esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 5 Discussion  
In this paper, by means of virtual-real combination, the proposed LSTM-DQN-APF is verified to be able to effectively solve the local minima, target unreachability, and time-varying problems in complex battlefield environments faced by the traditional artificial potential field method in UAV trajectory planning. The virtual-reality interaction system therein presents the physical entity in digitalization with a multi-dimensional, multi-space, multi-scale model and takes the multi-source heterogeneous data as a link, connects the physical entity with the virtual space operation in real time, maps it in real time, and refreshes it in real time; constantly improves the model and parameters of the simulation environment; gradually improves the realism of the simulation environment; improves the credibility of the simulation results; and reduces the difficulty of migrating from the simulation environment to the real environment. The final goal is to improve the simulation environment with the help of the virtual space simulation. The ultimate goal is to further improve the physical entity verification capability based on digital twin technology by means of simulation and visualization in virtual space.  
Conceptualization, Y.L. and Y.F.; methodology, X.W.; software, X.W.; validation, Y.L.; formal analysis, Y.L.; investigation, S.Q.; resources, Y.F. and Y.Y.; data curation, Y.Y. and M.D.; writing--original draft preparation, X.W.; writing--review and editing, Y.L.; visualization, M.D.; supervision, Y.L.; project administration, Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm Name** & **Ambient Condition** & \begin{tabular}{c} **Mean Time of** \\ **Flight/s** \\ \end{tabular} & \
\begin{tabular}{c} **Average Path** \\ **Length/m** \\ \end{tabular} &
\begin{tabular}{c} **Task Completion** \\ **Rate** \\ \end{tabular} \\ \hline APF & breezeless & 57.27 & 40.5 m & 36\% \\ Ge-Apf & breezeless & 49.21 & 36.4 m & 78\% \\ LSTM-DQN-APF & breezeless & 26.62 & 28.2 m & 99.2\% \\ APF & windiness & 71.1 & 48 m & 23\% \\ Ge-Apf & windiness & 65.43 & 42.3 m & 64\% \\ LSTM-DQN-APF & windiness & 34.35 & 35.3 m & 97.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Flight performance comparison between APF, Ge-Apf, and LSTM-DQN-APF in windy and windless conditions.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest.
## 3 Results
### Individual Conditional Expectation (ICE) Interpretation of the ML Models  
The ICE plots in Figures 14 and 15 show how the model predictions vary in relation to a certain input feature while all other features have fixed values. The input features for the generation of the ICE plots have been selected based on their impacts on the model outputs. The ICE curves are generated for each data sample separately, which clarifies the differences between the data samples, in terms of their interaction with the predictive model. In each ICE plot, the average of all curves is represented with a thick blue line. In the ICE plots, all of the curves being close to one another indicates a similar variation of the predicted quantity in all samples. Curves that lie farther away from the average indicate a larger deviation of the predictions from the average. For instance, in the ICE plots of Figures 14a and 15b, all of the generated curves lie relatively close to the average curve, whereas, in Figures 14c and 15a, the curves are further apart from each other, indicating a larger variation of the predictions. According to Figure 14a, the confined ultimate strength \(f_{\text{cc}}\), which is denoted with \(f(x)\) in Figure 14, continuously increases with \(f_{\text{co}}\). The largest increase in \(f_{\text{cc}}\) occurs in the interval \(75\text{ MPa}\leq f_{\text{co}}\leq 100\text{ MPa}\). A similar trend is also observed in \(f(x)|t_{\text{r}}\), and the largest increase happens for \(t_{\text{r}}\leq 2\text{ mm}\). Figure 15 shows the variation of the predicted \(\epsilon_{\text{cu}}\) denoted with \(f(x)\). The adverse effect of increasing the column length on the ultimate strain can be seen in Figure 14c. Although \(f_{\text{co}}\) has, overall, an increasing effect on the ultimate strength according to Figure 13a, a different trend can be observed in Figure 15b, where increasing the value of \(f_{\text{co}}\) in the range \(20\text{ MPa}\leq f_{\text{co}}\leq 80\text{ MPa}\) is shown to have a decreasing effect on the ultimate strain.  
