esa-sceva/satcom-chunk-collection huggingface dataset:
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 3 Neural Network Architectures
### Multi-Kernel 2d-Cnn  
A 2D-CNN with three parallel convolutional layers, each employing different kernel sizes, was used to extract the time- and frequency-domain features from the audio feature map. Typically, the receptive field of a CNN expands with additional layers. However, increasing the number of layers increases the number of parameters, which can lead to overfitting. Previous studies have demonstrated that the model performance can be improved by expanding the receptive field using different kernel sizes without increasing the number of layers [36; 37].  
The overall architecture, shown in Figure 6, employs three parallel branches at its input stage. Each branch consists of a Conv2D layer with distinct kernel sizes (11 \(\times\) 1, 1 \(\times\) 9, and 3 \(\times\) 3, as illustrated in Figure 7), followed by batch normalization, ReLU activation, and dropout layers. This parallel structure enables the simultaneous extraction of various temporal and frequency features at different scales. The features from these parallel branches are concatenated and processed through four additional convolutional blocks, each comprising Conv2D, batch normalization, ReLU activation, and dropout layers. This design allows the network to capture a comprehensive range of acoustic cues, from local patterns to extended temporal dependencies, which is particularly effective for distinguishing subtle age-related variations in Korean speech. The architecture concludes with a global max pooling layer and fully connected layers for final classification.  
Figure 5: 1D-CNN architecture.
# Negative Stiffness, Incompressibility, and Strain Localisation in Particulate Materials
## 5 Conclusions  
The failure of particulate materials such as rocks and concretes occurs in the form of splitting, shear failure, or spalling. In this study, we focused on a possible mechanism of strain localisation that precedes shear failure.  
Two mechanisms that are candidates for producing strain localisation in particulate materials under compression are the apparent negative stiffness caused by rotating non-spherical constituents in the presence of compression and the incremental compressibility caused by dilatancy. Using discrete element simulations (2D), we demonstrated that the apparent negative stiffness could be induced in the assembly of circular particles when not all the bonds were broken such that rotating clusters were formed. These clusters were not circular and, hence, in the presence of compression, they were capable of producing the apparent negative stiffness similarly to non-spherical (non-circular) particles.  
The dilatancy-induced incremental incompressibility in (incrementally) isotropic material was shown to produce localisation zones inclined at 45\({}^{\circ}\) to the direction of axial compressive loading. This mechanism of localisation in particulate materials in compression was illustrated through 2D and 3D simulations. The time window needed for determining the stage of incremental incompressibility (the incremental Poisson's ratio equal to 1 in 2D and 0.5 in 3D) was obtained from the step loading method. It was shown that, in the simulations, the peak stress (the onset of localisation) corresponded to the incremental Poisson's ratio, reaching the critical values of 1 (in 2D) and 0.5 (in 3D). The localisation zones obtained in the simulation were inclined at approximately 45\({}^{\circ}\) to the loading axis; the deviation from the predicted angle could be attributed to the small stress-induced anisotropy.  
The results of this study can provide insight into the mechanics of failure of heterogeneous and particulate materials in compression.  
A.V.D.--Conceptualisation, analytical modelling, writing; E.P.--Analytical modelling, writing; Y.X.--Numerical modelling, writing. All authors have read and agreed to the published version of the manuscript.  
The research was funded bu the Australian Research Council Discovery Grant DP210102224.  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: Localisation pattern of 3D virtual sample; the load direction is vertical. The broken line indicates the predicted angle of 45\({}^{\circ}\) to the loading direction.
## 4 Conclusions  
Taking gravel particles as the research object and particle morphology characteristics as the entry point, this paper uses discrete element software PFC 3D to analyze the macro-mechanical response and microscopic action mechanism of particle morphology characteristics on gravel particle deposits, and draws the following conclusions:  
(1) The more the particle shape deviates from the spherical shape, the greater the peak deviatoric stress, the more detailed the strain-softening phenomenon, the greater the deviatoric stress and residual strength, and the more detailed the dilatancy. Increasing the complexity of particle morphology is conducive to enhancing the bearing capacity of gravel aggregate.  
