esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Complexity of FL Approach  
The FL parameters add complexity to FL over a conventional ML system because of the distinct scenario they represent. The skewness of the heterogeneous data spread among the client devices, for instance, is a crucial parameter. In a multi-class classification issue, a severely skewed data scenario can involve each client having just one label of data. The number of participating clients, as well as each client's communication and processing budget, are further important FL variables.  
Provided that FL is supposed to function in the edge layer, it should have a low time complexity, as the software being run has a significant impact on the behavior of the associated processes. The time complexity of the global model depends on the time complexity of the clients, the time complexity of the aggregation server, and the complexity of the exchanged parameters, excluding transmission times, as these factors typically vary significantly across networks.  
The local client's time complexity can be defined by: \(O(E.n_{k}.(l_{0}.l_{1}+l_{1}.l_{2}+\ldots+l_{L-1}.l_{L}))\), where \(\mathrm{l_{x}}\) represents layer x, \(E\) represents the number of local epochs, and \(n_{k}\) represents the size of each client dataset. Now, the global server time complexity depends on the total number of clients (\(K\)) and also the cumulation of all local model parameters (\(W\)). So, we can define the time complexity of the global server by: \(O(K.W)\). Again, we will have many parameters exchanged between global and local clients. The complexity of the exchanged parameters we can define as: \(O(W)\).  
Figure 3: Interactions among clients’ participants of FL-based IDS.  
So, the total time complexity (\(O_{\text{total}}\)) of our proposed FL approach can be represented by the following equation (Equation (2)).  
\[O_{\text{(total)}}=O(K.(E.n_{k}.(l_{0}.l_{1}\ +\ l_{1}.l_{2}+\ldots+l_{L-1}.l_{L})))+O(K. W)+O(W) \tag{2}\]
## 2 Materials and Methods
### Post-Irradiation Analyses  
The irradiated samples were stored at 3 different temperatures in the post-irradiation period: 20 \({}^{\circ}\)C in a laboratory room, 40 \({}^{\circ}\)C in an oven (MOV-112S, Sanyo Electric Biomedical Co., Ltd., Osaka, Japan) and 5.5 \({}^{\circ}\)C in a refrigerator. Afterward, irradiation followed by storage at 20 \({}^{\circ}\)C was performed again to check the reproducibility of the coloration/decolorization properties. Color images of the irradiated samples were taken using a flatbed scanner (GT-X900, Seiko Epson Corp., Nagano, Japan) with certain time intervals. The acquired images were analyzed using a freeware application, ImageJ, which is a Java-based image processing program developed by the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation [33]. Measurements of color levels, color separations and calibrations were made by using the image processing functions of ImageJ. For examination of the dose responses of RGB components, the scanned color images were separated into three 8-bit images of blue, green and red components by using the built-in color split function of ImageJ.  
\begin{table}
\begin{tabular}{c c} \hline \hline Density & 1.075 g cm\({}^{-3}\) \\ Elemental composition 1 & K:1, Si:1, C:2, H:6, I:1, O:4 \\ Effective atomic number & 38.8 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Physico-chemical properties of PAISiN.  
Figure 1: Experimental setup for irradiation using \({}^{137}\)Cs source \(\gamma\)-rays; 3 samples each were irradiated with 5, 10 and 20 Gy (for water) with a dose rate of 0.75 Gy min\({}^{-1}\).
## 4 Conclusions  
_Cleistocalyx operculatus_ leaves contain various compounds, such as flavonoids, saponins, phenols, terpenoids, and alkaloids. The total flavonoid and terpenoid contents of leaves were high. For old leaves, they also were slightly higher than those of the young and mature leaves, which are those mainly and efficiently collected in the field. The herbal blend of _C. operculatus_ mixed with 30% of _Mentha arrensis_ leaves kept most of the initial specific unique aroma and taste and revealed a DPPH radical scavenging IC50 value of 35.65 (\(\pm\) 0.12) \(\mu\)g/mL and a lipid peroxidation inhibition IC50 value of 6.01 (\(\pm\) 075) \(\mu\)g/mL. However, the addition of _Mentha arrensis_ in a 7/3 ratio allowed a reduction in bitterness, which was not so much appreciated, mainly by the young participants of the hedonic test.  
This study reveals the potential of the combination of _C. operculatus_ (70%) and _M. arrensis_ (30%) as a natural drink appreciated by the panel of 19-23 years old consumers. This improved formulation (7/3 ratio) shows potential therapeutic benefits, more specifically, with a reduction in lipid peroxidation and brain function protection. We can conclude that this work should sustain the consumption of _Cleistocalyx operculatus_ and may thus contribute to health protection.  
