esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics
###### Abstract  
On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle's movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.  
vehicle parameter estimation; variational bayes; probabilistic robotics Article
## 3 Results and Discussion
### Fundamental Analysis of the SC Ni-Mn-Ga Alloys
#### 3.1.3 Crystal Direction Identification  
The SC Ni-Mn-Ga ingot, which was fabricated through an FZ method, is shown in Figure S3a in the Supplementary Materials. The disks, which were sliced down from the edge of the ingot in the floating zone, were revealed in OM images as shown in Figure S3b,c in the Supplementary Materials. In the OM images (Figure S3b,c), no obvious grain boundaries can be discerned, indicating SC Ni-Mn-Ga disks. In Figure S3b, no apparent martensite variants were found; on the other hand, in Figure S3c, some typical martensite variants were observed. This can be attributed to the slight inhomogeneous distribution of the chemical composition in the ingot, which was fabricated through an FZ method. This is a well-known phenomenon when specimens are synthesized through an FZ technique due to some evaporation of specific elements during the high-temperature process of the FZ method [39]. Therefore, this explains the different apparent phases while the disks were sliced down from different positions of the FZ-made SC Ni-Mn-Ga ingot. It is necessary to mention that the specimens for the examinations, such as the compression tests and \(M\)-\(H\) curves, were confirmed to be a single 5M martensite phase before the examinations were conducted.  
With regard to the determination of crystal directions, the disks shown in Figure S3b,c were measured by using an X-ray diffractometer. Both specimens possess the same crystal directions; therefore, again, it is reconfirmed that the alloy fabricated through the FZ technique is an SC. The pole figures of (004)\({}_{\rm m}\) and (202)\({}_{\rm m}\) are shown in Figure 3a,b, respectively. Based on the pole figures, as illustrated in Figure 3c, the SC [100]\({}_{\rm p}\) Ni-Mn-Ga cubes with certain dimensions were cut out of the disks by using an EDM. Please note that the determination of the [100]\({}_{\rm p}\) was based on the lattice correspondence of the 5M martensite phase and the parent phase. Here, a subscript \"m\" is used to indicate the martensite phase, while a subscript \"p\" is used to indicate the parent phase.  
Figure 2: The X-ray diffraction pattern of SC Ni-Mn-Ga particles at RT. A single 5M martensite phase is confirmed. (The triangle symbols indicate the 5M martensite phase).  
The as-cut SC Ni-Mn-Ga cubes were subjected to crystallographic direction identification again for the re-confirmation of their crystallographic directions, as shown in Figure 4. The (004)\({}_{\rm m}\) and (202)\({}_{\rm m}\) pole figures in three different planes, such as the (a) \(x\)-plane, (b) \(y\)-plane, and (c) \(z\)-plane, are shown in Figure 4, respectively. The digital images of the cubic specimens and the corresponding illustration, which indicate the specific planes measured, are shown in Figure 4d. It is observed that all the planes can be identified as the near-[100]\({}_{\rm p}\) plane.
## 6 Result and Discussion
### Cumulative Damage Index (CDI)  
Figure 17 demonstrates the increase in cumulative damage index (CDI) for the selected BRBs under the application of five sets of multi-hazard scenarios. As indicated in Tables 1 and 7, the estimated CDI for BRB#1-5 with an average strain amplitude of 0.05, 0.03, 0.025, 0.02, and 0.01% is about 0.64, 0.1, 0.03, 0.015, and 0.005, respectively, which  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Selected Damper**} & \multirow{2}{*}{**MH Scenarios**} & \multirow{2}{*}{**CDI**} & \multicolumn{2}{c}{**Strain Amplitude (\%)**} & \multirow{2}{*}{**Cycle**} & \multirow{2}{*}{**Energy**} \\ \cline{5-6}  & & & **Ave.** & & **Max** & & **No.** & **kN-m** \\ \hline \multirow{5}{*}{BRB\#4} & Set 1 & 0.015 & 0.019 & 0.654 & 129,787 & 385 \\  & Set 2 & 0.014 & 0.018 & 0.708 & 140,681 & 307 \\  & Set 3 & 0.015 & 0.018 & 0.664 & 134,786 & 365 \\  & Set 4 & 0.011 & 0.016 & 0.582 & 137,618 & 321 \\  & Set 5 & 0.011 & 0.016 & 0.698 & 126,165 & 234 \\ \hline \multirow{5}{*}{BRB\#5} & Set 1 & 0.005 & 0.009 & 1.210 & 142,085 & 99 \\  & Set 2 & 0.004 & 0.008 & 0.339 & 152,142 & 94 \\ \cline{1-1}  & Set 3 & 0.005 & 0.008 & 0.406 & 148,793 & 88 \\ \cline{1-1}  & Set 4 & 0.004 & 0.008 & 0.367 & 151,671 & 83 \\ \cline{1-1}  & Set 5 & 0.004 & 0.007 & 0.422 & 135,930 & 69 \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(Cont.\)  
Figure 16: Maximum story drift ratio and shear force under multi-hazard scenarios.  
