esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flame Detection Algorithm Based on Improved YOLOv7
## 5 Results and Analysis  
In our experiments, we measured the performances of our improved model as well as the original YOLOv5 and YOLOv7, and the results are shown in Figure 7. In order to effectively compare these models, we used the same data set and the same training parameters and methods in the experiments for each model. In Figure 7, the so-called _YOLOv7-Improved-1_ indicates the network only with the SimAM attention mechanism shown in Figure 4, the so-called _YOLOv7-Improved-2_ indicates the network only with ConvNeXt shown in Figure 5, and the so-called _YOLOv7-Improved-3_ indicates the network with both of SimAM attention mechanism and ConvNeXt. It can be easily seen from Figure 7 that there is no underfitting and overfitting during all experiments.  
In order to avoid the randomness, the experiments for each model are repeated three times, and we choose the average value as the final result. An overview of our experimental results is given in Table 1. It can be easily seen that the overall performances of the original YOLOv7 has been significantly improved compared to YOLOv5. Further, the performances of YOLOv7-Improved-1 and YOLOv7-Improved-2 are already higher than the original YOLOv7 in each metric, and YOLOv7-Improved-3 is still much stronger than YOLOv7-Improved-1 and YOLOv7-Improved-2. The mAP_0.5 of the YOLOv7-Improved-3 has been increased by 7% compared to the original YOLOv7, the detection accuracy (precision) has been increased by 5.3%, and the F1 score has been increased by 4.1%.  
Figure 7: Comparisons of mAP, recall curves, and loss curves of various models: (**a**) mAP curves; (**b**) Recall curves; (**c**) Loss curves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAP\_0.5** & **Precision** & **Recall** & **Parameters (M)** & **F1** \\ \hline YOLOv5 & 0.499 & 0.491 & 0.613 & 46.10 & 0.451 \\ \hline YOLOv7 & 0.544 & 0.543 & 0.621 & 36.40 & 0.560 \\ \hline YOLOv7-Improved-1 & 0.580 & 0.570 & 0.632 & 39.73 & 0.583 \\ \hline YOLOv7-Improved-2 & 0.585 & 0.577 & 0.630 & 40.52 & 0.576 \\ \hline YOLOv7-Improved-3 & 0.614 & 0.596 & 0.635 & 50.38 & 0.601 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with the original YOLOv5 and YOLOv7.  
For comparative analysis of various models, we randomly selected three images from the test set and used the models of YOLOv5, YOLOv7, and YOLOv7-Improved-3 to detect the flame in these images. The detection effects are shown in Figure 8.  
From Figure 8, we can see that for Image a, the confidence of YOLOv5 is 58%, the confidence of YOLOv7 is 72%, and the confidence of YOLOv7-Improved-3 has reached 82%. Obviously, the confidence of YOLOv7-Improved-3 has been significantly improved. For Image b, YOLOv5 cannot detect the target in the lower right corner, while YOLOv7 and YOLOv7-Improved-3 detected it successfully, and the confidence of YOLOv7-Improved-3 is higher than YOLOv7. For Image c, both YOLOv5 and YOLOv7 have serious false detections due to the complex background and environment, while YOLOv7-Improved-3 detected the flame with a high degree of confidence.
