esa-sceva/satcom-chunk-collection huggingface dataset:
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 5 Study Case: In-Vehicle BSM Data Services Platform
### Data Forecasting
#### 5.4.2 Model Development  
We performed the traffic volume prediction using the Multilayer Perceptron (MLP) artificial neural network which is a feedforward ANN. This kind of ANN relies on backpropagation for the training stage. It has multiple input layers connected as a directed graph with output layers. Analyzed dataset contains the following features shown in Table 13: 1-Date, 2-Time, 3-Number of Passenger car, 4-Number of Bus, 5-Number of Trailer, 6-Average speed of Passenger car, 7-Average speed of Bus, 8-Average speed of Trailer and 9-Traffic density. The whole inputs are significant except for the AST input, as is shown in Table 13 and is observable in Figure 15.  
_Generally measured in units of vehicles per mile (v/m), the traffic density is referred to the volume of vehicles on a road fragment._  
As a preprocessing step, we first randomized our dataset and then divided it into three sub-sets. The first sub-set represented 10% of the whole dataset and it was used for training. The second sub-set was taken for cross-validation in the ratio of 10%. The remaining part that represent 80% of the dataset was used for testing purposes.  
Distinct architectures of ANN have been designed to determine an efficient network. Table 14, presents several ANN models that were prepared to train on the dataset. The testing data sets along  
\begin{table}
\begin{tabular}{c l c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Features** & **Acronym** & **Sensitivity** \\ \hline
1 & Date & D & 3986 \\ \hline
2 & Time & T & 1.4418 \\ \hline
3 & NB Passenger car & NC & 1.1359 \\ \hline
4 & NB Bus & NB & 0.8068 \\ \hline
5 & NB Trailer & NT & 0.7001 \\ \hline
6 & AVG speed of Passenger car & ASC & 0.5584 \\ \hline
7 & AVG speed of Bus & ASB & 2.4001 \\ \hline
8 & AVG speed of Trailer & AST & 0.1083 \\ \hline
9 & Traffic density & DN & 1.0385 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Input variables considered by the Artificial Neural Network (ANN).  
Figure 15: Input variables sensitivity analysis.  
with cross-validation were used to identify the performance of every ANN architecture. As well as, to evaluate the predicted results, other parameters were exploited, such as Normalized Mean Square Error (NMSE), the coefficient of correlation (r), and Mean Absolute Error (MAE). The desired neural network is that of three hidden neurons, according to Table 14. Therefore, Figure 16 shows, in detail, the ANN structure used in our work.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 4 Numerical Simulation and Results
### The Results of the Proposed Algorithm and Comparison  
This section compares the above interference avoidance strategies in terms of different metrics, such as the total satellite communication capacity, the minimum off-axis angle of the LEO satellite beams, and the overlapping coverage between the adjacent satellites in the same orbit plane. In order to verify whether the interference avoidance strategy satisfies the corresponding constraints and to evaluate the total communication capacity of the LEO satellite, we performed the related simulations regarding different progressive schemes and the results shown in the Figure 6a-c.  
Figure 5: The scheme of progressive pitch: (**a**) pitch angle vs. the latitude of LEO satellite; (**b**) number of off beams vs. the latitude of the LEO satellite.  
In Figure 6a, the satellite communication capacities have the same trend in the above progressive pitch schemes. The communication capacities decrease slowly at the high latitude; however, when the satellite approaches the lower latitude, the communication capacity decreases significantly, mainly caused by turning off the beams. The shutdown of the satellite beam occurs when the pitch angle reaches its maximum in the interference-only and brutal force schemes. Because of the high dimensional action space, it is difficult to find a good enough solution without an excellent initial guess. While in the GA + brutal force scheme, we take the brutal force scheme as the initial guess and use the genetic algorithm to optimize the solution. Since the capacity of the brutal force scheme nearly reaches the limit of the interference-only scheme, the improvement of the optimized solution by the genetic algorithm is very limited.  
