esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
The weights assigned to each criterion and attribute as well as the variables were entered in a Microsoft Excel sheet using the SCB Associates AHP Excel Template. Once the comparison matrix was obtained, the eigenvector and criteria weights were automatically calculated by the SCB Associates AHP Excel Template. To reduce the subjectivity of the estimation and potential personal biases of the cocoa farmers, the comparison matrices were checked for consistency. According to [47], the consistency index (CI) is used to verify the consistency of the comparison matrix. If the CI is less than 0.1 or 10%, the set of judgments can be regarded as reliable, otherwise, it is reviewed to meet the consistency condition. The percentage contribution (weight) of each variable, attribute and criterion was retrieved. The scores assigned to the variables on a scale of 0 to 5 to standardise the measurement unit of each variable were averaged. The value of cocoa agroecosystem resilience was computed from the sum of the product of the 15 weighted variables and their average scores, where AgRe is agroecosystem resilience; V\(i\) is the average variable score and Wi is the weight of the variables.  
\[AgRe=\sum_{i=1}^{i=x}Vi*Wi \tag{1}\]  
A \(t\)-test analysis was conducted on the agroecosystem resilience values to determine whether or not there was a statistical difference between the two study areas. The \(p\)-value, mean and standard deviation values of the variables were calculated. A radar diagram was used to illustrate cocoa agroecosystem resilience in the Offinso Municipal District and Adansi North District.
## 3 Thermal Coupling of Power Components
### Losses Calculation and \(T_{c}\) Comparisons  
Power losses are regarded as the main cause of heat generation through power semiconductor devices. In the testing process, power components are in the on-state, and the conduction losses contribute to the temperature increments for the MOSFET and the diode [27; 28]. The losses for the MOSFET (\(P_{M}\)) and the diode (\(P_{D}\)) can be computed by [29]:  
\[\left\{\begin{array}{c}P_{M}=\ R_{D\text{ }S}l^{2}\\ P_{D}=V_{fd}\ I\end{array}\right. \tag{1}\]  
where \(R_{D\text{S}}\) is the on-state resistance for the MOSFET [24]. The value is 44 m\(\Omega\) for the MOSFET (IRF540N) at ambient temperature (\(T_{a}\)) and the relationship between \(R_{D\text{S}}\) and \(T_{j}\) is given in the datasheet [30]. \(V_{fd}\) is the forward voltage drop of the diode which can be collected by a digital multimeter in the steady-state and \(I\) is the working current.  
Figure 11 gives the comparisons of \(T_{c}\) between single device testing and thermal coupling testing at different separation distances between the power devices and different working currents. When the devices are individually tested, \(T_{c}\) in the steady state is lowest and when the devices are mutually tested, \(T_{c}\) for the MOSFET and the diode display increasing trends and the \(T_{c}\) increases in size with the reduction of the separation distances.  
\begin{table}
\begin{tabular}{c c c c} \hline \(d\) **[mm]** & \(I\) **[A]** & _T\({}_{c\text{-}M}\)_ **[\({}^{\circ}\)C]** & _T\({}_{c\text{-}D}\)_ **[\({}^{\circ}\)C]** \\
## 5 Discussion  
Predicting customers' buying behavior is a significant challenge [25; 26]. Compared to prior approaches, our work using YOLOv5 and DeepSORT demonstrates significant advancement in real-time trend recognition and store management. Conventional methods frequently depend on less complex algorithms or handbook observance, which causes slower processing times and inaccuracy. On the other hand, our technology combines the robust tracking of DeepSORT with the cutting-edge object identification of YOLOv5 to provide a real-time, high-precision understanding of customer behavior. This combination gives retailers more reliable data for well-informed decision making and achieves greater accuracy and efficiency than traditional methods. Moreover, our technology swiftly adapts to changes in consumer behavior or shop design to enormous data quantity across various cameras, although other approaches may confront problems with scalability and adaptation. Our work enhances the accuracy and speed of retail analytics and offers a scalable and adaptable solution that meets the demands of modern retail environments.  
Our method, which uses YOLOv5 and DeepSORT, can be easily incorporated into existing retail infrastructures, such as inventory management platforms and Point of Sale (POS) systems, to address the practical applications of our study. This integration enables retailers to leverage real-time analytics with minimal disruption, providing a more advanced understanding of consumer behavior.  