Conceptualization, T.T.L.M.; Methodology, T.T.L.M., L.T.B.K., S.T.T.M., D.L.B.N., L.T.B.T., N.T.Q., T.T.A., L.V.H., N.V.P., C.T.M.D. and N.H.M.; Validation, T.T.L.M. and G.E.; Formal analysis, T.T.L.M.; Investigation, T.T.L.M., L.T.B.K., S.T.T.M., D.L.B.N., L.T.B.T., N.T.Q., T.T.A., L.V.H., N.V.P., C.T.M.D. and N.H.M.; Resources, T.T.L.M.; Writing--Original draft, T.T.L.M. and G.E.; Writing--Review & editing, T.T.L.M. and G.E.; Visualization, T.T.L.M.; Supervision, T.T.L.M. and G.E.; Project administration, T.T.L.M.; Funding acquisition, T.T.L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NONG LAM University under grant No. CS-CB21-CNSH-03.  
This research was approved by the National Vietnamese authorities with the approv-al No. 20210108 NLU.  
Not applicable.  
Data Availability Statement: Data can be transmitted on demand.  
The authors thank the Management of NONG LAM University and UMR transfrontaliere INRAE 1158 BioEcoAgro BIOPI-UPJV for their encouragement. Thanks to his research was funded by NONG LAM University under grant No. CS-CB21-CNSH-03.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Samples** & **IC\({}_{50}\) (\(\mu\)g/mL)** \\ \hline Herbal blend Mix 3 & \(6.01\pm 0.75\) \\ Trolox & \(27.85\pm 1.22\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Lipid peroxidation inhibitory activity of the lyophilized dried matter from the infusion of the herbal blend.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
###### Abstract  
In recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefore, numerous artificial intelligent methods such as machine learning and deep learning have been considered as solutions for accurate wind power forecasts. In addition to deterministic forecasting, the probabilistic forecasting becomes more important, because it indicates the level of uncertainty. In this paper, a hybrid forecasting model considering different Numerical Weather Prediction (NWP) models and the XGBoost training model is proposed for short-term wind power forecasting. The proposed forecasting algorithm includes data preprocessing, in which an autoencoder model is used to reduce the dimension of 20 NWP ensembles. The performance of the proposed method is investigated using historical wind power measurements and NWP results by the Taiwan Central Weather Bureau (CWB); the NWP includes spot wind speeds from WRFD, RWRF, and ensemble wind speeds from WEPS. Based on the forecasting results, the proposed model produces better performance and forecasting accuracy among other forecasting models, which reveals the importance of data preprocessing using autoencoders and the use of deep learning models in deterministic or probabilistic forecasts.
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 2 The Campaign Sites and Instrumentations
### Instruments
#### 2.2.4 HYSPLIT Air-Mass Trajectories  
The HYSPLIT model is a hybrid model between the Lagrangian and the Eulerian approach [53; 54]. The Lagrangian method is applied to calculate the advection and diffusion as backward trajectories of the air parcels, and the Eulerian method is applied to compute the pollutant air concentrations using a fixed three-dimensional grid as a frame of reference. The combination of these two methodologies allows the HYSPLIT model to compute air parcel trajectories, complex transport, dispersion, deposition, and chemical transformation. In this work, the HYSPLIT model was applied to estimate the backward trajectories of air parcels for 72 and 120 h, with a temporal resolution of 6 h, in order to estimate the influence of aerosol from long-range transportation during the Lorena and Volta Redonda campaigns.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 2 Preliminaries of Conventional Soft Decision Decoder  
### Message Encoding Process  
A message m is represented as m = [\(m_{0}\), \(m_{1}\),..., \(m_{k-1}\)], where m \(\in\) {0, 1}\({}^{k}\) and \(k\) is the number of bits in the message. The polynomial form is expressed as (1):  
\[m(x)=m_{k-1}x^{k-1}+\cdots+m_{1}x+m_{0} \tag{1}\]The generator polynomial of the code **C** is  
\[g(x)=x^{n-k}+g_{n-k-1}x^{n-k-1}+\cdots+g_{1}x+g_{0},\ g_{i}\in\{0,1\} \tag{2}\]  
The corresponding generator matrix **G** is  
\[\textbf{G}=\left[\textbf{I}_{k}|\textbf{P}\right] \tag{3}\]  
where \(I_{k}\) is the identity matrix of dimension \(k\) and **P** is the parity sub-matrix (p. 7, [15]).  
