esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 3 Test Case Setups
### Case 2: Two-Dimensional ASL on the Horizon
#### 3.2.2 Case 2 (ii): Sinusoidal Plane Wave (S2)  
A sinusoidal plane wave signal (Equation (2)) was generated from a random direction (real, random value of \(\theta\)) and fixed \(r\) (=10 m) from the origin. The signal was polluted with white Gaussian noise. The amplitude of the pressure signal and the white noise were equal and set to 10 Pa. We applied the generalized cross-correlation (GCC) algorithm to all microphone pairs to generate an input feature. GCC is a time delay of arrival (TDOA) approach and is known to be robust to noise and reverberation. The GCC vector between two signals \(r_{1}(t)\) and \(r_{2}(t)\) is defined as the inverse Fourier transform (IFFT) of the cross-power spectrum [35]:  
\[\text{GCC}=\text{IFFT}\;(R_{1}\odot R_{2}^{*})\]  
where, \(R_{1}\) and \(R_{2}\) are the STFT vectors for \(r_{1}\) and \(r_{2}\), \(\odot\) represents element-wise multiplication, and \({}^{*}\) represents the complex conjugate.  
Figure 9 shows a sample GCC pattern obtained using a pair of microphone signals polluted with uncorrelated noise. It can be seen that, while the signals are distorted, the GCC pattern is still very evident. The entire GCC vector was not used as input. For each microphone pair, there are a certain number of correlation coefficients near the center that contain useful information. These coefficients were extracted from the GCC feature map to be used as input to the CNN. The calculation for the number of coefficients is as follows. The maximum possible time delay (\(\tau\)) between two microphones for the four-microphone array shown in Figure 5 is given by the distance between two microphones (2 m) and the speed of sound (343 m/s) as \(\tau=2/343=5.83\) ms. For a sampling frequency of 16,000 Hz, the maximum delay in samples is \(n=16\),\(000\tau\approx 93\). So it is only the first 93 coefficients from the center of the GCC that contain useful information for each microphone pair. The first 93 GCC coefficients from the GCC of all microphone pairs (6) were concatenated to form a vector of size (\(558\times 1\)) and then reshaped into a matrix of dimensions (\(31\times 18\)). This reshaped matrix was used as input to the CNN. Of course, the vector of GCC coefficients can be directly fed to a multi-layer perceptron (MLP); however, a CNN is generally more powerful and accurate for classification problems. The CNN also has fewer learnable parameters compared with MLP, which reduces the risk of overfitting.
## 3 Hybrid Visual SLAM with Deep Keypoints, Depths, Poses, and Line Features
### Integrating Line Features and Deep Keypoints in the Front End  
#### 3.2.1 Line Feature Detection  
To enhance the stability and robustness of feature extraction, we detect line features alongside point features. Point features typically capture keypoints with significant gradient changes, while line features represent regions composed of sets of points with such changes. As a result, line features depict object edges in the image, providing stable and recognizable information from various perspectives. Introducing line features into the SLAM front-end tracking module significantly improves feature tracking stability, especially in scenes with drastic lighting variations.  
In our feature tracking module, we employ the line segment detector (LSD) [33], a method for extracting line segment features from images. We then compute binary descriptors for the line features using the line band descriptor (LBD) [34] and track the line segments accordingly. As shown in Equation (1), our LSD feature extraction method first utilizes a two-dimensional kernel function to calculate the image gradient, which includes the horizontal and vertical components of the image coordinates \((\mathbf{G}(u),\mathbf{G}(v))\). Here, \(\mathbf{I}(u,v)\) represents the pixel value at the coordinate point \((u,v)\) in the image.  
Figure 1: An overview of our proposed D3L-SLAM system that integrates deep keypoints, deep pose priors, deep depth estimates, and a line detector. Deep keypoints indicate the self-supervised keypoint extraction method. Deep pose and depth mean the self-supervised pose and depth estimates.  
\[\mathbf{G}(u) =\frac{\mathbf{I}(u+1,v)+\mathbf{I}(u+1,v+1)-\mathbf{I}(u,v)- \mathbf{I}(u,v+1)}{2} \tag{1}\] \[\mathbf{G}(v) =\frac{\mathbf{I}(u,v+1)+\mathbf{I}(u+1,v+1)-\mathbf{I}(u,v)- \mathbf{I}(u+1,v)}{2}\]  
We compute the gradient magnitude, \(\
abla\mathbf{I}_{u,v}\), at point \((u,v)\) in the image, as well as the angle \(\theta_{u,v}\) relative to the horizontal axis.  
\[\
abla\mathbf{I}_{u,v} =\sqrt{\mathbf{G}(u)^{2}+\mathbf{G}(v)^{2}} \tag{2}\] \[\theta_{u,v} =\arctan\left[\frac{\mathbf{G}_{u}}{\mathbf{G}_{v}}\right]\]  
and then, LSD uses non-maximum suppression to enhance real edges and reduce noise. Next, it connects adjacent edge points to form line segments, possibly involving clustering and sorting. Finally, it refines these segments by removing points that do not fit a line segment and merging close segments to create more accurate and complete line representations.  
