esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[13] used CFD to simulate the aerodynamic properties of close-formation flight for swarms of BWB-model UAVs. The study's findings demonstrated that, with a maximum increase of 25.4% over a single flight, the ideal position for the UAV to produce the maximum lift-to-drag ratio is at \(z/b=0\) and \(y/b=0.75\) (where \(z\) is the vertical distance, \(y\) is the lateral distance, and \(b\) is the wingspan). The study also proposed that the flow field behind the UAV will be more complex if it has the combination of a vertical and a horizontal tail. [PERSON] et al. [14] used the CFD approach to evaluate the aerodynamic properties of a BWB aircraft swarm flight. On the other hand, different analyses were performed on flights that had more than four, five, or seven elements. The study also examined the echelon formation in addition to the V-formation. Specifically, studies utilizing CFD analysis have typically used simpler geometries, such BWB, in their execution. In these investigations, the aircraft speeds are 0.7 Ma and higher. This study examines the aerodynamic effect between swarming UAVs using CFD analysis. In contrast to previous research in the literature, a UAV model including a fixed-wing with tail is used for geometry. It is preferred to adopt a tactical UAV model known as the Medium-Altitude Long-Endurance (MALE) class, which is currently employed in numerous military and civilian industries. When compared to studies in the literature, the speed value used in the analysis Is low because it was determined based on the UAV's model real-flight properties. The goal is to ascertain the close-formation flight performance values in V-formation for these commonly used UAVs. Data validation with analysis on a three-dimensional wing, a single UAV model's analysis and impact area determination, and analysis of two UAVs in different positions make up this study.
## 3 Results and Discussion
### Soil Characterization
Cd input in the environmental is due to different sources, such as vehicular traffic, combustion of fossil fuels and various industrial activities including the production of steel, pigments and batteries. As concerns Pb, this metal is used in manifold activities, e.g., the production of automotive batteries, the manufacture of metal alloys, the working of the crystal, the welding and the production of bullets [33; 34] and, in the past, in the production of paints and enamels, pipes, pesticides and as a gasoline additive for use as an anti-knock agent [35]. The anthropogenic sources of Cu should come from its use in agriculture (pesticides, agronomic use of zootechnical sewage, sewage sludge and amendment) and in industry, whereas the main sources of Zn are the industries (electrolating, foundries, battery production, etc.) and road traffic [36; 37; 38]. However, elements such as As, Cd and Cu, although more concentrated in the topsoil and therefore deserving of monitoring, the presented values below the Italian legal limits. Pb and Zn, instead, showed concentrations well above the limit for public green areas and their presence is probably connected to vehicular traffic, which represents the primary pollution source for the site of interest. \begin{table}
\begin{tabular}{c c c c} \hline  & **CA (Surface Layer)** & **CA (Deep Layer)** & **NOB** \\ \hline Organic carbon (\% w/w) & 1.66 \(\pm\) 0.01 & 0.47 \(\pm\) 0.02 & 2.05 \(\pm\) 0.05 \\ \hline Organic matter (\% w/w) & 2.86 \(\pm\) 0.01 & 0.80 \(\pm\) 0.03 & 3.54 \(\pm\) 0.08 \\ \hline pH & 7.1 & 7.0 & 7.3 \\ \hline \end{tabular}
\end{table} 
Table 2: General characteristics of the Turin central area (CA) and peripheral green area (NOB) soils. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sup A**} & \multicolumn{4}{c}{**Sup B**} & \multicolumn{4}{c}{**Sup C**} & \multicolumn{4}{c}{**Sup D**} & \multicolumn{4}{c}{**Prof A**} & \multicolumn{4}{c}{**Prof B**} & \multicolumn{4}{c}{**Snutical Samples**} & \multicolumn{4}{c}{**Mean Value**} & \multicolumn{4}{c}{**Mean Value**} & \multicolumn{4}{c}{**A**} & \multicolumn{4}{c}{**B**} \\ \hline Al & 44,3  
On the contrary, Co, Cr and Ni concentration is almost the same in surface and deep samples, because their presence in the soils of Turin area is mainly attributable to the lithological substrate of ultramafic rock of which they are the main constituents, as documented by other studies on the soil metal content in the Turin area [39; 40].
