esa-sceva/satcom-chunk-collection huggingface dataset:
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## Materials and Methods
### Configuration of Antenna  
The flexible antenna design had two separated ports with a high isolation between them to enable transmitting (Tx) and receiving (Rx) functions (i.e., full-duplex operation). Figure 1 shows the configuration of the proposed wearable antenna, which is similar to the design in our previously published work [23]. The thickness of the textile substrate was 2 mm, which was much thinner than other wearable antennas published in the literature [31; 32; 33; 34; 35]. In comparison to a large majority of patch antenna designs, our antenna design used a strip line placed perpendicularly across the two feeds to enhance the bandwidth and isolation between the two ports. The active and ground conductive layers show thicknesses of around 40 \(\upmu\)m and conductivity of \(1.3\times 10^{6}\) s/m. The dielectric constant of the antenna is 1.78 and dielectric loss is 0.021. Based on this information, the optimized design parameters listed in Figure 1 were determined using an ANSYS High-Frequency Structural Simulator (HFSS). In Figure 1, when Port-1 is excited, the electromagnetic energy couples to the strip near Port-2. This feature not only significantly suppresses the mutual coupling between the two feed ports, but also improves impedance matching of the patch antenna and broadens the bandwidth [23]. The same coupling effect can also be observed on the other strip load when Port-2 is excited, but with the orthogonally aligned surface current distribution. The unidirectional radiation patterns were also similar when either Port-1 or Port-2 was excited. The antenna design has a high isolation of more than 30 dB via the strip load coupling.
## 4 Time-Frequency Analysis for Feature Extraction
### Hilbert-Huang Transform (HHT)
#### 4.4.2 Hilbert Spectral Analysis (HSA)  
The HT is applied to each IMF, producing instantaneous frequency and amplitude over time. For a signal \(x(t)\), the Hilbert Transform is:  
\[\mathcal{H}\{x(t)\}=\frac{1}{\pi}\text{P.V.}\int_{-\infty}^{\infty}\frac{x( \tau)}{t-\tau}d\tau \tag{10}\]  
P.V. indicates the Cauchy principal value. The output is an analytic signal:  
\[z(t)=x(t)+j\mathcal{H}\{x(t)\}=A(t)e^{i\theta(t)} \tag{11}\]  
where \(A(t)=\sqrt{x(t)^{2}+\mathcal{H}\{x(t)\}^{2}}\) is the amplitude, \(\theta(t)=\arctan\Bigl{(}\frac{\mathcal{H}\{x(t)\}}{x(t)}\Bigr{)}\) is the phase, and the frequency is \(f(t)=\frac{1}{2\pi}\frac{d\theta(t)}{dt}\).
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.8. Qlaco  
In the paper, Ref. [52] the author used an ant colony with the aid of Q-learning techniques to address the energy efficiency and link reliability of multi-path routing protocols for underwater wireless sensor networks. Initially, the nodes create a Q-table with values of 0, while the forward ants are released. Each node sends beacons periodically, including their ID, energy, and depth. The source node sends ACKs to all the neighbors. The forwarding ants (FANTs) select the next hop depending on the max Q-values of the node. FANTs collect the information while traveling to the destination node. The sink sets a virtual topology and sets the optimal path. To calculate the Q-value for the path, the authors use the information from the FANTs. The FANTs become the back ants (BANTs) when they return to the source node from the destination node. The source node sends FANTs periodically to find the best possible path to maintain a stable link. For each successful transmission, the agent receives a reward and a demerit point for each unsuccessful transmission. The authors used binary phase-shift keying (BPSK) modulation for their system. The maximum reward is given to a node when its neighboring node is not empty, and the policy is calculated using rewards and penalties. The max Q-values are assigned when the max reward is achieved.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Hello Packet** & **No of Layers** & **Localization** & **Simulation Tool** & **No. of Sink** & **Possible Application** \\ \hline DBR & Not used & Not specified & Not needed & NS2 & Multiple & Monitoring sea \\ LDBR & Not used & Not specified & Not Needed & Not given & Multiple & Surveillance \\ SORP & Control packet & Not specified & Not Needed & NS2 & Multiple & Fish reservoir \\ RSAR & Network & 5 & Not needed & MATLAB & Multiple & Monitoring \\ RD & Successful & Not specified & Not needed & NS3 & Single & Surveillance \\ LETR & Not used & Not specified & Using TOA & Not given & Multiple & Coral life \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of depth-based routing protocol.  
