esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### LC-MS Analysis  
Mass spectrometry can perform quantitative metabolite analysis with high sensitivity and selectivity, as well as identify metabolites. The combination of liquid chromatography and mass spectrometry facilitates metabolite identification by reducing sample complexity and allowing metabolite separation prior to detection [47]. On a molecular weight basis, the spectra in the current study corresponded to glycosides, alkaloids, flavonoids, amino acids, esters, phenolics, organic acids, and terpenes (Figure S3 and Table 5).  
Figure 3: FTIR spectra of (**A**). _T. focus_ freeze-dried powder (**B**). of ethanol extract of _T. focus_ flesh.  
The alkaloid piperidine derivative 1-(4-aminophenyl)-3,3-dichloropiperidin-2-one could be a potential antioxidant. Piperidine was found to have a protective effect against oxidative damage in in vitro experiments by inhibiting free radicals, reactive oxygen species, and hydroxyl radicals in a study by [48]. [PERSON] et al. [49] also mentions the antioxidant capacity of the alkaloid. Chelidonine, another identified alkaloid, is the primary bioactive ingredient in _Chelidonium majus_ and has anti-cancer, anti-inflammatory, and antioxidant properties [50].  
Flavonoids 7-hydroxy-3-methylflavone and 6-hydroxyflavanone might also be powerful antioxidants. Flavonoids in plants are mostly found as glycosides, which are hydroxyl groups linked with sugar units and two aromatic rings (A and B). Many connecting sites exist between the basic skeleton C ring and B ring, and A ring and B ring frequently have substituents such as hydroxyl, methoxy, methyl, and isopentyl, resulting in a wide range of derivatives and active functions [51].  
Numerous studies have shown that the medicinal value of plants is closely related to the antioxidant activity of flavonoids. In general, flavonoids have high free radical scavenging activity. The mechanism of DPPH scavenging in vitro is that flavonoids' ring substituents, such as hydroxyl and methoxy, react with free radicals [52]. Anthocyanins are a type of water-soluble flavonoids found in a variety of fruits and vegetables. Petunidin, like europinidin and malvidin, is an O-methylated 3-hydroxy anthocyanin derived from delphinidin.  
Petunidin has high antioxidant activity because it is an anthocyanin. Phenolic acids are a type of dietary polyphenol that acts as a natural antioxidant [53]. Protocatechuic acid-3-glucoside has recently been shown to have antioxidant properties in a variety of diseases, making these \"old compounds\" a potential \"new application\" for medical therapies [54]. The identification of all these interesting individual phytochemicals indicates that the antioxidant activity of the fruit can be attributed to the synergistic effects of the multiple chemical components in the extracts.
# Blockchain Based Trust Model Using Tendermint in Vehicular Adhoc Networks
###### Abstract
Due to its high security, blockchain has been commonly used in the non-financial market, i.e., content delivery, key management [23], decentralized storage [24; 25; 26], etc. Some popular projects like Hyperledger can also be used as an application of byzantine fault tolerance [27; 28]. A blockchain based crowdsourcing program is also defined for the court adjudication [29; 30; 31]. The block generation undertaken by the attacker is slow as compared to the normal RSUs, due to the issue of trustworthiness. The benefit of using the cross blockchain through Tendermint is the interoperability between blockchains, which also reduces the latency and improves the transaction speed. The existing technique has worked on the proof of work consensus, which is not at all a power-efficient method. In the proposed method we have implemented the decentralized cross chain Tendermint protocol, in which the transaction speed is greater, and this allows us to use and send the data on any blockchain. The existing study has only shown transactions and ratings, but we also worked on the quality of services and calculated the effect on QoS. Voting-based consensus uses less power as compared to the existing study [32; 33]. The contribution of this paper is summarized below:  
(a) A decentralized trust management system has been proposed based on blockchain technology, which permits all the vehicles and RSUs to update the trust value in a decentralized manner, and the active participation of all vehicles and RSUs for the updating procedure;(b) We have proposed a proof of authority (PoA), which is better than proof of work (PoW) and practical Byzantine fault tolerance (pBFT), because of the high energy consumption and greater overhead, respectively. (c) We have proposed a system model and conducted a simulation which proves that our proposed model is efficient in practical vehicular networks.
## 6 Conclusions  
This publication presents an MR application that utilizes grayscale and color passthrough, developed to support therapeutic education for diabetic patients. A study was conducted to assess the effectiveness of the MR application in transmitting knowledge, as well as its usability and user satisfaction, comparing its two visualization modes with AR and traditional methods. The results show that patients' knowledge about short-term carbohydrate choices increased significantly across all four learning methods. However, the patients using the MR application using Meta Quest 3 achieved the highest post-learning outcomes. Additionally, those using the MR application using Meta Quest 3 provided significantly more positive feedback compared to those using the MR application using Meta Quest 2. This suggests that, given that the application itself is identical, the use of color passthrough had a positive impact on both learning and user experience. These findings highlight the growing potential of technology in educational contexts. In an increasingly digitalized society, interest in technology-mediated learning continues to grow, offering a valuable complement to traditional education and positioning it as an appealing and effective instructional approach.  
