esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Taguchi Design of Experiments Method
The signal-to-noise ratio that optimizes the photogrammetric flight is the one with the bigger value, which is the one that minimizes the error, and can be defined as it follows:  
\[\text{S}/\text{N}=-10\cdot\log_{10}\frac{1}{\text{n}}\sum_{i=1}^{\text{n}} {y_{i}}^{2}, \tag{3}\]  
where \(\text{S}/\text{N}\) is the signal-to-noise ratio, \(\text{n}\) is the number of repetitions, and \(\text{y}_{\text{i}}\) is the observed value. After obtaining the signal-to-noise ratio for each arrange, the average signal-to-noise ratio is calculated. \[\overline{\text{S}/\text{N}}=\frac{1}{9}\sum_{i=1}^{9}{(\text{S}/\text{N})}_{ i}, \tag{4}\]  
where \(\overline{\text{S}/\text{N}}\) is the average signal-to-noise ratio and \(\text{S}/\text{N}_{\text{i}}\) are the signal-to-noise ratios of each test. The results obtained from the signal-to-noise ratio can be analyzed with several procedures. In this particular case, the statistic technique that has been applied to determine the influence of each parameter is the Pareto-ANOVA Method [27]. It is a fast and simple technique that considers the signal-to-noise ratio of all the tests and allows for identification of the most influent parameter on the final result, which is the one with the greatest variability between the average signal-to-noise ratios for each level and parameter (\(\Delta\)). On that purpose, the sum of squares of the variation with respect to the average (SS) is calculated. \[\text{SS}=\sum_{\text{i}=1}^{9}\Bigl{[}\text{S}/\text{N}-\overline{\text{S}/ \text{N}}\Bigr{]}^{2}. \tag{5}\]  
After that, the sum of squares for every parameter (SS) is determined. \[\text{SS}_{\text{i}}=\sum_{\text{i}=1}^{3}\Bigl{[}{(\text{S}/\text{N})}_{ \text{ij}}-\overline{\text{S}/\text{N}}\Bigr{]}^{2}, \tag{6}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{5}{c}{**Factors**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** \\ \hline
2 & 1 & 2 & 2 & 2 \\
4 & 2 & 1 & 2 & 3 \\
5 & 2 & 2 & 3 & 1 \\
6 & 2 & 3 & 1 & 2 \\
7 & 3 & 1 & 3 & 2 \\
8 & 3 & 2 & 1 & 3 \\
9 & 3 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Orthogonal arrange with four factors and three levels \(\text{L}_{9}=(3)^{4}\). where \((\text{S}/\text{N})_{\text{ij}}\) are the average signal-to-noise ratios for each level of each parameter. Finally, the contribution of each parameter is calculated by the application of Equation (7). \[\text{Contribution }(\%)=\frac{\text{SS}_{\text{i}}}{\text{SS}}\cdot 100. \tag{7}\]
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 7 Results  
In this section we will include results to show the performance improvement due to the use of our improved bias models both using simulated data (in Section 7.1) and real data (in Section 7.2). Also, the consistency of the estimator using this model will be assessed.  
The evaluation of the results will be made evaluating the Root Mean Square (RMS) value of the difference between the positions of the measurements from different radars at the same time for the same target:  
\[error_{RMS}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}(x_{i,1}-x_{i,2})^{2}+(y_{ i,1}-y_{i,2})^{2}\Bigr{)}} \tag{73}\]  
where \((x_{i,1},y_{i,1})\) and \((x_{i,2},y_{i,2})\) are the \(i\)-th measures from first and second radar for the same target at the same time. This method is used because the evaluation of this model done principally with real data.  
Another way to test the results for real data is to have access to ADS-B o WAM data that can be supposed unbiased compared with radar measurements. As the only available information is the measurements of the aircraft, only the errors of the corrected plots can be evaluated, but it is almost impossible know the real value of the biases. In route tracking in ATC it is more important the alignment between plot than the accuracy in position of the measurement.  
For simulated data the value of the biases is known and the results are presented in convergence graphics (as a function of the number of samples used in the simulation).
# An EV Charging Scheduling Mechanism Based on Price Negotiation
## 4 EV Charging Scheduling Based on Price Negotiation
### Price Adjustment Strategy of Charging Station Operator  
After one round of price negotiation, the charging station operator can get all the bidding prices of EV users that negotiated with him in the last round. The price for the next round can be adjusted according to these data.  
