esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cross-Region Evaluation of Model Generalization  
To further evaluate the generalization capability of MSFNet under different geographic conditions, we conducted a cross-region test using images collected from a wheat field located in a different area to the training sets. The additional dataset was collected from the Experimental Base of the Shaanxi Hybrid Rapeseed Research Center (as shown in Figure 9), which is geographically distinct from the area used for the original MSWDD2024 dataset. The collection and processing process of the data is consistent with that described in Section 2.1.  
As shown in Table 10, MSFNet continued to exhibit excellent performance on the new test set collected from different fields. Specifically, the overall accuracy, precision, recall, and \(F1\)-_score_ reached 94.7%, 94.6%, 95.0%, and 94.7%, respectively. Among the four classes, the model performed best on the healthy and stripe rust categories, with both exceeding 97% in all evaluation metrics. The remaining categories also maintained metrics above 90%, demonstrating the model's strong robustness and generalization capability across different geographic conditions.  
Compared to the results obtained on the test set from the original field (as shown in Table 5), the performance drop was marginal--most metrics decreased by less than 1%. These results suggest that MSFNet maintains a reliable prediction accuracy even when applied to regions beyond the training domain, confirming its potential for practical deployment in varied agricultural environments.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metrics** & **Healthy** & **Others** & **Stripe Rust** & **Yellow Dwarf** & **Total** \\ \hline Accuracy & 98.1\% & 93.4\% & 90.3\% & 97.6\% & 94.7\% \\ Precision & 98.7\% & 91.2\% & 91.9\% & 97.2\% & 94.6\% \\ Recall & 98.0\% & 92.7\% & 92.0\% & 98.1\% & 95.0\% \\ \(F1\)-_score_ & 98.3\% & 91.9\% & 91.9\% & 97.5\% & 94.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Cross-region evaluation results of MSFNet.  
Figure 9: Wheat field at the Shaanxi Hybrid Rapeseed Research Center.
# PAPR Reduction in UFMC for 5G Cellular Systems
###### Abstract  
Universal filtered multi-carrier (UFMC) is a potential multi-carrier system for future cellular networks. UFMC provides low latency, frequency offset robustness, and reduced out-of-band (OOB) emission that results in better spectral efficiency. However, UFMC suffers from the problem of high peak-to-average power ratio (PAPR), which might impact the function of high power amplifiers causing a nonlinear distortion. We propose a comparative probabilistic PAPR reduction technique, called the decomposed selective mapping approach, to alleviate PAPR in UFMC systems. The concept of this proposal depends on decomposing the complex symbol into real and imaginary parts, and then converting each part to a number of different phase vectors prior to the inverse fast Fourier transform (IFFT) operation. The IFFT copy, which introduces the lowest PAPR, is considered for transmission. Results obtained using theoretical analysis and simulations show that the proposed approach can significantly enhance the performance of the UFMC system in terms of PAPR reduction. Besides, it maintains the OOB emission with candidate bit error rate and error vector magnitude performances.  
universal-filtered multi-carrier (UFMC); peak-to-average power ratio (PAPR); selective mapping (SLM) Article  
APR Reduction in UFMC for 5G Cellular Systems  
[PERSON]  
1 Applied Electrical Engineering Department, King Saud University, Riyadh 11451, Saudi Arabia; [EMAIL_ADDRESS]  
2 Department of Electronics and Electrical Communications, Faculty of Engineering, Tanta University, Tanta 31527, Egypt; [EMAIL_ADDRESS]  
3 KACST-TIC in Radio Frequency and Photonics (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia; [EMAIL_ADDRESS]  
4
# Merging Spatio-Temporal Objects and Business Processes: Land Reform Process Case Study
###### Abstract
This framework is designed to depict real-world geographical objects while also illuminating their associations with various management tasks. By combining a conceptual spatio-temporal model with business process diagrams, we deliver a solution that gives the user the ability to clearly illustrate, track, and explain how the geographical characteristics of objects are and will be impacted in response to well-defined external processes. In this paper, we present a comprehensive GIS framework that leverages the synergies between MADS (Modeling of Application Data with Spatio-Temporal features) and BPMN (Business Process Model Notation). Geographical data are conceptually represented using MADS, while associated management processes are conceptually described in BPMN. The MADS diagram forms the information model of related BPMN process models. This conceptual separation between objects and processes is novel in the domain of geographical data and vital for maintaining flexibility and adaptability as requirements evolve. The MADS-based conceptual model in our approach