esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Analysis of Auxiliary Input  
This section aims to evaluate the impact of the prior pronunciation auxiliary input on performance and to analyze in detail why this method is effective.  
The last two rows of Table 3 present the performance of vowel reduction detection and overall phoneme recognition in the case of two different input types. One is to use the acoustic feature input alone as in the usual E2E speech recognition method. The other is to add pronunciation prior knowledge as an auxiliary input. Experiments show that, when the auxiliary input is added, vowel reduction detection and overall phoneme recognition have been improved. This improvement is more significant than with simply using an E2E method.  
We randomly selected two sentences for decoding and analysis. Figure 3 shows the main and auxiliary encoders' weight change in the hierarchical attention mechanism during the decoding process. The horizontal axis is the decoding step, the vertical axis is the weight value, the yellow line (Att1) represents the auxiliary encoder, and the blue line (Att0) represents the main encoder. It can be seen that, when decoding phonemes contained in the prior articulation knowledge, the auxiliary encoder has a higher weight. When decoding phonemes that the prior articulation knowledge cannot provide enough information about, the main encoder that uses the acoustic feature input has a higher weight. We argue that the complementarity of these two types of information leads to the improvement. The input of the prior articulation knowledge improves the recognition rate while reducing the model's burden to recognize phonemes from acoustic features. It results in an improvement of the vowel reduction detection's performance.  
We also observed the convergence of the frame-level attention mechanism during the training process. The experiment is consistent with the analysis in Section 2.3. If two encoders with the same complexity are designed, over-reliance on the pronunciation prior encoder will lead to poor convergence of the acoustic encoder's attention mechanism.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{\(ih_{schwa}\)} & \multicolumn{2}{c}{\(ah_{schwa}\)} & \multirow{2}{*}{**PCR**} \\ \cline{2-2} \cline{4-5}  & **Pre/Rec** & **F1** & **Pre/Rec** & **F1** & **(\%)** \\ \hline Hybrid & 0.58/0.64 & 0.61 & 0.54/0.53 & 0.53 & 77.3 \\ CNN-RNN-CTC [22] & 0.64/0.67 & 0.65 & 0.57/0.56 & 0.56 & 74.7 \\ E2E & 0.59/0.68 & 0.63 & 0.53/0.54 & 0.53 & 77.9 \\ E2E + prior & 0.64/0.67 & 0.66 & 0.62/0.55 & 0.58 & 81.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of proposed method, CNN-RNN-CTC method and baselines.
## 5 Conclusions and Future Work  
By studying the time-varying line fault probabilities under typhoons, we develop a robust emergency dispatch strategy considering the spatiotemporal evolution of grid failures. The following conclusions can be achieved based on the case study results:  
* Different lines encounter different levels of fatigue damage under the passage of typhoons, causing time-varying fault probabilities of lines and dynamic spatiotemporal evolution of grid failures. The threshold-based method can quickly screen out the irrelevant or less severe scenarios, thus reducing the size of emergency scenarios and complexity of the emergency dispatch model.
* Compared with the non-robust emergency dispatch strategy, the proposed robust emergency dispatch strategy can guarantee the secure operation of power systems under all emergency scenarios of the scenario set without incurring extra load and wind power curtailment costs.  
In the future study, the influences of other types of climate disasters will be considered, and the corresponding spatiotemporal revolution of grid failures will be studied. Also, a reduction in the size of the emergency scenario set will be further investigated by achieving the trade-off between computational efficiency elevation and robustness. Optimal line reinforcement, line rerouting and other emergency planning measures under different climate disasters will also be investigated.  
Conceptualization, B.R. and D.W.; methodology, B.R. and C.W.; software, B.R. and D.W.; validation, Q.L.; formal analysis, Y.H.; investigation, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of State Grid Jiangsu Electric Power Company under Grant J2024021 \"Research on System Fault Evolution and Risk Assessment Technology Considering Source-Grid Spatio-Temporal Coupling under Extreme Weather Conditions\".  
