a}=[A_{1}^{a},A_{2}^{a},\cdots,A_{M}^{a}]\) is a vector consisting of peak amplitude, \(\mathbf{T}^{a}=[T_{1}^{a},T_{2}^{a},\cdots,T_{M}^{a}]\) is a vector consisting of peak position, and \(M\) is the number of peaks. So, the timing of the aortic component, \(t_{a}\), is defined as weighted peak position  
\[t_{a}=\frac{\sum\limits_{m=1}^{M}W_{m}^{a}T_{m}^{a}}{M} \tag{10}\]  
where the weight coefficient is determined by  
\[W_{m}^{a}=\frac{A_{m}^{a}}{\sum\limits_{m=1}^{M}A_{m}^{a}}\]  
Similarly, get the upper envelope of the absolute pulmonary component using the local maxima with the help of cubic spline interpolation  
\[en_{p}(t)=\text{upper\_envelope}(|\hat{p}(t)|) \tag{11}\]  
The peaks of the estimated pulmonary component can be determined by  
\[[\mathbf{A}^{p},\mathbf{T}^{p}]=\text{findpeaks}(\left[en_{p}(t)\right]^{k}) \tag{12}\]  
Figure 8: Example of discrimination aortic and pulmonary components for subject No. 4. (**a**) the collection of S2s shown in joint domains, (**b**) separated aortic components, (**c**) separated pulmonary components.  
where \(k\) is empirically set as 8. \(A^{p}=[A_{1}^{p},A_{2}^{p},\cdots,A_{N}^{p}]\) is a vector consisting of peak amplitude, \(\mathbf{T}^{p}=[T_{1}^{p},T_{2}^{p},\cdots,T_{N}^{p}]\) is a vector consisting of peak position, and \(N\) is the number of peaks. So, the timing of the pulmonary component, \(t_{p}\) is defined as weighted peak positon  
\[t_{p}=\frac{\sum\limits_{n=1}^{N}W_{n}^{p}T_{n}^{p}}{N} \tag{13}\]  
where the weight coefficient is calculated by  
\[W_{n}^{p}=\frac{A_{n}^{a}}{\sum\limits_{n=1}^{N}A_{n}^{p}}\]  
The split between the aortic and pulmonary component is then estimated by the timing difference  
\[\hat{u}(\theta)=\left|t_{a}-t_{p}\right| \tag{14}\]  
Here is an example in Figure 9. An S2 signal is shown in Figure 9a. In Figure 9b, the blue line is the estimated aortic component and the red line is the estimated pulmonary component. The powered envelopes of the two components are given in Figure 9c. The peaks are indicated by blue stars and red squares. The associated weighted timings are shown by a black bold star and a black bold square. Hence, the split is measured by the timing difference. This processing is repeated for each S2 signal. Then, the varying split modulated by respiratory phase is obtained.
## 2 Methods of Encapsulation  
### Emulsion-Based Encapsulation and Hydrogels  
Oil-in-water emulsions involve the suspension of small lipid droplets in a continuous aqueous phase. These emulsions are found extensively in applications in the food, pharmaceutical, and cosmetic industries as potential carriers for lipophilic bioactive compounds [13]. However, it is important to note that these emulsion systems can exhibit unpredictable behavior due to thermodynamic instability, leading to issues like flocculation, coalescence, cream, and even phase separation, which can hinder their practical use [14].  
Enhancing the stability of emulsions can be achieved by encapsulating the oil droplets within a three-dimensional solid matrix, such as hydrogels [15]. Polysaccharide hydrogels are commonly employed as carrier materials and have been shown to improve the stability of systems loaded with active compounds [16]. Additionally, reducing the size of emulsion droplets can enhance the migration and efficacy of bioactive substances. Nanoemulsions, characterized by low interfacial tension, offer thermodynamic stability and resistance to temperature fluctuations [17].  
Hydrogels can be classified based on the type of crosslinking forces into physical and chemical hydrogels [18]. Chemical crosslinking involves the formation of covalent bonds between polymer chains through the polymerization of low-molecular-weight monomers or crosslinking of polymer precursors. Hydrogels created through chemical crosslinking exhibit remarkable stability and the ability to maintain their structure over time, with variations in structure depending on the crosslinking degree and polymer-to-reagentproportions. Common chemical crosslinking methods include the use of crosslinking agents, graft polymerization, and radiation crosslinking [19].  
