esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results  
### MIMU Installation Angles Estimation Result  
When GNSS signal and motion constraints were all available, the installation angles were estimated. In other cases, the installation angles were kept at the original value, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Gyroscope & \begin{tabular}{c} Bias stability (\({}^{\circ}\)/h) \\ Scale factor accuracy (ppm) \\ Angle random walk(\({}^{\circ}\)/\(\sqrt{\text{h}}\)) \\ \end{tabular} & \begin{tabular}{c} 25 \\ 1000 \\ 0.3 \\ \end{tabular} \\ \hline Accelerometer & \begin{tabular}{c} Bias stability (mg) \\ Scale factor accuracy (ppm) \\ Velocity random walk (m/s/\(\sqrt{\text{h}}\)) \\ \end{tabular} & \begin{tabular}{c} 0.2 \\ 1000 \\ 0.05 \\ \end{tabular} \\ \hline Odometer & \
\begin{tabular}{c} Resolution(\(p\)/\(t\)) \\ Max number of revolution (r/min) \\ Wheel diameter (mm) \\ \end{tabular} &
\begin{tabular}{c} 100 \\ 6000 \\ 860 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor specifications.  
Figure 8: MIMU installation angles.  
Figure 7: Integrated navigation and data acquisition system.  
During the start-up phase of the train's movement, the acceleration is unstable and the speed is small. The observability of installation angles is closely related to train speed and acceleration [26; 27], so the estimated installation angles fluctuated significantly at this time. As the speed increased, the estimates of the installation angles gradually converged.The installation angles were basically stable during the constant speed driving phase. Compared with the pitch installation angle, the heading installation angle had a slower convergence, which may have been related to the lack of heading maneuver of the train and the low accuracy of the initial heading angle. There was a certain coupling relationship between the attitude misalignment angles and the installation angles [27].
## 3 Imaging Attitude Control  
### Coordinate Frames  
Coordinate frames are used to express the position of a point in relation to a certain reference. Five principal coordinate frames (Figure 3) were used for image attitude control and IMC analysis:
## 4 Analysis and Simulation
### Noise Analysis
In Frame 8, the mismatched positions exhibited power levels comparable to those of the correct positions, further indicating incorrect matching results. Figure 8d shows the simulation results of the original system under the influence of both input and amplifier noise. Errors were observed in the system outputs from Frames 3 to 7. In addition, an erroneous signal was generated in Frame 8. In particular, at the correct symbol position, only a pulse with a power of approximately 1 mW was observed;however, at the erroneous symbol position, a pulse with a maximum power exceeding 4.8 mW was output. As shown in Figure 8, the amplifier noise affects the original system more than the input noise. Because the amplifier is placed after the filter in the original system configuration, without fully leveraging the capability of the filter to reduce the amplifier noise,  
Figure 8: Simulation results of the original system in an ideal scenario and in various noise scenarios: (**a**) ideal scenario, (**b**) input OSNR = 6 dB, (**c**) NF of EDFAs = 4 dB, and (**d**) input OSNR = 6 dB and NF of EDFAs = 4 dB. even in the absence of input noise, the system may output erroneous signals. In addition, because of the two cascaded AND gates introduced to eliminate the system's mismatch noise, four EDFAs were added to the system, resulting in the generation of a large amount of noise. The noise generated by the two EDFAs in the recirculating loop also accumulated during the matching process and significantly affected the system output. Therefore, the noise-suppression scheme should focus on reducing the effects of the internal amplifier noise on the system.
## 4 Data Analysis and Results
### Failure Modes  
Table 4 shows the failure modes of each individual component that was observed in accordance with the same study of the failure rate of respective components. For instance, the three failures that usually happen in the positive displacement pump are vibration, parameter deviation, and failure to start on demand. Looking profoundly into the failure modes of the critical components, the positive displacement pump and positive displacement meter, it is noticed that these failures particularly, unlike other component failures, could be caused by deficiency of more than one part of the component construction, therefore the root cause of the failures could be very difficult to detect. On the other hand, unloading arm failures can easily be detected. Practically, valves are extremely important components in the system to either isolate or control the fluid flow. It has been found that almost all of the valve failures are a result of their construction integrity or excessive operation conditions; both of these failures cause an internal or an external leakage. Failure detection is out of the scope of this study; however, knowing the failure moods and their root causes are essential to reliability improvement.  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Failure Mode** \\ \hline Unloading Arms & Rupture \& Leak \\ \hline \multirow{4}{*}{Ball Valve} & Leakage in closed position \\ \cline{2-3}  & Internal leakage \\ \cline{2-3}  & External leakage \\ \cline{2-3}  & Low output \\ \cline{2-3}  & Structural deficiency \\ \hline \multirow{4}{*}{Positive Displacement Pump} & Vibration \\ \cline{2-3}  & Parameter deviation \\ \cline{2-3}  & Fail to start on demand \\ \hline \multirow{4}{*}{Check Valve} & External leakage \\ \cline{2-3}  & Opposite direction flow \\ \hline \end{tabular}
\end{table}
Table 4: Unloading system component failure modes.  
