esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multicolumn{2}{c}{**Dental Arch (N = 200)**} & \multicolumn{6}{c}{**MD/BL Tooth Ratio**} \\ \cline{3-13}
**(N = 100)** & \multicolumn{3}{c}{**16**} & \multicolumn{3}{c}{**12**} & \multicolumn{3}{c}{**11**} & \multicolumn{3}{c}{**21**} & \multicolumn{3}{c}{**22**} & \multicolumn{3}{c}{**26**} \\ \hline \multirow{7}{*}{
\begin{tabular}{} \end{tabular} } & **F** & **M** & **F** & **M** & **F** & **M** & **F** & **M** & **F** & **M** \\ \cline{2-13}  & NCA Mean/SD & 0.93/0.03 & 0.88/0.06 & 1.16/0.29 & 1.53/0.02 & 1.32/0.22 & 1.40/0.10 & 1.27/0.19 & 1.35/0.08 & 1.13/0.15 & 1.66/0.47 & 0.95/0.06 & 0.90/0.05 \\ \cline{2-13}  & MCA Mean/SD & 0.95/0.08 & 0.92/0.03 & 1.42/0.24 & 1.87/0.71 & 1.47/0.27 & 1.55/0.21 & 1.44/0.27 & 1.52/0.20 & 1.43/0.38 & 1.75/0.69 & 0.93/0.04 & 0.92/0.06 \\ \cline{2-13}  & SCA Mean/SD & 0.95/0.03 & 0.93/0.03 & 1.59/0.67 & 1.30/0.15 & 1.68/0.46 & 1.35/0.14 & 1.68/0.39 & 1.35/0.09 & 1.43/0.37 & 1.30/0.16 & 0.94/0.03 & 0.91/0.04 \\ \cline{2-13}  & SpA Mean/SD & 0.93/0.05 & 0.97/0.06 & 1.21/0.11 & 1.03/0.06 & 1.32/0.12 & 1.52/0.53 & 1.32/0.11 & 1.43/0.33 & 1.20/0.14 & 1.08/0.17 & 0.90/0.05 & 0.97/0.06 \\ \cline{2-13}  & \(p\) value & 0.617 & 0.006 * & 0.017 * & 0.003 * & 0.002 * & 0.550 * & 0.001 * & 0.414 & 0.010 * & 0.031 * & 0.062 & 0.041 * \\ \hline \hline \multicolumn{2}{c}{} & **46** & \multicolumn{3}{c}{**42**} & \multicolumn{3}{c}{**41**} & \multicolumn{3}{c}{**31**} & \multicolumn{3}{c}{**32**} & \multicolumn{3}{c}{**36**} \\ \hline \hline \multicolumn{2}{c}{} & **F** & **M** & **F** & **M** & **F** & **M** & **F** & **M** & **F** & **M** & **F** & **M** \\ \hline \hline \multicolumn{2}{c}{} & **NCA Mean/SD & 0.98/0.06 & 1.00/0.04 & 1.18/0.25 & 1.31/0.20 & 1.02/0.15 & 1.02/0.08 & 1.00/0.09 & 1.01/0.05 & 1.19/0.28 & 1.57/0.75 & 0.96/0.04 & 1.06/0.08 \\ \cline{2-13}  & MCA Mean/SD & 0.98/0.06 & 0.98/0.0 & 1.41/0.66 & 1.61/0 & 1.03/0.18 & 1.08/0 & 1.00/0.11 & 1.18/0 & 1.45/0.66 & 1.53/0 & 0.98/0.06 & 1.02/0 \\ \cline{2-13}  & SCA Mean/SD & 0.96/0.03 & 0.98/0.0 & 1.17/0.18 & 1.22/0.24 & 1.00/0.17 & 0.96/0.01 & 0.94/0.12 & 0.91/0.06 & 1.18/0.18 & 1.31/0.12 & 0.97/0.02 & 0.96/0.02 \\ \cline{2-13}  & SpA Mean/SD & 0.99/0.05 & 0.95/0.07 & 1.03/0.10 & 1.26/0.36 & 0.98/0.08 & 0.96/0.12 & 0.94/0.11 & 0.97/0.14 & 1.02/0.12 & 1.19/0.21 & 1.01/0.06 & 0.97/0.08 \\ \cline{2-13}  & \(p\) value & 0.498 & 0.169 & 0.033 * & 0.421 & 0.737 & 0.235 & 0.186 & 0.023 * & 0.020 * & 0.292 & 0.027 * & 0.024 * \\ \hline \multicolumn{2}{c}{} & \multicolumn{6}{c}{* \(p\) < 0.05 statistically significant.} \\ \end{tabular}
\end{table} 
Table 9: Comparison of crown proportions between normal, spaced, and crowded arches for female and male patients (statistical significance according to ANOVA analysis).
