esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Artificial Neural Network (ANN)  
An ANN is defined by input, hidden, and output layers. The number of nodes in the input layer corresponds to the variables evaluated, while the number of neurons in the output layer is related to the parameters. In the hidden and output layers, each neuron is connected to all the nodes by an associated numerical weight. The input layer receives the initial data (spectral segment), the hidden layer processes the data, and the output layer presents the results of the model [14]. The number of neurons in the hidden layers was determined herein once the maximum values of the correlation coefficients were observed. Neural structures characterized by 10 hidden layers were selected. The wavenumber interval [12,000-4000 cm\({}^{-1}\)] was segmented into 1154 data sets, which were used as the input data for the ANN model. The output layer (1) was similar for all the models (1154:10:1). Multilayer perceptron (MLP) was used for the regression models, namely, the backpropagation learning algorithm. The Levenberg-Marquardt algorithm was used to train the neural networks, using 70% of a total of 326 input spectra. For each validation and testing step, 15% (49 spectra) were used. The multilayer feed-forward was trained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) learning algorithm (200 epochs). According to the correlation and root-mean-square error (RMSE), the best ANN models were developed, as defined by \(n\) (the number of observations) and \(\hat{y}\) (the output values in the test data), while \(y\) corresponds to the predicted output value (Equation (8)). A significance level of \(\alpha=0.05\) was defined.  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y}-y)^{2}}{n}} \tag{8}\]
## 3 Problem Formulation  
Practically, the parameters of equal and variable order fractional models are unknown. Accurate estimation of these parameters is a problem of optimization which targets minimization of the gap between the original system and the estimated one. Therefore, developing new optimization variants to estimate the global unknown parameters optimally is a very crucial issue.  
The general fractional derivative differential equation of the PMSM are described as follows (3).  
\[D_{l}^{q}X(t)=f(X_{t},X_{0},\theta) \tag{3}\]  
where \(D_{t}=\frac{d}{dt}\) is the time derivative operator \(t\), the original system state vector is \(X=(i_{d},i_{q},\omega)^{T}\in R^{n}\) while its initial state vector is \(X_{0}\) and \(\theta=(\theta_{1},\theta_{2},...,\theta_{d})^{T}=(\sigma,\gamma)^{T}\) is the original parameters and \(q=(q_{1},q_{2},q_{3}......q_{n})^{T}\in R^{n}\) is fractional order derivatives.  
While, fractional derivative differential equation with identified parameters is (4)  
\[D_{l}^{\hat{q}}\hat{X}(t)=f(\hat{X}_{t},X_{0},\hat{\theta}) \tag{4}\]  
where \(\hat{X}=(\hat{i_{d}},\hat{i_{q}},\hat{\omega})^{T}\) is the estimation of the state vector, \(\hat{\theta}=(\hat{\sigma},\hat{\gamma})^{T}\) the vector of the extracted parameters and \(\hat{q}=(\hat{q}_{1},\hat{q}_{2},\hat{q}_{3}......\hat{q}_{n})^{T}\) is the estimated non-integer derivative orders.  
The objective function named Mean Square Error (MSE) is employed between original and identified state vectors as in Equation (5). The block diagram describing the process of the fractional model parameters estimation is emerged in Figure 1.  
\[MSE=\frac{1}{k}\sum_{i=1}^{k}|X(i)-\hat{X}(i)|^{2} \tag{5}\]  
where samples number is indicated by k.  
The core problem associated with the conventional optimization algorithms that may prevent them to converge to the global optimal parameters is occurred due to the local optima. Therefore, there is a persistent need to develop and test novel optimization techniques in this work to handle such these intricate non linear problems.
## 3 Results and Discussion
### Results of the Description Models on Feature Data Fusion  
The collected spectral data and image features were normalized to the range of 0-1, and the spectral data and image features with different properties were transformed into pure quantities for data fusion. The fusion of spectral and texture data were able to improve not only the model's convergence speed but also the model's prediction accuracy.  
The spectral features and texture features screened by CARS-SPA were fused as input variables of the SVR and PLSR models to build a prediction model about the LNC of corn. The model was run iteratively to obtain the final results, shown in Figure 14.  
Figure 14: Leaf nitrogen content of corn predicted by the models SVR (**a**) and PLSR (**b**) based on fusion features.  
Figure 13: Prediction of corn leaf nitrogen content by models SVR (**a**) and PLSR (**b**) based on image texture features.  
