esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method
### Distribution Characteristics of Temperature Field in Bearing Chamber-Bearing System  
As detailed in the temperature field numerical method outlined in Section 2.6, the computed convective heat transfer coefficients are applied to the respective chamber walls as boundary conditions for the thermal analysis of the temperature field. This is further elaborated in Tables 8-11. Additionally, the heat generation due to load and the convective heat transfer conditions on the bearings are incorporated to facilitate the steady-state temperature field calculation for the entire bearing chamber-bearing system. The temperature field distributions within the system under Operating Conditions 1, 2, and 3 were determined, and they exhibit general consistency. Taking the static temperature contour of the bearing chamber-bearing system under Operating Condition 1 as a representative example, the distribution characteristics are illustrated in Figure 8. It is evident from the figure that the solid region surrounding the oil supply pipeline is cooled, whereas the bearing chamber  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Convective Heat Transfer Coefficient (W/m\({}^{2}\cdot\)K)** & **Operating Condition 1** \\ \hline Wall & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Convective heat transfer coefficient of walls in Chamber 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Convective Heat Transfer Coefficient (W/m\({}^{2}\cdot\)K)** & **Operating Condition 1** \\ \hline
2-1 & 1190 \\
2-2 & 2725 \\
2-3 & 2301 \\
2-4 & 2290 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Convective heat transfer coefficient of walls in Chamber 2 and Chamber 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Convective Heat Transfer Coefficient (W/m\({}^{2}\cdot\)K)** & **Operating Condition 1** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 9: Convective heat transfer coefficient of walls in Chamber 5.  
is heated through convective heat transfer with the splashed oil. The highest temperature is observed in the central bearing chamber of the system. Given that the rotational speed remains constant across the three operating conditions, the bearing load varies minimally, and the oil supply conditions are uniform, leading to only slight variations in bearing heat generation and a comparable distribution of the oil-air two-phase flow within the bearing chambers. Consequently, the maximum temperature of the bearing chamber-bearing system under the three operating conditions is largely consistent, reaching approximately 179 \({}^{\circ}\)C.
# Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations
## 5 Conclusions  
In this work, a specialized algorithm was proposed to operate a V2G system composed of a PV array, a battery bank belonging to an electric vehicle, and an interface with the electrical grid. The algorithm optimized the currents being drawn or injected by each component while taking into account an objective that balanced the state of charge of the batteries and the monetary cost of operation. As the system heavily depended on the PV generation values and the electricity tariff throughout the day, those two quantities impacted the optimizer. To better cope with the changing nature of these two inputs, a fuzzy logic component was developed and tested in a number of different scenarios. The results shown here corroborated the suitability of the proposed scheme to be deployed in practice on a real V2G platform, a project that is currently under development.  
Directions for possible future investigations include the use of alternative methods for optimizing the cost other than the differential evolution methodology. From a practical viewpoint, the best optimizer will also clearly be a function of the available computing power in a real V2G scenario. Finally, the matter of imprecise measurements and forecasts should be investigated as these have an important influence on the overall results.  
Conceptualization and methodology, E.T.M. and R.B.G.; validation, N.D.d.A., R.B.G. and L.G.d.S.; formal analysis, R.B.G., E.T.M. and M.A.G.d.B.; investigation, L.G.d.S. and N.D.d.A.; resources, N.D.d.A. and L.G.d.S.; writing--original draft preparation, N.D.d.A. and L.G.d.S.; writing--review and editing, M.A.G.d.B. and E.T.M.; supervision, R.B.G.; project administration, R.B.G.; funding acquisition, R.B.G. All authors have read and agreed to the published version of the manuscript.  
This study was financed by the Research and Development Project--P&D ANEEL. PD-06961-0010/2019.  
Not applicable.  
The authors want to thank the Federal University of Mato Grosso do Sul--UFMS and the companies that sponsored the Research and Development Project--P&D ANEEL. PD-06961-0010/2019.  
The authors declare no conflict of interest.