(2) With the decrease in the particle shape parameters EI and FI, the coordination number and sliding contact ratio increase, and the particle interlock effect is also enhanced. The proportion of the strong contact of spherical particles is larger than that of non-spherical particles, and the microcosmic level explains the reason for the small macroscopic strength of spherical particles.  
(3) The analysis of the influence of particle shape on the friction mobilization index and the probability density of the contact force between particles in the critical state shows that the smaller EI and FI are, the larger the average value of the friction mobilization index is, the higher the degree of friction mobilization in the particle collection is, and the particle interlock is more likely to occur, which is also consistent with the macroscopic law.  
Gravel particles, as a critical engineering material, exhibit significant multi-scale effects, making their study crucial in advancing the understanding of rock and soil mechanics. The particle morphology research results in this paper contribute to the development of constitutive models for particulate materials and can also provide a reference for the design of high-performance fillers in geotechnical structures.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L. and Z.C.; formal analysis, Y.L. and Z.C.; resources, H.M.; data curation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, Y.L. and H.M.; visualization, Y.L. and Z.C.; supervision, Y.L. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (51808204, 52078195, U22A20232), and these supports are gratefully acknowledged.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.  
Figure 18: Deviatoric fabric-axial strain curves for specimens with different particle shapes (200 kPa): (a) EI; (b) FI; and (c) EI/FI.
## 3 Results
### Role of AceF in Bacterial Virulence in an Acute Pneumonia Model  
Since the _P. aeruginosa_ T3 SS plays an essential role in acute infections, we examined the virulence of the _\(\Delta\)aceF_ mutant in a murine acute pneumonia model by inoculating 4 \(\times\) 10\({}^{6}\) CFU of bacteria into each mouse. A pathology analysis of the infected lungs  
Figure 4: RsmY and RsmZ repress the T3 SS in the \(\Delta\)aceF mutant. (**A**) Cytotoxicity of wild-type PA14, the \(\Delta\)aceF mutant, the complemented strain, the \(\Delta\)aceFArsmYZ triple mutant and \(\Delta\)rsmYZ double mutant. A549 cells were infected with the bacteria for 3 h (MOI = 50). The cytotoxicity was determined by measuring crystal violet-stained cells. (**B**) mRNA levels of the T3 SS genes. Bacteria were grown in the presence and absence of 5 mM EGTA for 4 h. The relative mRNA levels of \(pcrV\), _exsA_, _exsC_ and _exoU_ were determined by real-time PCR. Data shown represent mean \(\pm\) standard error of mean from three samples. * \(p<\) 0.05; ** \(p<\) 0.01; *** \(p<\) 0.001 by ANOVA.  
showed that the mutation of _aceF_ alleviated the host inflammatory response, which is in agreement with the defective T3 SS in the _DaceF_ mutant (Figure 5A). However, the bacterial loads of the lungs infected by the _DaceF_ mutant were similar to those infected by wild-type PA14 (Figure 5B). These results indicate that mutations in _aceF_ might enhance a mechanism that promotes the bacterial survival independent of the T3 SS. To test our hypothesis, we compared the bacterial loads of a _AexsA_ mutant and a _AexsAaceF_ double mutant. The deletion of _exsA_ significantly attenuated the bacterial virulence. At the dose of 4 \(\times\) 10\({}^{6}\) CFU per mouse, both the _DexsA_ and _DexsAaceF_ mutants were cleared at 12 hpi (data not shown). Thus, we increased the dose to 4 \(\times\) 10\({}^{8}\) CFU per mouse. The bacterial loads in the _DexsA_ mutant infected mice were higher than those infected by the _DexsA_ mutant (Figure 5C). Given that the _P. aeruginosa_ T3 SS preferentially targets bactericidal neutrophils in the acute pneumonia model [36; 37; 38], these results suggest that the T3 SS-defective _DaceF_ mutant might be more tolerant to the killing mechanisms of neutrophils.
