esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 5 Simulation Results
### Comprehensive Comparison of Estimation Quality
This is because the  
Figure 4: NMSE of the estimated CSI versus received pilot symbols with sparseness \(L=5\), received \(SNR=10\) dB, angle quantization \(G=32\) and without hardware impairment. Figure 3: NMSE of the estimated CSI versus sparseness with received \(SNR=10\) dB, pilot symbol frames \(M=16\), angle quantization \(G=32\) and without hardware impairment. transmitter's hardware impairment can be regarded as the extra noise and it is the main component of noise at the high SNR range. It is worth mentioning that the hardware impairments will increase with the increase of transmission power, which means simply increasing the transmission power cannot avoid the hardware impairments. Therefore, estimation and compensation are necessary for a practical mmWave massive MIMO system to combat them. In Figure 7, we illustrate the success probability of channel estimation versus the number of antennas at the transmitter. Without loss of generality, we set \(2N_{RF}^{T}=N_{T}\), \(G_{T}=2N_{T}\) and \(\gamma_{th}=-10\) dB, which indicates the number of RF chains \(N_{RF}\) and the uniform grid of angle  
Figure 5: NMSE of the estimated CSI versus received angle quantization (\(G\)) with received \(SNR=10\) dB, sparseness \(L=5\), pilot symbol frames \(M=40\) and without hardware impairment. Figure 6: NMSE of the estimated CSI versus received SNR with hardware impairment = 13 dB, sparseness \(L=5\), angle quantization \(G=32\) and pilot symbol frames \(M=40\). increase when the antennas as transmitter since, and the channel estimation is successful when the estimated error is less than \(-10\) dB. We can observe that the success probability increases and then remains unchanged as the number of antennas increases for OMP algorithm because the angle quantization error at transmitter limits the success probability when the number of antennas is small, but the angle quantization error at receiver is the decisive factor when the antennas of transmitter is large enough. The same phenomenon can be observed in the BCS algorithm. However, different from the OMP algorithm, the number of available pilot symbols is the main factor that limits the success probability of BCS algorithm. As for BP algorithm, the number of antennas has little effect on the success probability, since the NMSE of BP algorithm is mainly affected by SNR at the receiver. Based on above simulation results and analysis, a comprehensive comparison of different algorithms can be formed. Specifically, the convex relaxation algorithms are mainly affected by received SNR and transmitter's hardware impairments, while the angle quantization error is the key factor that limits the performance of greedy iteration algorithms and all referred factors have an impact on Bayesian inference algorithms.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 2 Health Information Exchange  
HIE is a healthcare delivery approach that enables the sharing of important patient information among different healthcare systems. In today's ever-changing digital health landscape, HIE is crucial for enhancing the coordination and continuity of care. By leveraging electronic data exchange, HIE enables the secure transmission of patient health records, diagnostic test results, medication histories, and other relevant information among healthcare organizations [18]. This interconnected system empowers healthcare providers with comprehensive insights into a patient's medical history, engendering more informed clinical decisions, better treatment outcomes, and increased overall efficiency in healthcare service delivery. As HIE continues to evolve, it plays a vital role in advancing patient-centered care, reducing redundancies, and realizing a more interconnected and efficient healthcare ecosystem, thereby improving patient health outcomes [19; 20].  
The integration of HIE with cloud technology represents a significant advancement in the accessibility, scalability, and efficiency of healthcare information management [21]. Utilizing cloud infrastructures in HIE, healthcare organizations, providers, and patients can benefit from increased accessibility, enhanced scalability, and improved efficiency. The introduction of cloud technology in HIE presents many opportunities for healthcare stakeholders to better manage and deliver healthcare services [22; 23].  
Cloud-based HIE allows healthcare professionals to access patient information securely from anywhere online, facilitating timely decision making, especially in emergencies [24]. Due to its scalability, cloud technology enables healthcare organizations to adjust HIE systems on demand, accommodating variations in data volume and user requirements [25]. The use of this cost-effective alternative to traditional methods of record storage and sharing can help to evolve healthcare. Cloud service providers ensure the security of patient health information via encryption, access controls, and regular security audits of the HIE sphere. The use of cloud-based HIE promotes collaboration and interoperability across healthcare organizations via standardized data formats and communication protocols [26]. Hence,the use of HIE and cloud technology promises to create a more connected and efficient healthcare ecosystem [27].
