esa-sceva/satcom-chunk-collection huggingface dataset:
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Analysis of Results
#### 4.3.3 Throughput (kbps) vs. No of Nodes  
Throughput is a crucial performance metric for IoMT networks, reflecting the network's capacity to transmit data efficiently. Figure 8, presented in the study, provides a detailed comparison of the throughput of different protocols and network sizes. The results indicate that the SBAODV protocol has the highest throughput values across all network sizes, ranging from 87.3 kbps for 20 to 77.7 kbps for 100 nodes. The high throughput values obtained by SBAODV can be attributed to its ability to adapt to network topology changes and dynamically select the best path to the destination based on the available network resources.  
Comparing SBAODV with other protocols, it is evident that TPH, RPH, FDTRP, and DADRP exhibit lower throughput values than SBAODV for all network sizes. These protocols are not as efficient as SBAODV in capturing black hole devices and removing them from the network, which causes a reduction in their throughput values. On the other hand, LPNRP (routing protocol for low-power and lossy networks) demonstrates similar throughput values to SBAODV for all network sizes and maintains a throughput above 78%. However, other schemes suffer to maintain their throughput, with LPNRP offering the lowest values below 60% at 100 nodes. It is important to note that selecting a protocol for an IoMT network depends on various factors such as network topology, application requirements, and network reliability. At the same time, SBAODV has the highest throughput values compared to the other schemes because SBAODV utilizes a game theory algorithm, which makes it observe the behavior of end devices. If any device behavior is malicious, SBAODV utilizes the game model to block it as a result; other devices forward data efficiently through reliable routes, which increases the throughput of the proposed scheme. Further, the proposed scheme's result provides valuable insights into the performance of throughput as a result, the efficiency and quality of healthcare services in IoMT networks increase.  
Figure 7: Network routing load vs. no of nodes.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 2 Methodology
### Review of Some Basic Principles of Electromagnetism
#### 2.1.2 The Huygens Principle and Knife-Edge Diffraction  
The Huygens principle states that every point (red dots in Figure 4) on a primary wave front (blue curve in Figure 4) may be regarded as a new source of spherical secondary waves, and that at each point in space these waves propagate in every direction at a speed and frequency equal to that of  
Figure 3: GNSS-R scattering geometry in Figure 1 modified according to the principle of equivalence: the transmitter is replaced by its image. Computed electric fields are only valid in the region z > 0.  
Figure 2: Left: linear and loop currents over a perfect conductor. Right: the conductor has been replaced by the image currents. Note that the sign reversal of the currents parallel to the conducting plane are responsible for the polarization change of the “reflected” wave.  
the primary wave, and in such a way that the primary wave front at some later time is the envelope of these secondary waves (red curve in Figure 4).  
The Huygens principle can be applied to compute the electric fields when part of the trajectory is partially obstructed by a conducting plane as illustrated in Figure 5, leaving a clearance \(h\) with respect to the line of sight. This academic case is known as the \"knife-edge diffraction\", and it is used in radio communications to assess the propagation losses with respect to the free space caused by the obstruction by a mountain, building, etc. In the latter this will represent the transition from one medium to another [6].  
The development of an analytical solution can be found in many text books, and only the final result is provided below:  
\[E_{diff}=E_{frac}\cdot F(v) \tag{2}\]  
where:  
\[F(v)\hat{=}\frac{1+j}{2}\left[\int_{v}^{\infty}cos\left(\frac{\pi}{2}t^{2} \right)dt-j\int_{v}^{\infty}sin\left(\frac{\pi}{2}t^{2}\right)dt\right]=\ \frac{1+j}{2}\left\{\left[\frac{1}{2}-C(v)\right]-j\left[\frac{1}{2}-S(v) \right]\right\}, \tag{3}\]  
Figure 4: Graphical representation of the Huygens diffraction principle.  
Figure 5: Huygens principle applied to the “knife-edge diffraction” case. The light-gray area partially blocks the propagation of the signal, hiding parts of some Fresnel zones. \(h\) is the clearance of the obstruction.  
