esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 1 Introduction
[21] proposed learning tree-augmented naive Bayesian networks from flight data to augment aircraft diagnostic models, improving fault detection through data mining combined with expert input. [PERSON] et al. [22] proposed a fusion-learning approach that builds Bayesian networks separately from sensor data and maintenance logs, extracting causal relationships through natural language processing and human-assisted annotations to achieve multi-source information fusion. However, Bayesian networks inherently rely on prior knowledge, and their diagnostic performance can be compromised when such prior information is limited. To address this challenge, we developed a structured decision-level multi-source fusion framework based on Dempster-Shafer (D-S) evidence theory. First, diagnostic features are independently extracted from the gas path and vibration subsystems, and initial fault judgments are made based on predefined physical criteria. Then, a Bayesian network is constructed to perform decision-level fusion by integrating prior knowledge and capturing probabilistic dependencies among fault events. Finally, to mitigate the reliance on prior information, D-S evidence theory is applied to further optimize the fusion diagnosis process. A case study based on dual-shaft aero-engine experimental data demonstrates that the proposed method enables earlier fault detection and achieves higher diagnostic confidence levels compared to Bayesian-network-based approaches.
## 4 Experiments
### Effect of Training Sample Size  
We investigated whether the performance of the model is affected by the number of training samples and conducted an experiment by randomly extracting a certain percentage of samples from the original training set. The experimental results are depicted in Figures 4 and 5.  
As depicted in the above graphs, the accuracy and macro-F1 score reached their maxima when the number of training samples was seventy percent. When the number of training samples was too small, the model was not sufficiently trained, resulting in an under-fitted model. Conversely, when there were too many training samples, the model learned too much irrelevant information, causing a degradation in its performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Restaurant14 (\%)**} & \multicolumn{2}{c}{**Laptop14 (\%)**} & \multicolumn{2}{c}{**Restaurant15 (\%)**} & \multicolumn{2}{c}{**Restaurant16 (\%)**} \\ \cline{2-7}  & **Acc.** & **F1** & **Acc.** & **F1** & **Acc.** & **F1** & **Acc.** & **F1** \\ \hline MHAKE-GCN w/o MHSA & 82.16 & 74.28 & 77.64 & 75.44 & 80.29 & 64.28 & 87.64 & 68.87 \\ \hline MHAKE-GCN w/o MHIA & 81.59 & 72.47 & 74.21 & 71.80 & 79.03 & 61.94 & 86.83 & 68.96 \\ \hline MHAKE-GCN w/o Affective Knowledge & 81.04 & 71.36 & 75.91 & 71.63 & 79.95 & 60.87 & 87.38 & 68.21 \\ \hline MHAKE-GCN & **83.96** & **74.93** & **78.46** & **76.54** & **82.10** & **65.90** & **89.36** & **69.32** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation experiment of our MHAKE-GCN.  
Figure 3: Effect of GCN layers on macro-F1.  
Figure 2: Effect of GCN layers on accuracy.
## 3 Methodology
### Models
#### 3.3.6 LightGBM  
LightGBM is an open-source implementation of the Gradient Boosting Decision Tree (GBDT) algorithm. GBDT, a supervised learning method, aims to predict a target variable accurately by aggregating estimates from a collection of simpler and weaker models [54].  
For this study, LightGBM was optimized with the following values: max_depth = 7 limits the complexity of individual trees. n_estimators = 1000 builds 1000 decision trees. num_leaves = 10 restricts each tree to a maximum of 10 leaves. subsample = 0.5 randomly uses only half of the available data for each tree, reducing variance and preventing overfitting.
# (R-ISSUES) Rural Interoperable System of Systems for Unified Environmental Stewardship
## 1 Introduction
### System of Systems (SoS) Engineering  
Systems engineering (SE) is a methodological, multi-disciplinary, integrative approach to engineering systems. The discipline is described in the International Council on Systems Engineering (INCOSE) handbook adapted to the ISO 15288 standard [30].  
A system of systems (SoS) comprises systems with at least two conditions: operational independence, managerial independence, geographical distribution, emergent behaviour, or evolutionary development processes [31]. Each of the constituent systems of the SoS may have its lifecycle, which complicates the interoperability of the SoS's engineering. Specific Information and Communication Technologies (ICT) offer interoperability and configuration management capabilities [32], which help solve this SoS engineering problem, generally in an iterative manner [33].  
