esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Classes/Metrics** & **Acc** & **Pre** & **Rec** & **F1** & **Kap** \\ \hline [PERSON] et al. [28] & 98.6 & 98.2 & 97.8 & 97.9 & 94.2 \\ \hline [PERSON] et al. [29] & 99.4 & 98.5 & 98.6 & 98.7 & 95.0 \\ \hline [PERSON] et al. [35] & 97.5 & 97.1 & 97.5 & 97.2 & 93.2 \\ \hline [PERSON] et al. [37] & 98.4 & 97.5 & 97.9 & 97.8 & 92.8 \\ \hline [PERSON] et al. [36] & 98.5 & 97.6 & 98.2 & 97.9 & 93.8 \\ \hline EfficientNet B7 & 96.8 & 97.2 & 97.6 & 97.4 & 91.8 \\ \hline MobileNet V3 & 97.2 & 96.9 & 97.3 & 97.4 & 92.2 \\ \hline Proposed Model & 99.6 & 98.8 & 98.6 & 98.7 & 95.7 \\ \hline \end{tabular}
\end{table} 
Table 4: Outcome of comparative analysis—dataset 2. Figure 7: Findings of comparative analysis—dataset 1 [28; 29; 35; 36; 37]. The uncertainty analysis is presented in Table 5. The findings indicate the reliability of the proposed model. In addition, it reveals that the model is well calibrated and can support the physician in making effective decisions. Lastly, the computational configuration for the CAD detection model is listed in Table 6. The proposed CAD detection model demands a minimum number of parameters and floating point operations (FLOPs) for generating the outcome. It is evident that the recommended model can be deployed in healthcare centers with minimal computational resources. Figure 8: Findings of comparative analysis—dataset 2 [28; 29; 35; 36; 37]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classes/Metrics**} & \multicolumn{3}{c}{**Dataset 1**} & \multicolumn{3}{c}{**Dataset 2**} \\ \cline{2-7}  & **Loss** & **SD** & **CI** & **Loss** & **SD** & **CI** \\ \hline [PERSON] et al. [28] & 2.7 & 0.0021 & [96.62–96.71] & 2.3 & 0.0017 & [98.55–98.61] \\ \hline [PERSON] et al. [29] & 1.9 & 0.0019 & [97.41–97.49] & 1.8 & 0.0019 & [98.64–98.72] \\ \hline [PERSON] et al. [35] & 2.4 & 0.0026 & [96.57–97.23] & 1.6 & 0.0019 & [97.81–98.42] \\ \hline [PERSON] et al. [37] & 2.7 & 0.0027 & [95.81–96.34] & 2.5 & 0.0023 & [98.52–98.69] \\ \hline [PERSON] et al. [36] & 3.2 & 0.0029 & [95.82–98.91] & 2.4 & 0.0016 & [97.32–97.42] \\ \hline EfficientNet B7 & 1.9 & 0.0035 & [96.13–97.15] & 1.7 & 0.0018 & [97.56–98.21] \\ \hline MobileNet V3 & 2.2 & 0.0029 & [97.21–96.23] & 2.5 & 0.0021 & [98.12–98.26] \\ \hline Proposed Model & 1.2 & 0.0013 & [98.41–98.72] & 1.2 & 0.0011 & [98.57–98.89] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Findings of uncertainty analysis.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 9--Heterogeneity of Networks  
The improvement and demand on wireless and mobile communications enhance the various IoT applications by improving energy consumption of the devices, overall throughput, and communication techniques that apply directly or indirectly on the devices [108]. These communications include various networks (e.g., Wireless Sensor Networks (WSN), Wireless Mesh Network (WMN), SCADA, etc. Such highly dynamic network environments make an IIoT system more vulnerable, especially for the devices located at the edge networks [109]. One common security issue is the exposure of secret (private and sensitive) information via packet dropping attack [64]. A packet-dropping attack is one kind of DoS attack, where the compromised nodes drop packets to make the connection path unavailable between the source (i.e., sender) and destination (i.e., receiver) nodes.
