esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DCCMM Microblog Sentiment Analysis Model
### Dual-Stream Dynamic Coding Layer  
Chinese characters and words both contain important semantic information, and there are differences in the information they contain. Therefore, it is necessary to extract both character-level and word-level information when performing microblog sentiment analysis, to achieve complementarity between them and improve model performance. The character-based pre-trained language model does not rely on word separation tools, thus avoiding the problem of inaccurate word boundary delineation, does not include semantic noise caused by improper word separation, and does not need to consider the impact of unregistered words on downstream tasks; the word-based model can make the processingsequence shorter and speed up the model's operation. In addition, the semantic uncertainty of word meaning is lower than that of character meaning, and its semantic information is richer, which helps improve the accuracy of the text classification task. Therefore, we combine the advantages of character and word pre-training models, and propose a dual-stream dynamic encoding mechanism that uses WOBERT Plus and ALBERT to complete text dynamic word-level encoding and character-level encoding, respectively. Both the WOBERT Plus and ALBERT models need to add a CLS vector when encoding text. CLS does not represent the information of a single character or word, but contains the semantics of the entire sentence.  
1. WOBERT Plus word-level dynamic encoding  
The WOBERT Plus model is used to train word vectors on the input text, and its output contains two parts: the CLS vector and the word vector matrix.  
Here is the word vector matrix \(\mathbf{H}\in\mathbb{R}^{L\times d}\), \(\mathbf{H}=[\mathbf{w}_{1},\mathbf{w}_{2},\dots,\mathbf{w}_{L}]^{T}\), \(\mathbf{H}_{ij}=[\mathbf{w}_{i},\mathbf{w}_{i+1},\dots,\mathbf{w}_{j}]^{T}\) (\(j\geq i\)), where \(L\) is the number of words in the input text, \(d\) is the word vector dimension (\(d=768\)), and each row of the word vector matrix represents a vector of individual word maps.  
2. ALBERT character-level dynamic encoding  
The ALBERT model is used to train character vectors on the input text, and the CLS vectors imply the semantics of the whole sentence. To reduce the model parameters and complexity, only the encoded CLS vectors are output.  
The CLS vector \(\mathbf{X}_{\mathbf{C}-\mathbf{CLS}}\in\mathbb{R}^{d}\), \(d\) is the word vector dimension.
## 5 Conclusions  
This paper combines the specific engineering parameters of the CSEMP shallow low-rank coal project and establishes a coupled Thermal-Hydraulic-Mechanical model, which achieves the dynamic evolution of fluid physical parameters and is of great interest as a new guide for the preliminary design of subsequent CO\({}_{2}\)-ECBM projects based on shallow low-rank coal. Based on COMSOL Multi-physics software, the finite element method is used to solve the multi-physical field coupling. The following conclusions are obtained:  
1. During direct mining, the reservoir pressure shows a downward trend, and the downward trend gradually slows down and tends to be stable. During CO\({}_{2}\) injection, the reservoir pressure increases with time, increases rapidly at the initial stage and gradually tends to be stable. The injection of CO\({}_{2}\) has a significant enhancement effect on coalbed methane extraction.
2. For direct mining, the increase in permeability caused by matrix shrinkage deformation dominates, and the reservoir permeability gradually increases with time, and the closer to the production well, the greater the permeability. For CO\({}_{2}\) injection, the volumetric strain effect caused by adsorption and desorption is dominant in both production wells and injection wells. The permeability near the injection well decreases rapidly and the permeability near the production well increases.
3. Increasing the gas injection pressure can promote the production of CH\({}_{4}\) and the storage of CO\({}_{2}\), but the increase of gas injection pressure will also lead to the rapid attenuation of near-well reservoir permeability and the weakening of injection capacity. When the gas injection pressure reaches 3 MPa, the injection pressure continues to increase, and the recovery degree does not change much.
4. When the injection temperature increases, the CO\({}_{2}\) concentration is relatively reduced, and the replacement effect of CH\({}_{4}\) is weakened, resulting in a slight decrease in CBM production and CO\({}_{2}\) burial stock.
## 3 Crack Detection in Plates Using Dynamic Response (DR)  
In SHM studies, examining the DR is essential for detecting changes or damage in dynamic systems using vibration signal analysis. This analysis aims to identify, locate, and measure any structural damages present. Notably, a crack in a structure increases the flexibility locally, affecting its static and dynamic behaviors. Engineers have worked extensively to understand and model the changes caused by cracks and their effects on the structure's dynamic properties. It is important to mention that most models have been developed to explore changes in DR due to crack damage, addressing a forward problem. However, damage detection through vibration measurements, which is an inverse problem, is challenging and sometimes impractical. This discussion will further explore both forward and inverse problem research, with attention to the VT configurations in the following sections.
