esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Procedures
### Experimental Setup  
The signals from the subsurface defects were acquired using the standard specimen, and the sensor module of the ultrasonic inspection system for the work rolls was modeled with an ultrasonic C-scanner, as shown in Figure 4. Figure 4 shows a photograph of the ultrasonic C-scanner used in the experiments.  
The transducer used in the experiment was a water-immersion-type device (Olympus, 10 MHz, 0.25\({}^{\prime\prime}\)) that is used in actual industrial sites. Moreover, the gain of the ultrasonic pulser receiver was adjusted to detect and differentiate the defect signals. Figure 5 shows the ultrasonic A-scan signals of the defects in the standard specimen for the respective cases. Figure 5a shows signals with no defects, and Figure 5b-e shows the signals of the defects at depths of 1 mm, 2 mm, 3 mm, and 4 mm below the surface, respectively. In Figure 5d,e, the signals reflected from the surface and those from the subsurface defects are observed to overlap, which actually shows similar signal patterns to those in Figure 5a without the defects. This indicates that in such cases it is difficult to determine the presence or absence of defects by intuitive human judgment based on visual observations.  
Figure 4: Ultrasonic C-scanner used in this study.
## 2 Methodology
### Numerical Procedures
#### 2.1.1 Numerical Model Development  
The heat transferred from the laser source to the powder bed can be described by the energy Equation (1) according to [32]:  
\[\rho C_{p}\frac{\partial T}{\partial t}=\
abla\cdot\left(\mathrm{k}\;\
abla\; \mathrm{T}\right)+S_{h} \tag{1}\]  
where \(\rho\), \(C_{p}\), \(T\), \(\mathrm{k}\), \(t\), and \(S_{h}\) represent the density, specific heat, temperature, thermal conductivity, time, and laser heat source, respectively. The laser heat source per unit volume (\(\mathrm{W/m^{3}}\)) can be modelled according to [32] and was assumed to have a Gaussian distribution as described by Equation (2):  
\[S_{h}=\beta I_{o}\alpha\exp\left(-2\;\frac{\left(x-v_{x}t\right)^{2}+\left(y- v_{y}t\right)^{2}}{\omega^{2}}-\alpha z\right) \tag{2}\]  
where \(\beta\), \(I_{o}\), \(\alpha\), \(v\), and \(R\) are the material absorptivity, laser intensity, absorption coefficient, scanning speed, and laser radius, respectively. The parameters \(x\) and \(y\) were used to control the laser source movement on the powder layer (scanning strategy).  
The absorption coefficient was estimated according to [33]. The laser intensity can be calculated according to [32] as follows:  
\[I_{o}=\frac{2P}{\pi\;\omega^{2}} \tag{3}\]  
\[\omega=\frac{D_{b}}{2\times 2.146} \tag{4}\]  
where \(\omega\) is the characteristic laser radius and \(D_{b}\) is the laser beam diameter. It is worth mentioning that a User-Defined Function (UDF) for the governing equations was developed, compiled, and solved using ANSYS FLUENT 2020R1 to model the laser source as described by Equation (2) according to [32]. Additionally, the UDF considered the phase transformation from solid phase to sintered phase based on the sintering point of SiC where any region in the model with a temperature above the sintering point is considered a sintered region. User Defined Memory (UDM) available in ANSYS FLUENT 2020R1 was used to monitor and save the sintered region during the movement of the laser.  
The initial and boundary conditions used in this study are shown in Figure 2 and described by Equations (5) and (6):  
\[T(x,y,z)_{t=0}=T_{O} \tag{5}\]\[-\kappa(\frac{\partial T}{\partial z})=\dot{S_{h}}-h_{cov}(T_{a}-T_{s})-\sigma \mathcal{E}\Big{(}T_{a}^{4}-T_{s}^{4}\Big{)} \tag{6}\]  
where \(T_{a}\) is the ambient temperature, \(T_{s}\) is the powder surface temperature, \(\sigma\) is the Stefan Boltzmann constant, \(\mathcal{E}\) is the emissivity, and \(h_{cov}\) is the convection coefficient.  
Additionally, the developed numerical model considered the SiC material properties as a function of temperature according to our previous study [34] on the developed UDF.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Machine Learning Methods for Yield Prediction
#### 2.3.1 Linear Models and Regularization Methods  
In this study, two linear regression models were initially employed: ridge regression and elastic net regression, to predict winter wheat yield. Ridge regression effectively addresses the issue of multicollinearity among predictors by introducing a regularization term [43]. The regularization strength was set to \(\alpha=1.0\) to ensure robust performance in environments with highly correlated features. Elastic net regression, which combines L1 and L2 regularization (with l1_ratio = 0.5), excels in handling both sparsity and multicollinearity, making it particularly suitable for high-dimensional data.
