esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Experimental Results and Discussions
### Validation of Recommended Criteras of TO: A Case Study of an Aircraft Bracket  
To validate the effectiveness of the proposed recommendation criteria, this study employs an aircraft bracket as a verification case [54] and utilizes both hexahedral and tetrahedral meshes for discretization modeling. In the Abaqus general module, boundary conditions and loading configurations are established, as illustrated in Figure 12; loading points are positioned at the center of the aircraft bracket's ear hole ring with force components of \(F_{y}\) = 25,980.76 N and \(F_{x}\) = 15,000 N, while fixed constraints are applied to four bolt connection holes. Material properties are defined as \(E\) = 110.3 GPa and \(\mu\) = 0.33. Within the TO module, non-design regions encompass the ear rings and bolt holes, with the remaining areas designated as design domains and subjected to X-axis symmetry constraints at the base.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Original Model**} & \multirow{2}{*}{**Model in Ref. [43]**} & \multicolumn{2}{c}{**The Best Overall Solution**} \\ \cline{3-5}  & & & **Hexahedral Test 9** & **Tetrahedral Test 3** \\ \hline Volume (mm\({}^{3}\)) & 281,544 & 226,899 & 215,427 & 218,913 \\ Weight reduction ratio (\%) & 0 & 19.38 & 23.46 & 22.22 \\ Maximum stress (MPa) & 1332.3 & 1094.9 & 948.8 & 998.5 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Feasibility verification of the best overall solution.  
Figure 11: Finite element stress map of the geometric reconstruction model No. 3 for the tetrahedral mesh (unit: MPa).  
Figure 13 demonstrates the mesh generation and TO structural configurations of the aircraft bracket developed under the recommended criteria. Specifically, Figure 13b displays the optimization results employing 2 mm hexahedral meshes with the initial density set to 0.5, the filter radius equivalent to 2.4 times the average element edge length (4.8 mm), and the penalty factor configured to 3.5. Figure 13d presents the optimization results employing 3 mm tetrahedral meshes, with the initial density set to 0.5, the filter radius scaled to 1.4 times the average element edge length (4.2 mm), and the penalty factor configured to three. Experimental verification confirms the complete elimination of isolated bodies, disconnected branches, and thin bars in both optimized configurations, substantiating the proposed criteria's effectiveness in enhancing topology optimization manufacturability.
## 6 Real Jamming Results Analysis
### Low Jamming Scenario
#### 6.1.1 First Trajectory

Table 6: RMS error, mean, and Max positioning errors with low actual jamming [first trajectory]. Figure 21: (a) Vertical positioning error for different GNSS signal combinations [first trajectory]. (b) Zoomed view of vertical positioning error for different GNSS signal combinations [first trajectory]. Figure 22: Number of visible satellites for different GNSS signal combinations [first trajectory].
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 3 Experiments
### Loop Closure and Plane Detection  
In this section, loop closure and plane detection methods of the interactive SLAM algorithm are applied to the results of A-LOAM and HDL Graph SLAM, and the effects of these methods are investigated. Although loop closure and plane detection methods were applied to both A-LOAM and HDL Graph SLAM results in this study, only A-LOAM results are shown in the figures below. The accuracies of both algorithms are given in the Results and Conclusions section.  
Figure 5a shows the section with odometry drift in the outdoor environment. The point cloud corrected by the manual loop closure method is given in Figure 5b.  
Figure 6 shows the top and side views of the point cloud with odometry drift in the indoor environment and the point clouds corrected by the automatic loop closure method.  
In Figure 6c, the drift occurring predominantly in the direction of the Z-axis can be seen. Figure 6d shows that the drift in the Z direction is substantially eliminated with the automatic loop closure method, but there is a curve, especially in the middle section. It is believed that this error, which occurs mainly in the long corridor environment, can be eliminated by applying the plane detection method after the automatic loop closure method. Figure 6e is a side view of the corrected point clouds, following the ground and plane detection.  
When the results are examined, it is seen that the curvature of the ground after the automatic loop closure algorithm is corrected by plane detection. In addition, since the perpendicularity was defined between the floors and the corridor, irregularities on the walls were corrected.  
Figure 4: Results of the SLAM algorithms for an outdoor environment: (**a**) top view of LOAM results; (**b**) side view of LOAM results; (**c**) top view of A-LOAM result; (**d**) perspective view of A-LOAM results; (**e**) top view of HDL Graph SLAM results; (**f**) perspective view of HDL Graph SLAM results.  
Top and perspective views of the corrected final 3D maps of the indoor and outdoor environments are in Figure 7.  
