esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Sample and Methods
### Statistical Analysis  
After considering the confidence level (98%) and the margin of error (3.63%), the calculated minimum sample size was 624 subjects [47]. Statistica v. 13.3 statistical software was used to calculate the sample size and study power [48]. The independent samples t-test and power analysis were carried out. The variables used in the calculations were pHDI and nHDI, the variables used to extract the feeding patterns. The minimum sample size for an adequate study power was 286; alpha = 0.05 and power = 95%. All the variables were checked for normality using the Kolmogorov-Smirnov test. The \(\chi^{2}\) test was used to assess the distribution of the categorical variables. Principal component analysis (PCA) was used to isolate dietary patterns (DPs). The dietary patterns were identified by considering the following criteria: (1) the eigenvalues of the variable correlations \(>\)1.0, (2) the plot of the eigenvalues, and (3) the total explained variance [49]. Rotated factor loadings with an absolute value of \(\mid\) 0.50 \(\mid\) were considered to be specific to the pattern [49]. For each patient and each pattern, the scores were calculated as a product of the factor loading and food frequency consumption [49]. Next, for each dietary pattern, tertile intervals were calculated to measure the adherence to the patterns of each patient [49]. The logistic regression analysis searched for significant correlations among the obtained dietary patterns, trait intensities (TIPI-PLs), and factors (TFEQ-13s). The dependent variables were the three dietary patterns extracted from the studies performed, while the independent variables were the selected character traits that were analysed. Statistical analysis was also performed using Statistica v. 13.3 statistical software (StatSoft Polska Sp. z o.o. 2023) and Kit Plus version 5.0.96; available online: [[https://www.statsoft.pl/](https://www.statsoft.pl/)]([https://www.statsoft.pl/](https://www.statsoft.pl/)) (accessed on 14 December 2023) [48].
## 1 Introduction
### Literature Review
regard vehicle dynamics, physical constraints, and safety requirements as constraints, proposed a synchronous planning and control framework based on nonlinear model predictive control, and applied it to lane changing tasks [30]. [PERSON] et al. proposed an LSTM-MPC algorithm to predict the trajectory of surrounding vehicles through an LSTM network training data set to realize safe lane changing [31]. The many alternate path planning algorithms mentioned above can generally be divided into two categories: numerical-optimization-based and sampling-based. The method based on numerical optimization can simultaneously consider the constraints of the ego vehicle and the surrounding environment. Due to the non-convexity of most path planning, optimization problems are difficult to solve. Sampling-based methods generally have the ability to generate a large number of high-quality candidate paths. A random sampling method can be used to generate a lane change path quickly, but the smoothness of the path is often unusable and may not be optimal. To the authors' knowledge, only a few research findings account for the optimization problem of two or even more variables. In addition, the determination of cost function weight in existing references is relatively fuzzy. To solve the issue mentioned above, an algorithm based on NSGA-II and TOPSIS is proposed in this paper, which is convenient for solving the multi-objective optimization problem in the lane change process quickly. It not only retains sufficient reference trajectory, but also considers the constraints, making the lane change trajectory smooth and continuous. In fact, a genetic algorithm is widely used to solve optimization problems, but few scholars have applied it to lane change path planning. NSGA-II is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto Evolutionary Algorithm--two other elitist multi-objective evolutionary algorithms that pay special attention to creating a diverse Pareto-optimal front. While comparable to NSGA, the time complexity of NSGA-II is greatly reduced due to the adoption of fast non-dominated sorting and elite strategy.
## 4 Discussion
### Amendments and Soil Properties
#### 4.1.3 Soil Iron and Phosphorous in Absence of Plants  
Phosphorus and Fe are two important nutrients limiting plant growth in calcareous soils [86]. Although their soil total content may be high, both are largely unavailable to plants in this soil environment due to the poor solubility of cations at a high pH. For every pH unit increase, for example, Fe\({}^{3+}\) concentration decreases a thousand-fold [87]. Further, the high concentrations of Ca present readily precipitate with P as Ca-phosphate, which is released extremely slowly over time as the mineral weathers and dissolves, and also sorbs to Ca oxides in soil [88; 89]. Observed increases in soil available P concentrations, which were similar among all treatments, were likely the result of increased microbial activity [51]. Similar available P increases have been described in other research documenting changes in soil properties through amendment with biochar and compost [51; 90] However, usingthe Olsen P Method [69], an optimal range for available P in soil is 30-50 mg kg\({}^{-1}\)[91], thus the increases observed were likely not sufficient to overcome symptoms of deficiency.  
