esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In orthodontic treatments, extraction space closure is often performed [1]. In the two-step method, the canines are moved first, and then the incisors are moved. On the other hand, in the one-step method, the canines and incisors are retracted en masse. Even with en masse retraction, movement of the anchor teeth or anchorage loss does not increase as compared with the two-step method [2, 3]. En masse retraction is an easier way to close the extraction space.  
Sliding mechanics or retraction springs have been used to move the anterior teeth en masse [4, 5, 6, 7]. Both methods have advantages and disadvantages. In sliding mechanics, the anterior teeth could move bodily over a long distance because of a guide of the archwire. Friction arises when the archwire slides to the bracket slot, which decays the retraction force. In clinical situations, the magnitude of friction is uncertain, which makes it difficult to control the magnitude of the force acting on a root.  
In the method with retraction springs, a force created by the spring is applied directly to the root. It is difficult to control a tipping movement of the anterior teeth due to the elastic deformation of the spring. Additionally, as the teeth move, the spring returns to its initial shape, and the force decreases, which makes it difficult to move the anterior teeth over a long distance. It is necessary to design a suitable spring for efficient bodily movement.  
In many cases, loops are incorporated into an archive and used as a retraction spring. Such archives are also commercially available. Until now, various loops have been designed, and their force systems have been calculated by using the beam theory or the finite element method (FEM) [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. However, the force system changed as the tooth moves, and the tooth movement pattern also changed [17; 18]. It is difficult to estimate the efficacy of spring only from the force system created by activating the spring.  
In the present study, to clarify the mechanism of tooth movement caused by the loop springs, a long-term movement of the teeth in the extraction space closure was simulated by the FEM. First, it was shown how the teeth were moved with the retraction spring, and then how the loop shape and the wire size affected the movement. Lastly, the mechanical efficacies of various springs were compared to each other.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods
### Materials
#### 3.1.3 Trusted Automated Exchange of Intelligence Information (TAXII)  
Diversifying our infrastructure, we integrate the Trusted Automated Exchange of Intelligence Information (TAXII). As a standard protocol for the exchange of cyber threat intelligence, TAXII facilitates secure and standardized communication of threat information [54]. Its incorporation enhances interoperability and extends the reach of our threat intelligence sharing ecosystem.  
This section provides an overview of Hyperledger Fabric and TAXII and explores the possibilities of leveraging these two powerful technologies for threat information sharing. Hyperledger Fabric is a blockchain framework that allows organizations to build private permissioned blockchain networks to enhance the security, transparency, and efficiency of their transactions. TAXII, on the other hand, is a trusted automated exchange protocol that facilitates the sharing of threat information in a standardized and structured manner [48]. By combining Hyperledger Fabric's secure and scalable infrastructure with TAXII's ability to exchange contextualized threat intelligence, organizations can create a robust framework for sharing threat information among trusted parties. We will delve into the features, capabilities, and potential advantages of using Hyperledger Fabric and TAXII together to enable seamless collaboration and enhance cyber threat detection and response.  
Integrating Hyperledger Fabric with TAXII offers several compelling benefits for organizations involved in threat information sharing [31; 55]. Firstly, it enhances the security and privacy of sensitive threat data. With Hyperledger Fabric's robust framework and consensus mechanism, combined with TAXII's standardized exchange protocols, organizations can ensure the confidentiality and integrity of their shared information. Additionally, this integration enables real-time updates and improved data accessibility. Hyperledger Fabric's distributed ledger technology allows for the immediate propagation of threat information across multiple participants, ensuring that all stakeholders have access to up-to-date and accurate data. Furthermore, by leveraging the smart contract capabilities of Hyperledger Fabric, organizations can automate the enforcement of rules and policies in the sharing process, reducing the risk of human error or malicious activities. This integration also paves the way for improved collaboration and trust-building among organizations. The transparent and auditable nature of Hyperledger Fabric, combined with TAXII's standardized language for threat information, promotes cooperation, fosters trust, and simplifies data sharing between organizations. By integrating these two powerful technologies, organizations can unlock the full potential of threat information sharing, leading to more effective and timely responses to emerging cyber threats.
# Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
## 3 Wavelet-Based Deep Transfer Learning
### Overall Framework  
This section proposes a wavelet-based deep TL method to address the fault diagnosis problem using limited target samples. Figure 1 presents the proposed framework consisting of the pre-trained GoogLeNet, which uses the source data, and the partial retraining of the same pre-trained model, which uses the intermediate data and the target data. The continuous wavelet transform (CWT) constructs the time and frequency characteristics of the time-series signal and generates the image of the intermediate and target data.  
Figure 1: Proposed framework of fault diagnosis using limited target samples.  
The major components of the proposed framework are as follows:  
* Pre-trained model: We use the GoogLeNet trained on the ImageNet data set to classify 1000 categories of the typical images [20]. GoogLeNet is a 22-layer CNN, a variant of the inception network developed at Google for image classification and object detection.
* Training on the intermediate domain: The CWT converts the time-series signals of the intermediate domain to the wavelet images capturing the time and frequency characteristics. The intermediate network has nearly the same architecture as the pre-trained model of the source domain. Some subsequent parameters of the network are updated using the intermediate samples, while we reduce the amount of learning parameters to avoid overfitting by fixing most of the parameters of the preceding layers. Once the network is trained, it is used as the general fault diagnosis model to input the wavelet images.
* Training on the target domain: Similarly, CWT converts the time-series signals of the target domain to the wavelet images in terms of the time and frequency domains. The limited wavelet images of the target domain are passed to the intermediate network to fine-tune a few subsequent layers of the network for the fault diagnosis of the target domain.
* Fault diagnosis stage: The target network is eventually utilized to classify various fault types based on the extracted feature information of the wavelet images.
# Virtual Reality in Cultural Heritage: A Setup for Balzi
## 4 Conclusions and Future Works  
The Balzi Rossi VR experience has been used at the Balzi Rossi Museum since November 2023. Visitors can explore the ancient ruins in virtual reality, learning more about the area's history. The experience has been designed to be interactive, allowing visitors to explore and take photos of the ruins, as well as educational, providing information about the area's history. The final version of the experience was intended as PC VR because currently there is no possibility of taking advantage of Nanite in mobile VR. To avoid cable issues, typical of first generation of HMDs since 2016, we chose Meta Quest 2 and Air Link for the final form, leaving open the possibility of connecting the HMDs to a dedicated workstation via Meta Quest Link.  
The Balzi Rossi VR experience in this form is open and prepared for implementation or corrective operation, and we are already investigating new possible features to transform and improve the experience in a more immersive way, which could involve MetaHuman and generative AI.  
The Balzi Rossi Museum project provides an overview of how new technologies can be employed both in communication, through the immersive experience of the Balzi Rossi VR, and in conservation, by creating a digital twin and using a georeferenced point cloud and advanced photogrammetry techniques. However, our methodology goes beyond the Balzi Rossi Museum project; it is designed to be flexible and adaptable. Its purpose is not only to enhance and strengthen the archaeological site in question but also to demonstrate the transformative potential of innovative technologies in the cultural heritage sector. Our commitment highlights the versatility of these methodologies when applied to large structures, such as cliffs or archaeological sites, ensuring a level of scalability and quality equal, if not superior, to that for smaller-sized objects. To improve the experience for all the museum visitors, another future goal is to collect data on the main linguistic groups of visitors in order to add more languages.  
Conceptualization, S.I.; Methodology, S.I.; Software, S.I., M.S. and L.M.; Validation, S.I., C.P. and D.Z.; Formal analysis, S.I. and M.P.; Investigation, M.P.; Resources, C.P. and M.P.; Data curation, M.P.; Writing--original draft, S.I., M.S. and L.M.; Writing--review & editing, S.I., M.S., L.M., C.P., D.Z., M.P., A.T. and G.V.V.; Supervision, S.I., A.T. and G.V.V.; Project administration, A.T. and G.V.V.; Funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Ecosystem \"RAISE--Robotics and Artificial Intelligence for Socio-Economic Empowerment\" (NextGenerationEU code ECS 00000035) PNRR-M4C2-II.5.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to Museo Preistorico dei Balzi Rossi e Zona Archeologica.  