# Banana Peels: A Genuine Waste or a Wonderful Opportunity?
## 5 Other Uses of Banana Waste
### A Biosorbent of Water Pollutants  
With the industrial revolution, water pollution has also increased and many harmful pollutants are released into water without proper treatment [123]. Industries release heavy metals, dyes, pesticides, chiral chemicals, pharmaceuticals and oil particles into the aquatic environment [124]. These pollutants are causing severe threats to human and aquatic life because of their carcinogenic properties. The reproducibility of fish is affected by some elements, even at very low concentrations (1.0 ng/L) [125]. There are several treatment methods being used to decontaminate the water, such as reverse osmosis, filtration, solvent extraction, chelation, ozonation, skimming, adsorption and electrophoresis. Adsorption by activated carbon is one of the most effective methods, but its application is limited due to its high cost [126]. Natural and synthetic adsorbents can be used as alternatives, such as biochar, clay, inorganic materials, chitosan and carbon nanotubes [127,128]. Although efficient, these materials mostly have complicated and expensive procedures for preparation, enhancements and regeneration. Moreover, these processes release secondary pollutants and are non-biodegradable [128]. Hence, biomass materials are now being explored for use in the adsorption of water contaminants as they are easily available, eco-friendly, biodegradable and less expensive [129,130]. The biosorbents effectively being tested are rice husk, banana peel, corn cob, potato peel, coconut husk, cocoa pod, orange peel and sugarcane bagasse [131,132].  
Banana peel waste is getting attention to be explored as a biosorbent because it is available all over the world. The overall efficiency of banana peel as an adsorbent depends on factors such as water pH, temperature, contamination level, time of treatment and dosage, reusability, regeneration, thermodynamics and kinetics [133,134,135]. It has been observed that lower pH values are suitable for decontaminating anionic pollutants and high pH solutions for cationic compounds. The optimum time was 1 h of treatment, and banana peel is an effective biosorbent over a range of temperatures. It can undergo both exothermic and endothermic reactions, making it suitable for use in both tropical and temperate regions [136]. Biosorbent made from banana peel was tested for oil spill removal. The peel biosorbent can be reused up to 10 times after treating it with mechanical action and \(n\)-hexane to remove the absorbed oil [137]. Banana peel has been used to decontaminate many metals such as Cd, Cu, Pb, Fe, Sr, Zn, Ni, Mn and many dyes such as methylene blue, amido black, Congo red, etc. [128]. Further studies are required to make banana peel a more efficient biosorbent, focusing on the reusability of biomass, effectiveness on more pollutants such as Hg and As, and commercialising it on higher industrial scales [128].
## 3 Results and Discussion
### OAM Detection at Different Transmission Distances  
In free-space optical communication systems, the intensity distortion of vortex beams at the receiving end is affected by the transmission distance. The longer the transmission distance, the more severe the intensity attenuation, and the more difficult it is to extract features from the intensity map. In order to verify the effect of the detection model in the case of different transmission distances, this paper studies different transmission distances. As shown in Figure 7, the influence of Transfer OAM mode classifier on the detection accuracy of 15 distorted OAM modes at different transmission distances (500 m, 1000 m, 1500 m, 2000 m). When the transmission distance is 500 m, even if the turbulence strengths reach strong turbulence, the detection accuracy is still close to 100%. When the transmission distance is 1000 m, the detection accuracy is close to 100% when the turbulence strengths are between weak turbulence and moderate turbulence. However, as the turbulence strengths increase, the accuracy begins to decrease, but it can still remain above 90%. When the transmission distance is 1500 m, the accuracy decreases significantly with the increase in turbulence strengths, especially in strong turbulence where the detection accuracy drops to 86.73%. The performance is most obvious when the transmission distance reaches 2000 m, and the turbulence strengths have the greatest impact on the detection accuracy. Under strong turbulence, the detection accuracy significantly decreases, indicating that turbulence has a significant impact on OAM mode transmission during long-distance transmission, and the classifier performance is significantly limited. However, the detection accuracy can still reach around 75%. The results show that the accuracy of OAM mode detection decreases with the increase in transmission distance, but the Transfer OAM mode classifier designed in this paper can achieve high detection accuracy in long-distance  
Figure 6: The confusion matrix of the Transfer OAM model under three types of ATs, with a transmission distance of z = 1000 m.  
transmission, which is of great significance for reducing the number of relays, reducing costs, and improving the accuracy of beam mode detection in practical applications.
