esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Description and Data Preprocessing
### Data Preprocessing
#### 3.2.3 Feature Selection  
During the drilling process, there are many factors affecting the ROP, including the drilling parameters, drilling fluid properties, formation properties, wellbore geometry, drill-bit properties, drilling tool properties, etc. We know that if there are too many features, then the complexity of the model will be greatly increased. At the same time, adding features with low correlation will affect the accuracy of the model instead. Feature selection before training the model can improve the accuracy and efficiency of the model while reducing the number of features, reducing the complexity of the model, and avoiding overfitting. Therefore, we need to select features with higher correlation.  
Through correlation analysis, we only select parameters that have a greater correlation with the ROP to input into the model, filter or reduce variables with low correlation, and avoid introducing multiple parameters with high similarity at the same time.  
For proper feature selection, this paper chooses to use the Pearson correlation coefficient for correlation analysis. It can be used to measure the correlation between two variables, with values ranging from \(-1\) to \(1\).  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{\sum\limits_{i= 1}^{n}{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{\sqrt{\sum\limits_{i=1}^{n}{ (X_{i}-\overline{X})^{2}}}\sqrt{\sum\limits_{i=1}^{n}{(Y_{i}-\overline{Y})^{2 }}}} \tag{12}\]  
where \(\rho_{X,Y}\) is the Pearson correlation coefficient; \(X\) and \(Y\) are two random variables; \(\sigma_{X}\) and \(\sigma_{Y}\) are the standard deviations of \(X\) and \(Y\), respectively; \(\text{cov}(X,Y)\) is the covariance between \(X\) and \(Y\); \(\overline{X}\) and \(\overline{Y}\) are the means of \(X\) and \(Y\), respectively; \(X_{i}\) and \(Y_{i}\) are the samples of \(X\) and \(Y\), respectively.  
Generally, features with a Pearson correlation coefficient greater than 0.3 are considered correlated. We filtered out the features with a Pearson correlation coefficient less than 0.3, which finally left 13 input parameters, as shown in Table 2.
## 4 Experiment and Results
### Discussion  
In the literature, some hybrid model, cross validation, and feature selection techniques were used, which are missing in our study. A greater memory and longer compilation time are required for such complicated techniques [20]. To reduce compilation time of a model, we avoid a cross-validation technique or a hybrid model. Dropout, l2 regularization, and dense layers can be used to handle overfitting issues [21]. Instead of cross-validation techniques, we proposed our model with some balanced dense layers, and kernel regularizers are added to some neurons. Then, we disable a few parameters in order to balance our data. Issues with overfitting are handled via kernel regularizers and dropouts. Finally, we  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Models**} \\ \hline
**Performance Measure** & **OCNN** & **OANN** & **OLSTM** & **CNN** & **ANN** & **LSTM** \\ \hline AUC Score & 0.99 & 0.97 & 0.97 & 0.88 & 0.83 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental result of AUC score among the models.  
Figure 6: Runtime analysis among the models.4.7. Evaluation of Prediction Result.  
