esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
### The Potential of Cross-System Transfer Learning  
The discussion of cross-system transfer learning is particularly meaningful and important in the field of aerospace. Compared to other machines, aeroplanes are expensive assets that consist of complex systems with numerous components, which brings a unique challenge when trying to obtain the large amount of fault data required to develop fault diagnosis algorithms. To overcome this challenge, a common procedure to acquire fault data for aircraft systems is to design lab-based experiments or computational simulations based on the target systems, such as the three studies outlined in the following. The first system is the environmental control system (ECS). Designed by [PERSON] et al. [15], a simulation platform for ECS was created with components built through governing engineering equations. The simulation results were validated against the Boeing dataset to ensure it produced reliable, healthy cases. Then, faults were injected into the simulation to collect various fault data based on physics. The second system is the Auxiliary Power Unit (APU). [PERSON] et al. [16] ran experiments on a real APU for Boeing 747-400 to collect a validating dataset for the APU simulation platform, where a variety of fault modes can be simulated and studied. The third system is the aircraft's fuel system. Since the actual fuel system on commercial aircraft is too complicated, a benchtop test rig was constructed to include only a simple fuel feeding loop. By injecting various faults into the test rig, fault data were collected. Using this test dataset as a baseline, [PERSON] et al. [17] constructed a simulation platform that enabled complex fault modes to be injected and collected. Each dataset of the three systems was the result of years of work, as all steps described above are necessary to derive a reliable simulation that can generate enough fault data to train a fault diagnosis model. Therefore, there is a need for more efficient fault diagnosis solutions, and applying TL to transfer diagnostic knowledge between each of the three systems above could be a solution. Upon the successful application of cross-system TL, the workload described above could be partially relieved because less training data are required for an equally accurate diagnosis.  
In summary, the application of cross-system TL is a novel way to solve the challenges of fault diagnosis in a data-lacking situation, which bears the potential to improve the efficiency of CBM.
###### Abstract  
Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain-computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout--a variant of Monte Carlo dropout (MCD)--with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiological relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (_ShallowConvNet_, _EEGNet_, _TCNet Fusion_) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, _TCNet Fusion_ achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, _ShallowConvNet_ recorded the lowest peak at 72.7%, indicating TCNet Fusion's robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; _EEGNet_ and _TCNet Fusion_ showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification.  
**Keywords:** motor imagery; channel dropout; class activation maps; spatiotemporal uncertainty
# Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks
## 2 Background and Related Work
### Non-Independent and Identically Distributed Data Distributions  
Non-IID data occur when the training data on each client in FL vary significantly, resulting in differing data distributions among clients [17]. In real-world applications, such data are typically non-IID because of variations in user behavior, preferences, and environments. Managing non-IID data is a major challenge in FL [20]. However, previous works [9; 10; 11; 12] did not account for non-IID data and instead employed divisions where clients received either an equal number of samples or samples from all classes (i.e., types of attacks). This approach contradicts the nature of FL scenarios characterized by non-IID data distributions [7] and assumes an unrealistic data distribution [21]. Non-IID data distributions are only considered in [13; 14]. In [13], nine candidate clients are assumed, each possessing varying numbers of data samples (50, 100, 150, 1000, 1500, 2000, 2500, 3000, 3500), but the data distribution among clients and the distribution of samples across all classes are not clarified. In [14], to achieve a non-IID setting, data are allocated to vehicles using a Dirichlet \((\mu)\) distribution, with the \((\mu)\) parameter adjusted from 0.1 to 0.7.  
However, the majority of studies [9; 10; 11] did not consider the data heterogeneity (non-IID data) in CAN bus data when they distributed the data across clients, thus rendering their findings unrealistic [21]. This consideration is essential for two reasons: first, in real-world deployments, vehicles will not have uniform distributions of both attack and normal data; second, testing with varying non-IID levels enables a more comprehensive evaluation of IDS performance and robustness across diverse data distributions. To address these issues, we have adopted H-FL with a realistic non-IID data distribution. H-FL incorporates a central aggregator with multiple edge aggregators instead of having one central aggregator. Furthermore, both experimental results and theoretical analysis have shown that H-FL architecture leads to faster convergence and lower training time and energy consumption of the end devices compared with the conventional FL-based framework [18]. To the best of our knowledge, no prior work has deployed and evaluated the performance of an in-vehicle IDS in an H-FL environment to detect known and unknown attacks.
## 2 Preliminaries  
This section covers a few fundamental concepts. For a more in-depth exploration, we recommend referring to standard textbooks or monographs, such as the books of [PERSON] et al. (2019) and [PERSON] (2018).  
