esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Proteins Related to Isoprenoid Biosynthesis  
KEGG pathway enrichment analysis was performed among all DEPs to identify the proteins involved in isoprenoid biosynthesis. In the category of biosynthesis of secondary metabolites, 21 proteins were related to the isoprenoid biosynthesis pathway, of which 12 proteins were involved in methylerythitol phosphate and mevalonate. When compared to 15 d, the expression levels of DXS, DXR, HDS, HDR, IPPI, FPPS, and VTE4 were considerably down-regulated in 65 d. However, the expressions of MCT, MDS, AACT, GGPPS, VTE1, crtISO, PDS, AOG, NCED, ABA2, ZEP, wrpA, and alpha-terpineol synthase were significantly induced. Interestingly, in the MAS family, two DEPs were filtered, only one member was up-regulated, whereas the other was down-regulated in 65d (Figure 3 and Table S3). These results indicate that many proteins, such as MCT, MDS, ACCT, GGPPS, and alpha-terpineol synthase, may play pivotal roles in regulating the isoprenoid biosynthesis of _C.__gamma_.  
Figure 3: Identification of candidate proteins related to the isoprenoid biosynthesis pathway. The pathway shows the subset of metabolites and enzymes that constitute the process of isoprenoid biosynthesis.  
Red: up-regulated proteins in the 65d. Green: down-regulated proteins in the 65d. Yellow: in the MAS family, only one member was up-regulated, whereas the other was down-regulated in 65d. AACT, Acetyl-CoA C-acetyltransferase; IPPI, isopentenyl diphosphate isomerase; FPPS, farnesyl diphosphate synthase; DXS, 1-deoxy-D-xylulose 5-phosphate synthase; DXR, 1-deoxy-D-xylulose 5-phosphate reductoisomerase; MCT, 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase; MDS, 2-Cmethyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS, 4-hydroxy-3-methylbut-2-enyldiphosphate synthase; HDR, 4-hydroxy-3-methylbut-2-enyldiphosphate reductase; MAS, momilactone-A synthase; VTE1,tocopherol cyclase; VTE4, \(\gamma\)-tocopherol methyltransferase; wrbA, NADPH dehydrogenase; PDS, 15-cis-phytoene desaturase; crtlSO, prolycopene isomerase; ZEP, zeaxanthin epoxidase; NCED, 9-cis-epoxycarotenoid dioxygenase; ABA2, xanthoxin dehydrogenase; AOG, abscisate beta-glucosidasetransferase. GAP, D-glyceraldehyde 3-phosphate; DXP, 1-deoxy-D-xylulose 5-phosphate; MEP, methylerythritol phosphate; CDP-ME, 4-(cytidine 5\({}^{\prime}\)-diphospho)-2-C-methyl-D-erythritol; CDP-MEP, 2-phospho-4-(cytidine 5\({}^{\prime}\)-diphospho)-2-C-methyl-D-erythritol; McPP, 2-C-methyl-D-erythritol-2,4-cyclodiphosphate; HMBPP, 4-hydroxy-3-methylbut-2-enyldiphosphate; DMAPP, dimethylallyl diphosphate; GPP, geranyl diphosphate; Acetyl-CoA, acetyl-coenzyme A; Aceto-acetyl-CoA, Aceto-acetyl-coenzyme A; IPP, isopentenyl diphosphate; FPP, farnesyl diphosphate; GFPP, geranylfarnesyl diphosphate; GGPP, geranylgeranyl diphosphate.
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
## 2 Materials and Methods  
Within this section, all configurations of passive and active components used to develop the PLC system and propagate its waveform along the PV-DC loop are discussed. The location and PV system where the PLC system was mounted, is the same as that detailed in [4]; a grid-connected eleven module array with varying defects.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 3 Proposed Method
### Obtaining Feature Subsets  
After relevance calculation, two feature sets are formed called \(Subset1\) and \(Subset2\), respectively, according to the \(FF\_threshold\) and \(FC\_threshold\). The features, of which corresponding FF-MIC values are smaller than the \(FF\_threshold\), will be the elements of \(Subset1\), because the closer to 0 the FF-MIC value, the stronger the irrelevance between features. Additionally, the features, of which corresponding FC-MIC values are bigger than the \(FC\_threshold\), will become the members of \(Subset2\), because the closer to 1 the FC-MIC value, the stronger the relevance between features and categories. The details of obtaining feature subsets are shown in Algorithm 4.  
