esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 5 Handover and Load Balancing in HWLNets
### Load Balancing
In this method, the user's trajectory is known in order to make a calculation, which is almost impossible in real word scenarios. Another study [43] used dynamic iterative load balancing with AP and resource allocation, thus improving data rates and minimizing handover overheads. Using cell dwell time, a method is formed to measure handover costs [74] without channel state information, which is best for varying channels. Modification of this design was proposed in [66] with the addition of blockages, thus reducing its obstructive influence. Fuzzy logic can be used to balance traffic loads in a hybrid network [65]. This decision-making system can reduce the complexity of the above-mentioned systems relying only on velocity of movement, SINR, and data rates. This comprehensive system can be additionally upgraded with the fuzzy logic method for solving unbalanced traffic loads [53] imposed by light path blockages. Both of these algorithms use predefined rules, thus lacking real-life elasticity, whereas machine learning can provide a better model for real-time applications [75]. This method can achieve better results than all of the above-mentioned iterative algorithms.
## 3 Result Analysis and Discussion
### System Complexity
The transmitter complexity in conventional Flip F-OFDM remains high due to the large number of subcarriers and the necessity to conduct a full IFFT on each subcarrier. Table 3 provides a comparative example of the computational complications of the proposed Flip-F-OFDM, conventional Flip-F-OFDM, HSF Flip-OFDM, Flip-OFDM, ACO-OFDM, JM-DCO-F-OFDM, and DCO-OFDM methods, based on a transmitted signal length of 128. According to Table 3, the proposed Flip-F-OFDM technique attains a significant reduction of \(\approx\)66.5% in multiplication processes and around 57%  
Figure 9: Flip\(-\)F\(-\)OFDM multiplication complexity. both the transmission and reception sides, compared to the classical Flip-F-OFDM and JM-DCO-F-OFDM methods. This illustrates the substantial computational complexity savings provided by the proposed Flip-F-OFDM approach. However, the proposed Flip-F-OFDM procedure has more multiplications than the HSF Flip-OFDM, Flip-OFDM, ACO-OFDM, and DCO-OFDM procedures. This increase in multiplications is attributed to the additional complexity introduced by the filter. Consequently, the computational complexity of the proposed Flip-F-OFDM relative to these techniques depends on the filter size used.
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 3 Results and Discussion
### Analysis of Deterministic Prediction Results  
Deterministic prediction values can be obtained by using linear output in the output layer of ForecastNet. The RMSE and MAE metric values are calculated separately for the ForecastNet model and the other three prediction models: support vector regression (SVR), K-nearest neighbor (KNN), and light gradient-boosting machine (LightGBM). Tables 1 and 2 show the RMSE and MAE metrics for selecting the top 1-5 proximate wind turbine prediction results with the highest power outputs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Models** & **Top = 1** & **Top = 2** & **Top = 3** & **Top = 4** & **Top = 5** \\ \hline ForecastNet & 55.930 & 59.595 & 62.336 & 53.447 & 65.332 \\ SVR & 114.660 & 109.169 & 120.044 & 100.730 & 106.931 \\ KNN & 121.807 & 120.008 & 137.426 & 106.321 & 121.102 \\ LightGBM & 82.583 & 95.353 & 106.340 & 92.396 & 96.301 \\ \hline \end{tabular}
\end{table}
Table 1: RMSE metrics for wind turbine prediction results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Models** & **Wind Turbine 1** & **Wind Turbine 2** & **Wind Turbine 3** & **Wind Turbine 4** & **Wind Turbine 5** \\ \hline ForecastNet & 48.828 & 43.145 & 53.664 & 44.771 & 62.602 \\ SVR & 96.914 & 93.318 & 103.126 & 92.814 & 110.663 \\ KNN & 105.121 & 100.702 & 112.003 & 113.630 & 129.361 \\ LightGBM & 69.743 & 73.088 & 80.355 & 75.230 & 90.505 \\ \hline \end{tabular}
\end{table}
Table 2: MAE metrics for wind turbine prediction results.  
Figure 11 demonstrates the trend in RMSE evaluation metrics for selecting different numbers of wind turbines with the highest power output (THPO) in the multi-step prediction process.  
From Figure 11, it can be concluded that ForecastNet obtains similar error distributions when predicting proximity wind turbines. It indicates that the ForecastNet model achieves relative feature aggregation in dealing with the proximity of wind turbines, obtaining similar error outputs. The RMSE within the short-term prediction is controlled to be less than 60, and its prediction trend is relatively good and stable. However, after 200 predictions, a large error fluctuation occurs, and then the error fluctuation tends to be more stable. The reason for this may be that the wind speed and other characteristics have fluctuated more during this prediction. Overall, most of the prediction steps have good error performance, and from the data in the Figure 11 and Tables 1 and 2, it can be seen that better prediction results can be achieved by selecting the four wind turbines with the highest power outputs for prediction.
