esa-sceva/satcom-chunk-collection huggingface dataset:
# Tailoring the V-Model for Optics: A Methodology for Optomechatronic Systems
## 1 Introduction
### Introduction of the Application Example
Various measures can be implemented to correct these effects; for example, the combination of several lenses made of different materials. However, since losses occur at each optical element as a result of Fresnel reflections and material defects, which have a negative impact on the optical efficiency of the system, the use of as few optical elements as possible is advantageous. Consequently, a conflict of objectives arises between optical efficiency and image quality of the system [35; 36]. * **Light modulation:** So-called light modulators such as liquid crystal displays (e.g., LCD, LCoS) or digital micromirror arrays are used to generate high resolutions. LED matrices represent another way to develop active lighting systems, but are not in the focus of this contribution due to the significantly lower achievable resolution. While the aforementioned technologies are well established in the field of video projection applications, their deployment in the field of vehicle front lighting is new. When using a DMD, the limited tilt angle of the micromirrors constrains the aperture angle of the incident light beam, which thus limits the maximum luminous flux that can be modulated. The operation of liquid crystal displays is based on the use of polarized light, which results in special requirements for the light source and the use of special optical elements for polarization [37; 38]. Based on the challenges presented, it is obvious that a methodical approach is required for the targeted development of high-resolution lighting systems. The following sections of this paper will therefore explain how the challenges involved in the development of high-resolution lighting systems can be met by concretizing the V-Model (cf. Section 1.1) to develop optomechatronic systems. For this purpose, Section 2 describes the process for the development of optomechatronic systems by [PERSON] and the underlying V-Model [9]. Based on this, in Section 3 it is explained how the method for the preliminary design of high-resolution lighting systems presented in [16] by [PERSON] can be integrated into the process phase for the system design of optomechatronic systems according to [9] by [PERSON]. This is followed by the integration of the method introduced in [17] by [PERSON] for the detailed design of high-resolution lighting systems into the process phase for the domain-specific design of optomechatronic systems according to [9] by [PERSON]. Finally, the variant of the V-Model for high-resolution lighting systems elaborated in this contribution is discussed on the basis of application examples in Section 4.
# Resonant Asymmetrical Half-Bridge Flyback Converter
## 3 Design Considerations
### Determination of \(T_{r}\), \(C_{r}\) and \(f_{sw}\)  
The current \(i_{Lr}\) can discharge and charge C\({}_{\mathrm{oss1}}\) and C\({}_{\mathrm{oss2}}\), respectively. To extend the effect of ZVS turn-on to light load as much as possible, it is better to design \(T_{off}\) to be \(T_{r}/4\) so as to obtain \(i_{Lr}\) at its maximum point. The maximum frequency designed at 180 kHz with C\({}_{r}\) as 4.45 \(\upmu\)F and 5.06 \(\upmu\)F for input voltages of 48 V and 24 V, respectively.  
\[T_{r}=\frac{1}{2\pi\sqrt{C_{r}\cdot L_{r}}}, \tag{83}\]\[C_{r}=\frac{1}{L_{r}(2\pi\cdot T_{r})^{2}}, \tag{84}\]  
\[T_{r}=4\cdot T_{off}=20.48\ \upmu\text{s}, \tag{85}\]  
\[C_{r}=\frac{1}{L_{r}\cdot\left(2\pi\frac{1}{T_{r}}\right)^{2}}=5.059\ \upmu\text{F}, \tag{86}\]  
Finally, \(C_{r}\) is chosen to be a 4.7 \(\upmu\)r MLCC. Then, \(f_{sw}\) is recalculated to obtain 179 kHz and 155 kHz at input voltages of 24 V and 48 V, respectively.  
