esa-sceva/satcom-chunk-collection huggingface dataset:
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 4 InSAR Datasets and Methods  
We investigate ground displacements in the Perth Basin using temporally- and spatially-overlapping Sentinel-1A (C-band: \(\lambda\sim\) 56 mm) and TerraSAR-X (X-band: \(\lambda\sim\) 31 mm) datasets. All interferograms were produced using the JPL/Caltech InSAR Scientific Computing Environment (ISCE) software [44], with different processing chains implemented for each dataset to account for differences in the satellite acquisition modes. Multi-temporal analysis was then used to combine the interferograms in each dataset and increase the signal-to-noise ratio [45].
## 3 Proposed Method
### MFN Based on Bayesian Hyperparameter Optimization Algorithm (Bayes-MFN)  
The manual parameter adjustment can be time-consuming and labor-intensive and may not result in finding the optimal combination of hyperparameters due to the complexity of the deep neural network model, which is often considered as a black box method. To establish the optimal neural network model, it is particularly crucial to optimize the hyperparameters.  
Compared with the traditional grid search and random search methods, the algorithm framework of Bayesian optimization is sequential. That is, the Bayesian optimization method adjusts the optimal combination of hyperparameters by using prior knowledge to approximate the posterior distribution of unknown functions, thereby reducing a large number of unnecessary calculations. The Bayesian optimization method has gained popularity in many fields due to its ability to obtain the optimal value of complex objective function with fewer evaluation times, using limited function sampling values.  
Hyperparameters can be divided into two groups: those for model training and those for model structure. Since the design of the proposed model aimed to achieve better classification performance with fewer learnable parameters, the hyperparameters of model structure were fixed. Proper selection of the hyperparameters related to model training can enable the neural network to learn faster and to perform better. In the training process, the batch size affects the generalization performance of the model, and the learning rate (lr) determines the convergence state of the model [31]. In addition, the warmup and decay (wd) hyperparameters are closely related to the stability of the deep neural network model. An appropriate epoch size can help to avoid overfitting or underfitting of the model. Therefore, it is necessary to perform Bayesian optimization for those four hyperparameters, with the loss function as the objective function.  
This study adopted the Bayesian optimization algorithm provided by the HyperOpt package [32] in Python to achieve hyperparameter optimization with fewer steps [33]. In this paper, the tree parzen estimator (TPE) search algorithm [34] was chosen for efficient hyperparameter optimization. The process of optimizing hyperparameters is as follows: (1) define an objective function to minimize, (2) define the hyperparameters and search space to be optimized, and (3) select the search algorithm [35]. The TPE algorithm was used to find the optimal combination of hyperparameters by minimizing the loss function.  
Figure 3: Implementation details of the CNN block, Trans block, and FFU block in stage 3. (The green part represents the Trans block. The blue part represents the CNN block structure. The yellow part is the process from the output feature of the previous FFU to the next FFU through the down-sample operation).
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
The first component has significant positive associations with Cr, Cu, and Zn; thus, this component mainly measures the long-term bioaccumulation of these elements. The second component identifies a sizeable negative association with the element Cd; this component mainly indicates persistent bioaccumulation of this metal. The Figure 4 graphically shows the two components, the first on the \(x\)-axis and the second on the \(y\)-axis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample Area**} & \multicolumn{5}{c}{**Mean \(\pm\) SD (mg/kg)**} \\  & \multicolumn{5}{c}{**(Minimum–Maximum)**} \\ \cline{2-6}  & **Cd** & **Pb** & **Cr** & **Ni** & **Cu** & **Zn** \\ \hline \multirow{3}{*}{Puerto El Morro} & 3.75 \(\pm\) 2.56 & 0.39 \(\pm\) 0.34 & 0.19 \(\pm\) 0.17 & 0.80 \(\pm\) 0.51 & 6.70 \(\pm\) 1.64 & 53.78 \(\pm\) 11.10 \\  & (1.10\(-\)8.20) & (0.0\(-\)1.20) & (0.0\(-\)0.60) & (0.20\(-\)1.70) & (4.30\(-\)10.00) & (81.60\(-\)40.00) \\ \cline{1-1}  & 2.14 \(\pm\) 2.00 & 0.68 \(\pm\) 0.75 & 0.30 \(\pm\) 0.34 & 0.57 \(\pm\) 0.21 & 5.3 \(\pm\) 1.02 & 55.63 \(\pm\) 9.52 \\ \cline{1-1}  & (0.50\(-\)8.00) & (0.10\(-\)2.8) & (0.0\(-\)1.1) & (0.30\(-\)1.1) & (4.1\(-\)7.50) & (73.80\(-\)43.90) \\ \cline{1-1}  & 2.90 \(\pm\) 1.16 & 0.56 \(\pm\) 0.33 & 1.34 \(\pm\) 1.27 & 0.93 \(\pm\) 0.24 & 8.56 \(\pm\) 4.27 & 59.73\(-\)9.57 \\ \cline{1-1}  & (1.60\(-\)5.20) & (0.20\(-\)1.50) & (0.10\(-\)4.50) & (0.60\(-\)1.20) & (4.20\(-\)18.20) & (75.30\(-\)45.70) \\ \hline \end{tabular}
\end{table}
Table 1: Concentrations of Cd, Pb, Cr, Ni, Cu, and Zn in samples of bivalve mollusks _Anadara tuberculosa_ (n = 33) for each sector of the Gulf of Guayaquil (mg/kg) on a dry basis.  
