esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art and Opportunities for Bioactive Pentacyclic Triterpenes from Native Mexican Plants
## 2 Pentacyclic Triterpenes from Mexican Plants
### Drug Delivery Strategies  
As indicated previously, PCTs have shown a potential therapeutic effect. Nevertheless, this application has not been further exploited since many of these compounds have been recently discovered, and partially because of their disadvantageous properties such as low solubility, stability, and bioavailability. The latter limits intestinal absorption from oral administration [52; 53]; hence, adequate drug delivery systems for improving their assimilation in the organism must be developed. In this aspect, the formulations employed for PCT delivery until now are liposomes, emulsions, nanoparticles, and cyclodextrin-drug complexes. In most cases, these have been used with successful results. In a minor degree, dendrimers, capsules, and tablets have been used too [52]. All of them are applicable to diverse administration routes (e.g., oral, intraperitoneal, transdermal, or intravenous). The development, characterization, and evaluation of novel formulations for PCTs, in particular for cancer treatment, have been assayed with oleanolic **(1)**, unsolic **(14)**, and betulinic **(36)** acids, as representative triterpenoids. In vivo studies have shown that formulated PCTs presented better efficiencies than free-form triterpenes and in human preclinical reports, UA liposomes with **(14)** have shown good tolerability and action [53]. In Mexico, scarce work has been carried out that is related to the formulation of PCTs. Two efforts in this aspect include collaborations with other countries and belong to the Chemical Faculty of the Universidad Autonoma del Estado de Morelos with Spanish and Portuguese research groups. In one of these reports, the team formulated nanoemulsions of **(1)** and **(14)** (0.2%) for its topic administration while in the second report, polymeric nanoparticles were loaded with the same PCTs [54; 55]. Nanoemulsions consisted of castor oil as the non-polar phase, labrasol as surfactant, transcutol-P as co-surfactant, and propylene glycol in the polar phase together with **(1)** and **(14)**, which were obtained from _Plumera obtusa_ leaves, an ornamental tree collected from Campeche, Mexico. The formulation based on this technology is oriented to offer local non-systemic effects since the retention was higher than the permeation, and the inflammation in the mouse ear was inhibited by 76.19% [54]. The poly (DL-lactide-coglycolide) acid (PLGA) nanoparticles containing the PCTs were elaborated by the solvent displacement method, showing good stability and anti-inflammatory activity in rabbit eyes [55]. More recently, in a third study, [PERSON] and collaborators at Tecnologico Nacional de Tepic, Nayarit and the State University of West Parana in Brazil proposed a nanoemulsion loaded with _Coccoloba vifera_ (sea grape) leaf extract from Veracruz rich in lupel, \(\alpha\) y \(\beta\)-amirin [56]. The ultrasound-formed nanoemulsion was stabilized with 1.25% of jackruit leaf protein hydrolysate, with the intention of being used in the food industry.
# Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction
## 1 Introduction
[21] developed a time-reversal backward spreading algorithm to efficiently locate the source of a diffusion-like process and proposed a general locatability condition. For multiple sources detection, [PERSON] et al. [22] investigated a maximum-minimum strategy based on backward diffusion, which has been further extended by [PERSON] et al. [23] via integer programming. In this paper, we present the Gaussian-based localization and deduction (GLAD) as a simple and efficient framework for locating the diffusion source in networks based on partial timestamps. We mainly considered a simple diffusion process associated with time delay and provided a probabilistic method to locate the source of diffusion via parameter estimation and maximum likelihood estimation. To be more concise, we derived an optimal solution of the source localization problem on arbitrary trees and extended it to general graphs via approximations and simplifications. Experimental results were conducted on synthetic networks (including arbitrary trees and general graphs) and real networks, and the results all verified the good performance of our algorithm.
## 3 Results and Discussion
### Characterization
#### 3.1.2 SEM-EDX Analysis
Figure 2: SEM-EDX analysis of Al-TMC, (**A**) electron Image without mapping, (**B**) compositional mapping, (**C–F**) elemental distribution for Al, O, C, and N.  
