esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Challenges in Agricultural Vehicle Path Tracking Control
### Vehicle Model  
Most existing research develops path-tracking controllers on the basis of vehicle kinematic models because controllers designed based on kinematic models can meet most of the operating requirements under low-speed operating conditions. However, most kinematic models are constructed under certain approximate conditions, so ignoring these assumptions and directly applying kinematic models to agricultural machinery navigation control will bring greater uncertainty and influence on control accuracy. Compared with kinematic models, control methods based on dynamic models can fully consider the dynamic characteristics of agricultural machinery. However, the complexity of the contact between the agricultural machinery and the ground increases the difficulty of obtaining the model parameters accurately. On the other hand, there is a lack of further analysis of dynamic models, which makes it difficult to ensure the accuracy of the control results.
## 4 Evaluation Methods
### Experimental Setup  
The experimental methodology employed in this study involves a 10-fold cross-validation process. Specifically, the dataset is divided into 10 equal-sized sub-datasets. In each of the 10 folds, 9 subsets are used for training the model, while the remaining subset is reserved for testing. This procedure is repeated 10 times, with each subset serving as the test set in one fold and as part of the training set in the other nine folds. At the end of these cycles, the results from all 10 folds are aggregated to provide a thorough evaluation of the model's performance. This approach not only ensures the reliability of the model by testing it across different subsets but also helps in assessing the consistency of its predictive accuracy across various segments of data.  
The food nutrients evaluated in the experiments include Cal and CPF, totaling four components. Each of these components has a different scale. To ensure consistent evaluation across all components, we normalized the data using Z-score normalization. This method adjusts the data such that all features have a mean of 0 and a standard deviation of 1, conforming to the properties of a standard normal distribution.
## 4 Experimentation and Results
### Heterogeneous ECU Pairing  
In ECU pairing, the types of pairing can be broadly categorized as homogeneous and heterogeneous. Homogeneous pairing involves pairing electronic components or sensors of the same type. For example, a vehicle may feature multiple sensors of the same kind, such as various cameras (front-facing, side-view, rear-view) or LIDAR and radar sensors, aimed at collecting environmental data. Similarly, external sensors in ITS serve various functions like vehicle detection, monitoring road surface conditions, or assessing weather conditions.  
Conversely, heterogeneous pairing involves pairing electronic components of different types that share the same information. For instance, within a vehicle, the navigation system, ADAS, and telematics components all rely on GPS for location data. Similarly, components like the tire pressure monitoring system, electronic stability control, and anti-lock brake systems utilize wheel speed sensors. Outside the vehicle, sensors serve multiple components such as supplying data to access control, toll collection, or parking management systems, as well as sharing weather and air quality information with traffic management systems, variable message signs, and road condition monitoring systems.  
In our research, we focused on the heterogeneous pairing of ECUs due to limitations in vehicle architecture that may not accommodate multiple ECUs or sensors for a single task. However, in future studies, homogeneous pairing could be explored to address time constraints and congestion issues in transportation systems more effectively.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 3 2\(\pi\) Ambiguity Problem
### Modulation Frequency Reduction  
One way to mitigate the 2\(\pi\) ambiguity problem is to increase the unambiguous range by reducing the modulation frequency. It is obvious that the unambiguous range increases as the frequency decreases according to Equation (8). However, the uncertainty of the measured distance also increases as the modulation frequency decreases, as can be described using the standard deviation of distance measurement [12] as  
\[\sigma_{d}=\frac{c}{4\sqrt{2}\pi f}\frac{\sqrt{B+N_{pseudo}}}{c_{d}A}, \tag{10}\]  
where \(c_{d}\) is referred to as demodulation contrast; \(A\) is the amplitude of the sample \(I_{m}(\delta_{n})\) in Equation (5) (\(\frac{R_{A}M_{A}T_{\mathrm{int}}}{2}\)); \(B\) is DC component of sample \(I_{m}(\delta_{n})\) in Equation (5) (\(R_{DC}M_{DC}T_{\mathrm{int}}\)), which partially stems from the DC component of the demodulation signal \(M_{DC}\); and \(N_{pseudo}\) represents other noise sources which are not related to the DC component [12]. According to this equation, a reduction of the modulation frequency increases the standard deviation. Therefore, the unambiguous range and the accuracy of the distance measurement have a trade-off relationship.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 3 Channel Clustering Based on the K-Means++ Algorithm
### Complexity Optimization of the End-to-End Distortion Model Based on the Clustering Algorithm
It is often necessary to traverse all combinations of codingparameters, and the worst case computational complexity is \(O(|S|\times|C|\times|M|)\). However, the JSCC scheme based on rate allocation proposed in this paper divides the selection method of the parameter combination into two steps. Firstly, the channel environment is estimated by the clustering algorithm, that is, the code rate \(r(k)\) of the channel coding can be obtained by simple clustering before encoding, and then the obtained channel-coding parameters are brought into the minimum end-to-end distortion estimation to obtain the source-coding parameters and the choice of the best reference frame. In the worst case, the computational complexity is \(O(|S|\times|M|)\). In addition, in practice, since not all of the source-coding options can be used in any of the FCC coding modes of the channel-coding candidate set \(C\), the computational complexity may be even lower.
