esa-sceva/satcom-chunk-collection huggingface dataset:
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Scenario 2-The Cluster Tree Topology with the Variable Depth
This is because the retransmission or the transmission attempts by the collision or the frame error of the proposed scheme is the lowest.
## 3 Simulation Results and Discussion
### Effect of Thermal Resistance
In this case, Yakutsk, the coldest city in this study, would be the only city where this limit should be established, this value being approximately 0.10 m\({}^{2}\) K/W. In another vein, since creating comfortable conditions is the primary purpose of heating systems, it was necessary to determine the percentage of cases of all the dwellings included in this analysis where the minimum comfort requirement of 14 h a day was fulfilled for each thermal resistance value in each city. Figure 12 shows this percentage plotted versus thermal resistance for five cities. In La Paz and New York, an increase in thermal resistance led to a minor decline in the percentage of cases meeting the minimum comfort requirement. That was also true for all the cities with winter severity lying between the climates prevailing in La Paz and New York. As observed in the figure, even at the highest resistance, 90% of the dwellings met the comfort requirements. For dwellings in colder climates such as Moscow, a rise in thermal resistance perceptibly lowered the percentage of cases meeting comfort conditions, from approximately 99% for the granite floor to 94% for the wooden floors with the lowest thermal resistance and 66% for the wooden floors with the highest resistance. In the two coldest cities analysed here, Irkutsk and Yakutsk, thermal resistance had a large impact on the percentage of dwellings attaining comfortable conditions. In Yakutsk in particular, granite floors were compliant in 83% of the cases, compared with only 10% with wood floors presenting the highest thermal resistance. As observed, the percentage of dwellings meeting the comfort conditions even at the highest covering thermal resistance is not negligible, including the coldest climates. Therefore, once again, there is no justification for rejecting any thermal resistance value of the wooden flooring. Figure 11: Effect of thermal resistance of wooden flooring on energy demand, thermal comfort, and start-up lag time in five representative cities. In summary, probably the most important implications to be drawn from all the results are:  
* Wooden floors can perform similarly to granite in the vast majority of buildings and climates, although depending on the climate and the quality of the building, the choice of wood properties will be more relevant. In general terms, it can be said that the more severe the winter and the less insulated the house, the more relevant the proper choice of wood properties. In any case, for most situations it is possible to find a wood that offers performance comparable to that of granite. * On the other hand, it was found that, although in most cases the appropriate choice of wood is the one with the highest thermal conductivity, this is not always the case, and there are cases in which a performance comparable to or even better than that of granite is achieved with low thermal conductivity wood.
## 4 Topology-Based Routing Protocols in FANETs  
When it comes to network topology-based routing methods, the information from the nodes' links is utilized to distribute packets. Routing protocols use the IP addresses in the network to identify nodes that employ topology-based routing. The system's complexity and great mobility force regular topological changes. However, existing routing is unsuitable for highly dynamic FANETs since it was designed for MANET or VANET. Therefore, several researchers have tried to develop the classical routing protocol by adding extension features or modifying the control messages to adopt in FANETs. In fact, routing protocols in FANETs networks are extremely difficult to design. Furthermore, FANETs are uniquely different from MANETs, hence several of the MANET routing protocols are tailored to their specific characteristics. Proactive, Reactive, Hybrid, and Static are FANET's subcategories of topology-based routing protocols, as shown in Figure 20.  
Figure 20: Categories of Topology-Based Routing Protocols for FANETs.
## 5 Simulation Results
### Comparison of the Throughout Capacity  
As depicted in Figure 7, a comparison of transmission rate performance is made between DUDA and DUCA. Figure 7 reveals that the DUDA system's throughput capacity achieves a maximum value of about 10 Gbps at \(\frac{\rho_{L}}{\rho_{H}}=3\), while in comparison, utilizing DUCA yields merely an operational rate of 9 Gbps. Specifically, analysis illustrated within Figure 8 underscores the superiority of DUDA, demonstrating its potential to amplify the uplink transmission speed by a significant 20 percent. Additionally, it is noteworthy that the overall system performance significantly enhances by approximately 17 percent when \(P_{H}\)= 23 dBW is in place. Significantly, both the overall system's efficiency as well as the rate ratio improve proportionally when the ratio \(\frac{\rho_{L}}{\rho_{H}}\) increases.  
