esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Reflectance Values at a Wavelength of 400-540 nm
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**Oil A: Conover–Iman post hoc test; \(p=\)**} \\ \hline
**Intertemporal comparison** & **A\_0** & **A\_20\_min** & **A\_60\_min** & **A\_120\_min** \\ \hline
**A\_0** & 0.2154 & 0.1047 & 0.0919 \\ \hline
**A\_20\_min** & 0.2154 & 0.0053 & 0.0044 \\ \hline
**A\_60\_min** & 0.1047 & 0.0053 & 0.09477 \\ \hline
**A\_120\_min** & 0.0919 & 0.0044 & 0.9477 \\ \hline \multicolumn{4}{c}{**Oil B: Conover–Iman post hoc test; \(p=\)**} \\ \hline
**Intertemporal comparison** & **B\_0** & **B\_20\_min** & **B\_60\_min** & **B\_120\_min** \\ \hline
**B\_0** & 0.0124 & 0.0013 & 0.0000 \\ \hline
**B\_20\_min** & 0.0124 & 0.0000 & 0.0000 \\ \hline
**B\_60\_min** & 0.0013 & 0.0000 & 0.1356 \\ \hline
**B\_120\_min** & 0.0000 & 0.0000 & 0.1356 \\ \hline
**Oil C: Conover–Iman post hoc test; \(p=\)** & & & \\ \hline
**Intertemporal comparison** & **C\_0** & **C\_20\_min** & **C\_60\_min** & **C\_120\_min** \\ \hline
**C\_0** & 0.3066 & 0.0604 & 0.0003 \\ \hline
**C\_20\_min** & 0.3066 & 0.0046 & 0.0000 \\ \hline
**C\_60\_min** & 0.0604 & 0.0046 & 0.0604 \\ \hline
**C\_120\_min** & 0.0003 & 0.0000 & 0.0604 \\ \hline
**Oil D: Conover–Iman post hoc test; \(p=\)** & & & \\ \hline
**Intertemporal comparison** & **D\_0** & **D\_20\_min** & **D\_60\_min** & **D\_120\_min** \\ \hline
**D\_0** & 0.9465 & 0.1114 & 0.0017 \\ \hline
**D\_20\_min** & 0.9465 & 0.0976 & 0.0014 \\ \hline
**D\_60\_min** & 0.1114 & 0.0976 & 0.0976 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of post hoc tests on the reflectance at a wavelength of 400–540 nm after the application of Oils A, B, C, and D.  
Moreover, it was found that at a wavelength of 400-540 nm after the application of Oil D (Table 4), the reflectance value after 120 min of exposure was statistically significantly higher compared with the value before and after 20 min of exposure. Comparing the type of oil, there was no statistically significant difference in the reflectance values at a wavelength of 400-540 nm before exposure (\(p\) = 0.8076), after 20 min of exposure (\(p\) = 0.8822), after 60 min of exposure (\(p\) = 0.8331), and after 120 min of exposure (\(p\) = 0.7438) between the type of oil used.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model
### Semi-Markov Process Model for 3Z
#### 3.2.4 States for Semi-Markov Process and Transition Diagram  
A summary of all the defined states is in Table 1.  
The zone registered most recently by the UE can be the edge or center zone as shown in the table above. Additionally, all the states of the UE can be defined by classifying its state with respect to the last and current zones.  
If the UE's current zone is an edge zone, regardless of whether or not the edge zone is 1 or 3, the last zone is unconditionally a center zone. As a result, these states are expressed as E\({}_{21}\) or E\({}_{23}\). If the state is E\({}_{23}\), this means that the UE's most recently registered location is in an edge zone and it moved to the other edge zone that is already in the zone list. If the status is E\({}_{21}\), this means that the UE's most recently registered location is in an edge zone and it moved to another zone that is already in the zone list and its current zone is an edge zone in which it registered its location most recently.  
