esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Ensemble-Based Detection of In-Vehicle Network Intrusion
## 4 Proposed Scheme  
We propose a multi-classification scheme capable of detecting four types of attacks on CAN bus networks. We use machine learning to design and evaluate an effective IDS framework. This framework utilizes multi-classification algorithm-based ensemble methods to enhance the IDS's ability to identify intrusions. We used three supervised ML models (RF, DT, and XGBoost) along with three ensemble classifiers (stacking, voting, and bagging). Well-known evaluation metrics were used to assess the effectiveness of the proposed approach. Accuracy, precision, recall, F1-score, and ROC were the metrics utilized in this study. Figure 6 is an overview of our proposed model's workflow, which includes the CAN bus input dataset, data preprocessing, supervised models building, ensemble modeling, attacks classification, and scheme evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Citation** & **Model** & \
\begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} & **Balanced** & **Hyperparameters** &
\begin{tabular}{c} **Ensemble** \\ **Methods** \\ \end{tabular} & **Type of Attacks** \\ \hline
[48] & DCNN & Not reported & – & + & – & DoS, fuzzing, and spoofing \\ \hline
[49] & RNN & Not reported & Not reported & + & – & DoS, fuzzing, and impersonation \\ \hline
[50] & LSTM & – & – & + & – & DoS, fuzzing, and spoofing \\ \hline
[51] & RNN &
\begin{tabular}{c} Manual feature \\ extraction \\ \end{tabular} & – & – & – & – & spoofing \\ \hline
[52] & CNN and GRU & \
\begin{tabular}{c} deep feature \\ selection \\ via CNN \\ \end{tabular} & – & + & – &
\begin{tabular}{c} DoS, fuzzing, and impersonation \\ \end{tabular} \\ \hline
[53] & ML & RQA & – & + & – & spoofing \\ \hline
[54] & CNN-LSTM &
\begin{tabular}{c} Implicit via \\ CNN layers \\ \end{tabular} & – & + & – & DoS and fuzzing \\ \hline
[55] & SVM & \begin{tabular}{c} Heuristic Feature \\ KNN \\ \end{tabular} & \begin{tabular}{c} -- \\ Selection \\ \end{tabular} & – & – & – & \
\begin{tabular}{c} DoS, fuzzing, and spoofing \\ \end{tabular} \\ \hline Ours & Stacking & Extra Trees & + & + & + &
\begin{tabular}{c} DoS, fuzzing, spoofing, and replay \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Our proposed method compared to recently proposed promising approaches.
# Mechanical Strength Study of a Cranial Implant Using Computational Tools
## 2 Materials and Methods
### Skull Defect Creation and New Mesh  
The base finite element head model used in this work is the YEAHM (YEt Another Head Model), initially developed by [PERSON] et al. [22] and afterwards improved by [PERSON] et al. [23], [PERSON] et al. [24], and [PERSON] et al. [25]. The model comprises a skull, brain, CSF, bridging veins (BV), and superior sagittal sinus (SSS). In the original model, the bone that makes up the skull was homogeneous and did not feature the distinction between cortical bone, trabecular bone, or cranial sutures. These details were added by [PERSON] et al. [25], including a damage model. The material of the brain was modeled as hyper-viscoelastic, the CSF was considered to be a hyperelastic material, the SSS and BV were modeled with an elastoplastic law, and, in the model proposed by [PERSON] et al. [25], the trabecular bone was modeled as elastic-plastic material, and the cortical bone and cranial sutures as quasi-brittle material.  
The head model previously mentioned was manipulated with the software Hypermesh (Altair Engineering, Michigan, MA, USA) in order to obtain the skull model after the craniectomy. The option was to create a large skull defect with approximately 50 cm\({}^{2}\) at the parietal zone to reproduce a realistic situation. Then, nodes were selected and an approximated contour of the implant at the desired skull location was performed and used to create a spline surface, which acted as a start point to obtain the skull defect and the implant. Once this surface was completed, it was used to create the defect in the skull to place the implant. The skull elements intersecting this surface along the thickness were deleted and then, to smooth the skull defect contour, the border elements were projected to this surface. Then, the skull model was exported to Abaqus. Using the tool Verify mesh from the Abaqus toolbox, some poor elements with near-zero volume on the defect's border were identified and eliminated so that the analysis could be performed.  
