esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Piezoelectric Ceramic Actuator with Scissoring Composite Vibration for Medical Applications
## 2 Design Principle and Modeling
Simultaneously, the beam tip  
Figure 1: The schematic illustration of the as-designed scissoring actuator with (**a**) assembly drawing and (**b**) detailed view of vibration wave propagation at converter, 1 input longitudinal elastic wave, 2 propagating wave and 3 mode conversion. can be replaced by a mass (m\({}_{\rm a}\)), connected at the right end of the bar, because the beam tip has no distinct longitudinal vibration. In bending beam tip, the groove can be replaced by a simple beam because the bending deflection focuses on relatively thin grooves and the left end of the beam can be connected to a torsional spring (k\({}_{\rm t}\)) to simulate the bending movement. Similarly, the beam tip, except the groove, can be replaced by another mass (m\({}_{\rm b}\)). Therefore, only one beam tip is modeled due to the symmetrical structure at the end effector. During vibrations, the longitudinal vibration force, transmitted through the bar, is converted into bending torque at the beam tip, altering the vibration mode from longitudinal mode to scissoring mode. Hence, the vibration conversion principle can be clearly described by the simplified model. Presumably, the deformation of the bar and beam tip occurs only in the x-y plane. Therefore, the motion equations of the system, including longitudinal vibration of the bar and bending vibration of beam tip, can be expressed as:  
\[\left[\rho A_{1}+m_{a}\delta\left(x-l_{1}\right)\right]\frac{\partial^{2}u}{ \partial t^{2}}-EA_{1}\left[\frac{\partial^{2}u}{\partial x^{2}}+c_{a}\frac{ \partial^{3}u}{\partial x^{2}\cdot\partial t}\right]=F_{1}\delta(x-0)-k_{s}u \delta(x-0) \tag{1}\]  
\[\left[\rho A_{2}+m_{b}\delta\left(x-l\right)\right]\frac{\partial^{2}y}{ \partial t^{2}}+\frac{\partial^{2}}{\partial x^{2}}\left[El_{2}\left(1+c_{b} \frac{\partial}{\partial t}\right)\frac{\partial^{2}y}{\partial x^{2}}\right] =M_{p}\delta\prime(x-l_{1})-k_{t}\theta\delta\prime(x-l_{1}) \tag{2}\]  
where \(A_{1}\) and \(A_{2}\) refer to the cross-sectional area of the bar and converter, respectively, \(\rho\) denotes the density, \(E\) represents [PERSON]'s modulus, \(u\) denotes the longitudinal vibration displacement of the bar, \(y\) refers to the vertical displacement of the beam tip, \(\delta\) corresponds to the Dirac coefficient, \(F_{1}\) represents the input driving force at x = 0, \(M_{p}\) denotes the input torque at the torsional spring, \(\theta\) refers to the rotational angle of the torsional spring and \(I_{2}\) denotes the moment of inertia of beam tip. Moreover, \(l=l_{1}+l_{2}\), where \(l_{1}\) and \(l_{2}\) represent the length of the bar and beam tip, respectively. \(c_{a}\) and \(c_{b}\) refer to the damping coefficient of the bar and converter, respectively.
## 3 Reliability Verification of Finite Element Model  
To ensure the accuracy of the finite element model, the fault frequency obtained from the simulation was compared with the experimental fault frequency and the theoretically calculated fault frequency while keeping the bearing structural parameters, boundary conditions, loads, and rotational speeds consistent. Additionally, the simulation rotational speed of the cage was compared with the theoretical rotational speed.