ISO 21839 [34] provides a definition of SoS and its constituent system as follows:  
_System of Systems (SoS)--A set of systems or system elements that interact to provide a unique capability that none of the constituent systems can accomplish independently. Note: Systems elements can be necessary to facilitate the interaction of the constituent systems in the system of systems._  
_Constituent Systems--Constituent systems can be part of one or more SoS. Note: Each constituent is a valuable system, with its development, management goals, and resources, but it interacts within the SoS to provide the SoS's unique capability._  
The definitions of SoS are diverse and depend on the particularity of an application area. The types of SoS can be directed, acknowledged, collaborative, or virtual. Each is based on the degree of independence of constituents, origin, and relationships. This is the Taxonomy of Systems of Systems presented in 15288 Annex G and ISO 21841 [35].  
Systems of systems are generally characterized as complex [36; 37; 38; 39; 40; 41], as noted in the system of systems (SoS) knowledge area of the SEBoK [42]. According to INCOSE, dealing with complexity needs system thinking principles, such as meta-cognition, encouraging variety, considering the relationships of the possible solution, and humility. A combination of courage and humility enables the complex systems engineer to risk genuine innovation and learn fast from iterative prototyping of solutions in context [42].
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 3 Methods
### Basic Principles of Situation Assessment  
The purpose of transforming the situation assessment into binary classification is to assess the air traffic situation more scientifically. In addition, we can obtain the quantitative assessment value. Support vector classification (SVC) is the main tool to deal with the classification of low-dimensional data. It has the advantages of a high classification accuracy and fast calculation speed. It can calculate the distance from the sample to the classification hyperplane. The distance will be taken as the situation assessment value of each sample.  
Support vector classification (SVC) can divide samples into two parts through a hyperplane. The classification hyperplane is a straight line in two-dimensional space and a surface in three-dimensional space. In two-dimensional space, the classification hyperplane can be expressed as:  
\[g(\mathbf{x})=\mathbf{w}^{T}\cdot\mathbf{x}+b=0 \tag{18}\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\), \(T\in\{(\mathbf{x}_{1},\mathbf{y}_{1}),(\mathbf{x}_{2},\mathbf{y}_{2}),\cdots(\mathbf{x}_{N},\mathbf{y} _{N})\}\), \(b\in\mathbf{R}\), \(\frac{2}{\left\lVert\mathbf{w}\right\rVert}\) represents the distance between \(H_{1}\) and \(H_{2}\). The classification boundary interval of the sample is \([-1,1]\). According to \(g(\mathbf{x}_{i})>0\) and \(g(\mathbf{x}_{i})<0\), the air traffic network situation is divided into two categories. \(g(\mathbf{x}_{i})>0\) indicates that the sample is positive. The air traffic situation represented by this sample is stable during this period. \(g(\mathbf{x}_{i})<0\) indicates that the sample is negative. The air traffic situation represented by this sample is risky and unstable. The situation assessment principle based on SVC is shown in Figure 2.  
SVC can find an optimal hyperplane \(H\) that maximizes the margin between the two classes. The air traffic situation in different periods was divided into two categories by SVC. The margin between samples and the classification hyperplane \(H\) is different in Figure 2. It can be used to quantitatively assess the air traffic state in a certain time. Therefore, it is very important to obtain an accurate and reasonable classification hyperplane.
