esa-sceva/satcom-chunk-collection huggingface dataset:
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
## 1 Introduction
The phytochemicals present in soybean are of great interest for health because studies have shown that they lower the amount of cholesterol, have an anticarcinogenic capacity, and contribute to bone health [24]. Soybean and lentil flour often appear in specialized studies due to the possibility of being used as an ingredient in various bakery products [1,7]. The use of lentil and soybean flours as partial substitutes in wheat flour is justified by both nutritional and sensory aspects. Their use, particularly in the germinated form, may improve the quality of bakery products even from a technological point of view, especially if the wheat flour used has enzymatic deficiencies. According to the results obtained by [PERSON] et al. [25], native red lentil flour incorporation in wheat dough led to higher water absorption and mechanical weakening, while dough development time, stability and minimum torque, and cooking stability were lower compared to the control and increased with the addition level, due to the influence of the chemical compounds of the ingredient added. In the study of [PERSON] et al. [26] on the effects of lentil flour in the wheat dough, it is stated that the addition level increase caused water absorption rises, dough stability reduction, delayed protein weakening, and worsening of dough pasting consistency which could be related to the lower swelling power of pulses compared to wheat. The incorporation of germinated lentil flour in Sangak bread determined water absorption, dough development time, and softness degree increases compared to the control, while the stability of dough did not differ significantly. Wheat bread fortification with defatted soybean led to higher water absorption and dough extensibility and lower dough stability compared to the control according to results presented by [PERSON] et al. [27]. The addition of native and germinated soybean flours in wheat dough increased water absorption, maximum consistency time, and dough stability, while dough maximum resistance to extension and extensibility was not significantly affected [28]. The rise of the 7S protein fraction extracted from native and germinated soybean in wheat dough was related to the increment of water absorption and extensographic maximum resistance to extension, especially in the case of protein extracted from native soybeans [29]. This study aimed to optimize the formulation of germinated soybean and lentil flours that can be added to refined wheat flour of low alpha-amylase activity to improve dough rheological properties. For the optimal combination between the soybean and lentil germinated flour, the dough microstructure was analyzed by using epifluorescence light microscopy (EFLM). To our knowledge, no other studies have examined soybean and lentil germinated flour addition to wheat flour in a combined form. The importance of their use in bread making derives from the valuable nutritional composition of these legumes, but also the technological advantages of their use in a germinated form.
## 1 Introduction  
### Motivation and Background  
As environmental issues and energy crises have increasingly negative impacts on human society, targets such as energy conservation and emission reduction, energy efficiency improvement, and the effective utilization of renewable energy become research hotspots in the field of power system [1, 2]. In recent years, as the representative for new energy vehicles, electric vehicles (EVs) are recognized as the main direction for the transformation and development of the 21 st century automobile industry, owing to the fact that EVs are characterized by high energy efficiency, low CO2 emissions, and little oil dependence [3]. However, the uncoordinated charging behavior of large-scale EV owners will have a profound influence on the power system [4]. If effectively controlled, the orderly charging behaviors of EV clusters does not only realize the purpose of peak-shaving, but also solves the problems of low utilization rate of renewable energy and imbalance between power supply and demand, which will bring positive effects to the safe and stable operation of the electricity grid [5].  
According to the investigation, the number of EVs in the U.S. will reach 1.8 million by 2020, and the proportion of EVs is expected to reach 51% and 62% of the total number of vehicles by 2030 and 2050 [6]. Considering the stochasticity of heavy EV charging load, it is necessary to fully explore the controllability of EVs in the time dimension, in order to decrease the huge impact of EV charging behaviors on the power system [7]. Reference [8] indicates that the uncertainty caused by battery degradation cost and real-time electricity price has a great influence on the economy of EV users. Reference [9] solves the parking-lot EV charging scheduling problem through a game-theoretic approach. Each EV acts as a player with the objective of maximizing its own utility function, and the change of electricity price seriously affects the equilibrium. As the temporal characteristic of EV charging load is closely related to the charging price mechanism, reasonable charging schedules can be arranged for EVs through the economic lever of charging price [8; 10]. Furthermore, the micro-grid is a good platform for large-scale access of renewable energy generation and EVs to the grid, which can promote both sides [11]. Since the source and load of the micro-grid containing distributed generation and EVs have considerable uncertainty, which leads to the imbalance between power supply and demand and the low utilization rate of renewable energy, it is necessary to formulate an attracting tariff mechanism based on the dispatching demand of the micro-grid to availably guide the charging behaviors of a great quantity of EVs.
