esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] believed that the excessive roll could be avoided effectively by minimizing yaw deviation and then limiting the increase of drift angle. However, due to the limitations of the experimental equipment capabilities, [PERSON] et al. did not perform the model test with rudder manipulation to verify his inference, let alone self-propelled model tests. In order to verify the assumptions about yaw instability and further explore the influence of propeller on roll stability, the author employs the CFD method to carry out the numerical simulation of submarine emergency rising maneuvers. A self-propulsion simulation with moving rudders and a rotating propeller is accomplished by using Star-CCM+ solver, combined with overset grid technique and S surface control method. The effects of rudder and propeller on roll stability are analyzed to provide technical support for submarine emergency rising maneuvers. Therefore, the structure of this paper is organized as follows: In Section 1, a brief background about the model test is presented firstly, followed by the preliminary analysis of the strong coupling effect between roll and yaw motions, which lays the foundation of this study. Section 2 describes the numerical simulation method in detail established in this paper, including modeling analysis and hydrostatic attribute calculation, meshing methodology for the small gaps, superimposed rotation of moving objects, etc. In Section 3.1, an equivalent simulation with the same state as the model test is conducted, while the accuracy of the numerical simulation is verified by comparing it with the experimental data in Section 3.2. Section 3 introduces an S surface controller based on the conditional determination mechanism firstly. Then, using an identical grids model, a nonpropelled model simulation with rudder steering is carried out to analyze the influence of course keeping on the roll stability. Section 3.4 further accomplishes the self-propulsion simulation to preliminarily explore the effect of the propeller on its course keeping capability and roll stability. Finally, the Section 4 summarizes the innovative conclusions of this paper, and proposes the next research plans.
## Appendix D Description of General Purpose Mechanisms
### Sybil-Proof Wireless Network Coordinate Based Byzantine Consensus [167]  
The Sybil attack resistance mechanics of Sybil-Proof Wireless Network Coordinate Based Byzantine Consensus (SENATE) are designed with the intuition that Sybil identities are likely to occur in close geographic proximity (to the attacker). To prevent such attacks, Jiang et al. [167] propose to geographically partition the space in which users of the system are active, and achieve meta-consensus from the results of the subgroups. While this approach may be effective against small-scale Sybil attacks, those attackers that have the means to deploy Sybil nodes in a geographically dispersed fashion would still be successful.
## 3 Methods

Table 1: InSAR processing parameters. For ground motion hazard monitoring in the Lake Sarez area, it is possible to conclude that it would be more advantageous to perform separate interpretations of the ascending and descending pass surveys where, in combination, 88% of the area can be assessed, instead of a stereo analysis where only 32% coverage is possible. A broader conclusion would be that, in high mountain terrain, data from both ascending and descending tracks is needed to guarantee the fullest possible coverage over steep slopes. However, opposite pass surveys are not always possible due to a lack of data in some parts of the world. For instance, the Sentinel-1 mission prioritises stereo (ascending and descending) surveys over Europe and tectonically active areas only, in order to support scientific and policy objectives, and so routine access to both ascending and descending acquisitions cannot be relied upon. Other SAR satellites are available, but these are usually at a premium, meaning that obtaining a pair of InSAR stacks over an area using such data may not be as cost-effective. Figure 3: Areas of layover and shadow for the (**a**) ascending (track N\({}^{\circ}\) 100) and (**b**) descending (track N\({}^{\circ}\) 5) geometries. The significant difference between the coverage of the ascending and descending geometries illustrates the importance of the incident geometry and the specific slope and aspect of the terrain. If there are multiple options for surveying an area, via a choice of different satellites and geometries, a simple _apriori_ estimation of the layover and shadow may, therefore, help to determine which of those are optimal for a given location. Although a full simulation, as performed in this study, is necessary to completely delineate layover and shadow, estimating it using an approximate geometry is sufficient to identify the potential problems in achieving adequate coverage. This can be readily performed in a Geographic Information System (GIS) environment using only an estimate of the radar incident geometry and a DEM [42].
## 2 The Unbroadened Spectrum  
Using [PERSON]'s golden rule the unbroadened spontaneous emission spectrum is obtained from [16]:  
\[R_{sp}(E)=\frac{ne^{2}}{\epsilon_{0}h^{2}\pi c^{3}m_{0}^{2}}|M_{T}|^{2}\times E \cdot\rho_{Idos}(E)\cdot S(E)\cdot f_{c}(E)\cdot(1-f_{v}(E)), \tag{3}\]  
where \(E\) is the photon energy, \(n\) the refractive index, \(|M_{T}|^{2}\) the squared transition matrix element and \(\rho_{Idos}(E)\) the unbroadened joint density of states (JDOS). \(S(E)\) is the Sommerfeld enhancement for the exciton effects, and \(f_{c}\) and \(f_{v}\) are the Fermi-Dirac occupancy functions for the conduction band electrons and valence band holes, respectively. For quantum well LEDs, \(\rho_{Idos}(E)\) follows the Heaviside step function \(H(E_{0})\)[17], where \(E_{0}\) is the difference between the lowest and highest energy levels in the conduction and valence band quantum wells, respectively. Without significant error, the product \(f_{c}\cdot(1-f_{v})\) can be approximated with the Boltzmann distribution [15], (\(\exp(-E/k_{B}T)\)) and members before the multiplication sign (\(\times\)) assumed to be constant or slowly varying throughout the studied energy range.  
