esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cost Model of Predictive Maintenance
### Risks
#### 2.3.3 Environmental Risks  
A scenario of failure may lead to environmental damages by emission of harmful pollutants. We consider for a scenario of failure:  
* \(l\): the total number of chemicals emitted during a failure scenario.
* \(\{p_{j,i}^{e}\}_{1\leq j\leq l,1\leq i\leq N_{in}}\): the emission probabilities of pollutants where \(p_{j,i}^{e}\) is the emission probability of pollutant \(j\) if system fails between inspection \(i\) and \(i\)+1.
* \(\{v_{j}^{e}\}_{1\leq j\leq l}\): the emission volumes of pollutants where \(v_{j}^{e}\) is the emission volume of pollutant \(j\) during a failure scenario.  
* \(\{\rho_{j}\}_{1\leq j\leq l}\): the density vector of chemicals where \(\rho_{j}\) is the density value of chemical \(j\) emitted during a failure scenario.
* \(\{d_{aj}\}_{1\leq j\leq l}\): the cost of damage per tonne emission where \(d_{aj}\) is the cost of damage per tonne emission of chemical \(j\) during a failure scenario.  
Several methods exist in the literature to evaluate the cost of harm of pollutants to environment: for example, DALYs measure the reduced quality of life due to illness in years in order to quantify the ecological impact on human health [36]. DALYs for a disease or health condition is the sum of the years of life lost because of premature death in the population, and the years lost because of disability for people living with a disease or its consequences [37]. The evaluation of DALYs allows realizing that in Europe, psychiatric illnesses represent the third cause of healthy life year lost (10.9% of DALYs) behind cardiovascular diseases (26.6% of DALYs) and cancers (15.4% of DALYs) [38] (World Health Organization. Disease Burden and Mortality Estimates, [[https://www.who.int/](https://www.who.int/)]([https://www.who.int/](https://www.who.int/)) (accessed on 8 October 2020)). Environmental Burden of Disease (EBD) quantifies the burden of disease due to environmental risk factors [39], while the methodology for Cost-Benefit Analysis is used mainly by the Clean Air For Europe program (CAFE-CBA) in order to quantify the damage of some chemicals to crops and to human health [40].  
The environmental risks \(R_{e}\) of a failure scenario \(i\) by taking into account the expected number of failures and by considering one of the previously cited methods for damage cost evaluation can be expressed as follows:  
\[R_{e}=\sum_{j=1}^{i=l}\sum_{i=1}^{N_{in}}v_{j}^{e}\cdot\rho_{j}\cdot d_{aj} \cdot p_{j,i}^{e} \tag{8}\]  
where the term \(p_{j,i}^{e}\) contains the emission probability of \(j\)th pollutant due to system failure multiplied by the probability of system failure between inspection \(i\) and \(i\)+1. This latest probability is of course equal to \((1-N_{i})\int_{i_{i}}^{t_{i+1}}f_{i}(t\backslash T>t_{i})\cdot dt\).
## 4 Methodology
### Attention-Based Spatial-Temporal Fusion Graph Module (ASTFG)
#### 4.2.2 Temporal Dependency Modeling  
Acquiring the temporal dependence is another crucial problem in trajectory anticipation. Although the AST-GCN block has amassed the overall spatial-temporal features, its primary focus is on other agents, and it does not emphasize inherent historical affiliated relations. In fact, prospective motions of a vehicle intensely hinge on the historical status since the driving behavior and decision-making ability remain consistent over time and thus steady for certain drivers. To this end, numerous existing research studies make an effort to model the temporal dependence issue by data-driven methods. Among them, the LSTM and other RNN-based methods are mostly developed to extract the features for trajectory prediction. However, these studies have the drawback of low training efficiency and tend to cause gradient disappearance, resulting in the prediction consequence being barely satisfactory compared to expectations.  