## 2 Materials and Methods
### Nutrients Removal  
Nutrients removal was evaluated in terms of nitrogen and (N) phosphorus (P) present in the culture medium/effluent. Nitrogen was assessed in the forms of nitrate and nitrite ions, whereas phosphorus was monitored through the presence of phosphate ions. From each assay, 5 mL of the microalgal suspension were periodically collected (days 0, 1, 2, 4, 7, 9, and 11). These samples were centrifuged at 4000 rpm, for 10 min, and the supernatants were filtered through 0.45-\(\upmu\)m nylon membranes (Specanalitica, Portugal). Nitrate, nitrite and phosphate concentrations were determined in an ion chromatograph (ICS-2100, Dionex) equipped with an anion analytical column (4x 250 mm, AS11-HC) and a self-regeneration suppressor (4 mm, AERS 500). The values obtained in the first and last day of culturing were used to calculate the following removal parameters: (i) removal efficiencies (%_R_, %); (ii) average removal rates (\(RR\), mg L\({}^{-1}\) d\({}^{-1}\)); and (iii) mass removal (\(R\), mg L\({}^{-1}\)), as shown in Equations (5), (6) and (7), respectively:  
\[\%_{\rm R}=\frac{S_{f}-S_{i}}{S_{i}}\times 100, \tag{5}\]  
\[RR=\frac{S_{f}-S_{i}}{t_{f}-t_{i}}, \tag{6}\]  
\[R=S_{f}-S_{i}, \tag{7}\]  
where \(S_{f}\) and \(S_{i}\) correspond to the nitrogen (nitrate + nitrite) or phosphorus (phosphate) concentration (mg L\({}^{-1}\)) in the end (\(t_{f}\), d) and beginning (\(t_{i}\), d) of the cultivation period, respectively.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 5 Conclusions  
In conclusion, transmittance and standing wave field distribution are studied for three classic kinds of graded-index AR coating which are potentially applied in high energy system.  
The transmittance of these graded-index coatings are designed by a searching method which is proposed in this work. We find that the searching method is working well and transmittance of the designed coatings are good at the designed wavelengths 200-1200 nm, which are the working wavelengths for laser induced inertial-fusion research. Re-divisions for the obtained optimize thicknesses of these three kinds coatings show that the designed coatings are fabricable. Our simple searching method can be widely used to design graded-index coating.  
The standing wave field distributions of these graded-index coatings are numerical calculated by the electromagnetic wave equation, which are significant for the study of intense laser induced damage of coating. We find that standing wave field distributions of these three graded-index AR coatings are quite different. For the coating with linear index distribution intensity of standing wave field decreases periodically from surface to substrate with narrow oscillation range and the period is proportional to the incident wavelength. For the coating with exponential index distribution, intensity of standing wave field decreases periodically from surface to substrate with large oscillation range and the period is also proportional to the incident wavelength. Finally, for the coating with polynomial index, intensity of standing wave field is quickly falling down from surface to substrate without an obvious oscillation. We also find that the intensity of standing wave field in the interface between coating and substrate for linear index and polynomial index are lower than exponential index. It means that linear index coating and polynomial index coating have large damage thresholds than exponential index coating. Our findings can be used to design the high damage threshold graded-index AR coating.  
The present simulation results are useful for the design and application of graded-index AR coating in high energy laser system.  
This work was financially supported by the Fundamental Research Funds for the Central Universities (Grant No. ZYGX2014J036); the National Natural Science Foundation of China (No. 61505023); China Postdoctoral Science Foundation funded project (No. 2015M582570 and No. 2016T90871). [PERSON], [PERSON] and [PERSON] conceived and designed the study; [PERSON] performed the simulation; [PERSON], [PERSON], [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Effect of Chitosan Nanoemulsion on Enhancing the
## 4 Discussion
PAL plays a key role in different phenolic biosynthesis pathways called the phenyl-propanol pathway [60]. It has been demonstrated that PAL activity decreases during the maturity and postharvest stages [61]. In addition, with the increase in the activity of the PPO activity during the senescence process, the consumption of polyphenols increases, and as a result, the content of total phenolic compounds decreases with the aging of the fruit tissue [62]. The results of this study show that the treatment enhanced the activity of PAL, resulting in the production of different phenolic compounds. On the other hand, edible coatings such as chitosan, by protecting the surface of the products, have been shown to reduce oxygen and thereby reducing the oxidation of phenolics [63]. Guaiacol peroxidase is one of the important antioxidant enzymes that were increased by an edible coating containing nanoparticles of chitosan in this study. This enzyme has been reported to show high activity in biological systems against hydrogen peroxide. Hydrogen peroxide is an important ROS-attacking cell membrane, and the increase in GPOX activity is pivotal for protecting the fruit cell membranes against peroxidation of lipids and DNA hydroperoxides [64]. Different enzymatic antioxidants are the cells' weapons against the oxidation of biological molecules such as lipids, proteins, carbohydrates, and deoxyribonucleic acid by free radicals and ROS [65; 66]. The free radicals and ROS are produced during normal cell metabolism and attach the biomolecules of the cells called oxidative damage. To prevent oxidative stress, plant cells activate their antioxidant systems and respond to oxidative damage through the activation of different antioxidant systems [67]. Our results indicate that the antioxidant capacity, measured with both DPPH and FRAP methods, increases in the fruit treated with 5 g L\({}^{-1}\) chitosan nanoemulsion. The use of edible coatings increases the capacity of the fruit antioxidant system and protects the cells against oxidative stress and pathogen attack [68; 69].
