esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Figure 5: FRAP model analysis: (**a**) Perturbation plot; (**b**) contour surface plots of FRAP against hydro module (ratio) and duration (min); (**c**) contour surface plots of FRAP against hydro module (ratio) and temperature; (**d**) contour surface plots of FRAP against hydro module (ratio) and extractant; (**e**) contour surface plots of FRAP against temperature and extractant; (**f**) contour surface plots of FRAP against duration (min) and temperature. The model describing the dependence of DPPH on the independent variables is quadratic (Table 11). The coefficient of determination R\({}^{2}\) and the standard error in this case are, respectively, 0.96 and 2.44. It can be seen that the coefficient of determination is high and the error value is small. This standard error value means that when estimating the DPPH value, a mean error of less than 2.44 uM TE/100 g fw can be expected. Diagnostics of the model are shown in the plots of the normal distribution of residuals, residuals versus predicted values, and predicted versus actual values, presented in Figure 6. Based on them, it can be said that the residuals are normally distributed and there are no extreme values among them. Figure 7 presents the perturbation and contour plots. The perturbation plot (Figure 7a) showed that the extractant and duration have the most significant influence, while the impact of temperature had no effect. Figure 7b revealed the effect of the hydro module and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & \multirow{2}{*}{**df**} & **Mean** & **F-** & \(p\)**-** \\  & **Squares** & & **Square** & **Value** & **Value** \\ \hline Model & 3.684 \(\times\)\(10^{5}\) & 14 & 26,310.90 & 0.3124 & 0.9814 & not significant \\ A-hydromodule (ratio) & 29,020.71 & 1 & 29,020.71 & 0.3446 & 0.5665 & \\ B-Duration & 25,594.23 & 1 & 25,594.23 & 0.3039 & 0.5901 & \\ C-Temperature & 40,626.75 & 1 & 40,626.75 & 0.4824 & 0.4987 & \\ D-Extractant & 18,531.15 & 1 & 18,531.15 & 0.2200 & 0.6462 & \\ AB & 739.27 & 1 & 739.27 & 0.0088 & 0.9267 & \\ AC & 71,752.47 & 1 & 71,752.47 & 0.8520 & 0.3716 & \\ AD & 12,646.52 & 1 & 12,646.52 & 0.1502 & 0.7042 & \\ BC & 18,765.71 & 1 & 18,765.71 & 0.2228 & 0.6442 & \\ BD & 46,171.53 & 1 & 46,171.53 & 0.5483 & 0.4713 & \\ CD & 30.89 & 1 & 30.89 & 0.0004 & 0.9850 & \\ A\({}^{2}\) & 21,446.49 & 1 & 21,446.49 & 0.2547 & 0.6217 & \\ B\({}^{2}\) & 73,547.65 & 1 & 73,547.65 & 0.8733 & 0.3659 & \\ C\({}^{2}\) & 14,051.90 & 1 & 14,051.90 & 0.1669 & 0.6891 & \\ D\({}^{2}\) & 15,509.35 & 1 & 15,509.35 & 0.1842 & 0.6743 & \\ Residual & 1.179 \(\times\)\(10^{6}\) & 14 & 84,214.25 & & & \\ Lack of Fit & 1.099 \(\times\)\(10^{6}\) & 10 & 1.099 \(\times\)\(10^{5}\) & 5.48 & 0.0577 & not significant \\ Pure Error & 80,189.56 & 4 & 20,047.39 & & & \\ Cor Total & 1.547 \(\times\)\(10^{6}\) & 28 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: ANOVA for quadratic model Response 4: CUPRAC.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 2 Methods
### Experimental Research Using Particle Image Velocimetry  
The experiment with the Particle Image Velocimetry (PIV) technique was carried out in the wind tunnel at the Institute of Thermomechanics of the Academy of Sciences of the Czech Republic. The measurements set-up is presented in Figure 4. The analysed configuration of building models was located within a ground boundary layer generated by the facility output from the contraction nozzle of a cross-section of 10D in width and 10D in height. The test section of the wind tunnel is 120D long but the measurements were performed in the central part of the tunnel. The lower building was placed 60D upstream the test section. A laser beam from a dual-head Nd:YLF laser with a pulse energy of 10 mJ at 2 kHz illuminated the particles. Dantec CCD camera (SpeedSense 611) was used to capture pairs of particle images at a frame rate of 100 Hz. The SAFEX fog generator was used to seed \(\sim\)1 \(\upmu\)m oil droplets. Seeding particles were introduced into the flow in a sufficient and stable concentration. The parameters of PIV experiments were set on the base of [31]. Instantaneous and time-averaged flow velocity fields were measured within the areas, marked in Figure 4 with green fields, lying in the plane \(Z=\) const. The PIV measurements provided the velocity vector field with two velocity components (Ux and Uy) in horizontal (X) and vertical (Y) direction. Time-averaged PIV results were used for comparison with CFD results.
