esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Global Forces
The time histories and corresponding frequency contents are shown in Figure 12. Also, the animations created from fine and medium grid DDES revealed a frequency of approximately 160 Hz for the sheet-cavitation shedding. If we approximate the reference length of the Strouhal number as a quarter of the chord length, the Strouhal number would be \(St=fc/U_{\infty}=0.30\). If instead we take the reference velocity as \(U_{ref}=U_{\infty}\sqrt{1+\sigma}\), the Strouhal number would be \(St=0.20\). Compared to the case with the lower cavitation number, we now see more distinct peaks even up to the 4 th harmonics of the lift and drag. We note that the conducted simulation time was not enough with the fine grid for the case with \(\sigma=0.53\) to provide a sufficient time history to conduct a reliable Fourier analysis. The resolution on the coarse grid was not enough to obtain a reasonable frequency for the cavitation shedding with \(\sigma=1.27\). There was a small deviation between the mean lift coefficients of the left and right foils with the same cavitation number, while the mean drag coefficients were the same for both foils on each grid. We noted small differences also in the frequencies exhibited in the time histories. A decrease of approximately 40% of the mean lift and an increase of approximately 40% of the mean drag are seen in the results with \(\sigma=0.53\) compared to the results of \(\sigma=1.27\). This is because there is considerably more suction side sheet cavitation for the case with \(\sigma=0.53\), since the cavitation number is lower. The increase in the amount of the vapor phase on the suction side in turn decreased the lift and increased the drag of the foil. This difference was visible in the results using each of the three different grid densities. Figure 12: Time history and frequency content of the lift and drag coefficients. Fine grid, \(\sigma=1.27\), DDES.
## 3 Results and Discussion
### Spontaneous Evaporation Rate Dependency on Surface Temperature
The obtained temperature profiles are shown in Figure 8. As expected, the temperature profiles show that the liquid surface temperature in the vacuum simulations linearly increases from 88.31 K to 90 K when the thermostat region boundary position at the right-side interface changes from \(-2\) to \(+1\). Furthermore, Figure 8 demonstrates that the liquid surface temperature can be maintained at the desired temperature of 90 K only when the thermostat region boundary is placed beyond the liquid surface position, i.e., at position \(+1\). At this point, the heat is induced by the thermostat directly to the liquid surface to compensate for the heat sink effect as if the thermal energy was conducted from the liquid phase to the surface infinitely fast. The varying liquid surface temperature and the spontaneous evaporation rate with changing thermostat region boundary position provide a possibility to investigate the correlation between these two quantities. The results given in Figure 9a show that the spontaneous evaporation rate can be approximated as an increasing linear function of the liquid surface temperature in a given temperature range. Furthermore, the spontaneous evaporation rate decreases from 64.48 to 50.93 \(\mathrm{gcm^{-2}s^{-1}}\) as the liquid surface temperature drops from the targeted value of 90 K to 87.55 K. This example illustrates the sensitivity of the evaporation rate to the chosen temperature control approach and small changes of liquid surface temperature in virtual vacuum simulations. As a result, the evaporation coefficient value estimated from the MD simulations can be influenced by more than 21% with the relatively small deviations of liquid surface temperature from the targeted temperature, as illustrated in Figure 9b. Such small temperature deviations might have an even greater impact on the evaporation coefficient estimation at the higher temperatures as the evaporation rate is an exponential function of the temperature. Figure 8: (**a**) The temperature profiles from simulations with different thermostat region boundary positions at the right-side interface, and (**b**) the zoomed view of these profiles in the liquid region near the liquid surface. The black dotted lines denote the liquid and vapor boundary positions \(z^{*}=-0.9\) and \(z^{*}=3\) for both left- and right-side interfaces, while the blue dotted line denote the liquid surface position \(z^{*}=0\). For comparison, the temperature profile from the phase equilibria simulation is also plotted.
