esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation of Surface Deformation Induced by Backfill Mining
### Model Establishment and Simulation Schemes  
According to the specific geological conditions of the backfill panel in Tangshan Coal Mine and combined with the stratal movement characteristics, a model measuring 1500 m \(\times\) 1200 mm \(\times\) 725 m (length \(\times\) width \(\times\) height) was established. For the convenience of mesh generation, the thicknesses of the strata were rounded according to the actual conditions, and the grids of strata near the surface were refined. A total of 4,220,601 nodes and 4,140,000 elements were generated in the model. Horizontal constraints were applied to four sides of the model, the horizontal and vertical constraints were applied to the bottom boundary, and no constraint and load were applied to the top boundary. The  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Applied Stress/MPa**} & \multicolumn{6}{c}{**Model Parameter**} \\ \cline{2-7}  & \(\mathbf{K_{e}/MPa}\) & \(\mathbf{G_{e}/MPa}\) & \(\boldsymbol{\eta_{v}^{\delta}/MPa\_{\text{-}min}}\) & \(\boldsymbol{\alpha}\) & \(\mathbf{G_{ey}/MPa}\) & \(\boldsymbol{\eta_{\text{ev}}^{\beta}/MPa\_{\text{-}min}}\) & \(\boldsymbol{\beta}\) \\ \hline
5 & 51.71 & 11.08 & 40,565.17 & 0.36 & 208.63 & 4745.79 & 0.77 \\
10 & 74.61 & 15.99 & 71,385.55 & 0.38 & 267.21 & 3454.55 & 0.66 \\
15 & 94.79 & 20.31 & 100,886.91 & 0.38 & 286.92 & 2580.14 & 0.56 \\
20 & 115.26 & 24.69 & 91,656.83 & 0.38 & 320.48 & 4405.27 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter identification results of the fractional Burgers’ model.  
Figure 9: Comparison of simulated and measured results.  
Mohr-Coulomb model was adopted to coal, and the fractional Burgers' model was used for the backfill. The numerical model is displayed in Figure 10.  
The coal seam mined in the backfill panel was buried at an average depth below ground of 725 m, where the in situ stress was about 18 MPa. Therefore, the compressive creep deformation of gangue backfill materials was measured under an axial stress of 18 MPa. The fractional Burgers' model was used for parameter identification of test data (Table 2), and the identified parameters were used as values of parameters of the backfill in the numerical simulation.  
The mining and backfilling were simulated based on the actual conditions of the backfill panel. The surface deformation 30, 60, 90, 180, 270, and 360 d after mining and backfilling was simulated. Two measuring lines were arranged at central positions along the dip and strike directions of the backfill panel on the surface to monitor the horizontal and vertical displacement along the measuring lines.
## 4 Experimental Results and Discussion
### Performance Changes after Adjusting Hyperparameters  
In this section, we experimentally evaluate the performance changes in different algorithms under different loss weights \(\lambda\) and analyze the results. We set the undersampling neighbor count K to 3, the imbalance ratio to 0.5, and vary the loss weights as \(\{1\times 10^{-7},\)\(1\times 10^{-6},2\times 10^{-6},4\times 10^{-6},6\times 10^{-6},8\times 10^{-6},1 \times 10^{-5}\}\). Each experiment is conducted three times, and the results are averaged.  
From the experimental results in Figures 6-8, a conclusion can be drawn: in the Cora, Citeseer, and PubMed datasets, the improved ESA-GCN model outperforms the original  
Figure 4: Changes in AUC-ROC and F1-Macro when adjusting the imbalance ratio on the Citeseer dataset.  
Figure 5: Changes in AUC-ROC and F1-Macro when adjusting the imbalance ratio on the PubMed dataset.  
Figure 3: Changes in AUC-ROC and F1-Macro when adjusting the imbalance ratio on the Cora dataset.