In bulk materials, the exciton continuum enhancement for the oscillator strengths is given by the Elliot formula [18]:  
\[S(E)=\pi\alpha\frac{e^{\pi\alpha}}{sinh(\pi\alpha)}, \tag{4}\]  
where \(\alpha=\sqrt{E_{b}/(E-E_{g})}\) and \(E_{b}\) and \(E_{g}\) are the exciton binding and bandgap energies respectively. For quantum wells, the exciton enhancement \(S(E)\) depends on the polarization and propagation directions of the emitted photons. However, for the most common direction, perpendicular to the quantum well plane it is necessary to consider only the TE-polarization, i.e., electric field vector parallel with the qw-plane.  
A similar analytical oscillator strength formula is not at hand for quantum wells in diode pn-junctions (i.e., areas with high electric field). Therefore, the available enhancement factor for two-dimensional excitons [19; 20] is now applied as an approximation. At usual ambient temperatures, we can neglect the discrete hydrogen-atom-like exciton peaks and the related exciton quasicontinuum in the bandgap. Then we get the 2-D exciton true continuum TE-polarization enhancement from:  
\[S_{TE}(E)=\frac{e^{\pi\alpha}}{cosh(\pi\alpha)}. \tag{5}\]  
Figure 1: Comparison of four lineshapes: Equation (1): (\(\sigma\) = 15 meV), Equation (2): (\(\tau\) = 50 fs), Equation (6): (\(E_{u}\) = 15 meV), Equation (10): (\(\tau_{be}\) = \(\tau_{blt}\) = 35 fs).  
\(E_{b}\) is typically bigger in quantum wells than in bulk semiconductors. Values between 6 and 15 meV have earlier been used for GaAs and related materials [21]. It has been commented that Equation (5) exaggerates the exciton effect in real quantum wells [22]. However, it does not make a significant error here since Equation (5) only varies between 2 and 1, when E increases from \(E_{g}\) to \(\infty\) and especially if we examine only the low energy side broadening and the lineshape in the bandgap.
## 3 Results and Discussion
### Material Removal Rate and Surface Roughness Achieved by Lapping  
Figure 13 shows variations in the average removal rates of the SiC when using different plates. The MRR for Lapping-2 (copper plate + self-modified diamond slurry) was 1.9 \(\upmu\)m/min, which was higher than the 1.7 \(\upmu\)m/min for Lapping-1 (copper plate + industrial diamond slurry). As shown in the figure, the SiC removal rate was higher for Lapping-1 and Lapping-2 (copper plate) than for Lapping-3 and Lapping-4 (resin plate impregnated with diamond), which were 0.96 and 1.5 \(\upmu\)m/min, respectively, as expected. Figure 14 shows the \(Sa\) and the root-mean-square roughness (\(Sz\)) of the SiC samples polished by the conventional copper plate and the resin plate impregnated with diamond. According to these measurements, as shown in Figure 15, the average \(Sa\) and \(Sz\) values of the SiC samples after Lapping-1 were 22 and 617 nm, while those for Lapping-2 were about 21 and 543 nm, respectively. The average \(Sa\) and \(Sz\) values for Lapping-3 were approximately 4.75 and 218 nm, respectively, and these values for Lapping-4 were 4.6 and 395 nm, respectively. The surface finish of the samples polished by the resin diamond plates was much better than what could be achieved with the copper plate. [PERSON] et al. [22] suggested that sharp diamond grit particles will remain on the plate surface and cause the deep scratches and pits found on the polished wafer surface. To determine the actual situation, scanning electron microscopy (SEM) was used to examine the surface of the lapping plate (see Figure 16). It was discovered that the residual diamond abrasive particles on the copper plate could not be removed after diamond disk dressing of the copper plate. This result could be due to the fact that the two-body lapping process could easily scratch the SiC wafer. The self-made lapping plate did not have diamond particles adhering to it, and the SiC substrate had fewer scratches and a better surface roughness value, as shown in Figure 17. When using a traditional metal lapping plate, abrasive particles could easily fall from the plate and create an uneven stress distribution resulting in a poor surface roughness value, as shown in Figure 18 [23]. [PERSON] et al. [24] pointed out that the excellent self-sharpening ability of the lapping plates causes even material removal with the least surface roughness.  