Figure 13: CatBoost ultimate strain prediction feature dependence plots for (**a**) \(f_{\text{r}}\), (**b**) \(f_{\text{co}}\), (**c**) L and (**d**) \(t_{\text{f}}\).  
Figure 14: ICE plots of the XGBoost ultimate strength prediction for (**a**) \(\rm f_{\rm co}\), (**b**) \(\rm t_{\rm r}\), (**c**) L and (**d**) \(\rm f_{\rm f}\).  
Figure 15: ICE plots of the XGBoost ultimate strain prediction for (**a**) \(\rm f_{\rm f}\), (**b**) \(\rm f_{\rm co}\), (**c**) L and (**d**) \(\rm t_{\rm r}\).
## 3 Results
### Real-World Application Results
#### 3.1.2 Computation Time  
The second diagram in Figure 6 shows a comparison of the computation times of the different methods. As assumed, the landmark and shifting-window methods showed a continuous increase in computation time, due to the models' need to be retrained at each evaluation iteration. By contrast, the conventional batch learning approach exhibited a much lower processing time because of its singular model training phase. On the other hand, the OML algorithm achieved time-efficient results as well. This was because OML updates models incrementally, rather than training from scratch with each evaluation.
## 4 Simulation
### Onshore AC Fault  
At t = 3 s, a three-phase AC grounding fault occurred on the onshore AC grid, causing the AC voltage to drop to zero, as shown in Figure 15a. An overcurrent can be observed during an AC fault, as shown in Figure 15b. Consequently, the active power generated by the wind turbines could no longer be transmitted to the grid and accumulated at the converters. Due to the constant DC current control employed by the onshore converter, the DC current in the HVDC link remained stable during the first phase of the AC fault, assuming no additional control strategies were applied. The active power from the wind turbines continuously feeds into the onshore converter, leading to a steady increase in the capacitor voltage of the submodule, as seen in Figure 16c. The onshore converter is composed of the FB submodule, and under the modulation effect, the DC voltage in the HVDC link remains unchanged in the early phase of the fault, while the submodule capacitor voltage continues to rise. As a result, neither the MMC-HVDR nor the MVDR can detect the fault, and the active voltage limiting feature in the DR is not triggered. Without an additional control strategy, the \(\mathrm{v_{out1}}\), \(\mathrm{V_{dc1}}\), and \(\mathrm{V_{c1}}\) values remain constant, as shown by the pink curve in Figure 16.  
If the onshore converter reduces the DC current in the HVDC link, the active power transmission of wind turbines to the onshore converter can be suppressed, as shown by the red curves in Figures 16 and 17. However, the active power accumulates in the previous stage converter, namely the MMC-HVDR, causing the submodule capacitor voltage in the MMC and Vout to rise correspondingly. Under the active voltage limiting characteristic in the DR, the active power of the wind turbine begins to decrease slowly. However, energy still accumulates in the submodule capacitors of the MMC-HVDR during this period, causing the submodule capacitor voltage to continue to rise.  
Once the overvoltage of the capacitor is detected, the active voltage rapid injection method proposed in this article will operate immediately, and force the intermediate variable AC voltage in the grid-side converter to be lower than the DC voltage. The active power transmission path is blocked quickly. This reduces the accumulation of active power in each stage and reduces the overvoltage, as shown by the blue curve in Figure 16c. Under the control strategy proposed in this article, the system achieved onshore AC fault ride-through.  
