esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Serum Biochemical Analysis  
The results of the serum biochemical analysis showed that aspartate aminotransferase (AST) and alanine aminotransferase (ALT), which are liver function test components, tended to increase in the HFD group compared to those in the normal group. However, AST and ALT levels in the group administered the two types of seaweed extracts significantly decreased compared to those in the HFD group. Gamma Glutamyl Transferase (GGT) levels did not identify significant differences in all groups. The level of total cholesterol (CHO) was high at 193.7 \(\pm\) 11.3 mg/dL in the HFD group compared to that in the normal  
Figure 5: Effects of _S. horneri_ and _U. australis_ on body weight in C57 BL/6 mice fed a high-fat diet for 12 weeks. (**A**) Weekly changes in body weight; (**B**) body weight gain. ND, normal diet; HFD, high-fat diet; HFD+SH, administration of 1% S. _horneri_ extract in the drinking water and HFD; HFD+UA, administration of 1% U. _australis_ extract in the drinking water and HFD. Each value represents the mean \(\pm\) standard deviation (n = 6). Significant differences: \({}^{*}\)_p_ \(<\) 0.05, \({}^{**}\)_p_ \(<\) 0.01 vs. the HFD group, and \(\#\)_#_p_ \(<\) 0.01 vs. the ND group.  
\begin{table}
\begin{tabular}{c c c} \hline
**Groups** & **Food Intake (g/Day)** & **Drink Intake (mL/Day)** \\ \hline ND & 2.5 \(\pm\) 0.3 & 4.9 \(\pm\) 0.6 \\ HFD & 2.3 \(\pm\) 0.1 & 5.2 \(\pm\) 0.5 \\ HFD+SH & 2.3 \(\pm\) 0.1 & 4.9 \(\pm\) 0.6 \\ HFD+UA & 2.2 \(\pm\) 0.1 & 5.0 \(\pm\) 0.4 \\ \hline \end{tabular} Data are expressed as the mean \(\pm\) standard deviation (n = 6).  
\end{table}
Table 3: Changes in food and drink intake in C57 BL/6 mice during 12 weeks of HFD-induced obesity.  
group. However, it significantly decreased to 142.4 \(\pm\) 6.6 and 156.3 \(\pm\) 9.7 mg/dL in the SH and UA extract administration groups, respectively. TG levels were 50.9 \(\pm\) 6.0, 32.6 \(\pm\) 6.1, and 39.0 \(\pm\) 5.1 mg/dL, in the HFD, SH, and UA groups, respectively; TG levels significantly decreased in the groups treated with the extracts compared to those in the HFD group. HDL cholesterol (HDL-C) levels tended to increase in the high-fat diet group compared to the normal diet group. LDL cholesterol (LDL-C) levels, analyzed by total cholesterol, triglyceride, and HDL cholesterol levels, were higher in the HFD group at 71.9 \(\pm\) 4.2 mg/dL compared with the normal group. However, in the SH and UA extract treatment groups, they were significantly reduced to 40.7 \(\pm\) 4.4 and 40.8 \(\pm\) 3.1 mg/dL, respectively. Blood glucose (GLU) levels, too, significantly decreased in the experimental groups compared to those in the HFD group (Table 4).
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments
### Results
#### 3.3.2 Results for Univariate Setting  
The results of the experiments in the univariate setting are presented in Table 2. Except for two instances, i.e., ETTm2 with a forecast horizon of 96 and exchange with a forecast horizon of 720, our method surpasses existing baselines in all other settings. The average improvement in the overall MSE value in the ETTm2 dataset is 4%, and in the exchange dataset is 12.5%. While considering the individual settings, the best improvement in performance is achieved in the exchange dataset at a forecast horizon length (FHL) of 96, with a 61.8% relative improvement compared to the previous best. Also, in the univariate setting there does not seem to be any striking degradation in performance as the length of the forecasting horizon increases.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model’s**} & \multicolumn{2}{c}{**Our Method**} & \multicolumn{2}{c}{**Autoformer [20]**} & \multicolumn{2}{c}{**SCINet [21]**} & \multicolumn{2}{c}{**Informer [4]**} & \multicolumn{2}{c}{**LogTrans [15]**} & \multicolumn{2}{c}{**N-BEATS [60]**} & \multicolumn{2}{c}{**DeepAR [32]**} & \multicolumn{2}{c}{**ARIMA [7]**} \\ \cline{3-14} \multicolumn{2}{c}{**Dataset Metric**} & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **Dataset** \\ **192** \\ **192** \\ **192** \\ **1936** \\ **194** \\ **192** \\ **194** \\ **195** \\ **196** \\ **197** \\ **198** \\ \end{tabular} } &
\begin{tabular}{c} **Our Method** \\ **190** \\ **190** \\ **198** \\ **199** \\ **19
## 4 Conclusions  
The low-velocity impact test and residual compression behaviors for the optimized fiber loading, fiber length, and alkaline treatment content of the banana-fiber reinforced sandwich structures were obtained. In this study, glass/epoxy, carbon/epoxy, and Kevlar/epoxy composites were also tested for comparison. This work contributes several key findings, as follows:  
1. The maximum stress and tensile modulus of the optimal banana-fiber-reinforced epoxy composite were increased up to 90% and 22%, respectively, compared to the epoxy-resin system.
