esa-sceva/satcom-chunk-collection huggingface dataset:
# Surgical Repositioning of an Inverted Developing Incisor Assisted by 3D Technology
## 2 Case Report  
### Preoperative Evaluation and Preparation  
A 5-year-old male patient was referred from the department of pediatric dentistry for surgical extraction of an impacted mesiodens. A radiographic examination was performed including panorama, periapical view, and CT. The inverted mesiodens was impacted between the developing permanent maxillary central incisors. Three-dimensional (3D) CT imaging showed that the left maxillary central incisor was dislocated upside down and rotated buccopalatally (Figure 1).  
The treatment plan consisted of extraction of the impacted mesiodens and repositioning of the displaced developing incisor in place. It was decided that a 3D simulation should be conducted prior to the actual surgery. Digital image files from the 3D CT were imported into Mimics(r) 19.0 (Materialise, Leuven, Belgium). A 3D image file of the impacted developing permanent teeth was acquired (Figure 2A). The dislocated maxillary central incisor was moved into its original position in the virtual image (Figure 2B). It was confirmed that there was enough space between the adjacent embedding permanent teeth, and a bony interference was confirmed. The STL file was exported to CHITUBOX 64(r) (CBD-Tech, Shenzhen, China) for pre-processing of 3D printing (Figure 2C). All supports were set to a point size of 0.8 mm, a density of 70%. We set the printing layer thickness to 100 \(\upmu\)m and used a printing orientation of 45\({}^{\circ}\). A rapid prototype model of the left maxillary developing central incisor was printed using a Phrozen Sonic Mini Desktop 3D printer(r) (Phrozen Tech Co. LTD., Hsinchu, Taiwan). A commercial Bello C&B Hybrid(r) (Bello 3D Co., Daejeon, Korea) resin was used to make the 3D printing replica. The support was removed with disc bur after printing. The replica was cured for 10 min using CureM U102H(r) UV LED post curing system (SONA GLOBAL Co., Ltd., Seoul, Korea) (Figure 2D).  
Figure 1: Preoperative X-ray, (**A**) Preoperative panoramic view shows the inverted mesiodens and maxillary central incisor impaction; (**B**) Axial view of computed tomography (CT) shows buccolingual inverted central incisor; (**C**) Coronal image of the CT, arrow: inverted central incisor.
## 3 Proposed Methodology
### Joint Loss Function
#### 3.2.1 Categorical Loss  
Categorical loss, also known as classification loss, quantifies the difference between predicted emotion category probabilities and ground truth emotion labels. A categorical cross-entropy loss is employed for discrete emotional representations. It measures the  
Figure 4: Joint model block diagram.  
Figure 3: Overview of the proposed joint model architecture.  
dissimilarity between predicted probability distribution of emotion categories and true distribution, typically represented as one-hot encoded labels.  
\[L(\mathbf{x},y;\mathbf{\theta})=-\underset{j}{\sum}y_{j}logp\big{(}c_{j}\big{|}\mathbf{x} \big{)}\]  
where \(y\) is a one-hot encoded vector representing the true label, \(p\) is a vector of predicted probabilities for each class, and \(log\) is the natural logarithm.  
Another categorical loss function considered is Kullback-Leibler (KL) Divergence, which measures how one probability distribution differs from a reference distribution, as follows:  
\[KL(P||Q=\sum p_{i}(x)log\bigg{(}\frac{p_{i}(x)}{q_{i}(x)}\bigg{)}\]  
where \(P\) represents the true distribution and \(Q\) represents the predicted distribution.
# Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
## 4 Experimentation and Discussion
### Case 3  
This example considers a general case involving multiple multi-antecedent rules. In particular, four rules with each involving three antecedent variables are selected as the closest neighbouring rules. Importantly, the values of two variables within the given observation are located between these rules, whereas one is located beyond the area flanked by them. This relates to a hybrid case, reflecting the needs for both interpolation and extrapolation [11]. All of the involved fuzzy sets are listed in Table 3.  
