esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis
### Analysis of Network Data Augmentation and Pattern Recognition Effectiveness
#### 4.3.2 Comparison of Classification Effectiveness of Different Data Augmentation Methods  
To verify the effectiveness of the proposed data augmentation method for partial discharge pulse samples, five oversampling techniques--ROS, SMOTE, CWGAN-GP, BEGAN, and VAE--were selected for comparison. Each of these methods, along with the proposed sample generation approach, was used to balance and expand the capacity of each partial discharge sample type to 1500. The classifier used was then trained using the different augmented sample libraries. The evaluation metrics for the classifier trained on the original samples and on the samples augmented using different oversampling methods are shown in Table 6.  
As shown in Table 6, the classifier trained on the original samples exhibits low recognition accuracy due to the imbalance and small size of the partial discharge dataset. ROS does not significantly improve classification performance. While SMOTE, BEGAN, and VAE can enhance performance, their effects are moderate. CWGAN-GP produces more diverse samples, resulting in a greater improvement in classification accuracy. After DAE-GAN data augmentation, the accuracy, F1 score, and G-mean metrics are improved by 8.24%, 9.20%, and 7.69%, respectively, compared to the pattern recognition metrics trained on the original samples. The classification accuracy also shows varying degrees of improvement compared to other data augmentation algorithms.
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
## 2 Mathematical Model  
### Computational Fluid Dynamics  
In computational fluid dynamics, the continuity and Navier-Stokes governing equations are used to simulate the flow behavior. Given the small size of the geometry, and hence the small hydrodynamic length scale, the flow can be assumed to be laminar. Also, assuming MR fluids are incompressible, the continuity equation of the fluid can be written as:  
\[\
abla\cdot\mathbf{u}=0 \tag{1}\]  
where \(\mathbf{u}\) is the vector field of the mixture velocity.  
The momentum of the fluid is governed by the Navier-Stokes equation:  
\[\frac{\partial\mathbf{u}}{\partial t}+(\mathbf{u}\cdot\
abla)\mathbf{u}=-v_{eff}\
abla^{2} \mathbf{u}+\frac{1}{\rho_{eff}}\
abla p+\mathbf{S} \tag{2}\]  
where \(t\) is the flow time; \(v_{eff}\) and \(\rho_{eff}\) are the respective effective kinematic viscosity and density of the MR fluid; \(\
abla p\) is the flow pressure gradient, which contributes to the internal source term; and \(\mathbf{S}\) is the external source term due to the magnetic response of the MR fluid.  
The effective kinematic viscosity and density of the MR fluid is considered to be the weighted average between the carrier fluid (subscript \(f\)) and the particle suspensions (subscript \(p\)). The effective density of the MR fluid is given as:  
\[\rho_{eff}=(1-\alpha)\rho_{f}+\alpha\rho_{p} \tag{3}\]  
where \(\alpha\) is the volume fraction of the magnetic-responsive particles.  
The effective dynamic viscosity \(\mu_{eff}\) of a two-phase mixture containing rigid spherical particles was first predicted by [PERSON] [43] as:\[\mu_{eff}=\mu_{f}(1+k_{1}\alpha) \tag{4}\]  
where \(\mu_{f}\) is the dynamic viscosity of the continuous phase (carrier fluid), and \(k_{1}\) is the [PERSON] shape factor. For rigid spheres, the [PERSON] shape factor is given by \(k_{1}\) = 2.5. However, this approximation is only ideal for low particle volume fractions (\(\alpha<0.05\)).  
For higher particle volume fractions, [PERSON] [44] proposed the following expression, which accounts for both mutual hydrodynamic interactions and particle collisions:  
\[\mu_{eff}=\mu_{f}\exp\left[\frac{k_{1}\alpha+\theta(k_{2}-k_{1})\alpha^{2}}{1 -B_{H}\alpha}\right] \tag{5}\]  
where \(k_{1}\) is the [PERSON] shape factor, \(k_{2}\) is the shape factor for collision doublets, \(\theta\) is the collision time constant, and \(B_{H}\) is the hydrodynamic interaction constant. For spherical particles [44], \(k_{1}\) = 2.5, \(k_{2}\) = 3.175, \(\theta\) = 4, and \(B_{H}\) = 0.609.  
