esa-sceva/satcom-chunk-collection huggingface dataset:
# MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization
## 2 Topology Optimization
### The Solid Isotropic Material with Penalization (SIMP) Approach  
In the past, several approaches have been proposed for solving the STO problem. The main ones are as follows [12]: (i) level-set method, (ii) density method, (iii) phase field method, (iv) topological derivative method, and (v) evolutionary method. SIMP, proposed in 90 s [7; 8; 9], is the most well-known representative of the density method and is commonly used for dealing with the STO problem. The structural system's compliance \(C\) is the most widely adopted performance indicator used in STO problem formulations. If \(n\) finite elements are used to discretize the design domain \(\Omega\), the distribution of material over \(\Omega\) is denoted by \(x_{i}\) that are the density values of each finite element, \(i\ \in[1,\ldots,n]\) and \(x_{i}\ \in(0,1]\). When \(x_{i}\approx 0\), it denotes no material to the \(i\)th finite element, while if \(x_{i}=1\), then the \(i\)th finite element is fully filled. Thus, Equation (2) becomes as follows:  
\[\begin{split}\min_{x\in(0,1]}C(x)&=U(x)^{T}\cdot P \\ subject\quad to:\\ K(x)\cdot U(x)&=P\\ \frac{V(x)}{V_{0}}&=V_{t}\end{split} \tag{3}\]  
where \(C(x)\) denotes the compliance of the structural systems for specific material distribution \(x\), while \(V(x)\), \(V_{0}\) and \(V_{t}\) express the volume corresponding to density vector \(x\), the initial volume for \(x=x_{0}\) and the targeted volume of the optimized domain, respectively. According to SIMP, [PERSON]'s modulus \(E\) is correlated based on a power law expression to the density value \(x_{i}\) of each element of the FE discretization as follows:  
\[E_{x}(x_{i})=x_{i}^{p}\times E_{0}\ \Longleftrightarrow\ K_{x}(x_{i})=x_{i}^{p} \times K_{i,0} \tag{4}\]  
where the penalization coefficient \(p\) is often set to \(p=3\), while \(K_{x}(x_{i})\) and \(K_{i,0}\) denote the local stiffness matrix of the \(i\)th element when its density is equal to \(x_{i}\) and its original stiffness matrix, respectively. In the literature, various search algorithms were combined with SIMP for solving the problem of Equation (3); the method of moving asymptotes (MMA) and the optimality criteria (OC) algorithm are the most commonly used ones.
# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 4 Results
### Model Performance Evaluation and Feature Importance Analysis
Four explanatory variables were selected: proportion of residents aged 80 and older, number of ambulances assigned to the center, total population, and alcohol consumption rate. This model structure reflects localized factors that influence response time and can serve as a tool for assessing operational adequacy across different administrative regions. The adoption of a Lasso-based weighting scheme has proven effective in multiple dimensions: enhancing model interpretability, quantifying variable importance, and enabling user-driven scenario analysis within the simulation interface. These features position the proposed framework as a valuable decision-support tool for both short-term resource allocation and long-term strategic planning in emergency medical services. These results underscore a key methodological advancement of this study by demonstrating that the integration of Lasso regression into the simulation framework improves predictive performance while generating sparse and interpretable coefficients tailored to the operational context of emergency medical services (EMS). Unlike traditional simulation models that often rely on expert heuristics or manually calibrated parameters, this data-driven approach derives variable weights through statistically grounded learning, thereby enhancing both reproducibility and empirical rigor. The Lasso-based weighting scheme supports transparent scenario-specific adjustments and maintains model stability \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAPE** & **RMSLE** \\ \hline LinearRegression & 0.195 & 0.442 & 0.361 & 0.248 & 0.192 \\ Ridge & 0.182 & 0.427 & 0.349 & 0.241 & 0.183 \\ Lasso & **0.168** & **0.410** & **0.328** & **0.232** & **0.174** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of linear regression models in simulation weight estimation. in high-dimensional settings, contributing to the development of scalable and accountable decision-support tools for EMS resource planning.