Regarding the MR application, a possible improvement would be to replace the virtual table with a real physical table and a real dish, while keeping only the food and additional information virtual.  
Finally, MR applications using headsets have great potential, as the activity can be performed anywhere and at any time. The user remains fully connected to reality at all times. In this publication, we have explored the potential of MR for therapeutic diabetes education by comparing it with other learning methods. However, our MR application could be used in other studies in the future.  
**Author Contributions:** Conceptualization, M.C. and M.-C.J.; data curation, M.C. and M.-C.J.; formal analysis, M.C. and M.-C.J.; funding acquisition, M.-C.J.; investigation, M.C., F.A. and M.-C.J.; methodology, M.C., F.A. and M.-C.J.; project administration, M.-C.J.; resources, M.-C.J.; software, M.C., F.A. and M.-C.J.; supervision, M.-C.J.; validation, M.C. and M.-C.J.; visualization, M.C., F.A. and M.-C.J.; writing--original draft, M.C., F.A. and M.-C.J.; writing--review and editing, M.C., F.A. and M.-C.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University of Cuenca (Ecuador).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank [PERSON], the Diabetes House (Ecuador), and all of the patients who participated in the study. We would like to thank the editor and reviewers for their valuable suggestions.  
The authors declare no conflicts of interest.
# Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques
## 2 Materials and Methods
### State Variables  
The state variables \(S\) are essential during the reinforcement learning process (7). One important property is that the state variables must be capable of generalization so that the policy obtained after the learning process can solve unseen scenarios. Therefore, absolute variables like the absolute distance traveled by the robot are avoided. A set of relative distances (\(d_{1},\ldots,d_{n}\)) with respect to the robot's position, the relative orientation to the destination (\(\theta_{dest}\)), and dynamic variables such as robot linear and angular velocities (\(v_{rob},w_{rob}\)) and accelerations (\(\dot{v}_{rob},\dot{w}_{rob}\)) are selected. The relative distances come from a lidar sensor, which covers 180 degrees of the robot's front view. To smoothly reduce the linear velocity before reaching the destination, the relative distance between the robot and the destination (\(d_{dest}\)) is also included.  
\[S=\left\{d_{1},\ldots,d_{n},v_{rob},w_{rob},\dot{v}_{rob},\dot{w}_{rob},\theta_ {dest},d_{dest}\right\} \tag{7}\]  
Figure 2 shows an image of the previously mentioned state variables:  
For the learning process to be stable, the state variables must fulfill the Markov property. The Markov property is formulated in (8):  
\[p(s_{t+1}|s_{t})=p(s_{t+1}|s_{1},s_{2},\ldots,s_{t}) \tag{8}\]  
According to (8), the future state \(s_{t+1}\) only depends on the current state \(s_{t}\). This means that all the information from the history (\(s_{1},s_{2},\ldots,s_{t-1}\)) is irrelevant for computing the future state \(s_{t+1}\). To make it possible for the state variables to fulfill the Markov property,  
Figure 2: State variables.  
they must include not only relative distances but also dynamic variables like the linear and angular velocities and accelerations. Therefore, they are included in the list of state variables in (7).  
Finally, it is necessary to make sure that all states variables can be measured with sensors. The relative distances come from a lidar sensor mounted on the top of the robot, covering 180 degrees of the front view. The linear and angular velocities and accelerations of the robot can be measured with encoders mounted on the driven wheels and IMU sensors. The distance and orientation to the destination pose can be calculated based on camera images. The destination pose can be recognized by using some special color that the camera recognizes. Based on that recognition and after proper calibration of the camera parameters, the distance to the destination pose and the relative orientation of the robot to the destination pose can be calculated.
## 5 Experimental Results and Analysis
### Given a Sinusoidal Signal (Experiment)  
In Figure 18, when the sinusoidal signal \(x=sin4\pi t\) is applied, the ADRC controller has a maximum absolute error of \(\mid\!E_{\max}\!\mid\!=1.11\) rad, RBF-ADRC has a maximum absolute error of \(\mid\!E_{\max}\!\mid\!=0.034\) rad, and FRBF-ADRC has a maximum absolute error of \(\mid\!E_{\max}\!\mid\!=0.0022\) rad. In conclusion, the error of the FRBF-ADRC controller is the lowest among the three.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Step Signal** & **ADRC** & **RBF-ADRC** & **FRBF-ADRC** \\ \hline
1 rad & 0.1124 s & 0.0369 s & 0.0112 s \\
1.5 rad & 0.1429 s & 0.0459 s & 0.0446 s \\
2 rad & 0.1545 s & 0.0547 s & 0.0526 s \\ \hline \end{tabular}
\end{table}
Table 4: Experimental comparison of ADRC, RBF-ADRC, and FRBF-ADRC controllers under different step signals.  
