esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Specimens used for the CU test were saturated and regarded as soaked samples, whereas those used for the UU test were unsaturated and regarded as unsoaked samples. The compression test only determined the soil strength q\({}_{\text{u}}\) value. Soil shear strength parameters consisted of the c value and \(\phi\) angle. According to the Mohr-Coulomb theory, soil shear strength is calculated using the Equation (1). \[\tau_{\text{f}}=\text{c}+\sigma\tan\phi, \tag{1}\]  
where \(\tau_{\text{f}}\) is shear strength, \(\sigma\) is normal stress, and c and \(\phi\) represent cohesion value angle and internal friction angle, respectively. The swelling of soil can be assessed by identifying the effect of infiltration saturation and vertical stress on GMZ bentonite swelling indicated by [PERSON] et al. [37]. The present study adopted the California bearing ratio (CBR) test. As clay, which is rich in smectite, possesses a high swelling potential and a low hydraulic conductivity, it is commonly used as a liner for landfills. The hydraulic conductivity of clay causes desiccation cracking and adverse effects. Therefore, one-dimensional swelling deformation apparatus introduced by [PERSON] and [PERSON] [38] can be used as a research instrument. Swelling can also be determined through the soaking process of the CBR test [24; 30]. The results of related studies indicate volume swelling. Clay clods and desiccation cracks also affect the mechanisms of hydraulic conductivity [18; 29; 31; 39; 40; 41; 42]. We used the indoor permeability test to clarify the hydraulic conductivity mechanism, while consolidation and compression were also intensively studied. Since the void/cement ratio is considered as a main factor that contributes to the strength and compression, clay-based lightweight aggregate concrete improved with cement addition [43; 44]. In summary, (1) for the application of compaction test results, the moisture content of the specimens prepared in the present study was similar to the OMC level on the wet side; (2) soil strength was compared based on the CU and UU test results of the unconfined compression and triaxial compression tests, and the Mohr-Coulomb shear strength formula was used to determine the difference between mudstone and mudstone-cement samples; (3) after the compression test was completed, stress-strain curves were used to determine E\({}_{50}\), and the relationship between elastic modulus E\({}_{50}\) and unconfined compression strength q\({}_{u}\) was clarified; (4) the indoor permeability test was used to explore the factors related to on-site clods that could affect mudstone hydraulic conductivity; (5) the one-dimension consolidation test was used to explore the compression variation of mudstone before and after the addition of cement paste; and (6) the CBR test was used to clarify the swelling of mudstone after compaction and soaking.
## 3 Results
### Influence of the Size of Stator Hub Clearance on the Evolution of Flow Topologies
#### 3.3.1 Results at the DE Condition
When the stator hub clearance grows to 0.50\(\uptau\), the corresponding \(\lambda^{2}Ree\) equals to 100, where the flow viscosity cloud be nearly ignored, as a result, the clearance flow at the clearance outlet was strong enough to push away the secondary flow and resist the streamwise adverse pressure gradient, so the backflow, even together with the corner separation, disappeared. The distributions of the normalized axial velocity at the stator outlet are shown in Figure 7. It can be seen that there was almost no difference in the tip corner separation induced flow blockage between different stator hub clearance cases. But for the hub corner separation induced flow blockage, when a small stator hub clearance (0.25\(\uptau\)) was introduced, the shape of the blockage area changed little while the blockage area was enlarged and the velocity inside the blockage area becomes smaller; when the stator hub clearance grows to 0.50\(\uptau\), the spanwise scale of the blockage area becomes much smaller but the pitchwise scale became larger; the situation at 1.00\(\uptau\) was similar to that at 0.50\(\uptau\). Combined with the oil-flow visualization results and the five-hole probe results, it can be seen that at zero clearance the flow structures at the hub corner were predominated by the corner separation. When a small stator hub clearance (0.25\(\uptau\)) was introduced, though some hub leakage flow was brought about (see the separation line SL4 in Figure 6), it was too weak to suppress the migration of the boundary layer low energy flows from the blade pressure side to the suction side, on the contrary, the very small streamwise velocity component separated and flowed backward under the large streamwise adverse pressure along the blade suction side, which led to an even stronger hub corner separation compared with that at zero clearance. Hence, the hub corner was predominated by both the corner separation and the tip leakage flow. When the stator hub clearance grew to 0.50\(\uptau\), the hub leakage flow was strong enough to restrain the secondary flows on the hub, hence, the separation line SL1 on the blade suction side disappeared and an attachment line AL2 on the blade suction side arose  
Figure 7: Distribution of the normalized axial velocity at the stator outlet. and the separation line SL3 on the hub left far away from the hub corner, so in this case the hub corner was predominated by the hub leakage flow and that is why the hub corner blockage area at the stator outlet becomes smaller in the spanwise direction and larger in the pitchwise direction; when the stator hub clearance increases to 1.00\(\uptau\), the hub leakage flow becomes stronger and the hub corner is still predominated by the hub leakage flow.
