esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models
## 2 Materials and Methods
### Bayesian Model Averaging  
Bayesian model averaging is a scheme for the integration of outputs from multiple models. More detail regarding the theory can be found in the literature [10]. In the BMA method, the posterior distribution of the simulation variable, y, can be calculated using the following expression:  
\[p(y|D)=\sum_{i=1}^{k}p(f_{i}|D)\,p_{i}(y|f_{i},D) \tag{2}\]  
where y is the variable to be simulated, \(D=\left[y_{1}^{obs},y_{2}^{obs},\cdots,y_{T}^{obs}\right]\) is the training data with length T, \(f=[f_{1},f_{2},\cdots,f_{k}]\) is the ensemble of outputs from k different models, \(p(f_{i}|D)\) is the posterior probability of a simulation given the observation D, and \(p_{i}(y|f_{i},D)\) is the posterior distribution of y given the model simulation \(f_{i}\) and the observed dataset D.  
Finally, the posterior mean is estimated using the following formula:  
\[E[y|D]=\ \sum_{i=1}^{k}p(f_{i}|D)\cdot E[p_{i}(y|f_{i},D)]=\sum_{i=1}^{k}w_{i}(a _{i}+b_{i}f_{i}) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Main Formula** & **Input Variables** \\ \hline GLO-PEM & GPP = APAR \(\times\) LUE\({}_{\max}\times\) f(T) \(\times\) f(SM) \(\times\) f(VPD) & PAR, NDVI, T, SM, VPD \\ VPM & GPP = APAR \(\times\) LUE\({}_{\max}\times\) f(T) \(\times\) f(LSWI) \(\times\) f(P) & PAR, EVI, T, LSWI \\ C-Fix & GPP = APAR \(\times\) LUE\({}_{\max}\times\) f(T) \(\times\) f(CO\({}_{2}\)) & PAR, NDVI, T, CO\({}_{2}\) \\ EC-LUE & GPP = APAR \(\times\) LUE\({}_{\max}\times\) f(T) \(\times\) f(EF) & PAR, NDVI, T, EF \\ MODIS-PSN & GPP = APAR \(\times\) LUE\({}_{\max}\times\) f(TMIN) \(\times\) f(VPD) & PAR, NDVI, TMIN, VPD \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Light Use Efficiency (LUE) models used in this study.  
where \(w_{i}\) is the weight for model \(i\) and \(\sum_{i=1}^{k}w_{i}=1\); \(a_{i}\) and \(b_{i}\) are bias correction terms that are derived using a simple linear regression of \(y\) on \(f_{i}\).  
Measurements obtained from the five sites in 2014 were used to train the BMA method, and measurements obtained in 2015 were used to validate the performance of the BMA method.
## 2 Materials and Methods
### Electrochemical Characterization  
#### 2.4.1 Electrochemical Impedance Spectroscopy (EIS)  
Electrochemical impedance spectroscopy (EIS) was used to assess the ionic conductivities of membranes introduced with a commercial liquid electrolyte. These experiments were conducted utilizing an alternating current (AC) amplitude of 5 mV, encompassing a frequency range from 0.1 Hz to 1 MHz. Within a coin cell configuration, a gel polymer electrolyte was interposed between two blocking electrodes, composed of stainless steel (SS). Subsequently, the determination of ionic conductivity was executed by employing the provided mathematical formulation.  
\[\sigma=\frac{h}{RbS}\]  
In the presented mathematical equation, the symbol \(\sigma\) denotes the ionic conductivity, \(Rb\) is the bulk resistivity, \(h\) is the membrane thickness, and \(S\) is the contact area between the membrane and the SS electrodes.
