esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods  
### Image Processing  
Absolute radiometric calibration and orthorectification of TSX data was done with GAMMA(tm) software. The best available digital elevation model (DEM) for our study region, the National Elevation Dataset (NED) with 60 m spatial resolution, was used to perform orthorectification. The performance of this correction was assessed visually by overlaying geocoded TSX images on the terrain-corrected Landsat-5 TM image. Speckle removal in the TSX images was performed using IDRIS(tm) image processing software by applying a 5X5 window mean filter. Since GAMMA provided the radar backscatter (\(\sigma^{\mathrm{o}}\)) values in linear unit (intensity values), they were converted to decibels (dB) after we performed filtering and extracted the mean backscatter value of drained basins for further analysis. A NDVI dataset was produced using Landsat-5 TM band-3 (red) and band-4 (near infrared) using Equation (1) [53].  
\[\mathrm{NDVI}=\frac{\mathrm{Near\ Infrared\ -Red}}{\mathrm{Near\ Infrared\ +Red}} \tag{1}\]  
Since the objective of the study was to investigate the relationship between backscatter and NDVI with basin age, remnant water bodies in all DTLBs were masked from all TSX scenes, as well as the Landsat NDVI image using IDRISI software. To mask water bodies, a threshold method separating land from water surfaces was applied to the TSX images, and the same method was applied to band-5 (first shortwave infrared band) of the Landsat image. A threshold of \(-14\) dB was chosen to mask out water bodies from TSX images, and a threshold of 31 DN (digital numbers) was chosen for Landsat band 5.
## 3 Results
### Classification and Evaluation Criteria  
Random Forest (RF) machine learning algorithm [12] was employed to compare results between \(V_{entropy}\), \(V_{merge}\), and \(V_{general}\), where the time gap of AKI onset to 48 h before AKI onset and a feature window of 24 h was used. After finding the critical point in the time gap, we used it in the feature window variation task, studying feature windows of 24 h to 48 h with a time gap of 24 h.  
The RF model can predict whether a patient will have AKI onset or not during ICU admission, by providing a probability. The probability is determined by the ratio of the decision trees that give positive results in the total number of trees. In our RF model, we set the number of multiple decision trees as 300 and the number of splits to 21. Ten-fold validation was used.  
The performance was evaluated using the accuracy, area under the ROC curve (AUROC), and net reclassification improvement (NRI) metrics. The accuracy measures the percentage of correctly classified samples of the total samples under a given threshold. The AUROC evaluates the overall model performance. The NRI aims to provide an objective method to quantify improvements in categories in models [18]. It measures how well a new model correctly reclassifies subjects that were not correctly classified in the old model. In our case, the new model was the model with increment in time \(t\), while the old model for comparison was the model with prior time (i.e., \(t-1\)). F value from F test was conducted in order to compare the variance between two groups.  
Figure 4: Visualization plot for setting time between different vital signs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Vital Signs** & **Mean Setting Time (h)** \\ \hline Pulse pressure (SBP, DBP) & 2.93 \\ Okimetry & 8.39 \\ Respiratory rate & 3.00 \\ Pulse rate & 2.99 \\ Temperature & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mean setting time for vital signs.
# A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model
## 3 On-Axis Tracking Control Method based on the Orbital Motion Model  
### On-Axis Tracking Control Model  
During the operation of the spectrometer, the turntable control is conducted in three steps: sun pre-pointing, locking, and tracking. The encoder data is employed for pre-pointing as feedback to realize the closed-loop position control. After the sun enters the optical field of view of the tracking sensor, the calculated angular offset is used to adjust the turntable to make the sun lie in the center of the field of view of the spectrometer and to keep the boresight axis of the spectrometer pointing to the sun. Owing to certain factors, such as the frame rate and image processing algorithm, the angular offset output from the tracking sensor lags behind its imaging time, which leads to the offset obtained by the control system lagging behind the current target position. This lag link is equivalent to adding a delay in the position loop, which has a negative influence on the stability and tracking accuracy of the system.  
