esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Extract Feature Paths
#### 4.1.1 Estimate Principal Curvatures of Each Vertex  
At a point \(v\) on a surface \(\Pi\), the principal curvatures, denoted as \(K_{1}\) and \(K_{2}\), represent the maximum and minimum normal curvatures, occurring in orthogonal directions known as principal directions, denoted as \(T_{1}\) and \(T_{2}\)[32]. \(R_{1}\) and \(R_{2}\) represent the curvature radii associated with \(K_{1}\) and \(K_{2}\), respectively, as depicted in Figure 3. Each point on a smooth surface possesses a unique tangent plane that offers a linear approximation of the surface at that location. By examining the normal curvature in various directions within this tangent plane, one can determine the principal directions, which correspond to the eigenvectors of the shape operator. These principal directions are orthogonal to each other. Understanding the principal curvatures and principal directions is essential for defining feature paths and cross-sectional sketches in our CAD reconstruction methodology, as they provide detailed insights into the local geometric properties of the surface.  
This study employs the curvature estimation method introduced by [PERSON] et al. [34], which involves interpolating three endpoints and their respective normal vectors of each triangle to form a curved patch. Subsequently, the curvature per triangle is computed for the neighboring triangles surrounding a specific mesh point of interest. Similar to deriving per-vertex normal vectors from adjacent per-triangle normals, the per-vertex curvature is calculated by determining a weighted average of the per-triangle curvatures.  
Upon determining the principal curvatures \(K_{1}\) and \(K_{2}\) of the vertices, the computational approach presented by [PERSON] [35] is employed to derive the principal directions \(T_{1}\) and \(T_{2}\). This method involves constructing symmetric matrices to identify the two eigen  
Figure 3: Determining the principal curvature and principal direction of a continuous surface [33].  
Figure 2: The four typical solid features can be consolidated into a unified mathematical representation, such as GSV.  
vectors that correspond to the principal directions \(T_{1}\) and \(T_{2}\). Following this, the directions of the feature sections are determined utilizing the previously obtained principal directions.
## 4 Experimental Results
### Quantitative Comparison
Specifically, DRST-Net outperforms Swin-Unet by 3.59%, 6.04%, and 1.85% in \(m1 ou\), \(mPre\), and F1_score, respectively. It also leads MISSFormer by 0.36% in \(mRecall\) and 0.65% in F1_score._ This performance highlights DRST-Net's unique advantage in combining local details with global semantics, allowing it to maintain high recognition accuracy in complex backgrounds. DRST-Net's superior performance can be attributed to its innovative architecture and design, which include a dual-branch structure integrating ResNet50 and Swin Transformer for effective local and global feature extraction. The cross-attention feature fusion (CaFF) module enhances this by selectively focusing on the most relevant features while sup \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **m1 ou (\%)** & **mPre (\%)** & **mRecall (\%)** & **F1\_Score (\%)** \\ \hline Unet [18] & 80.48 & 85.10 & 91.34 & 88.11 \\ Unet++ [19] & 81.81 & 86.14 & 92.26 & 89.10 \\ nnU-Net [40] & 85.05 & 90.88 & 91.62 & 91.25 \\ Swin-Unet [41] & 82.58 & 86.28 & 93.69 & 89.83 \\ MISSFormer [42] & 86.15 & 92.30 & 90.68 & 91.03 \\ ACC-UNet [29] & 80.57 & 92.21 & 84.58 & 88.23 \\ DCSAU-Net [36] & 84.11 & 86.12 & 86.12 & 86.12 \\ Att-Unet [20] & 81.41 & 86.20 & 91.47 & 88.76 \\ BRAU-Net++ [30] & 85.38 & 91.17 & 91.79 & 91.48 \\ DRST-Net (ours) & 86.17 & 92.32 & 91.04 & 91.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weld seam recognition results using different methods. pressing noise, improving robustness in complex and noisy industrial environments. By capturing multi-scale features and efficiently fusing them across different contexts, DRST-Net ensures both fine details and broader structural information are accurately incorporated. Additionally, training with noise augmentation enables the model to handle real-world disturbances such as sensor noise and lighting variations, further enhancing its robustness. Overall, DRST-Net's combination of dual-branch architecture, attention-based feature fusion, and noise-resistant training strategies provides significant advantages in weld seam recognition, leading to improved accuracy and reliability compared to other models. In the quantitative comparison analysis, DRST-Net shows excellent effectiveness in several key metrics, outperforming other hybrid transformer networks. It performs well in \(mlou\), precision, and \(F1\_score\). Although its recall rate is slightly lower than that of attention U-Net, its overall capability is still better than many hybrid transformer networks. This shows DRST-Net has great potential for complex weld seam recognition tasks.