In contrast, hydrogels formed through physical crosslinking are generally non-toxic since no chemical reagents are involved in their preparation [20]. This makes them suitable for producing safe and eco-friendly natural hydrogels based on polymers, which are highly favored, especially in the food industry. Physical crosslinking relies on non-covalent forces to create reversible hydrogels. The strength of these non-covalent forces between polymer chains significantly influences the hydrogel's structure. By controlling factors such as pH, temperature, and polymer concentration, one can achieve the desired properties in physically crosslinked hydrogels [21]. Various physical crosslinking methods have been developed, including processes such as heating/cooling, freeze-thaw cycles, and complex coaervation, for the preparation of natural polymer-based hydrogels [22]. A diagram of the emulsion-based encapsulation method is shown in Figure 1.
## 7 Bioprocess for Xanthophylls Production by Microalgae
### Factors Determining Xanthophylls Production
#### 7.2.4 Nutrient-Related Stresses  
The concentration of nitrogen in the culture medium and xanthophyll content of microalgae were correlated in many species [164]. The production of xanthophylls was increased by nitrogen limitation in _Neochloris oleoabundans_, _Chlorella zofingiensis_, _Dunaliella salina_, and _Muriellosis_ sp. [25; 165; 166]. In addition to ASX and lutein, \(\beta\)-carotene level was enhanced to 2.7% DW in _Dunaliella salina_ cells in nitrogen-depletion [167]. Under the same conditions, xanthophylls, namely ASX and canthaxanthin, initiate their accumulation in aerial microalgae (_Coalstrella_ sp.); after this, the color of the cells changes from green to red [56]. Nitrogen starvation can promote the concomitant accumulation of ASX in microalgae [120; 168] as well as lutein accumulation. The latter is highly dependent on nitrogen concentration in the culture medium [165]. In general, nitrogen deficiency has a greater impact than excess nitrogen on carotenoid production, mainly that of ASX in _Haematococcus pluvialis_, because a culture growing in a nitrogen-rich medium requires carbon to assimilate the nitrogen. On the other hand, high competition for carbon required for xanthophylls synthesis would be established in low nitrogen concentrations [169]. A mixture of urea and other nitrogen sources has led to a maximal lutein production in _Auxencholorella protothecides_[77]. A two-step mode where nitrogen enriched and nitrogen deficient media were used consecutively stimulated the microalgae growth during the first phase and carotenoid enrichment during the second phase.  
Iron Supplementation  
Iron is needed for microalgae growth such as _Dunaliella_. Of all the micronutrients, iron was the best for accumulation of ASX in _Haematococcus pluvialis_ cysts [144] because iron acts as a chelating agent and can scavenge hydroxyl radicals in the Fenton reaction, which is widely used in the enzyme system of animals, microbes, and plants [170]. It acts as a limiting factor under hyper saline conditions. The site for assimilation of iron is usually the plasma membrane [171]. [PERSON] et al. (2007) reported that ASX accumulation depends on many nutrients, such as phosphorus, iron, and sulfur [171]. However, adding Fe\({}^{2+}\)- EDTA to culture medium produced ASX up to 3.1% dry cell in _Haematococcus pluvialis_[172].
## 3 Methods  
### Satellite Remote Sensing Mapping and Multi-Criteria Analysis (MCA)  
According to available statistics, between 2014 and 2018, there were 327 fires in Son La [21]. Forest fires were mostly concentrated in the Muong La, Son La and Sop Cop districts of the province  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Land Use** & **Area (ha)** & **Area (\%)** \\ \hline
1 & Residential & 4873 & 0.29 \\
2 & Agricultural land, other land & 390,917.70 & 22.88 \\
3 & Vacant land & 605,797 & 35.46 \\
4 & Water surface & 25,728.40 & 1.51 \\
5 & Rocky mountains without trees & 7147.6 & 0.4 \\
6 & Mixed bamboo, wood forest & 16,928.30 & 0.99 \\
7 & Rich evergreen broad leaved forest & 11,023 & 0.65 \\
8 & Poor evergreen broad leaved forest & 62,970.80 & 3.69 \\
9 & Restored evergreen broad leaved forest & 368,903.20 & 21.59 \\
10 & Medium evergreen broad leaved forest & 46,886.70 & 2.74 \\
11 & Rocky mountain forest & 91,500.30 & 5.36 \\
12 & Bamboo forest & 56,748 & 3.32 \\
13 & Planted forest & 19,147.90 & 1.12 \\ \hline \end{tabular}
\end{table}
Table 1: Land use in Son La Province [20].  