Figure 5: The effect of the manual loop closure method in the outdoor environment: (**a**) odometry drift, and (**b**) corrected point cloud.  
Figure 6: The effect of the automatic loop closure method in the indoor environment: (**a**) odometry drift (top view); (**b**) corrected point cloud (top view); (**c**) odometry drift (side view); (**d**) corrected point cloud (side view); (**e**) side view of the corrected point clouds after ground and plane detection.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 2 System Model and Problem Formulation
### System Model
\(\mathbf{g}_{b}=[\mathbf{g}_{b1},\mathbf{g}_{b2},\ldots,\mathbf{g}_{bK}]^{T}\), \(\mathbf{g}_{c}=[\mathbf{g}_{c1},\mathbf{g}_{c2},\ldots,\mathbf{g}_{cK}]^{T}\), where the elements are defined as \[g_{a\phi}=\eta\sum_{n=1}^{K}\left|\alpha_{\phi,n}\beta_{\phi,n} \right|^{2}\bigg{(}\frac{x_{\phi}-x_{R}}{d_{\phi R}}+\frac{x_{n}-x_{R}}{d_{nR}} \bigg{)}^{2}, \tag{7}\] \[g_{b\phi}=\eta\sum_{n=1}^{K}\left|\alpha_{\phi,n}\beta_{\phi,n} \right|^{2}\bigg{(}\frac{y_{\phi}-y_{R}}{d_{\phi R}}+\frac{y_{n}-y_{R}}{d_{nR}} \bigg{)}^{2},\] (8) \[g_{c\phi}=\eta\sum_{n=1}^{K}\left|\alpha_{\phi,n}\beta_{\phi,n} \right|^{2}\bigg{(}\frac{x_{\phi}-x_{R}}{d_{\phi R}}+\frac{x_{n}-x_{R}}{d_{nR} }\bigg{)}\] (9) Figure 1:0  
where \(\eta=\frac{8\pi^{2}\beta^{2}}{\sigma_{\mathrm{F}}^{2}c^{2}}\) and \(d_{nR}\) is the distance between the buoy \(n\) to the location of the target \((x_{R},y_{R})\). To achieve better localization accuracy, the CRB needs to be as small as possible. After observing Equations (2) and (6), the UAV's location and buoy transmit power affect the performance of the communication and radar sensing, which should be jointly optimized to obtain a better trade-off. However, conventional methods to deal with such problems generally optimize one performance and treat the others as constraints which can not provide an optimal performance trade-off between the dual functionalities.
# Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging
###### Abstract  
Parking duration analysis is an important aspect of evaluating parking demand. Identifying accurate distribution characteristics of parking duration can not only enhance parking efficiency and parking facility planning, but also provide essential support for parking delicacy management. Previous studies have proposed various statistical distributions to depict parking duration data. However, it is difficult to find a certain type of distribution to describe the characteristics of parking duration in diverse parking facilities, since model uncertainty is caused by stochastic parking behaviors and diverse parking environments. To address the model uncertainty, a Bayesian model averaging (BMA) was applied to integrate the advantages of different statistical distributions to depict parking duration characteristics. The parking dataset was collected from a commercial parking lot in Chengdu, China, and the dataset was categorized into two groups (i.e., temporary users and long-term users) to analyze. A set of statistical distributions was chosen as candidate models, and their corresponding unknown parameters were estimated. The posterior model probability for each candidate model was calculated according to the goodness-of-fit (GOF) metric. The findings of the study illustrate that there is no universally applicable distribution form (e.g., log-normal distribution) to depict the parking duration distribution for both user types, whereas the BMA approach assigns weights to candidate models and always provides an accurate description of the parking duration characteristics. The parking duration analysis is useful for improving parking management strategies and optimizing parking pricing policies.  
