esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computation of PDs of Surface-Wave Phase Velocities
\begin{table}
\begin{tabular}{l l} \hline \hline
**Pseudo-code 1** The computation of \(\partial F/\partial c\) \\ \hline \hline
**Input**: angular frequency \(\omega\), phase velocity \(c\), earth model \(\mathbf{Q}_{\mathrm{R}}\) or \(\mathbf{Q}_{\mathrm{L}}\) \\ \hline
1: & Initialize four vectors: \(\mathbf{xold}=\overline{\mathbf{X}}_{1}\), \(\mathbf{yold}=\overline{\mathbf{Y}}_{1}\), \(\mathbf{xnew}=\mathbf{0}\), and \(\mathbf{ynew}=\mathbf{0}\) \\
2: & **for**\(m=1\)-\(n\) **do** \\
3: & calculate \(\overline{\mathbf{T}}_{m}\) and \(\partial\overline{\mathbf{T}}_{m}/\partial c\) \\
4: & update \(\mathbf{xnew}\): \(\mathbf{xnew}=\mathbf{xold}\times\overline{\mathbf{T}}_{m}^{*}\) \\
5: & update \(\mathbf{ynew}\): \(\mathbf{ynew}=\mathbf{yold}\times\overline{\mathbf{T}}_{m}+\mathbf{xold} \times\partial\overline{\mathbf{T}}_{m}^{*}/\partial c\) \\
6: & update \(\mathbf{xold}\): \(\mathbf{xold}=\mathbf{xnew}\) \\
7: & update \(\mathbf{yold}\): \(\mathbf{yold}=\mathbf{ynew}\) \\
8: & **end for** \\
9: & compute \(\overline{\mathbf{V}}_{n+1}\) and \(\partial\overline{\mathbf{V}}_{n+1}/\partial c\) \\
**Output**: \(\partial F/\partial c=\mathbf{ynew}\times\overline{\mathbf{V}}_{n+1}+\mathbf{xnew }\times\partial\overline{\mathbf{V}}_{n+1}/\partial c\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pseudo-code for computation of \(\partial F/\partial c\). sequence number \(t\) (whether it equals to \(n+1\)). Therefore, by using these two algorithms and (4) in conjunction, the PDs can be computed analytically. We only show the elements of \(\partial\overline{\mathbf{V}}_{n+1}/\partial c\), \(\partial\overline{\mathbf{T}}_{m}/\partial c\), \(\partial\overline{\mathbf{V}}_{n+1}/\partial p_{n+1}\), and \(\partial\overline{\mathbf{T}}_{t}/\partial p_{t}\) for Love waves in Appendix B. As for Rayleigh waves, due to the lengthy formulas and the limited space, we suggest readers go directly to the source code according to the description in Section 5.
## 3 Results and Discussions
### Principal Component Analysis (PCA)  
Spectral data from the 64 samples of the dye solutions can be represented as a matrix X with \(m\) row-vectors and \(n\) length, where \(m=350\) number of variables (UV-Vis spectroscopy analysis wavelength range), and \(n=64\) number of observations (samples), resulting in a size of X matrix (\(64\times 350\)).  
The mathematical method underlying the principal component analysis was used in the spectral data from the 64 samples of dye solutions to reduce the dimensionality of the dataset. PCA was performed in Python using the scikit-learn library; the cumulative total variance of the top three principal components equaled up to 99.4%; in other words, three new noncorrelated variables (principal components) retained a maximal total variance explained by the 350 variables from the original data.  
In order to visualize the results of the PCA, the score plots of PC1 versus PC2 and PC1 versus PC3 were plotted, as can be seen in Figures 5 and 6, respectively; the points are labeled based on dye concentrations of the sample ID, as can be seen from Table 1.  
From the score plots shown in Figure 5, we noticed that the first and second principal components had a strong relationship with the B1 dye concentration, given that the data separated into three appreciable clusters (Figure 5c). Figure 6 shows three clusters for TZ and AR concentration classes along the PC2 dimension (Figures 6a and 6b, respectively).