## 2 System Model  
This paper considers a MIMO-OFDM system with \(N_{t}\) transmit antennas and \(N_{r}\) receive antennas, operating on \(K\) subcarriers. Each data frame of the transmitted signal contains \(N_{f}\) OFDM symbols. Let \(x_{n,k}^{p}\) represent the transmitted signal at the \(p\)th transmit antenna and the \(k\)th subcarrier of the \(n\)th OFDM symbol in one frame, where \(1\leq p\leq N_{t}\), \(1\leq n\leq N_{t}\), \(1\leq k\leq K\), the average power \(E\left\{\left|x_{n,k}^{p}\right|^{2}\right\}=\sigma_{x}^{2}\), and \(E\{\cdot\}\) denotes the expectation. The transmitted signal propagates through the channel and arrives at the receiving side, where the received signals are the sum of transmitted signals and noise. The transmitted signal at the \(q\)th receive antenna and the \(k\)th subcarrier of the \(n\)th OFDM symbol can be expressed as  
\[y_{n,k}^{q}=\sum_{p=1}^{N_{t}}h_{n,k}^{q,p}x_{n,k}^{p}+w_{n,k}^{q}, \tag{1}\]  
where \(1\leq q\leq N_{r}\), \(h_{n,k}^{q,p}\) represents the channel frequency response between the \(p\)th transmit antenna and the \(q\)th receive antenna at the \(k\)th subcarrier of the \(n\)th OFDM symbol, \(x_{n,k}^{p}\) and \(y_{n,k}^{q}\) are the frequency domain representations of the transmitted and received signals, respectively, and \(w_{n,k}^{q}\) is a complex Gaussian noise sample with zero mean and variance \(\sigma_{w}^{2}\).  
By defining \(\mathbf{x}_{n,k}=\begin{bmatrix}x_{n,k}^{1}&x_{n,k}^{2}&\cdots&x_{n,k}^{N_{t}}\end{bmatrix} ^{T}\in\mathbb{C}^{N_{t}\times 1}\), \(\mathbf{y}_{n,k}=\begin{bmatrix}y_{n,k}^{1}&y_{n,k}^{2}&\cdots&y_{n,k}^{N_{t}} \end{bmatrix}^{T}\in\mathbb{C}^{N_{t}\times 1}\), \(\mathbf{w}_{n,k}=\begin{bmatrix}w_{n,k}^{1}&w_{n,k}^{2}&\cdots&w_{n,k}^{N_{t}} \end{bmatrix}^{T}\in\mathbb{C}^{N_{t}\times 1}\), \(\mathbf{H}_{n,k}=\begin{bmatrix}\mathbf{h}_{n,k}^{1}&\mathbf{h}_{n,k}^{2}&\cdots&\mathbf{h}_{n,k }^{N_{t}}\end{bmatrix}^{T}\in\mathbb{C}^{N_{t}\times N_{t}}\), and \(\mathbf{h}_{n,k}^{q}=\begin{bmatrix}h_{n,k}^{q,1}&h_{n,k}^{q,2}&\cdots&h_{n,k}^{q,N_ {t}}\end{bmatrix}^{T}\in\mathbb{C}^{N_{t}\times 1}\), where \((\cdot)^{T}\) stands for transpose, (1) can be represented as follows:  
\[\mathbf{y}_{n,k}=\mathbf{H}_{n,k}\mathbf{x}_{n,k}+\mathbf{w}_{n,k}. \tag{2}\]
## 3 Lemurs Optimizer (LO)
### Mathematical Model of the Lemur Optimizer Algorithm
Algorithm 1 shows the LO algorithm's pseudocode, while the general steps are presented in Figure 6.  
```
1: Set up the LO parameters (Number of iterations, Number of dimensions (Dim), Number of solutions, Lower Bound (LB), Upper Bound (UB), Low-Risk Rate, High-Risk Rate). 2: Generate Lemurs population. 3:while the current iteration does not equal the number of iterations do
4: Evaluate the objective function for all Lemurs. 5: Calculate free risk rate (\(FRR\)) using Equation (4). 6: Update the Global Best Lemur (\(gbl\)). 7:for each lemur indexed by i do
8: Update the Best Nearest Lemur (\(bnl\)). 9:for each decision variable in Lemur \(i\) indexed by \(j\)do
10: Set \(random([0,1])\) to \(rand\). 11:if\(rand<\)\(lumpingRate\)then
12: Use Equation (3) case number one to update the decision variable \(j\). 13:else
14: Use Equation (3) case number two to update the decision variable \(j\). 15:endif
16:
17:endfor
18:endfor
19:endwhile
20: Return the Global Best Lemur. ```  
**Algorithm 1** The LO algorithm's pseudocode. Figure 6: Lemur Optimizer Flowchart. The LO algorithm begins by generating a swarm of lemurs randomly. At each iteration, the decision variables of the neatest lemur with a better fitness value try to transfer towards the lemurs with a lower fitness value via dance-hup. In the LO algorithm, creating a set of random lemurs is the first step in the optimization process. The \(FRR\) value starts close to \(Low_Risk_Rate\), which means the lemur tends to move toward the best neatest lemur via dance-hup. During the LO execution, the \(FRR\) will decrease close to \(High_Risk_Rate\), which means the lemur tends to move toward the best global lemur via leap up. This procedure shall be iterated until an end condition is met. The number of lemurs, iterations, and decision variables affects the algorithms' computing complexity. To summarise, the proposed algorithm has the following computational complexity:  
\[O(LO)=O(MaxIter\times s\times d) \tag{5}\]