Firstly, the charging station operator needs to calculate the average bidding price of all EV users.  
\[\overline{p_{k}^{u}}=\frac{1}{N_{n}}\sum_{i=1}^{N_{n}}p_{i,k}^{u} \tag{10}\]  
where \(N_{n}\) is the total number of current bidding EV users.  
Secondly, the charging station operator needs to find the relationship between the number of current bidding EV users and the average historical number \(\overline{N_{n}}\) of bidding EV users for the same daily time period. If \(\overline{N_{n}}\leq N_{n}\), the charging station operator may reduce the reduction ratio of the bidding price for the next round or even reduce the price. Otherwise, the charging station operator may increase the reduction ratio.  
The bidding price of charging station operators for the new round of price negotiation is formed as follows.  
\[p_{j,k+1}^{cs}=p_{j,k}^{cs}\times(1-\omega\times\frac{\overline{N_{n}}}{N_{n}} \times\frac{(p_{j,k}^{cs}-\overline{p_{k}^{u}})}{p_{j,k}^{cs}}) \tag{11}\]  
where \(\omega\) is the decision-making coefficient of the charging station operator, \(\omega\in(0,1)\). If \(\omega\) turns to 0 it means that the charging station operators have a strong desire for little or no price reduction, otherwise they will reduce the price. Apparently, the lowest price that a charging station operator can accept is the average bidding price \(\overline{p_{k}^{u}}\) of all EV users.
## 4 Experiments and Results
### Post-Processing  
To further improve the performance of liver segmentation. We applied the connected components algorithm and the median filter to remove noises. The connected components algorithm labels contiguous regions of the segmented volume and isolates the largest connected component, corresponding to the liver. This process is particularly effective in addressing false positives that often occur near the edges of the liver or in regions with similar intensity values. By identifying and retaining only the largest connected region,  
Figure 4: Two example CT images from the LiTS dataset (**left**), Sliver07 dataset (**middle**), and the 3D-IRCADb dataset (**right**), respectively.  
Figure 5: CT image preprocessing to enhance the contract between the liver and other tissues and remove irrelevant tissues by adjusting the Hounsfield unit values to the range of \([-200,200]\).  
the algorithm ensures that the segmentation remains focused on the target organ. Then, the median filter is applied to refine the segmentation by smoothing out high-frequency noises and irregularities. Unlike other smoothing techniques, the median filter preserves the edges of the anatomical structures, ensuring that the boundaries of the liver remain intact while removing noises. The combination of the connected components algorithm and the median filter effectively enhances the segmentation by eliminating irrelevant structures and improving both the reliability and accuracy of the output. Figure 6 shows that noises are removed after post-processing to improve liver segmentation accuracy.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.7 Example Designs  
The proposed design, which bridges the gaps in integration with front-end circuitry and performance evaluation, involves dynamically tunable metasurfaces applied to adaptive beamforming for multi-user scenarios. For example, using a dynamic metasurface antenna (DMA) system (ref. [72]) in combination with FPGA-based real-time controls enables the antenna to perform highly accurate user-specific beamforming and allows dynamic adaptation to environmental changes in operational conditions. Such a design would find quite practical applications in smart healthcare and remote sensing. The evaluation results reveal that this approach might provide better performance, which shows a spectral efficiency increase of more than 150% compared with the conventional MIMO system (ref. [70; 74]). The example proposed here will make use of advanced digital precoding and beamforming algorithms in association with holographic metasurfaces for robust and scalable solutions toward next-generation wireless communication systems.
# Perspective of Using Vertically Oriented Graphene as an Electrochemical Biosensing Platform
## 3 Results and Discussions  
### Raman Spectroscopy  
Raman spectroscopy can provide information on the graphitic structure and quality of VG. The G peak at around 1580 cm\({}^{-1}\) and the 2D peak at around 2700 cm\({}^{-1}\) indicate the film has a graphitic nature. Moreover, the appearance of the D peak at about 1350 cm\({}^{-1}\) and the D+G peak at around 2910 cm\({}^{-1}\) reflects the presence of defects in the graphitic structure. A multitude of defects result from disorderliness, which in turn is caused by numerous edge states, sp\({}^{3}\)-bonded C-H species, a base layer composed of nanographite, and defects that are ion-induced due to plasma exposure during the growth of VG film.  