Figure 3: Histogram of Cd, Pb, Ni, Cr, Cu, and Zn concentrations (mg/kg) in _A. tuberculosa_.  
Table 2 identifies the differences in the concentrations of heavy metals detected in the _Anadara tuberculosa_ species. The information presented in the table serves as a valuable frame of reference for interpreting the results of this research and provides an overview of the problem. There is sufficient evidence to determine the species as a bioindicator of heavy metal contamination in the Gulf of Guayaquil.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 7 Conclusions  
Aiming at the real-time observation requirements in marine science and ocean engineering, based on underwater acoustic communication and satellite communication technology, a seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO) is proposed in this paper. Based on the current underwater acoustic communication and satellite communication technology, the SRSS/ILMO has an acceptable data transmission capability. The system overall design, hardware composition design, key module design, and performance analysis are given. Based on the harbor environment, an application test of the submarine engineering geological observation was carried out. The preliminary test results show the effectiveness of the developed system. In the future, the deep-sea environment test of SRSS/ILMO will be carried out, and the system will be extended to other seabed in-situ long-term real-time multi-parameter observation applications.  
Conceptualization and methodology, L.L., Y.J., and X.G.; Software, Z.L., C.C., and J.C.; Validation and data curation, J.N., Z.C., and L.D.; Writing, L.L. and Z.L.; Data curation, X.G., H.X., T.L.  
The research is supported by National Key Research and Development Program of China (2017 YFC0307701), National Natural Science Foundation of China (41427803) and Open Fund of Qingdao National Laboratory for Marine Science and Technology (QNLM2016 ORP0414).  
The authors declare no conflict of interest.
## 4 Welding Multi-Source Sensing Systems and Their Information Processing and Knowledge Modeling
### Vibration Sensing Time Domain Feature Analysis and Acquisition  
Vibration sensing is mostly used in troubleshooting machine tools or rotating machinery [24]; however, it is seldom used in welding. In fact, vibration is generated when welding, because we can hear the sound of the welding process, and the sound is generated from the vibration, through the monitoring of welding. The vibration signals during the process can be used to understand the fusion of the weld and the quality of the weld. Therefore, this paper attempts to use vibration sensors to detect the vibration signals emitted during the welding process, together with the current signals and molten pool images to achieve the identification of welding defects. Vibration signals for 01 porosity, 02 incomplete penetration, 03 burn-through, 04 incompletely filled grooves, 05 satisfactory weld, and 06 weld misalignment are collected. Due to environmental and equipment influences, the collected signals often contain noise. In this paper, we subject the signals to various processing methods, such as removing the DC component, wavelet thresholding and noise reduction (db3, 4-layer decomposition), signal frame analysis, and smoothing.  
This helps in extracting the statistics of the vibration amplitude signals in the original X, Y, and Z directions. The statistical time-domain features of the signals with vibration amplitude in three directions are 16 dimensions, which are variance, maximum, peak, root-mean-square, absolute mean, peak indicator, skewness, waveform indicator, minimum, impulse indicator, margin indicator, square root magnitude, skewness indicator, mean values, and crag metrics. Since there are 16-dimensional features in one direction, combining the features from all 3 directions results in 48-dimensional features. For example, Figure 10 shows the time-domain waveform of the \(X\)-axis after removing the DC component and wavelet thresholding noise reduction.  
