esa-sceva/satcom-chunk-collection huggingface dataset:
# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 3 Method  
### YOLOv5  
The algorithm presented in this paper is related to YOLOv5, so in this section we will briefly review YOLOv5. As shown in Figure 1, YOLOv5 architecture is divided into four distinct components. The first part is the input, which performs the necessary pre-processing operations on the input image to prepare for the subsequent stage of the network.  
The second part is the _backbone_ network, which is mainly composed of _Conv_, C3 and Spatial Pyramid Pooling (SPP) modules [32]. _Conv_ is the basic convolution unit of YOLOv5, which performs Conv2d convolution, BatchNormalization layer (BN), and SiLU activation operations on input features. The C3 module consists of three Conv layers and multiple Bottleneck layers. It contains two branches: one for deep feature extraction through multiple Bottleneck layers, and the other through a _Conv_, and then the two branches are connected through residual connection, which effectively increases the depth and receptive field of the network, and improves the capability of feature extraction. SPP uses the maximum pooling method of different sizes, and then concatenates the feature maps of different scales to further enhance the feature extraction capability.  
The third part is the _neck_ network, which is mainly responsible for feature fusion. Using a FPN+PAN structure, features of different levels are fused to better detect targets of different scales. FPN (Feature Pyramid Network) conveys semantic information from the top down, and PAN (Pyramid Attention Network) [33] conveys positioning features from the bottom up.  
Figure 1: YOLOv5 network structure.  
The fourth part is the _head_, which is mainly responsible for outputting detection information, including the category, location and confidence of the target.
## 4 Experiments  
### Datasets  
Medical image data are a valuable resource, yet accessing medical image datasets is challenging due to patient identity protection and the complexity of data sharing. Fortunately, some researchers and research organizations have made publicly available datasets for the development of medical image segmentation tasks. In our study, we selected three widely recognized biomedical image datasets to validate our model: (1) The BUSI dataset [39] comprises breast ultrasound images of 600 female patients aged between 25 and 75. The dataset consists of 780 images, each with a corresponding truth mask. It features 133 normal breast images, 437 benign tumor images, and 210 malignant tumor images. For our study, we selected 647 images of benign and malignant tumors, which are provided in three-channel color format (commonly referred to as RGB in the context of digital imaging). This color information is crucial as it enhances the visibility of various tissue characteristics that are important for accurate tumor segmentation. (2) Kvasir-SEG [40] is an endoscopic dataset for pixel-level segmentation of colon polyps. It includes 1000 images of gastrointestinal polyps and their corresponding segmentation masks, which have been personally labeled and verified by senior gastroenterologists. (3) The ISIC 2018 [41] dataset is a large-scale dermoscopic image dataset published by the International Skin Imaging Collaboration (ISIC). The mask image data are generated using a variety of techniques and reviewed and curated by professional dermatologists. Due to the limited resources of computer equipment, we resized the input images of the BUSI, Kvasir-SEG, and ISIC 2018 datasets to 256 \(\times\) 256 to ensure the experiments could be conducted. Additionally, we randomly partitioned the datasets into training and test sets in a 4:1 ratio for experimental evaluation, as shown in Table 1.
## 6 Conclusions  
The performance and merits-demerits of the KF, EKF, UKF and PF along with the implementation of a fractional order controller have been presented in this work. The methods have been validated by simulations in MATLAB. This research has focused on the improved performance of SFL and SFJ with respect to the conventional PID controller. The performance is mainly focused on the smooth and quick response of the model along with the noise removal, which can be validated form the responses shown in the graphs. In order to do this, fractional order controller and state estimation methods have been used for the following reasons: (i) a fractional order depicts the system more accurately, and (ii) state estimation methods provide better tracking of signals from noisy measurements. Therefore, it can be concluded that the combination of both fractional calculus and state  
Figure 14: Comparison of filter responses with fractional order control for Joint 1.  
estimation methods provides more robustness, performance and stability to the system than the conventional PID controller for our 2 DSFL and 2 DSFJ system.  
