esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detection of Coal Burst Risk Active-Passive Seismic CT
### Active-Passive Combined CT Technology and System
When a massive seismic ray path passes through the grid cells, arranging the travel time, distance, and slowness for each grid into matrices, the velocity can be determined through inverse theory, as shown in Equation (3). \[\textbf{AS = T} \tag{3}\]  
\(\textbf{A}=(a_{\text{ij}})_{\text{M}\times\text{N}}\) is the distance matrix (M \(\times\) N); **S** is the slowness matrix (M \(\times\) N); **T** is the travel time per ray matrix (1 \(\times\) N). Solving the equations, one can obtain the slowness distribution, thereby achieving the velocity structure inversion in the research zones. Matrix inversion methods are effective but require considerable computational power for large datasets. Usually, the inverse problem is either underdetermined (more cells than rays) or overdetermined (more rays than cells) [30]. The most effective way to solve this problem is an iterative process. Currently, the most referenced iterative methods are Algebraic Reconstructive Technique (ART) [31] and Simultaneous Iterative Reconstructive Technique (SIRT) [32]. In order to combine the advantages of passive and active CT, equipment called KJ 470 was designed and developed, which could receive not only the artificial seismic sources, such as mechanical hammer and blasting, but also natural seismic events induced by mining activities, so the inversion accuracy and area are both boosted enormously. The KJ 470 system, normally 32 channels, is composed of the waveform acquisition and analysis system, monitoring substation, and geophones, all data is transmitted by optical fiber. The schematic diagram is shown in Figure 3. The frequency range of the geophones used in KJ 470 is 2 to 400 Hz. The geophones were installed on the bolts in the sidewall. Figure 2: Schematic diagram of seismic velocity inversion.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 2 System Model  
### Descriptions of The Heterogeneous Satellite Network  
The GEO-LEO heterogeneous SATCOM network discussed in this paper is shown in Figure 1. To be specific, we consider a two-user uplink scenario in which one user is located at the beam center of the LEO satellite and the other is assumed to be a beam-edge user. The beam-edge user can connect the LEO or GEO satellite. Reflector antennas are applied at the satellites and users.  
As the orbit height of LEO satellites is relatively low, the beam gain on-board for the beam-center user may be much higher than that for the beam-edge user. Denote by \(G_{l}\) and \(\theta_{i}\) as the normal-direction gain of the receive antenna on-board and the angle between user \(i\) and beam center compared to the LEO satellite, respectively. The uplink channel models of the LEO and GEO satellite can be derived as  
\[h_{i}=\rho_{ij}l(d_{i})\sqrt{G_{i}}\sqrt{G_{l}(\theta_{i})} \tag{1}\]  
and  
\[g_{i}=\rho_{i,g}l(d_{g})\sqrt{G_{i}}\sqrt{G_{\rm s}}, \tag{2}\]  
respectively, where \(G_{i}\) and \(G_{s}\) represent the transmit antenna gain of user \(i\) and receive antenna gain of the GEO satellite, respectively. The free-space transmission coefficient is denoted as \(l(d)=\sqrt{(\lambda/(4\pi d))^{2}}\), where \(d\) is the orbit height. More specifically, \(G_{l}(\theta_{i})=G_{l}\Big{(}\frac{h_{i}(u_{i})}{2u_{i}}+36\frac{h_{i}(u_{i} )}{u_{i}^{2}}\Big{)}^{2}\), where \(u_{i}=2.07123\frac{\sin(\theta_{i})}{\sin(\theta_{3 dB})}\) and \(\theta_{3 dB}\) denotes the angle of 3-dB for the satellite beam (in this case, the receive beam gain of the beam-center user and the beam-edge user satisfies \(G_{l}(\theta_{2})/G_{l}(\theta_{1})\approx 0.5\)). \(I_{1}(\cdot)\) and \(J_{3}(\cdot)\) represent the first-kind Bessel function with order one and three, respectively [22]. Since the transmission environment is mainly considered as line-of-sight (LoS) in SATCOM, \(\rho_{i,g}\) in (1) and (2) follows Shadowed Rician distribution, i.e., \(f_{|p_{ij}|^{2}}(x)=\frac{1}{2b_{ij}}\Big{(}\frac{2b_{ij}m_{i,j}}{2b_{ij}m_{i,j}+U_{ij}}\Big{)}^{m_{ij}}e^{-\frac{2b_{ij}}{2b_{ij}}x}{{}_{1}F_{1}}\left(m_{ ij};1;\frac{x\Omega_{ij}}{2b_{ij}(2b_{ij}m_{i,j}+U_{ij})}\right)=\epsilon_{ij}e^{- \kappa_{ij}x}{{}_{1}F_{1}}(m_{ij};1;v_{ij}x)\)[23]. In detail, \(\Omega_{ij}\) and \(2b_{ij}\) denote average power of the LoS  
Figure 1: The GEO–LEO heterogeneous SATCOM network.  
