esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Mechanical Damage in Green-Making Process on Aroma of Rougui Tea
Continuous mechanical injury during the manufacturing process of RGT can upregulate the expression of CsMYC2, the key transcription factor in jasmonic acid signal transduction; increase the content of jasmonic acid; and promote the formation of aroma compounds [20]. Aliphatic alcohols and esters with a natural flowery and fruity aroma that can be formed during green-making have a significant positive correlation with CsADH gene expression [21]. The green-making process includes turnover and spreading. Some studies have found that most of the aroma compounds are formed during static spreading after turnover, and transcription factors play an important role in the response to mechanical injury during the manufacturing process of RGT [22]. During the withering and turning over treatments of oolong tea, the expression and abundance of phenylalanine ammoniase, peroxidase and polyphenol oxidase were positively correlated with the dynamic changes in their corresponding catalytic products, such as carbohydrates, amino acids and flavonoids [23]. Sun withering can induce the expression of protease in phenylpropanol biosynthesis, flavonoid biosynthesis, phenylalanine metabolism, sesquiterpenoid and triterpenoid biosynthesis and other metabolic pathways in tea; reduce the content of flavonoids, xanthine alkaloids and other metabolites; accumulate volatile terpenoids and phenylpropanoids/benzenoids; and promote the formation of good flavor characteristics in oolong tea [24]. Spraying exogenous methyl jasmonate on fresh tea leaves could significantly affect the expression of the \(\alpha\)-linolenic acid degradation pathway, terpene backbone biosynthesis and phenylalanine metabolism-related proteins and promote the synthesis of green leaf volatiles, volatile terpenoids and volatile phenylpropanoids [25]. The combined transcriptome and metabolome analysis method was used to investigate the changes in volatile components, the expression of related genes in the anabolic pathway of the main aroma compounds and the function of single key genes during the processing of RGT. However, transcriptional regulation is a very complex biological process, and there are few reports on the changes in post-transcriptional protein regulation during the RGT manufacturing process. The aim of this research was to use transcriptome, proteome and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) to explore the mechanism of aroma formation during the green-making process of RGT, and to analyze the differentially expressed genes (DEGs) and proteins (DEPs) and the protein interaction network, which can provide an exhaustive and precise understanding of the flavor formation process and further elucidate the influence of the green-making process on the characteristic quality metabolites of RGT.
## 5 Performance Evaluation of the TPCT
### In Situ Observations  
In situ observation in TPCT embankments is crucial for understanding their performance and effectiveness in different climatic conditions, particularly in permafrost regions [70; 71]. For example, Figure 7 shows the plane distributions of the observation system and Figure 8 presents the cross-sections of the two embankments. [PERSON] et al. [71] firstly established an innovate spatial thermal-deformation observation system and evaluates the spatiotemporal thermal-deformation characteristics of a composite embankment with LTPCTs on a permafrost slope. It provides valuable data for understanding the performance of TPCT embankments in permafrost regions. [PERSON] et al. [72] suggested that L-shaped TPCTs should be adopted in the remedial engineering to ensure the cooling effect and reduce the differential settlement of the embankment. It provided valuable data for understanding the performance of TPCT embankments in permafrost regions. [PERSON] et al. [57] evaluated the influences of protective measures, including an insulation layer and TPCT, on the thermal and mechanical response of frozen soils and buried pipelines. It provided valuable insights into the mitigation of permafrost degradation and ensuring pipeline safety in permafrost. [PERSON] et al. [70] provided a comprehensive analysis of the thermal characteristics and the cooling effect of TPCTs based on the Zhangling-Mohe highway TPCT test section in Da Xing'anling Mountains. It also discussed the limitations of using existing TPCTs in high-latitude permafrost regions of China and presented potential improvements. [PERSON] et al. [73] presented long-term monitoring data for a road section of the Qinghai-Tibet highway before and after installing the TPCTs.  