\[D_{(2\upmu\text{V})}=\frac{V_{O}\left(\frac{L_{m}+L_{r}}{L_{m}}\right)}{V_{in }\cdot\frac{N_{2}}{N_{1}}}=23.2\%, \tag{87}\]  
\[D_{(48\upmu\text{V})}=11.6\%, \tag{88}\]  
\[T_{on(24\upmu\text{V})}=\frac{V_{O}\left(\frac{L_{m}+L_{r}}{L_{m}}\right)}{f_ {sw}\cdot V_{in}\frac{N_{2}}{N_{1}}}=1.491\ \upmu\text{s}, \tag{89}\]  
\[T_{on(48\upmu\text{V})}=0.648\ \upmu\text{s}, \tag{90}\]  
\[f_{sw(24\upmu\text{V})}=\frac{1}{T_{on(24\upmu\text{V})}+T_{off(24\upmu\text {V})}}=155.63\ \text{kHz}, \tag{91}\]  
\[f_{sw(48\upmu\text{V})}=179.1\ \text{kHz}, \tag{92}\]
## 4 Discussion
### Limitations and Future Research Directions  
A number of limitations have to be considered when integrating these findings. Substantial heterogeneity of intervention study design, duration, strength of intervention, and measure of outcome among the thirteen studies included in this review made it difficult to conduct formal meta-analyses. Methodological critique determined that many of these studies did not have adequate blinding of assessors and lacked statistical group comparisons. In addition, most of these studies utilized relatively short follow-up periods and hence did not allow for assessment of duration of these benefits. Publication bias could not be formally assessed due to methodological heterogeneity, though the high proportion of positive results (10/12 studies) may indicate potential publication bias.  
Future research should highlight the importance of longitudinal follow-up studies to determine if early improvements in cardiometabolic biomarkers translate into lasting gains in health benefits and lower prevalence of chronic disease in adults. Properly powered research with standardized measures would make informative meta-analyses possible and help make more accurate estimates of effect sizes. There is a critical need for implementation science to clarify how effective interventions can be scaled and sustained in diverse learning settings. Finally, an examination of intervention dosage and cost-effectiveness would help with evidence-based program development and resource allocation plans [60; 61]. Intervention research in pediatrics has recently emphasized the role of biomarker assessment due to their potential for providing more sensitive measurement of intervention effectiveness than anthropometric assessments alone [62].
## 3 Atomistic Simulation Methodology  
As the focus of the present review is the diffusion in energy materials, we will briefly introduce here molecular dynamics (MD) and density functional theory (DFT) methods. The quantum mechanical formulation offers the most complete description, however, the analytical solution of Schrodinger's equation for a large number of electrons is practically impossible due to the complexity of many-electron interactions [85]. DFT is in essence an approximation that allows the efficient modelling of solids [86; 87]. Presently in DFT, the common way to address the exchange-correlation energy is by the local density approximation (LDA), the generalized gradient approximation (GGA) and hybrid functional, which incorporate a part of the exact exchange from Hartree-Fock theory [88]. Typically, a plane-wave basis set is used with the pseudopotential method, with the core electrons being described by effective potentials (known as pseudopotentials) and the valence electrons evolving explicitly. Diffusion energetics and mechanisms can be investigated using DFT as the activation energy of diffusion can be calculated by identifying the minimum energy path, using methods such as the nudged elastic band (NEB) [89]. The main issue of DFT simulations is the relatively small number of atoms that can be modelled (presently a few hundred atoms) and this is an issue particularly when modelling complicated diffusion mechanisms (for example, involving the cooperative motion of local polyhedral structures in the lattice).  
MD using classical potentials is a very common method to study the energetics of diffusion in energy materials. MD the state of the system can be described by the positions and the momenta of all the constituent particles, with [PERSON]'s equations of motion for an ensemble of particles being solved iteratively. The interactions between particles is through potential energy functions, typically within the classical Born-like description of the crystal lattice [90]. In these the ionic interactions are defined by a long-range Coulombic term and by a short-range parameterized pair potential (for example the Buckingham potential [91]). The key advantage of MD as compared to DFT is that extended systems can be modelled, however, this is not a method that can be used when the description of the electronic structure of the system becomes important [92; 93; 94; 95; 96]. In essence, not unlike experimental techniques different methodologies are more appropriate for different length scales and properties.  