## 3 Results and Discussion
### Principal Component Analysis  
PCA was applied to detect the structure in the correlation between the independent variables' parameters (time and osmolality) and the microorganisms' (_E. coli_, _L. monocytogenes_, _Salmonella_ spp., Enterobacteriaceae, and TPC) viability responses [46].  
Figure 3 presents the results of PCA. The goal was to determine the trend visualization in the shown data and the discriminating efficiency of the used descriptors (time, osmolality, and the microorganisms' viability). Therefore, a scatter plot was produced, presenting the first two principal components from the PCA of the data matrix, the first principal component at the \(x\)-axis, and the second at the \(y\)-axis.  
A neat separation of 42 tested samples according to different exposure times and osmolality levels can be seen from the presented scatter plot. The position of the samples in the figure was influenced by the exposure time and osmolality level, where with the increasing exposure times and osmolality levels, the location of the samples moved from negative to positive first principal component (PC) values. The samples in the area with higher negative first principal component values were characterized by higher levels of all the tested microorganisms' numbers and viability. Quality results showed that the first two PCs accounted for 99.21% of the total variance and could be considered more than sufficient for data representation.  
The contribution of the responses to the first principal component was almost equally distributed among all the microorganisms' viability responses. Only the contribution from the response for _L. monocytogenes_ was slightly lower. However, in the case of the second principal component, the highest contribution was from the _L. monocytogenes_ response.  
Figure 2: Color correlation diagram between the parameters of the independent variables and the responses of microorganisms’ viability.
## 3 Model Validation
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\Theta_{exp}-\Theta_{mn}\right)^{2}}, \tag{15}\]  
The Mean Relative Absolute Error (\(MRAE\)) is also available, an indicator that refers to the absolute average value of the differences produced between the experimental data and those calculated by the model. \[MRAE=\frac{1}{n}\sum_{i=1}^{n}\abs{\frac{\Theta_{exp}-\Theta_{mn}}{\Theta_{ exp}}}, \tag{16}\]  
Figure 1: Temperature distributions in the tank axis comparing modeling predicted results with experimental data from [36]. An essential criterion corresponds to the Relative Root-Mean-Square Error (_RRMSE_) measurement. It was calculated by dividing the _RMSE_ by the average of the experimental data. The accuracy of the model was considered excellent when the _RRMSE_\(<\) 10%, good when 10% \(<\)_RRMSE_\(<\) 20%, reasonable if 20% \(<\)_RRMSE_\(<\) 30%, and poor when it takes a value greater than 30% [42]. \[RRMSE=\frac{RMSE}{\sum_{i=1}^{n}\Theta_{exp}}\cdot 100\%, \tag{17}\]  
Table 2 shows the results obtained for these statistical variables. The _RRMSE_ for each of the analyzed curves is reasonable enough in magnitude to establish that the numerical model consistently fits the experimental data. The other indicators were presented as dimensionless and decimal. The data expressed in a small magnitude tend to be associated with that characteristic. However, these values translated to the Celsius scale are intrinsically associated with a loss associated with the environment since the experimental tank is open and has carbon steel walls. The present numerical model works as an almost adiabatic system, according to the current technology applied in thermocline TES. Finally, the numerical model is satisfactorily validated.
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
## 3 Results
### Joint Moment  
The joint moment results calculated with inverse dynamics tool in OpenSim are presented in Figure 5, and statistical analysis results are listed in Table 4. Although the results showed that differences among hip joint moments (flexion, adduction, and rotation moment) and knee flexion moment were not statistically significant (\(p>0.05\)), differences in knee adduction moment and ankle plantarflexion moment were statistically significant (\(p<0.05\)). Compared to the natural gait, [PERSON]'s test revealed that the first peak of KAM was reduced by 11.7% in the toe-in gait but that the toe-out gait showed an increase of the first peak by 0.8%. The second peak of KAM was reduced by 5.7% in the toe-out gait. Compared to the natural gait, the toe-in gait and the toe-out gaits decreased the ankle joint plantarflexion moment by 3.7% and 1.7% respectively (\(p=0.04\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GRF (BW)** & **Natural** & **Toe-In** & **Toe-Out** & \(p\)**-Value** \\ \hline Max posterior force & 0.134 (0.02) & 0.142 (0.02) & 0.128 (0.02) & 0.23 \\ Max anterior force & 0.099 (0.02) & 0.095 (0.01) & 0.098 (0.01) & 0.42 \\
1 st peak in the SI direction & 0.97 (0.02) & 0.96 (0.04) & 0.96 (0.03) & 0.57 \\
2 nd peak in the SI direction & 0.99 (0.03) & 0.99 (0.02) & 0.99 (0.03) & 0.44 \\
1 st peak in the ML direction & 0.05 (\(<0.01\)) & 0.05 (0.01) & 0.05 (0.01) & 0.43 \\
2 nd peak in the ML direction & 0.05 (0.05) & 0.04 (0.02) & 0.06 (0.01) \# & 0.04 \\ \hline \hline \end{tabular} SI: supior-inferior, ML: medial-lateral, # significantly different from toe-in gait.  
