esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mechanisms by Which Sprouted Grains Improve Insulin Sensitivity
### Prebiotic Effects of Sprouted Grains on Gut Health  
The gut microbiota is a key regulator of host metabolism, inflammation, and insulin sensitivity [78]. Sprouted grains exhibit prebiotic properties by reshaping microbial composition, enhancing intestinal barrier integrity, and promoting the production of SCFAs.  
Intervention with germinated brown and black rice increased the abundance of beneficial microbes such as _Megamonas_, _Murbaculaceae_, and _Alloprevotella_, while decreasing potentially pathogenic _Veillonella_[79]. Autoclaved GBR (cv. 'Suliing 18') further enhanced microbial diversity and selectively promoted the growth of _Bacteroides_, _Faecalibacterium_, _Dialister_, _Prevotella_, and _Bifidobacterium_, while suppressing pro-inflammatory taxa including _Escherichia-Shigella_[72]. GBR intake also modulated immune responses by increasing the regulatory T cell (Treg)/T helper 17 cell (Th17) ratio and lowering circulating levels of IL-6, LPS, and interleukin-8 (IL-8), all markers of systemic inflammation [68]. These changes were accompanied by a reduced _Firmicutes/Bacteroidetes_ ratio and improvements in gut barrier integrity [73].  
Additionally, germination stimulated colonic production of SCFAs, particularly isobutyric and valeric acids, which were inversely associated with IR and inflammatory cytokines such as TNF-\(\alpha\) and MCP-1 [21]. Compared to white or non-germinated rice, GBR feeding led to increased _Lactobacillus_ abundance and elevated SCFA levels in mice [19].  
Collectively, sprouted grains reshape the gut ecosystem and promote SCFA production, contributing to improved metabolic homeostasis. Beyond their anti-inflammatory and insulin-sensitizing effects, SCFAs also support lipid regulation by inhibiting hepatic cholesterol synthesis and activating AMPK signaling [80], underscoring the gut-liver axis as a key mediator of the metabolic benefits of sprouted grains.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 1 Introduction
This study firstly constructs muti-source observation links with satellite-to-ground, inter-satellite and satellite-based observation data to gain a suitable observation geometry condition and to weaken the correlation of the inter-epoch observation equations in the process of orbit determination. Then, an orbit determination method for the BDS-3 and LEO satellites is proposed based on the analysis of the correction models and strategies. Finally, the accuracy of orbit determination for the BDS-3 satellites and LEO satellites is evaluated, with the support of different observation data.
## 2 Materials and Methods
### Rear-Impact Loading Conditions and Injury Measurement
The Neck Injury Criterion (NIC) is calculated based on the relative acceleration difference between the head's center of gravity and the first thoracic vertebra (T1) and is associated with the S-shaped deformation of the neck. N\({}_{\text{ij}}\), N\({}_{\text{km}}\), and NIC have been incorporated into standards. For instance, N\({}_{\text{km}}\) and NIC are included in the Euro-NCAP whiplash test regulations [23], and N\({}_{\text{ij}}\) is included in FMVSS 208 [36]. In addition to the above injury indices, the neck ligament injury is a type of complicated and important injury. This type of neck injury can be calibrated via the THUMS 1 but not via the anthropomorphic test device (ATD) model. The THUMS has a complete neck ligament structure, and the stress and strain statuses can be evaluated at the tissue level, which intuitively reflects the relationship between head and neck injuries and the flexion and stretching of the neck. During our study, the maximum principal strains of the anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), capsular ligament (CL), ligaments flava (LF), and interspinous ligaments (ISLs) were also recorded in the THUMS. After the simulation, the neck ligament injury was evaluated according to corresponding injury thresholds measured by [PERSON] et al. [37] via quasi-static tensile tests (10 mm/s) of cadaveric cervical ligaments. Although the study did not consider the effect of the loading rate, which may be important for impact scenarios, the study utilized a large sample size, up to 25 human cadavers for the in situ biomechanical properties, and systematically characterized the ligament length and cross-section in terms of a heavy-duty cryomicrotome device. Those threshold values have been widely utilized to evaluate ligament injury in the safety community. For instance, [PERSON] et al. [38] cited these thresholds when the THUMS was used to study the protective effect of active rotating seats on occupants. [PERSON] et al. [13] cited these thresholds when the THUMS was used to study occupant injuries in different postures during rear-end collisions. Meanwhile, the mechanical properties of human neck ligaments obtained in the study of [PERSON] et al. [37] have been widely used in the development of finite element models of occupant heads and necks. [PERSON] et al. [39] referenced the ligament mechanical properties from the study of [PERSON] et al. [37] during the development of muscle activation of the head and neck in the Global Human Body Models Consortium (GHBMC) human body models. Figure 6: Acceleration pulses for 16 km/h, 40 km/h, and 56 km/h rear impact.
# SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
###### Abstract  
Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph are extensively adopted. However, existing LBP-based methods that do not utilize node attributes often assume a global or predefined homophily strength of edges in the social graph, while different user's discrimination and preferences may vary, resulting in local homogeneity differences. Another issue is that the existing message-passing paradigm uses the same edge potential when propagating belief to both sides of a directed edge, which does not agree with the trust interaction in one-way social relationships. To bridge these gaps, we present SybilHP, a Sybil-detection method optimized for directed social networks with adaptive homophily prediction. Specifically, we incorporate an iteratively updated edge homophily estimation into the belief propagation to better adapt to the personal preferences of real-world social network users. Moreover, we endow message passing on edges with directionality by a direction-sensitive potential function design. As a result, SybilHP can better capture the local homophily and direction pattern in real-world social networks. Experiments show that SybilHP works with high detection accuracy on synthesized and real-world social graphs. Compared with various state-of-the-art graph-based methods on a large-scale Twitter dataset, SybilHP substantially outperforms existing methods.  
social network; sybil detection; semi-supervised learning; belief propagation
# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 8 Conclusions  
In this paper, to classify black ice, hyperspectral imaging data were used for the first time with the help of a 2D-3D CNN model that provided the highest overall accuracy of 97% for objects at a 10 m distance from the camera. The data dimension was reduced with the help of Principal Component Analysis, which helped the model to work faster. Higher overall accuracy and a higher kappa score of 94% were achieved at the 10 m distance. The same process was done for images taken from distances of 20 m and 30 m, and the accuracy rate was also quite high. A comparison was made with color images of black ice and the state-of-the-art model for hyperspectral image classification. The difference between the results was higher than expected and might be due to the different dimensions of features. On the other hand, our dataset was collected in the daylight of winter, but the daylight (the brightness) does not stay consistent for a long time. Therefore, our data do have lighting differences in every image, which makes the model more robust. For the application of moving cars and other real-life cases, the dataset can be collected accordingly. The cameras can be mounted in front of the car in a position where they can have a proper view of the road. According to our experiments, the model is detecting the ice quite well until 30 m. Owing to the high-dimensional and abundant information about the material from the image, using hyperspectral imaging showed great results. However, due to dimension reduction, some information might have been missing in this case. For future aspects of this research, efforts will be made toward finding ways to protect important information from bands and to calculate the distance from the camera to any other object. We keep the further experiment of black ice detection at night for our future scope.  
The contributions were distributed between authors as follows: [PERSON] wrote the text of the manuscript, programmed the method, and implemented the idea. [PERSON] provided the database and operational scenario, performed the in-depth discussion of the related literature, and confirmed the accuracy experiments that are exclusive to this paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (IRIS RS-2023-00240109). This work was supported by the 2023 Yeungnam University Research Grants.  
Not applicable.  
The dataset was collected with our own lab instruments and camera and with the \"Info Works\" team. Therefore, there is no need for copyright since it is our own dataset. Data are available upon request from the authors. The dataset is not available online.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Apparatus  
The samples underwent characterization using various techniques to analyze their morphology, crystal structure, and optical properties. The morphologies of the samples were examined using a high-resolution scanning electron microscope (SEM), specifically a Hitachi S-4800 model (Hitachi High-Tech Corporation, Tokyo, Japan). Crystal structures analyses were performed using a D8 Advance Eco X-ray diffractometer (Bruker Corporation, Karlsruhe, Germany, Germany) employing the Cu K\(\alpha\) with the wavelength of 0.154 nm, the potential radiation of 40 kV, and the amperage of 25 mA. An Agilent Cary 60 UV-VIS Spectrophotometer (Agilent Technologies, Santa Clara, CA, USA) was used to investigate the optical characteristics of the sample through UV-VIS absorption spectroscopy. The pH of the solutions was precisely adjusted using a Hi98115 Hanna pH meter (Hanna Instrument, Smithfield, Rhode Island, USA) for the synthesis process.  
