esa-sceva/satcom-chunk-collection huggingface dataset:
# Induction Motor Dynamics Regimes: A Comprehensive Study of Mathematical Models and Validation
## 3 Models of Induction Motors  
### Induction Motor Model in the \(\alpha\beta\) Coordinate System  
For \(\alpha\beta\) (\(\omega_{x}\) = 0) fixed coordinate system with axes linked to the stator, Equations (33) and (36) solving to flux linkages derivatives take the following form:  
\[\begin{array}{c}\frac{d\dot{v}_{1x}}{dt}=U_{m}\text{cos}\left(\tau\right)- R_{1}i_{1a}\\ \frac{d\dot{v}_{1\beta}}{dt}=U_{m}\text{cos}\left(\tau\right)-R_{1}i_{1\beta} \\ \frac{d\dot{v}_{2\alpha}}{dt}=-R_{2}i_{2a}+\omega\psi_{2\beta}\\ \frac{d\dot{v}_{2\beta}}{dt}=-R_{2}i_{2\beta}+\omega\psi_{2\alpha}\end{array} \tag{37}\]  
The rotor speed variation is described by the following equation:  
\[\frac{d\omega}{dt}=\left(T_{e}-T_{l}\right)/T_{M} \tag{38}\]  
where \(T_{e}\) is the electromagnetic torque, \(T_{l}\) is the load torque, and \(T_{M}\) represents the motor's moment of inertia.  
The current components are determined using the flux linkage expressions:  
\[\begin{array}{l}i_{1\alpha}=\frac{(X_{2}\psi_{1\alpha}-X_{ad}\psi_{2\alpha})}{ \Delta}\\ i_{1\alpha}=\frac{(X_{2}\psi_{1\beta}-X_{ad}\psi_{2\beta})}{\Delta}\\ i_{2\alpha}=\frac{(X_{1}\psi_{2\alpha}-X_{ad}\psi_{1\alpha})}{\Delta}\\ i_{2\beta}=\frac{(X_{1}\psi_{2\beta}-X_{ad}\psi_{1\beta})}{\Delta}\end{array} \tag{39}\]  
where  
\[\Delta=X_{1}X_{2}-X_{ad}X_{ad}\]  
The electromagnetic torque is given by:  
\[T_{e}=X_{ad}\big{(}i_{1\alpha}i_{2\beta}-i_{1\beta}i_{2\alpha}\big{)} \tag{40}\]  
and the load torque is the following:  
\[T_{l}=SM\omega_{2}^{2}+SMC \tag{41}\]  
where  
\(\psi_{1\beta},\ \psi_{1\alpha},\ \psi_{2\beta},\ \psi_{1\alpha}\) are the stator and rotor flux linkage components in the \(\alpha\beta\) coordinate system,  
\(i_{1x},\ i_{1y},\ i_{2x},\ i_{2y}\) are the stator and rotor current components in the \(\alpha\beta\) coordinate system,  
\(\omega\) is the rotor angular speed,  
\(U_{m}cos(\tau),-U_{m}sin(\tau)\) are the stator applied voltage,  
\(R_{1},R_{2},X_{1},X_{2},X_{ad}\) are the induction motor parameters in per-unit values,  
\(T_{e},T_{l}\) are the electromagnetic and load torques, respectively,  
\(SM,SMC\) are the motor's variable and constant torques coefficients.  
The developed induction motor model in the \(\alpha\beta\) coordinate system provides a framework for simulating various dynamic operating modes, including direct starting and direct starting with current displacement in the slots. This model (_model I_) enables direct comparisons between the single-phase simulation results and the experimental data, without additional transformations.
# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 3 Hazard Assessment Results for the Study Area
### Construction of Hazard Assessment Index System for Ground Subsidence
#### 3.1.3 Inducing Factors
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Road Name** & **Pipeline Type** & **Value** \\ \hline
1 & Xinhuan Road & Electricity, communication, water supply, heating, sewage pipes severely damaged & 5 \\
2 & Nanxin Road & Electricity, communication, water supply, and a few pipes damaged & 3 \\
3 & BeixinRoad & Electricity, communication, water supply, heating, sewage pipes severely damaged & 5 \\
4 & Jianshe South Road & Electricity, communication, water supply, multiple drainage pipes damaged & 5 \\
5 & College south Road & Electricity, communication, water supply, heating, multiple stormwater pipes damaged & 4 \\
6 & Jianhua Road & No electricity, heating, drainage, or pipe damage & 1 \\
7 & Longze Road & Electricity, communication, water supply pipes, minor pipes damaged & 3 \\
8 & Hexi Road & Electricity, communication, water supply, reclaimed water, gas, sewage pipes severely & 2 \\
9 & Binhe Road & damaged & 5 \\
10 & Dacheng Road & Electricity, communication, water supply, heating, gas pipelines in need of renovation & 3 \\
11 & Zensheng Road & Electricity, communication, water supply, heating, sewage pipes severely damaged & 5 \\
12 & Other Roads & A few pipes damaged & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Distribution of underground pipelines. Figure 19: Rainfall of study area. (**a**) Distribution of rainfall. (**b**) Classification of rainfall. (4) Seismic Peak Ground Acceleration  
As per the \"Seismic Ground Motion Parameter Zonation Map of China\" (GB18306-2015 [36]), the seismic peak ground acceleration in the study area was vectorized. As indicated in Table 13, the seismic peak ground accelerations in the study area are 0.2 g and 0.3 g, signifying an eighth degree of seismic fortification intensity in Tangshan City. In light of the seismic peak ground acceleration in the study area and referencing the Chinese seismic intensity scale, the study area was bifurcated into two levels. The value of 0.2 g was assigned a value of 1, and the value of 0.3 g was assigned a value of 2. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Peak Acceleration** & **0.10 g** & **0.20 g** & **0.40 g** & **0.5 g** \\ \hline Seismic Design Intensity & Level VII & Level VIII & Level IX & Level X \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Seismic intensity in China. Figure 21: Earthquake liquefaction of the study area. (**a**) Earthquake liquefaction. (**b**) Classification of Earthquake liquefaction. Figure 20: Leakage of underground pipelines in the study area. (**a**) Leakage of underground pipelines. (**b**) Classification of underground pipeline leakage. classification map of the study area is presented in Table 14 and Figure 22. Figure 22 reveals that the seismic peak ground acceleration is 0.3 g in the southern part of the study area, while it is 0.2 g in the remaining areas.
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 2 Materials and Methods
### Construction of Rock Physical Prior Information
#### 2.2.1 The Inversion Objective Function of Lithological Constraints  
When incorporating prior information that encompasses lithological information, the inversion object function is structured as follows:  
\[Q_{L}(\mathbf{m})=Q(\mathbf{m})+\lambda_{L}\underset{i=1}{\overset{N}{\sum}} \Phi_{L}(\mathbf{m}) \tag{5}\]  
where \(Q_{L}(\mathbf{m})\) is the objective function under lithological constraints, \(\lambda_{L}\) is the lithological regularization factor, \(\Phi_{L}(\mathbf{m})\) is the parameter penalty term, and \(N\) is the total number of spatial positions of model parameters. If the prior geological information indicates that there are n potential rock types in the study area, and the physical property parameters of each type of lithology are normally distributed, the average value \(\mathbf{m}^{*}\) and standard deviation \(\sigma\) of the model parameters of each type of lithology are derived from the outcomes of laboratory analyses.  
In order to integrate the clustering characteristics of physical parameters offered by the prior information, model parameter \(\Phi_{L}(\mathbf{m})\) is defined as  
\[\Phi_{L}(\mathbf{m})=\left(\frac{\mathbf{m}_{i}-\mathbf{m}_{n(i)}^{*}}{\sigma _{n(i)}}\right)^{2} \tag{6}\]where \(i\) is the index of the model element, and \(n(i)\) represents the category of the cluster with the smallest penalty term for the \(i\)th element. Logging data and laboratory analysis generally facilitate the retrieval of parameters such as velocity and density. In this regard, the corresponding model penalty term can be formulated as follows:  
\[\Phi_{L}(\mathbf{m})=\left(\frac{v_{i}-v_{n(i)}^{*}}{\sigma_{m(i)}}\right)^{2}+ \left(\frac{\rho_{i}-\rho_{n(i)}^{*}}{\sigma_{pn(i)}}\right)^{2} \tag{7}\]
## 4 Experimental Results and Discussion
### LP Characters Recognition Results Analysis
#### 4.2.2 Multi-Channel CNN Architecture Module  
Multiple image fields are the essential requirement for efficient object classification as image features have direct impact on performance of object recognition framework. Here we propose multi-channels based multi-CNN architecture along with concatenated output feature vector, that is capable to deal with comprehensive and more appropriate features to enhance the original image's feature information. First, red, green and blue channel images are obtained by decomposing the original RGB image, then it is fed to separately trained neural networks for each channel image and finally the fully connected layer is obtained by concatenating one-dimensional eigenvectors of three single image domains, as shown in Figure 11.  