The Raman spectrum of VG film obtained at 700 \({}^{\circ}\)C is represented in Figure 1 and shows the specific bands D, G, and 2D centered at D\(\sim\)1340 cm\({}^{-1}\), G\(\sim\)1568 cm\({}^{-1}\), and 2D\(\sim\)2665 cm\({}^{-1}\), respectively. They are shifted from their usual positions. The I\({}_{\rm D}\)/I\({}_{\rm G}\) ratio is 1.17 and is still in the range of low defect density. Also, the I\({}_{\rm 2D}\)/I\({}_{\rm G}\) ratio with a value of 0.58 indicates that the nanosheets are formed from a few graphene layers. The importance of FWHM for D and G shows some defects in the crystalline lattice of the graphene due to nucleation centers. The analysis confirmed the specific bands of VG and its crystallinity, with some structural defects.
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Contemporary Use of 3D-Printed Accessories in Orthodontics
#### 4.1.7 Temporary Anchorage Device (TAD) Insertion Guides  
Temporary anchorage devices (TADs), also referred to as mini-screws or mini-implants, are small devices used in orthodontics to offer additional anchorage and support during tooth movement. They are temporarily inserted into the bone and removed once they are no longer needed [67]. Their usage enhances the biomechanics and procedures in orthodontics [30].  
Three-dimensional printers have been utilized to create customized surgical guides that ensure the precise placement of TADs. This helps to avoid critical anatomical structureslike dental roots, nerve-vascular bundles, and thin bone areas. The guides can provide clear images of hard tissue (bone-teeth) for selecting mini-implant locations, while the contours of teeth-brackets-mucosa ensure the guide's proper fit in the mouth [17]. Several studies provided evidence of the successful implementation of 3D-printed TAD insertion guides in their practices [30; 68; 69; 70; 71]. The key benefits of using a digital workflow in guide production include reducing the risk of complications, minimizing chair time, and enhancing patient comfort [72].
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 6 Conversion of Halophytes into Biogas
### Inhibiting Effects of Halophytes on the AD Process
#### 6.3.2 Co-Digestion of Halophytes
[98] suggested that the addition of osmoprotectants, glycine betaine, offers an effective alternative for co-digestion to minimize the toxic/inhibitory effects caused by high salinity. Ultimately, AD of halophytes can be significantly improved by adjusting the co-digestion ratio of halophytic biomass with other biomass or waste streams under varying salinity concentrations to an optimum with high biogas/biomethane yields and process stability. Thereby, co-digestion of halophytes, together with other biomass, can offer a solution to the unfavorable conditions in mono-substrate digestion, and halophytes can become a welcome feedstock to boost biogas production in existing large scale biogas plants.
# Photonic Technology for Precision Metrology
## 2 From the Beginning of Spectroscopy to High-Spectral-Resolution Measurements
Accuracy is sometimes confused with precision; that is, the ability of a device to make consecutive, close measurements. The instrument can produce extremely precise measurements that are not at all accurate. Precision is a direct result of the repeatability and reproducibility of the measurements. The differences between these parameters are shown in Figure 2.  
The manner by which the accuracy and precision of the measuring instrument, in this case a spectrophotometer, influences the results is presented. In the presented definitions, there is a term that is very important for all measurements, uncertainty, which describes the doubt concerning the validity of the measurement result. It generally characterizes a dispersion of values that can reasonably be assigned to the measurand, such as, for example, standard deviation or the half-width of an interval  
Figure 1: Mid-infrared absorption spectra of nitrous oxide from the HTRAN 2008 database and measured with an IFS 125 HR spectrophotometer (**a**), and enlarged part of the spectra to show the differences (**b**). The theoretical spectra were obtained for the following settings: resolution 0.025 cm\({}^{-1}\) (as a result of the maximum datapoints in the range of 4–5 \(\upmu\)m), temperature 296 K, and partial pressure 3.2 \(\times\) 10\({}^{-7}\) atm. The measurement was carried out in the following conditions: resolution 0.0035 cm\({}^{-1}\); 10 cm gas cell, including CaF\({}_{2}\) windows; 10 mbar; temp. 23 \({}^{\circ}\)C; beam splitter CaF\({}_{2}\); and liquid-nitrogen cooled InSb detector. with a certain confidence level [4]. In the presented example, the standard deviation (\(\sigma_{x}\)), calculated from Formula (1), is \(2.51\times 10^{-5}\)\(\upmu\)m.  
\[\sigma_{x}=\sqrt{\frac{1}{N-1}\sum(x_{i}-\overline{x})^{2}} \tag{1}\]  
where \(N\) is the number of measurements, \(x_{i}\)--results of the subsequent measurements, and \(\overline{x}\) is the mean value.
