esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
###### Abstract  
In the past few years, research efforts have focused on plant exploitation for deriving some valuable compounds. Extraction has been usually performed using petrochemical and volatile organic solvents, but nowadays, increased recognition of environmental pollution has prompted the utilization of green solvents as alternatives. Therefore, the aim of the present study was to exploit deep eutectic solvents (DES) (choline chloride: citric acid and choline chloride: glucose) as solvents for extracting valuable phenolic antioxidants from grapes. Investigation was conducted on ten grape varieties, observing seeds and skin as different matrix. Total polyphenol content (TPC) was determined by _Folin-Ciocalteu_ spectrophotometric microassay. Antioxidant activity was investigated using four different tests and results were combined in a unique Antioxidant Composite Index (ACI) to reveal comprehensive information about this biological activity. Polyphenol compounds were identified and quantified with the aim of HPLC-diode array detector (DAD). Overall results support that DES (particularly choline chloride: citric acid) were comparable to conventional solvent, and in most cases even outperformed acidified aqueous ethanol (concerning extraction efficiency and antioxidant activity). Regardless of varietal distinctions, grape seeds have higher antioxidant capacity compared to grape skin, and such findings are according to their phenol compound concentrations.  
g article Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 1 Introduction  
The Metropolis Monte Carlo method [1] is the first computational procedure that was shown capable of sampling an ensemble of configurations suitable for statistical-mechanical treatment. While current simulation work in macromolecular systems generally employs the molecular dynamics approach with significant success, there are applications where Monte Carlo was found to be more efficient [2]. Also, the ease of restricting the sampling to a limited number of degrees of freedom makes Monte Carlo an attractive choice [3]. However, it is important to recognize that the Metropolis method actually encompasses a large family of methods [4] and thus there is still room for significant improvements and for 'niche' applications [5].  
One of the factors determining the computational efficiency of Monte Carlo (MC) simulations is the proper selection of the range of changes from which trial moves will be sampled, commonly represented by the so-called stepsize parameters, e.g., the edge of the cube around a molecule within which the trial position must fall or the width of a torsion angle interval within which the trial angle should fall. Generally, the larger these parameters the larger conformational change is made in each step, but the smaller the acceptance probability will be. However, selection of stepsizes resulting in a targeted acceptance rate is a nontrivial process for system having several degrees of freedom of different types.  
The present paper proposes an automated method that employs the proportional-integral controller (PIC) technique, well-known in engineering application. It has recently been shown to work extremely well in controlling the density during grand-canonical ensemble simulations [6].  
In general, it is difficult to establish the optimal acceptance rate since the sampling efficiency is only known with any certainty after several long simulations. For systems with smooth free energy landscape, the accumulated experience in the field leads to target acceptance rates around 30% instead of the assumption of the early practitioners that 50% is the best [7; 8]. For systems with a rugged free energy landscape, however, the optimum shifts toward smaller acceptance rates since larger stepsizes may cross easier barriers separating separate free-energy basins [9].  
The present paper leaves the question of establishing the optimal acceptance rates for further study. Instead, it provides an automated solution of the problem of determining the stepsize parameters that produce a targeted acceptance rate, e.g., 30%. The availability of such technique, however, could help in establishing the optimum acceptance rate in a different study.  
It should also be emphasized that while optimizing the stepsize selection is likely to produce a significant improvement in the ease and efficiency of simulating biochemical systems with the Monte Carlo approach, other developments, such as the generalized ensemble approach (e.g., Ref. [10]) or the development of more complex moves may prove more important. However, such developments can also employ the stepsize tuning technique described in the present paper.
## 4 Experiments and Discussion
### Self-Assembling Experiments  
Using the controller, docking experiments at different distances and angles were performed. The front robot was placed in situ and the docking robot was laid behind it. As shown in Figure 14, it can be observed that the robot docked with the front one successfully and the path of the moving SMMRob is depicted in a red curve. The lateral distance, longitudinal displacement, and heading angle are depicted in Figure 15. At far, middle, and near distances within certain angles, 70 points were selected as starting locations for test docking. The success or failure cases are presented in Figure 16. A close distance and a small angle proved to be easy for docking, whereas a small distance and large angle was difficult because there not enough travel distance to adjust lateral displacement and heading. According to the data, within a distance of 30 cm and angle of 20\({}^{\circ}\), the success rate was about 50%.  
Figure 12: Pitch and yaw angles of the joint and maximum payload.  
Figure 13: Video screen in the master computer from the stereo camera of the head module.  