Conceptualization, A.P.S. and D.D.; methodology, S.G. and D.D.; software, S.G.; validation, S.G.; formal analysis, A.P.S. and S.G.; writing--original draft preparation, S.G. and D.D.; writing--review and editing, S.G., A.P.S., D.D. and S.O.; supervision, D.D.; funding acquisition, S.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Regional Development Fund in \"A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration\" project, Reg. No. \(CZ.02.101/0.0/0.0/17_049/0008425\) within the Operational Programme of Research, Development and Education.  
Not applicable.  
The authors declare no conflict of interest.
# An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW
## 1 Introduction  
As one of the most efficient welding methods, gas metal arc welding (GMAW) is widely used in industrial production due to the large range of controllable heat input levels [1; 2; 3]. With the rapid promotion of automation in the welding industry, the traditional direct-current (DC) welding method at constant voltage fails to meet the increasing manufacturing requirements; hence, pulsed GMAW was developed recently, and it is especially used in robotic welding systems [4].  
A special pulsed projected metal transfer process plays an important role in the stability of pulsed GMAW [5; 6; 7]. Under the periodic action of a pulse current, the droplets periodically transit to the molten pool, the speed of which has a great effect on the welding quality. As a result, a better adaptability of all-position welding can be obtained during the pulsed GMAW process [8; 9]. Therefore, it is significant to observe and study the metal transfer process of pulsed GMAW.  
In addition, under the drive of intelligent manufacturing, automatic welding technology is gaining importance. As a powerful tool to extract information from digital images, image processing is applied in automatic welding equipment to monitor the welding process and amend parameters automatically [10; 11; 12]. It is of great significance to obtain intuitive information of the metal transfer process using image-processing technology to improve the quality of pulsed GMAW. Many recent investigations focused on the mode of metal transfer and tried to determine the mathematic description of droplet flight trajectory or kinematic characteristics, such as droplet size and velocity [13; 14; 15; 16]. Both arc and droplet behaviors are necessary for studying metal transfer. However, it is difficult to obtain high-quality pictures of the arc and clear droplets at the same time, and the extraction of droplet information is not precise enough due to the interference of the bright arc.  
Thus, in this paper, a monitoring system of the metal transfer process is proposed to acquire clear images during pulsed GMAW. Furthermore, a novel image-processing algorithm is designed to recognize the edge and centroid coordinates of a droplet automatically from the metal transfer image. To this end, kinematic characteristics of droplets can be extracted based on the proposed algorithm for possible applications in revealing the droplet transfer mode and developing precise automatic welding systems.
## 3 System Model
### Computation Model  
Tasks that have been offloaded are processed in parallel by the edge server upon reception. Due to the edge server's relatively high computational performance [5], it is assumed that the delay consumed in processing tasks and waiting in the serial queue of tasks is not considered. Setting the terminal device's central processing unit (CPU) has a multicore function. The device utilizes multicore computing to process multiple tasks concurrently, allowing for adjustable computing frequencies for core \(k\in\{1,2,\ldots,K\}\). Additionally, the regulation of its computing frequency is continuous. The computing frequency of the core \(k\) is in the frequency range \(F\in(f^{\text{min}},f^{\text{max}})\). According to the offloading ratio, the size of the task data computed locally on the terminal device \(i\) is given by  
\[I^{u}_{i,t}=\left(1-\sum_{j=1}^{N}O_{i,j,t}\right)t^{a}_{i,t}. \tag{6}\]  
Define the size of the task data allocated to the core \(k\) in the terminal device \(i\) as \(I^{u}_{i,k,t}\), \(I^{u}_{i,t}=\sum_{k=1}^{K}I^{u}_{i,k,t}\). The computing delay for tasks in the core \(k\) can be expressed as  
\[T^{u}_{i,k,t}=\frac{I^{u}_{i,k,t}s}{f_{i,k,t}}, \tag{7}\]  
where the CPU requires \(s\) cycles to process every individual byte.  