Once the effective dynamic viscosity \(\mu_{eff}\) is determined, the effective kinematic viscosity \(\
u_{eff}\) can be calculated via:  
\[\
u_{eff}=\frac{\mu_{eff}}{\rho_{eff}} \tag{6}\]
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
###### Abstract
It is emphasized that, despite the abundance of available studies focused on the sorption capacity of zeolite [16; 17; 18; 19], to date, no research has been found that works with real irrigation waters, which are considered complex due to the diversity of chemical and biological elements they contain. Furthermore, none of these studies concurrently focused on the two chemical species that are most \"problematic\" in agriculture, sodium and boron, given their negative impact on plant growth and, consequently, on production.
## 2 Materials and Methods
### Optimization and Validation of the Created CVFZ  
As there is no identifiable vegetation functional area endmember to reference, it is difficult to evaluate the performance of the VCA-MLC algorithm using indicators such as root mean square-Signature Angle Error (rms-SAE). Therefore, the algorithm's performance is indirectly evaluated by assessing the discrimination effect of CVFZ on vegetation function, with the key indicators for evaluating image classification accuracy being interclass distance and in-class distance [74]. Missing rate (\(MR\)), interclass distance (\(D_{1}\)), in-class distance (\(D_{2}\)), and discrimination (\(D_{3}\)) are proposed to optimize and validate CVFZ. \(D_{1}\), \(D_{2}\), and \(D_{3}\) are also used to evaluate MCD-2011, CLCD-2011, MCD-unchanged, and MCD-CVFZ. These formulas are as follows:  
\[MR=1-\frac{\sum_{k=0}^{9}c_{k}}{c} \tag{7}\]  
\[D_{1}=\frac{\sum_{k_{1}=0}^{9}\sum_{k_{2}=k_{1}+1}^{9}D_{mean_{k_{1}}-mean_{ k_{2}}}}{\sum_{k_{1}=0}^{9}\sum_{k_{2}=k_{1}+1}^{9}1} \tag{8}\]  
Figure 2: VCA–MLC algorithm flowchart. The process of extracting vegetation endmembers was conducted 1000 times for the sample of MOD-2011, which was also used as the sample for subsequent MLC.  
\[D_{2}=\frac{\sum_{k=0}^{9}\sum_{j=1}^{c_{k}}D_{mem_{k}-pixel_{k_{j}}}}{\sum\limits_ {k=0}^{9}\sum_{j=1}^{c_{k}}1} \tag{9}\]  
\[D_{3}=D_{1}-D_{2} \tag{10}\]  
where \(k\) is the category in which the pixel belongs to, ranging from 0 to 9 for a total of 10 classes. \(c_{k}\) is the number of pixels in this class, \(C\) is the total number of pixels to be classified after filtering, \(D_{mem_{k_{1}}\text{-}mem_{k_{j}}}\) is the \(D\) of the LAI mean vector between the \(k_{1}\) and \(k_{2}\) categories, and \(D_{mem\text{-}pixel_{k}}\) is the \(D\) between the LAI mean vector of the \(k\) category and the pixel in it.  
\(D_{1}\) measures the average difference between different vegetation functional areas. \(D_{2}\) measures the average difference between different pixels in the same vegetation functional area. The larger \(D_{1}\) and the smaller \(D_{2}\) represent a better classification effect. \(D_{3}\) combines \(D_{1}\) with \(D_{2}\) to directly characterize the degree of discrimination of vegetation functional zones. The larger \(D_{3}\), the better the classification effect.  
Table 2 shows that adjusting the Threshold to optimize the classification results under the VCA-MLC algorithm is essential. At a Threshold of 2.05, \(D_{3}\) reaches a maximum of 0.0516, indicating that the classification effect reached is the best. The \(MR\) at this time is only 0.0318, meaning most of the pixels to be classified have already been classified.
## 2 Study Site and Data Processing
### Regional Data  
We used the China Meteorological Forcing Dataset (CMFD) [46; 47] for regional climate forcing, which is at the spatial resolution of 0.1\({}^{\circ}\), and temporal resolution of 3 h. CMFD was produced by merging a variety of data sources, including CMA (China Meteorological Administration) weather station observation data, TRMM satellite precipitation analysis data (3B42), GEWEX-SRB downward shortwave radiation data, the Modern Era-Retrospective Analysis for Research and Applications (MERRA) (surface pressure), and GLDAS data (wind, air temperature, relative humidity, air pressure, and precipitation) [46].  