Finally, it should be stressed that the use of thermodynamic models (for example the cB\(\Omega\) model) can be used to gain further insights from the experimental and computational modelling methods as it was demonstrated for other systems in previous work [97; 98; 99; 100; 101; 102; 103].
###### Abstract  
An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.  
dam deformation; attention mechanism; long short-term memory; dam safety monitoring; prediction 2021  
15 July 2021  
19 July 2021  
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**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
###### Abstract  
This paper introduces the localization method of an Autonomous Underwater Vehicle (AUV) in environments (such as harbors or ports) where there can be passing ships near the AUV. It is assumed that the AUV can access the trajectory and approximate source level of a passing ship, while identifying the ship by processing the ship's sound. This paper considers an AUV which can localize itself by integrating propeller and Inertial Measurement Units (IMU). Suppose that the AUV has been moving in underwater environments for a long time, under the IMU-only localization. To fix long-term drift in the IMU-only localization, we propose that the AUV localization uses sound measurements of passing ships whose trajectories are known a priori. As far as we know, this AUV localization method is novel in using sound measurements of passing ships of which the trajectories are known a priori. The performance of the proposed localization method is verified utilizing MATLAB simulations. The simulation results show significant estimation improvements, compared to IMU-only localization. Moreover, using measurements from multiple ships gives better estimation results, compared to the case where the measurement of a single ship is used.  
Autonomous Underwater Vehicle; passing ships; underwater localization; IMU-only localization; sound measurements; source level +
Footnote †: journal: Applied Sciences
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 3 System Model and Mathematical Background
### Mathematical Model for Performance Measurement  
For the purpose of this study, the network's performance is evaluated using three metrics: average resource efficiency, average acceptance rate, as well as average bandwidth and CPU consumption.  
* Average Acceptance Rate: This metric is calculated by dividing the number of successful NRs by the number of unsuccessful NRs during the specified transmission time interval \((T_{max})\), which yields the direct performance measurement for the provided physical infrastructure. \[\eta_{AR}=\frac{sNR}{tNR}\] (5) where sNR and tNR refer to the total number of successful and unsuccessful NRs in the specified time interval, respectively.
* Average CPU and Bandwidth Utilization: The total CPU and bandwidth utilized by the successful NRs in the given transmission time interval is determined by, \[CPU_{utilized}=\sum_{i=1}^{sNR}CPU_{NR}(i)\] (6)\[BW_{utilized}=\sum_{i=1}^{sNR}BW_{NR}(i) \tag{7}\]
* Average Resource Efficiency: This metric is defined as the ratio of the amount of revenue generated to the amount of money spent on building the physical infrastructure. The revenue can be calculated by accurately calculating the amount of CPU and bandwidth used per NSR. The physical infrastructure provided by NSR determines the cost of investment. The following Equation (6) is used to calculate the network's resource efficiency for the given transmission time interval \((T_{max})\). \[\eta_{RE}=\sum_{i=1}^{sNR}\frac{CPU_{NR}(i)+BW_{NR}(i)}{(CPU_{NR}(i)+BW_{NR}( i).L(i))}\] (8) where \(CPU_{NR}(i)\) and \(BW_{NR}(i)\) refer to the total CPU and bandwidth requested by the \(i\)-th NR, respectively. \(L(i)\) refers the shortest path length utilized for \(i\)-th NR, and sNR refers the total number of successful NRs.
# Augmented Reality as a Didactic Resource for Teaching Mathematics
## 3 Results
### Classroom Design Using AR  
Now we will explain the didactic proposal to teach the didactic unit of polyhedra. For its teaching, AR is used as a novel teaching resource that aims to facilitate learning, and to improve the visualization of geometric concepts and spatial capacity. In addition, using AR to teach will serve as a catalyst to improve the motivation of students to mathematics.  
