esa-sceva/satcom-chunk-collection huggingface dataset:
# CarTwin--Development of a Digital Twin for a Real-World In-Vehicle CAN Network
## 2 Background and Related Work
### Related Work  
Since the CAN specification published by Bosch has been standardized, there have been many research papers addressing various topics related to it. The first studies were focused on the message scheduling [15; 16] and response times due to message arbitration or error frames [17]. They were followed by proposals, such as TT-CAN [18] and CANOpen [19] that address its possible extensions so it can be adopted to other applications or updates to its original specification [20; 21], with the goal to improve its communication bandwidth. Due to the increasing need for more data bytes in the message frames and faster communication between nodes, the automotive industry together with Bosch have published a newer version of CAN called CAN-FD (flexible data-rate) [13; 14]. This extension proves that CAN is going to remain present in cars in the long run.  
There were practical implementations of digital twins a few years after the concept was proposed [5], mainly in manufacturing processes as a basis for product lifecycle management [8]. Considering that digital twins represent an authentic copy of the real physical object with regards to specific properties, monitoring and diagnosing digital twins can reveal solid information, helpful for monitoring the real physical object behavior under certain conditions or stimuli. Digital twin frameworks and designs have been proposed by research works for a broad area of use cases, such as cyber-physical systems production [22; 23], aerospace machining [24], drilling wells for the oil and gas industry [25], Internet of Things with 5G/6G networks [26] or even for healthcare services [27]. Automotive system digital twin proposals have emerged during the last couple of years from simulations of brake systems [28] to battery systems [29] and wiring harnesses [30]. A recent study provided details regarding the development and implementation of a digital twin architecture for an autonomous driving simulation [31]. Hence, our proposal from this work is a car twin with a real vehicle network defined by the wiring harness and monitored data as a digital twin.  
From a security standpoint, authors from [32] described and detailed industrial control system testbeds, compared and evaluated existing datasets and reported which intrusion detection systems perform best. Furthermore, recent works proposed digital twin designs for evaluating privacy enhancement in automotive [33], prediction of cybersecurity incidents [34] and protection of critical infrastructure for intelligent transport systems [35]. Since none of the existing CAN standards propose any security requirements, the CAN bus is exposed to threats, so there are numerous research topics and proposals in this area. Thereby, the automotive industry requires vehicle manufacturers to embed rolling counters and message authentication codes inside the data field of the CAN frames that carry safety-critical data according to the AUTOSAR Secure On-board Communication standard [36].
## 1 Introduction
The key performance indicators evaluated include solution availability, convergence time, positioning accuracy, and ZTD estimation for GPS, Galileo, and combined GPS+Galileo solutions. Importantly, HAS data were collected in real time, enabling a realistic simulation of near-real-time PPP performance. BRDM and CNES RTS corrections were also used in post-processing, representing additional simulated real-time scenarios. The final CODE MGEX products serve as the most precise reference solution and are used as a benchmark for the highest achievable PPP accuracy. This study also examines the availability of HAS products and PPP solutions, highlighting practical challenges in real-time applications. In addition, 1 s observation intervals were employed with final CODE products--an uncommon approach in post-processing that enables a more detailed analysis of short-term convergence behavior. Following the introduction, Section 2 describes the data, product sets, and processing methodology. Section 3 presents the results of the conducted analyses. Section 4 provides a discussion, and Section 5 offers a summary of the key findings.
## 4 Mechanism of Extraction in FPSE  
Classical microextraction techniques such as SPME, SBSE, and TFME preferentially employ highly viscous pristine polymeric sorbents including PDMS, PEG, and PA, etc., as the extracting phase. During extraction, the analytes are solvated by the extracting polymeric phase. The diffusion coefficient in the highly viscous polymeric coating enables the analytes to penetrate the whole volume of the coating if enough time is allowed. As such, the mass transfer rate as well as the extraction kinetic is relatively slow in the viscous polymeric sorbent coating. When the analytes are heavier (high molar mass), the diffusion into the polymeric extracting phase is even slower. The extraction kinetic can be enhanced by impregnating the viscous polymeric phases with high surface area carbonaceous particulates material such as divinyl benzene (DVB) and Carboxen. These particles act as a bridge inside the liquid polymeric phases and facilitate faster extraction kinetics.  