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
## 4 Simulation and Analysis
### Training Set and Test Set of DnCNN  
In our simulation, the number of sampling points of the observation \(\text{y}(\text{n})\) is fixed to N = 513. The midpoint of the signal is the origin of time. The sample frequency is 256. There are only chirp signals and additive white Gaussian noises in the system without other interference or noise. The parameter change scopes of the training set and validation set are \(\text{f}_{0}\in[9,13]\), \(\text{k}\in[4,7]\), and 0.2 as an interval, as shown in Figure 6a. The parameter scope of the test set is the same as the training set, but the parameters are randomly generated, and accurate to one decimal place.  
The SNR of the training set varies from \(-8\) dB to \(-3\) dB at an interval of 1 dB. We randomly generate \(\text{m}_{\text{i}}\) signals with the same parameter \(\text{f}_{0}\) and k under the specific SNR as shown in Table 1. The parameter combination has covered all values within the parameter setting range. The validation set size is one-fifth of the training set. For the test set, we randomly generate 500 signals, and SNR varies from \(-8\) dB to \(1\) dB for each signal as shown in Figure 6b.  
In the training process of DnCNN, we set the reshaped signal image array \(\text{Y}^{0}\in\mathbf{R}^{36\times 57}\). The mini-batch size is 128. The maximum number of the epoch is 4. Before each training epoch, we shuffle the training data. The initial learning rate is 0.0002 and updates the learning rate every epoch by multiplying with the learning rate drop factor 0.9. The training is completed on NVIDIA TITANV GPU with MATLAB Deep Learning Toolbox.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & \(\mathbf{-8}\) **dB** & \(\mathbf{-7}\) **dB** & \(\mathbf{-6}\) **dB** & \(\mathbf{-5}\) **dB** & \(\mathbf{-4}\) **dB** & \(\mathbf{-3}\) **dB** \\ \hline \(\text{m}_{\text{i}}\) of training Set & 150 & 140 & 130 & 120 & 110 & 100 \\ \(\text{m}_{\text{i}}\) of validation Set & 30 & 28 & 26 & 24 & 22 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of \(\text{m}_{\text{i}}\) in the training set and validation set.  
Figure 6: Training set and test set parameter distribution diagram. (**a**) describes the training set, (**b**) describes the test set.
## 2 Materials and Methods
### Breaking News Detector  
The instant detection of breaking news, as well as the collection of data related to the breaking news, is of great significance for journalists. In this scope, a module that detects breaking news and collects relevant news data is included within the platform's architecture. Within the EarthPress platform, the important news that we are trying to detect is related to either natural or human-made disasters. To detect breaking news related to disasters, the platform uses the Twitter social network.  
The breaking news detector is composed of several sub-modules that perform different functionalities. Two of the sub-modules are related to the data retrieval from Twitter. At first, the trending topic-monitoring sub-module downloads tweets that contain certain, pre-defined, keywords related to disasters, and second, the topic (disaster) classification sub-module monitors the trending topics of certain areas and recognizes trending topics related to disasters.  
Figure 1: Platform’s architecture.  
From the tweets that have been extracted and are related to a natural disaster, it is important to extract information such as the type of disaster, the location, and the date of the event. For this reason, the keywords and main entities extraction sub-module is used to extract the most important words and phrases from the posts retrieved, along with the aforementioned information. For this task, the YAKE algorithm has been used [30]. These keywords are used as input by the article retrieval sub-module, whose aim is to collect news articles that will contain these certain keywords.  
