esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Consistency between Simulations and Experimental Measurements  
Figures 8 and 13 illustrate the scatter plots of \(\mathrm{SIF}_{canopy}\) and \(\mathrm{APAR}_{chl}\) for both O\({}_{2}\)-A and O\({}_{2}\)-B bands based on the simulated dataset and experimental dataset, respectively. The results clearly show that the regression relationship between \(\mathrm{SIF}_{canopy}\) and \(\mathrm{APAR}_{chl}\) is significantly different due to different canopy structures, which results in the discrepancies of the values of the ratio of \(\mathrm{SIF}_{canopy}\) to \(\mathrm{APAR}_{chl}\). Furthermore, both simulated dataset and experimental measurements indicate that the ratio of \(\mathrm{SIF}_{canopy}\) to \(\mathrm{APAR}_{chl}\) increases when canopy structure becomes more horizontal. The corresponding statistical models are listed in Table 3. As illustrated in Table 3, at the O\({}_{2}\)-A band, for the linear regression models that have an offset of 0, the ratios of \(\mathrm{SIF}_{canopy}\) to \(\mathrm{APAR}_{chl}\) with confidence level of 95% are 0.0092 \(\pm\) 0.0020, 0.0076 \(\pm\) 0.0036 and 0.0052 \(\pm\) 0.0004 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{\SIUnitSymbolMicro m}\  
Although the ratios of \(\mathrm{SIF}_{canopy}\) to APAR\({}_{chl}\) derived from the simulations agree with those from the field measurements, there is still a small discrepancy between the two datasets. In the simulations by the SCOPE model, only Cab, LAI, SZA and the leaf inclination distribution type were considered, which does not correspond to the much more complicated natural conditions. For example, the fluorescence quantum yield, \(\epsilon_{F}\), was set to 0.01 [54]. However, the fluorescence quantum yield depends on the light intensity and other environmental conditions [14; 28; 35]. Therefore, although the effect of the radiative transfer mechanism within the canopy on the relationship between \(\mathrm{SIF}_{canopy}\) and APAR\({}_{chl}\) was explored in this paper, the effect of the physiological mechanism remains unclear and will be a challenge for future studies.
## 3 Machine Learning Algorithms
### Support Vector Regression (SVR)  
Unlike commonly used regression machine learning algorithms (e.g., linear regression) that minimize the error between the predicted values and actual observations, the SVR algorithm fits a hyperplane to represent the training data within a threshold value. This threshold value (called the margin, \(\epsilon\)) is a hyperparameter of the model. The SVR algorithm uses a kernel function to transform the data instances into multi-dimensional input feature space; the radial basis function (RBF) kernel is used in this study. As highly nonlinear data with multiple input features cannot be completely represented by a hyperplane and a margin, a tolerance is used for the margin. Another hyperparameter (called the penalty parameter, C) of the SVR algorithm controls the magnitude of this tolerance across all dimensions in input feature space. When making a prediction on test data, the SVR model simply uses the hyperplane to make the prediction.
###### Abstract  
In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images.  
image registration; keypoint matching; synthetic aperture radar; deep neural network; generative adversarial networks +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Dynamic Mechanical Analysis  
Figure 9 shows the dynamic mechanical properties of the neat mPPO and GF/mPPO composites. The storage modulus was maintained in the glassy state and can indicate the stiffness of the materials. The storage modulus was increased with increasing GF content. This trend is similar to the results of the flexural modulus in Figure 4. As the temperature increased, the storage modulus began to drop in the glass transition region where the mPPO transitioned from a glassy to a rubbery state. This is related to the molecular chain mobility. When the movement of the molecular chain occurs due to having enough energy, the elastic response decreases, while the viscous response increases. A high glass transition temperature means that the energy required for the movement of molecular chains is high. Generally, the Tg increases as the fiber content and fiber length increase because the fibers hinder the movement of the molecular chains. The Tg determined by the onset in the storage modulus was shifted to higher temperatures at the GF/mPPO  
Figure 8: Typical TGA results of neat mPPO and GF/mPPO composites; (**a**) weight loss curves, and (**b**) derivative thermogravimetric curves.  
30 compared with the neat mPPO. However, it was found that the T\({}_{\rm g}\) was shifted to the lower temperatures with increasing GF content, which is consistent with the DSC results (Table 5). As mentioned in the thermal analyses, a chain scission results from thermal degradation. Short molecular chains are less entangled, and this would lead to the easy movement of molecular chains. The lower T\({}_{\rm g}\) indicates that the lower energy is needed for the molecular chain flexibility.  