Inspired by the outstanding performance of the GRU for sequence modeling because of its fewer parameters and cheap computation cost, we adapt the GRU to propose the Enc-GRU block to extract temporal dependency. The responsibility of Enc-GRU block is to analyze the upper layer information generated by the AST-GCN block and make a valid integration with context scenes to produce latent representations. Figure 4 depicts the process of temporal features modeling using the GRU approach.  
The concrete procedure of encoding the temporal features based on GRU is formulated as follows. For each ongoing horizon, we take the spatial-temporal GCN output denoted as \(f(x_{t},A)\) and the previous hidden state \(h_{t-1}\) as the input for the Enc-GRU block. With iterative updating, the output serviced as current hidden state \(h_{t}\) at \(t\) moment can be obtained by the following:  
\[\begin{cases}z_{t}=\sigma(W_{z}\cdot[f(x_{t},A),h_{t-1}])\\ r_{t}=\sigma(W_{r}\cdot[f(x_{t},A),h_{t-1}])\\ \tilde{h_{t}}=tanh(W\cdot[r_{t}\odot h_{t-1},f(x_{t},A)])\\ h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\tilde{h_{t}}\end{cases} \tag{9}\]  
where \(W_{z},W_{r},W\) are the parameters, \(\sigma(\cdot)\) is the sigmoid function, and \(z_{t}\) controls the degree of previous information entering the current state. The \(r_{t}\) is the reset gate, which is leveraged to determine the previous step information ignoring degree. \(\tilde{h_{t}}\) represents the contents reserved at time \(t\), and \(\odot\) is the Hadamard production.  
Through several rounds of recurrent updates with Equation (9), the temporal dependencies of trajectories and traffic rules are extracted from the primary state to the final state and ultimately integrated with the output \(Enc_{out}\). In this way, the \(Enc_{out}\) simultaneously contains all features of the multi-agent interaction mechanisms and context scene, where spatial dependencies and temporal attributes are operated by the AST-GCN and Enc-GRU block separately in the fusion graph module of the proposed strategy.
## 6 Conclusions  
The current study examines the spatiotemporal changes in vegetation ecological quality during the growth season in Anhui Province from 2000 to 2020. Utilizing a geographic weighted regression model, we explore the spatial heterogeneity of climate, terrain, and human activities on EQI. We draw the following conclusions.  
Between 2000 and 2020, the vegetation ecological quality during the growth season in Anhui Province has significantly improved. The overall EQI reveals an upward trend, with an annual growth rate of 0.61%. Significantly increasing areas accounted for 43.49%, while significantly decreasing areas accounted for 3.60%.  
EQI is positively correlated with precipitation (\(p<0.1\)) and negatively correlated with average temperature. Precipitation exhibits a greater impact, with rising altitudes and slopes leading to an increase followed by a decrease in EQI. Elevated human activity levels result in a deterioration of EQI with a significant negative correlation (\(p<0.01\)). The spatial dependence between human activities and EQI is mainly of the \"high-low\" type, where high EQI values are surrounded by low human activity areas.  
EQI displayed significant spatial heterogeneity in its responsiveness to climate, topography, and human activities. The impact of human activities and topography on EQI was stronger at the local level, whereas climatic factors tended to exert a greater influence on EQI at the global level. In terms of magnitude, elevation had the strongest effect on EQI, followed by human activities, slope, average temperature, and precipitation.  