## 8 Environmental Impact  
The implementation of solar power has been greatly encouraged due to its influence on reducing carbon emissions. Thus, it is important to measure the environmental benefit of using solar energy instead of fossil fuel. Figure 6 shows the amount of CO2 emission saved during the lifetime of the project. The calculations depend majorly on the value of the life cycle emissions (LCA), which represent CO2 emissions associated with a given component or quantity of energy. This includes the total life cycle of a component or the amount of energy, including production, operation, maintenance, disposal, etc. The rationale behind the carbon footprint tool is that the electricity generated by the photovoltaic system will replace the same amount of electricity in the existing grid. If the carbon footprint of the electricity generation on the grid is more than the PV system per kWh, carbon dioxide emissions will be reduced. From Figure 6, CO2 emission is negative until the last 10 years of the project; then, it becomes positive due to the fact that the PV system installed satisfies only 16% of the consumer demand. The rest of the demand is met by the grid of utilities using traditional fossil fuel energy that causes CO2 emissions in the air.
## 3 Application Cases  
An earthquake science experience facility is selected as a case study. The facility simulates earthquake disaster phenomena through a combination of physical and virtual methods, allowing visitors to experience earthquakes. This enables them to gain in-depth knowledge about the causes of earthquakes, early warning systems, earthquake resistance, and response measures. The earthquake science experience facility serves not only as a platform for science education but also provides the public with an intuitive and vivid learning environment. It helps people better understand earthquake science, enhances their self-protection capabilities, and promotes the improvement of society's ability to respond to earthquake disasters. Therefore, earthquake science experience facilities play a crucial role in public education.  
Currently, most earthquake science experience facilities on the market focus mainly on simulating the scene and the tremors, highlighting functionality while overlooking the effectiveness of product form design communication. Product form features are important factors influencing users' understanding and judgment of design intent, directly affecting the comfort and experiential feel during product use. Therefore, it is essential to study the evaluation and prediction of design intent cognition for earthquake science experience facilities. In this case, the application of relevant theories in sensory engineering was utilized, and through multidimensional scaling and systematic clustering analysis, six typical samples of earthquake science experience facilities were identified. (For detailed methods and procedures, see Sections 3.1 and 3.2). Subsequently, the typical samples were subjected to the division of morphological feature categories and the extraction and encoding of feature factors. Experiments were conducted based on six dimensions: morphological function orientation, behavioral intention, recognizability, cognitive load, attention allocation, and experiential sensation. This facilitated the assignment of feature category weights and the measurement of feature factors and integrated cognitive intent evaluation, ultimately leading to the construction of the cognitive intent prediction model.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 5 Experimental Results
### Prediction Algorithm Analysis  
We evaluate the performance of WCSAD by comparing the amount of energy actually harvested during each timeslot against the predicted energy intake. To this end, we implement WCSAD into the wind-powered sensor node and execute an outdoor 7 day continuous running. The invoked duty-cycle is set to be 30 min, parameters \(v\), \(\tau\), \(\varepsilon\) and \(\omega\) are 0.6, 0.45, 0.55 and 0.62, respectively, \(D\) is considered as 5 regarding the tradeoff between computational accuracy and limited working resources. The predictor error has been compared with WCMA [36] and the selective statistical range is from slot 3 to slot 28, i.e., from 07:30 to 20:00, the result is shown in Figure 9.  
Here in the test the wind speed keeps in hybrid magnitude from day 1 to day 4, however, in the 5 th day the air turns to breeze, therefore the harvested energy decreases dramatically, but at last, the 6 th day and 7 th day just take on a strong breeze. As a result of the severe change between the 5 th day and its previous days, WCMA cannot adapt to this variation and leads to a major error in Figure 9a, while WCSAD considers the past data of the harvested energy for reference ingeniously and keeps the error under 15%. In Figure 9b, WCMA slightly eases the impact of weather variation but still cannot offer a predictive value with high confidence, however, WCSAD maintains the accuracy as in the previous. In Figure 9c, the wind category keeps near the same with the day before, thus WCMA and WCSAD both gain a relatively low error rate, whereas WCSAD performs even better with the highest error of only 5%.  
As can be seen from the above analysis, the WCSAD algorithm shows ideal predictive accuracy and can provide reliable support for the \"ENO\" working requirement.  