2. The effect of optimizing the compounding parameters of banana-reinforced epoxy was significantly comparable in terms of impact, damage tolerance, and residual impact due to higher bonding between fibers and matrices.  
Figure 15: Cross-section of failure modes for the CAI testing of the (**a**) banana, (**b**) glass, (**c**) carbon, and (**d**) Kevlar epoxy sandwich structures.  
* The low-velocity impact response of the optimal banana/epoxy composite gave a lower peak load. However, a significantly large damage area, higher energy absorption, and greater dent depth were obtained.
* The banana fiber sandwich structure recorded a lower CAI resistance and a greater reduction in normalized strength than those using synthetic fibers.
* Delamination and core fracture were mainly observed in the optimal banana/epoxy structure. However, under the CAI testing matrix, shear cracking dominated in the Kevlar, carbon, and glass composites.  
Finally, the results depicted significantly lower values for the impact resistance and residual damage tolerance of the optimum banana composite structures. Further investigation through matrix modification by adding nanofillers to natural fiber composites may yield interesting discoveries.  
**Author Contributions:** Formal analysis, Z.A.R.; Funding acquisition, R.D. and M.Y.M.D.; Investigation, M.Z.H. and A.F.M.N.; Supervision, S.M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Universitat Teknologi Malaysia under \"Geran Universiti Penyelidik\" (GUP) Tier 2 Scheme Q.K.130000.2656.15J85, Tier 2 Scheme Q.K.130000.2656.17J66, and UTMER Scheme Q.K.130000.2656.18J24.  
**Acknowledgments:** The authors are thankful to the Ministry of Education Malaysia and Universiti Putra Malaysia for supporting this work through a Visiting Scholar (Post-Doctoral) scholarship.  
**Conflicts of Interest:** The authors declare no conflict of interest.
Since the DAVAR requires instant calculation of Allan variance, its calculation burden is a critical shortcoming [19; 20; 21; 22]. [PERSON] and [PERSON] developed a fast algorithm for DAVAR to simply the DAVAR algorithm and accelerate the calculation speed [23]. An MWD system keeps working for several days underground. The output signal of the gyroscopes in the MWD system would be interrupted due to error in the mud pulse communication or sudden power outages. In this case, both the classical DAVAR and the fast DAVAR algorithms are unable to deal with discontinuous time series. Hence, in order to deal with the discontinuous gyroscope data, a further improvement is made on the basis of the fast DAVAR. The recursion characteristic of the fast DAVAR will judge whether the data contains unreadable code or not. It makes the fast DAVAR available to deal with discontinuous gyroscope data. Thus, the improved fast algorithm of the DAVAR is more valuable in engineering applications. This article is organized as follows. In Section 2, the structure of the FOG-based MWD and its working characteristics are introduced. In Section 3, the theory of Allan variance is briefly presented. In Section 4, we sum up the calculation process of the classical DAVAR. In addition, a 2D diagram illustrating the noise characteristics of FOGs is reported. In Section 5, the fast algorithm of the DAVAR is derived step by step. In Section 6, a further improvement has been made and the improved fast DAVAR is extended to discontinuous data. In Section 7, in order to test that the improved fast DAVAR is superior to the classical method, three sets of simulation data have been analyzed by the two methods. In Section 8, a set of discontinuous time series collected from a vibration experiment with the FOGs-based MWD is analyzed by the fast DAVAR. The conclusions are presented in Section 9.