## 5 Conclusions  
### Visibility Graphs Are a Helpful Tool to Leverage Time Series with Network Analysis  
This study confirms the usefulness of the proposal by [PERSON] et al. [3] to transform time series into complex networks using visibility graphs. It is a novel way to incorporate the time dimension as an object of study within a complex network. Visibility graphs, as also confirmed by [PERSON] et al. [54], preserve useful information present in the time series onto the resulting complex network.  
Figure 5: Best power law fit using _curve_fit_ with the average of clustering coefficients per degree for (**a**) IOTA VG, (**b**) IoTeX VG, (**c**) IOTA HVG and (**d**) IoTeX HVG.  
IOTA and IoTeX Markets Are Still at Their Infancy in Terms of Development--IoTex Appears to Develop Faster  
The number of data points studied in these networks is reduced. The history of both IoT tokens is still short. The density and transitivity figures obtained in Section 4.1 confirm this point. Similar VGs in Bitcoin, Ethereum and Litecoin [54] display stronger powerlaw fits. Their time series are 5 years long compared to the 4 and 3 year-long time series history for IOTA and IoTeX, respectively. IoTex VG, although one year younger than IOTA, seems to display a slightly better power law fit than IOTA. Figure 4 confirms this point. This could hint a faster path to maturity for IoTeX. In terms of HVG, given that the number of edges is more limited, we do not draw conclusions on maturity based on Figures 3 and 4.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Emulator
#### 3.1.3 Conclusions  
The channel capacity determines the upper bound of the communication system rate, and therefore, the accuracy of the channel estimation determines the communication efficiency of the actual system. Complex VLC channels should be accurately predicted thanks to the widespread use of powerful ML techniques in channel estimation. ML algorithms will guide IVLC to break through its own bottlenecks and complete the comprehensive integration of high-speed communication and large-scale heterogeneous networking to achieve technical solutions for next-generation communication.
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
## 1 Introduction
Inspired by ZCC [29] and MDW [30], this paper proposes a Multi-Weight-Zero Cross-Correlation (MW-ZCC) code for use in WON-OCDMA systems that is capable of carrying simultaneously SHM, including video surveillance data. The paper is organised as follows. Section 2 describes the MW-ZCC code construction, Section 3 the architecture of WON-OCDMA for a sensor system and explains the code detection and design. Section 4 focuses on the main results, and Section 5 presents analytical results; this is followed by Sections 6 and 7.
## 7 Conclusions  
In this work, void hole problem is alleviated using two proactive routing protocols. These routing protocols perform reliable and interference-free routing (using the greedy approach) in IoT-UWSN. Both protocols use sender based approach to minimize the EC and E2E delay. The proposed protocol EP-VIR-Three selects the next forwarder node with a minimum number of neighbors (to provide collision and interference-free communication using greedy forwarding) using 3-hop neighbors information. Meanwhile, the proposed protocol BF-SPR-Three selects the fastest and shortest path using the 'BF' algorithm (to provide reliable data delivery with the binary tree generation approach for greedy forwarding) using 3-hop neighbors information. Additionally, the feasible regions using linear programming are also computed for optimal EC and to improve the network lifetime. Moreover,  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocols** & **Achievements** & **Compromised Parameters** \\ \hline LETR [14] & \begin{tabular}{c} Void hole avoidance and error \\ resilient communication \\ \end{tabular} & Network faces high EC and high E2E delay \\ \hline Hydrocast [26] & PDR is achieved & Network has faced E2E delay \\ \hline Improved Hydrocast [27] & Network faces less E2E delay & EC on fixed nodes deployment \\ \hline Co-Hydrocast [27] & Network faces high PDR & Network faces high EC due to opportunistic cooperative routing \\ \hline Co-Improved Hydrocast [27] & Network faces low E2E delay with high PDR & EC on fixed nodes deployment and on opportunistic cooperative routing \\ \hline GEDAR [23] & Void hole avoidance & High EC during void hole avoidance with affordable E2E delay \\ \hline LMPC [24] & Reliable DPs transmission & High EC due to binary tree generation from the source node \\ \hline EP-VIR-Three & \
\begin{tabular}{c} Void hole avoidance with \\ reliable data delivery \\ \end{tabular} & EC during interference and collision-free path selection with affordable E2E delay \\ \hline BF-SPR-Three &
\begin{tabular}{c} Void hole avoidance and reliable \\ data transmission (using shortest and fast path given by bellman ford algorithm) \\ \end{tabular} & EC due to binary tree generation with affordable E2E delay \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance trade-offs between benchmark and the proposed protocols.  
