esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**A Microwave-Assisted Simultaneous Distillation**  
**and Extraction Method for the Separation of**  
**Polysaccharides and Essential Oil from the**  
**Leaves of _Taxus chinensis_**Var. _mairei_**  
[PERSON] 1,2,3, [PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1,2,3, [PERSON] 1,2,  
[PERSON] 1,2 and [PERSON] 1,2  
Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China; [EMAIL_ADDRESS] (C.Z.); klp13 [EMAIL_ADDRESS] (X.H.); [EMAIL_ADDRESS] (L.Y.); [EMAIL_ADDRESS] (Y.F.); klp14 [EMAIL_ADDRESS] (K.W.); klp14 [EMAIL_ADDRESS] (Y.Z.); klp14 [EMAIL_ADDRESS] (Y.N.)  
State Engineering Laboratory of Bio-Resource Eco-Utilization, Northeast Forestry University, Harbin 150040, China  
Collaborative Innovation Center for Development and Utilization of Forest Resources, Harbin 150040, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-451-82190848; Fax: +86-451-82102082  
Academic Editor: [PERSON]  
Received: 6 October 2015; Accepted: 8 January 2016; Published: 27 January 2016  
**Abstract:** An efficient microwave-assisted simultaneous distillation and extraction (MA-SDE) method has been developed for the separation of polysaccharides and essential oil from _Taxus chinensis_ var. _mairei_. The key operating parameters for MA-SDE were optimized by single factor and central composite design experiments, and the optimal conditions were found to include a particle size of 60-80 mesh, liquid/solid ratio of 22.5 mL/g, extraction time of 17.5 min, microwave power of 547 W, and dichloromethane was used as the extraction solvent of the essential oil. The yields obtained for polysaccharides and essential oil under the optimized conditions were 6.39% \(\pm\) 0.12% and 0.27% \(\pm\) 0.03%, respectively. The MA-SDE method was also compared with conventional heat reflux extraction (HRE) and hydrodistillation extraction (HDE). The MA-SDE method not only allowed for the simultaneous extraction of polysaccharides and essential oil, but also completed the task with a much shorter extraction time of 17.5 min (HRE and HDE required 3 and 6 h, respectively). Furthermore, the MA-SDE method gave increased extraction yields for polysaccharides (1.14-fold higher than HRE) and essential oil (1.23-fold higher than HDE). Based on these results, this MA-SDE method represents a rapid and efficient technique for the simultaneous extraction of polysaccharides and essential oil.  
**Keywords:** essential oil; microwave-assisted simultaneous distillation/extraction; polysaccharides; _Taxus chinensis_ var. _mairei_
## 4 Control Strategies
### PMSG-Based Wind Turbine Control
#### 4.2.2 Control for Proportional Load Sharing  
As explained in Section 3, when the microgrid is islanded, if the battery is full, a full charge and the DG units' generated power exceeds the total load power demand, and the excessive produced energy starts to flow to the microgrid DC bus capacitors, making the DC voltage rise. A scheme can again be applied to reduce the PMSG output power proportionally to the DC bus voltage increase per unit of the maximum allowed voltage increment.  
Based on the wind generation efficiency characteristic shown in Figure 7, it can be observed that if the tip speed ratio \(\lambda\) is increased from the optimum value for MPP (\(\lambda^{opt}=6.9\)), the efficiency factor \(C_{p}\) decreases from the maximum value (0.42), becoming zero when \(\lambda\cong 15.5\). Therefore, an adjustment in the value of the tip speed ratio can be made, according to Figure 8, to force a reduction of generated power in proportion (approximately) to the increase in the DC bus voltage increase.  
It is essential to mention that, similarly to the PV power reduction method proposed, wind power is reduced from the level corresponding to the MPP condition in a linear relation with the microgrid DC bus voltage increase per unit of the maximum DC bus voltage variation. Thus, the total load power-sharing among all DG units will be approximately proportional to their respective instantaneous MPPs.