## 2 Methodology
For instance, considering the \(k\)NN method, \(w_{i}(\mathbf{x}_{0})\) can be defined as:  
\[w_{i}^{k\text{NN}}(\mathbf{x}_{0})=\frac{1}{k}\mathbb{I}[\mathbf{x}_{i}\text{ is a }k\text{NN of }\mathbf{x}_{0}]. \tag{4}\]  
Accordingly, (1) can be reformulated as:  
\[\mathbf{z}_{N}^{k\text{NN}}(\mathbf{x}_{0})\in\arg\max_{\mathbf{z}\in\mathcal{N}_{k}(\mathbf{x }_{0})}\frac{1}{k}c(\mathbf{y}_{i},\mathbf{z}), \tag{5}\]  
where \(\mathcal{N}_{k}(\mathbf{x}_{0})=\{i=1,\ldots,N:\sum_{j=1}^{N}\mathbb{I}\big{[}\| \mathbf{x}_{0}-\mathbf{x}^{i}\|\geq\|\mathbf{x}_{0}-\mathbf{x}^{i}\|\big{]}\leq k\}\) is the set of \(k\) historical data samples that are the closest to \(\mathbf{x}_{0}\). Note that \(k\) is a hyperparameter in \(k\)NN regression, and there are multiple methods for measuring the distance between data points in \(k\)NN, such as Euclidean distance, Mantattan distance, and Minkowski distance. Our interest lies in the performance of the data-driven decision under the wSAA framework. To this end, we assess the performance of a data-driven decision for an out-of-sample \(\mathbf{x}_{0}\) through the minimum relative regret, defined as  
\[\mathcal{R}_{N}^{\text{wSAA}}(\mathbf{x}_{0}):=\frac{\mathbb{E}\big{[}c\big{(}\bm {y},\mathbf{z}^{*}(\mathbf{x}_{0})\big{)}\big{]}-\mathbb{E}\big{[}c\big{(}\mathbf{y},\mathbf{ z}_{N}^{\text{wSAA}}(\mathbf{x}_{0})\big{)}\big{]}}{\mathbb{E}\big{[}c\big{(}\mathbf{y},\mathbf{ z}^{*}(\mathbf{x}_{0})\big{)}\big{]}}, \tag{6}\]  
where \(\mathbf{y}\) remains a random variable given \(\mathbf{x}_{0}\), and the expectation is taken over \(\mathbf{y}\) given \(\mathbf{x}_{0}\). Here, \(\mathbb{E}\Big{[}c\big{(}\mathbf{y},\mathbf{z}^{*}(\mathbf{x}_{0})\big{)}\Big{]}\) denotes the expected profit from the oracle, and \(\mathbb{E}\Big{[}c\big{(}\mathbf{y},\mathbf{z}_{N}^{\text{wSAA}}(\mathbf{x}_{0})\big{)} \Big{]}\) denotes the maximum expected profit obtained by the wSAA decision. Observing the above equation, if the minimum relative regret approaches 1, it indicates a total divergence between the expected profit from the oracle and the profit obtained by the data-driven decision under the wSAA framework. Notably, the wSAA framework tends to discard data that is not close to the new observation, thereby requiring a substantial amount of data, particularly data that is close to the current observation, to accurately estimate the distributions of the uncertain variables and subsequently maximize the given profit function [2]. This suggests that the wSAA framework may struggle when extrapolating to new observations that lie outside the range of known data samples. To comprehensively characterize this minimum relative regret, we design two distinct CSO problems. One problem features a new observation that is outside the range of known data samples, and the other features a new observation that is within the range of known data samples.