Unlike pristine polymers used in classical microextraction techniques, FPSE utilizes sol-gel sorbent coating technology that chemically binds the organic/inorganic polymer to the fabric substrate via sol-gel precursor as a cross-linker. The resulting sol-gel sorbent is a 3D polymeric network possessing random linkage between the sol-gel precursor and the inorganic/organic polymer. Sol-gel sorbents are inherently porous with sponge-like porous architecture containing numerous mesopores and micropores. During the analyte extraction, the fabric substrate attracts aqueous sample/analytes via its hydrophilic/hydrophobic surface property. As the analytes approach towards the FPSE membrane, multiple intermolecular interactions between the sol-gel sorbent and the analytes come into play, resulting in successful extraction of the analytes into the sol-gel sorbent. The sorbent loading in the FPSE membrane is very high compared to SPME/SBSE/TFME. Due to the high sorbent loading, only a fraction of analyte retention capacity is utilized during the extraction process (even after the exhaustive extraction) as demonstrated by [PERSON] et al. (2010).
# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
###### Abstract  
With the development of Industry 4.0, as a pivotal part of the power system, large-capacity power transformers are requiring fault diagnostic methods with higher intelligence, accuracy and anti-interference ability. Considering the powerful capability for extracting non-linear features and the sensitivity differences to features of deep learning methods, this paper proposes a deep parallel diagnostic method for transformer dissolved gas analysis (DGA). In view of the insufficient and imbalanced dataset of transformers, adaptive synthetic oversampling (ADASYN) was implemented to augment the fault dataset. Then, the newly constructed dataset was normalized and input into the LSTM-based diagnostic framework. Then, the dataset was converted into images as the input of the CNN-based diagnostic framework. At the same time, the problem of still insufficient data was compensated by the introduction of transfer learning technology. Finally, the diagnostic models were trained and tested respectively, and the Dempster-Shafer (DS) evidence theory was introduced to fuse the diagnostic confidence matrices of the two models to achieve deep parallel diagnosis. The results of the proposed deep parallel diagnostic method show that without complex feature extraction, the diagnostic accuracy rate could reach 96.9%. Even when the dataset was superimposed with 3% random noises, the rate only decreased by 0.62%.  
adaptive synthetic oversampling; convolutional neural networks; Dempster-Shafer evidence theory; dissolved gas analysis; deep parallel diagnosis; long short-term memory; transformer; fault diagnosis +
Footnote †: journal: Applied Sciences
## 2 Mathematical Model and Controller Design
### Parameter Identification  
There are two parameters reflecting control effectiveness, defined as:  
\[\textit{Peak}=\frac{max\theta_{1}-max\theta_{2}}{max\theta_{1}} \tag{3}\]  
\[\textit{RMS}=\frac{s_{1}-s_{2}}{s_{1}} \tag{4}\]  
where \(s\) is root-mean-square, \(s=\left(\frac{1}{n}\sum\limits_{i=1}^{n}\left(\theta_{i}-\overline{\theta} \right)^{2}\right)^{\frac{1}{2}}\) and \(\overline{\theta}\) is angle mean, \(\overline{\theta}=\frac{1}{n}\sum\limits_{i=1}^{n}\theta_{i}\), \(\theta_{i}\) is the angle of \(i\) moment. \(\theta_{1}\) is the swing angle without control. \(\theta_{2}\) is the swing angle with the ARID control. \(s_{1}\) is the root-mean-square without control. \(s_{2}\) is the root-mean-square with the ARID control. The _Peak_ reflects the control effect of the ARID system on a peak swing angle. The _RMS_ (root-mean-square) reflects the control effect of the ARID system on swing angle dispersion.
