esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impacts of Climate and Human Activities on Grassland Productivity Variation in China
## 2 Materials and Methods
### Data  
#### 2.2.1 Remote Sensing Data  
The GPP data in this study were provided by the Global Land Surface Satellite (GLASS) dataset, which was downloaded from the National Center for Earth System Science Data (data source: [[http://www.geodata.cn/index.html](http://www.geodata.cn/index.html)]([http://www.geodata.cn/index.html](http://www.geodata.cn/index.html)), accessed on 8 October 2022) [37]. The GLASS dataset has high accuracy and combines multi-source ground observation data with remote sensing data [38]. This dataset is a global-scale remote sensing data product with the longest time series, of which the temporal resolution is 1982-2018 and the spatial resolution is 0.05\({}^{\circ}\)[37]. In addition, the GLASS GPP product was calculated based on the  
Figure 1: Land use types (**a**) and main grassland areas (**b**) in the study area.  
Eddy Covariance-Light Use Efficiency model and has better accuracy than the moderate resolution imaging spectroradiometer (MODIS) GPP product [39; 40]. The GIMMS dataset widely serves for vegetation dynamic monitoring and climate change studies and land surface models driven at global, continental, and regional scales.  
The TEM, PRE, and RAD were downloaded from the China Meteorological Forcing Dataset (CMFD) [41], which is a regional high spatial and temporal resolution surface meteorological dataset. This CMFD dataset is a set of gridded near-surface meteorological datasets, assimilated remote sensing products, and meteorological station observation datasets. In addition, this dataset spans nearly 40 years, ranging from 1979 to 2018, with a spatial resolution of 0.1\({}^{\circ}\), and it has seven climate variables, including 2 m air temperature, precipitation, shortwave and longwave downward radiation, and so on. Based on the above advantages, the CMFD dataset has been widely used in hydrological models, climate models, and data assimilation. The land use dataset was obtained from the CLCD dataset, which has a long temporal period (from 1985 to 2020), a high spatial resolution (30 m), and a high accuracy compared to common land use datasets, such as the moderate resolution imaging spectroradiometer (MODIS) land cover product (MCD12Q1) and global land cover datasets at 30-meters resolution (GlobeLand30), [42]. To unify the spatial resolution of multi-source data, the climate factor data and land use data were resampled to 0.05\({}^{\circ}\), which was consistent with the GPP data, using the \"bilinear\" and \"majority\" interpolation methods, respectively.
## 1 Introduction
Thirdly, the parameter estimation method should be weakly dependent on initial values. Finally, the computation efficiency and convergence rate of the parameter estimation method must be sufficiently fast to adapt to the demands of RTHS. However, very few adaptive delay compensation strategies satisfy the properties as mention above. Conversely, the Kalman filter is found to exhibit promising performance for applications in RTHS, and can satisfy these requirements. In this study, an adaptive delay compensation method based on the Kalman filter (KF) is proposed, where the compensated commands of actuators are generated using the desired displacements and previous displacement commands. The remainder of this paper is organized as follows. Section 2 briefly describes the Benchmark problem in RTHS. Then, the compensation method proposed in this study, namely the KF-based adaptive delay compensation strategy, is explained in detail in Section 3. The performance of KF in RTHS, including the proof of convergence and influence of initial values, are presented in Section 4, while Section 5 provides the results of virtual RTHS using the Benchmark model. The results reveal that, the estimated parameters can converge to the true values by the KF, and there is a little fluctuation of the estimated parameters. Finally, the proposed method is compared with other delay compensation strategies in Section 6.