indicates that CDI is decreasing in upper stories. Accordingly, none of the selected BRBs are reaching the failure status (CDI = 1.0) according to Miner's rule.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
They constructed a system of equations for 30-day time-series of the CYGNSS measurements, which is indeed an under-determined system with 29 equations. Hence, they incorporated the SMAP maximum and minimum SM values into the algorithm for bounding the system. They also assumed that changes in vegetation and surface roughness occur much slower compared to changes in SM. This paper proposes a physics-aware machine learning approach through capturing the nonlinear dependencies of the CYGNSS observables to SM values and several bio/geophysical parameters that represent vegetation and ground effects. An Artificial Neural Network (ANN) is employed to learn the complex nonlinear relations. The term \"physics-aware\" in this manuscript refers to the use of several ancillary data sets and International Soil Moisture Network (ISMN) measurements to represent the vegetation and ground dynamics in the learning process. The details of the data usage will be explained in the next section. Daily SM measurements are used both in training of the model and validation of the SM predictions. One of the main objectives of this study is to initiate a novel, practically applicable SM retrieval algorithm that can provide sub-daily SM products within a few kilometers by utilizing the individual CYGNSS observations as the algorithm inputs. Once trained on a reference data set, this method neither requires SM information from other satellite missions, nor operates on spatial or temporal averaging of the CYGNSS observations. SM retrieval performances for multiple ISMN sites are visually and quantitatively demonstrated. The rest of the paper is organized as follows: Section 2 describes the theoretical background of the GNSS-R based SM retrieval, possible use of CYGNSS data products, and the related challenges, followed by Section 3 with the explanation of the SM retrieval methodology, details of the ANN model, acquisition of the data sets, as well as training and validation. Section 4 provides the SM estimation results along with the visualizations and statistical performance metrics achieved. Section 5 gives a comprehensive discussion of the findings and points to be improved in the future. Section 6 concludes the study.
## 2 Study Area and Data Sources
### Data
#### 2.2.5 Forest Area Data  
The forest area data for the study region is derived from the 2013 land cover dataset of China, sourced from the China 30 m annual land cover dataset and its dynamic changes from 1985 to 2022 [38]. This dataset is provided by the National Glacier, Snow, and DesertData Center ([[http://www.ncdc.ac.cn](http://www.ncdc.ac.cn)]([http://www.ncdc.ac.cn](http://www.ncdc.ac.cn)) (accessed on 16 July 2024))) and has a spatial resolution of 30 m.