\(C(v)\) and \(S(v)\) are the so-called Fresnel integrals, which are given be:  
\[C(v) =\int_{0}^{v}cos\Big{(}\frac{\pi}{2}t^{2}\Big{)}dt, \tag{4a}\] \[S(v) =\int_{0}^{v}sin\Big{(}\frac{\pi}{2}t^{2}\Big{)}dt, \tag{4b}\]  
and \(v\) is the Fresnel-Kirchhoff parameter:  
\[v=h\cdot\sqrt{\frac{2\cdot(R_{T}+R_{R})}{\lambda\cdot R_{T}\cdot R_{R}}} \tag{5}\]  
Figure 6 shows the absolute value of \(F(v)\) in Equation (3) in linear units. When \(h=0\), half of the electric field is blocked, which translates into a power loss of 6 dB with respect to the free-space conditions. Note the ringing for \(h<0\) due to the different Fresnel zones being blocked or not and contributing to the total electric field with different phases.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Software
#### 4.1.2 Context-Specific Performance  
The suitability of a DES platform varies depending on the healthcare environment. Studies by [PERSON] and [PERSON] [30] found that in emergency department (ED) modeling, Arena and Simul8 performed particularly well due to their robust queue-handling capabilities, while AnyLogic's agent-based features provided a more detailed representation of patient behavior variations. Despite these differences, all three platforms demonstrated comparable accuracy in throughput prediction.  
For outpatient clinics, Simul8 proved especially effective, with its healthcare templates reducing model development time by 40%. Arena's process analyzer facilitated superior scenario analysis, while AnyLogic offered better integration with scheduling systems, making it a strong choice for environments requiring dynamic appointment management.
## 2 Optical Data Information Encoding
### Data Encoding Combined Structure-Specific Slope Angle and Azimuthal Variation  
The slope angle of the structure was combined with the azimuth angle to improve storage density and encoded as \"\(\alpha\) v,\" where \(\alpha\) represents the slope angle and \(\psi\) represents the azimuth angle of the structure. We chose a total of 12 slope angles from 25\({}^{\circ}\) to 66.25\({}^{\circ}\) and 10 azimuth angles from 0\({}^{\circ}\) to 90\({}^{\circ}\). Figure 6 shows the 30 spectral responses of the 120 data. The codes \" 2 0 \" up to \" 2 5 \" indicate a structure with a fixed slope angle of 28.75\({}^{\circ}\) and an azimuth angle increasing from 0 to 50\({}^{\circ}\). The codes \"4 0\" and \"6 0\" indicate a structure with a fixed azimuth angle of 0\({}^{\circ}\) and slope angles of 36.25\({}^{\circ}\) and 43.75\({}^{\circ}\), respectively. Our findings showed that the changes in spectral response increased with the changes in slope angle and azimuth angle. This finding is beneficial for subsequent decoding work.  
Figure 5: Simulated wavelength-dependent spectra for 18 data sequences (Fixed slope angle).  
Figure 6: Simulated wavelength-dependent spectra for 30 of the 120 data sequences (slope angle combined with azimuth).
## 4 Practical Application
### Structural Optimization  
As a further use case of the RooTri() function, we consider a typical problem from the area of so-called topology optimization, a subarea of structural optimization. The focus is on the search for a suitable material distribution in a given design domain with defined boundary conditions and external loads in order to fulfill a certain objective criterion. As a benchmark problem, compliance is often minimized. Figure 17a shows such a problem. Here, a linear elastic body that has corresponding Young's modulus \(E\), density \(\rho\) and Poisson's ratio \(\
u\) is considered. On the left side, the body is clamped, and on the right side, a force \(F\) acts. The extensions in the \(x\)- and \(y\)-directions are marked with \(2L\) and \(2H\), respectively.  
Figure 16: Determination of equipotential lines of constant torques.  
Figure 17: Benchmark problem in topology optimization; (**a**) problem definition, (**b**) numerically determined values of the higher-dimensional \(\phi\)-function in a certain iteration step.  
