esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 3 Methods
### Statistical Analysis
#### 3.2.4 Analysis of Principal Component Analysis (PCA)  
PCA is used to enhance computational efficiency and identify dominant patterns in water quality data while minimizing information loss [45]. The PCA methodology follows the steps outlined in Figure 5.  
Step 1: Data Standardization  
All water quality parameters (pH, DO, COD, BOD, TSS, AN) were standardized to ensure uniformity, eliminating scale biases by transforming them to zero mean and unit variance.  
Step 2: Covariance Matrix Calculation  
A covariance matrix quantified inter-variable relationships, identifying patterns of correlation. Each element represented the covariance between two variables, revealing how changes in one affected the other.  
Step 3: Eigenvalue Decomposition  
Eigenvalues and eigenvectors were computed, ranking principal components in descending order of variance explained. The first principal component (PC1) captured the highest variance, guiding dimensionality reduction.  
Figure 5: PCA implementation process.  
Step 4: Cumulative Variance and Scree Plot  
Eigenvalues determined the variance explained by each principal component. A scree plots visualized eigenvalues to identify the \"elbow point,\" aiding optimal component selection while retaining maximum information.  
Step 5: Heatmap of Correlation Coefficients  
A heatmap visualized feature correlations, using color gradients to indicate relationship strengths, ensuring that PCA effectively transformed correlated factors into orthogonal components.  
Step 6: Projection of Data onto Principal Components  
Following the determination of the primary components, the data that had been standardized were projected onto these new axes. The dataset was changed into a space with less dimensions as a result of this projection, which resulted in the assignment of a new feature to each of the basic components. By keeping the variability that was represented by the components that were selected, the transformation ensured that the computational efficiency was maintained and improved the efficiency of following modeling operations.  
Step 7: Visualization of Principal Components  
Several plots were generated to interpret the results of PCA:  
* PC1 vs. PC2 scatterplot: Showed data distribution in a reduced two-dimensional space.
* Scatterplot matrix (PC1-PC4): Examined higher-dimensional relationships, uncovering latent structures.  
The step-by-step method ensures that PCA effectively reduces dimensionality while retaining critical information, enabling efficient analysis and interpretation of water quality data.
## 3 Results
### Pigment Productivity  
The carotenoid composition of _H. rubescens_ KNUA214, cultivated in the DSW, was analyzed, focusing on astaxanthin, lutein, zeaxanthin, canthaxanthin, and beta-carotene (Figure 4). On day 4, the highest concentrations of astaxanthin, canthaxanthin, and beta-carotene were observed in BG-11 medium, with values of 0.147 mg g\({}^{-1}\), 0.039 mg g\({}^{-1}\), and 0.171 mg g\({}^{-1}\), respectively. A notable decrease in these concentrations was observed as the DSW level increased. By day 8, an overall increase in the carotenoid content was evident in the media supplemented with the DSW, except for beta-carotene in the BG-11 + 25% DSW mixture. Notably, the BG-11 + 50% DSW mixture showed the most significant increase, with concentrations reaching 0.190 mg g\({}^{-1}\) for astaxanthin, 0.064 mg g\({}^{-1}\) for canthaxanthin, and 0.260 mg g\({}^{-1}\) for beta-carotene. The lutein concentration was relatively stable across all conditions on day 4, ranging from 0.71 mg g\({}^{-1}\) to 0.85 mg g\({}^{-1}\); however, by day 8, the lutein content had doubled to 1.54 mg g\({}^{-1}\) under the BG-11 + 50% DSW condition. Zeaxanthin showed the highest concentration in 100% DSW on day 4, with a remarkable fivefold increase observed in the BG-11 + 25% DSW mixture by day 8. On day 8, the other carotenoids, except for zeaxanthin, reached their peak concentrations under the BG-11 + 50% DSW condition. The astaxanthin concentration increased 2.71-fold, from 0.07 mg g\({}^{-1}\) to 0.19 mg g\({}^{-1}\), while lutein showed a 2.09-fold increase, from 0.74 mg g\({}^{-1}\) to 1.54 mg g\({}^{-1}\). The canthaxanthin concentration increased 2.56-fold, from 0.03 mg g\({}^{-1}\) to 0.06 mg g\({}^{-1}\), and beta-carotene doubled, increasing from 0.13 mg g\({}^{-1}\) to 0.26 mg g\({}^{-1}\). These results  
Figure 1: **(a)** Light microscope image of _H. rubescens_ KNUA214. **(b)** Phylogenetic relationship of _H. rubescens_ KNUA214 and its closely related species based on 188 rRNA sequence data. Numbers at nodes indicate percentage values derived from 500-bootstrap-analysis samples. The scale bar represents differences in nucleotide sequences.  
indicated that 50% DSW supplementation offers an optimal environment for enhancing the production of carotenoids in _H. rubescens_ KNUA214, suggesting a potential for scaled bioproduction under controlled dilution conditions.  
