esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this paper is to compare von [PERSON] stress levels and displacements between two possible designs of a class II [PERSON] unilateral removable partial denture. Finite element analysis was used to compare two unilateral class II [PERSON] removable partial dentures replacing a mandibular second premolar, and first and second molars and with the same design, except a distal implant. The 3D model was created for a mandibular RPD with three artificial teeth and double ball attachment on the abutum teeth and a horizontal ball attachment for the implant, using Autodesk Inventor 2022 software. On the saddle, significant differences appear only when force is applied on the second molar. Association with implant increases von [PERSON] stresses on the acrylic saddle, where displacements values are similar, and decreases von [PERSON] stresses on the muco-osseous support, where displacements values are also similar. Regarding metal reinforcement of the denture, von Mises stress values are greater for forces applied on the second premolar in the implant situation, and smaller for forces applied on the first and second molars. Association with implant decreases von [PERSON] stresses on the metal reinforcement of the denture only if the force is applied distally, on the two molars; in this case, the metal reinforcement is more stressed above the mesial double ball attachment, where displacements values are similar. Regarding the artificial teeth, adding a distal implant does not significantly influence the stress and displacements values, except on the second premolar where the von Mises stresses values are greater, where displacements values are similar. Although an association with an implant increases the risk of fracture for the denture, the results demonstrate that in the posterior area of the dentulous space, the denture exerts less pressure on the mucosa and subjacent bone, protecting the area against resorption.  
Keywords:unilateral removable partial denture; dental implant; finite element analysis +
Footnote †: journal: Applied Sciences
# Analysis of Hybrid Ship Machinery System with Proton Exchange Membrane Fuel Cells and Battery Pack
###### Abstract  
As marine traffic is contributing to pollution, and most vessels have predictable routes with repetitive load profiles, to reduce their impact on environment, hybrid systems with proton exchange membrane fuel cells (PEMFC-s) and battery pack are a promising replacement. For this purpose, the new approach takes into consideration an alternative to diesel propulsion with the additional benefit of carbon neutrality and increase of system efficiency. Additionally, in the developed numerical model, control of the PEMFC-battery hybrid energy system with balance of plant is incorporated with repowering existing vessels that have two diesel engines with 300 kWe. The goal of this paper is to develop a numerical model that analyzes and determines an equivalent hybrid ship propulsion system for a known traveling route. The developed numerical model consists of an interconnected system with the PEMFC stack and a battery pack as power sources. The numerical model was developed and optimized to meet the minimal required power demand for a successful route, which has variable loads and sees ships sail daily six times along the same route--in total 54 nautical miles. The results showed that the equivalent hybrid power system consists of a 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, a hydrogen tank of 7200 L holding 284.7 kg at pressure of 700 bar is required, compared to previous system that consumed 1524 kg of diesel and generated 4886 kg of CO\({}_{2}\).  
proton exchange membrane fuel cell; system modeling; balance of plant; hybrid marine system +
Footnote †: journal: Applied sciences
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 3 Comparative Analysis of the Construction Techniques
### Coding & Decoding Delays  
In order to optimize throughput, it is essential to have codes with optimal coding delay and QODs have an advantage of it. The decoding delay, denoted here as \(\xi\), is an important performance measure for STBCs. Essentially, the decoding delay signifies the total number of time slots a receiver has to wait to receive a complete block of code before starting the decoding process. This implies that higher order code matrices require larger decoding delays. The performance of different STBCs schemes, that is, symmetric-paired designs 2.1, 2.2, 2.3 and [PERSON]'s approach 2.4 are compared. We enlist values of \(\xi\) for codes in case of \(3,4\) and 5 dual-polarized antenna arrangements in Table 1. In the table, \(N_{t}\) denotes the number of dual-polarized transmit antennas. From the table, it is seen that for 3 and 5 dual-polarized antenna systems there is no QOD obtainable from iterative techniques which we represent with \(*\).
