esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Regular RBF Method  
The conventional radial basis function method can be used to interpolate given \(N\) data \(f(x_{k})\) at \(x_{k}\) locations by defining the interpolant function \(I(x)\).  
\[I(x)=\sum_{k=1}^{N}\alpha_{k}\phi(r(x,x_{k}),\varepsilon), \tag{1}\]  
where \(\phi\) denotes the radial basis function, \(\alpha_{j}\) are coefficients associated with each basis, and \(\varepsilon\) refers to the shape parameter. \(r(x,x_{k})=\sqrt{(x-x_{k})\cdot(x-x_{k})}\) signifies the distance between two points in space, as shown in Figure 1. Although the matrix generated from Gaussian RBF is ill-conditioned especially when the shape parameter approaches zero, the Gaussian radial basis function (i.e., Equation (2)) is selected in this work because it is one of the most accurate RBFs when used for approximation [43]. That is because it is strictly positive definite and infinitely differentiable [2]. In addition, it has the ability to evaluate its derivatives without the risk of dividing by zero.  
\[\phi(r,\varepsilon)=exp\Big{(}-\varepsilon^{2}\;r^{2}\Big{)} \tag{2}\]  
Figure 1: Schematic diagram of the distance \(r\).  
Coefficients \(\alpha_{j}\) can be found by imposing the interpolation constraints (equating the interpolant at points \(\mathbf{x}_{k}\) to \(f(\mathbf{x})\)) on the interpolant:  
\[I(\mathbf{x}_{k})=\sum\limits_{j=1}^{N}\alpha_{j}\phi\big{(}r\big{(}\mathbf{x}_{k},\mathbf{ x}_{j}\big{)},\varepsilon\big{)}=f(\mathbf{x}_{k}), \tag{3}\]  
which results in the following system of equations:  
\[\phi_{ij}\alpha_{j}=f(\mathbf{x}_{i}). \tag{4}\]  
The matrix \(\phi_{ij}=\phi\big{(}r\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)},\varepsilon\big{)}\) is a symmetric square matrix, which represents the coefficients of a system of equations that can be solved for \(\alpha_{j}\). The coefficients \(\alpha_{j}\) are substituted in the interpolant (Equation (1)) to interpolate the given function \(f(x)\) at any location \(\mathbf{x}\). Thereafter, the derivatives of the function \(f\) can be approximated by taking the derivatives of the interpolant. For example, the first and second derivatives are presented in Equations (5) and (6), respectively.  
\[\frac{\partial f(\mathbf{x})}{\partial x_{\mathbf{x}}}\approx\frac{\partial I(\mathbf{x}) }{\partial x_{\mathbf{x}}}=\sum\limits_{j=1}^{N}\alpha_{j}\frac{\partial\phi\big{(} r\big{(}\mathbf{x},\mathbf{x}_{j}\big{)},\varepsilon\big{)}}{\partial x_{\mathbf{x}}} \tag{5}\]  
\[\frac{\partial^{2}f(\mathbf{x})}{\partial x_{\mathbf{x}}\partial x_{\beta}}\approx \frac{\partial^{2}I(\mathbf{x})}{\partial x_{\mathbf{a}}\partial x_{\beta}}=\sum \limits_{j=1}^{N}\alpha_{j}\frac{\partial^{2}\phi\big{(}r\big{(}\mathbf{x},\mathbf{x}_ {j}\big{)},\varepsilon\big{)}}{\partial x_{\mathbf{a}}\partial x_{\beta}} \tag{6}\]  
In this section, it is shown that the interpolation and derivatives approximations depend on \(\alpha_{k}\), which cannot be stably calculated when the shape parameter approaches zero because the matrix \(\phi_{ij}\) is ill-conditioned. A way to resolve this problem is proposed in the next section.