Figure 15: The waveforms of onshore AC voltage and current. (**a**) AC voltage (**b**) AC current.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
###### Abstract  
Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for example, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as _DTN-assisted FANET_. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks.  
flying ad hoc networks (FANET); non-terrestrial networks (NTN); terrestrial networks; unmanned aerial vehicles (UAV) +
Footnote †: journal: Advances in neural networks  
1
Footnote 1: [[https://www.mdpi.com/journal/drones](https://www.mdpi.com/journal/drones)]([https://www.mdpi.com/journal/drones](https://www.mdpi.com/journal/drones))  
2022
# High-Precision Manufacture and Alignment of Image Slicer Based on Thin Glass Bonding
## 4 Error Analysis
### Surface Error  
The shape error is the height mismatch between the image slice and ideal spherical surface. The image slice deformation during stacking results in changes in the surface shape, which can be monitored using a 4D interferometer. As the shape error causes speckle change during the focusing process, controls of less than 40 nm on the surface shape RMS of each slice sphere are needed during stacking. In addition, the surface roughness \(\mathrm{R_{a}}\) should be less than 2 nm to reduce scattering.
# Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches
## 5 Conclusions  
This study investigated the influence of side branches on FFR estimation. First, the influence of side branches on FFR estimation was investigated with three idealized models and further interpreted with circuit models. The understandings from the idealized models were further validated with two patient-specific models. The inclusion of side branches caused a significant increase in the inlet flowrate. The side branches at the downstream of the stenosis will significantly reduce the FFR value, while the side branches at the upstream of the stenosis have a minimal effect on the FFR estimation. The side branches with a smaller diameter have less influence on the FFR estimation. The findings of this study could enhance the understanding of the relationship between pressure, the anatomy of narrowing vessels, and the anatomy of a vessel tree, which are not limited to OCT-derived vessel geometries. Accounting for the blood flow into side branches will enhance FFR estimation accuracy and likely produce a better quantitative agreement with patient-specific FFR measurements compared to the models that ignore the side branch flow.  
Conceptualization, L.G.; Data curation, P.T.G., P.D., J.L., Y.G. and V.N.Z.; Funding acquisition, H.G.B., D.L.W. and L.G.; Investigation, P.T.G., P.D., J.L. and L.G.; Methodology, P.T.G. and P.D.; Project administration, D.L.W. and L.G.; Resources, J.L., Y.G., V.N.Z., H.G.B. and D.L.W.; Software, P.T.G.; Supervision, H.G.B., D.L.W. and L.G. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the National Heart, Lung, and Blood Institute through grant R01 HL143484-01. The grant was obtained via collaboration between Case Western Reserve University, University Hospitals of Cleveland, and Florida Institute of Technology. The content of this report is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  
Not applicable.  
Thanks to [PERSON] for proofreading this manuscript.  
H.G.B.: Consulting Honorarias from Abbott Vascular; All other authors declare no conflict of interest.