Retailers have implemented concrete actions based on real-time insights, such as relocating popular items to high-traffic areas, adjusting promotional displays, and optimizing inventory management processes by analyzing product interactions. By tailoring store layouts to foot traffic and product interactions, retailers may improve accessibility and encourage impulsive purchases. For instance, positioning popular items in critical areas based on traffic patterns may enhance the shopping experience and boost revenue. Additionally, our system's real-time inventory management feature guarantees that popular goods are always available, immediately raising consumer satisfaction. Retailers gain a significant advantage over traditional methods by quickly adapting to shifts in customer behavior, enabling them to respond promptly to emerging trends and preferences. Retailers can make real-time, data-driven decisions, creating a more efficient and enjoyable shopping experience.  
Figure 22: Performance comparison of YOLOv5 + DeepSORT vs. other methods.
# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
## 4 Research on Road Pattern Recognition Based on YOLOv8
### Analysis of Pavement Pattern Recognition Results Based on YOLO v8n  
According to the 3178 photos of 21 different modes of pavement taken by the experiment, the pavement pattern recognition model based on YOLO v8n is used for pattern recognition research. Consistent with the pavement pattern recognition based on Resnet 18, 2550 photos were randomly selected from 3178 photos as the training set, and the remaining 628 photos were used as the test set. The recognition results are shown in Table 4.  
It can be seen from the table that the accuracy of pavement pattern recognition can reach more than 90% when the pavement pattern recognition model based on YOLO v8n identifies the small data samples of 21 different modes of pavement. Compared with the pavement pattern recognition model based on Resnet 18, the recognition accuracy is improved by 3.1%, and the recognition accuracy is further improved. Therefore, the pavement pattern recognition accuracy based on YOLO v8 is higher. At the same time, after the training of the recognition model is completed, the recognition time of the 628 pavement  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Imagesize** & **Parameters** & **GFLOPS** & **Top1 acc-val** & **Infer-Time** & **FPS** \\ \hline YOLO v8 & 224 & 1,111,317 & 0.19 & 0.915 & 0.8 & 1250 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of road pattern recognition based on YOLO v8n.  
Figure 4: Classification principle of the YOLO v8n algorithm.  
image data in the test set is only 0.8 s. Compared with the road pattern recognition model based on Resnet 18, the test recognition time is shortened by more than 200%.  
In order to verify the effectiveness of YOLO v8n, the yolov8l model with moderate computational load and comprehensive performance was selected from the other four series models of YOLO v8, including yolov8s, yolov8m, yolov8l, and yolov8x, for pavement pattern recognition. The results were compared with those of YOLO v8n.  
According to the comparative analysis of Tables 4 and 5, the recognition accuracy of Yolov8l is 0.825. Compared with the road pattern recognition model based on YOLO v8n, the recognition accuracy is reduced by 9%, the test recognition time is extended by 150%, and the response speed and recognition accuracy are far lower than those of Yolov8n.  
Therefore, the pavement pattern recognition model based on YOLO v8n has the advantages of better recognition accuracy, response speed, and recognition effect than Resnet 18. However, although the recognition accuracy of the pavement pattern recognition model based on YOLO v8n reaches 91.5%, the recognition accuracy still needs to be further improved in order to improve its applicability.
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 3 The Mechanism and Effect of Vibration Reduction Through Waveform Interference
### Analysis of the Vibration Reduction Effect
When the delay time exceeds 3 ms, the superposition of blasting seismic waves progressively weakens while the vibration duration remains largely unchanged. Within the 0.6 ms \(<\)\(\Delta\)t \(<\) 3 ms range, principal seismic wave trains exhibit staggered main periods yet maintain superimposed vibration phases, resulting in mutual wave interference that reduces vibration energy. Specifically, at 2 ms delay, the vibration reduction effect proves moderately superior to that under other cases. Comparing Tables 6 and 7, point B1 is more suitable for vibration reduction through main-period superposition, while point a and point B2 are more suitable for vibration reduction through main vibration phase superposition. In summary, the vibration reduction effect can be more reliably predicted through main-period superposition analysis. When the condition \(\frac{T}{3}<\Delta t<\frac{2T}{3}\) is satisfied, blasting seismic waves undergo mutual interference, enabling effective vibration reduction via main-period superposition. In contrast, vibration reduction relying on main vibration duration superposition exhibits strong spatial dependency--the same millisecond delay \begin{table}
\begin{tabular}{c c c} \hline \hline
**Point** & **Optimal Delay Time** & **Vibration Reduction Mode** \\ \hline A & 2 ms & Vibration reduction through main vibration phase superposition \\ B1 & 1 ms & Vibration reduction through main-period superposition \\ B2 & 5 ms & Vibration reduction through main vibration phase superposition \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimal delay time and vibration reduction mode at three points. Figure 8: Variation of vibration reduction rate at each node. (**a**) Point A, (**b**) Point B1, (**c**) Point B2. time may yield significantly varied effects across different locations. Consequently, by utilizing site-specific blast seismic wave parameters (e.g., \(\Delta\)t\({}_{\text{m}}\) and T at protected structures), optimal millisecond delay times can be selected to achieve targeted superposition mechanisms. This approach concurrently minimizes vibration impact while maintaining construction efficiency. Parameter Optimization of Millisecond Delay Blasting for Vibration Control in Classified Surrounding Rock  
During propagation, blasting seismic waves undergo attenuation effects from the surrounding rock medium. The attenuation characteristics vary across different rock mass classes, ultimately causing divergence in seismic wave superposition outcomes. Consequently, the optimal delay time becomes contingent on site-specific geological conditions.