Figure 3: Mean time between failures of unloading system components.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analog-to-Digital Converter \\ AGC & Automatic Gain Control \\ ANOVA & Analysis of Variance \\ APSK & Amplitude and Phase Shift Keying (modulation) \\ BLE & Bluetooth Low Energy \\ BPF & Band-Pass filter \\ BPSK & Binary Phase Shift Keying (modulation) \\ CDMA & Code Division Multiple Access \\ CNR & Carrier-to-Noise Ratio \\ CNN & Convolutional Neural Networks \\ CVT & Continuous Wavelet Transform \\ DAC & Digital-to-Analog Converter \\ DE & Dispersion Entropy \\ DQPSK & Differential Quadrature Phase Shift Keying (modulation) \\ DWT & Discrete Wavelet Transform \\ ESA & European Space Agency \\ ESTEC & European Space Research and Technology Centre \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ GLRT & Gaussian Likelihood Ratio Test \\ FE & Front-End \\ FIR & Finite Impulse Response \\ GNSS & Global Navigation Satellite System \\ GSM & Global System for Mobile Communications \\ HHT & Hilbert-Huang Transform \\ HW & Hardware \\ IoT & Internet of Things \\ I/Q & In-Phase /Quadrature \\ LDA & Linear Discriminant Analysis \\ LPA & Low Power Amplifier \\ LO & Local Oscillator \\ LRT & Likelihood Ratio Test \\ LTE & Long-Term Evolution \\ MDA & Multiple Discriminant Analysis \\ MSACN & Message Structure Aided Attentional Convolution Network \\ OXCO & Oven Controlled Crystal Oscillator \\ PA & Power Amplifier \\ PCA & Principal Component Analysis \\ PE & Permutation Entropy \\ PN & Phase Noise \\ \end{tabular}
## 4 Results and Discussions
### Schlieren Imaging Analysis
This can cause the boundary layer to thicken, making it more susceptible to separation and instability. As the boundary layer slows and becomes unstable, vortices within it may detach from the surface because they no longer have enough momentum to remain attached and migrate downstream. As the flow is dominated by chaotic and disordered motion, one common finding that applies to  
Eventually, Figure 16a-j shows consecutive frames which belong to the ejector nozzle operated at 115k RPM. Twice as many consecutive frames presented in both original and processed images were analyzed in comparison to the chevron configuration. This approach highlights the advantage of advanced image processing in capturing and analyzing the instantaneus flow behavior more comprehensively, especially in cases where turbulence is less structured. Figure 16: Cont. Figure 15: Chevron and baseline nozzle—Regime 3: 115k rpm, consecutive frames. (a–e)—baseline, (a’-e’)—chevrons. The additional frames clearly illustrate the broader, less confined nature of turbulence in the ejector configuration. The processed images reveal larger-scale vortices distributed over a wider area, indicating that the mixing process is less localized and less efficient compared to the chevron nozzle. These frames also emphasize the absence of fine-scale turbulence near the nozzle wall, a feature that is consistently observed in the chevron configuration, where turbulence remains confined and structured. When compared to the chevron nozzle, the ejector configuration generates larger, less organized vortices that dominate the flow field, leading to a noisier low-frequency profile. In contrast, the chevrons promote earlier mixing and finer turbulence scales by disrupting large coherent structures immediately at the nozzle exit. This localized mixing shifts the energy spectrum toward higher frequencies, effectively reducing low-frequency noise that propagates farther. This comparison aligns with the findings from the acoustic analysis, which demonstrated the chevrons' superior efficiency in noise reduction. The high-frequency turbulence generated by the chevrons attenuates more rapidly, leading to reduced far-field noise compared to the ejector's broader low-frequency mixing. This consistency between the visualizations and acoustic results reinforces the conclusion that the chevron nozzle provides better overall performance in controlling noise and turbulence.
