esa-sceva/satcom-chunk-collection huggingface dataset:
# Intra-oral 3D Scanning for the Digital Evaluation of Dental Arch Parameters
## 2 Methods
### Data Processing  
The proposed algorithm of the processing of point cloud acquired by the intra oral scanner and exported into the STL file includes the following steps:  
1. Data acquisition, their visualisation and transform into the 3D space in the selected coordinate system,
2. Data preprocessing using linear and nonlinear filters to reject noise components,
3. Estimation of the dental plane defined by tops of individual teeth using the least square method,
4. 3D data rotation into the horizontal plane and image registration,
5. The use of the digital model for evaluation of selected dental arch parameters.  
Data acquired by the intra-oral scanner in the STL format were imported into the computational environment of MATLAB 2018a to reject noise components by median filtering using the moving 5 by 5 mask. The 3D digital model presented in Figure 2b was then processed to find contour levels presented in Figure 2c for a selected tooth (molar).  
The contour plot of the acquired dental arch model was used to obtain 3D coordinates of tops of individual teeth to define the dental plane. Its parameters were evaluated by the least square method from these positions. Figure 3a presents the spatial locations of these points related to individual teeth.  
The dental plane is in general defined by relation  
\[a(1)\ x+a(2)\ y+a(3)\ z+a(4)=0 \tag{1}\]  
in the selected Cartesian coordinate system and the coefficients \(\{a(i)\}_{i=1}^{4}\) are evaluated by the least square method. The normal vector to this plane and its direction cosines were then evaluated by relation  
\[cos(\alpha){=}a(1)/r,\ \ \ cos(\beta){=}a(2)/r,\ \ \ cos(\gamma){=}a(3)/r \tag{2}\]  
for \(r=-sign(a(4))\ \ \sqrt{a(1)^{2}{+}a(2)^{2}{+}a(3)^{2}}\) defining the angles of the normal vector with positive half-axis in each direction. These angles were then used to find the parameters for rotating all the objects around individual coordinates into the horizontal plane.  
To follow the progress of the treatment, it is necessary to compare objects or the complete dental arch of the same individual recorded at different instants. This registration process should be applied  
Figure 3: Data processing presenting: (**a**) original centers of separate teeth acquired by the 3D scanner in the 3D space with the original and rotated plane, (**b**) original centers of separate teeth in the 2D space, (**c**) rotated centers of separate teeth in the 2D space with their approximation by a polynomial of the second order.  
to images rotated into the same planes. From the computational point of view, it assumes the combined transform including scaling, rotation, translation and shear of data points \(\{x(i),y(i)\}\) into their new position \(\{X(i),Y(i)\}\) performed [34; 35; 36] by matrix multiplication  
\[[X(i),Y(i),1]=[x(i),y(i),1]\ \left[\begin{array}{ccc}r1&s1&0\\ r2&s2&0\\ r0&s0&1\end{array}\right] \tag{3}\]  
with properly evaluated values of the transform matrix. For the rigid transform, corresponding fixed points in associated images can be selected for specification of the registration.
## 3 Proposed Method
### 2D with 3D Grid-Based Network Construction  
The cloud-based VANET was initially established within a structured 2D and 3D grid framework. This grid divides the network into manageable m*m grid segments, resulting in reduced complexity and enhanced scalability. Within this grid selection, strategic deployment of both the base station (BS) and edge-assisted roadside units (Edge-RSUs) optimizes coverage and connectivity. Each grid is equipped with Edge-RSUs to efficiently harness vehicle interactions within their coverage areas, facilitating information transfer to the mini lanes situated within each grid. Furthermore, this architecture promotes direct communication between vehicles (V2V), vehicles and infrastructure (V2I), and vehicles and pedestrians (V2P), effectively mitigating communication overhead. By adopting this 2D and 3D grid network construction approach, the system significantly reduces the burden of high communication overhead, resulting in a more streamlined and efficient VANET infrastructure.