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts
### Machine Learning
Convolutional neural networks and deep belief networks are two examples of analytical deep learning models that provide computational intelligence solutions by studying sizable datasets in circumstances when shallow learning is unable to investigate the necessary meaningfulness of trends. These learning models are frequently used in precision medicine for disease diagnosis and therapeutic development processes [33]. The ML algorithms used to anticipate and classify health data exhibit an analytical patient and provide results that are generally acceptable. AI and edge computing integration in smart healthcare enhances latency, efficiency, and tailored healthcare delivery, promoting a responsive, dependable, and effective system. They are regarded as a key enabling technology in this SLR due to the frequent use of categorization and prediction models in smart healthcare applications.
## 3 Experiments and Results  
In this work, binary and four-category classification experiments were designed at the individual and group levels. We randomly selected 10 subjects from PhysioNet, who were denoted as Sbj1-Sbj10 in the experiments, and used 67% of signals of each subject as the training set and the other 33% as the testing set, respectively.
## 4 Case Study--Numerical Experiments
### Inter-Facility Material Transfer Process
#### 4.1.4 Sensitivity Analysis  
Sensitivity analysis is a valuable tool used in decision-making processes to evaluate the influence of various factors on the outcomes of a system or model. Sensitivity analysis provides insights into the robustness and stability of decision-making frameworks by systematically varying these factors and observing the resulting changes in the outputs. In this section, we employ sensitivity analysis techniques to investigate the impact of critical factors on our decision-making model. Through this analysis, we aim to identify the most influential factors, understand their effects on decision outcomes, and enhance the reliability and effectiveness of our decision-making processes.  
In this sensitivity analysis section, we examine the variations in utilisation rates, throughput, and buffer allocations resulting from introducing an additional server to node 6 in scenarios 1 and 3. We aim to assess the impact on the system's performance by introducing this additional server. The results of this analysis are presented in Table 6, which provides insights into the changes observed in utilisation rates, throughput, and buffer allocations under these specific conditions. Through this examination, we gain a deeper understanding of how the system responds to introducing an extra server and its implications for overall system performance in scenarios 1 and 3.  
Table 6 unveils intriguing insights regarding the factors influenced by the introduction of an additional server in node 6. In scenario 1, the results demonstrate a slight increase in throughput, reaching 3126 tons, representing a modest improvement of approximately 0.8% compared to the original case. Conversely, scenario 3 exhibits a more substantial boost in throughput, surging to 4199 tons with the inclusion of a dual server in node 6, signifying a significant increase of approximately 14% from the baseline. Furthermore, both scenarios showcase a reduction in the total number of servers utilised and a decrease in the utilisation rate of node 6. These findings emphasise the interconnected nature of design factors within the system, whereby modifications in certain factors influence other performance metrics to varied extents. Consequently, this analysis provides valuable insights for informed decision  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario \#**} & \multicolumn{2}{c}{**Node Utilisation Rate**} & \multicolumn{2}{c}{**Throughput**} & \multicolumn{2}{c}{**Total \# of Buffers**} \\ \cline{2-7}  & **1 Server** & **2 Servers** & **1 Server** & **2 Servers** & **1 Server** & **2 Servers** \\ \hline Scenario 1 & 84\% & 42\% & 3101 & 3126 & 22 & 10 \\ \hline Scenario 3 & 99\% & 67\% & 3677 & 4199 & 24 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of adding an extra server in node 6.  
making when designing system topologies, enabling practitioners to consider the interplay of factors and their implications on system performance.
# Dual-Band Shared-Aperture Antenna with Pattern and Polarization Diversity
###### Abstract  
This paper proposes a dual-band co-aperture antenna, which covers 2.09-11.61 GHz (9.52 GHz, 138.9%) and 21.6-29.6 GHz (8 GHz, 31%). The overall size of the proposed antenna is 0.76 \(\times\) 0.76 \(\times\) 0.37 \(\lambda_{0}\)\({}^{3}\), where \(\lambda_{0}\) is the free space wavelength of the lowest operating frequency of 2.09 GHz. By removing the internal metal of the monopole antenna and adding a Vivaldi antenna at the bottom of the bowl monopole antenna, dual-band radiation characteristics can be achieved. A medium polarization converter is used to achieve circular polarization and improve the broadside gain in the high-frequency band. The simulation results show that the antenna has a stable omnidirectional radiation pattern in the low-frequency band and a directional radiation pattern in the high-frequency band.  
