esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Nigella sativa_ L. (generally known as black cumin) is a medicinal plant prized for its therapeutic and nutritional benefits. Its seed oil is used extensively in pharmaceuticals, nutraceuticalals, cosmetics, and cooking. However, extracting oil to satisfy the world's needs leaves behind plenty of solid residues. The seeds of _Nigella_ are loaded with health-benefiting phytoconstituents, but so might their extraction residues. While much research on seeds and oil has been carried out, there is relatively little information about solid residue, particularly regarding health-benefiting phytoconstituents. Additionally, there is a knowledge gap relating to how phytoconstituents transfer from seeds to solid residue during oil extraction and any loss of key phytoconstituents that may occur during this transfer. Understanding the health-benefiting phytoconstituents in _Nigella_ solid residue is crucial for unlocking its full potential for value-added applications in health and nutrition. Moreover, understanding the dynamics of these phytoconstituent transfers is essential for optimizing extraction processes and preserving the nutritional and therapeutic value of the derived products. Therefore, this study investigated the composition of the screw-press solid residues of different _Nigella_ genotypes grown under similar environmental conditions. The results showed moderate variation in the levels of potential health-benefiting phytoconstituents in _Nigella_ solid residues regarding total phenolic content (TPC) (720.5-934.8 mg GAE/100 g), ferric reducing antioxidant capacity (FRAP) (853.1-1010.5 mg TE/100 g), cupric reducing antioxidant capacity (CUPRAC) (3863.1-4801.5 mg TE/100 g), thymoquinone (TQ) (156.0-260.1 mg/100 g), saturated fatty acid (SFA) (2.0-2.2 mg/g), monounsaturated fatty acid (MUFA) (2.0-3.6 mg/g), and polyunsaturated fatty acid (PUFA) (8.2-12.1 mg/g). Notably, TPC, FRAP, and CUPRAC had high transfer rates into the solid residue (78.1-85.9%, 65.4-75.7%, and 84.5-90.4%, respectively), whereas TQ, SFA, MUFA, and PUFA showed lower transfer rates (15.9-19.3%, 75.8-9%, 12.0-18.3%, and 6.5-7.5%, respectively). When summing the values of individual phytoconstituents transferred into oil and solid residue from their respective seeds during processing, it was found that only 80.6-88.3% of TPC, 74.2-84.4% of FRAP, 86.3-92.3% of CUPRAC, 54.4-64.9% of TQ, 68.5-92.4% of SFA, 76.2-90.6% of MUFA, and 51.6-76.6% of PUFA were transferred from the total value present in their respective seeds.  
Nigella sativa_ L.; screw-pressed solid residue; health-promoting phytoconstituents; transfer; loss
## 5 Results and Discussion
### Compared with Other Methods
Further, the diagnostic results of the above six intelligent fault diagnosis methods in five random trails were compared with the proposed GIN-ST, as shown in Figure 16. The performance of GIN-ST is noted for its remarkable stability, consistently achieving peak performance across all datasets. The average diagnostic accuracies of GIN-ST for each dataset are 97.59%, 97.75%, 96.70%, 96.62%, and 98.28%, respectively. These figures underscore that the proposed method delivers the most comprehensive and optimal results in terms of efficiency and accuracy, particularly under conditions of significant noise interference. To enhance the complexity of fault diagnosis, white noise with varying signal-to-noise ratios (SNR) was introduced into the dataset, mimicking the noise found in various operating conditions of axial piston pumps. Subsequently, the resulting dataset was randomly divided into a training set (80%) and a testing set (20%). Illustrated in Figure 17, the diagnostic accuracies of the aforementioned methods were assessed under different levels of random noise. The classification results demonstrate the superior noise resistance performance of the GIN-ST model compared to other fault diagnosis methods, primarily attributed to the design of the spatio-temporal attention module, which emphasizes fault features. Thus, these experimental findings indicate that the proposed model shows enhanced robust stability and noise resistance performance for diagnosing variable working conditions in axial piston pumps.
## 5 Results
### Seismic Refraction Technique  
Results obtained correspond to the identification of five geophysical levels as a function of P-wave velocity values, except for SR-1, which only has three. The last level in all profiles corresponds to the semi-infinite space (Figure 5).  