Through a time-consuming process, a state transition diagram for all the states was obtained, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**State** & **The Last Zone** & **Current Zone** & **Most Recently Registered Zone** & **No. of Paging** \\ \hline E\({}_{21}\) & 2 & 1 & edge (1) & 1 \\ E\({}_{32}\) & 3 & 2 & edge (1) & 2 \\ E\({}_{12}\) & 1 & 2 & edge (1) & 2 \\ E\({}_{23}\) & 2 & 3 & edge (1) & 3 \\ C\({}_{21}\) & 2 & 1 & center (2) & 2 \\ C\({}_{12}\) & 1 & 2 & center (2) & 1 \\ C\({}_{32}\) & 3 & 2 & center (2) & 1 \\ C\({}_{23}\) & 2 & 3 & center (2) & 3 \\ I\({}_{21}\) & 2 & 1 & edge (1) & 1 \\ I\({}_{12}\) & 1 & 2 & center (2) & 1 \\ I\({}_{32}\) & 3 & 2 & center (2) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: States for the semi-Markov process model.  
Figure 8: State transition diagram for a 3Z system.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Key Parameters Selection
#### 5.2.4 Reader Distance to Shelves  
Following that, we adjusted the separation of the reader antenna and the shelf from 1.5 m to 3 m. The increase of the distance between the tags and the antennas decreases the received signal strength (RSS), the results are plotted in Figure 10. We discovered that the localization accuracy of DNCL increases as the above distance increases, and the localization effect is stable. This indicates that our proposed algorithm has good stability in the environment of multipath interference.  
Figure 8: Interrogation interval selection.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 6 Dynamic Simulation Results
### Case 1: Wind Speed = 10.0 m/s  
Figure 7 presents simulation results for Case 1 (the incoming wind speed of the DFIG is 10.0 m/s). The rotating kinetic energy available is 4.041 s. In the conventional method, \(\Delta P_{\text{con}}\) is 0.34 p.u. and the setting of \(\omega_{c}\) is 0.87 p.u. In addition, the setting of \(\Delta t\) in the proposed method is 12.5 s.  
As shown in Figure 7a, the system frequency nadirs for MPT, the conventional method, and the proposed method, respectively, are 59.391 Hz, 59.574 Hz, and 59.574 Hz. The system frequency nadir of the proposed method is the same as in the conventional method due to the same output power from a DFIG (which is limited by the torque limit), as in Figure 7b. The NBFR in the proposed method is 164.3 MW/Hz, which is the same as in the conventional strategy because of the identical system frequency nadir. The second frequency nadirs for the proposed method and the conventional method are the same (59.693 Hz). This is because \(\Delta t\) is set to 12.5 s so that the second frequency deviation in the proposed strategy is the same as in the conventional strategy.  
Due to the second part of Equation (13), \(\omega_{r}\) rapidly converges to 1.00 p.u. at 31.4 s, as shown in Figure 7c. However, the rotor speed in the conventional method converges to 0.87 p.u. at 52.7 s, which is slower than in the proposed method by 21.3 s. Thus, the proposed method starts recovering \(\omega_{r}\) earlier than in the conventional strategy. Moreover, the rotating kinetic energy cost in the proposed method is 1.491 s, which is significantly less than in the conventional method due to the high \(\omega_{c}\).  
During the deceleration period, the released rotating kinetic energy around the system frequency nadir for the proposed strategy is 0.583 s, which is the same as in the conventional method (see Figure 7c); however, the released rotating kinetic energy in the rest acceleration period is 0.908 s, which is 0.43 times greater than that of the conventional strategy. Thus, after supporting the system frequency nadir, the proposed strategy releases the rotating kinetic energy significantly less than in the conventional strategy.  
As displayed in Figure 7c, when the proposed method produces the same second frequency deviation as in the conventional method, the rotor speed in the proposed method is recovered at 72.2 s, which is 41.7 s faster than the conventional method because of less released kinetic energy, rapid rotor speed convergence, and the power reference in Equation (14). Furthermore, the harvested mechanical input power from 10.0 s to 110.0 s in the proposed strategy is 6.96 MWh, which is 0.29 MWh more than in the conventional strategy. This is due to less released rotating kinetic energy and rapid rotor speed recovery.
