esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 4 Experiment
### Comparison of Algorithm Experimental Results and Analysis on Different Test Problems
The results indicate that the algorithms NSGA-II, MOEA/D, NSGA-III, LMEA, S3 CMAES, FDV, and DVA-TPCEA achieved optimal or suboptimal counts of 1, 3, 5, 6, 0, 7, and 8, respectively, with the DVA-TPCEA algorithm ranking first, demonstrating exceptional stability, followed by FDV and LMEA. From the data, it is evident that the performance of the DVA-TPCEA algorithm is particularly outstanding with 500 and 1000 decision variables. This is attributed to the qualitative analysis method for decision variables employed in this study, which effectively groups large-scale decision variables. Furthermore, the integration of the dual-population cooperative evolutionary mechanism allows the algorithm to specifically optimize the convergence and diversity of the population, thereby improving convergence efficiency and distribution. This further confirms the effectiveness and overall performance advantages of DVA-TPCEA in addressing challenging large-scale multi-objective problems (MaOPs).
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Research Potential for Applications
#### 3.3.4 Collaboration of Smartphone Applications with Vehicles  
Automotive companies including Volvo, Bosch, and many others are utilizing smartphone applications to provide new innovative services. For instance, a smart phone application can unlock the vehicle and start/stop the engine, provide access to authorized riders (e.g., in ridesharing scenarios), notify the owner of any upcoming services, and remotely check the fuel level [126; 127]. Another example is the digital driver map that drivers can use to locate/reserve parking spaces [126]. The potential of these applications will provide a myriad of new innovative services within the vehicle.  
Table 2 provides a summary of the applications that have been discussed in this article along with the available references to gain a better understanding of these applications.
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 1 Introduction
### FREEDM Architecture with Multiple Levels
#### 2.1.2 Solid State Transformer (SST) Level  
The SST level contains a power electronic transformer, which manages the interface between the customer loads, DRERs, and DESDs [41]. The SST is different from the legacy distribution transformers because of the involvement of power electronic converters, which offers typical functionalities, such as multilevel power conversions (high voltage AC/DC power conversion for DC buses, DC/DC conversion for high-frequency DC regulated buses, and DC/AC conversion stages for low-voltage regulated AC buses), and makes it a three-level power exchange [42]. The size of the SST transformer is reduced considerably compared with that of the conventional transformer because of the employment of high-frequency silicon carbide (SiC) insulated gate bipolar transistor (IGBT) technology, which makes it lighter and more compact [43; 44]. SST will act as a smart and intelligent plug-and-play interface device that supports different interfaces (AC/DC) via AC and DC ports.  
A block diagram of SST is shown in Figure 3. It interfaces different levels of DC bus outputs and aids in voltage sag restoration, fault current limiting, power factor correction, and reactive power flow balancing [45]. There can be multiple SSTs under one ER, which acts in coordination.
# An Exploratory Landscape Analysis-Based Benchmark Suite
## 1 Introduction
Consequently, landscape analysis measures can fluctuate due to the sampling method. It is, therefore, imperative that the landscape analysis measures produce results that do not fluctuate significantly, since this can affect decisions on which algorithm to select to solve a problem. If a landscape analysis measure does not have a large variance, it is deemed to be robust [18]. Landscape analysis measures must be robust, because if these measures are intended to be used as input to machine learning models for automated algorithm selection, reproducibility of the models is desirable. There is little focus in the literature on how to adequately choose the sample size for ELA. [PERSON] et al. [19] noted that a sample size of \(50\times D\), where \(D\) is the dimensionality of the decision space, is sufficient to train a classifier on the BBOB benchmark suite to differentiate problem instances based on the number of funnels in the fitness landscape. However, this may not necessarily generalize to other problem characteristics. The main contributions of this paper are (1) an approach to determine the sample size needed to produce robust measures from exploratory landscape analysis (ELA), and (2) a proposal of a single-objective boundary-constrained benchmark suite, using commonly found benchmark functions in the literature, that has better coverage of the single-objective, continuous-valued optimization problem space than benchmark suites that are currently used in the literature. The remainder of this study is organized as follows: Section 2 contains an introduction to the concepts used within the study, Section 3 investigates the robustness of ELA measures and provides the sample size used for the remainder of the study, Section 4 contains the benchmark suite proposal. Finally, Section 5 concludes the study and discusses avenues for future work.