The NDVI and albedo data used in this study were derived from the MODIS products MOD13Q1/MYD13Q1 and MCD43A3, which have a spatial resolution of 250 m and 1000 m, respectively. Due to the existence of cloud cover, the MODIS NDVI/Albedo values are often missing or underestimated. To remove the outliers and fill the missing gaps in the original time series, we used the technique of Harmonic Analysis of Time Series (HANTS) [48]. The LAI was estimated using the NDVI data. LAI is estimated from NDVI in this study due to its relatively high spatial resolution (250 m). First, vegetation coverage \(\mathrm{f_{c}}\) is estimated using the NDVI, i.e., \(\mathrm{f_{c}}=(\mathrm{NDVI_{max}}-\mathrm{NDVI})/(\mathrm{NDVI_{max}}- \mathrm{NDVI_{min}})\), where \(\mathrm{NDVI_{max}}\) and \(\mathrm{NDVI_{min}}\) are taken as 0.95 and 0.01, respectively. LAI is then inverted using the relationship between the LAI and vegetation coverage, i.e., \(\mathrm{f_{c}}=1-\exp(-0.6*\mathrm{LAI})\)[49; 50]. Land cover data in the years 2000, 2005, 2010, and 2015 were provided by the National Earth System Science Data Center of China ([[http://auth.geodata.cn](http://auth.geodata.cn)]([http://auth.geodata.cn](http://auth.geodata.cn)), accessed on 30 June 2020). All data were resampled to 250 m and a daily scale.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 1 Introduction
**Re-usability:** Once a PHY is designed and tested in software, its deployment can be implemented in several hardware platforms. 5. **Reconfigurability:** Current SDR implementations offers the capability of reconfigurable hardware to support several wireless standards and waveforms. 6. **Lower Cost:** Fewer hardware components are necessary to be acquired in order to build a communication system from the scratch. Such devices are usually more expensive than the acquisition of SDR products. A complete description of the developed SDR is outlined in Section 2, where special focus is given to the adopted firmware connectivity between the embedded system and both Giga Samples  
Figure 1: Block diagram of the ideal software-defined radio transceiver (image adapted from [6]). Per Second (GSPS) DAC/ADC boards. In addition, an extensive SDR characterization in terms of SNR, Spurious-Free Dynamic Range (SFDR), Signal-to-Noise and Distortion Ratio (SINAD), and Effective Number Of Bits (ENOB) is given, along with the proposed hardware design for Orthogonal Frequency Division Multiplexing (OFDM) systems based on the LTE-Advanced downlink PHY layer is given in Section 3. The OFDM performance in both Back-to-Back (B2B) and Over-The-Air (OTA) at 60 GHz scenarios was assessed in terms of Error Vector Magnitude (EVM) and the received scatter constellation plots for 4-/256-QAM. Finally, a showcase considering the proposed mmWave testbed with Ultra-High-Definition (UHD) multi-stream video, as usage case application, is outlined in Section 4.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Mechanism of Al-S957 for Fluoride  
Figure 7a shows the infrared spectra before and after fluoride adsorption. After adsorption, the peaks at 3414 cm\({}^{-1}\) and 1169 cm\({}^{-1}\) were weakened, which was attributed to the stretching vibration of \(-\)OH [49]. This indicates that the adsorption process is accompanied by the breaking of \(-\)OH [50]. At the same time, the bending vibration of Al-O at 702 cm\({}^{-1}\) was weakened, while a new Al-F peak appeared at 838 cm\({}^{-1}\) after adsorption, indicating that the fluoride and Al-O bond interacted to form a new complex [51].  
The XPS wide-scan spectra of Al-S957 before and after adsorption, shown in Figure 7b, confirmed the presence of F 1s, indicating fluoride adsorption onto the material [52; 53].  
Figure 7: FTIR spectra (**a**), XPS full survey spectra (**b**), Al 2p spectra (**c**), and O 1s spectra (**d**) of Al-S957 before and after fluoride adsorption.  