Not applicable.  
Data Availability StatementData can be accessed from case studies.  
Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company State Grid Jiangsu Electric Power Company Ltd. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Failure in Confined Brazilian Tests on Sandstone
## 1 Introduction
Using this method, [PERSON] and [PERSON] [1] interpreted results obtained with different confining pressures along with post-failure examination of the fracture angle and surface as clearly demonstrating a hybrid fracture between a tensile and shear fracture. A challenge with this method is the very detailed sample preparation necessary to produce the dog bone geometry, e.g., [3]. Further, numerical evaluation of the test suggests that the specimen geometry can produce a non-uniform state of stress that deviates from the analytical solution used to interpret the tensile strength [4]. Figure 1: Failure curve in principal stress (\(\sigma_{1}\)–\(\sigma_{3}\)) space. The confined tension region is shown as a shaded area. The stress paths of different strength tests are shown, including the stress path followed by 3 confined Brazilian tests conducted under 3 different confining stresses. Figure modified from [PERSON] and [PERSON] [4]. Another method to test rock strength under confined tension conditions is the confined Brazilian test originally described by [PERSON] and [PERSON] [13], and subsequently used to find strength of rocks [14] and cementitious materials [15] in the confined tension region. This test produces a triaxial state of stress, with the intermediate principal stress equal to the confining pressure. The stress state induced by the confined Brazilian test at failure is a function of the confining pressure; by testing at different confining pressures, the failure surface in the confined tension region can be defined as shown in Figure 1. The stress paths of confined Brazilian tests are more similar to that of typical compression tests compared to extension tests on dog bone samples. Similar to the BTS test, the state of stress in the confined Brazilian test is not homogeneous and must be calculated from the applied load and confining pressure. Specim preparation is more straightforward than that for dog bone specimens, but similar to BTS tests, there are concerns with crushing-type failure near the location where the specimens are loaded [4]. Strength measured in the confined tension region by different test methods indicate that the intermediate principal stress affects the strength [4; 5; 13; 15]. This suggests strength data obtained from testing in the confined tension region should be evaluated with a criterion that includes the intermediate principal stress. We report on quasi-static confined Brazilian tests under a range of confining stresses to determine the strength of sandstone in the confined tension region. We seek to determine if the strength in the confined tension region is a function of the intermediate principal stress, and whether the strength can be described by a failure criterion that accounts for the intermediate principal stress. Numerical simulations from combined finite-discrete element method are compared to the experimental results to gain further insight into the behavior of brittle geomaterials in the confined tension region.
## 5 Methodology
### NowDeepN Approach
#### 5.3.1 Data Collection and Cleaning
Thus, the value \(V(i,j)\) will be approximated with the weighted average of its valid neighbors, as shown in Formula (1)  
\[V(i,j)=\sum_{ \begin{subarray}{c}(x,y)\in\mathcal{N}_{l}(i,j)\\ (x,y)\text{ }valid\end{subarray} }(w_{ij}(x,y)\cdot V(x,y)), \tag{1}\]  
Figure 4: The 2D data subgrid representing \(\mathcal{N}_{3}(3,3)\). Figure 3: The sample 2D data grid. The values for the product \(V\) are displayed. where \(w_{ij}(x,y)\) represent the weight of point \((x,y)\) and is computed as shown in Formula (2) by normalizing the similarity values \(sim_{ij}(x,y)\) such that \(\sum\limits_{ \begin{subarray}{c}(x,y)\in\mathcal{N}_{l}(i,j)\\ (x,y)valid\end{subarray} }w_{ij}(x,y)=1\). We note that this normalization assures that the approximated \(V\) values represent valid ones, that is, ranging in the interval \([-33,33]\). \[w_{ij}(x,y)=\frac{sim_{ij}(x,y)}{\sum\limits_{ \begin{subarray}{c}(x^{\prime}, y^{\prime})\in\mathcal{N}_{l}(i,j)\\ (x^{\prime},y^{\prime})valid\end{subarray} }sim_{ij}(x^{\prime},y^{\prime})}. \tag{2}\]  