The interpolated conclusion \(\bar{B}_{c}^{*}=<(7.52,8.72,9.77;0.6),(6.61,8.86,10.71;1)>\) is shown in Figure 6. It can be seen that the interpolation and extrapolation hybrid performs well.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{Rule 1} & \(\bar{A}_{11}=<(3,3,3;1),(3,3,3;1)>\) \\  & \(\bar{A}_{12}=<(2.5,2.5,2.5;1),(2.5,2.5,2.5;1)>\) \\  & \(\bar{B}_{1}=<(4,4,4;1),(4,4;1)>\) \\ \hline \multirow{2}{*}{Rule 2} & \(\bar{A}_{21}=<(12,13,13.5;0.6),(11,13,14.5;1)>\) \\  & \(\bar{A}_{22}=<(11.5,12.5,13.5;0.5),(10,12,14.5;1)>\) \\  & \(\bar{B}_{2}=<(10.5,11.5,12;0.5),(9,11.5,13;1)>\) \\ \hline \multirow{2}{*}{Observation} & \(\bar{A}_{1}^{*}=<(6,7,8;0.6),(5,7.5,9;1)>\) \\  & \(\bar{A}_{2}^{*}=<(5.5,6,7;0.5),(4,6,8;1)>\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Involved interval type-2 fuzzy sets for Case 2.  
Figure 5: Two multi-antecedent rules interpolation with singleton-valued conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline Rule 1 & \(\tilde{A}_{11}=<(1,2,3;0.7),(0,1.5,4;1)>\) \\ \(\tilde{A}_{12}=<(2,3,4;0.5),(1,2.5,5;1)>\) \\ \(\tilde{A}_{13}=<(7,8,9;0.6),(6,7.5,10;1)>\) \\ \(\tilde{B}_{1}=<(1,15,2,5;0.6),(0,1.5,3.5;1)>\) \\ \hline Rule 2 & \(\tilde{A}_{21}=<(16,17.5,19;0.7),(15,17.5,20;1)>\) \\ \(\tilde{A}_{22}=<(11.5,12.5,13;0.5),(10,13,15;1)>\) \\ \(\tilde{A}_{23}=<(21,22,23;0.6),(20,22,23.5;1)>\) \\ \(\tilde{B}_{2}=<(21.5,23,23.5;0.6),(20.5,23,24;1)>\) \\ \hline Rule 3 & \(\tilde{A}_{31}=<(21.5,22.5,24;0.7),(20,22,225;1)>\) \\ \(\tilde{A}_{32}=<(6.5,7,8.5;0.5),(5.5,7.5,9;1)>\) \\ \(\tilde{A}_{33}=<(12,12.5,14;0.6),(11,13,15;1)>\) \\ \(\tilde{B}_{3}=<(16.5,17,18;0.6),(15,17,20;1)>\) \\ \hline Rule 4 & \(\tilde{A}_{41}=<(11.5,12,13;0.7),(10.5,12,14;1)>\) \\ \(\tilde{A}_{42}=<(22,23,23.5;0.5),(21,22.5,24;1)>\) \\ \(\tilde{A}_{43}=<(17,18.5,19;0.6),(16,18.5,19.5;1)>\) \\ \(\tilde{B}_{4}=<(12,13,14;0.6),(11.5,13.5,14.5;1)>\) \\ \hline Observation & \(\tilde{A}_{1}^{*}=<(6,6.5,7.5,;0.7),(5,6.5,9;1)>\) \\ \(\tilde{A}_{2}^{*}=<(16.5,18,19.5;0.5),(15,18,20;1)>\) \\ \(\tilde{A}_{3}^{*}=<(1.5,2.5,4;0.6),(0.5,3,5;1)>\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hybridisation of interpolation and extrapolation for Case 3.  
Figure 6: Interpolation and extrapolation involving multiple multi-antecedent rules for Case 3.
# Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery
###### Abstract  
In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion.  
actified coronary artery; machine learning; support vector regression (SVR); stent expansion; finite element (FE) method +
Footnote †: journal: Applied Sciences
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 3 Optisystem Simulation Tool for Indoor VLC System
### NLOS Propagation Model
#### 3.2.2 Case 2. Dual LED NLOS Propagation Model  
We use two LEDs as transmitters in this case and consider the light's direct and reflected paths. The propagation model is shown in Figure 5.  
Figure 4: Single LED NLOS propagation model simulation environment in Optisystem.  
Figure 3: Experiment design for single LED NLOS propagation model. The experiment was 10 m long and considered a single LED and photodiode with multiple refection points.  
