esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[21] employed Response Surface Methodology-Genetic Algorithm (RSM-GA), Artificial Neural Network (ANN), and ANN-Genetic Algorithm (ANN-GA) hybrid tools to develop mathematical models. Their aim was to establish indirect correlations between various FDM process parameters and part accuracy for process parameter optimization, thereby enhancing the precision of printed products. [PERSON] et al. [22] explored the relationship between process parameters and the mechanical properties of the lattice structure in 3D-printed Polylactic Acid (PLA+) by employing Particle Swarm Optimization (PSO) to refine the settings of Random Forest and XGBoost models, thereby augmenting their predictive capabilities. In this study, we aim to overcome the limitations of current artificial verteb fabrication methods by integrating modern 3D printing technology, image processing, and intelligent prediction techniques. Our research focuses on developing a personalized 3D printing method based on bone density prediction to prepare artificial vertebrae that closely replicate patient-specific bone density. By applying methods such as the Response Surface Methodology, Back Propagation neural networks, and genetic algorithms, we can predict 3D printing parameters for artificial vertebrae more accurately, leading to significant improvements in the preparation and performance of artificial vertebrae for robotic-assisted surgeries. This approach addresses the current gap in the preparation of artificial vertebrae that accurately replicate both the geometric and bone density characteristics of natural vertebrae, which is critical for robotic-assisted surgeries. By doing so, we aim to provide a reliable method for producing artificial vertebra models that support both surgical planning and robotic tactile feedback, ultimately contributing to safer and more effective spinal surgeries. This study holds broad implications for addressing the challenge of obtaining isolated artificial vertebra models with matching bone density through a 3D printing preparation method grounded in bone density prediction.
## 4 Selection of Computational Intelligence and Reinforcement Learning Solutions
### Multi-Swarm Particle Optimization  
While PSO has been successfully applied in a wide range of applications, it has proven to be less effective in dynamic environments, where it suffers from outdated memory and lack of diversity issues [57]. To address these shortcomings, researchers have started investigating Multi-Swarm PSO (MPSO) constructions [58].  
More specifically, MPSO exploits two principle mechanisms for maintaining diversification--and, as a result, avoiding premature convergence and implementing effective exploration throughout the entire search space--in the optimization process: multiple populations and repulsion. Instantiating more than one swarm allows MPSO to simultaneously explore different portions of the search space, thus allowing it to achieve very good performance in the case of multiple optima, i.e., for so-called multi-peak target functions. In addition, MPSO adopts two layers of repulsion: among particles belonging to the same swarm and among all the swarms, to maintain diversity in the exploration process and to avoid premature convergence.  
In this context, a particularly successful multi-swarm PSO construction has proven to be the one based on the atom analogy [57]. Taking loose inspiration from the structure of an atom, each swarm is divided into a relatively compact nucleus of neutral (or positively charged) particles and a loose nebula of negatively charged particles that float around the nucleus. This is obtained by moving the neutral portion of the particles according to classical PSO dynamics, i.e., as defined in Equation (2), and the negatively charged rest of the swarm according to quantum-inspired dynamics (Some versions of MPSO use Coulomb-force-inspired repulsion between charged particles, but QPSO dynamics has proven to be computationally simpler and at least just as effective [58]), i.e., as defined in Lines 17-23 of Algorithm 2.  
In addition, swarms have and their diversity is preserved by two mechanisms: exclusion and anti-convergence, which lead to a continuous birth-and-death process for swarms. Exclusion implements local diversity, preventing swarms from converging to the same optimum (or peak). If a swarm \(S_{A}\) comes closer than a predefined exclusion radius \(r_{excl}\) to another swarm \(S_{B}\), it is killed and a new randomly initialized swarm \(S_{C}\) takes its place. Anti-convergence, instead, implements global diversity, by ensuring that at least one swarm is \"free\", i.e., patrolling the search space instead of converging to an optimum. Towards that goal, MPSO monitors the diameter of each swarm, and if all of them fall below a threshold (which is typically dynamically estimated to suit the characteristics of the optimization problem), it kills and replaces one swarm.  