## 5 Limits and Future Directions  
No meta-analysis that considered the different outcomes used, types of robotic protocols, or included patients with different arm impairments concerning severity or predominant motor pattern (weakness, incoordination) was performed on the five RCTs that were included in this evidence-based review. Moreover, due to the heterogeneity of treatment protocols in terms of duration and frequency, it is currently not possible to demonstrate how many weekly sessions or which therapy duration could lead to the best rehabilitative results. In addition, all of the studies analyzed the use of end-effector robots and did not consider the use of robotic exoskeletons, which are still not widespread in clinical practice. The reported studies also did not report on the cost-benefit ration or the evidence of efficiency. More RCTs with larger sample sizes and follow-up evaluations are required. Finally, MS-specific characteristics should be addressed more deeply in future studies. As previously reported, arm impairment is usually bilateral and usually represents different motor deficits (weakness/incoordination, learned non-use). Future studies would be more targeted on a specific impairment, and it is likely that a bilateral robotic arm approach would be helpful in this population. Moreover, even if severely disabled patients are more likely to be candidates for arm rehabilitation, functional reorganization processes could be limited by accumulating structural CNS damage [41]. It would also be essential to implement specific protocols for PwMS in terms of duration, intensity, dose, and cognitive-motor dysfunction according to the stage and type of the disease.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 5 Experiment
### Evaluation Metrics  
As shown in Table 1, we report the basic prediction parameters and evaluation metrics of this study to evaluate the experimental results. In the case where the prediction model generates more than \(K\) trajectories, we consider only the predictions with the top \(K\) probability scores. \(\mathbf{minADE_{k}}\) measures the displacement between the ground-truth future trajectory and the closest predicted trajectory (of the \(K\) joint predictions), and takes its mean squared average over the future time horizon. The formula for ADE is as follows.  
\[\text{ADE}=\tfrac{1}{K}\sum_{i=1}^{K}\left(\tfrac{1}{T_{p}}\sum_{t=T+1}^{T+T_{ p}}\sqrt{\left(\hat{y}-y_{t}^{\text{gt}}\right)^{2}}\right) \tag{15}\]  
where \(K\) is the number of predicted trajectories, \(T_{p}\) is the time of future trajectory prediction, and \(T\) is the observation time of the whole segment of true trajectory.  
The \(minFDE_{k}\) measures the minimum trajectory displacement error at the end point in the time range, which is used to evaluate the accuracy of the predicted trajectory at the last moment in the prediction range. Its formula is  
\[\text{FDE}=\tfrac{1}{K}\sum_{i=1}^{K}\sqrt{\left(\hat{y}_{\left(T+T_{p}\right) }-y_{\left(T+T_{p}\right)}^{\text{gt}}\right)^{2}} \tag{16}\]  
Finally mAP measures the average accuracy based on the type of vehicle movement, such as a friendly turn at an intersection or going straight. Our model achieves state of the art results, as shown in Table 2. Although our model and MID use the same backbone, our method performs better on all metrics due to the strength of the residual diffusion model.  
\begin{table}
\begin{tabular}{l|c c c c c} \hline \hline
**Method** & \(\mathbf{minADE_{1}}\) & \(\mathbf{minADE_{5}}\) & \(\mathbf{minFDE_{1}}\) & \(\mathbf{minFDE_{5}}\) & \(\mathbf{mAP}\) \\ \hline Social Gan [4] & 2.028 & 1.12 & 4.12 & 2.201 & 0.06 \\ CS-LSTM [3] & 2.00 & 1.04 & 4.03 & 2.14 & 0.07 \\ STA LSTM [31] & 1.93 & 0.99 & 3.85 & 2.02 & 0.08 \\ Trajecton++ [32] & 1.724 & 0.81 & 3.74 & 1.98 & 0.12 \\ VectorNet [33] & 1.976 & 0.89 & 3.96 & 1.698 & 0.14 \\ LaneGCN [34] & 1.69 & 0.80 & 3.93 & 1.89 & 0.10 \\ GANet [24] & 1.58 & 0.75 & 3.84 & 1.69 & 0.14 \\ MultiPath++ [35] & 1.55 & 0.72 & 3.65 & 1.76 & 0.18 \\ DenseTNT [36] & 1.66 & 0.84 & 3.80 & 1.78 & 0.16 \\ JFP [37] & 1.45 & 0.60 & 3.47 & 1.63 & 0.22 \\ MID [16] & 1.584 & 0.63 & 3.52 & 1.74 & 0.19 \\ mmTransformer [38] & 1.50 & 0.58 & 3.31 & 1.63 & 0.19 \\ Ours & 1.438 & 0.56 & 3.22 & 1.59 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trajectory prediction results based on NGSIM US-101 dataset.