Finally, to ensure that the output of the breaking news detector does not contain articles and tweets with false/misleading content, the collected information is checked for their reliability and the detection of fake news. It is essential to ensure that the output information of the breaking news detector does not contain articles and tweets with false/misleading content, as these data will be used to compose the final news article in the EarthBot module. For this reason, deep learning methods have been developed that automatically detect and filter out any text with misleading content to ensure the quality of the output information.
## 3 Model Solution  
The sampled signals are inevitably contaminated with noise. Assume the actual non-uniformly sampled signal is represented by the vector \(s_{\delta}\), where \(s_{\delta}=s+\delta\), \(\delta\) indicating the noise error term. In this case, the error term \(\tilde{n}\) should also include the effects caused by the noise, let \(\tilde{n}=\delta^{M}+\delta\). Therefore, according to (2), we can get  
\[s_{\delta}=AS+\tilde{n} \tag{10}\]Equation (10) represents the signal model that needs to be solved under the presence of noise. By setting \(AS_{\hat{n}}=AS+\tilde{n}\) and \(S_{\hat{n}}\) as the approximate solution of \(S\), Equation (10) can be equivalently expressed as  
\[s_{\delta}=AS_{\hat{n}} \tag{11}\]  
In this article, we only consider the case when \(N\geq 2M+1\), indicating that the signal's sampling rate is not less than the Nyquist sampling rate, and Equation (11) has a unique least squares solution.
## 3 Results
### Performance of the PRI-Based LUE Model and SIF-Based Linear Model for GPP Estimation
Comparison between the Abilities of the PRI-Based and SIF-Based Models under Different Environmental Variables  
R\({}^{2}\)\({}_{\text{PRI}}\) and R\({}^{2}\)\({}_{\text{SIF}}\) were compared under classified ranges of the environmental variables (Figure 7). R\({}^{2}\)\({}_{\text{PRI}}\) remained constant (\(>\)0.6) with the increase of PAR and Q (Figure 7a). R\({}^{2}\)\({}_{\text{SIF}}\) increased with the increase of the light-related variables (i.e., PAR and Q) and reached the highest values of 0.56 and 0.85 for the PAR range of 800-1000 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\) and the Q range of \(>\)2, respectively (Figure 7a,b). R\({}^{2}\)\({}_{\text{SIF}}\) was higher than R\({}^{2}\)\({}_{\text{PRI}}\) for the Q range of \(>\)2 (Figure 7b). The distributions of R\({}^{2}\)\({}_{\text{PRI}}\) and R\({}^{2}\)\({}_{\text{SIF}}\) were different for the two water-related variables (i.e., RH and SWC) (Figure 7c,d). R\({}^{2}\)\({}_{\text{PRI}}\) was conspicuously lower in RH \(<\) 60\(\%\) than that in RH \(>\) 60\(\%\). It reached the  
Figure 6: Relative contributions of the environmental variables for explaining (**a**) R\({}^{2}\)\({}_{\text{PRI}}\), the correlation between LUE\({}_{\text{PRI}}\)\(\times\)APAR and GPP\({}_{\text{EC}}\), (**b**) R\({}^{2}\)\({}_{\text{SIF}}\), the correlation between SIF\({}_{\text{CAN}}\) and GPP\({}_{\text{EC}}\). Figure 7: Distribution of R\({}^{2}\)\({}_{\text{PRI}}\) and R\({}^{2}\)\({}_{\text{SIF}}\) under the classified (**a**) PAR, (**b**) Q, (**c**) RH, (**d**) SWC, (**e**) T\({}_{\text{a}}\), (**f**) T\({}_{\text{s}}\). Error bars represent standard deviations of R\({}^{2}\) under the classified ranges of the environmental variations. highest value of 0.83 for the RH range of 70-80%. R\({}^{2}\)PRI had high values at low and high ends of SWC with a high value of 0.88 for the SWC range of 0.2-0.25 m\({}^{3}\) m\({}^{-3}\) and a high value of 0.81 for the SWC range of 0.4-0.5 m\({}^{3}\) m\({}^{-3}\). However, R\({}^{2}\)SIF fluctuated with the increase of RH and SWC. For the temperature-related variables (T\({}_{\text{a}}\) and T\({}_{\text{s}}\)), the distributions of R\({}^{2}\)PRIand R\({}^{2}\)SIFalso differed (Figure 7e,f). The higher soil temperature showed the higher R\({}^{2}\)PRI. The highest R\({}^{2}\)PRI was at the temperature range of 31-34 \({}^{\circ}\)C, while the highest R\({}^{2}\)SIF was at the temperature range of 25-28 \({}^{\circ}\)C.  