## 4 Results
### Machine Learning-Based IDS on the Cloud with FS Methods
#### 4.2.3 MLP-Based IDS Implementation on the Cloud with FS Methods  
PCA and chi-square are deployed on the MLP algorithm, and their results are recorded for 25, 15, and 10 features, respectively. On PCA, MLP testing accuracies are 93.9%, 94.0%, and 93.99% for 25, 15, and 10 extracted features, respectively. On chi-square, MLP testing accuracies are 93.88%, 93.83%, and 92.99% for 25, 15, and 10 features, respectively. The results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML Model** & **FS Technique** & **No. of Features** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LSTM & PCA & 25 & 94.07\% & 0.82 & 0.84 & 0.83 \\ \hline LSTM & Chi-square & 25 & 94.25\% & 0.84 & 0.84 & 0.84 \\ \hline LSTM & PCA & 15 & 94.4\% & 0.84 & 0.83 & 0.83 \\ \hline LSTM & Chi-square & 15 & 94.5\% & 0.84 & 0.83 & 0.83 \\ \hline LSTM & PCA & 10 & 94.4\% & 0.84 & 0.84 & 0.84 \\ \hline LSTM & Chi-square & 10 & 94.0\% & 0.84 & 0.82 & 0.83 \\ \hline RF & PCA & 25 & 97.50\% & 0.92 & 0.91 & 0.91 \\ \hline RF & Chi-square & 25 & 96.50\% & 0.90 & 0.87 & 0.88 \\ \hline RF & PCA & 15 & 94.64\% & 0.85 & 0.84 & 0.84 \\ \hline RF & Chi-square & 15 & 94.40\% & 0.84 & 0.84 & 0.84 \\ \hline RF & PCA & 10 & 94.44\% & 0.84 & 0.84 & 0.84 \\ \hline RF & Chi-square & 10 & 94.53\% & 0.85 & 0.82 & 0.85 \\ \hline MLP & PCA & 25 & 93.90\% & 0.84 & 0.82 & 0.82 \\ \hline MLP & Chi-square & 25 & 93.88\% & 0.84 & 0.81 & 0.81 \\ \hline MLP & PCA & 15 & 94.00\% & 0.84 & 0.82 & 0.83 \\ \hline MLP & Chi-square & 15 & 93.83\% & 0.83 & 0.80 & 0.80 \\ \hline MLP & PCA & 10 & 93.99\% & 0.84 & 0.81 & 0.82 \\ \hline MLP & Chi-square & 10 & 92.99\% & 0.82 & 0.81 & 0.81 \\ \hline XGBost & PCA & 25 & 97.67\% & 0.98 & 0.98 & 0.98 \\ \hline
**XGBoost** & **Chi-square** & **25** & **98.35\%** & **0.98** & **0.98** & **0.98** \\ \hline XGBoost & PCA & 15 & 95.58\% & 0.96 & 0.96 & 0.96 \\ \hline XGBoost & Chi-square & 15 & 98.13\% & 0.98 & 0.98 & 0.98 \\ \hline XGBoost & PCA & 10 & 95.55\% & 0.96 & 0.96 & 0.96 \\ \hline XGBoost & Chi-square & 10 & 95.69\% & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results of LSTM-, RF-, MLP-, XGBoost-based IDSs with FS methods.
## 4 Target and Frequency Sweeping Jamming Classification Experiment
### Experimental Evaluation Indicators  
In the application scenario of radio fuzes, only the echo signal needs to be dichotomized as targets or jamming signals. In this experimental section, the confusion matrix for classification recognition can be represented as shown in Table 3.  
TP (true positives) represents the sample that is actually a target and is classified as a target; FP (false positives) represents the that sample is actually a jamming signal and is classified as a target; FN (false negatives) represents the sample that is actually a target and is classified as a jamming signal; TN (true negatives) represents the sample that is actually a jamming signal and is classified as jamming signal.  