## 2 Experimental Details  
### Experimental Setup for Fire Extinguishment  
Figure 1 shows the schematic diagrams of the experimental setup and measurement locations for fire extinguishment. As shown in Figure 1a, the experimental setup was mainly comprised of a twin-fluid atomizer, fire source, water pump, water tank, nitrogen tank, air compressor, mass flow controller (MFC), gas analyzer, and data acquisition system. The fire-extinguishing experiments were performed in an enclosed chamber with dimensions of 1 m \(\times\) 1 m \(\times\) 1 m. To construct the frame of enclosed chamber, the aluminum profile was used. The upper and bottom walls of enclosed chamber were made of aluminum plates, and its side walls were made of transparent polycarbonate plates. The enclosed chamber was placed at the height of 0.58 m from the floor. A full cone twin-fluid atomizer was installed at the center of the ceiling. The fire source of the n-heptane pool was placed at the center of the bottom in the enclosed chamber, where it was vertically below the full cone twin-fluid atomizer. Hence, under these conditions, the water mist and atomizing gas were directly discharged into the fire source. Furthermore, deionized water was used. Water was supplied to the twin-fluid atomizer via water pump (micro gear pump or rotary piston pump) from the water tank. The water flow rates were obtained by measuring the change in the weight of the water tank, using an electronic balance, for a time interval of 1 min. Nitrogen and air were used as the atomizing gases to examine the effect of atomizing gas type on the fire-extinguishing phenomena. Nitrogen and air were supplied to the twin-fluid atomizer from the nitrogen tank and air compressor, respectively. To control their flow rates, an MFC was utilized. To measure the variation in oxygen concentration with time, a gas analyzer with the paramagnetic oxygen cell was used. As shown in Figure 1b, the gas sampling was performed via its probe at a height of 265 mm from the bottom of the  
enclosed chamber and 100 mm away from the fuel source center. Additionally, to measure the temporal variation in temperature near the fuel surface, initially, K-type thermocouple of 1.5875 mm (1/16 inch) in sheath diameter was placed under the surface of liquid fuel pool within approximately 1.6 mm in depth. The temporal variations in gas concentration and fuel surface temperature were monitored and stored, using a data acquisition system and a laptop computer, at a sampling rate of 1 Hz. In addition, to record the videos of experiments, the digital camera was used.  
The droplet size of water mist in the twin-fluid atomizer is a critical factor that significantly influences the fire-extinguishing performance. Before the fire-extinguishing experiments, the SMD of the water mist was measured utilizing the Malvern-type droplet measurement system. The measurements were conducted at 200 mm vertically below the exit of the twin-fluid atomizer under no fire conditions.
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 1 Introduction
The difference between the meta-evaluation function and the evaluation function is that the evaluation function targets all samples from each batch in the experiment, whereas the meta-evaluation function targets the query samples from each batch. The support samples from each batch are used to train the classifier and are not involved in the meta-evaluation. The main contributions of this study are as follows:  
* We propose a meta-learning method based on adaptive feature fusion and weight optimization. The adaptive feature fusion method is used to train and test classifiers by fusing features from each stage of the embedding model, and the feature fusion weight is optimized by combining the classifier's prediction results. * In terms of the feature fusion method, we propose a novel feature splicing strategy: the output features of each stage are pooled and flattened, and then unified in a certain dimension. Feature splicing is carried out in this dimension. In the splicing process, the weight of each stage feature is different. * For the optimization of the feature fusion weights, we combine the optimization algorithm with meta-learning and propose the use of a particle swarm optimization  
Figure 1: Comparison of heat maps for different stage features. The feature maps of the input images at each stage of the network are visualized, and the responses of neurons at different stages to different features are compared. algorithm [27; 28] to optimize the weights of the features at each stage of the embedding model. In addition, we use the parallel structure of the computer to optimize the feature fusion weights of multiple groups simultaneously to improve the algorithm's efficiency. In this paper, the proposed method is compared with the current state-of-the-art meta-learning methods. The experimental results show that the proposed method is effective and reliable.
## 5 Finite Element Models Arrangement
### Validation of the Finite Element Results
#### 5.4.1 Minor Axis  
The single-upright configuration for FE modelling has been used in this section. Figure 25 compares the normalized bending moment-deflection curves of the FE model and experimental results. Figure 26 also illustrates the linear regression of the FE model mean curve with the experimental mean curve. Table 5 presents the evaluation criteria of the accuracy of the FE model predictions.  
Figure 25: Finite element model and experimental curves for minor axis test set-up: (**A**) non-reinforced types; (**B**) 200 mm reinforced types; (**C**) 300 mm reinforced types.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{Non-reinforced model} & \multicolumn{2}{l}{Evaluation criteria} \\ \cline{2-3}  & Standard deviation & 0.0618701 \\ \cline{2-3}  & Pearson (r) & 0.998139545 \\ \cline{2-3}  & R\({}^{2}\) & 0.9963 \\ \hline \multirow{2}{*}{200 mm reinforced model} & \multicolumn{2}{l}{Evaluation criteria} \\ \cline{2-3}  & Evaluation criteria & \\ \cline{2-3}  & Standard deviation & 0.08034494 \\ \cline{2-3}  & Pearson (r) & 0.993668695 \\ \cline{2-3}  & R\({}^{2}\) & 0.998 \\ \hline \multirow{2}{*}{300 mm reinforced model} & \multicolumn{2}{l}{Evaluation criteria} \\ \cline{2-3}  & Standard deviation & 0.069798432 \\ \cline{2-3}  & Pearson (r) & 0.998023512 \\ \cline{2-3}  & R\({}^{2}\) & 0.9961 \\ \hline \end{tabular}
\end{table}
Table 5: Finite element model vs experimental results accuracy details in terms of evaluation criteria.  
