esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multiphysics-Coupled Analysis-Based Induction Hardening Process
### Fundamental Theories of Electromagnetic-Thermal Interaction
#### 2.1.4 Current Distribution  
The distribution of electric current within the workpiece during induction heating follows an exponential decay as the depth increases from the surface. This phenomenon can be described using the current distribution equation, where \(f(r)\) represents the current density at a given depth \(r\) and \(f_{s}\) is the current density at the surface. The penetration depth \(\delta_{w}\), as previously defined, governs the rate of this decay [13; 14].  
\[\delta=\frac{1}{\sqrt{\pi\cdot f\cdot\mu_{r}\cdot\mu_{0}\cdot\sigma}} \tag{15}\]  
This equation illustrates that current density decreases exponentially with depth, indicating that most of the induced current--and, consequently, the generated heat--is concentrated near the surface of the workpiece. The proper selection of operating frequency ensures that sufficient current penetration is achieved for uniform heating.
Why Is It Important to Consider Dust Aerosol in the Sevastopol and Black Sea Region during Remote Sensing Tasks? A Case Study  
[PERSON]  
1 Management of the Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical thickness of the atmosphere and the spectral slope, the so-called Angstrom parameter. Inaccurate determination of these parameters is one of the causes of error in the retrieval of remote-sensed reflectance spectra. In this work, a large array of field and satellite data measured in Sevastopol and the northeastern part of the Black Sea were used, including ship-based measurements of atmospheric characteristics and sea reflectance, MODIS Aqua/Terra, and VIIRS NOAA/NPP Level 2 remote-sensed reflectance and atmospheric data. In total, three episodes of Saharan dust transfer over the Black Sea region were considered, mainly in the autumn-winter period. The purpose of this study was to show the numerical differences between the atmospheric parameters measured at the surface level and by satellites, and show their relationship with the differences between in situ and satellite remote-sensed reflectance. Based on the information identified, we propose an algorithm for additional correction of satellite level 2 data that uses a two-parametric model of the Black Sea remote-sensed reflectance as a first approximation. Moreover, additional correction significantly reduces the discrepancy between in situ and retrieved remote-sensed reflectance, especially in short-wave spectral bands.  
MODIS; VIIRS; aerosol; remote-sensed reflectance; sun photometer; aerosol optical depth; atmospheric correction; Black Sea; AERONET; SPM
# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
###### Abstract  
This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study's overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos.  
disruptive series; deep learning; economic forecasting; stock market; time series Article  
11 Faculty of Physics and Applied Computer Science, University of Krakow, 30-059 Krakow, Poland; [EMAIL_ADDRESS]  
2 Data Science Institute, University of Technology Sydney, 15 Broadway, Ultimo 2007, Australia  
3 Chitkara University Institute of Engineering and Technology, Chikara University, Chandigarh 140401, India; [EMAIL_ADDRESS]  
4 Computer Science & Engineering Department, Punjabi University, Patiala 147002, India; [EMAIL_ADDRESS]  
5 School of Computer Science, UPES, Dehradun 248007, India; [EMAIL_ADDRESS]  
6 Department of Computer Science & Engineering, Guru Jambeshwar University of Science & Technology, Hisar 125001, India; [EMAIL_ADDRESS]  
7 Correspondence: r03 [EMAIL_ADDRESS] or [EMAIL_ADDRESS]
# Effect of Temperatures on Polyphenols during Extraction
## 7 Challenges in the Analysis of the Extractant
### Lack of Consensus in the TPC Correlation to AOA  
While most of the literature did find a strong correlation between TPC and AOA [19, 20, 28, 57, 89, 87, 88, 89], this is not always the case. There are quite a few publications that did not show such a correlation [43, 46, 47, 89, 90, 91, 92].  
[PERSON] and [PERSON] [48] extracted polyphenols using PLE (130-200 \({}^{\circ}\)C) and methanol/water extraction from bitter melon (65 \({}^{\circ}\)C). TPC increased with increasing temperature as did the specific polyphenols (catechin, gallic acid, gentisic acid and chlorogenic acid) that were studied, although gallic acid showed a slight decrease at 200 \({}^{\circ}\)C. As the AOA does not relate to TPC or the individual phenols here, the AOA must be exhibited by either phenolic compounds not tested for here or non-phenolic compounds such as vitamins and sugars that were decomposed at higher temperatures.  