The geophysical levels obtained are correlated with the area containing the observed geological materials and described from the top to the depth for all profiles. The first level, the shallowest one, corresponds to low-compact natural fill materials (\(V_{p}\) values obtained  
Figure 5: Two-dimensional refraction seismic interpretation profiles with \(V_{p}\) values (in blue) and overlayed MASW profiles results (\(V_{s}\) in red squared lines, 1D solution). Both profiles were made at the exact location.  
are between 303 and 322 m.s\({}^{-1}\)) and could be composed of some artificial fills used for road construction. Below this level, more compact materials appear with P-wave velocities between 605 and 768 m.s\({}^{-1}\) related to more coarse sediments. Both levels are correlated to the gravitational sediments observed around the soils and colluvium.  
The next level, the third one, is defined between 1024 and 1196 m.s\({}^{-1}\) P-wave values and would correspond to a more compact sediment level with boulders and/or rock blocks where probable humidity increases (Figure 5). The presence of a transition zone from these fillings to eluvial material could be correlated to the fourth level, where the compressional velocity is clearly over 1000 m.s\({}^{-1}\) (1390 to 1450 m.s\({}^{-1}\)). No more interfaces were defined under this level at the SR-1 profile, so it is a semi-infinite space here.  
The last geophysical level determined In the investigation (for SR-2 and SR-3, in Figure 5) has velocities of 1456 to 2440 m.s\({}^{-1}\), correlated with the basement's metamorphic substratum (this is the semi-infinite level). The impedance contrast values for this \(V_{p}\) velocities between sediments and substrate varies from 2.2 to 2.3.  
These materials' geometric distribution is undulated, showing a deeper thickness to the center of the profiles that turns thin at the sides. Its related P-velocities aim to determine an initial distribution model of geophysical levels in the area and provide an initial idea about the possible position of the landslide rupture surface at the sediments-basement interface separation (see Figure 5).
# Numerical Simulation of Co-Barrier Efficiency of Air Curtains in Mine Refuge Chambers
## 3 Simulation Results
### Effect of Jet Angles  
Keeping the hole diameter of 6 mm, the hole spacing of 15 mm, and the jet velocity of 22 m/s as constants. And the air curtain is located on the top side of the chamber door frame. Figure 17 shows that jet angle affects the barrier performance of the air curtain. When the air curtain jet angle is 10\({}^{\circ}\), that is, the air curtain into the outside environment at  
Figure 16: A mathematical relationship between hole diameter, hole spacing, and air curtain efficiency.  
an angle of 10\({}^{\circ}\), and 300 s transition room CO concentration of 2630 ppm, the air curtain at this time has the best barrier effect.  
When the air curtain jet angle is 0\({}^{\circ}\), the air curtain airflow flows vertically to the chamber door ground floor, which results in a lower concentration than when the air curtain jet angle is 10\({}^{\circ}\). The airflow from the air curtain will tilt toward the transition room because of the buoyancy created by hot pressing, partially reducing the air curtain barrier effect. As a result of heat pressing, the air curtain airflow blows vertically to the ground when the jet angle it has towards high concentration CO is 10\({}^{\circ}\). The air curtain has an excellent blocking effect, and the jet angle is 10\({}^{\circ}\). Figure 18 shows the velocity cloud diagram of airflow in the air curtain at a jet angle of 10\({}^{\circ}\). The air curtain is more effective at obstructing CO because, as shown in the picture, it blows to the ground vertically while being affected by the thermal pressure of the environment outside. Figure 19 shows the CO concentration cloud diagram at varying moments in the transition room now that the air curtain jet angle is 10\({}^{\circ}\). The cloud diagram shows that installing an air curtain to block CO has a significant impact as compared to not performing it. The length to be sealed also increases as the angle of the curtain jet increases since it increases the distance from the nozzle to the refuge ground. To create a complete air curtain, it is necessary to raise the jet velocity of the air curtain and the overall kinetic energy of the airflow. The CO barrier effect decreases if the jet velocity stays constant and the air curtain jet angle keeps rising.  
Figure 17: The CO concentration in transition room at different jet angles.  
Figure 18: 300 s air curtain velocity cloud diagram when the jet angle is 10\({}^{\circ}\) at Y = 1.805 m.  
By analyzing each factor affecting the performance of the air curtain in blocking CO gas, we can not only draw a regular relationship between each influencing factor and the blocking efficiency. A specific conclusion can also be drawn. The best CO barrier effect is achieved by installing an air curtain at the top with a jet velocity of 22 m/s and a jet angle of 10\({}^{\circ}\).