Since the delay required to find the energy consumption is the sum of the computing delay of all cores in the CPU, the delay consumption is given by  
\[T^{comp}_{i,t}=\sum_{k=1}^{K}T^{u}_{i,k,t}. \tag{8}\]  
Our research ignores the edge server's energy consumption and only considers that of the device [12]. As the edge server does not use any energy from the terminal device for its task computing, the base station is the only source of electricity for the edge server. The power of a task during core \(k\) calculation can be expressed as  
\[p^{u}_{i,t}=\kappa_{u}(f_{i,k,t})^{3}, \tag{9}\]  
where the CPU's effective capacitance parameter is denoted by \(\kappa_{u}\). The sum of the computing power of all cores of the CPU is  
\[p^{comp}_{i,k,t}=\sum_{k=1}^{K}p^{u}_{i,k,t}. \tag{10}\]  
The energy consumption for task computing can be expressed as  
\[E^{comp}_{i,t}=p^{comp}_{i,t}T^{comp}_{i,t}. \tag{11}\]
## 2 Related Work
### Detailed Structure of SE-GNNRM
#### 3.2.1 Item Embedding Layer
In natural language processing, it mainly solves the problem of long-distance dependence by calculating the mutual influence between words, filtering out important item information in the sequence, and assigning greater weight to it. In order to learn more representative session features, self-attention is used to encode session sequences in this paper. The self-attention network in this paper follows the mechanism, which consists of an attention network and a feed-forward network. The session sequence \(S=[v_{s,1},v_{s,2},v_{s,3},\ldots v_{s,n}]\) is input into the attention network, and the attention function \(C\) consists of a map of the query and key-value pairs outputs:  
\[C=softmax(\frac{(SW^{Q})\left(SW^{k}\right)}{\sqrt{d}})\left(SW^{V}\right) \tag{6}\]  
where \(W^{Q},W^{k},W^{V}\in R^{d\times d}\) is the learning weight matrices, and \(\sqrt{d}\) is the normalization factor in preventing the calculation value of the inner product from being too large. The feed-forward network consists of two layers of neural networks. _Relu_ is selected as the first layer activation function to add non-linearity to the self-attention module. At the same time, transmission loss may occur in the self-attention network. Using residual connections can make full use of the bottom layer information. The feed-forward network layer formula is as follows:  
\[F=H+Relu(CW_{1}+b_{4})W_{2}+b_{5} \tag{7}\]  
where \(W_{1}\in R^{d\times 4d},W_{2}\in R^{4d\times d}\) is the weight matrices that can be learned, \(b_{4},b_{5}\in R^{d}\) is the bias vector. For session S, \(V_{s}=[v_{s,1},v_{s,2},v_{s,3},\ldots,v_{s,n}]\) and \(F_{s}=[f_{s,1},f_{s,2},f_{s,3},\ldots,f_{s,n}]\) are obtained after encoding by the GNN module and the SAN module, respectively. Then, the two item embeddings are combined to complement each other. The joint function is described as follows. \[O_{s}=V_{s}+\theta F_{s} \tag{8}\]  
where \(\theta\) is a weight parameter, which represents the importance of the item embedding learned by the SAN module, and is used to adjust the proportions of the two modules.