Figure 3: (**a**) Nutrient concentration and (**b**) percentage of removal efficiency in 100% DSW by _H. rubescens_ KNUA214 over an 8-day period. Statistical significance between groups is denoted as follows: **_p_ < 0.01, ***_p_ < 0.001.  
Figure 2: (**a**) Optical density, (**b**) dry weight, and (**c**) chlorophyll \(a\) and (**d**) chlorophyll \(b\) concentrations of _H. rubescens_ KNUA214 under different concentrations of DSW for 8 days. Microscopy images of _H. rubescens_ KNUA214 in (**e**) BG-11 and (**f**) 100% DSW over an 8-day period.
## 2 Results and Discussion  
For the time being, biological monitoring for AD exposure presents many issues, including the need to detect low urinary levels and use different extraction methods for each class of analytes, which entails complex and time-consuming procedures. Furthermore, the complexity of the dermal route of exposure makes the correlation between surface contamination and urinary AD levels extremely difficult.  
The drugs in the study, cyclophosphamide and iphosphamide, represent a convenient starting point for new method development, thanks to their feature of being found unchanged in urine samples from exposed personnel and their strong response in electrospray source (ESI) mass spectrometry. As a matter of fact, for most Ads, only the metabolized drug can be found in urine in detectable concentrations, but since these metabolites are often commercially unavailable, a correct quantification is strictly tied to expensive and specific synthetic procedures.  
The developed method offers a fast and sensitive alternative to the currently used urine extraction procedures, along with comparable performances. The DoE approach is in different ways a key factor in the optimization of the mobile phase, minimizing the number of experiments and giving an intuitive response, and may be transposed in the near future for the development of extraction methods for similar analytes. The \(\upmu\)SPEed cartridges, containing 3 \(\upmu\)m particles, moves the solid-phase extraction (SPE) technique close to HPLC systems, both in regard to the cleanness of performances and automatability. They can be reused multiple times and coupled with the ePrep One workstation, which can eventually be directly connected to the HPLC injection port, allowing the method to be easily expanded to larger batch sizes. At the same time, the growing number of sorbent chemistries and different particle sizes might extend the application of this workflow, theoretically, to any kind of analyte in any biological fluid.
# Numerical and Experimental Analysis of the Oil Flow in a Planetary Gearbox
## 2 Materials and Methods
### CFD Modeling of the Oil Flow
#### 2.4.1 Geometrical Considerations  
The CFD model was derived starting from the CAD model by considering the areas where the lubricant can be present. Therefore, different from the structural analysis, the negative model of the solid parts was considered. Small-scale features such as screws, springs, and seals were neglected as they are not expected to have a noteworthy impact on the oil distribution. Further cleaning of the geometry included the removal of chamfers, edges, and small clearances. The final geometry consists of two ring gears, three planets, and the planet carrier. To avoid numerical singularities in the gear meshing region, the ring gears were scaled to 102% of their nominal size (in the radial direction only). This preliminary action is required for all finite volume-based simulations of intersecting objects and is not expected to significantly influence the main oil flow in the gearbox.
## 2 Materials and Methods
### DFT Calculation  
The band structure of the WS\({}_{2}\)/MOS\({}_{2}\) HS was calculated with the DFT method using VASP 5.1. For the modeling of WS\({}_{2}\)/MOS\({}_{2}\) HS, the AB-stack structure of ML-WS\({}_{2}\) and ML-MoS\({}_{2}\) with aligned metal atoms was chosen to construct a supercell including 6 atoms. The initial interlayer distance between the two monolayers was set to 3.3 A and optimized to 3.087 A. The electron-ion interaction was described using ultra-soft pseudopotential plane waves, and the interaction correlation energy was treated using a generalized gradient approximation (GGA) with the Periodic-Boundary-Embedding (PBE) functional form. To prevent the interaction between the supercell layers caused by the periodic boundary condition, a vacuum layer with a thickness of 15 A was added in the c direction. During the optimization and electron calculation processes, the internal interatomic interaction force and pressure thresholds were set to 0.03 eV A\({}^{-1}\) and 0.05 GPa, respectively. The k-point grid of 3 \(\times\) 3 \(\times\) 1, the energy convergence criterion of 10\({}^{-5}\) eV, and the cutoff energy of 650 eV were optimized for use.