# Soliton Content of Fiber-Optic Light Pulses
## 4 Methods to Verify Soliton Content
### The Inverse and Direct Scattering Transform
#### 4.1.2 Numerical Restrictions  
We have applied numerical DST to a case for which exact analytical results are known, i.e., the fundamental soliton Equation (3), to assess inaccuracies of the numerical procedure. They arise from various sources; we identify three main error sources.  
Truncation error: At the edges of the computational time window of finite width \(T_{\text{W}}\), the pulse wings are truncated. A fraction \(E_{\text{trunc}}\) of the energy is not represented in the numerical field:  
\[E_{\text{trunc}}=2\int\limits_{T_{\text{W}}/2}^{\infty}\hat{P}\,\text{sech}^{2 }\left(\frac{T}{T_{0}}\right)\,dT=E_{\text{tot}}\left(1-\tanh\left(\frac{T_{ \text{W}}/2}{T_{0}}\right)\right)\quad. \tag{28}\]  
Sampling error: Sampling error arises from coarse sampling when the temporal discretization step is too wide for the pulse duration.  
Floating point error: Computers store numbers in some format with a limited number of digits; 'double precision' usually offers 15 digits plus the exponent. After a large number of floating point operations, numerical errors accumulate.  
To disentangle these contributions, we varied the number of sampling points and the width of the time window for a given pulse width; the results are shown in Figure 9. To the right, the computational window is sufficiently wide (\(T_{\text{W}}\gg T_{0}\)). Then, the three curves shown are for discretization step numbers as indicated by the labels. For \(2^{12}\) points, sampling is increasingly coarse when these points are spread over a wider window, and the error rises. Larger numbers of points improve the situation quadratically because the error scales with the square of the sampling point separation. To the left, truncation error dominates; the prediction of Equation (28), plotted as a solid curve, fits well. Floating point error becomes relevant only where these two contributions are small and where the number of calculation steps is large; in our example, it appears as 'noise' in the central section of the curve for \(2^{22}\) points. We convinced ourselves that the transfer-matrix (TM) method presented in [65] yields the same results.
## 1 Introduction
The influence of environmental factors on GPR signals has also been analyzed [30]. Both chloride content in concrete and rebar corrosion were found to reduce GPR reflection amplitudes. Moreover, higher chloride content resulted in greater attenuation of GPR signals. Higher temperatures also resulted in stronger amplitude attenuation, with concrete containing higher chloride levels exhibiting greater sensitivity to temperature variations. In contrast, relative humidity was found to have an insignificant effect on GPR signal reflection amplitudes. The analysis and interpretation of GPR data are often challenging and labor-intensive, demanding substantial expertise and computational effort to derive meaningful insights. Implementing a fully automated GPR data interpretation system for identifying embedded rebars could effectively address this challenge [31; 32; 33]. [PERSON] et al. [34] proposed a new approach based on a synthetic aperture technique that has proven effective in providing rebar layout information, including rebar spacing, depth, and concrete cover. [PERSON] et al. [35] presented a revolutionizing methodology using curve fitting principles to simultaneously determine the rebars' horizontal location, depth, concrete cover, and size. This study aimed to identify the optimal transverse and longitudinal GPR profile line spacing offset. The approach involved calculating the depth and spacing of rebars on each face of the beam by measuring the electromagnetic wave travel time. A series of GPR scans was conducted at various offsets, and the resulting hyperbolas were analyzed. A computer code was developed using Delphi programing language to automatically compute the rebars' location by detecting all hyperbolic reflections from the GPR radargrams and best fitting them with proposed equations. This study has proven to be an efficient and reliable method for estimating rebars' horizontal and vertical locations in concrete structures. This study comprehensively explores GPR's capabilities in assessing reinforced concrete structures, providing valuable insights into advancements in nondestructive testing and evaluation methods for inspection and maintenance of the bridges.
# Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem
## 2 Materials and Methods  
### Study Site  
The experiment was conducted at one AmeriFlux site (US-Hn1; 46 deg24'32''N, 119 deg16'/30''W; Figure 1) located in the Hanford area of central Washington, United States. Site information was documented in detail elsewhere [15; 30]. Briefly, the area has a Mediterranean-type climate with generally cool, wet winters and hot, dry summers. The long-term mean annual precipitation is less than 200 mm, most of which occurs during winter and spring [15]. The site is characterized by a deep vadose zone in which the water input of the soil is primarily fed by precipitation. Vegetation is a mixture of scattered shrubs and short grasses (Figure 2). Shrub species include _Artemisia tridentata_ (big sagebrush) and _Chrysothamus viscidiformis_ (green rabbitbrush), with an average height of approximately 1.5 m. Grass species include _Bromus tectorum_ (chetagrass), _Salsola kali_ (Russian thistle), _Poa secunda_ (sandberg bluegrass), _Pseudorogeneria spicata_ (blueunch wheatgrass), and _Stipa comate_ (needle-and-thread grass) [15]. The soil texture in the top 30 cm layer is loamy sand (5% clay, 11% silt, and 84% sand) with small rocks and gravel interspersed, and the soil texture in the layer from 30 to 45 cm is sand (2% clay, 4% silt, and 94% sand) [15; 30]. An instrumented 10 m flux tower was installed at the site, allowing continuous eddy covariance and meteorological measurements [30]). The tower began to operate in January 2016 and a soil chamber system was installed in late 2018. For this study, we examined the measurements of the phenology camera, eddy covariance, and soil chamber systems collected during 2019 and 2020 with contrasting weather and vegetation conditions (Figure 2). The EC tower is located around 0.5 km from the river, at an elevation of 13 m higher than that of the river water surface [15]. Under the northwesterly and southeasterly dominated winds, the impacts of the river on the tower measurements are generally negligible, as indicated by the 90% contours of flux footprints (Figure 1). The flux footprint was determined using the tool developed by [PERSON] et al. [31].  
Figure 1: Locations of the US-Hn1 eddy covariance tower (red star) and two Washington State University AgWeatherNet (AWN) stations (red triangle), and photos of the EC tower and the automated soil chamber. The yellow, red, and blue lines denote the mean, daytime, and nighttime 90% contours of the footprint climatology, respectively, for the EC tower. Land cover type data were obtained from the National Land Cover Database (NLCD) 2019 products ([[https://www.mrl.gov/data](https://www.mrl.gov/data)]([https://www.mrl.gov/data](https://www.mrl.gov/data)), accessed on 17 October 2022). The map was made with QGIS 3.14.
# Towards Client Selection in Satellite Federated Learning
## 3 System Model
### Communication Model  
In the simulation, we only consider a set of clients \(\mathcal{I}\) with only one server (a GS or an LEO satellite). In our model, the satellites use wide beams in the \(Ka\) band (receiver between 20.1-21.2 GHz and transmitter between 29.9-31 GHz [26]) for inter-satellite communication. Mutual interference exists because the PS receives multiple clients at the same time. Considering large-scale fading and shadowed-Rician fading [27], we denote the channel power gain from client \(i\) to the server as \(g_{i}\). Hence, the transmission rate \(R_{i}\) for parameter aggregation can be calculated as follows:  
\[R_{i}=B\log_{2}\left(1+\frac{g_{i}p_{i}}{\sum_{j\in\mathcal{I}\backslash\{i \}}g_{j}p_{j}+\sigma^{2}}\right),\qquad i\in\mathcal{I} \tag{8}\]  
Figure 5: Space geometric relationship between the LEO satellites in circle orbit.  
where \(B\) is the available spectrum bandwidth, \(p_{i}\) is the transmit power of client \(i\), and \(\sigma^{2}\) is the additive white Gaussian noise (AWGN) power.  
Factually, the satellite communication model is more complex and there are differences between satellite-to-GS and satellite-to-satellite. However, in this simulation, we ignore many of the communication details such as atmospheric absorption loss and antenna pointing loss [28]. We only give a rough model, since the amount of data we are transmitting is not large and communication has a relatively minor impact on overall performance.