## 4 Attack Filtering Performance
### Attack Mitigation Performance  
As discussed above, if \(d_{K}>d_{W}\), then \(I_{v}\in L_{K}\), so \(V\) can filter \(K\)'s BSM. This holds true even if \(K\) increases the BSM transmission power or uses directional transmission to extend its transmission range towards farther victim vehicles. But as \(V\) comes closer so that its whisper can be decoded by \(K\), then \(K\)'s BSM can pass \(V\)'s test. Figure 7 shows the number of successful attacks as a function of \(d_{K}\). Namely, it plots how far from the road center the attacker can successfully mount the attack. Although the number of vehicles that fall to the attack is also a function of the vehicle traffic density, vehicle speed, and the duration of the attack, we fix them as in the previous section and focus on the effect of \(d_{K}\). Without the whisper check, the attacker can successfully achieve the attack from as far as \(d_{K}>600\) m at the BSM Tx power of 23 dBm. With Tx power-boosting or directional transmission, \(d_{K}\) could be even larger. This result suggests that with good line-of-sight (LoS), the attacker may well position himself safely apart from the highway, avoid visual detection by the passing victims, and still pose a significant threat. The reason that the number of vehicles exposed to the attack is approximately 3.5 inside the attack range is because we have a headway distance of 33.3 m between vehicles. The length of a vehicle is 5 m, so with four lanes, approximately 3.5 vehicles enter the attack range every second. With the whisper check, however, the attack enabling distance \(d_{K}\) is significantly reduced to \(d_{W}\approx 170\) m. The result confirms that the whisper check is effective in narrowing the attack range to \(d_{W}\). So, at least, the attacker should come near to the roadside in order to mount the false position attack under the whisper scheme.
# Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers
## 3 Proposed Method
### SVM-Based Goal Estimation  
For the goal estimation where the target vehicle cuts-in, the proposed method defines the estimation as the multiclass classification problem using SVM. The two features are input to SVM, and the goal of lane-changing is output as a class. Figure 2 represents how to define a goal as a class. There are four classes as a goal of the target vehicle. The \(A\) class represents the front space of the lead vehicle, \(B\) class is the space between the rear and lead vehicles. Moreover, the \(C\) class depicts the behind space of the rear vehicle. Lastly, \(D\) represents that the target vehicle maintains the current lane. The goal estimation is performed at each time step.  
Figure 6: The ROI (region of interest) for the interaction between drivers: the proposed method focuses on the region where two potential fields are overlapped. (**a**) shows the case that the target vehicle accelerates whereas the lead vehicle decelerates for giving passage. Conversely, (**b**) represents the case when the target vehicle is slowing down and cuts-in to the behind space of the lead vehicle. It is confirmed that the potential fields have the different distribution within the ROI.  
Figure 7: Definition of the proposed features: the proposed method uses two features for the goal estimation. The first feature is the difference of potential energies of the two lanes within the ROI. The second feature is the remained distance of the acceleration lane.  
The proposed method uses the radial basis function as known that normally shows the best performance. The radial basis function is defined by  
\[K(\mathbf{x},\mathbf{x}^{\prime})=\exp{(-\gamma||\mathbf{x}-\mathbf{x}^{\prime}|| ^{2})}, \tag{9}\]  
where \(\gamma\) is the kernel parameter. The proposed method uses a simple approach for the multiclass extension of the binary SVM using a one-versus-one strategy. In addition, LIBSVM, which is a library for SVM, was applied to implement the classification [16].
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### 5G Non-Public Network on the Industrial Shopfloor
#### 3.1.2 5G mmW System Deployment at 26 GHz  
The 5G mmW non-public network operates at 26 GHz (5G n258 band) using a system bandwidth of 800 MHz. The 5G mmW system is well-suited for applications with high data rate demands and requiring low mobility. The mmW frequencies are prone to high path loss, and hence, directivity gains with narrow beamwidths are used. We used Qualcomm \(\times\)65-based devices in our OTA tests.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 3 Aggregated Control Strategy for Multi-Type Loads in Industrial Parks
### Real-Time Predictive Control of Multiple Types Loads in Industrial Parks
#### 3.3.1 Real-Time Predictive Control Model  
During the operating stage, the controllable loads are predicted and controlled using a model. The prediction horizon is \(P\) and the control horizon is \(M=P-\tau\), where \(\tau\) is the response delay duration of the controllable loads during the operating stage.  