Experimental results proved that the proposed framework with multi-channel CNN module (M\({}_{\text{MC-CNN}}\)) can learn image features more effectively as compared to traditional convolutional neural  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Recognition Accuracy, \%**} \\ \hline Country & CNN\({}_{\text{B}}\) & CNN\({}_{\text{C15}}\) & CNN\({}_{\text{C25}}\) & CNN\({}_{\text{C35}}\) & CNN\({}_{\text{C45}}\) \\ \hline Australia & 89.67 & 88.49 & 74.10 & 91.14 & 91.55 \\ Canada & 78.76 & 82.47 & 63.72 & 82.41 & 84.62 \\ England & 85.12 & 84.9 & 72.14 & 84.89 & 88.43 \\ Mexico & 93.47 & 93.47 & 85.71 & 95.78 & 96.19 \\ Pakistan & 83.48 & 84.12 & 67.77 & 85.75 & 90.89 \\ Europe & 90.78 & 91.44 & 85.47 & 91.98 & 93.76 \\ UAE & 88.11 & 88.11 & 87.67 & 89.87 & 91.25 \\ USA & 80.64 & 81.97 & 63.03 & 82.00 & 85.69 \\ \hline Total & 86.25 & 86.87 & 74.95 & 87.98 & 90.30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of base CNN with modified CNN structures.  
Figure 15: Different layers aggregation structures of proposed CNN model.  
network (CNN-LA), and achieved 92.13% overall recognition accuracy which is 2% more than traditional CNN structure, as shown in Table 5.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 1 Introduction
This architectural model engages novel IoT technologies [10] and Wireless Sensor Networks (WSNs) capabilities to provide a sufficient view of precision agriculture. Furthermore, the proposed architecture enables a combination of modern remote sensing techniques such as UAV tracking, Global Positioning System (GPS) for location detection, Geographic Information Systems (GIS), real-time monitoring with different types of sensors and intelligent input control systems. These technologies have already been tested in various agricultural fields in different countries for the cultivation of rice, wheat, tomatoes, vegetables, potatoes, ornamental flowers, chilly, cacao, pepper, corn, olives, apples, lemons, grape and others. By incorporating new technologies into agricultural production and by using modern EO techniques growers will be able to manage their crops at a different and more advanced kind of level in detail that was not possible a few years ago. This paper is an extension of our work in [11]. Towards enhancing contribution a use case study is also presented regarding an ongoing research project known as Drone Innovation in saffron Agriculture Surveillance(DIAS). The DIAS architectural model is based on the proposed paradigm. This paper is organized as follows. Section 2 presents an overview of the proposed smart farming monitoring system architecture. In Section 3, the sensor layer is introduced, followed by the network layer and its suitable protocols and technologies regarding communication, routing, encapsulation techniques and interoperability mechanisms in Section 4. Section 5 is focused on the management layer and the provided services of the proposed monitoring system, while Section 6 focuses on IoT agricultural applications. Section 7 deals with energy-saving technologies and security mechanisms that can be implemented in cooperation with networking technologies of the system. The use case study of the DIAS architecture is presented in Section 8. Existing challenges are mentioned and discussed in Section 9. Finally, Section 10 concludes this study.