By (3), the message m is encoded as the codeword  
\[c=\textbf{mG} \tag{4}\]  
in a systematic form, where \(c=[c_{0},\)\(c_{1},\)\(\dots,\)\(c_{n-1}]\) and \(c_{i}\in\{0,1\}\). Here, \(n\) is the codeword length. The codeword \(c\) can be expressed as (4) in a polynomial form.  
\[c(x)=m(x)x^{n-k}+m(x)x^{n-k}\text{mod}\ g(x) \tag{5}\]  
The codeword \(c\) must satisfy  
\[c\textbf{H}^{T}=\textbf{0} \tag{6}\]  
where **H** is the parity check matrix, which is defined as follows:  
\[\textbf{H}=\left[\textbf{P}^{T}\middle|\textbf{I}_{\text{n-k}}\right] \tag{7}\]  
Here \(\textbf{I}_{\text{n-k}}\) is the identity matrix of dimension \(n-k\).  
Consider the (\(n\), \(k\), \(d\)) binary linear block code, where \(d\) is the minimum Hamming distance. The error-correcting capability \(t\) is thus  
\[t=\left\lfloor\frac{d-1}{2}\right\rfloor \tag{8}\]
## 4 Calibration and Compensation of Gyro Bias  
The aforementioned process predicted and estimated the influence of several typical error sources on the accuracy of the navigation algorithm. In a RINS, the accelerometer bias is usually smaller than 0.1 mg, and the initial alignment accuracy would be better than 0.001 degree. According to the simulation results from Figures 4 and 5, the accelerometer bias and initial attitude misalignment would not be the dominant error source in the navigation process. In order to improve the performance of navigation accuracy in a RINS, we concentrate on eliminating the influence of gyro bias error.  
A typical INS consists of at least three gyros and accelerometers. Since each inertial sensor would introduce a zero drift into the navigation calculation process and all the error source would couple to each other in the navigation process, it is hard to distinguish the types of error. To simplify the calibration process, we introduce the rotational modulation method to eliminate the bias influence of two inertial sensors in the direction perpendicular to the rotational axis. Under this condition, the main error source comes from one individual sensor along the rotational direction, making it easier to calibrate inertial sensor error and  
Figure 5: Simulation of the initial misalignment’s influence on the navigation algorithm.  
Figure 6: Simulation of the installation misalignment’s influence on the navigation algorithm.  
compensate its error. In addition, a rotational turntable would provide a highly precise rotational angle reference along the azimuth direction, which could be used to calibrate the gyro bias. In this section, the elimination of the gyro bias's influence in a rotational inertial system is introduced, and the calibration method was established.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Security Evaluation
#### 4.2.4 Security Improvements in LoRaWAN(tm) 1.0.4 Specification  
Version 1.0.4 brought several improvements from version 1.1. Specifically, the security procedures require the 32-bit frame counter size being stored in persistent memory, such as NVRAM, so that the value remains stored with the rest of the security context during the reboot for the ABP device. As a result, the counter value will not be reset, thus preventing possible threats, and the behavior of DevNonce was changed so that the DevNonce values of the device monotonically increase so that the work of a JS is much easier, and it is possible to monitor DevNonce to prevent replay attacks that are possible when using the same DevNonce.
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 3 The Spectral Solution
### The Special Case for Perfectly Conducting Conductors  
In the case of \(\delta_{iw}=0\), the term \(1/\zeta^{2}\) in the denominator of (40) and (42) grows without bound near \(k_{0}\) (due to the fact that the pole has been absorbed into \(k_{0}\)). However, in the common mode case, (39) is still integrable since the sum of Neumann functions in the denominator limits this growth. However, more care must be used to evaluate the integral. Note that this does not happen in the differential mode case because the difference in Neumann functions does not grow near \(k_{0}\). Given this difficulty, (40) is separated into two parts.  