A graph \(G=(V,E)\) consists of a set of vertices \(V\) and a set of edges \(E\). When the vertices and edges of a graph \(G\) are not explicitly specified, they are denoted by \(V(G)\) and \(E(G)\), respectively. Typically, a graph is assumed to have \(n\) vertices and \(m\) edges.  
Two graphs \(G_{1}=(V_{1},E_{1})\) and \(G_{2}=(V_{2},E_{2})\) are disjoint if \(V_{1}\cap V_{2}=\emptyset\) and \(E_{1}\cap E_{2}=\emptyset\). The union of \(G_{1}\) and \(G_{2}\) is defined as \(G_{1}\cup G_{2}=(V_{1}\cup V_{2},E_{1}\cup E_{2})\). For a nonempty subset \(S\) of the vertices in a graph \(G\), a subgraph of \(G\) induced by \(S\), denoted by \(G[S]\), is the graph with vertex set \(S\) and edge set \(\{(u,v)\in E(G)\mid u,v\in S\}\). The number of elements in a set \(S\) is called the cardinality (or size) of \(S\), denoted by \(|S|\).  
A subset of the vertices in a graph is a clique if it consists of only one vertex or if every two distinct vertices in the subset are adjacent. A clique in a graph is maximal if it cannot be contained in any larger maximal clique in the graph. A maximum clique is a maximal clique with the largest possible cardinality. The clique number of a graph \(G\), denoted by \(\omega(G)\), is the cardinality of a maximum clique in \(G\).  
A clique transversal set of \(G\) is a subset \(S\) of the vertices such that \(|S\cap C|\geq 1\) for every maximal clique \(C\in C(G)\). The clique transversal problem is to find a clique transversal set of \(G\) with minimum cardinality. The minimum cardinality of a clique transversal set of \(G\) is denoted by \(\tau_{C}(G)\).
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 8 Summary  
In this paper, we presented experimental verification of resonance frequency estimation with the use of modal analysis. The way of creating the estimation formula was described. Furthermore, the experimental verification of the estimation method was presented. The experiment was based on research of resonance for the flat CFRP specimens, which were with and without additional nonstructural mass, as simple geometry, and for the satellite antenna, as a complex object.  
The nominal geometry of the specimen was not changed during the tests, only the additional mass of the specimen. Three different specimens were manufactured to verify the test multiple times. The variable in the test for each of the probes was the contact distance measured from fixation.  
Based on the results, it could be assumed that the estimation formula predicted resonance frequencies with good accuracy with respect to the values obtained in experimental tests. The tested resonance frequencies differ a lot compared to the values obtained in numerical simulations with the use of always closed or neglected contact modal simulations.  
A conclusion may be assumed, based on the presented results, that when the contact effect is more dominant in the structure, the modal simulation errors will increase. The presented formula estimates the resonance frequency much better and might be applicable in industry applications, while further research will be completed.  
The formula was also tested for more complex shapes. The satellite antenna was selected for verification. The results of the experimental tests were in good agreement with the estimated values. However, the case was demonstrated that for more complex geometry, the mode shapes in two different half-cycles should be recognized and matched.  
The applicability limit that was observed is the contact edge in the model. If the contact edge changes during vibrations, the method is not applicable, because it assumes that there are two visible half-cycles. In addition, closing second or third contact is also not included, for the same reason.  
Despite the limitations, the method seems to have potential in industrial application. Further research should be completed in terms of testing more geometries with nonregular shape.  
Conceptualization, M.W.; Investigation, M.W.; Methodology, A.I.; Project administration, A.I.; Resources, M.W.; Supervision, A.I.; Validation, M.W.; Writing--original draft, M.W.; Writing--review & editing, A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 4 Discussion
### Insights from SHAP Value Analysis  
The SHAP value analysis offers a more granular view of feature contributions. The SHAP summary plot revealed that topographical and geographical features such as slope_deg and dist_geo_sens_area had the highest mean SHAP values, signifying their dominant influence on the model's predictions. This finding aligns with the Random Forest's identification of similar key features, indicating consistent results across both methods. The visualization of SHAP values highlighted the in-depth effects of each feature, providing deeper insights into their contributions compared to the simple variable importance ranking from the Random Forest model.  