# Transition Analysis and Its Application to Global Path Determination for a Biped Climbing Robot
## 1 Introduction
As far as we know, our proposed algorithm is the only one capable of determining both the possibility and operational regions for transition in tens of microseconds, considering the safety, reachability, and accessibility of grips. Secondly, we propose a novel path planning algorithm based on transition analysis, for a rapid generation of all feasible global paths on trusses from a start point to the given destination. The remainder of this paper is organized as follows. We firstly introduce global path planning and transition problems of BiCRs in Section 2. We then analyze the transition requirements and constraints to compute the operational region in Section 3. We apply the transition analysis to fast determination of global climbing paths, with algorithm implementations presented in Section 4. In Section 5, we conduct  
Figure 2: Climbot and its kinematic diagram. simulations with Climbot to verify the proposed analysis and algorithms. Finally, we conclude our work in Section 6.
# Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake
## 3 Results and Discussion  
### Time-Invariant SISO TF Models  
In Figure 1, the gathered energy intake and bodyweight curves from the 32 participants are displayed. The obtained data have shown clear interpersonal differences between participants in their energy intake and bodyweight curves during the course of the experiment. Even though during the control phase it is attempted to reduce the variability in the bodyweight among the participants, it can be seen that the individual variability in energy intake during the starvation stage of the experiment induced different responses in the bodyweight time course. Similar energy intake patterns lead to different bodyweight time courses, which highlights the individual characteristics of the biological process, emphasizing the importance of the aim of this study. Therefore, using the Minnesota starvation experiment dataset, 32 sub-datasets are created using the bodyweight and energy intake from each individual participant. Using each sub-dataset, a system identification procedure is performed to define the best time-invariant parameters model structure  
Figure 1: Energy intake in kcal day\({}^{-1}\) (**a**) and bodyweight in kg (**b**) curves collected for each one of the 32 participants in the Minnesota starvation. needed to relate energy intake and bodyweight. In Table 1, a summary is shown of the first and second order model performances in terms of _YIC_ and the goodness of fit, \(R_{T}^{2}\). Table 2 shows the Pearson correlation coefficients (\(r\)), to assess the similarity between the model outputs and the weight curves from the participants. A representative example of the agreement between the measured and estimated bodyweight curves for one individual test participant (participant 8) in the Minnesota starvation experiment is displayed in Figure 2.  
The results have shown that a first order model is sufficient to characterize the impact of energy intake in bodyweight, as reflected by the superior _YIC_, \(R_{T}^{2}\), and a high Pearson correlation coefficient results (see Tables 1 and 2). During the system identification process, the presence of delay was allowed, but the best results were always obtained when no delay (i.e., \(\delta=0\)) was present in the models. From a biological viewpoint this make sense, as metabolizing the ingested energy takes about 12 h, while the used time interval is one week (i.e., 168 h). Thus, the time delay between energy intake and bodyweight change reduces to nearly zero in terms of the used measurement interval of one week. In addition, the YIC value also indicates that the parameters estimation when using a first order model is reliable. Ideally, it would be possible to define a first order model, using the average values of the different estimations of \(a\)- and \(b\)-parameters obtained for each subject, which would allow
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Reality
#### 4.4.2 Virtual Reality (VR)  
Virtual Reality (VR) has been implemented to generate an immersive and interactive environment and it can be full-immersive or semi-immersive. Full-immersive VR displays a virtual image while the real environment is omitted from vision, on the other hand semi-immersive VR displays the virtual image while the real world is partly excluded from vision [63]. Recently, the use of VR has been developed in a fast pace benefiting the area of healthcare industry by supporting 3D visualization from many angles, without covering details compared to 2D models. VR allows doctors to virtually interact with patients and intensify the regularity of therapeutic treatment. In such a way, this technology can assist to the rehabilitation of patients with several health problems, and moreover, in the study of viruses that may lead to serious vaccine research. VR can be used in the training of surgeons and doctors mainly in performing virtual surgical procedures.  
VR technology exploits the potentiality of user interaction and immersion. In such a way, the manipulation of a system becomes simpler and intuitive. Thus, the implementation of VR technologies can help to achieve superior image quality and smooth interaction with the patient, facilitating a faster and more precise diagnosis, as the doctor can move around the system and visualize patient's body with more details. Evidently, it is meaningful to thoroughly examine areas that are considered of utmost importance for accurate medical diagnosis [64].
