esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Additionally, the current definition of sight distance may not always align with drivers' decision-making processes, highlighting the need to enhance relevant standard regulations [22]. Regarding driver behavior in diversion zones, some scholars have analyzed driving behavior by gathering data on vehicle speeds, trajectories, and deceleration. They have observed variations in driving behavior across different types of diversion zones and among different drivers [23; 24; 25; 26]. Presently, there is scarce literature directly studying relevant indicators and driver behavior characteristics in the context of right-turn ramp left placement at interchanges. Therefore, investigating the left exit decision sight distance for right-turn ramps in conjunction with actual traffic scenarios and driver behavior can enhance relevant design parameters and improve operational efficiency and traffic safety levels in traffic flows under the left-turn ramp configuration. The purpose of this study is to analyze the vehicle speed, time headway, and lane-changing behavior within the RTRL at the Xi'an X interchange and L interchange. This analysis is achieved by integrating driver behavior simulation experiments with actual traffic data measurements. Furthermore, the traffic behavior characteristics and driving comfort of drivers in RTRL are evaluated quantitatively through a driver behavior simulation experiment. Finally, the decision sight distance calculation model is established. The parameters within this model are determined by combining the measured traffic data and the results of the driver simulation experiments sequentially to propose the recommended values of the decision sight distance of the RTRL under different design speeds. The remainder of this paper is structured as follows: Section 2 delineates the collection and analysis of the measured data, including the correlation analysis of the three indicators of vehicle speed, time headway, and lane-changing behavior. Section 3 describes the preparation, simulation modeling, process, and results of the driver behavior simulation experiment in detail. The calculation model for the decision sight distance is established, and the calculation results for determining the relevant parameters and recommended values for the decision sight distance based on the model are provided in Section 4. Finally, conclusions are given in Section 5.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 6 Conclusions  
In this paper, we proposed an on-demand virtual network slicing and orchestration environment for selective MEC services associated with a multitier architecture over a wide-area SDN infrastructure. MEC can improve latency and bandwidth utilization by enabling technologies such as SDN and virtual network slicing. Furthermore, SDN-based resource orchestration can automatically and intelligently allocate computing and storage resources integrated with virtual network slices for the dynamic service demand of various UEs, end-hosts, and user devices. Using VDN and VDNO as virtual network slicing and orchestration technologies, a selective MEC service model was proposed in this paper to solve an arising issue of restricted and insufficient resource availability at the MEC on-site and edge site, particularly in the case of on-demand and dynamic needs of computing and storage resource deployment. Our future work includes the design and implementation of the proposed selective MEC service architecture which combines an automated and intelligent virtual network environment as a new network service over KREONET-S.  
**Author Contributions:** Supervision, D.K.; Writing-original draft, D.K. and Y.-H.K.; Writing-review & editing, D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from the Korean government.  
**Acknowledgments:** This research was supported by the Korea Institute of Science and Technology Information (KISTI).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Theory and Methods
### Model Improvements
#### 2.2.3 WIMIoU
The WIMIoU loss function enhances localization accuracy in object detection, particularly for small targets, by integrating three key components: MPDIoU, InnerIoU, and WiseIoU. MPDIoU extends traditional IoU by introducing a corner distance penalty term normalized by image dimensions, which balances sensitivity across objects of varying scales. InnerIoU constrains the model to focus on central regions by adaptively shrinking bounding boxes using a ratio factor, effectively reducing noise from peripheral areas. WiseIoU dynamically adjusts loss weights through a compensation coefficient based on the center offset between predicted and ground-truth boxes, reinforcing alignment precision. These components are integrated through weighted summation to formulate the final loss function. The advantages of WIMIoU lie in its multi-level geometric constraints and adaptive mechanisms: MPDIoU establishes foundational localization constraints, InnerIoU refines sensitivity to central regions, and the dynamic compensation term mitigates class imbalance. This design ensures heightened responsiveness to minor positional deviations while maintaining smooth penalty gradients for large misalignments, ultimately improving robustness and accuracy in small-object detection.
