esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### UV-Vis Absorption Spectrum Analysis
#### 3.3.2 Light Absorption Spectra of the MXene Based Ionanofluid
This illustrated a similar trend with less efficiency to that of the previous ionanofluid as indicated in Figure 8. As seen in the image, the indisputable absorbance enhancement was found in the UV region of 200 to 380 nm wavelength, implicating the ionic liquid-based MXene nanofluids' ability to harvest solar energy. However, less concentrated MXene nanofluid (0.1 wt%) showed an obvious reduction of light absorbance after 330 nm. On the other hand, the increase of nanoparticle concentration led to many UV and visible region peaks. Therefore, increasing the concentration of MXene nanoparticles could make better light absorbance in the visible region than the UV region, which is favorable for solar energy harvesting. The presence of many absorbance peaks, especially on the first day, demonstrated that an increase in optical absorption in the UV and visible regions of MXene nanofluids could effectively capture solar energy, which would enable the prepared samples to have successful photovoltaic applications. Figure 8: Absorption spectra of ionanofluids with different weight concentrations on the first day. Figure 9: Absorption spectra of ionanofluids with different weight concentration on the seventh day. The Beer-Lambert law was applied for calculating the extinction coefficient of the as-prepared samples. Figure 10 shows the calculated extinction coefficient results at different wavelengths and nanoparticle concentrations on the first day. From the figure, it is clear that the extinction coefficient of the MXene based ionanofluids improved linearly with an increase in nanoparticle concentration. Furthermore, the extinction coefficient was higher in the UV region due to the excitation of localized surface plasmon resonance on the MXene surface [20]. While at longer wavelengths in the visible and IR regions, the optical properties resulted in less efficiency. The utmost extinction coefficient was 7.6 cm\({}^{-1}\) for 0.4 wt% nanoparticle concentration, while the lowest value was obtained for 0.1 wt% concentration. On the other hand, the extinction coefficient of each sample decreased with an increase in wavelength. Figure 11 illustrates the results of the extinction coefficient of the synthesized MXene ionanofluids of 0.1, 0.2, 0.3, and 0.4% weight concentrations on the seventh day. Most researchers proved that the extinction coefficient of fluids was improved by adding nanoparticles [21; 22]. It is important to accurately predict the spectral characteristics of nanofluids' extinction coefficient to obtain significant optical properties at low concentrations. From the above figure, the maximum extinction coefficient value of 5.6 cm\({}^{-1}\) was acquired for 0.4 wt% at 300 nm, whereas others remained below 2.6 cm\({}^{-1}\). From 200 nm, the values fell desperately to 800 nm for the studied MXene concentrations. The obtained results were in good agreement with previous literature [3].
## 4 Results and Discussion
### Water Balance Evaluation between 2020 and 2029
#### 4.7.1 Annual Water Balance without the Consideration of Ecological Water Requirements  
The annual water balance evaluation results without the consideration of ecological water requirements under the two scenarios and conditions are reported in Table 24.  