leveraged geostatistical methods and the inverse distance weighting method to analyze the spatial distribution of soil nutrients in China's Pearl River Delta region. Their findings showed that the overall spatial distribution of readily available potassium and total nitrogen increased from the northeast to the southwest, while the distribution of available potassium and available silica was higher in the northeast than in the southwest [16]. This research offers insights into region-specific crop selection. [PERSON] and his team, focusing on the Nanxiong Basin in South China as a case study, employed geostatistical methods to analyze the spatial variation characteristics of soil pH values. Their results identified that land-use patterns and topographical factors were the primary and secondary contributors, respectively, to the pH values of the soil in the area [17]. In recent years, most studies examining the spatial variation characteristics of soil nutrients have been based on low-density sampling data over larger scales. Moreover, the nutrient indices researched for the soil in China's Huai River Basin region have been relatively singular, with comprehensive comparative studies between nutrients being rare. This has made it challenging to offer practical guidance for modern precision agriculture production and the prevention and control of non-point source pollution. This study is centered on Fengtai County, situated in the Huai River Basin of China, which is represented as a grain-producing region. Soil nutrient data from concurrent sample collections were analyzed. Using ArcMap 10.8, the best semivariogram model parameters were determined to gauge spatial autocorrelation. The global [PERSON]'s \(I\) index was utilized to verify the relevance of soil nutrient autocorrelation. Based on the optimal model, the Ordinary Kriging interpolation was employed to craft nutrient content spatial distribution maps. The local [PERSON]'s \(I\) index was also utilized to fashion LISA cluster maps. This approach is characterized by the marriage of geostatistical methods with GIS technology. Subsequently, the author investigated the wheat yield of typical crops under the same sampling point in the second year, analyzed the correlation between yield and soil nutrients, and used PCA principal component analysis to score the overall soil quality of Fengtai County, and determined the influencing factors that restrict the soil quality in this area. The research results of the combination of geostatistics and GIS technology were further demonstrated to provide theoretical support for the rational fertilization of soil in this area, the precise development of agriculture, and the consideration of soil ecological service functions.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Frequency Sampling Methods
#### 3.1.4 Cruz-Roldan's Method II  
In [123], a multi-objective optimization technique based on the Cruz-Roldan's method I in [120] is proposed. The difference between [120; 123] lies in the objective function, and the objective function of [PERSON]'s method II is  
\[\psi =\frac{1}{2\pi}\int_{\omega_{s}}^{2\pi-\omega_{s}}|H(e^{\mathrm{i }\omega})|^{2}d\omega\] \[=\mathbf{h}\mathbf{S}\mathbf{h}^{\mathrm{T}}\] \[=\frac{1}{N^{2}}\mathbf{H}^{\mathrm{T}}(\mathbf{W}_{N}^{-1})^{ \mathrm{T}}\mathbf{S}\mathbf{W}_{N}^{-1}\mathbf{H}, \tag{36}\] \[\text{s.t.}\left\{\begin{aligned} &\max_{\omega\in[0,\pi]}(|T_{0}(e^{ \mathrm{i}\omega})|)-\min_{\omega\in[0,\pi]}(|T_{0}(e^{\mathrm{i}\omega})|) \leq\delta_{pp}^{ini},\\ & MSA\geq MSA^{ini},\end{aligned}\right.\]  
where \(\mathbf{h}=[h(0),h(1),\cdots h(N-1)]\), \(\mathbf{h}^{\mathrm{T}}=(1/N)\mathbf{W}_{N}^{-1}\mathbf{H}\), \(\mathbf{W}_{N}^{-1}\) is the IDFT matrix, and the elements of \(N\times N\) matrix \(\mathbf{S}\) are given by  
\[[\mathbf{S}_{i,j}]=\left\{\begin{aligned} & 1-\frac{\omega_{s}}{\pi},& i=j,\\ &-\frac{\sin[\omega_{s}(i-j)]}{\pi(i-j)},&\text{ otherwise}.\end{aligned}\right. \tag{37}\]  
The values of \(\delta_{pp}^{ini}\) (amplitude distortion [121]) and \(MSA^{ini}\) (minimum stopband attenuation) are the initially fixed goals of the problem. The overall distortion transfer function \(T_{0}(e^{\mathrm{i}\omega})\) is obtained as  
\[T_{0}(e^{\mathrm{i}\omega})=\frac{e^{-\mathrm{i}\omega(N-1)}}{F}\sum_{k=0}^{ 2F-1}|H(e^{\mathrm{i}(\omega-k\cdot\pi/F)})|^{2}, \tag{38}\]  
where \(F\) is the number of subcarriers. This method requires the values of \(\delta_{pp}^{ini}\) and \(MSA^{ini}\) to be selected appropriately, and the MATLAB function _fgoalattain_ can solve the multi-objective optimization problem.