## 2 Methodology
### Data Processing Methods  
Electromagnetic wave scattering from the sea surface is influenced by wind speed and wind direction. As shown in Figure 3, the wind direction has a significant effect on the scattering coefficient. Therefore, in order to study the influence of wind speed on the scattering coefficient, the values of the scattering coefficient should be calibrated to the same wind direction. In this work, the uniform wind direction is set to \(0^{\circ}\). After calibrating the influence of the wind direction, the value of the scattering coefficient acquired by the scatterometer at wind direction \(\phi_{1}\) can be expressed as follows:  
\[\sigma=\sigma_{m1}+\Delta\sigma \tag{11}\]  
Here, the difference \(\Delta\sigma=\sigma_{0}-\sigma_{1}\) reflects the influence of wind direction on the scattering coefficient. \(\sigma_{0}\) and \(\sigma_{1}\) denote the scattering coefficients when the wind directions  
Figure 3: Schematic diagram of the scattering coefficient calibration.  
Figure 2: Changes in scattering coefficient under different conditions of (**a**) permittivity of seawater, (**b**) sea surface temperature, and (**c**) seawater salinity.  
are 0\({}^{\circ}\) and \(\phi_{1}\). \(\sigma_{m1}\) is the measured scattering coefficient. \(\sigma_{0}\) and \(\sigma_{1}\) are both evaluated by the Ku-band GMF [16; 17].  
In this study, the anomaly data (i.e., _SSTA_, _SATDA_, and _SCA_) are calculated by subtracting the mean background field from the raw data (i.e., _SST_, _SATD_, and scattering coefficient), as demonstrated in Equation (12):  
\[DATA_{anomaly}=DATA_{original}-DATA_{mean} \tag{12}\]  
Here, \(DATA_{anomaly}\) is anomaly data. \(DATA_{original}\) represents the downloaded raw data, such as the _SST_, air temperature, _SATD_, and scattering coefficient. \(DATA_{mean}\) denotes the mean background field, which is obtained by mean filtering with a sliding window size of 3\({}^{\circ}\)\(\times\) 3\({}^{\circ}\) along the longitude and latitude directions.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 6 Conclusions and Outlook  
Beamforming and beamsteering through OBFNs provide promising enabling technologies for mmWave wireless communications. In this review, we analyzed typical OBFNs with TTD architectures and phase shifter architectures, introduced their principles and basic features, and conducted a performance comparison of different architectures. Furthermore, several technologies that can scale-up OBFNs were recommended, which include wavelength multiplexing using a microcomb source, MRR/microdisk-based photonic field-programmable gate arrays, the use of low-loss material platforms such as Si\({}_{3}\)N\({}_{4}\), and the heterogeneous and monolithic integration of electro-photonic systems. In addition, integrated devices such as high-linearity modulators and UTC-PDs on Si/Si\({}_{3}\)N\({}_{4}\) platforms are also key components in OBFNs for meeting the demands of mmWave PAAs with massive antenna elements and high excitation power. These two research topics may receive much more concern in the future. For practical applications, the convergence of an OBFN-based PAA and A-RoF is a competitive candidate technology for mmWave wireless communications.  
Conceptualization, F.D.; investigation, F.D., Y.G., Z.G. and Y.Y.; writing--original draft preparation, F.D.; writing--review and editing, F.D., Y.Y., Y.W. and T.C.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Huawei Technologies Co., Ltd.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to thank [PERSON] and [PERSON] from the Huawei Technologies Co., Ltd., for their help with academic writing of this paper.  