## 3 Methodology
### Statistical and Machine Learning Methods
#### 3.2.4 Regression Models
By measuring each feature's contribution to reducing prediction error, RF not only provides accurate predictions but also identifies key factors influencing pollutant concentrations, such as WS or RainF. These strengths make RF a versatile and reliable tool for environmental modeling, providing both predictive power and critical insights into the dynamic interactions between meteorological parameters and air pollutants. Its robustness and adaptability ensure its effectiveness in addressing the complex challenges of air quality management. The RF machine learning approach enhances temporal awareness by introducing lagged features for the variables, which are shifted by one time step. These lagged features help the model capture short-term patterns, such as pollutant buildup or weather effects, which improve pollutant prediction. Since RF does not inherently handle time, these engineered features add valuable context. Rows with missing values from the lagging process are then removed to ensure clean data for training. Both MLR and RF models were trained using the dataset of air pollutants and meteorological parameters collected between 2021 and 2023. A rigorous technique for adjusting hyperparameters like \"max_depth\" and \"n_estimators\", 5-fold cross-validation, was used to optimize the model. In addition, cross-validation guarantees that the model does not overfit and that it performs effectively when applied to new data. The performance of these models was evaluated using the coefficient of determination (R2), which measures the proportion of variance in pollutant concentrations explained by the predictors. By combining the strengths of both models, this study provides a comprehensive understanding of how meteorological factors impact air pollutants. The insights gained contribute to the development of robust predictive models, which are essential for informed air quality management.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 6 Discussion  
This study systematically introduces the overall structure, workflow, and control architecture of a fully servo-controlled four-roll bending machine. The machine integrates a modular roll system architecture with a distributed multi-stage motion control system, enhancing equipment scalability and forming stability. The system employs a multi-axis control strategy that integrates the motion controller and servo drives. This approach achieves a positioning accuracy of \(\pm\)0.01 mm. Experimental results demonstrate that the equipment maintains an error control within 1%, with an average rolling error of 0.798%. Compared to conventional roll bending processes, the residual straight edge length is significantly reduced, validating the effectiveness of the proposed solution. This machine offers advantages including high precision, intelligent operation, and excellent arc integrity, demonstrating broad application prospects in aerospace, rail transportation, and other advanced manufacturing sectors while providing robust support for high-end equipment production.  
It is noteworthy that this study merely proposes and establishes a database conceptual framework covering partial profile specifications. Future research directions may focus on deeply integrating machine learning technologies, optimizing data integration within the database, and enhancing the real-time invocation mechanism of the machine towards the database. By incorporating machine learning algorithms, intelligent analysis and prediction of complex data generated during the roll bending process can be achieved, enabling dynamic adjustment of rolling parameters to further enhance bending accuracy.  
Figure 19: Unilateral Residual Straight Edge Comparison: (**a**) Conventional Roll Bending Residual Straight Edge; (**b**) Proposed Machine Roll Bending Residual Straight Edge. The red boxes indicate the residual straight edge regions.
## 3 Results  
### Repeatability Test  
To assess the repeatability of the measurements, a specific test was conducted on a subset of samples. Measurements were taken in triplicate for each sample, in the same operating conditions and using the same instrument (Sauter machine). Repeatability was analyzed by calculating the Intraclass Correlation Coefficient (ICC), which yielded a value of 0.72, indicating good measurement repeatability. This sufficient repeatability allowed for the use of the average measurements in subsequent tests.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 3 Background: iSDVN System
### Single-Hop and Multi-Hop Network Connectivity in SDVNs  
Single-hop connectivity refers to direct connectivity from source to destination nodes without intermediate nodes. Here, packets are transmitted directly to the destination node with minimum delay, as there are no intermediate nodes between the source node and the destination node, as shown in Figure 1. The apparent benefit of single-hop connectivity in a VANET is that there will be direct vehicle-to-vehicle communication, ensuring low latency [88]. But, the limitation of single-hop connectivity is the limited transmission range. When the source node and destination node are far apart, with the limited transmission range of single-hop networks, data traffic must be routed to the destination over a number of hops through intermediate nodes to reach its destination node.  
A multi-hop network with intelligent message broadcasting can reduce the burden on the communication channel, as shown in Figure 1. Also, multi-hop routing could significantly improve network capacity and throughput in wireless networks. In VANETs, most of the safety-related information is broadcasted to all nodes in the network, thereby increasing the traffic congestion in the network [89]. However, researchers have proposed multi-hop routing protocols to resolve the problem of congestion in VANETs.
# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
###### Abstract  
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture-condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.  
OSA-CBM; rolling element bearing fault; the internet of things; arrowhead framework +
Footnote †: journal: Applied Sciences
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
###### Abstract  
Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.  
bearing fault diagnosis; transfer learning; graph convolution network; domain adaptation +
Footnote †: journal: Applied sciences
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 2 Material and Methods
### Predicting Wheat PY Based on the EEO Theory
#### 2.2.3 Predicting the Optimized Sowing Date  
The prediction of optimized sowing dates is based on the theory that an adequately managed agricultural system will optimize the crop calendar to maximize PY. We use two thresholds to constrain sowing dates. First, winter wheat cannot survive extreme cold; therefore, we set a threshold of \(-10\)\({}^{\circ}\)C for the mean temperature of the coldest month to determine whether winter wheat or spring wheat should be planted [57]. Second, heavy monsoon precipitation can hinder wheat germination, so a monthly precipitation exceeding 150 mm is considered unsuitable for winter wheat sowing [58]. We assume that winter wheat can be sown at any time, and we simulate PY (using the methods described in Sections 2.2.1 and 2.2.2) for all possible sowing dates to identify the optimal date that maximizes PY. The effectiveness of this approach has been validated through a global-scale evaluation [48].
# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 2 Triboelectric Nanogenerator
### Working Modes of Triboelectric Nanogenerator  
There are four basic working modes of a TENG: vertical contact-separation (C-S) mode, lateral-sliding (L-S) mode, single-electrode mode (S-E), and freestanding-layer (F-E) mode (Figure 2a). This review mainly introduces the common vertical contact-separation mode and single-electrode mode. The vertical contact-separation mode is the most fundamental mode of the TENG. It is composed of two friction layers with different electronegativities and electrodes stacked, where the electrodes are located at the opposite ends of the contact surfaces of the two friction layers. Under the action of an external force, the two friction layers approach each other and electron exchange occurs, which causes the two friction layers to carry equal amounts of opposite charges, respectively. When the two friction layers separate, the equal amounts of opposite charges between them form a potential difference. If the two electrodes are connected through an external load, electrons flow from the high-potential electrode to the low-potential electrode to achieve charge balance. When an external force is applied to the friction layers again, the transferred charges return to the original electrodes and generate a reverse current. When the friction layers repeatedly make contact and separate, the TENG can generate periodic alternating current (Figure 2(bi)).  
In the single-electrode mode, the friction layer consists of a freely moving object and a friction-layer material with different electronegativity. Compared with the vertical contact-separation mode, it has one fewer electrode (Figure 2(bii)). When the contact surfaces of the moving object and the friction layer approach or move away from each other, the local electric-field distribution can be changed, creating a potential difference between the electrode and the ground wire, thus driving the flow of electrons and generating current output. The simplification of the structure in this mode has advantages for harvesting mechanical energy in certain specific situations. However, due to the limitation of the  
Figure 2: Working modes of the TENG. (**a**) Four classic working modes of the TENG. (**bi**) Working mechanism of the vertical contact-separation mode. (**bii**) Working mechanism of the single-electrode mode.  
electrostatic shielding effect, the electrical output of the single-electrode is weak, making it unsuitable for application scenarios that require a relatively strong electrical output.
## 3 Results
### Additional Findings  
There was a total of seven discrepancies between the conventional H&E staining and eFuCM regarding pattern classification. Two of them were considered to be major discrepancies, corresponding to acute hepatitis which was misclassified as cirrhosis. In these cases, extensive areas of confluent necrosis were not easily identified on the eFuCM images, being confused with bands of fibrosis. The remaining discrepancies were considered to be minor and they consisted of a normal liver being misdiagnosed with very mild acute hepatitis on eFuCM (two cases), cirrhosis being classified as chronic hepatitis with advanced fibrosis (two cases) and mild steatosis not being identified on the eFuCM images (one case). Some of these situations are represented in Figure 4.  
Additionally, cytoplasmatic changes in hepatocytes (ballooning, pigments or Mallory-Denk bodies) were hardly identifiable, even retrospectively as they were very subtle. Also, bilirubinostasis could not be identified in the eFuCM images. Low degrees of fibrosis (less than F3) were not visible on the eFuCM images, or the H&E slides, with a Masson's Trichrome stain being necessary in these cases.  