## 2 Materials and Methods
### Mathematical Modeling  
For the FSI simulations in this study, the framework of IBFE [29] is adopted. In this formulation, the Navier-Stokes equations are used in an Eulerian framework to model the fluid dynamics; on the other hand, the stress, elasticity, and deformation of the whole structure are governed by Lagrangian equations. Consider a physical domain \(\Omega\in\mathbb{R}^{3}\) with Cartesian coordinates \(\mathbf{x}=(x,y,z)\in\Omega\). The initial coordinate system for the structure is denoted as \(U\subset\mathbb{R}^{3}\), and the curvilinear Lagrangian coordinates are represented as \((q,r,s)\in U\). At a given time \(t\), the position of a material point \(\mathbf{X}\) is described by \(\mathbf{\chi}(\mathbf{X},t)\). Therefore, the fluid and the structure occupy the domains \(\Omega_{f}(t)=\Omega\backslash\Omega_{s}\) and \(\mathbf{\chi}(\mathbf{X},t)=\Omega_{s}\), respectively. The governing equations for the coupled system are as follows:  
\[\rho\bigg{(}\frac{\partial\mathbf{u}}{\partial t}(\mathbf{x},t)+\mathbf{u}( \mathbf{x},t)\cdot\
abla\mathbf{u}(\mathbf{x},t)\bigg{)}=-\
abla p(\mathbf{x },t)+\mu\
abla^{2}\mathbf{u}(\mathbf{x},t)+\mathbf{f}(\mathbf{x},t) \tag{1}\]  
\[\
abla\cdot\mathbf{u}(\mathbf{x},t)=0 \tag{2}\]  
\[\mathbf{f}(\mathbf{x},t)=\int_{U}\mathbf{F}(\mathbf{X},t)\delta(\mathbf{x}- \mathbf{\chi}(\mathbf{X},t))\mathrm{d}\mathbf{X} \tag{3}\]  
\[\frac{\partial\mathbf{\chi}}{\partial t}(\mathbf{X},t)=\mathbf{u}(\mathbf{\chi}( \mathbf{X},t),t)=\int_{\Omega}\mathbf{u}(\mathbf{x},t)\delta(\mathbf{x}-\mathbf{ \chi}(\mathbf{X},t))\mathrm{d}\mathbf{x} \tag{4}\]  
Equations (1) and (2) represent the momentum and continuity equations for incompressible flow. In these equations, \(\rho\) and \(\mu\) stand for fluid density and dynamic viscosity, respectively. The Eulerian fields of velocity and pressure are denoted by \(\mathbf{u}(\mathbf{x},t)\) and \(p(\mathbf{x},t)\), and the Eulerian elastic force density exerted by the structure onto the fluid is given by \(\mathbf{f}(\mathbf{x},t)\). Equations (3) and (4) describe the interaction between the Lagrangian and Eulerian frameworks using integral transformations with a three-dimensional Dirac delta function, denoted as \(\delta(\mathbf{x})=\delta(x)\delta(y)\delta(z)\). Specifically, Equation (3) converts the Eulerian force density \(\mathbf{f}(\mathbf{x},t)\) into its Lagrangian elastic force density \(\mathbf{F}(\mathbf{X},t)\). Equation (4) imposes the no-slip condition at the interface between the fluid and the structure, where, at the interface, both are forced to move with the same velocity.
# Identifying Real Estate Opportunities Using Machine Learning
## 5 Evaluation
### Results and Findings
#### 5.2.2 How Are Models Affected by Setup?  
The impact of parameters in the median absolute error for the different machine learning techniques is shown in Figures 10-12 for the ensembles of regression trees, the \(k\)-nearest neighbors and the multi-layer perceptron, respectively.  
As we can see, in the case of ensembles of regression trees, the number of trees in the forest has little impact on the performance. It seems that the mean MedAE slightly decreases as the number of estimators grow, but the variation is very small. Variance also suffers a reduction as a result of introducing more trees, although again this effect is barely perceptible. In the case of the use of bootstrap sampling, it seems clear that its use negatively affects the model performance in a significant manner. As for the split criterion, a slight reduction in MedAE follows the use of the median squared error (MSE), but, as it happened with the number of estimators, the effect is small.  
Figure 10: Distribution of the median average error based on the different parameters for the ensembles of regression trees.  
Figure 9: Boxplot of the distribution of median average errors per machine learning technique.  
