esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spectral Calibration  
Before analyzing the obtained multispectral images, spectral calibration was performed to assign wavelengths to the spectral information pixels to obtain the relative reflectance of the sample images. To accomplish this, dark and white reference images were used to remove the dark current noise generated by the charged-coupled device sensor of the camera and to prevent light saturation with high intensity [52]. This step can enhance the reliability and acceptability of the spectral data of multispectral image information and its systems [53].  
A dark reference image (0% reflectance) was acquired by covering the opaque cap, and a white Teflon plate was used to acquire the white reference image (99% reflectance) [49]. The reflectance values were calculated based on the raw reference data according to the following equation [54].  
\[\text{I}_{\text{R}}=\frac{\text{I}_{\text{r}}-\text{I}_{\text{b}}}{\text{I}_ {\text{w}}-\text{I}_{\text{b}}} \tag{2}\]  
where \(\text{I}_{\text{R}}\) is the relative reflectance image and \(\text{I}_{\text{r}}\), \(\text{I}_{\text{w}}\), and \(\text{I}_{\text{b}}\) are the scanned Chinese cabbage images, white reference images, and black reference images, respectively. The spatial step size was set to 0.3 s and 0.5 mm.
# An Improved Product Code-Based Data Hiding Scheme
## Product Code-Based Data Hiding  
Two 1-D linear block codes \(C1\) (\(n1\), \(k1\)) and \(C2\) (\(n2\), \(k2\)) can be used to generate a 2-D (\(n1\)\(\times\)\(n2\), \(k1\)\(\times\)\(k2\)) linear code (called product code), such that each codeword (called code array) is an array of size \(n2\)\(\times\)\(n1\), as shown in Figure 2. After sequentially encoding the \(k2\) rows of the \(k2\)\(\times\)\(k1\) array of information bits (**IB**) by \(C1\) to generate the array **CR** (Check on Rows), and the \(k1\) columns of array **IB** are sequentially encoded by \(C2\) to generate the array **CC** (Check on Columns). Then, the array **CC** (Checks on Checks) can be obtained by performing either \(C1\) encoding on the \(n2\)-\(k2\) rows of array **CC**, or \(C2\) encoding on the \(n1\)-\(k1\) columns of array **CR**[13]. Note, the L-shaped parity check block that is the union of the arrays **CC**, **CCH**, and **CR** and is a function of array **IB**, denoted as **P(IB)**. Take the (7, 4) Hamming code as the example. That is, \(n1\)\(=\)\(n2\)\(=\) 7 and \(k1\)\(=\)\(k2\)\(=\) 4, then a (49, 16) product code can be generated, and there are 33 parity check bits. For the (49, 16) product code, similar to Table 1, the size of the standard array is up to \(2^{33}\)\(\times\)\(2^{16}\). Each element of the standard array is an array of size \(7\)\(\times\)\(7\). There are \(2^{16}\) code arrays and \(2^{33}\) coset leaders in the standard array.
## 4 Results  
### Confirmatory Factor Analysis (CFA)  
A Confirmatory Factor Analysis (CFA) was performed to evaluate the measurement model's fit and construct validity. The model hypothesised three latent factors: PEU (four items), PU (five items), and BI (three items). With 11 items and a sample size of 84, the item-to-sample ratio was approximately 7.6:1, which is deemed adequate [90]. Model fit was evaluated using multiple fit indices. The chi-square test of model fit was notsignificant, indicating an overall good fit to the data (\(\chi^{2}(51)=54.326\), \(p=0.349\)). The relative chi-square (\(\chi^{2}/\mathrm{df}\)) was below the recommended threshold of 3, indicating a good fit (\(\chi^{2}/\mathrm{df}\) = 1.065). All additional fit indices indicated were within recommended margins (CFI = 0.992, TLI = 0.99, RMSEA = 0.028 (90%; 0.00-0.077; \(p=0.717\)), and SRMR = 0.052), indicating a good fit. The results thus suggest that the hypothesised measurement model adequately represents the data.  