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 6 Model Test Result
### EMI Classification Performance  
In order to further analyze the recognition ability of the network for different EMI signal categories, four cases with better output performance are selected to plot the confusion matrix, as shown in Figure 8. Each row is the real attribution category, and each column is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Input Channel**} & \multicolumn{2}{c}{**Input Channel**} & \multicolumn{2}{c}{**Classification**} & \multicolumn{2}{c}{**Prediction**} & \multirow{2}{*}{**Inferring Time (ms)**} \\ \cline{2-5} \cline{7-7}  & **Input1** & **Input2** & **Input3** & **Accuracy** & **RMSE** & **MAPE** \\ \hline \multirow{5}{*}{Double-input} & - & STFT & constellation & 68.18\% & 0.91 & 16.26\% & 9.60 \\  & - & STFT & NDC & 74.62\% & 0.94 & 24.09\% & 10.03 \\ \cline{1-1}  & VPP & STFT & - & 73.11\% & 0.73 & 17.05\% & 8.64 \\ \cline{1-1}  & VPP & - & constellation & 90.15\% & 0.72 & 16.03\% & 10.67 \\ \cline{1-1}  & VPP & - & NDC & 93.94\% & 0.62 & 22.60\% & 9.89 \\ \cline{1-1} \cline{2-5} \cline{7-7}  & VPP & STFT & constellation & 91.67\% & 0.65 & 6.50\% & 10.11 \\ \cline{1-1}  & VPP & STFT & NDC & 95.45\% & 0.49 & 10.83\% & 14.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification and regression results on the test set.  
the modulation result predicted for that category. The cells on the diagonal correspond to the correct classification result, while the cells which are not on the diagonal correspond to the wrong classification result. Therefore, for the confusion matrix, the greater the value of the elements on the diagonal, the better the classification effect of the model is.  
Among them, the precision rate at the bottom of each confusion graph (in green) shows the proportion of correctly classified samples of the EMI types obtained by the network classification. The column on the right, usually called the recall rate (in green), shows the proportion of different categories of EMI that are correctly classified for the actual classification target. The cell at the bottom right of the matrix shows the overall accuracy _Acc_.  
Among the classification results of the four networks, the classification accuracy is higher than 90%. The three-input network with NDC as an input has the best classification performance, while the double-input network with constellation diagram and VPP as inputs has a slightly poor performance. For different EMI types, compared with CW and BPSK interference, the recall rate classified as WGN interference is the highest, which is more than 50 %, indicating that the model is more accurate in identifying WGN interference. Besides, the probability of falsely predicting WGN as CW is low, which only occurs in a double-input network with the input of VPP and constellation diagram.
# Concept for Generating Energy Demand in Electric Vehicles with a Model Based Approach
###### Abstract  
Hybrid energy storage systems (HESS) for electric vehicles, which consist of lithium-ion batteries and supercapacitors, have become an increasing focus of research and development in recent years. The combination of the two combines the advantages of each storage technology (high energy density in batteries and high power density in supercapacitors) in one system. To effectively manage the energy flow between these two different storage technologies, an intelligent energy management system (EMS) is required. In the development of the EMS, it is usual to run preliminary checks in a simulation environment that is as close to reality as feasible already during the development process. For this purpose, this paper presents a concept for the creation of a simulation environment consisting of realistic routes and a holistic vehicle model. The realistic route data are generated by a route-generating algorithm, which accesses different map services via application programming interfaces (API) and retrieves real route data to generate a simulated route. By integrating further online services (e.g., OpenWeather API), the routes are further specified with, for example, real weather data, traffic data, speed limits and altitude data. For the complete vehicle model, components including the suspension, chassis and auxiliary consumers are simulated as blackbox models. The components that can be accessed during the simulation are simulated as white box models. These are the battery, the supercapacitor, the DC/DC converter and the electric motor. This allows the EMS to control and regulate the HESS in real time during the simulation. To validate the simulation environment presented here, a real BMW i3 was driven on a real route, and its energy demand was measured. The same route was simulated in the simulation environment with environmental conditions that were as realistic as feasible (traffic volume, traffic facilities, weather) and the vehicle model of the BMW i3. The resulting energy demand from the simulation was recorded. The results show that the simulated energy consumption value differs by only 1.92% from the real measured value. This demonstrates the accuracy of the simulation environment presented here.  
