esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Numerical Validation of the Stability of \(E_{6}\)  
We start by demonstrating that the proposed system is asymptotically stable by using the n-dimensional phase portraits by state combinations [40]. According to this method, Figure 3 illustrates the phase portrait views for the \(m=6\) combinations of states where \(m=\frac{m!}{2(n-2)!}\) with \(n=4\). In addition to the vector fields, we plot the system trajectories using different colored lines (blue, brown, orange, purple, green, cyan, and maroon) based on the seven initial values described in Table 5. Figure 3a-f shows that the vector fields converge to \((x,y)=(1,1)\), \((x,z_{1})=(1,0)\), \((x,z_{2})=(1,0)\), \((y,z_{1})=(1,0)\), \((y,z_{2})=(1,0)\), and \((z_{1},z_{2})=(0,0)\), respectively. The analysis of the view of each state combination reveals that the vector fields converge to the Nash equilibrium \(E_{6}=(1,1,0,0)\). Figure 4a,b shows the evolution of population fractions \(x\), \(y\), \(z_{1}\), and \(z_{2}\) over time under the initial values \(initval_{1}\) and \(initval_{7}\), respectively. We can confirm from Figure 4a,b that the system converges to the Nash equilibrium \(E_{6}\). The convergence of the directional fields and the asymptotic stability of the evolution of \(x\), \(y\), \(z_{1}\), and \(z_{2}\) over time validate the correctness of our theoretical analysis regarding the stability of \(E_{6}\).  
In the following, we examine the effects of various parameters on the ESS, i.e., \(E_{6}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline \(P(A_{1}/T)\) & 0.3 \\ \(P(A_{1}/\tilde{T})\) & 0.6 \\ \(P(A_{2}/S)\) & 0.1 \\ \(P(A_{2}/\tilde{S})\) & 0.8 \\ \(C_{12}\) & 0.1 \\ \(C_{13}\) & 0.2 \\ \(C_{14}\) & 0.6 \\ \(C_{20}\) & 0.2 \\ \(C_{21}\) & 0.8 \\ \(C_{30}\) & 0.4 \\ \(C_{31}\) & 0.15 \\ \(C_{32}\) & 0.7 \\ \(C_{33}\) & 0.25 \\ \(P_{20}\) & 0.25 \\ \(P_{21}\) & 0.1 \\ \(R_{11}\) & 0.2 \\ \(R_{20}\) & 0.15 \\ Seven kinds of initial values & (0.05, 0.1, 0.4, 0.5): (0.1, 0.05, 0.2, 0.3); \\ \((x(0),y(0),z_{1}(0),z_{2}(0))\): & (0.01, 0.2, 0.6, 0.3); (0.4, 0.1, 0.3, 0.6); \\ \(initval_{1},\ldots,initval_{7}\) & (0.2, 0.3, 0.4, 0.1): (0.1, 0.7, 0.1, 0.2); \\  & (0.7, 0.1, 0.3, 0.1). \\ \hline \end{tabular}
\end{table}
Table 5: List of parameter values used in the numerical results.
## 3 Results
### Diffusion Coefficients and Activation Energies of Diffusion  
The migration of BPA from the PET bottles into the simulants follows [PERSON]'s law. Therefore, diffusion coefficients (D\({}_{\text{P}}\)) can be calculated from the linear increase in migration over the square root of time (Figure 1) according to Equation (1).  
