esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Carbon Emissions in the Whole Construction Process of Prefabricated Floor Slab
## 4 Case Study
### Transportation Stage  
The density of the slab is 2500 kg/m\({}^{3}\). Considering the possible losses in the transportation process, the number of PFS required in this case was estimated as 7600. Based on above parameters, the total volume, total mass, and required transport vehicles were calculated. The vehicles are required to be provided by the prefabrication plant. The basic data of carbon emissions such as fuel consumption are shown in Table 8.  
Table 8 shows that in the transportation stage, we only considered the full load and no-load situation. Therefore, in the calculation of specific carbon emissions, the values of C\({}_{e}\) in Equation (3) can only be 1 and 0.67. The fuel carbon emission factor is 2.73, and the final calculation result is shown in Table 9.  
In terms of artificial consumption, we assumed that two transportation personnel travel in the vehicle, the average speed of the vehicle is 60 km/h, and the total working time is 278.67 h. The basic data and the calculated carbon emissions are shown in Tables 10 and 11, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Total Working Time (hours)** & **Carbon Emission Factor (kg CO\({}_{2}\) eq/h)** & **Total Carbon Emissions (kg)** \\ \hline
278.67 & 0.645 & 179.74 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Basic data of carbon emissions of artificial consumption in the transportation stage.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Numbers of PFS units & 7600 \\ Single volume of PFS (m\({}^{3}\)) & 0.33 \\ Density (kg/m\({}^{3}\)) & 2500 \\ Total volume (m\({}^{3}\)) & 2508 \\ Total mass (t) & 6270 \\ Vehicles back and forth (times) & 209 \\ Total transportation distance with a full load (km) & 4180 \\ Total transportation distance with no load (km) & 4180 \\ Fuel consumption (L/100 km) & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Basic data of carbon emissions of energy consumption in the transportation stage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fuel Consumption with** & **Fuel Consumption with** & **Total Carbon Emission** & **Carbon Emission per m\({}^{3}\)** \\
**Load** & **No Load** & **Total Carbon Emission** & **Carbon Emission per m\({}^{3}\)** \\ \hline
5135.13 & 3440.54 & 8575.67 & 3.42 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Carbon emissions of energy consumption in the transportation stage (unit: kg).  
As such, we calculated the total PFS carbon emission in the transportation stage as (3.42 + 179.74)/2508 = 3.49 kg/m\({}^{3}\) by using Equation (3).
## 4 Flexible Route Planning Method Combining Q-learning and Graph Search
### Combination of \(A^{*}\) and \(Q\)-learning
#### 4.2.3 Definition of State Space  
In the conventional Q-learning methods for route planning problems, it is common to use the full transportation system as the state space in Q-learning [49]. However, the learning process is time-consuming due to the increased number of states where a large transportation system is treated. Therefore, in this study, the reduced state space representation is applied. The state \(s\) of AGVs used in Q-learning is defined as shown in Figure 5. The state space is defined for each AGV related to the center position of each AGV and is prepared for the number of AGVs. By defining the state space as relatively small compared to the entire transportation system, Q-learning need not be relearned when the number of AGVs increases or when the transportation system is enlarged.  
The state space for each AGV is defined as 13 nodes around itself as defined in Figure 5. Each AGV repeats the observation of those nodes in each action step. By adding the path information explored by the A* algorithm to the state space definition and reward setting in Q-learning, we combine the rule-based A* algorithm and Q-learning. The state space in Q-learning is defined for each AGV, which enables the generation of Q-functions that do not require learning again in response to changes in the problem environment or in the number of AGVs. The proposed method does not need to regenerate the plan when any of the motions of AGVs is delayed. It indicates that when unexpected events occur, traffic congestion is avoided for each AGV using the Q-learning, and the impact on the surrounding area is minimized.  