electric vehicle (EV); energy storage system; energy demand; model based development; virtual vehicle model; route generation application +
Footnote †: journal: _sciences_
## 3 Materials and Methods
### Methodology  
Figure 3 presents the flow chart applied in this study. After capturing 14 Sentinel-2 images, they were resampled at a B2 resolution of 10 m. Subsequently, for each Sentinel-2 image, 12 various spectral indices were calculated. Section 3.3.1 describes the computation of these indices in more detail. Many of these indices are correlated; therefore, principal component analysis (PCA) was utilized to remove redundancy of the data and to check whether the application of only first principal components without any data redundancy achieved the best results. Then, the detection of crop types based on random forest classification was carried out, as described in Section 3.3.3. Crop type classification was performed using the tools available in Esri ArcMap (version 10.8.1). This was performed separately for each index, for all indexes, for a group of selected indexes (MCARI, NDI4, and WDVI), and for different combinations of principal components (PCs). The accuracy of the classification was evaluated using various accuracy measures, as presented in Section 3.3.4.  
Figure 3: Methodology workflow for crop type classification.
# Internet of Things for Sensing: A Case Study in the Healthcare System
## 6 Results and Discussion
### Classification Results
#### 6.1.2 Data Classification Using KNN and RF  
The data collected using S-Band sensing technique was then classified using KNN and RF algorithms [45; 46; 47]. The former classification method is based on supervised learning where voting criteria classifies the new objects. The nearest object \(k\) from the training datasets is considered that assigns a new object to a particular class based on the majority votes. The training procedure of the KNN classification method includes storing of features and class label of the particular training datasets. In classification procedure, the most frequent training samples \(k\) assign an unlabeled object of a particular class. Various distance parameters are used in KNN algorithm as discussed in Equations (10)-(13). In our case, we have used Euclidean distance as in Equation (12) and set the value of \(k\) to 1, which implies that the selected class label was exactly the same as the one nearest to the training dataset On the other hand, the RF classification method primarily integrates a set of independent decision tree classifiers [48]. A decision tree in an RF classifier with \(N\) number of leaves divides the feature space into \(N\) number of regions such as \(R_{m},1\leq m\leq N\). The prediction function _f_(_x_) for each tree is described as  
\[f(x)=\sum_{m=1}^{N}c_{m}\Im(x,R_{m}),\]Here, \(N\) indicates the total number of regions in a feature space, \(R_{m}\) corresponds to the \(m\) region, \(c_{m}\) represents a constant number that corresponds to \(m\), and \(\Im\) describes the indicator function.  
\[\Im\left(x,R_{m}\right)=\left(\begin{array}{cc}1,&if\ x\ \varepsilon R_{m}\\ 0,&otherwise\end{array}\right.\]  
The final decision function for random forest algorithm is based on majority vote of all the trees. In our case, we have used an RF classification method with 100 trees.  
We now discuss the data obtained using KNN and RF algorithms in terms of accuracy, precision, recall, specificity, Cohen's Kappa coefficient, and F-measure. The formulae for six performance metrics are discussed as follows:  
\[Accuracy=\frac{Tp+Tn}{Tp+Tn+Fp+Fn} \tag{14}\]  
Accuracy is a fraction of instances that are correctly classified [46],  
\[Precision=\frac{Tp}{Tp+Fp} \tag{15}\]  
Precision is estimated as a ratio of true predictions pertaining to a class [46].  
\[Recall=\frac{Tp}{Tp+Fn} \tag{16}\]  
Recall is the fraction of relevant instances that have been retrieved over the total amount of relevant instances [47].  
\[Kappa=\frac{\left(Accuracy-Accuracy_{expected}\right)}{1-Accuracy_{ expected}} \tag{17}\]  
Kappa is the accuracy of the system to the accuracy of the random system.  
\[Specificity=\frac{T_{n}}{F_{p}+T_{n}}, \tag{18}\]  
Specificity measures the proportion of negatives that are correctly identified.  
\[F-measure=\frac{2 Tp}{\left(2 Tp+Fp+Fn\right)} \tag{19}\]  
F-measure is the harmonic mean of precision and recall.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 3 System Model
### Deployment Model  
We consider a mmWave 5G NR deployment model in an industrial environment, such as an automated factory with multiple production lines, as illustrated in Figure 1. Here, various industrial robotized machines are installed along the production lines (conveyors) performing some actions while producing goods. During the production process these machines are expected to require communications between each other to exchange the current state information leading to D2D communications scenario. On top of this, remote monitoring of the production process via cameras installed at machines may further require UE to BS connectivity. These are two communications scenarios we address in what follows.  