# A Survey of Advances in Landscape Analysis for Optimisation
## 4 Applications of Landscape Analysis
### Understanding and Explaining Algorithm Behaviour  
The introduction of new metaheuristic algorithms is commonly justified on the basis of competitive experimental results on a limited set of benchmark problems. One of the problems with this approach is that these studies provide no scientific understanding as to _why_ the algorithm performs well on the given problems, and more importantly, no understanding of when the algorithm will perform poorly.  
Landscape analysis of optimisation problems provides a mechanism for explaining algorithm behaviour and identifying classes of problems that are suited to particular algorithms. Studies that describe the use of landscape analysis for understanding algorithm behaviour include: explaining evolutionary algorithm behaviour in dynamic environments [8] and the dynamics in coevolutionary games [12]; understanding the behaviour of local search algorithms [105; 106]; explaining performance differences between search-and-score algorithms for learning Bayesian network structures [107]; explaining the effect of different mutation operators [108] and different function sets [109] in genetic programming; explaining the performance of different real-valued evolutionary algorithms [87]; explaining the performance of multiobjective evolutionary algorithms [52; 110]; understanding evolvability in grammatical evolution [111]; understanding the effect of funnels in the landscape on metaheuristic performance [112]; and explaining the performance of evolutionary algorithms in generating unit tests for software [93].  
The list above shows that landscape analysis has mostly been used to understand different evolutionary algorithms, but there are examples where other types of algorithms have been studied, such as local search algorithms and search-and-score algorithms.
# Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns
## 1 Introduction
The University of Colorado carried out an analysis and a simulation of relevant interference parameters and soil moisture, and obtained real-time results of soil moisture estimation based on GPS SNR data, and published the results on the relevant websites of PBO GPS station network data [23]. At the same time, with the development of other GNSS systems and UAV remote sensing platforms, there has been some UAV-based GNSS-R soil moisture retrieval studies [24]. [PERSON] et al. explored the feasibility of soil moisture estimation based on GLONASS SNR data and studied the effect of satellite elevation angle on the accuracy of soil moisture inversion [25]. [PERSON] et al. proposed soil moisture inversion method based on Support Vector Regression (SVRM) from the BeiDou Geostationary Orbit (GEO) satellite reflection signal [26]. Based on the receiving and processing modes of the reflected signals, [PERSON] et al. categorized the GNSS-R remote sensing observation into two modes: single-antenna observation pattern and multiple-antenna observation pattern [27]. For soil moisture observation, the ultimate goal of the two observation patterns is to extend the scientific findings to business applications. Whether using the single-antenna pattern that separates the reflected components from interference signals, the multi-antenna pattern that measures the reflected signal separately, ground-based GNSS-R observation, or air- or satellite-based measurement, three common key issues need to be addressed for practical applications: The geographical location of the signal reflection, the earth surface range of the reflected signal, and the soil depth represented by the carried information, i.e., the specular reflection point of GNSS-R, the spatial resolution, and the detection depth. These three key factorsthat are involved in soil moisture remote sensing with GNSS-R have been sporadically investigated. Currently, comprehensive, systematic, and quantitative analyses are still lacking. In this study, first, from the theoretical perspective, we present the interpretation, the general mathematical descriptions, and the considerations for some special observation conditions. Then, we introduce the applications for the ground- and air-based observations in the single-antenna pattern and in the multi-antenna pattern using the Plate Boundary Observatory (PBO) soil moisture product from the United States and the aviation observation data for farmland around Zhengzhou City, Henan Province, as examples. Thus, this study provides implications for the research and business applications of soil moisture remote sensing via GNSS-R.
# Olive Oil with Ozone-Modified Properties and Its Application
## 3 Materials and Methods
### Determination of Antimicrobial Activity of Olive Oil  
The agar disc diffusion method was used to estimate the antimicrobial activity of the refined and ozonated olive oils. Sterile paper discs of 6 mm in diameter (Whatman No. 40, Lab-System-Service, Szczecin, Poland) were soaked with the oil. The discs were placed onto the surface of the appropriate medium previously inoculated with the specific strain of the microorganisms. The media, TSA and SDA for bacteria and fungi, respectively, assuring the optimal growth of the microorganisms were applied. The amount of 0.1 mL of the standardised suspensions (10\({}^{8}\) CFU mL\({}^{-1}\)) of the tested microorganisms was transferred onto the agar medium. Petri dishes were kept at 4 \({}^{\circ}\)C for 2 h and then incubated at the temperatures optimal for specific microorganisms for 24-72 h, and the zones of inhibition were measured. The antimicrobial activity of the olive oil samples against the specific microorganisms was classified by the diameter of the inhibition zones as follows: not active for diameter less than 8 mm, active for diameter 9-14 mm, very active for diameter 15-19 mm [13]. Results were presented as an arithmetic mean of six determinations with standard deviation and were analysed using a 3-way ANOVA test at a confidence level of \(p<0.05\).