Our data suggests that none of the amendments were a significant source of Fe. Further, the availability of Fe was largely unaffected by the higher pH in BIOC, which was unexpected. Nonetheless, the difference in pH between BIOC and the other treatments did not appear to affect ecologically meaningful differences in terms of Fe availability. General decreases in available Fe observed across all treatments were also observed by [PERSON] et al. (2014) [92]. Given the high concentrations of DOC and DON in COMP at the end of the experiment, the significant decrease in Fe in COMP and INF may be attributable to microbial immobilization and microbial biomass decomposition.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 4 Prospect  
To improve the output efficiency of the MEMS scale PEHs, it is possible to start from the aspects of the piezoelectric materials, the piezoelectric modes and the structure of the energy harvester. At present, the piezoelectric material is developing towards the lead-free and high-voltage coefficient direction. In general, compositing material is used to improve the piezoelectric constant and dielectric permittivity of the material while reducing the Curie temperature and the difficulty of sintering. The preparation methods of the material also have a great influence on the quality and parametersof the material. The SPS method can be used in the laboratory research, but the cost of large-scale preparation is high. By contrast, the mixed oxide and solid-state liquid-phase sintering technique are more suitable for industrial production in the future. For the sake of simplicity in the manufacturing process, most piezoelectric energy harvesters take \(d_{31}\) mode electrodes, while the thickness of the piezoelectric material is thin in MEMS devices so the electrode with the \(d_{33}\) mode can provide a higher voltage. This also reduces the energy lost during the collection process through the rectifier circuit. Therefore, the \(d_{33}\) mode should be mainly studied in the energy harvester. However, there is no report about combining \(d_{31}\) with \(d_{33}\), which may be a promising piezoelectric mode in the future. As to a larger energy harvester, \(d_{15}\) mode should be considered due to its higher power harvesting efficiency. Besides, the fabrication of \(d_{15}\) mode device still needs to be optimized. Furthermore, the choice of energy harvester structure depends on the environment. Reducing the resonant frequency of the PEH and up-converting the frequency can be chosen to match the resonant frequency and external excitation of the PEHs for external excitation with relatively concentrated frequency. The frequency bandwidth of the PEHs should be expanded for many non-fixed external frequencies. This means that nonlinear structure will be a better choice to get higher conversion efficiency. In addition, if the harvester structure is designed to be adjustable in frequency, it can also improve the energy efficiency of the PEH combined with the negative feedback system. To harvest the maximum power from PEH, the interface circuit should be carefully designed to achieve impedance matching for the PEH.  
This work was supported by the National Natural Science Foundation of China: 61741406 and 51702249. The authors would like to thank the valuable advices of the reviewers for this review.  
[PERSON] guided other authors to complete this paper. He also supplied the latest materials to the article. [PERSON] wrote the paper. [PERSON] and [PERSON] provided guidance on piezoelectric materials. [PERSON] also helped to proofread the manuscript.  
The authors declare no conflict of interest.
## 3 Results  
The relative reliability of myotonometry over therapy was found to be high (ICC = 0.842). Muscle stiffness for flexor carpi radialis decreased significantly from before therapy (\(295.4\pm 49.8\) N/m) to the 10 th day of therapy (\(246\pm 29.7\) N/m) (\(p=0.04;\eta^{2}=0.47\)). For the biceps brachii, there was no significant difference between muscle stiffness before therapy (\(191.4\pm 35.9\) N/m) and the 10 th day of therapy (\(210\pm 27.6\) N/m) (\(p=0.4;\eta^{2}=0.39\)) (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sessions** & **m. Flexor Carpi Radialis** & **m. Biceps brachii** \\ \hline Before therapy & \(295.4\pm 49.8\) & \(191.4\pm 35.9\) \\ After therapy & \(246\pm 29.7\)* & \(210\pm 27.6\) \\ \hline \hline \end{tabular}  
* Statistically significant changes.  
\end{table}
Table 1: Mean \(\pm\) SD muscle stiffness (N/m) before and after transcranial direct current (tDCS) therapy.  
Figure 1: Experimental protocol.  
In the control group, muscle stiffness of both investigated muscles did not change significantly throughout the 10 days of the experiment (Table 2).  