# RNA Modification Related Diseases and Sensing Methods
## 5 Detection Methods for RNA Modifications
### Illumina Sequencer  
To comprehensively investigate the epistranscriptome, high-throughput techniques are required. In the past, RNA modification mapping was mostly carried out by enriching the modification sites before sequencing and immunoprecipitation the modification sites with modification-specific antibodies [27; 97]. However, the m6A modification was initially studied using methylated RNA immunoprecipitation and subsequent sequencing (MeRIP-seq), which revealed unexpectedly high levels and dynamic modulation of the modification in mRNA [27; 97]. Reverse transcription (RT) from RNA to cDNA is necessary for NGS, which subsequently relies on synthetic methods for sequencing, such as second-strand synthesis with fluorescently tagged DNA molecules [98]. As a result, NGS methods cannot directly identify the existence of RNA modifications. However, some modifications (m1A, m3C, m3U, m1G, m2,2G, and m1 acp3Y) (Figure 3) disrupt Watson-Crick base pairs and cause reverse transcription errors, such as RT emissions and an increase in error frequency [99; 100]. This has been shown to be effective in identifying these modifications [101; 102]. Reverse transcription can be chemically stopped by treating RNA with substances that react only with modified bases [103], enabling read-end analysis to detect RNA modifications. Examples of such procedures include treatment with CMCT [104], treatment of 5 mC with hydrogen sulfite (Figure 3) [105], treatment of ac4C with sodium cyanoborohydride [106], treatment of m3C with hydrazine and/or aniline (Figure 3) [107], and treatment of inosine with glyoxal (Figure 3) [108].
## 1 Introduction
Genetic manipulation of AsA concentrations in different plant species have been successfully achieved by the overexpression of genes encoding AsA biosynthetic and recycling enzymes, or by the suppression of degrading enzymes [3; 7; 29]. Nonetheless, these transgenic approaches have mainly focused on the whole plant, and results on edible fruits are still limited [7]. In fruits, tomato transgenic lines with altered expression of genes of the L-galactose or D-galacturonic acid have been successful in increasing AsA accumulation, but sometimes with detrimental effects on fruit development [4]. Transcription factors (TFs) have already been identified as key regulators for other metabolic pathways [30], and their manipulation has been more effective for the control of metabolic pathways than using a single (functional) genes [31]. TFs that are directly affecting the expression of genes involved in the AsA metabolic pathways have been described in tomato and Arabidopsis [3]. Moreover, multiple TFs that are co-expressed with genes of the AsA metabolism in tomato fruits have been reported [32]. However, such an approach in other agronomical important crops, as _Citrus_, are scarce. These experimental strategies are required in order to understand the large variability in AsA content and its accumulation pattern among the different species and plant tissues as well as the particular developmental stages of each type of fruit. Therefore, the aim of the present study was to carry out a detailed comparative analysis of the changes in AsA content and in the transcriptional regulation of its metabolism during development of different organs of oranges (_Citrus sinensis_ cv. Valencia Late), namely petals, leaves, flavedo, and pulp of fruit. Additionally, and in order to provide insights on the potential upstream regulators of AsA metabolism in fruits, an analysis of gene co-expression networks and the _cis_-acting elements contained in the promoters of key genes of AsA metabolism has been also accomplished.
## 3 Results
### Scaffold's Geometrical Parameters and Main Findings
#### 3.5.2 Porosity  
In this review, it was possible to observe that, except for the study of [PERSON] et al. (2022) [32], which used BCP scaffolds with a porosity of 40%, all other studies opted to use scaffolds with more than 50%, with [PERSON] et al. (2006) [28] utilizing scaffolds with the highest porosity (60-80%) (CaP).  
According to [PERSON] et al. (2006) [28], a CaP matrix with high porosity can be beneficial for osteoconduction and bone integration, as it facilitates cell migration and bone growth. However, balancing porosity with the material's mechanical strength and structural integrity is crucial. The sintering of CaP ceramics at high temperatures can negatively affect the porous structure by destroying pores of different sizes and reducing connectivity between them [28]. This reduces the material's osteoconductive effectiveness, as partially connected pores hinder cell migration. [PERSON] et al. (2006) [28] observed that when exposed to a maximum temperature of 700 \({}^{\circ}\)C, CaP ceramics largely maintain their natural  
Figure 3: Distribution of the ceramic materials used for scaffold fabrication.  
crystal structure and degree of porosity, resulting in better biological properties. However, high sintering temperatures can make the production of CaP ceramics more challenging, compromising the structural integrity necessary for adequate performance [28]. Three of the eleven studies [28,30,34] did not mention the scaffolds' porosity.
