esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 5 Conclusions  
We proposed a deep learning-based fire detection method, called DTAIF, which imitates the human detection process, one step further from DTA in our previous paper. We assumed that DTAIF can reduce erroneous visual fire detection by combining all the information from different sensors. As the same way as the method in the previous paper, Faster R-CNN is used to detect SRoFs, and LSTM accumulates the local features within the bounding boxes to decide a fire in a short-term period. Then, majority voting of the short-term decisions is taken to make the decision reliable in a long-term period. In addition, to make the final fire decision more robust, this paper proposed to use a Bayesian network that combines environmental information with the visual information. As an example, we have constructed a simple Bayesian network to combine the humidity state with all extractable visual information from video sequences including the place category, and the existence of smoke, and the long-term majority voting-based fire decision.  
Our experiments showed that even a simple Bayesian network can improve the fire detection accuracy compared to our previous video-based method and can achieve state-of-art performance for the public dataset. The proposed scheme with the Bayesian network also reduced the latency for perfect fire decisions when compared against our previous method. We believe that the proposed fire detection approach is so general that it can be applied for both indoors and outdoors, even though our example Bayesian network was confined to outdoor fire in the experiment.  
We did not consider time complexity and practical implementation in the work. For example, a recent object detection method, such as YOLOv2 [39], instead of Faster R-CNN, can be chosen for further work to make more fast and effective realization of DTAIF. Furthermore, there are several issues to improve our work including learning Bayesian network based on dataset, and graph convolutional network (GCN) to combine domain knowledge instead of Bayesian net.  
Conceptualization, B.K. and J.L.; Data curation, B.K.; methodology, B.K. and J.L.; Project administration, B.K.; Resources, B.K.; Writing original draft, B.K. and J.L.; Writing review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A6A1A09031717).  
Not applicable.  
The original contributions presented in the study are included in the article [3; 26], further inquiries can be directed to the corresponding author/s.  
[PERSON] and [PERSON] designed the study, and performed the experiments and data analysis, and wrote the paper.  
The authors declare no conflict of interest.
## 5 Simulation Results and Analysis
Figure 7 shows the relationship between the average channel capacity of the system and the constellation priority parameters under different signal transmission powers. The average channel capacity of the system increased with the increase of the constellation priority parameter, and the higher the signal transmission power, the higher the average channel capacity of the channel. This is because the increase in transmission power and constellation priority parameters ensures the signal-to-noise ratio of high priority data and improves the reliability of system data transmission, so the average channel capacity of the system increases. Figure 6: Comparison of the improvement of BER performance of the cooperative FSO communication system by this scheme under different turbulence intensities. Figure 8 shows the relationship between the system average channel capacity and constellation priority parameters under different atmospheric turbulence intensities. In Figure 8, the atmospheric turbulence intensity is reflected by the refractive index structure constant C1. The larger the refractive index structure constant, the stronger the atmospheric turbulence intensity. Under different atmospheric turbulence intensities, the average channel capacity of the system increased with the increase of constellation priority parameters. The strong atmospheric turbulence led to the deterioration of the channel conditions, which interfered with the growth of the average channel capacity of the system, so the average channel capacity performance of the strong atmospheric turbulence channel deteriorated. In conclusion, the layered modulation, combined with physical-layer network coding scheme, can improve the average channel capacity performance of the system, and the higher the constellation priority parameter, the better the average channel capacity performance of the system. Figure 8: System average channel capacity under different turbulence intensities and constellation priority parameters. Figure 7: Average channel capacity of the system under different transmission powers and different constellation priority parameters.
## 6 Insulator Surface Charge and Electric Field Distribution Under Polarity-Reversal Voltages
### Insulator Surface Charge and Electric Field  
The variation in charge density on the insulator surface during voltage polarity reversal is shown in Figure 17. The charge density distribution on the convex and concave surfaces of the insulator reaches a stable state after 1000 h of pre-pressurization. During the polarity-reversal process, the voltage decreases from the positive working voltage to zero and then increases to the negative working voltage in the reverse direction, whereas the charge accumulated on the insulator surface cannot be readily dissipated in a short time period.  