In conclusion, in situ observation is vital for understanding the performance of TPCT embankments, assessing their cooling effects, evaluating their impact on infrastructure stability, and guiding potential improvements.  
Figure 7: The plane distribution in the observation system of the two embankments [71]. Composite embankment (AA\({}^{\prime}\) cross section); Contrast embankment (BB\({}^{\prime}\) cross section).
## 3. Sacrificial Anode CP
### Iron-Based Alloy Anode Material  
For some corrosion-resistant metals, such as copper alloys, stainless steel, etc., the above-mentioned sacrificial anode potential is too negative, and the potential difference with the protected metal is too large. This causes an increase in the rate of consumption of the sacrificial anode, resulting in increased costs and waste of resources. [PERSON] et al. [29] used Fe-Mn-Cr alloy anode and Zn-Al-Cd alloy anode to protect B10 copper alloy commonly used for naval seawater piping in flowing seawater, respectively. It was found that the over-negative potential of the Zn-Al-Cd alloy anode accelerated the consumption rate of the sacrificial anode and shortened its service life, while the surface of the B10 copper alloy generated speckled corrosion pits. The consumption rate of the Fe-Mn-Cr alloy anode was slower, and the surface of the B10 copper alloy showed no corrosion traces and was well protected. The potentiometric difference between the sacrificial anode and the protected metal may also cause hydrogen precipitation of the protected metal and hydrogen embrittlement. In a 3.5 wt.% NaCl solution, the self-corrosion potential of iron-based alloy anodes is about \(-0.73\) V (v.s.SCE), the self-corrosion potential of B10 copper alloy is about \(-0.26\) V, the general self-corrosion potential of the zinc-based sacrificial anode is about \(-1.1\) V, and the self-corrosion potential of steel is around \(-0.7\) V. The CP effect of iron alloy on copper alloy is comparable to that of zinc alloy on steel. A new Fe-Zn anode developed by [PERSON] et al. [30] provides a protection potential of \(-0.778\) V (v.s. SCE) for mild steel in seawater, with a current efficiency higher than 80%, comparable to that of commercial zinc alloy anodes. In addition to the CP of the protected metal, the dissolved iron ions can generate an iron-rich protective film on the surface of the iron-based alloy, causing the anode to be passivated to a certain extent, thus reducing the CP current and prolonging the service life of the anode.
## 5 Methodology: AGO Construction
### Second Phase: Build the Taxonomy of Classes  
5.2.1. Technologies and Tool  
The Semantic Web focuses on enabling machines with the capabilities of providing formal structured information using the encoded knowledge from ontologies [22]. There are several languages that enable formalizing and encoding knowledge. For the purpose of this work, the chosen ontology representation language was the Web Ontology Language (OWL) [11]. OWL was developed as a vocabulary extension of the Resource Description Framework (RDF) specification. This linking structure forms a directed, labeled graph, in which the edges represent the named link between two resources, represented by the graph nodes [5].  
There are several tools specifically designed to create and manage ontologies: Protege, Protege Web, Fluent Editor (FE), OWLGrEd, etc. For this approach, the ontology was defined with Protege ([[https://protege.stanford.edu/](https://protege.stanford.edu/)]([https://protege.stanford.edu/](https://protege.stanford.edu/)) accessed on 11 March 2021) because this tool enables intuitive construction of complete ontologies. Similarly, the ontology can be exported into different ontology representation languages, including RDF and OWL. This tool also provides the user with a Taxonomy Tree viewer (see Figure 4) and other visualization tools, such as OWLViz, which generates a diagram of the selected elements in different layouts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Year** & **Object Classes** & **Action Classes** \\ \hline nuScenes & 2019 & 23 & 5 \\ H3D Honda & 2019 & 8 & - \\ LyftLevel5 & 2019 & 9 & 18 \\ Waymo & 2019 & 4 & - \\ Apolloscape & 2019 & 65 & - \\ Berkeley Deep-Drive & 2020 & 40 & - \\ Audi A2-D2 & 2020 & 52 & - \\ Mapillary-Vistas & 2020 & 66 & - \\ SafetyPool (Taxonomy) & 2020 & 126 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the analyzed datasets for defining the taxonomy of the ontology.  
