esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents an IDHIL simulator to develop and evaluate cooperative eco-driving systems of CHEV. Using the IDHIL simulator, the systems can be tested under realistic driving environments without huge costs and endangering human lives.  
The IDHIL simulator consists of QualNet for V2X communication among nodes, dSPACE simulator Mid-Size for HIL simulation, dSPACE softwares for HEV models and simulation scenarios, dSPACE MicroAutoBox for HCU development, Cohda Wireless MK5 for cooperative eco-driving application development, and a driving controller for manual driving of a user. An example application to demonstrate the IDHIL simulator is cooperative eco-driving speed guidance application using V2I communication between traffic lights and HV. The application notifies the eco-speed calculated using received SPaT message to a driver. As a driver drives at the eco-speed, a CHEV operates driving mode decision and power distribution control algorithms, and thus a CHEV can reduce fuel consumption. In the results, the fuel efficiency has improved by 14.34% when driving with eco-speed. The results demonstrate that the developed algorithms of CHEV are effective, and furthermore, the IDHIL simulator is useful.  
To further improve a fuel efficiency of CHEV, a high-level automation of vehicle systems is needed. Therefore, we suggest future research project of implementation of a simulator to develop and evaluate cooperative eco-driving systems for connected and automated vehicle by adding partially and fully automated driving functions.  
Conceptualization, J.-i.J.; methodology, J.-i.J., G.L., and S.H.; software, G.L. and S.H.; validation, J.-i.J., G.L., and S.H.; formal analysis, G.L. and S.H.; investigation, G.L. and S.H.; resources, J.-i.J.; data curation, G.L. and S.H.; writing--original draft preparation, G.L. and S.H.; writing--review and editing, J.-i.J.; visualization, G.L.; supervision, J.-i.J.; project administration, J.-i.J.; funding acquisition, J.-i.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Republic of Korea (No. 20000578) and the Institute for Information & communication Technology Planning & evaluation (IITP) funded by the Republic of Korea (No. 2019-0-00148). The APC was funded by KEIT (No. 20000578).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Total Fuel (L)** & **Fuel Efficiency (km/L)** & **Improvement (\%)** \\ \hline Normal HEV & 0.3855 & 12.34 & \\ CHEV & 0.3368 & 14.11 & 14.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuel efficiency comparison of normal HEV and CHEV.  
Figure 12: Cumulative fuel consumption.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 6 Conclusions  
In this article, we have proposed a novel DNN to remove the reflection artifacts in reconstructed PAT images under different noise levels and different media. By directly comparing the proposed network to popular iterative reconstruction algorithms with simulated PAT data from CT scans, the results have showed that the proposed network is able to reconstruct superior images over the conventional iterative reconstructions in typical scenarios in terms of computational efficiency and noise reduction.  
The results can be further strengthened in several aspects. One practical and significant question is how to make the network robust to potential malignant attacks. It is well known that DL models are vulnerable to adversarial examples. A more stable training procedure is thus critical to the clinical application of DL methods. Next, the effectiveness and efficiency of the network can be further improved for constrained resources and cloud-end processing. Some other factors, such as limited view, acoustic attenuation, and fluctuation of sound speed, can greatly impact the quality of PAT images. It would be interesting to extend the DL approach to these situations as well.  
Figure 7: Reconstruction in a heterogeneous Medium.  
**Author Contributions:** [PERSON] and [PERSON] initiated the project and designed the experiments. [PERSON] performed machine learning research. [PERSON] performed iterative reconstruction research. [PERSON] and [PERSON] wrote the paper, and [PERSON] participated in the discussions and edited the paper.  
**Funding:** Y.Y. was partly supported by the NSF grant DMS-1715178, the [PERSON] travel grant, and the startup fund from the Michigan State University.  
**Acknowledgments:** The authors thank NVIDIA Corporation for the donation of GPUs used for this research. The authors would also like to express their gratitude to the anonymous reviewers for the valuable suggestions and comments which helped considerably improve the exposition of the paper.  
**Conflicts of Interest:** [PERSON] and [PERSON] have received unrelated industrial research grants from General Electric and Hologic Inc.