Figure 16. Success and failure results of docking experiments in different ranges.  
Five SMRRobs were made to perform self-assembling experiments to build a snake-like composite entity. An SMRRob head module with a lidar and a stereo sensor was placed in situ. Four motion modules docked with the front one according to a self-assembly  
Figure 14. SMRRob docking experiments and the path of the moving robot.  
Figure 15. Visual feedback output of a moving SMRRob docking with the front one, depicting the lateral distance, longitudinal displacement, and heading angle.  
sequence and then the assembled entity was able to move in a chain as a whole. As shown in Figure 17, the time required for self-assembly was 155 s, which was single-test data. The time of docking of each robot was different according to its initial position and heading. To form a quadruped structure, a head module with four passive docking panels was placed in situ, four motion modules docked with the head, and then the quadruped was able to lift the four legs to stand, as shown in Figure 18.
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 3 Results
### GPP Changes  
Since 2000, the overall trend of the annual mean value of GPP in the growing season in the study area increased significantly, with a change rate of 3.65 gC\(\cdot\)m\({}^{-2}\)year\({}^{-1}\) (\(p\) < 0.01). From 2000 to 2019, the average GPP in the growing season increased from 553.77 gC\(\cdot\)m\({}^{-2}\) to 624.33 gC\(\cdot\)m\({}^{-2}\), an increase of 12.74% (Figure 4a). Among them, the average value of GPP in the growing season was the lowest at 553.77 gC\(\cdot\)m\({}^{-2}\) in 2000, and the average value was the highest at 728.83 gC\(\cdot\)m\({}^{-2}\) in 2016. During the study period, the GPP of the three land cover types showed an increasing trend. Cropland and forest showed a significant increasing trend (\(p\) < 0.01), while grassland showed a slight upward trend (\(p\) > 0.05). The GPP of different land cover types showed a great difference in growing seasons, and the GPP of forest and cropland had a high coincidence with the overall GPP trend in southwest China. Forest was the land cover type with the highest gross primary productivity, and the highest average GPP in the growing season was 654.7 gC\(\cdot\)m\({}^{-2}\); the second was cropland, which was 571.63 gC\(\cdot\)m\({}^{-2}\); and the lowest value of grassland was 420 gC\(\cdot\)m\({}^{-2}\). Since forest had the highest average GPP, the transfer of forest to other land cover types led to a decrease in GPP, and the conversion of cropland and grassland to forest led to an increase in GPP. In general, the carbon storage of land cover increased, indicating that the ecological environment in southwest China improved.  
The average value of the growing season in southwest China from 2000 to 2019 was 594.61 gC\(\cdot\)m\({}^{-2}\), which showed a decreasing trend from southeast to northwest, corresponding to the distribution of cropland, forest, and grassland, with significant spatial differences. The high-value regions of GPP were mainly distributed in northwestern Yunnan, southeastern Guangxi, Sichuan Basin, etc. In addition, the GPP was relatively large in central and southern Guizhou, which was covered by forest and cropland. However, the concentrated distribution region of grassland in northwestern Sichuan, the concentrated distribution region of cropland in the Sichuan Basin, and the GPP value were generally lower than the average value (Figure 4b).  
Figure 4: **(a)** Interannual GPP changes in different land use types; the dashed line shows the variation trend of GPP in different land use types, where the black is GPP mean, the formula is y = 3.65x \(-\) 6742.5; the yellow is cropland’s GPP, the formula is y = 4.36x \(-\) 8195.02; the cyan is forest’s GPP, the formula is y = 3.97x \(-\) 7322.35; the light green is grassland’s GPP, the formula is y = 0.94x \(-\) 1463.16 and (b) spatial distribution pattern of GPP values of different land cover types in Southwest China from 2000 to 2019.