## 3 Results
### Dominant Meteorological Factors Driving the Projected Terrestrial GPP Variations
VPD-dominated regions occupy approximately 16% of the study area during April and 21% during both July and October, but are virtually absent in January and July. Spatially, Tmin-dominated areas concentrate over central China and Yunnan Province in January, and extend to northeastern China, eastern Inner Mongolia, and the western Sichuan province in April and October. VPD-dominated regions focus primarily on southern China in April and October, and on central China in July. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **January** & **April** & **July** & **October** \\ \hline Tmin (\({}^{\circ}\)C) & 0.15 \(\pm\) 0.22 & 0.06 \(\pm\) 0.25 & 0.03 \(\pm\) 0.23 & 0.19 \(\pm\) 0.29 \\ Srad (MJ/m\({}^{2}\)) & 1.18 \({}^{\ast}\)\(\pm\) 0.49 & 0.27 \({}^{\ast}\)\(\pm\) 0.34 & 0.45 \({}^{\ast}\)\(\pm\) 0.52 & 0.61 \({}^{\ast}\)\(\pm\) 0.40 \\ VPD (Pa) & 22.77 \({}^{\ast}\)\(\pm\) 14.77 & 0.02 \(\pm\) 13.50 & 16.80 \(\pm\) 30.10 & 28.19 \({}^{\ast}\)\(\pm\) 24.03 \\ PM\({}_{2.5}\) (\(\mu\)g/m\({}^{3}\)) & \(-49.92\)\({}^{\ast}\)\(\pm\) 25.52 & \(-9.61\)\({}^{\ast}\)\(\pm\) 8.71 & \(-7.55\)\({}^{\ast}\)\(\pm\) 8.82 & \(-18.31\)\({}^{\ast}\)\(\pm\) 13.19 \\ O\({}_{3}\) (ppbv) & \(-2.04\)\({}^{\ast}\)\(\pm\) 6.35 & \(-3.02\)\({}^{\ast}\)\(\pm\) 5.10 & \(-9.36\)\({}^{\ast}\)\(\pm\) 5.72 & \(-3.10\)\({}^{\ast}\)\(\pm\) 6.48 \\ \hline \end{tabular}
\end{table} 
Table 3: Changes in meteorological factors and pollutant concentrations due to PER in China. An asterisk represents significant differences at a 95% confidence interval. Figure 5: Main factors causing GPP variations influenced by carbon-neutral PER. Pie charts indicate the percentage of the region occupied by the dominant factors.
## 2 Method
### Trend Analysis
#### 2.4.1 Mann-Kendall Test  
The Mann-Kendall test reflects the variation of the latter part of the observed value compared with the previously observed value [49]. It can be described as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sgn\big{(}x_{j}-x_{i}\big{)} \tag{1}\]  
where \(n\) is the number of research years, \(x_{i}\) and \(x_{j}\) are the GPP values in year \(i\) and \(j\) (\(j>i\)), respectively, and the definition of \(sgn\big{(}x_{j}-x_{i}\big{)}\) is:  
\[sgn\big{(}x_{j}-x_{i}\big{)}=\left\{\begin{array}{c}+1,\textit{ if }x_{j}-x_{i}>0\\ 0,\textit{ if }x_{j}-x_{i}=0\\ -1,\textit{ if }x_{j}-x_{i}<0\end{array}\right. \tag{2}\]  
The variance \(Var(S)\) is calculated as:  
\[Var(S)=\frac{n(n-1)(2n+5)-\sum_{i=1}^{m}k_{i}(k_{i}-1)(2k_{i}+5)}{18} \tag{3}\]  
where \(n\) is the length of years, \(m\) represents the number of tied groups, and \(k_{i}\) is the number of ties in extent \(i\). The standard normal deviate (\(Z\) statistics) is calculated as:  
\[Z_{S}=\left\{\begin{array}{c}\frac{S-1}{\sqrt{Var(S)}},\textit{if }S>0\\ 0,\textit{if }S=0\\ \frac{S+1}{\sqrt{Var(S)}},\textit{if }S<0\end{array}\right. \tag{4}\]  
A positive value of \(Z_{S}\) means an upward trend while a negative \(Z_{S}\) value indicates a downward trend. We tested the trend at the significance \(\alpha\) level. If \(|Z_{S}|>Z_{1-\alpha/2}\), it means rejecting the null hypothesis at the \(\alpha\) level and that there is a significant trend in the GPP time series.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 4 Materials and Methods