is thoughtfully designed to be shared among a wide array of organizations, each with distinct BPMN-based business models. This thoughtful design allows it to seamlessly integrate with various business models, thereby fostering data reuse, promoting extensibility, and establishing a clear separation between data and process modeling. The overall conceptual framework is implemented and used in the relational database management system PostgreSQL/PostGIS. MADS diagrams are first encoded as a relational database; therefore, the BPMN diagram boosted with OCL logical rules is encoded as a Petri net to define the link with PostgreSQL/PostGIS [6; 7]. Indeed, to ensure the executability of our framework, OCL (Object Constraint Language) contracts are added to BPMN to specify which SQL statements must be applied to the database when an activity is executed [8]. Petri nets are formal tools that follow rigorous mathematical principles (e.g., place, transition, input, and output), which we have chosen to formalize the control flow of annotatedBPMN models [9]. The logical implementation of the conceptual and business model into PostgreSQL/PostGIS, meticulously adhering to standard SQL practices, serves as a practical demonstration of the adaptability of our framework to handle data on a larger organizational scale. The rest of the paper is structured as follows. After a quick review and comparison of existing conceptual models, Section 3 introduces the main principles behind the MADS and BPMN models. In Section 4, we discuss the technique and benefits of merging MADS and BPMN. In Section 5, we apply our approach to an illustrative case study. Lastly, we give concluding remarks in Section 6.
## 5 Discussion
### Verification  
Three comparison methods were used in this research to verify our approach. These were: (1) unsupervised classification using the SPOT-3 satellite multi-spectral state (XS) image map of Chen Youlanxi in each period to determine changes in river-terrace area; (2) comparison of river terraces in various periods with satellite images and aerial photos; (3) comparison of historical disaster data from the Chenyulan River, covering a total of 87 floods linked to 42 discrete weather events from August 1959 through October 2009 [43].
###### Abstract  
The kinematic and kinetic performance of tennis players differs across skill levels, with joint range of motion (ROM), moments, and stiffness being strongly linked to injury risk. Focusing on the biomechanical characteristics of lower-limb joints throughout the landing stage, especially among athletes of different skill levels, aids in understanding the link between injury risk and performance level. This study recruited 15 male campus tennis enthusiasts and 15 male professional tennis players. The kinematic and kinetic differences between amateur and professional players during the landing phase of the tennis serve were analyzed using SPM1D 0.4.11 and SPSS 27.0.1, with independent-sample \(t\)-tests applied in both cases. Throughout the tennis serve's landing stage, the professional group exhibited significantly greater sagittal plane hip-joint stiffness (\(p<0.001\)), horizontal plane moment (59-91%; \(p=0.036\)), and a significantly higher peak moment (\(p=0.029\)) in comparison with the amateur group. For the knee joint, the professional group exhibited significantly larger ROM in flexion-extension (0-82%; \(p=0.003\)); along with greater ROM (0-29%; \(p=0.042\)), moment (12-100%; \(p<0.001\)), peak moment (\(p<0.001\)) in adduction-abduction; and internal-external rotational moments (19-100%; \(p<0.001\)) were markedly higher. The professional group showed significantly higher ankle joint ROM (\(p<0.001\)) and moments (6-74%; \(p=0.004\)) in the sagittal plane, as well as greater horizontal-plane ROM (27-67%; \(p=0.041\)) and peak moments (\(p<0.001\)). Compared with amateur tennis players, professional tennis players exhibit greater ROM, joint moments, and stiffness in specific planes, potentially increasing their risk of injury during the landing phase.  
kinematics; dynamics; tennis; platform serve; lower limbs; sports injury +
Footnote †: journal: Applied sciences
# Immersive Safe Oceans Technology: Developing Virtual Onboard Training Episodes for Maritime Safety
## 3 Identifying the Research Challenges in Virtual Training in Maritime Safety
### Needs  
Growing demand for onboard training in maritime safety is a recognized need all over the world. Various shipping companies are not able to train their seafarers in sophisticated simulation centers due to the lack of centers availability and time. To offer onboard VR training opportunities, there is a lack of novel VR training episodes collections to be used in maritime safety. The use of learning analytics in onboard training will be needed to give immediate feedback with highly motivated learning content. In addition, there is a need for proving the effectiveness of VR training in onboard environments, and the shipping industry needs to learn how to utilize VR training efficiently in their business to improve maritime safety.