Consequently, at the moment \(t_{4}\) after the polarity reversal is completed, residual charge remains on the insulator surface. By moment \(t_{5}\) following reversal, the residual surface charge has dissipated and the system has again reached a stable state, with surface charge distribution exhibiting opposite polarity compared to the pre-reversal state.  
We show the transient characteristics of the electric field distribution along the insulator surface during voltage polarity reversal in Figure 18. In the \(t_{2}\)-\(t_{3}\) stage, the electric field strengths at the convex and concave surfaces of the insulator decrease gradually as the applied voltage decreases. At \(t_{3}\), when the voltage applied to the central conductor is 0, the surface electric field reaches its minimum value, which is generated by the residual charge  
Figure 16: Polarity-reversal voltage waveform.  
Figure 17: Transient characterization of surface charge density at polarity-reversal voltages: (**a**) convex surface; (**b**) concave surface.  
present prior to reversal. In the \(t_{3}\)-\(t_{4}\) stage, the surface electric field gradually increases with the increasing negative polarity voltage. At \(t_{4}\), the residual charge present prior to polarity reversal causes significant distortion in the surface electric field distribution. Specifically, the maximum electric field at the convex surface increases by 13.6% compared to its pre-reversal value, while at the concave surface, it increases by approximately 47.2%, indicating that the degree of electric field distortion is significantly higher on the concave surface than on the convex surface. When the surface charge accumulation reaches a new steady state, the electric field distribution along the insulator surface becomes identical to that observed prior to polarity reversal.
###### Abstract  
Friction dampers are widely used in structural vibration suppression in various fields, such as aeronautics, astronautics, robotics, precision manufacturing, etc. Traditional friction dampers are mainly used in a passive way to optimize vibration suppression with an immutable pressure around certain excitation. In this manuscript, a hybrid control strategy by considering both the friction force in the active control law and a nonlinear velocity compensation force is put forward: First, the normal force applied on the friction damper was adjusted to ensure its vibration reduction effect under different excitation for a first passive control; second, the active control law was established by combining the dry friction force and the velocity control force in the state space; lastly, the stability of the nonlinear control law was determined by Lyapunov criterion. Numerical simulations were conducted on a three degree-of-freedom system (3-DOF) based on the proposed hybrid control strategy, to show the control efficiency in vibration suppression and economic efficiency in energy input into the system. Simulation results showed that the proposed control law could reduce the amplitude of the active control force by about 5% without degrading the control efficiency.  
hybrid nonlinear active control; dry friction force; nonlinear velocity compensation force; Lyapunov criterion +
Footnote †: journal: Applied Sciences
## 3 System Architecture
### Detailed System Implementation
#### 3.4.2 Reinforcement-Learning Implementation Details  
The recommendation engine implements a Deep Q-Network architecture adapted from OpenAI's Spinning Up implementations [15]. The neural network processes 47 input features through three hidden layers (256, 128, and 64 neurons) with ReLU activation and dropout regularization (with rates of 0.3 and 0.2, respectively).  
Feature engineering transforms raw listing data into numerical representations, thus:  
* Spatial features: haversine distance to each university, zone one-hot encoding.
* Temporal features: days since listing, update frequency, day-of-week encoding.
* Textual features: TF-IDF vectors from descriptions, title-keyword similarity scores.
* Interaction features: cross-products of price-distance, area-zone relationships.  
The training process uses experience replay with a buffer of 10,000 transitions, sampling mini-batches of 32 for gradient updates. The target network updates every 1000 steps to ensure stable learning. Epsilon-greedy exploration starts at 1.0 and decays to 0.1 over 100,000 steps following an exponential schedule.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 3 Results
### Indoor Laboratory Environment
#### 3.2.4 Impact of Through-Wall Sensing  
We explore the impact of through-wall gait recognition in the meeting room. The experiment scenario is shown in Figure 13. The transceiver was located on one side of the wall, which was a 17 cm thick concrete wall. The target was located 5 m away from the wall on the other side. The experiment results are shown in Figure 14. In the case of 2-8 people, the recognition accuracy was 99.9-95.6%. Compared to line-of-sight-based lab scenes, the recognition accuracy decreased by only 1%. It shows that the system still has strong robustness in the scenario of penetrating the wall.
# Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
## 1 Introduction
This informative frequency band is further utilized to design a band pass filter to filter the original signal. [PERSON]'s transform-based envelope spectrum is applied to demodulate the filtered signals for extraction of the fault frequency.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 7 Results and Discussion
### Results from Applying DBSCAN  
DBSCAN serves as the main step when the RS receives a large number of MRs regarding misbehavior in offline scenarios. Its role is to analyze these MRs and group them into clusters that represent consistent patterns.  
Figure 14 shows the DBSCAN results of clustering the received 100 MRs in each scenario. In _ACD-OCC_, multiple distinct clusters (blue and green) represent consistent groups of MRs agreeing on the accident's occurrence. Noise points (yellow) are conflicting MRs that cannot fit into any cluster.  
In _ACD-ABS_ and _ACD-RSL_, clustering is less distinctive, with a higher number of noise points and smaller cluster sizes, indicating higher variability or disagreement among the reporters. This then underscores the difficulties inherent in establishing the truth behind scenarios when reporting is more inconsistent. Below is a detailed explanation of each scenario:  
* DBSCAN _ACD-OCC_ In _ACD-OCC_ (Top Figure), DBSCAN identified five clusters; the largest cluster, Cluster 0, contained 65 MRs. In total, it flagged 17 MRs as noise out of 100 MRs, which accounts for 17% of all the MRs. This represents moderate consistency among the \(Rep_{\text{Vehs}}\) since 83% of the MRs fell into meaningful clusters.
* DBSCAN _ACD-ABS_ In the second scenario _ACD-ABS_ (Middle Figure), DBSCAN grouped the MRS into four clusters, with Cluster 0 dominating by including 70 MRs. However, this scenario also shows a high level of noise, with 20 MRs flagged as outliers, accounting for 20% of the total.
* DBSCAN _ACD-RSL_ In the case of the final scenario, _ACD-RSL_ (Bottom Figure), DBSCAN identified three clusters, with Cluster 0 containing 80 reports. Only 8 MRs were classified as noise out of 100 MRs, which means 8% of the total. The scenario shows the highest reliability in reporting, as 92% of the received MRs fall well within the identified clusters, showing strong agreement among the \(Rep_{\text{Vehs}}\).  
Generally, DBSCAN effectively segregates consistent MRs into clusters while identifying noise points for further refinement using Isolation Forest to distinguish between serious anomalies and erroneous reports.  
Figure 14: _Cont_.
## 4 Metallic MEMS Cantilevers: Nonlinear Dynamics of the Pull-In Behavior
### Simulation of Pull-In Voltages and Displacements for Electrostatic Actuations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(I\)\(\mu\)m & **50** & **100** & **200** & **300** & **400** \\ \hline \multicolumn{5}{c}{Group 1: \(h\) = 1 \(\upmu\)m, \(E\) = 70 GPa, \(\rho\) = 2700 kg/m\({}^{3}\) (Al cantilever)} \\ \hline \(V_{\rm pi}\)/V & 99.66 & 24.41 & 6.28 & 2.74 & 1.53 \\ \hline \(w_{\rm pi}\) / \(\upmu\)m & 1.37 & 1.34 & 1.36 & 1.31 & 1.31 \\ \hline \multicolumn{5}{c}{Group 2: \(h\) = 2 \(\upmu\)m, \(E\) = 70 GPa, \(\rho\) = 2700 kg/m\({}^{3}\) (Al cantilever)} \\ \hline \(V_{\rm pi}\)/V & 291.21 & 71.02 & 17.54 & 7.82 & 4.30 \\ \hline \(w_{\rm pi}\) / \(\upmu\)m & 1.32 & 1.36 & 1.35 & 1.36 & 1.39 \\ \hline \multicolumn{5}{c}{Group 3: \(h\) = 1 \(\upmu\)m, \(E\) = 35 GPa, \(\rho\) = 2700 kg/m\({}^{3}\) (Cantilevers with half \(E\) of Al)} \\ \hline \(V_{\rm pi}\)/V & 70 & 17.26 & 4.44 & 1.94 & 1.08 \\ \hline \(w_{\rm pi}\)/ \(\upmu\)m & 1.34 & 1.34 & 1.36 & 1.36 & 1.30 \\ \hline \multicolumn{5}{c}{Group 4: \(h\) = 1 \(\upmu\)m, \(E\) = 70 GPa, \(\rho\) = 5400 kg/m\({}^{3}\)(Cantilevers with double \(\rho\) of Al)} \\ \hline \(V_{\rm pi}\) / \(\upmu\)m & 99.03 & 23.84 & 6.25 & 2.71 & 1.54 \\ \hline \(w_{\rm pi}\) / \(\upmu\)m & 1.34 & 1.30 & 1.36 & 1.23 & 1.26 \\ \hline \end{tabular}
\end{table} 
Table 2: Pull-in voltages \(V_{\rm pi}\) and displacements \(w_{\rm pi}\) of cantilevers for different \(l\), \(h\), \(E\) and \(\rho\). Figure 7: Displacement of the free end (tip) of an Al cantilever as a function of voltage. case 3 with halved \(E\) = 35 GPa and case 4 with halved \(l\) = 50 \(\upmu\)m are represented by green, orange and purple curves, respectively. The pull-in voltage of the reference case is named \(V_{\text{pil}}\) and the three cases are described by \(V_{\text{pil2}}\), \(V_{\text{pil3}}\) and \(V_{\text{pil4}}\). The \(V_{\text{pil}}\) value is 1.41 times that of \(V_{\text{pil3}}\), while the \(V_{\text{pil2}}\) and \(V_{\text{pil4}}\) values are 2.91 and 4.08 times that of \(V_{\text{pil1}}\). Thus, it is demonstrated that the degree of influence of these parameters on the \(V_{\text{pil}}\) is in descending order in the sequence of \(l\), \(h\), and \(E\). In Figure 6, outside of the orange metal \"bubble\", the properties of Al\({}_{2}\)O\({}_{3}\) are also shown. Even when Al is only shortly exposed to oxygen or water, a thin natural oxide layer of about 4 nm thickness is growing on the Al surfaces. This oxide layer is sealing the underneath pure metal and is not revealing ongoing oxidization if no artificial anodization (eloxal) processes are applied. Al\({}_{2}\)O\({}_{3}\) belongs to the \"bubble\" of the dielectric materials [42]. We checked that the existence of the natural oxide is influencing our pull-in voltage only by 3.8%. We compared the pull-in voltage for a pure Al cantilever with \(h\) = 1000 nm thickness with an Al cantilever with natural oxides having the same total thickness \(h\) = (4 + 992 + 4) nm.
# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
## 2 Methodology
### A Spatiotemporal Mann-Whitney Scan Statistic
#### 2.1.1 Spatiotemporal Distance  
The authors of [5] suggested that the Mann-Whitney Scan Statistic can be applied to spatiotemporal data using the spatiotemporal distance introduced in [13]. This was the major motivation behind the improvement of this concept. The authors of [13] introduced the spatiotemporal distance as a weighted combination of the spatial and temporal Euclidian distances along with a parameter establishing the correspondence between space and time. The procedure can be explained as follows.  
Let \(|A|\) denote the observation domain area and \(|T|\) the time observational interval length. Let \(D=2\sqrt{\frac{|A|}{\pi}}\) the diameter of a disc whose area is \(|A|\). Therefore, \(D\) is the maximal spatial distance between two points in the disc. Correspondingly, the spatiotemporal distance is defined as follows [13].  
\[d^{ST}[(x,y,t),(x_{0},y_{0},t_{0})]^{2}=d^{S}[(x,y),(x_{0},y_{0})]^{2}+\frac{D^ {2}}{|T|^{2}}d^{T}[t,t_{0}]^{2} \tag{1}\]  
where \(d^{S}\) and \(d^{T}\) are the Euclidian spatial and temporal distances, respectively.  