The authors declare no conflict of interest.
# The Rapid Establishment of Large Wind Fields via an Inverse Process
## 3 Simulations  
Step One: Velocity Distribution Generation for the Large Area & Step Two: Selecting a Sub-Domain and Extracting POD Basis Vectors  
Wind velocity snapshots were generated. To generate the snapshots of the velocity distribution by CFD, a cuboidal computation domain was set up with a ratio of the dimensions of 1:3:5, i.e., height/width/length. Inside the domain, several models imitating small hills were arranged to generate a number of flow patterns, as shown in Figure 1 and Table 1.  
As a prerequisite for POD, CFD was used to generate a large amount of velocity data for a simulated wind field. For a steady state flow condition and ignoring temperature variations, the conservation equations used were  
Continuity equation:  
\[\frac{\partial(\rho u)}{\partial x}+\frac{\partial(\rho v)}{\partial y}+\frac{ \partial(\rho w)}{\partial z}=0. \tag{6}\]  
Momentum equations:  
\[\left\{\begin{array}{l}\frac{\partial\left(\rho u^{2}\right)}{\partial x}+ \frac{\partial(\rho w)}{\partial y}+\frac{\partial(\rho w)}{\partial z}=-\frac {\partial p}{\partial x}+\frac{1}{Re_{r}}\bigg{(}\frac{\partial x_{\pi n}}{ \partial x}+\frac{\partial x_{\pi y}}{\partial y}+\frac{\partial x_{\pi n}}{ \partial z}\bigg{)}\\ \frac{\partial(\rho w)}{\partial x}+\frac{\partial(\rho v^{2})}{\partial y}+ \frac{\partial(\rho w)}{\partial z}=-\frac{\partial p}{\partial y}+\frac{1}{ Re_{r}}\bigg{(}\frac{\partial x_{\pi y}}{\partial x}+\frac{\partial x_{\pi y}}{ \partial y}+\frac{\partial x_{\pi y}}{\partial z}\bigg{)}\\ \frac{\partial(\rho w)}{\partial x}+\frac{\partial(\rho w)}{\partial y}+ \frac{\partial(\rho w^{2})}{\partial z}=-\frac{\partial p}{\partial z}+\frac {1}{Re_{r}}\bigg{(}\frac{\partial x}{\partial x}+\frac{\partial x_{\pi y}}{ \partial y}+\frac{\partial x_{\pi z}}{\partial z}\bigg{)}\end{array}\right. \tag{7}\]  
where \(u\), \(v\), and \(w\) are the wind velocities in \(x\), \(y\), and \(z\) coordinates, respectively, \(p\) is pressure, \(\rho\) is the fluid density, \(\mu\) is the fluid dynamic viscosity, \(\tau\) is the stress tensor, and \(Re\) is the Reynolds number.  
velocity, i.e., about 10 m/s, while the velocities immediately before the models became slightly lower, owing to the blocking effect caused by the object. The velocities behind the object seemed also slightly lower, which is consistent with the principles of fluid mechanics.  
A sub-domain was selected as indicated by the square in the lower middle part inside the large area, as shown in Figure 4. The number of mesh points within this sub-domain was 1270. The sub-domain was centered at (0,2).  
Figure 4: The sub-domain within the large area.  
Figure 3: Wind velocity distribution in the simulation domain.  
Figure 2: (**a**) Three-dimensional view of discretization of the computational domain. (**b**) Top view of discretization of the computational domain.