Writing--original draft, T.W.; writing--review and editing, M.Z. and T.W.; data curation, Y.G.; investigation, Z.Y.; supervision, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This project was funded by Anhui Provincial Natural Science Foundation, grant number 2208085 MD88; National Natural Science Foundation of China, grant number 41501226; Research Fund for Doctoral Program of Anhui University of Science and Technology, grant number ZY020.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Development of the Generic Implementation Strategy
### Methodological Fundaments of the Development
#### 3.1.3 Model Framework and Components  
The framework of the GIS (see Figure 5) was derived from the theoretical foundations of Section 2 as well the qualitative content analysis in Section 3.1.2. Hereby, the reference process model of the IEEE Standard 15288:2015 was chosen as a fundament [22]. To apply this standard, it was tailored to the requirements of its application object, i.e., the research objectives of this work. Therefore, the adaptation measures derived are the relevant influencing factors, which are represented by the GIS requirements described in Figure 4. Secondly, relevant decision-makers and experts of the DB were included through explorative and iterative interviews. Lastly, it was determined that the development of the GIS, due to the resources available within the scope of this work, focus on the technical processes of IEEE 15288:2015(E) only [22]. Therefore, non-technical processes were only marginally dealt with in the context of this work, but their importance is to be noted. To meet the requirement of completeness, all relevant processes and aspects of the strategy were included in the GIS.  
Conceptually, the GIS is oriented towards the life cycle phases of the DT as well as the corresponding asset. Within the GIS framework, a distinction is made between a GIS in the _narrower_ and _broader sense_ (see Section 2.2). Contrary to the existing scientific opinion, the decommissioning phase is also considered here in the context of GIS in the broader sense,  
Figure 5: Model approach of the GIS.  
since DTs have data and experience at their disposal when they are decommissioned, which can be relevant for future implementations [17]. Furthermore, the concept of a DT, which consists of the virtual and physical space as well as the environment of the DT, is crucial for the modelling approach. This results in three core components within the life cycle phases: the _environment_, the _virtual_ and _physical space_. Derived from this, the framework of Figure 5 results as the model approach of the GIS.
## 5 The SDRE Integral Type Servo Controller
### SDRE Integral Type Servo Control Design  
In order to track a time-variant reference attitude, we will augment the system with the integral state of quaternion, satellite velocity, antenna joint angle, and antenna joint angular velocity. Consequentially, we augment the state vector and system (Equation (28)) as follows:  
* SDLR \[\mathbf{\check{x}}=\begin{bmatrix}\mathbf{q}_{e}^{T}&\mathbf{\omega}_{e}^{T}&\mathbf{ \phi}_{e}^{T}&\mathbf{\dot{\phi}}_{e}^{T}&\mathbf{\Omega}_{e}^{T}&\mathbf{\Omega}_{e}^{T} &\mathbf{\int q}_{e}^{T}\mathrm{d}t&\mathbf{\omega}_{e}^{T}\mathrm{d}t&\mathbf{\int\mathbf{ \phi}}_{e}^{T}\mathrm{d}t\end{bmatrix}^{T}=\begin{bmatrix}\mathbf{x}_{e}^{T}&\mathbf{ \Omega}_{e}^{T}&\mathbf{\dot{x}_{e}^{in}}^{T}\end{bmatrix}^{T}\] (34) \[\mathbf{u}=\begin{bmatrix}\mathbf{\Omega}^{T}&\mathbf{\tau}_{\phi}^{T}\end{bmatrix}^ {T}\] (35) \[\mathbf{\check{x}}=\mathbf{\tilde{A}}_{c}(\mathbf{\check{x}})\mathbf{\check{x}}+ \mathbf{\tilde{B}}_{c}(\mathbf{\check{x}})\mathbf{u}\] (36) \[\mathbf{\tilde{A}}_{c}(\mathbf{\check{x}})=\begin{bmatrix}\mathbf{A}_{c}(\mathbf{x })&\mathbf{0}_{14\times 11}\\ \mathbf{E}_{11\times 11}&\mathbf{0}_{11\times 3}&\kappa\mathbf{E}_{11\times 11}\end{bmatrix}, \mathbf{\tilde{B}}_{c}(\mathbf{\check{x}})=\begin{bmatrix}\mathbf{B}_{c}(\mathbf{x})\\ \mathbf{0}_{11\times 5}\end{bmatrix}\] (37)
