esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Each CASTSR output SNR is taken as the objective function, the chaotic ant colony algorithm is used to calculate the optimal parameter combination when the output SNR of each stage is maximum, and the optimal results of each stage are input into the stochastic resonance system to achieve a sufficient noise reduction of weak signals. After that, EMD is used for decomposition, and the characteristic frequency is extracted from the IMF for fault diagnosis. The innovations and main contributions are described as follows:  
* A novel fault diagnosis method based on CASTSR and EMD is proposed. * The restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. * The chaotic ACO with a global optimization ability is used to adaptively adjust the parameters of the CASTSR system to obtain the optimal stochastic resonance. * The noise reduction pre-treatment technology based on CASTSR is realized to enhance the weak signal characteristics of low frequency. * EMD is employed to decompose the denoising signal and extract the characteristic frequency from the IMF so as to realize fault diagnosis.
# Study on Fire Smoke Distribution Characteristics in Mountain Spiral Highway Tunnels
## 1 Introduction
[PERSON] used a numerical method to reveal the distribution of temperature in curved tunnels and provided a new exponential decay model to predict longitudinal temperature in tunnels with various curvatures [16]. [PERSON] et al. performed numerical simulations and experiments to explore the critical velocity in a curved tunnel with different fire scenarios [17]. [PERSON] et al. performed numerical simulations to reveal the smoke distribution characteristics in a curved highway tunnel and pointed the distinct smoke distribution characteristics [18]. Meanwhile, some existing studies have explored the combined impacts of multiple variables, such as altitude and smoke outlet areas, slope and fire location, and slope and altitude. [PERSON] et al. explored the smoke distribution in a large-scale railway tunnel group in a high-altitude area and proposed suggestions on the design of disaster prevention and rescue structures such as an emergency rescue station and an emergency exit [19]. [PERSON] et al. conducted numerical simulations on tunnel fires under different wind velocities and different fire scales with Fluent to study smoke distribution when a car fire occurs in a tunnel with slope and curvature [20]. [PERSON] et al. established tunnel fire models with a slope of 4% after considering the fire location, downstream length, and upstream length and reduced the effect of longitudinal fire locations on smoke characteristics induced by inclined tunnel fires [21]. [PERSON] et al. explored the coupling effect of tunnel slope and ambient pressure on smoke diffusion and temperature distribution in full-scale tunnel fires \begin{table}
\begin{tabular}{c c c c c} \hline
**Name** & **Curvature of Radius** & **Length** & **Slope Gradient** & **Altitude** \\ \hline Ganhaizi Tunnel & 600 m & 1755 m & 29\% & 2500 m \\ Iron stockade Tunnel & 600 m & 2931 m & 25.5\% & 2215 m \\ Jinjiazhuang Tunnel & 860 m & 4228 m & 19\% & 1300 m \\ Wolonggou Tunnel & 700 m & 2620 m & 22\% & 3100 m \\ Laoyingpan Tunnel & 970 m & 5048 m & 25\% & 1000 m \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of existing spiral tunnels. with natural ventilation and proposed a height difference of stack effect after analyzing the mutual impact of the downstream length and tunnel slope on smoke movement [22]. Moreover, the characteristics of smoke distribution in a tunnel fire under the influence of three or more factors have not been realized, especially mountain spiral tunnel fires affected by slope, elevation, and curvature at the same time. This paper thus ascertains the characteristics of smoke distribution in mountain spiral highway tunnels. The focus lies on the smoke distribution and the interplay of different factors.