## 1 Introduction
While surface modification techniques have improved proppant transport by reducing density and enhancing sphericity and hydrophobicity [5], the stability of these coatings under coupled thermal, hydraulic, and chemical environments requires further investigation. These challenges necessitate the use of proppants with enhanced mechanical resilience, chemical inertness, and high thermal tolerance. Thermal cycling is a critical factor in EGS environments, driven by repeated injection-production cycles and operational shut-ins. Unlike the relatively stable thermal regimes in conventional oil and gas reservoirs, EGS environments are characterized by abrupt and repeated temperature fluctuations that can significantly influence material performance. These cycles induce micro-cracking [25], thermal fatigue, and interface delamination, especially in composite proppants (e.g., resin- or polymer-coated materials) where mismatches in thermal expansion coefficients between the core and coating exacerbate degradation. Over time, with the inclusion of the coupled thermo-hydro-mechanical-chemical (THMC) processes inherent in geothermal environments, such degradation compromises proppant integrity, potentially leading to loss of conductivity and reduced stimulation effectiveness. Figure 2 expands on the fracture network shown in Figure 1 by illustrating the coupled THMC processes that influence proppant stability within EGS environments. It highlights how thermal cycling, driven by alternating injection and production stages in geothermal operations, influences proppant behavior within the stimulated fracture system. Temperature plays a critical role in governing proppant-fluid-rock interactions, driving thermal stress, accelerating chemical reaction rates, and altering the physical and potentially mechanical properties of both the proppant materials and the surrounding geological formation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reservoir** & \begin{tabular}{c} **Permeability** \\ **[mD]** \\ \end{tabular} & **Temp. [\({}^{\circ}\)C]** & **MHS [psi/ft.]** & **Reservoir Rock Type** & **References** \\ \hline \begin{tabular}{c} Lower Eagle \\ Ford Group \\ \end{tabular} & 0.001 & 125–165 & 0.35–0.85 & \begin{tabular}{c} Carbonate-rich \\ lithofacies \\ \end{tabular} & [14; 15; 16] \\ \hline \begin{tabular}{c} Permian Basin \\ \end{tabular} & 0.01–1000 & 27–102 & 0.5–0.79 & \begin{tabular}{c} Dolomitized \\ marine carbonate \\ \end{tabular} & [17; 18; 19; 20] \\ \hline \begin{tabular}{c} Utah FORGE \\ \end{tabular} & 0.021 & 175–219 & 0.61–0.74 & \begin{tabular}{c} Granitoid and \\ metamorphic \\ \end{tabular} & [21; 22; 23] \\ \hline \
\begin{tabular}{c} Blue Mountain \\ geothermal field \\ \end{tabular} & Fault-controlled & 177–204 & N/A 1 &
\begin{tabular}{c} Metasedimentary and \\ granite-intrusive \\ \end{tabular} & [24] \\ \hline \hline \end{tabular}  
* Data Not Available.  
\end{table} 
Table 1: Comparison of subsurface conditions across oil and gas versus EGS reservoirs, highlighting parameters critical to proppant stability (temperature, minimum horizontal stress (MHS), and lithology).
## 2 Related Works  
Several studies solved the camera movement problem using the spatiotemporal domain of the image sequences [2; 3; 4; 5; 6]. [PERSON] et al. proposed a general framework for multiple-object tracking from a mobile vision platform [7], whereas [PERSON] studied a robust moving object detection method that uses a Markov random field (MRF) model [8]. [PERSON] et al. proposed a method to use tensor-based image processing for detecting moving objects [9]. [PERSON] et al. [10] suggested a moving object detection method using dense optical flow generated by moving thermal camera, whereby rotational movement is compensated with the IMU sensor. [PERSON] et al. [11] proposed a method estimating the global motion (egomotion) with grid-based key points by optical flow. An expectation maximization framework was used by [PERSON] et al. to detect the foreground region [12]. Some researchers studied image template matching to find the vehicles or pedestrians in the image [13; 14; 15; 16; 17]. [PERSON] et al. proposed a vehicle detection method using image stripe features [18], and an image registration method was used by [PERSON] et al. to detect a vehicle in video sequences [19]. [PERSON] and [PERSON] [20] compared various moving object detection methods with a focus on the case of a moving camera. Some studies utilized training techniques to detect objects from video [21; 22; 23]. [PERSON] et al. used a structural feature to construct a shape model to detect objects [24]. Some researches proposed a moving object detection method using a Harris interest point detector and optical flow [25; 26]. [PERSON] et al. also proposed a method using an epipolar line to classify the feature points of a moving object [27]. However, those methods used features that assumed that the motion of a static background is dominant in the image sequences and that the motion of the camera must be trivial, which is not appropriate for real on-road vehicle situations. Furthermore, the estimated parameters in feature-based methods are not very accurate since the feature points selected for use in parameter estimations are from not only the background but are also from moving object regions. The feature points selected from the moving objects might thus be outliers for the parameter estimation.  