The authors suggested using ACK from the neighbor within a set timeframe to avoid void regions. If the sender does not receive ACK from the next node, the sender reduces the Q-value of that node and updates the Q-table. It also sends the node's identification (ID) number and asks the other neighboring nodes to update their Q-table accordingly. The authors simulated their protocol and compared it against DBR and QLEAR. The result shows it performs better in packet delivery ratio against both QLEAR and DBR. The authors attributed this to the void avoidance function. They also claim that the QLACO has lower latency than QLEAR. As each node calculates its own Q-table, the complexity of the computations is high. The author did not mention the complexity throughout their paper.
# User Acceptance of Smart Watch for Medical Purposes: An Empirical Study
## 5 Findings and Discussion
### Hypotheses Testing Using PLS-SEM
A summary of the hypotheses testing results is shown in Table 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**H** & **Relationship** & **Path** & **\(t\)-Value** & **\(p\)-Value** & **Direction** & **Decision** \\ \hline H1 & SUF-\(>\) PU & 0.515 & 20.349 & 0.002 & Positive & Supported ** \\ \hline H2 & TIM-\(>\) PU & 0.382 & 18.238 & 0.000 & Positive & Supported ** \\ \hline H3 & REL-\(>\) PU & 0.478 & 18.608 & 0.000 & Positive & Supported ** \\ \hline H4 & PER-\(>\) PU & 0.281 & 4.125 & 0.038 & Positive & Supported * \\ \hline H5 & PER-\(>\) PEOU & 0.329 & 16.288 & 0.000 & Positive & Supported ** \\ \hline H6 & PU-\(>\) ASW & 0.668 & 12.433 & 0.004 & Positive & Supported ** \\ \hline H7 & PEOU-\(>\) ASW & 0.420 & 3.275 & 0.043 & Positive & Supported * \\ \hline \hline \end{tabular} Note: ASW, Adoption of SW; PEOU, Perceived Ease of Use; PU, Perceived Usefulness; CONR, Content Richness (REL, Relevance; TIM, Timeliness; SUF, Sufficiency); PER, Personal Innovativeness.  
\end{table} 
Table 8: Hypotheses-testing of the research model (significant at **\(p\) \(\leq\) 0.01, *\(p\) < 0.05). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Constructs** & **R\({}^{2}\)** & **Results** \\ \hline ASW & 0.756 & High \\ \hline PEOU & 0.828 & High \\ \hline PU & 0.773 & High \\ \hline \hline \end{tabular} Note: ASW, Adoption of SW; PEOU, Perceived Ease of Use; PU, Perceived Usefulness.  
\end{table} 
Table 9: R\({}^{2}\) of the endogenous latent variables.
## 2 Stereo Reconstruction Algorithm for Unstructured Scenes
### SAD-FAST Feature Detection and Recombination Stereo-Matching System  
As the most critical step in stereo 3D reconstruction technology, the commonly used algorithms are divided into the following broad categories: matching based on regions, matching based on phases, and matching based on feature points. Region matching uses feature vectors for matching, and it is computationally intensive, inefficient, and prone to mismatching. Phase matching is based on the assumption that the local phases between the corresponding pixels of two corresponding images should be equal and has a low bit error rate, but phase deviation has a huge impact on the matching accuracy. Feature matching is currently the most researched algorithm in the field of stereo matching and is based on special pixel points, such as corner points and edges. It has the advantages of low computation requirements, strong stability, and high real-time efficiency, and the probability of such feature points disappearing over time is very small, so the feature matching algorithm can basically meet the needs of 3D reconstruction projects.  
The algorithm for matching based on feature points can be divided into the following three steps, as shown in Figure 3: feature detection, feature description, and feature matching.  