## 5 Discussion  
This study demonstrates that the BDS POD accuracy with a regional network can be effectively improved to the cm level by a large LEO constellation with as many as 10-25 visible LEO satellites. However, the spaceborne observations can be seriously affected by an extreme space weather event, such as a solar storm. The real impaction time and magnitude of a solar storm on LEO satellites on different altitude and orbit planes is different through a calculation with real observations. This gives us a reference for the simulation of spaceborne observations when considering the observation errors. However, the real situation is much more complicated as the effect of a solar storm on a global operational capability LEO constellation used for, i.e., navigation augmentation is very irregular and impossible to be precisely simulated. It is just an approximate simulation in this study and demonstrates that an LEO constellation with about 10-25 visible LEO satellites is sufficient to reliably reduce or mitigate the observation errors on BDS POD in a combined adjustment procedure. For the real impaction magnitude and time, we can only hope to resolve these issues with real spaceborne observations from a global LEO constellation in the future.
## 3 Results and Discussion
### Antioxidant Activity Analysis of ZnO-NPs  
DPPH assay was applied to measure the radical scavenging activity of ZnO-NPs. The DPPH is a free radical that has been extensively used to investigate the antioxidant potency of inorganic compounds and nanomaterials [46]. The prepared DPPH solution had a deep violet color, which changed to pale yellow upon the addition of ZnO-NPs, which generally indicates the antioxidant capacity of ZnO-NPs [44]. The results of the antioxidant assay in Table 3 show an increase in % inhibition with increasing concentrations of both ZnO-NPs synthesized with or without _C. chinense_ fruit extract. The scavenging activity of ascorbic acid, which was used as reference/standard, was higher than that of both ZnO-NPs (Figure 8); thus, it indicates that the standard was more effective in capturing the DPPH's free radicals compared to both ZnO-NPs. However, the green ZnO-NPs showed higher inhibition potency compared to the ZnO-NPs prepared by the conventional route. Since green ZnO-NPs was shown to have smaller particle size, they contain larger surface area  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cell Lines** & **Test Samples** & & \multicolumn{3}{c}{**Sample Concentrations (\(\mu\)g/mL)**} & \multicolumn{2}{c}{**IC\({}_{50}\) (\(\mu\)g/mL)**} \\ \hline  & & 10 & 25 & 50 & 100 & \\ \hline HEK 293 &
\begin{tabular}{c} 5-FU \\ (Fluorouracil) \\ \end{tabular} & 77.36 \(\pm\) 0.048 & 51.92 \(\pm\) 0.003 & 35.34 \(\pm\) 0.010 & 11.33 \(\pm\) 0.017 & 6.05 \\  & Green ZnO & 86.34 \(\pm\) 0.054 & 58.22 \(\pm\) 0.057 & 50.64 \(\pm\) 0.013 & 37.06 \(\pm\) 0.022 & 49.35 \\  & Conventional & 91.33 \(\pm\) 0.040 & 77.93 \(\pm\) 0.015 & 59.26 \(\pm\) 0.041 & 34.33 \(\pm\) 0.012 & 63.09 \\  & ZnO & 78.40 \(\pm\) 0.03 & 58.89 \(\pm\) 0.04 & 50.47 \(\pm\) 0.02 & 37.99 \(\pm\) 0.02 & 17.48 \\  & Green ZnO & 87.10 \(\pm\) 0.063 & 57.64 \(\pm\) 0.080 & 38.92 \(\pm\) 0.014 & 27.76 \(\pm\) 0.023 & 37.30 \\  & Conventional & 74.61 \(\pm\) 0.021 & 65.65 \(\pm\) 0.056 & 52.34 \(\pm\) 0.043 & 42.78 \(\pm\) 0.067 & 62.34 \\  & ZnO & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cytotoxic activities (%) of green and conventional ZnO-NPs.  
Figure 7: The percentage cell viability of: (**a**) HEK 293, and (**b**) HeLa cells treated with various concentrations of green and conventional ZnO-NPs.  
to volume ratio than the conventional ZnO-NPs. Generally, large surface area to volume ratio of NPs results in high material performance due to domination of materials surface atoms [1; 4]. The obtained IC\({}_{50}\) values of 6.27 and 8.76 \(\upmu\)g/mL for green and conventional ZnO-NPs, respectively, were higher than that of ascorbic acid (4.73 \(\upmu\)g/mL). These results are in line with previous studies [66; 67], which reported lower IC\({}_{50}\) values of ascorbic acid than ZnO-NPs.