Figure 2: Study area: Son La Province and its typical landscape.  
(Figure 3). Satellite imagery was used to identify the hotspots in the thermal infrared channel and to construct a forest fire database through image interpretation and classification. Geospatial technology, including several GIS software packages, such as ArcGIS, QGIS, and multi-criteria spatial analysis in GIS (multi-criteria analysis--MCA), and AHP (the analytic hierarchy process) [22] were applied to mapping and integrating the information related to the forest fires. This enabled the identification of the causes and the system interactions between the forest fire parameters, the calculation of the weights of each parameter, and the integration into a general model to develop a forest fire risk map. The forest fire risk, classified into five levels, was calculated using the following multi-criteria integration function:  
\[\text{FR}=\sum\
olimits_{i=1}^{n}(\text{w}_{i}\text{x}_{i}) \tag{1}\]  
where:  
FR is the risk of forest fire;  
w\({}_{i}\): weight of indicator (i);  
x\({}_{i}\): indicator (i);  
n: number of indicators (from 1 to n).  
Depending on the conditions specific to each geographic area and on the availability of data, it was not always possible to apply exactly the same evaluation criteria and selected parameters, although this results in only minor disparities.
# A Classification Method for Academic Resources Based on a Graph Attention Network
## 3 Definition
### Academic Resource Networks  
In the construction of academic resource networks, this paper constructs a cooperation network based on the cooperative relationships between scholars and a citation network based on the citation relationship between papers. In the cooperation network, \(v_{i}\) represents an author, the node feature \(F\) is described by the keywords extracted from the author's published papers as the content attributes of the node, and the association information on the edge indicates the cooperation relationships between authors. In the citation network, \(v_{i}\) stands for a paper. The keywords of the papers are used as content attributes to represent the feature \(F\) of the node. The edge indicates that there is a citation relationship between papers. We provide two simple examples of a scholar cooperation network (Figure 1a) and a citation network (Figure 1b).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Descriptions** \\ \hline \(G\) & A graph. \\ \(V\) & The set of nodes in a graph. \\ \(v_{i}\) & A node \(v_{i}\in V\). \\ \(E\) & The set of edges in a graph. \\ \(N\) & The number of nodes, \(N=|V|\). \\ \(h\in\mathbb{R}^{F}\) & The feature vector of a set of nodes. \\ \(F\) & The dimension of the node features. \\ \(q_{ij}\) & The academic association commonality coefficient. \\ \(e_{ij}\) & The weighted coefficients of the association commonality. \\ \(\lambda_{ij}\) & The association influence coefficient. \\ \(a_{ij}\) & The semantic similarity coefficient. \\ \(A\) & The influence factor matrix. \\ \(D\) & The degree matrix, D. \\ \(d_{(v_{i})}\) & The degree of node \(v_{i}\). \\ \(M\) & The number of neighbors of a node \(v\). \\ \(\theta\) & The shared attention mechanism. \\ \(N_{v}\) & The neighbors of a node \(v\). \\ \(W\) & The sharing parameter. \\ \([\cdot\,|\cdot|\) & Splicing operation. \\ \(W_{e},W_{e},W_{D}\) & Learning parameters, including the academic semantic relevance, \\  & academic association commonality, and influence coefficient. \\ \(\lambda_{ij}\) & The final coefficient. \\ \(\sigma(\cdot\,)\) & The activation function. \\ \(K\) & The multi-attention number. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commonly used notations.  
In the scholar cooperation network, nodes represent scholars, and the edges between nodes represent the cooperative relationships between scholars. The final aggregation coefficient \(\lambda_{ij}\) is determined by the semantic similarity coefficient \(\alpha_{ij}\), association influence coefficient \(\beta_{ij}\), and weighted coefficients of the association community \(e_{ij}\). In the citation network, nodes represent papers, and the edges between nodes represent the citation relationships between papers. The black solid line represents the actual reference relationship, and the red dotted line represents the built reference relationship. Similarly, the final aggregation coefficient \(\lambda_{ij}\) is also composed of three coefficients.