## 5 Experiment and Evaluation
### Experimental Results
As shown in Table 3, the accuracies of ST-GCN were 70.40%, 72.40%, and 72.80% in cross validation, cross-object, and cross-view, respectively, whereas the results of 2s-AGCN are better, with 71.80%, 74.80%, and 78.16% respectively. By contrast, our LSTM-DGCN achieved better results under the same conditions. The accuracies of cross-subject, cross-view, and cross validation were 80.90%, 81.60%, and 83.70%, respectively. Although our method was originally designed for basketball action recognition, the experimental results in Table 5 show that our method also achieved better results compared with other mainstream human motion recognition algorithms based on the depth data on the NTU RGB+D dataset as the benchmark. Under the evaluation criteria of cross objects and cross perspectives, our method achieved accuracy rates of 87.0% and 94.6% on the NTU RGB+D dataset, which are better than most mainstream action recognition methods and similar to the accuracy rates of 88.5% and 95.1% by the most advanced 2s-AGCN. The NTU RGB+D dataset is a general human action dataset rather than a special basketball action dataset. Therefore, the LSTM-DGCN proposed here actually maps the NPU with 12 types of actions selected from the NTU RGB+D dataset. The results of the 12 kinds of basketball players' actions in NTU RGB+D are much better than those in our dataset, which not only proves the effectiveness of our method but also shows that NTU RGB+D's action categories are easier to distinguish than our basketball players' actions. The results of LSTM-DGCN on the NPU RGB+D dataset are not as good as those on the NTU RGB+D dataset for a few reasons. On the one hand, our dataset is smaller than NTU RGB+D, both in terms of research objects and perspectives and the number of videos. On the other hand, basketball players' actions are far more complex than daily actions, so they are more difficult to classify. Nevertheless, it is enough to prove that our method is also effective in the field of general action recognition. In addition, we provide the recognition accuracy of each type of action in Table 6. Interval accuracy is the accuracy of action sequence recognition based on LSTM-DGCN to distinguish from single-frame accuracy. The table indicates that we achieved excellent action recognition results in almost all basketball action categories. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Cross-Subject Accuracy** & **Cross-View Accuracy** \\ \hline Lie-Group [17] & 50.1\% & 52.8\% \\ \hline H-RNN [42] & 59.1\% & 64.0\% \\ \hline PA-LSTM [9] & 62.9\% & 70.3\% \\ \hline STA-LSTM [34] & 73.4\% & 81.2\% \\ \hline ST-GCN [6] & 81.5\% & 88.3\% \\ \hline AS-GCN [19] & 86.8\% & 94.2\% \\ \hline
2s-AGCN [20] & 88.5\% & 95.1\% \\ \hline LSTM-DGCN & 87.0\% & 94.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of action recognition performance on NTU-RGB+D. The classification accuracies on both cross-subject and cross-view benchmarks are presented.
## 4 Application and Results
### CSE Corpus Building
#### 4.1.4 Linguistic Analysis  
The linguistic analysis was performed according to the following levels of observation (see Figure 4). A sample of the CSE dialogues was analysed from a linguistic perspective and served as a baseline to ensure that the software tools and libraries were able to achieve the desired level of quality. Initially, as seen in Figure 4, a _lexical analysis_ was performed by breaking down a sentence into words, phrases, or other meaningful part, a task known as _chunking_. Afterwards, a _morphological analysis_ was performed where the structure and the form of each word was the main concern. Part-of-speech tagging (POS), stems and lemmas were identified, and a _syntactic analysis_ followed that focused on grammar and syntax patterns. Subsequently, the meaning of the words and phrases were examined, where the semantics of words and phrases were investigated. The most crucial step, though, was the _pragmatic analysis_ that took place to identify the _actual meaning_ of the utterances. This is reasonable because an automation tool cannot understand the hidden intent of a speaker or a writer.  
Figure 3: Ten most similar words of ‘password’ term.
# Institutional Adoption and Implementation of Blended Learning in the Era of Intelligent Education
## 4 Results and Discussion
### Fulfillment of the Intervention Objective
#### 4.1.2 Achievement on Blended Learning Structure  
As is described in Table 1, S university made several achievements in terms of the BL structure.  
Firstly, S university appointed an LMS admin to monitor all courses and continued to use its long-established school inspection to ensure the quality of the BL courses. It is noteworthy that video surveillance of the F2F sections and student surveys were added as supplements for course governance at Stage 3.  
Secondly, in the sub-category of models, S university put forward an Instruction Guide for BL: 151 (see Appendix C), which had gone through three rounds of revision until it finally met the reform need of the institution.  
Thirdly, S university provided a flexible scheduling system for BL courses, including exclusively online and BL learning.  
Last but not least, the evaluation at Stage 1 relied merely on a small-scale student survey and gradually evolved to include a large-scale student survey and a formative evaluation of courses (2.2 million-2.5 million records/semester) as well as a summative evaluation of courses (0.1 million-0.125 million records/semester).