The on-axis tracking technology based on predictive filtering calculates the offset data at the current moment by predicting and estimating the lag offset to compensate for the influence of the data lag on the control system. On-axis tracking is proposed on the basis of composite control, which combines the data of the tracking sensor and the position sensor to obtain parameters such as the position and speed of the target. The on-axis tracking control model of the spectrometer system is shown in Figure 3. Firstly, the offset data \(\Delta\theta\)(k-n) is fused with the encoder angle, where there is an artificial lag of n cycles \(\theta\)(k-n) to obtain \(\theta_{\text{ref}}\)(k-n), which is the angular position of the sun with n lag cycles in the field of view of the spectrometer. Then, the fused data is processed with a predictive filtering algorithm to obtain the estimated value of the target position e_\(\theta_{\text{ref}}\)(k) and velocity e_speed(k) at the current moment. Finally, the target position and estimated velocity are used in the control system as the input of the position loop and as the feed-forward signal of the velocity loop, respectively, to realize equivalent composite control.  
Figure 3: **The** spectrometer on-axis tracking control model.  
Figure 2: Single-axis control principle of the spectrometer.
## 4 Stability Analysis of GCI Controlled by Different Domains
### Influence of DC Voltage Loop Bandwidth BWv  
In order to study the influence of DC voltage loop bandwidth BWv on the stability of GCIs under different domain controls, given PLL bandwidth \(BW_{\text{PLL}}=20\) Hz, current loop control bandwidth \(BW_{\text{c}}=600\) Hz, and grid side inductance \(\text{Lg}=2\) mH, the Nyquist diagrams when the DC voltage loop bandwidth BWv is changed are shown in Figures 8 and 9.  
In Figure 8a, when the outer loop bandwidth \(BW_{\text{v}}\) is 30 Hz, 80 Hz, and 130 Hz, the Nyquist diagrams for the positive sequence impedance ratio \(ZR_{\text{p}}\)(s) and the negative sequence impedance ratio \(ZR_{\text{n}}\)(s) do not encircle the point \((-1,\,\text{j0})\). However, at an outer loop bandwidth of 180 Hz, the Nyquist diagrams of \(ZR_{\text{p}}\)(s) encircle the point \((-1,\,\text{j0})\), indicating instability.  
In Figure 8b, at outer loop bandwidths of 30 Hz, 80 Hz, 130 Hz, and 180 Hz, the Nyquist diagrams for both \(ZR_{\text{p}}\)(s) and \(ZR_{\text{n}}\)(s) do not surround point \((-1,\,\text{j0})\), suggesting stability across these settings.  
Figure 7: Nyquist diagram with \(BW_{\text{PLL}}\) changes when voltage feedforward control is considered. (**a**) PI control. (**b**) PR control.  
In Figure 9a,b, when the outer loop bandwidth \(BW_{\rm v}\) is 40 Hz, 90 Hz, 130 Hz, and 180 Hz, the Nyquist diagrams for \(ZR_{\rm p}\)(s) and \(ZR_{\rm n}\)(s) do not encircle the point \((-1,j0)\).  
As seen from Figures 8 and 9, when voltage feedforward control is not considered, the stability of GCIs under both control modes decreases as the outer loop bandwidth \(BW_{\rm v}\) increases.  
Additionally, with the introduction of voltage feedforward control, the stability of inverters using both time-domain PR control and \(dq\)-domain PI control is reduced, particularly at high outer loop bandwidths, which may lead to instability.