## 2 Materials and Methods
### Experimental Materials  
The dataset used in the experiments comprised of 3D MR spine images of 20 patients sourced from Spineweb dataset 10 [23; 24]. Among this dataset, the images used in actual experiments are 1 to 3 mid-sagittal images per patient, totaling 25. The pixel size of images is 1.5 \(\times\) 1.5 mm. Label data were made manually by a spine MR researcher and reviewed by a radiologist with an experience of more than 10 years. The experiments were implemented using 5-fold cross validation and each experiment had 5 test images and 20 training images. For fair validation of the network, all images from a single patient were used exclusively for either training or test.  
The segmentation accuracy was evaluated using a DSC [12], and to assess the accuracy of measurement of fine details the evaluation was divided into the following three parts: (1) whole area; (2) boundary area; (3) boundary area with 2 pixels' thickness. The first part evaluates segmentation accuracy of the entire area of intervertebral discs. The second and third parts evaluate the accuracy of the boundaries of the intervertebral discs whose boundary thickness was defined as 1 pixel and 2 pixels, respectively. A modified Hausdorff distance (MHD) was also used to evaluate the segmentation accuracy [25]. Smaller MHD indicates the better segmentation performance. Paired \(t\)-test [26] was used to compare the results for three types of measurements; \(p\)-values below 0.05 were considered statistically significant.  
Conventional U-net and dilated U-net were compared with BSU-net. Dilated U-net is a network in which dilated convolution is applied to conventional U-net. In the structure of dilated U-net used in this study, max-pooling layers used in conventional U-net are replaced with convolutional layers with stride 2, and dilated convolution blocks are placed before each convolutional layer with stride 2. Dilated convolution blocks are composed of three concatenated dilated convolutional layers whose rate is 1, 2, and 3 respectively, and a convolutional layer placed after them. Activation function (rectified linear unit (ReLU)) and batch normalization were used after each convolutional or dilated convolutional layer.  
The proposed network and all the neural networks used in our experiments were trained and tested using Google tensorflow library based on python 2.7 (Google, Mountain View, CA, USA) [27]. The computing hardware used in the experiments were as follows: GPU, NVIDIA GeForce GTX 1080 (NVIDIA Corp., Santa Clara, CA, USA); CPU, 3.60 GHz Octa core (Xeon, Intel, Santa Clara, CA, USA); Memory, 32 GB. Hyper parameters applied to the experiments were as follows: Learning rate was \(10^{-3}\), total training epoch was 200, and optimizer was Adam. All images used as input for the networks were resized to 256 \(\times\) 256 size matrix and normalized to values between 0 and 1.
## 3 Experimental Results
### Segmentation Results
The proposed method presents the highest execution time in the comparative as a trade-off for its high segmentation accuracy. The computational time demanded by the proposed method is directly related to the number of multiple scales of the Gaussian filter applied in the spatial domain and the Gabor filters performed in the frequency domain. Figure 11: **(a)** Ten angiograms from the testing set of 30 images, and **(b)** their respective ground-truth. The following rows are the segmentation response obtained by **(c)** the proposed method, **(d)** the the GMF/Entropy Maximization [8] method, **(e)** the U-Net ([PERSON] et al.) [39] method, **(f)** the Multiscale Top-hat/Background Unification [2] method, **(g)** the Top-hat/Background Unification [1] method, **(h)** the Multiline Detector/Threshold (0.56) [25] method, **(i)** the Hessian Matrix-based/Vessel Repair [5] method, **(j)** the Multiscale GMF/Otsu [17] method, **(k)** the Single-scale Gabor filters/Otsu [15] method, **(l)** the CNN ([PERSON] and [PERSON]) [24] method, **(m)** the GMF/Degree-based [38] method, **(n)** the CNN ([PERSON] et al.) [23], and **(o)** the GMF/Local Entropy [37] method. Finally, it is important to point out that the proposed method for the automatic segmentation of coronary arteries achieved the most accurate results using the present database and different evaluation metrics for each of the vessel detection and segmentation steps. The high segmentation accuracy can be attributed to the multiscale analysis performed in both spatial and frequency domains along with the statistically determined artificial neural network. In addition, given the efficiency obtained by the proposed method and the competitive computational time, it could be integrated into a computer-aided diagnosis system to support cardiologist during the decision-making process.
