esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [13] a variant of the firefly algorithm (FFA) is presented as a tuning method to obtain, in simulation, the gains of a PID controller, which is implemented for a linear model of an automatic voltage regulator. In fuzzy control, there have been also works on the applications of EA to adjust the fuzzy systems used; for instance, in [14] the optimization of a sectorial Mamdani-type fuzzy controller for a two degrees of freedom robot is reported. Particle swarm optimization (PSO) is employed to adjust the centers and standard deviations of the input and output membership functions; scopes of research are limited to simulation. In [15] a method for \"optimal design\" of an interval type-2 fuzzy control is presented. The control law is applied to a linear plant in simulation. The centers and standard deviations of the upper type-2 membership functions are adjusted in a restricted footprint of uncertainty, limiting in that way the search space of the optimization problem. The human evolutionary algorithm is used and three different objective functions are proposed. In [16], a combination of approximate feedback linearization and sliding mode control approaches is applied to stabilize a fourth-order under-actuated nonlinear inverted pendulum system. A new version of PSO is implemented. The obtained results are illustrated in simulation and do not show a stability analysis. In [17], an approach for automating and facilitating the inverted cart-pendulum (ICP) control in one step is proposed. A holistic optimization is performed by a simplified Ant Colony Optimization method with a constrained Nelder-Mead algorithm (ACO-NM). Simulation results on an ICP nonlinear model show that ACO-NM in the holistic approach is effective compared to other algorithms. Stability analysis is not proven and no real-time experiments are presented. In sum, our contributions can be described as follows:  
* The proposed algorithm does not use any knowledge of the plant. Unlike previous works, a formal stability analysis of the closed-loop system is proven via Lyapunov theory and therefore, boundedness of the solutions (position and velocity of the pendulum on a cart) is guaranteed. * A comparison among several evolutionary techniques are carried out in simulation, and then successfully exported to real-time experiments without any change in the optimized membership functions; only the gains of the controller were tuned again. This paper is organized as follows: In Section 2, the modeling of the inverted pendulum on a cart system is described, as well as a brief explanation of the fuzzy systems considered. A review of the evolutionary algorithms PSO, DE and FFA used in order to optimize the performance of the adaptive fuzzy controller is also presented. In addition, the proposed controller design is derived and the stability analysis is developed. In Section 3, control and optimization results are presented. Finally, Section 4 concludes about some contributions of the proposed control strategy.
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
## 1 Introduction
It is a recognized fact that due to the lack of a time-varying magnetic or electric field, in direct current (DC) systems it is more difficult to develop energy harvesting systems. However, due to their nature, TEGs allow us to bypass this difficulty, since heat is generated by the Joule effect in both DC and AC systems. Therefore, TEGs allow the design of energy harvesting units compatible with both DC and AC systems. Most commercial transmission line products are supplied by batteries and/or electromagnetic energy harvesters, so there are several works using TEGs for such applications. In [16], the possibility of using a TEG wrapped around the conductor to harvest the energy of the heat flux between the conductor and the environment is described. However, a heat exchanger connected to the cold side of the TEG is required to maximize the temperature gradient between the hot and cold ends. A similar approach was applied in [12] to power a wireless system mounted on a substation cylindrical busbar, which was composed of different sensors (voltage drop, current and temperature). When applied to power lines, the intensity of the current has a profound impact on the temperature of the conductor. As in the case of other energy harvesting approaches, i.e., solar or wind, thermoelectric energy harvesting depends on environmental variables. Therefore, it is difficult to generate a stable supply of electrical power [19], as it is necessary to apply specific energy management strategies to ensure a stable and reliable generation of electrical energy [20]. This paper presents an experimental study of the generated power and the efficiency of TEG devices installed in tubular busbars, which are characteristic of electrical substations. Due to their large diameters, there is very little temperature gradient between the ambient and the busbar, generally a few degrees Celsius. This is a challenging application that has hardly been analyzed in the technical literature, since most TEG applications are focused on high temperature gradients [21]. Under such unfavorable conditions, the TEGs generate very low voltage, so a suitable DC/DC converter is required to supply the sensors and communications module. This work analyzes four different combinations of TEG-DC/DC converters, which are tested under realistic operating conditions and their efficiencies are analyzed. This article is organized as follows. Section 2 reviews the basic principles of thermoelectric generators including aspects such as efficiency or their use in energy harvesting systems for transmission lines. Section 3 selects the most suitable TEG + DC/DC converter combination for this application from among four candidate options. Section 4 analyzes in depth the experimental behavior of the most appropriate TEG + DC/DC combination. Finally, Section 5 develops the conclusions of this work.