* Objective function \[J=\frac{1}{2}\int_{0}^{\infty}\begin{pmatrix}\mathbf{\check{x}}^{T}\mathbf{Q}(\mathbf{ \check{x}})\mathbf{\check{x}}+\mathbf{u}^{T}\mathbf{R}(\mathbf{\check{x}})\mathbf{u}\end{pmatrix} \mathrm{d}t\] (38)
* Riccati equation \[\mathbf{\tilde{A}}_{c}^{T}(\mathbf{\check{x}})\mathbf{\tilde{P}}(\mathbf{\check{x}})+\mathbf{ \tilde{P}}(\mathbf{\check{x}})\mathbf{\tilde{A}}_{c}(\mathbf{\check{x}})-\mathbf{\tilde{P}}(\bm {\check{x}})\mathbf{\tilde{B}}_{c}(\mathbf{\check{x}})\mathbf{R}^{-1}(\mathbf{\check{x}})\mathbf{ \tilde{B}}_{c}^{T}(\mathbf{\check{x}})\mathbf{P}(\mathbf{\check{x}})+\mathbf{Q}(\mathbf{\check{x}}) =\mathbf{0}\] (39)
* Optimal control input \[\mathbf{u}=-\mathbf{R}^{-1}(\mathbf{\check{x}})\mathbf{\check{x}}^{T}\mathbf{\tilde{P}}(\mathbf{ \check{x}})\mathbf{\check{x}}\] (40)
## 2 Materials and Methods
### Construction of Expression Vectors  
The expression vectors used in this study are based on the pRhon5 Hi-2 vector carrying the promoter of the _nifH_ gene for heterologous gene expression [39]. The sequence of \(\beta\)-caryophyllene synthase QHS1 from _A. annua_ (UniProt: Q8 SA63) was used to generate an appropriate synthetic gene whose DNA sequence is suitable for the codon-usage of _R. capsulatus_. For DNA sequence adaptation, the Codon Optimization Tool by IDT Integrated DNA Technologies and the Graphical Codon Usage Analyzer tool were used [86]. The 1.7-kb \(QHS1\) gene was obtained from Eurofins Genomics. The synthetic DNA fragment was flanked by appropriate restriction endonuclease recognition sequences (\(Xbal/Hind\)III). The final sequence of the synthetic DNA fragment is shown in the Supplementary Materials. For the construction, the \(Xbal/Hind\)III hydrolyzed \(QHS1\) fragment was inserted into likewise hydrolyzed pRhon5 Hi-2 as well as a variant, providing the additional isogen-prenoid biosynthetic gene \(ispA\). Thereby, the expression vectors pRhon5 Hi-2-QHS1 and pRhon5 Hi-2-QHS1-ispA were constructed, carrying the terpene synthase gene immediately downstream of the P\({}_{nif}\) promoter of the vector. Correct nucleotide sequences of all constructs were confirmed by Sanger sequencing (Eurofins Genomics, Ebersberg, Germany). The \(QHS1\) expression vectors are summarized in Table S1 (Supplementary Materials).
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 3 The Crypto-Engine
### Principles and Logical Architecture of the Engine
Furthermore, ECTAKS integrates the schemes for ECIES encryption and for ECDSA sender authentication, which also rely on NIST standards and are also referred to in the IEEE 1609.2 [41] and in the Transport Layer Security (TLS) [61; 62] protocols. The main benefits of ECTAKS can be summarized as follows:  
* Robustness against attacks on the topological integrity of the network;
* Generation of shared secrets on communication sessions with arbitrary topology;
* Scalability of multi-cast and converge-cast sessions;  
A reference design of the Crypto-Engine logical architecture is shown in Figure 3.  
Figure 3: Crypto-Engine block diagram.