Figure 17: The controller position curves under different step signals in the experiment.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 1 Introduction
Amplitude-related distortions should be pre-processed and recorded only if they have the potential to affect any device in the facility. Mostly, the likelihood of PQ event occurrence is evaluated based on the sine waveform Root-Mean-Square (RMS) change across a sliding window. According to IEC 61000-4-30, this RMS value should be measured over one waveform cycle, starting at a fundamental zero-crossing, and refreshed each half cycle (V\({}_{\text{RMS(1/2)}}\)). Thus, this standard establishes voltage sags, swells, and interruptions as a variation in RMS below 90%, above 110%, and below 10% of the nominal value, respectively.
## Abstract  
Coral innate immunity plays a key role in regulating coral microbiomes. These microbiomes may contribute to corals' resistance or susceptibility to environmental stress, predation or disease. Changes in immunity over coral evolution may, therefore, have driven changes in microbiome structure, which may in turn contribute to modern cross-species differences in disease susceptibility. We used phylogenetic comparative methods to test for correlations between coral innate immune gene repertoire, microbiome structure, and disease susceptibility across genera representing >240 million years of coral evolution. This analysis drew on 1440 microbiome 16S rRNA samples from coral mucus, tissue and endolithic skeleton from our Global Coral Microbiome Project (GCMP); three long-term regional disease datasets; and innate immune gene repertoires annotated from all publicly available coral genomes. Across sequenced coral genomes, we found that gene family expansions of TIR-domain containing innate immune genes strongly predict reduced microbiome richness (PGLS R\({}^{2}\) = 0.40, \(p\) = 0.021), especially within corals' endolithic skeletons (PGLS R\({}^{2}\) = 0.791 \(p\) = 0.0003). Indeed, gene copy number expansions of IL-1R genes alone explained an astounding -83.5% of overall variance in microbiome richness across coral genera in our data (PGLS R\({}^{2}\) = 0.84, \(p\) = 5.13 \(\times\) 10\({}^{-15}\)). We further found that across 40 coral genera with both microbiome and disease data available, disease susceptibility does not significantly correlate with microbiome richness. Instead, ecological dominance of the most abundant microbial taxon increased overall disease susceptibility (PGLS R\({}^{2}\) = 0.27, \(p\) = 0.0006), especially in corals with more \(\gamma\)- than \(\alpha\)- proteobacteria (PGLS R\({}^{2}\) = 0.40, \(p\) = 0.0003). We are investigating whether a growth/defense tradeoff in symbiotic association with _Endozoicomonas_ explains this correlation between ecological dominance in the microbiome and disease susceptibility. Overall, these results demonstrate the utility of consistently collected cross-species datasets for exploring the interactions between coral immunity, microbiome structure, and disease susceptibility.  
coral microbiome; innate immunity; coral disease
## 4 Application Case  
The developed theoretical framework of the finite state method has been applied in the case of power transformer manufacturing, in order to demonstrate its applicability in real-life. The manufacture of power transformers is a complex production process involving numerous operations, yielding a variety of technical solutions, and conditioned on unpredictable customer requirements. Each power transformer is composed of its active and passive parts, and here, we focus on the manufacture of its active part, the essential one. It includes elements in constant contact with voltage and is usually composed of the core, windings, insulating material, and other ancillary components. The core is manufactured out of thin steel sheets cut to a dedicated size using a press-cutter and stacked as a combination of legs (vertical parts) and yokes (horizontal parts) at the core stacking machine. On the other hand, the windings are usually handmade of copper and insulated by several layers of paper or varnish, applying high-quality standards. Upon manufacture, the windings are transferred to a drying device for the improvement of their thermal stability [48]. Once the core and the windings are manufactured, the active part of the power transformer is assembled manually at the assembly work-station and is, upon secondary drying, closed-up within a transformer tank. Usually, the insulating materials as well as the transformer tank are delivered as finished products by external suppliers.  