## 3 OLSR Attacks
### False Control Information Generation
#### 3.1.2 TC Messages Scope  
The significance of TC messages within the OLSR protocol is multifaceted. These messages are indispensable for various key functions, including the distribution of network topology information, efficient path calculation, adaptation to dynamic network conditions, maintenance of loop-free routing, and facilitation of fault detection and recovery. Here too, both Identity Spoofing and Link Spoofing are security threats that can undermine the trust and reliability of the protocol.  
* Identity Spoofing: It is possible for a node to send TC messages while pretending to be another node. In fact, it involves link spoofing. Indeed, a node assuming the identity of another node is effectively advertising incorrect links to the network [11].
* Link Spoofing: In this attack [12], a node can send TC messages, announcing an incorrect set of links. This can take two forms. If the SET is incomplete, that is, a node \"ignores\" the links to certain nodes in its SET of MPR selectors, this leads to a network with no connectivity with these \"ignored\" neighbors, \"as well as with neighbors who are accessible only by\" ignored \"neighbors. In addition, a node can add non-existent links (i.e., links to nodes that are not its neighbors) to a TC message.
## 6 Case Data and Simulation Results
### Optimal Scheduling Result Analysis  
This paper first takes the summer cooling scenario as an example for simulation. In the scenario, four cases are established: case 1: VES is not considered; case 2: Only EWHVES is considered; case 3: only ACVES is considered; case 4: both EWHVES and ACVES are considered. Then, in order to verify the effectiveness of the strategy proposed in this paper, simulations are performed in different seasons.
## 3 Results  
The data analyzed using ANOVA showed significant effects of treatments and exposure time to wood ash on slug's mortality, slugs feeding, ability to cross the barrier and attack plants, as well as damage potential (see Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Chemical**} & \multirow{2}{*}{**Unit**} & _Fagus_ & _Quercus_ & _Carpinus_ & _Abies_ & _Picea_ & _Almus_ & _Castanea_ \\
**Compounds/Element** & & _sylvatica_ & _petraea_ & _betulus_ & _alba_ & _abies_ & _glutinus_ & _sativa_ \\ \hline SiO\({}_{2}\) & \% & 6.87 & 4.94 & 1.02 & 11.53 & 7.46 & 3.04 & 2.78 \\ Al\({}_{2}\)O\({}_{3}\) & \% & 1.61 & 1.53 & 0.49 & 2.95 & 2.48 & 0.73 & 1.03 \\ Fe\({}_{2}\)O\({}_{3}\) & \% & 0.41 & 1.5 & 0.31 & 1.87 & 1.74 & 0.87 & 0.59 \\ MgO & \% & 7.36 & 3.4 & 1.87 & 6.83 & 4.44 & 2.34 & 1.92 \\ CaO & \% & 31.09 & 37.23 & 55.12 & 52.96 & 39.55 & 42.01 & 48.52 \\ Na\({}_{2}\)O & \% & 0.08 & 0.17 & 0.03 & 0.21 & 0.21 & 0.09 & 0.13 \\ K\({}_{2}\)O & \% & 12.6 & 11.11 & 6.23 & 4.98 & 7.54 & 5.14 & 3.24 \\ TiO\({}_{2}\) & \% & 0.09 & 0.13 & 0.02 & 0.21 & 0.16 & 0.04 & 0.19 \\ P\({}_{2}\)O\({}_{5}\) & \% & 1.86 & 2.12 & 1.77 & 2.24 & 2.36 & 1.07 & 2.03 \\ MnO & \% & 0.38 & 0.99 & 0.08 & 1.17 & 1.26 & 0.16 & 1.03 \\ Cr\({}_{2}\)O\({}_{3}\) & \% & \textless{}0.002 & 0.014 & \textless{}0.002 & 0.009 & \textless{}0.002 & \textless{}0.002 & \textless{}0.002 \\ Ba & \% & 0,1371 & 0,26 & 0.089 & 0.1907 & 0.2736 & 0.0712 & 0.0982 \\ Ni & \% & \textless{}0.002 & 0.0032 & \textless{}0.002 & 0.0039 & 0.005 & \textless{}0.002 & \textless{}0.002 \\ Co & \% & 0.00021 & 0.00081 & 0.00003 & 0.00117 & 0.0012 & 0.00006 & 0.00009 \\ Cs & \% & 0.00017 & 0.0001 & 0.00002 & 0.00009 & 0.0002 & 0.00003 & 0.00005 \\ Ga & \% & 0.00012 & 0.00019 & \textless{}0.00005 & 0.00038 & 0.0004 & \textless{}0.00005 & \textless{}0.00005 \\ Sr & \% & 0.03252 & 0.04565 & 0.05365 & 0.07348 & 0.0668 & 0.02165 & 0.03265 \\ Mo & \% & 0.00034 & 0.00023 & 0.00106 & 0.00023 & 0.0002 & 0.00083 & 0.00093 \\ Cu & \% & 0.00914 & 0.01786 & 0.00615 & 0.0135 & 0.0163 & 0.00725 & 0.00623 \\ Pb & \% & 0.00603 & 0.00393 & 0.00023 & 0.00101 & 0.0029 & 0.00043 & 0.00067 \\ Zn & \% & 0.0294 & 0.0370 & 0.0072 & 0.0157 & 0.1145 & 0.00089 & 0.000971 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compounds detected in tested wood ash.
# A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones
## 2 Drone Charging Station Design and Development  
The Aerial-Core H2020 [43] project included the research, design and development of a technological demonstrator for assessment of energy harvesting technology for drone charging stations and their advanced capabilities (Figure 1) on overhead power lines, including perching on cables and robotic manipulation. This section defines the design requirements and discusses the inductive coupling technique in detail. Then, the document covers the power electronics stages, which include a voltage rectifier with overvoltage protection and a DC/DC charger equipped with the MPPT algorithm. Lastly, the mechanical design, which is based on a clamp concept, is presented.  
* Overhead power line specifications  
High-voltage cables and towers designed for the European transmission and distribution grid reach up to 750 kV and 1 kA, respectively, but line voltages vary from country to country, with most being 50 Hz AC, except for some high-voltage DC links. The diameter of the overhead power cable varies based on power line current and length requirements. For the design of drone charging stations, the specifications for overhead power lines are constrained by the values shown in Table 1, which are based on established power grid standards.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Overhead Power Line** & **Value** & **Unit** \\ \hline Power line voltage 1  & \textless{}400 & kV \\ Frequency & 50/60 2  & Hz \\ Power line diameter & 9–24 & mm \\ Current & \textless{}600 & A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limit values considered for overhead power lines.  
Figure 1: Main local manipulation platform and charging station placed on the overhead power line (top-right corner) during integration experiments at the ATLAS Centre (Jaen) for the Aerial-Core project.  