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 1 Introduction
In [20], an MPC based current control of a switched reluctance motor is proposed, in which an adaptive controller is used to dynamically estimate and modify the motor inductance based on Kalman Filter theory. Paper [21] proposes a FCS-MPC based control method for voltage source inverter. This method is able to correct the correct parameters based on ADALINE estimator. However, the adaptive methods proposed in [20; 21] require additional algorithm installation and processing in controller, which inevitably increases the computational burden. In this paper, an Adaptive Model Predictive Control (A-MPC) based three-phase inverter current control method is proposed. The most important contribution of this method is the novel parameter estimation and adapting algorithm, which is simple and efficient. The parameter estimation and modification method is decoupled with the current control loop. No extra control algorithm is used in this estimation method. The parameter estimation can improve the control accuracy. The optimal switching state components in two-phase rotating coordinate system is generated and used as the input for parameter estimation and modulation. Since the variables (e.g., voltages, currents, switching states and their references) are all DC components, the optimal switching state can be calculated directly, avoiding the evaluation of all the available switching states. Therefore, the computational burden is effectively reduced. Simulations and experiments validate the effectiveness of the proposed method and the enhancement in system performance.
# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
###### Abstract  
In spite of excellent performance of deep learning-based computer vision algorithms, they are not suitable for real-time surveillance to detect abnormal behavior because of very high computational complexity. In this paper, we propose a real-time surveillance system for abnormal behavior analysis in a closed-circuit television (CCTV) environment by constructing an algorithm and system optimized for a CCTV environment. The proposed method combines pedestrian detection and tracking to extract pedestrian information in real-time, and detects abnormal behaviors such as intrusion, loitering, fall-down, and violence. To analyze an abnormal behavior, it first determines intrusion/loitering through the coordinates of an object and then determines fall-down/violence based on the behavior pattern of the object. The performance of the proposed method is evaluated using an intelligent CCTV data set distributed by Korea Internet and Security Agency (KISA).  
abnormal; behavior detection; action recognition; visual-based surveillance system +
Footnote †: journal: Applied Sciences  
identify the location of the pedestrian at a given interval, and the kernelized correlation filter (KCF) tracks the location in the rest of video frames. Since the KCF tracker [2] cannot deal with the occlusion problem by itself, we assigned weights to color values to improve the tracking performance. The proposed method consists of two main modules: object detection and abnormal behavior analysis. For accurate analysis of abnormal behavior, we additionally insert the same object identification and inter-module communication subsystems. Pedestrian detection and tracking method is used in a module for identifying the location of a pedestrian, and an intruding or loitering pedestrian in a specific area can be detected using the coordinates of the tracked pedestrian. In this module, pedestrian information is stored to analyze the abnormal behavior of the same pedestrian, and the image of the pedestrian is transmitted to the abnormal behavior analysis module through TCP/IP communication to detect violence or fall. Three-dimensional (3D) ResNet [3] was used for real-time action recognition. We build training data including normal, violent, and falling actions in a CCTV environment. The proposed system is implemented using a deep neural network that is suitable for a real surveillance environment. The performance of the proposed method was evaluated using datasets distributed by Korea Internet and Security Agency (KISA) [4]. This paper is organized as follows: Section 2 summarizes the related works to analyze abnormal behavior. Section 3 describes the proposed abnormal behavior analysis method. Section 4 presents experimental results using KISA datasets, and Section 5 concludes the paper.