## 4 TR-NOMS and TR-NSMS Channel Decoding Algorithm  
The TR decomposition has circular dimensional permutation invariance property and is more expressive than the TT decomposition. Better compression ratio and lower storage consumption are obtained in practice. Therefore, we also apply the TR decomposition to the NOMS algorithm, which is called TR-NOMS algorithm. We also transform the three-dimensional parameter tensors \(\beta_{v^{\prime},c^{\prime}}^{t}\), \(w_{v,c^{\prime}}^{t}\), \(w_{v}^{t}\) into four-dimensional tensors \(\beta_{v^{\prime},c^{\prime}}^{t}\), \(w_{v,c^{\prime}}^{t}\)  
\(\mathcal{B}(d)\in\mathbb{R}^{R_{d-1}\times x_{d}\times R_{d}}\) and rank set is \(R_{0},R_{1},\ldots,R_{D}\), \(\forall d\in[D]\). So, \([\iota^{1}_{v^{\prime},c^{\prime}},\iota^{1}_{v^{\prime},c^{\prime}},\ldots, \iota^{T}_{v^{\prime},c^{\prime}}]\)  
\(=\prod_{d=1}^{D}\mathcal{A}_{d}\), \([\beta^{1}_{v^{\prime},c^{\prime}},\beta^{1}_{v^{\prime},c^{\prime}},\ldots, \beta^{T}_{v^{\prime},c^{\prime}}]=\prod_{d=1}^{D}\mathcal{B}_{d}\). The TT-NSMS algorithm reduces the number of parameters of \([\alpha^{1}_{v^{\prime},c^{\prime}},\alpha^{1}_{v^{\prime},c^{\prime}},\ldots, \alpha^{T}_{v^{\prime},c^{\prime}}]\) and \([\beta^{1}_{v^{\prime},c^{\prime}},\beta^{1}_{v^{\prime},c^{\prime}},\ldots, \beta^{T}_{v^{\prime},c^{\prime}}]\) from \(2 EBT\) to \(2\sum_{d=1}^{D}x_{d}R_{d-1}R_{d}\), where \(x_{d}\), \(R_{d-1}\) and \(R_{d}\) are smaller than \(E\), \(B\) and \(T\). In order to reduce more multiplication, we also need to decompose the information in TT-NSMS algorithm. We reshape message tensor shape into high order \(y_{1},\ldots,y_{D}\). We use the product of a series of tensors in TT format \(U(1,2,\ldots,D)\) to replace original message, so \(\min_{v^{\prime}\in M(c\backslash v)}|u^{t}_{v^{\prime},c}|=\prod_{d=1}^{D}U_{d}\), where \(U(d)\in\mathbb{R}^{R_{d}-1\times y_{d}\times R_{d}}\) with rank set \(R_{0},R_{1},\ldots,R_{D}\). The multiplication in TT-NSMS algorithm comes from the multiplication of several tensors \(\mathcal{B}(1,2,\ldots,D)\) and multiplication of \(\mathcal{A}(1,2,\ldots,D)\) and \(U(1,2,\ldots,D)\). The total number of multiplication is \(\sum_{d=1}^{D-1}x_{d}y_{d}x_{d+1}y_{d+1}R_{d-1}R_{d}R_{d+1}T+\sum_{d=1}^{D-1}x _{d}x_{d+1}R_{d-1}R_{d}R_{d+1}\). The TR-NSMS algorithm and TT-NSMS algorithm have the same multiplication and parameters, but with more choices of rank set.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 7 Performance Analysis  
In this section, we analyze the proposed scheme's performance with the help of various experimental results. First, we discuss the performance of CMC under various traffic densities with the pseudonym update process. The proposed scheme CMC consists of both pseudonym change and exchange processes under diverse traffic conditions. We analyzed the anonymisation with the pseudonym change and exchange process as shown in Figure 10. During the pseudonym changing process, vehicles with high traffic density, i.e., have a higher number of neighbors in transmission range, the anonymisation of vehicles is improved. The pseudonym exchange process has lower anonymity due to fewer neighbors in transmission range. Depending on the process of pseudonyms update under various vehicle traffic conditions, the vehicle identity anonymisation is certainly different. A higher number of vehicles in a region that can change pseudonyms cooperatively will improve identity protection. Figure 11 shows the number of pseudonyms updated for various vehicle densities. We take two traffic densities. The lower traffic density case contains fewer vehicles in a road region, while higher density means a higher number of transmission range vehicles, as given in [52]. A higher number of vehicles taking part in the pseudonym update process increases identity protection. The figure clearly shows that a higher number of pseudonyms are updated during higher traffic conditions, which improves the anonymity of a vehicle in the crowd.