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Engineering Technology Summary  
In terms of the division of technical application fields, underwater operation in various turbid areas requires different technical support. The deep-sea engineering detection area is a typical 'deep and clear' water area, for which the main difficulty is the shortage of light, which leads to low-contrast and blurred images. Therefore, the construction of a model for the optical environment of the seafloor in necessary or an artificial light source or polarization imaging should be used for image processing. 'Shallow and turbid' waters often have plenty of light and do not required an artificial light source, but the turbidity of the water is high, and the background scattered light intensity is strong, so an image restoration model should be established, and target features extracted by removing background scattered light interference. Deep-water aquaculture farms represent a typical 'deep and turbid' area, with a turbid water body, poor light transmittance, and high aquatic biological density. Moreover, compared with deep-sea engineering work areas, it is more likely that the engineering requirements include identification of the quantity, type, health status, and movement tracking of aquatic organisms. These projects vary according to the actual conditions. Therefore, deep learning and ML should be flexibly used to achieve engineering automation. This combines underwater image restoration and enhancement techniques to improve the accuracy of depth learning methods.
## 2 Problem Description
### Dueling DQN-Based Reinforcement Learning Model  
The existing DQN model is an algorithm that combines deep learning and reinforcement learning by constructing a Q-network through a CNN. The conventional DQN has a structure that takes the state as input and outputs a Q-function [24; 25]. In this paper, we use Dueling DQN as a reinforcement learning technique, and Dueling DQN is a model that applies a dueling architecture that is more suitable for reinforcement learning instead of the existing neural network structure in the existing DQN model. Dueling DQN has the advantage of effective learning through state-value and noise-resistant learning [26]. The main feature of the Dueling DQN is that it explicitly separates the state-value and advantage functions and uses them to estimate the target. This allows the neural network to estimate the difference between state-value and advantage, which is the direct cause of its performance improvement over the DQN. The structure of a Dueling DQN is characterized by using the same inputs as a conventional DQN, but it adds a separate fully connected layer at the output that considers state-value and advantage. Dueling DQN is a structure that separates state-value and advantage and then combines them to output Q-values.  
In dynamic LEO satellite networks and heterogeneous device environments, the computational complexity should be fully considered when using Dueling DQN-based routing algorithms. Since Dueling-DQN involves a complex computation process by a neural network, it is necessary to adjust the amount of computation or accelerate the computation by adjusting it appropriately.
## 3 Problem Formulation and Proposed Solution  
The problem formulated in [14,22] is NP-hard to obtain the optimal number of selected antennas for each RF chain. Therefore, when there are a large number of APs in the cell-free network, and each AP is equipped with a large number of \(N_{r}\), this will lead to remarkably high computational complexity to determine the optimally selected antennas using an exhaustive search method across \(2^{N_{r}K}\). Additionally, the findings in [22] indicate that using channel quality to switch on antennas that have a large channel magnitude can overcome the quasi-coherent combining algorithm for fixed CPSs (FCPSs) [15] when the selected number of antennas for each RF chain is 75% of \(N_{r}\) antennas; further, similar performance compared with [15] is achieved when 50% of the selected antennas are utilized. Based on these findings, it is reasonable to conclude that excluding a particular number of antennas from \(W_{m}\) can enhance SE\({}_{m}\), implying that these antennas contribute more to interference signal power than the desired signal power. In addition, these results motivated us to put forth the following question: How can we translate the optimization problem formulated in [14,22] from an NP-hard problem to an assignment problem to find the optimal number of selected antennas for each RF chain at each AP to enhance EE without a significant loss in SE based on the channel quality utilizing a low-computational complexity approach in an uplink cell-free mm-Wave MIMO network?
