esa-sceva/satcom-chunk-collection huggingface dataset:
# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 6 Virtual Construction Simulation and Visualization Technology
### Safety Training  
DT also focuses on the optimisation of physical entities. In addition to the optimisation of the construction site, the improvement of the workers is essential, which is mainly reflected in safety training. Based on the analysis of the performance of a digital twin, knowledge gaps can be identified. Conducting targeted training for problems that arise during work can help to efficiently upskill the construction workforce. The PCMS [86] mentioned above is able to improve safety training programs while judging on-site individuals' risky behaviours. The system identifies and records the unsafe behaviour of a worker during the construction activity. Then, targeted training is provided for the enhancement of the worker's safety knowledge and awareness [4]. However, traditional safety training still has some weaknesses, such as failure to stimulate interest in learning and lack of hands-on practice. Although on-the-job training has been proven to be more efficacious [90], it cannot be widely used because it is time-intensive, expensive, and potentially hazardous [91].  
The integration of game technology and visualisation technology emphasises interaction in the development. It provides an innovative training method that can replace on-the-job training to some extent. Visualisation technology creates a virtual construction environment that allows users to walk, observe, and interact with virtual objects. By generating various scenarios of real construction activities being synchronised in VCS in a virtual environment and allowing users to judge whether they conform to safety regulations, the safety knowledge and awareness of the workers can be accurately and objectively evaluated [92]. Using a mouse, keyboard, controller, and other devices, trainees can perform construction activities, such as installation, operating, and dismantling, in a virtual environment to improve their professional skills [93].  
VR has also been introduced for safety training. The immersive experience provided by VR allows trainees to perform the same operation as in reality and obtain visual, auditory, and tactile feedback [94]. [PERSON] et al. [95] created an online VR system framework, proving that trainees can learn safety knowledge effectively through role-playing, dialogic learning, and social interaction in a VR environment. More importantly, VR does not present the hazards of on-the-job training. It allows workers to make wrong decisions in the VR environment and know the consequences of the decision [27]. By observing unsafe behaviour and its negative consequences, individuals will avoid performing similar behaviours [96]. This kind of training provides game-like experience, which can fully mobilise the enthusiasm of trainees, thereby improving training efficiency and the effect of knowledge transfer [97].
## 4 Intravalley Relaxation and Fermi Degeneracy Formation
### Role of Optical and Acoustic Phonons in the Relaxation  
The time evolution of the density of states (DOS) with the electron density \(n_{\rm M}=0.003\) electrons per site is given in Figure 3. The time evolution of the mean energy is also given in Figure 4, where the mean energy is defined as \(\langle E\rangle\equiv E_{\rm tot}/n_{\rm tot}\), where \(n_{\rm tot}\) is the total photo-excited electron number. In the early stage to around 1200 fs, the system shows an extremely rapid relaxation, since the electrons can occupy the lower parts of the conduction band very quickly. As seen in Figure 4, this rapid relaxation is caused mainly by the electron-optical phonon scattering.  
At around 1200 fs, most of the electrons occupy around the CBM. In this stage, the energies and momenta that should be necessary for the rest of electrons to relax inside the conduction band are strongly restricted. In other words, the number of phonons that meet the electronic transition in the conduction band decreases drastically. As a result, the relaxation becomes very slow. As seen in the inset of Figure 4, the system cannot reach the completely Fermi degenerated state (CFDS) only by the  
Figure 4: The time evolution of the mean energy is shown. “Only acoustic (optical)” represents the result, considering the time evolution only by the e-acoustic (ac) (op (optical)) process.  
Figure 3: The time evolution of the density of states (DOS) is shown. CFDS represents the DOS at the completely Fermi degenerated state. Thick and thin lines are plotted at every 500 and 100 fs, respectively, from \(t=0\)–4000 fs. Adapted with the permission from [11], copyright Journal of the Physical Society of Japan, 2015.  
electron-optical phonon scattering. Thus, this slow relaxation is mainly caused by electron-acoustic phonon scattering.  
Although it is not clear in Figure 4, the system very slowly relaxes down toward the CFDS at a later time, as seen in Figure 5. Thus, the relaxation speed continues slowing down, and thus, it takes an infinite time to reach the CFDS.  