Figure 10: Vibration time domain signal.  
Figure 9: Current characterization error bar.
# Power Losses Investigation in Direct 3 \(\times\) 5 Matrix Converter Using MATLAB Simulink
## 2 Bidirectional Switch Analysis
We can express the power losses in this type of switch using this assumption as follows:  
\[P_{LOSSmos}=2\cdot\left(I_{RMS}^{2}.R_{DSOn}\right) \tag{3}\]  
Because the RDson values of SiC semiconductors can range from 20 to 110 m\(\Omega\), the power losses created in the matrix converter based on the MOSFET can be greatly decreased according to Equation (3) when compared to Equation (1) for the IGBT switch.
# Influence of the Radial Gap on the External Gear Pump Performance
###### Abstract  
The paper presents a numerical and experimental study of the radial gap influence on the external gear pump performance. The numerical study is performed with a two-dimensional (2D) computational fluid dynamics (CFD) model developed and advanced in previous authors' works. The experimental study is carried out on a laboratory test bench. The presented numerical results are accurate in the entire operating range (500-3500 RPM) of the pump, which is confirmed by comparisons between the CFD results, experimental data, and manufacturer's technical documentation. The comparative analysis shows that the differences obtained during the verifications are in the range of \(-6.44\%\) to \(2.48\%\). An original methodology has been developed that allows us to obtain the volumetric efficiency and overall efficiency characteristics as a function of the rotation frequency of the pump at different values of the radial gap, using the manufacturer's data for the same characteristics at a nominal radial gap and the results of CFD simulations. The analysis of the numerical and experimental results shows that a gap size of \(0.04\) mm is close to the limit value for the investigated pump, if it is not operated at a rotational frequency above the nominal. The presented methodology can also be applied to other types of hydraulic displacement pumps in order to evaluate their performance in the wear process and to predict the maximum allowable value of a specific design parameter under different operating modes.  
external gear pump; radial gap; CFD; flow rate; efficiency Article
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 2 Establishment of Dataset  
### Acquisition of Wind Field Data  
Lidar can accurately detect the velocity of the atmospheric wind field and can better capture the wind field structure of the wake [14; 15]. To obtain real aircraft wake data, this experiment used the wind field data of Hong Kong International Airport. For field detection, the Leosphere Windcube200S lidar was used [16; 17; 18]. The main parameters are shown in Table 1. The lidar has been successfully deployed in many locations globally. The radar was deployed at a vertical distance of approximately 1400 m from the south runway of Hong Kong International Airport, as shown in Figure 1. On the basis of the evolution characteristics and the aerodynamic structure of the wake, the lidar used the range height indicator (RHI) scanning mode to detect the wake, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Radar wavelength & 1.54 \(\upmu\)m \\ Pulse width & 200 ns \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lidar parameters.  
The lidar scanned the target airspace periodically with a certain frequency, obtaining the wind field data of the aircraft wake evolution under different weather conditions. Considering the scanning data from 03:15:23 to 03:16:37 on 1 February 2019 as an example, the lidar scanned the airspace above the runway at the airport nine times. The detected wind speed data were visualised, as shown in Figure 3. The evolution of the aircraft wake was seen. As shown in the early stage (Figure 3a-c), under the induction of the ambient wind and two vortices, the velocity of the two vortices in the wake decreased gradually. The wake intensity was decreasing, and it was expanding in shape. As depicted in the middle stage (Figure 3d-f), under the influence of the crosswind and ground effect, the distance between the left and right vortices in the wake kept increasing [5]. As depicted in the later stage (Figure 3g-i), the symmetrical structure started to disappear, the left vortex merged with the ambient wind, and the right vortex lasted for a long time.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pulse repetition rate & 20 kHz \\ Detection range & 50–6000 m \\ Range gate width & 25 m \\ Angular resolution & 0.5\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Lidar (red circle) at the Hong Kong International Airport.  
Figure 2: The lidar working in RHI mode for aircraft wake recognition.
