esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Tooth Movement Produced and Clinical Implications
Furthermore, this angular assumption was chosen based on a previous molar uprighting FE study [13] to aid in the comparative assessment. The use of 2D FE modeling has been proven as a valid approach in the orthodontic literature. Subjects such as tooth movement simulation under specific force systems, inflected stress assessment, and distribution in the periodontal ligament and alveolar bone were previously investigated using 2D FE models [38; 39]. In the current study, 2D modeling was used in order to simplify the simulation of long-term orthodontic tooth movement during molar uprighting, while maintaining accuracy to capture key biomechanical responses and provide reliable results for the iterative process. The rationale behind this was twofold. Firstly, we aimed to explore the sagittal-plane biomechanics of molar uprighting in an efficient and focused way. And secondly, we aimed to test the effectiveness of an iterative simulation process that updates tooth and PDL geometry over time while correlating simulation steps with real clinical treatment duration. Utilizing a 2D FE model represents a scientifically justified simplification and is particularly suited for analyzing movements that predominantly occur in a single anatomical plane, where 3D effects such as torque and rotation are minimal. [PERSON] and [PERSON] (1977) quantified the moment required for molar uprighting using a conventional uprighting spring as being within the range of 12-17 N-mm, a value consistent with present study data [40]. Moreover, [PERSON] et al. (2006) showed that the distribution of maximum principal stresses in both alveolar bone and the PDL was comparable between 2D models and the corresponding sagittal sections of 3D models, supporting the relevance of 2D analysis under planar movement conditions [41]. Complementarily, the 3D FE study by [PERSON] et al. (2007), which employed the same type of uprighting spring as in current study, demonstrated that when the spring arm is not pre-bent, the uprighting of a molar by 22\({}^{\circ}\) is associated with a bucco-lingual tipping angle of less than 1\({}^{\circ}\)[13]. This confirms that the displacement remains mainly within a two-dimensional configuration, thus supporting the relevance of a 2D modeling approach for the specific objective of simulating molar uprighting mechanics. This approach allowed for establishing a new framework for long-term orthodontic simulation, which could lay the groundwork for more complex 3D modeling in future studies.
## 3 Experiments and Results
### Generalization
#### 3.4.2 Varying Environment Stiffness  
Second, the learned policy was also evaluated in different stiffness environments. Figure 8 shows the three environments considered for evaluation. High stiffness was the default environment. Medium stiffness was achieved by using a rubber band to hold the cuboid peg between the gripper fingers, adding a degree of static compliance. In addition to that, for the low-stiffness environment, a soft foam surface was added to further decrease stiffness. The policies were evaluated from 20 different initial positions; results are reported in Table 4.
## 1 Introduction
Aiming at the uncertainty associated with random vibration signals, the fractional Fourier ambiguity function (FFAF) [37] was introduced, and the correlation function [38] was derived. Based on the cross-correlation theory, the relationship between the correlation function and TV physical parameters can be determined. Based on the preceding analysis, the combination of the mentioned methods (multi-level KRRD, PCT, FFAF) shows great potential for application in identifying TV parameters under random excitation, and the remainder of the paper is organized as follows. The analysis process and specific theoretical derivations of multi-level KRRD, PCT and FFAF are described in Section 2. In Section 3, the random vibration model with TV parameters is presented. In Section 4, numerical simulations of a three DOF TV structure are given. In Section 5, the conclusions are presented to close the paper.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 5 Architecture of the Framework
In the implementation phase, depending on the language adopted, different coding alternatives may be used. Class Residence is not the only modelled element in the system. All physical objects share certain properties such as constant, dynamic and derived attributes, a number of'setter and getter' methods, methods to calculate the derived attributes, and methods which are used for notification purposes. Adopting a standard interface for all physical objects enables interfacing with the object-oriented database smoothly. The query objects per class may differ considerably from each other. Nevertheless, these objects have a uniform interface with the UI and the database. For brevity, we do not show the other class diagrams in this article. Example 1: _Assume that the prospective data sources for sensor fusion are selected as follows:_  
* _Face recognizer: This is attached to the corresponding physical instance._
* _Collapse detector: This is attached to the corresponding physical instance._
* _UAV camera: This is attached to the corresponding geographical area._  
The data sources Face Recognizer and Collapse Detector are attached to the instances of class Residence using the following queries:  
* Q1 ATTACH_ENTRANCE WHERE physical_object == 'Residence' AND data_source == 'FaceRecognizer'Q2 ATTACH_BASEMENT WHERE physical_object == 'Residence' AND data_source == 'CollapseDedector' Q3 ATTACH_ENTRANCE WHERE physical_object == 'Residence' AND data_source == 'FaceRecognizer' AND physical_object.id == 'instance' Q4 ATTACH_ENTRANCE WHERE physical_object == 'Residence' AND data_source == 'FaceRecognizer' AND physical_object.robustness_factor '<3' Q5 ATTACH WHERE physical_object == 'GeographicRegion' AND physical_object.coordinates == 'coordinates' AND data_source == 'UAV-camera')  
In Q1, 'ATTACH_ENTRANCE WHERE' is the query command, 'physical_object' denotes to one or more target objects of the query, and 'data_source' specifies the data source to be attached. This query selects all the instances of class Residence. Q2 is similar to Q1; however, as a data source, 'CollapseDedector' is attached. In Q3, with the help of the statement \"physical_object.id == 'instance\"' only one instance is selected. The word 'instance' here is used as a pseudo variable and must be replaced with a real instance defined in the simulation environment. Q4 selects all the instances of class Residence provided that their robustness factors are less than 3. Q5 selects a geographical region identified with the specified coordinate values and attaches a UAV camera as a data source. The query specifications Q1 to Q5 are so-called 'write' queries. The system also supports'read' queries which are embedded into certain menu items. These queries are called in the implementation of the menu items for analysis purposes, for example, to display the effectiveness, cost, and timing values of certain data fusion compositions. For brevity, these are not shown here.