## 3 Concepts and Models
### Simulated Annealing  
A simulated annealing (SA) algorithm is a heuristic algorithm used to solve various complex optimization problems [27]. It takes inspiration from the cooling process in metal forging. The algorithm starts from an initial solution and generates new candidate solutions by making small random changes to the current solution. It introduces the concept of temperature \(T\), which controls the probability of accepting new candidate solutions. The probability of accepting a new neighborhood solution is shown as follows:  
\[P=\left\{\begin{matrix}1_{r}&F_{t+1}<F_{t}\\ e^{\frac{F_{t}-F_{t+1}}{kT}}&F_{t+1}\geq F_{t}\end{matrix}\right. \tag{5}\]  
Figure 3: Equal interval time slice model. The equal-time interval slice partitioning method divides time into equal periods and captures network snapshots at the beginning of each period.  
where \(F_{t}\) is the objective function of the current solution and \(F_{t+1}\) is that of the new neighborhood solution. \(T\) is the current temperature and \(k\) is an arbitrarily set constant.  
A simulated annealing algorithm has a wide range of applications and is capable of handling complex optimization problems with large search spaces and nonlinear objective functions. It has been successfully applied to various real-world problems, including the traveling salesman problem [28], vehicle routing problem [29], scheduling problem [30], and parameter optimization in machine learning [31].
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 3 Artificial Neural Networks
### Autoencoders (AE)
#### 3.2.1 Architecture  
The encoder and decoder are both feed forward neural networks, essentially artificial neural networks (ANNs). A code is a single layer of an ANN with the dimensionality of our choice. Before training the autoencoder, a hyper parameter (code size) is set for the number of nodes in the code layer [46].  
The input passes through the encoder to produce the code. The decoder then produces the output using only the code as shown in Figure 3. The goal is to get an output identical to the input. The only requirement is that the dimensionality of the input and output be the same. Before training an autoencoder, four hyper parameters must be set: (1) Code size: number of nodes in the middle layer. The fewer, the greater the compression. It is possible to have as many layers as required in the autoencoder. (2) Number of nodes per layer: with each extra layer in the encoder, the number of nodes per layer decreases, whereas the opposite happens in the decoder. (3) Loss function: using either mean square error (mse) or binary cross entropy. Cross entropy is used if the input values lie between (0, 1); otherwise, the mean square error is used [46].
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 2 Results
### Output Time-Frequency Power Distribution
#### 2.7.4 Distribution Comparison  
The claimed exponential shape of the MaxEnt Doppler spectrum may seem puzzling to experienced readers who have experience with Doppler spectra being commonly modeled as having a Gaussian shape. The reader is reminded that Equation (39) is not the spectrum the radar \"sees\"; the shape of the spectrum a (side-looking) radar will see is determined by the convolution (along \(\rho\)) of the Doppler spectra \(\underline{b}(\rho|\tau)\) with the antenna pattern \(\underline{G}^{2}(\rho|\tau)\), which is then convolved (along \(\tau\) and \(\rho\)) with the waveform ambiguity squared \(|\chi_{NT}(\tau,\rho)|^{2}\). Each of these processing steps alters the resolution of the radar.  
As an illustrative example, we will consider the Doppler spectra modeled in SS3.8.2 of [49]. Since the radar is stationary, \(\underline{G}^{2}(\rho|\tau)\propto\delta(\rho)\). The predicted spectrum in this scenario is described using the following parameters:  
* \(F_{\tau}=2\) kHz
* \(N=32\) pulses
* Dolph-Chebyshev window with \(-55\) dB sidelobes
* \(\mu_{\rho}=62.5\) Hz
* \(\sigma_{\rho}^{2}=20\) Hz\({}^{2}\)
* Overall clutter to noise ratio (CNR) = 20 dB  
The computed MaxEnt spectra for both the unknown and known variance cases are shown in Figure 5. The predicted power distribution \(P(\rho)=|\chi_{NT}(0,\rho)|^{2}\approx\underline{b}(\rho)\) is shown in Figure 6. It can be seen in Figure 5 that the specification of the variance narrows the Doppler pdf \(\underline{b}(\rho)\) significantly, but if too short of a CPI is used, as is the case in Figure 6, the observed spectrum after matched filtering will not change significantly.
