esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Operation Principle
### Correction Principle  
In Figure 2, the procedure for calculating the desired pointing in the MPEOT system is displayed. For further elaboration, several left-hand coordinate frames are defined first, which are the geographic coordinates \(g\), moving-platform coordinate \(m\), telescope base coordinate \(b\), and target coordinate \(L\). Based on the theoretical pointing angle \((A_{g},E_{g})\) of the target in the geographic coordinate, the control system can then calculate the expected direction \((A_{b},E_{b})\). Therefore, the target positioning equation of EOT can be formulated as  
\[G=R_{m}^{g}R_{b}^{m}=B \tag{1}\]  
where \(G=[X_{g},Y_{g},Z_{g}]^{T},B=[X_{b},Y_{b},Z_{b}]^{T},R\) is the rotation transformation matrix between the coordinate frames. In an ideal condition, the azimuth axis, the elevation axis, and the line of sight are three perpendicular axes in zero-elevation positions [28]. In this case, the pointing model of the telescope can be defined as  
\[B=R_{L}^{b}L=Rot(Z,A_{b})Rot(X,E_{b})L \tag{2}\]  
Assuming that \(L=[0,1,0]^{T}\) is the unit pointing direction referenced to the target coordinate, the pointing angle of the telescope can be determined from Equation (2) and expressed in the base coordinate as  
\[\begin{cases}A_{b}=\arctan X_{b}/Y_{b}\\ E_{b}=\arcsin Z_{b}\end{cases} \tag{3}\]  
Figure 1: Gimbal arrangement of the electro-optical telescope.  
The pointing error is defined as the angular displacement with which the pointing control system positions the actual object relative to its telescope boresight location after acquiring a suitable target for observation [29]. As illustrated in Figure 3, the angular pointing errors of the azimuth and elevation axes of the telescope can be denoted as \(\delta A\) and \(\delta E\)[27], respectively. They are given by  
\[\begin{cases}\delta A=\hat{A}-A_{b}=f(\cdot)\\ \delta E=\hat{E}-E_{b}=g(\cdot)\end{cases} \tag{4}\]  
where \(\hat{A}\) and \(\hat{E}\) are the actual azimuth and elevation angles in the base coordinate while \(A_{b}\) and \(E_{b}\) are the theoretical pointing angles. \(f(\cdot)\) and \(g(\cdot)\) represent error models for azimuth and elevation angles, respectively. In particular, both \(\delta A\) and \(\delta E\) contain the error mean and standard deviation, hence the pointing error is normally characterized using the root mean square (RMS) [30].  
Thus far, the correction of EOT pointing errors entails the following steps. (1) Developing a relatively accurate and complete pointing error correction model using error source analysis. (2) Given a series of reference stars with known coordinates, the pointing errors can be calculated from the above equations by the measured values of EOT. (3) Estimating the parameters in Formula (4) and optimizing the correction model in reverse using the results. (4) Predicting and correcting the pointing error of the telescope using the solved model.
## 2 Materials and Methods
### Methods
#### 2.5.1 Scenario Using PPS + RFE  
1. Step one: Choosing an algorithm; By default, this method computes results using a decision tree. A decision tree classifier will be used when obtaining data with categorical values. The decision tree regressor will then be used when numerical values are obtained. Data pre-processing: Use Label Encoding if the column whose value will be predicted or the target column has a definite value. However, if the column that denotes the alias value of the feature column has a substantial value, a one-hot encoding should be used. The metric value for evaluation: F1 score weights will be used as an evaluation metric if the chosen method is a classification method. F1 scores range from 0 to 1 on a scale of 1 to 10. The PPS value between two features  
Figure 1: Feature selection and elimination approach from this method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors/Year** & **Dataset** & **Number of Features** & **Machine Learning Algorithm** & **Feature Selection Algorithm** & **Accuracy** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} [PERSON] et al. \\ [20]/2021 \\ \end{tabular} } & \multirow{4}{*}{Shirazi Dataset} & \multirow{4}{*}{\(15\)} & Artificial Neural Network (ANN), DT, Gaussian NB, Gradient Boosting (GB), KNN, RF, SVM & URL and HTML based, URL based only, Transformer (BERT, ELECTRA) NLP. BERT and ELECTRA using ANN outperform method & More than 96\% \\ \cline{1-1}  & & & XGBoost, AdaBoost, RF, GradientBoost, Bagging, LightGBM & Ensemble, GA-SVM & 97.16\%, outperforms another & 98.58\%, 97.39\% \\ \cline{1-1}  & & & XGBoost, AdaBoost, RF, GradientBoost, Bagging, LightGBM & Ensemble, GA-based ensemble. GA-SVM & 97.16\%, 98.58\%, 97.39\% \\ \cline{1-1}  & & & XGBoost, AdaBoost, RF, GradientBoost, Bagging, LightGBM & Ensemble, GA-based ensemble. GA-SVM & 98.58  
is rated at this stage, and then ten features with a high correlation value between features are eliminated.
