esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Collaborative Control Algorithm  
### Full-time SOC Control  
The SOC of BESSs should be considered during the operation. The SOC of BESSs should be considered during the operation. Charging and discharging processes have different effects on SOC. The \(P_{char}\) represents the charging power, and the value is positive. The \(P_{dischar}\) represents the discharging power, and the value is negative. The characteristics of the BESSs are as follows:  
1. SOC dynamics \[SOC(t+1)=SOC(t)+\frac{P_{char}}{Cap_{b}}\Delta t\] (7) \[SOC(t+1)=SOC(t)+\frac{P_{dischar}}{Cap_{b}}\Delta t\] (8)  
Figure 2: Battery energy storage systems (BESS) model.  
where \(SOC(t)\) is the SOC at time \(t\); and \(Cap_{b}\) is the capacity of the battery, \(P_{char}\) and \(P_{dischar}\) is the charging and discharging power of battery, respectively.
2. SOC limits \[SOC_{\text{min}}\leq SOC(t)\leq SOC_{\text{max}}\] (9) where \(SOC_{\text{min}}\) and \(SOC_{\text{max}}\) are the minimum and the maximum SOC, respectively.
3. Power limits \[P_{char}(t)\in\left\{[P_{char}^{\text{min}},\,P_{char}^{\text{max}}],0\right\}\] (10) \[P_{dischar}(t)\in\left\{0,[P_{dischar}^{\text{max}},P_{dischar}^{\text{min}}]\right\}\] (11) where \(P_{char}^{\text{min}}\), \(P_{dischar}^{\text{min}}\) and \(P_{char}^{\text{max}}\), \(P_{dischar}^{\text{max}}\) are the minimum and the maximum charging, discharging power, respectively.  
For each node, the flow chart of SOC control is shown in the Figure 3.  
As shown in Figure 3, for each node, the control center collects the SOC data of the BESSs and counts the quantity of BESSs of which the SOC is in the normal range. This process helps determine the specific amount of regulation for each BESS, which will be used in the coordinated control.  
Figure 3: State of charge (SOC) controlling block diagram.
## 6 Results and Discussions
### The Outer Loops
#### 6.2.2 Integral Action (PI) vs. Feedforward Action (P + FF)  
In Figure 10, we can observe the function of the integral action and the feedforward of the force to reduce the error of the system.  
Although theoretically, the integral action guarantees a null error, it is not very popular, because it can have stability problems, wind-up, and slower convergence than a feedforward action.  
Figure 9: Proportional derivative (PD) vs. proportional with velocity feedback (PV) comparison.  
On the other hand, control with feedforward is challenging to implement since it requires knowledge of the active stiffness matrix of the position control. In this case, we consider a constant \(\mathbf{FF}\) gain for the feedforward action.  
Due to the low sampling period and the fast dynamics of the interaction, both the integral action and the feedforward action do not guarantee a zero error. However, even using a constant feedforward, like the one used in Figure 10, P + FF allows to reduce the error from 0.11% to 0.02%.
# Dandelion Flowers as an Additive to Wheat Bread: Physical Properties of Dough and Bread Quality
## 1 Introduction  
Bread is a staple food product in the daily diet of most people. A large portion of bread is made from refined flour. Such bread is attractive to consumers due to its soft texture, light-colored crumb, crispy crust and easy digestibility [1]. However, during the milling of wheat grain into white flour, many nutritionally valuable compounds such as vitamins, minerals, dietary fiber and other phytochemicals are lost, which means that bread made from such flour is energy-dense food but low in biologically active compounds, and consequently has limited ability to protect the human body from various diseases [2]. For this reason, it is reasonable to enrich wheat flour (WF) with natural and compound additives that are rich in phenolics. Flours from frombread cereals and pseudocereals [3, 4], legumes [5], oilseeds [6, 7], herbs and spices [8, 9, 10, 11], fruits and vegetables in various forms, e.g., dried or extracts [12, 13, 14], are valuable additives to wheat bread.  