## 6 Conclusions  
To facilitate the decision-making of optimal planning and optimally aggregate utilization of DSER, this paper proposes an evaluation model of DSER based on geographic information, that is, various DSER in a region are evaluated based on geographic information. Firstly, for five kinds of demand-side energy resources of WG, PV, EV, ES, and flexible load, select the evaluation indexes of all kinds of resources and divide all indexes into commonality indexes and individuality indexes. Then, AHP is used to determine the weight of each subindex under the commonality indexes, and the entropy weight method is used to determine the weight of each subindex under the individuality indexes. Finally, weighted overlay is acquired according to the weights and quantized values of each index, and a comprehensive score is obtained for the commonality indexes and individuality indexes of various DSER in a region. The following conclusions are obtained through the cases analysis.  
1. The evaluation results of commonality indexes are helpful to understand the development status and prospects of DSER, which is conducive to the future development and long-term optimization planning of DSER.
2. The evaluation results of individual indexes are helpful to understand the respective characteristics of DSER, which is conducive to achieving a good interaction between the DSER and the power grid, providing support for the medium-term optimization aggregation of the DSER.
3. The evaluation model of DSER based on geographic information proposed in this paper has good practicability and effectiveness, which matches the development of DSER with urban planning as well as power grid planning, providing a theoretical basis for the subsequent development of DSER.  
The author [PERSON] carried out the main research tasks and wrote the full manuscript, [PERSON] participated in the revision of the manuscript, and [PERSON] proposed the original idea, analysed and double-checked the results and the whole manuscript. [PERSON] contributed to data processing and to writing and summarizing the proposed ideas, [PERSON] and [PERSON] provided technical and financial support throughout, while [PERSON] modified the English grammar.  
The Nature Science Foundation of China (51607068); The Fundamental Research Funds for the Central Universities (2018 MS082); The Fundamental Research Funds for the Central Universities (2017 MS090).  
This work is supported in part by the Nature Science Foundation of China (51607068), the Fundamental Research Funds for the Central Universities (2018 MS082) and the Fundamental Research Funds for the Central Universities (2017 MS090).  
The authors declare no conflict of interest.  
Figure 12: The comprehensive scores of individuality indexes in various regions. (**a**) The scores of WG in various regions; (**b**) The scores of EV in various regions.
## 4 Results and Discussion
### Relationship among Static and Initial Chord and Dynamic Elastic Modulus
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Mix** & **Day 4** & **Day 7** & **Day 14** & **Day 28** & **Average** \\ \hline \multirow{4}{*}{ASTMLT} & Mix 1 & 0.70 & 0.75 & 0.81 & 0.83 & 0.77 \\  & (6.38\%) & (2.81\%) & (3.12\%) & (3.43\%) & (7.47\%) \\  & Mix 2 & 0.77 & 0.78 & 0.84 & 0.85 & 0.81 \\  & (4.69\%) & (3.42\%) & (4.69\%) & (3.19\%) & (5.94\%) \\ \hline \multirow{4}{*}{f1,f2.LT} & Mix 1 & 0.69 & 0.74 & 0.80 & 0.81 & 0.76 \\  & (7.02\%) & (3.35\%) & (3.51\%) & (3.91\%) & (7.63\%) \\  & Mix 2 & 0.75 & 0.77 & 0.83 & 0.84 & 0.80 \\  & (5.54\%) & (4.14\%) & (4.81\%) & (3.24\%) & (6.12\%) \\ \hline \multirow{4}{*}{ASTMLTR} & Mix 1 & 0.72 & 0.78 & 0.84 & 0.86 & 0.80 \\  & (6.25\%) & (4.17\%) & (4.29\%) & (4.79\%) & (7.32\%) \\ \cline{1-1}  & Mix 2 & 0.78 & 0.81 & 0.87 & 0.87 & 0.83 \\ \cline{1-1}  & (4.81\%) & (3.82\%) & (5.28\%) & (4.13\%) & (5.98\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correction factors and MAPE for ages and mixes between \(Ed\) and \(Ei\). Figure 9: Comparison of first frequency of LT mode and E-modulus. poor. The ratio of _f1_, _f2_, _f3_, and _f4_ at the bottom and top of the LT mode was similar to 1.9, 2.43, and 2.82, but in the TR mode, _f1_-_f2_ was 1.85, and f1-f3 showed a large error. Figure 10: Relationship between the frequencies and _Ei_ of the resonance frequency test. (**a**) LT frequencies-_Ei_, (**b**) TR frequencies-_Ei_. Next, the correlation between the frequencies of the LT and TR modes and initial chord elastic modulus (_Ei_) was analyzed, as shown in Table 9. In the LT mode, the correlation between the frequency and _Ei_ was _f1_ > _f2_ > _f3_ > _f4_. The correlation values of _f1_ and _f2_ were almost identical and had a high correlation with _Ei_. In the TR mode, f1-Ei had the largest correlation at 0.94, but the correlation greatly decreased from _f2_. It was confirmed that the _Ei_ can be predicted with _f1_ and _f2_ with high correlation, through analysis of the variability of the resonance frequency for each mode and correlation with the _Ei_.