the data forwarding algorithms for the proposed routing protocols are also presented. The scalability of the proposed routing protocols is also analyzed by varying the number of nodes. In the end, comparative analysis is performed with benchmark routing protocols. It is evident that the EC of the proposed protocols is minimized (25-56% of the state-of-the-art routing protocols) in a dense network. And, outperformed the benchmark routing protocols in counterparts.
## 3 Materials and Methods
### Acoustic Velocity Measurement  
Twenty-seven dry samples were used for acoustic velocity measurement (compressional and shear velocity) using a NER 500 machine. The measurement was carried out at four different confining pressures: 5, 10, 20, and 40 MPa. The NER 500 contains one P-wave transducer and receiver and two orthogonal S-wave transducers and receivers. Confining pressure is built through a connected oil reservoir that can provide pressure up to 100 MPa. A computer and oscilloscope are attached to the NER 500 machine to translate the signal to digital data that can be used to calculate the P- and S-wave velocities (V\({}_{\rm P}\) and V\({}_{\rm S}\), respectively).
## 9 Future Outlook toward 6G-Era
### NTNs Vision and The Forthcoming Energy-Awareness  
Figure 4 illustrates the various stages of 5G network upgrades towards 6G-IoT as per the 3 GPP Release cycles. Anticipating additional technical advancements, the evolution from 5G to 5G-Advanced standards is expected to address the limitations of non-terrestrial networks, particularly in areas with no terrestrial coverage. The ongoing development of 5G technology aims to enhance performance and cater to emerging use cases.  
The inherent versatility of 5G lays a robust foundation for enabling NTNs. Given the complexity of NTNs, especially satellite communication networks, a holistic approach is necessary for their design. Across multiple 3 GPP releases, considerable attention has been dedicated to NTN design [72], signaling a commitment to making 5G from space a reality. Activities beyond standards are deemed essential for integrating non-terrestrial networks with IoT [41]. Subsequent 3 GPP upgrades are poised to unlock possibilities for servicing remote locations.
# Safety and Energy Implications of Setback Control in Operating Rooms during Unoccupied Periods
## 2 Materials and Methods
### Tests Performed  
The tests are performed simultaneously in two ORs by implementing ventilation setback during nonoccupation periods. Occupation is monitored through an occupancy sensor; setback is activated after 20 min of nonoccupation, while regular ventilation is reactivated immediately when occupation is detected. It is checked that activation of the regular ventilation is almost instantaneous, taking only seven seconds from the moment when the OR becomes occupied.  
Ventilation setback consists of reducing the supply airflow while maintaining the required pressure relationships by controlling the return airflow. It is important to notice that increasing the air changes does not necessarily imply less of a risk of microbial contamination [11]. Moreover, [PERSON] et al. [17] demonstrated through particle counting that there is no contaminant source during nonoccupation periods. The ANSI/ASHRAE/ASHE Standard 170:2021 [9] stipulates a time delay of at least 20 min after the OR becomes unoccupied, which corresponds to a reduction in airborne contamination of approximately 90%, providing six air changes per hour (ach) with perfect mixing. Consequently, because the setback is activated after 20 min since the last occupation, the pressure relationship is considered sufficient to ensure the asepsis of the OR, thus justifying the reduction in the air change rate.  
The two tests are performed during the winter period (December and January), one during the midseason (April), and two further tests during the summer period (July and August). During the tests, the measured parameters (Table 1) are registered every five minutes. The tests are performed simultaneously at two ORs.  