```
0:\(FS=\{f_{1},f_{2},\ldots f_{m}\}\), \(FF\_threshold\), and \(FC\_threshold\).
0:\(Subset1\) and \(Subset2\).
1. for each \(f_{i}\) in \(FS\)
2. select the features corresponding FF-MIC values smaller than the \(FF\_threshold\) to form a \(Subset1\)
3. end for
4. for each \(f_{i}\) in \(FS\)
5. select the features corresponding FC-MIC values bigger than the \(FF\_threshold\) to form a \(Subset2\)
6. end for
```  
**Algorithm 4** Obtaining feature subsets
# Joining of Fibre-Reinforced Thermoplastic Polymer Composites by Friction Stir Welding--A Review
## 7 Conclusions  
The joining of FRTP composites by FSW and its variants is a topic on which there is still a lot to explore. Regarding the FSW technique, the literature is still reduced and only addresses the weldability of FRTP composites reinforced with chopped fibres. On the other hand, it is already possible to understand the influence of most of the parameters on the quality of the welds produced. With the current work, it is possible to draw the following main conclusions:  
* The optimization of rotational speed and welding speed is of utmost importance, as it has a great influence on heat generation and on the quality of the mixture, and has a great impact on the degree of fragmentation of the reinforcing fibres;
* The fibres used in composites should not be close to the critical length, in order to combat the loss of strength induced by the fibre fragmentation, caused by the shearing forces of the welding tool;
* The tilt angle only has a residual influence on the process, but it must be considered for the FSW with conventional rotary shoulder tools;
* The calibration of the axial force/pressure and plunge depth is important to avoid the formation of welding defects caused by lack of compactness of the softened material;
* Threaded or grooved conical pins achieved better results than other geometries;
* Stationary-based tools showed better performance when compared to conventional tools, as the rotating shoulders degrade the composite surfaces and favour the breakage of the surface fibres.  
The joining of FRTP composites by RFSSW is only mentioned twice in the literature. However, these are the only two publications concerning the joining of composites reinforced with continuous fibres. These studies are focused on the feasibility of the RFSSW on these composites, so the investigation of the influence of the different parameters is quite small. Still, some conclusions regarding the influence of rotational speed and holding time pressure were pointed out. Having demonstrated its viability, this technology needs further research aimed at understanding the influence of different parameters.
## 5 Conclusion  
In this study, the optical spectral vegetation indices (OSVIs), radar polarimetric parameters (RPPs), combined optical spectral vegetation indices and radar polarimetric parameters (the product of optical spectral vegetation indices and radar polarimetric parameters (COSVI-RPPs)), and multiple stepwise regression (MSR) and partial least squares regression (PLSR) methods were investigated to determine the most accurate empirical regression equations for LAI and biomass estimation in winter wheat. The results of this study revealed the following conclusions. Strong relationships existed between LAI, biomass and OSVIs, RPPs, and the OSVIs and RPPs could be used to estimate LAI and biomass in winter wheat based on the relevant regression equations. We found a highly significant correlation between the new COSVI-RPPs (RVI \(\times\) OSVIs and DERD \(\times\) OSVIs) and LAI and biomass. The estimation accuracy of LAI and biomass was better using RVI \(\times\) OSVIs and DERD \(\times\) OSVIs than using the OSVIs and RPPs values alone. The MSR and PLSR methods were used to estimate LAI and biomass in winter wheat based on the results of the COSVI-RPPs. The results demonstrated that the PLSR regression equations based on the COSVI-RPPs resulted in a better estimation of winter wheat LAI (R\({}^{2}\)= 0.76, RMSE = 0.61, and nRMSE = 18.13%) and biomass (R\({}^{2}\) = 0.85, RMSE = 137.21 g/m\({}^{2}\), and nRMSE = 18.96%). The MSR regression equations based on the COSVI-RPPs also resulted in good estimations of LAI and biomass of winter wheat. The LAI (R\({}^{2}\) = 0.78, RMSE = 0.58, and nRMSE = 17.42%) and biomass (R\({}^{2}\) = 0.87, RMSE = 134.68 g/m\({}^{2}\), and nRMSE = 18.61%) model obtained the best estimation results based on all the COSVI-RPPs, OSVIs, and RPPs using PLSR.  