## 3 Visible Light and Infrared Image Feature Fusion Algorithm  
Considering the disadvantages of using single-mode optical equipment, this study is based on image information captured by visible light and infrared equipment, and a feature fusion algorithm of visible light and infrared images is proposed in this paper; first, an image of the vehicle target is captured by visible light equipment. Then, the vehicle target on the image is recognized and segmented, infrared equipment is used to capture an image of the vehicle target, and the features of the vehicle target on the formed infrared image are extracted. Finally, the vehicle target features extracted from visible light images and infrared images are fused; the fusion result can provide technical support for subsequent target recognition.
## 4 Result and Discussion
### Pipeline Stratum Model under 3D Effect
#### 4.1.3 Embedding Depth  
To understand the influence of the pipeline embedding depth on the 3D effect, this study set two kinds of models with different depths of embedded pipeline. The embedding depth (the top of the pipeline) was 0 m (embedding at the surface) and 4 m, respectively. The n value in the model was set as 0.1, the section size of the pipeline was 1.5 (W) \(\times\) 2 (D) m, and there were five survey lines.  
Figure 7 shows the 2D ERT results of the small pipeline with an embedding depth of 0 m and 4 m. Compared with the L1 lines (the horizontal distance from the buried pipeline was 1.5 m), when  
Figure 6: The influence of the pipeline size of the pipeline stratum model on the 3D effect.  
the embedding depth was 2 m, we could obviously observe the mapping phenomena, but when the embedding depth increased to 4 m, the 3D effect was not as obvious. This shows that as the depth increased, the influence of the 3D effect weakened, and the influence range seems to be related to the distance from the line to the pipeline, and not the horizontal distance.
# Performance Analysis of RIS-Assisted SatComs Based on a ZFBF and Co-Phasing Scheme
## 2 System Model
### ZFBF and Co-Phasing Technique  
In (1), the term \(\sqrt{\rho_{i}}f_{k,k}^{\mathrm{H}}\mathbf{\Phi}_{k}\mathbf{D}_{k}\mathbf{k}\mathbf{w}_{i}x_{i}\) represents IRI caused by the \(i\)th on-board feed \(\mathbf{w}_{i}x_{i}\), while the term \(\sqrt{\rho_{i}}f_{i,k}^{\mathrm{H}}\mathbf{\Phi}_{i}\mathbf{D}_{i}\mathbf{B}_{i}\mathbf{w}_{i}x _{i}\) represents IRI resulting from the \(i\)th on-board feed reflected by the \(i\)th RIS. Note that the acquisition of full CSI for RIS-related interference channels, such as \(\mathbf{f}_{j,k}\) and \(\mathbf{D}_{i}\), is challenging in practice [15]. Also, ZFBF and the co-phasing technique can be designed solely based on information about the desired channels, without requiring any information about the RIS-related interference channels [18; 20]. Hence, we consider ZFBF at the ES to eliminate IRI while adopting the co-phasing technique at the RIS as a suboptimal strategy to mitigate IRI.  
The effective channel between the satellite and UE \(k\) via RIS \(k\) and the aggregate effective channel for \(K\) UEs are, respectively, given as:  
\[\mathbf{h}_{k,k}=f_{k,k}^{\mathrm{H}}\mathbf{\Phi}_{k}\mathbf{D}_{k}\mathbf{B}_{k}, \tag{10}\] \[\mathbf{H}=\left[\mathbf{h}_{1,1}^{\mathrm{T}},\mathbf{h}_{2,2}^{\mathrm{T}},\cdots,\mathbf{h}_{K,K}^{\mathrm{T}}\right]^{\mathrm{T}}. \tag{11}\]  
Consider that \(\mathbf{H}\) is perfectly known at the ES. Then, the corresponding ZFBF matrix is obtained as \(\mathbf{T}=\mathbf{H}^{-1}\). Then, the ZFBF vector for UE \(k\) can be expressed as \(\mathbf{w}_{k}=\mathbf{t}_{k}/(K\|\mathbf{t}_{k}\|)\), where \(\mathbf{T}=[\mathbf{t}_{1},\mathbf{t}_{2},\cdots,\mathbf{t}_{K}]\). Here, the equal power allocation is assumed because, in high transmit power systems such as SatCom systems, the equal power allocation can achieve the performance resulting from the water-filling solution [21]. Also, to maximize the desired signal power, the phase shifts at the RIS can be designed based on the co-phasing technique such that the reflected \(N\) signals from the RIS are aligned in phase as follows: \(\theta_{k,n}=-\angle f_{k,k,n}^{*},\forall k,n\), where \(f_{k,k,n}\) is the \(n\)th element of \(f_{k,k}\). Then, the received signal-to-interference-plus-noise ratio (SINR) at UE \(k\) can be obtained as follows:  
\[\gamma_{k}=\frac{\rho_{k}}{K^{2}\|\mathbf{t}_{k}\|^{2}\big{(}1+\sum\limits_{i\
eq k }^{K}\rho_{i}\big{|}\mathbf{g}_{i,k}^{\mathrm{T}}\mathbf{D}_{i}\mathbf{B}_{i}\mathbf{w}_{i} \big{|}^{2}\big{)}}, \tag{12}\]  
where we define  
\[\mathbf{g}_{i,k}=\big{[}\big{|}f_{i,k,1}\big{|}e^{i\theta_{i,k,1}}\big{|}f_{i,k,2} \big{|}e^{i\theta_{i,k,2}},\cdots,\big{|}f_{i,k,N}\big{|}e^{i\theta_{i,k,N}} \big{]}\]  