Dandelion (_Taraxacum officinal_ [PERSON]) is a plant with an exceptionally valuable chemical composition. It occurs in Europe as a common weed [15]. Dandelion contains protein, fiber, fat, phenolic compounds, flavonoids, terpenoids, glycosides and various vitamins and minerals, including \(\beta\)-carotene, provitamin A, vitamins C and D, B vitamins, iron, silicon, magnesium, sodium, zinc, manganese, copper and phosphorus [16; 17; 18; 19]. In food production, various anatomical parts of this plant (roots, leaves and flowers) can be used as a rich source of many phytochemicals and minerals for human nutrition [17]. Roots of _Taraxacum officinal_ are a rich in inulin, a polysaccharide with probiotic properties, used for the microbiological production of fructose syrup [20]. The roots are also used for the production of tea and, after roasting, as a coffee substitute [21]. Root extracts can also be applied as a natural preservative, delaying oxidation in food products [22]. Young leaves of dandelion are usually eaten raw as an ingredient in cocktails or salads, e.g., in combination with lettuce or chives. They can also be cooked and, after draining, eaten with butter and sprinkled with pepper and salt. Dried leaves are used to prepare various soft drinks and wine. Dandelion flowers (DF) can be used as an additive during production of wines and desserts, and their extracts are used as flavoring ingredients for various food products, e.g., dairy desserts, cheeses, candies and cookies [15]. In earlier studies [23], we successfully used dried and powdered _Taraxacum officinal_ roots as an additive to wheat bread. These are the first studies aimed at determining the possibility of using dried DF powder as an additive to bread. The specific objectives of this work were (i) to determine the physical properties of dough with the addition of DF and (ii) to study the influence of physicochemical and sensory properties of DF-enriched bread.
## 3 Results and Discussion  
### Development of MW-SPM  
#### 3.1.1 Strategy for Method Development and Its Design  
Since spectrophotometric analyzers are frequently employed for assessment of pharmaceutical formulations, particularly when examining the uniformity of contents and characteristics of dissolution of solid dosage forms, spectrophotometric methods are of significant importance in drug analysis [31]. The spectrophotometric methods based on the formation of colored CTCs are widely used in the field of pharmaceutical QC of many pharmaceutical compounds [32; 33; 34; 35]. However, the conventional protocols of these methods are performed manually and accordingly their throughput is low. In addition, these methods also consume large volumes of costly organic solvents, and more importantly, the analyst is exposed to the poisonous effects of the organic solvents [36; 37; 38; 39]. Briefly, many reports assured the strong link between the exposure to these solvents in laboratories and the occurrence of neurotoxic signs [36], elevated incidence of some fertility disorders [37], elevated chance of spontaneous abortion between all pregnant women [38], and elevated danger of leukemic and lymphocytic malignancy in both males and females [39]. Accordingly, the reduction of human contact to the organic solvents have become a concern to health/environment protection agencies and administrations.  
In a previous study, [PERSON] et al. [40] successfully developed a novel methodological for reducing the consumption of organic solvents in the spectrophotometric analysis involving the formation of CTCs. This approach involved the 96-well assay plate as vessels in which the reactions were accrued out in situ, and an absorbance plate reader for measuring the color intensity in the 96-wells assay plate, simultaneously in less than 1 min. This approach significantly reduced the consumption of organic solvents and displayed high throughput. Therefore, the present research was devoted to developing a similar methodology for DUV and SEL. The CT reaction was taken into account in this study because of the good electron-donating property of both DUV and SEL, which is due to the presence of multiple basic amino groups in their chemical structures (Figure 1). A previous study involving charge transfer (CT) reactions with some electron acceptors of polyhalo-/polycyanoquinone derivatives [41] revealed that DDQ (Figure 1) is a very reactive acceptor, as it reacts instantaneously and gives highly sensitive signals. Therefore, DDQ was selected to develop the method described herein for both DUV and SEL.