\[\hat{I}_{h}^{c}(z)=\hat{I}_{ha}^{c}(z)+\hat{I}_{hb}^{c}(z) \tag{47}\]  
where the first is  
\[\hat{I}_{ha}^{c}(z)=\frac{-4\omega_{\rm e}\Phi}{\pi}. \tag{48}\]  
and the second (following the method of [22]) is  
\[\hat{I}_{hb}^{\
u}(z)\cong\frac{-\hat{\
u}}{\eta_{0}}e^{-j\delta_{0}z}\Bigg{\{} \tan^{-1}\Bigg{[}\frac{\ln(e^{2\gamma_{\epsilon}}k_{0}^{2}ad\Delta/2)}{\pi} \Bigg{]}+\pi/2\Bigg{\}} \tag{49}\]  
\(\eta_{0}=\sqrt{\mu_{0}/\epsilon_{0}}\) is the impedance of free space, \(\gamma_{\epsilon}\) is the Euler constant, \(\Delta\) is small enough that \(\zeta^{2}=\left(k_{0}^{2}-\gamma^{2}\right)\cong 2k_{0}(k_{0}-\gamma)\) and small argument approximations can be used for the Bessel functions [24]. In this solution, the substitutions \(u=(k_{0}-\gamma)\) and \(v=\ln\left(e^{2\gamma_{\epsilon}}k_{0}a^{2}u\right)\) have been used. Note that (49) accounts for the mSG pole residue, which (for \(\delta_{iw}=0\)) has been absorbed into \(k_{0}\). Finally, (42) along the vertical portion of the branch cut (but with \(\delta_{iw}=0\)) can be evaluated numerically as earlier.  
Since the pole at \(\gamma_{p}\) is absorbed into \(k_{0}\), there is no separate residue term. Given these results, the current induced on a perfect conductor by the voltage source is  
\[\hat{I}_{h}^{c}(z)=\hat{I}_{ha}^{c}(z)+\hat{I}_{hb}^{c}(z)+\hat{I}_{v}^{c}(z) \tag{50}\]
## 4 Materials and Methods
### Model Validation Using Statistical Metrics  
The statistical metrics that were used to quantify the performance of the clear sky models are described here, one of which is the relative mean bias error (rMBE), which reflects the degree of error in percentage terms. A higher rMBE means that the model performed poorly, and a lower rMBE means that the model performed excellently. When the rMBE value is negative, it implies that the model overestimates GHI, while positive rMBE values imply that the model underestimates GHI. The relative root mean square error (rRMSE) is another percentage error measure that measures the degree of error in percentage terms. The rRMSE is not affected by the direction of the error, and it places additional emphasis on big errors. A greater rRMSE value means that the model performed poorly, while a smaller rRMSE value means that the model performed very well. The relative mean absolute error (rMAE), which quantifies the definite value of the error between the measured and estimated values, was also considered in the study as it gives a better outlook of estimation skill. A higher value of rMAE implies that the model has low estimation skill, while a low rMAE implies that the model has higher estimation skill and accuracy. The coefficient of determination (\(R^{2}\)) is a quantitative gauge of the extent of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Skill** & **rMBE** & **rRMSE** & **R2** \\ \hline Poor & \(\geq\)1\(\pm\)101\% & \(\geq\)15\% & \(\leq\)0.97 \\ \hline Average & \(\geq\)1\(\pm\)51 \%, \(\leq\)1\(\pm\)101\% & \(\geq\)10\%, \(<\)15\% & \(\geq\)0.97, \(<\)0.98, \\ \hline Good & \(\geq\)1\(\pm\)21 \%, \(<\)1\(\pm\)5 \% & \(\geq\)5\%, \(<\)10\% & \(>\)0.98, \(<\)0.99, \\ \hline Excellent & \(\leq\)1\(\pm\)21\% & \(<\)5\% & \(\gtrsim\)0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Four performance classes to rank clear sky GHI models.  
Figure 2: The schematic outline of the procedure used from model input data collection to clear sky detection, model configuration, and model data validation.  
correlation between the change of the estimated and measured variables in a certain period. The nearer \(R^{2}\) is to 1, the more suitable the accuracy of the prediction. The equations of the metrics are given in Equations (31) to (37) below.  
\[MBE=\frac{1}{n}\sum_{i=1}^{n}(Pi-Oi) \tag{31}\]  
\[rMBE=100*\frac{1}{n}\sum_{i=1}^{n}(Pi-Oi) \tag{32}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|Pi-Oi| \tag{33}\]  
\[rMAE=100*\frac{1}{n}\sum_{i=1}^{n}\frac{|Pi-Oi|}{\overline{O}i} \tag{34}\]  
\[RMSE=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(Pi-Po)^{2}} \tag{35}\]  
\[rRMSE=\frac{100}{\overline{O}i}*\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(Pi-Po)^{2}} \tag{36}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(Pi-Oi)^{2}}{\sum_{i=1}^{n}(Pi-\overline{O}i)^{2}} \tag{37}\]  
where \(Oi\) is the measured value, \(Pi\) is the predicted value, \(\overline{O}i\) is the mean of the measured values, \(Po\) is the observed value, \(i\) is the time point, and n is the total number of points used.