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 3 Model Description
### Hybrid Dilated Convolution Module (HDM)  
In traditional convolutional neural networks, the context information is usually augmented by extending the receptive field. One way to achieve this goal is to increase the depth of the network model and use a deeper network. Another method is to enlarge the size of the convolution kernel. They will both raise the computational burden and the training time of the network model. Therefore, [PERSON] and [PERSON] (2016) first proposed a multi-scale context aggregation dilated convolution model. The original proposal was to solve the problem of image semantic segmentation, because the context information between images is very helpful for object segmentation.  
Figure 1a is a one-dimensional convolutional neural network with three conventional convolution layers. The expansion rate r of each layer is 1, Figure 1b is a one-dimensional convolutional neural network with three dilated convolution layers. The expansion rates r of each layer are 1, 2, and 4, respectively. The top blue unit is regarded as the unit of interest, and the other blue units represent its receptive field in each layer. Compared with the conventional convolution, the dilated convolution expands the receptive field of the convolution kernel without increasing the parameters.  
As shown in Figure 2, the hybrid dilated convolution module(HDM) consists of three parts: input \(1\times 1\) convolution, feature fusion convolution, and output \(1\times 1\) convolution. The input \(1\times 1\) convolution scrolling reduces the number of channels to half, reducing the number of model parameters. In feature fusion convolution, dilated and conventional convolution outputs are directly added one by one according to the elements. In addition, both dilated convolution and conventional convolution keep the number of channels unchanged. The output \(1\times 1\) convolution doubles the number of channels, so that the input and output of HDM channel number are consistent. Finally, the output is combined with the input of the module to form a residual connection ([PERSON] et al., 2017), which makes the network training easier.  
Figure 1: Conventional convolution and dilated convolution.
## Appendix E Types of Artificial Neural Networks  
In this section, various architecture types of Artificial Neural Networks (ANN) are defined. The term architecture refers to the way in which neurons of the network are connected.  
* Feed-forward neural network: This is the most common type of ANN and is used in many practical applications. In this type of network, the information comes in the neural unit and flows in one direction through hidden layers until it reaches the output unit. The first layer is considered as the input, and the last layer represents the output of the network. If there is more than one hidden layer, it is called a deep neural network (DNN). DNN is a type of feed-forward ANN that has multiple hidden layers of units between the input and the output layers. They compute a series of transformations between the input and output, so that at each layer, you get a new representation of the input where things that were similar in the previous layers may become less similar or things that were dissimilar in previous layers may become more similar.
* Recurrent neural network: Another well-known architecture is called the recurrent neural network. In this type of neural network, the information can flow around in a circle. Generally, recurrent neural networks are more powerful than feed-forward networks. They have directed cycles in their connection graph, which makes it possible to get back to the start by following the arrows. These networks can remember information for a long time; furthermore, they can exhibit all sort of interesting oscillations, but they are much more difficult to train because of their complex dynamics. Recurrent, unlike feed-forward, neural networks can use their internal memory to process arbitrary sequences of inputs. Recurrent neural networks use time series information and are ideal for text and speech analysis.
* Recursive neural networks: A recursive neural network architecture is composed of a shared-weight matrix and a binary tree structure that allows the recursive network to learn varying sequences of words or parts of an image. Recursive neural networks can recover both granular structure and higher-level hierarchical structure in datasets such as images or sentences. These networks are mostly used in image scene decomposition and audio-to-text transcription.
## 4 Discussion
### Application of AI for Predicting Blood Loss Prior to OGS  
Similarly to other complex and invasive procedures, OGS is likely to have associated complications such as excessive blood loss [59]. Predicting these expected complications ahead of their occurrence can be beneficial in their management. Several studies have reported on the factors that can be analyzed for predicting and anticipating the blood loss in patients undergoing OGS [60, 61, 62]. A report by [PERSON] et al. demonstrated the application of AI model in predicting perioperative blood loss in OGS. AI based Random Forest algorithm assessed (Bimaxillary surgery, Preoperative hematocrit, Preoperative hemoglobin, Preoperative, erythrocytes, Surgical time, Blood volume, Bilateral Sagittal Split Osteotomy (BSSO), Sex, BMI, BSSO with genioplasty, Age, Le Fort 1 Osteotomy) for predicting the perioperative blood loss. This ML-based AI model showed statistically significant correlation between the actual and predicted perioperative blood loss. (\(p<0.001\)). Although this model demonstrated good performance on the data obtained from one particular clinic, the authors mention that the model may not perform precisely on data sets obtained from other clinics or another patient population [39].