## 3 Results and Discussion
### Antioxidant Capacities  
Evaluation of the antioxidant capacities by 2, 2-Azino-bis (3-ethyl-benzothazoline-6-sulfonic acid) diammonium salt (ABTS), ferric reducing antioxidant power (FRAP), cupric ion reducing capability (CUPRAC) and 1, 1-diphenyl-2-picrylhydrazyl scavenging radical (DPPH) assays showed the highest values by CUPRAC between \(103.81\pm 5.43\) and \(19.61\pm 0.93\)\(\upmu\)MT/g DW for all kiwifruit cultivars and persimmon (Table 1). The results calculated by ABTS, FRAP, CUPRAC and DPPH which connected with different kiwifruit cultivars are in agreement with several reports. So, the IC50 values of ABTS radical cation scavenging activities showed that _A. arguta_ determined to be 1.26 mg/mL in comparison with _A. deliciosa_ cv. Hayward of 22.72 mg/mL, presenting the strongest antioxidant activities among all tested kiwifruit cultivars, such as _A. chinensis_, _A. polygamma_ and _A. macosperma_(6; 20). The estimated results of AAC (Table 1) showed that the ABTS values were 2.4 times greater than ADH. The antioxidant capacities of kiwifruit flour 'Hayward' in vitro by DPPH and FRAP assays were 20 and 25 \(\upmu\)mol TE/g DW, respectively, and the free phenolic content was 14.57 mg GAE/g DW (7). These results are superior to those presented in Table 1, where the DPPH and FRAP values were 12.45 and 10.28 \(\upmu\)mol TE/g DW, respectively, and TPs were 6.27 mg GAE/g DW. These values exhibit good correlation between the polyphenols and their antioxidant capacities, showing that the cited results (7) were 1.6, 2.43 and 2.32 times higher for DPPH, FRAP and TPs, respectively, than the presented ones in Table 1. The values of persimmon 'Fuyu' showed TPs of 4.31 mg GAE/g DW, antioxidant capacities (\(\upmu\)mol TE/g DW) by ABTS, FRAP and DPPH methods--16.48, 9.03 and 9.58, respectively, and VC of 2.31 mg AA/g DW (Table 1). These results can be compared with cv. Vanilla, in which total polyphenols were estimated of 1 mg GAE/g FW, antioxidant capacities by ABTS--5 \(\upmu\)mol TE/g FW, FRAP--6 \(\upmu\)mol TE/g FW, and DPPH--0.3 mg GAE/g FW. VC content was in the range of 0.1-0.2 mg/g FW (44). Two innovative products (kaki and kiwi) were characterized by their bioactivity (8). The total phenolics have ranged from 2.1 mg GAE/g DW (kiwi) to 8.7 mg GAE/g DW (kaki), while dried fruit antioxidant
# A Theory of Slope Shear Scouring and the Failure Mechanism of PFC3D on a Gangue Slope
###### Abstract  
In this paper, scouring shear failure theory is optimized based on the gangue slope near the thermal power field in Baiguo Town, Panzhou City, Guizhou Province. Based on the particle flow PFC (particle flow code) 3D fluid-solid coupling method, the scouring failure mechanism of ditch no. 5 of the gangue slope is comprehensively analyzed from the perspectives of the failure mode, displacement, motion track, and stress-strain. We consider the scouring shear theory in respect of (c, \(\varphi\)); this theory is dominated by two types of scouring intensity factors and can effectively explain the internal mechanisms of gully formation. The rainfall scouring failure of gangue slopes can be divided into four stages: (1) integral splash erosion and local pitting at the bottom of the slope; (2) erosion diversion and pitting in the slope; (3) the tributary-slope crest extension schist erosion stage; and (4) integral gully erosion and the landslide stage. The failure process is not only characterized by discontinuous failure but also occurs in the order of bottom-middle-branch-top. A three-section stepped effect is observed during the process in which the gangue is scoured and destroyed, which fully verifies the intermittent characteristics of the scouring and destruction of gangue slopes. During the whole process, the maximum displacement is concentrated at the top of the slope, and its proportions are as follows: top of the slope > tributary > middle of the slope > foot of the slope. The peak displacement of the slope crest in the horizontal Y-direction accounts for 41.76%, and that in the Z-direction accounts for 45.84%. Scouring deposits can be divided into the arc erosion deposit mode and the sector erosion deposit mode. Mainstream gullies primarily control whether deposits are characterized as arc or straight erosion deposits. The later stage of the second phase of scouring is the incubation period of the tributary gully. The large accumulation makes the stress at the bottom of the slope increase sharply, and the fluctuation value is between 2 and 6.8 MPa. The generation of the branch notch is mainly determined by X-direction stress, and 8.6 MPa is the critical stress. In efforts to prevent and control rainfall and landslide, the slope foot area should be preferentially protected, and the soil mass in the slope should be reduced to prevent the maximum energy fluctuation caused by scouring, so as to prevent significant displacement damage of the slope top.  
particle flow; rainfall scouring; gangue; fluid-solid coupling; hydrodynamics 2023
## 4 Results and Discussion
### Analysis of the Results of Testing Mortar in the Hard State  
4.2.1. Flexural and Compressive Strength  
On standard specimens of prism shape, dimensions 4 \(\times\) 4 \(\times\) 16 mm, obtained values of flexural strength and compressive strength at the age of 28 days are depicted in Figure 6.  
Figure 6: Flexural and compressive strength of samples aged 28 days.  