## 2 System Model
### Two-Stage Precoder
It is obvious that the minimum mean squared error (MMSE) type of precoder can achieve better performance than the ZF precoder. However, with the MMSE precoder, the optimal regularization parameter for the inner precoder design is dependent on the outer precoder design, so it is not easy to find. Moreover, we consider limited feedback environment, so the effects of channel quantization errors make the optimal regularization factor much more difficult to find. Thus, we consider the ZF precoder for inner precoder design thanks to its simplicity and analytical tractability. Note that the ZF beamforming is asymptotically optimal among all downlink beamforming strategies in a high SNR region [6; 29], and it guarantees high spectral efficiency for large-scale antennas with low-complexity linear processing [2; 9]. Then, the inner precoder for the \(g\)-th group is given by  
\[\mathbf{V}_{g}=\left[\mathbf{v}_{g,1},...,\mathbf{v}_{g,K_{g}}\right]\in \mathbb{C}^{\mathbf{s}_{g}\times K_{g}}, \tag{11}\]  
where \(\mathbf{v}_{g,k}\) is a ZF beamforming vector of user \(k\) in the \(g\)-th group. Note that a beamforming vector \(\mathbf{v}_{g,k}\) is normalized such that \(\|\mathbf{v}_{g,k}\|^{2}=1\), since the two-stage beamforming vector for user \(k\) in the \(g\)-th group with the BD-based outer precoder, i.e., \(\mathbf{f}_{k}=\mathbf{T}_{g}\mathbf{v}_{g,k}\), should satisfy \(\mathbf{f}_{k}^{\dagger}\mathbf{f}_{k}=1\), which is given by  
\[\mathbf{f}_{k}^{\dagger}\mathbf{f}_{k}=\mathbf{v}_{g,k}^{\dagger}\mathbf{T}_{ g}^{\dagger}\mathbf{T}_{g}\mathbf{v}_{g,k}=\mathbf{v}_{g,k}^{\dagger}\mathbf{I}_{g }\mathbf{v}_{g,k}=\mathbf{v}_{g,k}^{\dagger}\mathbf{v}_{g,k}=\|\mathbf{v}_{g, k}\|^{2}=1, \tag{12}\]  
for equal power allocation at the BS in (1). The details of the construction of a ZF beamforming vector of user \(k\) in the \(g\)-th group is characterized in the following subsection.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 3 Proposed Dereverberation Method
### Low-Complexity Algorithm Based on the Kronecker Product
Moreover, the state equation and the observation equation of the improved low-complexity Kalman filter are given as follows:  
\[\mathbf{w}_{1}(l)=\mathbf{A}^{H}(l)\mathbf{w}_{1}(l-1)+\mathbf{w}_{\Delta}(l) \tag{42}\]  
and  
\[x_{\mathrm{b}}^{*}(l)=\mathbf{t}_{\mathrm{w}}^{H}(l)\mathbf{w}_{1}(l)+x_{ \mathrm{e}}^{*}(l) \tag{43}\]  
where the process noise \(\mathbf{w}_{\Delta}\) (\(l\)) can be modeled by the zero-mean Gaussian process with the covariance matrix \(\mathbf{\Phi}_{\mathrm{w}_{\Delta}}(l)=\mathrm{E}[\mathbf{w}_{\Delta}(l) \mathbf{w}_{\Delta}^{H}(l)]\). Let \(\hat{\mathbf{w}}_{1}\) (\(l\)) be the estimated result by the state Equation (42) and \(\hat{\mathbf{w}}_{1}^{+}(l)\) be the modified result with the observation Equation (43); their corresponding errors are obtained as follows:  
\[\mathbf{e}_{1}(l)=\mathbf{w}_{1}(l)-\hat{\mathbf{w}}_{1}(l) \tag{44}\]  
\[\mathbf{e}_{1}^{+}(l)=\mathbf{w}_{1}(l)-\hat{\mathbf{w}}_{1}^{+}(l) \tag{45}\]  
the covariance matrices of \(\mathbf{e}_{1}(l)\) and \(\mathbf{e}_{1}^{+}(l)\) are given as follows:  
\[\mathbf{\Phi}_{\mathbf{e}_{1}}(l)=\mathrm{E}\Big{[}\mathbf{e}_{1}(l)\mathbf{ e}_{1}^{H}(l)\Big{]} \tag{46}\]  
\[\mathbf{\Phi}_{\mathbf{e}_{1}}^{+}(l)=\mathrm{E}\Big{[}\mathbf{e}_{1}^{+}(l) \mathbf{e}_{1}^{+}(l)^{H}\Big{]} \tag{47}\]  
According to the state Equation (42), the observation Equation (43), and following cost function:  