## 6 Existing and Potential Industrial Applications
### Thermal Recovery of Heavy Oil  
With the increasing demand for oil and the irreversible reduction of light crude oil reserves, the development and utilization of heavy oil accounting for a large proportion of oil and gas resources in the world has become an important way to improve the energy structure. The high viscosity and the low freezing point of heavy oil lead to many difficulties including its flow in the reservoir, wellbore lifting, surface gathering, and pipeline transportation. Most commonly used visbreaking methods involve thin-oil blending visbreaking, heating visbreaking, upgrading visbreaking, and chemical visbreaking. The heating visbreaking consists of steam drive, steam soaking and steam-assisted gravity drainage (SAGD). Steam produced in a steam injection boiler on the ground is injected into the formation. Yet the whole operation process generally has some shortcomings, such as low efficiency, high pollution, and need for quality water and sewage treatment. In addition, with the exploitation of heavy oil expanding to deep wells, ultra-deep wells and ocean, the disadvantages like large footprint and high heat loss of gas injection boiler are more obvious, which make it difficult to meet the needs of the project.  
Combining the above requirements combine with the research results of HSD, the \"hydrothermal combustion steam-gas generation technology\" was proposed by Xi'an Jiaotong University to produce heat carriers needed in the process of heavy oil recovery. The fuel and oxidant enter the compound thermal fluid generator in the downhole and carry out hydrothermal combustion in the supercritical water environment. Then, the composite thermal fluids produced (steam, CO\({}_{2}\), N\({}_{2}\), etc.) are adjusted to the required parameters needed for thermal recovery before being injected into the stratum. CO\({}_{2}\) and other gases dissolve in crude oil, to reduce viscosity and increase fluidity. At present, the team has developed a complete system and multiple burners, and is building a laboratory-scale platform for feasibility verification. Generally speaking, the main challenges this technology are facing include: (1) ignition and flame stability under large flow conditions, (2) reactants' transport and signal feedback, (3) structure design of the reactor, (4) how to prevent explosion induced by overpressure caused by hard start-up, and (5) how to avoid backflow and blockage after cessation.
## 2 Analysis of Stiffness and Damping Characteristics of Rubber-Based SMA Composite Dampers
The stiffness, damping, and energy dissipation of composite dampers with varying widths have been calculated, as shown in Table 4. Moreover, the variation curves of each parameter with the width of the composite damper are depicted in Figure 7.  
Figure 5: Curve of stiffness and damping characteristics of rubber-based SMA composite shock absorber with the thickness of outer rubber damping components. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Thickness of Outer Rubber** & **Stiffness** & **Damping** & **Dissipated Energy** \\
**Shock Absorber (mm)** & **(N/mm)** & **(N-s/mm)** & **(N-mm)** \\ \hline
8.5 & 162.6355 & 1.3106 & 581.4666 \\
12 & 168.812 & 1.3509 & 599.9545 \\
15.5 & 176.33 & 1.4408 & 639.8969 \\
19 & 192.9447 & 1.5442 & 685.7895 \\
22.5 & 215.0644 & 1.6502 & 732.6958 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stiffness, damping, and hysteresis energy dissipation parameters corresponding to the outer thickness of different rubber damping layers. As observed from Figure 7, the stiffness and damping of the shock absorber increase progressively with its width, though not in a linear manner. This nonlinearity arises because both rubber and SMA exhibit superelastic properties, and expanding the width of the shock absorber influences its internal stress distribution to some extent. Consequently, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composite Damper** & & & **Dissipated Energy** \\
**Width (mm)** & & & **(N\(\cdot\)mm)** \\ \hline
45 & 152.7763 & 1.1767 & 522.1919 \\
50 & 162.842 & 1.2874 & 571.6505 \\
55 & 176 & 1.4408 & 639.8969 \\
60 & 195.1771 & 1.5937 & 707.7873 \\
65 & 225.5769 & 1.8009 & 798.6611 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stiffness, damping, and dissipated energy parameters corresponding to different shock absorber widths. Figure 6: Force-displacement curves during loading and unloading process corresponding to different widths of shock absorbers. Figure 7: Curve of stiffness and damping characteristics of rubber-based SMA composite shock absorber with respect to the width of the shock absorber. the relationship between stiffness and damping and the width of the shock absorber exhibits certain nonlinear characteristics.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 4 Implementation Details
### Training  
The training method for both streams may be regarded as a modification of the method in reference [32]. The mini-batch stochastic gradient descent with momentum, set to 0.9, is used to learn the network weights. For the temporal stream, we calculate an optical flow volume I for the chosen training frame. A fixed size \(224\times 224\times 2L\) input is randomly chopped from corners and flipped from that volume, as discussed earlier. In the spatial stream, each batch generates 128 frames cropped down to \(224\times 224\) by sampling 128 videos (uniformly across all activities). The learning rate is first set to \(10^{-2}\) for both streams and then gradually reduced according to a predetermined schedule that is maintained throughout all training sets. When training both streams from scratch, models are run for 50,000 iterations, whereas the enhanced spatial stream is trained for just 2000 iterations (classification layer training + fine-tuning). In the end, softmax scores from both models are fused by averaging the softmax scores.