## 3 Proposed Hybrid Precoding-Companding Technique
### Companding  
Companding is a promising technique for PAPR reduction, and it has a moderate PAPR reduction gain with the smallest computational complexity among all other PAPR reduction techniques. However, the companding technique achieves PAPR reduction at the expense of degrading BER performance. There exist different companding transforms. For each companding transform, there is a point at which BER degradation is the minimum. It is the efficient operating point, regardless of PAPR reduction gain [41]. Among all companding transforms, Log companding with a threshold is the best in BER performance and OOB radiation reduction. BER and OOB radiation reduction are more important than PAPR reduction gain in companding techniques. Transfer functions of Log companding and de-companding with threshold are defined as follows [10]:  
\[x_{n}^{c}=\left\{\begin{array}{cc}x_{n}&,|x_{n}|\leq x_{th}\\ \mathrm{a}\ln\{1+(x_{h}\mathrm{b})\}&,|x_{h}|>x_{th}\end{array}\right. \tag{18a}\] \[r_{n}^{e}=\left\{\begin{array}{cc}r_{n}&,|r_{n}|\leq x_{th}\\ (exp\ ^{(r_{n}/\mathrm{a})}-1)/\mathrm{b}&,|r_{n}|>x_{th}\end{array}\right. \tag{18b}\]  
Log companding compresses signals with an amplitude larger than \(x_{th}\) while keeping signals with small amplitude unchanged. Two parameters \(\mathrm{a}\) and \(\mathrm{b}\) controlling Log transform slope (\(\mathrm{a}\times\mathrm{b}\)). The slope of the transform must equal to unity (\(\mathrm{a}\times\mathrm{b}=1\)), to keep OFDM symbol average power the same before and after the transform [3].  
This research assumes demanding with filtering to alleviate OOB radiation, although filtering may produce some peak regrowth [10]. Frequency domain filtering consists of two IFFT processes. The first FFT process converts the OFDM signal into a frequency domain. Then the in-band frequency domain components of the companded signal \(x_{0}\dots x_{(N_{DSC}/2-1)},x_{(LN_{DSC}-(N_{DSC}/2+1))}\dots x_{(LN_{DSC}-1)}\) are kept as it is, while OOB components of the companded signal \(x_{(N_{DSC}/2)}\dots x_{(LN_{DSC}-N_{DSC}/2)}\) are nulled. The second IFFT process converts the signal back to the time domain. Frequency domain filtering does not require a reverse process in the receiver [42]. In the transmitter, computational complexity in terms of \(RM_{s}\) and \(RA_{s}\) of the Log companding transform is given by:  
\[RM_{s} =\Big{(}i_{Taylor}+1\Big{)}6N_{T} \tag{19a}\] \[RA_{s} =\Big{(}4i_{Taylor}+1\Big{)}N_{T} \tag{19b}\]  
where \(i_{Taylor}\) is the number of terms in Taylor series that can be sufficiently set to 10.
## 4 Case Study for Signal Processing of Pulsed Thermography
### Advances of PCT  
With the PPT described in the previous section, PCT is most applicable for PT data processing. PPT transforms the image sequence into the Fourier (frequency) domain, and PCT applies PCA to the image sequence to extract the most meaningful frames.  
[PERSON] et al. [59] introduced Candid Covariance-free Principal Component Analysis (CCIPCA). This method is a fast Incremental Principal Component Analysis (IPCA), used to compute the principal components of a sequence of samples incrementally without estimating the covariance matrix. After that, [PERSON] et al. proposed Candid Covariance-free Principal Thermography based on CCIPCA. This approach decomposes the defects of the sample with the K-medoids clustering method based on the RGB of the PCT results. In addition, shorter calculations are applied to estimate the covariance command and singular value decomposition (SVD). The results of CCIPCT showed promising results in simplifying computational complexity while increasing the number of data acquisition frames. In addition, [PERSON] et al. [60] introduced Sparse Principal Component Analysis(RPCA)-based K-means clustering on improving the noise of PCT and CCIPCT results. This approach increased the robustness due to the norm addition terms to the PCA linear transformation and improved noise significantly compared to the existing technique. [PERSON] et al. [61] proposed the Sparse Principal Component Thermography (SPCT), an application of the SPCA to PT. They showed that SPCT outperforms existing techniques with high computational performance. [PERSON] et al. [62, 63] introduced an improved version of SPCT called Edge-Group Sparse Principal Component Thermography (ESPCT) that can preserve the spatial connectivity of thermal image pixels. They showed in experiments conducted on marquetry sample that ESPCT results provide higher contrast and SNR than PCT and SPCT.  