## 3 Results and Discussion
### Characterization and Replication of Microlens Array  
According to the optimal process parameters obtained above, a set of process parameters (hot embossing pressure of 400 N, hot embossing temperature of 130 \({}^{\circ}\)C, and holding embossing temperature of 50 \({}^{\circ}\)C) were selected to compress PMMA balls. A local 3 \(\times\) 3 MLA (microlens array) area of the lens array was characterized by the laser confocal microscope equipment to show good surface quality.  
Figure 6a,b show the surface morphologies of 3 \(\times\) 3 MLA features on the mold end face and MLA replicas, respectively. The copied MLA meets the requirements of shape  
Figure 5: (**a**) Comparison of aspheric lens profile and mold profile when the holding temperature is 90 \({}^{\circ}\)C; (**b**) comparison of aspheric lens profile and mold profile when the holding temperature is 70 \({}^{\circ}\)C and 50 \({}^{\circ}\)C; (**c**) the surface and imaging performance of aspheric microlens at holding temperatures of 90 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C; and (**d**) the heights and surface morphologies of the aspheric lens under different molding pressures.  
accuracy and surface finish. However, the local microlens' surface finish is not sound (for example, insufficient filling and undulations were discovered on the top of the aspheric surface) because of the low local surface quality of the mold or the surface defects caused by the adhesion between mold and PMMA during demolding. Surface defects can be solved by adding suitable coatings on the mold surface in subsequent research. Simultaneously, the measurement error caused by external noise interference during the laser confocal microscope measurement should be considered.  
Further shape characterization analysis is performed on a single local MLA, and the surface profile data extracted from the two perpendicular cross-sections in Figure 6c are compared in Figure 6d. The contours are in right consistency, proving that the MLA has good axial symmetry.
# Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles
###### Abstract  
The development of unmanned ground vehicles and unmanned aerial vehicles requires high-precision navigation due to the autonomous motion and higher traffic intensity. The existing L1 band GNSS receivers are a good and cheap decision for smartphones, vehicle navigation, fleet management systems, etc., but their accuracy is not good enough for many civilian purposes. At the same time, real-time kinematic (RTK) navigation allows for position precision in a sub-centimeter range, but the system cost significantly narrows this navigation to a very limited area of applications, such as geodesy. A practical solution includes the integration of dual-band GNSS receivers and inertial sensors to solve high-precision navigation tasks, but GNSS position accuracy may significantly affect IMU performance due to having a great impact on Kalman filter performance in unmanned vehicles. The estimation of dilution-of-precision (DOP) parameters is essential for the filter performance as the optimality of the estimation in the filter is closely connected to the quality of a priori information about the noise covariance matrix and measurement noise covariance. In this regard, the current paper analyzes the DOP parameters of the latest generation dual-band GNSS receivers and compares the results with the L1 ones. The study was accomplished using two types of antennas--L1/L5 band patch and wideband helix antennas, which were designed and assembled by the authors. In addition, the study is extended with a comparison of GNSS receivers from different generations but sold on the market by one of the world's leading GNSS manufacturers. The analyses of dilution-of-precision (DOP) parameters show that the introduction of dual-band receivers may significantly increase the navigation precision in a sub-meter range, in addition to multi-constellation signal reception. The fast advances in the performance of the integrated CPU in GNSS receivers allow the number of correlations and tracking satellites to be increased from 8-10 to 24-30, which also significantly improves the position accuracy even of L1-band receivers.  