## 2 Problem Formulation
### The Toy Problem Used in This Paper  
Instead of describing the problem by a partial derivative equation, we may instead assume that the solution of this equation is known explicitly. This is the approach we use in the simulations presented in this paper because this is quicker to simulate. We use for instance the following expression:  
\[\begin{cases} u(\mathbf{p},t+\Delta t)=\underbrace{\frac{1}{1+\left\| \mathbf{p}-\mathbf{p}_{s}(t)\right\|^{2}}}_{1}+\underbrace{(1-e^{-\frac{\Delta t }{\tau}})u(\mathbf{p},t)}_{2}\\ f(\mathbf{p},t)=u(\mathbf{p},t)+\underbrace{\beta(\mathbf{p},t)}_{3}.\end{cases} \tag{1}\]  
In these equations, \(\Delta t\) is a discrete time step. The Equation (1) contains three elements to represent real-world phenomena:  
1. a spatial term which decreases with the distance between target position \(\mathbf{p}_{s}\) and any position \(\mathbf{p}\) in the workspace; this is the inverse square law induced by mechanisms of conservation of power through propagation, modified with a constant additive term in the denominator to prevent the value from becoming unreasonably large when \(\left\|\mathbf{p}-\mathbf{p}_{s}(t)\right\|\to 0\);
2. a temporal term, representing a decay, inspired by the response to a first-order filter model parameterized by the time constant \(\tau\);
3. an additive white Gaussian noise \(\beta(\mathbf{p},t)\sim\mathcal{N}(0,\sigma)\) in the whole environment, with \(\sigma\ll\max(f)\), representing measurement noise.  
In our paper, dimension \(D\) can be 1 or 2 or more, and it will be indicated explicitly when it is necessary. Simulation results are not treated in higher dimensions, but we assume that the results we present should be close to the results displayed in Dimension 2 because there are no cross-dimensional terms in the PSO equation described below in Expression (2).  
We must stress that the exact mechanism behind the generation of function \(u\) does not need to be known for the problem to be solved. The only hypotheses that matter, are that (i) there is only one spatial maximum for \(u\) at each date and (ii) that the maximum is reached close to the actual position of the target at a given date. This way, all that matters is finding the maximum of \(u\) at each date by sampling \(f\) at the positions of the agents in the swarm.  
This model is less complex that in the state-of-the-art [31; 37; 43], where the plume model is dynamic and with multiple local maxima. Environmental sensors help the modelisation of this plume, an anemometer or ocean current sensor for example. However, in OSL, the source is static, and the important point we wanted to highlight is the unpredictability of the source behavior. Hence, study our algorithm with both the plume and source dynamics will complicate the analysis of our algorithm. For this reason, the analysis takes into account only the dynamics of the source, and an analysis with a dynamic plume model will be part of a future work. We note that the measurement noise can create multiple local maxima, but this phenomenon can disappear instantaneously at the next time step for the agent \(i\).
## 4 Case Study Investigation
### Asphal Mixture Compaction Monitoring System  
As part of the asphalt mixture compaction monitoring system, a real-time monitoring device was installed on the roller shaft of the road roller to monitor the main compaction indicators and collect parameters. The road roller was also equipped with BDS positioning components, temperature sensors, and vibration sensors, which collected key data such as the number of compaction passes, compaction speed, compaction trajectory, temperature, and vibration frequency. The system also transmitted all the collected data through the 5G transmission channel. The compaction monitoring date shown in Figure 19. In the data management system, the command center also directed staff to carry out a series of operations, such as compaction, through the vehicle-mounted display terminal. The asphalt concrete pavement construction quality monitoring platform monitored the data of the  
Figure 17: Paving site.  
Figure 18: Paving monitoring information.  
construction section, collected compaction degree and other data, and drew the compaction degree curve (as shown in the Figure 20). In addition, the relationship between the degree of compaction and the secondary resonance spectrum during vibration rolling was established, and the calculation parameters of vibration compaction degree were adjusted. The three colors of red, yellow and blue were used to calibrate the road compaction degree. The compaction monitoring system and compaction site are shown in Figure 21.  
Figure 19: Compaction monitoring information.  
Figure 20: Compaction curve.