## 4 Results and Analysis
### Field Measurement Results and Analysis
#### 4.2.3 Stretching Correction in Pre-Stack Depth Migration  
When the velocity model is iterated in the depth domain, the CRP gather should be flattened as the basic principle so that the imaging effect will be improved. When the model constraint velocity iteration reaches a certain accuracy, the global velocity model is optimized and adjusted by using the three-dimensional grid tomography method. Based on the tomographic optimization velocity model, the data attribute body in the depth domain (the continuity body of the seismic data event, the dip angle body, and the azimuth body; see Figure 11(a-1-a-3)) is extracted. According to the continuity of the formation, the internal reflection layer of the seismic data is automatically extracted to form multiple reflection internal layers in different regions. According to the common imaging point gathered and obtained by pre-stack depth migration, the depth residual velocity of the target line is picked up to form the depth residual velocity body. The above three seismic attribute bodies, depth residual velocity bodies, and previous depth migration layer velocity bodies are considered. Several data volumes such as the internal reflection layer are merged to create a pencil database (see Figure 11b). Each seismic record contains the above information, which lays the foundation for travel time calculation. A global grid tomography matrix containing multiple layers is established. The least squares method is used to solve the grid tomography matrix under the constraints of the above information. Finally, the optimized depth domain layer velocity body is obtained, and migration imaging is performed. Figure 11(c-1-c-3) show the flattening of CRP gathers at the same position during the velocity iteration. When the residual delay of each layer is basically zero, the CRP gathers are considered to have been fully flattened. At this time, the layer velocity is accurate, and the iteration of the velocity model is completed.  
Figure 10: Pre-stack migration velocity modeling: (**a-1,a-2**) time domain velocity modeling; (**b-1,b-2**) depth domain velocity modeling.
## 2 Resistance Mechanism  
Although multiple advances have been made in the treatment of cancer in the last decade, chemotherapy continues to be the most important option, especially in tumors that present metastases or are in advanced stages. The failure of this treatment is mainly determined by the resistance mechanisms presented by the tumor cells, which can be intrinsic or acquired [12]. Once chemotherapy treatment is started, many tumor cells die, but not all of them present in the tumor do. The cells that survive continue to grow and develop resistance to the treatment. This is why, after a while, to overcome this resistance, different antitumor drugs are used in combination with different mechanisms of action to try to kill all the tumor cells [13]. At this point, the cells that survive have MDR and are, to a greater extent, stem cells [14].  
This MDR (Figure 1) can be divided into mechanisms that expel the drug from the cytoplasm to the outside of the cell and mechanisms that prevent the drug from killing the tumor cell [15]. Within the first group, the most common resistance mechanisms are the expressions of P-glycoprotein (P-gp) and MDR-associated protein, representing the MRP family [16, 17]. Efflux pumps on the membranes of tumor cells allow the expulsion of the drug from the cells and drive this mechanism.  
In the second group, we find all the mechanisms that cells possess to avoid cell death, such as DNA repair, drug degradation or inactivation, or even the use of antioxidants or antiapoptotic proteins. There are many antitumor drugs that induce cellular DNA damage, so tumor cells have developed DNA repair mechanisms to limit this damage and thus survive treatment [18]. Enzymes of the glutathione s-transferase family are involved in drug inactivation, which are responsible for degrading the drug directly or indirectly by inhibiting the RAS-mitogen-activated protein kinase (MAPK) (RAS-MAPK) pathway [19]. For the last type of resistance, the involvement of the BCL2 protein is known as one of the most important antiapoptotic mechanisms since its absence is capable of substantially increasing the cytotoxic effect of antitumor drugs [20].
## 4 Outbound-Type Bundle Protocol
### Section between a Relay Node and the Receiving Node  
The message sending sequence between a relay node and a receiving node is shown in Figure 15. Details are described below.  
Figure 14: Structures of the messages exchanged between relay nodes.  
Figure 15: Procedure for relaying a bundle between relay nodes. Note: the bold arrow shows that a bundle copy is sent.  
* Relay node receives a \"Hello message\" from Receiving Node 1. It returns a \"bundle offer\", which contains the bundle identifiers of the bundles that are destined to the destination area in which Receiving Node 1 resides.
* Receiving Node 1 returns a \"bundle request\", which contains the bundle identifiers of those bundles in the \"bundle offer\" that it does not have.
* Relay node sends the requested bundles to Receiving Node 1.