## 5 Experimental Evaluation
### Clustering and Routing Mechanism Evaluation
This percentage is smaller for dense networks due to the greater overlapping among the cluster zones and the existence of multiple paths toward a CH. Nodes in the overlapping regions store routing entries towards more than one CH. The percentage also increases as the size of the network increases while when a variable probability is used (Figure 16(c)), the percentage is small for dense networks and large for sparse networks. In Figure 17(a), the total number of routing messages that are exchanged until the clustering formulation is completed is presented, while in Figure 17(b), the same number per node in the WSN is shown. More routing messages are exchanged in dense networks, due to the nature of the controlled flooding mechanism that has been adopted from the _MSolicitateCH_ message. Although cluster formulation messages are confined and cycle-prevented as discussed earlier, the existence of multiple connections for each node creates an analogous routing overhead that is avoided in sparse networks. Furthermore, in Figure 17(b) it is shown that the clustering formulation mechanism is scalable since the number of messages per node for different densities remain either stable or slightly increases as the network size increases. Figure 16: Percentage of total routes in the CHs routing caches for (a) \(P_{clust}=1\%\), (b) \(P_{clust}=4\%\) and (c) variable \(P_{clust}\). Upon completion of the clustering process, routing functionality exploits the available information in the routing caches of the sensor nodes. In Figures 18(a) and 18(b) the total number of _RouteRequest_ and _RouteResponse_ messages that are exchanged in order a node to identify a valid route, are depicted. The number of the generated _RouteRequest_ and _RouteResponse_ messages is radically reduced as the network density increases since more routing information is already available in the nodes. Thus, NEURON's scalability is addressed as the routing overhead (in number of messages) is considered low.
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 2 Development of the GPS Time-Synchronization Method
### Time-Synchronization Method
According to the time series, the reasonable workflows in the GPS serial interrupt (corresponding to \(T_{0}\)) and PPS interrupt (corresponding to \(T_{1}\)) service programs are shown in Figure 7:0  
The function of workflows is used to ensure that the same PPS interruption corresponds to the same GPS information on the premise of not affecting the main program to record seismic data. In GPS serial interruption, UBX packet is received and parsed for every second. After obtaining GPS information and parsing the UBX packet, it will detect the valid bit until the GPS data is valid. If GPS serial interruption checks the snapshot event from the main program at this time, it records the time series \(T_{1}(\text{nn, mm}+1000)\) as the current real-time GPS time. When the PPS interrupt service function detects that the serial interrupt has obtained valid GPS information, it sets the time synchronization valid bit to notify the main program that a snapshot event has been performed. In this way, the GEIWSR-III completes time synchronization for high-density seismic array surveys.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Sp
#### 2.1.2 Placement Decision  
The VM decision step decides which VMs should be placed or migrated to minimize the resource wastage of the over-loaded or under-loaded host as well as to satisfy user requirements such as reducing service response time, energy consumption, and cost. Thus, it is important to detect the VM state such as resource usage, and energy consumption in the load detection step, and to choose optimized metrics depending on user requirements for the VM decision.
## 4 Design and Simulation of the Conceptual Model for Capacity Planning of an OEM Plant
### Design of a Generic Conceptual Model Development Applying the VSM
* Increase external production capacities for peak or constant production requirements. * Increase the production capacity with new investments. * Calculate the daily production loss according to bottlenecks and extrapolate it for longer periods based on the available demand forecast. * Increase maintenance planning and coordination, as well as the number of tools, in order to reduce breakdowns and increase plant availability. Economic parameters: Within the conceptual model, three decisions are considered. * New investments for increasing production capacity. For this decision, the model requires a payback of less than two years. The return on investment is forecast to determine the increase in capacity that is pursued by the investment. * Operational costs related to extra hours or extra shifts of operational employees. Based on the gap between production capacity and demand, operational costs are optimized by adjusting the number of employees, working days, and shifts. * Customer demand loss based on the value of the customer order lead time. Customers buy from a company or not depending on the lead time. Therefore, the model assesses the customer requirements and evaluates the losses in sales due to a long delivery process. Based on this loss, a decision on whether to improve capacity and internal processes can be made. Decision-making: Decision trees are used as a function of the level, i.e., network, plant, line, machine group. For decision-making, a simulation inside the simulation is conducted in order to make a forecast of the decision, taking into account the risk of the decision with a qualitative approach of price sensitivity and new market entrance. To predict the behavior, quality standards for time series are used to analyze the probability that a demand pattern will continue over time (assumption: a demand pattern is only influenced by customer needs, not market influence, normative standards, price, promotions, disasters, etc. ).