## 4 Operational Strategy  
The operational strategy of the HEMS is designed to enhance the usage of RESs, manage backup systems efficiently, and ensure a continuous power supply to meet load demands. The process begins by assessing the load requirements of the residential building. This involves determining the total energy demand to guarantee the seamless operation of all electrical appliances and systems within the home. Following this, the power produced by the primary RESs, specifically PV and WTs, is computed. The total power generated is then compared against the load requirements. If the power produced by the PV and WTs surpasses the load demands, the system moves to verify the status of the backup sources. Conversely, if the power generated is less than the load requirements, the system proceeds to assess the power deficit and verify the constraints and availability of backup sources. In the next step, the system verifies the status of the backup sources, which include the BESS, HSS, and EVB. The parameters checked are the quantity of H2 in the tank and the SOC of the BESS and EVB. If there is excess energy, it is redirected to the backup sources based on their status. For instance, if the hydrogen tank is full and both the BESS and EVB reach their maximum SOC (SOCmax), the excess energy is sent to a dummy load.  
In cases where a power deficit is identified, the system checks the availability and constraints of the backup sources. If the SOC of the EVB (SOCEV) is between 70% and 80%, the system proceeds to load shedding. Similarly, if the SOC of the BESS (SOC Batt) is between 20% and 80%, the system evaluates the operational cost. Additionally, if there is stored hydrogen available, the system also evaluates the operational cost. For backup sources that meet the criteria, the system performs an operational cost evaluation. This involves calculating the cost of activating the backup sources to meet the load requirements. The operational cost analysis is further refined using advanced optimization techniques, specifically the RSA. This step ensures that the most cost-effective and efficient backup source is selected. Based on the optimization analysis, the system activates the optimal backup source to meet the load shortage, ensuring that the power supply remains uninterrupted. Finally, the system checks if the load shortage has been met. If the shortage is resolved, the process stops. If not, the system may loop back to reassess the available options and continue the process. The operational strategy of the HEMS provides a comprehensive framework for managing energy generation and storage as detailed in the flowchart in Figure 3. By leveraging advanced optimization techniques and systematically evaluating the status of primary and backup sources, the system ensures efficient energy management and continuous power supply.
# A Survey of Low-Cost 3D Laser Scanning Technology
## 3 Classification of the Prototypes
### Discussion of the Classification of the Prototypes
In the conversion process of the sampling point \(p\) from the 2D LiDAR coordinate frame \(L\) to the world coordinate frame \(W\), there are multiple space rigid body transformations. For a rotating 2D LiDAR, one space rigid body transformation is from the coordinate frame \(L\) of the 2D LiDAR to the coordinate frame \(P\) of the prototype, one is from the coordinate frame \(P\) of the prototype to the coordinate frame \(M\) of the mobile platform, another is from the coordinate frame \(M\) of the mobile platform to the world coordinate frame \(W\). For a push-broom 2D LiDAR, one space rigid body transformation is from the coordinate frame \(L\) of the 2D LiDAR to the coordinate frame \(M\) of the mobile platform, the other is from the coordinate frame \(M\) of the mobile platform to the world coordinate frame \(W\). The error of each space rigid body transformation will eventually lead to the error of the 3D point cloud. Among the above space rigid body transformations, the rotation matrices and translation vectors of some space rigid body transformations do not change with time, such as \(R_{p}^{M}\) and \(T_{p}^{M}\). The rotation matrices and translation vectors of some space rigid body transformations may vary with time. For example, for \(R_{L}^{P}\) and \(T_{L}^{P}\), the angular position of the motor shaft is different at different time points; for \(R_{M}^{W}\) and \(T_{M}^{W}\), the position and attitude of the mobile platform in the world coordinate frame at different time points may be different. The rotation matrices and translation vectors in Equations (3), (5), and (7) are corresponding to the time point when the sampling point \(p\) is collected by the 2D LiDAR. Through the above analysis, it can be known that, in order to accurately calculate the coordinates of the sampling points of a moving 2D LiDAR relative to the world coordinate frame, it is necessary to know the space rigid body transformation parameters of each sampling point from the 2D LiDAR coordinate frame \(L\) to the world coordinate frame \(W\) accurately in real time. From a perspective of engineering, this is difficult to realize. Therefore, there are errors in the 3D point clouds built by a moving 2D LiDAR. Many studies focus on the improvement of the accuracy. In the next section, we will discuss these studies in detail. In addition, another factor that restricts the application of a moving 2D LiDAR is its defect in real-time performance. A discussion of research on this issue is included in the next section.