and \(\theta_{i,k,n}=\angle f_{i,k,n}^{*}-\angle f_{i,n}^{*}\). We can observe from (12) that the IBI is perfectly eliminated, while the IRI still remains. However, we will demonstrate that the co-phasing technique at the RIS can asymptotically mitigate the IRI as well.
## 9 Optimization
### Geological Characterization and Sweet Spots  
A case by Shell Argentina is used to showcase the optimized selection of sweet spots. The company has been engaged in the Vaca Muerta formation since 2012, with a focus on optimizing dual-layer development, particularly in the complex upper Vaca Muerta strata. The company has honed its stratigraphic understanding of this upper layer, linking it to factors such as drilling sweet spots, reservoir containment, and pay thickness. A technologically advanced multi-well pad is in the planning stages, featuring fiber optics, chemical markers, microseismic monitoring, and the geochemical analysis of extracted oils. Two primary drilling targets have been identified in both the lower and upper Vaca Muerta layers. While the lower layer is relatively straightforward, the upper layer presents complexities due to its unique depositional environment.  
Micoseismic observations revealed event concentrations that varied by direction between the upper and lower layers, suggesting the presence of a fracking barrier. Geochemical analysis further confirmed that oil production was exclusive to each layer. Sweet spots were identified using a seismic-based static reservoir model. One of the primary challenges in the co-development of these layers is the risk of vertical interference, which is currently under evaluation. A robust data collection strategy is set to be implemented, encompassing fiber-optic monitoring, microseismic data, geochemical markers, chemical tracers, and interference tests.
## 4 The Proposed Method
### Integrating STPA into FMEA  
Although STPA and FMEA are two different methods, from the analysis process of the two methods, FMEA first identifies the failure mode after assigning functions to subsystems and components, and then determines the causes of the failure modes. This process is similar to the process of identifying unsafe control actions and then confirming the causal factors after establishing the safety control model of STPA. Therefore, in order to make full use of FMEA templates for causal factors analysis and risk assessment, some concepts of STPA are corresponding to FMEA in this paper. This is not to say that their essence is the same, but to enable STPA to make full use of the FMEA template in the analysis process. Figure 3 illustrates the correspondence between key concepts of STPA and FMEA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Terminologies** & **STPA** & **ISO26262** \\ \hline Safety Constraints & \begin{tabular}{c} System-level safety requirements \\ to prevent hazards from leading to \\ accidents and ensure safety \\ \end{tabular} & Not specifically defined \\ \hline Safety Goals & Not specifically defined & \begin{tabular}{c} Top-level safety requirements for an \\ item as a result of vehicle-level HARA, \\ expressed as functional objectives \\ \end{tabular} \\ \hline Malfunctioning Behavior & Not specifically defined & \begin{tabular}{c} An item’s failure or unintended \\ behavior with respect to its \\ design intent \\ \end{tabular} \\ \hline Unsafe control actions (UCAs) & \begin{tabular}{c} Inadequate control actions within \\ four types leading to hazards \\ \end{tabular} & Not specifically defined \\ \hline Causal Factors & \begin{tabular}{c} Scenarios that could explain how \\ inadequate control actions \\ might occur \\ \end{tabular} & Not specifically defined \\ \hline SCs for UCAs and \
\begin{tabular}{c} Causal Factors \\ \end{tabular} &
\begin{tabular}{c} Safety requirements derived from \\ the identified UCAs and \\ corresponding causal factors \\ \end{tabular} & Not specifically defined \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
Before the integration of the two methods, we will complete Step 1 and 2 of STPA first, that is, to determine an accident, identify the system-level hazards and the unsafe UCAs. Here, as shown in Figure 3, a control action in STPA can be equivalent to a function in FMEA, while a UCA can be equivalent to a failure mode in our paper. Combine Step 3 of STAP and Step 3 of FMEA to determine the causal factors (causes) of UCAs (failure modes), and Step 4 of FMEA can be used to assess risk according to ISO26262. The general steps of our proposed method are shown in Figure 4, and the specific steps of STPAF are described in detail below.  