# A Raisin Foreign Object Target Detection Method Based on Improved YOLOv8
## 5 Conclusions  
This study introduces a real-time detection approach for identifying foreign matter in mixed raisins, utilizing an enhanced version of YOLOv8. The proposed method integrates BoTNet's multi-head self-attention (MHSA) into YOLOv8's backbone, substitutes the neck layer's Conv module with RFAConv, switches from the original CIoU loss to EIoU loss, and uses a new raisin foreign matter dataset for training. After experiments, the F1-score and mAP0.5 of the improved model increased by 1.21% and 1.48%, respectively, compared to the original model, and its comprehensive performance is close to that of the YOLOv8m model with 96.1% more parameters.  
Comparing the average precision of the improved and original models for different categories, although the detection performance of this method has improved in various categories, there are still problems of being unable to detect or misjudge small or severely occluded foreign matter, especially in the detection of fruit stalks and branches. In comparison to various deep learning-based object detection algorithms, this approach delivers excellent detection outcomes with reduced parameter count and enhanced speed, striking an optimal balance between accuracy and efficiency. It is hardware-efficient and demonstrates robust practical utility.  
Using this method for rapid detection of foreign matter in raisins and combining it with a removal platform can greatly reduce labor costs, reduce hardware costs, and promote the intelligent development of the raisin industry, which has a positive significance for the promotion of agricultural products. Currently, this method still has shortcomings in detecting dense small objects and occluded objects. In our ongoing efforts, we plan to delve into refining and streamlining the model for implementation on mobile and embedded systems.  
**Author Contributions:** M.N.: conceptualization, methodology, writing--reviewing and editing. H.M.: data curation, writing--original draft preparation, visualization. Y.W.: supervision. L.C.: software. Y.C.: validation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China (2022 YFD2100304), the National Natural Science Foundation of China (Grant No. 52275001), and the National Science Foundation for Young Scientists of China (Grant No. 51705201).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Semantic 3D Mapping from Deep Image Segmentation
## 5 Limitations  
The 3D segmentation algorithm proposed in this work is efficient. It considerably improves the results obtained with a direct correspondence, or a preprocessing of the image, as we have shown in the previous section's experiments. However, it has certain limitations. These limitations arise from the fact that the method is based on the connection of 3D points corresponding to a segmented object in an image. We assume that the points that belong to the object have a certain continuity or neighborhood in space and that the points that do not really belong to the object are far from the rest of the detection points. These assumptions solve the _boundary point_ problem in virtually all cases, but false positives or false negatives could appear in the final result if these assumptions are not met. Another limitation of our approach is related to the point in space at which the expansion algorithm starts. If this point does not belong to the object to be detected, the algorithm will fail. This error can happen if the object has a hole in its middle, and the segmentation in the image indicates that this hole is also part of the object.  
The semantic mapping also has limitations if the localization is not accurate and the covariance matrix does not reflect this inaccuracy. During this wrong situation, the elements mapped could be misplaced.
## 6 Conclusions  
In this paper, a simplified, general finite element model to simulate unit load bending under warehouse load beam racking support was developed and validated. The following can be concluded:  
* The two-dimensional model presented can accurately replicate the load bridging effect that has been observed in physical experimentation for unit load segments of three and four columns with an average error of 8%.
* Unit loads with very large boxes, such as a two-column unit load, present different box movement behavior when deflecting. Refinement to the model must be developed in order to study these unit loads, considering the different contact interactions, possible box deformations, and the use of additional containment methods.
* This model is capable of replicating the behavior of the multiple unit load segments when modifying payload configurations, such as changing height, weight and number of columns.