The objective of this paper is to develop a new moving object detection method to overcome the shortfalls of the previous studies. Our approach is to use an inertial measurement unit (IMU) sensor to compensate for the movement of the camera and eliminate the outliers in the selected feature points. Figure 1 shows the overview of the proposed moving object detection method.  
The next sections of this paper are organized as follows. Section 3 explains the proposed approach to the background classification, whereas Section 4 describes the details of the overall procedure of the proposed method. Section 5 shows the experiments and results. Finally, this paper is summarized in Section 6.
## 5 Performance and Complexity Results  
Figure 5 shows a comparison in terms of estimation parameters between the algorithm proposed in [22; 23] and AdViSED. Another comparison in terms of computational complexity is provided in Figure 6. The latter is evaluated as normalized execution time when running the algorithms via SW on an Intel Core i3-4170 CPU, with Intel HD Graphics 4400 GPU, 8 GB DDR3 RAM, equipped with Windows 10 Pro x64 operating system. Results in Figures 5 and 6 represent the average of the results obtained for the whole test videos.  
Tables 3 and 4 compare the proposed smoke detection technique to the state of the art [20; 21; 36; 37; 38; 39; 40; 41; 36; 39; 41; 42; 43; 44] using the set of test videos, which is the one showed in Figure 8. The results in Table 3 show that AdViSED has improved recognition capabilities in terms of correctly detected frames and has no false alarms. Table 4 shows instead a comparison with [PERSON]'s [20], [PERSON]'s [42], and [PERSON]'s [36; 40; 41] methods in terms of delay (calculated in number of frames) to reveal the presence of smoke/fire. Table 4 shows that AdViSED is faster than the other video-based algorithms reported in the table, except for video n.5 due to the fence in front of the smoke. Even in the worst case of Table 4, an early-alarm is generated in 130 frames, i.e., about 4 s at 30 fps, x15 times faster than EN50155-standardized techniques that react in 60 s.
## 3 Results and Discussion
### Volatile Compounds
#### 3.1.2 Ketones
Acetoin (3-hydroxy-2-butanone) is a common flavor constituent in many fermented dairy products that is readily converted from diacetyl [6]. It has a mild creamy, butter-like flavor, similar to that of diacetyl but considerably weaker. Acetoin is readily formed from diacetyl by the enzyme diacetyl reductase. Typical acetoin concentrations in yogurt range from 1.2 to 28 mg kg\({}^{-1}\)[28]. In the present work, significantly higher concentrations of acetoin were recorded. Acetone (2-propanone) originates either from milk or from the yogurt bacterial cultures and is of minor importance to flavor contribution in dairy products [29]. It has a sweet fruity aroma and contributes positively to the flavor of yogurt. The typical acetone content in yogurt varies from 0.3 to 4 mg kg\({}^{-1}\)[27; 28]. In the present study, significantly higher concentrations of acetone were recorded. In general, the presence of Bifidobacteria has not been reported to affect the production of ketones by the microorganisms of the starter culture, whereas, on the contrary, the probiotic microorganism _Lactobacillus acidophilus_ affects the production of ketones [27]. This can be due to the increased rate of their citrate metabolism [30]. This was validated from our findings, since the cow dessert yogurts that were fortified with Bifidobacteria showed no increase in their flavor, compared to goat dessert yogurts that contained _Lactobacillus acidophilus_ and exhibited an improvement in flavor (Section 3.2). [PERSON] et al. [31] reported concentrations of 14.1 to 17.3 mg kg\({}^{-1}\) for acetaldehyde; 1.6 to 2.0 mg kg\({}^{-1}\) for diacetyl; 1.7 to 2.2 mg kg\({}^{-1}\) for acetoin and 0.66 to 0.75 mg kg\({}^{-1}\) for acetone in Bulgarian yogurts. 2-pentanone, 2-heptanone and 2-nonanone have also been recorded in the flavor profile of yogurts [6]. [PERSON] et al., [32; 33] determined the key aroma components in Switzerland yogurts and identified six volatiles (acetaldehyde, dimethylsulphide, diacetyl, 2,3-pentanedione, l-limonene, and undecanal) as having a high impact on yogurt flavor. These researchers noted that the obtained results should be interpreted with caution because the main ingredients (fat, proteins, and carbohydrates) in yogurt can significantly reduce the actual release of volatiles. Other carbonyl compounds including 2,3-pentanedione were found to contribute to the aroma of yogurt [32]. Specifically, 2,3-pentanedione is an impact flavor compound of yoghurt [34] and may be formed from a-aceto-a-hydroxyturate, an intermediate of isoleucine metabolism [33] 2,3-pentanedione was also determined in the flavor profile of all yogurt samples in the present study.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 2 Materials