The Ashworth Scale score for the wrist joint (functionally related to the flexor carpi radialis) decreased from before therapy (\(3.7\pm 0.5\)) to the 10 th day of therapy (\(2.3\pm 1.2\)) (\(p=0.02\)).  
In the control group, the Ashworth Scale score for the wrist joint did not change significantly throughout 10 days of the experiment.  
For all participants, the Spearman rank correlation showed statistically significant and positive relationships between muscle stiffness and the Ashworth Scale (\(p=0.04\)).
# Shear Behaviour of Aeolian Sand with Different Density and Confining Pressure
## 3 Results and Discussion
### Critical State
The figure shows that the \(M\) value increases with the increase in relative density, and the generation of shear band has great influence on the strength of aeolian sand. Figure 8: Undrained shear behaviour of aeolian sand. (**a**) The deviatoric stress-axial strain curves; (**b**) The effective stress paths. Figure 9: _Cont._  
The stress paths in the \(e\)-\(\ln p^{\prime}\) plane of aeolian sand specimens under drained conditions are shown in Figure 11. In this figure, \(e_{\rm c}\) is the void ratio after consolidation and \(e_{\rm f}\) is the void ratio at the end of test. Figure 11 shows the dilation of aeolian sand observed during tests at relative densities of 10% and 30% and the contraction observed during tests at greater relative densities. It is noted that the contraction of the specimen is accompanied by a rapid increase in \(p^{\prime}\), while the dilation of specimen \(p^{\prime}\) is almost constant. Figure 11: Cont. Figure 10: \(M\) value changes with relative density. The critical state lines in \(e\)-ln\(p^{\prime}\) plane for aeolian sand specimens are approximated by fitting the critical state points obtained from drained tests, as shown in Figure 12. The linear form of the critical state line (CSL) is considered here to better reflect the aeolian sand specimens in the \(e\)-ln\(p^{\prime}\) plane, which is expressed as:  
\[e_{\rm f}=e_{\rm f}-\lambda\ln p^{\prime} \tag{2}\]  
where \(e_{\rm f}\) is the void ratio of critical state, \(e\Gamma\) is the void ratio of critical state at \(p^{\prime}=0\) kPa, and \(\lambda\) is a material constant describing the material compressibility. In \(e\)-ln\(p^{\prime}\) plane, the aeolian sand specimens with different initial densities have different critical state lines, while for other types of sand, the critical state lines in \(e\)-ln\(p^{\prime}\) plane are thought to be unique lines irrespective of the initial state. It is apparent that the CSLs are not parallel to each other, which means that the material constant \(\lambda\) (the slope of the CSL) is dependent on the initial state, and the \(\lambda\) decreases with the increase in initial relative density \(D_{\rm r}\). Meanwhile, the CSL descends with increasing initial relative density \(D_{\rm r}\), that is, the material constant \(e_{\rm f}\) (the intercept of the CSL) decreases with increasing \(D_{\rm r}\). That is quite different from the unique CSL observed from clean sand and silty sand.
# Enhanced Drug Screening Efficacy in Zebrafish Using a Highly Oxygen-Permeable Culture Plate
###### Abstract  
Zebrafish are model organisms for drug screening owing to their transparent bodies, rapid embryonic development, and genetic similarities with humans. However, using standard polystyrene culture plates can limit the oxygen supply, potentially affecting embryo survival and the reliability of assays conducted in zebrafish. In this study, we evaluated the application of a novel, highly oxygen-permeable culture plate (InnoCell(tm)) in zebrafish development and drug screening assays. Under both normal and oxygen-restricted conditions, zebrafish embryos cultured on InnoCell(tm) plates exhibited significantly improved developmental parameters, including heart rate and body length, compared with those cultured on conventional polystyrene plates. The InnoCell(tm) plate enabled a significant reduction in medium volume without compromising zebrafish embryo viability, thereby demonstrating its advantages, particularly in high-throughput 384-well formats. Drug screening tests using antiangiogenic receptor tyrosine kinase inhibitors (TKIs) revealed enhanced sensitivity and more pronounced biological effects in InnoCell(tm) plates, as evidenced by the quantification of intersegmental blood vessels and gene expression analysis of the vascular endothelial growth factor receptor (_vegrt_, also known as _kdrl_). These results indicate that the InnoCell(tm) highly oxygen-permeable plate markedly improves zebrafish-based drug screening efficiency and assay reliability, highlighting its potential for widespread application in biomedical research.  
zebrafish; drug screening; oxygen permeability; high-throughput; culture plate; angiogenesis inhibitors 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), Vol., pp. 2020-2020, Document Cited by: SS1.