To determine the material distribution, different approaches are investigated in research, and a famous representative is the so-called level set method. Here, it is assumed that the component boundaries of the structure can be interpreted as the zeros of a higher-dimensional function, the \(\phi\)-function. At the component boundary, then, the condition \(\phi(x,y)=0\) holds. For example, [PERSON] et al. apply the level set method in [PERSON] et al. (2019) and also develop a so-called adaptive mesh. This method can be used to discretize the component boundaries more finely. However, the complexity of the optimization problem requires to develop the \(\phi\)-function iteratively to the local optimum in the framework. Due to the numerics, the \(\phi\)-function is then available as a point cloud; in Figure 17b, this is shown for a certain iteration step. The task, now, is to intersect the point cloud with the \(xy\)-plane in order to determine the optimized contour. We, therefore, pass point cloud \(\mathcal{P}\) depicted in Figure 17b to the RooTri() function and set parameter \(d\) to 0, i.e., we call RooTri(P,0,0,1,0). As a result, we finally obtain the associated component contour in the respective iteration step; see Figure 18. At this point, it should be noted that it is particularly useful, in the case of topology optimization, to obtain as many zeros as possible. Thus, the component contour can be described more precisely, which in turn is important for the discretization of the component in the context of finite element analysis.
## 4 Bigram and Traditional Results
### Advancements in Machine Learning for Water Contaminants and Soil Erosion
#### 4.1.1 Bigram Document Analysis  
In Figure 7, a thematic map displays the main concepts alongside a tree map, which illustrates the hierarchical visualization of the most frequent bigrams found in the article abstracts. Upon examining the tree map, we observe that arsenic, nitrates, iron, and manganese emerge as the primary components to be monitored. Moreover, the analysis context emphasizes groundwater, water quality, and water supply. Decision Tree and Random Forest algorithms stand out in this regard.  
Additionally, the thematic analysis reveals six groups. In the top-right quadrant, there is a group related to groundwater and nitrate contamination. In the bottom-right quadrant, two groups are present: the first one, colored in red, is associated with water supply, arsenic contamination, and ML techniques for predicting concentrations; the second one, in yellow, is connected to spatial analysis methods for monitoring water pollutants. Of the remaining three groups, two are in the top-left quadrant. The first group focuses on risk assessment in aquifers, considering geomorphological conditions and ML algorithms, while the second group in the same quadrant addresses soil erosion and spatio-temporal analysis techniques. Lastly, the final group in the bottom-left quadrant primarily concerns techniques used for monitoring or measuring pollutant concentrations, with remote sensing, hyperspectral imaging, and spectroscopy being the most prominent methods.  
In Figure 8, a conceptual map and a related dendrogram are presented, focusing on the topic of contaminants and soil erosion. In the conceptual map, two groups are identified. The first group, in light blue, is related to groundwater and nitrate contamination, which also has a strong connection to agricultural issues. The second group, in red, mainly concentrates on water quality and supply. In this context, Decision Trees and Random Forests reappear as the primary techniques used, and arsenic, nitrates, iron, and manganese are the minerals whose concentrations are sought to be measured. Upon examining the proximity of concepts in the dendrogram, it is evident that measuring contamination in groundwater is strongly related to agriculture, and nitrates are closely associated with the concept of prediction. On the other hand, soil and its erosion are connected to water supply and potable water topics. Arsenic is primarily linked to drinking water concerns.  
Figure 7: Tree and thematic map for a water contaminants and soil erosion topic.
# Dynamic Responses of Asphalt Concrete Waterproofing Layer in Ballastless Track
## 1 Introduction
Therefore, it is essential to evaluate the influence of the introduction of ACWL as a structural load bearing layer on the dynamic responses of CRTS III ballastless track structure. The main objective of this study is to investigate the dynamic responses of full cross-section asphalt concrete waterproofing layer in ballastless track. Firstly, the asphalt mixtures for the ACWL were assessed by the laboratory tests to ensure the service performance of ACWL. Secondly, the construction process of a test section of ACWL in north China was introduced and field tests were conducted to evaluate the construction quality. Then, the viscoelastic behavior of asphalt concrete extracted from the test section was characterized by the generalized Maxwell model. Finally, a 3D finite element model of vehicle and CRTS III track structure was developed to discuss the dynamic characteristics of ACWL, and the model was also validated by the field test data.