Figure 6b,c verify whether the above strategies satisfy the conditions of interference avoidance and seamless coverage, respectively. According to the results in Figure 6b, the minimum off-axis angle of the beams in the minor axis is greater than the angular threshold for all the approaches. There is a natural spatial separation between the LEO and GSO satellite systems at higher latitudes, and the off-axis angles of the LEO satellite beams are larger. The small bumps at low latitudes are mainly caused by turning off the satellite beams. However, in Figure 6c, interference avoidance-only progressive pitch strategy does not satisfy the constraint of seamless coverage. This also shows that designing an interference avoidance strategy by considering only interference avoidance is likely to result in a blind spot in satellite services. When designing a satellite interference avoidance strategy, seamless coverage consideration should not be neglected.  
Based on the above simulation results, the interference avoidance scheme proposed in the paper can avoid interference to the GSO satellite system while ensuring seamless coverage; and the capacity of the proposed schemes nearly equals the capacity of interference-only progressive pitch scheme.
## 3 Results and Discussion
### Sensorial Analysis  
Figure 6 shows the sensory evaluation of fresh apple juice and reconstituted cryoconcentrate sample (3 rd cycle). Results showed that cryoconcentration did not affect significantly (\(p\geq 0.05\)) the odor, aroma, flavor, and global assessment of the reconstituted sample, in comparison to the fresh apple juice, although there is an apparent lower score for the cryoconcentrated samples. These results were similar to studies on cryoconcentration applied to black current juice [55], since the cryoconcentrated juice was equivalent to the fresh sample for the same qualities analyzed in the present study. This advantage in the original characteristics preservation from the fresh sample has also been ratified by sensory evaluation on color, aroma, taste, and total quality of cryoconcentrated Andes berry pulp obtained by PCC [56].  
Figure 5: Process parameters: (**a**) efficiency; (**b**) percentage of concentrate; (**c**) solute yield.  
In the same way, the use of this concentration technology is advantageous from the sensory and organoleptic viewpoint, not only in juices and fruit pulps, but also in infusions as popular as coffee, since [PERSON] et al. [30] demonstrated a high retention in sensory qualities of coffee extract samples subjected to PCC and FFCC. These results confirm the advantage of CBCC technique to concentrate fruit juices, in particular for the retention of the fresh sensory characteristics [6].  
Generally, the sensory evaluation results showed that samples treated with CBCC were less appreciated than fresh apple juice, but there was no significant difference (\(p\geq 0.05\)) between the characteristics evaluated. Furthermore, the CBCC treatment had no negative effect on the odor, aroma, flavor, and global assessment. Therefore, the reconstituted cryoconcentrate is an excellent option for studies on commercialization.
# Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer
###### Abstract
[PERSON] and [PERSON] estimated surface-layer optical turbulence at Mauna Loa, Hawaii, and used a Monte Carlo-type approach to optimize the artificial neural network weights and subsequent prediction of \(C_{n}^{2}\)[31]. [PERSON] and [PERSON] et al. developed a new hybrid neural network that combines the backpropagation neural net-work (BPNN) and the adaptive niche-genetic algorithm (AGA) to forecast the near-surface \(C_{n}^{2}\)[32]. Compared with conventional empirical and parametric model methods, the network method has strong nonlinear mapping capabilities for learning adaptively and is data-driven rather than reliant on empirical formulas. This research proposes a new method for estimating \(C_{n}^{2}\) profiles with the use of machine learning. The backpropagation (BP) neural network, called BPNN, is designed to forecast \(C_{n}^{2}\) profiles in the offshore atmospheric boundary layer, with special consideration of input and output variables. The BPNN is validated against in situ measurements with the spatial evolution characteristic of the \(C_{n}^{2}\) profiles. Compared with the existing model, we concluded that the BPNN approach is reasonable and remarkable. The remainder of this paper is organized as follows: Section 2 introduces three methodologies associated with the investigation of the \(C_{n}^{2}\) profiles, Section 3 provides the experimental results and discussion, including the use of contrast experiments, and finally, Section 4 concludes the paper and proposes some limitations.