2. Step two: After deleting ten features, there are 38 remainings for dataset 1 and 77 for dataset 2. The remaining characteristics are obtained by conducting a ranking search using RFE with python sci-kit-learn and selecting rankings 1 to 10 by gradually reducing features.
3. Step three: The highest-ranked features are evaluated for accuracy using the RF algorithm.
## 4 Discussion  
Generally, the greenness and wetness VIs could reflect different aspects of the vegetation dynamics and consequently lead to differences in the VI profiles [33]. Our results showed that the NDVI began to decrease, while the wetness VI still stagnated at its maximum value (Figure 3a), which may suggest that the biochemical components of the rice canopy begin to asynchronously change when entering the heading stage. In the remote sensing context, the likely reason is that the increasing proportion of the spikes in the canopy could contribute to lower chlorophyll content but almost unchanged water content reflected on a pixel basis. Compared with the single-purpose VI, the phase-space method is theoretically advantageous in characterizing the development of rice because the greenness-wetness VI phase-space theoretically encompasses more information (e.g., pigment and water content). Thus, we suggest that the NDWI time series should not only be used to eliminate the influence of snow, and the phase-space partitioning may not only be applied throughout the whole year but also for the growth season, for example, crop cycles. As NDWI can be disturbed by soil moisture, especially in the case of low vegetation coverage, we did not use it as a separate VI in the phenology extraction and we also did not study the period from transplanting to active tillering.  
Heavy metal stress has been proven to cause a later heading stage in the rice development [4; 6], but it is still questionable when applying this theory to the remote sensing context. As a practical matter, the shifts of the heading stage induced by heavy metal stress are difficult to measure quantitatively due to the agricultural management and meteorological conditions. \(D_{\text{{{L}}}{\text{{i}}  
point method [50; 54]. We suggest that the inflection point method is more suitable to being transformed into the phase-space. Furthermore, we hoped that the RPI could be applied to other environmental changes that are causing intra-annual variability in phenology, not just for heavy metal stress in rice.
## 3 Results and Discussion
### The Chromatic Parameter Measurements  
The chromatic parameters were recorded using a special colorimeter. From the experimental data, the color features of the mosaic samples were calculated [26]. The obtained chromatic parameters are listed in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mineral Phase** & **Raman Characteristic Peaks [cm\({}^{-1}\)]** \\ \hline Calcite & 157, 283, 713, 1087 \\ Quartz & 352, 462, 1375 \\ Goethite & 304, 395, 480, 557 \\ Hematite & 304, 395 \\ Manganite & 395, 557, 637 \\ Orthosilicates & 395, 557, 880 \\ Carbon & 1325, 1610 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The tentative assignment of the observed Raman lines to the different mineral phases of the mosaics fragments.  
Figure 5: Raman spectra obtained from different points of samples 5(a) and 6(b,c). The Raman wavenumbers of the most intense Raman peaks are indicated. A not-homogeneous composition in different point of samples 5 and 6 is observed. The tentative assignment of Raman lines is present in Table 4.  
The color parameters (i.e., H, C, and \(\Delta\)E) determine the tendency to yellow hues (b) and green hues (a). However, the chromatic parameters a (red/green) and \(\Delta\)E (total color change) appear as decisive factors affecting the color variations over time, as the greatest changes took place in these parameters. This indicates that the color parameters such as a (red/green), b (yellow/blue), H and L have greater weight than \(\Delta\)E and C to distinguish these samples based on the pigments. Hue angle (h) derived from a and b color space, and therefore, is a more practical parameter in reflecting the pigments [48]. However, it should be noted that chromatics relate only to the surface of studied mosaic fragments. The color of the surface may be imperfect due to the interaction of the fragments of mosaic with the air or water vapor from atmosphere, which is a reason for the surface degradation and color changing of the mosaic stone from Roman Mosaic building. Most mosaic fragments with dark colored crusts were identified [49; 50]; these crusts can be biochemical and physical responses to diverse anthropogenic activities. It should be noted that the present chromatic data can be used together with experimental structural data obtained from the entire volume of the samples using high penetration methods as neutron diffraction or X-ray diffraction.