The spatial overlap between O, N, and C provides evidence for the presence of the biopolymer framework, whereas Cu is evenly distributed across the framework. The elemental composition for Fe-TMC showed, in contrast to both Al-TMC and Cu-TMC, increased sample inhomogeneity (cf. Table 2). This is further illustrated by SEM-EDX mapping of different sample spots in Fe-TMC (cf. Figure 4). A more homogeneous distribution of Fe on the seemingly more porous biocomposite surface was observed in Figure 4A, in contrast to the O-distribution (cf. Figure 4C). The second sample spot revealed a \"smooth\" surface (Figure 4F) with mostly O-content in conjunction with C, in addition to \"veins\" of Fe on the surface. The elevated presence of C, N, and O indicated a more biopolymer-rich framework on the surface, in contrast to spot 1 (Figure 4A). This may indicate that the mixing process for the Alg and Chi solutions for PEC formation could introduce small inaccessible regions where subsequently lower Fe-association occurs, which gives rise to a \"smoother\" surface appearance with different Fe-distribution compared to other spots.
## 1 Introduction
If only \(\alpha\)-amylase activity is to be determined, other specific methods must be used, which can be divided into several groups: (a) the saccharogenic method or the spectrophotometric determination of the reducing sugars formed by the action of \(\alpha\)-amylase on the substrate (starch), using different reagents, of which the 3,5-dinitrosalicylic acid (DNS method) is the most common; (b) amyloid methods to determine the reduction in the intensity of the blue coloration of the starch-iodine complex (e.g., Farrand and Wohlgemuth method); (c) nephelometric methods based on the reduction of turbidity of the reaction mixture of the tested starch sample; (d) ELISA test; (e) spectrophotometric methods using synthetic substrates that release color during the reaction, such as nitrophenyl derivatives of maltooligosaccharides (Ceralpha method) [20; 21]. All of the abovementioned methods are generally time-consuming and/or relatively expensive. Therefore, the possible adaptation of established rheological techniques commonly used in grain analysis, such as the FN method, in order to evaluate amyloid activity in malt flour represents a viable alternative. So far, to our knowledge, there has been only one study aimed at adapting the FN method to estimate amyloid activity in malt flour. In this study, the mixture of malted sorghum flour (0.2 g) with different amyloid activities and corn starch (5.8 g) was used in FN assay and the results were compared with the diastatic power of the malt flour samples [14]. The aim of this study was to modify the FN method, normally reserved for the assay of flour samples with low amyloid activity, to allow the quantification of high \(\alpha\)-amylase activity in malt flour. The modifications were performed in two ways: with different amounts of malted barley and wheat flour or by using malt extracts. In both cases, standard wheat starch was used as substrate and the results were compared with \(\alpha\)-amylase activity determined by the standard Ceralpha method.
# Spectral Correction of CPV Modules Equipped with GaInP/GaInAs/Ge Solar Cells and Fresnel Lenses
## 3 Analysis of Results  
In order to evaluate the proposed equations for the four CPV devices under study, four days with different atmospheric characteristics were selected and therefore the following were considered:  
* 21 June 2016--clear atmosphere with low \(AM\) (\(\overline{AM}\) = 2.0, \(\overline{AOD}\) = 0.06, \(\overline{PW}\) = 2.34 cm)
* 23 December 2016--clear atmosphere with high \(AM\) (\(\overline{AM}\) = 3.4, \(\overline{AOD}\) = 0.04, \(\overline{PW}\) = 1.35 cm)
* 29 June 2016--relatively hazy atmosphere (\(\overline{AM}\) = 2.0, \(\overline{AOD}\) = 0.28, \(\overline{PW}\) = 2.50 cm)
* 18 August 2016--relatively wet atmosphere (\(\overline{AM}\) = 2.1, \(\overline{AOD}\) = 0.12, \(\overline{PW}\) = 3.62 cm)  
These days exhibit different spectral conditions and, therefore, can allow the procedure's evaluation. Figure 2 shows an example of the measured and predicted \(SF\) of LM and LM + PMMA CPV devices during the selected days. It can be seen that both configurations exhibit a good fit during the four days. In order to quantify the accuracy of the procedure, different statistical metrics were calculated such as the mean percentage absolute error (\(MAPE\)) and the mean relative error (\(MRE\)). The LM and LM + PMMA present almost the same \(MAPE\) with values ranging from 0.7% to 1.0% and a \(MRE\) ranging from \(-\)1.1% to 0.4%. The MM and MM + SoG devices also present similar results with a \(MAPE\) ranging from 0.5% to 0.6% and a \(MRE\) from \(-\)0.7% to 0.2%. It is also important to remark that the accuracy of the method did not show any particular trend with the spectral conditions of the day considered. As such, it can be concluded that the proposed equations show a high accuracy for all the  
Figure 1: Irradiance (**left**) and spectral (**right**) sensors including component solar cells and the SolarSIM-D2.  