Figure 26: Comparison of finite element model against minor axis test results along with the linear regression: (**A**) and (**B**) non-reinforced model; (**C**) and (**D**) 200 mm reinforced model; (**E**) and (**F**) 300 mm reinforced model.  
According to Figure 26 and Table 5, the FE model results achieved outstanding accuracy and compatibility with the test results. In addition, Figure 27 compares the FE model failure modes of the uprights to the experimental failure modes where the developed FE model has well predicted the overall deformed shape. These uprights failed in distortional and local buckling mode.
## 2 Background and Theory
### Preprocessing Methods
#### 2.1.3 CAR Montage Filtering  
For the final stage of EEG data preprocessing, the common average reference (CAR) montage [60] is implemented. This technique, frequently applied in EEG signal processing, minimizes shared noise across channels by recalculating each channel's reference based on the average of all channels. Consequently, neural activities become more distinct relative to this combined average, thereby improving the signal-to-noise ratio, and enhancing the overall data quality. Expressed mathematically, if \(X\) represents the EEG signal matrix, the CAR method is described by Equation (5):  
\[\text{CAR}=X-\frac{1}{N}\sum_{i=1}^{N}X_{i} \tag{5}\]In this context, CAR refers to the EEG data matrix following the application of the common average reference filtering. The original EEG signal matrix is indicated by \(X\), with \(N\) representing the total number of EEG channels. Furthermore, \(X_{i}\) denotes the EEG signal from the \(i\)-th channel.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 4 Solutions
### Efficient and Secure Scaling of Information Networks
#### 4.2.1 5G and the Internet of Things  
One of the latest developments in wireless technologies and soon-to-be successor of the highly successful 4G and \"long-term evolution\" (LTE) technology is 5G. Although the current 4G LTE technology provides data transmission rates of up to 1 gigabit per second (Gbps), the technology is still relatively susceptible to disruptions due to interferences caused by other wireless signals and physical obstructions such as buildings [46]. The 5G technology will be able to transmit data at a rate of up to 10 Gbps, and will incorporate mechanisms that will provide even more reliable connections. Furthermore, 5G will provide a suitable platform to securely interconnect billions of IoT devices within a flexible networking architecture that is particularly suited for the interconnection of such a high number of devices [6].  
However, since it is currently still under development, the standardization process for 5G is only expected to be completed by 2020, with worldwide full 5G capable networks expected to be active by 2025 [47]. The technology is thus still in its infancy, but existing early 5G networks that are already active present very promising features that will have a profound impact on IoT-driven networks. There is therefore no better time than now for developers and researchers to focus on developing new networking strategies and security measures that are fully compliant and compatible with 5G. This will result in networking and security technologies being developed that can immediately be incorporated into 5G networks, while collaboration with developers and vendors can provide the necessary exposure for end-users of these technologies to change the mindset of viewing network security as a secondary priority, thus eliminating a problem at its roots.
# Effect of Wing Membrane Material on the Aerodynamic Performance of Flexible Flapping Wing
## 2 Materials and Methods
### Wing Design  
In this study, the wings presented in Figure 3 were designed to perform a hovering flapping motion with passive twisting. To test the influence of wing membrane materials on their aerodynamic performance, three different membranes with similar thicknesses were introduced, and their properties are shown in Table 1. Meanwhile, the shape of these wings is identical. These wings have an area of 4500 mm\({}^{2}\) and aspect ratio (AR) of 2.4. As there is an offset between the wing root and the flapping axis, the tip radius \(R\), from wing tip to the flapping axis, equals 120 mm, the radius of the second moment of wing area \(R_{2}\) can be expressed as [22]:  
\[R_{2}=\sqrt{\frac{1}{S}\int_{0}^{R}{c{r^{2}}dr}} \tag{3}\]  
where \(S\) is the wing area, \(r\) is the distance from the flapping axis to any radial position along the wingspan, and \(c\) is the local chord length at the given radial position \(r\). For these three wings, the \(R_{2}\) is the same value and equals 70.8 mm. Consequently, the non-dimensional radius of the second moment of wing area \(\hat{r}_{2}=R_{2}/R\) equals 0.59, which is similar to the insect wing parameter in nature [23]. The Reynolds number is given by:  
\[Re~{}=~{}\frac{U_{ref}\overline{c}}{v} \tag{4}\]  
where the reference velocity \(U_{ref}\)= 4\(f\psi_{m}R_{2}\),\(f\) is the flapping frequency, \(\psi_{m}\) is the flapping amplitude and is set to 60\({}^{\circ}\), and \(\overline{c}\) is the mean chord length and is given by \(S/R\), \(v\) is the kinematic viscosity of air. As the flapping frequency in this experiment ranges from 10 Hz to 22 Hz, the Reynold number \(Re\) varies from 7650 to 16,900.  