## 7 Conclusions  
The research question focused on designing a concept and system architecture for enabling real-time uninterrupted streaming data transfer from moving vehicles towards a control center. The presented concept (Section 2) enables continuous ML-based route optimization in emulated and real-world underground mining environments. The system architecture was designed from several viewpoints (Section 3) based on the conceptual view for emulated environments. Additionally, the architecture was realized in research artifacts related to the continuous ML pipeline and emulation environment. The artifacts enabled streaming data transfer from moving vehicles towards the control center in an emulated environment. Additionally, behavior of the moving vehicles can be demonstrated with a 3D visualization. Finally, the artifacts were evaluated based on DSRM research criteria (feasibility, operationality, effectiveness, and fidelity with a real-world environment). Several lessons learnt were presented regarding operationality/feasibility. The ML-based model (based on RF regressor) enabled \(\sim\)5-13% lower OWD in streaming data transfers, when compared to a simpler heuristic model (effectiveness). Comparison to earlier literature indicated the novelty of the proposed architecture design for emulating data transfers of moving vehicles within an underground mining environment when routing decisions are facilitated within a continuous ML architecture.  
Conceptualization, P.P., D.P. and K.S.; methodology, D.P. and P.P.; software, P.P., J.P., K.S. and K.A.; validation, P.P. and J.P.; formal analysis, P.P.; writing--original draft preparation, J.B.; writing--review and editing, J.B. and P.P.; visualization, K.S.; supervision, D.P.; project administration, P.P.; funding acquisition, P.P., K.S. and D.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by VTT Technical Research Centre of Finland Ltd.  
Not applicable.  
Authors acknowledges support from [PERSON] and [PERSON], VTT Technical Research Centre of Finland Ltd.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyzes, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Distance Vector-Hop (DV-Hop) and Differential Evolution (DE) Algorithms  
In this section, the process of the DV-Hop and DE algorithm is briefly introduced.
## 2 Materials and Methods
### Comet Assay  
The protective effect of azelaic acid against DNA damage was assessed using the comet assay. The fibroblast cells were cultivated according to protocols described in the previous paragraph. The experimental design to evidence the protective effects of azelaic acid against DNA damage induced by hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) consisted of the following steps:  
1. Cells treatment. The fibroblasts were incubated with 75 \(\mu\)M H\({}_{2}\)O\({}_{2}\) for 10 min to induce DNA damage, then a part of cells was washed with HEPES preheated to 37 \({}^{\circ}\)C and treated with 10% azelaic acid, and the cells were maintained in incubator (5% CO\({}_{2}\), 37 \({}^{\circ}\)C) for 60 min (H\({}_{2}\)O\({}_{2}\) + AZE sample). Other parts of cells after treatment with 75 \(\mu\)M H\({}_{2}\)O\({}_{2}\) were used immediately for comet assay and represents the positive control (CTRL +). The negative control (CTRL -) consists of the cells treated with PBS (solvent where azelaic acid was dissolved).