## 5 Experiments  
The primary objective of our experimental design is to rigorously validate the efficacy of our cluster-based adversarial training approach. We benchmark our methodology against standard adversarial training (AT). The straightforwardness of AT makes it an ideal baseline, as it allows for isolating the impacts of our novel contributions in clustering over its foundational principles.  
In each training epoch, the model undergoes two separate training phases: an initial training phase using clean data, followed by a subsequent training phase that utilizes adversarial examples. The model is dynamically trained on both clean and adversarial instances within the same epoch to improve its generalization capabilities. We employ the \(L_{\infty}\) norm as the metric to quantify the magnitude of perturbations induced by the adversarial attacks. The experimental evaluations were conducted on a single NVIDIA (Santa Clara, CA, USA) GeForce GTX 1080 Ti graphics processing unit.  
To precisely address the issue of adversarial data in training, we employ various clustering algorithms to augment the training data with clustered adversarial samples. The generation and clustering of adversarial samples are achieved through a hierarchical process: Initially, adversarial samples are generated, followed by clustering these samples. Finally, they are integrated into the training set as per the selected sampling strategy. We empirically set the number of clusters for K-means and agglomerative clustering within the range [1; 10]. For DBSCAN, which does not require a predetermined number of clusters, only the best results are reported for comparative evaluation with other clustering algorithms.
# Study of the Failure Mechanism of Soft Rock Mining Roadways Based on Limit Analysis Theory
## 2 Analysis of the Deformation and Failure of a Soft Rock Mining Roadway
### Energy Consumption Analysis of Roadway-Adjacent Rock  
After the failure model is constructed, according to the upper bound theorem of limit analysis, an energy consumption balance equation is established by calculating the work power of the external force and the internal energy dissipation rate, and the upper bound solution of the height of the velocity discontinuity line is solved. The external forces discussed in this paper include the gravity \(\gamma\) of the coal and rock mass, the supporting forces q and \(\sigma\) of the surrounding rock, and the load of the \(\sigma_{\rm s}\) overlying strata. The energy consumption balance equation is established as shown in Formula (3).  
\[P_{\gamma}+P_{\rm q}+P_{\sigma}+P_{\sigma_{\rm s}}=D \tag{3}\]  
In this formula, \(P_{\gamma}\) is the gravity power of the slider in the velocity discontinuity line; \(P_{\rm q}\) is the power of the roadway roof support pressure; \(P\sigma\) is the power of the support pressure of the roadway side; \(P_{\sigma{\rm s}}\) is the power of the overlying strata load; and \(D\) is the energy dissipation rate on the velocity discontinuity line.
# Indirect Assessment of Railway Infrastructure Anomalies Based on Passenger Comfort Criteria
## 4 Indirect Method for Infrastructure Condition Assessment Based on Comfort Criteria
### Condition-Based Maintenance Identification Methodology
#### 4.2.2 Data Analysis  
The data analysis was performed in MATLAB 2022a [57] using an algorithm developed for this purpose. The algorithm starts by calculating instantaneous floor discomfort (discomfort level at each second) according to ISO 2631 recommendations for each rail journey. A slight adaptation of the standard was conducted as discomfort levels were divided into only two categories instead of the nine defined for the standard. Those categories were defined based on the first discomfort threshold of the ISO 2631 standard. Table 3 presents all comfort levels defined according to the standard. The first category determines that accelerations equal to or under 0.315 m/s\({}^{2}\) are ranked as \"Not uncomfortable\", whereas, above 0.315 m/s\({}^{2}\), accelerations are rated as \"Uncomfortable\". The uncomfortable locations were identified and mapped in all journeys.  
To remove the interference of eventual railway vehicle maintenance needs, it was assumed that only the common discomfort sections required maintenance, i.e., segments with the same location and simultaneously reported by different trains.  
The GPS signal was perfectly obtained at any location inside the IC train. This way, geographic location and acceleration records were acquired at the same location. However, the same conditions were not present in AP vehicles, where the best GPS signal was obtained by installing the device in the train driver's cabin. Therefore, considering the AP train length, a maximum distance of 158.9 m between the accelerometer and GPS model could be observed for measurements taken at the opposite end of the train. Moreover, considering the worst-case scenario where the AP travels at 220 km/h, the distance it achieves in 1 s is approximately 61.1 m. Thus, a maximum offset of 220 m (158.9 + 61.1 m) between AP and IC was used to obtain the matching segment.  
Figure 8 illustrates the steps between taking the measurements and producing the final report.  