Since they have been specifically designed for dynamic optimization problems and are currently considered state-of-the-art solutions in this context [14], MPSO techniques represent a particularly promising candidate for our investigation.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Spatial Distribution Factors
#### 3.3.1 Feature Occupancy  
Feature occupancy is utilized to assess the sufficiency of objects within the visual images and HD map. The feature occupancy 3D \(Occ^{c}_{3d}\) is determined by image \(I_{m}\) which is projected to the image plane from HD map \(M_{c}\) in camera frame using (1):  
\[I_{m}=\pi M_{c} \tag{1}\]  
where \(\pi\) represents the image perspective projection from the camera coordinate system to the image plane. This projection is formulated using the camera intrinsic matrix \(\mathbf{K}\), given by (2):  
\[\pi=\mathbf{K}\begin{bmatrix}x_{c}\\ y_{c}\\ z_{c}\end{bmatrix}=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix}\begin{bmatrix}x_{c}\\ y_{c}\\ z_{c}\end{bmatrix} \tag{2}\]  
where \(f_{x}\) and \(f_{y}\) are the focal lengths. \(c_{x}\) and \(c_{y}\) are the principal point offsets. \((x_{c},y_{c},z_{c})\) is a point in the HD map \(M_{c}\). This feature occupancy 2D \(Occ^{c}_{2d}\) quantifies the ratio of occupied pixels by specific object categories \(c\) relative to the total number of occupied pixels in the segmented image \(I_{s}\), as shown in Figure 4a. \(Occ^{c}_{2d}\) and \(Occ^{c}_{3d}\) are calculated using (3):  
\[\begin{cases}Occ^{c}_{2d}=\frac{Occ(I_{s},c)}{Occ(I_{s})}\\ Occ^{c}_{3d}=\frac{Occ(I_{m},c)}{Occ(I_{m})}\end{cases} \tag{3}\]  
\(Occ(I_{s},c)\) represents the number of occupied grids for object category \(c\) in the segmented image \(I_{s}\). \(Occ(I_{s})\) represents the total number of occupied grids in \(I_{s}\) (the union of all occupied grids across categories). Similarly, \(Occ(I_{m},c)\) and \(Occ(I_{m})\) are for the HD projected image \(I_{m}\).
## 4 Experimental Analysis
### Qingjiangpu Religious Architecture Cultural Landscape Reproduction
#### 4.3.4 Three-dimensional modeling  
First, we collected historical documents, remote sensing images, mapping drawings, and high-definition images of the cultural heritage landscape of the ancient city of Qingjiangpu. Secondly, based on these data, the preliminary 3D models were constructed using the SketchUp modeling software. Third, the models were textured and mapped using high-definition images. Finally, model beautification was performed to make it look more realistic.  
Figure 6: Two-three dimensional fusion scene realization. (**a**) Ciyun Temple, (**b**) Confucious Temple.  
Figure 7: Cultural landscape 3D model construction and visualization workflow.  
Model integration  
Scene element initialization: There are several model import methods in Unity 3D, and this study uses drag-and-drop to import the 3D model built in step (1). After importing, select the model file and set its material import method to Use External Materials (Legacy). In order to get a better light baking effect, it is necessary to check its Calculate Normals module when importing the model, and check the option to automatically generate Lightmap UVs. Considering the problem of inconsistent unit scale between Unity and SketchUp, set the model Scale to 0.001 in Unity 3D.  
3D model mesh surface optimization: On the window interface, classify and combine the 3D model files, and use the integrated Simple LOD plug-in to simplify and optimize the mesh surface of the sub-objects in the combination, so as to reduce the draw call value and avoid the problem of scene jam when the program is loaded.  
Realistic light baking: Select the model after mesh surface optimization, set it as Static object, and select the scene lighting object, set its rendering mode to Baked, in the Lighting component, check Baked Lightmap, and set the baking option to Shadow Mask, and keep the rest as default parameters, and perform scene lighting baking to get a realistic scene lighting rendering effect.  
