esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information
## 2 Materials and Methods  
### Feature Extraction Methods of Speech Data  
#### 2.1.1 One-Dimensional Spectrum Feature Extraction  
Extracting useful features is the most critical factor in implementing a model that uses speech data. In this study, the one-dimensional spectrum features of speech data were extracted using MFCCs and gammatone cepstral coefficients (GTCCs).  
The MFCC is one of the most frequently used feature extraction methods in speech recognition. It extracts feature coefficients from the audio based on a mel filter bank reflecting the characteristics of the human auditory frequency range [12]. This is derived from the discrete cosine transform of the log power spectrum and is used to represent the short-term power spectrum of a sound. The purpose of extracting MFCC features from the speech is to determine the most compressed and beneficial set of features for improving efficiency [13]. In this study, the speech data were segmented into a frame unit, and these frames were overlapped by 25 ms and applied with a hamming window of 70 ms to extract the features of the MFCCs.  
Unlike MFCC, GTCC is calculated by applying an equivalent rectangular bandwidth (ERB)-based gamma-tone filter bank rather than a mel filter bank. The ERB is a frequency scale used in psychoacoustics to measure the width of the auditory filter at each point. The gamma-tone filter bank is defined as an impulse response in the time domain, which provides an approximation to the bandwidth of the auditory filter of humans [14]. Similar to MFCCs, the speech data are segmented into a frame unit, and these frames are overlapped by 25 ms and applied with a hamming window of 70 ms to compute the GTCC values.
## 5 Results and Discussion
### Results for the IM Operating at 31 Hz
Since the failure harmonics are mounted on the leakage, their amplitudes are affected and they are not reliable. Finally, when the proposed GA-MCSA methodology is applied, the amplitude ranges appreciated in Figure 4c are: from 0.045 to 0.06 A for the high load operation; from 0.032 to 0.04 A for the medium load case; and from 0.018 to 0.025 A. This means that none of the values from one operative condition appear in more than one case. In this sense, results provided by the GA-MCSA methodology are more reliable than those presented for the other two methodologies. This situation can be explained by the fact that, when the proposed methodology is applied, the leakage surrounding the FFC is greatly mitigated, therefore, the amplitude of the failure harmonic is not affected by the leakage, allowing the proper measurement of the failure severity. The amplitude measurements could be used as an indicator of the failure severity. However, the implementation of a methodology for determining the severity based on the amplitude results would require a few extra steps. For instance, it is necessary to carry out a considerable number of tests of the motor operating at healthy conditions. With these tests, the amplitude of the frequencies related to the failure harmonics at a specific operative frequency must be measured. The mean value of these amplitudes could be considered as the expected value for the healthy conditions and the maximum value could be use as the threshold limit for this condition. If the definition of thresholds for different failure severities is required, it would be necessary to perform tests with the failure conditions and obtain the mean values to define the boundaries of every threshold. If there exist overlaps in the amplitude values for different fault severities, this boundary definition cannot be correctly performed, because there is no certainty in the thresholds. Then, periodical samples of the IM must be acquired and the amplitudes of the failure harmonics have to be compared with the established ranges to determine the existence of a failure. The use of periodical samples also allows to visualize the time evolution of a failure. However, such methodology goes beyond the scope of this work and is left for future research.
## 2 Materials and Methods  
### Training Datasets Generation and Site Selection  
#### 2.1.1 Study Area  
Daejeon City, located in the central part of the Korean peninsula, was selected as the study area. The city has an area of approximately 539 km\({}^{2}\) and is a transportation hub connecting the southern and northern regions. As depicted in Figure 1, most of the areas illustrate urban landscapes, where large and small buildings are clustered. Rice paddies/fields and mountainous areas are distributed in minimal areas. Middle areas, primarily covered with many complex environments such as urban buildings and roads, are the areas where the SR approach is challenging to apply.  
Figure 1: Location of Daejeon and Sejong depicted with Google map and Sentinel-2 images (band 2 image acquired on 2018/04/18).  