## 4 Results and Discussion
### Microscopic Characterization of CaCO\({}_{3}\) NFs
#### 4.1.2 XRD Analysis  
Figure 3 demonstrates the XRD pattern of original CaCO\({}_{3}\) NPs and solvent-free CaCO\({}_{3}\) NFs. The characteristic diffraction peaks appeared in both curves at 23.12\({}^{\circ}\) (012), 29.50\({}^{\circ}\) (104), 35.97\({}^{\circ}\) (110), 39.49\({}^{\circ}\) (113), 43.26\({}^{\circ}\) (202), 47.60\({}^{\circ}\) (018), and 48.59\({}^{\circ}\) (116) by comparing the PDF standard card (PDF NO.05-0586). The results not only suggest that the nano-CaCO\({}_{3}\) used in this study was calcite type, but also indicate that the surface modification of the liquid-like behavior technology did not destroy the CaCO\({}_{3}\) NPs' crystal structure. Additionally, curve B had some new weak diffraction peaks around 20\({}^{\circ}\), indicating that there were substances with amorphous structure in addition to calcite nano-CaCO\({}_{3}\). Context analysis suggests that this material was a chemical long-chain DC5700 and NPES organic substance.  
Figure 3: XRD pattern: (**A**) original CaCO\({}_{3}\) NPs; (**B**) solvent-free CaCO\({}_{3}\) NFs.  
Figure 2: FTIR spectra: (**A**) original CaCO\({}_{3}\) NPs; (**B**) CaCO\({}_{3}\) NPs grafted DC5700; (**C**) solvent-free CaCO\({}_{3}\) NFs.
## 4 Conclusions  
In conclusion, **M1**, a TPD-based conjugated small molecule with a D-A-D-A-D framework, was synthesized and optimized with a combination of Stille cross-coupling and direct heteroallylation reaction. Some of the fragments for the target molecule showed low reactivity and low stability under the bromination conditions used in this study. Some stannylated or brominated fractions that can be easily obtained with more than 50% yield or are commercially available were conjugated by a Stille cross-coupling reaction while some fractions that were not simply brominated or stannylated were coupled with direct allylation with an improved chemical yield. Therefore, we demonstrated the facile synthetic route applicable for various D-A-D-A-D with TPD derivatives. Furthermore, **M1** shows strong absorption of visible light with an optical band gap of 1.82 eV in the solid state and clear vibronic shoulders observed in the UV-vis absorption spectra of the **M1** film suggesting a strong tendency to form \(\pi\)-\(\pi\) stacking. Together with UV-vis, CV measurements demonstrated the excellent semiconducting properties of **M1** for the organic BHJ solar cells by determining HOMO and LUMO energy levels. According to the concept of band alignment, as proven in CV, series of BHJ solar cell devices were fabricated with PC\({}_{71}\)BM upon varying the blend ratio between **M1** and PC\({}_{71}\)BM, solvents, and the type of additives so that **M1** showed fairly stable p-type semiconducting properties with the highest PCE of 1.86%, \(V_{\rm{oc}}\) of 0.85 eV, \(J_{\rm{sc}}\) of 3.53 mA/cm\({}^{2}\), and FF of 62% achieved in this study. These moderated PCEs of **M1**:PC\({}_{71}\)BM BHJ solar cells relative to polymers and other types of small-molecule BHJ solar cells appear to be caused by uneven surface morphologies of **M1**:PC\({}_{71}\)BM. Therefore, from a broader perspective, this contribution implies that a wider choice of solvents and additives may be pursued to address the challenge in obtaining targeted BHJ morphologies.  