## 2 Results and Discussion  
### Data Quality  
This study evaluated four types of SPME coatings and nine different compositions of desorption solvents; in total, 36 unique conditions were tested. Each coating-desorption solvent combination was tested in triplicate. Due to the diverse nature of samples present in different solvents (e.g., unique ionization intensity in electrospray ion source), every result was stacked against the mean value (\(n=4\)) that was recorded for the reference sample prepared with a mixture of analytes spiked into the corresponding type of desorption solvent.  
Several calibration runs in the expected concentration range of 5-100 ng/mL were performed in all acetonitrile-based, isopropanol-based, and methanol-based desorption solvents. The resulting 7-point calibration curves always provided coefficients of determination exceeding R\({}^{2}\) = 0.9912 for every analyte with a 1/a\({}^{2}\) weighting (see Table S1 in Supplementary Materials for more details). Additionally, no problems with carry-over or poor peak shapes were found with any of the analytes, and good reproducibility of the results was achieved. Relative standard deviation (RSD) was below 10% for over 98.6% of the results (median RSD = 3.1%) and did not exceed 15% at any time.  
Analysis of the signal recorded for internal standards spiked into desorption solvents confirmed the stability of the detector throughout the experiment. The relative standard deviations in each desorption solvent tested were 4.1-10.1% (median = 5.5%) for oxycodone D\({}_{3}\), 2.9-8.4% (median = 3.8%) for cocaine D\({}_{3}\), 8.9-12.5% (median = 10.8%) for alprazol D\({}_{5}\), and 3.8-14.9% (median = 5.0%) for THC-COOH D\({}_{3}\).
## 5 Simulation Results
### Simulation Results
To make a tradeoff between the complexity and performance, we can use only three PMFs under the unique SNR with no need to adjust the input distribution under the conditions that a small reduced shaping gain can be accepted. As discussed in Equation (14), the input distribution has a significant impact on the error performance. The average SER upper bound from Equation (10) and from the Monte Carlo simulation are shown in Figure 6. As the entropies of the probability distributions increase, the performance of the SER gradually deteriorates. This is because increasing entropies increase the probabilities of the outermost constellation points, which are dominant sources of errors. Moreover, the gain between the two PS schemes and the uniform distribution increases with the increase of the SER. As we can see, the PS scheme  
Figure 4: RBMD optimized entropy at each \(E_{s}/N_{0}\). Figure 5: RBMD for PS-16 QAM over the Gamma-Gamma channel with fixed entropy for each line. with \(H=3.7964\) achieves about 1.5 dB gains over the uniform distribution at an SER of \(4\times 10^{-1}\). It is shown that the theoretical curves and simulation curves are basically consistent, because PEP is calculated to approximate the average SER. This result shows that the PS schemes outperform the uniform distribution with the performance of the average SER. To further investigate the performance of PS schemes, we also evaluate the post-FEC BER under the turbulence channel in Figure 7, which is consistent with the trend of the average SER. Compared with the average SER, post-FEC BER falls sharply when its value is about \(4\times 10^{-2}\). The post-FEC BER would converge asymptotically as the BERs approach zero. As we can see, the PS with \(H=3.7964\) firstly approaches the falling region. It can also be concluded that the smaller the entropy of the PS, the earlier the curves converge. At the post-FEC BER of \(1\times 10^{-2}\), the proposed PS scheme with \(H=3.7964\) (resp. \(H=3.9451\)) obtains nearly a 1.3 dB (resp. 0.5 dB) gain over the uniform QAM. The reason is that the PS with \(H=3.7964\) increases the probability of the constellation points with better BER performance to a greater extent, and reduces that with poorer BER performance.
# Radiometric Identification of Signals by Matched Whitening Transform
## 2 Framework for Radiometric Identification  
The received signal is first corrected for phase offset, oscillator frequency offset and symbol timing errors before application of the whitening transform. The whitening transformation is an orthogonal projection based on a variation of the PCA and is related to the orthogonal subspace projection [43]. One whitening transformation matrix per source is estimated from the training data. There is no need to know the modulation type, frequency, phase or anything else about the signal. Identification of the unknown source is based on the observation that a data set is \"most white\" when projected on its own whitening matrix than on any other, hence _matched whitening_. Projection of the unknown data on the whitening transforms whitens the data only if there is a match between the whitening matrix and the data. Even when the data matches its own whitening transform, the projected data is never truly white. A \"whiteness\" measure is developed by choosing a divergence metric for the comparison of covariance matrices. This measure is the sum of the squared logarithms of joint eigenvalues of the reference and test covariance matrices; the Forstner-Moonen distance. Whitening is well known in signal detection and it is often formulated as the Whitening Matched Filter. The goal is to decorrelate noise samples at the filter output. A 3D implementation of WMF is used for environmental impact studies in hyperspectral imagery [44]. Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral appears in [45]. Examples of such whitening approaches mostly apply to signal and object detection and are not relevant to radiometric identification as proposed here.