As previously mentioned, in the cleaning process we considered a length \(l=13\) for the neighborhood. But, if all data points from the neighborhood of length \(l\) (\(\mathcal{N}_{l}(i,j)\)) are invalid, we are incrementally increasing \(l\) until the neighboring area will contain at least one valid point. In this case, Formula (1) is applied again using the new length \(l\) for estimating the value \(V(i,j)\). After the data was cleaned as previously shown, the data set is prepared for further training the \(NovDeepN\) regressor, using the representation described in Section 5.2. We denote, in the following, by \(Prod=\{p_{1},p_{2},\ldots,p_{np}\}\) the set of radar products we are using in our approach. The radar data set cleaned as previously described is split into \(np\) subsets \(D_{k},\ 1\leq k\leq np\), a data set corresponding to each radar product. A DNN will be afterwards trained on each \(D_{k}\), for learning to predict the value of the radar product \(p_{k}\) at a time moment \(t\) on a certain geographical location \(l\), based on the radar products' values from the neighborhood of \(l\) at time \(t-1\). A training example from a data set \(D_{k},\ 1\leq k\leq np\) is in the form \(<x_{k},y_{k}>\), where:  
* an instance \(x_{k}\) is the vector \(V_{t-1}(i,j,l,Prod)\) (see Section 5.2) representing the linearized 3D data grid expressing the neighborhood of length \(l\) surrounding a point \((i,j)\) at a time moment \(t-1\);
* the label \(y_{k}\) of the instance \(x\) is the value for the radar product \(p_{k}\) for the location \((i,j)\) from the map and time moment \(t\) (the time moment following the timestamp corresponding to instance \(x\)). Each data set \(D_{k}\) will contain, for each data point from the analyzed map, examples in the form \(<x_{k},y_{k}>\) (as previously described). As a preprocessing step before training, the data sets \(D_{k}\) are normalized to [0,1], using the _min-max_ normalization.
## 1 Introduction
### Aim of the Article  
The aim of the article is to propose a CNN structure to classify SSVEPs for a significantly limited training dataset. An important element of our research was the development of an augmentation method dedicated to SSVEP detection. The data augmentation method that we applied is based on the spectral analysis of the EEG signal. Then we compared the efficiency of the proposed CNN with the methods commonly used for SSVEP detection, such as: CCA, MLP, sMP, LDA, and QDA. All comparisons were made under the same conditions: window width, number of testing examples, etc. The idea of our research is presented in Figure 1.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems  
#### 3.6.1 General Description  
Satellites play a vital role in wireless communications, facilitating smooth data transfer, high speeds, and providing internet access and media broadcasting across the globe. Designing, testing and deploying novel and effective antennas is essential to satisfy the increasing demand for high-speed data transfer. LEO satellites are progressively becoming more and more prominent due to their reduced latency and high data throughput. Antenna designs for LEO satellites must adapt to face challenges such as swift beam switching, interference reduction, and scalable deployment for extensive constellations.  
As research progresses, antenna developments have come to utilize metasurfaces and phased arrays with compact feed systems for enhanced performance. Metasurfaces designs excel by offering lightweight and economical solutions that enable dynamic beam steering and increased gain. Among metasurface technologies, tensorial metasurfaces have emerged as an advancement. Unlike scalar metasurfaces, which are limited in the polarization control, tensorial metasurfaces enable independent amplitude, phase, and polarization manipulation. Similarly, phased arrays provide real-time beam adjustment, allowing reliable tracking with minimized signal interruptions. These advancements play a vital role in optimizing satellite applications such as global internet availability, Direct-to-Cell communications, and remote sensing.  
Advanced antenna designs for satellites are to be employed beyond the LEO applications to facilitate the 5G-beyond and 6G non-terrestrial communication networks. Furthermore, in the mmWave spectrum, antenna designs need to evolve to exhibit broaderbandwidths and improved efficiency while preserving cost-effectiveness, compactness, and low characteristics. The terminology can be visually explained in Figure 4.