Keywords:share-aperture antenna; dual-band; circular polarization; high gain +
Footnote †: journal: Applied sciences
## 3 Results  
### Descriptive Statistics for Soil Samples  
The plough and compacted layer depths of 132 sampling points are shown in Table 2 and Figure S3. The average depths of the plough layer and compacted layer were 16.29 cm (5.00-27.50 cm) and 7.39 cm (2.50-20.00 cm), respectively. The third quantiles of the two layers were 20.00 and 10.00 cm, indicating the depths of almost all sampling sites belonged to the intervals. Hence, we determined the plough layer as the 0-20 cm depth and the compacted layer as the 20-30 cm depth, respectively. In the plough layer, SOC varied from 2.78 to 48.65 g kg\({}^{-1}\), with the average and median SOC at 16.33 and 14.93 g kg\({}^{-1}\), respectively. In the compacted layer, SOC varied from 0.96 to 36.40 g kg\({}^{-1}\), with the average and median SOC at 13.21 and 12.09 g kg\({}^{-1}\), respectively (Figure 2 and Table 2). The skewness values were 1.12 and 0.72 in two layers, indicating a slightly positive skewness distribution. The kurtosis of 4.56 and 3.19 showed that the data was lightly tailed. There were high variations in two layers, with the %CV of 53.88 and 52.91 % and the SD of 8.80 and 6.99 g kg\({}^{-1}\)[61]. As a result of the Kolmogorov-Smirnov tests, SOC in two layers slightly deviated from normality [62].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Layer** & **Min** & **1 st Qu** & **Median** & **Average** & **3 rd Qu** & **Max** & **SD** & **\%CV** & **Skewness** & **Kurtosis** \\ \hline SOC PL & 2.78 & 9.95 & 14.93 & 16.33 & 20.76 & 48.65 & 8.80 & 53.88 & 1.12 & 4.56 \\ SOC CL & 0.96 & 7.75 & 12.09 & 13.21 & 17.41 & 36.40 & 6.99 & 52.91 & 0.72 & 3.19 \\ \hline \end{tabular}  
\begin{tabular}{c c c c c c c c c} \hline
**Layer** & **Min** & **1 st Qu** & **Median** & **Average** & **3 rd Qu** & **Max** & **SD** & **\%CV** & **Skewness** & **Kurtosis** \\ \hline SOC PL & 2.78 & 9.95 & 14.93 & 16.33 & 20.76 & 48.65 & 8.80 & 53.88 & 1.12 & 4.56 \\ SOC CL & 0.96 & 7.75 & 12.09 & 13.21 & 17.41 & 36.40 & 6.99 & 52.91 & 0.72 & 3.19 \\ \hline \end{tabular}  
\end{table}
Table 2: Descriptive statistics for SOC content of 132 sampling points in Northeast Plain (g kg\({}^{-1}\)).