Overall, the skull's new mesh contained 223,401 linear hexahedral elements of type C3D8R, of which 124,714 are from cortical bone, 86,703 from trabecular bone, and 11,984 from cranial sutures. Figure 1 depicts the modeling steps of the defective skull.  
Figure 1: Creation of the skull defect: (**a**) skull after elements were deleted; (**b**) skull after the projection of the border elements; (**c**) final skull model after the deletion of the poor elements on the defect’s border.
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 4 Subjective Logic Trust Model  
### Trust Attributes  
Many behavioral characteristics can be observed to evaluate nodes trust level during the nodes interaction process. In the following, we discuss these trust attributes and provide how we calculate them.  
Availability: As stated in [34; 45; 46], it is a measure to ensure that node \(j\) is operational and accessible to legitimate nodes whenever needed in a reasonable time to achieve the expectations. Node \(j\) might be unavailable if (1) it is busy processing other requests or (2) it refuses to respond to requested task. Availability \(A^{t}_{i,j}\) denotes the availability of \(j\), as observed by \(i\) at time \(t\). \(A^{t}_{i,j}\) is calculated as follows:  
\[A^{t}_{i,j}=\frac{\text{Accepted tasks}}{\text{Total tasks}} \tag{1}\]  
Reliability: As stated in [34; 45; 46], it is a measure to ensure that node consistently operates as per its specifications in a defined time. Reliability \(R^{t}_{i,j}\) represents \(j\)'s success rate in the completion of the tasks that it has accepted from \(i\). \(R^{t}_{i,j}\) is computed based on the following equation:  
\[R^{t}_{i,j}=\frac{\text{Successful performed tasks}}{\text{Accepted tasks}} \tag{2}\]  
Turnaround Efficiency: As stated in [34; 46], it is a quality measure that ensures \(j\) performs a requested task within the time that it promises to \(i\). Irregularities and high latency in response time predict potential intrusions in the system. Turnaround is a time frame that begins when \(i\) sends a processing task to \(j\) until \(j\) accomplishes the task successfully and sends it back to \(i\). Turnaround Efficiency \(E^{t}_{i,j}\) of \(j\) at time \(t\) is computed based on the following equation:  
\[E^{t}_{i,j}=\frac{\text{Promised Turnaround time}}{\text{Actual Turnaround time}} \tag{3}\]  
If the promised turnaround time is greater than the actual turnaround time, the turnaround efficiency is 1.  
Ownership: Every node in the Thing/Fog has an owner. The inclusion of this measure is based on the belief that devices belonging to the same owner have mutual trust. As a result, the value of the trust attribute \(Oi,j\) is set to 1 if a node comes across another node that the same owner owns; otherwise, it becomes 0.
# Generation and Inhibition of SO\({}_{3}\) in Lead Smelting Flue Gas
## 3 Results and Discussion
### Generation of SO\({}_{3}\) in Experimentally Simulated Lead Smelting Flue Gas  
Based on the simulated SO\({}_{3}\) generation data in lead smelting flue gas, the reaction temperature (200-1200 \({}^{\circ}\)C), O\({}_{2}\) content (3-15%), and SO\({}_{2}\) content (5-20%) were identified as dominant factors, with minimal influence from other flue gas components. Experimental studies under homogeneous (gas-phase) and heterogeneous (catalytic) conditions were conducted to analyze SO\({}_{2}\)/SO\({}_{3}\) conversion mechanisms. These investigations, combined with catalytic SO\({}_{3}\) generation patterns and inhibitor-based suppression strategies, provide actionable insights for SO\({}_{3}\) control in non-ferrous smelting operations. The key findings revealed that catalytic conditions significantly enhance SO\({}_{3}\) yields compared to homogeneous reactions, particularly at mid-range temperatures (400-600 \({}^{\circ}\)C). These results underscore the importance of temperature management and catalytic material selection in SO\({}_{3}\)-suppression strategies.