Under the dry year scenario, the annual water balance in the normal condition indicates a surplus in all years. The water surplus varies from 29.15 million m\({}^{3}\) to 79.70 million m\({}^{3}\), with an average water surplus of 54.12 million m\({}^{3}\). In addition, the annual water balance in the new normal condition also indicates a surplus in all years. The water surplus varies from 43.38 million m\({}^{3}\) to 79.70 million m\({}^{3}\), with an average water surplus of 58.19 million m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Area in km\({}^{2}\)**} & \multicolumn{4}{c}{**Water**} & \multicolumn{4}{c}{**Area in km\({}^{2}\)**} & \multicolumn{4}{c}{**Water**} \\ \cline{2-13}
**Year** & **Field** & **Paddy** & **Para Rubber** & **Total** & **Demand** & **Evergreen** & **Mangrove** & **Scrub** & **Total** & **Demand** \\  & **Crop** & **Field** & **Trees** & **Total** & **(Million m\({}^{3}\))** & **Forest** & **Forest** & **Forest** & **(Million m\({}^{3}\))** & **(Million m\({}^{3}\))** \\ \hline
2020 & 3.47 & 0.13 & 182.16 & 185.76 & 264.84 & 72.96 & 24.68 & 26.85 & 124.49 & 159.58 \\
2021 & 3.52 & 0.13 & 180.01 & 183.66 & 260.98 & 71.79 & 24.66 & 26.84 & 123.29 & 157.37 \\
2022 & 3.82 & 0.10 & 177.87 & 181.79 & 258.14 & 70.62 & 24.44 & 26.81 & 121.87 & 155.36 \\
2023 & 3.76 & 0.09 & 175.74 & 179.59 & 255.03 & 69.47 & 24.44 & 26.79 & 120.70 & 153.70 \\
2024 & 3.99 & 0.07 & 173.55 & 177.61 & 252.87 & 68.27 & 24.44 & 26.74 & 119.45 & 152.42 \\
2025 & 4.19 & 0.06 & 171.46 & 175.71 & 249.25 & 67.15 & 24.19 & 26.76 & 118.10 & 150.00 \\
2026 & 4.30 & 0.05 & 169.35 & 173.70 & 246.31 & 65.98 & 24.10 & 26.75 & 116.83 & 148.19 \\
2027 & 4.44 & 0.03 & 167.20 & 171.67 & 243.33 & 64.83 & 24.01 & 26.74 & 115.58 & 146.40 \\
2028 & 4.39 & 0.02 & 165.21 & 169.62 & 241.19 & 63.71 & 24.01 & 26.77 & 114.49 & 145.27 \\
2029 & 4.67 & 0.00 & 162.90 & 167.57 & 237.35 & 62.49 & 23.82 & 26.69 & 113.00 & 142.74 \\ \hline Average & 4.06 & 0.07 & 172.55 & 176.67 & 250.93 & 67.73 & 24.28 & 26.77 & 118.78 & 151.10 \\ \hline \hline \end{tabular}
\end{table}
Table 23: Area of each agriculture and forest type and water demand between 2020 and 2029.  
Similarly, the annual water balance in the normal condition reveals a surplus in all years under the wet year scenario. The water surplus varies from 749.61 million m\({}^{3}\) to 799.98 million m\({}^{3}\), with an average water surplus of 774.89 million m\({}^{3}\). In addition, the annual water balance in the new normal condition also indicates a surplus in all years under this scenario. The water surplus varies from 763.95 million m\({}^{3}\) to 799.98 million m\({}^{3}\), with an average water surplus of 778.97 million m\({}^{3}\).
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results
### Model Performance with Different Sets of Input Features
#### 3.2.1 Full-Length Features  
We trained all the six machine learning models with full-length features that include NDVI and EVI, GPP, climate variables (temperature, precipitation, VPD, and mean dew point temperature), and soil data. We present the R\({}^{2}\) and RMSE of all six models in Figure 5. The derived R\({}^{2}\) ranges from 0.791 to 0.901. The R\({}^{2}\) and RMSE of GWRFR are 0.901 and 0.764 MT/ha, respectively. The results also illustrate that GWRFR outperforms other five models by 0.04-0.11 in terms of R\({}^{2}\). DTR has the lowest performance (R\({}^{2}\) = 0.743 and RMSE = 1.231 MT/ha). GWRFR improves the yield prediction by 0.073 in terms of R\({}^{2}\) and 0.241 MT/ha in terms of the RMSE when compared with its non-spatial version, RFR (R\({}^{2}\) = 0.828 and RMSE = 1.006 MT/ha). The scatterplots of the observed and predicted yield values are also presented in Figure 5. Overall, the yield predictions by machine learning models are more similar to the observed yield as most points follow the dotted blue line; however, there are some under- and over-estimations in different models. Such cases are rarely observed in GWRFR. DTR has the lowest performance when compared with other machine learning models because the points in its scatterplot are more dispersed.  