## 3 Experimental Results
### Analysis of Strain Response
#### 3.2.1 The Strain Differences between Two Layers  
The characteristics of strain curves were significantly different between two layers. Taking the strain curves of specimen A1 and B1 for example, the two strain gages are at the middle of the upper and lower layers, respectively (Figure 12). The strain response of the upper strain gage was more intense than the lower strain gage, and the initial response time of the lower gage was later than that of upper gage, because the stress waves produced by the pressurization of perforation pressure propagated to the upper gage first. So, the strain curve of upper gage could better reflect the propagation of the fractures, and in the later section, only strain curves of upper gage were used to analyze the results of experiments.
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 1 Introduction
Once the residual energy of all member nodes in a subspace gets below the threshold level, then the RC node election is based on node's fitness value that considers the factors of average distance of member nodes and residual energy of a node. This protocol utilizes the periodic data gathering at an RC node from its member nodes and hierarchical data forwarding from an RC node to the sink. An RC node selects an intermediate RC node that has a higher fitness value and minimizes the number of hops towards the sink. With the benefit of uniform node distribution, RC node concept and the proposed data forwarding strategy, the EDR protocol prolongs the steady state and the network lifetime adequately. The rest of the paper is organized as follows. Section 2 reviews the related works. Section 3 presents the EDR protocol with the proposed network model and assumptions. Section 4 presents the performance evaluation. Finally, Section 5 concludes this work.
## 3 Results
### Comparisons with Related Methods  
Considering that there are some attributes with more than two categories for an attribute in the NEU-dataset, the existing algorithms are binary classifications for a certain attribute. Thus, the proposed method is not compared with other algorithms in the NEU-dataset. In order to compare this algorithm with the state-of-the-art algorithms, we used RAP datasets that are large and frequently applied to compare with other algorithms. In Ref. [23], the attribute was selected when the positive examples' ratio in the dataset was higher than 0.01. To prove the performance of the proposed method, we compared the following methods to the benchmark of the RAP dataset.  
Figure 4: Different attributes’ ROC and PR curves on NEU-dataset. The first and third rows are ROC curves, and the second and fourth rows are the corresponding PR curves.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Attributes** & _Acc_ & _PRR_ & _NRR_ & _mA_ & _Prec_ & _Rec_ & _F1_ \\ \hline
**Handbag** & 78.02 & 70.86 & 79.18 & - & 35.47 & 70.86 & 47.28 \\
**Carrying** & 84.77 & 87.77 & 82.33 & - & **80.12** & 87.77 & **83.77** \\
**Calling** & 88.33 & **95.95** & 87.88 & - & 31.99 & **95.96** & 47.98 \\
**Upcolor 1** & 69.95 & - & - & - & - & - \\
**Upcolor 2** & 77.76 & - & - & - & - & - \\
**Lowercolor** & 78.90 & - & - & - & - & - \\
**Average** & **80.92** & **78.23** & **81.81** & **75.23** & **47.84** & **80.44** & **58.33** \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._SVM and features based methods_: Considering each attribute independently, a linear SVM model is trained for each attribute through features ELF, FC6 and FC7, which are used in Reference [23], i.e., ELF-mm, FC7-mm and FC6-mm.  
The _ACN (Attribute Convolutional Net)_[7] method uses the CNN model to learn different attributes and recognize multiple attributes simultaneously with a jointly-trained holistic method.  
DeepMAR (Deep learning-based Multi-attribute joint recognition model) [9] method, whose object function for multi-attribute recognition is based on prior knowledge.  
M-Net method and HP-Net method [25] method: M-Net is a plain CNN architecture, and the HP-Net method is a multi-attribute recognition with multi-directional attention modules and M-Net.  