All PEC experiments were performed on a DY2100 mini potentiostat (Digi-Ivy, Inc., Austin, TX, USA) with a rhodium-plated counter-electrode (CE, model 7-metal foam, Redoxme AB, Norrkoping City, Sweden) and (_Ag/AgCl_ reference electrode (RE) (Redoxme AB, Norrkoping City, Sweden). A light-emitting diode (LED) with a light intensity of 20 mW\(\cdot\)cm\({}^{-2}\) was used as an irradiation source with a wavelength of 395 nm. Electrochemical impedance spectra (EIS) were recorded using a PGSTAT101 electrochemical workstation (Metrohm Autolab, Utrecht, The Netherlands) in the frequency range spanning from 0.01 Hz to 100 kHz.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.3 Coverage Efficiency  
Coverage efficiency or coverage maximization is one of the key issues of sensor network deployment which is affected by other desirable objectives which may or may not conflict. Various multi-objective formulations have been proposed in the literature to maximize the coverage while considering other desirable objectives at the same time. For example in [55], the authors have proposed a hybrid multi-objective optimization technique for the design of wireless sensor networks to maximize the coverage and to minimize the energy consumption. Maximization of coverage, minimization of active sensor nodes and energy consumption have been simultaneously optimized in [56] by suggesting a multi-objective optimization technique. Simultaneous optimization of the coverage efficiency and energy consumption is one of the key design problems. In [57], the authors have proposed a solution inspired from the nature called, multi-objective evolutionary algorithm based on decomposition to simultaneously optimize the coverage control and energy consumption. A hybrid routing protocol design has been proposed in [58] by using a multi-objective optimization approach to improve the coverage efficiency and to reduce the energy consumption.
# A Novel Fiber Optic Sensor for Microparticle Velocity Measurement Using Multicore Fiber
## 4 Discussion  
Compared with our previous work in which a fiber array probe was used to measure particle velocity in one-dimensional space, the MCF probe in this work not only achieved the velocity measurement in 2D space, but also increased the measurement accuracy. In order to prove that, a brief comparison was conducted between the MCF probe and the fiber array probe by measuring the velocity of a same particle. The fiber array probe was constructed from eight discrete fibers (with 62.5 \(\upmu\)m core diameters and 125 \(\upmu\)m cladding diameters), which were manually arranged in a plastic plate having a v-groove and fixed with adhesives. Table 2 shows the comparison result of the velocity measurement. We can see that the measurement accuracy of the MCF probe was generally better than that of the fiber array probe. That is because according to the principle of SFV, the interval of the spatial filter \(p_{j}\) affects frequencies of signals received by photo detectors, so the inhomogeneous interval leads to inaccuracy in the determination of the velocity components. For both the MCF probe and fiber array probe, the more consistent the distance (\(p_{j}\)) between cores, the better the measurement will be. As core distances are set during the fiber preform fabrication, they can be checked precisely, so the uniformity of core arrangement is better than the fiber array probe, which is manually assembled. Compared with the method used by [PERSON] et al. [10], the probe only measures the one-dimensional velocity of the particle, and each optical fiber used to receive the reflected light signal must be connected to a photodetector; that is, three photodetectors must be used for each measurement direction detector. In our method, the probe can measure the two-dimensional velocity of the particle, and only one photodetector is needed for each measurement direction. Compared with the work using a probe composed of thirty-seven ordinary optical fibers to measure the two-dimensional velocity of particles [11], we used only a single MCF instead of thirty-seven ordinary optical fibers as a probe. Moreover, only three PDs were used in our method while seven PDs were used in the study in [11]. The total cost of the sensor is thus much lower.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Actual Velocity (mm/s)**} & **Velocity Measured by MCF (mm/s)** & **Relative Error Measured by MCF (\%)** & **Velocity Measured by Fiber Array (\%)** & **Relative Error Measured by Fiber Array (\%)** \\ \hline
13.07 & 13.25 & 1.38 & 12.58 & 3.75 \\ \hline
15.44 & 15.62 & 1.17 & 15.27 & 1.10 \\ \hline
17.82 & 17.84 & 0.11 & 17.16 & 3.70 \\ \hline
20.2 & 20.4 & 0.99 & 19.76 & 2.18 \\ \hline
22.57 & 22.93 & 1.60 & 23.09 & 2.30 \\ \hline
23.73 & 23.75 & 0.08 & 24.63 & 3.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the particle velocity measurement between the MCF probe and the fiber array probe.  