Comparing the Random Forest and SHAP analyses reveals several key insights. Both methods consistently identify topographical and geographical features as highly influential. However, the SHAP analysis provides a more detailed understanding of individual feature impacts, illustrating how specific feature values affect model predictions. This granularity helps in understanding the diverse influences on the model, guiding future feature engineering and model refinement efforts. The ability to see how each feature value impacts predictions enhances their interpretability, providing actionable insights for domain experts.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Approaches to Energy-Aware Security
#### 4.3.2. Software-Based Approaches  
* _Lightweight cryptographic algorithms._ Lightweight encryption algorithms are designed to utilize fewer computational resources when performing encryption and decryption operations to lower power consumption. This is extremely important for battery-powered devices, as these devices can perform security operations with a reduced amount of time and a lower energy consumption [94].
* _Optimized security protocols._ Optimized security protocols that require less computation can diminish the power consumption when performing security-related tasks. For example, security protocols for specific low-power communication standards, such as Bluetooth low-energy (BLE) security, can ensure secure communication with minimal energy consumption [95].
* _Context-based adaptive security levels._ Encryption algorithms that adaptively adjust the strength according to security requirements and energy availability are conducive  
Figure 9: An overview of approaches to energy-aware security.  
to achieving energy efficiency [96]. Specifically, When network conditions are stable or the data transmitted are non-sensitive, energy-efficient communication methods will be adopted. Conversely, when sensitive data need to be transmitted, secure but energy-intensive protocols will be utilized. Likewise, lower-strength encryption will be utilized in low-risk environments to conserve energy, whereas strong encryption algorithms will be adopted in high-risk environments to ensure data protection.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 4 Results and Discussion
### Performance under Various Conditions  
We used two scenarios for validating the performance of the proposed method. Each test also included a comparison with a conventional dispatch-based control. In the first scenarios, the operation of the diesel generator is totally turned off by the controller, because the battery and hydrogen system can fulfill the load the demand in case of not enough from solar PV and wind turbine. The second scenario is used to test for the operation of a diesel generator when all other energy resources run out of energy. It starts with less energy from PV and WT, so the operation of battery and hydrogen are required. Finally, diesel generation must be turned on to ensure the operation of the power system.  
The simulation period was two days long using one-hour intervals between consecutive steps. WT, PV, and load demand were randomly generated from the year-round data. SOC and hydrogen levels were initialized with random values. Training based on random inputs shows the proposed DQN method can make effective schedules for the EMS in a deterministic environment from any initial conditions. The SOC minimum level was set to 30% in order to avoid running into deep discharging, thereby increasing battery lifetime. The minimum hydrogen level was set to 0. The simulation was implemented in the Reinforcement Learning Toolbox of MATLAB/Simulink software.  
Figure 9: The training process of the DQN-based EMS.
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 5 Discussion
### Uncertainty Caused by Ground-Measured Parameters  
Ground-measured parameters include ground spectra and atmospheric parameters. As no ground-measured spectral and atmospheric parameters of the Golmud test site are available in this paper, it was assumed that the Golmud test site has the same spectral and atmospheric characteristics as Dunhuang test site, which will affect the accuracy of SBAF. Consequently, in order to analyze the influence of ground measured parameters, new sand spectrum, atmospheric parameters, and aerosol models were used to calculate new SBAF and new cross-calibration coefficients from the Golmud test site. The Dunhuang ground-measured spectrum was replaced by the typical sand spectrum (as shown in Figure 7). The aerosol type was set to be rural type, the surface meteorological range of PMS was modified from 10 KM to 23 KM (a default value in MODTRAN software when the aerosol type is set to rural type) and the water vapor column was changed from 0.2077 gm/cm\({}^{2}\) to 3.00 gm/cm\({}^{2}\). Then, the new cross-calibration coefficients were calculated, and the uncertainty was analyzed, as shown in Table 7. The results show that, among the four influence factors, the site ground spectrum had the greatest impact, with the first band at 4.79%, and the other three bands at 0.67%, 1.05%, and \(-\)1.40%, respectively. This is because the RSRs of PMS and MOIS in blue bands have larger difference than other bands. The total uncertainty of SBAF is 4.85% in the first band and less than 2% in the other three bands.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 2 Mathematical Model