Before conducting line feature tracking and matching, a brute force matching method is employed to identify corresponding line features based on the Hamming distance. Next, the 3D line features' start and end points in world coordinates are projected onto the current frame using the initial pose estimation.
## 4 Results and Discussions
### Influence of the Dimensionless Standoff Distance on the Rock-Breaking Effect
Moreover, the downward trend of the SPWJ is weaker than that of the CWJ. As shown in Figure 17c, when the continuous water jet is at the optimal dimensionless standoff distance S = 100 in the experiment, the dimensionless standoff distance is offset by 50 and the jet erosion volume is reduced by 25.8%. However, for SPWJ, when it is offset from the experimental optimal standoff distance S = 200 by 50 to 150 or 250, the jet erosion volume is reduced by 87.5% and 78.7%, respectively. Furthermore, in the cumulative comparison of the spalling volume of granite with different dimensionless standoff distances, the CWJ was significantly better than the SPWJ. As shown in Figure 17d, the sensitivity of the SPWJ to the target distance is stronger than that of the CWJ in the process of engineering practice. In other words, the SPWJ has higher requirements for the target distance in the process of erosion and crushing because it is intermittently sprayed from the nozzle and is not only in a high-pressure state but also has strong pressure disturbance and pressure pulsation. As shown in Figure 4, the erosion drilling ability of this jet on granite is better than that of the CWJ because of its periodic water hammer effect. However, because of its intermittent emission, when the jet enters the fracture channel to crack granite, it cannot maintain a continuous crack, and the water wedge effect on granite is weaker than that of the CWJ. When both jets are at the optimal target distance, the distance of the SPWJ is considerably longer than that of the CWJ, and its application range is wider. It can be seen from Figure 17e that when the dimensionless target distance is between 100 and 150, the rock-breaking volume of the unit input pressure of SPWJ is lower than that of CWJ. When the dimensionless target distance is 200, 250, or 300, the rock-breaking volume of the unit input pressure is higher than that of CWJ.
## 5 Diagnosis Performance Analysis and Comparative Performance Evaluation  
### Diagnosis Time Characteristics Under Different Fault Occurrence Times  
To investigate the influence of fault occurrence time on diagnosis speed, Figure 15 illustrates the diagnosis time results at 20 fault instants uniformly distributed within one fundamental period. Here, \(t_{s}\) denotes the diagnosis time for the single-switch fault (\(T_{a1}\) OC fault), while \(t_{d}\) represents the diagnosis time for the double-switch fault (\(T_{a1}\) and \(T_{a3}\) OC fault). Specifically, when the \(T_{a1}\) fault occurs in the early segment of the positive half-cycle, rapid diagnosis can usually be achieved. However, as the fault occurrence time shifts towards the latter segment of the positive half-cycle and throughout the entire negative half-cycle, instantaneous fault diagnosis becomes more challenging, and the diagnosis time increases accordingly. This phenomenon primarily arises because \(T_{a1}\) OC faults in T-type inverters don't affect normal operational states during negative half-cycle operation. Consequently, effective diagnosis must be postponed until the subsequent positive half-cycle initiation. Notably, the simultaneous \(T_{a1}\delta T_{a3}\) OC faults exhibit more distinct signatures, generally enabling shorter detection times compared to single-switch faults.
# Eye State Identification Based on Discrete Wavelet Transforms
## 3 Proposed System  
Figures 2 and 3 show the hardware components of the developed system and its procedure for eye state identification, respectively. First, the brain activity of the user is captured by the EEG device, then this activity signal is processed and decomposed by the DWT. The obtained coefficients are then employed to extract the features, which finally feed the classification algorithm that estimates the user's ocular state. The following sections describe this procedure in detail.