**Author Contributions:** Performed the preparation and characterization of materials: O.K.; the fabrication and characterization of BHJ solar cells: J.L.; designed the material concepts in this study and drafted the manuscript: J.K.P.; designed BHJ solar cell concepts and also drafted the manuscript: D.H.W.  
**Funding:** This research was funded by the Hankuk University of Foreign Studies Research Fund of 2018. This research was also supported by the Chung-Ang University Research Scholarship Grants in 2018.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Work-Flow
### Semantic Concept  
The semantic concepts are generated using Social Network Analysis's (SNA) community detection algorithm (Figure 1, step 5) [41; 42; 43; 44]. The nodes \(n\) that have high mutual inter-connectivity are colored the same color to mark a group of nodes belonging to the same structural network feature--a community C  
\[C_{k,d}=\{n_{i}\mid n_{i}=Q_{k},\ \forall\ i\ \epsilon\ T_{d}\} \tag{1}\]  
is defined by the node membership in a community \(k\) is \(C_{k,d}\) that consists of all nodes (representing tokens in a document \(d\)) with the same modularity class \(Q_{k}\)[43].  
We equate the communities to semantic concepts as these structures identify nodes with common co-occurrences. Previously published research has shown that the semantic concepts emerge as the network's community structures and are identified as the semantic concept carrying structures [25]. Note that each node can be assigned to exactly one community in a document with a unique time stamp. The node size is proportional to the token's role within the network and can be selected to correspond to the node's connectivity (degree), Eigenvector centrality (centrality) [42; 45] or betweenness centrality (bridges) [42; 46].  
The same way the SNAP's raw text processing relies on mature NLP tools, the semantic concept processing uses mature SNA tools to discover semantic concepts in the lexical chains of tokens. The SNAP framework uses Gephi open source software (OSS) for the SNA processing through its headless application programming interface (API) engine [42] which implements the above cited network analysis algorithms.  
The above definition of a semantic concept extends the basic definition of key concepts that are commonly identified by the TF-IDF or manual coding by giving contextual information for each token. For example, Figure 1 shows the POS _16 YearOld_ being commonly used with _teen_, _mauled_, and _bear_ within the same semantic concept, and _troopers_ and _trail_ (through _teen_) in adjacent semantic concepts. Modeling the semantic concepts using lexical chaining and subsequent SNA of the semantic networks retains the full richness of how semantic concepts relate to each other and the supporting concepts.
## 7 Conclusions  
Four HSPMSMs with different rotor topologies were designed, analyzed, and compared in this paper. To reduce the operational frequency of HSPMSMs, all these HSPMSMs were designed with 6 stator slots and 2 rotor poles. The following conclusions could be drawn from the results of multiple physical field simulations:  
1. As the rotor diameter of M1 was the smallest, it presented better electromagnetic and mechanical performance. However, it showed poor temperature behavior due to its high loss density. Meanwhile, this topology was the most difficult solution to implement. So, it is only suitable for low-power applications.
2. The rotor structure of M2 was very simple to implement; thus, its manufacturing process was easier, and its loss was much lower than that of other HSPMSMs. It achieved quite good electromagnetic, thermal, and mechanical performance. In most applications, M2 is a popular solution.
3. Both M3 and M4 were relatively easy to fabricate since no sleeve was required. On the other hand, as high-silicon sheets were adopted, the rotor loss of these two machines was high. Considering the rotor temperature distribution, M4 faced a great risk of irreversible demagnetization. Though M3 and M4 are not suitable for fuel-cell electric-vehicle air compressor applications, they could be designed with better comprehensive performance under different weights.
4. The weighted-average method was used to judge the performance of these HSPMSMs. For different applications, a different weighted coefficient could be applied, and the best topology could be obtained.
5. In further work, an interesting issue could be to refine the optimal analytical results, so that it could be better adapted to other HS applications while following the same process.  
Conceptualization, B.L., C.L. and J.Z.; writing--original draft preparation, B.L.; writing--review and editing, C.L., J.Z. and G.L.; supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under project 52007047, and in part by the Outstanding Youth Innovation Project funded by State Key Laboratory of Reliability and Intelligence of Electrical Equipment EERI_OY2021005.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.