esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Node-Location Patterns
#### 4.2.1 One-Dimensional Scenario  
For the highway scenario, nodes were assumed to be located on a rectangle of which the width was the width of the road, and the length was infinite. The width of the road was small compared to the broadcast range, and it was acceptable to consider that the nodes were on a line. In this case, our model was a line with node density \(\lambda\), expressed in vehicles per meter. Since all nodes at distance \(j\) were in a segment, starting from the position of the farthest node at distance \(j-1\) from \(i\), nodes at distance \(j\) were in a segment of length \(R\). Thus, the mean number of nodes at distance \(j\) is the mean number of nodes in a segment of length \(2R\) (one segment to the right of \(i\) and one segment to the left of \(i\)).  
Figure 4 shows the location of nodes at \(n\) hops according to the hypotheses above. For example, the grey rectangles, i.e., those at a distance between \(2R\) and \(3R\) from the center (i.e., node \(i\)), show the location of nodes at three hops.  
Figure 4: Node locations in 1D highway scenario.  
The mean number of nodes at various distances from \(i\) is  
\[E(N_{i,0})=1\quad\text{and}\quad E(N_{i,j}) = 2\lambda R\quad\forall j>0. \tag{3}\]
## 4 Experimental Evaluation  
In this section, we use a graph-based SLAM method called GTSAM [35] to build the map of the environment during exploration.  
Our experiments are designed to support our claims that our approach (i) chooses more reasonable target points by taking into obstacle information; (ii) can better adapt to the exploration problem using temporal and spatial information and lead to more accurate exploration; (iii) has better robustness and generalizability to new environments against state-of-the-art approaches; (iv) converges faster and obtains more rewards.
# Wildfires in the Siberian Arctic
## 2 Materials and Methods
### Sources of Environmental Data  
Air temperature, precipitation, and ground cover moisture content (0-7 cm depth) were obtained at a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) from the ERA5-Land dataset [34]. The standardized precipitation evapotranspiration index (_SPEI_) values were obtained from the Global Drought Monitor ([[https://spei.csic.es/map/maps.html](https://spei.csic.es/map/maps.html)]([https://spei.csic.es/map/maps.html](https://spei.csic.es/map/maps.html)) (accessed on 21 February 2022)). _SPEI_ is a measure of the air moisture content and is an estimation of drought intensity and is defined as the difference between total precipitation and potential evapotranspiration for the that month or year [35]. We calculated the lightning strike index (_LSI_) for the Siberian Arctic from the annual lightning frequency for the entire Arctic, provided by the World Wide Lightning Location Network for 2010-2020 [36]. Lightning frequency was not available for earlier periods. We assume that the frequency of lightning strikes was uniform throughout the Arctic. The _LSI_ is a normalized unitless metric which varies between 0.0 (no lightning) and 1.0 (maximal number of strikes for the entire period. To estimate vegetation recovery after fire, we analyzed the temporal dynamics of the gross (GPP) and net (NPP) primary productivity and the enhanced vegetation index (EVI) for 13,940 distinct burnt areas within Siberian Arctic. We calculated GPP, NPP, and EVI temporal trends within each hotspot. The trend was determined as the tangent of the angle between the abscissa (i.e., time interval) and the regression line of the mean annual GPP or NPP values. We used a conservative approach and considered that vegetation recovery begins after the second year following the fire event. Thus, post-fire trends were calculated from the second year after the fire. In addition, we calculated GPP, NPP, and EVI temporal trends for the whole Siberian Arctic. GPP and NPP values were obtained from the Terra/MODIS product MOD17A2H at temporal and spatial resolutions of 8-days and 500 m\({}^{2}\), respectively ([[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)) (accessed on 5 May 2022); [37]). EVI values were obtained as MODIS/Terra product MOD13Q1 at temporal and spatial resolutions of 16-days and 250 m\({}^{2}\), respectively ([[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)) (accessed on 5 May 2022); [38]).
# A Digital Twin System for Adaptive Aligning of Large Cylindrical Components
## 5 DT Aligning System Development and Validation
### Application Validation of the DT Process System  
Based on the software framework shown in Figure 12, the Human Machine Interface (HMI) of the developed DT process system is shown in Figure 13, which mainly includes virtual scenes, monitoring function panels, and control panels. The virtual scene reflects the motion state of the twin model of the large component aligning system driven by twin data. The monitoring function panel is responsible for the digital display and control of the aligning status of large components and displays monitoring data in real-time and dynamically. The control panel provides various control functions for the execution control of the large component aligning process.  
* Click the action button in the monitoring system to start communication (1) in Figure 13); the DT process system establishes communication with the process sensing system, and the sensing system collects multi-source heterogeneous data during the aligning process in real time, including the current pose information of large components and the data of various sensors (e.g., the aligning stress and the servo motor data of the motion shaft).