This simulation model takes into account all direct and reflected pathways and includes two LEDs on the transmitter side. The system becomes more complex, and the signal distortion is more significant than in the other two propagation models as the number of LEDs used as the transmitter increases. In all scenarios listed in Table 2, the distance between the transmitter and receiver, the angle of incidence, and the angle of irradiance are determined.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**LED1** & **LED2** \\ \hline \(l_{1}=15m\); & \(m_{1}=15.5m\); \\ \(\theta_{1}s=15^{\circ},\theta_{1}d=15^{\circ}\) & \(\theta_{1}s=23^{\circ},\theta_{1}d=23^{\circ}\) \\ \hline \(l_{2}^{{}^{\prime}}=16.5m\); & \(m_{2}^{{}^{\prime}}=6m\); \\ \(\theta_{2}^{{}^{\prime}}s=60^{\circ},\theta_{2}^{{}^{\prime}}d=65^{\circ}\) & \(\theta_{2}^{{}^{\prime}}s=50^{\circ},\theta_{2}^{{}^{\prime}}d=38^{\circ}\) \\ \hline \(l_{2}^{{}^{\prime\prime}}=12m\); & \(m_{2}^{{}^{\prime\prime}}=15m\); \\ \(\theta_{2}^{{}^{\prime\prime}}s=35^{\circ},\theta_{2}^{{}^{\prime\prime}}d=60^{\circ}\) & \(\theta_{2}^{{}^{\prime\prime}}s=45^{\circ},\theta_{2}^{{}^{\prime\prime}}d=45^{\circ}\) \\ \hline \(l_{3}^{{}^{\prime}}=9m\); & \(m_{3}^{{}^{\prime}}=20m\); \\ \(\theta_{3}^{{}^{\prime}}s=50^{\circ},\theta_{3}^{{}^{\prime}}d=45^{\circ}\) & \(\theta_{3}^{{}^{\prime}}s=55^{\circ},\theta_{3}^{{}^{\prime}}d=35^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the dual LED NLOS propagation model.  
Figure 5: Experiment design for dual LED NLOS propagation model. The experiment was 10 m long and considered a single LED and photodiode with multiple refection points.  
In this model, the simulation used the exact configuration of a single LED propagation model with an additional LED. The system used four power splitters with ten diffuse channel models on the transmitting side. All attenuators use a 5 dB attenuation during signal transmission. On the receiver side, the simulation has five power combiners, and the output from this is fed to the PIN photodiode. The simulation uses the same lowpass filter configuration used in the other propagation model for the filtering process.
## 3 Results and Discussion
### Machining Time and Material Removal Rate
This was due to the debris becoming difficult to remove from the sparking area for the smaller electrode due to the incredibly small gap clearance and some debris accumulated on the machining surface due to the action of gravity [51]. Therefore, the sparking process was impeded, extending the machining time, which led to the decrease in material removal rate for the small electrode. The decreased area of the secondary spark on the side electrode and elimination of debris particles in the sparking area caused by the modified shape of electrode improved the material removal rate. The machining performance of the step cylindrical electrode clearly increased by approximately 215.7%, 203.8%, and 130.4% compared with the conventional electrode for electrode diameters of 6, 9, and 12 mm, respectively. However, the material removal rate of the step cylindrical electrode with a diameter of 3 mm was slightly higher than that for the conventional electrode. This can be explained by the height of the flank electrode (height at the end of the electrode) for small electrodes affecting the ability of debris particles and bubbles to flow out from the sparking area:  
\[\text{MRR}=\frac{M_{w1}-M_{w2}}{\rho_{w}\times t}\times 10^{3}, \tag{1}\]  
where the MRR is the material removal rate (mm\({}^{3}\)/min); \(M_{w1}\) and \(M_{w2}\) are the workpiece weight (g) before and after machining, respectively; \(\rho_{w}\) is the density of workpiece (7.78 g/cm\({}^{3}\)); and \(t\) is the machining time (min). Figure 5: Photography of secondary spark on the electrode. (**a**) Conventional electrode (CE); (**b**) step cylindrical electrode (SCE). Figure 6: Material removal rate of holes drilled with the conventional electrode (CE) and the step cylindrical electrode (SCE).
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
## 3 Results  
Communications in typical wireless sensor networks are bidirectional. Therefore, the present study assumed that both sides of the communication link have similar conditions. In this way, the transmitter and the receiver used the same type of antenna during the experiments with XBee. Considering the maximum value of EIRP, the transmit powers of the systems with omnidirectional and directional antennas were +8 dBm and 0.5 dBm, respectively. The goal was to determine the maximum range in each environment for both cases. This objective considered that the sensing range is greater than the communication range in many practical NLOS transmission situations.