## 3 Results
### Ablation Experiments  
To investigate the impact of each module on the model, ablation experiments were conducted. Specifically, key modules were removed from the complete model to compare the changes in model performance. The complete model includes four modules: conv1, mv2, transformer, and conv2. Here, conv1 and conv2 are convolution modules with \(1\times 1\) kernels, used for linear combinations of features across different channels, increasing cross-channel information interaction, and performing dimensionality reduction or expansion to adjust the number of output channels.  
In this experiment, comparisons were made by removing either the Mr2 module or the Transformer module from the complete model. Table 8 shows the results of the ablation experiments, where Accuracy refers to the accuracy on the test set. As seen in Table 8, removing each module leads to a decrease in the model's accuracy. Notably, the removal of the Transformer module causes a significant drop in accuracy, indicating the crucial role of the global attention mechanism in spectral classification. The Transformer module, combined with the convolution modules, can more effectively learn spectral features and improve accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cross Entropy Loss** & **Weighted Cross Entropy Loss** & **Focal Loss** & **Dice Loss** \\ \hline Acc (\%) & 99.24 & 98.54 & 98.32 & 98.65 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Effect of Different Loss Functions on SpectraViT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mv2** & **Transformer** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 (\%)** \\ \hline ✓ & ✓ & 99.24 & 99.25 & 99.06 & 99.23 \\ × & ✓ & 98.91 & 98.91 & 98.91 & 98.91 \\ ✓ & × & 93.18 & 93.18 & 93.06 & 92.50 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The Results of the impact of different modules on model performance. × indicates the removal of a module, ✓ indicates the retention of a module.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 3 Methodology
### The Main Network WLdetectNet
#### 3.1.1. Building a Perceptive Block as the Receptive Field of WLdetectNet  
Given the visual complexity of waterline in varied inland water environments, we intentionally build a block to specialize in perceiving the contextual information relevant to waterline segments, and make use of the perceptive block as the receptive field of WLdetectNet, i.e., as the first layer of the deep main network in our paradigm DeepWL. As shown in Figure 2, the premeditated block, termed WLpeephole, is designated to be an image region of size \(r\times r\), which consists of two different size fields (i.e., \(r\times r\) and \(s\times s\), besides \(r>s\)). Specifically, in order to more conveniently and precisely distinguish various segments relevant to a waterline, i.e., waterline segments, we take advantage of two different scale squares with the same central point, called observing field (\(r\times r\)) and recognizing field (\(s\times s\)), respectively, and further mandate the candidate segments of the waterline to emerge only in the smaller square (i.e., _recognizing field_). Correspondingly, the area between the _observing field_ and the _recognizing field_, namely the area _observing field_ surrounding the _recognizing field_, may be filled with various contextual information associated with a waterline segment, e.g., water streak, plants or buildings at the waterfront.  
Intuitively, the special design on the block can draw visual attention to the waterline within a receptive field. In practice, by feeding an image patch in accordance with the block-based design into our deep network WLdetectNet, we can get hold of the more discriminative waterline feature that avails final accurate decision-making on whether this image patch contains a waterline or not. The reason is that the block based on the design carries better characteristics for making distinctions between waterline and non-waterline by paying attention to necessary contexts associated with a waterline in such a receptive field. In addition, the disparity information mingled in a block facilitates WLdetectNet (whose architecture is specified in Table 1) to bolster the ability of the deep network regarding waterline-relevant feature representation, which will be also interpreted further in Section 5. Moreover, in our waterline detection algorithm (presented in Section 3.4), the perceptive block WLpeephole actually behaves as a peephole that successively diagnoses each region across an image captured by USVs to tell whether the current diagnosed region (i.e., current receptive field of WLdetectNet) contains a waterline or not.  
Figure 2. An illustrative example of WLpeephole (here, \(r=64\) and \(s=30\)).
## 3 System Model and Assumptions
At the same time, we assume that the average channel gain \(|G_{MUE_{i},F_{l}}|^{2}\) from an \(MUE_{i}\) to a neighboring \(F_{l}\) is the same as \(|G_{F_{l},MUE_{i}}|^{2}\) that was sent from an \(F_{l}\) to \(MUE_{i}\). Since the path loss and shadowing components are the same, then the channel gain \(|G_{F_{l},MUE_{i}}|^{2}\) is known easily from an \(F_{l}\) even though this possibility is not used neither in Frequency Division Duplex (FDD) nor in Time Division Duplex (TDD) system [24], while the \(|G^{k}_{F_{1},MUE_{i}}|^{2}\) on a specific RB \(k\) can be calculated directly when the \(RSS^{k}_{F_{1},MUE_{i}}\) is known from a neighboring \(F_{l}\).