###### Abstract  
This paper presents an experimental and numerical evaluation of the pre-chamber induced HCCI combustion concept (PC-HCCI) in terms of engine performance, emissions, and controllability. In this concept, a spark-initiated combustion in the pre-chamber is utilized to trigger the kinetically controlled combustion of an ultra-lean mixture in the main combustion chamber. The experimental measurements were performed on a single-cylinder engine with a custom-made active pre-chamber. A high compression ratio of 17.5 was used, which limits the maximum achievable engine load due to high knocking tendency but enables both standard PCSI combustion (flame propagation) at very high dilution levels and HCCI combustion at reasonable intake temperatures. The analysis of combustion characteristics and the resulting performance is performed at indicated mean effective pressures (IMEPs) of 3.5 and 3.0 bars, and three different intake temperatures of 80 \({}^{\circ}\)C, 90 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C. The variation in engine load was achieved by adjusting the excess air ratio in the main chamber. On each combination of intake temperature and engine load, a spark sweep and an injected PC fuel mass sweep were performed to obtain the highest indicated efficiency while satisfying the restrictions in terms of combustion stability and knock intensity. It was shown that, unlike in a conventional HCCI engine, the combustion phasing can be directly and reliably controlled by adjusting either spark timing or the reactivity of the pre-chamber mixture, ensuring adequate combustion stability and eliminating potential misfires. A similar indicated efficiency as with conventional HCCI combustion was obtained, while the NOx emissions, although slightly elevated, are still insignificant. Compared to PCSI combustion at the same engine load, a 4-percentage-point increase in indicated efficiency and two times lower NOx emissions were achieved. Compared to the most efficient PCSI operating point, it was 1 percentage point lower, indicating that efficiency was achieved, but the specific NOx emissions are reduced by approximately 70%. Most importantly, very similar performance was obtained with significant variations in intake temperature, proving the reliability and adaptability of this combustion concept.  
SI engine; pre-chamber; combustion; PCSI; HCCI; PC-HCCI +
Footnote †: journal:  
Article
## 2 Methods and Materials
### Signal Propagation through Free Space  
The free space path loss model [40] provides details or means of quantifying the loss that can be attained when radio signals are propagated without considering the effects of several other external impediments in the propagation paths. Thus, the power density \(S_{p}\)[2; 40; 50], attained over a communication distance \(r\) in free space, is related to the received power \(P_{r}\), the transmit power \(P_{t}\), and the antenna gain \(G_{t}\), given by (1), and the received power is given by (2)  
\[S_{p}=\frac{P_{t}G_{t}}{4\pi r^{2}} \tag{1}\]  
\[P_{r}=S_{p}A_{e}=\frac{P_{t}G_{t}}{4\pi r^{2}}A_{e} \tag{2}\]  
where \(A_{e}\) is the antenna aperture area defined in (3)  
\[A_{e}=\frac{G_{r}\lambda^{2}}{4\pi} \tag{3}\]  
\(P_{r}\) can be rewritten with respect to \(A_{e}\) as in (4):  
\[P_{r}=\frac{P_{t}G_{t}}{4\pi r^{2}}A_{e}=\frac{\lambda^{2}}{\left(4\pi r\right) ^{2}}G_{t}P_{t}G_{r} \tag{4}\]  
where \(\lambda=\) transmission wavelength in meters.  
Thus, the path loss, \(P_{l}(dB)\) over the free space channel can be computed as in (5) using Equation (4):  
\[P_{l}(dB)=20\ log\left(\frac{4\pi r}{\lambda}\right)=20\ log\left(\frac{4\pi f_{ ca}r}{C_{speed}}\right) \tag{5}\]  
where \(C_{speed}\) and \(f_{ca}\) define the speed of light and transmission frequency, respectively.  
Given that \(C_{speed}=3\times 10^{8}\) m/s, \(\pi=3.142\), then Equation (5) can be expressed as (6):  
\[P_{l}(dB)=130+20\ log(f)+20\ log(r) \tag{6}\]  
The expression in (6) reveals that signal loss in free space attenuates 20 dB in value. However, this is certainly not true in other propagation environments, such as built-up terrains, a suburban or an urban area.  