component and multi-path component, respectively. \(m_{i,j}\) is the Nakagami-\(m\) parameter ranging from zero to infinite. In addition, \({}_{1}F_{1}(m_{i,j};1;v_{i,j}x)\) represents the confluent hypergeometric function [24]. That is, the cumulative distribution function can be derived as \(F_{|\rho_{i,j}|^{2}}(x)=\varepsilon_{i,j}\sum_{k=0}^{\infty}\frac{(m_{i,j})v_{ i,j}^{k}}{(k)^{2}x_{i,j}^{k+1}}\gamma(k+1,xx_{i,j})\), where \(\gamma(\tau,x)=\int_{0}^{x}t^{\tau-1}e^{-t}dt\) denotes the lower incomplete Gamma function.
## 2 Solar Energy Charging System Design  
The aim of the solar energy charging system was the storage of energy in the reserve battery, as shown in Figure 1. In this study, a NI Single-Board RIO device (FPGA 9642) (Xilinx, San Jose, CA, USA) was used as a controller to implement the control scheme. The NI sbRIO-9642 device features an industrial Freescale MPC5200 real-time processor with speeds up to 400 MHz for deterministic real-time applications. The real-time processor (IBM, New York, NY, USA) is combined via a high-speed internal PCI bus with an onboard reconfigurable Xilinx Spartan-3 field-programmable gate array (FPGA) [3]. The voltage/current exported by the solar panel flows through the feedback circuit, the FPGA extracts data, and these data are fed back to the notebook PC for MPPT and calculation of the charge control on-off cycle. The calculated on-off cycle is fed back to the FPGA so that the FPGA exports the calculated PWM (pulse width modulation) (Xilinx, San Jose, CA, USA) through the gate drive circuit for signal output. When the PWM signal is sent to the charging circuit, the lead-acid battery feeds back the real-time charging voltage/current/temperature data synchronously. Finally, the data extracted by FPGA are fed back to the notebook PC for real-time data display.  
The solar charge control system process is shown in Figure 2. The automatic solar tracking system (NCUT, Taichung, Taiwan) used is that proposed by [PERSON] _et al._[3]. The solar tracking system positioning is implemented from the start. The MPPT is started before the charge control system, when the sun's rays are normal to the panel. The feedback circuit provides real-time battery voltage/current data during charge to judge the present battery charging condition. \(I_{\mathrm{a}}\) is defined as the charge current for CV charging, and \(I_{\mathrm{b}}\), and \(I_{\mathrm{c}}\) represent the charge current and discharge current for Reflex charging. When the battery side current \(I>I_{\mathrm{a}}\), CV charging is used; if the battery side current \(I_{\mathrm{b}}>I>I_{\mathrm{c}}\), Reflex charging is used. Finally, judgment of whether the battery is full is made according to SOC > SOC\({}_{\mathrm{a}}\), where SOC is the state of charge.  
Figure 1: Schematic diagram of the solar power system circuit.  
Figure 2: Solar charge control system flow chart.
## 5 Results and Discussion
### Carbon Footprint Analysis  
In this study, the total equivalent warming impact (TEWI) analysis is employed to estimate the CO\({}_{2}\) equivalent (CO\({}_{2}\),eq) emissions. The direct and indirect emissions resulting from the refrigerant leakages and electricity consumption, respectively, during the refrigeration unit operation [24]. To calculate TEWI, the heat pump's lifespan and the recovery factor are considered 15 years and 0.7, respectively, and the annual leakage rate is assumed to be 12% [8; 25]. The indirect emission factor depends on the sources of energy used for electricity generation. As this parameter differs from country to country, six countries with various indirect emission factors are selected to evaluate the CO\({}_{2}\).eq emissions. The indirect emission factor value is taken from ref. [26]. It is noted that the carbon footprint of the MT system is studied in this section, and the power consumption of the supermarket refrigeration system, used in the calculation of the CO\({}_{2}\).eq emissions, belonged to the case when the middle evaporating temperature was \(-15\)\({}^{\circ}\)C. This temperature was selected as the average operating middle evaporating temperature in the system. So, as an example condition, the corresponding power consumption was extrapolated to 15 years to estimate the system's carbon footprint.  