## 3 Speaker Clustering  
In order to evaluate the effect of RBM vectors in a speaker clustering task, we considered the conventional bottom-up AHC clustering system with the options of single and average linkages. We did not consider the model retraining approach because it is costly in terms of computations as compared to the linkage approaches to clustering [28]. The system starts with an initial number of clusters equal to the total number of speaker segments. Iteratively, the segments that are more likely to be from the same speaker are clustered together until a stopping criterion has reached. The stopping criterion can be thresholding the score in order to decide to merge clusters or it can be a desired (known) number of clusters achieved. The clustering algorithm is based on computing a distance/similarity matrix \(M(X)\) between all the speakers' segments where \(X\) is the set of segments to be clustered. Hence, the RBM vectors of all the segments are extracted, the matrix \(M(X)\) is computed by scoring all the RBM vectors against all. Thus, for \(N\) RBM vectors, the matrix \(M(X)\) has dimensions \(N\times N\). In every iteration, the segments with minimum/maximum distance/similarity scores are clustered together and the matrix \(M(X)\) is updated. The corresponding rows and columns of the clustered segments are removed from \(M(X)\) and a new row and column are added. The new row and column contain the distance scores between the new and old clusters. The new scores are computed according to the linkage algorithm used. For example, segments \(S_{a}\) and \(S_{b}\) are clustered in \(S_{ab}\). Then the scores between new cluster (\(S_{ab}\)) and old segment (\(S_{n}\)) are computed as follows:  
(a) Average Linkage:  
\[s(S_{ab},S_{n})=\frac{1}{2}\{s(S_{a},S_{n})+s(S_{b},S_{n})\} \tag{1}\]  
(b) Single Linkage:  
\[s(S_{ab},S_{n})=max\{s(S_{a},S_{n}),s(S_{b},S_{n})\} \tag{2}\]  
where \(s(S_{ab},S_{n})\) is the score between new cluster \(S_{ab}\) and old segment \(S_{n}\) while \(s(S_{a},S_{n})\) is the score between old segments \(S_{a}\) and \(S_{n}\).  
In this way, the process is iterated until a stopping criterion is met. There are two methods to control the iterations: (1) to fix a threshold and (2) to add an additional information to the system about the desired (known) number of clusters. The system stops when this number is reached. In this work, we did not let the system know any desired number of clusters and we have used the thresholding method. We have tuned a threshold in order to see the performance of the system at different possible working points. The system performance is measured with respect to a ground truth cluster label.  
Figure 2: Examples of 400-dimensional RBM vectors. The figure shows two pairs of RBM vectors from the test audios. Each pair belong to the same speaker. We rearrange the RBM vectors in the form \(10\times 40\) for the convenience of visualization. The ordering of the RBM vector is the same for all.
# Cloud-Enabled Deployment of 5G Core Network with Analytics Features
## 4 The Testbed
The UDR implemented by Open5 GS does not store subscriber data directly. Instead, it interacts with MongoDB, a NoSQL document-oriented database that stores data in a JavaScript Object Notation (JSON) structure [39]. Since an emulated Access Network (AN) and UE are necessary for the proper verification of the 5 GCN, an additional virtualized node is deployed for the 5 GAN. UERANSIM is an open-source framework that provides 5 GAN functionality, offering a software implementation of the gNB, which serves as the base station for the fifth-generation AN, along with a 5G-enabled UE. In the deployed 5G network, SBA communication through SBI interfaces and message exchanges over the N2/N1/N4 links are routed through a private address space. This setup requires minimal effort when utilizing public cloud network services, such as Azure Virtual Network [3] or Amazon VPC [40]. In summary, the architecture depicted in Figure 2 has been meticulously designed to ensure repeatability and support cloud migration. As a result, both the 5 GCN and 5 GAN can be fully deployed in a public cloud environment. To establish this reliable setup, we addressed several challenges detailed in the paper and proposed effective solutions, which are outlined below.