Figure 3: Wings designed for the test. (**a**) Photograph of three wings; (**b**) Description of the camera angle; (**c**) Wing assembled on the mechanism.  
Wing planform consists of membrane and CFRP stiffeners. The wing surface is stiffened by carbon bars of 0.5 mm in diameter to maintain the shape of the wing during its flapping period. Of note, the CFRP stiffeners are placed at the same location for all three wings. An expanded polystyrene glue is used to bond CFRP rods and wing membranes together.  
The wing contains two sleeves, the leading edge sleeve and the wing root sleeve. The diameter of the leading edge sleeve is about 2 mm, so the wing can rotate freely around the leading edge CFRP rod. A steel bar with a diameter of 1 mm is inserted through the sleeve of the wing root to fix the wing root. A camber angle, defined as the angle between the wing root edge and a normal to the leading edge, is introduced to achieve the ideal wing twisting deformation. In this study, the camber is set to 15\({}^{\circ}\), which has been proven to be better for the aerodynamic performance [24].
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
## 4 Discussion
### One MI vs. Two MIs on Molar Distalization  
In the recent review, studies that compared the single with a dual miniscrew support for maxillary molar distalization concluded that the dual unit produced a greater molar distalization rate in relation to the single MI. [PERSON] et al. [24] compared two different groups, in which showed 1.83 mm of distal bodily movement and 3.19\({}^{\circ}\) of distal tipping with a single MI in the time that found 2.91 mm and 1.55\({}^{\circ}\) with two MIs, respectively. However, the same author recently published improved results with 4.2 mm of distal movement and 0.6\({}^{\circ}\) of distal tipping using the same intraoral mechanics with an individual MI [35]. Another study by [PERSON] et al. demonstrated a particular miniscrew providing 2.8 mm of distal movement and 4.3 degrees of distal tipping [20]. [PERSON] et al. [37] performed a clinical trial that is in accordance with [PERSON] et al. while opposing the results of [PERSON] et al. [24], introducing 4.09 mm and 2.48\({}^{\circ}\) distalization movement together with tipping with a closed coil spring and a buccal ML. Mainly, the greater success of the two MIs may be assigned to an increased magnitude of force in comparison with the single unit [40]. Although the infra-zygomatic process, which inserted the miniscrews in the infrazygomatic alveolar crest, appears to be more effective, our results primarily focused on the interradicular method, which was the method mostly provided in our included articles [58].
## 2 Materials and Methods
### Experimental Equipment and Experimental Scheme  
#### 2.2.1 Test System  
The experimental device used in this experiment consists of custom-made freeze--thaw cycle equipment, which mainly encompasses a temperature control system, a data acquisition system, and a water replenishment system. The temperature control system comprises two high- and low-temperature constant-temperature baths (with the accuracy of bath 1 reaching \(\pm\)0.01 \({}^{\circ}\)C and that of bath 2 reaching \(\pm\)0.1 \({}^{\circ}\)C) and upper and lower guide plates. The No. 1 constant-temperature bath is connected to the upper guide plate through a circulating liquid conduit, and the No. 2 constant-temperature bath is connected to the lower guide plate. The upper and lower guide plates are placed above the soil sample and below the water replenishment tank, respectively. Different temperatures are attained to simulate the temperature difference between the upper and lower soil parts in the actual roadbed construction process. The water replenishment system comprises a Markov bottle, a water guide pipe, and a water replenishment tank. The external water replenishment amount is determined by the water level change in the Markov bottle. The data acquisition system relies on a CS655 soil three-parameter sensor, with a temperature accuracy of \(\pm\)0.5 \({}^{\circ}\)C, a volumetric moisture content accuracy of \(\pm\)3% WWC, and a conductivity accuracy of 0.05 dS/m. The data collector is a CR1000X device and is set to automatically collect data at 30 min intervals. A diagram of the overall device structure of the equipment is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Section** & **Sampling** & **SO\({}^{\text{2
## 4 Health Prognostics
### HI Construction
#### 4.1.1 Single HI  
Where there is a complete degradation mechanism shown to be related to the equipment or the whole system, and its fault physics could be represented by a single sensor signal which reflects most characteristics of the potential degradation process, it is an easy and reliable method to use the single HI to characterize the health condition of MSAE [159]. Single HI could be statistical features directly extracted from the time series of monitoring data, such as root mean square (RMS), skewness, kurtosis, and so forth. [160].  