## 2 Challenges, Trends and Constraints in WSAN Hardware Design
### Open-Source-Hardware Architectures versus Open-Architecture Systems
#### 2.3.4 Programming and Debugging Constraints  
1. _Peripherals and energy charge_: One of the most convenient and low cost methods to download the firmware to the program memory of a MCU is the in-circuit programming, or else, in-system programming. The hardware implementation of this programming method requires the usage of a certain number of the MCU's pins (i.e., Vdd, Vss, Reset, SPI pins, UART pins, etc.), which have to be connected accordingly, in order the MCU to be programmed directly by a personal computer via a USB port or by inserting specific external serial programming devices. Whilst, the programming operation takes place once in-house and lasts for a few minutes, the programming circuitry remains permanently on-board. Furthermore, this circuitry may cause electrical conflicts with other shields, because the programming pins are physically routed to the rest of the shields through the BECs, so in most of the cases, it is mandatory to remove any connected shields before a MCU-based shield programming take place.
2. _Limited debugging capabilities_: Since, the development of the firmware is mostly based on a combination of ready-made open-source parts of code, written by someone else, it is very critical for the system to be able to support real-time debugging with all the shields engaged.
3. _Lack of support for multi-MCU development_: OSEP expandable architectures cannot support multi-core developments. Practically, the main-board and each one of the expansion shields which incorporate a MCU must host their own programming and debugging circuits on-board.
## 2 Classification of Nanostructures  
The development and use of nanomaterials is a growing area. One of the nanomaterial classification systems is based on the type of material and is divided into: (1) carbon-based nanomaterials, (2) inorganic-based nanomaterials, (3) organic-based nanomaterials excluding carbon, and (4) nanomaterials based on composites [11; 12]. The second classification obeys dimensionality (Figure 2). In this case, the classification includes four groups: (1) Zero-dimensional (0D) nanomaterials have magnitudes less than 100 nm in all directions (nanoparticles, nanopores, quantum dots, proteins, and micelles). (2) First-dimensional (1D) nanomaterials are less than 100 nm only in two directions (nanorods, nanotubes, nanofibers, nanowires, collagen nanofibers, and cellulose nanofibers). (3) Second-dimensional nanomaterials (2D) correspond only to nanomaterials that are smaller than 100 nm in the thickness direction (thin films, nanocoatings, nanoplatelets, and cell membranes). (4) Third dimension nanomaterials (3D) have all dimensions outside the nanometer scale but are composed of individual nanometer-scale blocks smaller than 100 nm known as nanocomposites and nanostructured materials [13; 14]. Recently, some works have proposed classifying the nanomaterials according to their toxicity and considering some attributes (material, physicochemical, toxicological, and quantum-mechanical properties) through in silico methods [15; 16]. Results based on the Bayesian information criterion (BIC) analysis show that three hazard categories were defined: (1) o to low (NoL), (2) medium (M), and (3) high (H) hazard. This classification proposal resulted from a study that introduced a predictive computational framework based on the molecular and phenotypic effects of a large panel of engineered nanomaterial across multiple in vitro and in vivo models [17]. This novelty classification considers measurable physicochemical characteristics and their correlation through mathematical approximations. Research into this topic is still in progress, so the biological variables should add to have an extensive analysis.  
Figure 2: Nanomaterials classification. Comparison between inorganic structures with biological molecules in a nanoscale.
## 1 Introduction
The main technical contributions of this work comprise: (i) the development of a novel algorithm for globalized size reduction of antenna structures that combines several algorithmic approaches (variable-resolution EM simulations, metamodeling, and reducing the dimensionality), (ii) a demonstration of superior performance of the algorithm, which surpasses what was achievable using existent local and global techniques, especially with regard to attainable rates of miniaturization, (iii) a demonstration of the reliability of the size reduction process, in particular, minor result variability across independent algorithm runs. To the best authors' knowledge, no similar antenna miniaturization-oriented optimization framework has been presented in the literature so far, neither in terms of the algorithmic solutions used nor the level of performance and reliability.