## 2 Bolt Tensile and Stress Wave Detection Test
### Test Method  
To prevent the uneven contact surface between the bolt end face and the acceleration sensor from affecting the stress wave data, the bolt end face was polished to be rather smooth and flush. A scale was drawn on the bolt surface as a reference for the bolt deformation. The test is performed by controlling the axial displacement of the bolt through the bolt tension test bench and applying an axial load to the bolt. The stress wave data detection process is controlled by the roller shaker through the bolt nondestructive tester. Then, the acceleration sensor receives the stress wave data and transmits it back to the bolt nondestructive tester. The waveform data are recorded six times per test. The choice of displacement loading speed determines the rate of deformation of the bolt in the tensile process. The application of low stretching speed is conducive to adequate deformation of the bolt to ensure that ductile fracture occurs after the bolt has been stretched through the four stages of stretching. According to the tensile test standard regarding the test rate [27], the displacement loading speed was set to be 10 mm/min. Bolt nondestructive tester parameters mainly include the sampling rate, sampling points, channel gain, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bolt No.**} & **Diameter** & **Length** & **Yield Strength** & \\  & **(mm)** & **(mm)** & **(Mpa)** & **Ultimate Elongation** \\ \hline mg1 & 20 & 1200 & 500 & 15\% \\ mg2 & 20 & 1200 & 500 & 15\% \\ mg3 & 20 & 1200 & 500 & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bolt test parameters.  
Figure 3: Test bolts.  
Figure 2: Schematic of the test system.  
emission energy. The experimental sampling rate is set at the maximum sampling rate of 1 MHz for the bolt nondestructive tester to ensure the accuracy of the stress wave signal obtained from the test. The number of sampling points determines the total length of the sampled signal, and the total length of the sampled signal should contain at least 2 stress wave propagation cycles of information, which are set to 6 k according to the experimental bolt length. The transmitting energy mainly controls the amount of energy emitted by the excitation, and the larger the energy value, the larger the signal amplitude generated by the excitation. The channel gain cooperates with the setting of the emission energy to achieve normal signal acquisition. In order to ensure that the stress wave signal acquired during the bolt tensioning process will not be clipped, the stress wave signal was debugged before the test. The channel gain and emission energy were set to be 50 dB and 10, respectively, and the signal could thus be acquired normally.
## 1 Introduction  
### Quality Control of the CFRP for Concrete Reinforcement  
The utilization of carbon-fiber-reinforced polymer (CFRP) for the reinforcement of concrete is a progressively favored technique for the production of structures that exhibit enhanced resistance to structural damage [1; 2; 3; 4]. The application of CFRP reinforced typically involves the adhesion of an epoxy-resin-bonded layer composed of multiple carbon mats onto the cement substrate. The primary concern in this methodology pertains to the quality of the cement-CFRP adhesion [5; 6]. The absence of adhesive bonding or the occurrence of delamination results in a localized reduction in the strength of the reinforcement, thereby creating a favorable environment for potential harm to the constructed edifices [7].  
Consequently, it is of utmost significance to control said structures with regard to evaluating the quality of the bond between the concrete and composite. Various non-destructive testing techniques can be employed to monitor the quality of the cement-composite joint. These methods include radiographic, ultrasonic, and thermo-vision techniques [8; 9; 10; 11]. In light of the dimensions of the structures under scrutiny, techniques that facilitate a comprehensive evaluation of vast regions are favored in this instance. In this aspect, active thermography was shown to be highly advantageous due to its ability to ensure prompt analysis of large structures and can be a non-contact method, which holds significant relevance in the domain of field research. The present study employed the active thermography technique with microwave excitation (MIRT).