## 5 Algorithm Design for \(\mathcal{OP}\)
### Problem Conversion
#### 5.3.2 Proposed Task Splitting Algorithm Based on DDPG
parameters of the target actor network and target critic network (corresponding parameters are \(\eta^{\prime}\) and \(\theta^{\prime}\)) are slowly updated from the actor network and critic network. The most commonly used parameter update is soft update, and the update method is as follows [7]:  
\[\eta^{\prime} =\tau\eta+(1-\tau)\eta^{\prime}, \tag{23}\] \[\theta^{\prime} =\tau\theta+(1-\tau)\theta^{\prime}, \tag{24}\]  
where \(\tau\) is the soft update factor, and \(\tau\) satisfies \(\tau\ll 1\), which is beneficial to improving the stability of training. Based on the above analysis, we can summarize the intelligent task splitting and resource allocation algorithm proposed in this paper as Algorithm 1. ```
1:Randomly initialize the parameters of the actor network and critic network \(\eta\), \(\theta\). 2:Initialize the parameters of the target actor network and target critic network \(\eta^{\prime}=\eta\), \(\theta^{\prime}=\theta\). 3:Initialize the replay buffer \(\Lambda\). 4:for\(i=1,2,\dots\),do
5: The actor network generates action decisions \(a_{t}\) based on the current state \(s_{t}\). 6: Execute action \(a_{t}\) and obtain the corresponding reward \(r_{t}\) based on solving \(\mathcal{SP}2a\), an obtain new state \(s_{t+1}\). 7: Store \(\{s_{t},a_{t},r_{t},s_{t+1}\}\) into the replay buffer \(\Lambda\). 8: Randomly sample small samples \(\tilde{\Xi}\) from the replay buffer \(\Lambda\). 9: The critic network calculates the target value \(y_{t}=r_{t}+\gamma Q^{\prime}(s_{t+1},\phi(s_{t+1}|\eta^{\prime})|\theta^{ \prime})\)
10: Update the critic network parameters by calculating the loss function \(L(\theta)=\sum_{t\in\Xi}(y_{t}-Q(s_{t},a_{t}|\theta))^{2}\), and update critic network by one step of gradient descent using \(\frac{1}{|\Xi|}\
abla_{\theta}\sum L(\theta)\). 11: Update the actor network parameters by one step of gradient descent using \(\frac{1}{|\Xi|}\sum(\
abla_{\eta}\phi(s)\
abla_{a_{t}}Q(s_{t},a_{t}|\theta))\). 12: Update the parameters of the target actor network and target critic network through soft update (23) and (24). 13:endfor
```  
**Algorithm 1** Task splitting and resource allocation algorithm
## 2 Model for Attitude, Antenna PCO/PCV and Its POD Strategies  
This section elaborates the models for Jason-3 attitude, receiver PCO/PCVs and POD strategies. We designed a combined attitude model for Jason-3, and clarified the model of receiver PCO estimation and its correction involving attitude; the receiver PCVs is modeled and described. The models and strategies of POD, including attitude information models and receiver PCO/PCV modeling, are given in this section.
## 3 Numerical Model  
To investigate the propagation behavior of fractures at the rock-coal boundary and achieve optimal construction results, a numerical model was developed using XFEM software 2020. This model focused on a horizontal well situated in the rock above a coal seam roof. The utilization of XFEM software offers several advantages, such as accurate calculation results and low computational requirements. This software enables simulation and analysis of various physical parameters related to reservoirs and hydraulic fracturing construction. Consequently, it serves as an effective tool for studying fracture behavior at the rock-coal boundary and optimizing the hydraulic fracturing process while minimizing redundancy.
## 1 Introduction  
High-speed rail (HSR) and stations are important vehicles for urban interaction, and their rapid development has shortened the spatial and temporal distances among cities, improved the accessibility of cities, and affected the spatial agglomeration and diffusion of populations and of socioeconomic activities. From the perspective of the urban interior, the \"catalyst effect\" [1] and \"agglomeration and diffusion effect\" [2] of HSR stations can improve the transportation infrastructure in cities. Moreover, HSR causes increases in social and economic activities and leads populations to gather in cities with stations [3], which promotes the diversified development of industries, especially business services, communication and entertainment, retail, real estate, and tourism [4]. This increase can not only promote the improvement of the industrial structure and of urban socioeconomic levels but also lead to the development of new comprehensive hub areas with HSR stations at their pore, inducing resetting of the urban spatial structure. The continuous expansion of HSR networks and the development of station areas can promote the flow of materials, energy, and information and strengthen regional exchanges, which can help cultivate new urban economies and cause significant changes in urban and regional spaces. It is highly important to study the relationship between HSR and urban development and identify the mechanism by which HSR impacts urban space.  