achieve a balanced performance from all the different optimizers, and our model generates the findings in a very short time. In Table 6, the proposed model is compared with other models, including ANN, ensemble of deep learning based clinical decision support systems (EDL-CDSS), simple RNN, DNN, ANN-SVM, 1-D corrNN-LSTM, and SVM. The table displays the experimental results when the proposed model was tested. Our proposed model provides better accuracy, compared with the existing models.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 4 Source of EEG Information and Data Handling
### BCI Competition III
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Database** & **Author(s)** & **Year** &
\begin{tabular}{l} **Signal** \\ **Technique** \\ \end{tabular} & **Characteristics** & **Related Work** \\ \hline Physionet & & & & & & \\ Motor/Imagery & [PERSON] & 2000 & EEG & 109 Subjects. 14 trials: 1 Baseline. & [PERSON] et al. [24] \\  & et al. [19] & & & Upper limb: 3 Movement, & \\  & & & & 3 Imagery Lower Limb: & \\  & & & & 3 Movement 3 Imagery & \\ BCI Competiton & & & & & \\ III & & & & & \\ Dataset I & [PERSON] et al. [25] & 2004 & ECoG & Motor Imagery of 1 subject in two days with a lapse of 1 week between trials. & [PERSON] et al. [26] \\ Dataset II & [PERSON] & 2004 & EEG & P3 Speller Paradigm (Evoked potentials), estimate to wich letter of 6 \(\times\) 6 matrix. 2 subjects & [PERSON] et al. [28] \\  & et al. [27] & & & 185 trials (85 testing 100 trials training) & \\ Dataset IIIa & [PERSON] & 2005 & EEG & Motor Imagery. 4 classes (left hand, right hand, foot, tongue). & [PERSON] et al. [30] \\  & et al. [29] & & & 3 subjects. & \\ Dataset IIIb & [PERSON] [31] & 2005 & EEG & 3–4 Session of 3 subjects. Motor Imagery of left hand and foot. 1 subject. & [PERSON] et al. [32] \\ Dataset IVa & [PERSON] & 2004 & EEG & Motor Imagery of left hand and foot. 1 subject. & [PERSON] et al. [30] \\ Dataset IVb & [PERSON] & 2004 & EEG & Motor Imagery of left hand and foot. 1 subject.4 hrs later than & \\  & et al. [33] & & & IVB \\ Dataset V & [PERSON] [34] & 2004 & EEG & 3 subjects. 4 session. & \\  & & & & Imagination of left and right hand. Generation of words & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Databases’ characteristics. Once the information sources are available, they must be processed to obtain the needed components that could processed in the BCI implementations. These processing tools include feature extraction and feature selection tools. These are described ahead in the next two sections.
# Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem
###### Abstract  
In this study, deterministic and robust optimization models for single artillery unit fire scheduling are developed to minimize the total enemy threat to friendly forces by considering the enemy target threat level, enemy target destruction time, and target firing preparation time simultaneously. Many factors in war environments are uncertain. In particular, it is difficult to evaluate the threat levels of enemy targets definitively. We consider the threat level of an enemy target to be an uncertain parameter and propose a robust optimization model that minimizes the total enemy threat to friendly forces. The robust optimization model represents a semi-infinite problem that has infinitely many constraints. Therefore, we reformulate the robust optimization model into a tractable robust counterpart formulation with a finite number of constraints. In the robust counterpart formulation with cardinality-constrained uncertainty, the conservativeness and robustness of the solution can be adjusted with an uncertainty degree, \(\Gamma\). Further, numerical experiments are conducted to verify that the robust counterpart formulation with cardinality-constrained uncertainty can be made equivalent to the deterministic optimization model and the robust counterpart formulation with box uncertainty by setting \(\Gamma\) accordingly.  
deterministic optimization; robust optimization; single artillery unit fire scheduling; threat level +
Footnote †: journal: Applied Sciences
## 3 Conditions of the Field Survey
### Ground Control Points and Check Points  
GCPs are an essential input not only for data georeferencing, but also to refine the camera parameters, the accuracy of which is critical to limit bowl effects. In some cases, clearly identifiable features of the survey area can be used as \"natural GCP\", provided they are stable over time and present a strong contrast with the rest of the environment for unambiguous identification [1]. As coastal contexts are exceptionally dynamic environments, it is rare to encounter natural GCPs and complicated to set up permanent ones.  