## 6 Conclusions  
Addressing the problem of difficult signal detection and CFO estimation faced by the inter-station communication of MPDSs in interference environments, a method for signal detection, starting position estimation, and CFO estimation is proposed based on a virtual array in this paper. It is a data-aided method that uses a three-segment training sequence. It first implements coarse signal detection and CFO estimation from virtual array snapshots constructed from the first two sub-training sequences. Then, the virtual beamforming technique is introduced to process the virtual array snapshots constructed from the third sub-training sequence to protrude the expected signal and suppress the in-band interferences, which is beneficial to improve signal detection performance and CFO estimation accuracy. The simulation results show that the proposed method can achieve very low missed detection probability and false detection probability, as well as achieve very high CFO estimation accuracy in the situations of low SIR and \(E_{b}/N_{0}\), which validates the proposed method and shows its promising application prospects in networked passive detection scenarios.  
Conceptualization, methodology, validation, writing--original draft preparation and editing, X.C.; writing--review and supervision, H.M.; verification of results, H.Z. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation, grant number 2023M731211, and by the National Natural Science Foundation of China, grant number 62071188.  
The original contributions presented in this study are included in the article, and further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Concentrations of Li  
The statistical parameters of Li concentrations from the total of 3628 records (or composite samples) are listed in Table 1. The concentrations of Li vary from 10.2 \(\upmu\)g/g to 279 \(\upmu\)g/g, with a range more than an order of magnitude. Therefore, the logarithm of Li is used to illustrate its parameters.  
The values of lgLi of the total of 3628 records in the Mufushan area are shown in Figure 2a as a histogram, along with its box plot according to the illustration of [PERSON] et al. [21]. The box plot is used to show the distribution, focusing on the cumulative frequency, and its typical parameters are listed in Table 1. The histogram focuses on the distribution on the log-normal, with its parameters also listed in Table 1. The distribution of lgLi is close to the normal distribution except for outliers with clearly large values.  
In order to delete the outliers, the method of Avg \(\pm\) 3 Std (where Avg and Std are the abbreviations of average and standard deviation) was adopted. That is to say, the outlier data of lgLi were excluded repeatedly when they were outside the range of Avg \(\pm\) 3 Std until no outliers were found. Finally, 3458 records were left. The lgLi data without outliers are illustrated in Figure 2b and their statistical parameters are also listed in Table 1. The average, standard deviation, and median of the 3458 records (outliers excluded) of lgLi are 1.677, 0.129, and 1.668, respectively (Table 1), corresponding to the average and the median of Li concentrations of 47.5 \(\mu\)g/g and 46.6 \(\mu\)g/g.