Figure 3: Flow of feature matching algorithm.  
Figure 2: Steps in 3D scene reconstruction.  
In recent decades, research scholars have not stopped studying feature detection algorithms, and many algorithms with excellent performance have emerged, such as AKAZE, FREAK, and BRISK, which focus on corner point detection; the Harris and features from accelerated segment (FAST) algorithms, which have been the most commonly used in recent years; and SIFT, SURF, and ORB. Considering the uncertainty regarding the strength of the texture information in different regions of unstructured, outdoor scenes, we propose a combination of FAST feature detection + SURF feature description + a FLANN matcher for stereo matching under the general framework of the existing stereo-matching algorithms based on feature points for the sake of both matching accuracy and effectiveness, and we used the Mahalanobis distance instead of the Euclidean distance to determine the matching degree. For the contrast uncertainty affecting illumination and environmental information, an improved SAD-FAST feature detection method is proposed that uses the contrast of the image to adaptively adjust the threshold value during feature point detection and resets the process of determining feature points, solving the problem of too many feature points being adjacent to each other.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 7 Main Progresses, Perspectives, and Prospects
### The Increasing Importance of RS Data in DSM
#### 7.2.2 Incorporation of RS Products as Covariates in DSM
Thanks are expressed to the authors who allowed some figures of their original articles to be reproduced and/or adapted in this article (see references in the legends of Figures 5-9), and to ISRIC-Word Soil Information, Elsevier(r). **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
AVIRIS  
Airborne Visible/Infrared Imaging Spectrometer  
AWC  
Available Water Capacity  
CES  
Scientific Expertise Center  
CHIME  
Copernicus Hyperspectral Imaging Mission for the Environment  
CLAPAS  
CLAssement des Paysages et Segmentation  
clHS  
conditionnal Latin Hypercube Sampling  
CNES  
Centre National d'Etudes Spatiales (French Space Agency)  
CNS  
Cartographie Numerique des Sols (=DSM)  
COP21  
21 st Conference Of the Parties of UN Climate Change Conferences  
DEM  
Digital Elevation Model  
DSA  
Digital Soil Assessment  
DSM  
Digital Soil Mapping  
EnMAP  
Environmental Mapping and Analysis Program  
ESA  
European Space Agency  
ETM+  
Enhanced Thematic Mapper Plus  
EU  
European Union  
GSD  
Ground Sampling Distance  
HSUI  
Hyperspectral Imager SUIte  
INRAE  
Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (France)  
IPCC  
Intergovernmental Panel on Climate Change  
L-MEB  
L-band Microwave Emission of the Biosphere  
LST  
Land Surface Temperature  
MIR  
Middle Infrared  
MIRS  
Middle Infrared Spectroscopy  
MODIS  
Moderate Resolution Imaging Spectroradiometer  
MW  
MicroWave  
NDVI  
Normalized Difference Vegetation Index  
NIR  
Near Infra-Red  
PLSR  
Partial Least Squares Regression  
PRISMA  
Precursoe IperSeptrale della Missione Applicativa  
PTF  
PedoTransfer Function  
RMSH  
Root Mean Surface Height  
RS  
Remote Sensing  
RZSM  
Root Zone Soil Moisture
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## Results
### System Validation in Commercial Farms
At night, ewes were closer to each other, and the number of tag messages included in each collar message was higher. Therefore, the differences between day and night increased when the number of collars was higher. In the beef cattle farm, the number of messages was quite stable over the day. A peak was observed in the morning, when cows left their rest areas and met each other at the feeding and watering areas. Figure 7 represents the evolution of the proportion of collar messages with and without tag data over the day:  
Figure 6: Evolution of the number of messages received hourly from GPS collars (orange lines) and BLE tags (blue lines) over the day: (**a**) messages from the sheep farm with five collars and 25 tags; (**b**) messages from the beef cattle farm with five collars and 25 tags; (**c**) messages from the sheep farm with 15 collars and 25 tags; (**d**) messages from the beef cattle farm with 15 collars and 25 tags; (**e**) messages from the beef cattle farm with 25 collars and 25 tags; and (**f**) messages from the beef cattle farm with 15 collars and 25 tags. In the sheep farm, not only the number of tags included in each collar message was higher during the night, but also the proportion of collar messages without tag data was lower. Few differences over the day were observed in the case of the beef cattle farm.