The authors declare no conflict of interest.
## 4 Results
### Main Phase
#### 4.2.2 Assessment of Model Prediction Accuracy

Table 3: Summary of linear models used for Chl-a and phycocyanin estimation (Using Sentinel-2 data filtered by QA60 bitmask). The results shown in Table 4 demonstrate that using concurrent models 70% and 65% of the predicted Chl-a values are correctly categorized as moderate and high, respectively, and 70% and 86% of the phycocyanin prediction values are correctly categorized as moderate and high, respectively. Using a one-day time window prediction model, the accuracy of moderate and high levels of Chl-a values were 74% and 71%, respectively, and for phycocyanin concentrations the levels were 83% and 96%, respectively. Our primary goal in this evaluation was confirming that we did not have false negatives reported, i.e., field points that were known to have Chl-a or phycocyanin above 8 ug/L predicted by the remote sensing-based method as having low concentration. We note that one point in the concurrent dataset and three in a one-day dataset predicted low phycocyanin concentrations while the field phycocyanin concentrations were moderate or high (above 8 ug/L). After viewing the Sentinel-2 images for the four erroneous data points, we found that the errors were largely due to cloud contamination, which suggests that there may be issues with using the QA60 bitmask approach.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 3 Artifacts Correction by Deep Learning
### Loss Function  
The parameters of the network need to be optimized by minimizing an appropriate loss function. The loss function we choose is the combination of the mean-squared error (MSE) and structural similarity (SSIM) [43].  
The MSE between the output of the network, \(\mathbf{I}_{\text{est}}\), and the initial reference source, \(\mathbf{I}_{\text{IS}}\), is formally defined as  
\[\text{MSE}(\mathbf{I}_{\text{est}},\mathbf{I}_{\text{IS}})=\frac{1}{w\times h}\Big{\|} \mathbf{I}_{\text{est}}-\mathbf{I}_{\text{IS}}\Big{\|}_{F}^{2}, \tag{4}\]  
where \(w\) and \(h\) are the width and height of the image, respectively, and \(\|\cdot\|\) refers to Frobenius norm. Although MSE is the most straightforward loss function to optimize the network, the resultant images are usually over-smoothing and lose some details.  
In contrast to MSE, the SSIM can measure the similarity between two images in terms of their structures and textures. SSIM index is calculated on various windows of an image. The measure between the window \(x\) over \(\mathbf{I}_{\text{est}}\) and the window \(y\) over \(\mathbf{I}_{\text{IS}}\), based on a common size \(k\times k\), is defined as  
\[\text{SSIM}(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+c_{1}} \frac{2\sigma_{xy}+c_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}}, \tag{5}\]  
Figure 2: The proposed network structure for reflection artifacts correction. It comprises three parts—feature extraction, artifacts reduction, and reconstruction. In particular, we use a modified version of U-net as the backbone of artifacts reduction part.  
where \(\mu_{x}\) and \(\mu_{y}\) are the averages of \(x\) and \(y\) respectively, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of \(x\) and \(y\) respectively, and \(\sigma_{xy}\) is the covariance of \(x\) and \(y\). Also, \(c_{1}=0.01^{2}\) and \(c_{2}=0.03^{2}\) are two constants, which are used to stabilize the division with a weak denominator. The windows size \(k\) is 11, as suggested. The SSIM between two images \(\mathbf{I}_{\text{est}}\) and \(\mathbf{I}_{\text{IS}}\), \(\text{SSIM}(\mathbf{I}_{\text{est}},\mathbf{I}_{\text{IS}})\), refers to the average of the SSIM index over all windows.  
The loss function is defined as  
\[\min_{\mathbf{\theta}_{G}}\mathbb{E}_{(I_{\text{est}},I_{\text{IS}})}\sqrt{1+\text {MSE}(\mathbf{I}_{\text{est}},\mathbf{I}_{\text{IS}})}\times\big{[}1-\text{SSIM}(\mathbf{I }_{\text{est}},\mathbf{I}_{\text{IS}})\big{]}, \tag{6}\]  
where \(\mathbf{\theta}_{G}\) denotes the parameters of the network \(\mathcal{G}\). This loss function not only reduces the noise in terms of MSE but also preserve the image structures as measured by SSIM [44]. Various algorithms can solve this minimization problem. In this work, we adopt the Adam algorithm to update the parameters [45]. The gradients of the parameters are computed using a back-propagation algorithm [46].