## 2 Methods
### Double Interpolation and Mutation Interval Reconstruction Local Mean Decomposition (DIMIRLMD)
#### 2.3.2 DIMIRLMD Algorithm  
Based on the above analysis, the DIMIRLMD algorithm which integrates the double interpolation PF optimization process and demodulation mutation interval reconstruction process is detailed as follows:  
1. Calculate the local extreme point sequence of the signal x(t).
2. Apply the CSI method to construct the upper envelope \(\mathrm{E}_{\mathrm{u}}^{\prime}(\mathrm{t})\) and lower envelope \(\mathrm{E}_{\mathrm{u}}^{\prime}(\mathrm{t})\) using the extreme point sequence.
3. The mutation intervals where the upper and lower envelopes overlap should be determined; add the extreme symmetry points to the original extreme point sequence in the mutation interval to form a new extreme point sequence.
4. Reconstruct new envelopes \(\mathrm{E}_{\mathrm{u}}^{\prime}(\mathrm{t})\) and \(\mathrm{E}_{\mathrm{u}}^{\prime}(\mathrm{t})\) with the refreshed extreme point sequence; if the reconstructed envelopes still exhibit mutation intervals, return to step (3) until the exclusion of mutation intervals.
5. Calculate the continuous local mean function \(\mathrm{m}_{11}(\mathrm{t})\) and the envelope function \(a_{11}(t)\) using the reconstructed upper envelope \(\mathrm{E}_{\mathrm{u}}^{\prime}(\mathrm{t})\) and the lower envelope \(\mathrm{E}_{\mathrm{l}}^{\prime}(\mathrm{t})\): \[m_{11}(t)=\big{(}E_{u}^{\prime}(t)+E_{l}^{\prime}(t)\big{)}/2\] (15) \[a_{11}(t)=\big{|}E_{u}^{\prime}(t)-E_{l}^{\prime}(t)\big{|}/2\] (16)
6. Subtract \(m_{11}(t)\) from the original signal x(t) to obtain \(\mathrm{h}_{11}(\mathrm{t})\), and then the resulting signal \(\mathrm{h}_{11}(\mathrm{t})\) is divided by \(a_{11}(t)\). \[h_{11}=x(t)-m_{11}\] (17) \[s_{11}(t)=h_{11}(t)/a_{11}(t)\] (18)  
This sifting process continues until \(s_{\mathrm{ln}}(\mathrm{t})\) becomes a pure frequency-modulated signal. Multiply \(\mathrm{a}_{1}(\mathrm{t})\) by \(\mathrm{s}_{\mathrm{ln}}(\mathrm{t})\) to obtain the first CSI-based product function \(\mathrm{PF}_{\mathrm{l}}^{\prime}(\mathrm{t})\) of the original signal:  
\[PF_{1}^{\prime}(t)=a_{1}(t)s_{1n}(t) \tag{19}\]
7. Instead of the CSI method, employ the MPCHI method and repeat steps (1) to (6) to obtain the first MPCHI-based product function \(\mathrm{PF}_{1}^{\prime\prime}(\mathrm{t})\) of the original signal.
8. Calculate the \(IO^{\prime}\) of \(\mathrm{PF}_{1}^{\prime\prime}(\mathrm{t})\) and \(IO^{\prime\prime}\) of \(\mathrm{PF}_{1}^{\prime\prime}(\mathrm{t})\), and then select the PF component with lower \(IO\) as the first optimal \(\mathrm{PF}_{\mathrm{l}}(\mathrm{t})\) component.  
(9) Subtract PF\({}_{1}\)(t) from the original signal x(t) and obtain a new signal u\({}_{1}\)(t); the above procedure is repeated k times until u\({}_{k}\) becomes a monotonic function, and then we obtain a series of optimal PF components.  
The flowchart of the DIMIRLMD algorithm is shown in Figure 8.