\end{table}
Table 2: GRFs in three directions (mean \(\pm\) SD).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Muscles** & **RF** & **VM** & **VL** & **PL** & **TA** & **ST** & **BF** & **GM** & **GL** \\ \hline Toe-in & 3/7 \(\uparrow\) & 2/3 \(\uparrow\) & 1/1 \(\uparrow\) & 1/1 \(\uparrow\) & 1/2 \(\uparrow\) & 1/1 \(\uparrow\) & 3/5 \(\downarrow\) & 1/2 \(\uparrow\) & 1/1 \(\downarrow\) \\ Toe-out & 2/2 \(\uparrow\) & 2/3 \(\uparrow\) & 3/3 \(\uparrow\) & 2/3 \(\uparrow\) & 2/2 \(\downarrow\) & 3/4 \(\downarrow\) & 4/6 \(\downarrow\) & 2/2 \(\downarrow\) & 4/5 \(\downarrow\) \\ \hline \hline \end{tabular} RF: rectus femoris, VM: vastus medialis, VL: vastus laterals, PL: peroneus longus, TA: tibialis anterior, ST: semitendinosus, BF: biceps femoris, GM: gastrocnemius medialis, GL: gastrocnemius lateralis, \(\uparrow\) increase of activation, \(\downarrow\) decrease of activation.  
\end{table}
Table 3: Frequency of increase or decrease of activation of each muscles/number of significant EMG data.
## 5 Experimentation and Analysis
### Analysis of Experimental Arithmetic
#### 5.4.3 Comparison of Four-Season Load-Forecasting Models without Weather  
For the purposes of this research project, the dates selected were for a random week of data during the spring, summer, fall, and winter seasons of 2018: 4.1-4.7 for spring, 8.11-8.17 for summer, 11.11-11.17 for fall, and 1.11-1.17 for winter. The prediction accuracy of the model applying the MVMO-LSTM proposed in this research was compared with the prediction accuracy of the conventional model. Figure 6 and Table 2 show the results of the evaluation. Among them, the performance metrics of the improved model proposed in this study have been bolded in bold.  
(a) The table in this paper compares the MVMO-LSTM with the metrics of each algorithm from top to bottom. The metrics for each of the four seasons are compared from left to right, and \"Target\" is the original data. Compared with the MVMO-LSTM, the prediction performance of LSTM and the combination model of various optimization algorithms in the spring, autumn and winter seasons is not highly evaluated, and some indicators are slightly lower in the early period, which is susceptible to environmental  
Figure 6: Comparison of the various models without weather.  
changes. The average RMSE, MAE and R\({}^{2}\) of LSTM in each season were 1.0959 KW, 0.9388 KW and 84.72%, respectively. Smoothing the data with VMD can reduce the noise interference. As a result, the average RMSE, MAE and R\({}^{2}\) of the MVMO-LSTM were 0.2530 KW, 0.2195 KW and 99.27%, respectively. From these three indicators, this multi-strategy dung beetle algorithm is more advantageous compared with the combined models of other algorithms.  
(b) In addition, most of the other conventional algorithms optimize LSTM with RMSE values in the range of 0.3676 KW to 1.0519 KW. Comparing LSTM with the MVMO-LSTM from Table 2, these models are also closer to the predicted results. Compared to other conventional algorithms with optimized LSTM, the MVMO-LSTM integrated prediction model has R\({}^{2}\) over 99% for different datasets. In comparison, it has strong stability and adaptability.
## 3 Effect of Model Constant on Critical State
### Dilatant Behaviour in Dense State  
As mentioned earlier, the unification of critical state under various confining pressures is still under discussion. According to Figure 10, it appears that the critical mean effective stress p\({}^{\prime}\) increased with the increase in initial confining pressure for all density indexes.  