# An Asymmetric Bargaining Model for Natural-Gas Distribution
## 5 Results and Discussion  
Figure 3 shows the results of the Nash bargaining solution. The Nash bargaining is applied for all three cases from 2020-2028. In the first case, equal bargaining weights are assigned to each province. Whereas in the second case, the parameter of gas production is used to assign the bargaining weights. In the last case, the number of consumers (domestic, commercial, and industrial) are used to assign the bargaining weights for each province. Figure 3 shows the gas distribution among each province as a percentage of gas demand \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Provinces**} \\ \cline{2-6}  & **Punjab** & **Sindh** & **Baluchistan** & **KPK** & **Other Areas** & **Total** \\ \hline Percentage share in & & & & & \\ total production & 4 & 62.6 & 21.4 & 11 & 1 & 100 \\
**Bargaining weight** & **0.04** & **0.626** & **0.214** & **0.11** & **0.01** & **1.00** \\ Number of consumers & & & & & \\ (domestic, commercial and industrial), in millions & & & & & \\
**Bargaining weight** & **0.588** & **0.274** & **0.028** & **0.088** & **0.022** & **1.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Production of gas in provinces of Pakistan, number of consumers, and their bargaining weights. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year 2020** & **Year 2021** & **Year 2022** \\ \hline \(x(Pun)+x\) (\(Sin)+x(Bal)+x(Pan)+x(Sun)+x(San)+x(Pan)+x(Pun)+x(Sun)+x(Bal)+x(Pun)+x(Bin)+x(Bin)+x(Pun)+x(Bin)+x(  
under all three cases for the years 2020-2028 using the Nash bargaining solution. Table 5a-c show the results in tabular form. It can be seen from Figure 3 and Table 5a-c that when the gas allocation is done using the homogenous weights, the allocation of Punjab and Sindh is reduced, whereas the smaller provinces/administrative units get 100% of their claims. It is due to the reason that the claims of these provinces are already less. In the second case, when the allocation of gas is done using bargaining weights 2 (based on the gas production), the allocation of Punjab is considerably reduced, whereas the allocations for Sindh and Baluchistan are considerably increased for all the years. It is because these two provinces have the country's largest gas reserves, which is also evident in Table 4. Finally, in the third case, when the gas allocation is done using the heterogeneous weights 1 (based on the number of gas consumers), the allocations of Punjab and Sindh are increased. It is due to the reason that both these two provinces have the largest number of consumers. On the other hand, other provinces have considerably fewer consumers than Punjab and Sindh; hence, their allocation is reduced. Figure 3: Gas allocation as a percentage of demand using Nash bargaining solution under equal and bargaining weights (for years 2020–2028).
## 3 Results  
### Parametric Analysis of Hybrid ANN-PSO on Telemedicine Queuing System  
This sub-section focuses on the parametric analysis of the hybrid ANN-PSO model, designed for optimization purposes. The input parameters for the ANN-PSO hybrid model are the same 100 scenarios used in the ANN analysis. Each ANN-PSO model developed within this framework generates a single output variable.  
The dataset was divided into two sets, with 90% allocated for training the configured network and the remaining 10% used for testing. MATLAB was employed to conduct parametric analysis of the hybrid ANN-PSO model.  
Optimization Trials and Performance Evaluation  
Table 2 presents the selected values for acceleration factors (C\({}_{1}\) and C\({}_{2}\)), swarm population size, and the number of neurons, which were systematically varied to generate distinct ANN-PSO model configurations, as determined by the researcher [PERSON] [16].  
For each test, the three parameters were adjusted while the ANN-PSO algorithm was loaded into MATLAB. To determine the optimal combination of PSO and ANN parameters, a total of 36 trials were conducted, with each test running for a maximum of 1,000 iterations.  
Figure 2: ANN-PSO process flowchart.  
The training and prediction performance of each configured network was assessed using the MSE and the R\({}^{2}\) to validate the effectiveness of each ANN-PSO model configuration.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 2 Related Works
### Learning-Based Methods  
Deep learning has witnessed significant success in various computer vision applications. LLNet [21] stands out as the first deep learning-based image enhancement network, utilizing a Stacked Sparse Denoising Autoencoder (SSDA) [22] variation to simultaneously reduce noise and enhance brightness in low-light images. This groundbreaking work marked the onset of the deep learning wave in image enhancement. Moreover, EEMEFN [23] breaks down image enhancement into two stages: The first stage employs a multiple-exposure fusion module to generate an initial enhanced image with strong contrast and minimal color bias from low-light images. In the second stage, an edge enhancement module is employed to obtain a final well-exposed image with improved edge and structural details.  
These deep learning-based methods generally outperform traditional methods in terms of enhancement effectiveness and robustness. However, their performance is notably influenced by the availability of paired datasets. Synthetic paired datasets often fall short of accurately depicting the degradation problem encountered in real low-light scenarios. Collecting real paired data are constrained by high costs and limitations related to the diversity of scene types. Therefore, EnlightenGAN [24] takes a different approach by leveraging unpaired low-/normal-light image data for training. While this method alleviates some of the data constraints, it still necessitates a careful selection of unpaired image pairs.  
Moreover, unsupervised methods, not relying on any paired data, depend on hand-crafted priors to guide network training. RRDNet [16] decomposes low-light images into illumination, reflection, and noise. It then generates a new reflection component by removing the noise and a new illumination component through the application of a gamma function. As for PairLIE [17], it considers two low-light images as inputs and performs luminance enhancement and denoising based on the invariance of the reflection component and the randomization of noise. Furthermore, RUAS [18] enhances images by modeling and optimizing the reflection component in low-light images, while SCI [19] establishes a cascaded illumination learning process with weight sharing to handle this task. Finally, Zero-DCE [20] successfully enhanced low-light images by applying carefully designed high-order curves and loss functions.  