As shown in Table 1, the only information that can be obtained through the SCADA regarding the fans' operation is their frequency. To know the actual electric consumption of the fans, measurements are performed with the net analyzer Fluke model 1730 on a minute-by-minute basis.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 2 The Methodology Followed for This Study  
### Research Gap and Scope of the Review  
The current reviews [8; 9] on medical image analysis using deep learning focus primarily on the application of convolutional neural networks (CNNs) to static medical images, such as CT scans, MRI, and X-ray images. These reviews discuss various deep learning architectures, such as U-Net, DenseNet, and ResNet, and their application to medical image analysis tasks such as segmentation, classification, and detection. However, these reviews do not discuss the analysis of spatial and temporal features in medical images. Spatial features refer to the arrangement and distribution of pixels in an image, while temporal features refer to changes that occur over time in a sequence of images, such as in videos or dynamic medical imaging modalities such as ultrasound and endoscopy.  
While CNNs can be applied to temporal medical images, such as in video segmentation and classification tasks, their ability to capture spatiotemporal features is limited. To address this limitation, researchers have developed various deep learning architectures, such as 3D CNNs, that can capture both spatial and temporal features simultaneously. Thus, the current reviews on medical image analysis using deep learning mainly focus on static medical images and do not discuss the analysis of spatial and temporal features.  
The focus of this article is to provide a comprehensive review of the application of deep learning techniques for the analysis of medical diagnostic videos, i.e., ultrasound and endoscopy. The article reviews the deep learning techniques used for extracting spatial and temporal data from these videos and discusses publicly available datasets for medical diagnostic videos. This article also includes a detailed literature review of deep learning techniques applied to each type of medical diagnostic video for classification, segmentation, and detection tasks. Finally, this article discusses the challenges, future implications, and conclusions for deep learning and medical video analysis.
## 3 Results and Discussion
### EMI SE Computational Analysis  
The numerical study of the EMI shielding effect on polyurethane foam was carried out by RF module in COMSOL Multiphysics(r). The high-frequency waves used in this are formulated by [PERSON]'s and [PERSON]'s laws,  
\[\
abla xH=J+\partial D/\partial t \tag{9}\] \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Matrix and Fillers Used**} & \multicolumn{2}{l}{**Dominant Mechanism**} & \multicolumn{2}{l}{**Frequency Range (GHz)**} & \multicolumn{2}{l}{**Thickness (mm)**} & \multicolumn{2}{l}{**EMI SE (dB)**} & \multicolumn{1}{l}{**Ref.**} \\ \hline PU Foam/0.75 wt.\% & & & & & & & \\ MWCNT/1.5 wt.\% & Absorption & 8–12 & 20 & 22.6 & [31] \\ CuO/1.5 wt.\% BC & & & & & & & \\ \hline PU Foam/30 wt.\% Gr & Absorption & 8–12 & 5 & 44 & [54] \\ \hline Epoxy/1 wt.\% & & & & & & \\ MWCNT/5.5 wt.\% BC/ & Absorption & 8–12 & 3 & 24.52 & [55] \\
5.5 wt.\% CuO & & & & & & \\ \hline PU Foam/18.7 wt.\% GO & Absorption & 8–12 & 9 & 30–35 & [56] \\ \hline Epoxy/Graphene & Absorption & 8.2–12.4 & \(0.8\times 10^{-6}\)–1.1 \(\times 10^{-6}\) & 21 & [57] \\ \hline Fe\({}_{3}\)O\({}_{4}\)/GN & Reflection & 8–12 & 0.2–0.25 & 21–24 & [58] \\ \hline Fe\({}_{3}\)O\({}_{4}\)/RGO/PEI & Absorption & 8–12 & 2.5 & 14–18 & [59] \\ \hline MWCNT/PC & Absorption & 8–12 & 1.85 & 25 & [60] \\ \hline RGO films & Reflection & 8–12 & 0.0084 & 18–22 & [61] \\ \hline CVD graphene/PDMS & Absorption & 8–12 & 1 & 20 & [62] \\ \hline PU/0.3 wt.\% GNP/0.3 wt.\% & & & & & \\ ZrO\({}_{2}\)/2.5 wt.\% BC & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of EMI SE based on materials, dominant mechanism, frequency range, and thickness. Figure 8: Optimization curvature of factor for different nanoparticles influencing EMI value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & \multicolumn{2}{l}{**Graphite**} & \multicolumn{2}{l}{**Zirconium**} & \multicolumn{2}{l}{**Bamboo**} & \multirow{2}{*}{**EMI SE FIT**} & \multirow{2}{*}{**Desirability**} \\  & **Nanoplates** & & & & & \\