applied sciences; traffic management; parking delicacy management; parking demand; parking duration; Bayesian model averaging +
Footnote †: journal:  
doi:10.3390/app132413245
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 6 Analysis
### Models by Input Type
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** &
\begin{tabular}{c} **Speed** \\ **(FPS)** \\ \end{tabular} & **RGB** & **RGB-D** & **3D** & **Mod** & **Lidar** & **Depth** & **Pose** & **Gray** \\ \hline Improved CDPN [77] & 1.3 & ✓ & & & & & \\ SSD6D [6] & 10 & ✓ & & & & \\ Pix2 Pose [10] & 10 & ✓ & & & & \\ DeepIM [61] & 12 & ✓ & & & & \\ SO-Pose [23] & 20 & ✓ & & & & \\ PoET [43] & 20 & ✓ & & & & \\ CoS-PVNet [51] & 21 & ✓ & & & & \\ SE-UF-PVNet [49] & 23.6 & ✓ & & & & \\ ASPP-DF-PVNet [18] & 25 & ✓ & & & & \\ RNNPose [86] & 26.6 & ✓ & & & \\ Compressed YOLO-6D [40] & 27 & ✓ & & & \\ Focal Segmentation [53] & 29.2 & ✓ & & & \\ HybridPose [16] & 30 & ✓ & & & \\ BB8 [5] & 31 & ✓ & & & \\ DPOD [12] & 33 & ✓ & & & \\ Lite-HRPE [55] & 33 & ✓ & & & \\ CDPN [8] & 33.3 & ✓ & & & \\ [PERSON] et al. [24] & 33.3 & ✓ & & & \\ SC6D [33] & 33.3 & ✓ & & & \\ [PERSON] et al. [46] & 34 & ✓ & & \\ PVNet [32] & 35.8 & ✓ & & & \\ CRT-6D [41] & 38.5 & ✓ & & & \\ YOLOX-6D-Pose [56] & 50.5 & ✓ & & & \\ [PERSON] et al. [54] & 70.9 & ✓ & & & \\ YOLO-6D+ [17] & 71 & ✓ & & & \\ RePOSE [22] & 92 & ✓ & & & \\ SelfBO [64] & & ✓ & & & \\ [PERSON] et al. [68] & & ✓ & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Single Image Models and Input Types (Part I).
## 1 Introduction
In the case of LPBF, the residual stress is caused by the localized melting and solidification during the process. As the laser melts the metal powder, it creates localized regions of high temperature that can cause thermal expansion and deformation. As the material solidifies and then cools, the different regions of the part cool at different rates, which can cause large strain misfit and internal stresses to form [17; 18; 19]. The level and distribution of residual stress within a part can be influenced by a variety of factors, including the laser power, scanning speed, powder layer thickness, and part geometry [18; 19]. Residual stress can have a significant impact on the mechanical properties and performance of parts produced using LPBF. High levels of residual stress can cause warping and distortion of the part, leading to dimensional inaccuracies and reduced functionality. In addition, residual stress can also affect the fatigue life of the part, making it more susceptible to cracking and failure [20]. Therefore, understanding and managing the residual stress is critical to ensuring that parts produced using LPBF meet the required performance standards. There are already a few studies on the residual stress of the 18 Ni-300 marging steel produced by LPBF. For instance, [PERSON] et al. [21] have studied the residual stress of the maraging steel produced by LPBF using cantilever distortion tests. [PERSON] et al. [22] have investigated the effect of LPBF processing parameters on the residuals stress state of maraging steel using cantilever distortion and X-ray diffractions. Recently, [PERSON] et al. [23] have studied the influence of phase transformation in the formation of the residual stress during the LPBF of the maraging steel by a finite element (FE) simulation. However, the effects of aging heat treatment on the residual stress in the maraging 18 Ni-300 steel produced by the LPBF have not been investigated yet in the literature, although the aging heat treatment is a necessary procedure in most cases of applications of the maraging steels produced by LPBF. The aim of this study is to investigate the influence of the post heat treatment conditions on the residual stress in the maraging 18 Ni-300 steel produced by LPBF. Cantilever-shaped samples were designed and fabricated by LPBF to evaluate the residual stress. The samples that underwent different heat treatments were used for the tests.