Based on the above confusion matrix for target and jamming signals classification, the experimental evaluation indicators are set to include 'Accuracy', 'Precision', 'Recall' and 'FalseAlarm'. The calculation is shown below.  
\[\text{Accuracy}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{8}\] \[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
Figure 7: Flow chart of the target and jamming signals classification and identification method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual Type of Samples** & **Targets** & **Jamming Signals** \\ \hline Targets & TP & FP \\ Jamming signals & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for target and interference signal classification and identification.  
\[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\] \[\text{FalseAlarm}= \frac{\text{FP}}{\text{TP}+\text{FP}} \tag{11}\]  
where Accuracy measures the rate of correct classification; Precision measures the percentage of results classified as positive samples that are correctly classified; Recall measures the rate of true targets detected correctly as a rate of all true targets; FalseAlarm measures the probability of a fuze being cheated by jamming signals. In the course of the following experiments, we selected the above indicators to evaluate the effectiveness of the method in this manuscript.
## 5 Conclusions  
In this work, a new register control method using the pulse-width subdivision detection algorithm is proposed to improve the accuracy of register error detection without increasing the resolution of external encoder pulse signals. The specific contributions are as follows:  
1. We studied the principle of register color mark detection. It was identified that the resolution of the encoder pulse, sensor electrical edge jitter, color mark printing quality and mechanical vibration are the main interference factors that show that the most traditional detection method based on the encoder and photoelectric sensor cannot effectively work.  
Figure 16: Comparison with different error compensation methods: (**a,b**) traditional error compensation method and (**c,d**) optimized error compensation method.  
2. The proposed pulse-width subdivision detection algorithm can effectively improve the accuracy of register error detection based on the same hardware platform configuration.
3. Different compensation output intensities can greatly affect execution time; we also found that for all output policy parameters, an optimal value exists for the control speed and stability, but if this is too fast or too slow, the compensation time reduces production efficiency.  
The current detection system mainly solves the problem of insufficient positioning accuracy of the color mark; however, the color mark printing quality must meet the requirements of relevant printing process technical indicators, so further works on the detection system should identify more types of register color marks. In addition, the calibration and quantification of photoelectric sensor detection results cannot be realized yet, which leads to uncertainty in the register reference position. Future work should study how to realize automatic register, further reduce manual operation and apply the machine vision method to calculate the calibrated register error.  
[PERSON] designed the algorithm, performed the experiments and wrote the paper. [PERSON] organized the industrial application production and prepared the test materials. [PERSON] and [PERSON] supervised the research and modified the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Hubei Provincial Major Scientific and Technological Innovation Plan, No. 2016 AAA073.  
Not applicable.  
Data Availability StatementData from Simulation and experimental are available upon request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### 1L Scale Batch Fermentation
#### 3.3.2. Fermentation of L. fractiminis  
When L. _fractiminis_ was exposed to fermentation at the same conditions and in the same waste mixture of pear pomace and RCW, the results were surprisingly different. Figure 3 shows the sugar depletion and L-lactic acid production profiles in the 72 h of the process. Interestingly, lactose and sorbitol uptake did not occur with L. _fractiminis_ in the same conditions. This meant that, out of the total 31.60 g/L theoretically available in the waste, only 6.4 g/L were effectively consumed to produce the product of interest. Moreover, L. _fractiminis_ was shown to prefer glucose and only when this was completely depleted did it switch to fructose. Biomass yield factor was 15%, corresponding to 0.96 g/L of cells.  
Figure 3: Batch fermentation of L. _fractiminis_ in the presence of glucose, fructose, lactose and sorbitol, derived from pear pomace and RCW mixtures, for the production of L-lactic acid.  
Figure 2: Batch fermentation of L. _casei_ in the presence of glucose, fructose, lactose and sorbitol, derived from pear pomace and RCW mixtures, for the production of L-lactic acid.  
Considering the previous results and taking into account the high yield factor (calculated on the total amount of sugars consumed, \(Y_{\text{LA/S}}\)), it is reasonable to conclude that all the lactic acid produced was the l-enantiomer. In our study, optical purity was not measured and was not considered as a key point since recent research has reported that the stereocomplex PLA, which is composed of both poly-l- and -D-lactic acid, has been attracting a lot of attention due to its high thermostability. Stereocomplex-type polymers show a melting point (ca. 230 \({}^{\circ}\)C) that is approximately 50 \({}^{\circ}\)C higher than that of the respective single polymers [46]. Therefore, D-lactic acid, in addition to l-lactic acid, which has been the focus of production to date, is becoming increasingly important [47].  