# Enhanced Adsorption of Methyl Orange by Mongolian Montmorillonite after Aluminum Pillaring
## 4 Discussion
### Surface Area of Modified MMt  
The increase in the BET surface area (Table 3) of Al-intercalated MMt (P-MMt) and Al-pillared MMt (P-MMt-C400) is mainly due to the micropores (0.6-2.0 nm), with threepossible reasons: (a) the micropores are formed from the voids in the Keggin Al\({}_{13}\) complex that is intercalated into MMt, and from the voids between two Keggin Al\({}_{13}\) complexes; (b) the micropores in the interlayer of MMt could be detected after the Keggin Al\({}_{13}\) complex expanded the interlayer of MMt; and (c) the particles of P-MMt are more dispersed and the surfaces are coarser than those of MMt, which can be observed in SEM images (Figure 6c-f). According to Figure 3, when P-MMt was calcined at 600 \({}^{\circ}\)C and above, the SSA of P-MMt significantly decreased. In addition, Figure 2A shows that when the calcination increased to 600 \({}^{\circ}\)C, the d\({}_{001}\) peak shifted to higher 20 degree and the intensity of the d\({}_{001}\) peak decreased. Therefore, the decreasing SSA is related to the changes of the Keggin Al\({}_{13}\) complex in the interlayer with the increasing temperature. This might be due to the continuous loss of hydroxyl groups of the Keggin Al\({}_{13}\) complex, which provides voids, resulting in the decrease in micropores of P-MMt-C\({}_{x}\), and consequently the decrease in the surface area.  
Compared to the surface area calculated by BET, the surface areas calculated by the Langmuir theory of P-MMt and P-MMt-C400 are 20% larger. This is because the micropores, even though they contributed to the increase in surface area, may restrict multiple-layer adsorption, influencing the BET calculation. Although BET calculation is good for the computation of finely divided porous materials, in the case of the samples used in this work, it is better to use the Langmuir theory to determine the SSA for the P-MMt series (Table 3) [46; 47].
## 5 Datasets and Model Performance Evaluation Metrics
### Qoe  
QOE reflects the gap between the actual situation and user expectations. Compared with the unicast-based routing algorithm, the cache hit ratio (CHR) of the DBN-based IRA proposed by [PERSON] et al. [27] increases by 19%. Compared with the traditional AC algorithm [61] (TACA), the standard policy gradient-based resource allocation algorithm (SPGRA) and the non-learning scheme [97], the soft-ACDRL algorithm proposed by [PERSON] et al. [80] converges faster, and the performance improvement related to video quality is close to 15.2%, 16.5% and 32.5%, respectively.
## 3 Results and Discussion
### Volume Fraction of Minor Components
As shown in Figure 6a-d, Mode A has greater extinction efficiency, lower absorption efficiency, greater scattering efficiency, and lower asymmetric parameter values than Mode B when \(m_{2}=0.15+3.15\)i and \(m_{2}=1.46+2.75\)i, while both modes have similar values in all radiometric parameters when \(m_{2}=3.102+0.0925\)i. These phenomena are consistent with those obtained in Section 3.1; namely, the dispersion of a strong absorptive minor component in an aggregate can enhance absorption efficiency and decrease scattering efficiency.
## 2 Results
### Correlations among Main Variables
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Treatment** & **BAF** & **TF\({}_{\rm S}\)** & **TF\({}_{\rm P}\)** \\ \hline \multirow{4}{*}{Common bean} & Lead & 1.09 \({}^{\rm a}\)\(\pm\) 0.05 & 0.33 \({}^{\rm c}\)\(\pm\) 0.02 & 0.05 \({}^{\rm c}\)\(\pm\) 0.0004 \\  & Copper & 0.56 \({}^{\rm b}\)\(\pm\) 0.006 & 1.33 \({}^{\rm b}\)\(\pm\) 0.15 & 0.95 \({}^{\rm b}\)\(\pm\) 0.05 \\  & Cadmium & 0.16 \({}^{\rm c}\)\(\pm\) 0.008 & 3.05 \({}^{\rm a}\)\(\pm\) 0.85 & 3.20 \({}^{\rm a}\)\(\pm\) 0.77 \\ \hline \multirow{4}{*}{Faba bean} & Lead & 4.07 \({}^{\rm a}\)\(\pm\) 0.89 & 0.11 \({}^{\rm c}\)\(\pm\) 0.01 & 0.11 \({}^{\rm c}\)\(\pm\) 0.05 \\  & Copper & 1.79 \({}^{\rm c}\)\(\pm\) 0.34 & 0.35 \({}^{\rm b}\)\(\pm\) 0.02 & 1.88 \({}^{\rm b}\)\(\pm\) 0.14 \\ \cline{1-1}  & Cadmium & 1.88 \({}^{\rm b}\)\(\pm\) 0.45 & 0.62 \({}^{\rm a}\)\(\pm\) 0.05 & 0.28 \({}^{\rm a}\)\(\pm\) 0.05 \\ \hline \hline \end{tabular} BAF: bioaccumulation factor (\({}^{\rm a,b,c}\)); TF\({}_{\rm S}\): shoot translocation factor (\({}^{\rm a,b,c}\)); TF\({}_{\rm P}\): pod translocation factor (\({}^{\rm a,b,c}\)).  