\[\frac{m}{A}=\frac{2}{\sqrt{\pi}}c_{P,0}\quad\rho_{P}\sqrt{D_{P}\ t} \tag{1}\]  
with m/A as area-related migration, cp\({}_{0}\) as concentration of BPA in the PET bottle wall, \(\rho_{\text{P}}\) as density of PET, D\({}_{\text{P}}\) as the diffusion coefficient of BPA in PET, and t as storage time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Food Simulant** & **Storage Time [d]** & \multicolumn{4}{c}{**Concentration [\(\mu\)/L]**} \\  & & **30 °C** & **40 °C** & **50 °C** & **60 °C** \\ \hline
20\% ethanol & 10 & 0.205 \(\pm\) 0.005 & 0.383 \(\pm\) 0.004 & 1.05 \(\pm\) 0.02 & 2.88 \(\pm\) 0.02 \\  & 20 & 0.286 \(\pm\) 0.009 & 0.491 \(\pm\) 0.004 & 1.30 \(\pm\) 0.06 & 3.58 \(\pm\) 0.06 \\  & 30 & 0.244 \(\pm\) 0.005 & 0.603 \(\pm\) 0.013 & 1.57 \(\pm\) 0.03 & 4.25 \(\pm\) 0.07 \\  & 40 & 0.259 \(\pm\) 0.004 & 0.626 \(\pm\) 0.018 & 1.74 \(\pm\) 0.06 & 4.98 \(\pm\) 0.10 \\  & 50 & 0.336 \(\pm\) 0.010 & 0.700 \(\pm\) 0.007 & 1.72 \(\pm\) 0.07 & 4.26 \(\pm\) 0.12 \\  & 60 & 0.298 \(\pm\) 0.012 & 0.576 \(\pm\) 0.026 & 1.39 \(\pm\) 0.04 & 4.40 \(\pm\) 0.17 \\  & 90 & 0.386 \(\pm\) 0.019 & 0.736 \(\pm\) 0.078 & 2.19 \(\pm\) 0.10 & 6.20 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: Migration kinetics into 3% acetic acid (**a**), 10% ethanol (**b**), and 20% ethanol (**c**).  
The activation energies of diffusion (E\({}_{\rm A}\)) and the pre-exponential factor (D\({}_{\rm 0}\)) were then determined from Arrhenius plotting of the temperature-dependent diffusion coefficients versus the reciprocal temperatures (Figure 2).  
The lowest diffusion coefficients were measured in the migration kinetics in 3% acetic acid. The diffusion coefficients from the migration kinetics in 10% ethanol were slightly higher, and those in 20% ethanol were slightly higher again. It is very likely that the surface of the PET bottles is swollen by the ethanol, which increases the migration out of the PET and thus the diffusion coefficients (D\({}_{\rm P}\)). This swelling effect of the food simulants was also found in previous studies [29; 30; 31; 32]. Real food does not show this source effect, so the migration of BPA into 20% ethanol can be regarded as a worst-case scenario. Migration into real beverages (instead of ethanolic food simulants) will therefore always be lower. Therefore, more realistic migration of BPA is expected for 3% acetic acid or 10% ethanol as food simulants at ambient temperatures. In addition, the activation energy from 3% acetic acid is adequate for the modeling of migration into water or carbonated beverages.
# Long-Term Forecasting Potential of Photo-Voltaic Electricity Generation and Demand Using R
###### Abstract  
For micro-grid cost-benefit analyses, both energy production and demand must be estimated on the long-term of one year. However, there remains a scarcity of studies predicting energy production and demand simultaneously and in the long-term. By means of programming in R and applying linear, non-linear, and support vector regression, we show the in depth analysis of the data of a micro-grid on solar power generation and building energy demand and its potential to be modeled simultaneously on the term of one year, in relation to electricity costs. We found solar power generation is linearly related to solar irradiance, but the effect of temperature on total output was less pronounced than anticipated. Building energy demand was found to be related to multiple parameters of both time and weather, and could be estimated through a quadratic function in relation to temperature. Models for both solar power generation and building energy demand could predict electricity costs within 8% of actual costs, which is not yet the ideal accuracy, but shows potential for future studies. These results provide important statistics for future studies where building energy consumption of any building type is correlated in detail to various time and weather parameters.  
energy demand; long-term forecasting; machine learning; R programming; solar power generation; support vector regression +
Footnote †: journal: Applied Sciences
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 4 T-Move
### Mobility Support in T-Move  
A major problem with information-centric networks is the mobility of producers (or providers) of content. To illustrate this, we use a simple network where the producer and consumer are separated by two routers \(R_{1}\) and \(R_{2}\), such that the route from the producer to consumer is _producer-R1-R2-consumer_. Let the interests for a particular data object \(d_{i}\) arrive at router \(R_{2}\) as a Poisson arrival rate of \(\lambda\) per second and the router's cache hold \(N\) data objects at a time. Assuming that interests for data objects other than \(d_{i}\) arrive at a rate of \(\mu\) per second, authors of [35] give an expression for the rate of interest propagation from one router (\(R_{1}\)) to the next router (\(R_{2}\)) as \(\lambda(\frac{\lambda}{\lambda+\mu})^{N}\). If the transmission range of the router \(R_{1}\) is \(R_{tx}\) meters and the producer moves at a uniform rate of \(vm/s\), it stays within the router's range for a maximum of \(\frac{R_{tx}}{\
u}s\). After this time, all the interests sent by \(R_{1}\) to the producer expire.  