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 4 Experiments
### Complexity and Security Performance Analysis  
In the processes of encryption, we analyze the complexity of encryption part including compressive sensing and scrambling. Assuming that the image size is \(M\times N\) and the block size is \(B\times B\), and the size of the two projection multiplication matrices for each block are \(\beta B\times B\) and \(B\times B\), respectively, where \(\beta\leq 1\) is the compression rate, the projection complexity is \(O(\beta B\times B\times B\times l)\), where \(l\) is the number of blocks in the image. The complexities of scrambling and data hiding are related to the size of the stego-image. So their complexity are both \(O(\beta M\times N)\). Therefore, the complexity of the processed of encryption and data embedding is \(T(n)=O(\beta M\times N)+O(\beta M\times N)+O(\beta B\times B\times l)\). In the processes of data extraction and image recovery, the main time consumption the CS recovery. It is a convex optimization problem that can be solved with different kinds of methods by the receiver with abundant resource.  
The security of our scheme mainly is apparent in compressive sensing and scrambling. In BCS reconstruction, matrix \(\mathbf{\Phi}_{B}\), which is the same as the encryption, was used in the decryption. Moreover, we also used the random number to arrange the measurement values. If an unauthorized user cannot accurately obtain this matrix and the random number, then the original protected image cannot be reconstructed. However, the data volume is huge. If a user does not know in advance, it is impossible to guess the right matrix and measurement values.
## 4 Results and Analysis
### Effect of Sentiment Dictionaries on Model Accuracy  
In this study, two different sentiment dictionaries were used to analyze model correctness: Method 1 used the NTUSU sentiment dictionary, and Method 2 used an online sentiment dictionary. The results show that the accuracy rate of Method 1 was higher than that of Method 2. A comparison of the accuracy rates is shown in Table 3. Because the original model does not use a sentiment dictionary, the accuracy rate is not different when using different sentiment dictionaries. In Models D and E, the accuracy rate was improved by 23.26% and 7.75%, respectively, using NTUSU's sentiment dictionary. This indicates that a more suitable emotion dictionary will help improve the emotion discrimination accuracy. Therefore, the development of an effective sentiment analysis model can be facilitated by creating a dictionary suitable for students' learning emotions.  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c|}{**(1)**} & \multicolumn{2}{c|}{**(2)**} \\  & **Dictionary \#1 NTUSU Dict.** & **Dictionary \#2 Sentiment Dict.** & **(1)–(2) Improvement** \\ \hline Original model & Accuracy & Accuracy & Accuracy \\ \hline Model A & snowNLP & 40.31\% & 40.31\% & \\ \hline Model B & jieba & 87.60\% & 87.60\% & \\ \hline Model C & nltk & 78.29\% & 78.29\% & \\ \hline Our proposed revised model & & & \\ \hline Model D & snowNLP + training & 89.15\% & 65.89\% & +23.26\% \\ \hline \multicolumn{3}{p{113.8 pt}}{* Model E} & snowNLP + jieba & 95.35\% & 87.60\% & +7.75\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the accuracy rates of different dictionary models.
## 2 Synthesis and Characterization of MIP
### Polymerization Parameter in MIP Synthesis
#### 2.1.4 Porogenic Solvent  
All of the polymerization components are contained co-solvent, including the template, functional monomer(s) and initiator. Solvents plays a supporting role in the creation of macroporous polymer pores. Porogen was developed to describe the solvent for this reason. Depending on the type and concentration of the porogen, the shape and total pore volume of the macroporous polymers can be varied. To produce polymers with a well-developed pore structure and high specific surface areas, a thermodynamically favorable solvent is used. A thermodynamically ineffective solvent, on the other hand, produces polymers with poorly formed pore structures and low specific surface areas. During the polymerization step, the solvent also influences the time it takes for the developing polymer phase to separate. According to [PERSON] and [PERSON] (1998), greater solubility phase porogens would be preferable for applications in liquid conditions, such as solid phase extraction (SPE), since larger surface areas enhance cavity accessibility. However, as only a minimal volume of solvent is required, the polymer has a low surface area, making it thick and impenetrable (1999). [PERSON] et al. (2000) observed that using dichloromethane to increase selectivity resulted in a polymer with low porosity and a small surface area.  
According to a research by [PERSON] et al. (2005), optimal results are usually obtained with little or no porosity in a chloroform-based polymer. This demonstrates that there is no direct link between material identification properties and polymer topology. Solvation shows that porosity is not required for substrate diffusion through MIP. Furthermore, the polarity of the solvent is important as a solvent with a stable polarity is crucial for monomer-template assembly. Water is a favored solvent in situations when the template and monomers interact via hydrophobic interactions ([PERSON] et al., 2000). In addition, polar solvents, such as alcohol-water combinations, can imprint hydrophilic and weakly polar templates ([PERSON] et al., 2000; [PERSON] et al., 2000).