## 5 Conclusions
Conceptualization, C.G.-S.; methodology, C.G.-S.; software, C.G.-S.; validation, A.d.L.-C., E.A.-V., F.D.-F. and A.R.d.I.O.-P., formal analysis, A.d.L.-C. and E.A.-V.; investigation, C.G.-S. and E.L.-F.; resources, C.G.-S., E.L.-F., A.L.-C., E.A.-V., F.D.-F. and A.R.d.I.O.-P.; data curation, A.d.L.-C., E.A.-V., F.D.-F. and A.R.d.I.O.-P.; writing-original draft preparation, C.G.-S. and A.d.L.-C.; writing-review and editing, C.G.-S., E.L.-F., A.d.L.-C., E.A.-V., F.D.-F. and A.R.d.I.O.-P.; visualization, C.G.-S., E.L.-F., A.d.L.-C., E.A.-V., F.D.-F. and A.R.d.I.O.-P.; supervision, E.L.-F., A.d.L.-C., E.A.-V., F.D.-F. and A.R.d.I.O.-P.; project administration, A.d.L.-C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors thank the 'Fundacion DOMING MARTINEZ' for financial support. Conflicts of Interest: The authors declare no conflict of interest. Nomenclature BA Battery E\(\mathrm{EL}\)Consumed energy by the electrolyzer per kg of hydrogen, 45.29 kWh/kgH\({}_{2}\) ICurrent, A\(\mathrm{\eta}\)BAEfficiency of battery, 0.9 \(\mathrm{\eta}\)TC Efficiency of fuel cell, 0.65 D\(\mathrm{\Delta}\)Electrical demand, Wh/day or kWh/day S\(\mathrm{EL}\)Electrollyzer area ELElectrolysis cell EEnergy, kWh E\(\mathrm{\Delta}\)BaeEnergy of the extra battery, kWh/day E\(\mathrm{e}\)Excess energy, kWh/day BA\(\mathrm{e}\)Extra battery FC Fuel cell HT Hydrogen tank E\(\mathrm{i}\)Input energy, kWh/day E\(\mathrm{i}\)E\(\mathrm{i}\)Input energy, kWh/day E\(\mathrm{i}\)E\(\mathrm{i}\)Input energy of electrolyzer, kWh/day N\(\mathrm{EL}\)Number of EL cells in series NPV Number of PV modules in series PV\(\mathrm{i}\)mOne photovoltaic module S\(\mathrm{o}\),ELOptimal area of electrolyzer NP\(\mathrm{V}\)OP Optimal number of PV modules in series E\(\mathrm{o}\)Output energy, kWh/day E\(\mathrm{o}\)BAOutput energy of battery, kWh E\(\mathrm{o}\)PC Output energy of fuel cell, kWh/day E\(\mathrm{o}\)PV\(\mathrm{\Delta}\)mOutput energy of \(n\) photovoltaic module, kWh/day; \(n\): number of photovoltaic modules. Po\(\mathrm{o}\)FC Output power of fuel cell, kW OT Oxygen tank SPV\(\mathrm{p}\)Photovoltaic array area SPV\(\mathrm{m}\)Photovoltaic module area P Power, kW \(\mathrm{I}\)MPP PV Current at the maximum power point (MPP), A V\(\mathrm{V}\)MPP PV Voltage at the maximum power point (MPP), V f\(\mathrm{EL}\)Sizing factor of electrolyzer f\(\mathrm{p}\)V\(\mathrm{p}\)Sizing factor of photovoltaic panel PV\(\mathrm{p}\)P Solar PV array G Solar radiation, W/m\({}^{2}\) S\(\mathrm{SEL}\)Stack electrolyzer area T Time, hour or day V Voltage, V
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Classifiers
#### 3.2.4 Rf  
Classification of sensed data and sensor nodes is performed in the RF method through collective or collaborative learning classification and decision making. It creates a group of decision points at training time. This also generates a class for predicting the network structures. The number of trees and its accuracy of classifications are proportional ([PERSON] et al.) [10]. It works on two steps of operation functionally in WSN as fault finding and failure node identification:  
RF tree creation process is the first step.
* The number of total features available in the system is referred to as m. From these available features, some of them are selected randomly and they are represented as K.
* Few nodes are chosen from the selected features K, by the method of best split-point. The selected nodes are divided into daughter nodes using the best split method. The above procedure is repeated to get the best tree output from the entire network.  
After the RF tree formation, the data sensed and communicated by these TF Trees are to be classified. This classification is computed by the following stages for classification.  
In the RF tree, sensed data and communication processes are given for voting in the entire WSN.  
* The prediction target where the maximum voted has been considered as the best result of this RF algorithm.