* Click the operation button to start synchronization (1) in Figure 13); the virtual space of the aligning system synchronizes with its physical space. On the right side of the HMI interface is display information, such as the aligning pose, pose error, and aligning stress of two large components (1) and 1 in Figure 13).
* When starting the aligning process, the DT process system synchronously reads the pose and stress state information of large components, displays them on the pose display panel and curve graph, and distinguishes the aligning error of two large components by color. When the error decreases to a controllable range, the error value changes from red to yellow, and when the error meets the aligning accuracy requirements of large components, the error value changes to green. The display of the aligning status also changes with the aligning process of large components. After the aligning process is completed, the process status displays \"aligning completed\", and the process status signal indicator light turns green (1) in Figure 13).  
Figure 13: HMI of the developed DT process system.  
The experimental results show that the proposed method can greatly enhance the alignment accuracy of large cylindrical components. On the other hand, by analyzing time statistically, the proposed method can considerably reduce the aligning time of large components, enhance aligning efficiency, and significantly boost the level of automation and intelligence for the aligning system.
## 3 Results
### Evaluation of DSR View  
#### 3.2.1 Subjective Assessment by Dentists  
In total, 12 dentists working at the University Hospital experienced DSR View and commented on its use.  
* \"It looks more three-dimensional than smart glasses.\"
* \"It is lovely to be able to grasp complex three-dimensional structures easily.\"
* \"It allows us to grasp the volume of the lesion and realize the need for measurement.\"
* \"It is convenient to be able to display three-dimensional images from DICOM data immediately.\"
# Active Learning Strategy for Surrogate-Based Quantile Estimation of Field Function
## 2 Surrogate Model for Function with Mono-Dimensional Domain Random Field Output
### Quantile Estimation  
The resulting surrogate model \(\hat{\mathcal{Q}}_{\mathbf{X}}(\cdot)\) allows to define the output stochastic process as:  
\[\hat{\mathbf{X}}(t,\mathbf{U})=\hat{\mu}(t)+\sum_{k=1}^{N_{KL}}\sqrt{\hat{ \lambda}_{k}}\hat{\xi}_{k}(\mathbf{u})\hat{\mathcal{L}}_{k}(t) \tag{20}\]  
combining KL decomposition and Gaussian processes. It is possible to use this surrogate model to carry out uncertainty quantification studies instead of the exact computationally intensive function \(\mathcal{Q}_{\mathbf{X}}(\cdot)\). For instance, it is possible to estimate an \(\eta\)-quantile over the stochastic process surrogate model \(\hat{X}(\cdot,\cdot)\) defined by:  
\[\hat{q}_{\eta}(t)=\inf_{v\in\mathbb{R}}\{\mathbb{P}\big{[}\hat{\mathbf{X}}(t, \mathbf{U})\leq v\big{]}\geq\eta\} \tag{21}\]  
For that, a Mont-Carlo simulation technique may be used by sampling according to \(\phi_{\mathbf{U}}(\cdot)\) as the computational cost is affordable using the surrogate model. Therefore, the quantile is estimated following these steps:  
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Figure 1: Example of Gaussian process prediction and associated confidence interval.  
2. Compute the empirical distribution function: \[\hat{\Phi}_{\hat{\mathbf{X}}(t,\mathbf{U})}(\mathbf{u})=\frac{1}{N_{q}}\sum_{i=1}^ {N_{q}}\mathbb{I}_{\hat{\mathbf{X}}(t,\mathbf{u}_{i})\leq\hat{\mathbf{X}}(t, \mathbf{u})}\]
3. Estimate the quantile \(\hat{q}_{\eta}(t)\) by \[\hat{q}_{\eta}(t)=\hat{\Phi}_{\hat{\mathbf{X}}(t,\mathbf{U})}^{-1}(\eta)\]  
However, due to the limited size of the training dataset used to build the surrogate model, the estimation of the quantile \(\hat{q}_{\eta}(\cdot)\) may present a large degree of uncertainty. In order to control the accuracy in the estimation of the quantile of \(\mathbf{X}(\cdot,\cdot)\) using the surrogate model instead of the exact function, an active learning strategy is proposed in the following sections. It allows to follow an adaptive sampling strategy by adding relevant samples into the dataset in order to improve the accuracy of the estimation of the quantile while limiting the number of evaluations of the exact costly function \(\mathcal{Q}_{\mathbf{X}}(\cdot)\).
## 4 Methodology
### User Spacecraft Orbit Determination  
This section delves into the intricacies of the orbit determination modeling employed to generate BLS solutions for all user spacecraft using GTDS. Currently, GTDS is the operational tool used by FDF for DOWD processing, and it has been used to support all the missions mentioned above. The analysis used the same process used by the FDF for operational navigation support to guarantee the accuracy and consistency of the results.  