### Data Source  
The Landsat images utilized in this study were sourced from the United States Geological Survey (USGS) Resource Probe dataset [35], featuring a spatial resolution of 30 m. Images with less than 10% cloud cover and encompassing the three study areas were selected. Radiometric calibration and atmospheric corrections were conducted using the FLAASH module of the ENVI 5.3 software package, eliminating atmospheric effects and converting DN values to surface reflectance. Subsequently, the study areas were delineated from these OLI images based on regional boundaries. Additionally, available high-quality images with a cloud cover of less than 10% for each month were obtained from Google Earth Engine (GEE) to cover the entire Inner Mongolia region from March to October. It is worth noting that no images were obtained for January, February, November, and December due to the lack of available images. High-resolution satellite data (3 m) from the  
Figure 1: The study area and BC fraction map in different coalfields (The legends to the sub-figures on the right indicate the range of the BC fraction).
###### Abstract  
In mobile ad-hoc networks (MANETs), nodes are mobility naturally which renders a great challenge in topology controls. Connected dominating set (CDS) has been considered as an efficient solution to solve such a problem by constructing a virtual backbone network to achieve the scalability and efficiency of wireless networks. In this paper, we propose an efficient connected dominating set clustering based routing protocol with dynamic channel selection (CRD) in multi-channel cognitive radio MANETs to obtain high packet delivery ratio (PDR), low control overheads, low delay and low energy consumption when node is moving in high speed. Moreover, CRD protocol can apply for Vehicular Ad Hoc Network, IoT and 5G. The proposed CRD protocol includes clustering and routing phases under multi-channel cognitive radio approach. In the clustering phase, we propose CDS selection, CDS size reduction and CDS based clustering algorithms to formulate clusters and provide a set of intermediate nodes which is latter used as a route searching space for the routing phase. In the routing phase, a sending channel based focus region selection (CFS) algorithm is adopted dynamically to establish an efficient route from a set of intermediate nodes that avoids the affected region of a primary user. The CRD routing protocol is then implemented in environmental conditions close to reality (OMNET++ platform) to demonstrate its superiority over the conventional AODV protocol in terms of PDR, control overhead, delay and energy consumption.  
c pacs: 2019
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 5 Conclusions  
The design and architecture of an efficient implementation for the 2D DCT/IDCT computation using the B.G. [PERSON] algorithm was proposed in this research article. Incorporating the concept of pipelining, error-tolerant adders, fixed-point multipliers, shifters, and the row-column decomposition technique improved the computational efficiency and reduced the circuit complexity. The synthesis was performed with gpdk-45 nm and gpdk-90 nm standard semi-custom ASIC design flow using the Genus tool of Cadence. The synthesis results performed using 90 nm technology libraries indicated that the proposed implementation outperforms the existing architecture in [25] in terms of power, area, and PDP by 31.01%, 12.17%, and 21.11%, respectively. In comparison to the design in [25], there was a reduction in power, area, and PDP of 28.21%, 5.95%, and 19.62% when the synthesis was carried out using gpdk-45 nm technology libraries. The PSNR of the proposed design was better than the PSNR of the architectures specified in [9; 25; 26] by 14.61%, 5.85%, and 4.55%, respectively. A significant reduction in power, delay, and an improved PSNR was observed in the synthesis results. Future work will involve replacing the standard libraries with the optimized digital libraries from TSMC and realizing the design up to chip fabrication. The design can be extended further to make it suitable for high real-time performance required for video compression coding.  