Traditional IP-based schemes using tunneling and FIB updates have been proposed by some authors to handle producer mobility in ICN. However, most of them just deal with a way of rerouting interests to the producer that has moved. Since ICN uses in-network caching, we propose a caching decision policy to store the content of a mobile producer in the cache of a nearby _favored_ router with minimal FIB updates to route interests to this router. A favored router is an edge router that has content similar to the one at the producer in its cache. It has been shown by many researchers that content exhibits spatio-temporal popularity [34] and hence, caching content where similar content is frequently accessed gives better network performance. Storing data objects at favored routers results more interests being satisfied by the cached content, resulting in a higher cache hit ratio and lower retrieval time, as presented in Section 5.
# QPSK MMW Wireless Communication System Based On p-i-n InGaAs Photomixer
## 3 Experimental Setup  
### Optical and Receiver Channel  
The optical channel of the MMW wireless communication link consists of two CW laser sources, IQ-MZM, Modulator Bias Control (MBC), Arbitrary Wave Generator (AWG), Erbium-Doped Fiber Amplifier (EDFA), Fused Fiber Polarization Combiner/Splitter, and optical fiber cables. The two independent laser sources used to generate the two optical signals are necessary for generating the required THz frequency difference \(v_{THz}\). The first laser source is based on a Single-Frequency Laser (SFL), SFL1550P, from Thorlabs operating at \(\lambda_{1}=1550.07\) nm. The SFL1550P source is current- and temperature-controlled, which allows tuning to specific wavelengths and power, which is essential to obtain the required MMW or THz-carrier frequency difference. The second laser source \(\lambda_{2}=1549.35\) nm, is based on telecom modulation device, MX10B from Thorlabs. The MX10B is compatible with International Telecommunication Union (ITU) transmission channels in a 1.5 \(\upmu\)m band. Measurements of the two lasers' exact wavelengths carried out using an Optical Spectrum Analyzer (OSA), AQ6331 from Yokogawa. The OSA is configured at 1549.73 nm central wavelength and 0.05 nm (5 GHz) wavelength resolution, which is the lowest resolution available for this device.  
For the receiver channel, a high harmonic MMW mixer from Virginia's Diode, Inc. (VDI) integrated with MMW horn antenna of 23 dBi gain operating in the W-band used to downconvert the MMW signal into the low IF signal. The mixer has a 10 dB conversion loss at this frequency band. Using a stable signal synthesizer, the mixer LO is fed with 13.3333-14.6667 GHz frequency and 0 dBm to \(-\)3 dBm input power where the signal multiplied by 6 times to generate LO frequency in the range of 80 to 88 GHz. In this case, the received MMW signal and the LO signal produce the IF signal in the range of 2 to 10 GHz. The IF signal frequency, output power, noise, and the wireless link overall performance analyzed using Keysight Spectrum Analyzer (KSA) and processed by Vector Signal Analyzer (VSA) code, which performs real-time digital signal processing (DSP).