Overall, _L. fraciminis_ was shown to be inappropriate for fermenting pear pomace together with RCW, pear pomace alone, or waste containing, in a broader perspective, only monosaccharides as principal carbon sources. Thus, the need to find a low-cost diluting agent for pear pomace, besides fresh water, obliged us, at this stage of our research, to pay special attention to more versatile microorganisms, such as _L. casei_.  
Even in the case of _L. casei_, overall productivity values were lower in our experimental conditions compared to those reported in the literature and the reason could be principally due to the batch strategy chosen for the experiments. To limit operating costs, as well as minimizing the risk of contamination, continuous fermentation must surely be excluded, but a fed-batch process might be the best compromise to reach effective process performance.
## 4 Results
### Pearson Correlation
#### 4.1.5 Accuracy, Precision, Recall, F1 Score  
The accuracy, precision, recall, and F1 score shown in Figure 10 below were also calculated and printed for the predicted data evaluation metrics. These model performances were then saved in a text file.  
Accuracy: The number of correct predictions is divided by the total number of predictions.  
\[\text{Accuracy}=\text{(True Positives + True Negatives)}/\text{Total Instances} \tag{5}\]  
Precision: This measures the accuracy of positive class predictions by determining how many of them actually belong to the positive class.  
\[\text{Precision}=\text{True Positives}/\text{(True Positives + False Positives)} \tag{6}\]  
Recall (Sensitivity): This calculates the proportion of correct positive predictions out of all the positive examples in the dataset.  
\[\text{Recall}=\text{True Positives}/\text{(True Positives + False Negatives)} \tag{7}\]  
F1 Score: This provides a single score that balances precision and recall in one number. It is a harmonic mean of precision and recall.  
\[\text{F1 Score}=2\times((\text{Precision }\times\text{Recall})/(\text{Precision + Recall})) \tag{8}\]  
Accuracy: Achieving an accuracy of 0.996 indicates overall solid performance.  
Precision: A precision score 1.00 suggests that the model rarely misclassified true errors as non-errors.  
Recall: The recall score 0.500 indicates that the model identified only half of the actual errors. This trade-off between precision and recall is common in imbalanced datasets.  
F1 Score: The F1 score (0.667) balances precision and recall, providing a comprehensive view of the model's effectiveness.
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 4 Discussion
### Viscosity Effect on Vortex Cavitation and Potential Core
#### 4.1.2 Difference in Vortex Cavitation and Potential Core in the Chamfered Valve Seat Configuration  
As shown in Figures 7 and 8, for both water- and oil-flow case, cavitation morphology is also present inside the chamfered groove due to the vena-contraca phenomenon, contributing to increased volume of the vortex cavitation, particularly the bubble cloud in the water-flow case, as shown in Figure 7a. Consequently, the jet potential core is fragmented more violently than that in the poppet with a sharp valve seat. However, the puff feature in the water-flow case and the waving pattern in the oil-flow case are both distinguishable, as indicated by the sectional velocity contour in Figures 7 and 8.  
The most significant difference between the two cases in terms of vortex cavitation refers to the thin layer feature of the vortex cavitation ring within the free shearing layer and the paired vortex cavitation in oil flow case. The thin layer feature is a result of the strong shearing intensity, as mentioned in the sharp valve seat case. The persistent presence of paired configuration of vortex cavitation in both sharp and chamfered valve seat confirms that such discrepancy is a common flow phenomenon as a result of the viscosity effect irrespective of the valve seat configuration. As mentioned earlier, the vortex cavitation of water-flow case at 44 bar pressure drop could extend upstream to the attached cavitation within the chamfered groove, as shown in Figure 7a,b. Due to the detached cavitation from the chamfered groove, the vortex cavitation at the free shearing layer exhibits a clearly larger volume, with the appearance of a circumferentially-aligned bubble cloud with strong 3-dimensionality, as shown in Figure 7a,b. In contrast, the oil-flow case presents vortex cavitation with favorable circumferential symmetry, as exhibited in Figure 8a,b.  
Figure 6: Simulation results for oil cavitating flow inside a poppet valve with a sharp valve seat. Cavitation and vortex structure are identified with iso-surface of \(\alpha_{\text{l}}=70\%\) and Q = \(3\times 10^{9}\). Persistent presence of paired feature and waving pattern in each operating condition is mainly due to relatively mild transition process. (**a**) 45 bar pressure drop; (**b**) 42 bar pressure drop; (**c**) 40 bar pressure drop; (**d**) 38 bar pressure drop.