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
###### Abstract  
The implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a result of the difficulty in extracting attack information from extremely large datasets and obtaining an adequate number of examples for specific types of attacks. A robust Federated Learning method, CGFL, is introduced in this study to resolve the challenges presented by data distribution discrepancies and client class imbalance. By employing a data generation strategy to generate balanced datasets for each client, CGFL enhances the global model. It employs a data generator that integrates artificially generated data with the existing data from local clients by employing label correction and data generation techniques. The geometric median aggregation technique was implemented to enhance the security of the aggregation process. The model was simulated and evaluated using the CIC-IDS2017 dataset, NSL-KDD dataset, and CSE-CIC-IDS2018 dataset. The experimental results indicate that CGFL does an effective job of enhancing the accuracy of ICS attack detection in Federated Learning under imbalanced sample conditions.  
intrusion detection system; Federated Learning; data augmentation; generative adversarial networks
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 4 Experimental Results and Analysis
### Discussion and Comparison  
In comparison to existing methods such as FixMatch and CutMix, our HCLM framework shows significant improvements in segmentation accuracy, especially with limited labeled data. While traditional semi-supervised methods focus primarily on strong perturbations, our method incorporates both weak and strong perturbations, alongside contrastive learning, to fully utilize unlabeled data. This not only reduces the empirical  
Figure 8: Selection of ablation experiments demonstrating the impact of different modules on the model’s performance. The first column displays the original image, while the second column shows the ground truth. The remaining columns illustrate the segmentation performance after the addition of various modules.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **SGCS** & **HCP** & **3\%** & **7\%** & **20\%** & **40\%** \\ \hline \#1 & \(\times\) & \(\times\) & 0.864 & 0.889 & 0.904 & 0.915 \\ \#2 & \(\times\) & \(\surd\) & 0.868 & 0.891 & 0.908 & 0.912 \\ \#3 & \(\surd\) & \(\times\) & 0.871 & 0.899 & 0.912 & 0.918 \\ \#4 & \(\surd\) & \(\surd\) & 0.891 & 0.906 & 0.915 & 0.926 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of image segmentation under different settingsdistribution gap but also enhances the model's ability to generalize to unseen data. From the experimental results, it is evident that our method achieves superior Dice scores, particularly under lower label proportions, validating the effectiveness of our approach. However, the computational cost of adding a strong perturbation branch (HCP) may increase training time, which is offset by faster convergence and more robust segmentation results. Moreover, due to the uniformity in the imaging process and the structural consistency of organs in medical images, the data distribution is relatively homogeneous. This reduces the need for complex image augmentations to simulate real-world scenarios, further mitigating the time cost. As a result, the additional modules do not impose a significant training burden and remain acceptable in terms of computational overhead.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 4 Calculation of TDMP Normalization Factor Based on Density Evolution
### Normalization Factor Calculation
Extend \(\tanh^{-1}\) into a series form  
\[\tanh^{-1}(x)=\frac{1}{2}[\ln(1+x)-\ln(1-x)]=\sum_{k=0}^{\infty}\frac{x^{2k+1} }{2k+1}, \tag{32}\]  
According to the series expansion [18]  
\[E(L_{3})=2E\left[\tanh^{-1}(\prod_{j=1}^{dc-1}\tanh(\frac{|v_{j}|}{2}))\right] =2\sum_{k=0}^{\infty}\frac{\left[\left(\prod_{j=1}^{d-1}\tanh(\frac{|v_{j}|}{2} )\right)^{2k+1}\right]}{2k+1}=2\sum_{k=0}^{\infty}\frac{\left[\left(\prod_{j=1 }^{(\tanh(\frac{|v_{j}|}{2}))^{2k+1}}\right)\right]^{dc-1}}{2k+1} \tag{33}\]  
According to mathematical knowledge and relevant literature, such as [21; 24; 25], the high-order expansion terms of the Taylor series are very small (negligible), so the first few terms of the series are usually taken for approximate calculation. In this paper, the first five terms were selected as approximate results by referring to Reference [25].
# A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution
## 4 Proposed Shadowed Fuzzy using DE Algorithm  
The main contribution of this article is the utilization of a novel optimization technique, shadowed type-2 fuzzy systems (ST2-FS), for dynamically adapting the crossover parameter of differential evolution (DE). This adaptation aims to enhance the performance of the DE algorithm when applied to optimize the motor position control problem with interval type-2, specifically by identifying the optimal structure of the membership functions.  
Figure 4 displays a flowchart illustrating the procedure employed in this article's general proposal. It demonstrates the utilization of ST2-FS to dynamically adjust the CR parameter, followed by its simulation in the motor controller featuring an IT2-FS.  