# Mesh-Free MLS-Based Error-Recovery Technique for Finite Element Incompressible Elastic Computations
## 1 Introduction
The efficient and reliable error estimator based on the recently presented mesh-free radial point interpolation technique for the adaptive discretization of incompressible elastic problems motivated us to apply other mesh-free techniques for error-recover-based solutions and to present a comparison of mesh-free error-recovery results with mesh-dependent error-recovery results. In this study, a mesh-free interpolation error recovery technique is explored for incompressible elastic finite element analysis. Moving least squares (MLS) interpolation considering radial weights over circular support domains was implemented for the recovery of solution errors in incompressible elastic finite element analysis and the errors were quantified in an energy norm. The MLS interpolation technique utilizes the finite element method's solutions in weighted least squares to create a continuous displacement/stress approximation. The displacement/pressure-based mixed formulation was used in the finite element analysis. The most common element types, six-node triangular and four-node quadrilateral elements, were employed for the mesh generation. Analyses of numerical examples, including problems with singularity, were performed to illustrate the effectiveness and efficiency of the meshless MLS interpolation approach for displacement/stress-solution-error recovery in incompressibility conditions. The precision of the error estimator was measured by its convergence rate and effectiveness in error estimation, and the meshes were updated to ensure the desired accuracy. The quality of the mesh-less MLS interpolation approach for displacement/stress error recovery was compared with the least-squares interpolation approach for displacement/stress-solution-error recovery in incompressibility conditions. The node-based and element-based node patches were employed for the displacement and stress, respectively, in the least-squares-interpolation-recovery approach. The mesh-dependent patches of elements for displacement recovery consider all the surrounding elements of the specified element and mesh-dependent patches of node for stress recovery consisted of the union of the nodes surrounding the specified node [20].
## 5 Conclusions
The input is a mixed speech of two speakers saying Label-A and Label-B with temporal overlap, and the proposed SAT-ASR system generates two sequences: Result-1 and Result-2. The word sequences were the token level text generated by a Transformer-based language model [43]. Through these plans, we intend to achieve more accurate multi-speaker speech recognition in complex real-world environments. Conceptualization, M.K. and G.-J.J.; methodology, M.K. ; software, M.K. ; validation, M.K. and G.-J.J.; formal analysis, M.K. and G.J. ; investigation, G.-J.J.; resources, G.-J.J.; data curation, M.K. ; writing--original draft preparation, M.K. ; writing--review and editing, M.K. and G.-J.J.; visualization, M.K. ; supervision, G.-J.J.; project administration, G.-J.J.; funding acquisition, G.-J.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (no. RS-2021-II21068, Artificial Intelligence Innovation Hub; and R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding). Not applicable. The data presented in this study are openly available in [[https://github.com/JorisCos/LibriMix](https://github.com/JorisCos/LibriMix)]([https://github.com/JorisCos/LibriMix](https://github.com/JorisCos/LibriMix)) at [[https://doi.org/10.48550/arXiv.2005.11262](https://doi.org/10.48550/arXiv.2005.11262)]([https://doi.org/10.48550/arXiv.2005.11262](https://doi.org/10.48550/arXiv.2005.11262)) (accessed on 20 July 2024). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ASR & Automatic Speech Recognition \\ WER & Word Error Rate \\ DPCL & Deep Clustering \\ PIT & Permutation Invariant Training \\ SAT & Speaker-Attributed Training \\ SAT-ASR & Speaker-Attributed Training for Automatic Speech Recognition \\ UBM & Universal Background Model \\ GMM & Gaussian Mixture Model \\ PLDA & Probabilistic Linear Discriminant Analysis \\ DNN & Deep Neural Network \\ LSTM & Long Short-Term Memory \\ SISO & Single Input Single Output \\ SIMO & Single Input Multiple Outputs \\ MFCC & Mel-Frequency Cepstral Coefficient \\ CNN & Convolutional Neural Network \\ SS-ASR & Single-Speaker Automatic Speech Recognition \\ MS-ASR & Single-Speaker Automatic Speech Recognition \\ DPRNN & Dual-Path Recurrent Neural Network \\ TasNet & Time-Domain Audio Separation Network \\ \end{tabular}
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 5 Concluding Remarks  
This study has established a BPNN machine learning model for predicting external noise limits during motor vehicles' acceleration. The proposed model includes three layers, in which the input layer uses the first six historic data of the vehicle noise limits as inputs, the single hidden layer with 12 neural units id located in the middle, and the output layer is to output the predicted noise limits of motor vehicles.  