## 3 Experimental Programme
### The Suitability of Small-Scale Models Compared to the Full-Scale Prototype of the TSTs  
Due to the different storage materials, the suitability of the small-scale model needed to be validated with a full-scale prototype of the TST. While the density of steel balls is larger than a normal liquid, the volume of the steel balls in the small-scale model was far smaller than the volume of a normal liquid in a full-scale prototype. Therefore, the pressure caused by small steel balls in the small-scale model was expected to be equivalent to the pressure caused by a normal liquid in a full-scale prototype. A simple calculation was conducted to confirm the suitability of using a small-scale model to replicate a full-scale TST. The chosen real-world prototype, for comparison purposes, was the model highlighted in the paper by [PERSON] et al. (2019). The real prototype was a steel tank with a radius of 10 m and a total height of 9.6 m. The equivalent uniform thickness of the tank's wall was 9.6 mm. The total mass of the water in the tank (\(m_{l}\)) was \(2.51\times 10^{6}\) kg. The total mass of the tank wall (\(m_{w}\)) was \(4.3\times 10^{4}\) kg, and the mass of the tank's roof (\(m_{r}\)) was \(2.5\times 10^{4}\) kg. Table 7 shows the ratio between the mass of the water/steel balls to the total mass of the roof and tank wall of the small-scale tank and real-world prototype. The mass of the roof in the small-scale tank was the mass of the top of cylinders 1 and 2. The calculated results show that the ratios were nearly equivalent. Therefore, the small-scale model could be used for testing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Similarity Rules in 1 g (Model/Prototype)**} \\ \cline{2-4}  & **Scale: 1/\(A\)** & **Scale: 1/10** & **Scale: 1/40** \\ \hline Elastic modulus & \(\frac{\rho_{m}v_{s}}{\lambda\rho_{p}}\) & 0.46 & 0.12 \\ Pressure & \(\frac{\rho_{m}v_{s}}{\lambda\rho_{p}}\) & 0.46 & 0.12 \\ Acceleration & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Similarity rules for small-scale models.  
Another problem that needs to be considered when using steel balls to replace a normal liquid is that steel balls can cause a stress concentration on thin-walled shells. However, because the diameters of the applied steel balls were very small (only 1.2 mm) and had uniform circular shapes, the movement of the steel balls was smooth. Therefore, the stress concentration was very small. Moreover, 5 mm strain gauges were used to measure the strain values on thin-walled shells and the lengths of the strain gauges were much larger than the diameters of the steel balls. According to the results, the measured strain values could eliminate the stress concentration value if it occurs. Many points inside the strain gauges were measured and the final values are the average values from all the positions.
# Electronic Beam Steering Metamaterial Antenna with Dual-Tuned Mode of Liquid Crystal Material
## 3 Design, Simulation and Analysis of Dual-Tuned CRLH Metamaterial Antenna Based on LC  
### Design of CRLH Unit Cell  
On the basis of the dual-tuned mode of LC material, a CRLH unit cell is elaborately designed, whose schematic diagram is sketched in Figure 3. Figure 3a shows the cross-section of the CRLH unit cell. It is composed of three substrates, four metal layers and two LC layers. From bottom to top, the first metal layer is the ground, while the second metal layer is the parallel inductor, as shown in Figure 3b, and the first LC layer is between the two metal layers. The third metal layer is an interdigital structure, as shown in Figure 3c, and the fourth metal layer is the cover sheet; the second LC layer is between them. The  
Figure 2: Schematic diagram of four different states of dual-tuned mode based on (**a**) alignment of two LC layers, (**b**) loading bias voltage on the first LC layer, (**c**) loading bias voltage on the second LC layer and (**d**) loading bias voltage on two LC layers.  
second metal layer is connected to the third metal layer by a metallized via hole to formulate left-handed parallel inductance. The series capacitor is composed of two groups of series interdigital structures. In order to facilitate the feeding, the insertion of the finger is connected with a strip line. The corresponding equivalent circuit diagram of the CRLH unit cell is demonstrated in Figure 3d. The detailed parameters of the structure and the equivalent circuit (LC under natural state) in Figure 3 are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \(\mathbf{h_{LC}}\) & \(\mathbf{h_{m}}\) & \(\mathbf{h_{sub}}\) & \(\mathbf{p}\) & \(\mathbf{r}\) & \(\mathbf{L_{R}}\) & \(\mathbf{L_{L}}\) \\ \hline Value & 0.25 mm & 0.018 mm & 0.8 mm & 2.5 mm & 0.2 mm & 1.02 nH & 0.5 nH \\ \hline Parameter & \(w_{f}\) & \(w_{1}\) & \(w_{2}\) & \(l_{f}\) & \(w_{in}\) & \(C_{R}\) & \(L_{L}\) \\ \hline Value & 0.4 mm & 1.5 mm & 0.5 mm & 1.3 mm & 2.5 mm & 0.625 pF & 0.04 pF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the CRLH unit cells.  
Figure 3: Schematic diagram of the CRLH unit cell: (**a**) cross-section, (**b**) the parallel inductor structure, (**c**) the interdigital structure and (**d**) equivalent circuit diagram of the CRLH unit cell.