Figure 4: The spatial distribution of predicted yield of (**a**) MLR, (**b**) PLSR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using (1) full-length features, (2) vegetation indices derived from RS data, (3) GPP, (4) climate data, and (5) soil data.  
Figure 5: \(Cont.\)
## 2 Materials and Methods  
### Apparatus and Material  
An induction furnace (Type TNX 60, Plustherm Point GmBH, Wettingen, Switzerland) with a recrystallized alumina work tube was used. A schematic of the furnace is shown in Figure 1. A molybdenum crucible was used as the inner crucible for the preparation of the slag. The furnace was water cooled and had openings to facilitate the introduction of an inert gas in the crucible and thermocouple. The temperature was measured by a B-type thermocouple, which was checked against a calibrated standard couple. The synthetic samples were made from mixtures of TiO\({}_{2}\) (>99.5 pct), Ti\({}_{2}\)O\({}_{3}\) (>99.9 pct), FeO (>99.5 pct), MgO (>99.9 pct), MnO (>99.9 pct), SiO\({}_{2}\) (>99.9 pct), and Al\({}_{2}\)O\({}_{3}\) (>99.5 pct). The concentration of components used for each test is described in Table 1. The percentage composition of TiO\({}_{2}\) and Ti\({}_{2}\)O\({}_{3}\) selected for each test was comparable to the actual industrial slag. The presence of FeO can play an important role in the slag formation, so a wide range (1% to 15%) of composition was selected. The MgO composition in industrial slag is around 1-2%, but there can be an increase in composition because the ilmenite smelter operates with a freeze lining of a solidified slag against the furnace wall [15]. Any damage caused to the freeze lining may result in a higher concentration of MgO in the slag. To accommodate this variation, the percentage composition of MgO was from 0.6% to 10%. All the other impurities were within the range of industrial slag. A FeO-TiO\({}_{2}\)-Ti\({}_{2}\)O\({}_{3}\) ternary phase diagram at 1600 \({}^{\circ}\)C and 1500 \({}^{\circ}\)C is shown in Figures 2 and 3, respectively, which were prepared with FactSage version 6.4 using FactPS and FToxid databases. Figure 1: Schematic diagram of the induction furnace used for the preparation of the slags. Figure 2: FeO-TiO\({}_{2}\)-Ti\({}_{2}\)O\({}_{3}\) ternary phase diagram at 1600 \({}^{\circ}\)C using FactSage version 6.4. The constitution of the ternary phases in the system was similar between 1500 \({}^{\circ}\)C and 1600 \({}^{\circ}\)C. Pseudobrookite formation was more prominent at 1500 \({}^{\circ}\)C with a higher concentration of TiO\({}_{2}\) in the system [26]. However, the liquid slag phase was enlarged at 1600 \({}^{\circ}\)C, and the magneli phases were solidified, as shown in Figure 2. The bordering phase regions were shifted, accordingly, too. The temperature (in \({}^{\circ}\)C) mentioned was the liquidus temperature for each sample, which was determined based on the theoretically calculated liquidus contour diagram, as shown in Figure 4. The calculation of the diagram was carried out with MTDATA 6.0 software [27] using a non-commercial HTOX 9.0 database [28] for oxide and sulfide systems. It was important to keep a check on the liquidus temperature so that the complete melting of the slag was achieved. This was especially so in the laboratory furnace as the holding period was just half an hour to achieve equilibrium. Samples with low FeO started to melt at lower temperatures when compared to samples with higher FeO content.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
###### Abstract  
Structures for the evaluation of fast Fourier transforms are important components in several signal-processing applications and communication systems. Their capabilities play a key role in the performance enhancement of the whole system in which they are embedded. In this paper, a novel implementation of the discrete Fourier transform is proposed, based on a bit-slice approach and on the exploitation of the input sequence finite word length. Input samples of the sequence to be transformed are split into binary sequences and each one is Fourier transformed using only complex sums. An FPGA-based solution characterized by low latency and low power consumption is designed. Simulations have been carried out, first in the Matlab environment, then emulated in Quartus IDE with Intel. The hardware implementation of the conceived system and the test for the functional accuracy verification have been performed, adopting the DE2-115 development board from Terasic, which is equipped with the Cyclone IV EP4 CE115F29C7 FPGA by Intel.  