There are 4 events to be observed, as shown in Figure 6. First, each AGV detects whether another AGV exists or not for each node. In the example shown in Figure 6, the positions of AGV 1 and AGV 3 are detected. Then, the direction in which it cannot travel in the next state is determined. In the example, the leftward movement is detected as impossible. In addition, the possible interference of other AGVs is determined, and in the example, the potential for interference by moving to the right is detected. Finally, the A* algorithm determines the direction of the explored path. In the example, the direction is determined to be upward.  
The amount of information for each observed event is given by 13 bits, 4 bits, 4 bits, and 2 bits, respectively, and the number of states of the Q-function is \(2^{23}=8,388,608\).
## 4 Results and Discussion
### First Design
#### 4.2.2 Changing Thickness of Top Plate and Magnet  
The thickness of the top plate and the magnet of the prototype are 1.00 and 2.30 mm, respectively. Owing to the limited inner space of the linear vibration motor, the total thickness of the top plate and magnet is maintained. The analysis results with different top plate thicknesses are shown in Table 3.  
With an increase in the top plate thickness, the magnetic flux leakage reduced without a significant reduction in the force factor. This was because the thicker top plate could allow more magnetic fluxes to pass through, whereas the flux was reduced by a thinner magnet.
## 1 Introduction
### Application Status of Sound Simulation System  
The early sound simulation system was used in traditional fuel vehicles to enhance the sound of the engine. Even when driving a regular car, it can resemble the acceleration sound of a top sports car when accelerating. Volkswagen has installed an in-vehicle sound synthesis system on Golf, Suteng, Siborui, and other models to achieve the effect of dynamic enhancement of vehicle driving sound. The artificially simulated sound is installed in the Audi A3 pure electric version of the car and played through the car audio system. Figure 1 shows a Volkswagen Golf with an acoustic wave augmentation system.  
The device that simulates the engine sound is installed on the electric version of Smart by Mercedes-Benz, which can achieve the same acceleration sound effect as the gasoline version of the vehicle. The electronic device, named 'SLS cSound', is installed in the subsequent pure electric version of the SLS AMG Coupe model, and different sound levels are set to output the driving sound matching the vehicle running state. The car engine sound simulation system is installed on some models of the BMW M series by BMW, named 'Active Sound Design' (ASD) system. The sound is played through the sound system in the car, and both the driver and the passenger can feel the strong acceleration sound wave in the car. After the eight-cylinder engine was replaced with a more fuel-efficient four-cylinder engine, Ford added a sound amplification system to the Mustang for sound enhancements based on the original vehicle's accelerated sound. The sound simulation system is installed in the NX series of Lexus. In the motion mode, the vehicle can add additional sound to the original engine sound. The system will mix the two sounds in proportion and play the output through the sound system in the car. Traditional luxury car brands in the world have occupied a considerable market; almost all of their introduction of pure electric vehicles with the sound simulation function is to give users a better driving experience.  
At present, the new energy automobile industry is developing rapidly, and some models have successfully applied the acceleration sound simulation technology of pure electric vehicles. For example, G3 released by Xiaopeng, an electric vehicle brand, has the function of simulating throttle acceleration roar. The Xiaopeng G3 car has set up four different gear acceleration sounds; the car acceleration sound mode can be manually selected by the user.
## 3 System Construction
### Annotation Statistics
#### 3.4.1 Aspect Ratio and Motion  
Figure 4 shows the distribution of changes in aspect ratio between two annotated frames. Concretely, the aspect ratio change is \(\left(w_{t}/h_{t}\right)/\left(w_{t-1}/h_{t-1}\right)\), where \(w_{t}\), \(h_{t}\) are the width and height of the object at time t respectively. This metric can be used to understand the types of motion in tracking datasets.  
Figure 5a,b present the distribution of object motion proportional to the size of the object width. \(w_{t}\) is the width of the bounding box at time t. We define the distance in \(x\) as \(dx_{t}^{2}=\left\|x_{t}-x_{t-1}\right\|/w_{t-1}\), the distance in \(y\) as \(d_{t}^{y}=\left\|y_{t}-y_{t-1}\right\|/h_{t-1}\). Then, \(d_{t}=\sqrt{(d_{t}^{x})^{2}+(d_{t}^{y})^{2}}\).  