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 4 Conclusions  
This study was based on numerous previous studies by other researchers, and it integrated an optimization platform for topology optimization considering the anisotropy of AM. The anisotropy test and SEM analysis were carried out on the additive manufacturing samples in different printing directions, and the obtained anisotropy parameters were substituted into the optimization analysis. The following conclusions could be drawn:  
1. By testing 316L stainless-steel products of SLM manufacturing technology, it was found that the mechanical properties of the printing direction were the weakest.
2. Under electron microscopy observation, there were more dimples and second-phase particles at the fracture of the sample stretched along the printing direction, which may have been due to incomplete powder melting caused by the interval of scanning speed between layers.
3. The attribute parameters of the forming direction had a significant influence on the optimized stiffness and optimized shape of the structure. In the case of the node in this paper, when adding parameters in various directions for analysis, both had the same direction most beneficial to actual manufacturing.  
Figure 16: Deformation comparison between anisotropy and isotropy (yellow line is the deformation in six directions after isotropic optimization; red line is the deformation in six directions after anisotropic optimization).  
* 4. The deformation obtained by anisotropic optimization was about 1.09-2.19% smaller than that obtained by isotropic optimization. Therefore, when topology optimization is combined with additive manufacturing, it is necessary to consider the performance difference caused by the printing orientation.  
Conceptualization, X.W.; Data curation, X.J.; Investigation, R.W.; Methodology, Z.W.; Software, H.R.; Validation, F.Z. ([PERSON]); Writing--original draft, F.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The project was supported by the Key Laboratory of Civil Engineering Structure and Mechanics, Inner Mongolia University of Technology (No.: 2021 KY002). National Natural Science Foundation of China (No.: 52268037). Yunnan University Postgraduate Scientific Research Innovation Project (No.: 2021Y285).  
The authors declare no conflict of interest.
## 2 CS-1 Scenario and Rendezvous Phases  
The primary objective of the ClearSpace-1 (CS-1) mission is to demonstrate the rendezvous, capture, and de-orbiting of the VESPA payload adapter, launched aboard VEGA in 2013.  
Figure 1 provides an overview of the mission, showing the sequence of operations conducted, from the launch of the servicer to the deorbit of the stack configuration, including the key aspects of close-range rendezvous and target capture using a deployable robotic arm. In the bottom right of the figure, the target, the VESPA payload adapter, is shown.  
Figure 1: ClearSpace-1 mission overview.  
Note that the figure presents a simplified overview of the CS-1 mission. The full mission includes strategies for efficient station keeping and advanced capture strategies (co-rotation) for high-angular target motion. This simplification is deliberate to focus the testing and demonstration of results on the main close-proximity phases.  
Figure 2 shows the sequence of the CS-1 mission's phases, already illustrated in Figure 1, as well as the AOCS/GNC involved. A standard AOCS is combined with GNC for the rendezvous for the approach and capture; a mission-specific AOCS will be adopted for post-capture and de-orbiting. This paper focuses on the Rendezvous operations, that include Client Phasing, Far Rendezvous, Close Rendezvous, and Capture.  
In particular, Client Phasing decreases the difference in the true anomaly of the servicer with respect to the client in a safe way, using the Relative Orbital Elements (ROEs) approach of walking safety ellipses [30; 31]. A walking safety ellipse is a drifting out-of-plane elliptical periodic relative motion around the target that never crosses its velocity vector [32]. In this way, the chaser moves on the surface of a cylinder, approaching the target in a natural motion that avoids any collision with the target, as depicted in Figure 3. The technical details on ROEs are discussed in Section 4.3.  
Then, in the Far Rendezvous phase, Fly-Around trajectories with impulsive control are used, allowing the servicer to get closer to the client, while also serving the purpose of inspecting it. Following the client inspection, the servicer transitions to forced motion control to reach a holding point known as the Formation Keeping Point (FKP), where the relative position is maintained until the next phase begins. Finally, during Close Rendezvous and Capture phases, six Degrees-of-Freedom (6 DOF) forced motion guides the servicer towards the capture of the client.  
Figure 3: Illustrative example of relative motion with a walking safety ellipse [30]. Full line represents the chaser’s motion, and the dashed line represents V-bar direction.  