## 1 Introduction
Therefore, this paper further introduces a retransmission mechanism to deal with frame loss, and reduces data collection time by using different frequency channels for wake-up messages and data transmission. We implemented the proposed SC-Sched method in the network simulator ns-2 using the parameters of a real WuRx and a data transceiver. Evaluations show that: (i) It is possible to shorten data collection time (caused by wake-up latency) by more than 31% via using multiple channels; (ii) Compared with the conventional duty-cycling method, SC-Sched greatly reduces total energy consumption, by up to 96.1% in a WSN with 86 nodes; (iii) Network lifetime can be improved further, by up to 28.2%, when the number of data collection points is increased from 1 to 4 in the presence of sink mobility; and (iv) SC-Sched prolongs the network lifetime by 7.47 times compared with the conventional duty-cycling method when the number of collection points is 4 in a WSN with 300 sensor nodes. The contribution of this paper is fourfold, as follows:  
1. We propose an energy-efficient data collection method by integrating asymmetric communication and WuR. In the proposed method, the activation timing and routing of each sensor node are controlled by wake-up calls (WuCs) from the sink node. Thereby, each sensor node's active time is reduced to the minimum. 2. The proposed method has an adaptive retransmission mechanism to deal with frame loss of both a WuC and a data frame, which greatly improves system reliability. 3. This paper further proposes to transmit WuCs and data frames on different channels in parallel, which helps to reduce data collection time. 4. All proposed methods are implemented on a network simulator (ns-2) and evaluated by extensive simulations in different respects, e.g., network lifetime, the impact of sink mobility, and comparison with the conventional duty-cycling method. I and II were reported in a conference paper [6]. III is newly added, and IV is greatly enhanced in this paper. The rest of this paper is organized as follows. Section 2 describes related work. Section 3 explains the proposed method and Section 4 presents the simulation evaluation. Finally, Section 5 concludes this paper and points out future work.
## 4 Results and Discussion
### Overall Communication Delay Analysis  
Figure 10 shows the result of probability density estimation for the overall measured communication delay \(T_{\text{c}}\) samples. The parameter \(K\) was set to 2 to distinguish the PDs from ODs, and weights of the PDs and ODs were estimated as 98.67% and 1.33%, respectively. As expected, the result of the probability density estimation for the overall measured communication samples was divided into two distinct Gaussian mixture models (Figure 10). The first Gaussian component, including passive communication delay, dominated 98.67%, and only 1.33% of the total communication delay was considered outlier delay (Figure 10). On the other hand, the second Gaussian component, including outlier delay, had a maximum outlier delay of 323 ms with an average of 113 ms (Figure 10). From the perspective of teleoperation, the smaller the size of the communication delay, the better, which can better guarantee real time; but, on the other hand, it is considered better to compensate as much as possible for delays of 100 ms or more, which can be felt by the human operators.  
Figure 8: Vehicle setup of teleoperation using a CAN network to communicate with each low-level controller and camera communication.  
Figure 9: Experimental setup scene including feedback simulator.  