### Wave Spectrum and Significant Ship Motion Values  
This study used the three-dimensional energy wave spectrum of short-crested waves introduced by the International Towing Tank Conference (ITTC) and combined it with various RAOs to simulate 6 DOF motion responses of ships under different sea states, and to calculate the significant ship motion values. The ITTC energy wave spectrum comprised two major control factors: significant wave height (\(H_{1/3}\)) and average period (\(\overline{T}\)). Formulas for the energy wave spectrum are as follows:  
\[S\Big{(}\omega_{i},\mu_{j}\Big{)}=\frac{172.75H_{1/3}^{2}}{\overline{T}^{4} \omega_{i}^{5}}\exp\left(\frac{-691}{\overline{T}^{4}\omega_{i}^{4}}\right) \times\frac{2}{\pi}\cos^{2}\mu_{j} \tag{14}\]  
\[(\gamma)_{1/3}=2\sqrt{\int_{0}^{\infty}\int_{-\pi/2}^{\pi/2}\!\left(\frac{ \gamma}{a}\right)^{2}}S\Big{(}\omega_{i},\mu_{j}\Big{)}\mu d\omega \tag{15}\]  
\[\left(\dot{\gamma}\right)_{1/3}=2\sqrt{\int_{0}^{\infty}\int_{-\pi/2}^{\pi/2} \omega_{i}^{2}\!\left(\frac{\gamma}{a}\right)^{2}}S\Big{(}\omega_{i},\mu_{j} \Big{)}\mu d\omega \tag{16}\]  
\[\left(\ddot{\gamma}\right)_{1/3}=2\sqrt{\int_{0}^{\infty}\int_{-\pi/2}^{\pi/2 }\omega_{i}^{4}\!\left(\frac{\gamma}{a}\right)^{2}}S\Big{(}\omega_{i},\mu_{j} \Big{)}\mu d\omega \tag{17}\]  
where \(H_{1/3}\) is the significant wave height, \(\overline{T}\) is the average period, \(\mu_{j}\) is the wave heading angle of the \(j\)th wave component with a range of \(-\frac{\pi}{2}<\mu_{j}<\frac{\pi}{2}\), \((\gamma)_{1/3}\) is the significant ship motion displacement value, \(\left(\dot{\gamma}\right)_{1/3}\) is the significant ship motion speed value, \(\left(\ddot{\gamma}\right)_{1/3}\) is the significant ship motion acceleration value, \(\frac{\gamma}{a}\) is the RAO of each motion (i.e., swaying, heaving, rolling, pitching, and yawing), \(a\) is the wave amplitude, and \(S\Big{(}\omega_{i},\ \mu_{j}\Big{)}\) is the energy wave spectrum.
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
## 2 Analysis Method (Prototype)
### Electromagnetic-Mechanical-Acoustic Coupled Analysis  
The electromagnetic and mechanical domains were coupled with a force factor. The force factor is in the governing equations as a form of back EMF and Lorentz force in Equations (1) and (6). In the acoustic domain, the volume velocity was coupled with the velocity from the mechanical domain. The acoustic pressure was calculated using the force. The relationships can be written as  
\[P=\frac{F_{Lorentz}}{S_{d}} \tag{7}\]  
\[v_{volatime}=S_{d}\frac{dz}{dt} \tag{8}\]  
Figure 8: Experimental setup for the measurement using Klippel equipment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Name** & **Value** & **Unit** \\ \hline \(M\) & Mass & 54.90 & Mg \\ \(C\) & Damping & 0.083 & kg/s \\ \(K\) & Stiffness & 1.130 & N/mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured values of 1610_SPK using Klippel equipment.  
where \(P\) is the acoustic pressure, \(S_{d}\) is the effective diaphragm area, and \(v_{rollume}\) is the volume velocity. These two equations are coupled with the effective diaphragm area \(S_{d}\). Thus, an effective diaphragm area of 1610_SPK should be found. [PERSON] et al. suggested a method to obtain an effective diaphragm area [16]. Figure 9 illustrates how to determine the effective diaphragm area (\(S_{d}\)) and its boundary conditions for mechanical FEM analysis.  
Figure 9a is adapted from \"Analysis and Design of Helmholtz Protector to Improve High-Frequency Response of Insert Earphone,\" [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] 2019, _Appl. Sci._, 9(12), p. 2541.  
The \(S_{d}\) of 1610_SPK was found using a FEM-based commercial software (Ansys). The material properties needed for the analysis are listed in Table 4. The effective diaphragm area of 1610_SPK was found to be 95.96 mm\({}^{2}\).  
For the SPL analysis, the electromagnetic and mechanical domains were modeled as equivalent circuits, and the acoustic domain was modeled in 3D using the FEM-based software, as shown in Figure 3. Because the microspeker SPL was measured in an anechoic chamber, the sound emitted from the microspeker was not reflected and was absorbed by the chamber walls. To simulate the experimental conditions in the FEM software, we made a sphere-shaped air region with its boundary condition of spherical wave radiation directed outward in the acoustic domain. In addition, the baffle plate used in actual experimental conditions was simulated as a hard boundary wall in the software. The SPL measurement point was made at a distance of 0.1 m away from the speaker. After obtaining all the parameters needed for the coupled analysis, we calculated the SPL using FEM software (Virtual.Lab). In the software, each domain was modeled as shown in Figure 3.