Operations on live power lines require compliance with power line safety clearances. Additionally, the charging station was designed to minimize stress on the power line by adjusting its weight, size and shape. Overhead power lines typically support installed accessories such as bird diverters, line spacers or Stockbridge dampers. However, the weight restriction imposed by drone payloads is higher than that of the power line. The utilization of low-abrasion materials and the adjustment of the shape to deter birds from nesting were also taken into account.
* Multirotor drone specifications Professional unmanned aerial vehicles (UAVs) are used for electrical grid inspection and maintenance due to their ability to carry equipment. To determine the average values for commercial multirotor UAVs, the specifications shown in Table 2 are considered.  
The drone charging station and installation end effectors are designed to fit the drone payload, while the charging station is integrated into the drone to minimize risks during flight.
## 3 Results and Discussion
### Analysis of Experimental Results
#### 3.4.1 Results of Prediction Analysis Metrics
It could more accurately distinguish between positive and negative samples and had better classification ability. Compared with linear regression, linear discriminant, random forest, and ordinary ChebNet models, it has higher application value in the prediction and analysis of reservoir fluid PVT.
# Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night
## 5 Discussion
Figure 17 shows the vertical profiles of the \(Z\) values and rawinsonde data. It is observed that there is a layer of enhanced reflectivity at the entrainment layer, which is consistent with the known results [63]. Some scholars also conject that dust and other particulate matter can be the cause of the clear-air echoes. At the beginning of this study, we also inferred that nocturnal pollutant accumulation is the reason for the echo diurnal variation because of the uncleanliness of the atmosphere of the megacities. Yet, it is hard to explain the variation in the echo signal. One piece of evidence is based on seasonal variation. In winter, the air quality is the worst because of heating, which uses fossil fuels, but the clear-air echo is hardly observed by radars during winter. The annual and monthly variation in the sand and dust also showed that the sand and dust weather is most frequent in spring, whereas the signal of the spring clear-air echo was generally weaker than summer and autumn. The other piece of evidence comes from the daily change in pollutants. The concentration of pollutants has two peaks because of traffic congestion in the metropolis, which is different from the \(Z\) value. The wind, which is related to pollutant diffusion and dust, also does not show a significant correlation with the clear-air echoes. Although it is known that fire plumes can cause clear-air echoes [65; 66], it is unrealistic to detect plumes of wild fires in an urban area. Thus, dust and the pollutants are unlikely to be the main causes of clear-air echoes. The issue of the influence of meteorological factors on troposcatter communication remains unknown and requires future examination. In troposcatter, the value of the signal level depends on the refractive index and its gradient, which are affected by the intensity of the turbulence fluctuation and atmospheric stratification. Thus, [PERSON] proposed that the inversion of temperature leads to the diurnal variation in the signal [54]. However, few studies focus on the effect of turbulent mixing on signals level. Thus, we plan to provide a more in-depth explanation of reflecting layers forming and the structure of the nocturnal boundary layer in future studies. Meanwhile, [PERSON] pointed that bistatic radars have an advantage in the sensitivity of clear echoes [67]. Technically, experiments using bistatic radars are closer to the principle of troposcatter propagation. Moreover, experiments with bistatic radar data would provide more information for the analysis in future research.
## 3 Results
### Impact of Meteorological Factors on CO\({}_{2}\) Fluxes
#### 3.2.2 Contribution of Meteorological Factors to CO\({}_{2}\) Fluxes  
The machine learning algorithm, random forest (RF), has the advantage of being able to handle large amounts of mixed data with high noise immunity and assess the importance of each variable factor [43]. Therefore, the contribution of meteorological factors to CO\({}_{2}\) fluxes at half-hourly and daily scales was calculated using the RF model.  