## 1 Introduction
Thus, the following molecular DNA markers: inter-simple sequence repeat (ISSR), amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), microsatellites and single nucleotide polymorphisms (SNP) have been developed and used to assess the relationships and levels of genetic diversity in wheat germplasm [32]. ISSR (Inter-simple sequence repeat) is a molecular biology technique discovered in 1994 by [PERSON] et al., which aims at DNA fingerprinting, by amplifying, using the PCR technique, repetitive regions within the nuclear and organelle genomes (chloroplasts and mitochondria) [33]. ISSR primers have a dominant character, but occasionally, they show codominance [34]. Interest in using ISSR primers is caused by the reproducibility of the results and the low costs of development and usage [35]. In this study, we evaluated the genetic diversity by utilizing ISSR markers (Inter-Simple Sequence Repeats) of 31 wheat varieties cultivated in Romania, with different origins and kept in the active collection of the \"[PERSON]\" Suceava Plant Genetic Resources Bank. The vast majority of common wheat varieties, taken in this study, of Romanian origin, such as Izvor, Glosa, Andrada and Dumbrava, or foreign ones, such as Sosthene, Amicus, Apache and Anapurna, were listed in the Romanian Official Variety Catalog, published in 2021. The varieties listed in this catalog are admitted for certification and commercialization based on tests of distinction, uniformity and stability for agronomic value and use. These wheat varieties are representative for Romania, and in particular, for the intensive wheat cultures in the northeast region, which resulted from applying breeding programs that aimed to develop wheat with certain characteristics such as higher yield, superior quality for bread-making, and resistance to biotic and abiotic stress. Additionally, the use of ancient wheat species from the gene bank collection, such as _Tritcium monocytogenes_ and _Tritcium spelta_, highlights important aspects regarding ancient wheat species. To our knowledge, no other study has been reported so far on the genetic diversity of wheat varieties cultivated in Romania, one of the most important wheat producers in Europe. Moreover, the physico-chemical characteristics of wheat samples of different species (_Tritcium aestivum L._, _Tritcium monocytogenes_ L._, _Tritcium spelta_ L.) cultivated in Romania have been determined in order to allow us to compare them by using multivariate principal component analysis (PCA) and to analyze the differences between wheat species. The physicochemical characteristics analyzed in this study, namely moisture, ash, protein, wet gluten, lipid, starch, falling number, and damaged starch, are the most relevant ones for the bread-making industry. This allows us to recommend the best wheat species and varieties that may be used in the future by farmers in order to obtain raw materials of a high quality for bread-making.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 1 Introduction
Subsequently, these trust values are adjusted through various interrelationships, enhancing their credibility and safety. Moreover, the proposed scheme further combines the interrelationships and interactions between vehicle nodes to augment the accuracy and robustness of trust assessments. The main contributions of this paper are as follows:  
1. Proposing a method for the dynamic segmentation of behavior-homogeneous clusters. 2. Investigating the weights of interrelationships among vehicles within a cluster. 3. Introducing a method for identifying misbehavior nodes in complex networks within dynamic behavior-homogeneous clusters based on a hybrid trust model. The organization of the rest of the paper is as follows: Section 2 introduces the system model, laying the foundation for the proposed framework. Section 3 explains the segmentation of dynamic behavior-homogeneous clusters. Section 4 focuses on mining complex relationship weights within dynamic vehicle clusters using a hypergraph model. Section 5 presents the computation of hybrid trust values based on weighted vehicle behavior and interrelationship weights. Section 6 details a collaborative node-centric approach for misbehavior detection using hybrid trust values. Section 7 provides a comprehensive performance analysis of the proposed methods. Finally, the conclusion discusses future research directions.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 6 Conclusions  
In this paper, we discuss a multi-UAV-assisted D2D communication model incorporating NOMA to address the channel allocation, trajectory planning, and power control problems, respectively, and design a DH-MDQN algorithm, in which the channel allocation problem is solved by a dynamic hypergraph and the MDQN algorithm is utilized to solve the trajectory planning and power control problems. Our simulation evaluates the performance of the proposed algorithm through numerical results in terms of the number of D2D clusters, D2D cluster communication spacing, UAV size, trajectory planning, and NOMA decoding order in multiple dimensions. These results also demonstrate the superiority of the UAV-assisted D2D-NOMA framework, and the proposed DH-MDQN algorithm is able to achieve higher system throughput compared to other benchmark algorithms. In future work, the research will be further extended to the sky-ground integrated network system to explore more heterogeneous and dynamic environments in depth in order to meet the practical needs of next-generation wireless networks in terms of high efficiency, high reliability, and strong adaptability.  
Conceptualization, G.C.; methodology, G.W.; validation, G.W.; formal analysis, X.G.; investigation, X.G.; data curation, G.W.; writing--original draft, G.W.; visualization, G.W.; project administration, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61540022, Weapons and Equipment Advance Research Programs No. 6141B021322, No. 6141B012826, and Jilin Provincial Special Project for Research and Development of Industrial Technology No. 2022C047-8.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Appendix A  
A brief description of the FAO-56 dual crop coefficient approach is presented here from [40]. For more details, the reader is referred to the original paper.  