## 2 Methodologies and System Description
### Spyder Compile Software  
The system was developed using the Spyder compiler software in an environment based on the Python language. Based on an interactive development environment for algorithm development, data analysis, and numerical computation, Spyder is an integrated development environment (IDE) that uses an open-source code for cross-platform scientific computing based on the Python language. It provides functions such as advanced code editing, interactive testing and debugging, computational science, data processing, and predictive analysis, and it supports multiple programming languages and operating systems.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 3 OFDM/OQPSK System Model
### OFDM-IOTA System Model
In practice, the OFDM/OQPSK system utilizes an improved minimum mean square error (IMMSE) method to suppress the noise impact on the initial CIR estimation, and the estimated CIR \(\mathbf{\tilde{h}}_{m,n}\) is expressed as [16]:  
\[\mathbf{\tilde{h}}_{m,n}=\frac{\hat{h}_{m,n}\cdot|\hat{h}_{m,n}|^{2}}{\alpha\cdot |\hat{h}_{m,n}|^{2}+(1-\alpha)\cdot A_{i}^{2}}, \tag{7}\]  
where \(A_{i}=\max\limits_{0\leq n\leq N-1}(|\mathbf{\hat{h}}_{m,n}|)\) denotes the maximum amplitude, and \(\alpha\) denotes the suppression factor. In practice, \(\alpha\) can be environment. Table 3 represents the main simulation parameters for OFDM/OQPSK, OFDM/QPSK, OFDM/OQAM, and OFDM/QAM systems. The signal modulation patterns are OQAM, QAM, OQPSK, and QPSK, respectively. The symbol rate is 12 kbit/s, the length of FFT is 512, and the carrier frequency is 2 GHz. The fast fading model is the Jakes spectrum and the channel coding is a convolutional encoder with a code rate of 1/2. The inter-leaver is a block inter-leaver with 300 OFDM symbols. The channel decoding algorithm in the systems is Viterbi decoding.
# Dynamic Responses of Semi-Flexible Pavements Used for the Autonomous Rail Rapid Transit
## 2 Materials and Methods
### Test Section Implementation and In Situ Test  
The test section was constructed based on the Zhuzhou ART line. The surface layer of the pavement structure consisted of three layers: the SFP layer, the asphalt concrete (AC) layer, and the stress absorbing layer (SAL), as shown in Figure 2. It needs to be noted that the porous asphalt mixture used for SFP-13 was the high-viscosity modified porous asphalt mixture, the styrene-butadiene-styrene (SBS) modified asphalt was used in AC-13, and crumb rubber modified asphalt and basalt aggregate with the size of 9.5-13.2 mm were used in SAL.  
In order to monitor the stress-strain state of the semi-flexible ART pavement under vehicles of the ART system, sensors were buried in the test section of the pavement to continuously track and observe the mechanical response of the pavement with a sampling frequency of 5 Hz. Figure 3 shows the photos of the buried sensors on the test section site. Using the monitoring system, the stress-strain response of different layers of the ART pavement could be recorded automatically when the ART vehicle passed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Modulus (MPa)**} \\ \cline{2-3}  & **SFP-13** & **AC-13** \\ \hline
20 & 8545 & 8654 \\
30 & 4756 & 5104 \\
40 & 2346 & 3270 \\
50 & 1091 & 2278 \\
60 & 207 & 1703 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The modulus of SFP and AC materials.  
Figure 2: The structural composition of ART pavement incorporating an SFP layer.