The players on the field concerned in NPSPT dataset have more running and sprinting than the pedestrians in the MOTChallenge, so there will be more changes between two bounding boxes in adjacent frames of the same character. From the statistical results of the figures, the aspect ratio of NPSPT is more widely distributed, and the motion distribution is more uniform.  
Figure 3: (**a**) is the visualization of players’ motion trajectories in the camera image. (**b**) is the visualization of players’ motion trajectories under the real physical plane. The b-boxes in (**a**) and the circles in (**b**) represent the position of the players at the current moment, and the solid dots depict the trajectories of the players in the past 100 frames. The red boxes marked in (**a**,**b**) correspond to the trajectories of the same two players in the camera image and the real physical plane respectively.  
From Figures 4b and 5b, larger field size is generally associated with wider the aspect ratio distribution, and more uniform the movement distribution. When the field is larger, the players need to use more runs to cover the field, so there will be more changes in the bounding-boxes of two adjacent frames.
## 2 State of the Art
### Robust Object Recognition  
The approach used of monitoring multiple layers requires more robust object recognition techniques because the metal surfaces of the fermentation chambers. Several publications deal with object detection and recognition of three-dimensional point clouds. [PERSON] and [PERSON] present a simulation platform for multi-view sensor fusion of synthetic time-of-flight (ToF) images to serve as the base for following object recognition tasks [13]. The sensor outputs are fused to one three-dimensional point cloud to obtain a suitable field of view. [PERSON] et al. propose a novel structure of a neural network called PointNet, which allows a direct object recognition and segmentation of three-dimensional point clouds [14]. The generation of training data is very time-consuming because the ground-truth data has to be generated manually, which means that every point belonging to a particular object has to be marked as such. In comparison to the mentioned methods, we use an instance segmentation network Mask Region-based Convolutional Neural Network (Mask R-CNN), proposed by [PERSON] et al. and originally developed for RGB-images [15]. To our knowledge, we are among the first who propose an approach of using this network structure for the instance segmentation of depth images for a tangible application.  
Different kinds of shape representations have been developed for object recovery, such as the extruded generalized cylinder [16], the recognition-by-components based on so-called geons [17], or the representation by superquadrics [18; 19]. Due to the flexibility and simplicity, superquadrics are currently used most frequently in computer graphics and computer vision. In Thamer and Scholz-Reiter, a method for the segmentation and object recognition of point clouds generated by laser scanners is proposed [20]. The segmentation is based on the local curvature using the surface normal. By fitting superquadrics to the segmented data, the type of the object is detected based on the shape. [PERSON] et al. present a method for a superquadric fitting of objects to serve as a base for robot grasping applications and achieve promising results. We chose the approach of superquadric fitting to estimate the shape parameters and hence the volume of detected objects.
## 2 Methodology
### Bayesian Optimization Methods  
Various model parameters, including black box models, were optimized via Bayesian optimization methods [32; 33]. The calibration of the groundwater flow model presented in this paper required adjustment of the hydraulic conductivity in four different regions. The manual adjustment of parameters was time-consuming and challenging, so a Bayesian optimization method was chosen to perform the calibration. For the sample set consisting of n hydraulic conductivity vectors [\(p_{1}\), \(p_{2}\), \(\cdots\), \(p_{s}\), \(\cdots\), \(p_{r}\)], where \(p_{s}\) denotes the \(s\)-th hydraulic conductivity vector, the Bayesian optimization process was as follows:
# Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
## 4 Experiments
### Datasets and Baselines  
We report experimental results on two attributed graph datasets: Cora and Citeseer. They are citation networks with varying sizes where nodes and edges represent publications and citations. Node attributes of Cora and Citeseer are bag-of-words representing the content of the publication. The statistics of the datasets are summarized in Table 2.  