# Some Key Aspects in the Mechanics of Stress Transfer Between SRG and Masony
## 4 Conclusions  
Bond behavior between SRG composite and masonry substrate was analyzed by testing masonry blocks strengthened with SRG composites using the classical push-pull single-lap direct shear test set-up. In total, thirty-two masonry blocks were tested. In addition to masonry, three concrete prisms strengthened with the same SRG system were tested for comparison, using the same test set-up.  
Three failure modes were observed: interlaminar failure at the matrix-fiber interface, rupture of steel fibers, and detachment of the SRG strip from the substrate with a thin layer of substrate attached to it only in a limited portion.  
Four parameters were analyzed to determine their influence: width of the SRG matrix, type of SRG matrix, test rate, and role of the substrate. Test results suggested that:  
1. Increasing the width of the SRG matrix (without increasing the width of the fibers), from 50 mm to 70 and 90 mm, the average load-carrying capacity \(P_{crit}\) increased by 52% and 69%, respectively, due to a higher amount of energy required for the crack to initiate and propagate.
2. Results of masonry blocks strengthened with cement-based mortar SRG were less scattered with respect to blocks strengthened with lime-based mortar SRG. Compared to specimens with the same matrix width but strengthened with lime-based matrix SRG, the average load-carrying capacity (\(P_{crit}\)) and peak load (\(P^{*}\)) of specimens strengthened with cement-based matrix SRG increased by 33% and 20%, respectively.
3. The average peak load of SRG-masonry joints tested at a rate ten times the standard rate increased by 37%. Therefore, it appears that for this type of composite a rate effect might be present.  
Figure 9: **(a)** Load response of SRG-concrete joints and **(b)** photo of failure mode due to the rupture of steel fibers [43].  
Comparing masonry and concrete substrate strengthened with the same SRG system, the mode of failure changed: failure of SRG-concrete joints was due to rupture of steel fibers, while SRG-masnorry joints exhibited predominantly interlaminar failure. Therefore, for concrete specimens, it was not possible to evaluate \(P_{crit}\). When the averages of peak load \(P^{*}\) were compared, the highest average value was observed in concrete specimens since rupture of the fibers occurred.  
Conceptualization, G.B. and C.C.; Methodology, T.D. and C.C.; Investigation, G.B.; Validation, T.D.; Writing--Original Draft, G.B.; Writing--Review and Editing, T.D. and C.C.; Funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Kerakoll S.p.A. of Sassuolo, Italy, is gratefully acknowledged for providing the composite materials.  
The authors declare no conflict of interest.
## 4 Discussion
### Crop Classification  
The results using single groups of polarimetric observables showed that polarimetric decompositions (ND, FD, and CP), backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with OAs greater than 87%. This is because these polarimetric observables were found to be very sensitive to crop structure and growth parameters [7; 17; 27; 38; 39; 41]. In previous studies,  
Figure 16: Normalized variable importance of RF classification with all polarimetric observables using all RADARSAT-2 images. Different types of polarimetric variables are represented as follows: LP (purple), Pauli (dark green), Span (light green), Ra (blue); Ro (orange), Pha (green), FD (pink), CP (yellow), ND (red), RVI (cyan). A variable name consists of three parts, with the prefix, center, and suffix respectively representing acquisition date, date source (see Table 1), and a certain variable (none if only one variable from data source, such as Span and RVI). For example, the first variable name 0927-Pauli-HH-VV represents the backscattering coefficient HH-VV from Pauli channel using SAR image on 27 September.  
the common way for constructing the feature set was by stacking all polarimetric observables from various sources [7; 17; 27; 39]. However, in our case, the classifications using an optimal combination of polarimetric observables with reduced number of polarimetric observables, i.e., eight groups (NM, FD, Ra, Pha, Ro, CP, RVI and Pauli), produced better overall accuracies for crop classification. Fewer images acquired on certain critical dates could produce better overall accuracies for crop classification, which is consistent with previous studies [10; 27]. The best combination of SAR images was selected from the SAR dataset by the forward selection procedure, rather than combinations by adding images one by one sequentially along the SAR acquisition time as in previous reports [39; 40]. The best result was achieved using an optimal combination of polarimetric observables and SAR images (including eight groups and six images), with an OA of 94.04%. This indicates that this strategy (a forward selection procedure for deciding the optimal combination of polarimetric observables and SAR images) is recommended for practical classification in operational scenarios.