This study was supported by the Natural Science Foundation of China (41471285, 41271345, 41471351), the Beijing Natural Science Foundation (4141001), the Special Funds for Technology Innovation Capacity Building sponsored by the Beijing Academy of Agriculture and Forestry Sciences (KJCX20140417, KJCX20150409), Yangzhou University Excellent Doctoral Foundation. We are grateful to China Meteorological Data Sharing Service System and Canada Weather and Climate Data Service System for environment data collection. We are grateful to staffs for field data collection.  
[PERSON] analysed data and wrote the manuscript; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] gave comments, suggestions to the manuscript, and checked the writing; [PERSON], [PERSON], and [PERSON] provided data and data acquisition capacity.  
The authors declare no conflict of interest.
## 4 Simulation and Results
### Case A
In Figure 13, the wind power utilization rate in Case 6 is the highest, and the wind power utilization rate in Case 5, where three ME applications are all employed, is only slightly higher than in Case 1, where no ME application is deployed, while much lower than in Case 2-4, where only one application is deployed. Compare Case 5 and Case 6, the RES utilization gap is caused by the different dispatch schemes, as shown in Figure 12, which implies that:  
1. The accommodating effects of the CCHP, P2H/G2P, and demand response of EVs are remarkable, the RES utilization rate raises from 2.6%, to 90.73%, 91.48%, and 91.77%. With all the three ME applications, even more RES could be adopted, a 93.14% RES utilization rate gain is achieved in Case 6. 2. However, without a comprehensive energy management method, ME may not function in a coordinate way, as shown in Figure 5, thus fail to improve the utilization of the RES, and even worsen other performances of the MES. The other two performances in Figure 13 shows the similar patterns, the operation performance in Case 6 is superior in all the three concerned aspects, while the operation performance in Case 5 is far lower, and even lower than those in Case 2-4, where each of the ME applications functions separately. Compare Case 2-6 with Case 1, the RES utilization rate in each case have respectively increased by 88.13%, 88.87%, 89.17%, 33.55%, and 90.54%. As to the energy saving performance (ESP), ESP\({}_{6}\) is the highest, while ESP\({}_{3}\) is the second lowest, only higher than ESP\({}_{1}\). It is because the P2H and G2P processes involve multiple times of energy conversions, which will inevitably lead to energy loss. As to the economy, profit\({}_{6}\) is the highest, and profit\({}_{3}\) is the second, which owes both to the TOU pricing scheme and to higher wind power utilization rate. With the six single objective optimization problems in Case A, we may safely arrive at the conclusion that though the ME applications can greatly enhance the MES's ability in accommodating RES, its realization counts on a comprehensive energy management method to coordinate their orderly operation. Otherwise, they may function uncoordinatedly, which not only fails to raise the RES utilization, but also degrade the operation performance of the MES, such as greatly increasing the operation cost.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
###### Abstract  
Vehicle-to-everything (V2X) communications allow a vehicle to interact with other vehicles and with communication parties in its vicinity (e.g., road-side units, pedestrian users, etc.) with the primary goal of making the driving and traveling experience safer, smarter and more comfortable. A wide set of V2X-tailored specifications have been identified by the Third Generation Partnership Project (3 GPP) with focus on the design of architecture enhancements and a flexible air interface to ensure ultra-low latency, highly reliable and high-throughput connectivity as the ultimate aim. This paper discusses the potential of leveraging Information-Centric Networking (ICN) principles in the 3 GPP architecture for V2X communications. We consider Named Data Networking (NDN) as reference ICN architecture and elaborate on the specific design aspects, required changes and enhancements in the 3 GPP V2X architecture to enable NDN-based data exchange as an alternative/complementary solution to traditional IP networking, which barely matches the dynamics of vehicular environments. Results are provided to showcase the performance improvements of the NDN-based proposal in disseminating content requests over the cellular network against a traditional networking solution.  