CPV devices during all the different days. Table 2 summarizes the results of each CPV configuration, obtained during the selected days.
## 3 Results and Discussion
### Effects of the Operating Conditions on the SCWG of Cellulose
#### 3.1.2 Influence of Oxygen Coefficient  
The results from the SCWG of cellulose without any oxidant and up to an excess of stoichiometric oxygen, n = 1.1, at 250 bar of pressure, and a temperature of 535 \({}^{\circ}\)C with a cellulose initial concentration of 0.3 g/L are shown in Figure 5.  
As already mentioned in the introduction, when SCWG processes are not added to an oxidant, a significant amount of energy is required to preheat and maintain the desired temperature, since the gasification reactions that take place in SCWG processes are globally endothermic. Therefore, a certain amount of oxidant is added to produce both oxidation and gasification, which in turn reduces the energy requirements of the whole process. It is well known that when cellulose is gasified under oxygen defect (n \(<\) 1, i.e., a lesser amount of oxygen than the minimum required for a complete oxidation) a considerable amount of CO and intermediates are generated from the reactions, which later on will produce H\({}_{2}\) in the water gas shift reaction [49; 53]. It can be seen from Figure 5 that the hydrogen yields increase with an increasing oxygen coefficient until n = 0.5. From that point, adding more oxidant may have a negative effect, since hydrogen yields would decrease from the moment that the oxidation reactions become predominant over the gasification ones. This effect becomes more evident as residence time is increased. Similar results were obtained in previous studies where glucose was used as feed [25]. As can be seen in Table 2, CO is only obtained from the experiments with n \(>\) 0.5. Furthermore, an increment of the LHV has been obtained when n = 0.5.
## 3 Results and Discussion
### Mixture Ratio Prediction  
As already mentioned, in the input sequence of the RNN, the last two parameters are the NTO and MMH flow rate of the thruster with the orifice because these are the main control parameters that can be used for the mixture ratio adjustment in the last step. The influence weight of the NTO flow rate after welding is smaller than the SMD value, so it is in the first place in the sequence. The input sequence is shown in the following order in Figure 6. Through mutual transmission between the input layer, hidden layers, and output layer, the information of the data is propagated backward, and the classification result of the predicted mixture ratio classification is given after the last hidden layer.  
Figure 5: The typical structure of the NTO flow channel with an annular nozzle and MMH flow channel with a circular nozzle.  
Figure 6: The prediction network structure based on RNN with high-weight input sequence.  
Figure 7 shows the curves of the classification loss and accuracy of the training dataset and test dataset as the number of epochs increased. After many hyperparametric comparisons, at dropout value of 0.1 and weight decay value of 0.01, the loss and accuracy of the test set were stable, slightly lower than that of the training set. When the batch size was 500, the fluctuation of the test accuracy in the iteration process showed its sensibility, but the complete prediction accuracy could still reach above 52%. Considering the accepted deviation of 1, which may fall into an adjacent classification, this percentage increased to around 75%. Table 2 indicates the prediction accuracy of the enhanced dataset and the raw dataset, respectively. As the amount of training and test data needs to be further accumulated, it is currently considered that this prediction percentage is acceptable.  