## 3 Validation of MANTiS-32 Sensing Performance
### Integrated Measurement Performance Evaluation of the MANTiS-6050 Sensor and MANTiS-32 System  
The MANTiS-32 system is designed to interface with up to eight MANTiS-6050 sensor modules, as illustrated in Figure 11. When all eight sensors are simultaneously connected, their measurement data are transmitted through a single MANTiS-32 hub, which may introduce a communication bottleneck. Such a bottleneck can lead to data loss or transmission delays, thereby compromising the reliability of the acquired data.  
This experiment aimed to verify whether measurement data from all eight MANTiS-6050 sensors could be transmitted reliably and without loss from the MANTiS-32 hub to the PBM system. Considering that the MANTiS-32 platform was designed primarily for monitoring real bridge structures with dominant frequencies below 100 Hz, in accordance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Distance**} & \multicolumn{4}{c}{**Data Acquisition Rate**} \\ \cline{2-5}  & \multicolumn{4}{c}{**Sampling Rate**} \\ \cline{2-5}  & **20 Hz** & **50 Hz** & **100 Hz** & **200 Hz** \\ \hline
20 m & 100\% & 100\% & 100\% & 100\% \\ \hline
40 m & 100\% & 100\% & 100\% & 100\% \\ \hline
60 m & 100\% & 100\% & 100\% & 100\% \\ \hline
80 m & 100\% & 100\% & 100\% & 100\% \\ \hline
100 m & 0\% & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data acquisition performance with respect to transmission distance and RSSI.  
Figure 11: Communication architecture between MANTiS-6050 sensors and the MANTiS-32 system.  
with the Nyquist-Shannon sampling theorem, the system's performance was validated at up to a maximum sampling rate of 200 Hz.
## 4 Privacy-Aware Selection Modes
### Privacy-Aware Selection Modes
#### 4.2.2 Stranger Then Familiar Selection Mode  
This next-hop selection mode addresses complex social situations where privacy needs vary. Nodes forward to least familiar/trusted neighbors to maximize anonymity (see Figure 2b). Indeed, the selection is based on categorizing node relationships as \"stranger\", \"weak tie\", or \"familiar\" by user-preferred thresholds based on NCF and NEM metrics. Multimetric scoring allows for the handling of more complex privacy needs than binary trust thresholds. The \"stranger then familiar\" method acts as follows:  
1. The source node selects initial hops that are strangers or weak ties to maximize anonymity.  
Figure 2: Privacy modes’ flowcharts (**a**) for the trust threshold-based mode, (**b**) for stranger then familiar selection mode, and (**c**) for familiar then stranger selection mode.  
Subsequent intermediary nodes prioritize familiar, well-connected neighbors to relay messages.  
This mode ensures the originator can hide messages from known people by routing through strangers first, and the intermediaries protect privacy by passing them to trusted parties later, who will probably be strongly unfamiliar to the sender since they are familiar with the chosen stranger node. Therefore, this approach suits social contexts where sources want privacy without involving familiars, while intermediaries only rely on trusted parties. Moreover, it is suitable when sources want to hide messages from familiars but do not mind strangers receiving copies.
## 3 Datasets and Methodology
### Methods
#### 3.2.4 Calculate the Vegetation Ecological Quality Index  
The Vegetation Ecological Quality Index (EQI), constructed from the vegetation NPP and FVC, can reflect both the ability of vegetation to produce and its cover capacity per unit area. The NPP and FVC form an integrated model using a weighted approach [29]. The formula for the EQI is as follows:  
\[\text{EQI}=100\times\left(\text{f}_{1}\times\text{fvc}+\text{f}_{2}\times \frac{\text{NPP}}{\text{NPP}_{\text{max}}}\right) \tag{3}\]  
In Equation (3), the EQI ranges from 0 to 100. NPP\({}_{\text{max}}\) stands for the highest value of the vegetation NPP that was documented throughout the designated assessment period. f\({}_{1}\) and f\({}_{2}\) are weight coefficients, where the sum of f\({}_{1}\) and f\({}_{2}\) is 1, and the values of f\({}_{1}\) and f\({}_{2}\) are adjusted according to the region; in this study, both f\({}_{1}\) and f\({}_{2}\) are set at 0.5.