System function realization  
This study mainly reproduces the scenes of religious buildings in Qingjiangpu and shows the cultural characteristics and connotations of religious buildings, and realizes the following functions: full-scene roaming of religious buildings, real-time weather change, voice explanation, and architectural text introduction; the system interface is shown in Figure 8.  
As shown in Figure 9, the system scene roaming page, the right menu bar has six parts: start roaming, pause roaming, continue roaming, voice explanation, system settings (sound), and return to the map; the small map in the lower left corner of the interface shows its location within the scene; the bottom of the interface shows the introduction of the architectural and cultural landscape when roaming.  
Figure 8: Initial interface of religious scene reproduction system based on a micro-cosmic scale.
## 1 Introduction  
Secondary tunnel linings frequently develop construction quality defects such as vault voids during construction. Over time, these may evolve into various lining deteriorations during operation [1, 2, 3, 4]. Among these, water leakage represents one of the most prevalent tunnel defects, accelerating corrosion of the lining and internal reinforcement, reducing load-bearing capacity, and ultimately compromising operational safety [5, 6, 7]. Consequently, timely detection and precise treatment of tunnel lining leakage are critical for tunnel safety management.  
Conventional leakage detection primarily relies on visual inspection and periodic monitoring. While operationally simple, these methods face inherent limitations: stains on lining surfaces and inadequate tunnel lighting often impede reliable differentiation between leakage zones and stained areas. During large-scale, long-term monitoring--exemplified by the Khimti Headrace Tunnel (Nepal) where geological logs were recorded at 5-20 m intervals over its 7.88 km length--inspectors encounter substantial accuracy challenges [8]. Moreover, methodological subjectivity stems from inter-inspector variability [9; 10]. As tunnel construction scales expand and monitoring requirements intensify, traditional approaches increasingly fail to meet modern demands for precision and efficiency.  
Recent advances in deep learning for medical image segmentation have prompted applications in structural defect detection [11; 12; 13; 14; 15]. Current tunnel leakage identification predominantly employs CNN-based semantic segmentation. Although CNNs effectively integrate multi-level features for object recognition, studies indicate their bias toward local textures over global shapes [16], hindering accurate leakage detection in complex tunnel environments. Similarly, standalone Transformer-based models exhibit degraded local feature representation due to absent CNN-like inductive biases [17]. Thus, most existing leakage detection models demonstrate constrained capability in segmenting target fine contours under complex conditions.  
To overcome the subjectivity and inefficiency of manual inspection, as well as the inadequate recognition accuracy of existing models in challenging environments, a Vision Transformer (ViT) was integrated into the UNet architecture. By embedding SE-Block modules at both the encoder-decoder skip connections and the ViT output layers, the SE-TransUNet model is proposed. The model is trained on a hybrid leakage dataset comprising mountain drill-and-blast tunnels and shield tunnels. Ablation studies and Score-CAM heatmaps confirm the effectiveness and contribution of the SE-Block and ViT modules. This work proposes a novel approach for tunnel leakage identification, offering methodological insights for related research.
# Borophene Is a Promising 2D Allotropic Material for Biomedical Devices
## 2 Main Properties of Borophene
An experimental study based on first-principles calculations investigated the mechanical, dynamic, and thermodynamic stabilities of striped \(\beta_{12}\) and \(\chi_{3}\) borophene. The results showed that \(\beta_{12}\) and \(\chi_{3}\) borophene have good mechanical, dynamic, and thermodynamic stabilities; on the other hand, striped borophene is not equally stable. The origin of the high stiffness and high instability observed in striped borophene, along the \"a\" direction, can be attributed both to strong directional bonding and to high stiffness along the \"a\" direction [13]. Another study conducted by [PERSON] et al. also confirmed the thermodynamic stability of \(\beta_{12}\) borophene and \(\chi_{3}\) borophene [14]. On the contrary, [PERSON] et al. showed that both \(\beta_{12}\) borophene and \(\chi_{3}\) borophene could show naive phonon-mediated superconductivity [15]. Furthermore, interesting results obtained by first-principles calculations and STM image simulation indicated that \(\beta_{12}\) and \(\chi_{3}\) sheets are thermodynamically unstable [16]. Due to the structural instability of borophene, some researchers have theoretically hypothesized a fully hydrogenated structure of borophene defined as \"borophene\", a structure that showed excellent stability in silico [17]. Finally, the good optoelectronic properties and ability to be a superconductor allow borophene-based nanostructures to be strategic for the production of nano-devices that are applicable in different medical fields [18].