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 5 Conclusions  
This article presented a tensor framework for reconstructing the NDVI on the basis of tensor decomposition and a sliding-window-based reconstruction algorithm from multitemporal optical remote-sensing images. We proposed a generic algorithm and used it successfully with NDVI that was extracted from Landsat series optical remote-sensing images for an estuary of Salween River in Southeast Asia. Our results showed that the proposed algorithm was able to reconstruct NDVI with small ranks and less execution time than the general Tucker decomposition method can. Compared with RFR, SWTSA achieved better accuracy and reconstruction capabilities. Moreover, experimental results demonstrated that SWTSA and MLR had almost the same reconstruction effect corresponding to rank size \(3\times 10\times 10\). Lastly, this approach was capable of describing spatial distribution patterns and temporal evolution at different levels (rank sizes). In addition, we determined that large core tensors may not mean significant improvement in accuracy for reconstruction of NDVI, but a significant increase in the execution time of the algorithm. Conceptualization, Z.S. and L.W. ; methodology, Z.S. ; software, Z.S. and C.C. ; validation, Z.S. and C.C. ; formal analysis, Z.S. and C.C. ; investigation, Z.S. and C.C. ; resources, Z.S. and C.C. ; data curation, Z.S., C.C. and Y.Z. ; writing--original draft preparation, Z.S. ; writing--review and editing, Z.S. and L.W. ; visualization, C.C. and Y.Z. ; supervision, L.W. All authors have read and agreed to the published version of the manuscript. This work was supported by National Natural Science Foundation of China (NSFC) under Grant Nos. 41906148 and 61966036, and Project of Innovative Research Team of Yunnan Province grant/award No. 2018 HC019. Not applicable. Datasets used to support this study are available at [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 19 September 2020). The authors are very grateful to the reviewers for their valuable suggestion in improving this paper. The authors would like to thank [PERSON], [PERSON], and [PERSON] for the insightful comments on the early drafts of the manuscript. The authors declare that they have no conflict of interest.
## 2 Proposed Data-Driven Approaches
### Hyperbolic Tangent Neural Network  
Neural networks can be defined as a mathematical function that manipulates a set of derived inputs, commonly known as hidden nodes. The concealed nodes can be represented as non-linear transformations of the initial inputs. The researcher is provided with the choice to define how many hidden layers they prefer, where each layer can be customised to include any desired quantity of hidden nodes. The neural network architecture encompasses a fully connected multi-layer perceptron, consisting of either one or multiple layers. Neural networks are utilised in order to predict one or more response variables by employing a flexible function of the input variables. Neural networks demonstrate robust predictive ability in scenarios where there is no need to explicitly define the functional structure of the response surface or establish a clear description of the relationship between the input variables and the response variable [19]. Figure 2 displays the proposed neural network design that showcases 45 inputs derived from the dataset, with the only output being the prediction result for RUL.  
The activation function is a mathematical operation that applies a non-linear transformation to the weighted sum of the input variables, sometimes denoted as \(x\) variables. The function used for the response entails either a linear combination in the case of continuous responses or a logistic transformation for nominal or ordinal responses [20]. One significant advantage of utilising a neural network model is its capacity to efficiently record and depict a wide range of response surfaces [21]. By employing an ample quantity of hidden nodes and layers, it becomes feasible to approximate any given surface with a high degree of accuracy. One significant constraint linked to neural network models is the difficulty of comprehending their results. The behaviour in question can be attributed to the inclusion of intermediate layers, which differ from the typical regression model where a straight link exists from the \(x\) variables to the \(y\) variables.  
Figure 2: Display of a neural network diagram with 45 inputs.
## 2 System Overview  
### Overall Design of the Proposed System  
The overall design of the proposed cooperative environment perception approach based on on-board sensors and vehicular wireless communications is illustrated in Figure 1. The system is designed to serve as a flexible platform that enables reliable vehicle driving environment recognition and to provide appropriate road safety applications. Automotiveremote sensors (e.g., radars and cameras) are often connected to the controller area network (CAN) bus such that the data from these sensors along with other data generated by in-vehicle electronic control units (ECUs) are collected in the form of CAN messages. The DSRC messages received with an on-board unit (OBU) are collected via Ethernet interfaces. For the enhancement of GNSS positioning performance, RTCM corrections, which contain differential corrections for the GNSS as defined by the Radio Technical Commission for Maritime Services (RTCM) Special Committee 104, can be obtained either through Networked Transport of RTCM via Internet Protocol (NTRIP) when an Internet connection is available, or through DSRC RTCM messages as defined in SAE J2735 [29] when it is possible to utilize roadside units (RSUs) capable of RTCM applications.  
For cooperative relative positioning, and particularly for the fusion of the on-board sensors and V2X communications, it is necessary to transform the GNSS positioning information (i.e., latitude and longitude) of the host vehicle and the remote targets from the World Geodetic System 1984 (WGS 84) reference frame to the host vehicle reference frame. For this, WGS-84 coordinates are converted to earth-centered, earth-fixed (ECEF) coordinates and then to east-north-up (ENU) coordinates. The GNSS course heading of the host vehicle is utilized to finally rotate the remote target positions and find their relative positions in the host vehicle reference frame. The relative positions and other dynamic information on remote targets acquired from V2X communications can be used in conjunction with the measurements from on-board sensors for more accurate and reliable target state estimation, classification, and trajectory prediction. Some of the safety applications that can be offered with this cooperative approach are shown in the safety applications block in Figure 1.