It should be highlighted that the developed methodology only identifies abnormalities in the railway track infrastructure and, therefore, does not distinguish the type of abnormality presented. Moreover, the developed methodology is more accurate for identifying abnormalities within 10-120 m of wavelength once it is based on comfort analysis. Lower wavelength irregularities may not be precisely detected.  
Figure 7: Experimented vehicles: (**a**) AP train; (**b**) IC train.
## 2 Materials and Methods
### Temperature Loss Modelling
#### 2.5.1 Ross Model  
The Ross model [61] is simple in its assumptions, as it considers that the difference between cell temperature and ambient temperature only depends on the plane-of-array irradiance through an empirically determined coefficient \(k\) (Equation (5)). This model was adopted in the IEC 61215-2 Standard [62] with _NOCT_ as the parameter to fit, which represents the temperature of the PV cells under open-circuit conditions at 800 W/m\({}^{2}\) irradiance, 20 \({}^{\circ}\)C ambient air temperature, 1 m/s wind speed, a tilt angle of 37\({}^{\circ}\), and open-air ventilation. Typical _NOCT_ values for crystalline silicon (c-Si) modules are around 45 \({}^{\circ}\)C.  
However, in BIPV systems, modules do not undergo such boundary conditions; thus, we will refer to the effective _NOCT_\({}_{\text{eff}}\) obtained under the specific tilt angle and limited ventilation conditions of a particular BIPV application. In the case that experimental data to fit the effective nominal temperature are not available, there are some recommendations in the literature, such as [63] from the National Renewable Energy Laboratory (NREL), which suggests increasing the NOCT by 2 to 18 degrees depending on the air gap thickness behind the modules, although not differentiating by mounting configuration or module structure.  
\[T_{c}=T_{a}+k\cdot G_{POA}=T_{a}+\frac{NOCT-20}{800}\cdot G_{POA} \tag{5}\]
## 3 Results and Discussion
### PASS Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Pa \textgreater{} 70\%** & & & & & **Bioconjugates** & & & & \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Cholesterol antagonist & 82 & 76 & 95 & 94 & 74 & – & – & 94 & 92 & 91 \\ Analeptic & – & – & 73 & 79 & – & – & – & 82 & 83 & 75 \\ Antihypercholesterolem & – & – & 91 & 84 & – & – & – & 78 & – & 72 \\ Respiratory & maleptic & – & 85 & 88 & – & – & – & 87 & 82 & 70 \\ Lipoprotein lipase inhibitor & – & – & – & 84 & – & – & – & 78 & 75 & – \\ Acylcarnitine hydrolase & & & & & & & & & & \\ Inhibitor & – & – & – & 71 & – & – & – & 75 & 72 & 75 \\ Glycerol-ether monooxygenase inhibitor & – & – & – & – & – & – & 73 & – & 71 & 74 \\ Hypolipemic & – & – & – & – & – & – & – & – & – & 74 \\ CYP3A1 substrate & – & – & 75 & – & – & – & – & – & – & \\ CYP3A4 inducer & – & – & 70 & – & – & – & – & – & – & – \\ CYP3A inducer & – & – & 70 & – & – & – & – & – & – & – \\ Alkylacetylglycerophosphatase & & & & & & & & & & \\ Inhibitor & & & & & & & & & & \\ Cytoprotectant & – & – & – & – & 70 & – & 70 & – & – & – \\ Antiecezematic & – & – & – & – & 71 & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Probability “to be active” (PA) values for the predicted biological activity of bioconjugates **1**–**10**. profile of natural compounds offers a promising alternative. These compounds have a safer use profile, fewer side effects, and potential health benefits, including cholesterol-lowering properties [61]. As shown in Table 3, an in silico analysis of compounds **1-10** revealed that all caffeine and steroid derivative bioconjugates demonstrated significant potential for cholesterol reduction. Analogics are stimulants that act on the central nervous system, particularly the respiratory and vasomotor centers. A notable example of a compound with anaeptic activity is caffeine. Caffe, a naturally occurring stimulant, has a long history of use in the treatment of barbiturate overdose [62]. This alkaloid acts on the cerebral cortex and has also been demonstrated to stimulate the respiratory, metabolic, and thermoregulatory centers. As a pharmaceutical agent, it is used to treat cardiovascular insufficiency, as a psychostimulant, and as a vasoconstrtictor to relieve headaches.