## 3 Prediction Framework  
### Model  
In order to accurately predict bus arrival time, we built a prediction network based on DA-RNN (a dual-stage attention-based recurrent neural network) [18]. The overall prediction framework is shown in Figure 1. The input \(x_{S}\) and \(x_{D}\) are the influencing factors of the arrival time on the last \(T\) road segments (including the current road segment). We take the calculation process of \(\hat{y}_{i}\) as an example to show the internal structure of the prediction network. The calculation processes of \(\hat{y}_{(i+1)}\) to \(\hat{y}_{(N-1)}\) are similar to that of \(\hat{y}_{i}\). The input (\(y_{i:T+1}\), \(y_{i:T+2}\)... \(y_{i:1}\)) is the travel time on the last \(T\)-1 road segments (not including the current road segment). The output \(\hat{y}_{i}\) is the predicted travel time of the current road segment. The role of the encoder in RNN is to encode the input sequences into a feature representation [20; 21]. The encoder with input attention module can adaptively select the relevant influencing factor series. Then we use the LSTM-based decoder to decode the encoded input information. The temporal attention module in the decoder is used to adaptively select relevant encoder hidden states across all time steps. The decoder output predicted travel time \(\hat{y}_{i}\). \(\hat{y}_{i}\) can be calculated by Equation (5). The predicted bus arrival time \(t_{i}\) can be calculated by Equation (9) (k = i).  
The application of LSTM units can solve the exploding and vanishing gradient problems that are common when training traditional RNNs. The specific structure of LSTM unit is shown in Figure 2. Each unit performs the following operations:  
\[i_{t}=\sigma(W_{ii}x_{t}+b_{ii}+W_{h}h_{t-1}+b_{hi}), \tag{10}\]  
\[f_{t}=\sigma(W_{if}x_{t}+b_{if}+W_{hf}h_{t-1}+b_{hf}), \tag{11}\]  
\[g_{t}=\tanh(W_{ig}x_{t}+b_{ig}+W_{hg}h_{t-1}+b_{hg}), \tag{12}\]  
\[o_{t}=\sigma(W_{io}x_{t}+b_{io}+W_{ho}h_{t-1}+b_{ho}), \tag{13}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*g_{t}, \tag{14}\]  
\[h_{t}=o_{t}*\tanh(c_{t}), \tag{15}\]  
where \(h_{t}\) is the hidden state at time \(t\), \(c_{t}\) is the cell state at time \(t\), \(x_{t}\) is the input at time \(t\), \(h_{t-1}\) is the hidden state of the layer at time \(t-1\) or the initial hidden state at time \(0\), \(i_{t}\) is the input gate, \(f_{t}\) is the forget gate, \(g_{t}\) is the cell gate, \(o_{t}\) is the output gates, \(\sigma\) is the sigmoid function, and \({}^{*}\) is the Hadamard product.  
Figure 1: The prediction model is a dual-stage attention-based recurrent neural network.  
Figure 2: A long Short-Term Memory (LSTM) unit structure.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 4 Adaptive Channel Estimation Scheme Based on Preamble
### Selection of Channel Estimation  
The key of the proposed scheme is to selectively use a channel estimation scheme that minimizes adaptation algorithm. Therefore, the last step compares the adaptation algorithm of each channel estimation scheme and determines the channel estimation scheme with the smallest resulting value. Comparing the STA to MMSE, it is found that the STA is more efficient than the MMSE at attenuating noise; thus, the STA has a resulting value less than MMSE in low-SNR regions. As a results, the probability that STA is selected increases in low-SNR signal regions. In contrast, as the SNR increases, the effects of noise decreases, and the STA causes an error floor because of the degraded channel estimation accuracy. Therefore, the resulting value of the STA increases as a result of this error floor in high-SNR regions. This leads to an increase in the probability that the STA is not selected. However, because MMSE exhibits excellent channel performance in high-SNR regions, they have a smaller resulting value as the SNR increases than STA, i.e., MMSE have a high probability of being selected. Comparing the STA to TRFI, since the channel estimate of TRFI is set to \(H(0,k)\), the modified MSE of TRFI is zero; thus, the adaptation algorithm depends on \(1/\overline{\gamma}\). The STA causes an error floor in high-SNR regions, but \(1/\overline{\gamma}\) decreases as the SNR increases. Therefore, TRFI is selected after the point where error floor occurs. Figure 5 shows that the channel estimation scheme selected through the last step is employed to equalize all data symbols in the packet.