The value of tan \(\delta\) is the ratio between the loss modulus and storage modulus. The temperature at the tan \(\delta\) peak in the glass transition region is known as the T\({}_{\rm g}\). The height of the tan \(\delta\) peak can be associated with the impact toughness. The height of the tan \(\delta\) peaks diminished with increasing GF content in the GF/mPPO composites. The lower tan \(\delta\) peak indicates less energy dissipation in the DMA tests due to the fiber-reinforcing effects by stress transfer between the fiber and matrix. Because the interfacial adhesion can  
Figure 9: DMA results of the neat mPPO and GF/mPPO composites; (**a**) storage modulus, and (**b**) tan \(\delta\).  
be assumed as the same for the composites in this study, the decreased height of the tan \(\delta\) peak would be attributed to the GF content, rather than the interfacial adhesion, leading to less energy dissipation of the GF-reinforcing effects. This may be correlated with the lower impact strength for the composites with higher CF contents. Similar results are found in other studies [23; 24].
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 6 Hardware Configuration and Experiment
### Wireless Remote Control (WRC)  
The RC-eye 650 is dully controlled through wireless remote control (WRC) that has the range of 2.400 to 2.5 GHz. In addition to this, this has been integrated with special Lightbridge controller module to make it flexible so that one can control it using cellular device too. The range in terms of distance through which one may transmit the control inputs is in between 1.65 to 4.55 km with receiver's sensitivity of \(-101\) dBm (+) (-) 2 dBm. Moreover, the temperature range at which one may operate it is \(-10\) to 45 degrees Celsius. This wireless control technique supports android as well as iPhone operating systems. The name of application is DJI app. The mentioned controller in this manuscript supports the high-definition (HD) camera view using zemnuse X3 gimbal camera, as illustrated in the Figure 23.
# Acceleration of OCT Signal Processing with Lookup Table Method for Logarithmic Transformation
## 1 Introduction  
Optical coherence tomography (OCT) is a noninvasive, high-resolution optical imaging technique that can provide a cross-sectional and three-dimensional structure of biological tissues in micrometer scale [1]. There are two kinds of OCT technologies including time domain OCT (TDOCT) and Fourier domain OCT (FDOCT). TDOCT acquires interference signal based on mechanical scanning of reference arm, hence the scanning rate is limited by the hardware. FDOCT that obtains the structural information by evaluation of the interference spectrum is capable of a much higher scan rate since no scanning of the reference arm is needed. It has been reported that a multi-MHz scan rate can be achieved in a swept source OCT (SSOCT) system by using an ultra-high-speed Fourier domain mode-locked laser [2].  
Despite the achievement of scan rate, the bottleneck of imaging display is the mass data processing. The signal processing of OCT includes data transfer, background subtraction, data resampling, Fourier transformation, and logarithmic transformation in which logarithmic-scaling takes a high proportion of total processing time. Due to strong absorption of light in biological tissues, the intensity of OCT signal from superficial and deep layers of tissue varies by several orders of magnitude. By mapping logarithmically transformed raw data to a series of fixed gray values to adapt the psychological properties of the human eyes, weak backscattered signals can be visualized more clearly with a compressed dynamic range [3]. For this reason, logarithmic transformation has become a standard procedure for OCT signal processing.  
In FDOCT, the use of a logarithmic amplifier after the photodetector to save logarithmic calculation time as in TDOCT [4] is inappropriate since Fourier transformation of the signal in log scale would distort imaging and degrade resolution [5]. Alternative methods of compressing the dynamic range to accelerate processing in OCT have been demonstrated. For example, a truncation logarithm method was adopted to achieve a predetermined dynamic range by selecting an appropriate threshold in logarithmic calculation, however at a cost of degraded imaging quality [6]. Parallel computing accelerated algorithms based on graphics processing units (GPU) can be used for real-time OCT data processing and imaging display [7; 8]. However, high-performance GPUs are limited in the market and costly. Therefore, a low cost and less complex technique to accelerate OCT signal processing, especiallylogarithmic calculation for real-time display, is attractive. In this paper, a lookup table method is proposed to improve the processing speed of logarithmic transformation in FDOCT.