Figure 4: General steps of the proposed method with the implementation steps of STPAFT on the left hand side and the outputs of STPAFT required in ISO26262 concept phase on the right hand side.  
Figure 3: Correspondence between key concepts of STPA and FMEA.
## 1 Introduction
This research primarily focuses on two aspects: steady-state interference protection and lightning overvoltage protection. Currently, solid-state decouplers are commonly used for steady-state interference protection, while limited research has been performed on lightning overvoltage protection for pipelines. This paper aims to investigate the impact of grounded, scattered lightning currents on nearby natural gas pipelines and assess the effectiveness of solid-state decouplers in reducing the voltage endured by the pipeline's insulating layer. The simulation results are then used to determine the optimal length for laying bare copper wire, as presented in the provided equation.
## 5 Methodology  
The authors investigated the literature regarding blockchain and the user-centric privacy of patients' health data records. The author also analyzed and compared the famous blockchain operating systems from several perspectives to choose the optimized one to meet the identified requirements. Based on our analysis, Tables 3 and 4 show the blockchain operating systems' characteristics. As seen in Tables 3 and 4, we selected various available blockchain operating systems for our analysis, comparing Ethereum, Hyperledger Fabric, ConsenSys Codefi, EOS, Overledger OS, Liberty OS, Ripple, and Corda. We analyzed them regarding language support, blockchain type, consensus algorithm availability, performance, integrated development environment, and transaction flow. In this analysis, the first aspect we selected is programming language support, as developing this support is the most important of any language. Blockchains use different languages that aid in frontend and smart contract connections [100, 101, 102, 103, 104]. Some common programming languages used by blockchains include Solidity, Vyper, Java, Go, Node.js, C++, and Kotlin. Secondly, the authors analyzed the type of operating system. In our use case, the concern is privacy, so we need to use a private blockchain with complete privacy that a network administrator manages--a permissioned blockchain. Although public blockchains are lighter and faster, they have an open environment, true decentralization, and anonymity. Thirdly, the authors analyzed the consensus algorithm, which achieves reliability in the blockchain network and establishes trust between peers [100, 101, 103, 104, 105]. Fourthly, the authors analyzed the performance [100, 101, 103, 104, 105, 106, 107, 108]. Based on our analysis, EOS has the best performance, followed by Hyperledger Fabric. However, in an EOS implementation, only C++ is available. Although C++ is being developed in the production environment, other scripting languages will provide more availability, support, and implementation in the future. This will limit the future performance of EOS. Fifthly, the authors analyzed the integrated development environment (IDE), which includes the applications used to develop other applications that include all programming tasks in one application and offer a central interface [100, 101, 103, 104, 105, 107, 109]. Finally, the authors analyzed the transaction flow or the process from the beginning to the end of the transaction. We found many similarities between the systems in terms of transaction flow. Since our goal was to develop a complete ecosystem for patient health records, we prioritized the performance, language support, and blockchain type. For this reason, we chose the Hyperledger Fabric, which has neither the highest nor lowest performance, the most accessible language, and high availability of consensus algorithms, including Raft support. It one of the best performing based on a consensus algorithm that is not available in EOS.
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 5 Sensitivity Analysis of Permeability to Cyclic Loading
### Evaluation Parameters for Cyclic Load Sensitivity  
The sensitivity of coal permeability to the frequency of cyclic dynamic loading is analyzed from two perspectives, primarily concerning the rate of permeability change and the permeability dynamic loading sensitivity coefficient. These two parameters reflect the magnitude and the rate of change in permeability, respectively.  
The rate of change in permeability described in this paper refers to the variation in permeability induced by cyclic loading frequencies during the static load phase and with a fixed type of gas.  
\[\mathbf{D_{v}}=\frac{\mathbf{k_{i}}-\mathbf{k_{0}}}{\mathbf{k_{0}}}\]  
where \(D_{v}\) represents the rate of change in coal permeability, indicating the magnitude of permeability variation. \(k_{0}\) denotes the initial permeability of the coal body, in \(\upmu\upmu^{2}\). \(k_{i}\) signifies the permeability after an increase in dynamic loading frequency, in \(\upmu\upmu^{2}\).  