## 2 Study Area and Data Preparation
### Data and Pre-Processing
#### 2.2.3 MODIS Data  
Three 500 m resolution daily MODIS surface reflectance products (MOD09 GA) and three 250 m resolution daily MODIS surface reflectance products (MOD09 GQ) were used in this study (Table 1). These MODIS images were reprojected from the native sinusoidal projection onto the UTM-WGS84 reference system, and were resized to the selected study area using MODIS Reprojection Tool (MRT) software. These MODIS data were then georeferenced using a second-order polynomial warping approach based on the selection of 28 or 32 GCPs from 500 m or 250 m GF-1 WFV images, respectively, with a nearest neighbour resampling method that has a position error of within 0.53 and 0.67 GF-1 WFV pixels, respectively. The 500 m and 250 m GF-1 WFV images were resized from georeferenced GF-1 WFV images using the pixel aggregate resampling method.
## 1 Introduction
### Existing Limits and Our Contributions  
Based on the above research, the method based on matching and probability prediction has certain limitations. The OptiTrack method requires the installation of a target ball, which is not universal. The method based on reflection group tracking only uses depth information, and it is easy to lose the target during the tracking process. The aforementioned algorithms have the problem of not being able to balance real-time and robustness.  
Since the 2D affine transformation group can make the rectangular frame change with the target scale, rotation and cross-cutting, the geometric particle filter algorithm has a good effect on target tracking. Under the geometric particle filter tracking framework, the resampling link is not improved; it will lead to the loss of particle diversity. After a long time of iterative tracking, the accuracy of tracking prediction will be greatly reduced. It will affect the success rate of robot tracking and grasping. In order to solve the above problems, the improved Gaussian resampling is combined with the improved geometric particle filter based on the geometric particle filter algorithm. A robot dynamic target capture method is proposed based on the affine group improved Gaussian resampling particle filter. The Gaussian resampling method is improved for the resampling link and merged with the geometric particle filter algorithm. It improves the loss of particle diversity and effectively ensures the tracking accuracy of the algorithm after long iterations. The robot-oriented position servo grabbing is presented. The difference between the 3D pose of the dynamic target and the 3D pose of the end is calculated. The end of the robot is adjusted to approach the dynamic target in real time. A method of robot dynamic target tracking and grasping is proposed based on affine group improved Gaussian resampling particle filter. Finally, the particle filter algorithm, geometric particle filter algorithm and the proposed algorithm in the paper are compared and tested. The robot's grasping of dynamic targets was simulated and experimented on within the simulation environment and actual environment to realize the robot's prediction and grasp of dynamic targets. It verified the effectiveness of the algorithm proposed in the paper.
## 4 Quality Assessment of Actual Gravity Datasets
### Uncertainty in Estimation of Scale Factor (SF)
#### 4.2.1 Calibration of Scale Factors  
The MBGA method can use AG values to calibrate the SF, but it can also use one meter with a known SF to calibrate another meter used synchronously, which is especially useful for a survey network with only a few AG values.  
1. Scale factor calibrated by another gravimeter  
For the example of survey data from March 2018, the CG-170 gravimeter was relatively stable, and its SF was calibrated using the long-baseline; the original SF of CG-169 and CG-170 was 1.000253 and 1.000066, respectively (ratio: 1.000187) (Table 1). Testing the SF of CG-169 calibrated by CG-170 revealed that the SF deviation of CG-169 was just 6 ppm, which could lead to a difference of <2 \(\mu\)Gal in the maximum GD (300 mGal). Then, the SF of CG-170 optimized using the MBGA method was used to calibrate the SF of CG-169. It was found that even taking account of the different SF of CG-170 in the calibration, the SF ratio between the two instruments was almost the same. Therefore, it was considered that if the SF of one instrument is known exactly, the SF of another instrument could be calibrated using the known one, and in the same survey campaign, there is a fixed proportional relationship between the SFs of instrument pairs. However, if the SF of the  
Figure 6: Nonlinear drift rate of different relative gravimeters obtained using the BGA method: (a) CG-169, (b) CG-170, (c) LCR-149, and (d) LCR-150.  
first instrument is not accurate, it will introduce inaccuracy into the calibration result of a second instrument.