## 2 Signal Processing and Height Inversion
### Signal Processing
#### 2.1.2 Coherent Integration  
The coherent integration time is set to \(T_{c}\) and the sampling frequency \(f_{\text{s}}\) is 80 MHz. In this case, \(f_{\text{s}}\times T_{c}\) samples constitute the sequence of each coherent integration, which is expressed as \(S_{\text{up}}^{nT_{c}}[K]=[S_{\text{up}}^{nT_{c}}(1),S_{\text{up}}^{nT_{c}}( 2)\dots S_{\text{up}}^{nT_{c}}(f_{\text{s}}\times T_{c})]\). Removing single-frequency interference is essential before coherent integration can be performed to prevent it from influencing the measurement result. Through fast Fourier transform (FFT), the time domain signal \(S_{\text{up}}^{nT_{c}}[K]\) and \(S_{\text{down}}^{nT_{c}}[K]\) are transformed into the frequency domain \(S_{\text{up}}^{nT_{c}}[f]\) and \(S_{\text{down}}^{nT_{c}}[f]\), and the amplitude anomalies in the frequency spectrum are identified and filtered out. The filtered direct signal and the reflected signal are cross-correlated in the frequency domain to obtain the complex waveform \(y(nT_{c},\tau)=F^{-1}(S_{\text{up}}^{nT_{c}}[f]\times S_{\text{down}}^{nT_{c }}[f])\). It should  
Figure 1: Simplified flowchart of airborne IF data processing.  
be declared that the phased array antenna is used in this experiment, and beamforming is usually employed to improve the SNR in the signal post-processing. However, the effect of beamforming may submerge the influence of the coherent integration time on the SNR of power waveforms. To reveal the role of coherent integration time, this research abandons beamforming.
## 2 Materials and Methods
### Analysis of Individual Phenolic Compounds  
Individual phenolic compounds were identified and quantified by high-performance liquid chromatography with diode array detection (HPLC/DAD, Agilent 1200 Series, Agilent Technologies, Santa Clara, CA, USA) analysis. Chromatographic separations were performed on a Zorbax Eclipse XDB-C18 column (4.6 \(\times\) 150 mm, 5 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA) using a gradient system of elution with solvent flow rate of 1 mL/min. The solvent system selected for elution was water/acetic acid adjusted to pH 2.8 (solvent A) and acetonitrile (solvent B). The solvent A/solvent B ratio was changed from 95/5 to 70/30. Detection was carried out at 272 and 350 nm, respectively. Standard curves of individual phenolic compounds were prepared and used for identification and quantification of phenolic compounds in our samples using ChemStation software 12.2 (Agilent Technologies, Santa Clara, CA, USA). Results were expressed as \(\upmu\)g per gram of sample (\(\upmu\)g/g).
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Ablation Study
#### 3.3.4 Skipping Span of SSE Module  
In order to find the relationship between the detection performance and the skipping span of the input feature maps of the SSE module, we keep the second input feature map unchanged and change the skipping span of the two input feature maps. The experimental results are shown in Table 5. It can be seen that the larger the input skipping span of SSE module, the higher the detection accuracy. The reason is that SSE module uses skip-connection strategy, which applies the channel weights obtained from the shallow feature map to the deeper feature map, and establishes the gradient connection between the shallow network and deeper network. Once the skipping span of the input increases, the gradient correlation between the shallow network and the deeper network increases, thereby increasing the transmission capacity of the network and further improving the performance of the network.
## 4 Prediction Model and Evaluation  
The prediction model is a deep neural network model with eight hidden layers, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Hidden** & **Sensitivity** & **Specificity** & **AUC** \\
2 & 0.33 & 1.00 & 0.78 \\
5 & 0.40 & 1.00 & 0.79 \\
6 & 0.42 & 1.00 & 0.82 \\
7 & 0.47 & 1.00 & 0.83 \\
8 & 0.50 & 0.98 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation according to hidden layers.  
For the evaluations, we selected sensitivity given by Equation (1), specificity given by Equation (2) and receiving operator characteristic-area under the curve (ROC-AUC) [43; 44; 45] which is the probability that a classifier would rank a randomly chosen positive instance higher than it ranked a randomly chosen negative one. The main consideration in predicting gastric cancer progression was sensitivity because less than 3% of all patients had gastric cancer.  
\[\text{Sensitivity}=\frac{Number\;of\;true\;positives}{Total\;number\;of\;gastric\;cancer\;patients} \tag{1}\]  
\[\text{Specificity}=\frac{Number\;of\;true\;negatives}{Total\;number\;of\;atrophic\;gastritis\;patients} \tag{2}\]  
As Table 1 shows, the sensitivity increased as the number of hidden layers increased. On the basis of our results, we selected a deep RNN model with eight hidden layers. Then we applied dropout and L2 regularization to overcome overfitting to the minority class (i.e., gastric cancer diagnosis). As a result, we achieved an AUC of 0.84, a sensitivity of 0.50, and a specificity of 0.98 with the optimal prediction model as shown in Table 1.  