RMS evaluates the overall condition of the equipment by tracking the progress of the fault and is not sensitive to early faults.  
\[s_{rms}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{{(x_{i})}^{2}}}}. \tag{22}\]Skewness represents the symmetry of the probability density function of the signals of a time series.  
\[\gamma_{skewness}=\frac{N\cdot\sum_{i=1}^{N}(x_{i}-\overline{x})^{3}}{\left\{ \sqrt{\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}}\right\}^{3}}. \tag{23}\]  
Kurtosis describes the peak or flatness of the signal distribution.  
\[g_{kurtosis}=\frac{N\cdot\sum_{i=1}^{N}(x_{i}-\overline{x})^{4}}{\left\{\sqrt{ \sum_{i=1}^{N}(x_{i}-\overline{x})^{2}}\right\}^{2}}. \tag{24}\]  
In Equations (22)-(24), \(x_{i}\) is the \(i\)th member of points in the dataset \(\mathbf{x}\), \(N\) is the number of data points in the dataset \(\mathbf{x}\), \(\overline{x}\) is the mean value of the dataset \(\mathbf{x}\).  
[PERSON] et al. presented the peak values and RMS for mechanical RUL prediction and analysis [161]. RMS and kurtosis values are also used as single HI to predict the RUL of equipment such as bearings [162; 163], compressors [164]. The following Refs. [165; 166] used the statistical features extracted from time-domain signals to realize the RUL prediction. In addition, the degradation information in the frequency domain signal, such as the power density, the average amplitude, and so forth, could also be used as a single HI to predict the RUL of gears, thrust bearings, and pumps [167; 168; 169]. [PERSON] et al. proposed two model-based performance indicators--heat loss and power deficit--as single HI to monitor the performance degradation of gas turbines [170]. Other kinds of literature have completed a lot of models and analysis researches on a single HI [171; 172; 158]. However, the equipment uniqueness of a single HI cannot be extended to other systems and completely describe the degradation characteristics of the complex system, which limits its application in MSAE.
## 4 Mathematical Formalism of Consensus Through Path-Laplacian Matrices
### Consensus Dynamics on Undirected Networks
#### 4.2.2 Discrete-Time Consensus  
In discrete time, consensus is achieved by iterative averaging. At each time step, \(t\to t+1\), every agent \(v_{i}\) updates its state as a convex combination of its own and its neighbors' previous states (\(v\in V_{i}\)). A common discrete model can be formulated as  
\[\phi_{i}(t+1)=\phi_{i}(t)+\varepsilon\sum_{j\in V_{i}}\bigl{(}\phi_{j}(t)-\phi _{i}(t)\bigr{)}, \tag{9}\]  
where \(0<\varepsilon<1\) is a small step size, also interpreted as an interaction weight [21]. In matrix form, this update can be expressed as  
\[\boldsymbol{\phi}(t+1)=\bigl{(}I\ -\ \varepsilon L\bigr{)}\ \boldsymbol{\phi}(t), \tag{10}\]  
where \(I\) is the identity of order \(n\). Let \(W=I-\varepsilon L\) denote the update's weight matrix. Other generalizations of diffusion that include subdiffusion and super diffusion in networks were stated in [42].  
By construction, each row of \(W\) sums to \(1\) and \(W_{ij}>0\) if \(j\in V_{i}\) (with \(W_{ii}>0\) as well). If \(0<\varepsilon<1/\) deg\({}_{\max}\), where deg\({}_{\max}\) is the maximum degree in the network, then \(W\) has all non-negative entries and no entry exceeds \(1\), meaning that \(W\) is a stochastic matrix. In such a matrix, only the rows are normalized to sum to \(1\). On the one hand, if \(W\) is _doubly stochastic_, that is, both its rows and its columns sum to \(1\), then the average of the states is invariant under the update, that is \(\sum_{i=1}^{n}\phi_{i}(t+1)=\sum_{i=1}^{n}\phi_{i}(t)\) for all \(t\geq 0\), and the consensus is reached at \(\boldsymbol{\phi_{0}}\) with \(\phi_{01}=\ldots=\phi_{0n}=c\), with \(c\) equalling the initial average \(\frac{1}{n}\sum_{i=1}^{n}\phi_{i}(0)\). On the other hand, if \(W\) is only row-stochastic, then the limiting consensus state will be a weighted average determined by the left eigenvector of \(W\) corresponding to eigenvalue \(1\)[43]. The convergence of (10) is again related to the spectrum of \(L\) analogously to the continuous case.