This tool also provides the means to include Annotation properties, which comprise ontology metadata and general properties of the classes. For the metadata, the _versionInfo_ (owl:versionInfo) is defined as a built-in annotation property of OWL [11]]. Furthermore, some of the _Dublin Core Metadata Initiative_ (DCMI) [23] terms are adopted: _Contributor_, _creator_, _description_, _title_, _date submitted_, _license_, and _publisher_. In addition, to complete the description of the classes, a brief _description_ and a _label_ are included as defined by the RDF schema [10]: rdfs:comment and rdfs:label.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 1 Introduction
The basic SampEn algorithm for computing the complexity of a signal is \(O(N^{2})\) and the calculation time is closely related to the length of the time series. With the growth of the time series length, the computing time grows almost exponentially and, therefore, this technique is not realistic for applications with long data sets. For that reason, a fast SampEn algorithm based on a kd tree is adopted to detect whether the rolling bearing contains faults or not in a timely manner. The WPEE is a feature extraction method that can be used in conjunction with wavelet packets and the Shannon entropy. The vibration signal is decomposed into several sub-signals by using a wavelet packet. Then, the energy entropy values of the sub-signals are calculated to generate the fault feature vector. The WPEE reflects the evenness of the signal energy distribution in various frequency bands. The WPEE can effectively describe the characteristics of a vibration signal and is a powerful tool for vibration signal analysis. In this paper, the proposed method extracts the fault features of bearing vibration signals by the WPEE and forms a feature vector for further fault identification and severity analysis. The mRVM is used to identify the fault type of rolling bearings and indicate the level of fault severity. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments and comparative studies in this paper. The innovative contributions of this paper can be summarized as follows:  
1. This paper systematically presents a rolling bearing fault diagnosis and severity analysis method. The fault severity analysis of different fault types can be realized on the premise of accurate fault type identification. 2. A fast sample entropy algorithm based on a kd tree is adopted to improve the real-time performance of the fault detection process for rolling bearings. 3. The wavelet packet energy entropy is used to describe the characteristics and severity of different fault types. This approach can solve the problem of a low fault diagnosis accuracy caused by the weak separability of feature vectors extracted from non-stationary and non-linear vibration signals. 4. A multiclass relevance vector machine is used to accurately classify different fault types and this tool is effectively extended to analyze the same fault with different fault severities. The rest of this paper is organized as follows: Section 2 briefly introduces the fundamental theories of the SampEn and the fast algorithm of the SampEn. The feature extraction method based on the WPEE is detailed in Section 3. In Section 4, the fundamental theories of the mRVM are briefly described. Section 5 provides a description of the proposed rolling bearing fault diagnosis and severity analysis method. In Section 6, the effectiveness of the proposed method is demonstrated by experiments. Finally, conclusions are drawn in Section 7.