# Continuous-Time Perfect Control Algorithm--A State Feedback Approach
## 6 Simulation Examples
### Three-By-One System  
Consider again the state-space continuous-time system, but now with three inputs, two state variables, and single output variable. Such a plant is described with the following matrices: \(\mathbf{A}=\left[\begin{array}{cc}0.4000&0.1200\\ 0.7000&1.1000\end{array}\right]\), \(\mathbf{B}=\left[\begin{array}{cc}0&-1.1000&0.5000\\ 0.4000&1.0000&1.8000\end{array}\right]\), and \(\mathbf{C}^{\mathrm{T}}=\left[\begin{array}{cc}0.2000\\ 0.6000\end{array}\right]\). Moreover, the initial condition is \(\mathbf{x_{0}}=[4\;\;-2]^{\mathrm{T}}\) and \(\Delta t=0.2s\). With the application of the minimum-norm right \(T\)-inverse, we obtain the unstable perfect control system with the corresponding signals presented in Figure 2. In this scenario, the closed-loop poles obtained by the expression (20) are equal to \(s_{1}=0.3307\) and \(s_{2}=-5.0000\), which also confirm the unstable behavior. Interestingly, the output variable remains at the reference value despite the obvious state instability. However, this feature was expected since, in the discrete-time manner, the perfect control algorithm has already revealed similar properties. It is also worth mentioning that the energy of control signal is equal \(E=||\mathbf{u}(t)||_{2}=2.2876\cdot 10^{6}\).  
Figure 1: Perfect control plots: \(T\)-inverse, case \(\Delta t=0.1\) s.  
On the other hand, with the application of right \(\sigma\)-inverse with degrees of freedom in the form of  
\[\beta=\left[\begin{array}{cc}-9.1000&-0.3000&2.0000\end{array}\right], \tag{25}\]  
the following stable state and control signals, presented in Figure 3, are obtained.  
In this examination, the closed-loop perfect control poles are equal to \(s_{1}=-5.0000\) and \(s_{2}=-5.4748\), whilst the control energy equals \(E=6.0802\cdot 10^{6}\). Here, the stability of closed-loop system is earned with higher energy expenditure. Of course, with a wider time horizon, the energy outcome will eventually favor the stable solution, as the steady state is obtained here after five calculation steps. Therefor, a stable continuous-time perfect control can be established using the generalized nonunique matrix inverse. This feature is coherent with results received for discrete-time systems.
## 3 State of the Art
### Graph-Based Design Languages Implemented with UML and DC43
#### 3.2.4 Applications of the Framework of Graph-Based Design Languages and DC43  
The engineering framework provides a powerful environment for design engineers because of the possibility of automating the systematic variation, simulation and calculation and to feed and integrate all relevant CAE tools. It permits automated machine execution and also the documentation and reuse of design and manufacturing knowledge, because this knowledge is included in the automated model generation, simulation and calculation. Consequently, the engineering framework allows the generation of a vast number of design alternatives. The generation of alternative solutions can be based on parameter variation across multiple simulation tools, but also on topological variation and can be carried out in a fast and automated manner [48; 51; 52; 53; 54; 55]. Figure 11 illustrates the successful applications of GBDLs.  
Figure 10: Design graph with digital models.  
One of the central advantages of GBDLs is that the \"members\" of the mentioned variety of included design alternatives may not only be different in terms of certain design parameters, but may also be topologically different. It is a main advantage of using GBDLs in connection with a design compiler that it is possible to realize fully automated processes that may contain calculation and simulation steps. This decisive characteristic enables the creation of a multitude of possible and feasible product configurations. Because of this, it also enables the realization of optimization cycles that may lead to a certain product configuration that may represent an optimum or close-to-optimum solution in a multi-dimensional solution space. In this context, the term \"multi-dimensional\" may refer to both the requirements space and the solution space. The ability to find optimum solutions is specifically advantageous for the product development of systems with many configuration possibilities, e.g., wire harnesses or pipe systems (e.g., exhaust systems) [55]. Another central advantage results from the ability to incorporate multiple physical domains; it is, for instance, possible to simultaneously address several issues, such as the mass and moment balance as well as the thermal validation of a satellite.  
The advantages of GBDLs mentioned above can only be used completely if these methods are employed covering all stages of product concretization and all phases of the lifecycle of technical systems. Additionally, issues such as the fault tolerance of systems and fault-tolerant design can be addressed. One important objective of employing these methods and the underlying framework is to accelerate innovation. Another central objective of this employment is to develop appropriate knowledge representations. Through this, an automated processing of the product lifecycle can be enabled, which realizes more human-independent technological process integration and therefore improves knowledge storage and transfer in product development processes.