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 3 Mobile Channel Simulations by IMT-2020 Channel Model  
The channel model is used to meet the requirements of evaluating IMT-2020 candidate radio-interface technologies (RITs) by allowing the realistic modelling of propagation conditions for radio transmissions in different environments [3]. Generally, a GBSM channel model is built in a 2D/3D co-ordinate system. The path loss model, shadow fading, small-scale parameters of clusters in typical scenarios (such as indoor hotspots (InH), UMa, urban micro (UMi), and rural macro (RMa)) are analyzed and collected from many empirical measurements. This can simulate and reproduce channel information that is similar with that in real conditions. In this section, the IMT-2020 channel model is used to simulate the measured channels. It follows the typical GBSM channel-model procedures in ITU-R M. 2135 [2], 3 GPP TR 38. 900 [21], and TR 36. 873 [22]. To get closer to the mobile channels, the spatial-consistency feature was implemented, which is based on the primary module of the IMT-2020 channel model. Simulation results, including moving trajectory, path loss model, and angle evolution of clusters, are given, as are comparisons between measurements and simulations with and without this feature.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 3 Inter-SubNet with New Loss Function
### Discussion of Some Other Losses
#### 3.3.2 Perceptual Loss  
In training SE networks, perceptual loss functions have been used to better align the model's output with human auditory perception. The following presents some key perceptual loss functions from the recent literature:  
1. Perceptual Evaluation of Speech Quality (PESQ) [36; 37]: PESQ is widely used for assessing speech quality. However, the original PESQ algorithm is non-differentiable, making it unsuitable as a direct loss function. To address this limitation, researchers have trained neural networks to mimic PESQ's behavior, creating differentiable surrogate loss functions that enable gradient-based optimization.
2. Short-Time Objective Intelligibility (STOI) [38]: STOI measures speech intelligibility and has been used as a metric to evaluate speech enhancement methods. Like PESQ, STOI's non-differentiable nature poses challenges for direct optimization. To overcome this, researchers have explicitly developed differentiable versions or approximations of STOI for training deep-learning SE models.
3. Phone-Fortified Perceptual Loss (PFPL) [39]: The PFPL integrates phonetic information through wav2 vec, a self-supervised learning framework that captures rich phonetic information. It uses Wasserstein distance to quantify the distributional differences between the phonetic feature representations of clean and enhanced speech. This approach allows for assessing how well the enhanced speech preserves phonetic attributes compared to the original clean speech. PFPL has also shown a strong correlation with PESQ and STOI.  
Similar to PESQ, STOI, and PFPL losses, the TAP loss employed in this study also considers various perceptual characteristics of enhanced signals. These perceptual losses are generally combined with conventional MSE loss related to estimated signals or features to further improve SE performance. As it integrates up to 25 temporal acoustic features, TAP loss is regarded as more comprehensive, offering a deeper insight into how enhancements affect speech compared to other perceptual losses. However, the effectiveness of each loss function ultimately depends on the specific applications.
## 3 Results and Discussion
### Uncertainty of NH\({}_{3}\) Emission Factor at Biogas Production Facility  
#### 3.4.1 Uncertainty of NH\({}_{3}\) Emission Factors Based on Waste at Biogas Production Facilities  
To assess the uncertainty of the NH\({}_{3}\) emission factor of the biogas production facility determined in this research, the Monte Carlo simulation was applied using \"Crystal Ball\", and the results are shown in Table 7.  
The PDF of the NH\({}_{3}\) emission factors for A biogas production facility based on the waste processing amount was analyzed as a Gamma distribution. The average was 0.0012 kgNH\({}_{3}\)/ton-waste. The bottom 2.5% was 0.0011 kgNH\({}_{3}\)/ton-waste at the 95% confidence level, and the top 97.5% was 0.0014 kgNH\({}_{3}\)/ton-waste. The range of uncertainty of the NH\({}_{3}\) emission factor determined using these values was between \(-\)4.3 and +3.7% at the 95% confidence level.  