* When Receiving Node 1 comes in contact with another receiving node (Receiving Node 2) while it is still in the destination area, it sends a copy of the bundle to that receiving node.
* Receiving Node 2, which has received the bundle from Receiving Node 1, also sends a copy of the bundle to another receiving node that comes in contact with it, just as Receiving Node 1 did, while it still stays in the destination area.  
Even after the relay node has sent bundles to receiving nodes in the destination area, it continues to relay bundles. Receiving nodes also continue to send bundles to other receiving nodes in the same destination area. Relay nodes can move only on fixed routes. Therefore, relaying of bundles between receiving nodes is necessary if the bundles concerned are to reach all receiving nodes in the destination area. The structures of the messages exchanged above are shown in Figure 16.
## 2 Related Works
In [29], a spatiotemporal enrichment module was proposed that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Moreover, a relational self-attention module was proposed in [30] to dynamically compute attention weights through spatiotemporal self-correlations. Moreover, video-based recognition and action detection methods, like IJD [31] and SlowFast [32], extend 3D CNNs to jointly model spatial and temporal dependencies, demonstrating state-of-the-art performance in video analysis. More recently, ViTs have revolutionized spatiotemporal modeling by leveraging attention mechanisms to capture long-range dependencies. Extensions of ViTs, such as TimeSformer [33] and Video Swin Transformer [34], incorporate spatiotemporal attention to further enhance performance on video tasks. Despite these advancements, applying such methods directly to 3D object recognition with sequential views remains challenging due to the high computational cost of attention mechanisms and the lack of effective multiscale feature integration.
# Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n
## 4 Experiment and Result Analysis
### Comparison of Detection Effects  
To clearly demonstrate the enhanced model's detection capability, multiple sample images with different textures, colors, and shooting angles are randomly selected from the test set in the experiment to ensure the comprehensiveness and diversity of the evaluation. By comparing and analyzing the original YOLOv8n with the improved YOLOv8n, key performance indicators such as detection precision, false detection rate, missed detection rate, and confidence are evaluated to more comprehensively reflect the superiority of the improved method. The comparative results include the original image, YOLOv8n detection result image, and improved YOLOv8n detection result image, as shown in Figures 13-15, respectively. Subfigures a-f in Figures 13-15 represent different sample images.  
According to the detection results in the above figures, the improved algorithm proposed in this paper shows significant improvement in seamless fabric defect detection performance compared to the YOLOv8n. The improved algorithm significantly reduces the missed detection rate and false detection rate and improves the precision of detection. In Figure 14c, detected by YOLOv8n, there is one missed detection and one false detection, while in Figure 14f, there is also one missed detection. These errors indicate that the YOLOv8n algorithm may miss some defects or incorrectly identify certain areas in certain situations. However, the comparison between Figures 14 and 15 shows that the improved algorithm effectively reduces these problems, significantly increases the overall confidence of the improved model, accurately identifies more defect areas, and significantly reduces the occurrence of failure to detect and erroneous detections. These improvements make  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sampling Method** & **P/\%** & **R/\%** & **AP\({}_{\text{f}}\)** & **AP\({}_{\text{a}}\)** & **[EMAIL_ADDRESS]/\%** & **Parameters/M** & **FLOPs/G** \\ \hline Nearest & 89 & 84.3 & 85.3 & 97.7 & 91.5 & 3.0 & 8.2 \\ DySample & 90.5 & 83.2 & 84.9 & 96.3 & 90.6 & 3.0 & 8.2 \\ CARAFE & 91.3 & 90.4 & 89.3 & 97.3 & 93.6 & 3.1 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Upsampling comparative analysis.  
