esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Numerical Results for S-O Performance Metrics  
The S-O statistical measures of an FSO RIS-ACs model over F-S \(\mathcal{F}\) TI fading propagation channels are presented in Figures 6 and 7. The variances in (40) were evaluated as \(\sigma^{2}_{\gamma_{h_{1}}}=\sigma^{2}_{\gamma_{h_{2}}}=f_{0}^{2}\pi^{2}\sigma^{2 }_{\gamma_{h}}\langle\gamma_{h}\rangle\) ([30], Equation (13)), where \(\sigma^{2}_{\gamma_{h}}=\sigma^{2}_{\gamma_{h_{i}}}\) was given by (11) and \(\langle\gamma_{h}\rangle=\langle\gamma_{h_{i}}\rangle=1\). Furthermore, \(f_{0}=\frac{1}{\pi\gamma_{0}\sqrt{2}}\) was the quasi frequency of the FSO RIS-ACs path ([30], Equation (15)). Additionally, \(\gamma_{0}=\frac{\sqrt{\lambda S}}{U}\) was the turbulence correlation time, \(\lambda\) was the wavelength, \(S\) was the distance and \(U\) was the average wind speed of the RIS-ACs transmission system. The S-O metrics were evaluated for \(\lambda=\lambda_{i}=532\,\)nm, \(U=1\,\)m/s and \(S=980\,\)m [46].  
The \(N_{\gamma}\) versus \(\gamma_{th}\) for weak (\(a_{1}=7.433,b_{1}=3.265,a_{2}=7.433,b_{2}=3.265\)), moderate (\(a_{1}=5.75,b_{1}=2.58,a_{2}=5.75,b_{2}=2.58\)) and strong (\(a_{1}=2.7,b_{1}=2.1,a_{2}=2.73\), \(b_{2}=2.1\)) TI fading severity values [13] and for various \(\gamma_{th}\) values is presented in Figure 6. The least severe (observed) TI fading conditions providing the lowest \(N_{\gamma}(\gamma_{th})\) values throughout the \(\gamma_{th}\) dB regime (e.g., the lowest \(N_{\gamma}(\gamma_{th})\) values were obtained for \(a_{1}=7.433,b_{1}=3.265,a_{2}=7.433\) and \(b_{2}=3.265\) TI fading severities). As expected, the shifts from more severe to less severe TI fading conditions cause the \(N_{\gamma}(\gamma_{th})\) to decrease. The increase in \(\hat{\gamma}_{th}\) (e.g., by increasing \(\hat{\gamma}\) from \(\hat{\gamma}=3\) to \(\hat{\gamma}=6\)) could caused \(N_{\gamma}(\gamma_{th})\) to decrease for lower \(\gamma_{th}\) dB values and \(N_{\gamma}(\gamma_{th})\) to slightly increase for higher \(\gamma_{th}\) dB values. It was also evident that the impact of TI fading severities on \(N_{\gamma}(\gamma_{th})\) was more dominant for lower \(\gamma_{th}\) dB values.  
The \(A_{\gamma}(\gamma_{th})\) for weak (\(a_{1}=7,b_{1}=3.265,a_{2}=7.433,b_{2}=3.265\)), moderate (\(a_{1}=6\), \(b_{1}=2.58,a_{2}=5.75,b_{2}=2.58\)) and strong (\(a_{1}=3,b_{1}=2.1,a_{2}=2.73,b_{2}=2.1\)) TI fading severity values [13], where \(a_{1}\) was an integer, and for various \(\hat{\gamma}_{th}\) values, is presented in Figure 7. It can be observed that, by shifting from less severe to more severe TI fading conditions, the \(A_{\gamma}(\gamma_{th})\) decreased for higher \(\gamma_{th}\) dB values (e.g., by shifting from \(a_{1}=7\), \(b_{1}=3.265,a_{2}=7.433\) and \(b_{2}=3.265\) to \(a_{1}=6\), \(b_{1}=2.58,a_{2}=5.75\) to \(a_{1}=6\), \(b_{1}=2.58,a_{2}=5.75\) and \(b_{2}=2.58\) to \(a_{1}=3,b_{1}=2.1,a_{2}=2.73\) and \(b_{2}=2.1\)). It can be furtherMoreover, the \(A_{\gamma}(\gamma_{th})\) was the least affected by TI fading severity conditions and \(\tilde{\gamma}\) for the threshold values around \(\gamma_{th}=0\) dB.