As shown in Figure 8, at the half-hourly scale, the 0 cm soil temperature had the greatest contribution to NEE and GPP, while the 20 cm soil temperature had the largest contribution to Reco. It is noteworthy that for the studied seasonal variations and control factors of water and CO\({}_{2}\) flux in alpine meadows in Lijiang, southwestern China, using eddy covariance technology on half-hourly and daily scales, photosynthetically active radiation (PAR) and air temperature are the primary meteorological factors determining net ecosystem production (NEP) [44]. At the daily scale, the 20 cm soil moisture contributed the most to NEE and GPP, followed by the 10 cm and 20 cm soil temperature, and the 5 cm soil temperature contributed the most to Reco. This indicates that soil temperature is a prerequisite for the variation in CO\({}_{2}\) fluxes in the Middle Tien Shan grassland ecosystem because temperature affects the enzyme activity of plant physiological processes, which in turn affects photosynthesis in the ecosystem. It also suggests that the meteorological factor  
Figure 7: Seasonal variation in meteorological factors in grassland ecosystems from Apr to Sept of 2018. (**a**) Seasonal variation in wind speed (WS); (**b**) seasonal variation in relative humidity (RH); (**c**) seasonal variation in photosynthetic active radiation (PAR); (**d**) seasonal variation in vapor pressure difference (VPD); (**e**) seasonal variation in air temperature (Ta); (**f**) seasonal variation in soil temperature (Ts); (**g**) seasonal variation in soil moisture content (SWC); and (**h**) seasonal variation in Soil heat flux (SHF).  
of soil temperature has the most significant influence on CO\({}_{2}\) fluxes, both at the half-hourly and daily scales.
## 4 Results
### Effect of Room Volume  
Participants had to answer questions about the boundaries and dimensions of the room they were in. Their answers were used to estimate the volume of the room in which they performed the task (see Figure 3). In the FW group, the estimated room volumes were larger on average and more dispersed than in the CW group. A nonparametric statistical test confirmed that both distributions were significantly different (Kruskal-Wallis: \(H=16.85\)\(p<0.001\)). Consequently, in the following analysis, the influence of volume on auditory distance perception is investigated separately in each group. For each group, separate linear regressions were applied to responses associated with a specific stimulus (27 different stimuli: 9 distance \(\times\) 3 rendering methods), with the room volume as a factor. In the CW group, significant correlations between the room volume and distances longer than 4 m generated with the intensity-based model were found (\(D=4\) m \(F=4.16\)\(p=0.034\); \(D=5\) m \(F=6.05\)\(p=0.017\) ; \(D=6\) m \(F=5.06\)\(p=0.028\) ; \(D=7\) m \(F=6.40\)\(p=0.014\)). For these specific stimuli, larger volumes were significantly correlated to a larger compression of the reported distances. No significant correlations were found for shorter source distances (\(p>0.05\)) nor for all distances in the FW group.  
Figure 2: Geometric means of reported distance according to the rendering method used to generate the stimuli and the visual spatial boundary condition. Clear dots represent the geometric mean of a single participant for a given distance, while opaque dots represent the geometric mean over all participants for a given distance.
## 5 Experimental Results
### Overall Power Saving with a Combination of BCC and CBA  
In this subsection we discuss the combined effect of CBA and BCC together for power saving. We evaluated the power consumption of a data center consisting of 800 servers with a higher injection rate traffic having an average flow rate of 400 Mbps. For this comparison, the power consumption of each server was modeled based on Odroid-XU3 board. The power consumption of the Odroid-XU3 at PowerNap was conservatively assumed to be 1 watt and the full load power consumption was 20 Watts. We also assumed that the power saving ratio per device followed the energy saving ratio due to CBA algorithm. In Figure 12 we showed the power consumption of the data center in different cases including, normal condition (NC), utilizing BCC only, utilizing CBA only and finally, utilizing both BCC and CBA together.  
At higher datarate, BCC consolidation could reduce the power consumption of the overall data center more for S2S-WiDCN compared to fat-tree network. The CBA algorithm working standalone outperformed BCC for fat-tree wired network while in S2S-WiDCN, BCC could reduce more power compared to CBA. Nevertheless, for both wired and wireless networks, combining both BCC and CBA could achieve maximum power reduction.