The mathematical contents correspond, mostly, to the official curriculum in force in Spain for the third year of compulsory secondary education. These mathematical contents are:  
Common contents to all math blocks:  
1. Planning of the problem-solving process: analysis of the situation, selection, and relationship between the data, selection, and application of the appropriate resolution strategies, analysis of the solutions and, where appropriate, extension of the initial problem.
2. Choice of strategies and procedures implemented: Use of appropriate language (graphic and geometric) and good notation; construction of a figure, scheme, or diagram. Experimentation using the error test method. Search for analogies and similar problems. Reformulation of the problem, subproblem resolution dividing the problem into parts; exhaustive counting, start by simple individual cases, search for regularities and laws; introduction of auxiliary and complementary elements; working back assuming the problem is solved.
3. Reflection on the results: review of the operations used, assignment of units to the results, verification and interpretation of the solutions in the context of the situation, search for other forms of resolutions, etc.
4. Verbal and written expression in mathematics.
5. Approach of mathematical school research in geometric contexts, appropriate to the educational level and the difficulty of the situation.
6. Practice of the processes of mathematization and modeling, in contexts of reality and in mathematical contexts.
7. Confidence in one's own abilities to develop appropriate attitudes and face the difficulties of scientific work.  
Related to the objectives to be achieved with this project using new technologies in the learning process, our targets are:  
* To elaborate and create geometric representations.
* To facilitate the understanding of geometric properties.
* To communicate and share, in appropriate environments, information and mathematical ideas.  
Specifically about the geometry content:  
* Use of technological tools to study and build shapes, configurations, and geometric relationships
* Space geometry: polyhedra
* Symmetry planes in polyhedra
* Euler's formula for simple polyhedra
* Regular polyhedra
* Dual polyhedra
* Calculation of areas and volumes of polyhedra
* Contextualization
* Truncation of polyhedra
* Archimedes solids
* Kepler-Poinsot solids
## 3 Settings for Prediction Accuracy Tests
### Terminology Clarification  
To clearly state the datasets used in the current paper, the following notations will be used. The letters \"G\" and \"O\", which stand for \"geometric\" and \"operational\", are used to point out the datasets for the prediction accuracy tests with geometric design variables and with geometric and operational design variables, respectively. The abbreviations \"FCCD\" and \"DLB\" are used to state the type of design of experiment in producing the datasets. After these symbols, one or two numbers can follow (FCCD is followed by one number, while DLB is followed by two numbers), where the first number indicates the total degree of freedom and the second number shows the value of \(m\) of the \(m\)-level-based method. For example, the notation \"G-FCCD-2\" means the FCCD-created datasets for the 2-degrees of freedom prediction accuracy test with geometric variables. Similarly, the notation \"O-DLB-4-5\" indicates the datasets were created by the discrete 5-level based method for the 4-degrees of freedom prediction accuracy test with geometric and operational variables.
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 4 Near-Real-Time Global Plasma Bubble Monitoring  
In addition to the opportunity to better explore the characteristics of low-latitude electron density distribution as described above, the increased F7/C2 occultation soundings also provide more closely sampled measurements that can be effectively utilized in monitoring occurrence and distribution of ionospheric irregularities [103; 104; 105]. This section describes the potential of applying the F7/C2 soundings as an effective tool for near-real-time global plasma bubble monitoring.  
The scintillation index S4 (S4-index) derived from the SNR of receiving GNSS L1-band (1.575 GHz) C/A code in the F3/C scnLv1 data product has been utilized to observe global plasma irregularity occurrence in the ionosphere [106; 107]. For ground-based and aviation communication applications, [PERSON] et al. [103] developed a method for integrating the scattered distributed F3/C S4 max in the ionosphere vertically to the ground, while [PERSON] et al. [104] constructed an empirical global model, the F3 CGS4 (F3/C Global S4) model, to calculate the climatological median S4-index for a designated year and day of the year, local time, location, and solar activity F10.7 index. [PERSON] et al. [108] further improved the F3 CGS4 model by using the whole S4-index profile data and constructing the global probability model for the S4-index of L-band scintillations. The dense F7/C2 radio occultation scintillation provides a good chance to observe equatorial and low-latitude ionospheric GNSS L-band S4 scintillations.