For validation and verification, the BPNN model was compared with the quadratic regression model. The maximum deviation of the predicted values of the BPNN was found much smaller than those obtained by the quadratic regression model. Those results also demonstrated that the predicted value of the noise limits by using the BPNN model is much close to the expected value of vehicle noises and superior to the quadratic regression model as the percentage errors are much smaller. The BPNN model can also provide a unified function suitable for all learned categories of vehicle and related to the previous noise limits of a car type.  
Limits of the external noise limits of all categories of vehicle during accelerating have been predicted by following the trend of noise limits in UNECE R51 regulations. The predicted results show that some categories of vehicles, including N2 and N3, have higher vehicle noise limits defined in the regulations currently and they were predicted to be significantly smaller than the current limits in their next revisions and meanwhile the noise limits of other vehicle categories with low noise limits might be changed slightly.  
The proposed BPNN model also has deficiencies, such as the determination of the structure of the neural networks, especially the algorithm of determining the number of hidden layer neurons, in which an optimal value cannot be determined. It would be part of the future work as an extension of current research. For improving the accuracy of the proposed BPNN model for prediction of noise limits of motor vehicles.  
Conceptualisation, G.T. and R.Y.; methodology, G.T. and R.Y.; software, G.T.; validation, G.T., R.Y.; data analysis, G.T., Q.C. and C.L.; investigation, G.T. and R.Y.; data curation, G.T. and R.Y.; writing--drafting, reviewing and editing, G.T. and R.Y.; visualisation, G.T.; supervision, R.Y.; project administration, R.Y.; funding acquisition, G.T. and R.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The first author is grateful for the financial support from the China Scholarship Council during his academic visit to School of Engineering, Western Sydney University. All the co-authors, G.T., Q.C., C.L., and R.C.Y. would like to show their gratitude to School of Engineering, Western Sydney University for the support and assistance on their research activities.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison with Other Methods  
To comprehensively estimate the developed method, it was compared to several other reported methods, which determined insecticides in vegetables (Table 3). Although the different sorbent materials were utilized in these works in similar matrices for insecticides, these sorbents generally demonstrated lower selectivity to only a few analytes. In comparison, 3D-rGOPFH was able to adsorb different classes of insecticides from the sample and provided satisfactory extraction efficiency with recoveries higher than 79.2%. Unlike other reported methods, no matrix effect was observed in our developed sample preparation method. Also, the MSPE operations of this work were simpler than those reported in the literature. No repeated desorption process, nitrogen blowing, and redissolution were employed, simplifying sample preparation. These results demonstrate that the current method is adequate for the pretreatment of the multiresidue insecticides in vegetable samples. Despite its obvious superiorities, the limitation of this method is that the sample extraction and separation/detection are carried out off-line as well as a drying process is required to remove moistures after magnetic separation of the sorbent. Moreover, the preparation process of 3D-rGOPFH nanohybrid is slightly  
Figure 4: MSPE-GC-MS/MS chromatograms for spiked/un-spiked blank vegetables. The chromatogram peaks marked 1 to 13 represent isocarbophos, quinalphos, phorate, chlorpyrifos, phosalone, pirimor, carbaryl, myclobutanil, diniconazole, lambda-cyhalothrin, bifenthrin, 2, 4\({}^{\prime}\)-DDT, and mirex, respectively.  
time-consuming. We will focus on these issues in future research to improve the efficiency of the current method for determining various pesticide residues in various samples.