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 4 State Feedback Controller for Nonlinear Magnetic Levitation System
### Comparison of Robust Pole-Placement and Nonlinear Controller for Magnetic Levitation
_Nonlinear continuous-time controller_  
Advantages:  
* The nonlinear control law follows the changing system parameters, for example, ball mass appears explicitly in control law (29). Therefore, the responses are very close to each other for different ball masses. This feature can be advantageous also for other parameter changes. Disadvantages:  
* in feedback linearization design, the exact closed-loop poles are to be prescribed, which are not easy to determine appropriately, since the closed-loop system proves as rather sensitive to pole-position, for example there is a problem when the complex poles are prescribed;
* control law is rather complicated and its implementation is more demanding, since several nonlinear feedback dependent formulas must be computed on-line;
* for simplification of the control law, the appropriate approximations of nonlinear terms should be used which must be found by optimization.
## 2 Circuit Design
### Schematics, Assemblies, and 3D Views  
Figure 9a,b displays the schematics and components lists of the proposed continuous Class-J mode quasi-MMIC PAs that utilize GaN and GaAs IPD technology. In Figure 9a, the design is based on a split CoB wire-bond assembly. On the other hand, Figure 9b represents a stack CoB wire-bond assembly design. In these PA designs, the transistors' gate widths are designed as 1000 (8 \(\times\) 125) \(\upmu\)m for the driver stage (\(Q_{1}\)) and 2000 (10 \(\times\) 200) \(\upmu\)m for the output stage (\(Q_{2}\)). The PAs are configured with a 1:2 gate periphery driving ratio, which guarantees sufficient driving to saturate the output stage. Both stages are biased at deep class AB. The resistors (\(R_{sp1}\) and \(R_{sp2}\)) of the stability networks are divided equally by two resistors to achieve a balanced layout configuration with a symmetrical signal path. The input matching network and interstage matching network utilize a band-pass matching technique to enhance the overall bandwidth of the PAs.  
Figure 10a,b presents the assemblies and 3D views of the quasi-MMIC PAs. Figure 10a depicts the split CoB wire-bond assembly design, while Figure 10b shows the stack CoB wire-bond assembly design. These figures provide a visual representation of the physical layout and arrangement of the components in the PAs.
## 4 Response Reconstruction for an Actual Continuous Bridge
### Data Analysis  
#### 4.3.1 Intersectional Relationship between High-Frequency Responses  
Initially, this study focused on exploring the potential correlation between the high-frequency responses of strain gauges within a single cross-section and across multiple cross-sections. Figure 10 displays the scatter plot of the high-frequency responses between 02-S01 and 02-S02, demonstrating a linear relationship between these two gauges. This linearity can be attributed to the strain being proportional to the distance of the gauge from the vertical axis line within the same cross-section. Furthermore, Equation (14) confirms that this linear relationship holds true for all bending modes.  
Figure 9: Typical strain monitoring data ((**b**) illustrates the high-pass filtered responses corresponding to (**a**) while the curve marked with a rectangle in (**b**) was zoomed in and is illustrated as (**c**)).  
In consideration of the high-frequency responses observed across different cross-sections, it was evident that the linear relationship that was previously discussed was not an appropriate fit. Figure 11a-c illustrate scatter plots between strain gauges 02-S01, 03-S01, and 04-S01, respectively. Compared to the pattern shown in Figure 10, identifying the intersectional relationship between strains at different cross-sections is more complex. According to Equation (5b), it was hypothesized that the high-frequency responses were composed of multiple modes, each with its own specific modal coordinates. These coordinates differed across these cross-sections. As a result, the responses at these cross-sections did not necessarily align with a linear relationship.
## 3 Formulation of the Mathematical Model
### Numerical Procedure  
It should be noted that the abovementioned governing equations, as well as the boundary conditions, are solved using the finite element method. The equations are solved numerically by the Galerkin weighted residual finite element method. The computational domain is discretized into triangular elements. Triangular Lagrange finite elements of different orders are used for each of the flow variables within the computational domain. Residuals for each conservation equation are obtained by substituting the approximations into the governing equations. To simplify the nonlinear terms in the momentum equations, a Newton-Raphson iteration algorithm was used. The convergence of the solution is assumed when the relative error for each of the variables satisfies the following convergence criteria:  
\[\left|\frac{\Gamma^{i+1}-\Gamma^{i}}{\Gamma^{i}}\right|\leq\eta\]  
where \(i\) represents the iteration number and \(\eta\) is the convergence criterion. In this study, the convergence criterion was set at \(\eta=10^{-6}\).