Figure 6: Association probability with \(\rho_{L}/\rho_{H}=2\).  
Figure 7: Throughout capacity of DUDA and DUCA, \(P_{H}\)=23 dBW and \(P_{L}\)=20 dBW.  
Figure 8 illustrates that while the uplink rate and throughout capacity of the DUDA approach exceed those of the DUCA scheme, the uplink rate in DUDA remains invariant as \(P_{H}\) augmentation occurs. We observe that the uplink rate of DUCA diminishes as \(P_{H}\) escalates. This is due to the fact that the DUCA scheme selects the satellite with highest SINR in downlink as the associated satellite for both links, consequently, the uplink transmission distance is longer than that of the \(L\)-th layer satellite.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 3 Methodology
### Simulators and Frameworks
#### 3.2.1 Simulation of Urban MObility (SUMO)  
SUMO is an open-source traffic modeling simulation tool developed by the German Aerospace Center, now maintained by the Eclipse Foundation. SUMO enables researchers to model road traffic and traffic management systems and to perform in-depth analysis prior to launching the solution in real-world scenarios. In addition to road traffic comprising autonomous and human-driven vehicles, SUMO is capable of simulating public transportation and pedestrians.  
SUMO provides a variety of tools with the ability to generate, execute, and evaluate traffic simulations that involve the importing or creation of road networks, route calculations with given parameters and constraints, visualization of the traffic, and emissions cost of each trip for the vehicles.  
SUMO has been used in prior research projects to conduct a variety of application studies with a diverse set of research questions and objectives. Examples include an evaluation of proposed algorithms for traffic light control systems to improve vehicle throughput and reduce waiting times, artificial intelligence (AI) training of traffic light schedules, traffic effects of autonomous vehicles, simulation of traffic parking scenarios, and so on.
# Computation of Implicit Representation of Volumetric Shells with Predefined Thickness
## 2 First-Order Offset Function  
Let \(f(\vec{x})=0\) be an implicit surface \(\Gamma\). Our aim is to find an implicit surface \(\Gamma_{h}\) as the solution of the offset function \(\phi(\vec{x},h)=0\) such that the minimal Euclidean distance between any point on \(\Gamma\) to \(\Gamma_{h}\) is \(h\). Further, we require that the distance between \(\Gamma_{h}\) and \(\Gamma_{-h}\) is \(2h\). An exact solution to this problem is difficult to find in general and might not even exist, but we can start from a low-order approximation by computing the Taylor expansion of \(f(\vec{x})\):  
\[\phi(\vec{x},h)=f(\vec{x})-h\partial_{n}f(\vec{x})+\mathcal{O}(h^{2}), \tag{1}\]  
where the index \(n\) denotes the normal to \(\Gamma\), which is approximated to lowest order by the direction of the gradient of \(f(\vec{x})\) such that Equation (1) becomes  
\[\phi(\vec{x},h)=f(\vec{x})-h\sqrt{(\
abla f(\vec{x}))\cdot(\
abla f(\vec{x}))} +\mathcal{O}(h^{2}). \tag{2}\]  
Omitting the error term and rewriting \((\
abla f(\vec{x}))\cdot(\
abla f(\vec{x}))=(\
abla f(\vec{x}))^{2}\) gives us the first-order offset function \(\phi^{(1)}(\vec{x},h)\), which can be interpreted as an eikonal equation:  
\[\phi^{(1)}(\vec{x},h)=f(\vec{x})-h\sqrt{(\
abla f(\vec{x}))^{2}}. \tag{3}\]  
From \(\phi^{(1)}(\vec{x},h)=0\), a first-order accurate approximation of the offset surface denoted by \(\Gamma_{h}^{(1)}\) can be computed.  