Internal consistency was examined using Cronbach's alpha (\(\alpha\)) and omega (\(\omega\)) coefficients. Both indicated acceptable reliability (\(\geq\)0.7) for each latent construct PEU (\(\alpha\) = 0.79, \(\omega\) = 0.797), PLB (\(\alpha\) = 0.861, \(\omega\) = 0.856), and BI (\(\alpha\) = 0.764, \(\omega\) = 0.768). In addition, all items loaded onto their respective constructs were statistically significant (\(p<0.001\)), with loadings ranging between 0.531 and 0.879. Convergent validity was assessed through AVE and CR, with all constructs meeting the recommended rule of thumb (AVE > 0.50 and CR > 0.70), including PEU (AVE = 0.504, CR = 0.793), PLB (AVE = 0.552, CR = 0.872), and BI (AVE = 0.521, CR = 0.762), indicating satisfactory convergent validity for all latent constructs. All HTMT values were below 0.85, indicating acceptable discriminant validity. The CFA results suggest that the measurement model demonstrated a good model fit and satisfactory convergent and discriminant validity, providing a solid basis for SEM analysis (Table 2).
## 3 Results
### Model Uncertainty  
The global uncertainty in model predictions was quantified using the SD of the predicted values for 100 iterations. The RF and XGBoost models with optimal variable selection (RF_sel and XG_sel) exhibited a lower uncertainty than the respective all-variable models (RF_all and XG_all; Figure 7). In the SOC prediction, the mean uncertainty estimates for the RF_sel model ranged from 0 to 0.11 g\(\cdot\)kg\({}^{-1}\), and the estimates for the XG_sel model were between 0 and 0.40 g\(\cdot\)kg\({}^{-1}\). In the STP prediction, the mean uncertainty estimates for the RF_sel and XG_sel models fell in the range of 0-25.16 mg\(\cdot\)kg\({}^{-1}\) and 0-101.87 mg\(\cdot\)kg\({}^{-1}\), respectively. In the SAP prediction, the range of mean uncertainty estimates was 0-2.36 mg\(\cdot\)kg\({}^{-1}\) for the RF_sel and 0-4.54 mg\(\cdot\)kg\({}^{-1}\) for the XG_sel model.  
The results showed that all the variable selection models exhibited low levels of uncertainty. Despite their similar accuracy, the RF model had a lower uncertainty and was more robust than the XGBoost model to predict the soil properties. With regard to the spatial distribution pattern of uncertainty in the models' predictions, relatively high estimates were found in the northwestern and east-central parts of the sub-watershed with fragmented land use types or large elevation changes (Figure 7).  
Figure 5: Spatial distribution of the soil properties predicted by different machine leaning models without (RF_all and XG_all) and with (RF_sel and XG_sel) variable selection.  
Figure 6: Relative importance of variables in the random forest model for soil properties. The variables are defined in Table 1.
## 4 Bi-Level Dispatch and Control Architecture
### Overall Operation Strategy  
As mentioned above, the inner- and outer-level operation strategies are designed with different objectives and depicted through optimization models. More specially, Equations (7), (14), and (21) are multi-objective optimization models, and Equation (26) is a mono-objective optimization model. The typical multi-objective optimization methods, e.g., the multi-objective particle swarm optimization algorithm [3], multi-objective genetic algorithm [35], and multi-criteria decision analysis [36], can be applied to Equations (7), (14), and (21). Moreover, the mono-objective optimization method, e.g., the particle swarm optimization algorithm [37] and genetic algorithm [38], is effective to solve Equation (26). So the commands of output power for controllable resources can be obtained by solving the optimization models. Furthermore, the overall operation logic of the bi-level dispatch and control architecture can be seen in Figure 10.  
According to Figure 10, the operation of the bi-level dispatch and control architecture is listed below:  
1. Collect monitoring information of the controlled power system in the outer-level dispatch and control platform.
2. If the disturbance is detected, execute the emergency operation strategy of the outer level; otherwise, execute the normal operation strategy of the outer level.
3. Broadcast commands from the outer-level dispatch and control platform to GVPPs and other controllable resources.
4. The outer-level commands are received and updated in the inner level.
5. If disturbance information is involved, start the emergency response agent and execute the emergency operation strategy of the inner level; otherwise, start the normal dispatch agent and execute the normal operation strategy of the inner level.
6. Broadcast commands to inner controllable resources, and return monitoring information to the outer-level dispatch and control platform.