Figure 13 represents the total CO\({}_{2}\).eq emissions of the supermarket refrigeration system operated with R404A and R449A. R404A has a higher direct contribution to CO\({}_{2}\).eq emissions than R449A due to the higher GWP values (about three times) of R404A than R449A. According to the results, R449A has a lower contribution in total CO\({}_{2}\).eq emissions. It means that the phase-out of refrigerants with high GWP values, such as R404A from the commercial refrigeration system, can reduce direct emissions. These results also indicate that the energy sources for electricity generation significantly contribute to the  
Figure 12: The cooling capacity versus COP of the supermarket refrigeration system at LT and MT applications.  
indirect emissions from the supermarket refrigeration system. When the primary sources of electricity are renewable clean energy, the amount of CO\({}_{2}\).eq emissions for each kWh would be much lower than in a country where the primary source of electricity generation is fossil fuels. It can explain much lower total CO\({}_{2}\).eq emissions in a country such as Sweden, with an emission factor value of 0.012 kgCO\({}_{2}\).eq kWh\({}^{-1}\), compared to other selected countries. The presented results support the statement that both the phase-down/out of refrigerants with high GWP values and moving toward clean energy sources for electricity generation are required to mitigate the carbon footprint in the commercial refrigeration industry.
## 4 Results
### Methane Adsorption Characteristics  
The methane adsorption test underestimates the actual adsorption capacity because it ignores the volume of the adsorption phase, and direct measurement of the actual adsorption capacity of methane is not possible. The measured gas adsorption volume is referred to as excess adsorption volume. Therefore, the influence of the adsorption phase volume must be considered to accurately calculate the absolute adsorption volume. [PERSON] et al. modified the binary Langmuir adsorption model by considering the adsorption phase density as a variable and proposed a ternary Langmuir model [50]. Through nonlinear fitting, the excess adsorption volume was used to directly obtain the Langmuir volume and Langmuir pressure. The equation of the ternary Langmuir model is:  
\[V_{ex}=\frac{V_{L}\times P}{P_{L}+P}\bigg{(}1-\frac{\rho_{g}}{\rho_{a}}\bigg{)} \tag{1}\]  
where \(P\) is the equilibrium pressure, MPa; \(V_{ex}\) is the excess adsorption volume at P, cm\({}^{3}\)/g; \(P_{L}\) is the Langmuir pressure, MPa; \(V_{L}\) is the Langmuir volume, cm\({}^{3}\)/g; \(\rho_{g}\) is the methane density at P, g/cm\({}^{3}\); \(\rho_{a}\) is the density of methane adsorption phase, g/cm\({}^{3}\).  
Figure 10: Pore size distribution of Taiyuan Formation shale in the SNCB.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & **BET SSA** & **PV** & **APS** & **Langmuir** & **Langmuir** \\  & **(m\({}^{2}\)/g)** & **(10\({}^{-3}\)m\({}^{3}\)/g)** & **(nm)** & **Volume** & **Pressure** \\  & & & & **(m\({}^{3}\)/h)** & **(MPa)** \\ \hline Z-1 & 19.10 & 21.79 & 4.57 & 2.82  
The adsorption curves of shale samples at 90 \({}^{\circ}\)C under pressures ranging from 0 to 10 MPa are presented in Figure 11, illustrating the excess and absolute adsorption of CH\({}_{4}\). Upon comparing these two curves, it is evident that the CH\({}_{4}\) adsorption volume experiences a rapid increase with increasing pressure at low levels (0-3 MPa). Moreover, there is minimal disparity between the excess and absolute adsorption volumes. As the pressure exceeds 4 MPa, the CH\({}_{4}\) adsorption volume continues to rise but at a slower pace, while the difference between excess and absolute adsorption volumes becomes more pronounced. Conversely, the absolute adsorption volume significantly surpasses the excess adsorption volume, with this distinction gradually widening. It is crucial to adjust the excess adsorption curve according to formation pressure and temperature when estimating actual methane adsorption capacity under formation conditions. Failure to do so may lead to an underestimation of geological reserves for shale gas [51]. The shale samples exhibit V\({}_{\text{L}}\) fitted by V\({}_{\text{ex}}\) within a range of 1.35 to 4.30 cm\({}^{3}\)/g and an average value of 2.57 cm\({}^{3}\)/g. P\({}_{\text{L}}\) varies from 3.34 to 5.70 MPa, with an average value of 4.35 MPa (Table 3).