It was analysed in the last section that the performance of numerical results in loose and very loose states were insensitive to variation in model parameter b due to the fact that their initial density indexes were almost located in the first part of proposed critical state line, which was mainly determined by model constant a. However, for dense specimens whose initial states fell totally within the second part of the proposed critical line, the determination of model constant b played a very important role instead of the model constant a. A case of density index of 0.70 under confining pressure equal to 200 kPa was investigated by keeping model parameter b constant. The stress-strain curve is shown in Figure 11, and it is clear that the result is not ideal because the state parameter was over-estimated.  
Figure 11: Stress–strain curve of Hostun RF sand on dense state under a confining pressure of 200 kPa.  
Figure 10: Critical state of Hostun RF sand under various confining pressures equal to 100, 200, and 400 kPa [6].  
Therefore, it seems better to suppose that model parameter \(b=b(p^{\prime},b_{0})\) is a function of initial confining pressure instead of a model constant, where \(b_{0}\) is determined by original model parameter identification method [9; 13]. For a well-studied sand, the variable model parameter \(b=b(p^{\prime},b_{0})\) can be directly determined by fitting the critical state line for different confining pressures. For a sand less-investigated, the authors recommend the following equation:  
\[b=\left\{\begin{array}{c}b_{0},\text{while }p^{\prime}_{0}<100\text{ kPa}\\ -0.12p^{\prime}_{0}{}^{-10\text{b}_{0}},\text{ while }p^{\prime}_{0}>100\text{ kPa}\end{array}\right. \tag{16}\]  
Figure 12 shows that with the correction of model parameter \(b\), the model was capable of correctly simulating a case for dense sand specimens. Some small differences can be attributed to the observation that deformation of dense sand was not homogenous and that the expansion of the sample's section was significant.
# Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
## 5 Conclusions  
This paper presents a novel approach to DR for vehicles. This method utilizes Lie theory by treating the position and velocity of the vehicle as elements of the Lie group \(SE_{2}(3)\). Previously widely used DR methods have faced challenges due to approximation errors when handling nonlinear transformations and uncertainties. However, this method enables accurate solutions without linear approximation errors by treating the position and velocity of the vehicle as elements of a Lie group. In the DR operation process, the increments for pose and velocity are expressed in the Lie algebra, which is a linear space. Real-world experimental results have confirmed that this method improves accuracy. Notably, even when the time interval is long and increments increase, this method significantly reduces errors compared to previously used DR. In other words, the proposed method can overcome approximation errors due to integration and uncertainty errors caused by forced approximation of attitude into a linear space. In future work, the proposed method will be used as a process model in recursive filters such as the KF to improve the accuracy of state prediction. Additionally, the convergence performance of internal parameters such as measurement innovation and Kalman gain will be enhanced based on the improved state prediction values.  
Conceptualization, D.B.J. and N.Y.K.; methodology, D.B.J. and N.Y.K.; software, D.B.J. and N.Y.K.; validation, D.B.J., N.Y.K. and B.L.; formal analysis, D.B.J. and N.Y.K.; investigation, D.B.J., N.Y.K. and B.L.; resources, D.B.J. and N.Y.K.; data curation, D.B.J., N.Y.K. and B.L.; writing--original draft preparation, D.B.J.; writing--review and editing, N.Y.K.; visualization, D.B.J.; supervision, N.Y.K.; project administration, N.Y.K. and B.L.; funding acquisition, N.Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1I1A3A01057691).  
Not applicable.  
The original data presented in the study are openly available in [35].  
The authors declare no conflicts of interest.