Figure 9: Seven-core end face.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 2 Results and Discussion
### Identification of Aroma Compounds
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Codes**} & \multirow{2}{*}{**RI 1** **} & \multirow{2}{*}{**Volatile Compounds**} & \multirow{2}{*}{**Chemical Formulas**} & \multicolumn{2}{c}{**Concentration for _Liteschukchuk_ (\%) 2** **} & \multicolumn{2}{c}{**Concentration for _Piber soluble_ (\%) **} & \multicolumn{2}{c}{**Concentration for _Piber soluble_ (\%) **} \\ \cline{3-10}  & & & & **HD 3** ** & **SDE 2** ** & **SPME 3** ** & **HD 3** ** & **SDE 3** [FOOTNOTE:3]Footnote 3: footnot  
For ketones, 6-methyl-5-hepten-2-one (B1) and camphor (B2) were both detected in all the cubeb berries samples. As irregular terpene, 6-methyl-5-hepten-2-one is probably derivative of carotenoids produced by enzymatic action [26]. Camphor (17.59-20.6%) was detected as the major ketones in all the extracts of _P. cubeba_. The contents of ketones in _P. cubeba_ (17.97-20.99%) with different extraction methods were significantly higher than in the _L. cubeba_ (1.49-3.24%) by one-way ANOVA (Figure 2). Alcohols were also present with a high proportion in cubeb fruit, and the contents of alcohols in _P. cubeba_ (16.68-28.97%) with different extraction methods were significantly higher than in the _L. cubeba_ (4.16-10.56%) by a One-way ANOVA (Figure 2). Terpinen-4-ol (C7) could be detected in all the extracts of _L. cubeba_ and _P. cubeba_. Linalool (C2, 14.89-21.31%) was the most abundant volatile and could be found in all the extracts of _P. cubeba_. Citral was present as the most abundant aldehyde compound in _L. cubeba_ extracts. This result is in accordance with the study by [PERSON] et al. [20], who reported that citral was one of the main constituents in the fruit oil of _L. cubeba_ extracted by HD. The contents of 3,7-dimethyl-2,6-ocatdional in _L. cubeba_ was higher than _P. cubeba_. Only 10 esters were identified in the current work, most were relatively high-boiling esters and with lower contents than other chemical families in cubeba berries. Only two kinds of acids, decanoic acid and dodecanoic acid, were mainly extracted by HD and SDE with longer extraction time and higher temperature. The lack of acids with low volatility in SPME extracts may be caused by the low extraction temperature during the extraction process. Most acids may exist as esters form or have been changed to aldehydes, alcohols, or other secondary metabolites [13]. The contents of acids in _L. cubeba_ (2.39-48.55%) with different extraction methods were significantly higher than in the _P. cubeba_ (0-18.69%) by one-way ANOVA (Figure 2).
## 1 Introduction
It can increase the integration and reliability of the monitoring system and can be deployed immediately after a sudden landslide. In the FDMS, ad-hoc network technology is used to improve the robustness of the monitoring network and the measuring equipment is special designed to make it able to be installed quickly. The FDMS was applied in the Baige landslide, Tibet. In the FDMS, Beidou receivers, which are based on China's Beidou Navigation Technology, and crack meters were used to monitor the displacements of the landslide. The KM-FFT-SVM model was trained by the displacement data, and the landslide precursor features were extracted successfully. Then, we used the testing data to verify the model. The results demonstrated that the KM-FFT-SVM model can make a real-time precursor predication with high accuracy and good practicability.