\end{table} 
Table 8: Bioaccumulation factor and translocation factor in bean and faba bean plants at pods stage. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RC** & **SC** & **Subst C** & **BAF** & **TFveg** \\ \hline RC & - & & & \\ SC & 0.98 \({}^{**}\) & - & & \\ Subst C & 0.80 \({}^{**}\) & 0.77 \({}^{**}\) & - & & \\ BAF & 0.95 \({}^{**}\) & 0.92 \({}^{**}\) & 0.66 \({}^{**}\) & - & \\ TF Veg & 0.37 \({}^{*}\) & 0.47 \({}^{**}\) & ns & 0.31 \({}^{*}\) & - \\ \hline \hline \end{tabular} RC: roots concentration, SC: shoots concentration (**), SubstC. (**): substrate concentration (**), BAF: bioaccumulation factor (**), TF\({}_{\rm veg}\): translocation factor (\({}^{\rm a}\)) at vegetative stage, ns: not significant.  
\end{table} 
Table 9: Pearson correlation between main variables at vegetative stage of _Vicia faba_.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 2 Proposed Methodology
### Prognostic Approach: Hybrid-Driven Remaining Useful Life Prediction
#### 2.2.3 Remaining Useful Lifetime Calculation  
The future degradation states could be estimated by the learned reliability function at the inspection time \(T\). The equipment should be shut down when the reliability is close to the specific threshold. Therefore, the RUL can be defined as:  
\[\text{RUL}(T)=inf\{r:R(r+T)\geq\omega|R\} \tag{8}\]  
where \(\text{RUL}(T)\) is the RUL at \(T\), \(R(r+T)\) is the estimated reliability at \(r+T\), and \(\omega\) is the failure threshold.
## 3 Multi-Source Information Fusion Method
### Leakage Detection Based on Kalman Filter and SPRT
#### 3.1.2 Sequential Probability Ratio Test  
SPRT is a statistical decision-making method that optimizes the selection of the sample size. It does not pre-specify the number of observational sample groups but continues to increase the data until the required threshold is met in the inspection process [21].  
The abnormality of the relay valve leakage is mainly the statistical mean shift [22]. Therefore, the simulation test in this paper has two hypothesis H\({}_{0}\) and H\({}_{1}\).  
1. Normally, the sequence satisfies the null hypothesis H\({}_{0}\): \(\mu=\mu_{0}\);
2. In leakage, the sequence satisfies the alternative hypothesis H\({}_{1}\): \(\mu=\mu_{0}+\Delta\mu\).  
where \(\Delta\mu\) > 0 means the internal leakage and \(\Delta\mu\) < 0 means the external leakage.  
The joint probability density of the random sequence is  
\[P_{0k}=\frac{1}{\left(\sigma\sqrt{2\pi}\right)^{k}}\exp{(-\frac{1}{2\sigma^{2} }\sum_{i=1}^{k}(x_{i}-\mu_{0})^{2})}, \tag{8}\]  
\[P_{1k}=\frac{1}{\left(\sigma\sqrt{2\pi}\right)^{k}}\exp{(-\frac{1}{2\sigma^{2} }\sum_{i=1}^{k}(x_{i}-\mu_{0}-\Delta\mu)^{2})}, \tag{9}\]  
where \(k\) is the sampling points.  
In the SPRT test, the decision on the hypothesis is based on the value range of Equation (10).  
\[LR=\frac{P_{1k}}{P_{0k}}, \tag{10}\]  
The SPRT algorithm is as follows:  
\[\lambda(k)=\ln(LR)=\ln\left(\frac{P_{1k}}{P_{0k}}\right)=\frac{\Delta\mu}{ \sigma^{2}}\sum_{i=1}^{k}(x_{i}-\mu_{0}-\frac{\Delta\mu}{2}), \tag{11}\]  
where \(\lambda(k)\) is the test index and \(LR\) (Likelihood Ratio) is the probability ratio. The upper and lower limits are specified according to the allowable false alarm rate \(\alpha\) and the missing alarm rate \(\beta\).  
\[A=\ln(\frac{1-\beta}{\alpha}), \tag{12}\]  
\[B=\ln(\frac{\beta}{1-\alpha}), \tag{13}\]  
where \(A\) is the upper limit and \(B\) is the lower limit.  