discrete Fourier transform; fast Fourier transform; FPGA; OFDM (Orthogonal Frequency Division Multiplexing); DVB (Digital Video Broadcasting) [table]capposition=top  
1][PERSON] [1], [PERSON] [2] and [PERSON] [3]
## 2 Context and Case Study of a \"Power Electronic-Less\" Powertrain for Aircraft Propulsion  
Based on the previous state-of-the-art analysis, it has appeared that most of the propulsive architectures in more electric (hybrid or full electric) aircrafts are based on power electronics with a coupling between power sources and propulsion device through a DC bus, as illustrated in Figure 1a. The main idea of this paper is then to suppress this DC bus and these power electronics by directly connecting power sources (generators) to propulsive devices (motors) through a dedicated AC bus, as illustrated in the Figure 1b (for single motor connection) and Figure 1c (for multi-motor connection). All electric machines considered in that study are surface mounted PMSMs.  
It could be said that the interaction between synchronous machines is a subject already considered in the context of large terrestrial networks and addressed by reference works as proposed by [PERSON] in [31]. However, some differences appear between this last approach and the one proposed in this paper. Indeed, [PERSON] has focused his work on ground transport networks of electricity, where large distances (long cables) are involved, which differs from the aeronautical application case which reactances are only composed of electrical machine reactances. Moreover, while the reference work [31] focuses only on the electrical dimension of the device, we aim here to also couple the mechanical dimension of the system, which is of major importance in terms of dynamics (stability).  
In the \"power electronic-less\" structure of Figure 1b,c, considering the direct connection of the synchronous generator and the motors, obviously all machines operate at the same electrical speed (in a steady-state situation). Only two mechanical controls exist, each one on a system side: on the generator side, the mechanical speed, thus the AC link frequency, is controlled through the gas turbine, which manages the mechanical operation of this system. Oppositely, on the propeller sides, a pitch control of blades would be necessary to manage the aircraft thrust given the rotation speed operation. However, this latter issue is not the aim of our contribution.  
This study may be applied to a wide spectrum of aeronautic applications. Two examples of topologies with six propellers are presented below, depending on whether one or more generators are placed on the wings in a series parallel architecture (Figure 2 left) or at the aircraft rear in the case of a series architecture (Figure 2 right).  
Such electric distributed propulsion may offer aerodynamic advantages as studied in [10; 11].  
Figure 1: Several topologies for the powertrain: (**a**) with power electronics coupling; (**b**) single-motor “power electronic-less” coupling; (**c**) multi-motor “power electronic-less” coupling.  
Figure 2: Two example of architectures (here with six propellers) with direct AC coupling between generators and motors.