## 3 Results
### Train-Test Split Models Results
#### 3.1.2 Neutral Stimuli Dataset  
In the context of the Neutral Stimulus Dataset, which is a well-balanced dataset comprising 103 samples, the dataset was stratified into a training cohort of 82 samples and a validation cohort of 21 samples. Analogous to the analysis performed on the Emotional Stimulus Dataset, machine learning models such as Random Forest, Multi-layer Perceptron  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**IA Model Name** & **Validation Accuracy** & **Validation Precision** & **Validation Recall** & **Validation F1-Score** \\ \hline Random Forest & **0.64** & **0.64** & **0.64** & **0.63** \\ Logistic Regression & 0.5 & 0.5 & 0.5 & 0.5 \\ AdaBoost Classifier & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance results of the emotional stimulus database in the validation group for the best classification models.  
(MLP), and AdaBoost exhibited performance metrics exceeding the 0.8 threshold in the training cohort. Notably, as delineated in Table 3, the Random Forest model surpassed the performance of other models, achieving precision and F1 score values of 0.81. This indicates that, contrary to observations made with the previous dataset, the issue of overfitting was markedly less pronounced in this instance.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### The QoS Parameters
#### 3.2.2 Link Quality  
In this paper, a new metric, i.e., link quality, is introduced which is measured as the length of time of the existence of a link between two nodes (link residual life). In our proposed scheme, we use the measurement value of the link quality to help reduce the route failure in a highly dynamic environment. Although the accurate depiction of wireless links in MANETs is a tedious task, link residual life can be relatively easily estimated based on the range of communication and a relative velocity between nodes. The time during which a link exists between nodes can be estimated as follows. First, we need to find the distance between nodes and its relative velocity. Considering a link between nodes A and B, let D be the distance between the two nodes. If \((x1,y1)\) and \((x2,y2)\) are the coordinates of nodes A and B, respectively, the distance can be calculated by using the following formula:  
\[D=\sqrt{\left((x2-x1)^{2}+(y2-y1)\right)^{2}} \tag{2}\]  
Figure 1: Adversary model. Route Request (RREQ); Route Reply (RREP).  
Let \(R\) be the communication range and \(\Delta V=V_{A}-V_{B}\) be the relative velocity, the residual life of the link T is then \((R-D)/\Delta V\).
## 4 Deriving mission requirements for ocean meso-scale applications
### Resulting requirements for mesoscale measurements  
From these and other (e.g. [25-26]) studies, the following refined requirements for mesoscale applications can be derived in terms of constellations of nadir altimetry satellites:  
1. The minimum requirement is to continue flying a two satellite configuration (after Jason-1 and ENVISAT) with one long-term reference mission (Jason series). A two-satellite configuration provides already a good representation of the mesoscale variability (sea level mapping errors of the order of 10% of the signal variance). However, it cannot provide a sufficiently accurate estimation of the velocity field (e.g. below 10% of the signal variance) and will not allow to track small eddies (e.g. diameter below 100/150 km).  
2. This can be very significantly improved with an optimised three satellite configuration. Compared with the Jason-1 and ENVISAT tandem (or T/P+ERS), such configurations would allow a reduction of sea level and velocity mapping errors by a factor of about 2 to 3. It is also likely to partly resolve the large scale high frequency barotropic motions (see for instance [27]). Possible orbit configurations could be based on the Jason pattern with interleaved ground-tracks (which would yield a track separation of about 100 km at the equator and a repeat period of 10 days), or an ENVISAT orbit with all three satellites overlying the same ENVISAT ground tracks (which would yield a track separation of about 80 km at the equator and a repeat period of 35/3 days). The Jason orbit based scenario is slightly better for mapping the meridional velocity field but the ENVISAT orbit based scenario has the advantage of providing measurements at high latitudes.  