## 1 Introduction
### Background
By integrating blockchain, IoT, and wireless sensor networks, the end consumer can track the product, verifying its origin, harvest date, mode of transport, and certification, increasing consumer confidence in the product they are purchasing. Consequently, technology can increase the demand for these products among those consumers who value such information [15; 16]. The information recorded by the blockchain is secure and can be publicly or privately accessible, depending on the preferred strategy, and most importantly, it is immutable. This allows the information to be accessible to retailers, auditors, governments, consumers, producers, and other stakeholders along the supply chain. Moreover, it provides for monitoring and auditing the data, assessing economic and environmental performance, or other indicators of interest [17]. Additionally, blockchain has the advantage of eliminating dependence on a central authority. Its decentralized nature elevates the importance of the network effect. The technology's potential benefits increase as the network's size expands, making it more robust against external attacks [18]. Finally, the interoperability of blockchain and the use of mobile devices, from computers to smartphones, put the power of decision-making in the hand of consumers [19]. However, the benefits and opportunities associated with using this technology and the advantages and disadvantages it provides to stakeholders influence managers' decisions to adopt blockchain [20]. Furthermore, business models need to rebuild existing systems, popularize the technology, and train staff to adapt to the new management process [21; 22].
## 4 Experimental Design and Data Collection
### Video Super-Resolution Reconstruction and Evaluation Based on BasicVSR++
#### 4.3.3 VSR Reconstruction Effect and Evaluation  
The real-world VSR reconstruction results of the UUT gas leakage event based on the BasicVSR++ model is shown in Figure 7. The reconstructed gas-leakage feather image is clearer, the edge details in the low-resolution image are restored, the noise is effectively reduced, and the information quality of the image is improved. In addition, NIQE and BRISQUE were used to further quantify the evaluation algorithm results. Due to the limited computing resources, only the first frame, middle frame, and last frame of each video sequence were extracted for indicator evaluation. The NIQE of the original image was 7.4006, and the NIQE after the output of the BasicVSR++ was 3.0021, which is a decrease of 4.3985 points. Similarly, the BRISQUE of the original image was 22.3987, and the BRISQUE after the output of the BasicVSR++ was 18.2362, which is a decrease of 4.1625 points. The above results prove that the VSR reconstruction based on the BasicVSR++ is successful, which provides a basis for subsequent video grading and recognition.
## 1 Introduction
Albedo and the solar reflectance index (SRI) of different precast concrete pavements have been measured. The results found in these concrete pavements are compared with the ones reported for the most commonly used materials to construct pavements. Finally, the effect of using high-albedo pavement materials on climate change mitigation has been discussed. \begin{table}
\begin{tabular}{c c} \hline \hline
**Pavement Constituent** & **Albedo** \\ \hline New asphalt & 0.05–0.10 \\ Polished asphalt & 0.10–0.20 \\ \hline White Portland cement & 0.87 \\ Blast-furnace cements & 0.71–0.75 \\ Grey Portland cement & 0.32–0.47 \\ Coal fly ash & 0.28–0.55 \\ \hline Fine grain natural gravel & 0.62 \\ Gold and white rock (chert, iron impurities) & 0.55 \\ White rock (plagioclase) & 0.49 \\ Limestone fine aggregate & 0.42 \\ Limestone coarse aggregate & 0.42 \\ Dark grey riverbed sand (quartz, clay minerals, mica) & 0.20 \\ Black and red rock (granite) & 0.19 \\ \hline Concrete composed of ordinary Portland cement, fine aggregate & 0.64 \\ fromcrushed limestone, and light-colored slag cement & 0.64 \\ Concrete composed of white cement and fine aggregate from & 0.64 \\ crushed limestone & \\ Smooth dry concrete made with white cement and fine aggregate & 0.41–0.77 \\ from crushed limestone & 0.35–0.40 \\ New grey concrete & 0.25–0.30 \\ Weatherped grey concrete & 0.25–0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Albedo of typical pavement materials and concrete constituents.