## 5 Data Preparation and Filtering
### Filtering of Raw Operational Data Techniques
#### 5.2.4 Kalman Filter  
The Kalman filter (KF) is a low pass filter that acts as an optimal estimator to minimize the MSE based on the measurement and estimated data. The filtering behavior depends on the hyperparameter known as the covariance matrix. If the covariance matrix is set to a low value, it will obtain a smooth function by removing a sudden spike in values; otherwise, it will trust the measurement data input if the covariance values are set as a high value. This  
Figure 14: Fast Fourier transform to detect and filter out the noise. (**a**) Noisy signal. (**b**) Frequency response of noisy signal. (**c**) Frequency response of filtered signal. (**d**) Filtered signal.  
approach was used in [63] to denoise all the ship operational data provided that the trend remains close to speed over ground (SOG) data shown in Figure 15.
## 2 System Model
### Receiver
\begin{array}{l}q^{(\alpha,\gamma),(m,m)}(lT,nT)\\ =\sum\limits_{\beta=1}^{2}\sum\limits_{k=-\infty}^{+\infty}\sum\limits_{\zeta= -\infty}^{+\infty}\sum\limits_{\rho=-\infty}^{+\infty}g_{\xi}(kT_{c}-\rho T_{ c}-lT)h_{\beta}^{\gamma,m}(\rho T_{c})g_{r}(\xi T-kT_{c})h_{\beta}^{\frac{- \alpha,m}{\beta}}(nT-\xi T)\\ \eta^{\alpha,m}(nT)=\sum\limits_{\beta=1}^{2}\sum\limits_{\zeta=-\infty}^{+ \infty}\sum\limits_{k=-\infty}^{+\infty}w_{\beta}(kT_{c})g_{\xi}(\xi T-kT_{c}) h_{\beta}^{\frac{-\alpha,m}{\beta}}(nT-\xi T)e^{-j\frac{\gamma_{2}}{M}mk}\end{array}  \tag{9}\]  
where \(h_{\beta}^{\frac{-\alpha,m}{\beta}}(nT)\) represents the time form corresponding to the frequency domain response \(\left[H_{\beta}^{\alpha,m}(\omega)\right]^{*}\). Equation (8) can be given by  
\[ \begin{array}{ll}r^{\alpha,m}(nT)&=q^{(\alpha,\alpha),(m,m)}(nT,nT)s^{\alpha,m}(nT)+\sum\limits_{l=-\infty}^{+\infty}\sum\limits_{\gamma=1,\gamma\
eq\alpha }^{2}q^{(\alpha,\gamma),(m,m)}(lT,nT)s^{\gamma,m}(lT)\\ &+\sum\limits_{l=-\infty}^{+\infty}q^{(\alpha,\alpha),(m,m)}(lT,nT)s^{\alpha,m}(lT)+\eta^{\alpha,m}(nT)\end{array}  \tag{10}\]  
Equation (10) shows that in addition to the desired signal component, the output signal of TR processing \(r^{\alpha,m}(nT)\) also contains noise, residual CCI, and ISI that need to be removed. In the proposed method, DA-based turbo equalization is used as the post-processor, which is described in detail in the following sections. Figure 3: Block diagram for TR processing.
###### Abstract  
The constant fluctuation of thermal loads on steel members, especially during construction, causes non-uniform distributions of temperatures, resulting in possible constructional and structural defects leading to unfavorable thermally induced responses and potential safety risks. The Canadian Highway Bridge Design Code (CHBDC) provides one thermal gradient variation profile without accounting for the differences in the geometrical parameters of the steel members and the variations in the climate regions of Canada. Therefore, in this study, three-dimensional finite element (FE) thermal simulations were conducted to investigate the maximum effective temperatures and positive vertical thermal gradients for different Canadian climate regions. Parametric studies were performed to conduct the FE thermal analysis using the thermal model validated in ANSYS. The comprehensive study results showed that Canada could be divided into two main zones for vertical thermal gradient calculations, meaning that one stationary thermal gradient profile cannot be applicable to all climate regions of Canada, as recommended by the CHBDC. Based on the FE thermal analysis results, empirical formulas as a function of the significant parameters were proposed to predict the maximum effective temperature and thermal gradient variations of the steel I-girder. The predicted maximum effective temperature and thermal gradient variation values were found to be highly correlated with the FE values with coefficients of determination R\({}^{2}\) of approximately 0.97 and 0.98, respectively.  