The time evolution of the electron occupation can be seen in Figure 6. In the slow relaxation process, the electron occupation is close to the Fermi-Dirac distribution, implying the e-e interaction is effectively working. Thus, the electron temperature is well defined in this stage.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Enhanced Pose Estimation
#### 3.5.2 Enhanced Pose Optimization
This accounts for objects with a high degree of similarity in their longitudinal distribution, maintaining their relative positions during the optimization process. 5. If the spatial distribution \(GDOP^{c}_{2d}>g\) or \(GDOP^{c}_{3d}>g\) (\(g=0.4\)): Decrease weights of all categories. This accounts for unbalanced object distributions, helping to mitigate potential registration errors in the pose estimation process. The geometric cost function \(G(p,c)\) is composed of several standard deviations with the details in Section 3.4, as given by (16). The residual block of \(G(p,c)\) is typically around 0.001. The geometric properties of map objects remain unaffected by the translation dimensions of the 6-DOF pose. \[G(p,c)=f_{SSC}(p,c)=\left\{ \begin{array}{l}SSC_{lm}(c=lanmarking)\\ SSC_{pole}(c=pole)\\ SSC_{sign}(c=sign)\\ d=(3,4,5)\end{array} \right. \tag{16}\]  
The mentioned values \(\alpha=100\), \(l=0.1(le(0,1))\), and \(g=0.4\) (\(ge(0,1)\)) are determined based on the theoretical range and experimental observations. Ceres Solver [30] is employed to facilitate the optimization process. Bi-cubic interpolation is used on the distance transform image to get a smooth gradient. The SoftOneLoss function is used to decrease the impact of outliers. The distance parameters are constrained within \begin{table}
\begin{tabular}{c l l} \hline \hline
**Scenario** & \multicolumn{2}{c}{**SDF Values**} & \multicolumn{2}{c}{**Weights and Dimensions**} \\ \hline Initialization & / & \(\left\{\begin{pmatrix}w_{lm},w_{pole},w_{sign}\end{pmatrix}_{0}=(1.0,1.0,0.5)\\ d_{0}=(0,1,2,3,4,5)\end{pmatrix}\right.\) \\ \hline Condition (1) & \(\text{Occ}^{c}_{3d}=0\) or \(\text{Occ}^{c}_{2d}=0\) & \(w_{c}=0\) \\ \hline Condition (2) & \(L_{lm}<l\) & \(\left\{\begin{pmatrix}w_{pole},w_{sign}\end{pmatrix}=2\begin{pmatrix}w_{ pole},w_{sign}\end{pmatrix}_{0}\right.\) \\ \(d=(3,4,5)\end{pmatrix}\) \\ \hline Condition (3) & \(L_{lm}\geq l\) and \(\text{Occ}^{c}_{3d}>\text{Occ}^{lm}_{3d}\) & \(\left(w_{pole},w_{sign}\right)=2\begin{pmatrix}w_{pole},w_{sign}\end{pmatrix}_{0}\) \\ \hline Condition (4) & \(L_{c}<l\) & \(d=(0,1,3,4,5)\) \\ \hline Condition (5) & \(GDOP^{c}_{2d}>g\) or \(GDOP^{c}_{3d}>g\) & \(w_{c}=0.5\)\(w_{c0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cost Function Composition and Constraints. the range of [\(-1.5\), \(1.5\)] meters, while the altitude angle parameters are bounded within [\(-0.2\), \(0.2\)] radians.
## 3 Materials and Methods
### Workflow Implementation and Data Availability  
All workflow modules were implemented in Python (version 3.12). Data cleaning and organization utilized pandas [49], with statsmodels [50] supporting the mixed-effects analyses. Retrieval and knowledge-graph components were managed by LangChain [51], gte-Embegginds model based on [52], Qdrant [43], and Neo4j [44]. The VectorRAG and VectorGraphRAG agents used Google Gemini 2.0 [42], and the Fine-Tuned LLM relied on Meta-Llama-3.1-8B-Instruct-finki-edu-4 courses [47]. The Meta Evaluator was GPT4o [48], supplemented by human experts to ensure alignment with genuine course standards. A.2 (Table A4) shows example questions generated by agents for Task 3.  
The dataset includes both human-generated exam questions, which represent the actual question pool for the courses, and LLM-generated questions. Since the human-generated questions are actively used for student assessment, they cannot be published. However, all data generated by LLMs--including questions, course materials, fine-tuning datasets with the code for the actual fine-tuning, datasets produced by agents (Agents1 and Agents2), and meta-evaluation datasets with human supervision (Meta1 and Meta2), along with the analysis code, will be shared on the project's GitHub repository (v1.0.0) [53] after acceptance, in accordance with the journal's data-sharing guidelines.
# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 6 Conclusions  
In conclusion, an ontology in SAR scenario is built successfully for the smart decision-making of robots. The ontology in our article consists of entity ontology, environment ontology, and task ontology. Aiming at the core task ontology, we designed the representation method for high-level tasks and atomic actions in smart decision-making of robot, which facilitates the development of a subsequent task planning algorithm. In addition, the environment plays an important role in robot smart decision-making. Aiming at the environment ontology, the semantic model and the representation of objects in the environment are carried out.  