Figure 13: Silicon carbide material removal rate of lapping processes.  
Figure 16: Surface morphology of the copper plate surface: (**a**) original; (**b**) after lapping; (**c**) after dressing.  
Figure 17: Surface morphology of the self-made plate surface: (**a**) original; (**b**) after lapping; (**c**) after dressing.  
Figure 14: Surface roughness of grinding SiC wafers: (**a**) Sa; (**b**) Sz.  
Figure 15: Zygo images of the SiC surface: (**a**) Lapping-1; (**b**) Lapping-2; (**c**) Lapping-3; (**d**) Lapping-4.
###### Abstract  
Global Navigation Satellite System (GNSS) is the most reliable navigation system for location-based applications where accuracy and consistency is an essential requirement. The LSE (least squares estimator) has been used since the start of GNSS for position estimation. However; LSE is affected by outliers and errors in GNSS measurements and results in wrong user position. In this paper; we proposed a novel three-phase estimator for enhancing GNSS positioning accuracy in the presence of outliers and errors; relying upon the robust MM estimation theory. In the first phase; a subsampling process is proposed on available observations. IRWLS (iterative reweighted LS) is applied to all subsamples up to a predefined number of observations to obtain a positioning estimate and a scale factor. Secondly; IRWLS is applied up to the convergence point on a set of selected subsamples. The third phase involves the selection of optimum positioning solution having minimum scale factor. An outlier detection and exclusion process is applied on a probabilistic set of outlying observations to maintain the integrity and reliability of the position. Multiple simulated and real scenarios are tested. Results show high accuracy and reliability of the proposed algorithm in challenging environments.  
multi-GNSS; GNSS navigation; LSE; IRWLS; urban canyon; outliers; MM estimation +
Footnote †: journal: Applied
## 5 Experiments and Results
### Validating Tests
#### 5.5.2 Validating Tests in the MAMuJoCo Environment  
In Figure 14, G-MAPPO outperforms MAPPO and acquires the optimal _return_. In Figure 14a the variance of G-MAPPO is bigger than MAPPO, while in Figure 14b, the variance of G-MAPPO is smaller than MAPPO. A possible reason is that the default group in Figure 14a decreases the necessity of cross-group coordination, while the \"independent\" agents in Figure 14b must cooperate with each other to move the robotic body. Similar to the MPE tests, it can be concluded that the GAPO method can dramatically increase the performance in highly cooperative tasks, but may be limited in tasks requiring little cooperation.
# Implant Model Generation Method for Mandibular Defect Based on Improved 3D Unet
## 2 Materials and Methods
### The Process of Constructing the Mandible 3D Model Dataset
#### 2.2.2 The Method to Generate a 3D Voxel Mandible Model with Defect
\[PPV=TP/(TP+FP) \tag{5}\]  
\[Recall=TP/(TP+FN). \tag{6}\]  
where \(TP\), \(FP\), and \(FN\) represent true positive, false positive, and false negative, respectively. 3.3. Test Results and Analysis  
3.3.1. Mandibular Restoration and Implant Model Evaluation  
Mandible reconstruction was performed on the divided eight test sets using four trained networks: 3D Unet, 3D RUnet, 3D DUnet, and 3D RDUnet. For each test set, the reconstruction-subtraction strategy was used to obtain the implant of the defect part according to Formula (1). The evaluation indices of the mandibular repair model obtained from four different network reconstructions and the evaluation indexes of the defect implant obtained after reconstruction-subtraction are shown in Table 2.  
Table 2 shows that 3D RUnet is superior to 3D Unet in mandible and implant indicators, indicating that incorporating the residual structure effectively improves the reconstruction performance of the model. However, compared 3D DUnet with 3D RUnet, only the mandible and implant precision (PPV) of 3D DUnet is superior to 3D RUnet, others indexes have no substantial improvement. Furthermore, compared with 3D Unet, there is even a considerable decrease in the recall rate (From 0.7315 to 0.6412). This shows that the dilated convolutional layer increases the trainable parameters but cannot improve the reconstruction performance of the network. The indicators of 3D RDUnet are better than those of 3D RUnet and 3D DUnet. This result shows that incorporating residual structures and dilated convolutional layers can remarkably enhance the performance of the network. The mandible-related evaluation indices of 3D RDUnet are all above 0.97 (DCS: 0.9873, IoU: 0.9750, PPV: 0.985, Recall: 0.9897), and the implant-related indices are all above 0.65 (DCS: 0.8018, IoU: 0.6731, PPV: 0.7782, Recall: 0.8330). Compared with 3D Unet, 3D RUnet, and 3D DUnet, the improvement is obviously clear, which shows that 3D RDUnet substantially enhances the ability of the network to generate implants. In all networks, the evaluation index of the implant is lower than that of the mandible. The main reasons are as follows: First, the defect part is a difficult part to reconstruct when the mandible is reconstructed, and contextual information is lacking. Especially when the volume of the defect is large, the reconstruction is likely to fail. Second, the implant is obtained by subtracting Formula (1), which is equivalent to adding the error of the mandibular reconstruction to the implant, resulting in a lower index. Finally, part of the surface of the implant must fit the defect, resulting in a complex, changeable shape, unlike the overall shape of the mandible. Inpainting these complex boundaries well, which is also the main source of implant errors, is difficult for the network.
# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 6 Discussion  
As shown in Table 2, the MIC-CNN method achieves the best recognition effect for coal types, with higher classification accuracy and faster computation speed compared with 1D-CNN and BP neural networks. The MIC method can quickly and accurately identify the correlation among data features and reduce the interference of redundant features. When combined with the powerful feature extraction and classification ability of CNN, the model accuracy can be further improved. The recognition effect of 1D-CNN follows, which is affected by the presence of redundant features that lower the classification accuracy and long computation time of the model. The BP neural network has the poorest recognition effect because of the high requirements of data quality and features, and it is prone to overfitting when handling large amounts of power plant data. These experiments demonstrate that data-driven deep learning methods have a good effect on coal identification, and combining with the MIC feature selection method can improve the identification accuracy of the deep learning model. This paper provides a new approach for coal identification in TPs, which has potential applications. A dedicated server is established on-site to host the software components. This server acts as the central hub for data processing, storage, and distribution, which ensures data accessibility and maintains a stable connection with the Oracle database. The Oracle database, provided by Oracle Corporation, is utilized to store and manage the data generated by the system. The software establishes a connection to the Oracle database using appropriate credentials, enabling data retrieval and storage. This integration ensures data consistency, security, and efficient management. The software is configured to display the data locally, which means that the data processed and retrieved from the Oracle database is visualized on a user interface accessible within the local environment. This local display provides real-time insights into the data's behavior and characteristics. The software's functionality extends to the Distributed Control System (DCS) monitoring interface. Through integration with the DCS, the software enables real-time monitoring and control of the processes. This integration enhances operational efficiency by allowing operators to monitor the system's performance and make informed decisions. The software is deployed using Python programming language, a versatile and widely-used scripting language. Python's robust libraries and frameworks facilitate seamless  
Figure 9: Clustering diagram of MIC-CNN classification.  
communication between various components, making it suitable for interfacing with the Oracle database, local display, and DCS monitoring.
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
## 3 Proposed Framework--BI-LSTM-GMM  
As a solution to the OSR problem in DDoS attack detection, this study proposes a framework integrating Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. The functional block diagram of the proposed BI-LSTM-GMM framework is given in Figure 2.  
Figure 2: The functional block diagram of the proposed BI-LSTM-GMM.  
With supervised learning, the BI-LSTM is employed for the discrimination of regular traffic and DDoS attacks. Based on a concept similar to unsupervised learning, the GMM is adopted to construct the training data's distribution model. With GMM, new attacks or new legitimate traffic can be captured if they fall beyond a specific range of the constructed distribution model. Captured new traffics are subject to the identification and labeling of the traffic engineer and then used to update the BI-LSTM and the GMM in the incremental learning phase. To a certain extent, the GMM can be regarded as a classifier of differentiating learned samples and novel instances and the BI-LSTM as a classifier for the discrimination of good and bad traffics.  
Our framework uses historical traffic data for deep learning training to generate models that can be used to detect traffic. We also introduce the Gaussian mixture module to make the model capable of identifying unseen attacks. Finally, unknown attacks or traffic classified by the Gaussian mixture model will be identified and labeled by experts, and the deep learning model will be updated through incremental learning.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 4 Test and Results
### Scyther  
Scyther performs the verification with an unlimited number of protocol executions. It was developed by [PERSON] in 2006 [19; 20]. The scenario in which the protocol is analyzed is not required to be specified. Scyther provides a graphical interface in which the protocol description and security parameters must be entered. As an output, it delivers a report of the resulting analysis and a graph for each attack. It can review all possible scenarios. In case one or more attacks are found during the verification, the tool creates and displays a tree, as shown in Figure 11, where possible attacks can be observed. It is capable of verifying man-in-the-middle attacks and characterization. In the same way, it can perform a protocol characterization analysis.  
Scyther uses the Security Protocol Description Language (SPDL). The language allows the verification of claims, manually and automatically, which allows the tool to be requested to validate certain aspects of the protocol (e.g., the secrecy of a value). This provides confidentiality or certain properties necessary for authentication. Finally, the protocol can be analyzed from the point of view of each role, so the tool takes a finite number of possibilities in the execution of the protocol.