Sejong City, Korea's administrative capital, is being developed into a city since 2012. The area of Sejong City is approximately 465 km\({}^{2}\), and most of the regions are still mountains and rice fields. However, due to construction, the impermeable layer is increasing rapidly every year. Daejeon City was selected to produce training datasets, and Sejong City was selected as a test site to analyze the generalization capabilities. Even if training samples and test samples are not overlapped, spatial autocorrelation within the same area cannot be avoided. Therefore, it was necessary to select an independent region with different characteristics.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 5 Results
### Comparison with SOTA Semantic Segmentation Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **mIOU (\%)** \\ \hline DeepLab-CRF [9] & ResNet-101+CRF & 77.69 \\ DeepLabv3 [12] & ResNet-101 & 76.5 \\ DeepLabv3+Res2 Net [40] & Res2 Net-101 & 79.3 \\ DeepLabv3+[13] & Aligned Xception & 84.56 \\ PSPNet [15] & RestNet-152 & 85.4 \\ SpineNet-S143 [24] & Dilated SpineNet & 85.64 \\ HPN [19] & ResNet-101 & 85.8 \\ ExFuse [41] & ResNeXt-131 & 85.8 \\ Eff-B7 NAS-FPN [42] & EfficientNet-B7+NAS+FPN & 86.6 \\ EfficientNet-L2+NAS-FPN [21] & EfficientNet-B7+NAS+FPN & 90.0 \\ \hline bf DTS-Net-Lite+NLF & MobileNetV2+DTS & 83.8 \\
**DTS-Net+NLF** & Xception+DTS & **91.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed method and SOTA methods on PASCAL VOC2012 validation benchmark. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **mIOU (\%)** \\ \hline DICENet [50] & ShuffleNetV2 & 63.4 & 17.2 \\ ContextNet [51] & Shallow+deep CNN & 65.9 & 18.3 \\ Template-Based NAS-arch1 [52] & NAS-Net [53] & 69.5 & 97.0 \\ HPN [19] & ResNet-101 & 71.7 & 4.0 \\ FasterSeg [54] & NAS-Net [53] & 73.1 & 6.1 \\ SqueezeNAS [55] & NAS-Net [53] & 75.2 & 0.0065 \\ Dilated-ResNet [11] & Dilated ResNet-101 & 75.7 & - \\ EEEA-Net-C2 [26] & NAS-Net [53] & 76.8 & 11.6 \\ MDEQ-large [25] & MDEQ & 77.8 & - \\ DeepLabv3 [12] & Dilated ResNet-101 & 78.5 & 2.0 \\ DeepLabv3+[13] & Dilated Xception-71 & 79.6 & 4.2 \\ PSPNet [15] & Dilated ResNet-101 & 79.7 & 1.6 \\ MDEQ-XL [25] & MDEQ & 80.3 & - \\ RepVGG-B2 [27] & VGG-like & 80.57 & 4.5 \\ \hline
**DTS-Net-Lite+NLF** & MobileNetV2+DTS & 61.5 & 11.7 \\
**DTS-Net+NLF** & Xception+DTS & **80.72** & 6.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the proposed method and SOTA methods on CITYSCAPES semantic segmentation validation benchmark in terms of mIOU and speed (FPS).
## 2 Related Work
### Domain Name-Based Methods for Character-Based AGDs  
For character-based AGD detection, domain name-based methods could be divided into methods based on feature engineering, methods based on deep learning-based methods, and methods based on clustering, according to the model they use.  
Methods based on feature engineering usually construct features manually according to the characteristics of AGDs and use machine learning models for detection [1; 2; 3]. [PERSON] et al. [1] analyzed the domain names of the NXDomain, extracted three kinds of features, including structural features, linguistic features and statistical features, and used random forest and SVM models for classification. [PERSON] et al. [2] extracted 11 types of features from domain names, including character entropy, n-gram median and consecutive character ratio, and trained a binary classification model (B_RF) for AGD detection and two multiclass classification models (M-RF and OVA-RF) for family classification based on a random forest model. Feature engineering-based methods are difficult to implement in feature design, and attackers can design new DGA categories to avoid detection.  
For methods based on deep learning [4; 5; 6], common detection models include RNNs and convolutional neural networks (CNNs). [PERSON] et al. [4] first proposed detecting AGDs through the traditional LSTM model. They used the LSTM model to realize binary classification for AGD detection and multiclass classification for DGA family classification. Later, [PERSON] et al. [5] further proposed LSTM.MI model to address the problem of imbalanced samples by adding different weights to different DGA families. Similarly, [PERSON] et al. [6] used LSTM, CNN models, etc., to extract features and perform classification based on the character sequences of the domain names.  