## 4 Experiment
### Experimental Results  
To verify our analysis and prove the effects of illumination polarization and structural features, the Au line array with the pitch of 1000 nm was inspected using TSOM with two illumination polarizations.  
Figure 8 presents the simulation results of the Au line array with 50-nm height. The TSOM images were simulated with a 0.8 INA, 0.8 CNA, and 555-nm illumination wavelength, which was identical to the experimental conditions. Figure 9 shows the experimental results of the Au line arrays with 50-nm and 70-nm height.  
The microscope system noise is dependent on the stray light, blurs on the wafer, aberration of the microscope, and imprecision of the motion and location component of the stage [2; 15]. The TSOM images of the system noise with two illumination polarizations were obtained by subtracting two background TSOM images corresponding to line heights of 70 nm and 50 nm. The TSOM images are demonstrated in Figure 10.  
Figure 6: Schematic of the TSOM nanoscale metrology device, where the red ray is the illumination ray and the green ray is the imaging ray. The INA of the illuminating system is defined by the iris and the CNA of imaging system is defined by the objective. The motor stage and charge-coupled device (CCD) were controlled by a computer so that the CCD took one picture as soon as the motor stage moved a certain distance.  
Figure 8: Simulated TSOM images of Au line arrays with illumination polarizations of 90\({}^{\circ}\) (**a**) and 0\({}^{\circ}\) (**b**).  
Figure 7: SEM of Au line array. In this figure, the width of the Au line is 23.6 nm, and the pitch of this group is approximately 1000 nm.  
Figure 9: Experimental TSOM images for the Au line arrays with illumination polarizations of 90\({}^{\circ}\) (the first column) and 0\({}^{\circ}\) (the second column). The line heights were 50 nm and 70 nm.  
Clearly, the intensity of signal and system noise are quantified by the OIR of the TSOM images of the target and noise, respectively. By dividing the OIR of the TSOM image of the target by that of the TSOM image of the noise, the signal-to-noise ratio (SNR) of TSOM is retained. The SNRs in the two illumination polarizations are presented in Table 4. According to the result, for the given target, the sensitivity and SNR apparently increase when the polarization is perpendicular to the Au lines.
## 8 Challenges and Future Directions
### Challenges
#### 8.1.1 Data Privacy and Security  
HAR relies on the collection of personal data on people's activities, which raises concerns about the privacy of the data in HAR systems, user consent, and the potential misuse of data. Data privacy is a crucial issue for any system that uses sensitive information from the user, and HAR systems are no different. Due to the intrusive and sensitive nature of data collected by HAR systems such as location, daily routines and biometric information, it can be used for surveillance and potentially infringe on the user's privacy rights. It is also important that these data be handled securely because data breaches can have severe consequences for users such as identity theft or financial loss.  
These data concerns also make it difficult for user adoption. With users becoming more aware and conscious of how their data are being used, without clear and user-friendly privacy policies, it would be difficult to promote user adoption of HAR systems.
## 3 Results and Discussion
### Analysis of the Effects of YLDI on the Volatile Compounds of Green Tea Using GC-MS/MS
#### 3.3.2 Multivariate Statistical Analysis  
PLS-DA was employed to further analyze the volatile components obtained from GC-MS. Favorable parameters (R\({}^{2}\)Y = 0.982 and Q\({}^{2}\) = 0.896) were attained, which suggested a robust explanatory capacity and predictive ability. As depicted in Figure 3A, a notable differentiation among the samples was observed, with the distribution patterns of tea samples subjected to five varying light intensities aligning with the findings from the GC-E-Nose analysis. Specifically, the samples exposed to 500 Lux and 2000 Lux were clustered primarily in the upper quadrant of the score plots, while those exposed to 4000 Lux and 6000 Lux were primarily located in the lower left quadrant. Conversely, samples treated with 1000 Lux were situated in the lower right quadrant. Additionally, the reliability of the PLS-DA model was assessed through 200 iterations of permutation testing, which confirmed the absence of overfitting, as depicted in Figure 3B.  