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Network Coding (NC)
The authors suggest a mathematical model and a heuristic algorithm to determine the number of network-encoding nodes and their location, which is insufficient for the aggregate received data at the gateway/gateways to be decodable, taking into account link failure scenarios. Protocols that utilize NC in WSN need to balance achieving the NC expedience and diverse performance criteria. On the one hand, the motivation is to send only coded packets, as sending uncoded packets degrades the NC gain. On the other hand, a strategy in which a relay waits for packets to arrive in order to code them can yield unacceptable delays. Furthermore, storing arriving packets while waiting for additional packets to arrive before coding them can result in a buffer overflow and packet loss. To overcome these drawbacks, [PERSON] et al. [173] suggest an opportunistic network coding (ONC) approach in which the relay can transmit either coded or uncoded packets. Each relay determines whether to transmit a coded or uncoded packet according to its queue state at each transmission opportunity. For the simple topology of a relay interconnecting two nodes that communicate with one another, [PERSON]. [173] presents an ONC strategy that can achieve the optimal delay/power tradeoff. [PERSON] et al. [174] adopt the ONC paradigm for data dissemination in vehicular ad hoc networks (VANETs), for which the mobile nodes (vehicles) employ a decode-and-forward scheme with subjective timers determined according to their distance from the source. In [PERSON] et al. [175], an opportunistic routing protocol with opportunistic network coding is proposed for correlated data gathering. Next, we provide more recent works that utilize NC in WSNs and related networks that are not particular to data-gathering applications. [PERSON] et al. [176] propose to use NC in a fog computing scenario, since in the fog computing system architecture, the data measured on a node should be delivered to many other destination nodes. The authors design a protocol for encoding and decoding and provide a design to incorporate it on the MAC level. [PERSON] et al. [177] consider a setup in which a WSN has been split into multiple disjoint partitions and suggests a polynomial-time heuristic algorithm based on space network coding termed relay placement using space network coding (i.e., rather than send additional coded data, additional relay nodes are deployed) for finding the optimal number and positions to place relay nodes for restoring the network connectivity. A network coding backpressure routing scheme for data aggregation in large-scale Internet of things (IoT) networks is explored by [PERSON] et al. [178]. The proposed routing scheme exploits network coding for the data aggregation process, which improves the throughput of the network by eliminating redundant packets. The paper relies on cluster-based routing that selects the cluster-heads (CH) based on the available energy and distance, which helps to minimize traffic congestion and provide efficient data transmission.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 6 Performance Results
### Massive MIMO Scenario  
By increasing the number of transmitting and receiving antennas \(P=32\) and \(R=64\), the system can be considered as a massimo MIMO system.  
Figure 5 shows the performance results for IB-DFE and MRC receivers. As for the MIMO system, the first iteration of the MRC presents weak values as shown in Figure 5a. For a BER of \(2\times 10^{-4}\), the simulated value for IB-DFE is 1.2 dB higher than MFB. In the case of MRC, when compared to MFB, the theoretical value is increased by 1.7 dB and the simulated value also increases by 3.8 dB. In Figure 5b the correlation factor is very similar to the one presented in Figure 3b. This is due to the fact that the \(R/P\) ratio is the same.  
When analyzing the results for the receiver that combines the IB-DFE with the MRC (Figure 6), the results obtained via simulation practically match with the theoretical values for the BER performance, as shown in Figure 6a. At the end of the second iteration, for a BER of \(10^{-4}\), the receiver is approximately 0.4 dB away from MFB and in its third iteration, the difference for the MFB is only 0.1 dB. The correlation factor, depicted in Figure 6b in the second iteration of the receiver, for an \(E_{b}/N_{0}\) of 5 dB, is practically 1.  
Figure 4: Theoretical/simulated BER performance and correlation factor for a receiver that combines IB-DFE and MRC in a MIMO system with 3 iterations. (**a**) BER performance. (**b**) Correlation factor (\(\rho\)).
## 5 Content Dependence of the Image Quality Metrics  
Another advantage of SSIM over RMSE is its consistency over different images. In this work, GS algorithm was used generate the phase-only holograms corresponding to the 29 natural images in the LIVE Image Quality Assessment Database. A total of 400 GS iterations were carried out for each image. The large number of iterations used in this test is to ensure that all the reconstructed images have a good quality. The reconstructed images were evaluated by both RMSE and SSIM. Figure 8 shows the RMSE and SSIM values for the 29 reconstructed images.  
