esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### E-CERP Routing Algorithm for a WSN  
The energy-efficient cross-layer-based expedient routing protocol (E-CERP) transfers data over the shortest paths in an energy-efficient and scalable manner. It increases communication link reliability while reducing packet delays. The existing cross-layer-based opportunistic routing protocol (CORP) algorithm has several drawbacks:  
* High data transmission complexity occurs when the number of constraints increases due to the limited computing power of the WSNs.
* They are challenging to integrate.
* They have a high power consumption, packet loss, delivery rate, and communication delay.  
The proposed E-CERP technology solves the problems of conventional routing protocols, and the goal is to maximize the nodes' transmission power by utilizing the network's remaining energy.  
(i) Local broadcast  
Each node sends a HELLO message that includes its ID, the number of hops to the BS, and the path cost. The base station path cost can change from round to round if it is initially 0 and each node is infinite. Each node organizes its list of neighbors based on the HELLO messages it receives. Signal strength is calculated using the received signal strength indicator (RSSI) of incoming messages. The average RSSI calculates the link reliability metric \(L(n,\,m)\), expressed in Equation (8).  
\[L_{n,m}=T_{mtp}-T_{arp} \tag{8}\]  
The Lagrange multiplier is used to estimate the link cost as follows:  
\[cos(n,\,m)=X_{cir}+X_{arp}+\frac{In\,\,I}{v}.\frac{1}{h_{nm}} \tag{9}\]  
(ii) Routing algorithm  
A parent \(Par(n)\) has to be chosen for a specified sensor node \(n\) to act as the next hop sensor node to send data from node \(n\) to the BS. The mathematical equation can be written as follows:  
\[Par(n)=arg_{m\in N_{i}(n)}\,\,min[cost(m)+cost(n,m)] \tag{10}\]  
Using the cost function and the parent route selection shown in Equation (10), a cost-based route is created. In each round, the parent preferences are updated.  
(iii) Transmission Power Control (TPC)  
The ideal transmit power can be determined using hop count, as represented in Equation (11):  
\[T_{tx(n)}=\frac{1}{h_{n}}\left[In\,\,I+ln\,\left(h_{n}\,\sum_{n=1}^{H}\frac{1} {h_{n}}\right)\right] \tag{11}\]  
For the transmitted execution, reasonable simplification is carried out, and the upper bound can be calculated using Equation (12):  
\[\left(\sum_{n=1}^{H}\frac{1}{h_{n}}\right)ln\,\,I\leq\sum_{n=1}^{H}X_{tx(n)} \leq\left(\sum_{n=1}^{H}\frac{1}{h_{n}}\right)(ln\,\,I+ln\,\,H) \tag{12}\]  
Thus, for the given sensor node, the upper bound is estimated using Equation (13).  
\[X_{tx}(n)=\frac{1}{h_{nm}}\,\,[ln\,\,I+ln\,\,H(n)] \tag{13}\]  
where \"\(I\)\" is the target end-to-end success probability and \"\(H\)\" represents the hop count. The ideal path for sending and receiving data has been determined after evaluating the transmitting power. It has negligible packet loss and delays, as well as minimal energy conservation.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 6 Simulation Results and Discussion
### Simulation Results of Case 2  
Tables 5 and 6 show the filter planning results for Case 2 considering the harmonic spectrum of DGs. The results show that two single tuned filters are optimally designed and planned to eliminate the third harmonic order. Table 5 shows the characteristics and parameters of the proposed filters. Table 6 illustrates the effect of the proposed filter planning in enhancing system performance (reducing the voltage deviation, power loss, and THD). Where these filters can reduce the maximum THD from 9% to 3.87% to meet the IEEE-519 standard. The power loss is also reduced from 54 to 32 kW.  
Figure 10: Fundamental voltage profile of the 69-bus system with and without filters in case 1.  
Figure 9: Total harmonic distortion (THD) of the 69-bus system buses with and without filters in case 1.  