We note here that computing an offset function via the propagation of a level set is far from new [13]. In the following, we demonstrate that this simple method can be improved to any specified order of accuracy.
## 2 Materials and Methods
### FTIR Description  
FTIR can be used to measure substances (solids, liquids, gases, powders, polymers, organics, etc.) that absorb in the mid-infrared (i.e., approximately 400-4000 cm\({}^{-1}\)). As a basic principle of the interaction matter-radiation, absorption can occur at intrinsic energy levels, specific for each molecule. When mid-infrared radiations cross a molecule, some specific frequencies (or wavelengths) will be absorbed if they correspond to the transition of vibrational levels of the molecule. Consequently, FTIR can be used for qualitative and quantitative analysis. However, it cannot detect noble gases, such as helium (He) and argon (Ar), or diatomic gases, such as oxygen (O\({}_{2}\)), nitrogen (N\({}_{2}\)), and hydrogen (H\({}_{2}\)), as their vibration does not create a dipole moment, thus do not have absorbance bands in the infrared region of the electromagnetic spectrum. Others molecules absorb very little radiation and are, therefore, not detectable at low concentrations (e.g., H\({}_{2}\)S > 200 ppm [110]). The heart of every FTIR instrument is an optical device called an interferometer (Figure 1) [3]. The oldest and most common type is the Michelson interferometer. The infrared source is usually a heated ceramic (at ca. 1200 \({}^{\circ}\)C). A collimating mirror collects light from the source and makes its rays parallel. A beamsplitter (in KBr) transmits approximately half of the light incident upon it and reflects the remaining half. A fraction of the light transmitted travels to a fixed mirror, while the other fraction travels to a moving mirror (see Figure 1). The lights are reflected by the two mirrors back to the beamsplitter, where they are recombined into a single light beam. This light beam interacts with the sample (exhaust gas) in a gas cell and finally strikes the detector. A multireflection cell is used to obtain a long optical path length with the minimum possible volume of the cell [111].
# Classification of Heart Sounds Using Convolutional Neural Network
###### Abstract  
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm's performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm's performance achieves an appropriate trade-off between sensitivity and specificity.  
automatic heart sound classification; feature engineering; convolutional neural network 2020 10 3956 10.3390/app10113956
# Modeling and Simulation of IGCC Considering Pressure and Flow Distribution of Gasifier
## 5 Conclusions  
The modeling of the IGCC considering the pressure and flow distribution of the gasifier has been proposed in this paper. A dynamic simulation was conducted. The following conclusions can be drawn:  
* The new volume-resistance model can be used in the simulation of the Shell gasifier and IGCC system, especially in the analysis of the pressure and flow distribution characteristics in the gasifier. It meets the requirements for both steady-state and dynamic simulations.
* The IGCC system model with the new gasifier model can show the volume inertia well, through calculating the pressure and flow distribution. The simulation results of the new model show an obvious delay compared with the results of the models without considering the pressure and flow distribution. It is more realistic to reflect the dynamic response of the IGCC power plant.
* The simplified control system causes complicated dynamic responses for the steam turbine, which reduces the stability of the IGCC power plant. The new model can be used for the design of the IGCC control system.  
This work is supported by Program of Science and Technology Committee of Shanghai Municipal (09 DZ1200702).  
[PERSON] and [PERSON] proposed the volume-resistance characteristics method. [PERSON] and [PERSON] built the new IGCC model. [PERSON] did the simulation experiment, analyzed the data and wrote the manuscript. [PERSON] supervised the study and proof read the manuscript.  