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
###### Abstract  
The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locations. This paper proposes a wind power probability density prediction method based on a time-variant deep feed-forward neural network (ForecastNet) considering a spatio-temporal distribution. First, the outliers in the wind turbine data are detected based on the isolated forest algorithm and repaired through Lagrange interpolation. Then, based on the graph attention mechanism, the features of the proximity node information of the individual wind turbines in the wind farm are extracted and the input feature matrix is constructed. Finally, the wind power probability density prediction results are obtained using the ForecastNet model based on three different hidden layer variants. The experimental results show that the ForecastNet model with a hidden layer as a dense network based on the attention mechanism (ADFN) predicts better. The average width of the prediction intervals at achieved confidence levels for all interval coverage is reduced by 34.19%, 35.41%, and 35.17%, respectively, when compared to the model with the hidden layer as a multilayer perceptron. For different categories of wind turbines, ADFN also achieves relatively narrow interval average widths of 368.37 kW, 315.87 kW, and 299.13 kW, respectively.  
time-variant deep feed-forward neural network; probability density prediction; spatio-temporal distribution +
Footnote †: journal: Applied
## 4 System Model and Description of the Proposed Protocols
### Transmission and Receiving Energy Calculations in BF-SPR-Three  
The EC of the sensor nodes during the packet transmission and reception is calculated using Equations (9) and (10).  
\[E_{trans}=\frac{T_{p}\times PL}{DR}\times Dis, \tag{9}\]  
\[E_{rec}=\frac{R_{p}\times PL}{DR}\times Dis. \tag{10}\]  
Here, the EC of the sensor nodes during the packet transmission is denoted as \(E_{trans}\). \(E_{rec}\) is used to represent the EC of the sensor nodes during the packet reception. Whereas, \(T_{p}\) and \(R_{p}\) denotes transmission and reception power of the sensor node, respectively. Packet size is represented as \(PS\) and \(DR\) is used to  
Figure 11: Absorption and frequency relation in the proposed protocols.  
represent the data rate of the acoustic channel. The total residual energy of the nodes in the underwater environment after forwarding all the DP is denoted as \(E_{trans}^{rem}\) and computed using Equation (11).  
\[E_{trans}^{rem}=\sum_{DP=1}^{N}\sum_{HC=1}^{THC}n_{i}-E_{trans}. \tag{11}\]  
Here, \(N\) represents the total number of \(DPs\) in the network. Where total hop counts (for a single \(DP\) from source to the destination) are denoted as \(THC\). Whereas, \(n_{i}\) is used to represent ith node. The total residual energy of the nodes in the underwater environment after receiving all the \((DPs)\) is denoted as \(E_{rec}^{rem}\) and computed using Equation (12).  
\[E_{rec}^{rem}=\sum_{DP=1}^{N}\sum_{HC=1}^{THC}n_{i}-E_{rec}. \tag{12}\]  
The initial energy of the node is represented as \(E_{i}\). Whereas, the total energy of the network is denoted as \(T_{E}\) and calculated using Equation (13).  
\[T_{E}=E_{i}-(E_{trans}^{rem}+E_{rec}^{rem}). \tag{13}\]
# Engineering Applications of Adaptive Kalman Filtering Based on Singular Value Decomposition (SVD)
## 1 Introduction
In addition, under continuous excitation conditions, when the stator frequency is close to zero and when the inductor rotor voltage carries small values, an estimation of the speed of an induction motor (IM) is not likely to be obtainable. In practice, compared with extended Luenberger observers and slipping modes, the Kalman filter appears to function well in these conditions with no divergence [13]. With this trade-off, the complexity of the Kalman filter is higher. Other important applications of the KF are in the signal extraction and fusion of sensors, self-driving cars, GNSS/INS integration, and biomedical sensors; for example, when extracting cardiorespiratory signals from noninvasive and noncontacting sensor arrangements, such as magnetic induction sensors. In those applications, the signals are separated for monitoring purposes; by using the KF, it is possible to measure the heart and breathing rates in real time [14], improving the estimation of results, whereas other filtering techniques fail to extract relevant information due to the mixing of respiratory and cardiac signals and disturbance by measurement noise [14]. The second objective of this paper is to promote the use of continuous adaptation changes in the measurement error covariance matrix \(R_{k}\). The Kalman gain \(K_{k}\) weights the innovation for the measurement error covariance matrix \(R_{k}\) (the estimation error covariance matrix \(P_{k+1}^{-}\) is influenced by \(Q_{k}\)), where higher values of \(R_{k}\) make the measurement update step differ a little from the predicted value when the Kalman gain \(K_{k}\) becomes very small [15]. On the contrary, high values of \(Q_{k}\) engender more trust in the estimation of the state; [PERSON] et al. [16] showed that inappropriate tuning parameters may lead to low convergence by introducing adaptive technology consisting of adjustments to the noise covariance matrices to improve the estimation accuracy. [PERSON] et al. [17] showed that the Kalman filter can be used to integrate high-level localization subsystems, where the covariance information plays an essential role as new observations become available. This approach will be used in this research. The proper selection of \(R_{k}\) and \(Q_{k}\) is vital for optimal filter performance, as the system model should describe as closely as possible the time evolution of the measured data, and the choices should reflect the system model error and measurement noise [15]. This research only explores changes in the measurement error covariance matrix \(R_{k}\). [PERSON] et al. [18] showed that when the system being considered has a stronger nonlinear action, the accuracy of the mode estimation will be poor. In order to address the shortcomings of the KF-based algorithm (K-SVD) and enhance the estimation precision, the adaptive filtering algorithm (K-SVD adaptive) was investigated, consisting of the adaptive estimation of \(R_{k}\), which is a function of the standard deviation.