From the results of the flexural strength tests on samples at the age of 28 days, it can be observed that:  
* The use of polypropylene fibers leads to an increase in the flexural strength values in series A-II (34.1%) compared to A-I, which can be explained by the fact that better adhesion with the fibers is achieved within the mortar matrix, resulting in improved resistance to bending;
* The application of fibers results in an increase in flexural strength values (17.77%) in series containing electrofilter ash B-II compared to B-I, although this difference is less prominent in these series;
* The flexural strength values of series B-I and series A-I differ by 5.78%. The addition of fly ash does not significantly affect the reduction in flexural strength values, which is in line with studies conducted by other authors [25]. Based on the test results of compressive strength at the age of 28 days and by comparing the recorded values, it was observed that:
* The series of mortar mixtures for cement grouts containing electrofilter ash B-I and B-II record a decrease in compressive strength of 23.28% and 25.74%, respectively, compared to series A-I and A-II that do not contain electrofilter ash as a partial replacement for cement;
* The application of polypropylene fibers causes an increase in compressive strength values for series A-II (7.93%) and series B-II (5.81%) compared to the series A-I and B-I;
* Mortar mixtures containing micro-reinforcement (series A-II and B-II) exhibit higher strengths compared to the standard (A-I and B-I) by approximately 10%, which is consistent with studies by other authors [26];
* By replacing cement with electrofilter ash in the amount of 20%, along with the addition of polypropylene fibers, the compressive strength obtained is lower by 14.17% compared to traditionally prepared mortar mixture for cement grouts, represented by series A-I.
## 2 Bidirectional FSI Numerical Model
### Bidirectional FSI Algorithm  
The multi-body dynamics theory was combined with the MPS theory to construct a bidirectional transient solid-liquid coupling model. During the model construction process, both the contact between the drill string and the wellbore wall and the FSI effects are considered simultaneously. The fluid domain and the structure domain achieve data exchange through their contact interface. The structure of the drill string mainly affects the drilling fluid through displacement and velocity, while the drilling fluid is mainly applied to the wall surface of the drill string in the form of wall force. The bidirectional coupling process is shown in Figure 7 and can be divided into the following steps:  
Step 1: Solve the structural domain (drill string) and fluid domain (drilling fluid) at time step t(\(i\)). Transfer the fluid-induced wall forces and torque calculated from the fluid domain to the corresponding structural boundaries as external loads.  
Step 2: Update the drill string configuration by solving the structural dynamics under applied fluid loads, obtaining nodal displacements and velocities at t(\(i+1\)).  
Step 3: Archive the updated structural parameters (positions, velocities) at t(\(i+1\)) for subsequent coupling iterations.  
Step 4: Propagate the t(\(i+1\)) structural boundary conditions (positions, velocities) to the fluid domain to redefine moving interfaces.  
Step 5: Advance the fluid domain transient simulation to t(\(i+1\)) using the updated structural boundary data.  
Step 6: Recalculate fluid-induced forces (pressure, shear) at the fluid-structure interface for t(\(i+1\)).  
Step 7: Map the recalculated fluid loads (forces, torque) to the structural domain as boundary conditions for the next time step.  
Step 8: Iterate Steps 1-7 until convergence or terminal simulation time.  
Figure 5: Drill string and wellbore wall models.  
Figure 6: Schematic diagram of the drill string-drilling fluid coupled model.
## 2 Basic Theory and Calculation Method
### Gap Parameter  
In order to analyze the effects of the above factors, [PERSON] et al. [69; 70] proposed the concept of gap parameter to reflect the stratum loss caused by shield tunnel construction, transforming the three-dimensional problem to a two-dimensional gap in plane. The composition of the gap takes into account the following factors: Elastic-plastic deformation of the shield at the tunnel excavation face, the over-excavation of the shield machine, the physical gap between the outer skin of shield shell and segment lining, and the construction technology. The formula for calculating the gap parameters \(g\) under undrained condition is as in Equation (1).  
\[g=G_{P}+U_{3D}^{\star}+\omega \tag{1}\]  
where shows that the gap parameters are composed of three parts [44]:  
The first part \(G_{p}\) is the physical gap, which represents the gap between the outer skin of shield shell and segment lining; its expression is as in Equation (2):  
\[G_{P}=2\Delta+\delta \tag{2}\]  
where \(\Delta\) is the thickness of shield shell and \(\delta\) is the space formed by segment lining installation.  