\(k+i|k\) represents the prediction from \(k\) to \(k+i\), and the \(i\)th predicted output value of the controllable load at the time \(k(k\in[  
The operation of controllable loads in industrial parks are influenced by various uncertain factors, including external uncertainty and internal uncertainty. External uncertainty includes random disturbances, while internal uncertainty includes model mismatch, parameter estimation errors, measurement errors, etc. These uncertainties result in errors between the predicted trajectory and the actual operating trajectory of controllable loads. When there are dynamic conditions such as fault conditions or sudden changes in load, the errors will be greater. Implementing the following measures can improve the stability and robustness of predictive control models:  
* Most controllable loads are first-order inertial objects with response-delay characteristics. The stability and robustness of these objects are influenced by the predicted time domain. It can be demonstrated that the stability and robustness of predictive control can be improved with a larger prediction time horizon. However, selecting a large prediction time horizon will lead to loss in control speed. In practical applications, it is advisable to select several hours as the appropriate prediction time horizon.
* Add a feedback correction process, utilizes real-time measured information to estimate and correct the effects of uncertainty. This approach can effectively overcome the impact of external uncertainty and internal uncertainty and improve the robustness of the proposed control model. Meanwhile, the robustness of the control model is influenced by the feedback correction coefficient. It can be demonstrated that the robustness of the control model can be improved with a smaller feedback correction coefficient.
## 4 Results and Discussions
### Geographical Exploration of Spatial Differentiation of Water Conservation in Yunnan Province
#### 4.4.3 Interactive Detection of Spatial Differentiation of Water Conservation in Yunnan Province
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{**2000–2010**} & \multicolumn{2}{c}{**2010–2018**} & \multicolumn{2}{c}{**2000–2018**} \\  & \(q\)**-Value** & \(q\)**-Value** & \(q\)**-Value** & \(q\)**-Value** & \(q\)**-Value** & \(q\)**-Value** & \(q\)**-Value** \\ \hline Natural & Precipitation change & 0.827 & 1 & 0.857 & 1 & 0.893 & 1 \\ Environmental & Evapotranspiration change & 0.404 & 3 & 0.668 & 2 & 0.531 & 2 \\ NPP change & 0.411 & 2 & 0.299 & 3 & 0.160 & 3 \\ \hline \multirow{4}{*}{Socio-economic} & GDP change & 0.101 & 4 & 0.044 & 4 & 0.011 & 6 \\  & Population density change & 0.021 & 5 & 0.039 & 5 & 0.047 & 5 \\ \cline{1-1}  & Change in distance from town & 0.005 & 7 & 0.013 & 7 & 0.004 & 7 \\ \cline{1-1}  & Land use change & 0.006 & 6 & 0.023 & 6 & 0.060 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dynamic time factor detection for geographic detectors. Although the socio-economic factors may not exhibit significant individual explanatory power in single-factor analysis, they demonstrate substantial influence when interacting with other natural environmental variables. This observation underscores that changes in socio-economic factors frequently exert their influence on the natural environment first, subsequently affecting the regional water conservation function. Thus, their role in elucidating spatial differentiation becomes apparent primarily through interaction analysis. In the process of actual ecological environment change in the study area, the interaction and complexity of factors in different regions are important reasons for the spatial differentiation of water conservation. The two-factor synergistic enhancement in the interactive detection of static years and dynamic periods of factors further illustrates this point. Regarding the dominant factors, both single-factor analysis and interaction assessment consistently underscore that natural factors serve as the primary driving force behind the spatial variations in water conservation. This aligns with findings from pertinent studies [44]. However, it is undeniable that other factors also play a role in the spatial differentiation of water conservation functions. Especially in interactive detection, it further explains that the impact of social and economic factors on the regional water conservation function is usually interactive. The existing research also shows that there are two situations of resource-based water shortage and engineering-based water shortage in Yunnan Province [20]. Therefore, in the future process of urban economic development, it is necessary to coordinate the regional ecological functions and minimize the impact on regional water conservation.