The highest AOA was observed at 150 \({}^{\circ}\)C and the lowest was at 200 \({}^{\circ}\)C, showing that higher extraction temperatures result in lower AOA. While the highest TPC was observed at 200 \({}^{\circ}\)C, it corresponds to the lowest AOA. However, even the lowest AOA observed at 200 \({}^{\circ}\)C in the PLE extraction is seven times higher than any of the traditional extractions performed (Soxhlet, methanol (at 65 \({}^{\circ}\)C) and water extraction (at 100 \({}^{\circ}\)C)).  
[PERSON] et al. [46] performed a PLE extraction of polyphenols from dried oregano leaves at 25, 50, 100, 150 and 200 \({}^{\circ}\)C. The study showed that the temperatures did not significantly affect the TPC, although a drop was observed at 200 \({}^{\circ}\)C. The authors concluded that PLE does not, therefore, lead to the degradation or oxidation of phenolic compounds until 200 \({}^{\circ}\)C. However, the AOA increased at higher temperatures, suggesting that TPC is not correlated to AOA. The authors suggested that although the number of phenolic compounds is relatively constant, the variety and structure of the compounds may be changing in a way that increases the AOA.  
The relationship between the structure of a polyphenol and its antioxidant capacity is not well understood. However, some studies have tried to establish a link between them. [PERSON] et al. [93] studied flavonoids in the citrus peels and established that 'the antioxidant capacity of any flavonoid will be determined by a combination of the O-dihydroxy structure in the B-ring, the 2,3-double bond in conjugation with a 4-oxo function and the presence of both hydroxyl groups in positions 3 and 5\({}^{\prime}\).
## 2 Materials and Methods
### Functional Annotation and Biological Interpretation  
All identified volatile metabolites were annotated using the KEGG database to assign metabolic pathways and functional categories. KEGG compound IDs were used to match metabolites to known biosynthetic and metabolic pathways, including terpenoid, fatty acid, alcohol, and aldehyde metabolism. Only metabolites with valid KEGG compound IDs were included in enrichment analysis. Some sesquiterpenes and norisoprenoids could not be mapped due to database limitations and were therefore excluded. KEGG enrichment analysis of differential metabolites was performed using a hypergeometric test, and significantly enriched pathways were visualized using bar plots or bubble charts. To interpret the potential sensory impact of differential volatile compounds, odor descriptors were assigned based on multiple public aroma databases, including The Good Scents Company ([[http://www.thegoodscentscompany.com](http://www.thegoodscentscompany.com)]([http://www.thegoodscentscompany.com](http://www.thegoodscentscompany.com)) (accessed on 1 November 2024)), Perflavory ([[http://perflavory.com](http://perflavory.com)]([http://perflavory.com](http://perflavory.com)) (accessed on 1 November 2024)), Odour.org.uk ([[http://www.odour.org.uk/odour/index.html](http://www.odour.org.uk/odour/index.html)]([http://www.odour.org.uk/odour/index.html](http://www.odour.org.uk/odour/index.html)) (accessed on 1 November 2024)), and Food Flavor Lab ([[http://foodflavorlab.cn/#/home](http://foodflavorlab.cn/#/home)]([http://foodflavorlab.cn/#/home](http://foodflavorlab.cn/#/home)) (accessed on 1 November 2024)), as well as supporting information from peer-reviewed literature. These descriptors were used for qualitative reference to group aroma-related metabolites into categories, such as fruity, floral, green, sweet, herbal, and spicy aromas. Annotated aroma attributes were visualized using Sankey diagrams to highlight compound-odor relationships and to distinguish species-and organ-related sensory profiles.