## 2 Materials and Methods
### Data Analysis  
All data analyses, data cleaning, and statistical analyses were conducted using the R programming language (version 4.2.1) in the Rstudio environment (version 2022.07.1). We fitted separate repeated measure-nested linear mixed-effects models, estimated using REML (ImerTest package 3.1-3; [53]), to examine the effects of each experimental intervention (Exercise and Chewing) and time on the following Myoton parameters: stiffness (reflecting the tissue's resistance to shape changes), relaxation (the time required for the muscle to regain its initial shape post-deformation), tone, creep (the deformation to relaxation time ratio), and decrement (logarithmic decrement indicating elasticity). The model included 'intervention' and 'time' as fixed effects and 'participants' as random effects. To test the assumptions of the mixed linear models, residuals were plotted against the fitted values to assess homoscedasticity, and Q-Q plots were used to verify the normality of the residuals. The visual inspection of residual plots revealed no obvious deviations from homoscedasticity or normality. Moreover, \(p\)-values were computed using a Wald t-distribution approximation, and Sidak-corrected post hoc tests were employed to identify differences in cases of statistically significant interactions or main effect differences. To quantify the magnitude of changes, we calculated effect sizes in terms of standardised beta for mixed effect effects and Cohen's d for post hoc measures. Standardised beta effect sizes between 0.10 and 0.29 were considered small, effects between 0.30 and 0.49 were considered medium, and effect sizes of 0.5 or greater were considered large. Coden's d effect sizes between 0.20 and 0.49 were considered small, effects between 0.50 and 0.79 were considered medium, and effect sizes of 0.8 or greater were considered large [54].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Exercise n** & **Aim** & **Instructions** \\ \hline Exercise intervention & & \\
1 & Tongue proprioception and control & Hold for 30 s. Rest for 10 s, repeat 2\(\times\) \\
2–4 & & Repeat at 0.5 Hz for 30 s. Rest for 10 s \\
5 & Activate orofacial muscle spindles & repeat for 2\(\times\) \\
6 & Facial muscle relaxation & Hold for 30 s. Rest for 10 s, repeat 2\(\times\) \\
7 & Relaxation of the mandible & \\
8–9 & Mobilisation exercises & \\
10–12 & Coordination exercises & Repeat at 0.5 Hz for 20 s. Rest for 10 s \\
13–18 & Isometric strength exercises & Repeat for 30 s. Rest for 10 s, repeat 2\(\times\) \\
19–24 & Isometric exercises for the cervical & Hold for 20 s. Rest for 10 s, repeat 3\(\times\) \\
25 & Scapular retraction & \\ Chewing intervention & & \\  & Chewing & 60 bites/min for 6 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intervention protocol exercise summary.
## 3 Experimental Setup
### Key Transmission Performance Metrics  
#### 3.2.1 Bit Error Rate (BER) vs. Error Vector Magnitude (EVM)  
Why we focus on EVM  
* In our experiments, we employed Error Vector Magnitude (EVM) as the primary performance indicator. EVM is widely used in optical and wireless communications as it provides a direct measure of constellation distortion and correlates closely with BER once we set a threshold (e.g., the first-generation forward error correction (GFEC) limit).
* Specifically, in systems that use QAM-based modulation (QPSK, 16-QAM), an EVM under certain percentages typically assures a BER level under \(10^{-3}\) or \(10^{-4}\), depending on the coding scheme.
* Estimated BER from EVM
* Although we did not explicitly measure BER in real time, it is possible to relate EVM to BER via analytical expressions or standardized thresholds. For instance, the first GFEC threshold for QPSK is typically around 26.3% EVM, correlating to a BER near 3.8 \(\times\) 10\({}^{-3}\). For 16-QAM, a 12.3% EVM is often used as a typical threshold for meeting a BER near \(10^{-3}\).
* As reported in our experimental results, when we keep EVM below those threshold values, we can infer that the resulting BER is within acceptable limits for standard FEC to correct any residual errors.
* Furthermore, a clear theoretical relationship exists between EVM and BER. Typically, a lower EVM indicates higher signal quality, directly translating to a lower BER. This relationship is mathematically defined as follows: \[BER=0.5\times erfc\bigg{(}\sqrt{1/EVM^{2}}\bigg{)}\] where \(erfc()\) is the complementary error function. Thus, our extensive EVM results effectively provide a strong indirect indication of BER performance under varying masking ratios and modulation schemes (QPSK and 16-QAM). Our results clearly demonstrate optimal masking ratios around 75%, balancing improved throughput and accurate signal reconstruction.  
\begin{table}
\begin{tabular}{c c} \hline
**Key Hyperparameter** & **Value** \\ \hline Patch size & 16 samples (selected via ablation over [8; 16; 32] \\ Masking ratio & 75–85\% (optimized based on lowest EVM with acceptable BER) \\ Transformer depth & 4 layers (evaluated over [2; 4; 6]) \\ Embedding dimension & 128 \\ Number of attention heads & 4 \\ Dropout rate & 0.1 \\ Training epochs & 200 \\ Optimizer & Adam with learning rate = 1 \(\times\) 10\({}^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 4: Key hyperparameters of sparse code mask modeling.
## 2 Monitoring Scheme
### Fiber Optic Monitoring System and Deployment  
The monitoring system shown in Figure 4 was adopted to establish a monitoring system that facilitates interlayer debonding detection, and its deployment is illustrated in Figure 5. The monitoring system mainly consisted of a signal generator, tunable laser, demodulator, and sensors. The selected sensors were interferometric fiber optic accelerometers with suitable bandwidth and high sensitivity [39]. Their sensitivity was tested to be 500 rad/g within the range of 0.1 to 1000 Hz. The measurement range was \(\pm\)2 g. The interrogation of the interferometric fiber optic accelerometers in this paper was realized by the Phase Generation Carrier (PGC) demodulation technique [51]. The sampling frequency of the monitoring system was set to 1000 Hz.  