For the present test survey, 37 circular targets of 20 cm in diameter were distributed along the beach. Among these, 19 red targets (Figure 4) were used as Ground Control Points (tagged as GCP in Figure 3) and used in the SfM processing chain. The others, 18 blue targets (Figure 4), served as Check points (tagged as Cp. in Figure 3). These check points were used to assess the quality of the DEM reconstruction. The position of each target was measured using post-processed Differential GPS (DGPS). The base station GPS receiver was installed in an open-sky environment and collected raw satellite data during 4 hours. During the survey, the base station transmits correction data to the rover, situated in a radius of 200 m. Measurements are post-processed using data from permanent GPS network, allowing to achieve an accuracy of 1 cm horizontally and 2 cm vertically.
# An Efficient BDS-3 Long-Range Undifferenced Network RTK Positioning Algorithm
## 2 Long-Range URTK Algorithm  
The long-range URTK algorithm is mainly composed of three modules, denoted as Module 1, Module 2, and Module 3, which are explained in detail in the following paragraphs.  
Module 1: Using an integer AR model that considers the atmospheric error, the baseline in the GNSS reference station network is solved in order to determine the double-difference integer ambiguity. If the double-difference ambiguity cannot be fixed, the next epoch is directly solved.  
Module 2: Then, the double-difference integer ambiguity is converted into the undifferenced integer ambiguity. Via substituting the undifferenced integer ambiguity of the reference station network into the observation equation, the undifferenced error correction of each baseline is realized, and the undifferenced error correction of the rover station is interpolated using the inverse distance weighting algorithm.  
Module 3: The classification undifferenced error correction data of the rover station are used in the observation equation of the rover station in order to calculate the position parameters, and the real-time high-precision positioning of a user is achieved. The flowchart of the long-range URTK is shown in Figure 1.  
As shown in Figure 1, Module 1 has the most complex structure among the three modules, and it includes two main parts. The first part indicates that, when constructing the pseudo-range and phase double-difference observation equations, the atmospheric delay needs to be constrained by an inter-epoch random walk model. However, this constraint is indispensable for long-range network RTK, which directly affects the accuracy of the floating solution of double-difference ambiguity, thus influencing the convergence speed of double-difference ambiguity. The second part relates the test of double-difference ambiguity between reference stations, which can ensure the accuracy of double-difference AR. Module 2 relates mainly to the regional error model construction, which is performed on the premise that Module 1 can obtain an accurate double-difference AR. In Module 2, the crucial tasks are the calculation of undifferenced classification error correction and the interpolation of undifferenced error correction for a rover station. The result of this task directly affects the final positioning result of Module 3.
## 3 Results
### SIMCA Analysis  
The distinction of authentic and counterfeit products can be associated with the \"asymmetric classification problems\", i.e., all those cases where the interest is on a specific category (authentic) rather than on the other class under examination. These kinds of problems are often solved by means of class-modeling approaches, which focus on identifying the (usually bound) portion of the multivariate space where it is more likely to find individuals from a specific category. For this reason, despite the relatively accurate results obtained by PLS-DA analysis, SIMCA was also used in order to model the authentic class of samples.  
The same pre-treatments investigated in the case of PLS-DA analysis were tested as well in the SIMCA context; nevertheless, different figures of merit were used for assessing the optimal preprocessing approach and the number of principal components (PCs) to be extracted. In particular, the outcome of the SIMCA models was reported in terms of sensitivity (the percentage of the authentic samples correctly accepted by the model), specificity (the percentage of the adulterated samples correctly rejected by the model), and efficiency (the geometric average of sensitivity and specificity). The optimal model parameters (pre-treatments and number of PCs) were defined on the basis of the efficiency values resulting from a cross-validation procedure with seven cancelation groups. Similarly to above, four different models were created, one for each pre-treatment; the results are reported in Table 3.  
Figure 2: Variable importance in projection (VIP) analysis. Average spectrum of training samples (black line). Spectral variables presenting VIP index \(>\)1 are highlighted in red.  