In this paper, eight attributes (the first eight listed in Table 2) are selected to compute the \(mA\), _Acc_, _Prec_, _Rec_, and \(F1\). In addition, we compare the proposed method with the benchmark of the RAP. The comparison results are shown in Table 4. As shown in Table 4, the proposed algorithm is obviously superior to other algorithms in the \(mA\), _Acc_ and _Rec_ indexes, especially the \(mA\) of the proposed algorithm is obviously improved compared with other algorithms. Therefore, the proposed algorithm has certain performance compared with other methods. However, the _Prec_ and \(F1\) indexes of the proposed algorithm are not superior to other algorithms and there is still room for improvement.
# A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model
## 1 Introduction
To solve the above problems, this paper proposes a novel MGRP-SS method for the reliability analysis of multiple failure modes. The main contributions can be summarized as the following four aspects: (1) an RMQGS method is proposed to improve the randomness and uniformity of initial sample points; (2) an adaptive MRGP model combined the novel learning functions and termination criterion is presented to handle multiple failure modes; (3) an MRGP-SS model is introduced into the reliability analysis to depict the system reliability under multiple failure modes; (4) Numerical and engineering examples are conducted for proving the effectiveness of the proposed method. The remainder of this paper is organized as follows. In Section 2, an RMQGS method is given based on random sampling. In Section 3, a novel reliability analysis approach to multiple failure modes is proposed by introducing an adaptive MRGP model. In Section 4, three numerical examples are employed to demonstrate the effectiveness of the proposed method, and two engineering examples are used for application. In Section 5, the key conclusions of the study are summarized.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 6 Numerical Study  
### Experimental Data and Settings  
In order to verify the proposed two-stage optimization model, one of the busiest hub airports in the world, Shenzhen Bao'an International Airport, is used as the research object. We consider the actual operational data of the flight, which involves a total of 82 pairs of flights in the time range of 17:00-20:00. All flights are also divided into three categories based on the aircraft wingspan length, including 59 flights for category C, 17 flights for category D, and 6 large flights for category E. The time is converted into minutes based on the difference from 17:00. More detailed flight information is shown in Table 4.  
A total of 31 stands at the airport were selected for the stand allocation model. According to the size of aircraft wingspan that the stand can accommodate, all stands are classified into three categories: 11 stands for category E, 1 stand for category D, and 19 stands for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Flight**} & **Aircraft** & **Arrival** & **Departure** & **Arrival** & **Departure** & **Arrival** \\  & **Type** & **Runway** & **Runway** & **Time** & **Time** & **Runway** \\ \hline
0 & 3 & 15 & 15 & 0 & 125 & 15 \\
2 & 1 & 16 & 15 & 0 & 80 & 16 \\
3 & 1 & 16 & 15 & 0 & 85 & 16 \\... &... &... &... &... &... &... \\
79 & 1 & 15 & 16 & 185 & 270 & 15 \\
80 & 2 & 16 & 15 & 190 & 270 & 16 \\
81 & 2 & 16 & 15 & 195 & 285 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic information of flight data.  
category C. Among these, 22 are contact stands, and 9 are remote stands. The taxi-in and taxi-out times for different aircraft types are derived from historical data. All stands are self-maneuvering in and pushback-out, requiring tug services for pushback operations. Figure 5 shows the taxi time of Class C and Class E aircraft from different runways and different stands.  
Based on the field investigation at Shenzhen Airport, Table 5 presents the specific parameter settings for vehicles used in the experiments of this study.  
The LS-NSGA-II algorithm parameters are shown in Table 6. All the models related to this paper were created on a PC consisting of an Intel Core [EMAIL_ADDRESS] GHz CPU and 16 GB RAM. With PyCharm as the implementation platform, Python 3.12.3 was used as the core tools to solve the Two-Stage Optimization Model and obtain the optimal results of stand allocation and ground support vehicle scheduling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Maximum** & **Population** & **Crossover** & **Mutation** & **Objective** \\
**Iterations** & **Size** & **Probability** & **Probability** & **Function Weights** \\ \hline
800 & 100 & \(Pc_{max}:\) & \(Pm_{max}:\) & \(1/3\) \\  & & 0.8 \(Pc_{min}:\) 0.7 & 0.3 \(Pm_{min}:\) 0.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The LS-NSGA-II algorithm parameters.  
Figure 5: Taxi times for different types of aircraft. (**a**) C-type aircraft, (**b**) E-type aircraft.