The recursive formula is  
\[\lambda(k)=\lambda(k-1)+\frac{\Delta\mu}{\sigma^{2}}(x_{k}-\mu_{0}-\frac{ \Delta\mu}{2}), \tag{14}\]Figure 3 shows the detection process based on the Kalman and SPRT method.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 3 Numerical Simulations and Discussion
The H-reversal trajectory has been also proposed in an asteroid deflection mission, as discussed by [PERSON] et al. [58]. Generally speaking, a multi H-reversal trajectory can be thought of as an extension of [PERSON]'s original idea [53; 54]. In fact, in a multi H-reversal trajectory, two (or more) H-reversal points are used to generate a non-Keplerian closed orbit that does not encompass the Sun. This trajectory is then maintained for a long time interval by exploiting the propulsive acceleration given by a propellantless propulsion system such as, for example, a solar sail. This concept is thoroughly illustrated in the recent review by [PERSON] et al. [26] and in [PERSON]'s book about the so called 'fast solar sailing' [59; 60; 61]. Recalling the results obtained in a solar sail-based scenario [26], an optimal COFT in a DT case can be regarded as half of a particular double H-reversal trajectory, in which the perihelion coincides with the starting position [47]. In contrast, the case where a SWA occurs during an optimal transfer generates a propulsive trajectory that is completely different from the results in the literature. Therefore, the results obtained in the case of a SWA represent a novelty from the point of view of the shapes of optimal trajectories generated by continuous-thrust propulsion systems. Figure 2: Conceptual scheme of a COFT of DT type or with a single SWA. The results of the optimization process, in terms of minimum flight time \(\Delta t\) and characteristics of the optimal COFT, are illustrated in the following two subsections for a DT scenario and a transfer with a single SWA, respectively.
# Model Tests of Soil Reinforcement Inside the Bucket Foundation with Vacuum Electroosmosis Method
## 4 Bucket Foundation Bearing Capacity before and after Soil Reinforcement
### Influence of Soil Reinforcement on Bucket Foundation Vertical Bearing Capacity  
Under a vertical load, the bucket foundation experiences general shear failure with significant soil have around the foundation (Figure 13). In this test, the vertical load corresponding to a vertical displacement of 0.07 D is used as the vertical bearing capacity of the bucket foundation, which gives a soil vertical bearing capacity before and after reinforcement of 675 N and 2085 N, respectively. The vertical bearing capacity increased by 2.1 times after reinforcement. The vertical load-displacement curve can be divided into three stages, including the compression stage I, shear stage II, and heave stage III (Figure 14). In the compression phase I, the load-displacement curve is approximately straight during elastic deformation, and the stiffness of the vertical load is 0.130 MN/m without reinforcement, and 0.423 MN/m after reinforcement. The vertical bearing stiffness of the foundation after reinforcement is 3.25 times that of the unreinforced soil. In the shear stage II, the load-displacement curve is nonlinear, and shear occurs at the bottom of the foundation. Test results show that the vertical displacement in the shear stage is approximately 12 mm before reinforcement and increases to 17.5 mm after reinforcement. In the heave stage III, an incremental load increase will cause a large foundation settlement. In this stage, the vertical load growth rate is 6.23 kN/m for soil without reinforcement and 9.40 kN/m for reinforced soil. The vertical bearing capacity and stability of the bucket foundation as well as soil stiffness are significantly improved after vacuum-electro-osmotic reinforcement.  
Figure 12: Changes of moisture content with soil depth for T1–T6: (**a**) Test T1; (**b**) Test T2; (**c**) Test T3; (**d**) Test T4; (**e**) Test T5; (**f**) Test T6.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges  
Handover decision-making in 6G and smart cities is a complex issue due to the ultra-dense, heterogeneous, and highly dynamic characteristics of future networks. In contrast to previous generations, 6G will involve terrestrial, aerial, and satellite communication, supporting a wide range of applications such as autonomous vehicles, smart infrastructure, and immersive tech.  
Seamless connectivity in such environments necessitates intelligent, adaptive, and energy-aware HO mechanisms to accommodate ultra-low latency, huge connectivity, and multi-faceted mobility [57]. This part provides an overview of the key research challenges of handover decision-making for 6G and smart cities (Figure 6).  
Figure 6: Key research challenges of handover in 6G.