### Runtime Adaptation and Fuzzy Logic  
For complex and rough solution spaces, GA convergence can slow down and avoiding local minima may become more challenging. In order to improve search efficiency, GAs can be enhanced using runtime parameter adaptations. These techniques generally try to analyze the current state of the GA parameters, population statistics and improvement trajectory to dynamically adjust parameters for future generations. In our work, we leverage a fuzzy logic controller [30] to analyze the _update history_ and current mutation rate and adjust the mutation rate parameter for future generations. Update history is defined as the number of new chromosomes discovered and added to the Pareto front in the last five generations, as a proportion of the total population. Updates are measured with respect to the total population since larger populations would intuitively produce more updates, as a bigger chunk of the solution space is searched each generation. The goal is to guide the GA to increase diversity in the population when updates stagnate, or if the GA already has high mutation rates, to slow down and exploit existing solutions. The update history and current mutation rate are converted into fuzzy variables with three membership categories: LOW, MEDIUM, and HIGH. These variables are processed by the fuzzy inference engine, which outputs a new fuzzified mutation rate value. This value is later defuzzified using the mutation rate's membership function to obtain a new mutation rate. Figure 9 gives a brief overview of the fuzzy controller for mutation rate.  
Figure 8: Example of (**a**) gate reduction through elimination, (**b**) gate reduction through gate fusion, (**c**) depth reduction through elimination and fusion.  
Fuzzification uses the membership functions presented in Figure A2, in Appendix B.2, to convert crisp values into fuzzy values, while defuzzification uses the centroid method [31] and the membership function from Figure A2b specifically to obtain a crisp value from a fuzzy mutation variable. We chose the triangle membership function due to its simplicity of implementation and because it is one of the most widely used functions in literature [80]. The fuzzy rules, detailed in Table 2, are designed to address observed evolutionary scenarios: when the GA demonstrates low exploration (few updates and low mutation rates), the system increases mutation rates to enhance solution diversity. Conversely, when the GA demonstrates high exploration (high mutation rates with limited updates), the controller reduces mutation rates to promote exploitation of already discovered chromosomes. The controller also attempts to maintain stable rates when the GA is making consistent progress.  
This adaptive mechanism ensures that the genetic algorithm can dynamically balance exploration and exploitation, improving overall search performance. While the membership functions were initially based on informed guesses from experimentation, fuzzy logic's flexibility allows them to be updated without changing the rule set.
## 5 Stability of System
### Effect of Extinction Ratio of MZMs  
Figure 16 shows the relationship between the extinction ratio of the MZM and the Q factor of the downlink. As seen in Figure 16, in the case of BTB and a 20 km transmission distance, the Q factor of the downlink remains unchanged when the extinction ratio is increased over 30 dB. In the case of a 40 km transmission distance, the Q factor of the downlink remains unchanged when the extinction ratio is increased over 40 dB.
## 7 SensorNC Applications
### Node Placement Strategies
#### 7.2.1 Node Placement Schemes  
During the design phase of WSNs, the designer only knows the number of sensor nodes, \(n\). A circular field with radius \(R\) is considered in our experiments. Next, we introduce three node placement schemes.
# Blur Kernel Estimation by Structure Sparse Prior
## 4 Experiments and Results
### The Competitors and Experimental Images  
To evaluate the proposed method, three different methods [8; 27; 28] were used as the competitors.  
* **[PERSON]_2008:** The first method is from [PERSON]. [PERSON] et al. [27]. The method recovers the clear image in a unified probabilistic model, including blur kernel estimation and image restoration.