## 1 Introduction
The authors of [32] investigated the mathematical model and the topology of a HESS based on superconducting magnetic energy storage (SMES) and battery, and a novel system-level control strategy for reasonable and effective power allocation between SMES and battery is proposed. In [33], the authors presented a methodology to size flywheel energy storage for grid-level applications using an optimal control law, which performs trade-offs between minimizing grid power consumption and flywheel energy storage size. The characteristics of flywheel--such as high efficiency, high power density and high self-discharge rate--are considered in the methodology, and case study in this work indicates that the flywheel reduced peak grid power by 8%. In these works, load is normally considered as a whole and power fluctuation is decomposed into high-frequency component and low-frequency component for power dispatch among ES devices in a HESS. However, different consumers are not equally important and have various requirements on power supply reliability. The authors of [34; 35] proposed the classification of load based on importance and the corresponding requirements on power recovery time. For instance, power outage longer than 200 ms is not acceptable for extremely important load (level 1 load); hence, fast-respond energy compensation devices shall be utilized to ensure uninterrupted power supply. In this aspect, although characteristics including power/energy density, efficiency and self-discharge rate are already discussed in existing works on power source sizing of microgrid, the respond speed or switching time of various compensation devices, including ES and diesel generator, are seldom considered. In order to obtain a reasonable planning scheme with integrated consideration of both power source and load characteristics, analysis and discussion on ES respond speed and load classification is done in this paper. The paper then focuses on the source-load matching mechanism with analysis on economic loss caused by power outage, and a capacity planning strategy considering combinations of ES devices is proposed and an _ACR_ index is used for balancing economic efficiency and reliability. With differentiated allocation of capacity of ES devices according to load importance, economic efficiency can be improved by slightly sacrificing the supply reliability of level 2 and level 3 load, which are less important. The rest of this paper is organized as follows. In Section 2, modeling of distributed energy resources as well as major characteristics of ES devices are introduced. In Section 3, the source-load matching mechanism and capacity planning strategy are proposed. The objective function based on life cycle cost (LCC) theory is established in Section 4 and case study on an autonomy microgrid is introduced in Section 5.
### Stimulus Onset Interval \"Jitter\"  
Despite a general understanding that short SOIs might pose a problem for the measurement of ERPs, there has been little to no effort to control for such interference in the context of RSVP BCIs. To be sure, it is altogether unclear whether overlapping evoked responses are necessarily detrimental to the function of such systems, since a classifier only needs to differentiate a target from non-targets, not quantify the true amplitude or latency of a specific ERP component. In fact, the rhythmic presentation of visual stimuli will often generate a steady-state visual-evoked potential (SSVEP), which can itself be used to guide BCI function [20]. However, assuming some relationship between fidelity of evoked responses and system performance, one classic approach to the problem of measuring overlapping ERPs has been to randomize or \"jitter\" SOI within a pre-determined range, such as in the adjacent response (ADJAR) technique [17; 21] (not to be confused with P300 \"latency jitter\" [22; 23; 24; 25]). Put simply, randomizing the amount of overlap between responses by jittering SOIs should minimize constructive and destructive interference between repeated stimulus presentations, reduce measurable presence of an SSVEP, and result in more accurate estimates of target-related responses.
# Raman Spectra of ZrS\({}_{2}\) and ZrSe\({}_{2}\) from Bulk to Atomically Thin Layers
## Appendix E Discussion of Flake Degradation  
During experiments, special care had to be taken in order to avoid and minimize flake degradation. Indeed, the use of a high laser power density and a long exposition time during Raman measurements has been found to have dramatic effects on the flakes surface. This can be seen in Figure 11, which shows an AFM tapping image of a ZrS\({}_{2}\) flake after exposure to the laser for some minutes. A hole of about 1.9 nm produced by the laser over the flake surface is clearly visible.  
In order to avoid this inconvenience, laser power density and exposition time have been optimized to avoid flake surface modification after measurement. An AFM tapping image of a flake before and after Raman measurement with these optimized parameters is shown in Figure 12 and no difference can be appreciated on the flake surface due to the laser exposure.  
Nevertheless, an increase in flake thickness can be observed in the second measurement. We also measured it after 10 days but no further increase on flake thickness was observed. This thickness increasing could be a hint of flake oxidation or just due to an increasing of the \"dead layer\" as a consequence of sample exposition to ambient humidity.  
Another hint suggesting the possible flake degradation due to ambient oxidation is represented by the fact that, especially for ZrSe\({}_{2}\), no Raman signal could be observed on the thin flakes after sample exposition to the air for some days. Moreover, the surface of ZrSe\({}_{2}\) flakes appears to be quite damaged even after a short exposure to the air (one day), as shown in Figure 13.  
By these observations, and taking into account previously reported results in the literature that show how the surface of ZrX\({}_{2}\) bulk crystals passivates (amorphous oxides) [37; 38], we are led to think that the oxidation process could play an important role in these systems (oxidation effects are expected to be much more dramatic in thinner flakes). Thus we suggest that further research should be performed in encapsulated samples or under inert gas conditions to exclude this problem.  