## 4 Experiments
### Significance of Adaptive Grouping Strategy  
In order to verify the significance of an adaptive grouping strategy, we conduct an ablation study and compare the prediction performance of NeuroTIS+ with and without adaptive grouping. As is evident from Tables 1 and 2, NeuroTIS+ (G) consistently outperforms NeuroTIS+ (nG) in all the datasets and measures, especially PRE, F1-score, auPRC, and MCC scores. Figure 4 also shows the training and test process of the second phase of NeuroTIS+ (G) and NeuroTIS+ (nG). It is observed that mixture of all the negative TISs leads to heavier predictive oscillation and more convergence time in the training and test process, while adaptive grouping strategy makes the training process more stable and converge quickly. It is worth noting that groups 1 and 2 show more stable and accurate predictive results. This is because the positive TIS is located in the first frame, so negative TISs in the first frame have higher false positives than in the other two reading frames. All the above analysis demonstrates that features of negative TISs are heterogeneous, and construction of homogenous features according to frame information not only facilitates more accurate prediction performance but also promotes the stability of the model training process.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Human** & **SN (\%)** & **SP (\%)** & **PRE (\%)** & **ACC (\%)** & **F1-Score** & **auROC** & **auPRC** & **MCC** \\ \hline _TITER_ & 0.02 & 100 & - & 98.31 & - & 0.9788 & 0.6186 & - \\ _TISRover_ & 92.52 & 93.77 & 20.26 & 93.75 & 0.3324 & 0.9760 & 0.3998 & 0.4167 \\ _NeuroTIS_ & 98.19 & 98.54 & 56.29 & 98.53 & 0.7156 & 0.9985 & 0.9150 & 0.7377 \\ _NeuroTIS+ (nG)_ & 98.38 & 98.94 & 84.29 & 98.91 & 0.9079 & 0.9989 & 0.9266 & 0.9052 \\ _NeuroTIS+ (G)_ & 99.08 & 99.56 & 92.87 & 99.53 & 0.9588 & 0.9996 & 0.9385 & 0.9569 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of NeuroTIS+ with the other state-of-the-art methods on human dataset.  
Figure 4: Training and test process of NeuroTIS+ on human dataset. (**A**) Without grouping; (**B**) CNN0 with group 0; (**C**) CNN1 with group 1; (**D**) CNN2 with group 2.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Mouse** & **SN (\%)** & **SP (\%)** & **PRE (\%)** & **ACC (\%)** & **F1-Score** & **auROC** & **auPRC** & **MCC** \\ \hline _TITER_ & 0.03 & 100 & - & 98.36 & - & 0.9766 & 0.5879 & - \\ _TISRover_ & 95.29 & 96.74 & 32.52 & 96.72 & 0.4849 & 0.9936 & 0.7399 & 0.5463 \\ _NeuroTIS_ & 98.12 & 98.31 & 48.90 & 98.30 & 0.6527 & 0.9982 & 0.9036 & 0.6865 \\ _NeuroTIS+ (nG)_ & 98.63 & 99.26 & 86.96 & 99.23 & 0.9243 & 0.9991 & 0.9363 & 0.9223 \\ _NeuroTIS+ (G)_ & 99.26 & 99.73 & 94.85 & 99.71 & 0.9701 & 0.9997 & 0.9460 & 0.9688 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of NeuroTIS+ with the other state-of-the-art methods on mouse dataset.
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 4 Mobile Robots Categories
### Land-Based
#### 4.2.1 Wheeled Mobile Robot (WMR)  
WMRs, or wheeled mobile robots, are the most widely used robots among the many well-developed mature robots. The body, wheels, wheel support mechanism, and wheel drive system are the main components of a standard WMR chassis. The number of wheels  
Figure 5: Experimental setup of the balanced manipulator arm [31].  
on a chassis determines its configuration, with typical alternatives including two-wheel, three-wheel, and four-wheel layouts. The four-wheel chassis (see Figure 6) is the most common of these [34].  
While studies on legged and caterpillar-based locomotion have been extensive, the majority of mobile robots in research, development, and evaluation rely on wheels for mobility. This preference for WMRs (wheeled mobile robots) is due to several significant advantages: (1) they are more energy efficient, because they generally move on smooth and firm surfaces; (2) their multiple applications in the industry; (3) they require fewer parts and are less complex, making them easier to build; (4) their control wheels are less complex; (5) they cause less wear on the surface where they move compared with the bands of caterpillars; (6) balance problems do not present a great difficulty since they are always in contact with the surface. The reason WMRs have been experiencing popularity is due to their growing applications in planetary exploration, mining, inspection, and surveillance [36], the rescue of people, hazardous waste cleanup, and medical assistance, among others [37].  