Figure 1: The considered deployment scenario and two-dimensional view of the abstracted model. (**a**) The considered deployment scenario. (**b**) Abstracted model.  
The considered factory deployment scenario is illustrated in Figure 1a. We assume that machines are located on the factory floor according to Manhattan Poisson Line process (MPLP) [22; 23]. The process is defined by two homogeneous Poisson processes along \(x\)- and \(y\)-axes with intensities \(\lambda_{x}\) and \(\lambda_{y}\), accordingly, where the mean inter-line distances, \(1/\lambda_{x}\) and \(1/\lambda_{y}\), correspond to the width and length of industrial machines. Each point is associated with a line parallel to coordinate axes as shown in Figure 1b. The rationale for choosing this process is that it includes stochastic factors related to random locations of production lines in different factories but still maintains a certain degree of regularity related to constant distances between machines on adjacent lines reflecting basics of industrial deployments [15].  
The resulting MPLP process organizes a stochastic lattice grid. To capture industrial deployments with different densities of equipment, we assume that the probability that a lattice cell contains an autonomous machine is \(\
u\). Thus, as one may observe, the overall number of machines in any limited area follows a binomial distribution. Without the loss of generality, BS or UE is assumed to be located at the height \(h_{A}\) in the bottom-left corner of the grid. The UE under analysis is located on the factory floor at \(l_{x}\) and \(l_{y}\) distance as measured from the bottom-left corner, see Figure 1b. This UE is assumed to be installed at height \(h_{U}\). Note that when the first UE is a machine itself, we consider D2D communications, \(h_{A}=h_{U}\), otherwise, the height of BS is assumed to be higher than that of UE, i.e., \(h_{A}>h_{U}\).
## 3 Results  
Here, some benchmark results demonstrating the capabilities for CDI of our system are presented. The object used for the CDI studies was a hole drilled by our tightly focused laser beam on a 13 um thin aluminum foil. It was selected among a series of objects, imaged by a scanning electron microscope, based on (i) the sharpness of its edges and (ii) its contour details to be less than 1 um. In Figure 7a, colorred a SEM image of the object is shown.  
The object was illuminated by the XUV beam just before its focus in order to cover as large area as possible of the object. Since the ROC of the spherical multilayer mirror is 1000 mm, this choice was not expected to affect the XUV phase front. Our automated system allowed the acquisition of a series of CDI images by moving the object in the plane perpendicular to the k-vector of the XUV beam. In Figure 7c,e, the CDI images for the wavelengths of 47.6 nm and 32.2 nm, respectively, are shown. These CDI images were selected in order to demonstrate the imaging capabilities of sub-micrometer details. In Figure 7b, the ideal calculated CDI image of the object is shown. Comparing the experimental CDI images for these two wavelengths, it is clearly shown that the smaller the wavelength, the finer the details of interference modulation in the k-space of the CDI image are, as expected.  
In Figure 7d,f, the images in real space are shown, for the wavelengths of 47.6 nm and 32.2 nm, respectively, as calculated using the reconstruction algorithm presented in Appendix B. From these images, it is evident that using the shorter wavelength, the reconstructed image has a higher resolution, resolving the details with a higher number of pixels. Moreover, the reconstructed image size of the object is \(\sim\)1.5-times larger for the wavelength of 32.2 nm as compared to 47.6 nm, reflecting the inverse ratio of the wavelengths.  
Figure 6: XUV radiation overall coherence measurements. (**a**) The 70 lines per inch copper mesh. (**b**) The diffraction image obtained after illuminating the copper mesh with the full XUV spectrum. (**c**) Reconstructed image of the copper mesh. Axes in (**b,c**) are in pixels.