\[\mathbf{w}_{1}(l)\otimes\mathbf{w}_{2}(l)=[\mathbf{I}\otimes\mathbf{w}_{2}(l)] \mathbf{w}_{1}(l)=\mathbf{W}_{2}(l)\mathbf{w}_{1}(l) \tag{39}\]0  
we can obtain update equations for Kalman filtering with low complexity similar to Equations (31)-(37) as follows:  
\[\mathbf{w}_{1}(l)\otimes\mathbf{w}_{2}(l)=[\mathbf{I}\otimes\mathbf{w}_{2}(l)] \mathbf{w}_{1}(l)=\mathbf{W}_{2}(l)\mathbf{w}_{1}(l) \tag{39}\]1  
\[\mathbf{w}_{1}(l)\otimes\mathbf{w}_{2}(l)=[\mathbf{I}\otimes\mathbf{w}_{2}(l)] \mathbf{w}_{1}(l)=\mathbf{W}_{2}(l)\mathbf{w}_{1}(l) \tag{39}\]2  
\[\mathbf{w}_{1}(l)\otimes\mathbf{w}_{2}(l)=[\mathbf{I}\otimes\mathbf{w}_{2}(l)] \mathbf{w}_{1}(l)=\mathbf{W}_{2}(l)\mathbf{w}_{1}(l) \tag{39}\]3  
\[\mathbf{w}_{1}(l)\otimes\mathbf{w}_{2}(l)=[\mathbf{I}\otimes\mathbf{w}_{2}(l)] \mathbf{w}_{1}(l)=\mathbf{W}_{2}(l)\mathbf{w}_{1}(l) \tag{39}\]4  
\[\mathbf{w}_{1}(l)\otimes\mathbf{w}_{2}(l)=[\mathbf{I}\otimes\mathbf{w}_{2}(l)] \mathbf{w}_{1}(l)=\mathbf{W}_{2}(l)\mathbf{w}_{1}(l) \tag{39}\]5  
where \({s_{1}}^{*}\) (\(l\)) is the final enhanced signal by the low-complexity algorithm with power spectral density \(\phi_{\rm s_{1}}(l)\) and \({\bf k}_{1}\) is the Kalman filter gain of the low-complexity algorithm. Thus, the coefficients of the interframe filter \({\bf w}_{1}\) (\(l\)) can be estimated. Through Equations from (38) to (55), the dimension of the filter coefficients \({\bf w}_{\rm r}\) (\(l\)) is significantly reduced so that the computational complexity is reduced effectively. The effectiveness of the low-complexity algorithm is discussed in the experiments.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design  
The following section summarizes the types of reward functions used. Different approaches use different combinations of the categories mentioned below, as seen on Table 5. Some approaches normalize the rewards [95; 152; 163], for example, using upper bound [17], min-max [207; 249], z-score [296] or average expected time [170].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Group** & **Features** & **References** \\ \hline \multirow{8}{*}{Mirror objective function} & & [19; 21; 22; 28; 33; 37; 51; 65; 72; 79; 93; 107; 114; 119; 131; 138; 149, 152; 163; 164; 204; 206; 214; 222; 223; 226; 230; 247; 264; 267; 279, 286; 307; 308] \\ \cline{2-3}  & Negative & [49; 50; 80; 81; 175; 199; 237; 295] \\ \cline{2-3}  & Weighted & [46; 59; 73; 89; 113; 159; 228; 249; 284] \\ \cline{2-3}  & Inverse & [16; 55; 87; 247; 280] \\ \cline{2-3}  & Multiple objectives in one & [23; 28; 46; 59; 89; 103; 109; 113; 134; 136; 141; 201; 232; 241; 249, 264; 285; 289; 296] \\ \hline \multirow{8}{*}{Relative rewards} & & [26; 29; 35; 49; 56; 62; 77; 78; 80; 82; 84; 98; 109; 112; 117; 122; 130, 139; 142; 144; 189; 190; 198; 204; 208; 211; 212; 220; 232; 259; 260, 266; 268; 269; 273; 275; 277; 278; 308] \\ \cline{2-3}  & Initial solution & [109; 178; 179; 181; 195] \\ \cline{2-3}  & Global best & [29; 188; 244; 259; 263; 286] \\ \cline{2-3}  & Other baselines & [32; 95; 138; 148; 190; 211; 212; 233] \\ \hline \multirow{8}{*}{Extra terms} & Scheduling & [75; 110; 154; 194; 195; 212; 245; 247; 280; 301] \\ \cline{2-3}  & Lateness & [71; 92; 105; 140; 153; 173; 184; 191; 208; 215] \\ \cline{2-3}  & Cost & [101; 108; 115; 150, 151; 215; 225; 227; 267; 280; 307] \\ \cline{2-3}  & Network-related & [46; 47; 128; 162; 187; 205; 213; 236; 246] \\ \cline{2-3}  & Constraint as penalty & [43; 44; 131; 155; 199; 213; 235; 239; 251; 258; 261; 267; 283; 292; 301] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Common reward designs.  