## 3 Materials and Methods
### Growth and Physiological Parameters Analysis
#### 3.4.2 Assessment of Growth and Physiological Activity Parameters  
Seed potatoes were observed every day since pathogen inoculation and during storage. After their planting in substrate in plant microcosms, the growth and physiological activity of the plants were observed every 2 days for the first 24 days and every 6 days for the remaining period. The following parameters were measured:1. Biological condition of the seed potatoes, in terms of their vigor, turgor, rotting, and infection by phytopathogens;
2. Percentage of germinating tubers;
3. Kinetics of plant growth, by measuring the stem height every 2 days during the first 24 days and every 6 days for the remaining period [17; 18; 21];
4. Quality of the stems on a 5-point valuation scale, where 5 indicates stems full of vigor and well growing, and 1 indicates dried out ones [15; 16];
5. Kinetics of root growth, by measuring its length every 2 days until it reaches 30 cm [17; 18];
6. Kinetics of root system development, by assessing every 2 days the percentage of the soil profile area filled by the roots;
7. Intensity of gas exchange (net photosynthesis, transpiration, stomata conductivity, and intercellular CO\({}_{2}\) content), measured with a TPS-2 apparatus (PP Systems, USA) in the highest positioned fully developed leaves, during the period of most dynamic plant growth [16];
8. Index of chlorophyll content, measured with a Minolta SPAD-502 apparatus (Japan) in the highest positioned fully developed leaves, during the period of most dynamic plant growth [16];
9. Fresh and dry (dried at 130 \({}^{\circ}\)C for 3 days) mass of tubers and withered stalks (haulms) at the end of the cultivation period [20; 22];
10. Infestation of seed potatoes, stems, and roots by phytopathogens, assessed throughout the entire plant growth period [14].
## 3 Role of Small RNA in Stress Response  
A bacterial pathogen faces a multitude of hostile conditions that constitute significant stress within a host [32]. Immediately upon entry, the pathogen encounters the host's innate immune defenses, including antimicrobial peptides and reactive oxygen and nitrogen species produced by phagocytes. In addition, it is faced with host-mediated nutrient limitation (nutritional immunity). Furthermore, bacterial pathogens are subjected to further stresses like acidic pH within phagosomes, lysosomal enzymes, and continued oxidative and nitrosative attack [128]. Beyond direct immune assault, pathogens must also adapt to the limited availability of essential nutrients like iron and specific carbon sources, competition with the host microbiota, along with fluctuations in temperature and osmolarity [129; 130]. These diverse and dynamic stresses necessitate sophisticated bacterial adaptation mechanisms to ensure survival and successful infection.  
Bacteria have evolved a diverse arsenal of defense mechanisms to counteract the stresses imposed by the host [32]. To combat ROS and RNS, bacteria employ antioxidant enzymes like superoxide dismutase [131; 132], catalase [133], and alkyl hydroperoxide reductase [134]. To survive within acidic phagosomes, bacteria utilize acid tolerance systems, including proton pumps to maintain intracellular pH [135] and urease to generate ammonia [136]. Additionally, certain bacteria form biofilms, providing a protective matrix against immune cells and antimicrobial agents [112]. Furthermore, certain pathogens can modulate host immune responses directly, inhibiting phagosome maturation, escaping into the cytoplasm, or suppressing cytokine production, effectively manipulating the host's defenses to their advantage. Finally, nutrient limitation is addressed through metabolic flexibility, allowing bacteria to utilize alternative carbon sources and specialized scavenging and transport systems for essential nutrients like iron [137]. Iron is a double-edged sword for living organisms. While indispensable for essential processes like respiration, DNA synthesis, and enzymatic catalysis, excess iron can be toxic due to the formation of ROS through the Fenton reaction. Therefore, maintaining iron homeostasis is critical for bacterial survival, especially within host environments where iron is often scarce due to host nutritional immunity. In _E. coli_, the transcription factor Ferric Uptake Regulator, Fur, acts as a master regulator of iron uptake. Fur regulates a small RNA, RyhB, that equips the pathogen with a stress response, as outlined below.
## 5 Frequency Hopping Techniques
### Channel-Aware, Reduced-Hop-Set Techniques
#### 5.2.1 Highest Gain Frequency Hopping  
The most obvious example of the reduced-hop-set techniques is the Highest Gain Frequency Hopping (HGFH), where the selected \(M\) channels are the ones with highest gain.