The THD of the system buses before and after applying the proposed method is shown in Figure 11. This figure shows that; the DG units' harmonics have a considerable effect in increasing the THD of the system. Where the system max. THD has increased by 13.9% due to considering the DGs harmonic spectrum. Moreover, the system voltage profile is improved due to the installation of the proposed filters as shown in Figure 12. Where all the buses' voltages become within the permissible limits and the under-voltage issue is solved.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Without Filter** & **With Filter** \\ \hline \(\mathrm{V_{max}}\) (p.u) & \(1\) & \(1.0062\) \\ \(\mathrm{V_{min}}\) (p.u) & \(0.9329\) & \(0.9837\) \\ \(\mathrm{P_{loss}}\) (\(\mathrm{KW}\)) & \(53.622\) & \(32.29\) \\ \(\mathrm{THD_{max}}\) (\(\%\)) & \(9\) & \(3.87\) \\ \hline \multicolumn{3}{c}{**Fitness Functions**} \\ \hline \(F_{1}\) & \(F_{2}\) & \(F_{3}\) & \(F_{total}\) \\ \(3.8657\) & \(0.4131\) & \(3.0866\times 10^{4}\) & \(0.3076\) \\ \hline \end{tabular}
\end{table}
Table 6: Results of objective functions of case 2.  
Figure 11: THD of the 69-bus system buses with and without filters in case 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Filter No.** & **Placement** & \(f_{t}\) & **Q\({}_{c}\) (MVar)** & **R (\Omega)** & **L (mH)** & **C (\(\mu\)F)** \\ \hline \(1\) & \(69\) & \(3\) & \(0.1\) & \(3.5617\) & \(0.1890\) & \(6\) \\ \(2\) & \(43\) & \(3\) & \(1.82\) & \(0.1952\) & \(0.0104\) & \(108.7\) \\ \hline \end{tabular}
\end{table}
Table 5: Results of control variables for filter planning in case 2.
## 5 Analysis of Simulation Results  
### Performance Analysis of OGSMP-MIMO System  
Figure 4 shows the received signal eye pattern with different antenna selection algorithms under the condition of SNR(signal-to-noise ratio) of 10 dB. Figure 5 presents the bit-error rate of the OGSMP-MIMO system using different antenna selection algorithms. The system parameters are presented in Table 2.  
From the simulation results, it can be seen that the openness and shape of the received signal eye pattern based on the Pearson correlation coefficient antenna selection algorithm have been expanded and improved, compared to the random antenna selection algorithm and norm antenna selection algorithm, thereby ensuring maximum signal quality and minimum interference.  
It can be deduced from the simulation results that, compared with the norm-based antenna selection algorithm and random selection algorithm, the OGSMP-MIMO system based on the Pearson coefficient antenna selection algorithm has better bit-error rate performance. When the bit-error rate reaches \(10^{-6}\), the signal-to-noise ratio of the antenna selection algorithms based on the Pearson coefficient, antenna selection algorithms based on norm, and random selection algorithms reaches 50.3 dB, 53 dB, and 54 dB, respectively. The antenna selection algorithm based on the Pearson coefficient exhibits better bit-error performance, which is improved by 2.7 dB and 3.7 dB, respectively.  
Figure 6 illustrates the theoretical and simulated bit-error performances of the OGSMP-MIMO system, based on the Pearson coefficients. The system parameters are the same as in Table 2.  
It can be inferred from the simulation results that (1) when the SNR is relatively low, the theoretical BER of the OGSMP-MIMO system is higher than the actual BER, and when  
Figure 5: Bit-error rate of OGSMP-MIMO system with different antenna selection algorithms.  
Figure 6: Theoretical and simulated bit-error performances of OGSMP-MIMO system based on Pearson coefficients.  
the SNR is relatively high, the theoretical BER coincides with the actual BER, and (2) under the same modulation method, increasing the number of transmitting antennas will reduce the bit-error performance.  