## 2 Theoretical Background
### Total Harmonic Distortion (THD)  
According to the standard IEEE 519-2014 [28], harmonics are sinusoidal components in voltage signals that have frequencies that are integer multiples of the fundamental frequency component. Harmonic components are related to waveform distortion and the level of distortion due to harmonics is quantified using the THD index. The mathematical expression for THD is presented in (3):  
\[THD=\frac{\sqrt{\sum\
olimits_{h=2}^{50}{P_{h}}^{2}}}{P_{1}}\;100, \tag{3}\]  
where \(P_{1}\) is the power of the fundamental component and \(P_{h}\) is the power of the harmonic component of order \(h\). The power of the harmonic components is found using the Fourier transform. The THD calculation must be performed using a 200 ms time window, i.e., 12 cycles for 60 Hz power systems or 10 cycles for 50 Hz power systems.
## 4 Cost of Energy Results and Discussion  
### Total System Cost of Energy  
The total system cost of energy per year TSCoE (k\(\epsilon\)/year) in the Near Future and Mid Century scenarios for Hamburg and Murcia is shown in Figure 7. The subsystems are grouped into hydrogen and electricity. The average component installed capacities and their total annual costs (TC\({}_{i}\)) are listed in Appendix E Table A7 and serve as input for Figure 7. The following major trends can be observed when comparing both locations and scenarios.  
* _The 70% reduction in TSCoE in the Mid Century_ compared to the Near Future scenario for both locations. Higher efficiencies, lower final energy consumption, and increased favorable match between solar electricity production and final energy consumption significantly reduce installed capacities, thus costs. Economies of scale also reduce both installed capital and operation and maintenance costs.
* _The 20-30% lower TSCoE for Murcia_ compared to Hamburg for both scenarios. For Murcia, the TSCoE is 1.9 million euros/year in the Mid Century scenario, whereas, for Hamburg, it is 2.6 million euros/year. The reason for this is the lower final transportation and building electricity demand and lower storage and reconversion requirements.
* _Variations in TSCoE from year to year are very small_, 2.2-4.0% (coefficient of variation CV in Table A7 in Appendix E). This can be explained by the variations in daily and annual wind and solar electricity production, as well as the varying mismatch between solar electricity production and consumption. Seasonal hydrogen storage has relatively higher cost variations (8-12%) in comparison to other components, as the SHS is responsible for coping with all the above-mentioned variations.
* _The cost of hydrogen components in the Mid Century scenario drops up to 75%._ For both locations, in the Near Future scenario, the hydrogen components represent about 70% of the TSCoE; this reduces to 63% on average. As hydrogen technology is relatively new, economies of scale have a bigger impact on future cost reductions than on solar and wind electricity technology. In addition, the increase in solar output reduces storage requirements.
* _Hydrogen transportation, seasonal hydrogen storage, and the solar system are the only components that share in the total costs' relative increase compared to all other components_. This is because the cost reductions for these components are relatively lower compared to the other components. The relatively higher use of seasonal hydrogen storage in the Mid Century scenario compared to the annual hydrogen production (see Section 3.1) is another contributing factor.  
Figure 7: Total system cost of energy (TSCoE) for the component categories in the Near Future and Mid Century scenarios for Hamburg and Murcia. The subsystems are grouped into “Hydrogen” and “Electricity”.
## 3 Results and Discussion
### Constructability Testing  
Figure 3 shows the laboratory printing results regarding the 3 DPFC samples' constructability. It can be observed that with the change in the formulation of 3 DPFC, the printing height of the samples also varies significantly. Both sample EP40S1 and sample EP80S1 have a maximum single-print height of 18 layers. The maximum single-print height of sample EP80S1 is two layers more than that of sample EP40S2, while the maximum single-print height of sample EP100S1 is three layers more than that of sample EP80S2. Within a similar density range, samples with a higher expanded perlite content exhibit superior constructability.