## 3 Results
### Relationship between the Capillary Water Absorption Coefficient and Pore Structure  
Specimens NC-1, NC-2, HPC-1, and HPC-2 were subjected to ETF, and concrete specimens UHPC-1 and UHPC-2 were not subjected to ETF cycles to investigate the relationship between the concrete capillary water absorption coefficient and porosity. The pore structure of concrete changed through the action of ETF cycles. For NC, HPC, and UHPC specimens, the porosity and most probable pore size were measured using the MIP method, and capillary water absorption coefficients were obtained using the gravimetric method; the results are shown in Table 5.  
The capillary water absorption coefficient of concrete with different porosities is shown in Figure 4. The capillary water absorption coefficient and the porosity were approximately linearly correlated (correlation coefficient R\({}^{2}\) = 0.749). The capillary water absorption coefficient increased by approximately 21.9 \(\times\) 10\({}^{-4}\) mm\(\cdot\)s\({}^{-1/2}\) when the porosity increased from 8.0 to 13.6%.  
Figure 3: Capillary water absorption coefficients for different preload levels.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Code** & **NC-1-1** & **NC-1-2** & **NC-1-3** & **NC-2-1** & **NC-2-2** & **NC-2-3** & **HPC-1-1** \\ \hline Porosity (\%) & 9.4 & 9.1 & 11.1 & 12.7 & 12.1 & 13.0 & 9.8 \\ Capillary water absorption (10\({}^{-4}\) mm\(\cdot\)s\({}^{-1/2}\)) & 42.1 & 37.8 & 44.9 & 56.2 & 50.8 & 59.7 & 39.8 \\ Most probable pore size (nm) & 6.4 & 12.6 & 6.1 & 27.1 & 28.6 & 42.4 & 12.0 \\ \hline Code & HPC-1-2 & HPC-1-3 & HPC-2-1 & HPC-2-2 & HPC-2-3 & UHPC-1 & UHPC-2 \\ \hline Porosity (\%) & 8.0 & 10.8 & 13.4 & 12.4 & 13.4 & 5.7 & 5.4 \\ Capillary water absorption (10\({}^{-4}\) mm\(\cdot\)s\({}^{-1/2}\)) & 32.3 & 37.1 & 51.5 & 44.7 & 50.4 & 54.7 & 47.5 \\ Most probable pore size (nm) & 4.2 & 4.2 & 30.8 & 6.0 & 42.2 & 4.1 & 4.2 \\ \hline \end{tabular} The first 12 groups of specimens were NC-1, NC-2, HPC-1, and HPC-2 after 0, 90, and 330 ETF cycles, and the latter two groups were concrete specimens UHPC-1 and UHPC-2 without ETF cycles.  
\end{table}
Table 5: Capillary water absorption coefficients for different porosities.  
The NC-2 specimens (after 0, 90, and 330 ETF cycles) and the UHPC-1 and UHPC-2 specimens (no ETF cycles) were evaluated using the MIP method to determine the internal pore distribution. The results are shown in Table 6.  
As shown in Table 6, although the porosity differed, the capillary water absorption coefficients corresponding to the cement-based materials were similar; namely, the distribution of the internal pore structure of the cement-based materials exerted a substantial effect on its capillary water absorption performance. The pore volume distributions inside the concrete specimens with similar porosities were also quite different.
## 4 Results and Discussion  
### GMM Clustering Based on Travel Time  
#### 4.1.1 Clustering Results Based on Travel Time  
Here, travel time is defined as the difference between boarding time and alighting time in one trip, which also means a usually specified period of time spent in the metro system. It usually reflects the travel patterns of intercity ridership. The travel-time distributions of intercity ridership are shown in Figure 5. To examine differences in intercity travel time between boarding and alighting ridership as well as between weekend and weekday ridership, \(T\)-tests were conducted. The results indicated no significant differences, suggesting that travel time does not vary significantly between intercity boarding and alighting ridership or between weekends and weekdays. In addition, it can be found that there are two obvious peaks in the travel-time distribution of intercity ridership, showing mixed distribution characteristics. Based on this, intercity ridership is clustered based on travel time, and the GMM algorithm is adopted.  