As can be seen in Figure 14, the \(\text{R}_{\text{P}}{}^{2}\) of the prediction set of the SVR regression model based on fusion features was 0.927, the RMSEP was 0.12, the \(\text{R}_{\text{P}}{}^{2}\) of the PLSR model was 0.987, and the RMSEP was 0.047. The validation sample of the SVR model was basically around the 1:1 line except for a few points of deviation, while the data points of the PLSR model were almost close to the 1:1 line. The PLSR model had a better prediction performance.  
We can see from Table 5 that the \(\text{R}_{\text{P}}{}^{2}\) of the prediction set of both models based on fused features was significantly improved. In particular, the PLSR regression model based on fused features improved the prediction set \(\text{R}_{\text{P}}{}^{2}\) by 0.11 compared to the model with single spectral information and 0.135 compared with the model with a single texture feature. The model has improved the accuracy and generalization ability by taking into account the spectral information and texture features and has improved the phenomenon of \"the same objects with the different spectrum, and the same spectrum with different objects\" in previous studies only for single spectral information features.  
Meanwhile, in terms of the prediction effect of each feature, the PLSR-based model outperforms the SVR model. The PLSR algorithm decomposed and filtered the variable information in a specific way, extracted the most explanatory composite variable in the dependent variable, and identified the information and noise in the model. Thus, it is able to better overcome the adverse effects of multiple correlations of variables in the model, have a specific anti-interference ability, and improve the generalization and robustness of the prediction model.
## 7 Conclusions  
1. We have developed a hydraulic closed core-cutting mechanism and a water-insulated core-protecting bit, both of which are compatible with the traditional S75 wireline coring tool, forming a complete hydraulic closed wireline coring tool that can be applied to core drilling in loose and fractured formations. The hydraulic closed coring mechanism can fully close under the action of flushing fluid, with a bottom sealing area reaching over 75%, effectively enhancing the core recovery rate in wireline coring for loose and fractured geological layers.
2. Under the conditions of a flushing fluid flow rate of 100 L/min and a pump pressure of 4 MPa, the maximum pressure inside the core-cutting mechanism's valve body reaches 0.152 MPa, and the maximum flow rate between the valve body and the base reaches 4.33 m/s. This design effectively eliminates the vortex phenomenon inside the valve body and at the lower end of the valve core. The flushing speed of the water-insulated core-protecting bit, designed to assist in bottom hole rock fragmentation, is 2.5 m/s, which sharply decreases to 0.6 m/s at the core, thereby achieving a significant water-insulated core-protecting effect. Additionally, it adequately envelops the drill bit face, providing a cooling and lubricating effect on the bit.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Depth (m)**} & **Recovery Rate (\%)** & \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Depth (m)**} & **Recovery Rate (\%)** & & **Number** & **Depth (m)** & **Recovery Rate (\%  
Under a pump flow of 100 L/min and a pump pressure of 4 MPa, the axial force provided by the compression core claw is 11.74 KN, and the minimum pressure required for the core claw to close is 6.42 KN. At this point, the hydraulic closed core-cutting mechanism can accomplish hydraulic coring. Due to the sealing area reaching 75%, the core protection rate is 95%, and a standard core claw is recommended. Field tests show that the new drilling tool significantly outperforms the traditional S/75 wireline coring tool, with an average core recovery rate of 97.83%, representing an increase of 88.13% compared to traditional tools. The developed hydraulic closed core-cutting mechanism provides a new idea and technology for wireline coring in loose and fractured formations.  