The actual evapotranspiration is given by:  
\[ET_{r}=(K_{cb}\ K_{s}+K_{e})*ET_{0}\]  
where \(ET_{0}\) is the reference evapotranspiration computed using the Penman-Monteith equation and the three coefficients \(K_{cb}\), \(K_{s}\) and \(K_{e}\) are the crop coefficient, the water stress coefficient and evaporation coefficient, respectively.  
The crop coefficient is determined from NDVI using the relationship established in [55]:  
\[K_{cb}=1.07\Bigg{(}1-\left(\frac{NDVI_{max}-NDVI}{NDVI_{max}-NDVI_{min}}\right) ^{0.84/0.54}\Bigg{)}\]  
\(K_{e}\) characterizes evaporation from the soil surface. It is almost nil for a dry soil and increases with soil moisture:  
\[K_{e}=min\ (K_{r}\ (K_{c\ max}-K_{cb}),\ f_{ew}+K_{c\ max})\]  
where \(f_{ew}\) is the exposed and wetted fraction of the soil from which the evaporation process occurs. \(K_{c\ max}\) is the maximum value of \(K_{c}\) after an event of irrigation or precipitation. \(K_{r}\) is the evaporation reduction coefficient. It is dimensionless and depends on the cumulative depletion of the soil surface. Therefore, the calculation of \(K_{e}\) requires the computation of the surface layer's (of depth \(Z_{e}\)) daily water balance. The soil parameters required as inputs are the field capacity \(\theta_{fc}\), the wilting point \(\theta_{wp}\), the readily evaporable water (REW) and \(Z_{e}\). Table 1 summarizes the parameter values used for each site.  
\(K_{s}\) is a coefficient between 0 and 1 (no stress) and thus it reduces \(K_{cb}\). It defines the effects of water stress on evapotranspiration and can be computed from the root zone depletion \(D_{r}\):  
\[K_{s}=\frac{TAW-D_{r}}{(1-p)TAW}\]  
where TAW is the total water available in the soil root layer. It is computed as the difference in water content between \(\theta_{fc}\) and \(\theta_{wp}\) and expressed in mm (\(TAW=1000\left(\theta_{fc}-\theta_{wp}\right)Z_{r}\)). p is the fraction of TAW that can be extracted from the root layer without suffering water stress.  
Figure 14: Statistical metrics of the synthetic experiment for the 15-days accumulative amounts using drip irrigation technique at different window length: 3 6 and 12 days and for different errors on the surface soil moisture (SSM error).
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
###### Abstract  
Twelve healthy male college-age students were recruited to investigate the effects of different noise exposure conditions on complex task performance and vigilance. During each noise exposure, the Multi-Attribute Task Battery (MATB) with low, medium, and high mental workloads were conducted in the order designated by the Latin square method. Meanwhile, a psychomotor vigilance test (PVT) was used to evaluate human vigilance. Heart rate variability (HRV) signals were also collected while participants performed the tasks. The generalized additive mixed-effect model (GAMM) results showed that the increased mental workload had an inverted U-shaped effect on MATB task performance. Noise exposure had no significant impact on the overall performance of MATB tasks. However, when exposed to increased noise sharpness at low mental workloads, Tracking Task (TRA) performance significantly decreased, whereas the System Monitoring Task (SYS) performance was significantly improved. In addition, higher noise sound pressure level and sharpness would impair human vigilance, which was reflected in a lower mean sample entropy of HRV and worse performance on the PVT. The results indicated that noise control in the workplace should consider both sound pressure level and sharpness.  