There are two Gaussian distributions identified. Table 4 shows their weights, means, and standard deviations. The weight of Gaussian distribution 1 is 18.5%, and the mean value is 32.9 min. The weight of Gaussian distribution 2 is 81.5%, and the mean is 68.9 min. That is, Gaussian distribution 1 and Gaussian distribution 2 correspond to the short-travel-time and long-travel-time intercity travels, respectively. Obviously, the short-travel-time group accounts for a small proportion and has a mean travel time of about half an hour,  
Figure 5: Travel-time distribution of intercity ridership.  
while the long-travel-time group accounts for a large proportion and has a longer travel time of about 70 min.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 3 Results
### Applications in Peace and Conflict Areas
The use of chemicals and explosive weapons can have devastating impacts on land, water, and air. It is hard to monitor these effects with the naked eye. Nevertheless, remote sensing helps detect both the short-term and long-term impacts of these weapons. It can help to determine the effects of a crisis on aquatic life and water quality. Besides, it is very pertinent in assessing the geomorphologic impact of the usage of explosive weapons. Remote sensing can also identify changes in soil texture and content as well as changes in the landscape. It can also be used to identify landmines during the post-conflict phase to minimize further damage. Moreover, remote sensing can be used in monitoring air pollution during armed conflict, which has been trivialized for a long time. After the devastating Gulf war in Kuwait, remote sensing was utilized to monitor sand encroachment and land deformation due to the usage of explosive weapons ([PERSON] et al., 2016). A case study of forestry conflict in Sumatra, which is a large island in Indonesia, has been presented in [PERSON] et al. (2016). The authors discussed how remote sensing techniques have been helpful in conflict crisis resolution between the communities and the associated organizations. The challenges in conflict management have also been presented exhaustively. The management of cross-border conflicts using remote sensing data collected with Google Earth layers is a good example highlighting the importance of remote sensing toward international peace and security [88].
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods
### Methods
#### 2.4.2 Spatial Method  
In contrast to the other two interpolation methods, fitting establishes the relationship between the data and LST by introducing external effective data to recover the invalid value of LST. [PERSON] et al. [26] fitted LST and NDVI using linear regression and quadratic polynomial regression, respectively. Therefore, the invalid value of LST can be calculated by the regression equation and the effective value of NDVI. For each LST image with missing pixel values, there is a corresponding NDVI image. During reconstruction, the relationship between LST and NDVI is fitted using the effective pixels on the target LST image and the NDVI image at the same position. As a result, the invalid pixel on the target LST can be calculated using the effective value of the same position on the NDVI image.
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 2 Hardware Architecture  
The hardware of the portable wireless sensing packaging could be separated into four primary parts: A computational computer, a portable battery for power supply, an accelerometer for monitoring ground motion, and a satellite-based communication device.
## 5 Results and Analysis  
The proposed EO-ELM and DNN model applied to R-R modelling and six other well-known models, namely ELM, KELM, PSO-ELM, SVR, ANN and GBM, are considered for their comparison. To evaluate the performance of models, six evaluation indicators MAE, MAPE, NSE, R\({}^{2}\), RMSE and VAF have been used. Further, the ranking of models is assigned based on the value of each performance evaluation metric i.e., better the value, higher the ranking. In case of same value of matric, corresponding model ranking is decided based on the value priority of R\({}^{2}\) > RMSE > NSE > MAE.  
Figure 6: Discrete wavelet transform (DWT) sub-series (D1 to D9) and approximation (A9) for Teifi at Glanteifi (**a**) discharge (**b**) rainfall and Fal at Tregony (**c**) discharge (**d**) rainfall.