Figure 4 shows the ROC-AUC graph, which is suitable for performance evaluation of prediction model for imbalanced data. The blue solid line represents the diagnostic ability of a binary classifier that is created by plotting the true positive rate against the false positive rate at various threshold settings. The orange dashed line illustrate random classifier's ROC points.  
Figure 3: A deep recurrent neural network architecture.  
The prediction model was evaluated on a personal computer with Intel Core i7-7500U [EMAIL_ADDRESS] Ghz and 32 GB RAM. For the implementation, the Python programming language 3.7.3 along with keras 2.2.4, sklearn 0.24.2, matplotlib 3.0.3, and seabrom 0.9.0 machine learning libraries was employed [46]. We performed all experiments using Windows 10.  
Among 18,846 atrophic gastritis patients and 610 gastric cancer patients, we used 70% of patient cases as the training data and 30% as the test data, which comprised 5650 patients with atrophic gastritis and 187 with gastric cancer (total n = 5837). Table 2 shows the confusion matrix of the test data, reflecting a prediction of 5650 cases of atrophic gastritis.  
Table 3 presents the precision, recall, and F1-score for the study data. As previously mentioned, the atrophic gastritis precision, recall, and F1-score were extremely high because the data set was highly imbalanced. We found that the precision, recall, and F1-score values were lower for gastric cancer than for atrophic gastritis.
# Three-Dimensional Force Characterizations in Maximly Molar Distalization: A Finite Element Study
## 1 Introduction
The vertical displacement of anchorage teeth was affected by the length and width of the wire. Another FEA simulation showed that the direction of the force applied to the second molar is essential to obtain proper tooth movement [33]. FEA simulations were also conducted for other specific functional appliances, such as Forsus [34], PowerScope 2 [35], clear aligners [29], and Advanced Mandibular Spring [36]. The displacement and stress distributions are usually investigated with those FEM simulations, while the reaction force and moment are rarely investigated. Some recent studies showed that the Class II elastics can reduce the lingual tipping of anterior teeth but can aggravate mesial tipping of the posterior teeth [37]. The CM3D appliance is efficient in correcting Class II malocclusion via tipping and rotational movement of maxillary canines [38], while the skeletal changes along the vertical dimension need more attention [15]. For the new JVB design, limited data are available on its efficacy and performance. In this work, we will utilize FEA to quantify the 3D reaction force and moment on canine and molar teeth induced by three different MSDs (JVB, CM3D, and CM3D Clear). A patient-specific mandibular model will be reconstructed based on the cone-beam computed tomography (CBCT) images. Three MSDs will be implemented in the FEA separately for a comparative study. Following computer simulation, the reaction force and moment, which relate to the tooth extrusion, tipping, and rotation, will be extracted and compared. The quantitative comparations will provide the mechanistic bases for the tooth movement and the optimal design of the MSDs. The stress distributions of the three MSDs were also compared to further support the observations made from the analysis of reaction forces and moments. The mechanistic understanding of reaction force and moment induced by different MSD appliances will provide insights into the optimal design of orthodontic devices, which supplies better understanding to achieve the desired tooth movements.
## 4 Conclusions  
This study optimized the mix ratio of RBFC based on the RSM. The influence of the preparation parameters on the working performance and mechanical properties of concrete was discussed and analyzed. The following conclusions can be drawn:  
* Based on RSM, an optimum RBFC design is proposed: water-binder ratio is 0.39, basalt fiber content is 4.56 kg/m\({}^{3}\), and rubber content is 10%. Compared with the test results, it possesses favorable and reliable accuracy.
* The incorporation of rubber aggregate has a significant impact on the compressive strength of concrete. The rubber aggregate partly replaces the fine aggregate, and the effective compressive strength of the rubber aggregate is much lower than that of fine aggregate, which leads to a decrease in the strength of concrete.