**(wt.\%)** & & & & & & \\ \hline
**1** & 0.1 & 0.5 & 2.5 & 28.61 & 1 \\ \hline \hline \multirow{2}{*}{**D.100**} & \multirow{2}{*}{
\begin{tabular}{c} High \\ Car \\ Low \\ \end{tabular} } & \multicolumn{2}{l}{**Graphite nanoplates (wt.\%)**} & \multicolumn{2}{l}{**Zirconium Oxide (wt.\%)**} & \multicolumn{2}{l}{**Bamboo Charcoal(wt.\%)**} & \multirow{2}{*}{**Bamboo Charcoal (wt.\%)**} \\  & & 0.50 & 0.50 & 2.50 \\
**Low** & & 0.10 & 0.10 & 0.50 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Optimum wt.% of graphite nanoplates, zirconium oxide and bamboo charcoal. \[\
abla xE=-\partial B/\partial t \tag{10}\]  
where \(J\) is the current density, \(E\) is the electric field intensity, \(D\) is the electric flux density, \(B\) is the magnetic flux density, and \(H\) is the magnetic field intensity; where the governing wave equation for the electric field intensity \(E\) is the electromagnetic waves.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
###### Abstract  
Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.  
radar; Kalman filter; deep learning; multilayer perceptron; convolutional neural network; long short-term memory +
Footnote †: journal: Applied  
_article_  
[PERSON], [PERSON], [PERSON], [PERSON]; [PERSON], W. Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter  
103390/app1488332
## 3 Results Analysis
### Electric and Magnetic Field  
Figures 11 and 12 show the e-field (V/m) and h-field (A/m) distribution of the designed MMA for the normal incident angle \(\theta=0^{\circ}\) of the TE and TM modes, respectively; the absorption mechanism can also be analyzed from this electromagnetic behavior. The electromagnetic property is enhanced and agitated at a different area of the absorber.  
Figure 11: E-field and H-field distributions at TE mode.  
Figure 10: Bending effect of proposed MMA: (**a**) TE mode and (**b**) TM mode.  
The e-field and h-field properties of 380 nm, 521.83 nm, and 750 nm are presented. The metamaterial property also significantly influences the electromagnetic behavior. The relationship between metamaterial properties and electromagnetic behavior can be understood from Equations (9) and (10).  
\[D=\varepsilon_{eff}\varepsilon_{0}E \tag{9}\]  
\[B=\mu_{eff}\mu_{0}H \tag{10}\]  
where \(D\) is the electric flux density, \(B\) is the magnetic flux density, \(\varepsilon_{eff}\) = effective permittivity, \(\varepsilon_{0}\) = permittivity of free space, \(\mu_{eff}\) = effective permeability, \(\mu_{0}\) = permeability of free space, and \(E\) and \(H\) are the electric and magnetic field intensities, respectively. In the TE mode, at a wavelength of 380 nm, the maximum e-field is assimilated in the bounding area of the patch, where a h-field maximum intensity appears in the metal region of the patch. A high e-field intensity is also visible in the nonmetallic area of the dielectric medium, and the high magnetic field appears in the left and right arms of the outer ring. The e-field intensity is reduced at 750 nm compared with 350 nm, which shows the side view of the absorber and causes a lower 92.2% absorption at 750 nm compared with 94% absorption at 350 nm. The peak of 99.99% absorption is achieved at 521.83 nm, the e-field intensity is less than those at 380 and 750 nm, but a high h-field is created in the dielectric substrate, shown in the side view at 521.83 nm wavelength, and leads to an increased absorption peak. Figure 12 shows the electromagnetic behavior of the TM mode, where the field distribution is similar to that of the TE mode, but it rotates vertically to horizontal in the TM mode.  
A comparison with the existing absorbers is presented in Table 3. The proposed SiO\({}_{2}\) and W-based metamaterial absorber achieves 70\({}^{\circ}\) stability for an absorption above 70% with a wider operational bandwidth. An absorption above 92.2% and a peak absorption of  
Figure 12: E-field and H-field distributions at TM mode.  
99.99% are also achieved. These excellent absorption results prioritize the proposed MMA over the listed absorber in Table 3.