Figure 7 presents the bit-error rate of the OGSMP-MIMO system based on Pearson coefficients under different modulation orders. The system parameters are presented in Table 3.  
From the simulation results and Equation (3), it can be observed that, with an increase in the modulation order, the transmission rate increases; however, the bit-error performance decreases. The 2 PAM, 4 PAM, and 8 PAM modulation modes were used to achieve transmission rates of 5 bpcu, 7 bpcu, and 9 bpcu, respectively (bpcu refers to the number of bits transmitted in each channel). The signal-to-noise ratios reached 10 dB, 12 dB, and 14 dB. Although the transmission rates were reduced by 2 bps and 4 bps, respectively, the bit-error performance was improved by approximately 2 dB and 4 dB, respectively.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Hardware Architecture of a Wireless Sensor  
A wireless sensor is a small electronic device capable of measuring a physical quantity (e.g., temperature, light, pressure) and communicating it to a collection center directly or via other sensor nodes that act as routers [10]. Given the advances in microelectronics, wireless transmission technologies and software have made it possible to produce microsensors of a few cubic millimeters in volume that can operate in a network at a reasonable cost [22]. A sensor node consists of four base units and two optional units [10; 33] (see Figure 2):  
* _Acquisition unit_. This consists of two subunits: a sensor and an analog-to-digital converter (ADC). The sensor collects digital measurements on environmental parameters and transforms them into analog signals. The ADC converts analog signals into digital signals.
* _Processing unit_. This is also composed of two units: a data-storage unit, and a processor responsible for data-processing and control procedures allowing the collaboration of the sensor with the others in order to carry out acquisition tasks.
* _Communication unit_. Its function is information transmission and reception. It is equipped with a transmitter/receiver pair. It allows communication within the network, in our case, by radio  
Figure 1: General architecture of a wireless sensor network (WSN).  
frequency (radio waves). However, there are other transmission possibilities (e.g., optical and infra red)
* _Power unit_. Power supply is a critical element of sensor architecture. It is responsible for supplying energy to all other units. It most often corresponds to a battery, feeding the sensor, whose limited resources make it a problem specific to this type of network. However the recent realization of solar-panel power unit is trying to provide a solution for extending sensor life [33].
* _Mobilizer_. Optionally, this is used to move the node to complete the task to be processed.
* _Location-Finding System_. Optionally, this provides information on the location required by the application and/or routing.
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 3 Results and Discussion
### STEPDISC Method for Selecting Factors  
The values of the factors obey multivariate normal distribution and meet the prerequisites of STEPDISC method [16; 17]. If all of them are used to build the discriminant function, it will increase the computational effort, and the possible correlation between the factors will also cause computational difficulties and decrease the accuracy and instability of the discriminant function, so we need to select the factors with significant discriminant ability from the 12 factors. With AQL as the predicted grouping variable, the 12 factors were gradually selected at the significance level of 0.01. The results are shown in Table 3.  
Step 1 performed an analysis of covariance for the 12 factors outside the model, and from Table 3, we can see that PM\({}_{10}\) corresponds to a Wilks' \(\lambda\)-value of 0.280, and \(F\)-value of 909.7, which is the largest among all factors. \(p\)-value \(<\) 0.0001 is also the smallest and meets the criteria for factors to enter the model (\(p<\) 0.01), which can be judged to be most closely related to the AQL. Therefore, PM\({}_{10}\) was selected to enter the discriminant model. Step 2 started with an analysis of covariance for the factors within the model, at which point there was only PM\({}_{10}\) within the model, so it was not eliminated, and then an analysis of covariance was performed on the remaining 11 factors outside the model, and PM\({}_{2.5}\) was selected for inclusion in the discriminant model. Steps 3 to 9 analyzed the factors in the model and outside the model, respectively, to determine whether they meet the criteria for exclusion or entry. In this process, AP, NO\({}_{2}\), CO, RH, SO\({}_{2}\), SH, and WS were selected into the model, respectively, and no factors were excluded. The remaining AT, DP, and O\({}_{3}\) outside the model were analyzed for covariance, and the corresponding \(p\)-values did not meet the criteria for entry into the model and could not be selected into the discriminant model. Finally, AP, WS, SH, RH, PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), and CO were selected from 12 factors.  