Furthermore, the detection of S 2p and P 2p peaks confirms the coexistence of sulfonic and phosphonic acid groups, verifying the bifunctional nature of the S957 resin. As shown in Figure 7c, after adsorption, a new peak corresponding to Al-F appeared at 75.23 eV, while the binding energy of the Al-O peak shifted to 74.73 eV, and the corresponding peak ratios decreased [54]. As shown in Figure 7d, the O 1s peak of pristine Al-S957 was resolved into three distinct components at 530.81 eV, 531.93 eV, and 533.15 eV, corresponding to Al-O, \(-\)OH, and H\({}_{2}\)O, respectively [55]. After fluoride adsorption, the binding energy of the O 1s peak decreased by 0.12 eV. The Al-O peak area percentage declined from 14.85% to 12.36%, attributed to the formation of Al-F compounds, where fluoride occupies the metal-binding sites [56]. This is consistent with the analysis of FTIR spectra. Additionally, the \(-\)OH peak area decreased from 60.45% to 48.27%, providing further evidence of \(-\)OH ligand exchange as a key mechanism in fluoride adsorption [8].  
Overall, the superior fluoride adsorption performance of Al-S957 is primarily attributed to the strong complexation between aluminum and fluoride, which effectively displaces the original \(-\)OH ligands. This proposed adsorption mechanism, illustrated in Figure 8, is in good agreement with previous related studies [10; 23; 57].
## 2 Results and Discussion
### Effect of TDZ on Gene Expression of Berries
At the first time point of the treatment with TDZ, the expression levels of _VvPNGer_ and _VvPNlinNer1_ were slightly higher in treated berries in comparison with CK in both years. After the second time point of treatment, the expression of _VvPNGer_ and _VvPNlinNer1_ was significantly higher in the control samples compared with the TDZ-treated samples, at both the pre-veraison and veraison stages. At the commercial maturity stage, the expression of _VvPNlinNer1_ was higher in the TDZ-treated berries as compared with controls; thus, from two weeks post-application until pre-harvest, treatment with TDZ results in a decrease in the expression of _TPS_ genes. Among the _TPS_ genes, _VvPNlinNer1_ was identified as the most highly expressed gene in all samples and its expression was the most reduced by TDZ at the veraison stage. These data were in agreement with previously published results showing that the expression of _VvPNlinNer1_ and other TPS genes were most influenced by hormone treatment during the beginning of the berry color changing stages [48]. Figure 3: Effect of TDZ on the gene expression of berries in 2018 with the progression of berry development. Mean \(\pm\) Standard error (\(n=3\)). Different letters indicate significant differences ([PERSON]’s multiple range test, \(p\leq 0.05\)). In our study, treatment with TDZ significantly decreased the concentrations of volatile monototerpenoids. Collectively, these compounds contribute to the Muscat flavor of berries, and the MEP related genes and downstream TPS genes and enzymes that contribute to the biosynthesis of monoterpenoids are all negatively affected by treatment with TDZ. We conclude that TDZ functions as a potent chemical that affects volatile compounds by reducing the gene expression patterns of several monoterpenes biosynthesis pathway genes as well as the TPS enzyme activity in Shine Muscat berries.
# General Periodic Cruise Guidance Optimization for Hypersonic Vehicles
## 2 Preliminary Preparation  
### Mathematical Model  
In this paper, the HL-20 hypersonic vehicle model is employed [15] as a benchmark. In the trajectory optimization and guidance design phase, we assume that the attitude control can be ideally realized; in other words, there is no control error and the hypersonic vehicle can be considered as a mass point. For the sake of brevity, the spherical model of the Earth is ignored without affecting the essence of the optimization algorithm. The dynamic model for the mass point can be described as  
\[\left\{\begin{array}{l}\frac{dh}{dt}=M\mathrm{a}\sin\gamma\\ \frac{dM}{dt}=\frac{T\cos\alpha-D-mg\sin\gamma}{ma}\\ \frac{dy}{dt}=\frac{T\sin\alpha+L}{mM\mathrm{a}}+\cos\gamma\Big{(}\frac{Ma}{ \mathrm{R_{e}}+h}-\frac{\mathrm{g}}{Ma}\Big{)}\\ \frac{dm}{dt}=-\frac{T}{\mathrm{g}l_{\mathrm{sp}}}\\ \frac{dr}{dt}=M\mathrm{a}\cos\gamma\Big{(}\frac{\mathrm{R_{e}}}{\mathrm{R_{e}}+h }\Big{)}\end{array}\right., \tag{1}\]  
where \(h\) is the altitude (m); \(M\) is the Mach number; \(\gamma\) is the elevation angle (rad); \(m\) is the mass of the flight vehicle (kg); \(r\) is the range (m); a is the normal sound speed and assumes the constant of 340 m/s for convenience; \(\alpha\) is the angle of attack (rad); \(T\) is the thrust (N); \(D\) is the drag (N); \(L\) is the lift (N); \(g\approx 9.81\) m/s\({}^{2}\) is the gravitational constant; \(R_{e}=6378\) km is the radius of the Earth; and \(l_{\mathrm{sp}}\) is the specific impulse of the booster. The detail of this model can be seen in [15].  