* **[PERSON]_2011:** The second method is from [PERSON] et al. [8]. The method introduces a new type of regularization term, namely \(l_{1}/l_{2}\) norm, which gives lowest cost for the true sharp image.
* **[PERSON]_2013:** The final method is from [PERSON] et al. [28]. The method provides a unified framework for removing the blur kernel. A generalized and mathematically sound \(l_{0}\) sparse expression is proposed.  
In fact, two kinds of experiments from different perspectives were conducted. First, the experiment on the recovery performance of the proposed method was conducted on the benchmark from [PERSON] _et al._[29]. Then, the experiment on robustness was conducted on Berkeley Segmentation Database ([[http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench)]([http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench))) [30]. We chose 40 images from this database and randomly divided them into four groups (10 images in each group). Then, we generated blurred images with different blur kernels, where the kernel size was 7-19 pixels (kernel size = 7, 11, 15, 19). To evaluate the performances of comparison, two quantitative metrics were considered: Structure SIMilarity (SSIM) and Peak Signal to Noise Ratio (PSNR). These metrics evaluate the performances by comparing the similarity between the original image and the deblured image.
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 2 Materials and Methods
### Soil Thermal Diffusivity Estimation Methods
#### 2.2.3 Min-Max Method  
Equation (2) gives the range of soil temperature variation can be calculated as (18)  
\[\mathrm{T}(z,t)=T_{ave}(z)\pm A(z) \tag{15}\]  
showing that the temperature features between a minimum and a maximum. Since the range of the sine function is from \(-1\) to \(1\), \(A(z)\) can be calculated as:  
\[A(z)=\frac{T_{max}(z)-T_{min}(z)}{2} \tag{16}\]  
Thus, using the min-max method, the soil thermal diffusivity (\(k_{MM}\)) can be calculated as  
\[k_{MM}=\frac{\omega(z_{1}-z_{2})^{2}}{2\left(\ln(A(z_{1}))-\ln(A(z_{2}))\right) ^{2}} \tag{17}\]  
where \(A(z_{1})\) and \(A(z_{2})\) are the amplitudes of temperature fluctuation determined from the min-max method at depth \(z_{1}\) and \(z_{2}\), respectively.  
Field-measured minimum and maximum temperatures recorded by temperature sensors at depths \(z_{1}\) and \(z_{2}\), can be used to determine the soil thermal diffusivity. Consequently, the amplitude values can be determined without the temperature necessarily behaving strictly in a sinusoidal manner.
## 2 Materials and Methods
### What Are the Proposed Architectures That Support Cognitive Radio LPWAN Based IIoT?
#### 2.2.3 The Architecture of U-LTE Cognitive Radio for IIoT  
Unlicensed LTE (U-LTE) bands provide an attractive opportunity to satisfy subcarriers, enabling access to 5 GHz for IIoT. These bands coexist with other Wi-Fi/IoT users. The coexistence between U-LTE and IoT requires a protocol to listen before talk (LBT) with cognitive radio [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72]. The differences between LTE and IoT in PHY/MAC can cause continuous interference to Wi-Fi/IoT systems. Unlicensed band operation needs the component to be within the regulatory requirements of a given area deployment state of affairs of U-LTE at the side of IIoT person device and devices, by using speaking with Wi-Fi to get AP to use the unlicensed spectrum, form a femtocell, and grow to be number one customers. United states in a small cell with eNodeB utilize unlicensed spectrum and grow to be secondary customers. In U-LTE, the unlicensed spectrum is handiest for downlink site visitors and the operations that include making sure reliable communications and checking the provision of intended unlicensed channels are achieved through eNodeB [89].  
In the coexistence of U-LTE with Wi-Fi/IoT system as shown in Figure 8, the LTE and Wi-Fi/IoT randomly generate multiple signals with respective energy and transmission time. Free channels could be allotted to the transmission between the respective source and sink where the machine architectures of U-LTE cognitive radio for IIoT occupy idle channels and transmit for its respective period [90]. The contention adoption via the wireless based on mac protocol with a random access mechanism will find that the medium busy most of the time and have high back off.  