The dynamic load sensitivity coefficient of permeability refers to the relative change in coal rock permeability caused by an increase of 1 Hz in the frequency of cyclic dynamic loading, while the static loading phase and the type of gas are held constant. Based on the characteristics of the functions presented in this paper, the dynamic load sensitivity coefficient of permeability can be expressed using the following equation.  
\[\mathbf{C_{v}}=\frac{\mathbf{1}}{\mathbf{k_{0}}}\frac{\partial\mathbf{k}}{\partial\mathbf{v}}\]  
where \(\mathbf{C_{v}}\) represents the coefficient of dynamic load sensitivity, in Hz\({}^{-1}\). \(\partial k\) signifies the variation in permeability, in \(\upmu\upmu^{2}\). \(\partial v\) denotes the change in dynamic load frequency, in Hz. A higher \(\mathbf{C_{v}}\) value indicates a greater sensitivity of permeability to dynamic loads, while a lower \(\mathbf{C_{v}}\) value reflects a reduced sensitivity.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Static Load Stage** & **Gas Type** & \(\mathbf{a_{0}}\) & \(\mathbf{b_{0}}\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{\(0.3\sigma_{c}\)} & He & 0.0123 & 0.0693 & 0.9973 \\  & N\({}_{2}\) & 0.0116 & 0.0688 & 0.9813 \\  & CH\({}_{4}\) & 0.0107 & 0.0683 & 0.9963 \\  & CO\({}_{2}\) & 0.0105 & 0.0667 & 0.9972 \\ \hline \multirow{4}{*}{\(0.45\sigma_{c}\)} & He & 0.0129 & 0.0705 & 0.9980 \\  & N\({}_{2}\) & 0.0121 & 0.0758 & 0.9967 \\  & CH\({}_{4}\) & 0.0116 & 0.0686 & 0.9732 \\  & CO\({}_{2}\) & 0.0113 & 0.0629 & 0.9651 \\ \hline \multirow{4}{*}{\(0.6\sigma_{c}\)} & He & 0.0133 & 0.0814 & 0.9584 \\  & N\({}_{2}\) & 0.0131 & 0.0777 & 0.9663 \\ \cline{1-1}  & CH\({}_{4}\) & 0.0122 & 0.0723 & 0.9913 \\ \cline{1-1}  & CO\({}_{2}\) & 0.0119 & 0.0695 & 0.9842 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of fitting of permeability.
## 2 COM Simulation for Sensing Device  
The reflective delay line is composed of an IDT and several reflectors positioned along the SAW propagation path. Larger SNR will contribute well to the improvement of wireless sensing performance [22]. To predict the performance of the sensing device, the typical COM model was employed [23; 24].  
In this work, a SAW device patterned by a reflective delay line with one IDT and two shorted reflectors was employed for sensing temperature, as shown in Figure 1b. The first reflector positioned \(\sim\)1.0 us away from the IDT in time domain to eliminate the noise from the testing environment. In addition, to obtain high temperature accuracy and SNR, the Y-cut \(Z\)-propagating Lithium Niobate (YZ LiNbO\({}_{3}\)) substrate with a larger temperature coefficient (94 ppm/\({}^{\circ}\)C) and high electromechanical coupling coefficient (4.35%) was used to build the sensing chip. Hence, to conduct the SAW chip simulation by using the COM model, the COM equations towards IDT and shorted reflector were considered and solved, respectively. Corresponding mix (\(P\)) matrixes were deduced and cascaded. The reflection coefficient, \(S_{11}\), demonstrating the device performance, was denoted by [25]  
\[S_{11}=\frac{R-1/P_{33}}{R+1/P_{33}}, \tag{1}\]  
where \(P_{33}\) denotes the admittance describing the acoustic and electrostatic currents due to the drive voltage, and \(R\) indicates the characteristic impedance of the device.  
The COM model described above was used to simulate a reflective SAW delay line device operating at 433 MHz for sensing temperature prior to device development. Corresponding parameters for simulation are listed in Table 1. Obviously, the number 1 and 2 reflection peaks in Figure 2b were from the number 1 and 2 reflector in Figure 2a, respectively, and other peaks in Figure 2b were interference peaks, which were more than 30 dB compared with the former two reflection peaks, so they had almost no effect on temperature measurement. The difference of the interference peaks between the simulation and the experimental result may be caused by preparation technology, such as the unevenness of the substrate surface, the electrode difference in width, thickness, etc. Compared with the simulation result, larger SNR (more than 50 dB) was observed from the \(S_{11}\) in the experimental result, as shown in Figure 2b.