## 2 Materials and Methods
### Data Preparation  
In the provided dataset, some of the annotations were incorrect, containing an annotation without the peak point or with it being mislabeled. These samples were removed from the data along with its paired signals. In total, there were 2377 signals with a length of 5000 samples. As mentioned, the annotation came in the form of point collection. To create suitable masks for our model, we transformed the points collection into masks matching the shape of the input signal. For the specific fragments corresponding to the annotations, we applied a label in the form of an integer number as follows:  
* \"0\"--background;
* \"1\"--QRS block;
* \"2\"--T-wave;
* \"3\"-- P-wave.  
Then, we took the generated sparse masks that are one-hot-encoded, and the final labels have the shape \(k\times 5000\times 4\), where k indicates the number of samples. The input signals were normalized using the min-max scaling technique, which is defined as:  
\[x^{{}^{\prime}}=\frac{x-min(x)}{max(x)-min(x)}. \tag{1}\]  
Then, the dataset was split into training, validation, and testing sets as follows: first, we divided the data into a training and testing set with 80% and 20%; then, we extracted 20% of the training set into the validation set. The distribution of the dataset is presented in the Figure 2.  
The distributions between categories across the dataset are as follows:  
* Background--7,309,154 points,  
Figure 2: Data distribution.  
* QRS complex--894,721 points;
* P-wave--1,516,929 points;
* T-wave--814,196 points.
## 2 System Framework
Among the 6 parameters, the coordinates of each point after correction (\(x^{\prime}\), \(y^{\prime}\)) can be articulated as follows:  
\[\left[ \begin{array}{c}x^{\prime}\\ y^{\prime}\end{array} \right]=\left[ \begin{array}{cc}a&b\\ c&d\end{array} \right]\left[ \begin{array}{c}x\\ y\end{array} \right]+\left[ \begin{array}{c}e\\ f\end{array} \right] \tag{4}\]  
The parameters \(a\)-\(d\) indicate the translation and rotation of the received signal, which can approximately express the impact of frequency offset. The \(e\) and \(f\) parameters indicate that the time shift can approximately express the effect of phase offset. Implementing a single correction only requires \(\omega\) and \(\varphi\) maintaining the corresponding parameter changes. (\(x\), \(y\)) represents the coordinates of each constellation point before correction. CNN, two GRUs, and a thick layer that supports feature extraction and classification make up Part B. The signal's spatial features are retrieved by the first convolution layer, which has 75 filters and a 2 \(\times\) 8 kernel size. The extracted features are then further compressed by the second convolution layer, which has 25 filters and a 1 \(\times\) 5 kernel size. The temporal characteristics of the signal are extracted using the following GRU layers using 40 units. Ultimately, the dense layer--which has the same number of modulation classes--completes the classification assignment. Repaired linear unit activation functions are used in the first two convolution layers, On the other hand, the final thick layer uses Softmax. In order to produce a model that is smaller and requires less computing power, the matching network in the third part is designed to control the model size using a restricted number of parameters in the CNN layers. In order to confirm that the suggested approach is genuine, a comparative study was carried out to analyze the signal constellation maps without prediction correction and with the corrected ones when the signal SNR is set to be 10 dB, as shown in Figure 3.  
The original dataset's constellation diagram is shown on the left side of Figure 3, while the right side represents the improved constellation diagram of Part A output after offset correction. It can be seen that compared to the original signals, after introducing a correction process, the signals are tightly distributed and more conducive to GRUs extracted bidirectional of temporal features. Figure 2: Light weight based on predictive correction module network. The RadioML2016.10b [15] experimental dataset, produced using the GNU Radio channel model, is generated by simulating the propagation properties in a harsh environment. The datasets included the same carrying data as the actual signal, as well as pulse shaping, modulation, and emission parameter characterization. Table 1 displays the parameters for the RadioML2016.10b datasets. There were 1,200,000 dataset samples in all, with ten distinct modulation types operating at twenty Signal-to-Noise Ratios (SNRs) and continuously balanced signal samples.