The operational support procedure uses DOWD measurements to solve for the spacecraft state using an iterative process. To ensure the reliability of orbit reconstructions, the force budget used in dynamic modeling must consider the relevant forces acting on the spacecraft, including gravitational and non-gravitational forces.
## 3 Results and Discussion
### ADRC Parameterization Results  
Parameter selection is a crucial aspect in the design of an auto-disturbance control system (ADRC), and these parameters directly determine the performance of the control system, including the response speed, accuracy, and robustness [33]. Suitable parameters can ensure the effective suppression of the system against external disturbances and internal dynamic changes [34]. Through the combination of theoretical analysis and experimental verification, the precise adjustment of the ADRC parameters plays an important role in improving the overall performance of the system. After testing each parameter individually, the parameter tuning results of the ADRC are shown in Table 3 below, when the requirements of a higher response speed, lower overshoot, lower steady-state noise, and lower high-frequency vibration were guaranteed at the same time.
# The UWB Radar Application in the Aviation Security Systems
###### Abstract  
In the process of our research, we have identified new methods of processing ultra-wide-band (UWB) radar signals and possibilities of the UWB radar use in aviation security systems. We paid our main attention to finding new algorithms for tracking the movement of a person behind an obstacle using the UWB radar. Such UWB radar application is typical for tracking the movement of people behind obstacles in case of security forces intervention at an airport. In the research process, we used methods of analysis, synthesis, and measured data from the performed experiment. The main contribution of the paper is the development of new algorithms for locating the movement of a person behind an obstacle using a straight- line method in the case of using two independent UWB radar systems. The article did not examine the accuracy of determining the position of a person behind the obstacle. We found that when applying the Kalman filter after signal processing by the straight-line method, the trajectory of the person's movement behind the obstacle was smoother. The results of processing the measurement signals of UWB radar by the linear method have shown that this method is applicable to tracking a person behind an obstacle and can be used in aviation security systems.  
modeling; simulation; UWB radar; aviation security systems +
Footnote †: journal: Applied Sciences
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 1 Introduction and Related Works  
The increase in the number of wireless networks users and applications has led to an increase in demand for the use of cellular network resources. This challenge has led to the formation of services that reduce the load on the central part of the network. D2D communications is a proximity-based service that enables two devices to exchange information without the need for a base station and standardized by the 3 rd Generation Partnership Project (3 GPP) Long-Term Evolution (LTE). D2D communications are divided into overlay in-band, underlay in-band, and out-band communications. In in-band communications, D2D users utilize the cellular network spectrum that is categorized to overlay and underlay [1].  
The underlay in-band is a D2D communications mode that allows the D2D users to use the cellular spectrum and causes the interference to cellular links and vice versa. On the other hand, the cellular spectrum is distributed between cellular and D2D users in overlay in-band mode with no interference. In out-band communications, users use a different spectrum other than the cellular spectrum. The main advantages of D2D service are the optimal allocation of cellular network resources, power consumption, the cellular network traffic offloading, and the possibility of creating public safety networks [2].
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 4 Design and Implementation
### The Components of the CoAP Handler  
A CoAP Handler is composed of the following components Figure 2:  
CoAP proxy Module: The proxy component of the CoAP Handler is responsible for IP connectivity between a NAP and CoAP endpoint(s). The communication is based on UDP. The proxy receives a CoAP  
Figure 2: An example of CoAP over ICN reference architecture. On the right there are Things offering resources. Each resource is specified by a color and also by shape. On the left there are the CoAP clients.  
request from a CoAP client. Upon reception of the CoAP messages, this component extracts the necessary and essential information from the CoAP messages, gives an instruction for a specific action (e.g., single request-single response, CoAP observe, etc.), manages states and allows for translating CoAP messages to ICN messages.  
Protocol Translation: This component is a minimal implementation of the CoAP protocol following the semantics of RFC 7252 which is utilized to translate CoAP messages to ICN messages.  
POINT Interface Module: This module advertises ICN messages (i.e., translation of CoAP requests) to the ICN network. The rendezvous node of ICN network takes the responsibility of forwarding these CoAP messages to the appropriate NAP(s). Finally, a NAP which receives an ICN message forwards the original CoAP request to the CoAP server. The CoAP server follows the reverse path to forward the response to the CoAP clients.
## 3 Formulation for Joint Design Problem
### Responsibility Rating  
For power efficiency and PU protection, power assignment is considered in this work. However, the action space will be fairly large if we treat power assignment as actions in the joint optimization problem. Huge action space results in intensive computation complexity and low learning efficiency due to the maximum calculation in Q-value updating [17]. In this case, the concept called responsibility rating was introduced in our previous work [17].  