## 4 Experimental Results and Discussion
### Experimental Results in Case 1
#### 4.2.4 Performance Evaluation  
Fewer misses occurred in the experiments because the cache size was bigger enough in our design, where the cache size of the in-memory cache allocated 5 GB as well as an in-disk cache of 20 GB. The probability of the occurrence of a cache miss was about 0.0034 (34/1000) for the case of the in-memory cache and 0.0016 (16/1000) for the in-disk cache. According to the computed data from Figures 14-25, the weighted average execution time for launching a SQL query command to each approach was evaluated and then a summary of the performance comparison among Hive, Impala, SparkSQL, and the proposed methods are listed in Table 9 where the value in each cell means the average of the execution time for four commands running four data sets in a specific test environment and eventually the average of execution time for a specific approach for three test environments. As a result, the proposed approach outperformed the other benchmarks to show its best performance for a SQL query command execution in the big data environment.  
Figure 28: Execution time of SQL command IV in test environment III.  
Figure 27: Execution time of SQL command III in test environment III.  
Figure 29: Execution time of SQL command V in test environment III.  
The following is to evaluate the performance index for the above-mentioned approaches. We first substituted the average execution times from Table 9 into Equation (3) to find the normalized performance index among different approaches in the test, as listed in Table 10. Next, we substituted those results into Equation (4) to find the average normalized performance index, as listed in Table 11. As a result, in Table 11 it is noted that the proposed approach in this paper obtained the best performance index when compared with the others.
## 1 Introduction  
The strive for the accurate assessment of individual elements of the environment and processes occurring in them is the driving force for the development of appropriate analytical tools, which are necessary for obtaining reliable information. This concerns:  
* control and measuring devices, which ensure the possibility of analysing the prepared samples;
* reference materials which accurately reflect the composition and character of the actual samples tested, which is necessary to ensure a proper system for the quality control and assurance of measurement results;* analytical methodologies which can be used in testing environmental samples often characterised by a complex matrix composition and low, and sometimes very low, analyte content levels.  
In the latter case, the stage of preparing samples for analysis is a significant element of analytical procedures. Within this stage, three basic tasks are implemented:  
* increasing the level of analyte concentrations in the analysed samples to a higher level than the limit of detection of the analytical technique used;
* removal of at least a part of interferents which can influence the result of the analysis;
* replacement or at least simplification of the matrix composition of samples for analysis.  
At this stage, meeting the requirements of Green Chemistry and Green Analytical Chemistry will become more and more important to make it possible to decrease the intensity of the impact of anthropopresion connected with the operations of analytical laboratories. In this case, the following options can be considered:  
* the use of solvent-free/solvent-loss techniques for preparing samples for analysis;
* the use of microextraction techniques which, due to a reduced scale of the analysis lead to a decrease in the quantity of reagents used, including solvents.  
This relatively broad introduction with a general description of the existing knowledge is appropriate also for organomercury and organotin compounds present in environmental samples. In this study, literature information on microextraction techniques (SPME, LPME) will be presented in procedures for the determination of various chemical forms of mercury and tin, their short description and possibility of using them in environmental research.
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
## Influence of Weak Grid Conditions on the APF System
### Influence of Grid-Side Voltage Frequency Deviation  
Under weak grid conditions, when the voltage frequency has a large range of deviation, the attenuation of the equivalent disturbance by the above NF-PLL will be greatly reduced. As shown in Figure 6, the grid-side voltage frequency \(\pm 4\) Hz deviation will cause the attenuation of the 2 nd, 6 th and 12 th harmonic interference to increase to \(-6.85\) dB, \(-14.8\) dB and \(-20.7\) dB, respectively. The decrease in attenuation will greatly weaken the ability of the phase-locked loop to suppress interference and reduce the accuracy of phase and frequency estimation. Furthermore, it has an indirect influence on the harmonic current given link and the current tracking control link.  
In theory, VPI current control can achieve zero steady-state errors in tracking the signal corresponding to its resonant frequency [29]. However, it relies on an accurate grid frequency estimation, and its transfer function is:  
\[G_{\text{VPI}}(s)=\sum_{h\in H}2\frac{k_{ph}s^{2}+k_{rh}s}{s^{2}+\left(h\omega _{0}\right)^{2}} \tag{26}\]  
where \(k_{ph}\) is the proportional gain, \(k_{rh}\) is the resonant gain, \(\omega_{0}\) is the grid frequency and H is the compensated harmonic set. At the resonant frequency of the VPI controller, there is an infinite open-loop gain in theory. According to reference [29], when \(k_{ph}\) in (26) is smaller, the frequency selectivity of the VPI controller is stronger, and for the reference current component of the corresponding resonant frequency, the VPI control can achieve better steady-state performance. Of course, considering the requirements of the dynamic performance of APF, \(k_{ph}\) should not be too small. However, the stronger the frequency selectivity of the VPI controller, the worse the adaptability to frequency deviation. When the grid-side frequency deviation occurs, the steady-state compensation performance will decrease significantly.  