Wheeled mobile robots (WMRs) are becoming more common in both industrial and service robotics, particularly when adaptive motion capabilities on reasonably smooth and flat surfaces are required [38].
## 4 Experiments and Results  
### Experimental Environment and Parameters  
In this study, a directional charger was used to conduct a simulation experiment. The experimental environment was to randomly generate sensor positions on a plane with a length of 100 units and a width of 100 units. The proposed algorithm was repeated 500 times to find the average result, and obtain the number of chargers and average execution time. As this study uses a directional charger, its related parameters are set to a chargeable angle of 90\({}^{\circ}\) and a chargeable radius of 16 units. Assume that the reception energy consumption of the sink node is 0.6 units/reception. The performance of the proposed algorithm is compared with that of the random position and random orientation (RPRO) and power balance aware deployment (PBAD) [10] algorithms using directional chargers.
# RMS Modeling and Control of a Grid-Forming E-STATCOM for Power System Stability in Isolated Grids
## 5 Results and Discussion
### Synchronous Power Plant Outage
Figure 13d shows the DC voltage and current at the supercapacitor, showing how the voltage decreases as the element discharges to stabilize the system. The E-STATCOM has a dual converter system with a total rated power of 2 \(\times\) 12.5 MVA and a DC-voltage base of 2200 V for each link. The energy exchange analysis for grid stabilization highlights a significant difference between the E-STATCOM and the synchronous condenser (SynCon). Over a 20 s period (see Figure 13c), the E-STATCOM delivers approximately 0.060 MWh to the grid, whereas the SynCon supplies only 0.0083 MWh. This energy transfer occurs both during the transient and steady-state responses. A key distinction is that the E-STATCOM sustains its energy delivery over a longer period, as it keeps the DC-link voltage of the VSC relatively constant for an extended duration. While the E-STATCOM provides a rapid dynamic response, its prolonged energy injection could be a crucial factor when assessing long-term operational efficiency.
## 3 Results and Discussion
### Emulsion Structure
#### 3.3.2 Particle Size Distribution Range  
To evaluate the effect of thermo-ultrasonic on the stability of the fish broth, average particle size (d\({}_{3,2}\)) and particle size distribution were characterized. As shown in Figure 3B, under the thermo-ultrasonic treatment condition, d\({}_{3,2}\) of the boiling water group showed a trend of increasing first and then decreasing. This phenomenon may be due to the fact that the nutrients were dissolved more and aggregated before 90 min, and the nutrient particles were dispersed after boiling for 120 min. The d\({}_{3,2}\) of the thermo-ultrasonic treatment group decreased over time, with little change in values at 90 and 120 min. The results indicated that thermo-ultrasonic treatment had a positive effect on  
Figure 3: \(L^{*}\) (**A**) and d\({}_{3,2}\) (**B**) values of fish broth by different treatments and times. Droplet size distribution of fish broth by boiling water (**C**) and thermo-ultrasonic treatment (**D**) with different treatment times (1–5–0, 30, 60, 90, 120 min). The capital superscript letters (A and B) represent significant differences between treatment groups A and B, while small superscript letters (a, b, c, d, e) represent significant differences among treatment times (\(p<0.05\)).  
the particle size reduction in the fish broth. The particle size characteristic of the fish broth was visualized by the particle size distribution for different treatments and times. The results in Figure 3C show that the nanoscale particle size distribution appeared at 120 min in the boiling water group. The results in Figure 3D show that the distribution of nanoscale particles in the thermo-ultrasonic group started at 30 min and increased with longer treatment time. [PERSON] et al. attributed the reduction in particle size to the ultrasonic effect induced by the collision of particles generating shock waves and to the collapse of cavitation bubbles in the solution [PERSON] et al. (2018). Moreover, the thermal effect of boiling accelerates inter-particle collisions, achieving a synergistic effect with the ultrasound. The results indicated that the fish broth system was promoted to be more stable by the synergistic effects of the ultrasonic and thermal treatment.