###### Abstract  
(1) **Background:** Condylar fracture healing pattern classification in children and adolescents is primarily based on the radiological assessment of condylar morphology; however, recent studies showed the presence of a poor correlation between post-treatment radiological findings and clinical temporomandibular joint (TMJ) dysfunction. The present case series aimed to correlate the condylar morphology, shape, and trabecular bone density with the skeletal asymmetry and the clinical recovery of two growing patients with unilateral condylar fractures undergoing orthopedic treatment with the Balters Bionator appliance. (2) **Methods:** Pre- and post-treatment (12 months) cone-beam computed tomography (CBCT) scans of two growing patients with unilateral condylar fracture were retrieved; both patients were treated with the Balters Bionator appliance for one year. Morphological evaluation of the condylar healing pattern was carried out on CBCT reconstructions of the mandible. Condylar remodeling and skeletal asymmetry were assessed through linear measurements performed on pre- and post-treatment CBCT scans; then, fractal analysis (FA) was employed to assess the condylar trabecular bone density on orthopantomographies (OPTs). Clinical and TMJ functional evaluation were retrieved from patients' records from before and at the end of the treatment (12 months). (3) **Results:** Conservative treatment of condylar fractures in growing patients led to an increased bone density of the condylar heads, regardless of the post-treatment size and morphology of the injured condyles. Patient one presented an unchanged condylar morphology on the affected side, while patient two's condyle was slightly spherical. The qualitative results were confirmed by quantitative measurements on CBCTs. The radiological healing patterns were associated with slightly different functional outcomes. Both patients also exhibited an improvement in skeletal asymmetry and TMJ function. (4) **Conclusions:** According to the findings in the present study, the condylar remodeling and bone apposition after conservative treatment of condylar fractures in growing patients can exhibit different radiological and functional outcomes. Indeed, an unchanged morphology of the condylar head is more likely to determine a physiological TMJ recovery.  
capular fractures; growing patients; orthopedic treatment; conservative treatment; fractal analysis; Balters Bionator; case report
## 4 Experimental Section
### Frequency Resolution and LED-OFC Recognition Result Analysis
#### 4.3.2 High Frequency Range  
As shown in Figure 10, the minimum frequency resolution in the high frequency range was eight times (40 Hz) that in the low frequency range as seen above, which also proves our previous analysis that, because of the excessively dense stripes of the LED-OFC, the minimum frequency resolution is greater in the high frequency range.  
From the above experiments, the conclusion can be drawn that the minimum frequency resolution to ensure high recognition accuracy is 40 Hz. The totally different types of LED-OFC can be calculated as follows: \(\frac{(10000-5000)}{40}+\frac{(5000-500)}{5}=1025\). Moreover, since the color features of the stripes, including the color and the color change cycle, would change if the frequency of each LED were different, more different LED-OFCs can be obtained; only the assumption that all the frequencies were the same was considered in the above experiments. Therefore, there are about 1025 types of frequency. As far as RGB-LED is concerned, since the frequencies of each LED are non-interfering, there will be three different frequencies. According to the principle of permutation and combination in mathematics, the total number should be: \(10^{9}\) (\(A_{1025}^{3}\)).  
Figure 9: The recognition accuracy rate at different frequency resolutions in the low resolution range.
## Key Management in Wireless Sensor Networks  
**[PERSON] 1,2, [PERSON] 1,2,* and [PERSON] 1,2**  
1 University Clermont Auvergne, 49 Boulevard Francois Mitterrand, BP 10448, F-63000  
Chermont-Ferrand, France;  
E-Mails: [EMAIL_ADDRESS] (I.M.); [EMAIL_ADDRESS] (P.L.)  
2 CNRS, UMR 6158, Campus des Cezeaux, LIMOS, F-63173 Aubiere, France  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +33-4-73-17-70-48; Fax: +33-4-73-17-71-11.  
Academic Editor: [PERSON]  
_Received: 17 April 2015 / Accepted: 14 August 2015 / Published: 7 September 2015_  
**Abstract:** Wireless sensor networks are a challenging field of research when it comes to security issues. Using low cost sensor nodes with limited resources makes it difficult for cryptographic algorithms to function without impacting energy consumption and latency. In this paper, we focus on key management issues in multi-hop wireless sensor networks. These networks are easy to attack due to the open nature of the wireless medium. Intruders could try to penetrate the network, capture nodes or take control over particular nodes. In this context, it is important to revoke and renew keys that might be learned by malicious nodes. We propose several secure protocols for key revocation and key renewal based on symmetric encryption and elliptic curve cryptography. All protocols are secure, but have different security levels. Each proposed protocol is formally proven and analyzed using Scyther, an automatic verification tool for cryptographic protocols. For efficiency comparison sake, we implemented all protocols on real testbeds using TelosB motes and discussed their performances.  
**Keywords:** renewing; revocation; authentication; wireless sensor network; security; multihop; verification; formal proof
## 3 Materials and Methods
### Participants and Procedure  
A total of 24 subjects participated in the study: 18 men and 6 women. The age of the participants ranged between 23-64 years old (M = 36.62, SD = 13.09). They were master students and professors from our university. They all had previous experience with 2D video conference systems (Microsoft Skype, Google Meet), though none had experience with immersive AR systems. Seventeen participants (70.83%) had previous experience of using AR, mostly from playing AR games on mobile devices. All users volunteered to participate in the study.  