GNSS; patch antenna; helix antenna; multi-constellation reception Article
## 4 Results
### Results of Monitoring Corrosion Using HCP  
Half-Cell potential measurements were taken using the iCOR device every 6 days on the same day as the ultrasonic testing. Five test locations were marked along the rebar on the surface of the specimen, as shown in Figure 3. The HCP data are presented in terms of volts. ASTM C876 [33] provides a standard, based on which the probability of corrosion presence can be interpreted, as indicated in Table 2. The dotted lines shown in Figures 11 and 12 demonstrate the limits specified by ASTM C876. These limits are also indicated in the text boxes shown on the plots, where each one shows the probability of corrosion. Sample measured amplitudes using HCP method is presented in Appendix A (Table A2).  
Figure 11: HCP measurements for Specim A.  
Figure 10: Specimen B- Day 132 condition verification: Very mild corrosion build-up on the rebar.  
Figure 11 shows that the HCP measurements for day 30 are mostly in the \"uncertain\" region according to the interpretations provided by ASTM C876 [33], which means that the probability of corrosion activity can be anywhere between 10 and 90%. Only the 30 cm (12 in) point on this specimen demonstrated a higher probability of corrosion activity per HCP data, which is aligned with the UGWL observations, where there was a significant change (53.5%) at this point as early as day 9. With the HCP readings, none of the points showed a probability higher than 10% for corrosion activity.  
The HCP data for Specimen B is presented in Figure 12. All the points measured on day 30 are in the lowest probability (less than 10%) range for corrosion activity per HCP data. While some corrosion activity was detected using the UGWL method earlier than the HCP method on Specimen B, the results of the two methods are in good agreement, in that there is less potential/evidence of corrosion activity for this specimen compared to Specimen A in general. This is also in alignment with the observations from the autopsy shown in Figure 10.
# Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data
###### Abstract  
Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of data is available, a logistic function may be fitted and used as an accurate model for such curves, with the advantage that it describes the power curve by means of a very simple mathematical expression. Building such a curve from data can be achieved by different methods, such as using mean values or, alternatively, all the possible values for given intervals. However, when using the mean values, some information is missing and when using all the values the model obtained can be wrong. In this paper, some methods are proposed and applied to real data for comparison purposes. Among them, the one that combines data clustering and simulation is recommended in order to avoid some errors made by the other methods. Besides, a data filtering recommendation and two different assessment procedures for the error provided by the model are proposed.  
wind power; power curve; logistic function; spline; Monte Carlo simulation; interior point algorithm; MAPE Article
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 2 Multi-Wavelength Path Loss Model with Human Blockage
### VLC Channel Model
Therefore, in this paper, considering the tradeoff between accuracy and complexity, we calculated the first reflection path for the NLoS component. The power of the NLoS component can be calculated as  
\[P_{NLOS}=P_{\mathrm{T}}\int_{0}^{\infty}h^{1}(t)dt. \tag{6}\]  
At the receiver, the non-imaging concentrator with gain \(g(\phi)\) and optical filter with gain \(T(\phi)\) are usually used to enhance the system performance. The gain of the concentrator \(g(\phi)\) can be given by  
\[g(\phi)= \begin{cases}\frac{n^{2}}{\sin^{2}\phi}&0\leq\phi\leq\phi_{c}\\ 0&\phi>\phi_{c}\end{cases} , \tag{7}\]  
where \(n\) denotes the refractive index of the lens at the PD. If the VLC system is not equipped with a concentrator nor an optical filter, then the corresponding optical gains can be set as 1, that is, \(g(\phi)=T(\phi)=1\). Therefore, the received power can be calculated by  
\[P_{R}=(P_{LOS}+P_{NLOS})\times g(\phi)\times T(\phi). \tag{8}\]  
Figure 1: Reflection coefficient of plaster material. Here, \(P_{R}\) indicates the optical power reaching the receiver after channel transmission which is the superposition of the LoS and NLoS components. If there is no blockage for all the rays, then the path loss can be obtained by  
\[PL[\mathrm{dB}]=10\mathrm{log}(\frac{P_{T}}{P_{R}}). \tag{9}\]
## 4 Conclusions  
A numerical flexible and comprehensive model has been presented to simulate the formation of the waveforms of satellite altimeters by taking into account the non-uniform distribution of the backscattering coefficient over the waves. The algorithm has been applied successfully to simulate the influence of whitecaps and floating foam, and results show that part of the SSB can be consistently attributed to these effects.  