Although we intentionally sampled non-tumoral livers, the biopsy from case 20 also contained a metastatic colorectal carcinoma. Neoplastic glandular structures were easily identified and compared with conventional H&E histology in that case (Figure 4e,f).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Histopathological Patterns**} & **Pathologist 1 with No eFuCM** & **Pathologist 2 with eFuCM** \\  & **Experience (Kappa 2)** & **Experience (Kappa 3)** \\ \hline Normal or minimal changes & 0.467 & 1 \\ Steatosis & 0.679 & 1 \\ Acute hepatitis & 0.834 & 0.603 \\ Chronic hepatitis & 0.834 & 1 \\ Tumor & 1 & 1 \\ \hline eFuCM: Ex vivo Fusion Confocal Microscopy. & & \\ \end{tabular}
\end{table}
Table 4: Reproducibility of histopathologic patterns by Ex vivo Fusion Confocal Microscopy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Histopathological Features**} & **Pathologist 1 with No eFuCM** & **Pathologist 2 with eFuCM** \\  & **Experience (Kappa 2)** & **Experience (Kappa 3)** \\ \hline Fibrotic septa & 0.597 & 0.838 \\ Cirrhosis & 0.667 & 0.840 \\ Portal inflammation & 0.597 & 0.565 \\ Grade of portal inflammation & 0.233 & 0.497 \\ Lobular inflammation & 0.247 & 0.802 \\ Grade of lobular inflammation & 0.158 & 0.492 \\ Steatosis & 0.519 & 0.750 \\ Grade of steatosis & 0.519 & 0.603 \\ Tumor & 0.779 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reproducibility of histopathologic features by Ex vivo Fusion Confocal Microscopy.
## 5 Performance Analysis and Discussion
### Performance Metric
#### 5.2.1 Normalized Deadline (\(Nd\))  
To investigate the quality of results and for the purpose of comparison, deadline is normalized, called \(N\!D\) (Normalized Deadline). \(MP_{W}\) is the minimum makespan or schedule length of the workflow DAG. It can be obtained by computing the makespan of the workflow on fastest VM types with a maximum level of parallelism and ignoring delays. With these values, the normalized user-defined deadline constraint is computed by Equation (23):  
\[ND=\frac{DL_{W}}{\min(MP_{W})} \tag{23}\]  
For successful schedule map that meet the deadline constraint, the value of \(N\!D\) could not be less than 1.
## 2 Strategies for Establishing Optical Links  
We designed an acquisition process for satellite-to-sea laser communication. The communication handshake is initiated by shipborne ATP. The terminals on each side of the communication drive their respective optical antennas to roughly align with each other's instantaneous positions according to the ephemeris and attitude information. Shipborne ATP has high-precision pointing ability and can transmit uplink beacon light to accurately cover the satellite terminal. After the satellite coarse Charge Coupled Device (CCD) detects the beacon light of the shipborne ATP, closed-loop tracking is carried out with high accuracy. The satellite terminal emits downlink beacon light. After the downlink beacon light is detected by the shipboard ATP coarse CCD, stable closed-loop tracking is conducted to complete the acquisition. After establishing a stable communication link between the satellite and the sea, laser communication is achieved until the satellite passes through. The acquisition strategy is summarized in Figure 1.
## 3 Results and Discussions
### Influence of Vacuum Impresation Parameters on Soluble Phenols into Apple Slices
impregnated TSP content increased with increasing impregnation and restoration times, independent of vacuum pressure (Figure 2A-C). However, at a higher (\(-0.4\) bar) vacuum pressure (Figure 2C), greater impregnation of soluble phenols was achieved with less impregnation time, considering seven minutes of impregnation time and three minutes of restoration time. Additionally, the most significant parameters during VI of soluble phenols from the HS extract in apple slices are presented in the Pareto Chart with a 95% confidence level (Figure 2D). The impregnation time (\(X_{IT}\)), restoration time (\(X_{RT}\)), and interaction between impregnation time \(\times\) vacuum pressure (\({X_{IT}}^{2}\)\({}^{*}\)\({X_{VP}}\)) were identified as the primary factors influencing the VI process. Previous research has indicated that increasing the processing time improves grape juice impregnation in apple cubes, whereas higher vacuum pressure facilitates the impregnation process [19]. The restoration time is critical during the VI process because it enhances the relaxation/deformation phase, allowing liquid influx due to capillary pressure [21]. Some authors have reported that long restoration times (>30 min) are needed to improve VI results [36]; however, it has been reported that five minutes of restoration time after VI is sufficient to increase the phenolic compounds in yam bean slices and mentioned that food matrix porosity and solution characteristics are critical factors involved in the effectiveness of VI [37]. Nevertheless, some authors have noted a reduction in the soluble phenol content within the food matrix during VI, associated with an increase in vacuum pressure during impregnation due to the breakage of plant cell walls and the lixiviation of phenolic compounds [38]. Therefore, optimization of the VI process in each food matrix is required.