Overall, R\({}^{2}\)PRI was higher at high RH or under soil water stress (i.e., SWC too low or too high) or high temperature, while the higher R\({}^{2}\)SIF occurred under high solar radiation or clear sky. The PRI-based LUE model exhibited better performance than the SIF-based linear model in estimating GPP in most of the classified ranges of the environmental variables, with the SIF-based linear model performed slightly better under clear sky (Q \(>\) 2).
###### Abstract  
The discrete element method (DEM) coupled with computational fluid dynamics (CFD) is used extensively for the numerical simulation of gas-solid fluidized beds. In order to improve the efficiency of this approach, a coarse grain model of the DEM was proposed in the literature. In this model, a group of original particles are treated as a large-sized particle based on the initial particle distribution, and during the whole simulation process the number and components of these particle-groups remain unchanged. However, collisions between particles can lead to frequent crushing and polymerization of particle-groups. This fact has typically been ignored, so the purpose of this paper is to rationalize the coarse grain DEM-CFD model by considering the dynamic particle-group crushing and polymerization. In particular, the effective size of each particle-group is measured by a quantity called equivalent particle-group diameter, whose definition references the equivalent cluster diameter used by the energy-minimization multi-scale (EMMS) model. Then a particle-group crushing criterion is presented based on the mismatch between the equivalent diameter and actual diameter of a particle-group. As to the polymerization of two colliding particle-groups, their velocity difference after collision is chosen as a criterion. Moreover, considering the flow heterogeneity induced by the particle cluster formation, the EMMS drag force model is adopted in this work. Simulations are carried out by using a finite volume method (FVM) with non-staggered grids. For decoupling the Navier-Stokes equations, the semi-implicit method for pressure linked equations revised (SIMPLER) algorithm is used. The simulation results show that the proposed dynamic coarse grain DEM-CFD method has better performance than the original one.  
coarse grain; DEM-CFD; particle-group crushing; particle-group polymerization; EMMS; fluidized beds +
Footnote †: journal: Applied Physics Letters
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 3 Definitions
### Essential Nodes  
Using the PRC set, it is possible to define the concept of essential node. Node \(n_{u}\) is defined as essential for the route \(\pi_{ij}\) if \(n_{u}\in c_{ij}^{(m)}\) for all values of \(m\) (\(1\leq m\leq M_{ij}\)). In other words, \(n_{u}\) is essential if it participates in all PRCs of \(\pi_{ij}\). Note that it is mandatory to place regenerators in essential nodes because, otherwise, some routes become unfeasible in terms of QoT. For this reason, the proposed algorithms in this work start by placing regenerators in the essential nodes.
## 3 System Model
### AC-RL-Based Power Control (ACRLPC) Framework  
In this system, the Actor-Critic RL model optimizes the channel allocation policy by balancing exploration and exploitation. The action \(a_{t}\) at each time step is selected based on the policy \(\pi\), and the value function is updated according to the Bellman equation:  
\[V^{\pi}(s)=\max_{a}\left(\sum_{s^{\prime}}P(s^{\prime}|s,a)\big{(}R(s,a)+ \gamma V^{\pi}(s^{\prime})\big{)}\right),\]  
where \(\gamma\) is the discount factor, \(P(s^{\prime}|s,a)\) is the transition probability, and \(R(s,a)\) is the reward function.  
The optimal policy \(\pi^{*}\) is derived as follows:  
\[\pi^{*}(s)=\arg\max_{a}Q^{*}(s,a)\]  
where \(Q^{*}(s,a)\) is the action-value function, which is updated by the RL agent to reflect the expected cumulative reward.