###### Abstract  
Based on drilling, core and seismic data, combined with the regional tectonic sedimentary evolution background, the sedimentary environment of the Triassic carbonate rocks in the Mangeshlak Basin was studied. A sedimentary facies model of this set of carbonate rocks was established. Research has shown that the Mangeshlak Basin underwent a complete large-scale marine transgression-regression sedimentary evolution process during the Triassic. During the early to middle Triassic, seawater gradually invaded the northwest region of the basin from northwest to southeast and gradually regressed in the late Middle Triassic. In the lower part of the Triassic carbonate rocks, the primary components are developed granular limestone or dolomite with politic structures, interspersed with a small amount of thin mudstone, which is a good reservoir; the upper part of the Triassic is mainly composed of sedimentary mudstone and mudstone, which can form good sealings. The hill-shaped reflections of the platform edge facies, along with the high-frequency, strong-amplitude, and moderately continuous reflections within the restricted platform interior, are clearly visible on the seismic profile. These features are consistent with the sedimentary environment and lithofacies characteristics revealed by drilling data along the profile. Drilling and seismic data revealed that the sedimentary environment of the early and middle Triassic in the basin is mainly composed of shallow water platform edges and restricted platforms, as well as carbonate rock slopes and open non-marine shelves in deep water areas. A sedimentary facies model of the Triassic carbonate rock segment in the basin was established, comprising restricted platforms, platform edges, carbonate rock slopes, and non-marine shelves. Unlike the modified Wilson marginal carbonate rock platform model, the carbonate rock platform edge in the Mangeshlak Basin does not develop reef facies. Instead, it is mainly composed of politic beach (dam) sediments, making it the most favorable sedimentary facies zone for the Triassic reservoir development in the basin.  
Mangeshlak Basin; Triassic period; Carbonate rock; Ooid beach (dam); Sedimentary environment; Facies model +
Footnote †: journal: Applied sciences  
Article  
A Study on the Sedimentary Environment and Facies Model of Triassic Carbonate Rocks in the Mangeshlak Basin
###### Abstract  
A novel nondestructive method called magnetic adaptive testing has been previously applied to detect the neutron-irradiation-generated structural changes in reactor pressure vessel steel material. This method has been found to be a useful tool for this purpose, and good correlation--as a tendency--has been found between the estimated ductile-to-brittle transition temperature and magnetic parameters. However, a significant scattering of measured points was also observed for the investigated set of Charpy specimens. The main result of the work was that by magnetic selection of samples, the scatter can be notably reduced. As a conclusion, the magnetically measured parameters seemed to be precise and reliable for the detection of embrittlement of the reactor pressure vessel steel, with lower scattering of points than in the conventionally used destructive mechanical characteristics (ductile-to-brittle transition temperature). This result is surprising and needs further verification. The purpose of the present work is to repeat the measurement on irradiated reactor steel blocks. In this work, instead of the DBTT transition temperature, individually measured Vickers hardness (VH) data were used to help characterize the mechanical properties of the material. The so-called \"property transformation\" is a known and applied technique in the nuclear industry. The mechanical property characterized by the transition temperature cannot be determined individually for each specimen; instead, it can be obtained only on a set of samples by statistical fitting. Therefore, the individually measured Vickers hardness values can be utilized in order to predict the individual transition temperature values by the help of the property transformation technique. In this paper, however, not these derived transition temperature values, but their origins, the Vickers hardness values, are studied in a direct manner. The same behavior of blocks was observed as in the case of Charpy specimens, which is considered to validate the previously published results. As a possible reason for the scattering of points, large magnetic inhomogeneity of samples cut even from the same block was also proved. The magnetic parameters and Vickers hardness correlate well with each other. This result justifies the potential future application of magnetic techniques in practice aimed at the regular inspection of nuclear reactors.  