## 3 Methodology
### MDDA Augmented GNSS/INS Integration
#### 3.6.2 INS Mechanization
\] (28) where \(\omega=[\omega_{x}\ \omega_{y}\ \omega_{z}]^{T}\) is the body rotation angular velocity. The transformation matrix from the body frame to the \(LLF\) using the direction cosine matrix and quaternion states is illustrated by (29) [49; 51; 52]. \[C_{b}^{l}= \begin{bmatrix}q_{2}^{2}+q_{1}^{2}-q_{3}^{2}-q_{4}^{2}&2(q_{2}q_{3} -q_{4}q_{1})&2(q_{3}q_{4}+q_{3}q_{1})\\ 2(q_{2}q_{3}+q_{4}q_{1})&q_{3}^{2}+q_{1}^{2}-q_{2}^{2}-q_{4}^{2}&2(q_{3}q_{4} -q_{2}q_{1})\\ 2(q_{2}q_{4}-q_{3}q_{1})&2(q_{3}q_{4}+q_{2}q_{1})&q_{4}^{2}+q_{1}^{2}-q_{2}^{ 2}-q_{3}^{2}\end{bmatrix} \] (29) The attitude angles \(\varepsilon_{3\times 1}^{l}\) can be demonstrated as follows: \[\varepsilon_{3\times 1}^{l}= \begin{bmatrix}\phi\\ \theta\\ \psi\end{bmatrix} = \begin{bmatrix}-\text{atan2}(C_{b31}^{l},C_{b33}^{l})\\ \text{asin}(C_{b32}^{l})\\ -\text{atan2}(C_{b12}^{l},C_{b22}^{l})\end{bmatrix} \] (30)
# Life-Cycle Oriented Risk Assessment Using a Monte Carlo Simulation
## 5 Discussion
### Academic Implications  
The application of a Monte Carlo simulation to support decision making is not new; however, described in this paper is an approach that promotes the identification of risk for change management along the lifecycle. The base process here is based on understanding and integrating the new product development process and the production process. It confirms the necessity to have a mature PLM process to manage product and process definitions, as these underpin the risk assessment process described here. The integration of the lifecycle to include the MOL and the warranty phase of the 'use' phase is crucial. It means that the consequential risks and their associated costs are captured. The use here of the lifecycle with data and information flows [24] is necessary, as it provides a common basis yet allows for the separation of the business processes.  
Risk management is often used as a semi-quantitative approach, where the likelihood and impact are multiplied to provide a risk-adjusted rating. However, all too often, a simple 1-10 score that is uncalibrated is used with scant regard to the commercial aspects. Using the lifecycle approach described in this paper with the process understanding coupled with both technical and commercial insights, it is proposed that the approach could be further adapted for EOT products and could be further calibrated with historical data.  
Decision making processes for process changes are poorly described in an Industry 4.0 context. This gap should be further investigated, and Industry 4.0 provides the opportunity to move from silo to systems approaches. Much of the Industry 4.0 approaches focus on process control and creating a history of transactions that, in general, support root cause analysis. Here, the risk is clearly described and quantified and then used to support decision making (i.e., to set up a test in a cell or not) via a form of prediction. Using 'advanced' statistics (or other approaches) in decision making requires further investigation as the form of the outputs can be confusing to those who are not used to statistical analysis.
## 5 Simulation Results
### Validation through GRAFAN  
In order to validate the link performance of the Sheil Road deployment illustrated above, we monitored Link 2 and Link 3 through a Grafana-based interface for 7 days. The testbed allows us to monitor and visualize in real time the link performance of the backhaul network at Sheil Road illustrated in Figure 3. Note that each node of the deployment is characterized by an ID defined in Grafana that will be illustrated together with the monitored results.  
Specifically, Link 2 connects the node with ID sheil-rd-003-f8627 to the node with ID sheil-rd-004-f3080, represented as Node 2 and Node 3 in Figure 4, respectively. Figures 7-9 illustrate, among other parameters, the SNR (green line in Figure 7), the performance in terms of PER (red line in Figure 8) and the MCSs (yellow circles in Figure 9) at the receiver node, i.e., node sheil-rd-004-f3080. Other parameters that can be monitored through Grafana and that we do not illustrate because they are out of the scope of this paper are:  
Figure 5: Link2 performance.  