Meanwhile, many previous studies have evaluated communication delays based on round-trip time (RTT), and their characteristics differ due to reasons such as region and application system setup. In general, looking at previous studies, RTT shows various communication delay results ranging from 0.1 s to 1 s and appears to vary depending on the communication distance and type of network used [42; 43]. Since this is about twice the difference from the one-way communication delay measured in this study, the actual one-way delay can be interpreted as having a range from 0.0 5 s to 0.5 s. In the case of 3G communication, it was found that there was an average RTT of 0.121 s and a maximum of 1 s or more, and, via a satellite network, there was a communication delay of about 0.75 s [42]. In addition, there was actually a study that measured one-way communication delay like this study, and it was found to have an uplink speed of 0.33 s and a video downlink speed of 0.55 s at which control commands are transmitted [43]. In other words, the communication delay results obtained in this study show relatively low communication speeds, suggesting that they are strongly dependent on the type of remote control application and the characteristics of the network used.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 1 Introduction
Finally, the performance evaluation section uses the ONE simulator to assess the proposed model's performance in various simulation scenarios, including message and user numbers, transmission speed, buffer size, and the number of copies. Results demonstrate the model's effectiveness with high delivery probability, low overhead, and reduced latency, comparing these performances against established routing algorithms. The paper concludes by summarizing key findings and discussing the implications of the proposed model for enhancing efficiency and sustainability in wireless communication systems, outlining potential avenues for future research and development.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 2 Background
### Deep Learning
#### 2.2.2 Graphic Neural Networks  
Graph structures are common in COPs, so we need to encode graphs reasonably. In addition, the graph-structured representation is more suitable for some COPs than sequence representation. For instance, the permutation invariant property in the TSP cannot be characterized in the sequence representation. GNNs belong to the category of NNs and are widely utilized in DL methods [55], especially in those scenarios where the data are expressed by graph structures. Considering the representation superiority of graph data, people have begun to leverage the characteristics of graphs to design various types of ingenious GNN structures through integrating other techniques, such as graph convolutional networks [194], graph attention networks [195], and graph recurrent networks [196] over different applications, which range from node-type, edge-type, to graph-type predictive assignments. In general, GNNs are customarily employed to resolve those issues in which the graph-structured data are represented. Five typical problem categories are listed as follows.  
(1) Graph Classification Problem  
Fundamentally, the goal of graph classification is to divide the whole graph into domains with disparate features. In other words, the entire graph is required to be separatedinto different segments in this classification scenario. There are numerous applications of this classification, such as document categorizing [197], social recommendation [198], protein function prediction [199], etc.  
(2) Graph Grouping Problem  
Graph grouping is a class of grouping methods, the task of which is to put together graph data that have similar features [200]. There are also two sub-fields of graph grouping. The first is node clustering, which is based on density. High edge weight or long edge distance corresponds to high density. On the other hand, the graphs are regarded as objects and, hence, grouped based on similarity.  
(3) Node Classification Problem  
For node classification cases, the nodes in the graph are used to represent the label of samples. The objective of this task is to decide which label each node belongs to via checking their neighbor labels. With each node being tagged a specific label, all nodes in this graph system can be grouped into different classifications. The training approach in this type of case is a semi-supervised learning method [201], where only a portion of nodes in the graph are tagged.  
(4) Link Prediction Problem  
To predict whether or not two nodes in a graph exist a link is the research objective of this scenario. This function is widely investigated in a recommendation system consisting of friend recommendation [202], movie recommendation [203] and commerce recommendation [204]. Some COPs are well structured and can be reflected in these typical problems, e.g., MIS can be regarded as a node classification problem with two categories representing whether a node is included in MIS. For more COPs, abundant works are reshaping the graph to using GNNs.
# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
## 4 Experimental Results and Analysis
### Experimental Results and Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Reconstruction**} & \multirow{2}{*}{**Bicubic [5]**} & \multirow{2}{*}{**A+ [11]**} & \multirow{2}{*}{**SRCNN [18]**} & \multirow{2}{*}{**EEDS [20]**} & \multirow{2}{*}{**Proposed**} \\  & & & & & & **DCCNN** \\ \cline{3-7} \cline{6-7}  & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \multirow{3}{*}{_Set5_} & \multirow{3}{*}{\(\times\)2} & 33.64/0.9296 & 36.55/0.9543 & 36.67/0.9541 & 37.30/0.9578 & 37.43/**0.9603** \\  & & 30.38/0.8681 & 32.57/0.9089 & 32.76/0.9091 & 33.46/0.9190 & **33.59/0.9204** \\  & & 28.41/0.8106 & 30.29/0.8602 & 30.49/0.8627 & 31.15/0.8782 & **31.32/0.8842** \\ \hline \multirow{3}{*}{_Set14_} & \multirow{3}{*}{\(\times\)2} & 30.23/0.8687 & 32.29/0.9058 & 32.43/0.9062 & 32.82/0.9104 & **32.95/0.9115** \\  & & 27.54/0.7743 & 29.14/0.8187 & 29.29/0.8208 & 29.61/0.8283 & **29.70/0.8307** \\ \cline{1-1}  & & 26.01/0.7028 & 27.31/0.7492 & 27.48/0.7502 & 27.81/0.7625 & **28.13/0.7696** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) at different reconstruction scales on Set5 and Set14 datasets. Figure 7: Super-resolution reconstruction results of the image “Comic” with”\(\times\)3” scale factor. (a) Bicubic [5]; (b) A+ [11]; (c) SRCNN [18]; (d) EEDS [20]; (e) The Proposed Method; (f) Original Image.