# A Pipeline Approach to Context-Aware Handwritten Text Recognition
###### Abstract  
Despite concerted efforts towards handwritten text recognition, the automatic location and transcription of handwritten text remain a challenging task. Text detection and segmentation methods are often prone to errors, affecting the accuracy of the subsequent recognition procedure. In this paper, a pipeline that locates texts on a page and recognizes the text types, as well as the context of the texts within the detected region, is proposed. Clinical receipts are used as the subject of study. The proposed model is comprised of an object detection neural network that extracts text sequences present on the page regardless of size, orientation, and type (handwritten text, printed text, or non-text). After that, the text sequences are fed to a Residual Network with a Transformer (ResNet-101T) model to perform transcription. Next, the transcribed text sequences are analyzed using a Named Entity Recognition (NER) model to classify the text sequences into their corresponding contexts (e.g., name, address, prescription, and bill amount). In the proposed pipeline, all the processes are implicitly learned from data. Experiments performed on 500 self-collected clinical receipts containing 15,297 text segments reported a character error rate (CER) and word error rate (WER) of 7.77% and 10.77%, respectively.  
Keywords:handwritten text recognition; Residual Network; Transformer model; object detection; named entity recognition +
Footnote †: journal:
## 3 Hybrid Stochastic Approach to Location Estimation
### Deductive Approach for Location Estimation  
On the other hand, the _deductive approach_ estimates the _likelihood function_ by using empirical formulas about the signal propagation in an indoor environment. In this approach, we need to know the location of each base-station, the described map of the environment (walls, obstacles, etc.) and a propagation model. Several propagation models can be seen in references [35; 36]  
If we assume that each observation \(o^{\prime}\), from the vector o, is mutually independent, we can write:  
\[\Pr(o\mid l)=\prod_{j=1}^{b}\Pr(o^{\prime}\mid l,B^{\prime}) \tag{3}\]  
where \(b\) is the number of base-stations; _B\({}^{\prime}\)_ is the base-station \(j\); and, _o\({}^{\prime}\)_ is the observation signal strength from base-station \(j\). In our study the base-station _B\({}^{\prime}\)_ is characterized by two variables (_l\({}^{\prime}\)_, _o\({}^{\prime}\)_). The variable _l\({}^{\prime}\)_ denotes the location of base-station \(j\), and o\({}^{\prime}_{o}\) denotes the mean signal strength measured to _d\({}_{0}\)_ distance from base-station \(j\).  
In this work, we assume that \((o^{j}|l,B^{j})\) follows a Gaussian distribution with standard derivation \(\sigma\). The following empirical propagation model, which supposes that signal strength is measured in dB, is used [36; 37]:  
\[p(o^{j}\mid l,B^{j})\approx N({o_{0}}^{j}+10n\log(\frac{d^{j}}{d_{0}})+{L_{w}}^ {j},\sigma^{2}) \tag{4}\]  
where, \(d^{j}\) is the Euclidian distance between the observation location (\(l\)) and the base-station \(j\) (\(l^{j}\)), \(n\) is the attenuation variation index (\(n\) value depends on the specific propagation environment) and \({L_{w}}^{j}\) is the attenuation caused by the obstacles. In our study the value of \({L_{w}}^{j}\) depends on the number of walls that the line of sight crosses from the base-station \(j\) to the location. We will use \({L_{w}}^{j}=w{L_{0}}\). Where \(w\) is the number of wall crossed and \({L_{0}}\) is the wall average attenuation. Finally, let \(N(\mu,\,\sigma^{2})\) be the normal distribution with mean \(\mu\) and variance \(\sigma^{2}\).