To identify the key differential components in tea samples exposed to YLDI, hierarchical cluster analysis was performed. A total of 27 key volatiles were identified based on thresholds of \(p<0.05\) and variable importance in the projection (VIP) \(>1\). As depicted in Figure 3C, (\(E\), \(E\))-2,4-nondienal, butanoic acid, phenylmethyl ester, safranal, and (\(E\), \(E\))-2,4-decadienal were identified as characteristic volatiles distinguishing the tea samples treated with 500 Lux from those subjected to other light intensities. Citral, nonanal, and hexanol were more abundant in the 1000 Lux samples than other treatments. Nerol exhibited a more pronounced differentiation in the 2000 Lux samples compared to other treatment conditions. Higher levels of theaspirane, (\(E\))-2-hexenol, 3-octanone, benzyl alcohol, \(D\)-limonene, and (\(E\))-2-hexenyl butanoate were attained in tea samples exposed to 4000 Lux. The contents of (\(E\))-2-nonenal, butanoic acid, hexyl ester, (\(3Z\))-3-hexen-1-yl hexanoate, hexyl isovalerate, (\(Z\))-3-hexen-1-yl (\(Z\))-3-hexenoate, _cis_-3-hexenyl-\(\alpha\)-methylbutyrate, and geranic acid displayed elevated levels in the tea samples exposed to 6000 Lux compared to other treatments.
## 2 Materials and Methods
### Methods
#### 2.2.1 Radiometric Resolution Compression  
Radiometric resolution refers to the number of bit depth divisions, which represent the reflected energy of targets and influence the analysis of targets' spectral features [35; 36]. At this time, the greater the spatial resolution, the more the radiometric resolution can lead to additional noise in the image processing results [36]. Furthermore, a lower radiometric resolution reduces the computational complexity and it is more time efficient, while the difference in the information content in the high and low radiometric data is negligible [35]. Therefore, in this study, the radiometric resolution of the images is compressed while using the linear rescale method, and the range is taken from 8-bit to 14-bit [35; 37]. The optimal radiometric resolution is set according to the results of multiple trials, with the goal to still balance the time cost and accuracy.
## 2 Description of the Experiment
### Experimental Equipment and Principle
Not only do the modes increase, but also different modes with wave velocity close to will mix and interfere, increasing the difficulty of signal processing and affecting the detection accuracy. Here, the phase velocity of the fundamental symmetric (S0) Lamb wave mode was used to establish the model for evaluating the extent of the damage. In this research, the center frequency of 210 kHz is selected as the excitation frequency after the verification of experiment and simulation. At this frequency, the guided wave mode is the least, and the wave velocity does not change significantly with the frequency, which is conducive to modal separation. At the same time, selecting a lower frequency can reduce the attenuation of signal transmission, which is suitable for the monitoring of relatively large composite structures.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 2
#### 5.3.2 Network Lifetime  
Figures 25-27 present a comparison of lifecycle metrics across the EEUC, CHEF, and FQ-UCR protocols, evaluated with varying node counts. For the 100-node network, both the EEUC and CHEF protocols experienced considerable node depletion around the 900 th iteration. In contrast, FQ-UCR exhibited significantly slower node depletion and maintained higher survival rates. Over 175.3% of nodes remained operational after other protocols failed, demonstrating the robustness of FQ-UCR. When scaling up to 200 nodes, the CHEF algorithm showed instability, whereas FQ-UCR lasted over 2492 iterations, out-lasting EEUC by 197.73% and CHEF by 181.06%. With 400 nodes, FQ-UCR continued to perform the best, extending the network lifetime by 169.39% compared to EEUC and 201.93% compared to CHEF. These results highlight that FQ-UCR outperformed the comparison protocols, particularly in dense networks with a distant BS, showcasing its superior energy efficiency and resilience.  
Figure 26: Network lifetime comparison for 200 nodes in Scenario 2.  
Figure 25: Network lifetime comparison for 100 nodes in Scenario 2.