## 3 Results  
### Step I: Construction of MS  
After collecting the necessary information (Table 5), we calculated the MD using the formulas in Section 2.2.1. To compute MD1, we utilized the HG data and the 11 selection features. The 15 healthy subjects were used as the benchmark (healthy) group. The average and standard deviation of every characteristic from the HG were computed, and the information from Table 5 was normalized using Equation (1). This normalized information was used to build the correlation matrix and its inverse. Next, the MDs were computed using Equation (4) as presented in Table 6. Likewise, using the inverse correlation matrix generated from the HG data, the MDs were computed for the 10 sets of PG data. The MDs of the HG were almost equal to one, as shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Variables** & **Operational Definition** \\ \hline Step Length (cm) & X1 & Anterior–posterior distance from the heel of one footprint to the heel of the opposite footprint. \\ \hline Stride Length (cm) & X2 & Anterior–posterior distance between the heels of two consecutive footprints of the same foot (left to left, right to right) and two steps (e.g., a right step followed by a left step) comprise one stride or one gait cycle. \\ \hline Step Width (cm) & X3 & Lateral distance from the heel center of one footprint to the line of progression formed by two consecutive footprints of the opposite foot. \\ \hline Stance Time (s) & X4 & The stance phase is the weight-bearing portion of each gait cycle beginning at heel contact and ending at toe off of the same foot; stance time is the time elapsed between the initial contact last contact of a single footfall. \\ \hline Swing Time (s) & X5 & The swing begins with toe off and ends with initial contact of the same foot; swing time is the time elapsed between the last contact of the current footfall to the initial contact of the next footfall of the same foot. \\ \hline Single Support Time (s) & X6 & Single support occurs when only one foot is in contact with the ground; single support time is the time elapsed between the last contact of the opposite footfall to the initial contact of the next footfall of the same foot. \\ \hline Double Support Time (s) & X7 & Double support occurs when both feet are in contact with the ground simultaneously; double support time is the sum of the time elapsed during two periods of double support in the gait cycle. \\ \hline Stance Time (\%GC) & X8 & Stance time normalized to stride time. \\ \hline Double Support Time (\%GC) & X9 & Double support time normalized to stride time. \\ \hline Gait Speed (m/s) & X10 & Calculated by dividing the distance walked by the ambulation time. \\ \hline Stride Speed (m/s) & X11 & Calculated by dividing stride length by the stride time. \\ \hline \hline \end{tabular} Note = \(\%\)GC = \(\%\) gait cycle.  
\end{table}
Table 4: Operational definitions of gait parameters and variables.