## 2 Materials and Methods
### Machine Learning Regression
#### 2.3.4 Decision Trees Regression (DTR)  
DTR is a supervised machine learning algorithm that involves constructing regression models in the form of a tree structure known as Decision Trees (DTs). DTs are widely used for both regression and classification tasks, with an objective to create a predictive model that estimates the target variable's value using straightforward decision rules derived from the data features [5; 72]. Conceptually, a tree approximates the data with piece-wise constants. A given dataset is partitioned into smaller subsets and a DT is developed accordingly, comprising decision nodes and leaf nodes. The root node, representing the best predictor, initiates the tree. The feature space is iteratively split into distinct regions to group similar target values together. The choice of split is determined by the mean squared error in regression problems. While building a DT, the process employs recursive binary splitting for the expansion of an extensive tree based on the training dataset. The expansion halts solely when each terminal node contains a quantity of observations that is below a specified minimum threshold. Recursive binary splitting is an iterative, top-down strategy utilized with the objective of reducing the residual sum of squares denoted as '\(\epsilon\)'. This approach is applied to a partitioned feature space characterized by'm' partitions, as outlined in Equation (12).  
\[\varepsilon=\sum_{m=1}^{M}\sum_{i\in R_{m}}(y_{i}-\hat{y}R_{m})^{2} \tag{12}\]  
DTR models are easily interpretable white box models which can also be visualized. They require minimal data processing and are robust to the effect of outliers [73]. However, DTR models may generate highly complex trees that may tend to overfit the data and this necessitates the pruning of the trees and tuning of hyperparameters such as maximum depth or minimum samples. Furthermore, with complex intricate relationships, DTR are prone to high variance and biased trees with an unbalanced dataset with dominant classes. Therefore, DTR models are sometimes referred as weak learners [74; 75]. Usually, to overcome these limitations and to increase the generalizability of the predictive models, a strong ensemble of several weak learners is created by training them either independently in parallel and combining the predictions through bootstrap aggregating (as in the Random Forest Regression and Extremely Randomized Trees Regression) or sequentially through boosting (as in Adaptive Boosting Regression and Extreme Gradient Boosting Regression).
# An Improved SIFT Underwater Image Stitching Method
## 4 Results
### Quantitative Comparisons
#### 4.2.2 Comparisons with Image Stitching Algorithms  
UIQM is grounded in the framework proposed by [PERSON] in 2015 [35], which takes into consideration three vital metrics for assessing underwater image quality: colorfulness measure (UICM), sharpness index (UISM), and contrast measure (UIConM). UIQM is computed as a linear combination of these three metrics. The higher the UIQM value, the more favorable the color balance, sharpness, and contrast of the image. UIQM serves as a comprehensive standard for evaluating the quality of underwater images, with a specific focus on their degradation characteristics. Widely recognized and authoritative in the realm of underwater image quality assessment, UIQM played a pivotal role in our study. Consequently, we conducted ten experiments on an image dataset and employed the UIQM metric to compare the performance of our proposed stitching method with that of several other stitching algorithms.  
The formula for calculating the UIQM is shown below:  
\[UIQM=c_{1}\times UICM+c_{2}\times UISM+c_{3}\times UIConM. \tag{20}\]  
Based on the results of [35], we took the scale factors as \(c_{1}=0.0282\), \(c_{2}=0.2953\), and \(c_{3}=3.5753\). The results are shown in Table 2. Generally speaking, the value range of UIQM was between 0 and 3. For images with good imaging quality and clarity, the value of UIQM was generally above 1.5. As shown in Table 2, except for image 2, the UIQM value was relatively low due to the blurriness and poor quality of the image itself. Our method showed significant improvements in UIQM metrics compared to the traditional SIFT algorithm, with all datasets having higher scores and an average improvement of 6.04%. This demonstrates that our algorithm enhances the quality and sharpness of underwater panoramic images. Additionally, our method outperformed other algorithms such as APAP, AANAP, and SPHP, with improvements in UIQM of 4.73%, 9.01%, and 11.06%, respectively.  
Figure 6: _Cont._