SU should improve the transmitting power in its next transmission for reducing the average TL if much time has been wasted in the current transmission. If the latency of the current data transmission is sufficiently short, then the power should be lessened at the next time step to decrease energy expenditure. Based on this principle, the responsibility rating of SU \(n_{i}\) at time step \(t\) is given by:  
\[\psi_{i}^{t+1}=\left\{\begin{array}{ll}\psi_{i}^{t}+1&u_{TD,i}>\Lambda_{i,t} ^{*}\\ \psi_{i}^{t}-1&u_{TD,i}\leq\Lambda_{i,t}^{*}\end{array}\right., \tag{11}\]  
where responsibility rating \(\psi_{i}^{t}\) is a nonnegative integer corresponding to one of the transmission power levels. In addition, if \(\psi_{i}^{t}=\max\{\psi_{i}^{t}\}\) and \(u_{TD,i}>\Lambda_{i,t}^{*}\), then \(\psi_{i}^{t+1}=\psi_{i}^{t}\); if \(\psi_{i}^{t}=0\) and \(u_{TD,i}\leq\Lambda_{i,t}^{*}\), then \(\psi_{i}^{t+1}=0\). \(u_{TD,i}\) denotes the single-hop TL of SU \(n_{i}\), and \(\Lambda_{i,t}^{*}\) is the average value of \(u_{TD,i}\) at time step \(t\), which can be calculated in a progressive form via historical information:  
\[\Lambda_{i,t}^{*}=\Lambda_{i,t-1}^{*}+\frac{1}{t}\big{(}u_{TD,i}-\Lambda_{i,t -1}^{*}\big{)}. \tag{12}\]Every responsibility rating \(\psi_{i}^{t}\) matches a transmission power \(p_{i}\) (\(p_{\min}\leq p_{i}\leq p_{\max}\)), and the association is given by:  
\[p_{i}\big{(}\psi_{i}^{t}\big{)}=\left(1-\frac{\psi_{i}^{t}}{|\Psi_{i}|}\right)p _{\min}+\frac{\psi_{i}^{t}}{|\Psi_{i}|}p_{\max}, \tag{13}\]  
where \(|\Psi_{i}|\) represents the size of \(\big{\{}\psi_{i}^{t}\big{\}}\), and \(p_{\min}\) and \(p_{\max}\) are the minimal and maximal value of the transmit power, respectively. The responsibility rating not only adjustments transmitting power for high energy efficiency but compresses huge action space to reduce the computation load.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Multi-Tasks and Joint Learning
#### 3.3.3 Task 3: VAE-Based Latent Representation Learning and Data Reconstruction  
Task 3 uses an LSTM VAE neural network to generate latent representation and to reconstruct the input to model the spatial patterns in the data. The VAE uses the inference and generating networks to model the conditional probability density function. A probability encoder models the latent variable distribution, and the latent space variability is considered in the sampling process; hence, the VAE has higher expressive ability than the AE. Therefore, on the basis of Task 2, Task 3 is designed to learn the VAE-based latent representation and to reconstruct the data, so as to improve the spatial-dependency capturing performance of the model.  
The Task 3 input is given in Equation (9) and the output is denoted \(\sigma_{\text{Task3}}\in\Re^{M\times K}\). Task 3 is expressed in Equation (16), where \(Encoder_{\theta 3}\) is the encoder network and \(\theta_{3}\) is the parameter to be trained. \(Encoder_{\theta 3}\) maps the original data to a latent representation and is sampled from its distribution, as detailed in Equation (17). \(Decoder_{\varphi 3}\) is the decoder network and \(\varphi_{3}\) are the parameters to be trained. \(Decoder_{\varphi 3}\) maps the latent representation to the original data \(X\in\Re^{M\times K}\).  
\[\sigma_{\text{Task3}}=Decoder_{\varphi 3}(Encoder_{\theta 3}(X)), \tag{16}\]  
\[z_{vae}=Encoder_{\theta 3}(X). \tag{17}\]  
The Task 3 reconstruction error is determined from the difference between \(\sigma_{\text{Task3}}\) and \(X\), and is calculated based on the MSE such that  
\[\begin{split} Loss3=L(\theta_{3},\varphi_{3};X)\\ =\sum\limits_{i=1}^{N}\Big{(}\frac{1}{L}\sum\limits_{l=1}^{L}||D _{\theta 3}(z^{(l)})-X^{(i)}||^{2}\\ -D_{KL}[Q(z|X^{(i)},\varphi_{3})||P(z|\theta_{3})]\Big{)}.\end{split} \tag{18}\]