## 4 Employability Scenario: Assessing the Significance of the Enhanced Visibility Estimation Process
### Particle-Concentration Equivalent of a Dynamic Meteorological Condition (Wind Velocity)  
As briefly stated earlier, [PERSON]'s [26] study demonstrated a correlation of path loss to the wind speed, which conversely can be understood as a link to the severity of the SDS. The reported wind speeds were 0.6 m/s, 3.6 m/s, 3.8 m/s, and 7.3 m/s, respectively, for the environmental conditions of clear sky, dusty sky, sand storm, and heavy sand storm. Although the specifications of the instrument used for wind speed measurements are not reported in [26], it is believed that it is of a similar make (JL-FS2 anemometer) to the one reported in this source [27] with an error of \(\pm\)3%. Therefore, the recorded speeds are expected to have reading errors of \(\pm\)0.018 m/s, \(\pm\)0.108 m/s, \(\pm\)0.114 m/s, and \(\pm\)0.219 m/s, respectively.  
Since wind is the dynamic condition of dust storms [28], and dust storms are fundamentally a concentration of particles in the air, it is plausible to establish a relationship between the number of particle concentrations and corresponding wind speeds, as has been demonstrated by the authors of [29] using the following expression:  
\[M=29.66e^{0.7u}\ \mathrm{\mu g/m^{3}}, \tag{19}\]  
where \(M\) is the dust concentration in \(\mathrm{\mu g/m^{3}}\) and \(u\) is the wind speed in m/s. By substituting the reported wind speeds (and estimated errors) in the order mentioned in the earlier paragraphs into Equation (13), we get the following values: 45.14 (\(\pm\)0.57) \(\mathrm{\mu g/m^{3}}\), 316.2 (-26.84, +28.95) \(\mathrm{\mu g/m^{3}}\), 424.03 (-32.52, +35.22) \(\mathrm{\mu g/m^{3}}\), and 4913.78 (-698.38, +814.09) \(\mathrm{\mu g/m^{3}}\), respectively.  
Equation (19) provided by [PERSON] et al. [29] was superficially mentioned without elaborating on its employability restrictions. However, assuming the same study environment as in [29], a recently published work [30] indicated that variation in SDS particle concentrations are not only limited to changing wind speeds but can be observed in their vertical distribution as well. [PERSON] et al.'s [30] real-time measurements revealed that during the SDS period for heights less than 100 m, the concentrations ranged between 1220 \(\mathrm{\mu g/m^{3}}\) and 42,146 \(\mathrm{\mu g/m^{3}}\). Meanwhile, the lowest measured concentration in the same event at its initial meteorological conditions leading up to the storm was an average of 31.62 \(\mathrm{\mu g/m^{3}}\) at heights less than 200 m.  
[PERSON] et al.'s [30] results confer greater credibility on the results obtained from Equation (13). Therefore, for evaluation purposes, the values may be adopted conveniently, considering that the observation height for Mujlid's [26] environment conditions could not have exceeded 1.7 m, which is the average human height [31].
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 3 Selection of Microbes for the Phytoremediation of Metal Contaminated Saline Soils
### Frequently Investigated PGP Properties
#### 3.1.2 Production of 1-Aminocyclopropane-1 carboxylate Deaminase  
The production and activity of the 1-aminocyclopropane-1-carboxylate deaminase (ACCD) is also often prospected [43; 114; 115; 116; 117; 118; 119]. The ACCD is intrinsically linked to the presence of IAA [107]. Indeed, the excessive production of IAA can limit root growth because of the production of 1-aminocyclopropane-1-carboxylate (ACC) and its transformation into ethylene [120]. The production of ACCD by microbes enables the plant to regulate the amount of ACC in the plant cells by hydrolyzing it into \(\alpha\)-ketobutyrate and ammonia, which may be furthermore a potential source of nitrogen for plants and microbes. This enzyme thus has a double positive effect for the plant: (1) reducing ethylene content responsible for the inhibition of root growth and (2) enhancing the plant nutrition [107; 119]. In the study of [PERSON] et al. [121], 25 of the 36 tested strains isolated from the rhizosphere of some halophytes had the ability to produce ACCD, and this activity varied among strains. The strains unfold diverse PGP features, and the inoculation of Canola under salt stress revealed that most of the strains could significantly enhance the root length and the total plant dry biomass.
# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 4 Experiment  
### Experimental Setup  
In this section, our proposed approach is compared to 11 state-of-the-art trackers on three challenging datasets: OTB-2013 [30], OTB-2015 [30] and Temple Color-128 [31]. These state-of-the-art trackers include the DCF-based trackers (i.e., SRDCF [17], ECOhc [18], Staple [15], DSST [13], KCF [12], CSR-DCF [39], SAMF [14], SAMF_CA [40]) and the trackers for long-term tracking (i.e., TLD [42], LCT [33]).  
The comparison experiments are performed in MATLAB 2018a on a PC with an Intel i5-8400 CPU at 2.80 GHz\(\times\)6. In addition, to compare the performance of these trackers, we employ precision and success rate as evaluation metrics.  
**Parameters**: In our experiments, the regularization parameter \(lambda\) is set to \(10^{-3}\) in Equation (2), the regularization parameters \(lambda_{1}\) and \(lambda_{2}\) are set to \(0.1\) and \(4\) in Equation (7). In Equation (9), \(\sigma\) and \(\gamma\) are set to \(0.2\) and \(10^{-32}\) respectively. For the reliability estimation, in Algorithm 1, \(\{\tau_{1},\sigma_{1}\}\) is set to \(\{0.6,0.8\}\), \(\{\tau_{2},\sigma_{2}\}\) is set to \(\{0.7,0.7\}\), \(\{\tau_{3},\sigma_{3}\}\) is set to \(\{0.7,0.9\}\).
## 5 Conclusions  
To improve the accuracy of feature extraction while reducing time-cost, this study investigates the structure of the CNN, the overfitting problem, and combines dropout and the SGD optimizer with the CNN. An improved CNN algorithm based on dropout and the SGD optimizer is proposed, and parallelization is achieved using TensorFlow. The use of SGD leads the MCNN-DS to work faster because it does not use the whole training instances in each integration and can dynamically adjust the estimation of the first- and second-order matrices of the gradient of each parameter according to the loss function, which can reduce the amount of calculations and improve the computing speed. Furthermore, by improving the activation function, the algorithm avoids the problem of the neuron node output becoming 0, improves recognition accuracy, and reduces time-cost. As the present test dataset is limited, future studies need to use additional test sets to improve the performance of the algorithm. Moreover, the algorithm should be combined with the service robot system [28] established by the research group to conduct application research and to develop and improve model robustness. In addition, reducing the number of neurons in the CNN can lead to the reduction of computations at the working and testing stages; however, the dropout algorithm used at the learning stage increases the time of processing. We focused on improving the recognition accuracy at the beginning of the research. The time consumption of both stages requires further investigation.  
This research was partially supported by the National Natural Science Foundation of China, under Grant 61640209 and 91746116, the Foundation for Distinguished Young Talents of Guizhou Province, under grant QKHRZ[2015]13, the Science and Technology Foundation of Guizhou Province under grant JZ[2014]2004, JZ[2014]2001, ZDZX[2013]6020, and LH[2016]7433, and the Basic Platform Program of Guizhou Province under grant QKHPTRC[2018]5702 and [2017]5788.  
[PERSON] conceived of and designed the study. [PERSON] made substantial contributions to the implementation of the algorithm and analysis of the data. The manuscript was written by [PERSON] with contributions from all the authors. All the authors read and approved the submitted manuscript, agreed to be listed, and accepted this version for publication.  
The authors declare no conflict of interest.
## 1 Introduction
### Motivations and Contributions
* The reduction of the number of nodes by using a single receiver and the ability to adjust the variables of the main system by the subsystem make the proposed system different from existing CRNs where low-power communication is adopted [11; 12]. * The channel capacity formula is derived for the scenarios of mutualism, idle, interference and silent BS-active HT and the average secrecy rate of the system is solved by the presented iterative algorithm. * The power reflection coefficient and the time parameters of the system were jointly optimized so that the symbiotic use of the communication channel increased the secrecy rate. * In the comparative simulation results, higher performance in terms of bit rate was achieved compared to the wireless communication models [53; 54] presented in the literature with only the energy-harvesting-then-bit-transmission protocol.