## 3 The Carrier-Launcher System Performance: Preliminary Analyses and Results
### Aerodynamic and Aeromechanical Analyses of the Carrier-Launcher System
where \(x_{np}\), \(x_{ac,w}\) and \(x_{cg}\) are the longitudinal coordinates of the neutral point, aerodynamic centre of the wing, and centre of gravity of the carrier-launcher system, respectively. It has been assumed that the longitudinal position of the neutral point is equal to the longitudinal position of the aerodynamic centre; all the data are reported in Figure 12:0  
The estimated stability margin after the release manoeuvre of the launcher, assuming a reduced fuel mass of about 37,300 kg, is  
Figure 12:1  
In both examined conditions, the stability margin is positive and reaches typical values of a transport aircraft. The system is statically stable from an aeromechanical point of view if, in a generic equilibrium condition, and assuming the centre of gravity as the pole of moments, the overall moment is zero and the aerodynamic moment at zero lift is sufficiently large and positive. Neglecting higher order infinitesimal terms and including the effect of the thrust, we have for the overall moment coefficient (Figure 14):  
Figure 12:2  
where \(h=x_{cg}/MAC\) and \(h_{n}=h_{ac,w}=x_{np}/MAC\) are, respectively, the dimensionless coordinates of the centre of gravity and of the aerodynamic centre of the carrier-launcher system. \(C_{T}\) is the thrust coefficient; \(z\) and \(ze\) denote two dimensionless quantities, such that, indicating with c = MAC the reference chord, \(zc\) is the vertical distance between the c.g. and the reference plane of the wing, and \(zec\) is distance between the thrust axis and the reference plane of the wing (Figure 14). The quantities \(zc\) and \(zec\) are positive in the upper half plane. For the system to be stable,  
Figure 12:3  
and \(C_{L,system}=C_{La,system}.\alpha_{w}\),  
Figure 12:4 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference Point** & \(x\)**-Coordinate [m]** & **Source** \\ \hline Aerodynamic Centre/Neutral Point & \(x_{np}=x_{ac,w}=9.60\) & AVL model \\ C. G.—Flying Wing/Carrier & 9.00 & CAD model \\ (empty) & C. G.—Fuel (full) & 7.20 & Geometry of the tanks \\ C. G.—Launcher & 3.61 & Launcher geometry \\ C. G.—Carrier-Launcher system & \(x_{cg}=7.64\) & Calculation result \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Coordinates used for the Stability Margin calculation (cruise flight condition). Figure 14: Main parameters involved in the equilibrium condition of a flying wing [30]. where \(\alpha_{w}\) is the angle of attack and \(C_{La,system}\) is the slope of the lift curve. The requirement of Equation (10) can be critical for a flying wing; this is the reason why it is almost obligatory to use profiles with a reflected trailing edge (Figure 6). Anyway, to ensure stability in the various phases of the flight, the aerodynamic control surfaces (Figure 10) can be used in an appropriate and combined manner.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 5 Conclusions  
In this study, a multipurpose geometric error measurement system mainly composed of a laser source and a self-developed optical module has been developed for the measurement of straightness, perpendicularity, and parallelism errors for a linear guideway assembly process.  
This includes two adjustment methods using an in-lab-developed optical module capable of calibrating the altitude angle of the pentaprism: The first method is designed for ease of operations based on Michelson principle and uses a laser interferometer as the light receiver, and the second one is aimed for high calibration-repeatability based on autocollimator principle using QD to replace the light receiver. The measured data are analyzed by the least-squares method in order to obtain corresponding geometric errors; meanwhile, the system uncertainty analysis is also conducted for the laser source, PSD, and pentaprism module for evaluating the system performance.  
The features of the multipurpose geometric error measurement system presented in this paper are described as follows:  
1. There is no need to reset the system if the laser source is sheltered during the measurement process;
2. The horizontal and vertical straightness errors can be obtained in one measurement;
3. The system is capable of applying to the linear guideway assembly process of various double-column, three-axis, and five-axis machine tools;
4. The residual error of the horizontal straightness, vertical straightness, perpendicularity, and parallelism are found to be \(\pm\)1.3 \(\upmu\)m, \(\pm\)5.3 \(\upmu\)m, \(\pm\)1.2 \(\upmu\)m, and \(\pm\)0.1 \(\upmu\)m, respectively, which are better compared to those detected by the commercial laser interferometer and granite reference blocks.  
The authors contributed equally to the manuscript.  
The present work was funded by the Ministry of Economic Affairs (MoEA) and the Ministry of Science and Technology (MoST) of Taiwan (Republic of China), the authors would like gratefully express their sincere acknowledgment to MoEA and MoST.  