The equations utilized for constructing the fuzzy parameters of the input and output are presented in Equations (19) and (20), respectively.  
\[\mathit{Generations}=\frac{\mathit{Current\ Generation}}{\mathit{Maximun\ of\ generations}} \tag{19}\]  
\[\mathrm{CR}=\frac{\sum_{i=1}^{r_{\mathrm{CR}}}\mu_{i}^{\mathrm{CR}}(CR_{ 1i})}{\sum_{i=1}^{r_{\mathrm{CR}}}\mu_{i}^{CR}} \tag{20}\]  
The number of rules in the fuzzy system for CR is denoted by \(r_{\mathrm{CR}}\); \(CR_{1i}\), represents the output result for rule \(i\) of CR, while \(\mu_{i}^{CR}\), represents the membership function (MF) of rule i of CR.  
The fuzzy system structure integrated into the DE algorithm consists of the following characteristics. We refer to the combination of ST2-FS with the DE algorithm as DE-ST2-FS. The system comprises an input representing the generations used in the DE algorithm, and the output is the CR parameter that represents the crossover. In the original algorithm, this parameter is defined within the range of [0,1]. Both the input and output are divided into three membership functions (MFs), as depicted in Figure 5.  
The rules that form the fuzzy system are presented below. These rules were developed based on experimentation, which demonstrated that decreasing the CR parameter during the algorithm's execution yields favorable results.  
1. If generation is low, then CR is low.
2. If generation is medium, then CR is medium.
3. If generation is high, then CR is high.  
Figure 4: Proposal flowchart.
# A Review of Oil Spill Remote Sensing
## 6 Concluding Remarks  
Recent years have shown some progress in oil spill sensor development. Sensor manufacturers see oil spill applications as a small sector, so limited development has occurred. Cameras have improved very much and could be enhanced by adding specific filters or polarizing lenses. The situation is similar for IR cameras. The claimed new results for near IR have not been exploited nor substantiated by practical application. Satellite application of optical techniques has been limited by cloud cover and lack of contrast, despite the increased availability of new platforms.  
There are now some commercial laser fluorosensors available and these are generally being applied to surveillance of illegal discharges. Satellite radar is now dominating offshore surveillance. Several satellites are now providing data in several different modes. Radar processing is now very advanced. The strategic advantages of radar satellite data are difficult to match. Ship-mounted radars has provided very good tactical support for oil spill recovery vessels.  
There is little technology improvement for near-shore and land spills in comparison to spills on the sea. There have been no improvements in two decades. A similar situation exists for sub-surface spills and for oil in ice situations. The passive micro-wave technique for thickness measurement remains the only commercial option at this time. The sensor requires more utilization to develop confidence with different oil types and states.  
The automated airborne drone may be the tactical and operational support platform for the future. At this time, drones are equipped with visible and infrared cameras.  
All sources of funding of the study should be disclosed. Please clearly indicate grants that you have received in support of your research work. Clearly state if you received funds for covering the costs to publish in open access.  
The authors declare no conflict of interest.
## 5 Experiment and Results Description
### Experiment and Result Analysis  
#### 5.4.1 Verification Experiment  
To verify that the network does not have translation invariance or linear transformation invariance, which is convenient for obtaining more intuitive observations, the following images were segmented by U-Net after being displaced and rotated, and the network performance before and after database enhancement was compared. The results are presented as follows in Figure 7.  
(a) Displacement  
The neural network was trained using the original database images and slightly jittered images as training data. The segmentation results of the images in the test database generated by the trained network were compared with the segmentation results of the network trained on the 5-pixel and 10-pixel displacement databases for the same displacement, as shown in Figure 8.  
Figure 7: The segmentation results with the original network.  
The neural network was also trained using the original database images and slightly jittered images as training data. Then, the trained network was applied to segment the rotated images in the test database. The results are shown in Figure 9.  
The above results show that, although the network can better segment displaced and rotated images after more data are added to the database, the segmentation effect is still poor, especially for larger displacements and rotations. This result verifies the theory proposed in Section 4.3. Thus, to better segment the displaced and rotated images, further experiments are needed.  
Figure 8: The segmentation results of the displaced image with the original network.  
Figure 9: The segmentation results of the rotated image with the original network.  
It is generally believed that the linear enhancement of images, that is, adding rotated and displaced data to the training database, enables networks to segment images with different displacements and rotation angles. Therefore, we investigated this approach and added data with random displacements and rotations, and the results are presented as follows.  
According to the experimental results, although image enhancement somewhat improves the image segmentation accuracy, the qualitative analysis shows that the accuracy is still poor.  
Thus, images with random displacements and rotations were added to the original training dataset, and the trained network then segmented the displaced images. Because the amount of data is too small, after tens of thousands of training steps, the model still did not converge. The training parameters were MIoU = 0.707, Precision = 0.941, Recall = 0.761, and F1 score = 0.798.