[PERSON] et al. [64, 65, 66] introduced the Low-rank SPCT and applied several Non-negative Matrix Factorization (NMF) approaches. They conducted a comparative analysis on low-rank matrix approximation using the NMF approach and showed more promising results than other component-based (PCT, CCIPCT, SCPT) methods.  
Many PCT studies have focused mainly on improving the contrast of defects and reducing the quantity of data. Recently, Independent Component Analysis (IPCA) has been attracting attention, and various approaches have been proposed to solve ICA [67]. In [68], [PERSON] et al. investigated several recent ICA methods and evaluated their influence on PT data compared with the state-of-the-art methods. They found that ICA outperformed PCT for small defects and that the frequency of data acquisition and ICA methods had a significant influence on the results.
## 3 Results and Discussion
### Optimization of the XUV Yield  
The optimization for the total XUV yield was performed for four different gas pressures in the HHG-HCF and the results are summarized in Figure 6, where the integrated signal between 23 nm and 43 nm was used as fitness parameter. At 10 mbar the obtained optimized HHG look very similar for the driving laser pulses broadened either in argon or SF\({}_{6}\). The 27 th and 31 st harmonics are slightly less pronounced, using SF\({}_{6}\) for broadening (Figure 6a). Inspecting the evolution of the fitness during the optimization as shown in the inset of Figure 6a it can be seen that the initial value for the fitness is much higher using SF\({}_{6}\), but the gradient is smaller. These observations are a first hint that the temporal shape of the SF\({}_{6}\)-broadened pulse is slightly different. For 20 mbar backing pressure an increase for the 27 th harmonic can be seen using SF\({}_{6}\). Since the gas pressure and the properties of the HHG-HCF are the same, this effect can only be attributed to plasma dispersion and therefore a different temporal pulse shape. Also, other lower order harmonics are increased, as shown in Figure 6b, which significantly increases the fitness compared to the use of an argon broadened spectrum for pulse shaping. The same is true for 30 mbar (Figure 6c), here phase matching prevents low order harmonics in case of argon which can be compensated in case of SF\({}_{6}\). For 40 mbar (Figure 6d) the absolute yield after optimization is similar for both gases, but the spectral distribution is different. In case of SF\({}_{6}\) lower order harmonics are more pronounced.  
Figure 6: Optimized high-harmonic spectra from an argon-filled HCF are shown for temporally shaped pulses after spectral broadening in argon (black curve) and SF\({}_{6}\) (red curve). The optimization was performed to achieve the maximum yield between 23 and 43 nm and the fitness of the fittest individual of each generation is shown in the inset. The optimizations were performed at **(a)** 10 mbar; **(b)** 20 mbar; **(c)** 30 mbar and **(d)** 40 mbar argon backing pressure in the high-harmonic generation (HHG)-HCF.  
To conclude, for higher gas pressures the XUV spectra of the in SF\({}_{6}\) broadened and subsequently shaped pulse show a larger amount of lower order harmonics, which increases the total bandwidth of the XUV spectrum. In a certain parameter range, the XUV yield can be doubled compared to the use of argon for spectral broadening.
# A Comprehensive Survey on Knowledge-Defined Networking
## 4 Management Plane
### Network Monitoring Frameworks  
A network monitoring framework known as Payless has been proposed for KDNs, which uses an adaptive network statistics collection algorithm at different aggregation levels using RESTful Application Programming Interfaces (APIs) to deliver highly accurate information in a timely manner with a lower communication overhead [255]. A Representational State Transfer (REST) API is an API that conforms to the constraints of the REST architectural style. OpenSample is a low-latency, sample-based measurement platform for network monitoring that has been designed to collect network load and flow statistics [256]. Another network monitoring framework called OpenNetMon monitors the network by collecting per-flow metrics to check whether end-to-end QoS parameters are actually met while delivering the per-flow metrics to the knowledge plane to compute paths using traffic engineering [257]. OpenNetMon also uses an adaptive rate for polling similar to Payless, where the rate increases when the flow rates differ between the samples and decreases when the flows stabilize. A joint HOst-NEtwork (HONE) traffic management framework is a traffic monitoring framework used in KDN using a collection of traffic measurement data that minimizes overhead by performing lazy materialization of fine-grained statistics and processes data locally on end hosts [258].