###### Abstract  
To address the weak adaptability of the passive X-type interconnection hydropneumatic suspension to different road surfaces and the poor performance of traditional single-control methods, an active controller based on the extended state observer (ESO) and model predictive control (MPC) was designed for the X-type interconnection hydropneumatic suspension of heavy-duty special vehicles. First, the structure of the X-type interconnection hydropneumatic suspension was analyzed. A three-degree-of-freedom (DOF) linearized hydropneumatic suspension model with disturbances was established based of the seven-DOF full-vehicle model of the active X-type interconnection hydropneumatic suspension. The disturbances were analyzed, and a disturbance ESO was developed. A controller for MPC was subsequently designed based on the linearized state space model, forming a controller for ESO-MPC. Simulations were conducted on both C-class random roads and convex pavement, with fuzzy PID control included for comparison. The simulation results demonstrated that, compared with the passive X-type interconnection hydropneumatic suspension, the active suspension with the controller for ESO-MPC achieved reductions in body vertical acceleration, pitch angular acceleration, and roll angular acceleration of 18.7%, 24.7%, and 26.1%, respectively, on Class C random roads. With fuzzy PID control, the reductions were 5.59%, 7.99%, and 15.54%, respectively. For convex pavement, the controller for ESO-MPC reduced body vertical acceleration, pitch angular acceleration, and roll angular acceleration by 36.5%, 21.2%, and 18.1%, respectively, whereas fuzzy PID control resulted in reductions of 14.04%, 10.6%, and 7.92%, respectively. Compared with fuzzy PID control, the controller for ESO-MPC significantly improved the performance of the hydropneumatic suspension system, achieving precise control of the X-type interconnection hydropneumatic suspension system for heavy-duty special vehicles, thereby enhancing ride comfort and stability.  
X-type interconnection; hydropneumatic suspension; ESO; active MPC; ride comfort; stability
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 3 Single Modal Extraction Algorithm  
When ultrasonic guided waves propagate in rails, dispersion occurs. That is to say, the number of the guided wave modes propagating in the rails will increase with the increase in excitation frequency; furthermore, the propagating velocity of each mode is different. With the increase in propagating distance, the wave packets of each mode will gradually stagger and overlap. Therefore, they cannot be distinguished, and so the group velocity cannot be used to locate defects. To solve this problem, an SMEA based on the accurate modal identification method is proposed.  
As mentioned above, the vibration displacement at any point on the rail is the superposition of the vibration displacement of all modes at this point. Conversely, the vibration displacement of each mode at any point can be split by the total displacement of the point.  
The amplitude and phase \(\Omega_{n}\) of mode \(n\) can be obtained with the accurate modal identification algorithm, and the vibration displacement generated by mode \(n\) at the longitudinal coordinate \(x\) can be expressed by Equation (10).  
\[V_{n}(y,z,f)=\bar{F}_{f}\cdot\Omega_{n}U_{n}^{Rup}e^{-i[\bar{\zeta}_{n}(x-x_{ s})]} \tag{10}\]  
According to Equation (10), the vibration displacement of mode \(n\) at this point in a frequency domain is calculated, and the time domain waveform of the mode is obtained by inverse Fourier transform. To verify the correctness of the method, the following simulations were carried out.  
A non-defective three-dimensional rail model with a length of 200 m was established. The vibrations of the railhead along the rail in a longitudinal, transverse, and vertical excitation direction were simulated using ANSYS. To show the results clearly, the excitation signal was selected as a low-frequency signal with a center frequency of 200 Hz and five cycles, which has only four modes. The modal amplitude of each mode at the 200 Hz frequency point after mode analysis is shown in Figure 7.  
According to Figure 7, when the rail was excited longitudinally, the proportion of the extensional mode was higher. When the rail was excited horizontally, the proportions of the horizontal bending mode and torsion mode were higher. When the rail was excited vertically, the proportions of the vertical bending mode and torsion mode were higher.  
Using the SMEA and choosing the frequency range 100-300 Hz, the total waveform at 30 m and four single-mode waveforms were obtained, respectively, as shown in Figure 8a-c.  
It can be seen clearly in the figure that only the extensional mode existed when the railhead was excited longitudinally. The horizontal bending and torsion modes existed when the railhead was excited horizontally, and the vertical bending and torsion modes existed when the railhead was excited vertically. The results are consistent with the accurate identification results.  
Figure 7: Modal identification results under three excitation conditions (200 Hz).