noise; task performance; PVT; MATB; HRV; sample entropy Article
## 5 Validation Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Model** & \(R^{2}\) & **MSE** & **RMSE** & **MAE** & **MARE** & **MSRE** & **RMSRE** & **MRMSE** & **MBE** & **erMAX** & **SD** & **t-Stat** & **\(U_{95}\)** \\ \hline \hline \multicolumn{11}{c}{Evaluation index for prediction of maximum lateral displacement} \\ \hline GA-BP & 0.996 & 3.380 & 1.839 & 1.598 & 0.073 & 0.012 & 0.108 & 0.411 & \(-\)0.057 & 0.267 & 1.838 & 0.093 & 3.639 \\ \hline RF & 0.575 & 402 & 20.064 & 18.866 & 1.091 & 2.351 & 1.533 & \(-\)47.685 & 8.590 & 3.394 & 19.113 & 1.421 & 38.794 \\ \hline DT & 0.835 & 104.153 & 10.206 & 7.289 & 0.258 & 0.185 & 0.430 & 1.847 & \(-\)6.159 & 0.916 & 8.138 & 2.270 & 18.274 \\ \hline GBM & 0.977 & 16.468 & 4.058 & 1.922 & 0.038 & 0.006 & 0.079 & 0.700 & 1.016 & 0.242 & 3.929 & 0.776 & 7.908 \\ \hline LR & 0.866 & 59.649 & 7.723 & 4.670 & 0.098 & 0.018 & 0.132 & 1.490 & \(-\)1.662 & 0.313 & 7.542 & 0.661 & 15.113 \\ \hline GR & 0.882 & 74.734 & 8.645 & 7.379 & 0.160 & 0.036 & 0.189 & 1.515 & \(-\)5.608 & 0.378 & 6.579 & 2.557 & 15.209 \\ \hline \multicolumn{11}{c}{Evaluation index for prediction of maximum vertical displacement} \\ GA-BP & 0.999 & 2.323 & 1.524 & 1.212 & 0.018 & 0.001 & 0.028 & 0.134 & \(-\)0.200 & 0.076 & 1.511 & 0.397 & 3.005 \\ \hline RF & 0.160 & 1967 & 44.355 & 37.244 & 0.704 & 0.891 & 0.944 & \(-\)47.445 & 28.154 & 1.623 & 36.128 & 2.464 & 80.089 \\ \hline DT & 0.995 & 11.220 & 3.350 & 2.148 & 0.033 & 0.003 & 0.055 & 0.287 & \(-\)0.425 & 0.137 & 3.323 & 0.384 & 6.605 \\ \hline GBM & 0.993 & 16.519 & 4.064 & 2.230 & 0.050 & 0.011 & 0.105 & 0.362 & \(-\)1.627 & 0.310 & 3.724 & 1.311 & 7.718 \\ \hline LR & 0.703 & 359.202 & 18.953 & 10.077 & 0.094 & 0.035 & 0.187 & 1.487 & 1.139 & 0.529 & 18.918 & 0.181 & 37.490 \\ \hline GR & 0.942 & 97.877 & 9.893 & 4.707 & 0.040 & 0.005 & 0.071 & 0.807 & 3.607 & 0.201 & 9.212 & 1.175 & 18.926 \\ \hline \multicolumn{11}{c}{Evaluation index for prediction of maximum stress in the XZ direction} \\ GA-BP & 0.990 & 5.270 & 2.296 & 1.649 & 0.024 & 0.001 & 0.037 & 0.271 & 0.770 & 0.105 & 2.163 & 1.068 & 4.415 \\ \hline RF & 0.686 & 153 & 12.405 & 10.776 & 0.131 & 0.026 & 0.163 & \(-\)13.511 & 0.460 & 0.357 & 13.067 & 0.111 & 25.224 \\ \hline DT & 0.695 & 119.498 & 10.932 & 6.347 & 0.057 & 0.008 & 0.089 & 0.996 & \(-\)4.395 & 0.220 & 10.009 & 1.317 & 20.750 \\ \hline GBM & 0.937 & 13.424 & 3.664 & 2.627 & 0.025 & 0.001 & 0.034 & 0.339 & 2.247 & 0.066 & 2.894 & 2.329 & 6.537 \\ \hline LR & 0.429 & 301.296 & 17.358 & 9.794 & 0.095 & 0.025 & 0.157 & 1.788 & \(-\)1.940 & 0.458 & 17.249 & 0.337 & 34.259 \\ \hline GR & \(-\)0.502 & 489.915 & 22.134 & 13.144 & 0.124 & 0.039 & 0.198 & 2.195 & 11.673 & 0.477 & 18.806 & 1.862 & 40.662 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical metrics calculation results.