### Cr-Vanet  
The CR technique can be extended to VANET to utilize unused spectrum bands to improve vehicular communication efficiency. Figure 1 depicts the CR-VANET architecture, which can sense and make decisions across a wide spectrum. The allocation and utilization of the spectrum for networks is a key task to complete. It can be calculated by taking into account several factors, such as unused and underutilized license bands. The suggested work's primary goal is to avoid a large amount of spectrum utilization in a VANET communication scenario. In a CR-VANET, the sequence of actions, such as spectrum sensing, decision, and switch-over, if necessary, is crucial. We worked on a spectrum decision model in this paper to create an efficient spectrum and reduce the number of unwanted handoffs, through which overall end-to-end delay will be minimized. The study is divided into several sections, including related works, DAHP-TOPSIS, a suggested suitable channel decision method, and performance analysis [6; 7].
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 3 Result and Discussion
### Vegetation Classification by Combining UAV-Based NDVI and Thermal Infrared Image
Figure 18 shows the vegetation area classified using only NDVI for the basketball court and the waterproof coating roof, along with the verification points. When applying only NDVI, it was confirmed that the basketball court and the waterproof coating roof coated with urethane were misclassified as vegetation. Figure 19 shows the vegetation area classified by combining NDVI and thermal infrared images for the basketball court and the waterproof coating roof, along with the verification points. When vegetation was classified by combining NDVI and thermal infrared images, it was confirmed that basketball courts and waterproof coating roof were well classified as non-vegetation. Figure 17: Validation points selected at 5 m intervals. Figure 18: Vegetation map classified by applying only NDVI around the basketball courts and the waterproof coating roofs. In order to compare the vegetation map classified by using only NDVI and the vegetation map classified by combining the NDVI and thermal infrared images, kappa coefficients were analyzed as shown in Table 9. As a result of the analysis, the kappa coefficient analyzed by applying only the NDVI was 0.830, and the kappa coefficient analyzed by combining the NDVI and the thermal image was 0.934. Therefore, it was found that it was more effective to classify vegetation by combining NDVI and thermal image compared to classify vegetation by applying only NDVI in urban areas where urethane coated coverings are distributed.
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 4 Results
### Objective Experiment  
In order to comprehensively analyze the image quality enhancement achieved by different algorithms, this study has chosen to employ three image quality evaluation metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Natural Image Quality Evaluator (NIQE).  
Figure 7: Results of subjective experiment. (**a**) Comparison of enhancement results on seasoning bottle images; (**b**) Comparison of enhanced results for wardrobe images; (**c**) Comparison of enhanced results for sports arena images.  
PSNR quantifies the level of distortion between the original and reconstructed images by calculating the ratio of peak signal power to mean squared error. A higher PSNR value indicates lower distortion in the enhanced image, signifying superior algorithmic enhancement effectiveness and greater proximity to the reference image. While it excels in measuring pixel-level discrepancies in image comparison, its ability to assess image structural information is limited. To overcome the limitations of PSNR, SSIM has been referenced. This metric evaluates the reconstructed image from three aspects: luminance, contrast, and structural information. It measures the degree of similarity between the enhanced image and the reference image under normal lighting conditions, thereby demonstrating the algorithm's performance. Generally, higher SSIM values indicate greater similarity to the reference image and improved enhancement outcomes. In tasks involving image decomposition and reconstruction, instances arise where some images exhibit higher PSNR or SSIM values but possess overall quality deviations. This discrepancy is due to the fact that images with high PSNR or SSIM values may not necessarily conform to human visual perception of texture details. To address this, the NIQE image quality assessment metric was also employed. NIQE considers factors such as noise and distortion, simulating the relationship between image quality features and perceived distortion to emulate the human visual system's perception of image quality. Smaller output values from NIQE indicate higher quality in reconstructed images.  