## 5 Numerical Examples
### Comparative Experiments
#### 5.2.3 Experiment 3  
In Sections 5.1, 5.2.1 and 5.2.2, the electricity consumption is low and the AMRs do not visit the charging station due to sufficient remaining electricity. In Experiment 3, we investigate the impact of distance on charging times and the number of AMRs. Let the travel distance be \(nd_{ij}\), where \(n=1,2,4,6,8\) and \(d_{ij}\) represents the distance between \(i\) and \(j\) given in Table 3 for \(i,\ j=1,\cdots,12\). The variances of \(T_{ij}\) and \(S_{i}\) are denoted by \(\sigma_{t}^{2}=40+160I_{ij}\) and \(\sigma_{s}^{2}=360\), respectively. All the other parameters are the same as those in Section 5.1.  
Note that long distance results in long travel time and increased power consumption. As a result, the number of AMRs engaged in service, or the frequency of charging times, shows a corresponding increase with the progressive increment of travel distance as shown in Table 10. These findings have practical significance for system design and optimization. Understanding the relationship between travel distance, power consumption, and the number of AMRs, decision-makers can make wise decisions to improve operational efficiency and optimize resource utilization in real-world applications.
## 3 The Proposed Rolling Bearing Fault Feature Extraction Method under Variable Speed Conditions
### The Optimization Algorithm Derivation
#### 3.2.2 The Solution of Sub-Problem (13)  
Before minimizing the nuclear norm, the SVD of \(\mathbf{X}^{(k+1)}-\mathbf{D}^{(k)}\) should be firstly operated, which can be marked as  
\[[\mathbf{U},\mathbf{S},\mathbf{V}]=\text{SVD}\Big{(}\mathbf{X}^{(k+1)}- \mathbf{D}^{(k)}\Big{)}, \tag{18}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are the unitary matrices, \(\mathbf{S}\) is a diagonal matrix composed of singular values. The nuclear norm can be defined as the total of all elemental absolute values in \(\mathbf{S}\), i.e., \(\|\mathbf{S}\|_{L1}\). Therefore, the iteration step of sub-problem (13) is  
\[\mathbf{Z}^{(k+1)}=\mathbf{U}\cdot\text{soft}\bigg{(}\mathbf{S},\frac{\lambda_ {0}}{\rho}\bigg{)}\cdot\mathbf{V}^{T}. \tag{19}\]  
Hereafter, the Algorithm 1 can be concluded to optimize the sparsity and low-rankness jointly enforced model (SLRJEM).  
```
0: measured vibration signal in OFSC \(\mathbf{Z}\), \(\lambda_{0}\), \(\lambda_{1}\), \(\rho\), \(K\) (\(K\) is the maximum number of iterations), \(\epsilon\) (\(\epsilon\) is the stopping rule)
1:Initialization:\(\mathbf{X}=\mathbf{O}\), \(\mathbf{D}=\mathbf{O}\) (\(\mathbf{O}\) is the zero matrix), \(k=1\)
2:loop
3:\(\mathbf{X}^{(k+1)}=\text{soft}\Big{(}\frac{1}{1+\rho}\Big{(}\mathbf{Z}+\rho \Big{(}\mathbf{Y}^{(k)}+\mathbf{D}^{(k)}\Big{)}\Big{)}-\mathbf{X}^{(k)}\), \(\frac{\lambda_{1}}{1+\rho}\Big{)}\) % according to Equation (17)
4:\([\mathbf{U},\mathbf{S},\mathbf{V}]=\text{SVD}\Big{(}\mathbf{X}^{(k+1)}- \mathbf{D}^{(k)}\Big{)}\) % according to Equation (18)
5:\(\mathbf{Z}^{(k+1)}=\mathbf{U}\cdot\text{soft}\Big{(}\mathbf{S},\frac{\lambda_ {0}}{\rho}\Big{)}\cdot\mathbf{V}^{T}\) % according to Equation (19)
6:\(\mathbf{D}^{(k+1)}=\mathbf{D}^{(k)}-\big{(}\mathbf{X}^{(k+1)}-\mathbf{Y}^{(k+1) }\big{)}\) % according to Equation (14)
7:if\(L(\mathbf{X}^{(k+1)},\mathbf{Y}^{(k+1)},\mathbf{D}^{(k+1)})-L(\mathbf{X}^{(k)},\mathbf{Y}^{(k)},\mathbf{D}^{(k)})\leq\epsilon\) or \(k>K\)then
8:break
9:endif
10:\(k=k+1\)
11:endloop
```  
**Output:** sparsity and low-rankness jointly constrained fault feature \(\mathbf{X}^{(k)}\)
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 5 Caching Transient Contents in V-NDN  
### Contributions of This Paper  
The above-mentioned caching schemes do not consider that many contents exchanged in a vehicular environment have a limited time validity, which can vary from a few seconds to a couple of minutes. Intelligent driving assistance systems, parking lots availability, high-definition maps are just a few examples of services that rely on contents that may change with time due to the variation of the driving environment [2; 3]. They entail fresh content retrieval and also short latency. Such a transient feature may largely influence the performance of a caching system and cannot be ignored in the caching decision. Therefore, in the following sections, we aim at exploring the impact of transient contents in the caching systems of V-NDN nodes. In particular:  
* We detail how the vanilla NDN forwarding fabric deals with transient data, by emphasizing the potential weaknesses, and report the few related literature studies in the field.