Figure 8: Voltage variation of supercapacitor under three different operation modes.
## 3 Proposed Method
### Learning Specific Data Augmentation Strategies  
Data augmentation is an important technique to improve the generalization ability of CNN models, and this technique is especially important in the case of insufficient labeled remote sensing images. Due to the differences between natural images and remote sensing images, it is important to learn data augmentation strategies that are specific to remote sensing images. However, in the absence of sufficient annotated remote sensing images, related data augmentation techniques have received relatively little attention. Considering the GPU hours consumed by searching for candidate policies, in this paper, we adopt an algorithm called Fast AutoAugment (FAA) [47] that can find effective augmentation policies via a relatively efficient search strategy based on density matching.  
The whole process is illustrated in Figure 5. First, the training dataset \(D_{train}\) is split into K folds, each of which consists of two datasets, \(D_{M}^{(k)}\) and \(D_{A}^{(k)}\). For each fold, we train a model based on \(D_{M}^{(k)}\) and obtain the model parameters \(\theta^{k}\). After the parameters \(\theta^{k}\) are trained, \(B\) bundles of augmentation policies are sampled from sets of predefined data augmentation operators. Then, based on the evaluation results for model \(\theta^{k}\), the top N policies in \(B\) are selected to obtain \(T_{*}^{(K)}\) on \(D_{A}^{(k)}\). Finally, the top N policies \(T_{*}^{(K)}\) obtained from each of the K folds are appended to an augmentation list \(T_{*}\).  
Unlike previous methods such as AutoAugment [48], FAA does not train the model parameters \(\theta\) many times, so it can find data augmentation strategies that are suitable for RSSC datasets in less time.  
Figure 5: Overall augmentation search procedure using the FAA algorithm. FAA splits the training dataset \(D_{train}\) into K folds, each of which consists of two datasets, \(D_{M}^{(k)}\) and \(D_{A}^{(k)}\). Then, the model parameters \(\theta\) are trained in parallel on each \(D_{M}^{(k)}\).
# An Initial Load-Based Green Software Defined Network +
## 6 Performance Analysis
### The improvement in the average bandwidth utilization  
The results for average bandwidth utilization of ICWGA are also compared with those of the MILP model and the Dijkstra algorithm. Figure 4 shows the results for average bandwidth utilization of the three methods. Coinciding with the energy saving results reported in Figure 3, the average bandwidth utilizations of the two green methods are higher than those of the Dijkstra algorithm. In each topology, for the same \(\lambda\), the more the energy saving the network realizes, the higher the average bandwidth utilization of the network is. For example, for the India topology, in the \(\lambda=80\) case, the energy saving ratio is 14% achieved by the Dijkstra algorithm, 61% by ICWGA, and 69% by the MILP model. The corresponding average bandwidth utilization of the links are 10%, 24%, and 49% respectively.
# SSDLiteX: Enhancing SSDLite for Small Object Detection
## 3 Comparison of SSD and SSDLite
### Prior Box  
In SSD and SSDLite, the location and size of an object are predicted based on the given prior boxes for each location. SSD extracts the feature maps of six kinds of scales for object detection, from 1/8 to 1/256. The prior boxes for the first, fifth, and sixth scale feature maps have the sizes and aspect ratios described as Type 1 in Table 1. For the other scale feature maps, the sizes and aspect ratios are described as Type 2.  
In the table, the size of 1 is the reference size, meaning 0.1, 0.2, 0.37, 0.54, 0.71, and 0.88 times the input image size for the VOC data set. For the MS COCO data set, the reference sizes are 0.07, 0.15, 0.33, 0.51, 0.69, and 0.87 [11]. The size \(\sqrt{2}\) is the geometric mean of the reference size of the current scale and that of the next scale.  