Author irradiation; reactor pressure vessel steel; magnetic nondestructive evaluation; magnetic adaptive testing
## Nomenclature  
\begin{tabular}{l l} \(\sigma_{yield}\) & yield strength \\ \(v\) & [PERSON]'s ratio \\ \(\rho\) & density of the rotor material \\ \(n_{0}\) & number of armature conductors per slot \\ \(I_{0}\) & total current per slot \\ \(r\) & outer radius of the rotor \\ \(l\) & effective length of the rotor \\ \(\Omega\) & mechanical angular speed \\ \(E\) & [PERSON]'s modulus \\ \(k\) & safety factor \\ \(S_{Cu}\) & cross-sectional area of the armature conductor \\ \(J_{Cu}\) & the allowable current density of the armature conductor \\ \(d_{slot}\) & slot height \\ \(w_{slot}\) & slot width \\ \(d_{Cu}\) & armature conductor height \\ \(w_{Cu}\) & armature conductor width \\ \(\beta\) & packing factor \\ \(P\) & rated output power \\ \(\sigma_{Fant}\) & tangential stress of the rotor \\ \(T\) & torque of the rotor \\ \(I_{S}\) & RMS value of the phase current \\ \(q\) & number of the slots \\ \(cos\,\varphi\) & power factor \\ \(\rho_{cl}\) & electrical resistivity of the current lead \\ \(l_{cl}\) & length of the current lead \\ \(I\) & transport current through the current lead \\ \(A_{cl}\) & cross-sectional area of the current lead \\ \(k_{cl}\) & thermal conductivity of the current lead \\ \(T_{H}\) & temperature at the high-temperature part \\ \(T_{L}\) & temperature at the low-temperature part \\ \(N_{l}\) & no. of signal lines or leading-in tubes \\ \(A_{l}\) & cross-sectional area of a signal line or a leading-in tube \\ \(k_{l}\) & thermal conductivity of a signal line or a leading-in tube \\ \(L_{l}\) & length of a signal line or a leading-in tube \\ \(\sigma\) & Stefan-Boltzmann constant (\(5.67\times 10^{-8}\) W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-4}\)) \\ \(\varepsilon_{H}\) & emissivity of the vessel \\ \(\varepsilon_{N}\) & emissivity of MLI \\ \(N\) & no. of MLI layers \\ \(A_{H}\) & surface area of the high-temperature part \\ \(A_{L}\) & surface area of the low-temperature part \\ \end{tabular}
## 9 Stokes Space Polarization and Field Recovery Principle
### Stokes Space Field Recovery
#### 9.2.3 3D Direct-Detection-Based Field Recovery
Furthermore, it will be appropriate to detect only two intensities of \(|X|^{2}\) and \(|Y|^{2}\) (i.e., \(S_{0}\) and \(S_{1}\)) that are expressed by the local polarization axes. Furthermore, the field \(X\) and \(Y\) can be recovered by means of the intensities. Then, the Jones space 2 \(\times\) 2 MIMO polarization rotation can be implemented. Nevertheless, the related carrier fading is the primary impediment of the approach [13]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & ID & 1 & 1 & IDPO & 10 & 25 & 4 & 4 & \multicolumn{3}{c}{**Men**} & \multicolumn{3}{c}{**Dennet**} \\ \hline  & ID & 1 & 1 & IDPO & 10 & 25 & 4 & 4 & \multicolumn{3}{c}{**study in implemented**} & \multicolumn{3}{c}{**comordinated BMOD is alongside of FR} \\  & & & & &  
It is noteworthy that the optical carrier polarization state is unknown at the receiver when it travels along with the signal. Consistent separation of the carrier to the two local polarization axes might be challenging for the receiver. Therefore, the process can give rise to a total carrier fading at one of the polarization. Moreover, it has been observed that an active polarization control is required in the optical domain for the conventional PDM-SSB direct detection systems. A notable solution for addressing this in the offset-SSB is to launch two carriers, but at different frequencies so as to prevent destructive interference. In this, rather than the \(2\times 2\) (in view of two carriers and two polarizations at the receiver), \(4\times 4\) MIMO that is cascaded after field recovery for polarization recovery is employed. Furthermore, regarding the gapless SSB, there is no general consensus on whether the field recovery has to be implemented first before polarization recovery. However, the following points are very imperative in the system design:  
* The polarization recovery demands field recovery in the Jones space;
* The gapless SSB-FR implementation demands a specified CSPR threshold;
* Furthermore, in a condition whereby the polarization states are arbitrary, it will be highly challenging to guarantee the CSPR beyond the threshold for the entire MIMO dimensions without the polarization recovery. Therefore, this results in unstable system performance for various polarization states. The issue can be prevented using the Stokes space. This is mainly due to it being isomorphic to the Jones space. Based on this, it offers exceptional potential for polarization recovery in its specific space, and field recovery is not required before polarization recovery. As a result, its implementation provides a reliable solution based on which polarization-diversity DD-FR can be employed without the need for an active optical polarization control [13].
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Data from OLI Spectrometric Parameters (DOLIPs)  
Table 5 presents the performance evaluation of three machine learning algorithms (Gradient Boosting, XGBoost, and Random Forest) applied to data from OLI (Operational Land Imager) spectrometric parameters. The results show that:  
The Random Forest shows the best performance with an accuracy of 52.59%, an F-score of 43.02%, and an MCC of 30.32% while being the fastest (2.60 ms).  
Figure 8: (**A**) Confusion matrix and (**B**) ROC curve for the Random Forest model on data from PCA Parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 8.68 & 43 & 41.97 & 5.95 & 41.97 & 43 & 29.16 \\ XGBoost & 4.19 & 43.34 & 42.71 & 5.59 & 42.71 & 43.34 & 30.09 \\ Random Forest & 2.60 & 52.59 & 43.02 & 5.12 & 43.02 & 44.03 & 30.32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of algorithm performance on Data from OLI Spectrometric Parameters.  
Figure 9: (**A**) Confusion matrix and (**B**) ROC curve for the Random Forest model on Data from OLI Spectrometric Parameters.