## 2 Related Work and Data Origin  
Since this works primary contributions are algorithms and methods for automatic data validation and ranking of newly manufactured sensors for application of specific detection tasks, we looked at similar work in this field.  
Many research groups like [PERSON] et al. [8] mention their data processing as using several individual pieces of heterogeneous, commercial software tools like LabView or Matlab, or scripts. Often, the used sensor-features and statistical methods are presented informally as incomplete textual expressions or as black boxes entirely [9]. This makes a reproduction of the algorithms difficult. The software Dave3 [10], is a toolbox with a graphical user interface, which comes close to the idea, we want to convey. The tool is, however, specialized for the evaluation of data obtained during temperature cyclic operation of gas sensors and not applicable for the validation and performance ranking for sensors according to their screening data. Another unnamed software for the evaluation of data obtained from an electronic nose could be found. The software is limited to a specific subset of sensors and also is not suitable for ranking or validating data [11]. Both tools have the major shortcoming of being based on commercial software like MatLab or LabView which requires additional licences.  
Our goal is to present the algorithms for validation and ranking as well as the needed features in a mathematically formalized way so that can they can be implemented in a variety of (open source) languages of choice, such as Python or R.
# Prediction of Theoretical Derailments Caused by Cross-Winds with Frequency
## 4 Evaluation of Derailment Safety under Cross-Wind  
### Operating Regulations of the KTX According to Wind Speed  
The operating regulations of the KTX for cross-wind speeds are enforced, as shown in Table 7, to prevent derailments [24].  
Table 8 shows the aerodynamic coefficient and side areas of the KTX, and air density. The values related to the aerodynamics of the KTX are based on the previous research and data from vehicle manufacturers [19]. Equation (30), which is based on Bernoulli's Equation, is used to calculate aerodynamic forces due to cross-winds [31].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Resonant Frequencies [Hz]**} \\ \cline{2-3}  & **Lateral Direction** & **Rolling Direction** \\ \hline Theory & 0.94 & 2.51 \\ \hline Simpack & 0.90 & 2.77 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of the resonant frequencies between the theoretical formula and Simpack.  
Figure 9: The vehicle behavior under the resonant frequencies.  
The regulations of Table 7 for wind speeds are converted into the equivalent lateral forces by using Equation (30) and the aerodynamic characteristics of the KTX in Table 8.  
\[F_{wind}=C_{y}\times A\times\frac{\rho v^{2}}{2} \tag{30}\]
## 3 Material and Methods  
In this study, high-resolution datasets were collected and analyzed. The analysis of the ALOS PALSAR digital elevation model (ALOS DEM) with a 12.5 m spatial resolution was at the core of this work. The remotely sensed data of the DEM's raw form were acquired from the Earthdata website (ASF Data Search Vertex), [[https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)]([https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)), accessed on 15 May 2023. Six ALOS PALSAR scenes were collected and mosaicked to set up one digital elevation image. Additionally, topographic maps (1:50,000) of the Wadi Al-Lith basin were digitized and processed. Integration between ALOS DEM and topographic data assists with extracting and delineating many basin characteristics. The collected data were processed and analyzed using ArcGIS 10.4 and QGIS software 3.16. These two geospatial programs were used broadly and successfully in processing related data and extracting morphometric results. The raw data went through several steps in order to be ready for processing and extracting the results. The basic methodology applied in this study is illustrated in Figure 2. The current methodology began with rectification, calibration, and dataset corrections. Gap filling correction, flow direction, flow accumulation, pour point, snap pour point, stream orders, stream numbers, and stream length were applied via the hydrology tools in geospatial software to detect drainage networks, watershed delineations, and the cumulative calculations. The drainage systems extracted from ALOS DEM accurately matched the real drainage systems digitized from the topographic sheets. In this study, the Wadi Al-Lith basin presented 15 sub-basins utilizing the third order.  
Figure 2: Flowchart of method applied in this study.