Furthermore, the study [11] stated that this defined distance can be considered as a spatial Euclidean distance in \(\mathbb{R}^{3}\) after rescaling the temporal axis. Hence, more precisely, the spatiotemporal distance \(d^{ST}\) can be illustrated as follows:\[d^{ST}[(x,y,t),(x_{0},y_{0},t_{0})]=d^{S}\Big{[}\bigg{(}x,y,\frac{D}{T}t\bigg{)}, \bigg{(}x_{0},y_{0},\frac{D}{T}t_{0}\bigg{)}\Big{]} \tag{2}\]
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 3 Results and Discussions
### SEM with EDX Analysis  
Figure 3A-C show the SEM images of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%). Furthermore, the EDX analysis of Zn-doped SnO\({}_{2}\) NPs (5%) is presented in Figure 3D. SEM images (Figure 3A-C) showed that all synthesized samples were spherical in shape with uniform distribution. The results show that Zn-doped SnO\({}_{2}\) NPs (1% and 5%) exhibited a morphology similar to pure SnO\({}_{2}\) NPs. This indicates that the low concentration of Zn doping did not significantly alter the particle size or shape. Moreover, the prepared samples were found to have successfully passed an effective synthesis process because there was no agglomeration of NPs. Figure 3D shows that the EDX spectra of Zn-doped SnO\({}_{2}\) NPs (5%) confirmed the existence of tin (Sn), oxygen (O), and zinc (Zn) elements. The percentages of these elements signaled good compatibility with the used proportions of elements in the synthesis process. The SEM and EDX results were excellent compared to those of previous studies [43,44].  
Figure 1: XRD spectra of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) (**A**). The high resolution of XRD spectra for (110) peak of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) (**B**).  
Figure 3: SEM micrographs of pure SnO\({}_{2}\) NPs (**A**), Zn-doped SnO\({}_{2}\) NPs (1%) (**B**), Zn-doped SnO\({}_{2}\) NPs (5%) (**C**), and EDX spectra of Zn-doped SnO\({}_{2}\) NPs (5%) (**D**).  
Figure 2: TEM images, HR-TEM images, and histogram distribution of the particle sizes of pure SnO\({}_{2}\) NPs (**A**,**D**,**G**), Zn-doped SnO\({}_{2}\) NPs (1%) (**B**,**E**,**H**), and Zn-doped SnO\({}_{2}\) NPs (5%) (**C**,**F**,**I**).
# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 4 Experiments and Analysis
### Dataset  
This experiment utilized two datasets. The first one is the public dataset panel, which contains 2400 defect images of solar panels provided by enterprises. These images were split into a training set of 1920 images and a test set of 480 images in a ratio of 4:1. The dataset includes three types of defects: scratches, broken grids, and dirtiness. This study addressed the issue of insufficient initial data samples by employing various data augmentation strategies, such as random cropping, scale transformation, and illumination adjustment. Through geometric and intensity domain transformations on the original training data, the sample size was effectively expanded, thereby enhancing the model's understanding of the feature space [33]. The experimental results demonstrated that by generating derivative samples with rich variations, the heterogeneity of the dataset was significantly improved. This not only inhibited the model's excessive reliance on specific sample patterns but also significantly enhanced its adaptability to new scenarios. The augmented dataset includes 7528 training images, 892 validation images, and 866 test images. The second dataset is the PVEL-AD dataset, jointly released by Hebei University of Technology and Beihang University [34], which contains 36,543 near-infrared images with various internal defects and heterogeneous backgrounds. It includes 1 type of normal image and 12 different types of abnormal defect images, such as cracks (linear and star-shaped), broken grids, black cores, misalignment, thick lines, scratches, fragments, broken corners, and material defects. We selected 9842 images from this dataset for testing our model.  
Before using these datasets, we carefully examined the data overlap and leakage risks between the Panel and PVEL-AD datasets. By cross-checking the image identifiers, collection times, and scene information, we confirmed that the two datasets were completely independent and had no intersection. Additionally, to evaluate the domain transfer ability and overfitting potential of the model, we trained the model on the Panel dataset and then tested its generalization performance in different defect types and complex backgrounds on the PVEL-AD dataset. At the same time, by monitoring the changes in the loss function during the training process and the performance of the validation set, we evaluated whether the model had an overfitting tendency, thereby ensuring the reliability and generalization ability of the model across different datasets.
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Parameter Criticality Assessment  
In this chapter, two essential space reduction methods--sensitivity analysis (SA) and Design of Experiments (DoE)--are categorized under the broader concept of Parameter Criticality Assessment. These methods analyze the influence each design parameter has on the overall system performance. If a parameter is found to have minimal impact, it is excluded from the optimization process, allowing only the critical parameters to remain. Ultimately, these techniques contribute to a significant reduction in computational time and provide the designer with valuable insight into which parameters need greater attention during the optimization process. They are particularly important in enabling the use of the FEM in SLDOM.