According to the definition of the periodic cruise, the period refers to the flight range rather than the flight time. Therefore, we can reformulate Equation (1) as  
\[\left\{\begin{array}{l}h^{\prime}=\frac{dh}{dr}=\tan\gamma\Big{(}1+\frac{h} {\mathrm{R_{e}}}\Big{)}\\ M^{\prime}=\frac{dM}{dr}=\frac{T\cos\alpha-D-mg\sin\gamma}{Ma^{2}m\cos\gamma} \Big{(}1+\frac{h}{\mathrm{R_{e}}}\Big{)}\\ \gamma^{\prime}=\frac{dy}{dr}=\Big{(}\frac{L+T\sin\alpha-mg\cos\gamma}{mM \mathrm{a}^{2}\cos\gamma}+\frac{1}{\mathrm{R_{e}}+h}\Big{)}\Big{(}1+\frac{h} {\mathrm{R_{e}}}\Big{)}\\ m^{\prime}=\frac{dm}{dr}=-\frac{T}{\mathrm{g}l_{\mathrm{sp}}Ma\cos\gamma}\Big{(} 1+\frac{h}{\mathrm{R_{e}}}\Big{)}\end{array}\right., \tag{2}\]  
which is the foundation for later optimization.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Capture
#### 2.3.1 UAV  
UAV images were captured from a DJI (Shenzen, China) Inspire 1 v2. This is a quadcopter, weighing around 3 kg. A MicaSense (Seattle, WA, USA) RedEdge camera was attached to the UAV. Four of the five camera bands were used and are specified in Table 1. The flight paths were automated using the Drone Deploy iPad application. The images were captured from an altitude of 75 m with along-track overlap of 80% and at least two passes along the channel length. The ground pixel size was 5 cm. Before each flight, images of a calibration reflectance panel were taken. The images were radiometrically calibrated and orthomosaics were generated with the Pix4D software.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Band**} & \multicolumn{2}{c}{**MicaSense RedEdge**} & \multicolumn{2}{c}{**World-View 3**} \\ \cline{2-5}  & **Center (nm)** & **Bandwidth (nm)** & **Center (nm)** & **Bandwidth (nm)** \\ \hline Blue & 475 & 20 & 481.9 & 40.5 \\ Green & 560 & 20 & 547.1 & 61.8 \\ Red & 668 & 10 & 660.1 & 58.5 \\ Near-infrared & 840 & 40 & 824.0 & 100.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the utilised bands of the MicaSense RedEdge UAV-mounted camera, and the World-View 3 satellite instrument.  
Figure 2: Common weed types in the MIA channels (**a**) Ribbonweed; (**b**) Pondweed; (**c**) Parrot’s feather and cumbungi.  
Images were captured over four channel stretches as part of a study of the effectiveness of different weed control strategies. To reduce the impact of shadowing, where possible, the images were taken within 2 h of solar noon. The treatments, channel dimensions and image capture dates are shown in Table 2. The untreated channel (U) had no weed control applied. The mechanical treatment channel (M) had excavator de-silling carried out on 23 July 2018. The two chemical treatment channels (C1 and C2) had Endothall herbicide applied on 11 July 2018.  
Throughout the study period, each of the channels also had a WiField logger [20] continuously measuring water depth and temperature. Water depth was measured using a MaxBotix (Fort Mill, SC, USA) MB7389 ultrasonic sensor, and the water and air temperatures were measured using digital Maxim (Sunnyvale, CA, USA) DS18B20 temperature probes.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.21 The Indicator for Centralized Water Management  
This indicator encourages the control of water consumption and centralized water systems. This is promoted only by SBToolPT Urban, but ISO 37120 and SDGs address it, which has led to it being recognized as a priority environmental concern. The SBToolPT Urban Guide provides a verification checklist to calculate this indicator. In this checklist, most of the criteria can be calculated using the CIM model, and the points can be determined by applying a Dynamo routine. Despite this, the CIM model cannot assess the criteria associated with energy goals and results dissemination. A CIM assessment is considered to be \"PARTIALLY\" appropriate for this indicator.