The authors declare no conflict of interest.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 2 Transmitter for Short-Reach Transmission Systems
### Monolithic Integrated Transmitter
reported a net rate of 222-Gbit/s OOK signal transmission over 200-m SMF link assisted by a small plasmonic-organic hybrid (POH) MZM, while the design cannot be entirely fabricated in commercial SiP processes [87]. [PERSON] et al. reported the 1-km optical transmission of an 800-Gbit/s (4 \(\times\) 200-Gbits) PAM-4 signal using a four-channel SiP modulator chip with 3-dB bandwidth of 40-GHz [88]. Figure 4a showed a micrograph of the chip-on-board SiP transmitter, which integrated a four-channel SiP MZM chip and a four-channel driver chip. As depicted in Figure 4b, the 3-dB modulation bandwidth of silicon photonics modulator of different channels (e.g., CH_1, CH_2, CH_3, and CH_4) can both reach about 60-GHz, while that of silicon photonics transmitter co-packaged with the driver would reduce to about 40-GHz as shown in Figure 4c. In addition, other SiP transmitters, such as silicon micro-ring modulator [89; 90] and hybrid silicon electro-absorption modulator [91], have also been reported. Part of these reports about monolithic integrated transmitter applied in beyond 100-Gbit/s data transmission is summarized in Table 5.  
Figure 4: (**a**) Micrograph of the chip-on-board silicon photonics transmitter; (**b**) S\({}_{21}\) characteristics of silicon photonics modulator under different channels (e.g., CH_1, CH_2, CH_3, and CH_4); (**c**) S\({}_{21}\) characteristics of transmitter co-packaged with the driver under different channels. FA: fiber array; (From [88] © 2021 Chinese Laser Press). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Tx** & **A** & **MF** & **BW** & **Rate** & **Reach** & **Fiber** & **FEC** & **Ref.** \\
**Device** & **(mm)** & **MF** & **(GHz)** & **(Gbit/s)** & **(km)** & **Type** & **Limit** & **Ref.** \\ \hline InP-Based DFB-MZM & 1542 & PAM-8 & 54 & 300 & 1.2 & SMF & \(1.9\times 10^{-2}\) & [15] \\ InP-Based MZM & 1550 & DMT & 80 & 333 & 20 & SMF + DCF & \(2.7\times 10^{-2}\) & [16] \\ SiP-based MZM & 1550 & PAM-4 & \textgreater{}50 & 128 & 10 & SMF + DCF & \(2\times 10^{-2}\) & [79] \\ SiP-based MZM & 1310 & PAM-8 & 20 & 112 & 10 & SMF & \(3.8\times 10^{-3}\) & [80] \\ \hline \end{tabular}
\end{table} 
Table 5: Incomplete summary of monolithic integrated transmitters applied in beyond 100-Gbit/s per lane data transmission.
## 5 Comprehensive Analysis of Model Robustness, Generalizability, and Interpretability
### Model Generalization and Statistical Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Fold** & \(R^{2}\) & _RMSE_ & _MAE_ & **RPD** \\ \hline PCA-IWOA-XGBoost & 1 & 0.99 & 8.12 & 5.05 & 24.25 \\ PCA-IWOA-XGBoost & 2 & 0.992 & 7.95 & 4.92 & 23.87 \\ PCA-IWOA-XGBoost & 3 & 0.989 & 8.22 & 5.1 & 24.92 \\ PCA-IWOA-XGBoost & 4 & 0.993 & 7.78 & 4.81 & 23.34 \\ PCA-IWOA-XGBoost & 5 & 0.991 & 7.96 & 4.95 & 23.65 \\ PCA-IWOA-XGBoost & 6 & 0.99 & 8.01 & 4.99 & 24.22 \\ Average & -* & 0.991 & 7.91 & 4.92 & 23.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Six-fold cross-validation results. Using the scipy.stats.friedmanchisquare function, the Friedman statistic was calculated to be 16.8, with a \(p\)-value of 0.0008, which is far below the significance level \(\alpha\) = 0.05. Thus, the null hypothesis (i.e., no significant difference in model performance) was rejected, indicating that there were statistically significant differences in the predictive performance of the four models. The average rankings of the models were as follows: PCA-IWOA-XGBoost (1.33), IWOA-XGBoost (1.67), PCA-XGBoost (3.00), and XGBoost (4.00). Among them, PCA-IWOA-XGBoost achieved the best ranking, demonstrating the strongest predictive capability. Based on this, the Nemenyi post hoc test was further employed to conduct pairwise comparisons of model differences. Under the conditions of \(\alpha\) = 0.05, number of models \(k\) = 4, and sample size \(n\) = 6, the calculated Critical Distance (\(CD=q_{\alpha}\sqrt{\frac{k(k+1)}{6n}}\)) was determined, where \(q_{\alpha}\) is the Studentized Range statistic, \(q_{\alpha=0.05,k=4}\)\(\approx\) 2.85, \(CD\)\(\approx\) 2.32. The comparison results were as follows: the ranking difference between PCA-IWOA-XGBoost and XGBoost was 2.67 (>CD, \(p\) < 0.01), indicating a significant difference; the difference between PCA-IWOA-XGBoost and PCA-XGBoost was 1.67 (>CD, \(p\) < 0.05), also significant; and the difference between PCA-IWOA-XGBoost and IWOA-XGBoost was 0.34 (<CD, \(p\) = 0.062), approaching the significance level. The Nemenyi test further validated the superior predictive accuracy of the PCA-IWOA-XGBoost model, particularly in showing statistical significance in comparisons with the traditional XGBoost and individually optimized models. These results demonstrate that PCA effectively reduces redundant feature dimensions, while IWOA optimization enhances the model structure. The synergistic effect of the two significantly lowers the RMSE and improves model performance.