Conceptualization, W.W.; methodology, W.W. and S.W.; software, B.L.; validation, W.W., B.L. and J.X.; formal analysis, J.X.; investigation, W.W.; resources, S.W.; data curation, J.X.; writing--original draft preparation, W.W.; writing--review and editing, S.W.; visualization, B.L.; supervision, J.X.; project administration, W.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 6 Discussion
Sensitivity and specificity are independent of the prevalence of positives in the population, whereas positive and negative predictive values are influenced by the prevalence of positives in the population. Therefore, if our predictive modeling were transported to a population with higher frequencies of dropouts, the sensitivity would remain the same because the sensitivity is the characteristic of a test, but the positive predictive value would increase because the positive predictive value reflects the population. In machine learning, the quality of training data is a critical factor that determines the performance of predictive models. The NEIS is an ideal database for developing the dropout early warningsystem in South Korea for several reasons. First, the NEIS database contains information about more than six million students' basic information, academic achievement, absence, health and so on. Therefore, the NEIS can provide various students' features to build an effective dropout early warning system. Second, the NEIS has been used during the past 20 years in South Korea. Therefore, teachers are well trained in using the NEIS system, which also improves the quality of data. There are some limitations to the present study. First, our access to the NEIS database was limited in this study. Although we included the key risk indicators for dropout prediction in our analysis, we were not able to access many other features in the NEIS database, such as teachers' evaluation of students, at the time of our analysis. We expect that the performance of our prediction model could be improved by adding those additional features in the future. Second, our predictive model only predicts students at risk of dropping out of school. Recent advances in predictive modeling enable us to estimate heterogeneous treatment effects to understand those who can effectively be intervened [36]. Such information would be very useful in designing and implementing prevention programs. In sum, we aimed to build a dropout early warning system that maximizes our chance of supporting the successful learning of all students, and minimizes the cost for intervention by addressing the class imbalance issue using the SMOTE and ensemble methods in machine learning, and also by evaluating the trained classifiers with both ROC and PR curves. ROC curves were not very informative, whereas PR curves were informative. According to our PR curves, BDT showed the best performance. Considering the prevalence of the class-imbalance issue in other educational outcomes, this study has implications for other educational studies using predictive modeling as well. Conceptualization, S.L. and J.Y.C. ; methodology, S.L. ; software, S.L. ; validation, S.L. and J.Y.C. ; formal analysis, S.L. ; investigation, S.L. and J.Y.C. ; resources, S.L. and J.Y.C. ; data curation, J.Y.C. ; writing--original draft preparation, S.L. ; writing--review and editing, S.L. and J.Y.C. ; visualization, S.L. ; supervision, J.Y.C. ; and project administration, J.Y.C. This research received no external funding. The authors declare no conflict of interest.
## 4 Feedback Control System and Field-Null Implementation
### Feedback Control System  
PID [28] is a widely used feedback-control method in industrial systems. On HL-3, PID controllers are integrated into the Plasma Control System (PCS) to handle different control objects. In actual plasma discharges, noises in diagnostic signals are inevitable and  
Figure 6: Typical examples of field-null optimization using the scheme presented in Section 3.1. (**a**) The oblate elliptic region case to prevent the plasma from being vertically unstable, (**b**) the HFS field-null region case to strengthen the robustness of breakdown with poor wall conditions. (**c**) and (**d**) show the CS and PF currents corresponding to the \(|B_{\perp}|\) contour plots in (**a**) and (**b**), respectively. The coils filled with orange and red colors in (**a,c**) refer to the “unknown” and “Known” coils, and those filled with white refer to coils that are not energized, respectively. Some of the current values in (**b,d**) has been multiplied by a constant for a clearer view, e.g., “10”IP6” in the legend means the curve referring to IFF6 in (**b,d**) is multiplied by 10.  
usually can lower the performance of PID controllers. To solve this problem, a low-pass filter can be added before the signal goes through PID controllers. In this way, the noises in the diagnostic signals can be eliminated, which improves the performance of the controller, and then, the processed error signals are proportionally multiplied, differentiated and integrated to form the P, D and I control quantities, respectively. The block diagram of the feedback control algorithm is shown in Figure 7.  
Considering that the control period of PCS on HL-3 is 1 millisecond, and discrete signals are used in actual control, the filtering algorithms employed in this control algorithm are all digital filtering algorithms. During the calculation process, the impulse transfer function \(G(s)\) of the analog filter can first be designed based on technical specifications, and then, the system function \(H(z)\) of the corresponding discrete system can be obtained by applying the bilinear transform method.
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 2 Materials and Methods
### Costs of a Photovoltaic Installation
#### 2.1.2 Mounting Structures  
The cost of mounting structures varies depending on the materials selected for the project, such as hot galvanised steel or aluminium. For example, aluminium structures were priced at 0.34 \(\upmu\)/Wp. This cost has decreased by 50% over the past few years because aluminium was more than 50% cheaper in 2016 than in 2007 [23].  
The cost of the mounting system is considered in our probabilistic calculation using a constant PDF, considered as fixed cost, where it depends on the three sizes of installation (500 kW, 50 kW and 5 kW).