The authors declare no conflict of interest.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 3 User Survey  
This study proposes a new air traffic control system. Prior to system development, Jeju International Airport was selected to extract sample data. The Jeju International Airport is considered among the airports with high traffic density. It has four runways. However, only two of them are functional for ILS CAT I, CAT Ioperations. A questionnaire survey was conducted with air traffic controllers at the airport to derive problems and matters to be complemented in the current control system. The air traffic control towers of all airports including Jeju International Airport are designated as restricted access areas, and there were many restrictions on the access to information on the control system and visits to the control system. After a process of requesting cooperation for visiting and getting permission from the Ministry of Land, Transport and Maritime Affairs, the activities to collect information were allowed only in a limited range. In addition, due to the nature of the work, which is directly connected with safety, the information collection and interviews were allowed within limits so as not to interfere with the control work. The survey was conducted with air traffic controllers who could respond to the questionnaire when we visited the control tower. Considering the insufficient number of participants, the questionnaire was composed of selected questions appropriate for in-depth interviews. The participants who responded to the questionnaire consisted of four males and one female aged 40 years on average, and their average service period was 16.2 years. The questionnaire was generally composed of the following contents. The detailed questionnaire results are presented in Table 1.  
* Identify basic work procedures of air traffic controllers and the flight information referred to for work. * Identify the problems faced while air traffic controllers are performing their work, and the related work procedures and countermeasures. * Identify the content of work procedures in emergency situations. * Identify problems with the current control system. * Survey of demand for the proposed system. \begin{table}
\begin{tabular}{l l} \hline \hline
**Question** & **Response Results** \\ \hline \hline \multirow{7}{*}{Please describe your job in detail.} & (P1) No response \\  & (F2) Prevention of aircraft collision in the airfield control area, flow control, \\  & order maintenance \\ \cline{1-1}  & (P3) Control of aircraft takeoff and landing, ground control \\ \cline{1-1}  & (P4) Aircraft separation, emergency aircraft control, search and rescue cooperation, \\ \cline{1-1}  & (P5) Safe separation between aircraft, landing aircraft order assignment, smooth \\ \cline{1-1}  & flow control \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Questionnaire results.
## 4 Simulation Results and Discussion
### Analysis of the Performance of the Reverse Combination (RC) Configuration  
#### 4.1.1 Characteristic Curves  
Figure 7 demonstrates the conventional configurations of the PV array on the airship: SP, Vertical TCT (VTCT), and Horizontal TCT (HTCT). To demonstrate the advantages of the RC, a comparative analysis is conducted on the characteristic curves with SP, VTCT, and HTCT. Typical operating conditions from Table 2 are taken into consideration, and the corresponding I-V curves and P-V curves were simulated for the four configurations. The simulation results are depicted in Figure 8. The main parameters of the PV module under standard test conditions (STC) are shown in Table 2, and the area of the PV module is 32 m\({}^{2}\). The PV array operates at an altitude of 20 km, and the environmental temperature is set to \(-80\)\({}^{\circ}\)C.  
For operating condition 1, based on Figure 8a,b, the maximum output powers of the PV array are as follows: SP--98.4 kW, VTCT--102.7 kW, HTCT--81.2 kW, and RC--118.3 kW. The HTCT configuration arranges the rows of the PV modules along the airship axis. The variation in irradiance between rows is primarily influenced by yaw angle and circumferential curvature, resulting in the poorest uniformity of row irradiance and the highest level of mismatch losses. This also leads to the presence of three distinct local MPPs. In contrast, the SP and VTCT configurations arrange the modules radially along the airship, resulting in lower irradiance variations between rows due to the radial curvature and pitch angle. However, there are still mismatch losses and two local MPPs. The RC configuration performs optimally, exhibiting smooth I-V and P-V curves, and achieving the highest maximum power among the array curves. This configuration is based on the reconfiguration of VTCT and minimizes the variation in irradiance between rows caused by the longitudinal curvature and pitch angle, thereby minimizing mismatch losses.  