Within the cognitive radio utility in such coexistence architecture, wireless/IIoT can be considered as a primary user (pu) and U-LTE because of the secondary consumer (su). The alerts come from IIoT assets that occupy the channels following their own MAC protocol employing the channel allocation device. While U-LTE indicators will comply with the requirements channels through the cognitive radio machine. The cognitive radio system can be prepared on LTE base station, which gets a transmission request from LTE resources and then discovers the unfastened channels utilizing estimated idle channel threshold by way of measuring the energy degree for the duration of the listening duration [91].
_Applied_  
_sciences_  
_Article_  
**The Influence of Different Pre-Treatments on the Quality and Nutritional Characteristics in Dried Undersized Yellow Kiwifruit**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 2,*\({}^{\copyright}\), [PERSON] 2, [PERSON] 2\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\) and [PERSON] 2\({}^{\copyright}\)**  
Received: 31 October 2020; Accepted: 22 November 2020; Published: 26 November 2020  
**Featured Application: Industrial application of the pre-treatments used prior to convective drying should take into account the time required for the pre-treatments themselves. In the case of osmotic dehydration (OD), the processing time could be relatively long (up to 120-150 min to reach a pseudo-equilibrium of the mass change), but it offers the possibility to handle underripe and very sour fruits and it could be carried out in batch or continuous lines with fair simple technologies. Moreover, it can be performed at near ambient temperature before drying to bring the partially dewatered products to the convective drying. The spent solution management can also be easily performed. On the other hand, the industrialization of PEF pre-treatment can be easily introduced also in a continuous process since the required time for PEF application is very short (in the present work--10 s) and nowadays there are several equipment suppliers able to scale-up industrial plants from laboratory scale.**  
**Abstract:** Undersized kiwifruits are considered waste; therefore, there is a need to develop sustainable processing technologies for their valorization, in order to have a positive economic impact on their entire production chain. Therefore, the aim of this study was to apply pulsed electric fields (PEF) combined with osmotic dehydration (OD) and air drying (AD) to develop a novel yellow kiwifruit dried snack with high nutritional functionality. In particular, the effect of the different pre-treatments, PEF (200 V/cm) and/or OD (trehalose at 40%), on the color, bioactive compounds content and antioxidant activity (AA) was evaluated on kiwifruit chips air-dried at three different temperatures (50, 60, 70 \({}^{\circ}\)C). The results showed that better color maintenance was observed at the drying temperature of 60 \({}^{\circ}\)C. Concerning the bioactive compounds, a good retention in total polyphenols (TP), vitamin C content and AA with the DPPH method was obtained on OD and PEF kiwifruit dried snacks, similar to that of control samples. Instead, the ABTS method showed the best AA in PEF and OD/PEF samples. In general, a shorter drying time in the OD- and PEF-treated samples could contribute to the higher sustainability of the process.  
**Keywords:** emerging technologies; pulsed electric field--PEF; osmotic dehydration--OD; fruit snack; waste valorization
# Integrative Assessment of Sediment Quality in the Sao Francisco River (Mina Gerais, Brazil)
## 3 Results  
### Physicochemical Analyses  
The summarized results of the physicochemical parameters analyzed are shown in Table 1. The BG4 station registered the greatest concentrations of compounds (nitrate, ammonium, ammonia, and ammoniacal nitrogen). However, the greatest element concentrations of Fe and Cr were found at SF1; those of Cu, Pb, and Zn, at SF3; and those of Co and Ni, at SF2. The ZM2 station displayed the highest concentrations of Al and As. The concentrations of metals Pb, Zn, Hg, and Cu showed a smooth gradient that increased from station SF1 as follows: SF1 \(<\) SF2 \(<\) SF3 \(<\) ZM2. Station ZM2 was located in the area of mixing between the SFR and the heavily factory-influenced stream, which could justify the high values of Al and As.  