# A Probabilistic Hyperspectral Imagery Restoration Method
###### Abstract  
Hyperspectral image (HSI) restoration is an important task of hyperspectral imagery processing, which aims to improve the performance of the subsequent HSI interpretation and applications. Considering HSI is always influenced by multiple factors--such as Gaussian noise, stripes, dead pixels, etc.--we propose an HSI-oriented probabilistic low-rank restoration method to address this problem. Specifically, we treat the expected clean HSI as a low-rank matrix. We assume the distribution of complex noise obeys a mixture of Gaussian distributions. Then, the HSI restoration problem is casted into solving the clean HSI from its counterpart with complex noise. In addition, considering the rank number need to be assigned manually for existing low-rank based HSI restoration method, we propose to automatically determine the rank number of the low-rank matrix by taking advantage of hyperspectral unmixing. Experimental results demonstrate HSI image can be well restored with the proposed method.  
hyperspectral imagery; probabilistic model; hyperspectral image denoising; low-rank data analysis +
Footnote †: journal: Applied Physics Letters
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 3 Results
### Formal Errors  
Figure 6 shows three-dimension (3D) coordinate formal errors for all of the solutions in two test periods independently and Table 5 shows the mean formal error of coordinates for all stations in both  
Figure 4: Comparison of weighting schemes for (**a**) pseudoranges and (**b**) carrier-phase measurements.  
Figure 5: Time series of coordinate residuals and coordinate formal errors obtained from different solutions for station KZN2 during DoY 198, 2017.  
test periods together. The presented results show that multi-GNSS solutions can reduce formal errors, however, the improper weighting may significantly affect the results. This is the case in the GREC_L50 scheme in which the mean of the formal error equals about 16 mm, while for other solutions, it is below 5 mm. When compared to G_L10 solution, 3D formal errors are decreased by 21%, 14%, and 40% for GREC_L10, GREC_GxN, and GREC_GxS solutions, respectively. On the other hand, in the GREC_N&S solution coordinate formal errors are multiplied by a factor of 2.5 on average.  
We found that the GREC_GxS scheme results in the smallest formal errors for all stations and all days. On average the formal errors are 0.6, 1.2, and 2.3 mm for the North, East, and Up component, respectively. In the GREC_GxS solution, formal errors for all of the coordinate components are reduced at the similar 40%-level when compared to the G_L10 solution, while in other multi-GNSS solutions the reductions vary among components and are mainly observed for the East component.
# Low-Profile Harmonic Transponder for IoT Applications
## 3 Diode Conversion Loss
### Diode Conversion Loss Analysis  
The matched transponder operation, compared to the biased one, is better understood from expression of the diode current-voltage relationship:  
\[I_{d}=I_{8}\left(e^{v_{d}/nV_{T}}-1\right)\]  
where \(I_{d}\) is the generated current from the diode; \(v_{d}\), \(V_{T}\) are the diode applied and thermal voltages, respectively; \(n\) is the diode ideality factor; and \(I_{s}\) is the diode saturation current, which is in the range of (3-5) \(\,\mathrm{\upmu A}\). For the matched diode, \(v_{d}\) is the voltage derived from the antenna at the fundamental frequency, yielding a diode current that contains a DC component and higher frequency components, according to the Taylor expansion. To maximize the second order components generated from the diode, it has to operate in the square  
Figure 8: CL variation with input power at 2.45 GHz fundamental frequency and a corresponding 4.9 GHz second harmonic.  
Figure 6: The developed biasing circuit for HSMS-2850.  
Figure 7: The implemented biasing circuit besides the utilized battery.  
law region. Therefore, the main role of the matching circuit is to conceal the capacitive diode reactance and bring the diode operation to that point from the rectified diode voltage. Thus, the matched diode is self-biased from the fundamental interrogation signal, and a DC path for the generated DC component is provided by the matching network.  