Therefore, the fixed-frequency notch filter in the phase-locked loop is replaced by an adaptive notch filter with an adaptive adjustment notch frequency. On the one hand, PLL can effectively suppress the influence of 2 nd, 6 th and 12 th harmonic interference signals under complex conditions of weak grid and obtain the phase angle and frequency information of the three-phase power grid more accurately. On the other hand, the phase angle and frequency information are used for Park transformation in VPI control and frequency parameters in VPI control law, respectively, to improve the performance of current tracking control.  
Figure 6: Bode diagram of PLL system disturbance to output.
## 4 Reliability-Based Optimum Design of Preform
### Development of an Approximation Model  
An approximation model is a technique used to predict other result values within a discrete space without the need for additional experiments or analyses, relying on findings derived from previous experiments or analyses conducted within that space. To improve the accuracy of the optimal design, the experimental points must adequately represent the space within the discrete area. This study established specific ranges for nine design variables, which included six selected through screening and three additional variables aimed at assessing the reliability of raw materials. The experimental points for the nine chosen design variables were generated using Optimal Latin Hypercube Design (OLHD) from various experimental design methodologies. In total, 300 experimental points were designated, of which 270 were utilized to train the approximation model and 30 were used as test data for verification, thus confirming the model's accuracy.  
This study employed five techniques to compare approximation models and select the most appropriate one. The methods used included the Kriging method, radial basis function (RBF), polynomial regression (PR), ensemble decision trees (EDT), and multi-layer perceptron (MLP) techniques. The tolerances of the generated approximation models were compared, assessed using the predicted coefficient of determination (\(R^{2}\)). In Equation (10), \(R^{2}\) serves as a metric for the effectiveness of the approximation model in representing the actual data. The accuracy of the model's predictions increases as the predicted coefficient of determination approaches one.  
\[Predicted\ R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i\ pred}\right)^{2}}{ \sum_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}} \tag{10}\]  
In this equation, \(y_{i}\) represents the actual observed value, \(\hat{y}_{i\ pred}\) denotes the value predicted by the model, \(\overline{y}\) signifies the mean of the observed values, and \(n\) indicates the number of data points. Upon evaluating the tolerances associated with the three parameters--the effective strain, defect occurrence, and forming load--using the predicted coefficient of determination method, the Kriging technique (KRG) was selected as the predictive model for all three parameters. The \(R^{2}\) values for each approximation model are summarized in Table 5.  
Figure 12: Sensitivity analysis based on ANOVA.
## 3 Results
### Comparison with a Different Benchmark Dataset Used by DeepTP  
In order to further evaluate the performance of BertThermo, the benchmark training set of DeepTP [12] was used to retrain the model, and the three benchmark independent test sets used by DeepTP were used to score the model. The scoring results are listed in Tables 5 and 6. First, compared with DeepTP, BertThermo shows better values for the 10-fold cross-validation with the same training set (see Table 5). Second, for independent testing of balanced data, the metrics of BertThermo, except for Sp, are greater than the other state-of-the-art methods as far as we know. In fact, the number of non-thermophilic proteins is far more than that of thermophilic proteins. This means that data imbalance is a common situation. From the independent test results of an imbalanced dataset in Table 6, it can see that BertThermo has better generalization performance in the case of data imbalance. At the same time, in practical application, proteins with homology may show different thermophilicity. In Table 6, the results of testing 100 thermophilic proteins and 100 non-thermophilic proteins with a similarity of more than 40% showed that BertThermo also had the best performance. Figure 4: Dimension reduction visualization of BERT-bfd and other deep representation learning features. Red dots represent thermophilic proteins and blue dots represent non-thermophilic proteins. Horizontal axis and vertical axis are the first feature and the second feature of the reduced 2-dimensional vectors, respectively. These dots are classified by LR (yellow area for positive samples, while cyan area for negative samples). (**A**) shows the SSA feature reduced by UMAP. (**B**) shows the UniRep feature reduced by UMAP. (**C**) shows the W2V feature reduced by UMAP. (**D**) shows the BERT-bfd feature reduced by UMAP. (**E**) shows the selected BERT-bfd (top 90) feature reduced by UMAP.