# A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging
## 3 Sparse Bayesian Algorithm for High Cross-Range Resolution
### Sparse Bayesian Superresolution Algorithm
To this end, we use the negative logarithm operation to transform the problem as:  
\[\mathbf{\hat{x}}=\arg\ \min_{\mathbf{x}}\left[-\frac{1}{2}\ln\left(p\left( \mathbf{g}|\mathbf{x}\right)\right)-\frac{1}{2}\ln\left(p\left(\mathbf{x} \right)\right)\right], \tag{13}\]  
and the objective function is transformed into:  
\[\mathbf{\hat{x}}=\arg\ \min_{\mathbf{x}}\left[-\frac{1}{2\sigma^{2}}\left\| \mathbf{g}-\mathbf{A}\mathbf{x}\right\|_{2}^{2}+\sum_{n=1}^{NK}\frac{1}{q} \left(\left|x_{n}\right|^{q}-1\right)\right]. \tag{14}\]  
In Equation (14), the noise parameter \(\sigma^{2}\) is unknown and needs to be estimated. The estimation of this parameter will be discussed at the end of this section. Regardless of the unknown parameter \(\sigma^{2}\), we first calculate the conjugate gradient of Equation (14) with respect to \(\mathbf{x}\) and make it equal to zero:  
\[\
abla f\left(\mathbf{x}\right)=\frac{1}{\sigma^{2}}\mathbf{A}^{H}\mathbf{A} \mathbf{x}-\frac{1}{\sigma^{2}}\mathbf{A}^{H}\mathbf{g}+\mathbf{P}^{-1}\mathbf{ x} \tag{15}\]  
where \(\mathbf{P}=diag\left\{p_{1},\cdots,p_{NK}\right\}\) and \(p_{n}=\left|x_{n}\right|^{2-q}\). Minimizing (15) by letting \(\
abla f\left(\mathbf{x}\right)=0\), the formula can be written as:  
\[\left(\mathbf{A}^{H}\mathbf{A}+\sigma^{2}\mathbf{P}^{-1}\right)\mathbf{x}- \mathbf{A}^{H}\mathbf{g}=\mathbf{0}. \tag{16}\]  
It is hard to get the optimal solution of \(\mathbf{x}\) from Equation (16) because the matrix \(\mathbf{P}\) is a nonlinear function of \(\mathbf{x}\). In order to solve this problem, we employ a heuristic approach. The coarse least-squares approximation solution \(\mathbf{x}=\left(\mathbf{A}^{H}\mathbf{A}\right)^{-1}\mathbf{A}^{H}\mathbf{g}\) is chosen as the initial value first. Then, we construct a simple solution of \(\mathbf{x}\):  
Poisson-based MAP algorithm because it has been shown in [19] that it provides better superresolution performance than some traditional forward-looking imaging methods. In the simulations, the added noise obeys a zero mean and complex Gaussian distribution, and the signal-to-noise ratio (SNR) is defined as:  
\[SNR=20\log_{10}\frac{\left\|\mathbf{x}\right\|_{2}}{\left\|\mathbf{\hat{x}}- \mathbf{x}\right\|_{2}}. \tag{21}\]  
Note that the SNR herein is that of the cross-range echo after matched filtering in range. The forward-looking radar transmits linear frequency-modulated pulses, and can provide significant SNR gain by matched filtering. Typically, when the bandwidth of the LFM signal is 50 MHz and the pulse width is 2 \(\upmu\)s, the match filtering can provide an SNR gain by 30 dB. When the output SNR of the radar receiver is 0 dB, the ultimate SNR of the azimuth echo can reach 30 dB. Therefore, in the following simulation, the SNR will be set to the range from 15 dB to 25 dB, which will be reasonable according to the practical case. Some main simulation parameters are given in Table 1.
# A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem
## 6 Application
### Simulation Results  
The Boltzmann exploration policy is adopted when choosing action, and the fast cooling policy (i.e., Equation (14)) is selected when iterating the temperature coefficient \(T\). According to the principle in Section 5.2, the relative parameters are set as follows: \(T_{0}=900\), \(\gamma=0.9\), \(\alpha=0.1\), and \(r(s,a)=-2c\_t_{i,j,l}+150\).  
Suppose 20 carrier aircraft (labeled with 1 to 20) with similar conditions come to the _detection and maintenance_ stage every 2 min, and the arrival time of the first one is 0. Thus, the arrival time to the first stage and the initial sequence of all aircraft is deterministic.  
One thousand episodes of simulation are conducted in the same computing environment with Section 4.3, costing 2.03 s, the curve of maximal ending time of carrier aircraft support with an episode is shown in Figure 5(a), and the former 200 episodes are shown in Figure 5(b).  