In addition to supervised machine learning methods, some studies used clustering methods to detect AGDs [7; 8]. [PERSON] et al. extracted features such as bigram frequency, n-gram score, and entropy from the domain names and used K-means methods for clustering and detecting AGDs according to Mahalanobis distance. The PHOENIX method proposed by [PERSON] et al. [8] extracted a set of linguist features, used the DBSCAN model for clustering to obtain a domain name set, and detected AGDs by calculating the Mahalanobisdistance between the new domain name and the centroids of clusters. The accuracy of the clustering-based method depends on the effect of clustering, and the accuracy varies in different types of botnets.  
The character-based LSTM method can effectively extract the character sequence features of the domain names. Therefore, we also used the LSTM model to detect character-based AGDs. In addition, some statistical features are valuable for AGD detection, and this information is difficult to be reflected in deep learning networks. Therefore, we propose multidimensional statistical features and fuse them with deep learning features to improve the effectiveness of the features.
## 8 Conclusions
\begin{tabular}{l l}
**Abbreviations** & \\ FGM & Functionally graded material \\ FG & Functionally graded \\ FSDT & First-order shear deformation theory \\ TSDT & Third-order shear deformation theory \\ HSDT & Higher-order shear deformation theory \\
3D & Three-dimensional \\
2D & Two-dimensional \\ FG-I & Volume distribution of top constituent according to Equation (12) \\ FG-II & Volume distribution of top constituent according to Equation (13) \\ C & Clamped boundary condition \\ S & Simply supported boundary condition \\ F & Free edge boundary condition \\ UT & Uniform or constant thickness \\ LVT & Linear variable thickness \\
**Nomenclature** & \\ \(\mathbf{\Phi}\) & 3D geometry of the shell element \\ \(\mathbf{\Omega}\) & Shell's mid-surface \\ \(\xi^{i}\) & Direction \(i\) of the curvilinear coordinates \\ \(p\) & Polynomial order \\ \(\psi_{\mathbf{\chi}}(\xi^{1},\,\xi^{2})\) & 2D Lagrange interpolation function of order \(p\) \\ \(\underline{\underline{\mathbf{x}}}^{k}\) & Position vector of \(k\)-th spectral node \\ \(\underline{\underline{\mathbf{n}}}^{k}\) & Unit normal vector of the \(k\)-th spectral node \\ \(h\) & Thickness at the \(k\)-th spectral node position \\ \(q\) & Number of spectral nodes per element \\ \((\bullet)_{,i}\) & First derivative with respect \(\xi^{ii}\) \\ \(\underline{\mathbf{g}}_{i}\) & Set of covariant basis vectors \\ \([\underline{\mathbf{J}}]\) & Jacobian matrix \\ \(J\) & Determinant of the Jacobian matrix \\ \(\underline{\mathbf{g}}^{i}\) & Set of contravariant basis vectors \\ \(\underline{\underline{\mathbf{u}}}(\xi^{a},t)\) & Vector of the mid-surface displacements \\ \(\mathbf{\varphi}(\xi^{a},t)\) & Difference vector \\ \(\mathbf{\Psi}\) & Seventh parameter \\ E & Green-Lagrange strain tensor \\ \(E_{\underline{\mathbf{g}}\underline{\mathbf{g}}}\) & Strain covariant component \\ \(\varepsilon^{(0)}_{\underline{\mathbf{g}}\underline{\mathbf{\beta}}}\) & Strain covariant component associated with constant terms according to \(\xi^{3}\) \\ \(\varepsilon^{(1)}_{\underline{\mathbf{g}}\underline{\mathbf{\beta}}}\) & Strain covariant component associated with linear terms according to \(\xi^{3}\) \\ \(n\) & Volume fraction exponent or power-law index \\ \(V^{i}_{top}(\xi^{3})\) & Volume distribution of the top constituent following the power-law form \(i\) \\ \(\mathrm{C}^{ijkl}\) & Contravariant components of the fourth-order elasticity tensor \\ \(\lambda\big{(}\xi^{3}\big{)},\,\mu\big{(}\xi^{3}\big{)}\) & Lame parameters as a function of \(\xi^{3}\) \\ \(S^{ij}_{,i}\) & Contravariant components of the Riemannian metric tensor \\ \((\bullet)\) & Partial derivative with respect to time \\ \(I_{i}\) & Mass inertia \\ \(A^{ijkl}\) & Effective extensional fourth-order stiffness tensor component \\ \(B^{jkl}\) & Effective extensional-bending coupling fourth-order stiffness tensor component \\ \(D^{jkl}\) & Effective bending fourth-order stiffness tensor component \\ \(\omega\) & Frequency of natural vibration \\ \(\Delta^{e}\) & Vibration mode vector of the element \(e\) \\ \(h_{m}\) & Average thickness \\ \end{tabular}
# Image Completion with Hybrid Interpolation in Tensor Representation
## 5 Conclusions  
In this study, we showed the relationship between the models of RBF interpolation and Tucker decomposition (Remark 1). We combined the exponential RBF interpolation and polynomial regression in one model and experimentally