# Overall Equipment Effectiveness: Systematic Literature Review and Overview of Different Approaches
## 2 Materials and Methods
### Search Process  
Web of Science (WoS) and Scopus were the two electronic databases used in this study because they contain relevant, updated and high-quality bibliographic information. WoS is a digital platform of Clarivate Analytics, in which Scopus is affiliated with Elsevier; both databases were formed based on thousands of peer-reviewed journals in the fields of science, technology, medicine, social sciences, arts and humanities.  
A generalized search for the term 'overall equipment effectiveness' was performed to obtain broad results. The keywords used for this search were 'overall equipment effectiveness' AND 'OEE'. The search string applied in the WoS electronic databases was topic (TS) = ('overall equipment effectiveness' AND 'OEE'). In Scopus, a combined field that searches abstracts, keywords and document titles was used, i.e., TITLE-ABS-KEY ('overall equipment effectiveness' AND 'OEE').  
The total number of documents obtained in the general search was 847, i.e., 281 from WoS and 566 from Scopus. Only articles were selected for this study. Compared with proceedings papers, articles are more influential and complete as they contain more information and citations [22]. The results were based on articles obtained after eliminating duplicates and applying the inclusion and exclusion criteria detailed in the following section.
## 4 LNSSCL Algorithm
### Determining Particle Resurrection Locations and Node Affiliation Selection  
In stochastic competitive learning, when a particle visits a node under its control, its energy increases. On the other hand, when it visits a node controlled by a competing particle, its energy decreases. This energy manipulation serves to constrain the particle's walking range, thus reducing long-range and redundant accesses in the network. If a particle frequently visits nodes controlled by competing particles, its energy will continuously decrease until it is exhausted and enters a dormant state. Subsequently, the particle will randomly jump to a node within its control range to revive and recharge.  
Clearly, the choice of the particle's revival location has a high degree of randomness, which can lead to unstable community detection results. To address this issue, based on the particle's control over nodes, we select the node with the highest control capability as the unique revival location among the nodes it already controls, eliminating the uncertainty in location selection. If a particle currently doesn't control any nodes, it will randomly choose  
Figure 2: Schematic of error community detection when particle control capacity increment is constant.  
any node in the network for revival. The improved particle revival location selection is shown in Figure 3, where the energy of particle 1 is depleted due to its traversal into the control region of particle 3. After the improvement, particle 1 will no longer randomly jump to any controlled node within the dashed box, but will instead jump to the node indicated by the dashed arrow (assuming that node has the highest control capability value for particle 1).  
Once the algorithm reaches the convergence criterion, particles will cease their walks. Based on the control capabilities of each particle over nodes, all nodes in the network are assigned to the corresponding communities represented by particles. However, due to the potential randomness and potential misclassifications in the steps executed by particles before stopping their walks, a node membership selection step is introduced. By considering the frequency of community labels among neighboring nodes, this step ensures that each node is correctly assigned to its appropriate community, further optimizing the community detection outcomes. Specifically, for each node, the occurrence frequency of community labels among its neighboring nodes is observed. If the most frequent neighboring community label is unique, it is selected as the community label for that node. If the most frequent neighboring community label is not unique, an influence score is computed for each community, and the community label with the highest influence score is selected. The influence score \(Effect_{C_{k}}\) for a community is calculated as shown in the equation below:  
\[Effect_{C_{k}}=\sum_{j\in N(v_{i})}Sim(i,j),j\in C_{k} \tag{16}\]  
where \(C_{k}\) is one of the most frequent communities, and \(N(v_{i})\) represents the set of neighboring nodes of node \(v_{i}\).
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 8 Conclusions  
In this paper, a QoS-aware resource reallocation algorithm was introduced that exploits software defined networking advantages to proactively prevent congestion by using traffic forecasting techniques. In this way, an optimization problem in the form of binary linear programming was proposed. The scheme that solves the proposed problem (i.e., QRTP) was compared with the conventional approaches from throughput and packet loss perspective. Based on the experimental result, QRTP can reduce the total packet loss more than 22%, and simultaneously improves the network throughput more than 1.2 times. In addition, we compared the two proposed schemes from different aspects. Three metrics were considered: (i) maximum link utilization; (ii) the size of routing matrix; and (iii) computational complexity (based on optimization time). Simulation results showed that, although the result of RQTP is comparable with the QRTP, the optimization time of relaxed solution is dramatically lower than the original solution. In order to prove this claim, it was shown that RQTP improved the optimization time more than 50 times with 400 flows in the network. Moreover, the size of routing matrix decreased more than 3 times while the maximum link utilization was approximately the same.  