As for the experimental tasks, we chose the social interaction tell-a-lie [52] and tic-tac-toe game (see Figure 3). At the beginning of the experiment, we presented the aim of the research and instructed the participants on how to use HoloLens glasses. The experiment was conducted with one participant driving on the simulator using one HoloLens 2, and a researcher from our laboratory playing the role of a remote collaborator using another HoloLens 2. The whole process took approximately 20 min, out of which about 5 min were allocated for the user to get accustomed to the mixed reality environment and the hand tracking capabilities of the HoloLens 2.  
Due to the fact that the driving task was secondary, the participants were instructed to focus their attention on the collaborative interaction with the avatar. Social presence was measured using [PERSON] and [PERSON]'s questionnaire [59], whose main aim is to highlight the sense of being together and assess the attention and behavior during the interaction with the AR avatar. The social presence questionnaire uses a 7-point Likert scale rating (1: Strongly disagree-7: Strongly agree).
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments
### Model Performance Comparison
#### 5.3.2 Qualitative Results
Although it has a hole in the center of the mask, overall, the mask generated by the UNet is more similar to the ground truth. The mask generated by the ThinDyUNet model is able to generate a proper mask, although the shape is different than the ground truth and the overall size of the mask is a little bit smaller than the ground truth. Figure 7: Model prediction results on the RGB test image. (**a,h,o**) Original RGB image. (**b,i,p**) Ground truth segmentation mask. (**c,j,q**) Proposed ThinDyUNet prediction results. (**d,k,r**) UNet prediction results. (**e,l,s**) TransUNet prediction results. (**f,m,t**) PAN prediction results. (**g,n,u**) PSPNet prediction results. Based on both quantitative and qualitative results, the proposed ThinDyUNet model demonstrates effective learning and generalization capabilities when trained on the UAV semantic segmentation dataset. Although the ThinDyUNet does not achieve the best performance metrics, it shows a reliable and stable performance on RGB and IR image inputs compared to other models, which are mostly only good for RGB image inputs and fail for IR image inputs. Despite its compact architecture and low parameter count, ThinDyUNet performs well on both RGB and IR input images, producing segmentation  
Figure 8: Model prediction results on the IR test image. (**a**,**h**,**o**) Original IR image. (**b**,**i**,**p**) Ground truth segmentation mask. (**c**,**j**,**q**) Proposed ThinDyUNet prediction results. (**d**,**k**,**r**) UNet prediction results. (**e**,**l**,**s**) TransUNet prediction results. (**f**,**m**,**t**) PAN prediction results. (**g**,**n**,**u**) PSPNet prediction results. Figure 9: Generated mask by the ThinDyUNet and UNet model on RGB image input (**a**) Original RGB image. (**b**) Ground truth segmentation mask. (**c**) ThinDyUNet prediction masks. (**d**) UNet prediction mask. masks with satisfactory accuracy. These results confirm the efficiency and robustness of the proposed model in handling VL and IR inputs.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 4 NSGA-II for the Multi-Objective Path Planning Problem
### Crossover Operator  
For two randomly-selected individuals in the population (_POPc_), the crossover operator is used to form new individuals by swapping parts of the individuals. The newly-generated individuals are stored in the child population (_NEWPOP_). Suppose that \(p=[S,p_{1},p_{2},...,p_{i-1},p_{i},...,p_{j},p_{j+1},...,p_{n},T]\) and \(q=[S,q_{1},q_{2},...,q_{k-1},q_{k},...,q_{m},q_{m+1},...,q_{n^{\prime}},T]\) are two randomly-chosen paths, \(p_{i}\) and \(p_{j}\) are two randomly-selected rotation points (RPs) on path (\(p\)). Similarly, \(q_{k}\) and \(q_{m}\) are two randomly-selected rotation points on path (\(q\)). The line segments connecting \(p_{i}\) with \(p_{j}\) and the line segments linking \(q_{k}\) with \(q_{m}\) are exchanged. Finally, two new paths (\(p^{\prime}=[S,p_{1},p_{2},...,p_{i-1},q_{k},...,q_{m},p_{j+1},...,p_{n},T]\), \(q^{\prime}=[S,q_{1},q_{2},...,q_{k-1},p_{i},...,p_{j},q_{m+1},...,q_{n^{\prime}},T]\)) are generated. Figure 2 shows the result of exchanging line segments with the crossover operator.