# High Bandwidth-Utilization Digital Holographic Reconstruction Using an Untrained Neural Network
## 1 Introduction
Driven by a large datasets, deep neural networks (DNN) can perform complete wavefront reconstruction of specimens efficiently [30]. However, obtaining datasets and corresponding ground truth, especially when the scheme is applied to bio-samples, can be pretty time-consuming and cumbersome. Deep Image Prior (DIP) [31] applies an untrained network to the solution of several inverse problems without a massive training dataset and ground truth, which can fit a randomly initialized DNN to a single corrupted image. Inspired by the DIP, an untrained network model named \"PhysenNet\" [32] is proposed, which incorporates a complete physical model into the conventional DNN to achieve QPI from a single-frame intensity map. Recently, an unsupervised blind phase retrieval method called Deep Digital in-line holography (Deep-DIH) [33] has been proposed. Unlike the traditional DL-based phase retrieval model, it can achieve highly robust, high-resolution phase retrieval from a single-frame, in-line hologram iteratively without a large number of datasets. Inspired by the successful application of the interplay between DNN and the physical model, in this work, we propose a high-SBU, artifacts-free, low-carrier-frequency fringe demodulation approach for the slightly off-axis system, which completes phase recovery iteratively using an untrained neural network. Unlike the massive-data-driven DL training model, our method utilizes a single-frame hologram as the input of the model. After the primary demodulation based on the FT method, the complex amplitude will be updated by an unsupervised network iteratively to reconstruct the final phase results. In this process, an auto-encoder network [33; 34] with an \"hourglass\" architecture is used to constantly learn the delicate phase details of objects instead of the artifacts. Furthermore, the physical model will be migrated to the cost function of the network, which can help the DL model to correct the network in the parameter space effectively. In short, our method can improve the DH system's SBU to 53.9% under the single-shot, multiplex-free situation while achieving high-resolution, artifacts-free QPI. The results of the phase resolution target and the simulation experiment can quantitatively demonstrate the artifact suppression and high-resolution imaging capability of our method, while the results of the live-cell experiment indicate the practicality of the method in biological research.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 2 Materials and Methods
### Antioxidant Studies  
The antioxidant activity of plant extract was carried out by examining the free radical scavenging (1,1-diphenyl-2-picrylhydrazyl (DPPH), nitric oxide, and hydrogen peroxide), reducing potential, and total antioxidant capacity assays, with slight modifications [26; 27]. The final values were determined by using ascorbic acid as the standard for each of these assays.
## 5 Analysis  
### Security  
#### 5.1.1 Authorized Access to Trajectory Data  
Since the locations of a vehicle can be regarded as personal information of a driver, driving trajectory data maintained on the cloud must not be exposed to not only outsiders but also SM illegally in the system. In our proposed system, a trajectory data \(trj_{d}\) consisting of pseudonym-location pairs of a vehicle \(V_{d}\) is maintained by SM in the encrypted form under \(V_{d}\)'s public key \(pk_{V_{d}}\) as \(C\ \leftarrow clEnc(trj_{d},\)\(pid_{d0},\)\(pk_{V_{d}})\). Essentially, nobody can gain \(V_{d}\)'s trajectory data as trying to decrypt \(C\) without knowing \(V_{d}\)'s private key \(sk_{V_{d}}\) corresponding to \(pk_{V_{d}}\). On the other hand, some sRSUs of \(V_{d}\)'s interested socialspots specified in \(SR_{d}\) need to know \(V_{d}\)'s driving trajectories for disseminating service messages through contact point RSUs along \(V_{d}\)'s driving routes. \(V_{d}\) can allow an \(sRSU_{j}\in SR_{d}\) to get its trajectory data by giving a re-encryption key \(rk_{V_{d}\to sRSU_{j}}\gets clReKeyGen(sk_{V_{d}},\)\(pid_{d0},\)\(sRSU_{j},\)\(pk_{V_{d}},\)\(pk_{sRSU_{j}})\) to SM instead of entrusting plaintext trajectory data. The encrypted trajectory data \(C\) are then re-encrypted to \(C^{\prime}_{j}\gets clReEnc\) (\(pid_{d0},\)\(pk_{V_{d}},\)\(C,\)\(rk_{V_{d}\to sRSU_{j}}\)) for \(sRSU_{j}\) under the key \(rk_{V_{d}\to sRSU_{j}}\) by SM. Hence, only a valid \(sRSU_{j}\) that possesses a private key \(sk_{sRSU_{j}}\) corresponding to \(pk_{sRSU_{j}}\) involved in \(rk_{V_{d}\to sRSU_{j}}\) can get \(trj_{d}\) by decrypting \(C_{j}\).  
In addition to an outside attacker and unauthorized RSUs, another concern is the security threat of SM to the trajectory data managed on the cloud since a semi-honest SM may be curious to know vehicle's driving locations for the purpose of collecting and profiling driver's preferences. At this phase, even though the encrypted trajectory data and re-encryption keys are given to SM, it is hard for SM to deduce the private key of \(V_{d}\) or \(sRSU_{j}\) for the purpose of recovering the trajectory data \(trj_{d}\) from \(C\) or \(C_{j}\) if we assume the security properties of the underlying proxy re-encryption scheme [27]. Therefore, neither SM nor unauthorized RSUs can access vehicle's trajectory data on the cloud unless the vehicle authorizes decryption rights for the encrypted trajectory data under re-encryption keys.