# A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems
## 2 Methods
### Fuzzy Wavelet Neural Network (FWNN)  
Wavelets may be defined as mathematical functions used for representing data or other functions. A wavelet is such a waveform possessing an effectiveness of finite duration with an average value equal to zero. The underlying theme connected to wavelet neural networks is related to the combination of wavelet structure and neural networks. This successful synthesis is called WNN. The novelty of these networks is that they prefer the wavelet as the HL activation function in classical neural networks. The output of the network is given in Equation (8).  
\[\mathrm{g}(\mathrm{x})=\sum_{\mathrm{k}=1}^{\mathrm{n}}\omega_{\mathrm{k}} \mathrm{y}\bigg{(}\frac{\mathrm{x}-\mathrm{b}_{\mathrm{k}}}{\alpha_{\mathrm{k }}}\bigg{)} \tag{8}\]  
where n is the size of the input, and \(\omega_{\mathrm{k}}\) the weight coefficient. \(\mathrm{b}_{\mathrm{k}}\) and \(\alpha_{\mathrm{k}}\) are free parameters to be initialized.  
By synthesizing the FIS and the WNN, the structure of the FWNN was created. FWNN incorporates fuzzy rules, wavelet functions, and neural networks. Therefore, it has some of the strengths of these structures. For example, the high reasoning capability of fuzzy logic or the high-precision approximation performance of the wavelet structure. A set of fuzzy rules that influence the operation of this network is shown in Equation (9) [21].  
\[\mathrm{R}^{\mathrm{i}}=\mathrm{IF}\ \mathrm{x}_{\mathrm{1}}\ \mathrm{is}\ \mathrm{A}_{1}{}^{\mathrm{i}};\ \ldots;\ \mathrm{x}_{\mathrm{m}}\ \mathrm{is}\ \mathrm{A}_{1 \mathrm{m}}{}^{\mathrm{i}}\ \mathrm{Then}\ \mathrm{y}_{\mathrm{i}}=\mathrm{w}_{\mathrm{i}}\sum_{ \mathrm{i}=1}^{\mathrm{q}}\mathrm{w}_{\mathrm{j}}\big{(}\mathrm{x}_{\mathrm{i} }\big{)}\ \ \ \ \ \mathrm{i}=1:\mathrm{c} \tag{9}\]  
where c and m are the number of fuzzy rules and the size of the input. The output of the FWNN is given in Equation (10) and its general structure is given in Figure 3.  
It has seven layers. In the first layer, nodes equal to the number of input signals send the signals. In the second layer, each node corresponds to a linguistic term. For each signal input into the system, the degree of membership to the fuzzy set to which the input value belongs is calculated. In the third layer, each node represents a fuzzy rule. Here, the AND (min) operation is used to calculate the values of the output signals of the layer. The fourth layer is where the wavelets are incorporated into the neural network mechanism. In layer five, the output signals from layer three are multiplied by output signals from the WNNs. Defuzzification is carried out in the sixth and seventh layers to determine the final output of the entire network [14].  
\[\hat{\mathbf{y}}=\frac{\sum_{\mathrm{i}=1}^{\mathrm{c}}\left[\prod_{\mathrm{j }=1}^{\mathrm{m}}A_{\mathrm{j}}^{\mathrm{i}}\big{(}\mathbf{x}_{\mathrm{j}} \big{)}\right]\mathbf{y}_{\mathrm{i}}}{\sum_{\mathrm{i}=1}^{\mathrm{c}}\left[ \prod_{\mathrm{j}=1}^{\mathrm{m}}A_{\mathrm{j}}^{\mathrm{i}}\big{(}\mathbf{x}_ {\mathrm{j}}\big{)}\right]} \tag{10}\]
## 3 Mechanosensing in Normogravity  
### Cytoskeleton/Nucleoskeleton  
These structures provide support to the cells and nucleus to maintain their shape and internal ordered organization. Cytoskeleton and nucleoskeleton are composed by different components such as: actin fibres (F-Actin), myosin and myosin-like filaments, microtubules (MTs), intermediate filaments (IFs), and fibres that interact all together for the maintenance of the cellular architecture and organelle localization and movements [57]. Contractile activity of the cytoskeleton is performed by the actin-myosin system with a number of accessory proteins determining its different organization and function [58]. Isoforms of actin (more than 21 genes plus other isoforms obtained by alternative splicing), tubulin, and myosin (a complex family including skeletal, cardiac, smooth muscle myosins and dynein and many other myosin-like proteins) have in common not only sequence similarities, but also the way to generate the contractile force at the expense of ATP hydrolysis performed by a specific molecular domain of myosin light chain. Different contractile efficiency and vectorial characteristics are determined not only by their supramolecular organization (such as sarcomeres, the fusiform fibres of smooth muscle, the cortical skeleton of plasmamembrane and nucleoskeleton, the tubulin/dynein system in cilia, spermatozoa, and axonal transport). Accessory proteins of cytoskeleton include \(\alpha\)-actinin, fascin, filamin, and a number of other proteins located in the Z-disk, in the fusiform densitiesof smooth muscle and in the line M of the sarcomere. All of them are necessary to assembly and maintain more complex supramolecular structures, such as sarcomeres, stress fibres, and microtubular systems [55]. Under mechanical stimuli, stress fibres and focal adhesion plaque interact with sensing and transducing gravity, muscle activity, and stretching. Other proteins can interact with actin, such as Arp2/3, profilin, and ADF/cofilin. Under mechanical stimulation, cofilin is phosphorylated by LIM domain Kinase (LIMK), a kinase activated by the Rho/ROCK pathway [59], a signalling pathway that causes the stabilization of the cytoskeleton through the activation of formin Diaphanous (mDia) and Arp2/3 that in turn produces actin polymerization [60; 61]. The proteins described below and included in Table 1, are considered the most well-known mechanosensors acting in normogravity conditions.