Figure 11: AFM tapping image of a ZrSe\({}_{2}\) flake after one day of sample exposure to the air.
# Research on Multi-Strategy Routing Protocol in Flying Ad Hoc Networks
## 6 Conclusions  
In this paper, the RGF routing protocol was proposed. Because of the high-dynamic topology and low density of nodes, the existing routing protocols could not provide a good performance. In order to make up for the shortcomings of a single forwarding strategy, multi-strategy forwarding can effectively reduce the packet waiting delay caused by the need for re-routing. Among them, reactive mechanism forwarding is provided by a reactive mechanism based on the network topology, such as AODV. The reactive mechanism is not only used for packet forwarding, but also for reactive location services, and it improves the efficiency of protocol operations. On the other hand, because the location service of active flooding provides the location information of neighbor nodes, when the reactive path is interrupted and the forwarding of the reactive mechanism cannot continue, it can quickly switch to greedy forwarding based on geographical location, which ensures the continuous transmission of data packets and avoids the impact of path interruption. When greedy forwarding encounters a local minimum problem, it can switch to projection face forwarding for recovery. In order to reduce the path expansion  
Figure 19: The effect of the number of nodes on routing overhead.  
problem caused by projection face forwarding, a minimum local node will also start reactive mechanism construction, which can jump out of the routing hole to switch to reactive mechanism forwarding.  
Conceptualization, N.X. and X.C.; methodology, T.C., N.X., and Y.L.; software, F.L. and K.L.; validation, X.C., T.C., and F.L.; formal analysis, F.L.; investigation, N.X.; resources, X.C.; data curation, K.L.; writing--original draft preparation, N.X.; writing--review and editing, Y.L.; visualization, T.C.; supervision, Y.L.; project administration, X.C.; funding acquisition, N.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2019 YFB1803100.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Experimental Procedure
### Characterization and Wear Tests  
Microstructural characterization of epoxy/GNPs nanocomposites was done by scanning electron microscopy (SEM) using a _Hitachi S-3400N_ to evaluate the dispersion of GNPs in the epoxy matrix.  
Figure 1: Scanning electron microscopy (SEM) micrographs of two types of graphene nanoplatelets: nonfunctionalised (**a**,**b**) and functionalised (**c**,**d**).  
A _Mettler Toledo_ balance (\(\pm\)0.001 mg) equipped with a density determination kit by means of the buoyancy technique (Archimedes method) was used to evaluate the change of density in the epoxy resin by the addition of different GNPs contents. Five measurements were made for each material, and the average value and standard deviation were determined.  
Vickers microhardness measurements were carried out using a microhardness indenter (_Micro Hardness Tester Shimadzu_) by applying a load of 300 mN (HV0.3) for 15 s. The results of this test are the average of at least 10 indentations.  
The samples surface preparation was performed prior to the wear tests. They were ground with different emery papers to obtain a similar surface roughness that would not influence the wear properties. The average roughness of the samples, determined by a profilometer _Mitutoyo SJ-301 Surftest_, was 0.37 \(\pm\) 0.05 \(\upmu\)m.  
Wear tests were carried out on a pin-on-disc tribometer in dry conditions and with the same environmental conditions. The main wear parameters affecting the test were optimized in previous research [18]: A 6 mm diameter alumina ball, 10 N load, 0.09 m/s speed and test distance of 1000 m. Five wear tests were made for each material, and the average value and standard deviation were determined. The tribological behaviour was evaluated by measuring the mass loss, friction coefficient (\(\upmu\)) and wear rate (Q) of the epoxy/GNP nanocomposites. The wear testing machine continuously recorded the friction coefficient. Mass loss was determined by weighing the samples before and after the wear test and volume lost was calculated from the same value using density properties previously determined. Moreover, [PERSON]'s law was applied to determine the wear rate, as it was linear with the sliding distance [33].  
Finally, worn surfaces were analysed by SEM and 3D optical profilometer to define the main wear mechanisms. The width of the wear track was measured by optical microscopy using digital image analysis software included in the profilometer. These measurements allowed us to assess the severity of the wear.