On the other hand, the biasing circuit brings the diode operation to that point directly from the applied DC external supply, and the optimum biasing value to maximize the second order components is found to be \(100\,\mathrm{mV}\), as also explained by the I-V curve shown in Figure 9. Therefore, in Figure 6, the conversion loss for the biased diode compared to the matched one is higher in the case of input power \(>\)\(-20\,\mathrm{dBm}\), and lower for the input power below \(-20\,\mathrm{dBm}\).
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 4 Baseline Clustering Strategies under Jamming  
In the face of a jamming attack, one of the first countermeasures must be to isolate the jammer. However, the location of the jammer is established by the attacker, and the most damaging position for the network is usually chosen, e.g., the sink node neighborhood. Therefore, it is often necessary to establish new sink nodes and clustering is used for this purpose.  
Once the jammer is detected, its location and transmission power are estimated (which is considered resolved and out of the scope of this paper). Then, the cluster formation and selection of CHs is carried out. Using the K-medoids technique [50], we generate the clusters assigning an optimum number of centroids or CHs. For that sake we use as a metric the interference-plus-noise (IPN) that would be perceived from the location of each cluster node. The CH position must meet the following requirements to be chosen as valid:  
* Reduce the attacker's impact on the majority of the nodes in the cluster.
* Minimize the number of isolated nodes.
* Provide the highest number of associations.  
The first two criteria can be incorporated into the clustering when considering the Chebyshev distances [50] and the third one is used to select each CH. The clustering process itself leads to a trade-off between two options: many small clusters whose internal communication is not affected by the jammer; and a larger number of clusters that allows data collection from emerging sink nodes, e.g., through a UAV.  
The clustering strategies that we propose take into account both purposes, which does not guarantee that all nodes of the same cluster can communicate directly and successfully with the selected CH using the same transmit power as when there is no jammer. To guarantee effective communication in such cases, next, we discuss baseline strategies exploiting transmit power control, cooperative communication, and a combination of both.
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 2 Related Works
### Deployment Method and Pattern  
In smart cities, all kinds of sensor devices will promote the intelligent level of a city; however, this poses a significant challenge to deployment [29], especially the deployment position and pattern. This challenge is the same as the RFID location scheme.  
The current range-based RFID localization system is mostly based on the vast deployment of reference tags [30; 31]. Reference tags and matrix deployment are the mainstream deployment pattern. In the reference tags model, the position of reference tags needs to be carefully designed [27]; their coordinates need to be pre-measured. Virtual reference tags, as the variant method, need a dynamically and robustly coordinate generation algorithm for the virtual tags [8]. In the matrix deployment pattern [26; 32], reference tags or readers are not necessary, but the two-dimensional matrix tags' or readers' deployment are introduced to determine the relative position of the targets. While it is a satisfactory solution for RFID localization with acceptable accuracy, it is not very appropriate nor always feasible on a large scale in a smart city context because of the large sensor deployment.  
For a large-scale application and emergency scenario, the aforementioned deployment methods and patterns are not practical to deploy [30]. Despite the large investment for many tags, some areas do not usually allow the installation of hardware infrastructure, for example, in museums and emergency situations. Another drawback is the pre-measurement of referenced tags location. In an emergent situation, the time is rarely for rescue.
## 3 Proposed Method
### GMM-Based Anomaly Score Calculation  
After extracting the sound features through the classification model, a GMM is used to establish the anomaly detection model. Its calculation formula is as follows:  
Figure 10: Schematic diagram of the Arcface Loss classification structure.  
\[p(x|\theta)=\sum\limits_{k=1}^{\text{K}}\phi_{k}\xi(x|\mu_{k},\sum\limits_{k}) \tag{15}\]  
In Equation (15), \(p\) represents the probability of the occurrence of \(x\); \(x\) is the input vector; \(\phi_{k}\) is the weight of the mixed Gaussian distribution; \(\xi(x|\mu_{k},\sum\limits_{k})\) represents the Gaussian mixture function; K is the number of Gaussian distributions; and \(\mu_{k}\) and \(\sum\limits_{k}\) are the mean and covariance matrix of the \(k\)-th Gaussian distribution, respectively.  