To validate and evaluate SAVGAE, we compare it with 7 baseline methods including DeepWalk, Spectral Clustering [25], node2 vec, GAE, VGAE, ARGA, ARVGA in link prediction task. In node clustering task, we include 6 methods K-means, Graph Encoder [26], DNGR [27], RTM [28], RMSC [29], TADW besides baselines in link prediction task.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
###### Abstract  
Integrated bearings, characterized by their unique structure, feature an inner ring that is seamlessly integrated with the shaft. This study is based on the theoretical framework of rolling bearing dynamics and considers bearing friction, lubrication, and Hertz elastic contact theory. A dynamic simulation model considering the interaction between the components of the rolling bearing is established. Additionally, a subroutine for calculating the interaction forces between the bearing components was written in C and compiled into a dynamic link library, which was then integrated with the dynamic simulation software. To solve and simulate the dynamics of the integrated bearing model, a sophisticated combination of a refined integration method and the predictor-corrector Adams-Bashforth-Moulton multistep technique was employed. The theoretical analysis offers insights into the vibration characteristics of the integrated bearings across different structural and operational parameters. Results indicate that a judicious selection of parameters, such as the curvature radius ratio of the inner and outer grooves and the gap of the cage pockets, can significantly enhance the bearings' vibration and noise reduction capabilities. Furthermore, the application of an appropriate axial preload effectively reduces bearing vibrations, and there exists an optimal range of rotational speeds that minimizes these vibrations.  
Keywords:integrated bearing; dynamic modeling; vibration characteristics; no inner ring design +
Footnote †: journal: Applied sciences
# DANet: Temporal Action Localization with Double Attention
## 4 Experiments and Results
### Ablation Study
#### 4.3.6 The Effectiveness of Loss Function  
This subsection delves into the impact of loss functions on model performance. Given the regression-based nature of TAL, our analysis focuses on the effect of various regression losses on model efficacy.  
The IoU loss [53] regresses the four boundary coordinates of a candidate box as a single entity, leading to efficient and accurate regression with excellent scale invariance. In our model, this two-dimensional loss is compressed into one-dimensional space. The Generalized Intersection over Union (GIoU) loss [54] computes the area of the smallest convex closure, C, between two shapes (like rectangle boxes), A and B, and then calculates the ratio of the area excluding A and B in C to the original area of C. Finally, the original IoU is subtracted from this ratio to obtain the generalized IoU value. The Distance-IoU (DIoU) loss [55] calculates the normalized distance between the center points of two boundary boxes to address the issues of insufficient precision in boundary box regression and slow convergence rate. The Control Distance-IoU (CDIoU) loss [56] mainly contributes to improving the regression accuracy of boundary boxes effectively.  
We evaluate the four aforementioned regression losses and the results are presented in Table 8. The experimental results show that, when DIoU is adopted, the average mAP increases 0.4% and 0.8%, respectively, compared with GIoU adopted and CDIoU adopted. Therefore, our design of loss function is reasonable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(L_{\text{att}}\) & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@Avg** \\ \hline
3 & 79.4 & 75.0 & 65.8 & 53.3 & 37.8 & 62.2 \\
4 & 79.9 & 76.1 & 68.6 & 55.4 & 40.7 & 64.2 \\
5 & 81.4 & 76.3 & 69.0 & 56.7 & 41.5 & 65.0 \\
7 & 80.4 & 76.9 & 69.3 & 57.7 & 43.1 & 65.5 \\ \hline
6 (adopted) & 81.5 & 78.2 & 70.3 & 58.7 & 44.0 & 66.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The ablation study on the effectiveness of the numbers of backbone layers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Design** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@Avg** \\ \hline Design2 & 57.3 & 40.6 & 22.5 & 10.5 & 4.1 & 27.0 \\ \hline Design1 (adopted) & 81.5 & 78.2 & 70.3 & 58.7 & 44.0 & 66.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The ablation study on the effectiveness of path aggregation.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 6 Potential Usage and Applications
### Benefits to Service Providers, the Environment, and Society  
Internet giants, such as Amazon, found that every 100 ms of latency cost them 1% in sales; Google found that an extra 0.5 s in search page generation time dropped traffic by 20%. The gaming industry, where latencies larger than even 80 ms can hurt gameplay, has even tougher latency requirements. The expectations of today's NOW customers continue to grow, and the amount of data generated and accessed is mind-boggling. CISCO has described how 3.2 quintillion bytes of data are generated every day and has stated, that over the last two years alone, 95% of the data in the world were generated. It is clear that the need for speed and scale is escalating, and we need to understand how we can support future networks to remain competitive from all aspects: optimized operations, maximized network utility, and enhanced user experience. The benefits are not purely for large corporations. Reduced latency improves the quality of gaming and video telephony, improving the lives of isolated rural and under-connected global populations. Moreover, the applicability of the outcomes to the internet of vehicles will also help combat traffic congestion, bringing benefits to both the environment and society.  