One of the most direct yet simple approaches is to mirror the objective function as the iteration reward or penalty. The reward value can also reflect the relative improvement of the solution over successive iterations. Both these approaches are often complemented with extra terms that help the agent better achieve the original objective.  
Some rewards are the result of multiple reward functions, where certain environment conditions decide which function is used in each iteration. Based on the problem to solve, specific objectives might also be applicable. Multi-agent approaches can use multiple reward functions per agent or have specific rewards based on local and global events. Finally, when it is not possible or convenient to measure the objective function, alternative goals can be used.
## 4 Discussion
Additionally, the geometric design of files directly affects the total mass of the instrument, which has proven to be statistically significant in determining the stiffness and, therefore, the resistance to the cyclic fatigue of the files. The length of the files increases the mass of the instruments and may also explain the impact of this particular variable on cyclic fatigue resistance. [PERSON] et al. experimentally found that the number of threads directly correlates with increased flexural stiffness [29]; meanwhile, [PERSON] et al. reported that a shorter pitch design is correlated with increased cyclic fatigue resistance of glide path instruments [30]. Additionally, [PERSON] et al., experimentally showed that a greater helix angle value has a positive effect on the mechanical behavior of the instrument under conditions of bending and torsion [31]. [PERSON] et al., studied the cyclic fatigue of NiTi alloy endodontic rotary files and discovered that instruments with shafts of larger diameter failed in fewer cycles than files with lower diameters under identical testing conditions [18]. Additionally, comparisons of the 31 mm length, 25 mm length, and 21 mm length 25.06 CM Gold wire NiTi alloy endodontic rotary files and the 17 mm length CM Blue wire rotary files also showed that CM Blue wire NiTi alloy endodontic rotary files had higher static cyclic fatigue resistance than CM Gold wire NiTi alloy endodontic rotary files. That being said, it is challenging to isolate the most determinant or relevant variable or variable combination when it comes to the resistance to cyclic fatigue of NiTi endodontic rotary files. These results are corroborated when comparing the cyclic fatigue resistance of the NiTi CM Blue wire alloy from the Reciproc Blue endodontic reciprocating system with the NiTi CM Gold wire alloy from the Wave One Gold endodontic reciprocating system. The Reciproc Blue endodontic reciprocating system had a higher cyclic fatigue resistance than the Wave One Gold endodontic reciprocating system, perhaps owing to its cross-sectional design [32; 33]. The Weibull analysis enables estimation of the probability of a material presenting fracture over time. A more vertical curve denotes greater predictability of the mechanical behavior of a material, since it would indicate that all samples fracture at the same moment. However, a more horizontal curve denotes greater unpredictability of the behavior of a material as it could fracture at any time. These are expressed with shape and scale parameters. A larger scale, a broader distribution, and a shape parameter greater than 1 indicate that the failure rate increases with time. Regrettably, the limitations of the present study prevented an analysis of additional lengths to standardize the NiTi alloy, pitch, helix angle, apical diameter, speed, taper, and manufacturing process. Furthermore, difficulties in standardizing samples meant that the study was not carried out in a clinical environment.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 3 Results  
To verify the effectiveness of the algorithm proposed in this paper, both subjective and objective experimental comparisons were conducted. All algorithms used in this study were implemented uniformly under the Python 3.7 software platform. The computer hardware configuration included an Intel Core i7 processor with 16 GB RAM, operating on the Windows 7 operating system.
## 4 Limitations and Future Work
### Optimizing the Path of the Robot Arm to Remove Rocks  
A limitation in the applicability of this study to real-world mining sites arises from the inhomogeneity of sizes of rocks in actual mining scenarios and the possibility of multiple rocks obstructing the hopper. Thus, the gripper arm cannot be reliably guided to a well-defined center point of the rock causing the blockage following a carefully pre-designed path. In addition, if the jammed rocks cannot be pushed out by the robot arm or if the rocks are larger than the size of the inlet in an actual mining application, the gripper of the robot arm will require to be equipped with a crushing mechanism, e.g., a drill. In addition, in this study, the identification of rocks and calculation of their distances were performed using only one camera placed vertically above the inlet. In actual applications, an additional camera should be installed, enabling immediate rock recognition. Finally, when pushing rocks using the robot arm, factors such as size and number of rocks, the direction of inclination, and the center coordinates of rocks should be analyzed while planning a movement path for the robot arm.