5G; vehicle-to-everything; information centric networking; 3 GPP; named data networking +
Footnote †: journal: _Future Internet_**2019**, _11_, 199; doi:10.3390/611090199
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
## 6 Conclusions  
In this paper, a static-voltage-stability-domain boundary tracking method was proposed that considers curve extrapolation and a correction conversion strategy. This method avoids the iterative divergence phenomenon of conventional continuous methods in constructing stability domains, promoting the performance in constructing the SVSRB. The relevant conclusions are as follows:  
1. The typical failure scenario with the conventional continuous method is pointed out, and the reason for tracking failure to construct the SVSRB is analyzed.
2. A boundary tracking method considering curve extrapolation and a correction strategy was proposed in the predictor and corrector stages, avoiding iterative divergence.
3. The intermediate data in the predictor stage were utilized for providing the exact analytical expression of the boundary curve, achieving segmented analysis of the SVSRB.  
This method can provide an effective voltage monitoring tool for the current power grid, helping to ensure the safe and stable operation of power grid voltage. The focus of follow-up work is to carry out research on voltage stability assessment and control decision-making in relation to large power grids based on the SVSRB technique, and propose a more practical voltage analysis method.  
Conceptualization, P.S. and Y.W.; methodology, X.W. and C.F.; software, J.B. and B.C.; writing, J.L. and D.G.; supervision, J.L. and D.G.; project administration, J.L. and D.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Project of the State Grid Sichuan Electric Power Company (52199723000P).  
Not applicable[PERSON]  
The data presented in this study are available on request from the author, [PERSON]. The data are not publicly available due to the first author's PhD thesis not having been submitted at the time of publication of this work.  
The main author ([PERSON], [PERSON]) would like to thank the team ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]) for their continuous support and  
Figure 20: SVSRB of IEEE 300-bus power system.  
contributions towards this research, and to the State Grid Sichuan Electric Power Company, Sichuan, China, for their funding.  
Author [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ware employed by the Sichuan Electric Power Company, Sichuan, China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Implementing Ethical AI in Healthcare  
Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift in how medical services are delivered and managed. AI can improve diagnostic accuracy, personalize treatment plans, optimize resource allocation, and predict patient outcomes more precisely. However, the deployment of AI in healthcare must be cautiously approached due to the sensitive nature of medical data and the ethical implications surrounding its use [11]. Safeguarding patient data privacy is a critical issue in the healthcare sector, particularly with the integration of AI technologies that rely on medical records replete with sensitive personal information. Mishandling such data could result in significant privacy violations and misuse [7,12]. To mitigate these risks, a suite of sophisticated technologies has been developed to maintain the confidentiality and integrity of patient information while harnessing AI's capabilities.  
Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, utilizing a distributed ledger system to ensure that patient recordsare immutable and verifiable, thereby thwarting unauthorized access and potential data breaches. It also enables secure data sharing among verified entities. In parallel, Federated Learning (FL) allows for training AI models on decentralized devices or servers that hold local data samples, negating the need to transfer sensitive patient data and thus reducing centralization concerns [13]. Homomorphic Encryption (HE) offers a solution for performing computations on encrypted data without decryption, allowing AI to process and learn from the data. At the same time, it remains encrypted, thus upholding privacy even when third-party analysis is required [9]. Lastly, Differential Privacy (DP) introduces controlled noise to data sets to obscure individual identities, permitting the training of AI models on population-representative data without compromising individual privacy [10]. Collectively, these technologies form a robust framework for protecting patient privacy in the age of AI-driven healthcare. Implementing these technologies reflects a commitment to ethical standards in healthcare AI. They address technical challenges and demonstrate a dedication to respecting and protecting patient rights, particularly regarding privacy and autonomy. As AI continues to evolve and become more integral to healthcare delivery, the role of these technologies in maintaining a balance between innovation and ethical responsibility becomes increasingly vital. They represent solutions to current challenges and a vision for a future where healthcare AI advances with ethical considerations, ensuring that patient welfare remains at the core of technological progress in the healthcare sector.
## 2 Background
### Rpl
#### 2.2.2 HOP Metric  
For this metric, the preferred parent selection decision is based on the number of hops between the sending node and the collecting node. This simple metric only selects the path with a minimum number of hops. Clearly, this is a network-based metric that tries to emphasize the number of hops, rather than node or link statistics [8].