3. To further improve the mapping quality (which is needed for some of the envisioned scientific and operational applications), it is required to resolve the high frequency and high wavenumber signals, i.e. sample the ocean with a time sampling below 10 days and 100 km. This is likely to require a constellation of more than six satellites and/or use different concepts for satellite altimetry such as wide swath techniques as described in [4-5].  
Given the previous results, the coming sections will then describe the technical details of an original Ka band altimetric payload that could be embarked on micro-platforms so that getting constellations of such payloads in orbit could be cost-effective.
# Biosynthesis of Silver Nanoparticles Using _Tabernaemontana ventricosa_ Extracts
## 3 Results and Discussion  
### Visual Inspection of Synthesized AgNPs  
The visual confirmation of synthesized AgNPs using leaf methanol (LM), stem methanol (SM), fresh leaf (FL), fresh stem (FS), powdered leaf (PL), and powdered stem (PS) extracts of _T. ventricosa_ was visually evident in terms of the color variation of the reaction solutions. The methanol suspensions (extract + AgNO\({}_{3}\) before incubation) of _T. ventricosa_ displayed a greenish solution for the leaves and a murky yellow coloration for the stems (Figure 1-1A).  
The fresh suspensions (extract + AgNO\({}_{3}\) before incubation) for the leaves appeared pale yellow, and the stem solution was colorless (Figure 1-1B). While the powdered leaf and stem suspensions (extract + AgNO\({}_{3}\) before incubation) both displayed a bright yellow color (Figure 1-1C). However, after the addition of AgNO\({}_{3}\) and the incubation period for 3 h at 80 \({}^{\circ}\)C, all solutions revealed a color change, except for the synthesized AgNPs using fresh stem extracts. The synthesized AgNPs using leaf and stem methanolic extracts turned deep brown/ruby brown (Figure 1-2A). Interestingly, the synthesized AgNPs using fresh stem extracts did not display any prominent color changes and appeared extremely pale yellow in coloration (Figure 1-2B); however, the fresh leaf extracts appeared murky brown. The synthesized AgNPs using powdered leaf and stem extracts both appeared dark brown and golden brown, respectively (Figure 1-2C).  
The subsequent color changes of the above-mentioned solutions confirmed the synthesis of AgNPs, since the brown coloration is a characteristic indication of the formation of AgNPs [25; 26; 34]. According to [PERSON] et al. [35], the observed color changes occurred due to the excitation of surface plasmon vibrations with AgNPs. Moreover, studies have shown that AgNPs synthesized using medicinal plant extracts often display extreme color changes following incubation [15; 36]. The current study exhibited variable coloration among the synthesized AgNPs using leaf and stem extracts following incubation (Figure 1). The most notable color variation (slight visual change) was observed using fresh stem extracts for the synthesis of AgNPs. It has been suggested that these variations occur due to the use of various plant extracts (methanol, fresh, and powder) from different plant organs, since each part of the plant (leaves, stems, bark, root, and flowers) may vary in the type, concentration, and grouping of organic reducing agents, which are responsible for the capping of AgNPs [15; 37; 38].  
Figure 1: (**1**) A representative image displaying various extracts + AgNO\({}_{3}\) before the incubation period; (**2**) Synthesized Ag nanoparticles (various extracts) after the incubation period with AgNO\({}_{3}\) for 3 h at 80 \({}^{\circ}\)C. (**A**) Leaf methanol (LM) and stem methanol (SM); (**B**) Fresh leaf (FL) and fresh stem (FS); and (**C**) Powdered leaf (PL) and powdered stem (PS).
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods  
In this section, two wind retrieval models are developed based on the collocated data sets. For the compact RV polarization, we employ the CMOD framework to derive a new GMF by optimizing each coefficient. The resultant formulation is denoted the CoVe-Pol model. In this derivation, sensitivity analysis is used to avoid unnecessarily huge calculations [13]. Through the sensitivity analysis, the computation efficiency of the process to generate the new GMF generation is increased by more the \(10^{20}\) times and the accuracy of computed NRCS reaches \(10^{-2}\). For the compact RH polarization, we propose the CoHo-Pol wind retrieval model which we derive by using a quadratic regression function.