2. Comet assay (neutral single cell gel electrophoresis) was performed according to [PERSON] et al. (2018). Briefly, the fibroblasts (1.5-2 \(\times\)10\({}^{5}\) cells) were embedded into 0.7% low-melting-point agarose gel (Thermo Scientific, Waltham, MA, USA) and deposited on microscopic glass slide pre-covered with 1% normal-melting-point agarose, covered with other glass slide and left for 5 min on ice. Then, the slides were immersed in lysis buffer (2.5 M NaCl, 100 mM EDTA, 10 mM Tris-HCl, 1% N-lauroilsarcozine, pH 9.5) with freshly added 0.5% Triton X-100 and 10% DMSO (dimethyl sulfoxide) for 1 h at 4 \({}^{\circ}\)C. Subsequently, the slides were washed three times for 5 min/time with electrophoresis buffer (300 mM sodium acetate, 100 mM Tris-HCl, pH 8.3) and then subjected to horizontal electrophoresis (14 V, 0.5 V/cm, 11-12 mA) for 1 h, in the dark at 4 \({}^{\circ}\)C. The slides were then washed three times with cold distilled water (neutralization step) and stained with 2 mg/mL ethidium bromide. Cells were visualized using Bio Systems fluorescent microscope equipped with a 546 nm excitation filter and a 590 nm emission filter and images were photographed and processed with Comet Score software (Comet Score 2.0.0.38; TriTek Corp., Sumerdock, VA, USA).
## 4 Results and Discussion
### Feature Importance and Effects  
To analyze the meaningfulness of rules identified by the classifier, we accomplished an evaluation of the feature importance and the effects of the features on the matching probability. In general, the rules identified by the classifier aligned with our previous expectations presented in Section 3.6.3. The results of all features can be found in the supplement of the article.  
The high feature importance of the 3D displacement and the vertical displacement go along with our prior expectations. Figure 5(a) illustrates that the highest probabilities were achieved for vertical displacements below 5 m.  
Since the distances between the closest tree pairs indicate the agreement of the point patterns, these form the most important group of features. Figure 5(b) shows that, as expected, low distances to neighbored trees result in high matching probabilities. The ascending importance from the first pair to  
Figure 6: Effect of (**a**) the vertical displacement of the plot center; (**b**) the distance between the first point pair; (**c**) the correlation coefficient for tree height; and (**d**) the NND for surveyed trees on the matching probability (using just one simulation for each plot). The numbers in brackets correspond to the number of tree pairs. The whiskers extend to ten times the interquartile range. Outliers are marked by circles.  
the fourth pair and the descending importance from the fourth to the fifteenth pair (Figure 7) can be explained by the influence of the number of linked trees. The co-registration probability increases with an increasing number of linked trees, because the certainty that the point patterns match increases with an increasing number of (probably correct) linked trees.  
The highest matching probabilities are achieved if a positive correlation between the survey tree heights and ALS tree heights occurs (Figure 6c), which goes along with our previous expectations and the heuristic used by [PERSON] and [PERSON] (2017), [PERSON] et al. (2014) or [PERSON] et al. (2010). Since the NND is an indicator of the tree separability, the matching probability slightly increases with increasing NNDs (Figure 6d).
## 4 Trends and Challenges
### Challenges  
Although spaceborne InSAR theories and techniques have experienced nearly 60 years of development and have broken through many technical bottlenecks, they still face many technical difficulties and challenges. These are reflected in the following aspects.  
1. Multi-platform synchronization and multi-baseline keeping.  
The multidimensional InSAR system consists of a radar transmitter and multiple space-separated radar receivers. This system has the characteristics of flexible observation baseline configuration and diverse observation angles, but also faces many technical challenges,including how to achieve high-precision synchronization between multiple platforms, how to achieve the accurate positioning of multi-phase centers, and how to achieve multi-baseline measurement and maintenance. In recent years, synchronization has become a key issue that needs to be urgently addressed in distributed spaceborne SAR systems. By analyzing the time synchronization error and the beam synchronization error, guidance can be provided for system design [126]. In order to improve the phase synchronization accuracy of LT-1 in the bistatic interferometry mode, a robust phase error estimation and compensation method was proposed, which utilized the Kalman filter to enhance the synchronization accuracy [127].  
Storage, transmission, and on-board processing of massive amounts of data.  