Figure 6: Link3 performance.  
the Received Channel Power Indicator (RCPI) (blue line in Figure 7); the Automatic Gain Control (AGC) (orange line in Figure 7); the MAC transmit rate (Tx Rate) (green line in Figure 8); and the Modulation error ratio (MAR) (Iilac line in Figure 8). From these figures we can note that some parameters present a high oscillation amplitude and, therefore, we also included the average value of each parameter at the bottom of the diagrams. In summary, we can conclude that the average SNR is 14 dB, the PER on average is 0.18% and the average MCS value is 9.  
Link 3 connects the node with ID sheil-rd-005-f8627 to the node with ID sheil-rd-006-spfhc represented as Node 3 and Node 4 in Figure 4, respectively. Figures 10-12 illustrate again, among other parameters, the SNR (green line in Figure 10), the performance in terms of PER (red line in Figure 11) and the MCSs (yellow circles in Figure 12) at the receiver node, i.e., node sheil-rd-006-spfhc. From these figures we can conclude that the average SNR is 18.5 dB, the PER on average is 0.56% and the average MCS is 11.  
Figure 8: Performance in terms of PER of Link 2.  
Figure 7: Performance in terms of SNR of Link 2.  
Figure 11: Performance in terms of PER of Link 3.  
Figure 10: Performance in terms of SNR of Link 3.  
Figure 9: Performance in terms of MCS of Link 2.
## 3 Communication Characteristics of a QADA Receiver  
### Channel Gain  
The channel gain of the optical channel in a VLC or VLP system is the ratio between transmitted and received optical powers. The transmitter used in this paper is assumed to be a Lambertian source with order \(m\)[34]. In this paper, and in line with other work [33], only the line of sight component is considered. This is because of the properties of VLC. In VLC systems, unlike RF systems, the multipath components are very small and are negligible compared with the line of sight component [34]. Additionally, our previous work [14] has shown that due to their directionality, the  
Figure 1: QADA (**a**) receiver structure; (**b**) an example of the overlap between the light spot and the quadrant PD.  
communication performance of aperture receivers is mostly unaffected by the diffuse component. Therefore, the channel gain for the \(k\)-th quadrant of the quadrant PD is given by  
\[h_{k}=\frac{m+1}{2\pi l^{2}}A_{\rm o}^{(k)}\text{cos}^{m}\phi\cos\varphi \tag{3}\]  
where \(l\) is the distance between the luminaire and the receiver, \(A_{\rm o}^{(k)}\) is the overlap area between the light spot and the \(k\)-th quadrant of the PD, and \(\phi\) is the emission angle.  
The overlap area between the light spot and each quadrant is given by  
\[\begin{split} A_{\rm o}^{(k)}=&\max\left\{0,\min \left\{x^{\prime}_{\rm s}+L_{\rm PD},x^{\prime}_{k}+\frac{L_{\rm PD}}{2}\right\} -\right.\\ &\left.\max\left\{x^{\prime}_{\rm s}-L_{\rm PD},x^{\prime}_{k}- \frac{L_{\rm PD}}{2}\right\}\right\}\times\\ &\max\left\{0,\min\left\{y^{\prime}_{\rm s}+L_{\rm PD},y^{\prime }_{k}+\frac{L_{\rm PD}}{2}\right\}-\right.\\ &\left.\max\left\{y^{\prime}_{\rm s}-L_{\rm PD},y^{\prime}_{k}- \frac{L_{\rm PD}}{2}\right\}\right\}\end{split} \tag{4}\]  
Figure 2 illustrates the normalized 3-D channel gain plot for the fourth quadrant of the PD in QADA when \(d=L_{\rm PD}\). In this plot, the normalized channel gain for the light coming from a specific direction is represented by the radial distance from the corresponding point on the plot to the origin. It shows that each quadrant of the PD in a QADA has its own specific range of directions from which the light can be received with high gain.
## 4 Results  
Following the analysis approach that is described in the previous section, the daily variation of dielectric constant from CYGNSS has been examined and compared with the SMAP data. The calculation and comparison processes are applied to three locations in the grass field category. The characteristics of dielectric constant along with the change of the season are also explored by comparing with the reference SMAP datasets.