As illustrated in Figure 6, the curve converges from around the 90 th episode, and the mean and variance of support time of the last 900 episodes are 135.3 min and 0.26, respectively, which prove the robustness of this method. To make clear the distribution of each carrier aircraft on the station at each stage, the Gantt chart of carrier aircraft scheduling of the 1000 th episode is illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Detection and Maintenance** & **Refuel** & **Rearm** & **Ejection** \\ \hline station 1 & 11 & 15 & 20 & 2 \\ station 2 & 9 & 13 & 15 & 2 \\ station 3 & 10 & 12 & 17 & 3 \\ station 4 & 13 & 16 & 13 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The support time of each station for a similar carrier aircraft (minute).  
Figure 5: Flow chart of carrier aircraft aviation support scheduling.  
In Figure 7, the \(y\)-label is the location of carrier aircraft, for instance, 'S2_1' indicates the first station of the second stage or _refuel_ stage. The number in the chart manifests the stage of security and the tag of aircraft (e.g., '2-1' demonstrates that the aircraft with tag 1 is in the second stage or _refuel_ stage). The terminal support time is 134.9 min this episode.  
For deterministic initial sequence HFSP, the main factors affecting the convergence speed and the result quality are the setting of parameters. Moreover, the stochastic transferring time between two stages also has some influence on the optimal scheduling time.
# Rethinking Figure-of-Merits of Liquid Crystals Shielded Coplanar Waveguide Phase Shifters at 60 GHz
###### Abstract  
The demand for reconfigurable millimetre-wave (mm-Wave) components based on highly anisotropic liquid crystals (LC) is higher than ever before for the UK and worldwide. In this work, 60 GHz investigation on a bespoke shielded coplanar waveguide (SCPW) phase shifter structure filled with 16 types of microwave-enabled nematic LCs respectively indicates that the patterns of the device's figure-of-merit (FoM, defined as the ratio of maximum differential phase shift to maximum insertion loss) reshuffle from those of the characterised LC materials' FoM (defined as the ratio of tunability to maximum dissipation factor). To be more specific, GT7-29001- and MDA-03-2838-based phase shifters exhibit the highest FoM for devices, outperforming phase shifters based on GT5-28004 and TUD-566 with the highest FoM for materials. Such a mismatch between the device's FoM and LC's FoM implies a nonlinearly perturbed wave-occupied volume ratio effect. Furthermore, the relationship between insertion loss and the effective delay line length is nonlinear, as evidenced by measurement results of two phase shifters (0-\(\pi\) and 0-2\(\pi\), respectively). Such nonlinearities complicate the established FoM metrics and potentially lead to a renewed interest in the selection and material synthesis of LCs to optimise reconfigurable mmWave devices, and promote their technological exploitation in phased array systems targeting demanding applications such as inter-satellite links and satellite internet.  
figure-of-merit; insertion loss; liquid crystal; mmWave; phase shifter; shielded coplanar waveguide; tunability; tunable dielectrics; 60 GHz +
Footnote †: journal: 17 August 2021
## 2 Materials and Methods
### Methods  
#### 2.3.1 Calculation of Fractional Vegetation Coverage  
The Dimidiate Pixel Model based on NDVI was widely applied to calculate _FVC_ (Fractional Vegetation Coverage) because of the need for sample calculation and the independence of the field data collected during the study [31]. The _FVC_ between May and August from 2005 to 2007 was stable; therefore, the average _FVC_ between May and August over three years was computed to represent the pre-quake level of vegetation coverage. The vegetation coverage level immediately following the earthquake was expressed by the average _FVC_ between May and August in 2008, and the vegetation coverage level after the earthquake was the average _FVC_ between May and August for every year (2009-2013). Due to high levels of cloud cover in 2010, the image for this year was not included in the analysis.