While the InSAR system has many advantages, it generates a large amount of remote sensing data. In particular, the formation of an SAR system such as PIESAT-1 will generate an unprecedented amount of data and face huge challenges in storage, transmission, and processing. Efficient data compression, fast data transmission, and on-board real-time processing have become urgent problems to be solved in the industry. The on-board storage of InSAR data relies on the spaceborne solid-state storage system, which will greatly increase the cost of satellite development while increasing the storage capacity. InSAR data transmission is limited by the power and bandwidth of the on-board data transmission antenna and the design of the ground receiving station. The real-time transmission and storage of large volumes of data is still challenging. One example is to exploit the inherent parallelism of field-programmable gate arrays (FPGAs) [128]. Furthermore, the integration of artificial intelligence and machine learning techniques can be used to enhance data analysis and decision-making capabilities [129; 130].
## 6 Conclusions  
In this work, a strategy has been developed for physical damage properties (position, severity and size) identification based on bending stiffness reduction. The procedure makes use of natural frequencies only and of FE models of the structure. It is applied on a cantilever beam but nothing prevents its use with more complex structures. In this context, the minimization of the Frequency Shift Coefficient is used in two ways. First, we are able to build a damage library by correlating 2D and 3D model of damaged beam. The library was built for a rectangular damage and links depth and width in the 3D model with a bending stiffness reduction in the 2D model. The library does not depend on the beam position. Second, a simple model of the damaged beam is updated in order to identify position and severity of the damage. The damage library is then used to obtain more information about the geometry of the damage. The strategy has been tested numerically by recovering information about numerical damaged beam modeled in 2D or 3D. Single and double damage have been investigated. The lowest severity quantified in this numerical study was 2%. The strategy has also been successfully tested in an experimental cantilever beam embedding a saw-cut damage. In the future, this methodology will be tested with other types of structure, structural behavior and damage geometries (hole, oblique or triangular cut) in order to validate its efficiency.  
Conceptualization and methodology, A.D., V.D. and R.S.; validation, numerical analysis, investigation, A.D., V.D. and R.S.; resources R.S.; writing--original draft preparation, A.D.; writing--review and editing, A.D., V.D. and R.S.; visualization and supervision, V.D. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Region Centre-Val de Loire with the Grant Number: Doctoral grant 2017-2020. The APC was funded by the INSA Centre Val de Loire and the Laboratoire de Mecanique Gabriel Lame E.A. 7494.  
The authors are thankful to the INSA Centre Val de Loire through a doctoral contract and for all means provided.  
The authors declare no conflict of interest.  
Figure 17: Experimentally tested rectangular geometry case where FSC is a function of position and severity (**a**) the white circle indicates 34.93% estimated damage severity at position 0.72 m; (**b**) width 1.3 mm and depth 1.62 mm for 34.93% severity extracted from the damage library (blue dots: raw extraction, red line: cubic fit, black cross: recovered parameters).
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Node-Level-Focused Energy Conservation Schemes
#### 4.1.1 Power Management Techniques
Detailed information about these techniques is found in [61; 63; 86]  
Power management techniques, such as sleep/wake-up strategies and energy-efficient MAC protocols, are effective in LWSNs due to the minimal overhead associated with the node synchronization imposed by the linear topology. However, these techniques have a limited impact on reducing the number of multi-hop communications. In contrast, radio optimization techniques are more effective in LWSNs because the linear topology favors the use of directional antennas (beamforming in the \(0^{\circ}\) and \(180^{\circ}\) directions), which increase antenna gain without raising power consumption. This is particularly advantageous in LWSNs, especially when coverage needs to be extended due to the failure of a direct neighbor along the linear path. Traditional WSNs, on the other hand, typically require omnidirectional antennas, which consume more energy in long-range communications compared to directional antennas. Processor power management schemes are less effective in LWSN applications, where the processing load of the sensor nodes remains constant, and minimal processing is performed at the sensor node level.
## 3 Experimental Results and Analysis  
The researchers conducted many experiments to prove the validity, robustness, effectiveness, efficiency and high performance of the proposed approach. Matlab 2018 software was used to do perform the experiments on an Intel (R) Core (TM) i7 CPU @ 2.30 GHz processor, 8.00 GB RAM laptop. The employed image forgery dataset, performance metrics and results are elaborated in the following subsections.