## 3 Test Results and Discussion
### Local Stress inside the Anchor Hole of Fe-SMA Plates  
Figure 13 shows the recovery strain inside the Fe-SMA anchor bolt hole (strain gauge 2) during the activation process. It can be observed that the overall trend in local strain at the Fe-SMA plate anchor bolt hole is that the growth rate and increment are greater during the cooling process. Finally, the local stress inside the Fe-SMA anchor bolt hole in the adhesive-bolted hybrid connection is 183.81 MPa, the local stress of the Fe-SMA in the bolted connection is 245.36 MPa and the local stress of the Fe-SMA in the adhesively bonded connection is 130.35 MPa. However, in addition to the decrease in recovery strain caused by slip and softening of the structural adhesive, the strain at the inside of the anchor bolt hole is highest in the bolted connection, followed by the adhesive-bolted hybrid connection, and lowest in the adhesively bonded connection. The local stress of the Fe-SMA in the adhesively bonded connection is significantly lower than the other two connection methods. This is due to the stress concentration problem near the anchor bolt hole. Therefore, in the condition of similar total strain in the Fe-SMA, the strain at the anchor bolt hole of the adhesive-bolted hybrid connection should be smaller than that of the bolted connection. In the adhesively bonded connection, there is no problem of stress concentration, which also confirms that structural adhesive can effectively alleviate the local stress concentration problem in the anchor bolt holes of the Fe-SMA plate.  
Figure 12: Activation boundary strain and stress of Fe-SMA plate: (**a**) strain–activation temperature curve; (**b**) recovery stress.  
Figure 13: Strain and stress inside the anchor bolt hole of Fe-SMA: (**a**) strain–activation temperature curve; (**b**) local stress.
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 3 Results
### Magnetic Dataset
#### 3.2.1 Magnetic Sample Analysis  
The laboratory analysis of these samples was performed in the MFK1-FA Multifunction Frequency Kappapbridge (AGICO), one of the most sensitive laboratory instruments for measuring both anisotropy of magnetic susceptibility (AMS) and bulk magnetic susceptibility (Figure 8). In addiction to magnetic susceptibility, analysis have also included magnetic hysteresis measurements, IRM (Isothermal Remanent Magnetization) acquisition curves, FORCs (First Order Reversal Curves) and thermomagnetic curves. This set of analysis allows the understanding of the magnetic behavior of these rocks as well as to characterize the main magnetic minerals present in the samples [22].  
According to [22], the results of the magnetic susceptibility analysis for the carbonate samples show low values due to the prevailing presence of the diamagnetic minerals (i.e. calcite) in their composition. Measurements for magnetic hysteresis, IRM acquisition, FORCs and thermomagnetic curves for the carbonates have shown extreme noise level due to the low intensity of the magnetic signal. Despite the noise level, the magnetic hysteresis results show curves typical of low coercivity ferromagnetic minerals, while results for both IRM acquisition and thermomagnetic curves were considered as inconclusive. Although weak, the FORC signal of some samples show characteristics of multidomain grains which suggests the presence of detrital magnetite.  
For igneous samples, the magnetic susceptibility values were considered lower than would be expected for these kind of rocks. This was interpreted by [22] as consequence of the high alteration degree of the samples. However, the intensity of the magnetic signal was strong enough to allow the magnetic hysteresis and the IRM acquisition to show curves typical of low coercivity ferromagnetic minerals for most of the samples. Two samples, one of basalt and the other of diabase, have shown curves with characteristic behavior of high coercivity ferromagnetic minerals. Analysis of the FORC diagrams have exhibited bi-dimensional data indicating the presence of low coercivity multidomain grains. Thermomagnetic results have indicated that magnetite is the main mineral phase formed but maghemite, greigite, titanatemagnetite and possibly pyrite were also identified.  
Figure 8: The MFK1-FA instrument used to measure the magnetic susceptibility in 58 core samples collected in well 1-RJS-755-RJ ([[https://www.agico.cz/text/products/olddev/olddev.php](https://www.agico.cz/text/products/olddev/olddev.php)]([https://www.agico.cz/text/products/olddev/olddev.php](https://www.agico.cz/text/products/olddev/olddev.php)), (accessed on 31 January 2025)).