The robot can perform suitable tasks to adapt to different environment states. In addition, the environment is known partially, and the environment ontology is constantly updated in the process of task execution to understand the environment. The task planning algorithm based on ontology is designed. Then, we have an experiment in the SAR scenario based on TurtleBot3. The experimental results show that the robot can successfully complete tasks and realize smart decision-making.  
Future research work mainly focuses on the following aspects. First, the real SAR scenario is inaccurate, random, and incomplete. Therefore, it is necessary to study the task planning in uncertain environments. Then, multiple heterogeneous robots can cooperation to help with more complex tasks that a single robot cannot do. Therefore, it is of great significance to study cooperation task planning and its application based on multi-robot programming. Finally, the application of cloud-based knowledge will reduce the dependence of robots on specific hardware, which is conducive to the research of multi-agent and swarm intelligence.  
Conceptualization, Y.Z.; Funding acquisition, Y.Z.; Investigation, X.S.; Methodology, X.S.; Project administration, Y.Z.; Supervision, J.C.; Validation, X.S.; Writing--original draft, X.S.; Writing--review & editing, Y.Z. and J.C.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61806212, No. 61603403, No. U1734208 and No. 61702528).  
The authors declare no conflict of interest.
## 3 Numerical Examples  
Use enrichment function in Equation (3) and take the center of the circle as the origin of cylindrical coordinate. The boundary of the bubble in the field can be described using the level set function.  
\[\phi(\rho,z)=\frac{(\rho-\rho_{0})^{2}}{a^{2}}+\frac{(z-z_{0})^{2}}{b^{2}}-R^{ 2}, \tag{6}\]  
where \((\rho,z)\) is the coordinate point in the cylindrical coordinate.  
Three numerical models are used to verify the reliability of XFEM. Figure 3 shows an electric field analysis model of a bubble in the middle of cylindrical liquid nitrogen. The cylinder domain is simplified to an axisymmetric domain for more convenient calculation. The width of the axisymmetric domain is 7.5 mm and the height is 25 mm. The imposed voltage \(U\) is 8 V. The radii of bubbles are different in each model. The liquid nitrogen's relative permittivity is 1.434, while the bubble's is 1.  
Mesh is important for the finite element method, and the quantity of elements is one of the key factors affecting the accuracy of the calculation. The more elements and nodes there are, the higher the accuracy of calculation there is. However, at the same time, it will consume more computing resources. Therefore, the appropriate mesh needs to be selected. Four meshes with different densities are selected in Figure 4. The electric potentials at all nodes are compared, and ten results are shown in Table 1.  
Figure 3: Electric field analysis model.  
The results of mesh-c and mesh-d at these nodes are basically the same. The solution can be confirmed as an exact one. The results of numerical solution change little as the number of elements increases. Therefore, mesh-d is the most appropriate choice. The selection of XFEM mesh is based on the CFEM. Additionally, the size of the element is the same at the position far away from bubbles, which ensures the mesh of the two calculation methods is of the same level.
###### Abstract  
In order to study the axial compression behavior of concrete-filled steel tubular (CFST) columns filled with high titanium slag, a total of 32 specimens, including normal CFST columns, half-high titanium slag CFST columns, and full-high titanium slag CFST columns, were used as experimental samples in this study. The axial compression behaviors of high titanium slag CFST columns and normal CFST columns with various parameters such as length-diameter ratio, strength grade of concrete, strength grade of steel tube, steel content ratio, etc., were evaluated and compared through axial compression testing under monotonic static loading. The results showed that the axial compressive behaviors of high titanium slag CFST columns with various length-diameter ratios were not significantly different from those of normal CFST columns, both of which showed good axial compression performance. In addition, the length-diameter ratio limit between short and medium long column was from 3.5 to 4.4. The length-diameter ratio was the main factor influencing the shape of load-deformation curve of CFST columns. The casing hoop coefficient also had a great influence on the bearing capacity of short columns, while the influence on that of middle and long columns was not obvious. In the end, the bearing capacities of all specimens were calculated by bearing capacity formulas in European EC4, American AASC360-10, and Chinese GB50936-2014 standards. The calculated values were in good agreement with the test results.  
high titanium slag concrete; CFST column; axial compression behavior; experimental study; bearing capacity analysis +
Footnote †: journal: Applied Soft. 2019, 9, 2021; doi:10.3390/APP9102021
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 4 QKD Applications
### Internet of Things  
Another relevant market segment is the internet of things (IoT), which interconnects a massive number of sensors, actuators, and humans, and exchanging data over the Internet. IoT aims to increases the quality of life by permeating the world with connected sensors and actuators, and enabling novel services. However, this can lead to security issues because those devices must be small, simple, and cheap, even when acquiring and communicating critical sensitive data. Even though securing those devices is an urgent problem, maintaining low cost and size is a priority, meaning that adopting QKD is only feasible with highly integrated solutions, such as silicon photonic devices, attenuated single-photon LEDs, and decoy techniques, in order to remove the constraints of using pure single-photon sources. Since the devices can be mobile and are spread over a wide area, guided communication is not an option, and in-air protocols are more likely to be used in this context together with polarization encoding. CV-QKD may be envisioned to reduce the costs, but the dynamic nature of the environment in short-reach in-air links, often in urban scenarios, may impair its performance, reducing the key rate to unacceptable values [127].