The second part \(U_{3D}\) is the elastic-plastic deformation of shield at the tunnel excavation face; its quantitative expressions are as in Equations (3) and (4):  
\[U_{3D}=\frac{k}{2}\delta_{y}=\frac{k}{2}\frac{\Omega RP_{0}}{E} \tag{3}\]  
\[P_{0}=K_{0}^{\prime}P_{v}^{\prime}+P_{w}-P_{f} \tag{4}\]  
where \(k\) is the soil cutting resistance coefficient; \(\Omega\) is the displacement coefficient; \(R\) is the tunnel radius; \(E\) is the elastic modulus; \(K_{0}\) is the effective static lateral pressure coefficient; \(P_{v}^{\prime}\) is the effective vertical stress at depth of tunnel center; \(P_{w}\) is the pore water pressure at depth of tunnel center; and \(P_{f}\) is the supporting force provided by tunnel.  
Figure 2: Surface displacement caused by shield tunnel.  
The third part \(\omega\) is the shield construction technology coefficient, of which the value is as in Equation (5):  
\[\left\{\begin{array}{l}\frac{U_{i}}{R}=1-(\frac{1}{1+\frac{2(1+ \mu_{u})C_{u}}{E_{u}}\bigg{[}exp(\frac{N-1}{2})\bigg{]}^{2}})\frac{1}{2}\\ N=\frac{\gamma H-p_{f}}{C_{u}}\\ \omega=min\bigg{\{}0.6G_{p},\ \frac{1}{3}U_{i}\bigg{\}}\end{array}\right. \tag{5}\]  
where \(U_{i}\) is the elastoplastic plane strain displacement at the tunnel crown; \(\mu_{u}\) is the soil Poisson's ratio under undrained condition; \(C_{u}\) is the soil shear strength under undrained condition; \(E_{u}\) is the soil elastic modulus under undrained condition; \(\gamma\) is the unit weight of soil; and \(H\) is the buried depth of tunnel center.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 3 Results and Discussion
### Comparing Different Classification Methods  
#### 3.3.1 Classification Results of MLE  
After the dimension reduction of PCA, MNF, and ICA, the obtained data were classified by the MLE method. The overall accuracy (OA) of classification and Kappa coefficient are shown in Table 2. Compared with the different classification accuracies of different dimension reduction methods, data obtained after the MNF dimension reduction method have a higher classification accuracy, followed by data obtained after the PCA dimension reduction method, and data obtained by ICA have a lower classification accuracy. Their OAs and Kappa coefficients are 77.23%, 0.7339; 72.86%, 0.6831; and 64.74%, 0.5888, respectively. The overall classification accuracy difference between the first two optimal dimensionality reduction methods MNF and PCA is 3.84%, but the classification accuracy of ICA is much lower than that of MNF and PCA, and the difference between ICA and the optimal classification accuracy is 18.25%. The MLE classification accuracy of hyperspectral data without dimensionality reduction is only about 50%.  
The classification results of MLE can, respectively, be seen in Figure 10a-d. In Figure 10a, it is clear that there is a large number of misclassifications in each winter wheat cultivar, and salt-and-pepper noise is serious. In Figure 10b,c, the MLE classification results show less misclassification and salt-and-pepper noise after PCA and MNF dimensionality reduction. However, some types of winter wheat mix up together, such as type 2 and type 3, and the field bank between wheat cannot be classified. In Figure 10d, on the one hand, compared with PCA-MLE and MNF-MLE, the classification results of ICA-MLE is poor, especially the serious misclassification among type 3, type 4, and type 5. On the other hand, the classification of field banks is better, especially the classification of the longitudinal filed bank is much better than that of PCA and MNF. In summary, for different dimension reduction and classification combinations, MNF-MLE is the best, followed by PCA-MLE, and ICA-MLE is poor. Among them, MNF-MLE and PCA-MLE have few differences in classification results, but their classification results are far superior to ICA-MLE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensionality Reduction** & **Classifier** & **Overall Accuracy** & **Kappa Coefficient** \\ \hline Original & MLE & 49.51 & 0.4101 \\ PCA & MLE & 72.86 & 0.6831 \\ MNF & MLE & 77.23 & 0.7339 \\ ICA & MLE & 64.74 & 0.5888 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification results based on MLE.  
Figure 10: MLE results of different dimensionality reduction methods: (**a**) Original-MLE; (**b**) PCA-MLE; (**c**) MNF-MLE; (**d**) ICA-MLE.