###### Abstract  
With increasing interest in the W-band, there is growing focus on parabolic reflector antennas that are known for efficiently inducing high-gain radiation characteristics. There is particular focus on parabolic antennas with diverse defocus-fed applications, including monopulse tracking, multi-beam formation using multiple feeds, and aperture-shared antennas for multi-band operation. Thus, a new simplified method is presented in this paper to analyze the radiation characteristics of defocus-fed parabolic antennas. The presented method is based on the discrete division of a parabolic reflector surface and considers only simplified wave propagation theory and the effect of the scalar function pattern from the feeder. Additionally, array theory is exclusively applied for the analysis of radiation characteristics. Therefore, the presented method uses a very simplified formula to calculate the radiation characteristics of a defocused parabolic reflector antenna. The performance of the presented method was evaluated by comparing the results with commercial EM tools. The results of the analysis confirm the applicability of the presented method for the analysis of defocus-fed parabolic antennas.  
parabolic antennas; defocus-fed; radiation characteristics; simplified formula; W-band +
Footnote †: journal: Medical Informatics  
**Citation:** [PERSON]; [PERSON]; [PERSON] A Simplified Radiation Characteristic Analysis Method for Defocus-Fed Parabolic Antennas in a W-Band Communication System. _Appl. Sci._**2024**, _14_, 1622. [[https://dot.org/10.3390/app1401622](https://dot.org/10.3390/app1401622)]([https://dot.org/10.3390/app1401622](https://dot.org/10.3390/app1401622))  
Academic Editor: [PERSON]
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
Figure 2 shows the power consumption of sensor node subsystems. Energy consumption for the communication subsystem is higher than the computation subsystem. It is proved that sending 1-bit of information consumes energy as much as executing thousands of instructions [6], and the need to compromise between communication and processing is inevitable. Therefore, the most crucial issue is to decrease the energy consumption of sensor nodes.
Future Internet_**2013**, \(5\), 398-428; doi:10.3390/fi5030398  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Cross-Network Information Dissemination in Vehicular _Ad hoc_ Networks (VANETs): Experimental Results from a Smartphone-Based Testbed**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2,3}\), [PERSON] \({}^{4}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{4}\)**  
\({}^{1}\) Guglielmo srl, Strada Parma 31/H, Langhirano 43010, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) UPMC Sorbonne Universites, LIP6, 4 Place Jussieu, Paris 75005, France;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Thales Communications & Security SA, 4 Av. des Louvresses, Gennevilliers 92230, France  
\({}^{4}\) Department of Information Engineering, University of Parma, Viale G.P.Usberti 181/A, Parma 43124, Italy; E-Mails: [PERSON]SS] (M.P.); [EMAIL_ADDRESS] (G.F.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-0521-634324; Fax: +39-0521-634390.  
_Received: 1 March 2013; in revised form: 14 June 2013 / Accepted: 5 July 2013 / Published: 5 August 2013_  
**Abstract:** In this work, we present an innovative approach for effective cross-network information dissemination, with applications to vehicular _ad hoc_ networks (VANETs). The proposed approach, denoted as \"Cross-Network Effective Traffic Alert Dissemination\" (X-NETAD), leverages on the spontaneous formation of local WiFi (IEEE 802.11b) VANETs, with direct connections between neighboring vehicles, in order to disseminate, very quickly and inexpensively, traffic alerts received from the cellular network. The proposed communication architecture has been implemented on Android smartphones. The obtained experimental results show that an effective cross-network information dissemination service can entirely rely on smartphone-based communications. This paves the way to future Internet architectures, where vehicles will play a key role as information destinations and sources.  
**Keywords:** X-NETAD; cross-network dissemination; vehicular _ad hoc_ networks; Android; smartphone-based communications
## 4 Results and Discussion
### Varying \(C_{\rm corner}\) Coefficient  
The GEEO procedure is based on the \(k\)-\(\omega\) two-equation model employing the classical linear Boussinesq hypothesis (1), which leads to predicting identical Reynolds normal stresses near walls. That makes it difficult to accurately predict flows with high turbulence anisotropy, as it happens for the current aeronautical application featured by three-dimensional corner separation at the wing/fuselage junction subjected to adverse pressure gradient. For the current high incidences with largely separated flows, however, varying the \(C_{\rm corner}\) coefficient was empirically found not to substantially affect the RANS-based predictions, whereas the corner flow correction was demonstrated to play a crucial role at low angles of attack, e.g., [30].