Figure 2: Phases of the ClearSpace-1 mission.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 2 Background
### Cyclostationarity  
A signal \(x(t)\) is considered to be second order cyclostationary if its second order statistics exhibit hidden periodicities in time. Its autocorrelation function \(R_{x}(t,\tau)\) can thus be expressed as [12]:  
\[R_{x}(t,\tau)=E\{x(t+\frac{\tau}{2})x^{*}(t-\frac{\tau}{2})\}, \tag{1}\]  
where \(\tau\) denotes the time delay and \(E\{.\}\) the statistical expectation.  
Applying a Fourier series expansion to Equation (1), the \(T_{0}\)--periodic function \(R_{x}(t,\tau)\) can be represented as:  
\[R_{x}(t,\tau)=\sum_{\alpha}R_{x}^{\alpha}(\tau)e^{j2\pi\alpha t}, \tag{2}\]  
where \(\alpha=m/T_{0}\), \(m\in\mathbb{Z}\) are the cyclic frequencies, and the Fourier coefficients,  
\[R_{x}^{\alpha}(\tau)=\frac{1}{T_{0}}\int_{-T_{0}/2}^{T_{0}/2}R_{x}(t,\tau)e^{- j2\pi\alpha t}dt, \tag{3}\]  
are referred to as the Cyclic Autocorrelation Function (CAF).  
In the frequency domain, the signal \(x(t)\) is characterized by its cyclic spectrum \(S_{x}^{\alpha}(f)\), also known as the Spectral Correlation Function (SCF). It is defined as the Fourier transform of the CAF and is given by [21]:  
\[S_{x}^{\alpha}(f)=\int_{-T_{0}/2}^{T_{0}/2}R_{x}^{\alpha}(\tau)e^{-j2\pi f \tau}d\tau. \tag{4}\]  
The SCF cannot be directly estimated using Equation (4) because of its high computational complexity. Therefore, an efficient cyclic spectral analysis algorithm, called the Fast Fourier Transform (FFT) Accumulation Method (FAM), has been proposed to reduce this complexity. The first step consists of computing the complex demodulates \(R_{T}(n,f)\) using a sliding \(N^{\prime}\)-point FFT as follows:  
\[R_{T}(n,f)=\sum_{k=-N^{\prime}/2}^{N^{\prime}/2}a(k)x(n-k)e^{-j2\pi f/(n-k)T_ {s}}, \tag{5}\]  
where \(a(n)\) is a Hamming window of length \(T=N^{\prime}T_{s}\), and \(T_{s}\) is the sampling period. In the next step, the \(N^{\prime}\)--point FFT is hopped over the data in blocks of size \(L\). Then, the product between the complex demodulates and its conjugate is time-smoothed by a second FFT of length \(P\). Hence, the SCF estimate obtained by FAM is expressed as [21]:  
\[\mathbb{S}_{xT}^{\alpha_{i}+q\Delta x}(nL,f)=\sum_{k}R_{T}(kL,f)R_{T}^{*}(kL,f )g_{c}(n-k)e^{-j2\pi kq/P}. \tag{6}\]
## 4 Test Results and Interpretation  
### Types of Altered Minerals  
The samples collected from the Jiawula lead-zinc-silver deposit contained a total of 16 types of altered minerals (Figure 4). The types of minerals include Fe-Mg chlorite, Mg-chlorite Fe-chlorite, muscovite, paragonite, phengite, muscovite illite, paragonite illite, phengite illite, calcite, ankerite, kaolinite, and smectite. The highly crystalline white micas can be referred to by their mineral name muscovite, phengite, or paragonite. The illitic white micas also display compositional substitution and can be referred to as illite, phengitic illite, or paragonitic illite. In general, the mineral species of muscovite group, illite group, and chlorite group could not be accurately determined by rock specimens and microscopic identification. Examples include muscovite (muscovite, paragonite, and phengite), illite (muscovite illite, paragonite illite, and phengite illite), and chlorite (Fe-Mg chlorite, Mg-chlorite, and Fe-chlorite). Therefore, in this study, the muscovite group, illite group, and chlorite group minerals are not specifically distinguished when the altered mineral assemblages of the tunnel and borehole are zonated. Based on the identification by the TerraSpec mineral spectrometer and field-based manual alteration recognition, this study categorized muscovite, phengite, and paragonite as muscovite minerals and muscovite illite, phengite illite, and paragonite illite as illite. Furthermore, it grouped Fe-Mg chlorite, Mg-chlorite, and Fe-chlorite in the category of chlorite minerals.  