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 3 SOC Estimation Based on the HIF Algorithm
### State Space Equation of the Lithium-Ion Battery  
The establishment of system state space equations is the basis for controlling the parameters of dynamic systems in modern control theory. Establish a mathematical model that reflects the internal structure of the system and characterizes the system through the state equation and the output equation. Owing to the time-varying non-linearity of the battery system, its state space equation can be expressed as:  
\[x_{k+1}=A_{k}x_{k}+B_{k}u_{k}+\omega_{k} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RMSE** & **HPPC** & **DST** & **FUDS** \\ \hline PSO & 0.0061 & 0.0050 & 0.0057 \\ HPSO & 0.0036 & 0.0022 & 0.0035 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Root mean square error under different working conditions.  
Figure 8: Model voltage and simulation error under hybrid pulse power characteristic (HPPC) conditions.  
\[y_{k}=C_{k}x_{k}+D_{k}u_{k}+v_{k} \tag{10}\]  
This is the first step in the derivation of the state space equation of a lithium-ion battery to determine the relevant variable parameters of the battery system. According to the established battery model, the state variable of the system is set as \(x_{k}=[V_{CPE_{1}k}\ V_{CPE_{2}k}\ V_{W,k}\ SOC_{k}]\). The input of the system is \(u=l_{k}\), which is the current state of the battery. The output is \(y=U_{d,k}\), which is the terminal voltage of the battery.  
After determining the parameters, according to the fractional-order model established in Section 2.1, the state space equations of Equations (8) and (9) are derived, where \(A_{k}\), \(B_{k}\), \(C_{k}\), and \(D_{k}\) are defined as follows:  
\[A_{k}=\left[\begin{array}{cccc}\alpha-T^{\alpha}\frac{1}{R_{1}C_{1}}&0&0&0\\ 0&\beta-T^{\beta}\frac{1}{R_{2}C_{2}}&0&0\\ 0&0&\gamma&0\\ 0&0&0&1\end{array}\right],B_{k}=\left[\begin{array}{c}\frac{T^{\alpha}}{C_{1 }}\\ \frac{T^{\beta}}{C_{2}}\\ -\frac{T^{\gamma}}{W}\\ -\frac{nT}{C_{n}}\end{array}\right] \tag{11}\]  
The implementation of SOC estimation is to design the corresponding filter algorithm based on the above-mentioned state space equations to eliminate the effects of noise \(\omega_{k}\) and \(v_{k}\) as much as possible. With improvements in the accuracy of the system state estimation, the SOC estimation achieves the desired effect.
## 2 Mathematical Model
### Bed Efficiency and Contaminant Saturation  
The evaluation of breakthrough curves of contaminant allows the qualitative description of contaminant transport at varied \(\mu_{c}\) and \(\sigma_{c}\). However, obtaining the bed efficiency (\(\beta\)) and the contaminant saturation (\(\theta\)) from the evaluated breakthrough curves provides the quantitative description of breakthrough curves in each condition. The \(\beta\) value represents the fraction of adsorbed contaminant during its transport:  
\[\beta=1-\left(\sum_{j=1}^{PV_{f}}0.5\lambda\left(\left(\frac{C}{C_{0}}\right)_ {j}+\left(\frac{C}{C_{0}}\right)_{j+1}\right)\right)/PV_{f}, \tag{13}\]  
where \(PV_{f}\) (-) is the final elapsed pore volume (\(PV_{f}\) = 30 in this work), \(\lambda\) (-) is the pore volume interval, and \(j\) is an order of normalized concentration starting from 0 PV (\(j=PV_{f}/\lambda+1\)), and C\({}_{0}\) (M L\({}^{-3}\)) is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Note and Related Reference** \\ \hline \(n\) & 0.365 & Updated every time step. Decreased over time as colloidal particles retained \\ \hline \(N\) & 1 or 100 & \(N\) = 100 when \(\sigma_{c}\) > 0 \\ \hline \(D_{c}\) (10\({}^{-3}\) cm\({}^{2}\) s\({}^{-1}\)) & 6.1-6.4 & Calculated using equations proposed in [35], mean value was used if \(N\) > 1 \\ \hline \(D\) (10\({}^{-3}\) cm\({}^{2}\) s\({}^{-1}\)) & 2.9 & Experimentally obtained using non-reactive tracer (bromide) \\ \hline \(q\) (cm s\({}^{-1}\)) & 0.0123 & - \\ \hline \(\rho_{b}\) (g cm\({}^{-3}\)) & 1.68 & The specific gravity of sand was assumed 2.65 \\ \hline \(d_{c50}\) (cm) & 0.072 & Typical median size of coarse sand \\ \hline \(\mu_{c}\) & 0.1-2 & Typical median size of clay colloidal particles (\(r_{c50}\) = e\({}^{3}\)) \\ \hline \(\sigma_{c}\) & \(-2.3\) to 0.69 & Equivalent to the coefficient of uniformity ranged from 0 to 10 \\ \hline \(f\) (-) & 0.01 & Assumed value ([36]) \\ \hline \(K_{1},K_{2}\) (10\({}^{-3}\) L mg\({}^{-1}\)) & 2.202, 6.102 & Obtained by batch experiment ([9]) \\ \hline \(d_{\text{max(1)}}\) (\(d_{\text{max(2)}}\) (mg g\({}^{-1}\)) & \(1.081\times 10^{-3}\), 2.854 & Obtained by batch experiment ([9]) \\ \hline \(\omega_{1}\) & \(1.859\times 10^{-3}\) & Obtained by batch experiment ([9]) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters adopted in numerical simulation.  