In the field deployment scheme, there were six sensors in total for monitoring the two track segments. Three sensors were placed in the cable trough (Figure 5b) of each segment because the cable trough was the transversely nearest place to the debonding-prone area in the safety-securing zone allowed for sensor placement. As shown in Figure 2, the sensors were installed at the three cross-sections of each track segment. The white circular tube in Figure 5c was to prevent electric cables from touching the fiber optic accelerometers. The fiber optic transmission cable of the monitoring system was connected with the monitoring base (Figure 5d), which was about 1000 m away from the monitoring area, for signal demodulation and data accumulation.  
Figure 4: Schematic diagram of fiber optic vibration sensing system.  
Figure 5: The field deployment of vibration monitoring system.
## 2 Dynamic Modeling Process of the Fiber Woven Composite Shaft-Disk Rotor
### Energy Derivation of System Structure Units
#### 2.3.1 Potential and Kinetic Energy of the Rotating Shaft Units
\[\mathbf{r}_{\text{D}}\ =\ \mathbf{R}_{0}+(\mathbf{R}_{\text{D}}+\mathbf{U}_{\text{D}} \mathbf{A}_{1})\mathbf{A}_{\Omega} \tag{19}\]  
where \(\mathbf{R}_{0}\) (\(=[X,Y,Z]\)) is the origin's position of the rotating coordinate system \(\text{C}_{1}\) in the absolute coordinate system \(\text{C}_{0}\). \(\mathbf{R}_{\text{D}}\) denotes the position vector of the coupling nodes of the shaft and the disk, whose value is \(\mathbf{R}_{\text{D}}=[w_{\text{SC}}+x_{\text{SC}}\), \(u_{\text{SC}}\), \(v_{\text{SC}}]\), and the subscript \"SC\" denotes the coupling nodes of the shaft and the disk. \(\mathbf{A}_{1}\) and \(\mathbf{A}_{\Omega}\) are the coordinate transformation relation matrices from \(\text{C}_{2}\) to \(\text{C}_{1}\) and \(\text{C}_{1}\) to \(\text{C}_{0}\), and their expressions are detailed in the literature [38]. The symbol \(\mathbf{U}_{\text{D}}\) in Equation (19) denotes the position vector of a point (\(x_{\text{D}}\), \(r_{\text{D}}\), \(\theta_{\text{D}}\)) on the disk in the follower coordinate system with the following expression:  
\[ \begin{split}\mathbf{U}_{\text{D}}&=\ \mathbf{R}_{\text{D}}+\mathbf{U}_{\text{D}}\\ &=\ \begin{bmatrix}x_{\text{D}}\\ r_{\text{D}}\cos\theta_{\text{D}}\\ r_{\text{D}}\sin\theta_{\text{D}}\end{bmatrix}^{\text{T}}+\begin{bmatrix}w_{ \text{D}}\\ (u_{\text{D}}+x_{\text{D}}\varphi_{\text{D},\theta})\cos\theta_{\text{D}}-(v_{ \text{D}}-x_{\text{D}}\varphi_{\text{D},r})\sin\theta_{\text{D}}\\ (u_{\text{D}}+x_{\text{D}}\varphi_{\text{D},\theta})\sin\theta_{\text{D}}+(v_{ \text{D}}-x_{\text{D}}\varphi_{\text{D},r})\cos\theta_{\text{D}}\end{bmatrix}^{ \text{T}}\end{split}  \tag{20}\]  
Combining the above equations, the velocity of the node on the disk can be obtained as:  
\[\dot{\mathbf{r}}_{\text{D}}\ =\ (\mathbf{R}_{\text{D}}+\mathbf{U}_{\text{D}}\mathbf{A}_{1}) \dot{\mathbf{A}}_{\Omega}+\Big{(}\dot{\mathbf{R}}_{\text{D}}+\dot{\mathbf{U}}_{ \text{D}}\mathbf{A}_{1}+\mathbf{U}_{\text{D}}\dot{\mathbf{A}}_{1}\Big{)} \mathbf{A}_{\Omega} \tag{21}\]  
This, in turn, leads to an expression for the kinetic energy of the disk as:  
\[T_{\text{D}}\ =\ \frac{1}{2}\rho_{\text{D}}\iiint\dot{\mathbf{r}}_{\text{D}}\dot{ \mathbf{r}}_{\text{D}}\dot{\mathbf{r}}_{\text{D}}^{\text{T}}r_{\text{D}}d\theta_{ \text{D}}d\chi_{\text{D}} \tag{22}\]  
where \(\rho\) denotes the density. The kinetic energy formula for the disk is too lengthy to show specifically.