Inspecting Table 3, it is straightforward to see that the models provide very high sensitivity, but, on the contrary, their specificity is definitely low, indicating that adulterated samples were accepted by the model of the authentic class. This leads to generally low efficiencies: the highest one was 32.8%, and it was provided by the model built on NIR signals preprocessed by the second derivative. As expected, the application of this model to the test set was not completely satisfactory; in fact, it led to a sensitivity of 98.0% and a specificity of 11.0%. A possible explanation for these results can be identified in the high heterogeneity of the modeled category (the authentic samples); in order to account for the wide range of variability spanned by the class (different factories, different regions, and different seasons) and to obtain a high sensitivity, the model space needs to be expanded to the point that a high amount of samples of the alternative class is accepted. Altogether, these results indicate that the tested strategy is not suitable for the investigated classification problem.
## 3 Results and Discussion
### Effect of Pretreatment Time on BDMC, DMC, CUR Recovery of Fresh Turmeric  
In the current investigation, the effect of pretreatment time on BDMC, DMC and CUR will be further discussed. Processing time is an essential parameter that should be considered when applying a method in practice. Prolonging the treatment time may lead to increasing in the cost and reducing the quality of materials. However, the handling time of TP, UP, PEF were all lower than 5 min, so there was less evidence to claim the correlation between treatment time and the deterioration of curcuminoids.  
During pretreatment implementation, curcuminoids may be affected by environmental conditions such as oxygen, light and temperature. Depending on the structure of each molecule, different levels of degradation might be reached. The difference in molecular structure between BDMC, DMC and CUR is the number of methoxy group links with phenol rings; this characteristic can lead to different transformations undergo oxidation. CUR is a molecule with two methoxy groups, so it is exposed strongly to impact factors; however, DMC has one methoxy group remaining, and BDMC has no methoxy groups; hence they are less sensitive against oxygen [47]. With the difference in molecular structure, prolongedtreatment time has varying effects on the extract compounds. In the current study, the EP has the longest processing time, varies from 1-5 h, while for other pretreatments, the duration maximum is 5 min.  
With enzymatic pretreatment, the TC was not changed when increased treatment time (Figure 3); howbeit, the extracted amount of each curcuminoid had slightly decreased after 5 h of soaking (Figure 3), while BDMC and DMC did not change considerably (Figure 3b,c). In the observation of [47], in 2 h of soaking, the amount of CUR reduced 75%, while BDMC and DMC had less reduction than CUR (Figure 3a).  
Furthermore, when soaking, the impact of oxygen on CUR could not be ignored; Ref. [47] concluded that DMC and BDMC are less affected by autooxidation than CUR. Besides, BDMC and DMC could increase CUR stability.  
With the same observation, ultrasound treatment has resulted in the decline of CUR, DMC and BDMC recovery with increasing power (Figure 3a-c). According to [PERSON], [PERSON] and [PERSON] [39], the more intensive ultrasound power, the more deteriorated  
Figure 3: Effect of pretreatment techniques on Curcumin (CUR) (**a**), Demethoxycurcumin (DMC) (**b**) and Bisdemethoxycurcumin (BDMC) (**c**) recovery of fresh turmeric. In a panel, values with the same lowercase letter are not considering as significantly different (\(p<0.05\)).  
bioactive compounds. On the other hand, turmeric's pretreatments with PEF or thermal pretreatment did not reduce TC value when increasing W\({}_{\text{T}}\) (Figure 3). This occurrence might be explained as PEF was carried out within several us and without thermal effect. Thermal pretreatment in the current research was being done in 5 min, while curcuminoids can stable with a temperature of 70 \({}^{\circ}\)C in 10 min [38].
## 2 Materials and Methods
### Non-Destructive Measurement Models  
#### 2.2.1 Multi-Scale Convolutional Neural Network Model  
CNN is widely applied in the field of machine vision and has powerful functions in classification and recognition. CNN is better-suited to dealing with non-linear and complex systems compared with conventional methods used to process NIRS data. The basic structure of CNN includes an input layer, convolutional layer, downsampling layer, and fully connected layer [24]. However, some information maybe lost during convolutional processing by a basic CNN, while the multi-scale convolutional layer was able to extract more feature information [25]. Therefore, a multi-scale convolutional neural network (MS-CNN) model structure was constructed to estimate the moisture and nitrogen content of _Pinus massoniana_ seedlings leaves with NIRS.  