* The presented model can provide a better understanding of the load bridging effect on unit loads without the need for complex and time-intensive data collection for each model run. The model simplifications allow for it to be more widely adopted in commercial applications.  
Further exploration is required to determine how each of the many variables affects pallet bending. Load bridging in unit loads has been extensively studied in physical environments. The model developed allows for the exploration of the effects and interactions which is not feasible to conduct in controlled experiments.  
E.M. and L.H. research conceptualization. E.M. and R.L.W. simulation model development. E.M. data collection, experimentation and writing. E.M., L.H. and R.L.W. data analysis collaboration. This research project was conducted under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The research was financially supported by the Industrial Affiliate Program of the Center for Packaging and Unit Load Design at Virginia Tech. The corrugated boxes were donated by the Roanoke Facility of Packaging Corporations of America.  
The data presented in this study are available on request form the corresponding author.  
The authors declare no conflict of interest.
## 3 Theoretical Analysis and Experiment
### Experiment
#### 3.2.2 Experimental Process  
The measured value of OPM is the sum of the modulus of the geomagnetic field vector and other magnetic field vectors. It is numerically equivalent to the sum of the modulus of geomagnetic field vector and the projections of other magnetic fields in the direction of the geomagnetic field [26]. So, the direction of the geomagnetic field may affect the OPM's measured value. To verify this conjecture, as shown in the Figure 12, the UAV was placed in different directions and repeat experiments to observe the experimental phenomenon.  
Figure 11: Environmental diagram of UAV interference test site.  
Figure 10: Photograph of the fluxgate magnetometer [26].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Diameter of core & \(\Phi_{\text{core}}\) & 100 \(\mu\)m \\ Length of core & L & 20 mm \\ Wire diameter of excitation coil & \(\Phi_{\text{exc}}\) & 0.05 mm \\ Turn number of excitation coil & N\({}_{\text{exc}}\) & 900 \\ Wire diameter of pick-up coil & \(\Phi_{\text{pick}-up}\) & 0.08 mm \\ Turn number of pick-up coil & N\({}_{\text{pick}-up}\) & 1045 \\ Feedback resistance & \(Rfb\) & 6 k\(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structural parameters of the fluxgate sensor [26].  
The ground experiment process was as follows:  
1. Install the UAV on the test bracket and place it in the north-south direction. Turn on the power supply but keep the engine off and the propeller still. All sensors start measuring synchronously;
2. Static test for 5 min to ensure that the experimental instruments are normal and there is no other magnetic interference in the environment;
3. Start the engine and keep the propeller turning for 30 s. Repeat 3 times, with an interval of 3 min each time;
4. Place the UAV in the east-west direction and repeat steps (2) and (3).
###### Abstract  
Considering that smart vehicles are becoming interconnected through the Internet of Vehicles, cybersecurity threats like Distributed Denial of Service (DDoS) attacks pose a great challenge. Detection methods currently face challenges due to the complex and enormous amounts of data inherent in IoV systems. This paper presents a new approach toward improving DDoS attack detection by using the Gini index in feature selection and Federated Learning during model training. The Gini index assists in filtering out important features, hence simplifying the models for higher accuracy. FL enables decentralized training across many devices while preserving privacy and allowing scalability. The results show that the case for this approach is in detecting DDoS attacks, bringing out data confidentiality, and reducing computational load. As noted in this paper, the average accuracy of the models is 91%. Moreover, different types of DDoS attacks were identified by employing our proposed technique. Precisions achieved are as follows: DrDoS_DNS: 28.65%, DrDoS_SNMP: 28.94%, DrDoS_UDP: 9.20%, and NetBIOS: 20.61%. In this research, we foresee the potential for harvesting from integrating advanced feature selection with FL so that IoV systems can meet modern cybersecurity requirements. It also provides a robust and efficient solution for the future automotive industry. By carefully selecting only the most important data features and decentralizing the model training to devices, we reduce both time and memory usage. This makes the system much faster and lighter on resources, making it perfect for real-time IoV applications. Our approach is both effective and efficient for detecting DDoS attacks in IoV environments.  
Keywords:DDoS attack detection; Internet of Vehicles (IoV); Gini index; feature selection; Federated Learning (FL); cybersecurity; smart vehicles +
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