The considered manufacture process is summarized in Figure 14 including a mathematical model of the production process. The reliabilities at the level of each work-station as well as the capacities of the associated buffers acquired using factory-floor measurements are presented in Table 3. The production rate, the work-in-process at each buffer, and the probabilities of blockage and starvation were determined using the finite state method, as outlined in Section 2.2. The obtained results are presented and compared with data provided by the manufacturer in Table 4. A quite satisfactory agreement between the results could be noted, especially in terms of the production rate, while some discrepancies regarding the work-in-process were noted due to a rather large amount of fabricated elements. Thus, the discrepancy between the calculated and measured production rate amounted to \(-4.3\%\) concerning the measured one, while discrepancies between the respective valuesof the work-in-process at each buffer ranged between \(-13.2\%\) (_WIP\({}_{2}\)_) and \(40.7\%\) (_WIP\({}_{6}\)_). However, in most cases, the discrepancy between the calculated and measured values of the work-in-process remained negligible. Additionally, in some cases, significant probabilities of blockage and starvation could be noted (e.g., \(BL_{10}\), \(BL_{7}\), \(BL_{8}\), \(BL_{3}\), \(BL_{12}\), \(BL_{1}\), \(BL_{4}\), \(BL_{5}\), \(BL_{6}\), \(BL_{2}\), \(ST_{11}\), \(ST_{10}\), \(ST_{16}\)), which indicates that this assembly system could be significantly improved in terms of bottleneck identification.
## 3 Results
In contrast, in diabetic patients (Group 2) characterized by already low BRS values in the supine position (\(4.07\pm 2.98\), see also Figure 5), its comparable relative reduction in the orthostatic position led to much lower absolute \(BRS\) values (\(2.73\pm 1.03\), see also Figure 6). According to these data, one could expect higher blood pressure variability in Group 2, as their autonomic nervous system is less sensitive to the changes in blood pressure, and their feedback response to blood pressure variations is weaker than in Group 1. Surprisingly, patients in Groups 1 and 2 demonstrated rather comparable blood pressure variability (SBP standard deviations were \(7.83\pm 4.13\) vs. \(5.58\pm 3.08\) mmHg in the supine and \(7.69\pm 4.71\) vs. \(6.15\pm 1.84\) in the orthostatic position, see also Figures 5 and 6, respectively). Figure 6: The values of the six studied mutual information metrics: (**a**) Sync, (**b**) TDS, (**c**) _TAU_, (**d**) _CE_, (**e**) _Coher_ and (**f**) BRS obtained during the orthostatic phase of the tilt test. The horizontal axis denotes group number (also denoted by color): 1 (blue)—orthostatic hypotension; 2 (red)—diabetes mellitus with autonomic neuropathy; 3 (green)—healthy volunteers (control group). Moreover, the synchronization coefficient Sync demonstrated significant negative correlations with the standard deviation of the systolic blood pressure during the orthostatic phase of the tilt test. This seems to be rather a universal phenomenon, as it is reproduced well not only qualitatively but also quantitatively in all studied groups ([PERSON]'s correlation coefficient \(\rho\approx-0.5\), \(p<0.05\)). The above  
Figure 7: The star-style diagrams representing the overall responses to the tilt test based on a series of studies mutual dynamics indicators averaged for the overall duration of the tilt test records: (**a**) for the orthostatic hypotension patients (group 1), (**b**) for the diabetes patients with autonomic neuropathy (group 2) and (**c**) for the healthy subjects (control group); also for the supine tilt test phase: (**d**) for the orthostatic hypotension patients (group 1), (**e**) for the diabetes patients with autonomic neuropathy (group 2) and (**f**) for the healthy subjects (control group); as well as for the orthostatic tilt test phase: (**g**) for the orthostatic hypotension patients (group 1), (**h**) for the diabetes patients with autonomic neuropathy (group 2) and (**i**) for the healthy subjects (control group). Numbers above the diagrams also indicate the patient/subject groups (also denoted by different colors). The filled areas represent the interquartile ranges (IQR), while small stars within the filled areas represent the medians. indicates that in addition to the established BRS, synchronization patterns between blood pressure and pulse intervals also appear important markers of an adequate cardiovascular response to the orthostatic stress.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Gap Filling Service
#### 4.2.1 Papoulis-Gerchberg Algorithm  
The P-G algorithm is a multi-gap filling algorithm, meaning it is capable of correcting more than one gap at the time. This makes the algorithm perform good in conditions with many gaps and few available data points between gaps. This is due to its ability to collect information about the signal across multiple gaps [32]. The P-G algorithm works under the assumption that the signal is a periodic stationary signal with a known bandwidth. The signal will, therefore, consists of \(\mathcal{F}\) frequency components, and everything outside the band is assumed to be noise.  
The true bandwidth is also unknown in the signal. The P-G algorithm is very depended on the bandwidth for a correct reconstruction. A modified version of the algorithm that estimates the bandwidth, by varying the frequency components \(\mathcal{F}\) and analyzing the mean square error on the known signal is therefore used [33]. This approach is fairly good at estimating the true value of \(\mathcal{F}\), but is time-consuming. The implementation of the algorithm used in this work can be found in [34].