* The content of basalt fiber has a significant effect on the slump and flexural strength of concrete. When the fiber content is small, it can slow down the growth of concrete cracks. Excessive fibers will cause the fibers to form agglomerates, which is disadvantageous in the bending strength.
* Rubber aggregate and basalt fiber have been proved to help modify concrete. Waste rubber materials can be recycled to reduce environmental pollution. On the other hand, it can also improve the performance of concrete. In the long run, RBFC will produce good economic benefits.  
Conceptualization, G.T.; formal analysis, Y.G., G.T., and J.S.; investigation, Y.G.; data curation, G.T. and J.S.; writing--original draft preparation, J.S. and S.L.; writing--review and editing, Y.G. and S.L.; project administration, Y.G. and Y.H.; funding acquisition, Y.G., J.Y. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Transportation Technology Program of Jilin Province of China (Grant No. 2018-1-9), the Education Department's \"13 th Five-Year\" Science and Technology Program of Jilin Province (Grant No. JJKH20190015 KJ), the Special Funding for Basic Scientific Research Operation Fees of Central Universities, Supported by Graduate Innovation Fund of Jilin University and the Scientific and Technological Developing Scheme Program of Jilin Province (Grant No. 20200403157 SF).  
We would like to acknowledge the following people from Jilin University. Thank you to [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for technical support in the testing procedure and data processing.  
The authors declare that there is no conflict of interest regarding the publication of this paper  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Resposes**} & \multirow{2}{*}{**WBR**} & **BFC** & **RC** & **SP** & **CS** & **FS** \\  & & **(kg/m\({}^{3}\))** & **(\%)** & **(mm)** & **(MPa)** & **(MPa)** \\ \hline Prediction & 0.39 & 4.56 & 10 & 64 & 39.96 & 3.8 \\  & 1 & 0.39 & 4.56 & 10 & 67 & 38.27 & 3.9 \\ Experimental & 2 & 0.39 & 4.56 & 10 & 68 & 39.98 & 3.8 \\  & 3 & 0.39 & 4.56 & 10 & 65 & 38.11 & 3.8 \\  & Mean & 0.39 & 4.56 & 10 & 66.67 &
# A Novel Piezoelectric Energy Harvester Using a Multi-Stepped Beam with Rectangular Cavities
## 2 Harvester System Overview  
The harvester designed using a multi-stepped cantilever beam with rectangular cavities is shown in Figure 1a. Broad bandwidth and enhancement in the output was achieved by introducing multiple steps and rectangular cavities in the structure. Three piezoelectric patches were attached on the beam and positioned between \(L_{1}\) and \(L_{2}\), \(L_{3}\) and \(L_{4}\) and \(L_{6}\) and \(L_{7}\). The piezoelectric patches were fastened to the maximum strain region of the harvesting structure. The harvester was divided into sections I-VIII as shown in Figure 1a. Sections I, III, V, VI and VIII had only a regular beam. Sections II, IV and VIII were composite sections having a regular beam attached with piezoelectric patches. To increase the strain, rectangular cavities in the thickness direction was shaped in sections II and IV. Three piezoelectric patches were connected appropriately to obtain high output. The harvester performance was assessed by a tip exciter [30].
# A Light-Weight Autoregressive CNN-Based Frame Level Transducer Decoder for End-to-End ASR
###### Abstract  
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the probabilities of the current token at the token level, at the token transition of the output token sequence. Two probabilities of the current token, one from the encoder and the other from the CNN are added to the frame level to reduce the decoding step to the number of input frames. An encoder composed of an 18-layer conformer was combined with the proposed decoder for training with the Librispeech data set. The forward-backward algorithm was used for training. The space and re-appearance tokens are added to the 300-word piece tokens to represent the token string. A space token appears at a frame between two words. A comparison with the autoregressive decoders such as transformer and RNN-T decoders demonstrates that this work provides comparable WERs with much less decoding time. A comparison with non-autoregressive decoders such as CTC indicates that this work enhanced WERs.  
speech recognition; autoregressive speech recognition; end-to-end; CNN; transducer decoder