* We perform a first basic simulation campaign to quantify the effects of caching transient contents in V-NDN by studying crucial metrics such as the cache hit ratio and the cache inconsistency, which is due to the wrong awareness about the content lifetime supported by the vanilla NDN implementation.
* We design a new autonomous strategy, named Freshness-Driven Caching (FDC), which addresses the cache inconsistency issues and takes caching decisions based on the content lifetime. The rationale behind our proposal is pretty intuitive: caching contents that are ready to expire (possibly at the expenses of contents with a larger lifetime) does not efficiently use the storage space. Indeed, regardless of the request pattern, short-lasting contents will be quickly removed from the CS. Therefore, FDC aims at caching long-lasting contents with a higher probability.
* Performance of FDC is evaluated in two different mobility scenarios, urban and highway, and compared against two NDN benchmark schemes, CEE and random caching.
# Hierarchical Episodic Control
## 3 Hierarchical Episodic Control
### Theoretical Analysis
#### 3.3.1 Non-Overestimation Property  
The algorithm's attribute of not overestimating value estimation errors during the bidirectional propagation process is investigated as a fundamental concept in value-based methods. Theorem 1 demonstrates that the OptionEM method does not overestimate the true maximum in expectations.  
**Theorem 1**.: _Given an independent unbiased estimation_  
\[\tilde{M}_{\theta}^{(1,2)}(s_{t+h},\omega_{t+h},a_{t+h})=M^{\pi}(s_{t+h}, \omega_{t+h},a_{t+h})+\epsilon_{h}^{(1,2)} \tag{8}\]  
_and_  
\[\tilde{M}_{\Omega,\alpha}^{(1,2)}(s_{t+h},\omega_{t+h}))=M_{\Omega}^{\pi}(s_{t +h},\omega_{t+h})+\epsilon_{h}^{(1,2)} \tag{9}\]  
\[\mathbb{E}_{\tau,\mathcal{E}}\Big{[}R_{t}^{(1,2)}(s_{t})\Big{]}\leq\mathbb{E}_ {\tau}\Big{[}\max_{0\leq h<T-t}Q_{t,h}^{\pi}(s_{t},\omega_{t},a_{t})\Big{]} \tag{10}\]  
\[M_{t,h}^{\pi}(s,\omega,a)=\sum_{i=0}^{h}\gamma^{i}r_{t+i}+\left\{\begin{array} []{ll}\gamma^{h+1}M^{\pi}(s_{t+h+1},\omega_{t+h+1},a_{t+h+1}),&\text{ if }h<T-t,\beta_{t+h+1}=0\\ \gamma^{h+1}M_{\Omega}^{\pi}(s_{t+h+1},\omega_{t+h+1}),&\text{ if }h<T-t,\beta_{t+h+1}=1\\ 0,&\text{ if }h=T-t\end{array}\right. \tag{11}\]  
\[\tau=\{(s_{t},a_{t},a_{t},r_{t},s_{t+1},\omega_{t+1})\}\text{is a trajectory.}\]  
Appendix A gives the proof of Theorem 1. The bidirectional propagation process maintains the non-overestimation property of the double DQN, ensuring the reliability of the proposed value propagation mechanism. The subsequent analysis further demonstrates the convergence property of the OptionEM algorithm.