The annual averages of PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), CO concentration, and AQI for the years 2015-2019 are shown in Figures 3 and 4.  
According to Figure 3, PM\({}_{10}\) and PM\({}_{2.5}\) were the top pollutants in each year, SO\({}_{2}\) and NO\({}_{2}\) decreased significantly after 2018, and CO had little change in its impact in each year. From Figure 4, we know that the highest mean AQI value was 81.66 in 2015, the next highest value was 77.63 in 2016, and the lowest mean AQI value was 68.62 in 2019, which is a decrease of 15.97% compared to 2015.
# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies  
In this section, we present a brief description of the NOMA-VLC systems integrated with different emerging technologies. We mainly focus on the benefits of this coexistence with these technologies as discussed in the literature.
###### Abstract
Risks and relationships constitute a network structure of mutual influence, and evaluation of project risks from an isolated perspective will ignore the types of risks that are prone to secondary risks but do not cause much damage themselves [7]. This results in the frequent occurrence of secondary risks in engineering projects. Third, the risk evaluation process ignores the impact of complex systems on the accuracy of expert evaluation data. Existing research approaches mostly consider the fuzziness of the collected data at the computational stage and attempt to reduce its effect on the results using specific data processing methods. Few studies have reduced data ambiguity from the data collection process. Unfortunately, reducing the adverse effects of data subjectivity through a complex calculation process has limited effect. When the evaluation object is the relationship between construction project risks, a new method of data collection is required to improve the data quality. The current work is motivated by these research gaps and combines innovation relating to both theory and methodology. In terms of theoretical innovation, this study attempts to consider engineering project risks as a network system. The risk weights are evaluated by calculating the ability of the risk to elicit other risks in the network system. This is more consistent with engineering reality, where project risks exist as interacting systems rather than as independent events. In terms of methodology innovation, this research incorporates the idea of interference into the MCDM method to create an interference-MCMD method. The interference-MCDM replaces the traditional one-time direct evaluation of risk relationships with a two-time indirect evaluation of risk relationships under different risk assumptions. The eliciting relationships among risks are reflected by the difference between these two sets of indirect evaluations. This method is closer to the essence of risks and causes subjectivity in expert survey data to cancel each other out. The data processing result shows that data calculated by the interference fuzzy analytical network process (IF-ANP, one of the interference-MCDM methods combined with the fuzzy technique) have much better credibility than traditional MCDM methods. The remainder of this paper is organized as follows: Section 2 is a review of the existing literature related to this work. Section 3 clarifies the study framework, offers a brief introduction to factor analysis fuzzy measure, the IF-AHP method, and the DEMATEL method, and constructs the construction engineering risk analysis model. In Section 4, empirical experiments of the Chengdu-Chongqing Middle Line High-speed Railway construction engineering project are studied using the proposed model. The analysis result is shown in Section 5. Section 6 presents the conclusions obtained through the research results. The conclusions are discussed in Section 7.
## 3 Methods  
The processing chain (Figure 1) designed to analyze the S2 data is composed of three main phases: (1) preprocessing; (2) burned area detection procedure; (3) validation.  
Figure 1: Workflow of the procedure for identifying burned areas from Sentinel-2 data.  
All the analyses were done using the following free software: SNAP, R-cran (and the libraries \"raster,\" \"ncdf4,\" igraph,\" Hmisc,\" \"parallel,\" and \"doParallel\"), GRASS GIS, and Quantum GIS.