## 4 Results and Discussions
### Analysis of Load Bearing Characteristics of Adjacent Pile Bases with Immersion
#### 4.2.2 Axial Force and Shaft Friction of Piles  
The results of axial force and shaft friction are shown in Figures 12 and 13, respectively. And the calculated results according to [PERSON] et al. [52] are shown in Figure 14. The neutral point was the position of the pile where the shaft friction was zero. After 8 h of immersion, the axial force of the pile first increased and then decreased with the increase in buried depth, showing a \"D\"-shaped distribution. The shaft friction of pile bases was negative in the range of pile top and decreased gradually along the direction of pile buried depth after it reached the maximum. The neutral point was located at a depth of 20 mm since the value of shaft friction at this position transformed into positive from negative. With the increase in immersion time, the range of negative shaft friction gradually expanded downward, and the neutral point moved to the lower part of the pile. After 20 h of immersion, all the loess in the model box was saturated with water, and the range of negative shaft friction reached the maximum, while the neutral point reached the deepest (0.35 m). The neutral point depth ratio was 0.63 in this case.  
Figure 11: Load–settlement curve of pile bases with immersion.  
Figure 12: Mean axial force of 4 single piles with immersion.  
After the model box was flooded, the APCL with strong collapsibility in the model box would sink significantly after encountering water. Therefore, the collapsibility settlement of the APCL layer near the surface began to be greater than that of the pile bases. The pile bases were subjected to the downward pull of the soil around them, resulting in negative shaft friction, which decreased bearing capacity of pile bases, and the axial force of the  
Figure 14: The calculated pile shaft friction with immersion according to [PERSON] et al. [53].  
Figure 13: Mean shaft friction of 4 single piles with immersion.  
pile gradually increased with the buried depth. As the immersion time increased, the water on the surface of the soil layer gradually permeated downward, making the lower APCL gradually collapse. The settlement of APCL exceeded that of pile bases in that case. Therefore, the range of negative shaft friction also expanded downward with time, so the axial force gradually increased. As the overlying pressure of the soil increased gradually along the depth, the soil was compacted, and the deformation of it diminished, resulting in a decrease in the loess collapsibility. The settlement value of APCL was smaller than that of pile bases, which made shaft friction return to a positive value.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results  
The proposed system was evaluated in both simulated and real scenarios. Four simulated environments including roads, vehicles, lakes, houses, forests, and various other common elements were created, and the algorithm was tested using the Gazebo 9.0 simulation software. Additionally, a five-day experiment was performed under five different real-world scenarios. The system's performance metrics, including modeling accuracy and landing accuracy, were assessed via data logging and analysis at each experimental site and under varying conditions. The applicability of the proposed method was evaluated with respect to site selection, the timing of landing, proximity to obstacles, and UAV safety indicators.  
In a real-world deployment scenario, the algorithm was deployed on an computer (NX, NVIDIA, Santa Clara, CA, USA) equipped with a six-core 64-bit CPU and a 384-core Volta architecture GPU, running on a 64-bit Ubuntu 18.04 operating system. It functioned as the motion algorithm and vision processing unit for the UAV. A flight control computer (Pixhawk, 3 DR Solutions, Berkeley, CA, USA) was responsible for motor control and speed feedback as the UAV motion controller. Additionally, the NVIDIA NX was tasked with computing the proposed localization method, map construction, and landing site selection, and communicating the motion requirements of the UAV to the Pixhawk flight control computer, as illustrated in Figure 7.