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 2 Numerical Simulation
### Grid and Calculation Conditions  
#### 2.2.1 Geometric Model  
Based on the structure of the tube heat exchanger [12], the calculation model for heat transfer performance of tube heat exchanger is established. The tube heat exchanger is composed of an inner dimpled tube and a smooth outer tube. To improve the computational efficiency, only part of the tube is cut off for analysis on the premise of ensuring the full development of the flow. The dimpled tube is shown structure Figure 1. Inner tube fluid flows from left to right and outer tube fluid flows from right to left. On one section, there are 6 dimples in the circumference. On the next section, stagger by 30 degrees. The specific dimensions of a series of dimple structures are shown in Table 1. All units of length are in mm.  
Figure 1: Structure of dimpled heat exchange tube.
## 3 System Model
### Performance Metrics  
In this work, we aimed at evaluating the TDM-induced end-to-end packet delay in the IAB access network. Thus, we are interested in the following metrics:  
* The total packet delay in the uplink defined as the time from the instant the packet joins the inbound access buffer in its sector to the instant it leaves the system;
* The total packet delay in the downlink defined as the time from the instant the packet joins a buffer in Node 1 to the instant it leaves the system via an access link.
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 1 Introduction
The number of data entries in this dataset reaches 46,686,579 and there are as many as 33 attack types. Among them, most of the examples in this dataset are related to common malicious attacks: DDoS and DoS attacks [14];
2. We not only use the six DL models used in [6], but also use a Transformer model [15] to handle binary and multi-class classification tasks. Compared with [1; 7], we further implement the multi-class classification on our model;
3. On the ToN_IoT dataset, compared with [7], our Transformer model achieved an accuracy of 88.25%, which is 0.46% higher than the 87.79% of [7];
4. Compared with [10; 11; 13], which also use the CIC-IoT-2023 dataset [16; 17], the accuracy of our Transformer model in the multi-class classification reaches 99.40% accuracy; when compared with 95.60% [10], 98.75% [11], and 99.11% [13], our results are 3.8%, 0.65%, and 0.29% higher, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline
**Paper** & **Dataset** & **Classification** & **DL Method** & **Accuracy** & **Inference Time 1** \\ \hline
[1] & CIC-IoT-2023 & Binary & DNN based on Federated Learning & 99.00\% & \\
[6] & CIC-IDS-2018 & Binary, Multi-class & DNN, RNN, CNN, LSTM, CNN + LSTM, and CNN + RNN & Multi-Class: LSTM: 3.451 (ms) \\
[7] & ToN-IoT & Binary & LSTM, RNN, and Transformer & 87.79\% & LSTM: 27 (s) \\  & & & & RNN: 35 (s) \\ \hline \end{tabular}
\end{table} 
Table 1: Related works/baseline schemes. The second part of this paper is methodology, which describes the dataset and data preprocessing methods in detail. The third part will introduce six neural network models and Transformer models, and the fourth part will show the experimental results. The fifth part is the conclusion of this paper.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 2 Design and Analysis of Rail-Mounted Automatic Welding Equipment
### Mechanical Design of the Equipment's Core Mechanisms
#### 2.1.3 Mechanical Design of the Quick-Plug Mechanism and Welding Fixtures  
The quick-plug mechanism includes connector components, as shown in Figure 4. This mechanism enables rapid coupling between the welding torch and the robotic arm, facilitating disassembly, transportation, maintenance, and replacement. A connecting base that can be flange-mounted is installed at the end of the robotic arm. A vertical plate is set at the terminal of the robotic arm to mount a welding torch clamp for holding the welding torch. A visual sensor is installed inside a protective box on the connecting base. Locking screws press and secure the welding torch clamp and the connecting base together. The mating surfaces of the connecting base and the welding torch clamp are equipped with axially parallel and corresponding conical male and female positioning pins. Bosses and corresponding matching grooves are arranged on the surface of the vertical plate; the locking screw holes and through-holes penetrate the bosses and grooves, respectively. These features work together to enhance the reliability of the connection. Three conical male positioning pins are arranged on the vertical plate, corresponding and matching with threaded sleeves and female positioning pins within threaded holes. This ensures the installation precision of the vertical plate and the welding torch clamp, improving the control accuracy of the robotic arm over the welding torch. An opening and closing cover is installed at the end of the protective box, controlled by a spring hinge and a micro  
Figure 3: Schematic of fixing mechanism.  
telescopic cylinder. Its movement direction is aligned along the torque arm of the cover, providing protection for the visual sensor.  
By organizing the composition and material selection of the mechanical structure [17], the overall mechanical components involved in this equipment and the materials selected in the design are shown in Table 1. The materials listed in the table are the actual materials chosen for the mechanical structures.