When studying the number of neighbors, a clear pattern is shown where the error grows proportionally to the number of considered neighbors. In addition, the variance in MedAE grows as well. This is indicating that considering more similar instances is not only increasing variability but also adding confusion to the regression. From the obtained results, it is left as future work to test this technique with a number of neighbors smaller than five (like one or two) to test whether the error keeps descending. Considering the weights of the different features when computing the nearest neighbors, it is clear that weights that are inversely proportional to the distance of the target instance to the neighbors work better than uniform weights. In addition, regarding the distance metric, [PERSON] has also reported significantly better results than a cosine distance.  
Finally, in the case of the multi-layer perceptron, results show that performance is very sensitive to the architecture. It is interesting to see that the minimum MedAE is constant between different alternatives, but the mean and median MedAE is much smaller as more layers with less units each are introduced. As a result, variance is also affected, and we check that the configuration using three hidden layers with 128 units in the first one is the one reporting the best average MedAE. Nevertheless, as we concluded earlier, errors are always much larger than those obtained using ensembles of regression trees or \(k\)-nearest neighbors.
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
## 4 Numerical Results
### Comparison of FD Results and FE Solutions  
In this section, the reliability of the non-uniform meshes FD algorithm is confirmed by the COMMEMI 2D-0 testing model. The COMMEMI 2D-0 model, proposed by [PERSON] et al. for comparing of numerical modeling methods for electromagnetic induction [19], is illustrated in Figure 5. It consists of three segments of different conductivities with horizontal contrasts of 1:10 and 2:1, lying on a perfectly conducting basement.  
With a testing period of 30 s, the simulated apparent resistivities by the presented FD approach are compared with the FE solution [7] and the averaged volume integral results from the COMMEMI project [19]. The computational size of magnetotelluric domain was designed as 100 km \(\times\) 100 km. Using the technique of the non-uniform meshes, we set discrete elements in given domain as 100 \(\times\) 80 (i.e., \(N_{y}\) = 100 and \(N_{z}\) = 80), and extended 50 km to air space for TE-mode. Figure 6 shows the results of the FD computations along a profile.  
One can see (Figure 6) that the results calculated by the FD method and the FE method are in close agreement to each other. It shows that the apparent resistivities have no significant difference. Compared to the FE solution, the average relative error for the apparent resistivity is 0.05, which are in the acceptance limits. Meanwhile, our FD numerical results were quite comparable those of the COMMEMI 2D-0 projects.  
Figure 4: Comparison of FD solution and analytical solution for the two-layered model. (**a**) Apparent resistivities of TM-mode; (**b**) Apparent resistivities of TE-mode; (**c**) Phases of TM-mode; (**d**) Phases of TE-mode.  
Figure 5: Schematic drawing of the COMMEMI 2D-0 model.
# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks
## 3 Proposed Fuzzy-Logic-Assisted AODV Routing Protocol
### Fuzzy System Realization Flow Chart  
The fuzzy inference system established in this paper takes the normalized delay, stability, and residual energy value calculated by the node as input, and outputs the reliability level of the node after fuzzy processing. The higher the reliability, the more likely the node is chosen as a relay node. Through the fuzzy system shown in Figure 7, the relay node can be determined in real time. The choice of route can be determined by the highest average output reliability level of the route. Finally, a routing path with low latency, high link connectivity, and long network lifetime is obtained.
## 5 Medical Imaging-Based Kinematics
### Computerized Tomography (CT)  
CT consists of a mobile X-ray source that rotates around a focal point to produce scans from different angles. The result is a high-resolution stack of 2D images that can be time-resolved. The use of intravascular contrast agents is common for studies of the vascular system to improve the visibility of the blood vessels. CT scans can provide better resolution than all the other techniques described above with pixel sizes of about 0.5 mm (Figure 7b). There is no special feature to assess kinematics from CT scans, although its superior temporal and spatial resolution has been used to measure the dynamic change in cross-sectional area and shape of blood vessels during the cardiac cycle, from which homogenized values of circumferential strain for a given cross-section can be estimated. From there, kinematics can be inferred from tracking a given anatomical feature or making reasonable assumptions about rigid body rotation and torsion [11,222].  
The use of ionizing radiation makes this technique potentially hazardous; thus risk-benefit of a CT study should be seriously considered. This limits its use in serial follow-up, particularly in pediatric patients, to avoid repetitive exposure to radiation [223]. However, it avoids the risk of unknown or contraindicated implanted metallic object/devices associated with MRI and is typically capable of much shorter scan times than MRI, making it ideal for trauma or other acute emergencies.