Objective evaluation results of various algorithms on the low-light dataset are presented in Table 1. From the table, it is evident that the algorithm proposed in this paper achieves the best results across all three metrics.
_Appl. Sci._**2012**, \(2\), 396-442; doi:10.3390/app2020396  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Ion Beam Formation and Modification of Cobalt Nanoparticles**  
**David J. Sprouster * and Mark C. Ridgway**  
Department of Electronic Materials Engineering, Research School of Physics and Engineering,  
Australian National University, Canberra, ACT 0200, Australia; E-Mail: mcr109@physics.anu.edu.au  
* Author to whom correspondence should be addressed; E-Mail: djs109@physics.anu.edu.au;  
Tel.: +61-2-612-51056; Fax: +61-2-612-50511.  
_Received: 7 March 2012; in revised form: 9 April 2012 / Accepted: 20 April 2012 / Published: 27 April 2012_  
**Abstract:** This article reviews the size-dependent structural properties of ion beam synthesized Co nanoparticles (NPs) and the influence of ion irradiation on the size, shape, phase and structure. The evolution of the aforementioned properties were determined using complementary laboratory- and advanced synchrotron-based techniques, including cross-sectional transmission electron microscopy, small-angle X-ray scattering and X-ray absorption spectroscopy. Combining such techniques reveals a rich array of transformations particular to Co NPs. This methodology highlights the effectiveness of ion implantation and ion irradiation procedures as a means of fine tuning NP properties to best suit specific technological applications. Furthermore, our results facilitate a better understanding and aid in identifying the underlying physics particular to this potentially technologically important class of nanomaterials.  
**Keywords:** cobalt; ion irradiation; phase transformation; EXAFS; SAXS; XTEM
## 5 Conclusions  
The objective of the study was to explore the effect of a bird strike event, which may cause a blade-off condition or blade loss, on the critical frequency behavior of an engine rotor. The blade-off condition due to deformation induced as a result of a bird strike introduces unbalanced forces and non-linearities into the engine rotor system.  
An aspect of the study was to model the rotor blades of the engine in ANSYS. The blade geometry impacts the dynamic behavior of the rotor significantly, therefore, it was important to capture as many geometric details of the engine blades as possible. Because of the introduction of cross section deformabilities and the predominance of centrifugal stiffening, one-dimensional models are no longer valid. Therefore, a 3D element formulation is adopted in the analyses. The best suited element type is the 3D SOLID 185 element with eight nodes. This element not only helps in capturing all of the geometric details of the engine rotor, but also helps to get accurate results at a low computational power.  
In the critical frequency simulation of the engine rotor after the bird strike event, the critical modes are still conical and bending modes. However, a change in the critical frequencies of the modes is observed. An increase in the critical frequencies and excitation RPMs of each mode is observed. As the mode order increases, a greater rise in the critical frequency and excitation RPMs is observed. Also, a change in the whirl direction of the different modes is noted. A change in frequency and excitation RPM of the conical mode is not very noticeable. However, there was a significant change in the critical frequency and excitation RPM of the bending mode. Therefore, in the engine design phase, it is necessary that the engine must avoid working on a critical RPM as a result of the blade-off condition generated by the bird strike. This study only covers the blade-off condition, which means that the blade is deformed because of the bird strike making the engine rotor asymmetric; therefore, a blade loss condition may produce different results.  
Conceptualization, S.B., A.N. and A.F.R.; Formal analysis, A.N.; Funding acquisition, A.F.R.; Investigation, A.N.; Methodology, I.U.H. and S.A.M.; Project administration, S.B.; Resources, A.F.R., I.U.H. and S.A.M.; Software, A.N. and A.F.R.; Supervision, S.B.; Writing--original draft, A.N.; Writing--review & editing, S.B., A.F.R., I.U.H. and S.A.M.  
Figure 22: Campbell diagram after the bird strike.  
**Funding:** This work and APC was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (DF-804-135-1441).  
**Acknowledgments:** This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (DF-804-135-1441). The authors, therefore, gratefully acknowledge DSR technical and financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.