The PDF of the NH\({}_{3}\) emission factor of B biogas production facility was analyzed as a Beta distribution. The average was 0.0035 kgNH\({}_{3}\)/ton-waste. The bottom 2.5% at the 95% confidence level was 0.0034 kgNH3/ton-waste, and the top 97.5% was analyzed as 0.0037 kgNH\({}_{3}\)/ton-waste. The uncertainty range of the NH\({}_{3}\) emission factor determined using these values was between \(-\)2.9 and +5.7% at the 95% confidence level.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Site**} & **Mean** & **95\% Lower** & **95\% Lower** & **Probability** & **Uncertainty** \\  & **(kgNH\({}_{3}\)/Non-Waste)** & **Confidence Level** & **Confidence Level** & **Density** & **Range** \\  & **Waste** & **(kgNH\({}_{3}\)/Non-Waste)** & **(kgNH\({}_{3}\)/Non-Waste)** & **Function** & **Range** \\ \hline A biogas production facility & 0.0012 & 0.0011 & 0.0014 & \
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} Gamma distribution & \(-\)4.3\(\rightarrow\)+3.7\% \\ \end{tabular} \\ \hline B biogas production facility & 0.0035 & 0.0034 & 0.0037 & Beta distribution & \(-\)2.9\(\rightarrow\)+5.7\% \\ \hline C biogas production facility & 0.0075 & 0.0070 & 0.0081 &
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} Lognormal distribution & \(-\)6.7\(\rightarrow\)+8.0\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Uncertainty of the waste base, NH\({}_{3}\) emission factors at biogas production facility.  
Figure 3: Changes in NH\({}_{3}\) emissions in the waste sector by the reflection of biogas production facilities.  
The PDF of the NH\({}_{3}\) emission factor of C biogas production facility was analyzed as the Lognormal distribution. The average was analyzed to be 0.0075 kg NH\({}_{3}\)/ton-waste. The bottom 2.5% was 0.0070 kgNH\({}_{3}\)/ton-waste at the 95% confidence level, and the top 97.5% was 0.0081 kgNH\({}_{3}\)/ton-waste. The uncertainty range of the NH\({}_{3}\) emission factor determined using these values was \(-\)6.7\(-\)8.0% at the 95% confidence level.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Summary of Results  
Table 6 provides a detailed comparative analysis of the ANN and RF models' performance in forecasting HVAC and heating demand for both the Lucia and FUHEM Buildings. The evaluation incorporates three key metrics: Mean Absolute Percentage Error (MAPE),  
Figure 14: Training time for ANN and RF: (**a**) (LUCIA Building, nZEB-HVAC), (**b**) (FUHEM Building-Heating).  
Figure 15: Sensitivity analysis and feature importance for two buildings: (**a**) LUCIA Building ANN and RF, (**b**) FUHEM Building (Heating) ANN and RF.
# Effect of Vaneless Diffuser Shape on Performance of Centrifugal Compressor
## 2 Numerical Method
### Mesh Division and Boundary Condition Setting  
In this paper, the wall surface is set to adiabatic and non-slip boundary conditions and frozen rotor interface is selected to connect the static interface between the impeller and the diffuser. The inlet surface is given a total temperature of 288.15 K and a total pressure of 101,325 Pa. Pressure inlet and mass flow outlet conditions are set at the inlet and outlet surface of the centrifugal compressor, respectively. Three types of mass flow are set in order to investigate the influence in detail, which are 60%, 100%, and 112.5% of the design flow. The rotational speed of the impeller is the design speed (120,000 rpm). The ideal gas medium is selected for the internal medium to ignore the changes in gas density and the effect of heat exchange. The momentum equation, continuity equation and energy equation were solved by coupling solver. The difference scheme of high resolution controlled the accuracy of the solution, and the convergence criterion was set to the residual value less than \(10^{-4}\).  
The impeller computational domain was discretized automatically using structured mesh by means of a component of CFX named Turbogrid and the diffuser computational domain was divided by software Gambit. To ensure that the value of Y+ near the diffusers wall was around 1, the boundary layer grids of vaneless diffusers were set to fifteen layers, and grow rate was set to 1.2. Then mesh independence verification was proposed in order to make sure the result of simulation correctly. By changing the minimum and maximum mesh number of compressor, three mesh sets were implemented with a total number of 1.5 million up to 3.5 million cells. The effects of the compressor grid on the calculation performance are shown in Table 2. It can be seen that the pressure ratio difference of fine grid level and medium grid level is smaller than the difference between medium grid level and coarse grid level from Table 2. For the limitation of calculation source, the medium grid level concluded 2.33 million cells was selected in this article.