In contrast, SSDLite uses _Type 3_ prior boxes for the first scale feature maps and _Type 2_ prior boxes for the other feature maps [24]. The reference sizes are also changed to 0.2, 0.35, 0.5, 0.65, 0.8, 0.95 for the MS COCO data set. From the prior box configurations, it can be known that SSD uses more various prior boxes than those of SSDLite for the first extracted feature maps, whose scale is small and resolution is high. The prior boxes of SSD has the size 0.7 with the aspect ratios of 1, 2, 1/2 and the size 0.10 with the aspect ratios of 1. SSDLite, however, uses the prior boxes of size = 0.1 and the aspect ratio = 1 and also size = 0.2 and aspect ratio = 2 and 1/2. Instead, SSDLite uses more prior boxes in large-scale and low-resolution feature maps.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.12 QLEAR [35]  
In [35], the authors described a Q-learning-based energy-efficient and lifespan-aware routing protocol (QLEAR). QLEAR calculates the Q-value depending on a packet's successful transmission. The assigned action determines the performance of the agents in Q-learning. Depending on the residual energy and energy distribution determine the reward  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & **Hello** & **No of** & **Localization** & **Simulation** & **No. of Sink** & **Possible** & **Energy Saving** \\  & **Packet** & **Layers** & & **Tool** & **Application** & **Strategy** \\ \hline EEDBR & Yes & Not given & Not needed & NS2 & Multiple & Military application & Node selection based on residual energy \\  & & & & & & Time- & \\ EEEDBR & Yes & 3 & Not needed & Not given & Multiple & sensitive applications & Selects nodes closer to the sink node \\  & & & & & & Supireillance application & Relay nodes at the edge of layers. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of depth-based routing protocols.  
function. The protocol chooses the path with more residual energy over a shorter distance. When the agent chooses a path with lower residual energy, it receives a negative reward for that particular action. Although the network lifespan is more extended than Vector-Based Forwarding (VBF), excessive energy consumption arises due to packet overhearing.
## 2 Materials and Methods
### Implant Position Planning Procedure and Surgical Guide Fabrication  
A duplicated model was randomly selected and scanned with an intraoral scanner (Straumann(r) Virtuo Vivo(tm), Institute Straumann AG) and a Cone-Beam Computed Tomography (CBCT) machine (J.Morita Inc.) that generate STL and DICOM files, respectively. Afterwards, STL and DICOM files were merged with coDiagnostiXTM software. Then, the digitally diagnostic wax-up was obtained by an STL file of a standard maxillary dentoform. The \(4.1\times 10\) mm Straumann(r) BLT implant was virtually planned in the desired position (Figure 1).  
The full-arch tooth-supported surgical guides with a 5-mm-diameter Straumann(r) T-sleeve were designed with 2.5-mm thickness and 0.20-mm offset. Two inspecting windows were used to ensure that surgical guides fit well on the models, which were located at the buccal of the maxillary first premolar and the mesiobuccal cusp of the maxillary second molar (Figure 1). This experiment divided samples equally into 3 groups, each containing 10 samples, and the \(4.1\times 10\) mm Straumann(r) BLT was placed with 3 surgical guides fabricated by different 3D printers in each group. To fabricate the static surgical guides, each type of 3D printer was calibrated at the manufacturer's recommendation. Finally, all surgical guides were printed with 3 different 3D additive manufacturing printers following the manufacturer's instructions including DLP, SLA, and Polylet (Figure 2). The overview of 3D printers was shown in Table 1.  
Figure 1: (**a**) 3D and (**b**) 2D cross-sectional illustration of tooth 16 implant position and surgical guide planning on a model.
# Non-Probabilistic Reliability Analysis of Slopes Based on a Multidimensional Parallelepiped Model
## 4 Example Analysis
### Calculation Results and Analysis
#### 4.2.1 Feasibility Validation of the Non-Probabilistic Reliability Analysis Method for the Slope
As shown in Figures 7 and 8, with an increase in the coefficient of variation of the parameters, the difference between the non-probabilistic reliability indexes of the slope obtained under different correlation coefficients also changes to a certain extent, exhibiting a tendency to decrease. This shows that the influence of parameter correlation on slope stability is also related to the variability of parameters, and this effect is more obvious when the parameter variability is small. Both variability and correlation of parameters need to be taken into account when performing non-probabilistic reliability analysis of slopes.