The structure of the MS-CNN model is shown in Figure 2, which includes a convolutional layer near the input end of the network, three multi-scale convolutional layers arranged alternately, and two multi-scale downsampling layers. They all contain several branches. The three multi-scale convolutional layers can be used for a multi-scale collection  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maximum (\%)** & **Minimum (\%)** & **Average (\%)** & **Variance (\%)** \\ \hline
**Calibration** & 70.740 & 48.260 & 60.255 & 22.753 \\
**Prediction** & 69.250 & 59.130 & 64.580 & 4.731 \\
**All** & 70.740 & 48.260 & 61.302 & 21.796 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the moisture content analysis of _Pinus massoniana_ seedling leaves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maximum (\%)** & **Minimum (\%)** & **Average (\%)** & **Variance (\%)** \\ \hline
**Calibration** & 1.354 & 0.072 & 0.502 & 0.033 \\
**Prediction** & 1.334 & 0.126 & 0.768 & 0.118 \\
**All** & 1.354 & 0.072 & 0.563 & 0.066 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the nitrogen content analysis of _Pinus massoniana_ seedling leaves.  
of the samples' characteristics, and the two multi-scale downsampling layers can reduce the computational complexity of the network and maintain the characteristics contained in the spectral information without losing information.  
After multi-scale convolution and downsampling, the downsampling layer and a plurality of fully connected layers were applied. Due to the large number of output feature channels in multi-scale convolutional layer 3, the sampling core's size in the downsampling layer was set as 4, which reduced the dimensionality of input data in the fully connected layer. The number of neurons in the two fully connected layers was 200 and 2. The output layer was connected behind it and was composed of one neuron, and the Sigmoid function was used as the activation function to ensure that the output value was constrained to a reasonable range. The input of the loss function for network optimization is the output feature of the rear fully connected layer.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Summary of Shared Challenges for Electric Propulsion Technology Development
#### 4.4.1 Technologies High on The S-Curve  
Most mature EP thrusters are those in the moderate power range, 1-20 kW, as this has been the primary power level researched in the past several decades. In general, most of these technologies are limited by incomplete understanding of subtle physical processes and the mounting inadequacy of ground test facilities for long-lived designs. For example, although many flight Hall thrusters are operated \(>\)1 kW, poor understanding of electron transport and instabilities in these thrusters prevent the development of predictive models that might enable the more rapid and intelligent design of 10-20 kW test articles. And as a result, assessing the long-term performance of moderate-powered Hall thrusters must mostly be done in ground test facilities. Compounding the logistical strain such tests impose, the effect of the facility on the thruster performance casts some level of doubt on the extensibility of ground test results to on-orbit behavior, as discussed briefly in Section 4.2.1. We summarize these challenges as follows:  
* Understanding anomalous processes and instabilities to allow for self-consistent modeling;
* Modeling or mitigating facility effects to allow for more meaningful ground testing;
* Finding alternatives to long-duration testing to characterize reliability, long-term performance, and time-dependent failure mechanisms.  
A solution to the challenges faced by mature EP technologies, then, is improved modeling and ground test facilities. The former will help close the final gaps in the understanding of mature thrusters, and the latter will allow for more thorough and definitive testing of these devices. Specifically, we make the following recommendations for addressing the challenges to the \"high s-curve\" technologies:  
* Requirements for a test environment to adequately represent space-like conditions must be formulated accounting for the unique characteristics of modern mature EP technologies;
* Formulate strategies to make ground facilities more flight-like;
* Improved predictive and validated models for qualification efforts to address problems related to facility interactions and stability.