Figure 13: Original images.  
the performance of the model in seamless fabric defect detection more stable and reliable, better meet practical application needs, and provide more efficient industrial automation detection solutions. Therefore, the improved algorithm proposed in this study has greater potential in practical applications, which can effectively improve the detection precision and efficiency of seamless fabric defects.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 3 Proposed Method  
The proposed method, which is illustrated in Figure 2, consists of three main components:  
* The feature extractor CNNs: These CNNs extract the image semantics and deliver deep feature maps. Both proposed architectures, DTS-Net and DTS-Net-Lite, are presented in this section. We also discuss our reasons for choosing depth-wise separable convolution-based architectures.
* DTS layer: This layer aggregates the small feature maps to form a higher-resolution segmentation map (or depth map).
* The nearest label filtration (NLF): This filtration employs multi-label classification labels to filter the segmentation mask patch by patch.
## 3 Results and Discussion
### Chlorophylls and Polythenols Content
In convection drying, materials were used at the temperature of 40-60 \({}^{\circ}\)C; the losses of polyphenols were also significant, higher than in the work of [PERSON] et al. [2]. High losses of polyphenols at low drying temperatures result from the extension of the drying time, with a decrease in the temperature of the drying agent. This causes a longer duration of the exposure of the material to atmospheric oxygen. [PERSON] et al. [2] studied the effect of the drying method and temperature on the retention of polyphenols and indicated the convection method as the one in which the losses are the highest. They found that the vacuum and freeze drying \begin{table}
\begin{tabular}{c c c} \hline
**Drying Method** & **TPC (mg GAE/g d.m.)** & **Chlorophyll a and b Content (mg/g d.m.)** \\ \hline Fresh & 89.087 \(\pm\) 8.515 a & 5.362 \(\pm\) 0.473 a \\ \hline CD 40C & 32.262 \(\pm\) 0.094 d & 2.499 \(\pm\) 0.277 de \\ CD 50C & 33.676 \(\pm\) 1.229 d & 2.311 \(\pm\) 0.085 e \\ CD 60C & 28.701 \(\pm\) 2.191 de & 2.362 \(\pm\) 0.347 de \\ CD 70C & 23.489 \(\pm\) 1.683 & 2.132 \(\pm\) 0.607 e \\ MW-CD 100W 30C & 46.298 \(\pm\) 0.808 b & 3.124 \(\pm\) 0.224 b \\ MW-CD 100W 40C & 41.904 \(\pm\) 2.857 c & 2.970 \(\pm\) 0.138 c \\ MW-CD 200W 30C & 44.246 \(\pm\) 0.744 bc & 2.940 \(\pm\) 0.137 c \\ MW-CD 200W 40C & 42.652 \(\pm\) 2.566 bc & 2.619 \(\pm\) 0.254 cd \\ MW-CD 300W 30C & 39.173 \(\pm\) 2.035 cd & 2.479 \(\pm\) 0.083 d \\ MW-CD 300W 40C & 41.427 \(\pm\) 0.091 c & 2.365 \(\pm\) 0.031 e \\ \hline \end{tabular} Mean values followed by the same small letter (in columns) do not differ significantly at \(\alpha\) = 0.05.  
\end{table} 
Table 2: Polyphenol and chlorophyll content in fresh and dried Moldavian dragonhead leaves. methods are better than convection in terms of preserving the biological activity of the dried material. The higher retention of polyphenols in microwave-convection-dried leaves has been noted by many authors. This is most often explained by a shorter time of exposure of the material to oxygen and higher temperatures. However, it is also possible that microwaves can cause the destruction of plant cells, which increases the extractability of polyphenols. [PERSON] et al. [14] noticed that the degradation of polyphenols in microwave-convection-dried herbs was different in different herbs. Drying caused a significant degradation of polyphenols in malt, oregano, and basil (56, 40, and 34%, respectively, compared with fresh leaves). In turn, lavage, parsley, and rocket leaves were not influenced significantly by the total phenolic content. Among the analyzed variants of microwave-convection drying, the best retention of polyphenols was found in dried leaves in which the microwave power was 100 and 200 W (Table 2). Increasing the microwave power to 300 W caused a decrease in the content of polyphenols in dried leaves of Moldavian dragonhead, which could be the result of the material overheating.