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## Nomenclature  
\begin{tabular}{l l l}
**Symbol** & **Unit** & **Description** \\ \(a_{0}\dots a_{5}\) & 1 & Coefficients of main inductance polynomial \\ \(\Delta E_{\rm L}\) & & Difference in energy lost \\ \(E_{1}\) & & Energy lost of new approach \\ \(E_{2}\) & & Energy lost of reference approaches \\ \(L_{1}\) & & A & Stator current phasor \\ \(I_{1d}\) & & A & Stator d-current component \\ \(I_{\rm Id,opt,so}\) & & A & Stator optimal d-current component in static operation \\ \(I_{\rm Id,ref}\) & & A & Reference stator d-current for the current controller \\ \(I_{\rm Id,act}\) & & A & Actual stator d-current for the current controller \\ \(I_{1q}\) & & A & Stator q-current component \\ \(I_{1,{\rm max}}\) & & A & Maximum stator current phasor length \\ \(I_{2}\) & & A & Rotor current phasor \\ \(L_{L_{p}}\) & & A & Current phasor through main inductance \\ \(L_{R_{\rm re}}\) & & A & Current phasor through iron resistance \\ \(J\) & & 1 & Objective functional \\ \(J_{\rm d}\) & & 1 & Discretized objective functional \\ \(q\) & & \({\rm W}/({\rm rad}\,{\rm s}^{-1})^{2}\) & Weightage factor for specifying optimization priorities \\ \(J_{\rm M}\) & kgm\({}^{2}\) & Moment of inertia of the motor \\ \(J_{\rm L}\) & kgm\({}^{2}\) & Moment of inertia of the load \\ \(J_{\rm T}\) & kgm\({}^{2}\) & Total moment of inertia of the entire drive train \\ \(k\) & 1 & Sample number counter \\ \(N\) & 1 & Total number of samples \\ \(L_{\sigma}\) & H & Stray inductance \\ \(L_{\mu}\) & H & Main inductance \\ \(\omega_{1}\) & rad/s & Stator angular frequency \\ \(\omega_{2}\) & rad/s & Slip angular frequency \\ \(\omega_{\rm Rm}\) & rad/s & Rotor mechanical angular frequency reference for the speed \\ \(\omega_{\rm Rm,ref}\) & rad/s & controller \\ \(\omega_{\rm Rm,act}\) & rad/s & Actual rotor mechanical angular frequency for the current controller \\ \(P_{\rm L}\) & W & Power loss \\ \(\Psi_{\rm 1d}\) & Vs & Stator flux linkage d-component \\ \(\Psi_{\rm 1q}\) & Vs & Stator flux linkage q-component \\ \(\Psi_{\rm 2}\) & Vs & Rotor flux linkage phasor \\ \(\Psi_{\rm 2d}\) & Vs & Rotor flux linkage d-component \\ \(\Psi_{\rm 2q}\) & Vs & Rotor flux linkage q-component \\ \end{tabular}
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 4 Discussion
### 6G Specifications and Gains for AIoT Systems in Aviation
6G networks are expected to have AI deeply integrated into their core architecture which will provide the following benefits for aviation:  
* Enable edge AI capabilities on aircraft, allowing for real-time data processing and decision-making;
* Support dynamic network optimization, ensuring optimal performance of AloT systems under varying conditions;
* Facilitate Al-driven predictive maintenance by processing vast amounts of sensor data in real-time. The specifications of 6G technology offer transformative benefits for AloT systems in aviation. These advancements will enable a new era of intelligent, responsive, and highly efficient aircraft operations. From ultra-precise health monitoring to AI-driven predictive maintenance and enhanced safety measures, 6G will play a crucial role in shaping the future of aviation. As these technologies mature, we can anticipate a paradigm shift in how aircraft are monitored, maintained, and operated, leading to increase levels of safety, efficiency, and sustainability in the aviation industry.
# Grouped Contrastive Learning of Self-Supervised Sentence Representation
## 5 Further Investigation of GCLSR
### Effect of the Region Size of TextCNN  
The region size is a crucial parameter of TextCNN. Therefore, we design different region sizes to investigate their impacts. The results are shown in Table 9.  