Compared with the other methods, the BP-RNN chain method can not only obtain more and more accurate predictions with the accumulation of data, but it is also possible to find the key features in the testing process to guide product optimization. With the updated network, we are able to predict the mixture ratio range in advance by tracking historical data, especially the NTO flow rate after welding and the SMD value. When the prediction results do not meet the qualified expectations of classifications 6 or 7, the orifice size can be adjusted by improving its predicted classification in the network calculation until it finally falls into the qualified range.
## 2 Overview of the Power Ramp-Rate Control Methods
### Enhanced Linear Exponential Smoothing (ELES)  
The ELES method performs two complementary exponential smoothing operations. Although this method has high mathematical complexity, it is known to have a better performance compared to the aforementioned methods [19]. The mathematical expression for the ELES is given by [20; 21; 22; 23]:  
\[\mathrm{P}_{\mathrm{pv}}^{\prime}[\mathrm{k}] =\alpha\mathrm{P}_{\mathrm{pv}}[\mathrm{k}]+(1-\alpha)\mathrm{P}_{ \mathrm{pv}}^{\prime}[\mathrm{k}-1] \tag{9}\] \[\mathrm{P}_{\mathrm{pv}}^{\prime\prime}[\mathrm{k}] =\alpha\mathrm{P}_{\mathrm{pv}}^{\prime}[\mathrm{k}]+(1-\alpha) \mathrm{P}_{\mathrm{pv}}^{\prime\prime}[\mathrm{k}-1]\] (10) \[\mathrm{P}_{\mathrm{ess}}[\mathrm{k}] =2\mathrm{P}_{\mathrm{pv}}^{\prime}[\mathrm{k}]-\mathrm{P}_{ \mathrm{pv}}^{\prime\prime}[\mathrm{k}]-\mathrm{P}_{\mathrm{pv}}[\mathrm{k}] \tag{11}\]  
where \(\mathrm{P}_{\mathrm{pv}}^{\prime}\) and \(\mathrm{P}_{\mathrm{pv}}^{\prime\prime}\) are auxiliary variables for computing the double complementary filter. Constant \(\alpha\) is the smoothing factor, which is required to be selected between 0 and 1.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis
### Path Optimization  
We apply the path optimization approach proposed in Section 3.3 on the 200 \(\times\) 200 and 400 \(\times\) 400 maps, respectively. Figures 11 and 12 show the planned paths.  
Figure 10(a) shows that there are several turns on the planned path between position \(a\) and the goal node. However, optimized by the algorithm, the path between position \(a\) and the goal node is a straight line in Figure 10(b). This is because when the USV arrives at position \(a\), it finds that there is no obstacles on the straight line between position \(a\) and the goal node, and the distance of the straight line is shorter than the planned path. Therefore, the USV will move directly along the straight line to the goal node. We obtain a similar result on the 400 \(\times\) 400 map (Figure12). Figure 13 shows that the length of the optimized path on the 200 \(\times\) 200 map is decreased from 224.6 to 212.5 (5.4% reduction). The path length is also decreased from 488.5 to 475.7 (2.6% reduction) on the 400 \(\times\) 400 map.  
Figure 11: Path optimization on 200 \(\times\) 200 map.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Map Size** & \multicolumn{3}{c}{**200 \(\times\) 200**} & \multicolumn{3}{c}{**400 \(\times\) 400**} \\ \hline \multirow{2}{*}{Search approach} & \multirow{2}{*}{8-neighbor} & \multirow{2}{*}{48-neighbor} & \multirow{2}{*}{48-neighbor with constrained steering angle} & \multirow{2}{*}{8-neighbor} & \multirow{2}{*}{48-neighbor} & \multirow{2}{*}{48-neighbor} \\  & & & & & & \\ \hline Number of & & & & & & \\ searched nodes & 745 & 1813 & 1869 & 896 & 2516 & 2521 \\ Planning time (s) & 1.16 & 4.07 & 4.14 & 2.07 & 5.47 & 5.63 \\ Number of & 25 & 5 & 0 & 29 & 7 & 0 \\ sharp turns & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of exceeding steering angles.  