Similar results are observed for condition 2 (Figure 8c,d), condition 3 (Figure 8e,f), and condition 4 (Figure 8g,h). In all three conditions, the RC method demonstrates an obvious advantage with output powers of 145.3 kW, 92.6 kW, and 152.2 kW, respectively. These values are 8.3%, 28%, and 14.8% higher than the second-best VTCT configuration. Furthermore, a noticeable difference in the smoothness of the I-V and P-V curves for the RC configuration can be clearly observed compared with the other three configurations.
# The Influence of Bone Bruises on Bone Tunnel Enlargement Regarding ACL Rupture
###### Abstract  
The purpose was to determine the impact of the size of bone bruises (BBs) on bone tunnel enlargement (BTE) occurrence. **Materials and methods:** Twenty-four (24) patients who underwent anterior cruciate ligament reconstruction (ACLR) were included in this retrospective study. The measurements of BBs based on the initial MRI scan, bone tunnel size based on the control MRI scan, and the spatial determination of BB in relation to the bone tunnel location were evaluated. To analyze the relationship between BBs and BTE in homogeneous groups regarding the time from injury to ACLR (t(I-S)), the largest subgroup B (n = 15), in which t(I-S) was 31 to 60 days, was isolated for further investigation. **Results:** Based on subgroup B, a weak correlation (r = 0.33) existed between the BB volume and BTE size in the femur and tibia. Considering the relationship between the distance from the BB to the bone tunnel in the femur (_f_-1) and its enlargement (_A_/d), there was a moderate and statistically significant (_p_ < 0.05) negative correlation (_r_ = \(-\)0.64). The correlation between those parameters was even stronger (_r_ = \(-\)0.77) in subgroup B (time interval between injury and surgery ranged from 31 to 60 days). **Conclusions:** A retrospective analysis of MRI data in patients after ACL reconstruction surgery showed a relevant association between the distance from the BB to the bone tunnel and BTE in the femur. The relationship was not confirmed in the tibia.  
bone bruises; anterior cruciate ligament reconstruction; bone tunnel enlargement +
Footnote †: journal: Applied Sciences  
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**[PERSON]**  
1 Galen-Orthopedics, Jerzego 6, 43-150 Bierun, Poland; [EMAIL_ADDRESS]  
2 Galen Lab, Jerzego 6, 43-150 Bierun, Poland 2  
3 Faculty of Biomedical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; [EMAIL_ADDRESS]  
4 The Jerzy Kukuczka Academy of Physical Education, Mikolowska 72A, 40-065 Katowice, Poland  
4 Correspondence: [EMAIL_ADDRESS]; Tel.: +48-785-721-897  
**Featured Application: The close proximity of bone bruises (BBs) to the bone tunnel created during ACL reconstruction in the femur may contribute to the occurrence of BTE as an undesired side effect. Determining the spatial position of the BB in regard to the location of the femoral tunnel by imaging during preoperative planning may reduce BTE incidence and the risk of not achieving knee stability.**  
**Citation:** [PERSON]; [PERSON]; [PERSON] The Influence of Bone Bruises on Bone Tunnel Enlargement Regarding ACL Rupture. _Appl. Sci._**2021**, _11_, 2828. [[https://doi.org/10.3390/appl11062482](https://doi.org/10.3390/appl11062482)]([https://doi.org/10.3390/appl11062482](https://doi.org/10.3390/appl11062482))
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 3 New Traffic Forecasting Model Using Multi-Module Deep Neural Networks
### Initial Traffic Predictor  
Figure 2 shows the structure of the initial traffic predictor containing two blocks inside the hidden layers. In the first block, DNN modules extract hidden features from the input data through fully connected layers. The CNN module (marked as 1D CNN) performs one-dimensional convolutions parallel to the DNNs, to obtain features of the daily weather of each region. The second block of DNN combines individual features before generating the desired traffic data. The output layer predicts the speed and single-lane flow, using a sigmoid activation function to produce the output value in the range of \([0,1]\). The outputs are then converted at the end to recover the original speed and single-lane flow.  
Figure 1: Computing procedure of the TW-DNN. \(R\) is a predefined constant.