## 2 Spatial Distribution Characteristics of the Signal and Noise Fields
### Investigation of the Spatial Distribution of the Signal Field
\begin{array}{rl}\overline{\mathbf{p}^{2}}&=\frac{1}{2}\frac{1}{\left(r^{2} +\frac{5}{4}z_{s}^{2}\right)^{2}-z_{s}^{4}}\left[\left(1+G_{10}^{2}\right) \left(r^{2}+\frac{5}{4}z_{s}^{2}\right)+\left(1-G_{10}^{2}\right)z_{s}^{2} \right.\\ &\qquad\left.-2G_{10}\sqrt{\left(r^{2}+\frac{5}{4}z_{s}^{2}\right)^{2}-z_{s}^ {4}}\frac{\sin\theta}{\theta}cos\left(\frac{2 bz_{s}z}{\sqrt{r^{2}+z^{2}}} \right)\right]\end{array}  \tag{10}\]  
where \(\theta=2\pi z_{s}z\cdot\Delta f/\left(c\sqrt{r^{2}+z^{2}}\right)\) and \(k_{0}=2\pi f_{0}/c\). It is worth noticing that when the signal has a single frequency, i.e., \(\Delta f\) approximates \(0\), \(\sin\theta/\theta\) approaches \(1\); furthermore, when the signal bandwidth increases, \(\theta\) increases and \(\sin\theta/\theta\) approaches \(0\). Given this, when the signal has sufficient bandwidth, the fluctuation of the sound field of the signal can be effectively smoothed. When \(z\approx z_{s}/2\), the approximation can be given as  
\[\theta\approx\frac{\pi z_{s}^{2}\cdot\Delta f}{c\sqrt{r^{2}+z^{2}}}\approx \frac{\pi}{c}\frac{z_{s}^{2}}{r}\cdot\Delta f \tag{11}\]  
According to Formula (11), it can be obtained that the fluctuation range of the signal is proportional to the distance \(\mathbf{r}\) at the transmitting and receiving ends and inversely proportional to the center frequency \(f\), the bandwidth \(\Delta f\), and the modem depth \(z_{s}\).
## 4 Remote Base Unit  
This remote unit controls when and who can transmit within the wireless network. It is based on the high performance microcontroller PIC18F6722 and is constituted of three main modules: power source module, RF communications and HTTP Embedded server. In distinction to the RTUs, there will be only one BTU for each wireless sensor network. In the next sections BTU's hardware and software is described.
## 3 Seismic Behavior of Micropiles
### Factors of Influence  
As for other structures, the behavior analysis of MPs should be performed based on the main influential factors. Based on the literature study and making a synthesis of the main findings [1; 3; 6; 7; 10; 11; 12; 13; 14], the authors' view on the main factors influencing MP behavior is presented in Table 2, grouped into four main categories: factors related to micropile itself, soil-related factors, connection with the structure, and loading-related factors. These factors are detailed and further analyzed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Subfactors** & **Details** \\ \hline \multirow{4}{*}{MP-related factors} & Grouting & Material, pressure, technology, etc. \\ \cline{2-3}  & Bearing elements & \\  & and Casing & \\ \cline{2-3}  & MP shape & Vertical, inclined, reticulated, uniform/nonuniform \\  & & section, etc. \\ \hline \multirow{4}{*}{Soil-related factors} & Soil properties & Cohesive, cohesionless, density, etc. \\ \cline{2-3}  & Soil stratigraphy & Homogeneous, well-graded, weak soil overlying strong \\ \cline{1-1}  & Soil dynamic properties & Influence on the seismic wave propagation \\ \hline \multirow{4}{*}{MP-to-structure connection-related factors} & Ficity condition & Between two extremes: fully fixed and fully pinned \\ \cline{2-3}  & Connection type & Concrete pile cap, steel plate, steel base \\ \cline{1-1}  & Contact with soil & For example, in the case of pile cap, it can be fully \\ \cline{1-1}  & & embedded in soil, partially embedded, frictional contact, or \\ \hline \multirow{4}{*}{Loading-related factors} & Loading type & Axial, lateral, static, dynamic, complex, etc. \\ \cline{1-1}  & Loading intensity & Low, medium, and high (usually described in function of \\ \cline{1-1}  & & resulting strains) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Synthesis of factors and subfactors of influence for MP behavior (after [1; 3; 6; 7; 10; 11; 12; 13; 14]).