## 4 Data Products and Processing
### Format and Data Structure  
The complex waveform product is stored in netCDF (network Common Data Form) format, for which compatible libraries with several programming languages exist. Each netCDF file is generated by following the processing procedure shown in Section 3.2, which contains the complex waveforms and relevant metadata for the reflected signal from a  
Figure 3: Geographical distribution of the complex waveform product tracks processed from the raw IF data collected by (**a**) the UK TechDemoSat-1 satellite and (**b**) the CYGNSS constellation. The color of each track indicates the source of the GNSS transmitter (GPS, Galileo or BDS-3).  
given GNSS transmitter. The variables are organized by groups in the netCDF file as shown in Figure 4. The root group (\"/\") includes the basic information of the track, e.g., the raw IF collection, the PRN of the GNSS transmitter. The complex waveforms of the direct and reflected signals are stored in the \"cWF\" group along the time and waveform lag dimensions. The \"cWF\" group also includes some intermediate results for the complex waveform generation, such as the direct signal tracking results and the OL tracking model. It is noted that there can be more than one \"cWF\" groups corresponding to the complex waveform of the reflected signals at different frequency bands. The \"MetaData\" group mainly includes the positions and velocities of the transmitter, receiver and specular point along the time dimension. Table 2 provides the detailed explanations of some important variables, and how these variables can be linked to the raw IF processing in Section 3.2. Note that the brief explanations for all the variables can be found in the self-describing attributes in the netCDF file.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Variable** & **Dim** & **Description** & **Unit** & **Symbol** \\ \hline \multirow{3}{*}{coh\_int\_time} & \multirow{2}{*}{-} & Coherent integration time of the direct and reflected signal & \multirow{2}{*}{Second} & \multirow{2}{*}{\(t_{\text{c}}\) in (1)} \\ \cline{1-1} \cline{5-5}  & & & Range delays of the complex waveform lags with respect to the specular point & & \multirow{2}{*}{Meter} & \multirow{2}{*}{\(c\tau_{\text{i}}\) in (1)} \\ \cline{1-1} \cline{5-5}  & & & Start time of the cross-correlation, which is used as the time tag of each complex waveform & & \multirow{2}{*}{Second} & \multirow{2}{*}{\(t_{\text{0}}\) in (1)} \\ \cline{1-1} \cline{5-5}  & & & Start raw IF sample index of the cross-correlation & & & \multirow{2}{*}{-} & \multirow{2}{*}{\(t_{\text{0}}\)/s} \\ \cline{1-1} \cline{5-5}  & & & in (1) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition and explanation of the variables in the complex waveform netCDF file of the spaceborne GNSS-R raw IF data products.  
Figure 4: Structure of the complex waveform product derived from GNSS-R raw IF data collected by different spaceborne missions. The product is in netCDF (network Common Data Form) format. Each netCDF file contains the complex waveforms and relevant metadata in different groups.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 2 Background
### RNNs  
Recurrent neural networks (RNNs) were proposed to handle time-series data. An RNN is formulated by means of state and hidden state variables, which depend on the previous states and hidden states.  
Let \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{T})\), be a sequence. Each hidden state \(\mathbf{h_{t}}\) is recurrently updated by:  
\[\mathbf{h_{t}}=\left\{\begin{array}{cc}0&\text{t}=0\\ \varphi(\mathbf{h_{t-1}},\mathbf{x_{t}})&\text{otherwise}\end{array}\right. \tag{2}\]  
where \(\varphi\) is a nonlinearity, such as the composition of a logistic sigmoid with an affine transformation. Optionally, the RNN may have an output \(\boldsymbol{y}=(y_{1},y_{2},\ldots,y_{T})\).  
The update of the recurrent hidden state in Equation (2) is often implemented as:  
\[\mathbf{h_{t}}=\text{g}(\mathbf{Wx_{t}}+\mathbf{U}\mathbf{h_{t-1}}) \tag{3}\]  
where \(g\) is a smooth, bounded function such as the hyperbolic tangent or the logistic sigmoid.  
RNNs often fail to capture long-term dependencies due to the vanishing gradient effect. Two main directions were followed to cope with this. The first direction consists of alternatives to stochastic gradient descent [15; 16; 17]. The second direction involves the design of a sophisticated activation function, which consists of affine transformation and a simple element-wise nonlinearity obtained by gating units. The earliest attempt in thisdirection resulted in a recurrent unit, called long short-term memory (LSTM) [18]. Later, another type of recurrent unit, gated recurrent unit (GRU), was proposed [13; 19]. RNNs employing either of these recurrent units have been shown to perform well in tasks that require capturing long-term dependencies, such as speech recognition [22; 23] and natural language processing [24; 25]. Starting from time-series of sensor measurements obtained under various driving conditions, we investigate the application of both LSTMs and GRUs in the context of driving behaviour analysis.