The flexible numerical model presented here is already useful to improve the understanding of SSB, and might eventually prove also a tool to investigate into the mechanism of whitecaps formation and floating foam movement. Future developments should involve the introduction of a non-linear sea surface model to correctly represent wave skewness. Simulated results should be compared with available altimeter wind speed calibration curves and--when available--with altimeter waveform data.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Parallel Model Training and HPC Integration
#### 2.4.1 Apache Spark for Distributed Data Handling  
Spark was used to manage the data feeding into model training and to parallelize the training of the Random Forest model. The RF training was implemented via Spark's MLlib library, which can distribute tree-building across the cluster of executors. In our configuration, even though we run on a single machine, Spark creates one executor per CPU core, allowing the 100 decision trees (estimators) of the Random Forest to be built in parallel across threads. Spark's Catalyst query optimizer automatically planned an efficient execution for reading the training data (stored in Parquet) and piping it into the MLlib routines [19] We also used Spark to parallelize the hyperparameter tuning for the Random Forest: multiple models with different random seeds were evaluated concurrently on different executors, and the best configuration (in terms of validation error) was selected. This demonstrates Spark's strength in high-level parallelism without requiring explicit MPI-style code for certain tasks. Additionally, Spark oversaw the storage of intermediate results (like validation metrics) into a distributed SQL table (backed by Spark's in-memory tables), which allowed for quick aggregation and analysis of results after training.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 2 Security Challenges in 5G Network
### Access Control
The authors also developed a secure and efficient key assignment protocol based on threshold public-key encryption. In ad-hoc networks, unique characteristics exposed the system to potential vulnerabilities. Access control such as ad-hoc network group is important because it can prevent any unauthorized individual or group from accessing the system. Authors in [42] investigated a robust access control protocol attack that depends on a new proactive RSA signature scheme. The authors emphasize that the adversaries can recreate the shared secret usingthe information obtained in the RSA signature scheme. [PERSON] et al. studied the cooperative relaying using multiple-input single-output (MISO) for multiple distance vehicles [43]. Techniques such as signal superposition and cooperative jamming are used where an adjacent user decodes the signal and performs as a remote user relay. After a detailed examination of the eavesdropping security threats, the authors propose an improved secure transmission scheme. In [44], the authors examined the reliability and security in the downlink of cloud radio access network (C-RAN) practical scenario with channel estimation (CE) errors as consideration and remote radio heads (RRHs) node selection. [PERSON] et al. presented a novel framework in implementing semiautomatic verification for access control policy [45]. The proposed technique developed for Industrial Network Systems (INS) applies a twofold model that considers two system views: access control policies abstract requirement and specific details of physical target scheme. Furthermore, the authors also used role-based access control (RBAC) in the proposed framework to determine the policies. The utilization of micro 5G operators or local 5G networks can lead to frequent roaming events in 5G networks because most of the 5G operators have a lower level of security compared to main mobile network operators [46; 47; 48]. Therefore, the probability of running into malicious 5G operators that work as a serving network is highly possible [26]. Consequently, a strong authentication process in a 5G network is required to prevent the connection with such networks.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### Disaster Response and Recovery  
Essential to reinforcing smart grid resilience against disruptive forces like natural disasters and cyber threats, DTs have emerged as indispensable tools for comprehensive simulations, scenario analysis, and meticulous recovery planning. By virtually mirroring the physical grid infrastructure, these digital replicas can model the real-time impacts of potential disaster scenarios, empowering utilities to proactively formulate response strategies and restorative measures. This section explores how Digital Twins bolster grid resilience through their ability to conduct granular simulations and analyze diverse scenarios, fortifying preparedness and guiding efficient recovery initiatives.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 4 Technique Development and Implementation
### Monotonic Phase Correction
The time synchronization procedure consists of an iterative search for such a signal detuning (shift) for which the distance between the expected value of the cumulative signal phase and the measured value is the smallest. Due to the periodicity of the OFDM signal, said minimum is searched for in the set of distances determined for offsets ranging from 0 to 383 samples. There are many different methods of determining the distance between data sets [51]. The work is limited to determining the synchronization using the Euclidean distance, Mahalanobis distance [52; 53], and Frechet distance [54; 55]. Figures 6-8 show the total distance between the expected cumulative phase of the signal and the phase measured using the above-mentioned metrics. Additionally, the figures show the minimum value of the determined distance. In the analyzed case, the minimum was achieved in each case for shifting the signal by 184 samples. Figure 5: Cumulative signal phase, synchronization achieved. Figure 6: Cumulative normalized Euclidean distance between the expected cumulative phase and the measured phase.