The impact of HSR on urban space stems from changes in accessibility [5; 6]. In recent years, most studies have focused on the impact of HSR on accessibility [7], spatial equity [8; 9], travel patterns, urban sectoral employment [10], commuting behavior [11], spatial development [12], land use [13], and economic development [14]. Despite the importance of these previous studies, the impacts of HSR on station area spatial structure remain largely unexplored. China is rapidly urbanizing and is constructing HSR on a large scale. To cope with the rapid development of urban space, China's HSR stations are usually located at the edge or on the periphery of the city. Therefore, HSR station areas are regarded as important vehicles of urban expansion and structural optimization and adjustments for most cities in China. It has been proposed that new HSR cities centered on HSR stations be developed to help these cities become urban nodes in the HSR network. However, many HSR station areas have run into planning problems, such as the pursuit of large-scale construction and unreasonable expectations for growth. In view of the very large HSR network and the large number of HSR stations connecting cities in China, it is urgent to evaluate and analyze the development status and spatial structure of the areas surrounding HSR stations. Therefore, based on POI and land use data, this paper defines the spatial scope of the Zhengzhou East railway station area and explores the characteristics and influencing factors of its spatial structure from the perspective of industry.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Acoustic Emission Sensors in Port Crane Maintenance  
AE sensors are powerful tools in NDT and can detect acoustic emissions generated by materials under stress or deformation. These sensors are crucial for monitoring and assessing the structural integrity of materials and components in various industrial applications. Below is a detailed discussion of the working principles, types, applications, and performance characteristics of port crane maintenance.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Relationship between Static Force and Dynamic Force
#### 3.3.6 Contact Force Principle  
Based on the results obtained earlier, the average force during the collision contact is proportional to the thickness and velocity, and it is linear to the elastic modulus and added mass based on the elastic material. Thus, the force can be written as Equation (13):  
\[f_{p}=(k_{M}m+C_{M})(k_{t}t)(k_{v}v)(k_{E}e+c_{e}) \tag{13}\]  
Combining equations (8) and (12), and taking into account the data in Tables 4-11, the formula can be established precisely, and it can be used to solve for a variable when knowing the other parameters. Simultaneously, the collision contact forces have the almost same skewness ratio during collision. Thus, the mean force can be calculated by Equation (14):  
\[f_{p}=1.70f_{m} \tag{14}\]  
\[f(t)=f_{p}\sin(\frac{\pi}{\Delta t}t) \tag{15}\]  
In addition, since the contact forces in various cases using different impact velocities have the same shape function, the contact forces can thereby be described as Equation (15). Figure 17 shows the comparison of contact forces between the fitting results derived from Equation (15) and the numerical data. Excellent agreement can be established. Besides, according to Equation (15), the skewness ratio  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Case** & \(m\) & \(\Delta t\) & \(f_{p}\) & \(f_{m}\) & **Im** & **Case** & \(m\) & \(\Delta t\) & \(f_{p}\) & \(f_{m}\) & **Im** \\ \hline \(t\) = 0.04 & 0.5 & 0.033 & 13.1 & 7.8 & 0.26 & \(t\) = 0.06 & 0.5 & 0.027 & 20.3 & 12.2 & 0.33 \\ \(R\) = 2.0 & 1.0 & 0.042 & 18.3 & 10.4 & 0.44 & \(R\) = 2.0 & 1.0 & 0.031 & 26.7 & 15.2 & 0.47 \\ \(E\) = 210 & 1.5 & 0.050 & 21.4 & 12.5 & 0.63 & \(E\) = 210 & 1.5 & 0.034 & 32.2 & 19.4 & 0.67 \\ \(v\) = 4.0 & 2.0 & 0.057 & 24.4 & 14.2 & 0.81 & \(v\) = 4.0 & 2.0 & 0.039 & 36.4 & 21.6 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The statistic of force in different cases (unit: MN, GPa, meters).  
Figure 16: Effect of structural mass on forces and impulse. (**a**) Thickness 0.06; (**b**) Thickness 0.04.  
can be derived theoretically as \(k_{s}=\pi/2=1.57\), which has a 7.6% discrepancy compared with the numerical value 1.70.  
According to Equation (15), the dynamics equation can be written in Equation (16) correspondingly, where \(K\) is the stiffness of the spherical shell. The relationship depends on the thickness, elastic modulus, and radius of the shell:  
\[Ms^{*}+Ks^{\prime}=f_{p}\sin(\frac{\pi}{\Delta t}t) \tag{16}\]  
It can be evident that the function of displacement versus time is a harmonic function as well, and the duration time of collision depends on the thickness, elastic modulus, and density of the shell, but has little effect due to the radius and velocity of the spherical shell. This conclusion can be verified by the extensive data in Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10.