## 2 Hydroacoustic Signal Processing  
Before classifying hydroacoustic signals, which are sounds made by whales in an aquatic environment, they must be pre-processed, as the quality of the classification will depend on their quality. Hydroacoustic signal processing includes data preparation, as well as the use of further algorithms allowing the extraction of useful signals from certain directions. Preliminary processing includes de-noising, estimation of the degree of randomness, extraction of short-term local features, pre-filtering, etc. Preprocessing affects the process of further analysis within a hydroacoustic monitoring system [11; 12; 13]. Even though the preprocessing of hydroacoustic signals has been studied for a long time, there are several unresolved problems, namely: working in conditions of a priori uncertainty of signal parameters; processing complex non-stationary hydroacoustic signals with multiple local features; and analysis of multicomponent signals. Another set of problems is represented by effective preliminary visual processing of hydroacoustic signals and the need for a mathematical apparatus for signal preprocessing tasks.  
Current advances in applied mathematics and digital signal processing along with the development of high-performance hardware allow the effective application of numerous mathematical techniques, including continuous and discrete wavelet transforms. Wavelets are an effective tool for signal preprocessing, due to their adaptability, the availability of fast computational algorithms and the diversity of wavelet bases.  
Using wavelets for hydroacoustic signal analysis provides the following possibilities [14; 15]:  
1. Detection of foreign objects in marine and river areas, including icebergs and other ice formations, the size estimation of these objects, hazard assessment based on analyzing local signal features;
2. Detection and classification of marine targets based on the analysis of local signal features;
3. Detection of hydroacoustic signals in the presence of background noise;
4. Efficient visualization and processing of hydroacoustic signals based on multiscale wavelet spectrograms.  
Classification is an important task of modern signal processing. The quality of the classification depends on the noise level, training size and testing datasets, and the algorithm. It is also important to choose classification features and determine the size of the feature space. The classification feature is the feature or characteristic of the object used for classification. If we classify real non-stationary signals, it is important to have informative classification features. Among such features are wavelet coefficients.
# Current Challenges in _Yersinia_ Diagnosis and Treatment
## 1 Introduction  
_Yersinia_, along with _Shigella_ and _Salmonella_, belong to the family _Enterobacteriaceae_. The genus _Yersinia_ currently encompasses twenty-six species, three of which are pathogenic for humans: _Y. pestis, Y. enterocolitica_, and _Y. pseudotuberculosisis_[1]. The infection of _Y. enterocolitica_ causes yersiniosis in humans, _Y. pseudotuberculosisis_ is responsible for Far East scarlet-like feverdevelopment, and _Y. pestis_ leads to plague [2]. Human yersinioses most often occur as sporadic cases, but clusters of cases or outbreaks have also been described [3, 4, 5, 6, 7].  
Since the most common human enteropathogens are _Y. enterocolitica_ and _Y. pseudotuberculosisis_, which both have a worldwide distribution [2], only these will be discussed in this article.  
Bacillus from the genus _Yersinia_ have enterobacterial common antigen (ECA) in their cell envelope, which allows them to be classified as Enterobacteriaceaeaee. Both pathogens (_Y. enterocolitica_ and _Y. pseudotuberculosisis_) are foodborne diseases transmitted by the fecal--oral route. Unfortunately, they have become more common in recent years due to transmission by humans and farm animals; however, _Y. enterocolitica_ is detected more often than _Y. pseudotuberculosisis. Yersinia_ can grow over a wide range of temperatures (0-42 \({}^{\circ}\)C), whichincreases their virulence potential. Specifically, the nutritional infestation and infection development of _Y. enterocolitica_ after serotype O3-contaminated blood was stored at 4 \({}^{\circ}\)C for transfusion has been described in the literature [8]. Typically, enteric bacteria cause self-limited acute infections that spread from the intestine to the mesenteric lymph nodes. They cause infections mainly in children; however, severe illnesses and chronic conditions can also occur, especially in immunocompromised individuals [9; 10; 11; 12; 13; 14].  
Recently, many studies have broadly discussed bacterial virulence, their rapid reproduction, and the possibility of various diagnostic methods allowing for faster bacteria detection and effective therapeutic management [15; 16; 17; 18]. Unfortunately, such a management model is not always possible in clinical practice. The diagnosis is usually delayed due to late infection reporting by patients, a lack of access to appropriate diagnostic methods, and often excessive or too short treatment. Therefore, this review discusses the prevalence of _Yersinia_, pathomechanisms, different clinical variants of the disease, diagnostic and prophylactic methods, and current recommendations for treatment. The current status of the surveillance, detection, and prevention of yersiniosis is also described.