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 3 Reconstruction Algorithm
### Deep-Learning Algorithm  
In terms of the problem that the sparse hypothesis model in traditional compressed sensing cannot fully meet the application requirements, deep learning uses a data-driven method to learn signal features and design signal reconstruction in an end-to-end manner. The multiple iterative process of traditional compressed-sensing reconstruction can be replaced by the calculation of deep neural networks to conduct real-time reconstruction processing [70; 71].  
[PERSON] et al. presented a structured deep network called ISTA-Net that used the iterative shrinkage-thresholding algorithm (ISTA) to optimize a general \(\ell^{1}\) norm CS reconstruction model [72]. They converted ISTA into a deep-network form, and used nonlinear transformation to solve the near-end mapping problem related to the sparse-induced regularizer. The reconstruction performance of ISTA-Net was much better than that of existing optimization- and network-based CS methods while maintaining fast calculation speed.  
Existing deep-learning-based image CS methods need to train different models for different sampling ratios, which increases encoder and decoder complexity. A scalable convolutional neural network was proposed to achieve scalable sampling and scalable reconstruction with only one model called SCSNet [73]. The hierarchical reconstruction network in SCSNet contains a base layer that provides the basic reconstruction quality, and some enhancement layers that reference the lower reconstruction layers and gradually improve reconstruction quality.
## 2 System Model
### Suboptimal Noncoherent PPM Receiver  
The suboptimal PPM receiver first recovers the pulse signal through envelope detection and then determines the received information by judging which of the \(2^{M}\) time slots the pulse signal is in. In order to determine which time slot the PPM pulse is in, the suboptimal PPM receiver first needs to complete time synchronization; that is, it needs to determine the boundary of PPM symbols, usually including a transmission reference synchronization method, side-by-side synchronization method and full blind synchronization method [23; 24]. This paper does not study the synchronization method, and the subsequent analysis assumes that the system has been synchronized well. For the synchronized system, the decision flow of the suboptimal PPM receiver is shown in Figure 2.  
1. Start symbol decision;
2. Envelope value sampling starts from time slot \(i=1\);
3. Compare the sampling value \(U_{i}\) with the threshold value \(\gamma\);
4. If the sampling value exceeds the threshold value, the pulse is considered to be in the first time slot, the information represented by the first time slot is output and the current symbol decision ends;
5. If the sampling value is less than the threshold value, it is considered that the pulse is not in this time slot, and the envelope value of the next time slot is sampled and compared with the threshold value;  
Figure 1: \(Q\)-ary PPM signal. (**a**) is a binary PPM, (**b**) is a 4-ary PPM and (**c**) is a 8-ary PPM.  
This continues until the sampling value of the \(k\in[1,2^{M}]\) time slot exceeds the threshold value, the information represented by the \(k\)th time slot is output and the current symbol decision is ended;
7. In particular, if the sampling values from slot 1 to slot \(2^{M}\) are less than the threshold value, we agree that this symbol outputs the information represented by slot \(2^{M}\) and end the current decision.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 1 Introduction
### Related Literature
Accelerometer measurements and weather mast data suggest that temperature and rotation speed highly influence results. In addition, damage detection performed better for actuator excitation than for wind excitation. Last, [PERSON] et al. [16] studied the effect of crack propagation in a test facility and determined the frequency response function by using strain gauges and accelerometers. Their experiments demonstrate that damage only leads to minimal changes in eigenfrequencies. A variety of approaches has been proposed for analysing vibration measurements in related applications, e.g., monitoring of bearings [17; 18; 19] or bridges [20]. In many approaches, time-frequency-based methods such as empirical mode decomposition [17], wavelets [18] and correlation measures [20] were combined with machine learning and artificial intelligence (AI). For example, [PERSON] et al. [19] detected bearing defects by using a sparse cost function and convolutional neural networks and therewith addressed the challenges of a small training dataset. However, labelled datasets and precise physical models are unavailable for measurements on wind turbine blades--as occurring for many large-scale structures [21]. Hence, a different approach is needed to analyse those measurements with respect to environmental and operational variability.