Future work will be dedicated to applying the proposed scheme on service function chaining. Another field of future interest would be considering the queuing delay of the switches and the processing delay of the server to improve the QoSs. In addition, considering the energy consumption of the network devices as a new part of objective function would be of interest.  
Authors want to thank the editor and the reviewers for their valuable comments and suggestions that helped us to improve the quality of our paper. Also, they would like to thank [PERSON] Research Associate at the University of Padova, Italy for her kind comments and advices. All authors together proposed, discussed and finalized the main idea of this work. [PERSON] and [PERSON] proposed the idea, calculated and plotted the feasible regions, and implemented the algorithm and their comparisons. [PERSON] and [PERSON] helped in the paper preparations, English corrections and submission. The authors declare no conflict of interest.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Dataset Collection
#### 3.1.2 Stock Price Datasets  
In the subsection on stock price data, stock price datasets, including historical trading data of the stock market such as opening and closing prices, highest and lowest prices, and trading volume, are discussed. These datasets are typically used for training and testing stock price prediction models, to forecast future stock market trends. The stock price data for this study are obtained from various public financial market databases, including Yahoo Finance, Google Finance, and others [29]. Additionally, professional financial data service providers like Bloomberg and Reuters offer high-quality stock price data. The reason for using these datasets is that they provide detailed market transaction records, reflecting the collective behavior and sentiments of market participants. By analyzing the data, the model can learn complex patterns affecting stock price fluctuations, thereby enhancing prediction accuracy. After acquiring stock price data, data cleaning and preprocessing are conducted to ensure data quality and consistency. Specific to stock price prediction, various technical indicators such as moving averages, Relative Strength Index (RSI), and Bollinger Bands are calculated as input features for the model. Moreover, external factors like macroeconomic indicators and market news, which also influence stock price volatility, are considered.
## Channel Model  
### Channel Noise  
Noise in underwater communications is constituted by four components: shipping, wave, thermal and turbulence noise [26]. The power spectral density (PSD) of each component in decibels is denoted by \(N_{tab}\), \(N_{sh}\), \(N_{wv}\) and \(N_{th}\), respectively. The PSD of the total noise is the sum of the individual PSD of each noise component and is given by  
\[N=N_{sh}+N_{wv}+N_{th}+N_{tb} \tag{1}\]  
The individual PSD of each noise component is modeled by  
\[N_{sh}=40+20(s-0.5)+26 logf-60 log(f+0.03) \tag{2}\]  
\[N_{wv}=50+7.5w^{0.5}+20 logf-40 log(f+0.4) \tag{3}\]\[N_{th}=-25+20 logf \tag{4}\]  
\[N_{tb}=27-30 logf \tag{5}\]  
The unit of frequency \(f\) is kilohertz. The dimensionless variable \(s\) is called the shipping parameter and has values in the range \([0,1]\). It measures the shipping activities on the surface of water. These shipping activities generate shipping noise in the range of 10-100 Hz. When wind blows at the surface of water with a speed \(w\) in meters per second, the resulting generated noise in the range from 100 Hz to 100 kHz is called wave noise. Most of the acoustic communications are affected by wave noise. Thermal noise dominates above 200 kHz. Turbulence noise in the 0.1-10 Hz range is the result of turbulence in sea water.
## 2 Materials and Methods
### Methodology
#### 2.1.2 Phase II: Combination Model  
The combination model represents a first step toward the joint use of the statistical, displacement and damage analyses. To this aim, the \(TZU\)s intersected by the road network under investigation, \(TZU_{radd}\), are identified by introducing a buffer symmetrically disposed in the orthogonal direction with respect to the road centerline.  
The road susceptibility index, \(IS_{radd}\), is computed considering the \(IS_{TZU}\) derived from the zoning susceptibility map created at the end of the first phase.  
The DInSAR average velocity recorded along the line of sight (\(LOS\)) sensor-target direction is assumed as the intensity measure of each \(TZU\). A mean velocity value computed as the arithmetic mean of the yearly \(V_{LOS}\) of each coherent benchmark falling within the \(k^{\text{th}}\)\(TZU\) (if covered) or deriving from the interacting slow-moving landslides (for which enough coherent benchmarks are available) is assigned to each \(TZU\). Then, considering a threshold velocity value fixed according to the accuracy of the used DInSAR dataset, assumed as an indicator of movement [37; 38; 39], a \"moving\" or \"not moving\" condition is defined.  