Figure 11: Throughput results for Scenario 9 with RSF-DB: WiFi. Both paths had 54 Mbps bandwidth and 8 ms delay. The packet loss probability was low.
## 4 Challenges and Future Research Directions
### Challenges of Interference and Effective Spectrum Management  
Different interference mitigation techniques such as parallel interference cancellation and SIC can be integrated with a 6G-based smart grid network. In addition, deep learning-based decoding can be incorporated with NOMA-enabled 6G wireless communication instead of SIC to provide faster and more effective decoding of the respective end user signal without interference [136]. However, more feasible solutions are required for some open challenges of NOMA, such as multi-cell scenarios, mobility, imperfect channel state information, etc.  
The limited spectrum resources, along with interference mitigation challenges, require the effective management of 6G. Different spectrum-sharing approaches such as implementing spectrum sharing strategies and prolific spectrum management techniques are appropriate for 6G spectrum management. Ensuring efficient spectrum management is critical for optimal resource exploitation while maximizing the QoS for users [9].  
In addition, different suitable spectrum handling techniques can be incorporated with 6G-based smart grid communication networks, such as wide-spectrum access, carrier aggregation, and cognitive radio [9].
# YOLOv4-MN3 for PCB Surface Defect Detection
## 4 Experiment
### Training Details for YOLOv4-MN3  
Pre-training on large, natural-image datasets is a common strategy for DL-based detector training. Therefore, we pre-trained the proposed YOLOv4-MN3 on VOC2007 first. Then, the model training is split into two stages. In the first stage, pre-trained parameters are taken as the initial weights and the weights of the backbone network are frozen, then the parameters of the neck and head networks are trained and optimized. In the second stage, all the parameters are trained based on the first stage network weights. An Adam optimizer is employed to update the parameters with a weight decay of 5 \(\times\) 10\({}^{-4}\). A total of 100 epochs are performed in the model training, and both first and second stage last 50 epochs. The memory occupancy of the first stage is smaller than the second stage because of the pre-training. Accordingly, the batch sizes were set to eight and two for the two training stages, respectively, and their learning rates were set to 0.001 and 0.0001, respectively.  
Optimizing for anchor size that can match well with the size of detected defect facilitates the model's achieving better detection performance. The default anchor size of YOLOv4 is generated based on natural image objects that were not optimized for PCB surface defect detection. Therefore, K-means is employed to cluster and reset the anchor size based on the labeled data to better match the size of defect. Nine anchor boxes of sizes (26,23), (38,38), (52,48), (60,69), (94,83), (116,131), (140,41), (157,284), (239,149) were obtained after clustering. Different three-scale prediction output layers are employed to detect defects of different scales in YOLOv4-MN3. The three smallest anchor boxes were allocated to the 52 \(\times\) 52 prediction output layers, the three middle-sized anchor boxes were assigned to 26 \(\times\) 26 prediction output layers, and the 13 \(\times\) 13 prediction output layers used the three largest anchor boxes.