While these methods achieve impressive enhancement results, they often leave behind some undesirable issues, including high-level noise, local overexposure, and low color saturation in the image after enhancement. In contrast to previous research, our method is rooted in unsupervised techniques and introduces tailored solutions to address the aforementioned problems.
## 3 Results
### Footprint Estimation for Sentinel-2  
Sentinel-2 overpassed our study area at 10:56 UTC, therefore, we have selected the average of the footprint (x_peak according to Eddypro software) of all days at 11.00 UTC (110 m) calculated by the EddyPro 7.0.9 software (Li-Cor, Inc., Lincoln, NE, USA) from January 2018 to July 2020. A circle of 110 m was built centered in the tower. In addition, the histogram of wind directions at 11.00 UTC was built from January 2018 to August 2020 to consider the main directions of the wind at this time (Figure 9). The wind rose during the study period at 11.00 UTC as shown in Figure 10. As can be observed, the main wind direction is from the southeast. Finally, the pixels inside the green area, which was delimited by the main wind directions, were selected for calculating the spatial average of NDVI (Figure 11).  
Figure 8: Values for model the Gross Primary Production MODIS multiple linear regression model (GPP_M2) versus the tower Gross Primary Production (GPP_T) for the year 2020 (January to July) in the Mediterranean grassland of La Herreria (\(n=25\), R\({}^{2}=0.75\), ANOVA \(p\)-value = 0.00).  
Figure 9: Histogram of wind directions at 11.00 UTC from January 2018 to July 2020 in the Mediterranean grassland of La Herreria.  
Figure 11: The pixels selected for calculating the spatial average of NDVI (inside the green area, which was delimited by the main wind direction in the Mediterranean grassland of La Herreria. A circle of 110 m was drawn centered in the tower.  
Figure 10: The wind rose for the period using the wind values at 11.00 UTC from January 2018 to July 2020 in the Mediterranean grassland of La Herreria.
## 4 Conclusions  
A high performance sunlight photocatalyst of the type Au/TiO\({}_{2}\) nanocomposite has been successfully synthesized by the incorporation of gold nanoparticles (AuNPs) onto the TiO\({}_{2}\) surface using the plasma-liquid interaction method. This method is proven to be efficient and environmentally clean in terms of minimizing processing time and the usage of treating agents. There are two types of AuNPs confined in the treated Au/TiO\({}_{2}\) samples: the small size AuNPs, with the size of about 2 nm, may grow directly on the TiO\({}_{2}\) surface, while the larger size AuNPs with the particle size distributed around about 40 nm grow in the solution before being attached onto the TiO\({}_{2}\) surface.  
The plasma enhanced surface activation has brought a potentially strong improvement of the grafting of AuNPs onto the TiO\({}_{2}\) surface. The contact between the AuNPs and TiO\({}_{2}\) in the treated Au/TiO\({}_{2}\) sample is tight which results in a very strong red shift of the plasmonic resonant peak. The sunlight degradation rate of MB with treated Au/TiO\({}_{2}\) has increased 3.25 times in comparison to that of the non-treated Au/TiO\({}_{2}\) and even by the factor of 13 in comparison to that of the bare rutile TiO\({}_{2}\).  
**Author Contributions:** Conceptualization, J.H.K., S.I.K. and D.H.T.; investigation, N.T.T.T., L.H.M. and P.H.M.; writing--original draft preparation, N.T.T.T. and D.H.T.; writing--review and editing, N.T.T.T., N.T.H., J.H.K., S.I.K. and D.H.T.; supervision, D.H.T.; project administration, P.H.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the International Center of Physics under the auspices of UNESCO and project number QTRU01.01/19-20, and Russian Foundation for Basic Research (grant # 19-52-54003 Viet_a).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Kinetics of Methylene blue (MB) degradation (**a**) in the presence of bare TiO\({}_{2}\) and Au/TiO\({}_{2}\) nanocomposite in sunlight and decolorization of MB with Au/TiO\({}_{2}\) (5 min plasma treated) under sunlight radiation (**b**).