Figure 8: The RMSE and SSIM values of the reconstructed images after 200 GS iterations.  
Figure 6: Relationship between the perceived image quality and root-mean-squared error (RMSE) values.  
Figure 7: Relationship between the perceived image quality and structure similarity (SSIM) values.  
It can be seen that the RMSE values ranges from \(<\)0.01 to \(>\)0.05 while the SSIM values are between 0.92 and 0.99. In other words, the absolute values of RMSE are more dependent on the image content instead of the image quality. Since the termination of IFTAs usually is determined by a predefined threshold of the image quality metric, it would be impossible to set a universal RMSE threshold value for IFTAs given its large dependence on the image content. As a result, the direct use of RMSE as the image quality metric in IFTAs would become much more complicated. In contrast, the limited spread of the SSIM values makes it a more consistent image quality metric in IFTAs.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### RA Methods in Interweave CR Networks  
The authors of [89] consider the RA problem in interweave CR networks as a clustering of the SUs that have perceived common sets of channels as available for their communications. This formulation introduces conflicting objectives--clustering fewer SUs will yield higher throughput because they will have the similar sets of available channels, but at the same time, the number of clusters will be greater, which will increase the overhead. The cases of perfect (provided by spectrum availability database) and imperfect (via distance-dependent spectrum sensing) spectrum occupancy information are considered. Clustering is performed via biclique graphs (complete subgraphs within a bipartite graph) that are formed from bipartite graphs constructed by each SU, which connect the channels perceived by it, and all its neighboring SUs, as available. Then, three criteria for optimal matching of the SUs and channels are examined. For each SU, the number of matching channels is obtained and used as a characteristic, on the basis of which, via communication between the SUs, the clustering is performed. The number of bicliques (clusters) of certain size, the probability that one or more clusters are formed, and the average cluster size are the performance indicators for this algorithm. The cluster formation probability grows exponentially with the probability of channel availability, while it declines in the same manner as the biclique parameters increase. An interweave CR network with MIMO-enabled APs and single-antenna SUs is explored for the RA solution in [78]. Perfect channel occupancy knowledge at the CAPs is assumed, some SUs are not assigned a separate channel, and power control is not considered. The CR network is modeled as a graph with the SUs being vertices, with edges connecting two SUs if the degree of orthogonality between their channel gains is higher than the predefined threshold. To avoid interference with the PUs, each vertex is characterized by a weight coefficient which is the ratio of the norm of the channel gain vector between the SU and all PUs, and the average channel gain on each channel. RA is performed via a greedy graph coloring algorithm, which also considers the number of SUs that can be assigned to each channel. Only SUs, with a degree of orthogonality toward the PUs, with the PUs which are over a certain threshold report their feedback to reduce the overhead. The proposed solution's convergence time increases linearly with the number of SUs, while the total CR network throughput \(R_{CR,T}\) grows exponentially.
## 3 Methodology
### Validation with Benchmark Experimental Results  
The results of computational studies performed in this research project have been validated with previously published literature and shown in Figure 4a,b. Figure 4a shows the comparison of present numerical results with the experimental results of [PERSON] et al. [33]. They performed experiments to determine the drill tool bit temperature distribution in drilling aluminium 2024-T351 with minimum quality lubrication (MQL) and a flow rate of 60 mL/h. The plots indicate the temperature distribution from the drill centre, and the present CFD results are in closer agreement with the experimental findings of [PERSON] et al. [33]. [PERSON] et al. [44] analysed the convective heat transfer using clay nanoparticles for drilling operations. They proposed that by adding clay nanoparticles to coolants, the thermal conductivity of the resultant composition is enhanced, and thus makes the components susceptible to high temperatures. Accordingly, they analysed the change in temperature with respect to distance from the tip of the drill bit. It was observed that the temperature on the drill bit decreases with respect to distance. At a distance of 2 mm from the drill bit, the temperature on the drill bit reaches an ambient temperature value. Similar trends can be observed in the current research study. The only difference between the current research work and the studies performed by [PERSON] et al. [44] is that clay nanoparticles were deployed in the latter. Since this research study used Al\({}_{2}\)O\({}_{3}\)-doped nanoparticles, which have a different thermal conductivity than mineral oil, the temperature distribution varies slightly with previously published results. However, the behaviour of temperature with respect to distance from the drill bit tip is similar in both scenarios. Hence, it is safe to say that the computational settings for the current study are in accordance with the real-life scenario.