# Aptamer-Based Biosensors for the Analytical Determination of Bisphenol A in Foodstuffs
## 4 Detection Techniques Applied in the Analysis of Real Food Samples
### Capacitive Aptasensors  
Capacitive biosensors can be classified as non-Faradaic electrochemical biosensors because the use of a redox probe is avoided in this system [74]. The sensing event consists of a change of dielectric properties and/or thickness of the dielectric layer at the electrolyte-electrode interface, in which an analyte interacts with its receptor immobilized on the insulating dielectric layer. The sensing event can be monitored by measuring the interfacial capacitance. [PERSON] et al. [65] obtained a BPA capacitive aptasensor by means of printed circuit board material. The BPA level in solution was evaluated by measuring the sample/electrode interfacial capacitance of the sensor. The alternating current electro-thermal effect (ACET) was generated during capacitance measurements by a plate WE, and  
Figure 7: Schematic representation of the strategy used by [PERSON] et al. [73] to build up the electrochemuluminescent aptasensor through the functionalization of the indium-tin-oxide (ITO) working electrode. [Reprinted from Biosensors and Bioelectronics, 129, [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Signal-on electrochemuluminescence aptasensor for bisphenol A based on hybridization chain reaction and electrically heated electrode, 36–41, Copyright 2019, with permission from Elsevier].  
it significantly contributed to an increase in the rate of BPA transport towards the electrode surface, thus reducing the time of the analysis. The final BPA aptasensor (Figure 8) exhibits different advantages compared to Faradaic electrochemical biosensors developed for BPA detection in food matrices: (i) the adopted strategy to functionalize the plate electrode is simple and low-cost; (ii) the use of an external redox probe is avoided; (iii) the incubation time of the functionalized WE with BPA-containing solution is shortened by electrothermal effects during capacitance measurements; and (iv) repetitive analyses can be rapidly performed. However, as the sensing mechanism is conceived in capacitance biosensors, even slight changes in the environment surrounding the electrodes can significantly affect the measurements. Thus, the experimental condition of BPA detection should be strictly controlled. This phenomenon can limit the applicability of the system in more complex food matrices than in those tested (solid portion of canned food).
## 4 Case Study
### Discussions on Practical Applications  
With the expansion and diversification of prosumer aggregators, the challenges of practical DR are varied, numerous and continue to change. The rate at which the DR scale is changing is likely the greatest challenge to power grid companies struggling to gain a foothold or even to those leading the way. But a change in scale is paramount to actualization, for there are still many barriers to their practical application. A few of the challenges are listed below:  
(1) Problem complexity: In real-world scenarios with a higher number of prosumer aggregators and diverse RESs, the complexity of the implementation of the transactive DR framework is proportionable. The DR with a flexibility potential evaluation alone requires considerable computational resources, in addition to the data acquisition and information transfer. Since each prosumer aggregator can be regarded as a relatively independent part, a distributed methodology with massively parallel processing is recommended.  
(2) Cyber security: Since smart grid data acquisition infrastructures are vulnerable to cyber-attacks, DR digitalization may give rise to another considerable challenge in cyber-security. The attacker can manipulate meter readings/power consumption and launch false data injection attacks against DR implementation, having negative impacts on the normal operation. Resilience DR enhancement against cyber-attacks is recommended to maintain a reliable and secure energy system.  
(3) Prosumer management: Unlike other regulation schemes, such as energy efficiency, which focuses on the energy generation or consumption, DR depends upon a change to normal human behavior. Practically, DR may suffer from initial or halfway rejection, which may pose a more considerable barrier to its implementation than a lack of policy. An empowered DR contract with prosumers is recommended so that rewards and punishments that will result in arbitrary action are delivered.  
Figure 14: Daily output of P2G.  
Figure 13: Daily gas storage tanks.