The experimental results show that the performance can be influenced dramatically by region size. Specifically, region size 1 is crucial for obtaining good results, observed from region size (1,2,3,4,5,6) and (2,3,4,5,6). A possible explanation is that region size 1 can enhance the representation of every word itself in a sentence without noise from other words. Furthermore, we can increase the region size to study it. The results show that, although large region sizes can obtain a better result compared with a small region sizes (1,1,1,2,3,4) on STS tasks, worse performance is obtained in transfer tasks (a large region size will reduce by 0.3 percentage points). We argue that a large region size could obtain more context information, but at the same time, much noise is also added into the representation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Momentum/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline
0.8 & 55.47 & 72.45 & 66.50 & 74.84 & 67.32 \\
0.9 & 55.27 & 71.96 & 66.27 & 75.18 & 67.17 \\
0.99 & 54.76 & 70.86 & 65.69 & 73.93 & 66.24 \\ (0.9,0.8) (ours) & **55.79** & **72.72** & **66.73** & **75.40** & **67.66** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The effect of the SGD momentum (Mot). (0.9,0.8) means that the momentum is 0.9 before warm-up and 0.8 after warm-up.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Warm-Up/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline
1 & 55.62 & 72.52 & 66.54 & 74.75 & 67.36 \\
2 & 55.45 & 72.38 & 66.42 & 74.90 & 67.29 \\
3 & 55.62 & 72.54 & 66.56 & 75.09 & 67.45 \\
4 & 55.64 & 72.55 & 66.60 & 75.07 & 67.47 \\
5 (ours) & **55.79** & **72.72** & **66.73** & **75.40** & **67.66** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The effect of the warm-up. 1, 2, 3, 4, and 5 represent epochs that the LR starts to reduce.
## 4 Experiments
### Experimental Details  
The HR image is downsampled by a factor of 4 using bicubic interpolation to obtain the LR image for super-resolution reconstruction. To augment the training data, we applied horizontal flipping, vertical flipping, and random rotations of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). During hash-based adaptive matching, we set the number of hash computations to 4, with each computation generating a different hash value through independent operations. The size of each hash bucket is set to 144. Matching is performed at three scales: 160 \(\times\) 160, 80 \(\times\) 80, and 40 \(\times\) 40, thus utilizing the richness of multi-scale features. The progressive adaptive aggregation module performs progressive aggregation in a large-medium-small-medium-large scale order, with each scale being aggregated twice except for the smallest scale, which is aggregated once. By gradually refining and alternately enhancing local and global information, it is possible to restore image details while maintaining the global consistency and structural integrity of the image. During training, we optimize the model using the Adam optimizer, setting the parameters \(\beta_{1}\) and \(\beta_{2}\) to 0.99 and 0.999, respectively. The initial learning rate is set to 1e\({}^{-4}\), and the batch size is 9. Each batch contains 9 LR patches of size 40 \(\times\) 40 and 9 reference patches of size 160 \(\times\) 160. We use \(\mathcal{L}_{adv}\) as the sole loss function, with joint supervision from \(\mathcal{L}_{rec}\), \(\mathcal{L}_{per}\), and \(\mathcal{L}_{adv}\). The weights for \(\mathcal{L}_{rec}\), \(\mathcal{L}_{per}\), and \(\mathcal{L}_{adv}\) are set to 1.0, 10\({}^{-4}\), and 10\({}^{-6}\), respectively. \(\mathcal{L}_{rec}\) (1.0) is the main loss, ensuring the output closely matches the ground truth at the pixel level and provides stable training. \(\mathcal{L}_{per}\) (10\({}^{-4}\)) improves visual quality by comparing high-level features from a pretrained network. A small weight prevents it from overpowering the training. \(\mathcal{L}_{adv}\) (10\({}^{-6}\)) helps make the output more realistic. Due to its instability, it is given a very low weight to avoid disrupting training. The model is implemented in PyTorch 1.13.1 and trained on an NVIDIA 3090 GPU (NVIDIA, Santa Clara, CA, USA).
## 5 Simulation Examples  
This section demonstrates the performance of the proposed method via numerical simulation. In all simulation examples, we use the rotation array structure shown in Figure 2. The radius \(r\) is 0.124 m, the rotation frequency \(f_{z}\) is 15 Hz, and the noise background is AWGN. Select \(M=8\) and \(M=10\) elements for forming the UCA to estimate DOAs. To verify the performance of the R-MUSIC algorithm, some comparison simulations of MUSIC algorithm with five elements UCA and eight elements UCA are carried as well.