effective temperature; thermal gradient; thermal analysis; steel girder; finite element simulation; solar radiation; air temperature
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
## 2 Data and Methods
### Modeling Data  
The spatial modeling process of this study is shown in Figure 2. The first step was to generate a \(5^{\circ}\times 5^{\circ}\) grid within the model coverage range (\(110^{\circ}\)E-\(160^{\circ}\)E longitude and \(10^{\circ}\)-\(45^{\circ}\)S latitude) and fill in this grid with the IRI model's predicted data values. The second step was to put the actual observed foF2 values from the training stations into the corresponding adjacent grid to obtain the observed surface. The third step involved using the high-accuracy surface (HAS) modeling method to iteratively refine the observed surface to further improve the model's accuracy. The fourth step used Kriging interpolation to interpolate the \(5\times 5\) iterative surface of the completed HAS model to generate a \(0.5^{\circ}\times 0.5^{\circ}\) grid. Finally, cross-validation was used to verify and analyze the model's accuracy by selecting six out of the seven modeling stations as training stations and the remaining one as the validation station, followed by comparing the accuracy of the model when using the IRI model and Kriging interpolation to confirm the correctness and effectiveness of the proposed method.  
Figure 1: The spatial distribution of seven modeling stations within Oceania. These stations included Brisbane, Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville.  
To test the impact of the two models on reconstruction accuracy, this study used the RRMSE as the standard for method evaluation:  
\[\delta=\sqrt{\frac{1}{Y}\sum_{y=1}^{Y}\left(\frac{f_{\text{obs}}^{y}-f_{\text{ mod}}^{y}}{f_{\text{obs}}^{y}}\right)^{2}}\times 100\% \tag{1}\]  
where \(\delta\) is the RRMSE, \(Y\) is the number of cross-validations, \(f_{\text{obs}}^{y}\) is the actual observed foF2 value at the validation station, and \(f_{\text{mod}}^{y}\) is the reconstructed predicted foF2 value at the validation station for the \(y\)th iteration.
## 4 Methodology
The mobility data are used to update the node's SA view. The SA module has logic to determine if specific nodes are \"visible\" based on sensor range, which is a configurable parameter. Invisible nodes are excluded from the node's SA view computed by the module. The same procedure is applied when receiving SA updates from friendly nodes. The HDARP routing module receives a notification from the SA module every time the SA view is updated. For each notification, it determines whether routes must be recomputed as described in Section 3.3 above. To avoid stale routes, the HDARP module purges the existing routes in the IPv4 routing table before installing routes derived from the current SA view. Unfortunately, there is almost no support for antenna arrays components with beamforming in INET/INETMANET, with the notable exception of [38]. Furthermore, our interest was primarily in the behavior of the routing protocol, separated from the intricacies of the link layer and physical layer models. Consequently, we made a pragmatic simplification by configuring the wireless interface to use the AckingWirelessInterface module. This module contains two submodules: a unit disk radio (UnitDiskRadio) and a very simple MAC protocol (AckingMac). The unit disk radio is configured to perform interference modeling, which means that when a node transmits a MAC frame, all nodes within Tx range will be able to receive the frame, unless other nodes within Tx range are transmitting at the same time, in which case the reception will fail. The model implies the use of a isotropic antenna, which is at odds with our aim to use directional antennas. Instead, we emulate the effects of a directional antenna through the InterceptApp module, which is presented in Section 4.1. The AckingMac is trivial in the sense that it offers encapsulation and decapsulation of link layer frames, but no medium access procedure or retransmission. For over-the-air tactical links, it is common practice to avoid the use of the Address Resolution Protocol (ARP) [39] in order to reduce latency. Instead, IP addresses are mapped to MAC addresses in a static table available as a parameter in the MC, or the mapping is a deterministic function based on the assigned IP address of a node. We have configured the simulator to use GlobalArp in order to avoid ARP exchanges. The GlobalArp is a lookup table of IP to MAC address mappings available to all simulation nodes.