## 5 Case Study and Analysis
### Comparative Analysis
#### 5.3.2 Superiority of the Proposed Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Expeded Values** & **Ranking Order** \\ \hline \multirow{6}{*}{_VIKOR_[45]\(\omega_{1}=\alpha_{2}=\omega_{3}=\omega_{4}=\omega_{5}=\omega_{6}=0.1667\)} & \(S(\alpha_{1})=0.4986,S(\alpha_{2})=0.5748,S(\alpha_{3})=0.5913,S(\alpha_{4})=0.4641\). & \\  & \(S(\alpha_{3})=0.5913,S(\alpha_{4})=0.4641\). & \\  & \(S(\alpha_{1})=0.1658,R(\alpha_{2})=0.1407\), & \(\alpha_{1},\alpha_{2}\) are the compromise \\  & \(S(\alpha_{3})=0.1656,R(\alpha_{4})=0.1667\). & solutions \\  & \(S(\alpha_{1})=0.7355,Q(\alpha_{2})=0.2900\), & \\  & \(S(\alpha_{3})=0.9727,Q(\alpha_{4})=0.6667\). & \\  & \(S(\alpha_{1})=0.5350,C(\alpha_{2})=0.4605\), & \\  & \(S(\alpha_{3})=0.3035,C(\alpha_{4})=0.5670\). & \(\alpha_{4}>\alpha_{1}>\alpha_{2}>\alpha_{3}\) \\ \multicolumn{2}{c}{The proposed method (obtained weights by GBWM)} & \(\varphi(\alpha_{1})=0.0919,\varphi(\alpha_{2})=0.0827\), & \\  & \(S(\alpha_{3})=0.1468,\varphi(\alpha_{4})=-0.3214\). & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison results with assigning the criteria weights. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Ranking Values** & **Ranking Order** \\ \hline \multirow{6}{*}{_  
weight to each criterion directly in the TOPSIS method, we can acquire another weakness of the TOPSIS. That is, it does not consider uncertainty in weightings. Nevertheless, for deriving the reasonable weightings, our method attaches importance to DM's weights and relative preference degree for each criterion by utilizing the GBWM method. According to the above analysis, we can summarize that the improved GBWM-PROMETHEE II method is more comprehensive as well as has more powerful aggregation capabilities than some other existing methods.
# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 3 Approach  
### Problem Definition  
The approach presented in this work aims at enabling PdM in a hot rolling milling machine. The model of the rolling milling machine is presented in Figure 1. The rolling mill machine is composed of two rolling cylinders which rotate with the use of torque motors. The lower rolling cylinder has a fixed position and only the upper cylinder can move linearly (vertical), as shown in Figure 1. Each cylinder contains some coating segments. The segments have a wear-resistant coating that degrades over time. The segments' coating degrades due to the forces, friction, and high temperatures during the process. At the end of their useful life, the segments are replaced with exactly similar segments in terms of geometry and material. However, in the problem under study, since they are replaced with identical spare parts, the dimensions of the segments are not considered as parameters of the problem and do not have a contribution to the RUL estimation method.  
In many cases, the coating thickness cannot be measured during production, so an indirect measurement must be applied to indicate their wear. For this reason, temperature and hydraulic force  
Figure 1: Industrial rolling mill with three segments at their end of operational life (red color) on cylinder B and three new (blue color). Cylinder A can move linearly in the vertical direction.  
sensors are placed on the rolling mill, and the measurements are stored in a database. The analysis of the acquired data for RUL estimation is the main target of this study.  
In particular, this study discusses on an energy-based RUL estimation methodology based on the assumption that each product that is loaded on the rolling mill machine charges the working tools with an excitation dynamic energy, resulting to machine segments wear. As segment usage is quite short-term in this use case (approximately 15 days), corrosion phenomena due to oil supply in the rolling cylinders do not affect segment wear significantly. Thus, tool fatigue is mainly dependent on each product's energy contribution to degradation, resulting in the product's contact with the segments. After the product is processed, the tool's remaining embodied energy capacity decreases.