GMM builds the model by estimating the mean, variance, and corresponding mixture weights of each Gaussian distribution function. In this paper, the improved MFN sound feature extraction model is utilized, and all the collected normal data are taken as the input to extract the sound feature vectors from it. Subsequently, based on these feature vectors for training, a Gaussian Mixture Model capable of representing the normal operation state of the wind turbine gearbox is finally obtained. When detecting the \(j\)-th type of data, the negative maximum likelihood function of the GMM is used as the evaluation value of the anomaly score.  
\[\begin{split}& A_{GMM}(x_{t})=\Psi(x^{(j)},x_{t})\\ &=-\ln p(x_{t}\Big{|}\theta,x^{(j)})\end{split} \tag{16}\]  
In Equation (16), \(x^{(j)}\) is the feature vector corresponding to the \(j\)-th type of data extracted by the trained neural network and \(x_{t}\) is the feature vector of the sound to be tested.
## 3 Drone Routing Algorithms
### Trajectory Planning
#### 3.1.6 Exact and Heuristic Approaches to Truck-Drone Delivery Problem (EHTDDP)  
The proposed algorithm in [16] focuses on coordinated truck-drone delivery problem, in which a traditional delivery truck is combined with a drone to reduce delivery times and costs. A MIP formulation and heuristic approach were proposed to address the FSTSP which is basically TSP with drones. In brief, the MIP formulation addresses the short instances whereas heuristic approach successfully tackles large instances. The MIP formulation yielded better linear relaxation bounds than previously proposed formulations for all instances, while the hybrid heuristic was able to improve over 80% of best-known solutions for large-size instances.
# Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
###### Abstract  
One of the most effective methods to limit the mechanical vibrations of bladed disks is the use of friction damping at mechanical joint interfaces. Unfortunately, dedicated tests to assess the impact of mistuning and the effectiveness of friction dampers are uncommon. This paper presents an original design of an academic demonstrator to perform an experimental analysis of the dynamic response of a tip-free bladed disk with under-platform dampers (UPDs), including an identification of intrinsic and contact mistuning introduced by the UPDs. The 48-blade disk was tested in a vacuum spinning rig by using permanent magnets. Vibration measurements were performed with the Blade Tip-Timing system. Tests were simulated using the Policontac tool, which predicted the average experimental nonlinear response in the presence of UPD, confirming the tool's ability to capture the general nonlinear dynamic behavior of the mistuned bladed disk. This study presents a novel approach combining experimental Blade Tip Timing (BTT) with numerical simulations using Policontact (ver. 3.0) software and a model update based on experimental evidence to validate nonlinear dynamic responses. It distinguishes between intrinsic and contact mistuning effects, providing new insights into their impact on bladed disk vibrations. Additionally, a comparison of aluminum and steel UPDs reveals that steel offers a 26% greater damping efficiency due to its higher density and preload, significantly improving vibration reduction.  
bladed-disk vibration; friction damper; underplatform damper; nominal mistuning; blade tip timing; rotating test rig
## 3 Experiments
### Raspberry Pi and 5G Module Power Consumption
#### 3.3.1 Methodology  
Power draw was measured with an inline USB-C power meter placed between the 5 V/3 A supply and the Raspberry Pi 4 B. This meter logs real-time voltage and current, making it suitable for the watt-level loads of the gateway, where a millionhm shunt plus oscilloscope would have been cumbersome. Two test suites were run:  
1. Standalone Pi--the board was allowed to reach a steady idle desktop and then stressed with stress-ng on all four cores for 60 s to capture \"Load\" watts.
2. Pi + Waveshare SIM8200 EA-M2 5G modem--three operating points were profiled: * Idle (modem attached but no traffic); * Active use (two CPU cores stressed while downloading a 1 GB file over 5G); * Load (three CPU cores stressed while simultaneously downloading and uploading the file).  
A second pass repeated the Active-use and Load scenarios while throttling iperf3 [37] traffic to 100 Mbps, 500 Mbps, and 1 Gbps, revealing how modem demand scales with throughput.