## 1 Introduction
[25; 26] to study the aeroelastic response of isotropic panels under different boundary conditions, or the flutter properties of functionally graded (FG) panels in supersonic airflow. Similarly, in Refs. [27; 28; 29], the authors studied the flutter behavior of damped composite plates [10], the lattice sandwich beams under a supersonic airflow [28], and the aerothermoelastic behavior of buckled cylindrical composite shells [29]. Alternatively, a cell-based smoothed finite element method was proposed by [PERSON] et al. [30; 31] to assess the flutter response of FG panels in the presence or absence of cracks, whereas a Galerkin method was applied by [PERSON] et al. [32] for the flutter study of laminated composite panels. The Galerkin decomposition technique was combined to the multiple time scale method (MTS) in Ref. [33], to solve numerically the nonlinear partial differential equation of the problem, whereas a p-version finite element method (p-FEM) was combined to the third-order shear deformation theory in Ref. [34] for the aeroelasticanalyses of laminated hybrid multiscale composite plates, under a supersonic airflow. Various applications of finite strain-based theories have been presented in Refs. [35; 36; 37; 38; 39; 40] to analyze the superharmonic and subharmonic resonances of composite beams exposed to thermal environments and subjected to moving mass loads. In parallel, more recent investigations have concentrated on the flutter instability phenomena in composite beams [41; 42] and plates [43; 44], utilizing approaches such as the Galerkin method and the differential quadrature method. These studies consistently incorporate geometric nonlinearities within their theoretical frameworks, as exemplified in Ref. [45], which explores the nonlinear axial-lateral vibration behavior of functionally graded, fiber-reinforced composite laminated cantilever beams under combined aerodynamic and thermal loading conditions. Starting with the main idea from this last reference, the present work aims at extending the study to the nonlinear aeroelastic oscillations of CNT-based cantilever beams, by employing a modified third-order piston theory and the von Karman geometrical nonlinearities, whose nonlinear governing equation is solved in discrete form, based on the Galerkin decomposition and MTS methods. A numerical investigation evaluates systematically the sensitivity of the dynamic response of the composite beam-like structure to the reinforcement patterns and volume fraction, but also to the free air velocity, Mach number, slenderness ratio, and thickness ratio. The structure of the paper is as follows: after this literature review, the theoretical framework of the problem is presented in Section 2, with the corresponding numerical formulation detailed in Section 3. Section 4 discusses the numerical results, and concluding remarks are provided in Section 5.
# Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot
## 4 Results and Implementation
### Data Analysis Results  
#### 4.1.1 Parts, Angle, and Gender Combination Effects  
First, we analyzed the collected data from three factors: parts (shoulder, elbow and hand), angle (left and right), and gender combination (same or different), as shown in Tables 1 and 2. The reason why we use a gender combination factor is that a past study might suggest that pre-touch reaction distance increases with the opposite gender, although they did not provide strict evidence [16]. We conducted a three-factor mixed ANOVA test and only identified a significant main effect in the part factor (F(2,60) = 13.905, \(p<0.001\), partial n2 = 0.317). Multiple comparisons with the Bonferroni method revealed a significant  
Figure 3: Part of touches’ hand positions when evaluator clicked a button to stop the hand. (**a**–**c**) show each body part data from front view and (**d**,**e**) show all body part data from both side views. Plotted data are relative to positions of 3D model’s shoulder, elbow, and hands for visualization. Thus, actual postures of participants are different.  
difference for the distance factors: shoulder > hand (\(p\) = 0.001) and elbow > hand (\(p\) = 0.001). There was no significance between shoulder and elbow (\(p\) = 0.591). Thus, prediction 1 was supported.  
Note that other factors did not show significant effects in all combinations, as shown in Table 3. Thus, in this data collection, only the body parts significantly affected the pre-touch reaction distance. In other words, the angle and the gender combinations between the touchers and the evaluators did not significantly affect the distance, similar to the past study [16].