TSG was adopted to identify and interpret the types and mass fractions of altered minerals. During the identification and categorization of altered minerals, we only included minerals with mass fractions greater than 0.2 and ignored those with fractions lower than 0.2 to enhance the accuracy. The TerraSpec mineral spectrometer could not identify quartz silicified veins, so they were often recognized as \"NULL\" or \"Aspectral\". Therefore, the recognition and delineation of silicified zones were performed manually. Typical assemblages of altered minerals comprise muscovite-illite, muscovite-illite-chlorite, or illite-chlorite-calcite \(\pm\) ankerite, while the silicified zones comprise illite-smectite-chlorite and muscovite-kaolinite.
## 4 Experiments
### Implementation Details and Evaluation Metrics  
The deep network is trained with the Adam optimizer. The initial learning rate is \(1\times 10^{-4}\), and the learning rate is automatically attenuated according to the CosineAnnealingLR strategy. Until the 100 th epoch, the learning rate is attenuated to \(1\times 10^{-6}\). Due to the limitation of computing resources, images must be randomly cropped before input into the network. The patch size is set to \([96,96,96]\). Each image is randomly divided into eight patches, and the batch size is set to 4. The network is implemented using Pytorch 2.0 and MONAI 1.2.0 [59], and the server configuration is Intel(R) Xeon(R) CPU E5-16200, 3.60 GHZ, 64 GB RAM, Nvidia GeForce RTX 3090 (24 GB), Ubuntu18.04 (Intel, Santa Clara, CA, USA). To improve the generalization performance of the model and reduce overfitting, we use data augmentation during the training procedure, including random bias field, random Gaussian noise, random contrast adjustment, and random intensity value offset, with a probability of 0.5. We did not use any post-processing or ensemble methods for a fair comparison. In the self-supervised learning phase, a batch size of 4 is utilized. The learning rate begins at \(1\times 10^{-5}\), employing the Adam optimizer alongside a warm-up scheduler for the initial 500 steps. The model undergoes training for 200,000 iterations, with the final model being used for subsequent fine-tuning tasks.  
We use the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (95 HD), and Average Symmetric Distance (ASD) to evaluate the segmentation performance of the proposed model. The DSC coefficient measures the volumetric overlap between ground truth and segmentation results, while the 95 HD measures the distance between ground truth and the boundary pixels of the segmentation result. Additionally, the ASD provides a measure of the average distance between the boundary points of the segmentation and ground truth. These metrics give a well-rounded understanding of both the volumetric overlap and the boundary discrepancies between the ground truth and the segmentation results.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.4 User Mobility Estimation  
Predicting user's position, movement and trajectory can improve resource allocation and reduce signal overhead in 6G networks [16]. The authors in [83] used a discrete-time Markov chain based approach to predict the next cell a user is most likely to move into. Results show that the solution can accurately predict both the movement and trajectory of the users. Furthermore, in [84] the authors used HMM algorithm to predict user's location. The model addresses the mobile network as a state-transition graph. The efficiency and accuracy results of the approach were satisfactory. Two unsupervised algorithms for user equipment (UE) association are proposed in [85] in heterogeneous networks at RF and THz frequencies. The simulation results show that proposed algorithms can outperform conventional approaches in both data rate and balancing traffic load.
## 3 Results  
### Study Selection  
A total of 559 studies were identified through searches in various databases, including MEDLINE (_PubMed_) (n = 112), PEDro (n = 24), SPORTDiscus (n = 15), Scopus (n = 265), Elsevier (ScienceDirect) (n = 132), and Web of Science (WOS) (n = 11). After removing 150 duplicate records, 409 studies remained for screening.  
Subsequently, 186 studies were excluded based on title and abstract screening. The remaining 223 studies were retrieved for full-text review to assess their eligibility.  
Of these, 206 studies were excluded for the following reasons: publication in languages other than English or Spanish (n = 25), lack of full-text availability (n = 50), non-comparableinterventions (n = 13), different study populations (n = 20), study designs not aligned with the inclusion criteria (n = 80), and outcomes not relevant to the research question (n = 18). Ultimately, 17 studies met all inclusion criteria and were included in the qualitative synthesis. The study selection process is illustrated in Figure 1. Flow diagram of study selection (PRISMA 2020).