# Static Analysis of Composite Plates with Periodic Curvatures in Material Using Navier Method
###### Abstract  
Fiber-reinforced and laminated composite materials, widely used in engineering applications, may develop periodic curvature during manufacturing due to technological requirements. Given such curvatures in widely used composites, static and dynamic analyses of plates and shells under loads, along with related stability issues, have been extensively investigated. However, studies focusing specifically on the static analysis of such materials remain limited. Composite materials with structural curvature exhibit complex mechanical behavior, making their analysis particularly challenging. Predicting their mechanical response is crucial in engineering. In response to this need, the present study conducts a static analysis of plates made of periodically curved composite materials using the Navier method. The plate equations were derived based on the Kirchhoff-Love plate theory within the framework of the Continuum Theory proposed by [PERSON] and [PERSON]'. Using the Navier method, deflection, stress, and moment distributions were obtained at every point of the plate. Numerical results were computed using MATLAB. After verifying the convergence and accuracy of the developed MATLAB code by comparing it with existing solutions for rectangular homogeneous isotropic and laminated composite plates, results were obtained for periodically curved plates. This study offers valuable insights that may guide future research, as it employs the Navier method to provide an analytical solution framework. This study contributes to the limited literature with a novel evaluation of the static analysis of composite plates with periodic curvature.  
composite materials; curved composites; periodic curvature; laminated plates; static analysis; Navier method; Kirchhoff-Love theory Article
## 2 Data and Methods  
### Wind Data  
Daily wind speed at 10 m height was retrieved from regional climate simulations within the CODEX project [36] (Table 1) under the RCP8.5 greenhouse emission scenario. RCMs at a spatial resolution of 0.11\({}^{\circ}\) were considered from 2026 to 2045.  
A multi-model ensemble approach was applied by using a consensus criterion [49; 50] in order to reduce biases of each of the models that make up the multi-model. This consensus criterion determines the concordance of the multi-model ensemble wind speed difference (future minus past) with the different models, by imposing two conditions to evaluate the statistical significance of the pixels. The first condition states that at least 75% of all models present the same sign of the multi-model mean in every pixel. The second condition imposes that at least 80% of the models that met the first condition pass the Mann-Whitney-Wilcoxon test [51] (a 5% significant level was considered at each pixel).  
\begin{table}
\begin{tabular}{c c c} \hline
**GCM** & **RCM** & **INSTITUTE** \\ \hline CNRM-CM5 & CCLM4-8-17 & CLMcom \\ CNRM-CM5 & RCA4 & SMHI \\ EC-EARTH & RACMO22E & KNMI \\ IPSL-CM5A-MR & RCA4 & SMHI \\ IPSL-CM5A-MR & WRF331F & INERIS \\ MPI-ESM-LR & CCLM4-8-17 & CLMcom \\ MPI-ESM-LR & REMO2009 & MPI-CSC \\ \hline \end{tabular}
\end{table}
Table 1: Regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) project ([[http://www.cordex.org/](http://www.cordex.org/)]([http://www.cordex.org/](http://www.cordex.org/))) used in this study. GCM = global climate models.  
Figure 1: Study area with the main toponyms used in the study.  
For further information on the consensus criterion, the reader is referred to [49; 50]. The accuracy of this ensemble of RCMs was previously analyzed in the area under scope [52; 53].
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 2 Harmonic Detection Principles and Methods
### Based on 3-\(\varphi\) Instantaneous Reactive Power  
This technique has substantially aggrandized the development of active power filter (APF), as it is used in the detection algorithm in APF [14], initially aimed at three-phase circuits. In recent years, some scholars have developed improved methods based on this theory and extended the application range of this method to single-phase circuits. The technique can also have good detection accuracy for distorted grid voltage, but the asymmetric voltage will affect the detection performance. In general, the harmonic detection algorithm based on instantaneous reactive power has the advantages of simple circuit implementation and good real-time performance. Still, the circuit components greatly influence the algorithm's performance and have specific dependence. The parameter adjustment is more complicated, and the hardware costs more [16; 17]. This algorithm can be used to develop reactive power compensation devices and Control harmonics if the limitations are properly addressed.