Based on the observation above, we conclude that the path optimization algorithm can further optimize the path with a shorter length while almost not deteriorating the planning time.
## 4 Experiment and Validation
### Cross-Domain Fault Diagnosis Case 2: Production Line  
#### 4.3.1 Test Platform and Data Description  
Once the motor in the production line breaks down, the downtime and high maintenance costs will result in a significant loss of revenue for the company. Condition monitoring of the production line bearings was performed to verify the efficacy and engineering suitability of the method proposed in this paper. The measured signal data from the production line of an automotive company are used. Figure 11 depicts the experimental test platform. The four bearing operating states under test are NC, and the fault conditions are BE, IF, and OF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **VGGNet** & **Conv5** & **ProtoNet-10 Shot** & **Our Method-10 Shot** \\ \hline Train & 400s & 80s & 12s & 15s \\ Test & 6s & 2.6s & 1s & 2s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of time consumption of different models.  
Figure 10: The t-SNE visualization results under different methods. (**a–d**) correspond to the VGGNet, Conv5, ProtoNet-10 shot, and our method-10 shot.  
Throughout the experiments, the acceleration sensor was attached to the motor base. The motor speed was 1480 rpm during the test, and the acceleration sensor model was an IEPE vibration acceleration sensor. The instrument had a sampling rate of 46 kHz. We used the MiniImageNet dataset as the source dataset in the validation collected using the dataset gathered from this platform. For the target dataset, three few-shot training settings, 4-way 3-shot, 4-way 5-shot, and 4-way 10-shot, were employed. The detailed descriptions are displayed in Table 5.  
The fault data are subjected to multiscale morphological operations using MEMTF multiscale morphological filtering, and the analysis results for each scale are then processed using FFT to produce frequency-scale distribution images, the results of which are depicted in Figure 12 for the transformation under four operating conditions. The converted image results show that there are significant variances in the images of various fault states, which will help to significantly increase the fault classification accuracy.  
Figure 11: Schematic sketch of the experimental test platform structure.  
Figure 12: Converted images on four operating conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset Name** & \multicolumn{2}{c}{**Sample Category**} & **Sample Number** \\ \hline Source & \multirow{2}{*}{MinilmageNet} & Train & 100 & 100 \(\times\) 480 \\ dataset & & Val & 100 & 100 \(\times\) 120 \\ \hline Target & \multirow{2}{*}{Production data} & Train set & 4 & 4 \(\times\) N (N = 3/5/10) \\ dataset & & Test set & 4 & 4 \(\times\) (200-N) (N = 3/5/10) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Production line bearing dataset fault classification.
# A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL
## 2 Background
### Introduction of Aadl  
AADL is a design language defined by the standard AS5506 of the Society of Automotive Engineers. It is highly effective for model-based analysis and specification of complex real time embedded systems. The application software includes a thread, thread group, process, and subprogram. The execution platform includes a processor, memory, device and bus [15]. AADL describes the software and hardware architecture of the system through the concepts of component and connection, and models with components. In the AADL model, the component is the core element and the basis of modeling. The elements of the AADL architecture are shown in Figure 2[15], and the language elements of AADL are divided into different parts around the components, such as component type, components, component implementation, etc. On this basis, the detailed information of the elements is further described by the relationship between the elements.  
Figure 1: SOA working logic in avionics system.  
[PERSON] proposes that modes can be used to represent various states of a system or component in AADL and can also represent component configurations and connections in the system. Furthermore, a system or component can transit from one mode to another when a set condition is triggered [35]. The property of a system or component can have different values in different modes, and mode transitions can be triggered by events, data, or messages [15; 35]. Based on these characteristics, it can be observed that the mode is suitable for describing the working state of a system or component. In different modes, different values of the same attribute can express the characteristics of the system or component in different working states; a set of events triggers a mode transition that describes a change in a working state.