# Optimization of Coverage and Capacity Using Smart Antennae
## 3 Methodology
### Objective Function  
Based on Sections 3.2 and 3.3, we can define the objective function, denoted as _f(P, X)_, in this study shown in Formula (5) as follows:\[f(P,X)=Cov\cdot\sum_{k=1}^{K}u_{k}+\]  
\[Cap\cdot\sum_{k=1}^{K}B_{k}\cdot\left\{u_{k}\cdot log_{2}\left(1+\frac{p_{i}- \left[PL(d_{1}\ )+10\cdot log_{10}\left(\frac{d_{k}\ S_{i}}{d_{1}}\right)^{7}\right]+ \left(X_{i}^{i}\cdot G_{j}^{i}\right)}{\sum_{h\
eq i}\left\{p_{h}-\left[PL(d_{1 }\ )+10\cdot log_{10}\left(\frac{d_{k}\ S_{i}}{d_{1}}\right)^{7}\right]\right\}+ \sigma}\right)\right\} \tag{5}\]  
\[P=\{p_{i}\},\,\,\text{i}=1,\,\,2,\ldots,\text{n}\]  
\[X=\left\{X_{j}^{i}\right\},\,\,\text{i}=1,\,\,2,\ldots,\text{n};\,\,\text{j}= 1,\,2,\ldots,\text{m}\]  
where \(P=\{p_{i}\};\,\text{i}=1,\,2,\ldots,\text{n}\) represents the transmission power of each base station; and \(S_{1},S_{2},\ldots,S_{n},\,X=\left\{X_{j}^{i}\right\}\); \(\,\text{i}=1,\,2,\ldots,\text{n},\,\,\text{and}\,\,\text{j}=1,\,2,\ldots.\text{m}\) represent the antenna in the \(j\)-th direction of the base station \(S_{i}\). If the antenna is activated, \(X_{j}^{i}=1\); otherwise, \(X_{j}^{i}=0\). \(Cov\) and \(Cap\) are normalized factors.  
The trade-off between coverage and capacity is a well-recognized issue. When optimizing for coverage, it is challenging to achieve the specified transmission efficiency or capacity. Conversely, when optimizing for capacity, resources are prioritized for users very close to the base station, resulting in a decrease in coverage. Solutions to this multi-objective optimization problem have been proposed in research [17]. Our objective is to find a set of P and X that can maintain a high network coverage rate while reducing UE interference issues. By optimizing the objective function f(P, X), the goal is to achieve an efficient combination of transmission power settings and antenna configurations to enhance overall network performance, which is subject to the following factors:  
1. \(P_{min}\leq p_{i}\leq P_{max}\)
2. \(SINR_{k}\geq\theta,\,\forall k=1,\,\,2,\,\,\ldots,\,K,\,\,if\,\,u_{k}=1\),  
\[\text{where}\,\,SINR_{k}=\frac{p_{i}-\left[PL(d_{1}\ )+10\cdot log_{10}\left(\frac{d_{k}\ S_{i}}{d_{1}}\right)^{7} \right]+\left(X_{j}^{i}\cdot G_{j}^{i}\right)}{\sum_{h\
eq i}\left\{p_{h}- \left[PL(d_{1}\ )+10\cdot log_{10}\left(\frac{d_{k}\ S_{i}}{d_{1}}\right)^{7}\right]\right\}+ \sigma}\]
3. Each small cell can serve, at most, \(t\) users
4. There are \(t_{S_{i}}\) users covered by \(S_{i}\)
5. \(B_{k}=\frac{B}{t_{S_{i}}}\)
6. \(\gamma\geq 2\)
7. \(u_{k}=\left\{\begin{array}{c}1,\,\text{user}\,\,k\,\,\text{under}\,\,\text {the}\,\,\text{coverage}\,\,\,\text{area}\,\,R_{j}^{i}\\ 0,\,\,\,\text{otherwise}\end{array}\right.\)
8. \(X_{j}^{i}=\left\{\begin{array}{c}1,\,\,\text{Antenna}\,\,\text{beam}\,\,j\,\, \text{turns}\,\,\text{on}\,\,\text{small}\,\,\text{cell}\,\,\,\text{S}_{i}\\ 0,\,\,\,\text{otherwise}\end{array}\right.\)
9. \(R_{j}^{i}=\left\{\begin{array}{c}1,\,\,\text{if}\,\,X_{j}^{i}=1\\ 0,\,\,\,\text{otherwise}\end{array}\right.\)
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks  
In this section, we briefly describe the signal model of an IRS-assisted system and then describe the use cases approached in the state-of-the-art. For the best comprehension and organization, we divide the scenarios into two sub-problems: the channel estimation and the optimization and design of IRS phase shifts.