## 2 Materials and Methods
### Parameter Calibration and Model Validation at the Site Level
\begin{table}
\begin{tabular}{c l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Parameter Name**} & \multirow{2}{*}{**Unit**} & \multicolumn{5}{c}{\(GPP_{\tt BEAMS}\)**Module**} & \multicolumn{5}{c}{\(GPP_{\tt DIM}\)**Module**} \\ \cline{3-11}  & & & **DBF** & **EBF** & **ENF** & **GRS** & **DBF** & **EBF** & **ENF** & **GRS** \\ \hline \multirow{3}{*}{\(f_{tW}\)} & Fraction of \(APPFD\), absorbed by photosynthetic & \multirow{3}{*}{-} & 0.85 & 0.765 & 0.85 & 0.85 & 0.36 & 0.33 & 0.56 & 0.85 \\  & active vegetation part & & - & 0.94 & 0.86 & 0.94 & 0.94 & 0.94 & 0.94 & 0.94 & 0.78 \\  & Extinction coefficient of leaf nitrogen content & - & - & - & - & - & 0.33 & 0.3 & 0.36 & 0.3 \\  & Maximal photosynthetic light use efficiency & - & 0.02 & 0.02 & 0.0293 & 0.022 & - & - & - & - \\  & Leaf nitrogen content per leaf area at vegetation & g & \multirow{3}{*}{-} & m\textuperscript{-}2 & - & - & - & 1.57 & 2.34 & 3.1 & 1.69 \\  & canopy top & & m\textuperscript{-}2 & - & - & - & - & 1.57 & 2.34 & 3.1 & 1.69 \\  & Maximal carboxylation rate at 25 \(\lx@math@degree\)C & umd & 57.7 & 31.9 & 68.7 & 86.0 & - & - & - & - \\  & \(V_{max25}\textsuperscript{-}2\) at the top of vegetation canopy & m\textuperscript{-}2 & - & - & - & - & 42.0 & 31.9 & 62.5 & 46.2 \\  & \(P_{half}\) & Leaf response curvature to electron supply & s\textuperscript{-}1 & 0.63 & 0.63 & 0.7 & 0.77 & 0.5 & 0.56 & 0.51 & 0.63 \\  & Vegetation canopy reflection coefficient of \(PPFD\) & - & 0.09 & 0.08 & 0.07 & 0.09 & 0.1 & 0.11 & 0.07 & 0.11 \\  & Vegetation canopy transmissivity of \(PPFD\) & - & 0.045 & 0.04 & 0.05 & 0.05 & 0.05 & 0.06 & 0.05 & 0.07 \\  & Empirical coefficient of \(V_{conv25}\) variation & m\textuperscript{-}2 & - & - & - & - & 0.59 & 0.48 & 0.33 & 0.54 \\  & attributable to \(N_{half}\) & g\textuperscript{-}1 & - & - & - & - & 0.59 & 0.48 & 0.33 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Plant functional type-specific parameters used to calculate hourly \(GPP\). MODIS _LAI_ product (MCD15A3H [107]), the ERA-5 Land reanalysis dataset [110], and the SoilGrids250m dataset [111], respectively, same as the case of _GPP_. To set up initial _SOC_ conditions, hourly \(R_{e}\) simulations based on CENTURY [86; 87; 88] were first conducted in spin-up mode to accumulate organic carbon, until the carbon stock in the 0-28 cm soil layers approximately reached 6.95 \(\pm\) 3.25 kg m\({}^{-2}\), a range estimated based on the National Forest Soil Carbon Inventory of Japan [116]. Then, hourly \(R_{e}\) and _NEE_ were modeled to calibrate the parameters and to validate the \(R_{e}\) model. The details of the related equations and parameter settings are available in the Supplementary Materials.
# Data Traffic Modeling in RPAS/UAV Networks with Different Architectures
## 5 Results
This is due to the fact that it is necessary to simultaneously observe the data transmission in all models, which is difficult to ensure at large values of the TS parameters for both channels. The AU parameter increases with decreasing the bandwidth and at 6 Mbps bandwidth reaches \(\approx\) 70% for Model 4, \(\approx\)50% for Model 3, \(\approx\)23% for Model 2, and \(\approx\)19% for Model 1. For T3 and E3 bandwidths AU values for all models do not exceed \(\approx\)10%. However, as the bandwidth decreases, a significant increase in the AU parameter is observed, which makes it practically impossible to transfer data for Models 3 and 5 at the bandwidth below \(\approx\)10 Mbps. The results for Models 1 and 2 are very close and show the data transfer capability of such models over a wide range of data rates. Figure 4: Dependencies of AU on AU1 link bandwidth (Tactical Data—FTP, TS = 1 Kbits, TBT = 1 s, Const law; Common Data—inter LAN, TS = 10 Kbits, TBT = 1 s, Const law; and BER = 0%). The BER measured after Forward Error Correction (FEC) should be less than \(10^{-6}\) in accordance with ICAO requirements [36; 37]. Figure 5 shows the dependencies of the AU parameter on the BER for the \"BS-RPAS1\" channels of all models. The dependencies are given for TS = 1 Kbits for Tactical Data traffic and TS = 10 Kbits for Common Data traffic as in the case of Figure 4. The BER value for channels without additional error correction is usually \(10^{-4}\)-\(10^{-6}\). Bit distortion occurs both due to the presence of noise in the channel and due to the distortion of the waveform in channels with limited bandwidth. The BER value of 0.01% in Figure 5 corresponds to a value of \(10^{-4}\). The data presented in Figure 5 indicate a high sensitivity of channels to bit errors. As in previous cases, the results for Models 1 and 2 are very close. The results indicate that it is necessary to use effective error correction techniques in the case of complex networks (Models 3 and 4).