## 4 Simulation Analysis
### Validation of Model Generalizability  
In order to verify the universality of the proposed model, a real 10-kV distribution network in a region of China is used as an example. The structure of this distribution network is illustrated in Figure 13, which demonstrates its complex network topology, distributed power access locations, and SOP access points. The PV access capacities are 300 kW and 400 kW, while the WT access capacity is 500 kW. The remaining system parameters are listed in Table 7, including branch impedance and node load. The results of the planning process, as derived from the simulation, are presented in Table 8. The benefits radar chart is illustrated in Figure 14.  
Figure 12: Radar charts for 5 major indicators for 5 schemes.  
Figure 13: Grid structure of the actual distribution network.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Node \(i\)** & **Node \(j\)** & \
\begin{tabular}{c} **Branch** \\ **Impedance** \\ \end{tabular} & **Node \(j\)** & **Node \(i\)** & **Node \(j\)** &
\begin{tabular}{c} **Branch** \\ **Impedance** \\ \end{tabular} & **Node \(j\)** & **Node \(j\)** & **Node \(j\)** \\ \hline  
The data analysis presented in the above table indicates that the coordinated planning scheme of SOP and ESS reaches the minimum value of the objective function at the upper level and the maximum value at the lower level. This suggests that Scheme 4 has the most superior performance in terms of economy and flexibility. Concurrently, the scheme offers robust backing for practical applications.  
As illustrated in the figure, the area of the constituent graphics of the five schemes exhibits variability, with Scheme 4 exhibiting the largest area. This result indicates that Scheme 4 is the most superior in terms of overall performance and reflects its optimal balance in all aspects.  
It is noteworthy that the results are in accordance with the simulation outcomes of the IEEE 33-node, which serves to corroborate the universality of the proposed model. This phenomenon indicates that the model is capable of providing effective guidance and support in a variety of network structures and operating conditions. It can be concluded that Scenario 4 is not only applicable to the actual distribution network in the region under consideration, but also provides a valuable reference for the optimization of distribution networks in other regions.
## 3 Results
### Challenges and Future Directions
#### 3.6.3 Regulatory Approval  
The regulatory landscape for nanoparticle-based therapies remains complex, as these systems frequently fall under both pharmaceutical and medical device categories. Their nanoscale-specific properties may generate distinct toxicological risks compared to conventional materials, necessitating a tailored regulatory approach. Approval requires rigorous preclinical and clinical testing to demonstrate safety and efficacy--often a lengthy and costly process [97].  
To support the standardization of such procedures, international organizations such as the OECD and ISO have developed guidelines for the testing, characterization, and safety evaluation of nanomaterials.  
At the national level, the United States regulates nanomaterials through agencies such as the Environmental Protection Agency (EPA) and the Food and Drug Administration (FDA), which mandate detailed notifications and safety evaluations before product commercialization. The European Union applies REACH and CLP regulations, requiring mandatory registration and labeling of products containing nanomaterials. Countries such as Japan, South Korea, and Canada have adopted their own policies or aligned with international models to effectively manage associated risks.  
In parallel, the industry has introduced voluntary self-regulation initiatives such as the Responsible Nano Code and the Nano Risk Framework, aimed at promoting transparency and safety throughout the entire product lifecycle [99].  
Within this evolving framework, companies must carefully navigate a dynamic legislative environment. Collaborative efforts among researchers, manufacturers, and regulatory authorities are essential to facilitate the safe and timely approval of nanoparticle-based therapies, while ensuring patient protection and supporting innovation in nanomedicine [97; 99].