In a more general form, Equation (6) can be rewritten as (7):  
\[P_{l}(dB)=a_{1}+a_{2}\ log(f)+a_{3}\ log(r) \tag{7}\]  
Thus, Equation (7) is referred to as the general log-distance path loss model. In literature, several efforts have been explored to determine the loss coefficients: \(a_{1}\), \(a_{2}\), and \(a_{3}\). In this paper, these loss coefficients represent the identified parameters to be tuned (optimized) to reflect the true nature of the terrain where the signal is being propagated. This quest has also led to the introduction of several empirical models, such as the Hata [51], SUI [52; 53], COST 231 Hata [54; 55; 56], and ITU-R M2412-0 [57] models. The application of these models for path loss estimation in an environment different from environments where the models were developed produces significant errors. The results of the optimized loss coefficient (i.e., the identified parameters) of Equation (7) using the proposed RF-PS method compared to other popular existing methods are reported in this paper.
# Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network
###### Abstract
The method considers the influence of sentences and the relationship between a word and sentence [24]. In the field of transportation, [PERSON] et al. proposed a method to identify the cause factors from railway text reports in 2019 [25]. However, Ref. [25] did not discuss how to apply the cause factors where are from text data to risk analysis in the transportation field. Thus, in this paper, the most important task was to explore the application of the keywords extraction method in risk analysis. Specifically, we first define the system and the barriers to establish a graphical model. The graphical model not only reveals the accident occurrence principle and process but also shows the relations between barriers, which are the fundamental relations in BNs. Second, we perform text processing and characterize the text features and then establish fault trees based on the text representation information. In this step, the prior probabilities are determined. Next, based on the graphical model, we establish a BN (the text-based BN or TBN) using barriers, in which the relations are retrieved from the graphical model and the prior probabilities are obtained from the fault trees. Then, we perform a quantitative safety assessment of railway systems using the BN. This approach could help identify the key factors of accidents. Additionally, a dynamic BN shows the status changes of the barriers when certain conditions change. The method establishes the dependency of different parts, and, therefore, it could update the state of one subsystem using data from another subsystem. This ensures that managers obtain the latest state information to the greatest extent, thus facilitating risk reduction. The work demonstrates the inherent nonlinear relationships to improve the assessment results. We also study the barrier safety potential to determine high-risk barriers, which could provide information to managers about which barriers should be prioritized. The main contributions of our work are as follows:  
* to reduce the data uncertainty due to the prior probabilities of the BN by establishing fault trees to obtain relevant prior probabilities from numerous text data. In contrast, prior probabilities have been obtained in previous work from expert experience in practical applications, which increases the uncertainty;
* to quantitatively and accurately evaluate railway safety. The analysis results not only represent the quantitative risk but also reveal the key barriers and critical potential risks of defects;
* the proposed method has the advantage that the determined cause chains considering safety barriers better fit the accident characteristics than the cause chains directly determined from text records. This paper is organized as follows: Section 2 introduces fundamental information and describes the methodology. Section 3 presents the detailed steps of the TBN method. In Section 4, a case study is examined, and a corresponding graphical model, fault trees and BN are then established. Section 5 provides conclusions.
## 3 Proposed Methodology
### Analysing Failure Modes by Using Fuzzy Linguistic Variables and SAM
#### 3.2.1 Evaluating Risk Parameters of Failure Modes  
FMEA is used to analyze failure modes. The risk parameters for FMEA include occurrence, severity, and detection. To evaluate these risk parameters, fuzzy linguistic variables are employed. The fuzzy linguistic variables regarding occurrence, severity, and detection are given in Table 2, Table 3, and Table 4, respectively [49].  
\begin{table}
\begin{tabular}{l l l} \hline
**Failure Mode Category** & **Failure Mode Code** & **Failure Mode Description** \\ \hline Action & A1 & Operation too long/short \\  & A2 & Operation mistimed \\  & A3 & Operation in wrong direction \\  & A4 & Operation too much/little \\  & A5 & Misalign \\  & A6 & Right operation on wrong object \\  & A7 & Wrong operation on right object \\  & A8 & Operation omitted \\  & A9 & Operation incomplete \\  & A10 & Wrong operation on wrong object \\ \hline Checking & C1 & Check omitted \\  & C2 & Check incomplete \\  & C3 & Right check on wrong object \\  & C4 & Wrong check on right object \\  & C5 & Check mistimed \\  & C6 & Wrong check on wrong object \\ \hline Information communication & I1 & Information not communicated \\  & I2 & Wrong information communicated \\  & I3 & Information communication incomplete \\ \hline Information retrieval & R1 & Information not obtained \\  & R2 & Wrong information obtained \\  & R3 & Information retrieval incomplete \\ \hline Selection & S1 & Selection omitted \\  & S2 & Wrong selection made \\ \hline \end{tabular}
\end{table}
Table 1: Failure mode taxonomy in SHERPA.  