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
###### Abstract  
The paper explores the development and evaluation of algorithms for the positioning of ship hull cleaning robots, focusing on machine learning and sensor fusion techniques. The research employs Gradient Boosting, Kalman filters, and deep learning to enhance the accuracy of robot positioning. Gradient Boosting is used to predict displacement vectors and rotation angles, while the Kalman filter is applied to refine position estimates by integrating odometry and GPS data. Deep learning models are utilized to predict robot trajectories based on sensor inputs. Experiments conducted on the Rosario dataset and simulated environments demonstrate the effectiveness of these methods.  
ship hull cleaning; positioning; Kalman filter; odometry data; IMU data Article
# GPP of a Chinese Savanna Ecosystem during Different
###### Abstract  
Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China's savanna ecosystems constitute only a small part of the world's savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPP\({}_{\text{HLS-VPM}}\)) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPP\({}_{\text{MODIS-VPM}}\)) with GPP estimates from an eddy covariance (EC) flux tower (GPP\({}_{\text{EC}}\)) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MODI7A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.  
ed November 8, 2021, 16, 3475. [[https://doi.org/10.3390/rs16183475](https://doi.org/10.3390/rs16183475)]([https://doi.org/10.3390/rs16183475](https://doi.org/10.3390/rs16183475))
## 2 Materials and Methods
### Evaluation Methods
#### 2.5.2 Uncertainty Estimates  
Measurement errors are considered the primary source of uncertainty in the retrieved SIF signal. The uncertainty associated with the data-driven retrieval of SIF can be estimated using the following equation:  
\[\text{S}_{\text{e}}=\sigma_{\text{m}}^{2}(\text{J}^{\text{T}}\text{J})^{-1} \tag{7}\]  
where S\({}_{\text{e}}\) represents the inversion error covariance matrix with dimensions \((\text{n}_{\text{v}}+1)\times(\text{n}_{\text{v}}+1)\), \(\sigma_{\text{m}}^{2}\) denotes the measurement error, and J is a matrix composed of the first n\({}_{\text{v}}\) singular vectors and the state vector of Fs. Equation (7) effectively illustrates the propagation of measurement errors into the Fs.  
While a small portion of the uncertainty in our retrieval method is attributed to the introduction of atmospheric absorption bands rather than measurement errors, Equation (7) remains a robust tool for effectively estimating the majority of uncertainties in the retrieval results.
## 3 Optimal Feature Set Acquisition  
The Recursive Feature Elimination (RFE) method based on Random Forest (RF-RFE) employs a Random Forest as the estimator. It evaluates the importance of features using the random Forest model and selects the optimal feature subset by recursively eliminating those features deemed less important.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 7 Proof of Concept and Experimental Results
### Transmission of the Schedule
Due to the limited memory resources (only 4 MB), the nodes compute the entire schedule but they generate a list of transmissions (i.e., slots and SFs) only for their own id. The input transmission time, as well as the schedule computation time, are displayed in Table 5. The results show an up to 78% improvement in total transmission time in the case without duty cycle restrictions and an over 800% improvement when the duty cycle rules are followed. We also need to stress here that the energy consumption in processing mode is comparable to the receiving cost (i.e., \(\sim\)50 mA), so the nodes do not spend more energy compared to when the schedule was transmitted by the gateway.
## 3 Results and Discussion
### Interfaces and Morphology  
The SEM's analysis of the cross sections of the PMHM joints revealed the characteristic layered structure of 3D printed polymer-based materials where the air gaps between printed rasters are visible, as shown in Figure 5. The SEM results allowed for the estimation of the primer thicknesses present in the PMHM joints. The primer for the aluminium-ABS single lap-joint was applied manually and, therefore, was of an unknown thickness, while for aluminium-PA and aluminium-PA + CF single-lap joints, the primers were manufactured by FFF, and its thickness was set to be 0.1 mm in the software of the 3D printer. The SEM results indicated a thickness of 0.125 mm for the aluminium-ABS single lap joints, and validated the programmed 0.1 mm primer's thickness for the PMHM joints produced with PA and PA + CF. The values obtained for the thickness of the primer in the joints agrees with those obtained by [PERSON] et al. and their findings with regard to the influence of the thickness of the adhesive, on the extent of microcrack initiation, fracture propagation and, consequently, breaking stress [59]. In this work, the authors conclude that the optimal value of the adhesive thickness in order to achieve the maximum mechanical strength of the joint (a steel-steel single-lap joint glued with epoxy resin) was close to 0.1 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Surface Treatment**} \\ \cline{2-4}  & **None** & **SP** & **GB** \\ \hline Ra [\(\upmu\)m] & 0.16 \(\pm\) 0.04 & 0.32 \(\pm\) 0.05 & 1.65 \(\pm\) 0.46 \\ Rz [\(\upmu\)m] & 0.58 \(\pm\) 0.12 & 1.12 \(\pm\) 0.22 & 6.20 \(\pm\) 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of surface roughness of aluminium for the non-treated and treated surface conditions.  
Another SEM's analysis was conducted on the single-lap joints produced with PA + CF, with the objective of evaluating the possible infiltration of fibers on the surface of aluminium. However, the results from this analysis were inconclusive, as it was difficult to obtain a magnified SEM image of the aluminium-PA + CF interface.