demonstrated that such a hybrid method achieved the highest SIR scores in all the tests. The proposed algorithm (TI-IC) can be applied to a wide spectrum of image-completion problems. The incomplete images can contain many single missing entries or missing pixels distributed across the image, a large number of small-scale regions (holes), or regularly shaped missing regions, such as in resolution up-scaling problems. The TI-IC algorithm is also computationally efficient. It provides reconstructions of the highest quality and in a much shorter time than the tested low-rank tensor image-completion methods. Its runtime depends on the number of missing entries in an input tensor, and it is shorter if more entries are unknown. The computational complexity of the proposed method can be controlled by the block partitioning strategy, as proven in Remark 2. Assuming the overlapping in this partitioning strategy, we avoided visible disturbances around boundary entries of the blocks, which is an intrinsic effect of RBF interpolation methods. Furthermore, due to the use of RBFs, the overlapping blocks can be processed in parallel computer architectures, and our experiments demonstrated that the use of a parallel pool of workers in MATLAB considerably shortened the runtime of the proposed algorithm.  
Summing up, the proposed algorithm outperforms all the tested image completion methods for a wide spectrum of tests. Its computational runtime is also satisfactory and considerably shorter than that for the low-rank tensor decompositions. The proposed algorithm can also be efficiently implemented on parallel computer architectures.  
Conceptualization, R.Z.; Methodology, R.Z.; Software, R.Z. and T.S.; Investigation, R.Z. and T.S.; Visualization, T.S.; Writing--original draft, R.Z.; Writing--review and editing, R.Z.; Funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by grant 2015/17/B/ST6/01865, funded by the National Science Center in Poland. Calculations were performed at the Wroclaw Centre for Networking and Supercomputing under grant no. 127.
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 2 Methodology  
Figure 1 illustrates the proposed framework for the online _RUL_ prediction of the slurry pump impellers. There were three main parts of the developed methodology.  
**Part I. _Raw data filtering:_ This step generated a new criterion that can recognize the valid and invalid raw vibration signals. Four traditional statistical indicators, i.e., Root Mean Square (RMS), kurtosis, and Percentage Mean Difference (PMD) in the time domain, while kurtosis again, in the frequency domain, were selected as the selection criteria.  
**Part II. _Health degradation trend built:_ This step first calculated the eighteen traditional statistical features and constructed a feature matrix. Then, it used a principal component analysis along with a moving average method for reducing the dimensions of the feature matrix. The obtained first principal component was utilized as the health degradation tracking index. Afterward, the automatic selection of HDTs data points for initiating the prediction process was performed.  
**Part III. _Remaining useful life prediction:_ This step built the hybrid deep LSTM model, which was embedded with a novel smart learning rate mechanism. The developed model produced its prediction results, which were utilized for estimating the online _RUL_ of the slurry pump.
## 3 Theory and Methods
### Time Series Analysis Using Long Short-Term Memory (LSTM) Networks  
LSTMs are a sort of RNN that overcomes the vanishing gradient problem by controlling the flow of information through the network with a memory cell and numerous gating methods. The memory cell maintains information over time steps, while the gating mechanisms control the flow of information into and out of the cell. LSTMs have been demonstrated to be effective in time series analysis for road maintenance, mainly when dealing with data with long-term dependencies.  
CNNs are used to analyze road images and identify different types of road surface damage in the proposed approach in \"A Deep Learning Technique to Improve Road Maintenance Systems Based on Climate Change\". In contrast, LSTMs are used to analyze climate data, estimate the probability of further damage, and prioritize maintenance actions. The suggested approach captures spatial and temporal connections in the data by combining CNNs and LSTMs, which can increase the accuracy of maintenance suggestions. Previous research has demonstrated the usefulness of deep learning approaches, such as CNNs and LSTMs, in time series analysis for road maintenance.