the inlet concentration of contaminant. On the other hand, \(\theta\) quantifies the saturation of bed at \(PV_{f}\) (i.e., the fraction of occupied adsorption sites in the bed), expressed as:  
\[\theta=(C_{1}V_{PV}\beta PV_{f})/(\sum_{k=1}^{N}q_{\max(k)}W_{k}), \tag{14}\]  
where \(V_{PV}\) (L\({}^{3}\)) is the volume of 1 pore volume calculated by the porosity of the bed, \(N\) is the total number of materials (\(N\) = 2 in this study), \(q_{\max(k)}\) (M M\({}^{-1}\)) is the maximum adsorption capacity of contaminant, and \(W_{k}\) (M) is the weight of the \(k\)th material in the bed.
_Applied_  
_sciences_  
Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227 ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks  
**[PERSON] 1**, **[PERSON] 2**, **[PERSON] 3**, **[PERSON] 4,*, [PERSON] 5** and **[PERSON] 6**  
Received: 18 December 2015; Accepted: 12 January 2016; Published: 19 January 2016  
Academic Editor: [PERSON]  
1 College of Chemistry, Sichuan University, Chengdu 610064, China; [EMAIL_ADDRESS]  
2 College of Mathematics, Sichuan University, Chengdu 610064, China; [EMAIL_ADDRESS]  
3 College of Light Industry, Textile and Food Science Engineering, Sichuan University, Chengdu 610064, China; [EMAIL_ADDRESS]  
4 Software School, Xiamen University, Xiamen 361005, China  
5 Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China; [EMAIL_ADDRESS]  
6 School of Computing, Informatics, Decision Systems Engineering (CIDSE), Ira A. Fulton Schools of Engineering, Arizona State University, Tempe 85281, AZ, USA; [PERSON]ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-186-0609-9618  
**Abstract:** 1,1,1,2,3,3,3-Heptafluoropropane (R227 ea) is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM), and artificial neural networks (ANNs) were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227 ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs) (e.g., the RMSE of the SVM trained with data provided by [PERSON] _et al._[1] is 0.11, while the RMSE of the Song and Mason equation is 196.26). Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly.  
**Keywords:** 1,1,1,2,3,3,3-Heptafluoropropane; R227 ea; Song and Mason equation; machine learning; support vector machine; artificial neural networks
## 4 Materials and Methods
### Analysis of RNA-Seq Data  
Raw reads were filtered using fastp v.0.23.4 [64] for read adaptors, artificial reads, and other low-quality reads. FastQC v.0.12.0 [65] was used for the quality control of clean reads. Using HSIAT2 v.2.2.1 [66], the clean reads were mapped to the assembled genome of _P. dalae_ NBP-49626. Spliced RNA-Seq read alignments were generated using STAR v.2.7.3 [67] and processed into transcripts using StringTie v.1.3.4 [68]. The fragments per kilobase of exon per million reads (FPKM) method was used to calculate and normalize gene expression. Normalized gene expression levels in the submerged and semi-solid fermentation samples of _P. dalae_ NBP-49626 on different days were directly compared and collected using StringTie v.1.3.4 [68]. False discovery rate (FDR) control was performed using DESeq2 v. 1.42.0 [69] for differential gene expression analysis. Genes with a fold change \(\geq\) 2.0, \(p\)-value \(\leq\) 0.05, and FDR \(\leq\) 0.05 were identified as differentially expressed. GO functional enrichment and KEGG pathway enrichment analysis were performed using clusterProfiler v.4.10.0 [70] with \(p\)-adjust (FDR) < 0.05.