## 4 Experiment and Analysis
### Analysis of Ablation Experiment  
Ablation contrast experiments are scientific research methods designed to help researchers understand the effects of different components of a target detection model on its overall performance. This means that by modifying certain components, settings, or the model's parameters, the contribution of these factors to the model's performance can be fully deduced from the findings of the experiment. In this section, the detection performance effects of ISW, TAHNet, and C2f modules when used alone and in different combinations are discussed in detail. This experiment was verified on the public FLIR dataset, and a series of comparative tests were conducted based on the framework of the YOLOv5n model.  
As illustrated in Table 3, when the three proposed modules act on the network at the same time, the model performance is the best. However, the interaction between modules is not all shared facilitation; for example, the recall of ISW+TAH modules combined in a stack is 0.7% lower than that of TAH modules alone. The reason for this phenomenon is that when two modules are used at the same time, if there is a lack of complementarity, involving overlapping information or trying to deal with conflicting information, information disharmony occurs, resulting in performance degradation. The optimal incremental order of different module combinations is given and the growth curve of its mAP is intuitively displayed in Figure 7.  
**Benchmark module + TAH:** First, select the detection header of the optimized network to increase the positioning ability of the model. When the improved TAH policy is applied to the network alone, the weight value is obtained via function mapping, and the new parameter number and complexity are not introduced. Compared to the benchmark model, the precision, recall, F1, and mAP of the improved detection head increase by 0.7%, 2.0%, 1.0%, and 1.1%, respectively, reducing the problem of missing detection targets.  
**Benchmark module + TAH + C2f:** Secondly, to strengthen the feature extraction capability of the backbone, the C2f strategy is proposed to introduce more abundant gradient information. On the basis of TAH, C2f is added to network training to further improve the model's detection effect. The test outcomes showed that the recall increased by 3.3%, mAP increased by 1.9%, and the generalization capacity of the network increased.  
**Benchmark module + TAH + C2f + ISW:** In the end, to develop the feature fusion power of the network for multiscale targets, the ISW module was developed to capture spatial semantic information more comprehensively. The recall, precision, F1, and mAP of the overall model increased by 4.4%, 1.3%, 3.0%, and 3.1%, respectively, indicating that the detecting performance of the network on easily blocked infrared objects was effectively improved. Therefore, the experiment verified that these three modules have good complementarity and can make up for the defects of different modules to improve the overall performance and achieve optimal performance.
## 4 Results and Discussion
### Limitations  
From 37 images in the second group, ten pictures were discarded due to systematic errors that the experimenter reported during the experiment. In addition, two pictures from the first group were discarded due to the extreme camera rotation (90 degrees) around the Z-axis. This skewed rotation changed the coordination system for two pictures, and therefore the results were not calculable. Furthermore, in this experiment, the indicator regarding the Z direction (positioning only) was decided to be off to increase the speed of the system. The initial tests showed that the average transformations in the Z direction were very similar to transformations in the X direction. Considering this point and due to technical limitations, the indicator related to Z was decided to be put in the \"off\" setting during the experiment.  
A tripod was used for the initial study to understand the relationship between the camera's orientation and the ratios. The tripod was equipped with leveling and protractor tools. It was better if we used a digital one.  
It could have been better if we used the original tablet sensors instead of the tag sensors to monitor the orientation. However, the main issue that we observed in both systems (tag and tablet sensors) needed recalibration. Any time that the orientation sensors were used, the yaw had a slight error. Therefore, we decided to use the tag that generated data for both position and orientation.  
Figure 13: Cartesian coordination system of scene.  
The UWB system is a promising method that can penetrate walls. However, this experiment was conducted inside an open area. Thus, the potential impact of barriers concrete walls, steel structures, and building infrastructure (e.g., stairs, furniture, machines, etc.) that could have block line-of-sight were not considered.  
The intrinsic parameters of the camera for both tasks were the same. For both tasks, the same camera with the same zooming level was used. The participants could not change the zooming level. For extrinsic calibration, manual approaches were used, as are explained in the text. Minor errors could have be included, but since the same methods were implemented for both groups of pictures (i.e., sensor-based and non-sensor-based), the results were unbiased.  
The scene included a flat image of 172 \(\times\) 120 cm instead of a 3D object. The reason for this simplicity was reducing errors in calculations. This 2D scene could be enough to evaluate the precision and accuracy of the two approaches (sensor-based versus non-sensor-based) in retrieving the position and orientation of the camera.
# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
###### Abstract  
In order to understand the regularity of human motion, characteristic description is widely used in gait analysis. For completely expressing gait information and providing more concise indicators, parametric description is also particularly significant as a means of analysis. Therefore, in this paper, the mathematical models of gait curves based on the generalized extension-Bezier curve were investigated, of which the shape parameters were used as individual gait characteristics to distinguish whether the gait is normal or not and to assist in judging rehabilitation. To evaluate the models, angle data from three joints (hip, knee, and ankle) were recorded with motion capture system when participants (10 healthy males and 6 male patients with ankle fracture) were walking at comfortable velocity along a walkway. Then, the shape parameters of each subject were obtained by applying the mathematical models, and the parameter range of the normal group was further summarized. Through comparison, it could be found that most shape parameters of patients exceed the normal ranges in varying degrees, and are concentrated on specific parameters. The results can not only help to judge the recovery stages of patients but also figure out the corresponding abnormal postures, so as to provide guidance for rehabilitation training.  
Keywords: gait analysis; mathematical model; shape parameter; rehabilitation; walking posture +
Footnote †: journal: Applied Sciences
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 2 Methodologies
### State-Space Representation (SSR) Model  
The SSR model introduces an unobserved latent random variable \(x\), which is called state, and uses the state to explain the observed value \(y\). By using the SSR model, non-stationary data can be handled. The SSR model is a very flexible statistical model [34; 35; 36; 37; 38; 39; 40] that can create a combination of various statistical models without major restrictions. In this model, \"iter\" specifies the number of iterations in the given data, whereas \"chains\"  
Figure 1: Flowchart of the autoregressive integrated moving average (ARIMA) model [41].  
specifies the number of times the \"iter\" simulation is performed. The SSR model contains two variables, namely, the state and the observed value. When the time-series data are stationary, the value \(y_{t}\) observed at time \(t\) is generated from the state \(x_{t}\). The state \(x_{t}\) is then determined only by the state \(x_{t-1}\). The values can be expressed by Equations (5) and (6). Equations (3) and (4) are known as the state equation and the observation equation, respectively [39, 40].  
\[x_{t}=x_{t-1}+\varepsilon_{t-1} \tag{5}\]  
\[y_{t}=x_{t}+\delta_{t} \tag{6}\]  
where \(x_{t}\) is the state value at any time \(t\), \(y_{t}\) is the observed value at any time \(t\), \(\varepsilon_{t-1}\) is the process error at time \(t-1\), and \(\delta_{t}\) is the observation error in the state.  
Figure 2 presents the schematic representation of the SSR model. If the state is stationary, then the difference between the state at time \(t\) and the state at time \(t-1\) is expected to follow a normal distribution with a zero mean and a standard deviation of \(\sigma_{e}\). By repeating the same process, the reliability of the results can be checked. In this model, \"chains\" is specified four times and \"iter\" is predicted 2000 times.
# An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks
## 3 Intersection Signal Control Mechanism
### Fixed Phase and Period Signal Timing Improvement Scheme  
The traditional signal timing model adopts the decision mechanism of dynamically allotting an appropriate time within the fixed period to the signal phase of each traffic flow according to the conditions of the vehicles in the intersection area in the present. By incorporating the information of far vehicles, in this section, the fixed phase and period signal timing improvement scheme is proposed based on the waiting time of each traffic flow that we have discussed above.  
For the intersection in Figure 3, based on the common traditional classification, there are four phases, i.e., P1 (D1&D5), P2 (D2&D6), P3 (D3&D7), and P4 (D4&D8). As such, we can calculate the waiting time of each phase using Equation (10):  
\[\left\{\begin{array}{l}WT_{P1}=WT_{1}+WT_{5}\\ WT_{P2}=WT_{2}+WT_{6}\\ WT_{P3}=WT_{3}+WT_{7}\\ WT_{P4}=WT_{4}+WT_{8}\end{array}\right., \tag{10}\]  
and the total waiting time for all phases can be calculated using:  
\[WT_{s}=\sum_{i=1}^{8}WT_{i}. \tag{11}\]  
Then, the green light time \(T_{g-p_{i}}\) can be allotted to each signal phase according to the proportion of the waiting time of each phase using:  
\[T_{g-p_{i}}=\frac{WT_{P_{i}}}{WT_{s}}\times\Big{(}T_{p}-T_{y}\times 4\Big{)}, \tag{12}\]  
where \(T_{p}\) is the length of a complete signal period and \(T_{y}\) is the length of a yellow light period.