## 4 Probabilistic S-N Fatigue Strength Curve
### Generalities of S-N Percentiles Formulation  
The formulation to build a probabilistic S-N curve is based on the Weibull reliability function which in linear form is given as:  
\[y_{i}=\beta[\ln\left(\eta\right)+\ln\left(N_{i}\right)] \tag{15}\]  
Therefore, the predicted cycle to failure times \(N_{i}\) to determine the percentiles of the S-N curve are given as:  
\[\ln\left(N_{i}\right)=\frac{y_{i}}{\beta}+\ln\left(\eta\right) \tag{16}\]  
From the \(n\) predicted \(\ln\left(N_{i}\right)\) elements determine their standard deviation \(\sigma_{g}\) value. Notice this \(\sigma_{g}\) value is the same determined in Equation (14). Thus, based on the \(\sigma_{g}\) value and on the \(d(Z)\) value, which is the value of the normal distribution that corresponds to the desired confidence level (for example to \(CL=0.95\), \(d(Z)=1.6448536\)), the percentile for the \(\left(S^{\prime}\right)_{N}\) value is given as:  
\[d(\sigma_{g})=\exp\left(\ln\left[\left(S^{\prime}\right)_{N}\right]\pm d(Z) \sigma_{g}\right) \tag{17}\]  
Based on Equation (17), the method to determine the probabilistic percentiles for the S-N curve of the A572 Gr. 50 steel is as follows.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 3 System Model
### Task-Offloading Delay Evaluation Model  
When adaptively scheduling tasks to appropriate task processing locations according to user service requests, service delays in two different situations need to be considered, including local execution or offloading to the MEC server for execution.  
When the UE's service demand is executed locally, the UE does not need to transmit the task to the MEC server and can directly utilize the local computing resources for processing.  
Figure 1: Timing-dependent task scheduling architecture.  
Therefore, for UE \(u_{i}\) and the task \(v_{i,j}=(a_{i,j}c_{i,j}T_{i,j}^{tol})\) generated by \(u_{i}\), the processing delay for \(u_{i}\) to complete task \(j\) locally is obtained as:  
\[T_{i,j}^{loc}=\frac{a_{i,j}}{f_{l}}, \tag{1}\]  
where \(f_{l}\) denotes the UE local computation rate.  
When the local device cannot satisfy the service demand of the application program, the UE service request will be offloaded to the MEC server for execution. That is, if the local device is currently occupied by other programs that cannot provide the corresponding computing resources, or the processing time of the tasks required by the weak processing capability exceeds the deadline of the program response, the service request will be offloaded to the MEC server. At this time, the task goes through three stages: data transmission, task processing, and result return. In the data transmission stage and result return stage, the user transmits the task data to the corresponding MEC server through the channel. The uplink data transmission rate, downlink data transmission rate, uplink data transmission delay, and downlink data transmission delay can be calculated by Equations (2)-(5) [19]:  
\[R_{d,n}^{ul}=B_{d,n}^{ul}\log_{2}(1+\frac{P_{d,n}}{\eta^{2}}), \tag{2}\]  
\[R_{d,n}^{dl}=B_{d,n}^{dl}\log_{2}(1+\frac{P_{d,n}}{\eta^{2}}), \tag{3}\]  
\[T_{i,j}^{up}=\frac{a_{i,j}}{R_{d,n}^{ul}}, \tag{4}\]  
\[T_{i,j}^{down}=\frac{a_{i,j}}{R_{d,n}^{dl}}, \tag{5}\]  
where \(B_{d,n}^{ul}\) and \(B_{d,n}^{dl}\) represent the communication channel bandwidth of uplink and downlink, respectively. \(P_{d,n}\) represents the uplink/downlink channel transmission power. \(\eta^{2}\) represents the channel noise during the transmission process.  
The calculation delay of processing tasks on the MEC server can be calculated by Equation (6):  
\[T_{i,j}^{n,cal}=\frac{a_{i,j}c_{i,j}}{f_{n}}, \tag{6}\]  
where \(f_{n}\) represents the calculation rate of the MEC server.  
Therefore, if the service requirements of UE are offloaded to the MEC server for execution, the resulting task processing delay can be calculated by Equation (7):  
\[T_{i,j}^{sum}=T_{i,j}^{up}+T_{i,j}^{n,cal}+T_{i,j}^{down}. \tag{7}\]