###### Abstract  
In the present work, the freezing and melting characteristics of water seeded with chemically functionalized graphene nanoplatelets in a vertical cylindrical capsule were experimentally studied. The volume percentage of functionalized graphene nanoplatelets varied from 0.1% to 0.5% with an interval of 0.1%. The stability of the synthesized samples was measured using zeta potential analyzer. The thermal conductivity of the nanocomposite samples was experimentally measured using the transient hot wire method. A \(\sim\)24% (maximum) increase in the thermal conductivity was observed for the 0.5% volume percentage in the liquid state, while a \(\sim\)53% enhancement was observed in the solid state. The freezing and melting behavior of water dispersed with graphene nanoplatelets was assessed using a cylindrical stainless steel capsule in a constant temperature bath. The bath temperatures considered for studying the freezing characteristics were \(-\)6 \({}^{\circ}\)C and \(-\)10 \({}^{\circ}\)C, while to study the melting characteristics the bath temperature was set as 31 \({}^{\circ}\)C and 36 \({}^{\circ}\)C. The freezing and melting time decreased for all the test conditions when the volume percentage of GnP increased. The freezing rate was enhanced by \(\sim\)43% and \(\sim\)32% for the bath temperatures of \(-\)6 \({}^{\circ}\)C and \(-\)10 \({}^{\circ}\)C, respectively, at 0.5 vol % of graphene loading. The melting rate was enhanced by \(\sim\)42% and \(\sim\)63% for the bath temperatures of 31 \({}^{\circ}\)C and 36 \({}^{\circ}\)C, respectively, at 0.5 vol % of graphene loading.  
nanocomposite; melting; freezing; graphene; thermal conductivity +
Footnote †: journal: Applied Physics Letters
## 3 Data Set and Methodology  
### Data Set  
Facies and hydrocarbon detection were identified by using the integration of well log and seismic data (Figure 3). The dataset used for this research included the wireline logs and seismic data from the northern Malay basin. Post-stack and pre-stack seismic data were used to combine with the well-logs to improve the analysis completion. In this study area, there were eight wells used in this research, i.e., labelled as I-1, I-2- I-3, I-5, I-6, I-7, I-8, and ID-1. Eight well logs were used to do well seismic tie and facies interpretation. Other data from ID-1 and I-8 comprised a core, biostratiography, and Formation Micro-Imager (FMI) reports which was used to validate the facies classification.  
Based on 3D seismic data, this field comprised 398,750,000 m\({}^{2}\) area. The seismic data included post-stack and pre-stack data (near, mid, and far). The high resolution of structural and stratigraphic features in this research indicated a good quality of seismic data. The polarity of the seismic data was a zero-phase positive peak for increasing acoustic impedance. Furthermore, according to seismic data, the thickness could be captured at about 15 m. The integration of well log and seismic data was essential to increase the confidence in facies interpretation. Nevertheless, well log data showed the vertical changing and the seismic data was qualified to determine facies distribution.
## 4 Discussion and Results of Experiment
### Analysis of Experimental Results
#### 4.3.3 Model Performance on Imbalanced Datasets  
HVCBs have different probabilities of occurrence for different types of mechanical failures during operation, but some of them have a very low probability of occurrence.  
To evaluate the ability of our proposed method in solving the unbalanced sample problem, taking into consideration the impact of varying sample sizes across different classes on the performance of the model in fault diagnosis, we constructed the unbalanced sample dataset presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **State 1** & **State 2** & **State 3** & **State 4** \\ \hline
0 & 40 & 40 & 40 & 40 \\
1 & 40 & 40 & 40 & 20 \\
2 & 40 & 40 & 20 & 20 \\
3 & 40 & 40 & 20 & 10 \\
4 & 40 & 40 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Unbalanced sample datasets used in this study.  
Figure 9: Accuracy of each algorithm under different sample sizes.  
By setting the number of samples in states 3 and 4, the imbalance that may exist in practice is simulated. Under the condition of an imbalanced dataset, the performance of various methods was simulated and assessed on the test set. To conduct a detailed analysis of the fault diagnosis capability of the GWO-gcForest model in the presence of imbalanced sample conditions, a comparison was made with other models such as gcForest, GWO-RF, and additional relevant models.  
The experimental results are visually presented in Figure 10. As the sample imbalance increases, the accuracy of all the models decreases. However, in comparison to other models, our proposed method demonstrates a higher level of accuracy when handling unbalanced samples. When the imbalance ratio is 40:40:20:10, diagnostic accuracy of the proposed method is 90.91%, while the classification accuracies of other models are all <90%. In the case of dataset 4, the accuracy of the proposed model is still close to 90%. Figure 10 clearly illustrates that,