\begin{table}
\begin{tabular}{l l l} \hline
**Linguistic Variable** & **Triangular Fuzzy Number** & **Description** \\ \hline Very low (VL) & (0, 0, 2.5) & Failure modes are unlikely to occur. \\ Low (L) & (0, 2.5, 5.0) & Relatively few failure modes are likely to occur. \\ Medium (M) & (2.5, 5.0, 7.5) & Failure modes occur occasionally. \\ High (H) & (5.0, 7.5, 10.0) & Failure modes can be repeated. \\ Very high (VH) & (7.5, 10.0, 10.0) & Failure modes are almost inevitable. \\ \hline \end{tabular}
\end{table}
Table 2: Fuzzy linguistic variables regarding occurrence.  
\begin{table}
\begin{tabular}{l l l} \hline
**Linguistic Variable** & **Triangular Fuzzy Number** & **Description** \\ \hline Very low (VL) & (0, 0, 2.5) & The effect of failure mode on usability can be ignored. \\ Low (L) & (0, 2.5, 5.0) & The effect slightly impacts usability. \\ Medium (M) & (2.5, 5.0, 7.5) & The effect slightly but noticeably impacts usability. \\ High (H) & (5.0, 7.5, 10.0) & The effect significantly impacts on usability. \\ Very high (VH) & (7.5, 10.0, 10.0) & The effect seriously impacts usability. \\ \hline \end{tabular}
\end{table}
Table 3: Fuzzy linguistic variables regarding severity.
## 8 Conclusions  
An explicit FEM for solving the dynamic responses of saturated porous media is proposed based on the u-p formulation. In spatial domain, the global system is divided into the local node systems of all nodes, which means that the responses of each node only are related to the responses of the surrounding nodes. In time domain, the local node system of each node can be discretized by a completed explicit integration method, and further be simplify into three independent equations corresponding to each DOF of the node. The dimension of FE equations of each local node system is small and is solved fast. By cycling through all the nodes, the dynamic responses of the global computational domain can be obtained. In this way, the proposed method has the high efficiency for solving larger DOFs problem and strong nonlinear problem of the fluid-saturated porous media. Moreover, a viscoelastic artificial boundary is added to the method, and changes only the diagonal values of the coefficient matrix corresponding to boundary nodes and does not change the form of each coefficient matrix corresponding to each local node system. Additionally, the proposed method is validated by comparing with the analytical and numerical solutions of existing methods, and it has high computational efficiency. In the end, the application to the seismic responses of liquefiable site indicates that the proposed method is an effective method for solving the dynamic problems of saturated porous media.  
In this method, a complete explicit integration method with only first-order accuracy is chosen. However, based on the same spatial discretization, other suitable integration methods with high order accuracy can also be used in the FE method, such as the integration method used in the viscoelastic artificial boundary section. In addition, the computational efficiency of the proposed method only is verified by comparing with implicit Newmark method. The generality of the proposed needs to be confirmed for various types of analyses and comparisons.  
Figure 13: Contour plots of pore pressure ratio of the model.  
**Author Contributions:** Software, C.F.; Validation, F.W.; Writing--original draft, J.S.; Writing--review & editing, C.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (Grant No. 51808006 and No. 51722801); Natural Science Foundation of Beijing, China (Grant No. 8192012) and Beijing Municipal Education Commission Scientific Research Project (110052972027/146).  
**Acknowledgments:** The authors would like to thank the National Natural Science Foundation of China (Grant No. 51808006 and No. 51722801) and the Natural Science Foundation of Beijing, China (Grant No. 8192012) for funding the work presented in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.