The damages affecting the road network are assessed based on the results of virtual surveys carried out using a Google Street view image archive. To each identified road section (for which at least one Google Street view image is available in the time interval under investigation), a damage severity level is assigned adapting the damage ranking scale proposed by [PERSON] et al. [7], based on the classification provided by [PERSON] et al. [33], considering the following classes:  
* D0 (negligible): road pavement deformation and cracks are absent or rarely visible;
* D1(slight): deformation of the road pavement without the occurrence of cracks;
* D2 (moderate): cracks in the road pavement;
* D3 (severe): dislocation of the road pavement compromising its continuity.  
Starting from individual damaged (or undamaged) road sections, stretches of these sections exhibiting uniform levels of damage are merged, and a unique damage severity level is assigned to each one of them.  
Finally, the three retrieved types of information are associated to each \(TZU_{radd}\): susceptibility index, damage severity level (if the grid cell is covered) and velocity with associated state of movement (if the grid cell is covered).
## 3 Results and Discussion
### Site-Specific and Regional GlobAlbedo-Derived fAPAR and LAI  
Site and region comparisons between the GlobAlbedo-derived and MODIS fAPAR and LAI products are presented below, to explore the issue of temporal and spatial consistency between the products, as well as to identify differences. As described above, MODIS fAPAR and LAI estimates are derived using different assumptions from those in the GlobAlbedo JRC-TIP. A key difference is the fact that the MODIS products use a biome-specific 3D solution of radiative transfer in the vegetation canopy [7; 8]. Partitioning of the MODIS RT model-based fAPAR and LAI retrievals by biome (corresponding to IGBP land cover classes) allows the canopy type and structure to be considered in the retrieval process explicitly, as well as total % vegetation cover and canopy depth. The MODIS fAPAR/LAI retrieval process is further constrained by biome-specific consideration of clumping,  
Figure 1: (**Top**): global 25 km map of GlobAlbedo-derived fAPAR for DOY 185 2002; (**Bottom**): uncertainty \(\sigma_{\text{fAPAR}}\) of retrieved fAPAR for the same time period.  
Figure 2: (**Top**): global 25 km map of GlobAlbedo-derived LAI for DOY 185 2002; (**Bottom**): uncertainty \(\sigma_{\text{LAI}}\) of retrieved LAI for the same time period.  
layering (_i.e._, understory, overstory) and soil background. These constraints introduce fundamental differences between the GlobAlbedo-derived and MODIS products that are likely to be more significant at some times of year and location, than others. Examples of these constraints are the requirement for forest cover to exceed 70% to be considered \"forest\" in the MODIS retrievals, regardless of type, or the use of snow background at certain times of year and location. In the GlobAlbedo-derived products, the use of a prior based on a surface albedo climatology, allows the albedo retrieval to minimize the impact of outlier (snow, cloud) observations.  
Here, we show comparisons between the two datasets over the period 2002-2011 to explore the impacts of these different assumptions, in terms of why and where differences occur. We highlight areas of spatial and temporal consistency (or not as the case may be). We show comparisons at FLUXNET flux tower sites, selected to represent key (wide global coverage) vegetation cover types, namely: grasslands, crops/natural, deciduous broadleaf, evergreen needleleaf, mixed forest, as well as some miscellaneous sites in tropical and temperate regions. We then show regional comparisons over four 1200 \(\times\) 1200 km tiles, from W. Europe, N. America, Australia and Africa. Finally, we show whole-hemisphere seasonal comparisons.  
For the GlobAlbedo-derived parameter values we show the quantified uncertainty arising from the parameter retrieval; for the MODIS values, this is not so clear due to uncertainty being somewhat arbitrarily defined from the RT model assumptions, and so here we simply plot the standard deviation of the spatial average of the 3 \(\times\) 3 pixels surrounding the flux tower in each case.
# A Low-Cost CPW-Fed Multiband Frequency Reconfigurable Antenna for Wireless Applications
## 3 Results and Discussions
### Case 2: (All Switches Off)  
The simulated and measured S\({}_{11}\) in Case 2 are depicted in Figure 11. All the switches are OFF in this case; thus, current passes through the shortest path, and part of the antenna resonates. In this mode, the antenna resonates at five different operating bands with an impedance bandwidth of 2.1 GHz to 8.8 GHz. The radiation pattern of this case shows the same behavior, with an omnidirectional pattern in the H-plane and bidirectional in the E-plane as shown in Figure 12.  