###### Abstract  
As an emerging network paradigm, the space-air-ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and storage resources in mobile devices greatly impacts the quality of experiences for intelligent applications. Hence, we plan to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MECs). To facilitate efficient processing, we need to solve the optimal task offloading decisions. In contrast to existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capabilities for edge computing nodes, the uncertainty of transmission latency caused by heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we first describe the task offloading decision problem in environments characterized by these new challenges. However, we cannot use standard robust optimization and stochastic optimization methods to obtain optimal results under uncertain network environments. In this paper, we propose the 'condition value at risk-aware distributionally robust optimization' algorithm for task offloading, denoted as RADROO, to solve the task offloading decision problem. RADROO combines the distributionally robust optimization and the condition value at risk model to achieve optimal results. We evaluated our approach in simulated SAGIN environments, considering confidence intervals, the number of mobile task offloading instances, and various parameters. We compare our proposed RADROO algorithm with state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the Brute algorithm. The experimental results show that RADROO can achieve a sub-optimal mobile task offloading decision. Overall, RADROO is more robust than others to the new challenges mentioned above in SAGIN.  
space-air-ground integrated network; mobile edge task offloading; distributionally robust optimization; conditional value at risk +
Footnote †: journal: _Sensors_**2023**, 23, 5729. [[https://doi.org/10.3390/s23125729](https://doi.org/10.3390/s23125729)]([https://doi.org/10.3390/s23125729](https://doi.org/10.3390/s23125729))
## 2 Degradation of Materials in Space--Stability Issues on Mirrors
### Thermal Processes  
Thermal cycling may cause mechanical defects that can grow and degrade the optical system performance in orbit. For instance, strong thermal fluctuations may induce mechanical stress that may lead to alterations in the figure of the optics [69], modify the stress balance between the coating and substrate or even between different materials within the coating. Nowadays, most of the flight optics undergo a critical thermal cycling test for their space qualification. This test exposes optics to one or more cycles over temperature ranges typically within [-100 \({}^{\circ}\)C, +100 \({}^{\circ}\)C] for 24 h or more, although for some missions this test might be more extreme. As a reference, MIL-M-13508C specifies that protective Al coatings located in front mirrors have to survive at least 5h at -62 \({}^{\circ}\)C and 5 h at 71 \({}^{\circ}\)C. One example of an extreme temperature range test was the coating qualification of the oxide-protected Au-coated Be mirrors for JWST, in which witness samples were cryogenically cycled to down to 15 K four times and to 328 K one time [70].  
Among others, [PERSON] et al. reported on thermal characteristics of a classical solar telescope primary mirror [71] (similar investigations have been reported by [PERSON] et al. [72]). They used a heat transfer model that considers the heating caused by a smooth and gradual increase in the solar flux during the day-time observations and cooling resulting from the exponentially decaying ambient temperature at night. The thermal and structural response of SiC and Zerodur was investigated in detail. The low thermal conductivity of Zerodur mirror gives rise to strong radial and axial temperature gradients for daytime heating and nighttime cooling. Heat loss by free convection is very slow, so the mirror retains significant heat during the night. The observed thermal response of the SiC mirror is significantly different from Zerodur. The temperature within the SiC mirror substrate equilibrates rather quickly due to its high thermal conductivity. The thermal expansion of ceramic, silicon, and SiC optical substrate materials was also investigated in regard to Herschel (2009-2013) observatory [15]. In particular, SiC is one of the most investigated materials for an observatory in cryogenic environment [73; 74; 71; 75; 8; 71].  
Research on coatings and thin films demonstrated that the instability of properties in optical film was attributed both to the coating materials and their deposition process [76; 77; 78; 35; 78]. For example, with respect to metals, metal oxide compound coating materials possess large energy gaps and provide a high transmission to short, near-UV wavelengths because their optical absorption edge is outside (shorter than) the wavelength of interest. Therefore, they are intrinsically less vulnerable to damaging by thermal effect, ionizing, and UV radiation. The most commonly used coating materials are MgF\({}_{2}\), ZrO\({}_{2}\), TaO\({}_{5}\), TiO\({}_{2}\), HfO\({}_{2}\), and SiO\({}_{2}\)[79].