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 5 Results
### Sensitivity Analyses  
In Section 5.2, we observe an intriguing phenomenon: In Scenario I, the average SDGC of the BOMSDM is approximately 2.4 times higher than that of Model I and Model II (which produce consistent results in Scenario I), while the average SSR is 4.9% lower. In Scenario II, the average SDGC of the BOMSDM is approximately 1.4 times and 4.9 times higher than that of Model I and Model II, respectively, while the average SSR is 2.3% and 4.6% lower. These findings suggest that the BOMSDM exhibits the less effective optimization of objective values in both Scenario I and Scenario II, implying that community allocation strategies indeed exert a certain negative impact on the efficiency and equity of facility distribution. However, in Scenario III, BOMSDM's performance approaches or even  
Figure 5: The illustration of one selected solution generated by the 4 models.  
surpasses that of Model I and Model II. Specifically, for SDGC, the mean value of BOMSDM drops to 0.009, which is 50% higher than Model I but lower than the mean value of Model II; for SSR, BOMSDM's mean value is 5.2% higher than Model I, and 0.2% higher than Model II. This indicates that as the number of facilities increases, the negative impact of community allocation strategies gradually diminishes. This section analyzes the sensitivity of the parameter of facility redundancy \(R\) to the solutions. In this study, we defined the baseline facility quantity as one facility allocated per community, with redundancy defined as the excess number of facilities beyond the baseline, i.e., \(R=p^{t}-q\).  
We initiated \(R\) at 0 and incrementally increased it by five in ten cumulative measurements, with the mean value results reported in Figure 6 (See Table S3 for details). The results indicate that when R is between 0 and 10, the BOMSDM quickly optimizes both objectives. However, as R reaches 15 to 20, the SSR obtained by BOMSDM exceeds that of other models. This suggests that, in this instance, although BOMSDM partially sacrifices the two objective values due to the inter-community allocation constraints in the early stages, this shortfall can be compensated when redundancy reaches 15 to 20, which corresponds to approximately 20% of the total number of communities.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 5 Conclusions and Future Work  
This paper proposes a vector-tracking-based deep-coupled navigation system using dual-frequency GNSS and a grid strapdown inertial navigation system. The hardware simulator and semiphysical experiment results indicate that the proposed system can obtain better navigation accuracy and more stable tracking performance in polar areas compared with the traditional GNSS tracking method. In future work, more realistic experiments will be conducted to further assess the performance of the system, and multiconstellations can be included in the system with the coverage of GNSS satellites increasing in polar areas to further improve the navigation performance.  
Figure 14: The velocity errors of the dynamic test using different methods in polar areas.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMS Velocity Errors (m/s)**} \\ \cline{2-4}  & \(\mathbf{V_{E}}\) & \(\mathbf{V_{N}}\) & \(\mathbf{V_{U}}\) \\ \hline T-GNSS & 0.261 & 0.267 & 0.479 \\ T-DDC & 0.049 & 0.047 & 0.154 \\ IF-DDC & 0.048 & 0.047 & 0.153 \\ \hline \hline \end{tabular}
\end{table}
Table 8: RMS velocity error statistics of the dynamic test.  
**Author Contributions:** [PERSON] and [PERSON] conceived the idea and designed the proposed algorithm; [PERSON] designed, performed, and analyzed the experiments; [PERSON] supplemented the proposed algorithm and some simulation results; [PERSON] and [PERSON] wrote the paper jointly.  
**Funding:** This research was jointly funded by the National Natural Science Foundation of China (Nos. 61633008, 61773132, 61803115), the 7 th Generation Ultra Deep Water Drilling Unit Innovation Project sponsored by Chinese Ministry of Industry and Information Technology, the Heilongjiang Province Science Fund for Distinguished Young Scholars (No. JC2018019), the Fundamental Research Funds for Central Universities (No. HEUCFP201768), and the China Scholarship Council (file No. 201806680008).  
**Acknowledgments:** The authors would like to thank [PERSON] for the discussion and support regarding the GPS L2C code and also thank the editors and the anonymous reviewers for their constructive comments and suggestions to improve the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.