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results  
### Dataset  
We conducted experiments on a Mandarin multi-speaker speech dataset, AISHELL-3 [49], and a Mandarin multi-singer singing dataset, M4 Singer [50], to evaluate the effectiveness of the proposed method in both speech and singing voice conversion tasks.  
**(1) AISHELL-3**  
AISHELL-3 is a high-quality multi-speaker Mandarin speech synthesis dataset released by AISHELL Foundation. It contains 218 speakers (where the male-to-female ratio is balanced) and more than 85,000 speech sentences; the total duration is about 85 h, and the sampling rate is 44.1 kHz. A 16-bit, professional recording environment made clear sound quality possible. In order to construct the speech test set, in this study, we randomly selected 10 of the speakers to be excluded from training, and from each, we randomly selected 10 speech samples, totaling 100 speech samples.  
**(2) M4 Singer**  
M4 Singer is a large-scale Chinese singing voice dataset with multiple styles and multiple singers released by Tsinghua University. The dataset contains 30 professional singers (where the male-to-female ratio is balanced) and a total of 16,000 singing sentences; the total duration is 18.8 h, and the sampling rate is 44.1 kHz. It was generated in a 16-bit, clean, and noise-free recording environment and covers pop, folk, rock, and other styles. Similar to the above, 10 singers were randomly selected to be excluded from training, and 10 singing samples were selected for each singer, for a singing test set totaling 100 samples.
## 2 Methodology
### Bidirectional Cross Fusion  
The BCF module is designed to effectively integrate spatiotemporal features across both time and scale dimensions, enhancing temporal consistency and preserving details in remote sensing images. Unlike existing single-direction fusion modules, the BCF module captures temporal change and spatial details simultaneously, enabling bidirectional feature interactions. This approach improves robustness in scenarios with complex land cover changes. As shown in Figure 2, the module employs feature fusion strategies based on self-attention mechanisms for both temporal and scale dimensions. Furthermore, it integrates short-distance attention (SDA) and long-distance attention (LDA) [41] modules to reinforce local and global feature interactions.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 2 Methodology
### Sources of Uncertainty
Errors were then introduced into all sources randomly using the Monte-Carlo Simulation method to conduct random error tests, and the range or values of uncertainty for each variable is shown in Table 3. The purpose of the random error tests was to statistically analyze the effect of uncertainties on leak detectability. A total of 500 combinations were tested for each flow condition, steady state, flow decrease, and flow increase. The tuning weights in Equations (4)-(7) needed to be set differently and are listed in Table 4. When only a single variable contained uncertainty, its corresponding weight was set to a relatively small value compared to the weights for the other terms containing no error. For example, for tests with viscosity error alone, the tuning weight for _FPDC_ (\(W_{3}\)) was set to 1, which was the smallest value among all the tuning weights. \(W_{1}\) and \(W_{2}\) were set to 1 in all cases. This is because when \"measured\" data contain data noise, adjustment to the \"measured\" data is desirable and Statefinder was penalized less for making such an adjustment. The bound for the adjustment was determined by the sensor repeatability. For perfect data cases, repeatability of all sensors was set to be zero; thus, no adjustments to the measured pressures or flow rates would be made regardless of the values of \(W_{1}\) and \(W_{2}\). In the cases where error was introduced randomly in any uncertainty sources, the tuning weights were set similar to those used in real pipelines. Measurements, friction, and bulk modulus were considered the most likely uncertainty sources; thus, \(W_{1}\), \(W_{2}\), \(W_{3}\), and \(W_{6}\) were all set to 1. \(W_{7}\) was set to the second smallest value (5) so that Statefinder assigned a lower penalty when generating diagnostic flow to account for the leak. Higher penalties were assigned to the rate of change for pressure and friction by setting \(W_{4}\) to 10 and \(W_{5}\) to 500. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow Condition** & **Pressure Sensor with Time Skew** & **Flow Meter with Time Skew** \\ \hline \multirow{3}{*}{Steady State (with leak)} & US & none \\  & none & DS \\  & US & DS \\ \hline \multirow{3}{*}{Flow Decrease} & none & US \\  & none & DS \\  & DS & US \\ \hline \multirow{3}{*}{Flow Increase} & US & None \\  & none & US \\ \cline{1-1}  & none & US \\ \cline{1-1}  & none & US \\ \cline{1-1}  & none & US \\ \cline{1-1}  & use & US \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tested scenarios of time skew. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Range of Uncertainty** & **Tested Values** \\ \hline Viscosity & 1.31–11.53 mPa-s & random \\ Bulk modulus & 1.30–1.59 GPa & random \\ Data noise & - & 1\%, 2\% \\ Time skew & - & 5 s, 10 s \\ Polling time & - & 5 s, 10 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The range of uncertainty for each variable in random error tests.