## Appendix B Overview of U-Net-Based Autoencoder Architecture
### Training Metrics  
To evaluate performance, the initial experiments focused solely on spatial resolution enhancement without introducing noise into the data. The model achieved a mean squared error of 0.0081 on the validation set after 93 epochs of training when using all the simulations. Examples showcasing these results are provided below.  
The next validation error metrics show the results when training the main experiments (WB, EB, MID) under dense traffic conditions and with random white noise added to the input features (std = 0.005).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment (\(\downarrow\))** & **Mean Absolute Error** & **Mean Squared Error** & **Root Mean Squared Error** \\  & **(MAE)** & **(MSE)** & **(RMSE)** \\ \hline Westbound (WB) direction & 0.0119055 & 0.0014845 & 0.0385295 \\ Eastbound (EB) direction & 0.0459715 & 0.1898927 & 0.4357668 \\ Middle (MID) direction & 0.0520504 & 0.1350333 & 0.3674688 \\ \hline \end{tabular}
\end{table}
Table 10: Validation error metrics based on the datasets (initial experiments: resolution enhancement only, no added noise).
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 3 Uncertainty Estimation Approach
### Estimators Properties Analysis Using the Monte-Carlo Method  
After obtaining the models of the system and the perturbations, it is necessary to estimate the value of the fetal heart rate. Simulated samples from the inverse distribution function \(F_{\Xi_{n}}^{-1}F_{-}\) (\(\Xi_{n}\)) were used for this estimation. To carry out this task, the sample mean \(\overline{Y}_{n}\) and the sample deviation \(S_{n}\) were used. The mean is useful for determining the fetal heart rate value and the standard deviation for quantifying the quality of the estimate, known as uncertainty in the metrological area.  
However, it should be noted that for these estimators to be used properly, it is necessary to determine their properties specifically for each patient. The most important property to determine is how the length of sample \(K\) impacts the distribution function.  
On the other hand, the sample mean \(\overline{Y}_{n}\) and the standard deviation \(S_{n}\) are in turn random variables that are characterized by their probability distribution function \(F_{\overline{Y}_{n}}\) and \(F_{S_{n}}\), respectively. These functions in classical theory can be approximated with large samples and hypotheses of probabilistic independence, which turned out to be difficult to verify in this application. To solve this mathematical problem, the Monte-Carlo method is proposed as a tool to determine these distribution  
Figure 3: Estimation process of the error distribution function \(F_{\Xi_{n}}\).  
functions without the use of these hypotheses. The procedure is presented in Figure 4, where \(N\) is the number of samples used to simulate \(\Xi_{k}\) and \(K\) the number of samples to determine \(F_{\overline{Y}_{n}}\) and \(F_{S_{n}}\).  
This procedure allows us to estimate the variance \(\sigma^{2}\) because if \(K\) is large, the following is fulfilled  
\[E\{Y\}=E\{\overline{Y}_{K}\}, \tag{21}\]  
\[S_{K}^{2}=\frac{1}{K-1}\sum_{i=1}^{K}[Y_{i}-E\{Y\}]^{2}, \tag{22}\]  
\[\sigma^{2}=E\{S_{K}^{2}\}. \tag{23}\]