Figure 11: Measured and simulated S\({}_{11}\) for all switches OFF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Freq. (GHz)** & 2.7 & 4.4 & 8.1 & 9.4 \\ \hline
**RL (dB)** & \(-\)30 & \(-\)15 & \(-\)18 & \(-\)21 \\ \hline
**BW (MHz)** & 1200 & 900 & 600 & 500 \\ \hline
**Gain (dBi)** & 1.8 & 1.9 & 2.01 & 1.41 \\ \hline
**Dir. (dB)** & 1.7 & 2.1 & 2.2 & 1.54 \\ \hline
**Eff. (\%)** & 90 & 91 & 88 & 91 \\ \hline
**VSWR** & 1.09 & 1.2 & 1.03 & 0.9 \\ \hline
**App.** & \begin{tabular}{c} WLAN/WiMAX \\ \end{tabular} & S/C-Band & \
\begin{tabular}{c} Fixed/ \\ Mobile Satellite \\ \end{tabular} &
\begin{tabular}{c} Satellite/ \\ Radar \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarized results of Case 1.  
The first band achieved, in this case, is from 2.1 GHz to 3.3 GHz with an operating frequency of 2.7 GHz and an impedance bandwidth of 700 MHz. This band is used in earth exploration satellites, radio astronomy, and space research services whereas the 2.7 GHz to 2.9 GHz band is used by aviation services. The band from 3.1 GHz to 3.3 GHz band is used in by radiolocation services. This band is allocated for both federal and nonfederal use.  
The second band achieved is from 3.6 GHz to 4.3 GHz with an operating frequency of 4.1 GHz and an impedance bandwidth of 700 MHz. This band is useful for WiMAX applications. The third band achieved is from 5.2 GHz to 5.46 GHz with an operating frequency of 5.4 GHz and an impedance bandwidth of 260 MHz. This band can be used mainly for 5.15 GHz to 5.825 GHz standard WLAN applications. The fourth band achieved is from 6.4 GHz to 6.8 GHz with an operating frequency of 6.6 GHz and an impedance bandwidth of 400 MHz. This band is used for long-distance radio telecommunications. The last band of this case covers 8.4 GHz to 8.8 GHz with an operating frequency at 8.6 GHz and an impedance bandwidth of 400 MHz. This band is used for X-band Satellite communication. Table 4 presents the summarized results of this case.
## 5 Conclusions  
As the core device of the energy internet, PET plays a vital role in the effective consumption for renewable energy and the flexible conversion of electrical energy. Based on the existing literature of PET research, this paper proposes a novel PET with an improved structure that is applicable for AC/DC hybrid distribution network of multi-voltage levels, and corresponding control strategies are suggested. The reliability and effectiveness of the proposed structure and control strategies are verified by PSCAD/EMTDC simulation. The conclusions are summed as follows:  
(1) An improved structure of PET is proposed, in which the input stage utilizes MMC, and the isolation stage adopts a combined connection of ISOS and ISOP. The ISOS and ISOP hybrid structure facilitates the access of PET to the AC/DC distribution network with multiple voltage levels, which widens the application of PET.  
(2) The control strategy based on VSM is applied for the input stage, which increases the inertia and damping of the grid, and makes PET more friendly to the upper grid. In addition, it enables PET to provide power support and participate in the PFR by adjusting the output power of the output stage DC port when the frequency of the upper power grid fluctuates.  
(3) A power coordinated control strategy is proposed to coordinate the power distribution of all ports and ensure the stable operation of PET by adjusting the DC side voltage of the input stage and the input voltage of the isolation stage when the power demand of the output stage changes.  
Future works will be carried out to analyze the dynamic response characteristics of the control system and the system stability problems caused by parameters selection. The corresponding control strategy for PET participating in the secondary frequency regulation will also be investigated.  
Conceptualization, J.R. and X.A.; funding acquisition, X.A.; Investigation, J.R.; Methodology, J.R.; Supervision, X.A.; Validation, D.R.; Writing--original draft, J.R.; Writing--review and editing, Y.L. and D.R.  
This research was funded by the 2016 national key R & D program of China to support low-carbon Winter Olympics of integrated smart grid demonstration project (2016 YFB0900500); Beijing Natural Science Foundation (3182037).  
The authors declare no conflict of interest.  
Figure 16: Waveforms when the frequency fluctuates: (**a**) Frequency response before primary frequency regulation (PFR) and after PFR; (**b**) active power of AC output port and DC output port; (**c**) active power exchanged between PET and the upper grid.