Due to the placement of BG4, situated on the Barreiro Grande stream, where the Tres Marias water treatment plant discharges, the nitrogen species concentrations (nitrate, ammonium, ammonia, and ammoniacal nitrogen), well known as domestic waste indicators, were higher than the values obtained at the other stations. On the other hand, the sediments from the SF1 sampling point, although not directly influenced by the factory, received many contaminants from all over the drainage basin, since this sampling point was located under the Tres Marias reservoir. According to the results, we could establish a decreasing pattern related to domestic waste contamination as follows: BG4 \(>\) SF1 \(>\) SF3 \(\approx\) ZM2 \(>\) SF2.
## 3 An Improved Objective Function Based on SVDM
### Selection of Key Parameters in Improved Objective Function
#### 3.3.2 Selection of Excitation  
The frequency response \(h_{aq}(\mathbf{\mu},\omega)\) represents the response to the excitation \(q(t)\), so that the characteristics of \(h_{aq}(\mathbf{\mu},\omega)\) are closely related to the excitation. Since the excitation \(q(t)\) is applied numerically in the FE model, it can be freely selected. In order to obtain a frequency response that contains more accurate modal information, different excitations can be applied for each natural frequency to be identified. Here, the excitation \(q_{i}(t)=\mathbf{\varphi}_{i}\delta(t)\) is chosen for the \(i\)-th frequency \(\overline{\omega}_{i}\) to be identified, where \(\mathbf{\varphi}_{i}\) is the \(i\)-th mode shape of the theoretical undamaged FE model. The objective function of Equation (5) can be further improved as shown in Equation (8).  
\[f(\mathbf{\mu})=\alpha_{\varphi}\sum_{i=1}^{n_{\varphi}}(1-MAC(\mathbf{H}_{i}(\mathbf{\mu},\overline{\omega}_{i}),\overline{\mathbf{\varphi}}_{i}))-\alpha_{\omega}\sum_{i= 1}^{n_{\varphi}}\sum_{\alpha=1}^{n_{z}}|h_{ai}(\mathbf{\mu},\overline{\omega}_{i})| ^{2} \tag{8}\]  
where \(\mathbf{H}_{i}\) and \(h_{ai}\) represent the structural frequency response obtained by the theoretical FE model under excitation \(q_{i}(t)\).
## 3 An overview of AVs
### Sensor Technologies
#### 3.1.1 LiDAR  
LiDAR is considered the primary perceptive sensor in the self-driving car industry. Even though 3D-LiDAR has only been used in automobiles for slightly over ten years, it has already been shown to be crucial to the development of AVs and ADS [36]. Its exceptional ability to measure things precisely and its resilience to changing lighting conditions highlight how vital it is. Critical features of 3D scanning using lasers include its form aspect, affordability, tolerance to external variables, measuring range, precision, pinpoint density, scanning acceleration, configurational versatility, and spectral qualities [37]. These sensors work well in a range of weather conditions by using laser beams to measure distances and produce accurate 3D point clouds. However, there could be problems when there is a great deal of rain, snow, or fog since laser beams can disperse and potentially skew the data. Older types, such as the Velodyne HDL64-S2 [38], continue to function better in dense fog, while the performance of more modern laser scanners decreases. While making manual modifications can increase performance, it is still difficult to perceive reliably in deep fog. For this reason, alternative technologies, including gated imaging, should be considered for better performance in adverse conditions [39].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sensors** & **Advantages** & **Disadvantages** \\ \hline LiDAR & High resolution Long range Wide FOV & Sensitive to weather Expensive \\ \hline \multirow{2}{*}{Radar} & Long range Detection of velocity & \\  & Suitable for all types of weather & Low resolution Very sensitive \\ \hline Camera & High resolution Detection of colors & Sensitive to weather Sensitive to lighting \\ \hline Ultrasonic & Non-InvasiveReal-time feedback & Low resolution Expensive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of sensors [35].  
Figure 5: An architecture of AVs.