## 2 Materials and Methods
### Results
#### 3.1.1 Principal Component Analysis (PCA)  
All color features and all color average features were analyzed by principal component analysis, as shown in Table 1. Three principal component factors were extracted from all color features, and the cumulative variance contribution rate was 95.898%, indicating that the extracted factors can reflect \(\geq\)95% of the original information for all variables. Two principal component factors were finally extracted from all the color average features, and the cumulative variance contribution rate was 96.442%, which indicated that the extracted factors could reflect \(\geq\)96% of the original information for all variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **Component** & \begin{tabular}{c} **Characteristic** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Variance Contribution** \\ **Rate (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative Contribution** \\ **Rate (\%)** \\ \end{tabular} \\ \hline All color features & 1 & 12.745 & 63.723 & 63.723 \\  & 2 & 5.29 & 26.452 & 90.174 \\  & 3 & 1.145 & 5.724 & 95.898 \\ \hline Average features of & 1 & 8.523 & 85.234 & 85.234 \\ all colors & 2 & 1.121 & 11.208 & 96.442 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The principal component eigenvalues, contribution rate, and cumulative contribution rate of all color features and all color average features.  
Figure 3: Flowchart of the main procedures for extracting and analyzing the data.
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 3 Development of Supervisory Control Strategy
### ECMS Control Strategy without Thermal Limitations
When SOC is too low, the controller must restrict usage of the EM. On the other hand, when the SOC is near its upper threshold limit, the controller tends to use the EM power. Moreover, to accurately compare the fuel consumption of HEV, results should be indicated for the charge sustaining mode where SOC has the same level at the beginning and at the end of the driving cycle. Charge sustaining mode:  
Therefore, to maintain the battery SOC within the lower \(SOC_{low}\) and upper \(SOC_{high}\) limits, an s-shaped correction function \(PF_{soc}\) was introduced to penalize the equivalent fuel consumption of the battery [10]. If the SOC decreases below the threshold (0.6 in this case), the vehicle tends to restrict the usage of electric energy, hence the \(PF_{soc}\) starts to rise. Otherwise, the correction factor is equal to 1, meaning that no limitation to utilize the electric power is applied. In terms of an equation, the above condition can be represented as:  
\[PF_{soc}= \begin{cases}1-0.15\ \overline{SOC}^{3}+0.05\ \overline{SOC}^{4},&SOC <0.6\\ 1,&SOC\geq 0.6\end{cases}  \tag{20}\]  
where \(\overline{SOC}\) is a normalized state of charge that can be computed as [10]:  
\[\overline{SOC}=\frac{2\cdot SOC-\left(SOC_{high}+SOC_{low}\right)}{SOC_{high} -SOC_{low}} \tag{21}\]  
where, \(SOC_{high}=0.80\) and \(SOC_{high}=0.60\) values are used as upper and lower threshold limits for SOC of the battery. The coefficients in Equation (20) are chosen based on multiple trials to obtain the best fuel consumption. The plot in Figure 11 shows the variation of \(PF_{soc}\) as a function of SOC. Then the objective function in Equation (15) is modified with the penalization for low SOC values, as:  
\[J_{ecms}=\dot{m}_{ice}(P_{ice})+PF_{soc}\cdot\dot{m}_{eqv}(P_{bat}) \tag{22}\]  
By minimizing this objective function, the torque split is performed to minimize the fuel consumption without considering the thermal limitations of the battery. However, as was mentioned, the optimal operating temperature range for lithium-ion batteries is between 15-55 \({}^{\circ}\)C, otherwise, their safety, power and life are impacted [17; 18; 20]. Therefore, temperature limitation will be implemented in the ECMS controller in the next step.