## 4 Classification Results and Discussion
### Effects of Spatial Features  
The spatial features were added to take full advantage of increased spatial details from image sharpening and to fully evaluate the potential of image sharpening for LULC classification of Sentinel-2. Besides that, the effects of two spatial features on classification accuracy were also assessed using Sentinel-2 imagery for the first time. In general, GLCM and EAPs have an obvious advantage over image sharpening in classification accuracy of Sentinel-2. Table 5 and Figures 3 and 4 illustrate that all the accuracy statistics are greatly increased after EAPs and GLCM were integrated separately or together independent from data sets. For example, the lowest improvement of EAPs was achieved on the upscaled image at a 20-m GSD with the OA increased by 3.42%, and the highest improvement was obtained on downscaled data by PANSHARP with the OA increased by 4.83%. GLCM achievedthe lowest increase on the image sharpened by HPF with the OA increased by 4.25%, and its highest improvement was obtained on the image generated by GS with the OA increased by 6.23%.  
They differ not only in the OA but also in different classes. In Figure 7, the columns above \(x\)-axis represent that GLCM has a better performance than EAPs on its corresponding classes, and below \(x\)-axis are the opposite. The higher the column, the greater their differences in accuracy promotion. From this bar chart, EAPs and GLCM have a similar effect on agricultural land and forest, but in built-up area, water and road, GLCM performs better, and EAPs reverses its disadvantages in shrub and agricultural land.
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 3 Materials and Methods
### Crossover  
The crossover operation randomly selects some elements in the mutated solution vector and crosses them with the original solution vector to generate a new solution vector. The crossover operation helps the algorithm jump out of the local optimal solution in the search space, thereby better exploring the entire search space. Using different values  
Figure 2: The Relationship between population individual and CTU lines.  
will inevitably have a greater impact on the target problem. Choosing an appropriate crossover rate for a specific problem is crucial. The crossover rate controls the probability of occurrence of the crossover operation in the differential evolution algorithm, the degree of participation of each dimension of the individual in the crossover operation, and the balance of global and local search capabilities. The value range of crossover rate is usually [0, 1], and the more common values are 0.9 and 1.0. The choice of crossover rate needs to be adjusted based on the characteristics of the problem and the nature of the objective function. The smaller the CR, the lower the diversity of the population, resulting in insufficient search capabilities of the algorithm and premature convergence of the algorithm; the larger the CR, the greater the convergence speed, but if it is too large, the convergence speed may be slower due to the disturbance being greater than the difference in the population. Through verification analysis of different values of CR, the CR value is set to 0.5. The crossover operation used in this chapter is shown in (3). Among them, i represents the index of the current individual in the population, j represents the current dimension, and t represents the number of iterations; n is a random integer between [1, N], and N represents the population dimension; r is a random number between [0, 1]. The specific process of mutation operation and crossover operation is shown in Figure 3.  
\[\begin{cases}Y_{i,t}^{(j)}=V_{i,t}^{(j)}&\text{if }r\leq CR\text{ or }i=n\\ Y_{i,t}^{(j)}=X_{i,t}^{(j)}&\text{otherwise}\end{cases} \tag{3}\]  
Figure 3: Mutation operation and crossover operation.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### Results and Discussions
#### 7.4.3 Comparison of Convergence  
In Figure 4a-c, three values of \(\gamma\), i.e., \(\gamma=0.5,~{}1\) and \(1.5\) are used for simulation to demonstrate the effect of the relaxation factor on convergence with respect to the NMSE of the proposed Ex-ADMM algorithm. As the value of \(\gamma\) increases from 0.5 to 1 and then to 1.5, the convergence of proposed algorithm gets slightly better and converges to smaller NMSE values for all training symbol lengths (i.e., T = 400, 800 and 1200) as compared to ADMM and SVT. Effects on NMSE for different algorithms of multiple paths are shown in Figure 4d. Due to the poor scattering nature of mmWave, the performance of all algorithms gets worse with escalation of the number of paths, L\({}_{\text{P}}\). However, the performance of the proposed algorithm is still better than that of the others.  
Figure 3: (**a–c**) NMSE for different transmit SNRs for a 64 \(\times\) 64 mmWave MIMO channel at T = 400, T = 800 and T = 1200.  
Figure 4: _Cont._