Conceptualization, T.M. and S.G.N.; methodology, T.M.; software, T.M.; validation, T.M., S.G.N. and V.K.K.; formal analysis, T.M.; investigation, T.M. and S.G.N.; resources, T.M. and S.G.N.; writing--original draft preparation, T.M.; writing--review and editing, V.K.K., D.N. and H.S.M.; visualization, T.M.; supervision, S.G.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 11: Layout of the proposed design.  
**Acknowledgments:** The authors express their appreciation to Manipal Academy of Higher Education for offering them the TMA Pai scholarship necessary to complete this research. The authors would also like to express their gratitude to the Department of Electronics and Communication Engineering at Manipal Institute of Technology for providing the necessary laboratory environment and resources. The authors also thank [PERSON], MAHE, Manipal, for his valuable support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Firmness, Soluble Solid Content (SSC), pH, and Water Content  
The firmness, SSC, pH, and water content of 'Warrior' and 'Albion' parsnip cultivars are detailed in Table 2. 'Warrior' demonstrated a firmness of 7.84 \(\pm\) 0.03, which was significantly higher than the 7.18 \(\pm\) 0.17 observed for 'Albion'. SSC, which measures the concentration of dissolved solids including sugars (Tructose, glucose, sucrose), organic acids (malic, citric, succinic, acetic), and other trace components such as phenols, amino acids, ascorbic acid, and minerals [17], was 9.83 \(\pm\) 0.15 for 'Warrior' and significantly higher at 12.49 \(\pm\) 0.90 for 'Albion'. The pH values were 6.69 \(\pm\) 0.16 for 'Warrior' and 5.80 \(\pm\) 0.05 for 'Albion', with 'Warrior' showing a markedly higher pH. The pH levels can be influenced by factors such as cultivar type, the maturity stage at harvest, and the levels of organic acids present [18; 19]. [PERSON] and [PERSON] [18] found that the water content of parsnips was 78.66 \(\pm\) 0.48%, which is consistent with our results. Likewise, [PERSON] et al. [17] reported a water content of 83 \(\pm\) 0.70% in parsnips, aligning with our findings. However, 'Warrior' exhibited a higher water content of 81.71 \(\pm\) 0.83%, compared to 73.28 \(\pm\) 2.69% in 'Albion'. The 'Warrior' cultivar was harvested in winter on November 24, while 'Albion' was harvested in summer on July 27. These differences in water content may be due to variations in temperature and relative humidity during the respective harvest periods [20].
## 3 Materials and Methods
### Methodology
#### 3.2.2 Development of Vegetation Parameters  
The sensitivity of the five most commonly used vegetation parameters (i.e., GPP, NDVI, EVI, LAI, and fAPAR) was tested with PDSI to understand the most sensitive parameter in reference to PDSI for the long term under tropical or humid-subtropical climatic contexts. GPP is the rate at which chemical energy (typically expressed as carbon biomass or organic substances) is created by primary producers through capturing solar energy in a given unit of area and time during photosynthesis. GPP is considered as a very useful indicator of drought conditions in terms of productivity, as reported by several studies that were mentioned before. LAI and EVI both indicate the biomass condition of vegetations. However, LAI (LAI = leaf area/ground area, m\({}^{2}\)/m\({}^{2}\)) typically characterizes plant canopies, whereas EVI is highly responsive to plant physiognomy. EVI also indicates the water balance and atmospheric droughs of leaves that are the major eco-physiological parts of a plant that interact with the atmosphere. The formula used for computing EVI is  
\[EVI=G\times\frac{(NIR-RED)}{(NIR+C1\times RED-C2\times BLUE+L)} \tag{1}\]  
where NIR/red/blue are atmospherically corrected or partially atmosphere corrected (Rayleigh and ozone absorption) surface reflectance, L is the canopy background adjustment that addresses nonlinear, differential NIR and red radiant transfer through a canopy, and C1 and C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band. The coefficients adopted in the MODIS-EVI algorithm are; L=1, C1 = 6, C2 = 7.5, and G (gain factor) = 2.5. The NDVI (\(NDVI=NIR-RED/NIR+RED\)) is a widely used greenness (chlorophyll content) indicator of vegetation, which is often used to assess the water deficit-induced stress levels in plants. Lastly, fAPAR is one of the essential climate variables recognized by the UN Global Climate Observing System (GCOS) that has great potential to monitor and assess drought impacts on vegetation. fAPAR monitors the greenness and health of vegetationby quantifying the fraction of the solar radiation absorbed by alive leaves for the photosynthesis activity.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
###### Abstract  
Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.  
biomedical; eHealthcare; information and communications technology (ICT); telemonitoring; wireless body area network (WBAN); wireless technology +
Footnote †: journal: Sensors