# Performance Analysis of Hard-Switching Based Hybrid FSO/RF System over Turbulence Channels
## 2 System Model
### Ergodic Capacity  
The capacity of the hybrid FSO/RF channel under consideration is estimated as, [19]:  
\[C=C_{FSO}(\gamma_{th-FSO})+F^{FSO}(\gamma_{th-FSO})C_{RF}(\gamma_{th-RF}), \tag{21}\]  
where, \(C_{FSO}(.)\) or \(C_{RF}(.)\) is the capacity of the system when only FSO or RF link is active, respectively and is given as, [19]:  
\[C_{\Xi}(\gamma_{th-\Xi})=\int_{\gamma_{th-\Xi}}^{\infty}W_{\Xi}\log_{2}(1+ \gamma_{\Xi})f_{\Xi}(\gamma_{\Xi})d(\gamma_{\Xi}), \tag{22}\]  
where the subscript \(\Xi\) stands either for the FSO or RF link, while \(W_{FSO}\) and \(W_{RF}\) represent the corresponding bandwidths. Thus, by substituting (8) into (22) we conclude to the following integral for the estimation of average capacity for the FSO link, [12]:  
\[C_{FSO}(\gamma_{th-FSO})=\int_{\gamma_{th-FSO}}^{\infty}W_{FSO}\log_{2}(1+ \gamma_{FSO})\frac{(\alpha\beta/\sqrt{\gamma_{FSO}})^{\frac{(\alpha+\beta)}{2 }}\gamma_{FSO}(\frac{\alpha+\beta}{4})-1}{\Gamma(\alpha)\Gamma(\beta)}K_{ \alpha-\beta}\bigg{(}2\sqrt{\frac{\alpha\beta\gamma_{FSO}/2}{\sqrt{\gamma_{ FSO}}}}\bigg{)}d(\gamma_{FSO}) \tag{23}\]and the corresponding expression for the average capacity for the RF link, through Equation (22), is given as:  
\[C_{RF}(\gamma_{th-RF})=\int_{\gamma_{th-RF}}^{\infty}W_{RF}\log_{2}(1+\gamma_{RF} )\frac{1}{\gamma_{RF}}e^{-\gamma_{RF}/\overline{\gamma_{RF}}}d(\gamma_{RF}) \tag{24}\]  
By integrating by parts, the integral of Equation (24) can be solved and we conclude to the following closed-form mathematical expression for the average capacity of the RF link under consideration:  
\[C_{RF}(\gamma_{th-RF})=\frac{W_{RF}}{\ln(2)}e^{-\frac{\gamma_{th-RF}}{\gamma_{ RF}}}\bigg{[}E_{i}\bigg{(}\frac{\gamma_{th-RF}+1}{\overline{\gamma_{RF}}} \bigg{)}e^{\frac{\gamma_{th-RF}+1}{\gamma_{RF}}}+\ln(\gamma_{th-RF}+1)\bigg{]} \tag{25}\]  
where \(E_{i}(.)\) is the exponential integral function.  
By substituting (23) and (25) into (21), the average capacity of the whole hybrid FSO/RF system under consideration can be estimated.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 4 Finite Blocklength Analysis
### Orthogonal Allocation
Specifically, we consider \(\mathcal{S}_{\text{b}}\) to be operated at an error rate \(\epsilon^{*}\) that maximizes the information bits per channel use retrieved over a singleton slot:  
\[\epsilon^{*}=\underset{\epsilon}{\text{argmax}}\{(1-\epsilon)\mathsf{R}_{ \text{b}}^{*}(\epsilon)\}. \tag{22}\]  
Finally, recalling that mMTC traffic can enjoy a share \((1-\alpha)\) of the resources and that the fraction of singleton slots is bounded by \(e^{-1}\) (achieved for \(\lambda=1\)), the set of aggregate rates achievable by \(\mathcal{S}_{\text{b}}\) in the orthogonal configuration can be described as follows:  
\[\mathsf{R}_{\text{b}}<(1-\alpha)e^{-1}(1-\epsilon^{*})\mathsf{R}_{\text{b}}^{ *}(\epsilon^{*}).\]
# Camera Calibration with Phase-Shifting Wedge Grating Array
###### Abstract  
Planar targets with known features have been widely used for camera calibration in various vision systems. This paper utilizes phase-shifting wedge grating (PWG) arrays as an active calibration target. Features points are encoded into the carrier phase, which can be accurately calculated using the phase-shifting algorithm. The 2\(\pi\)-phase points are roughly extracted with edge detection and then optimized by windowed bicubic fitting with sub-pixel accuracy. Two 2\(\pi\)-phase lines for each PWG are obtained using linear fitting method. The PWG centers that are used as feature points are detected through computing the intersections of 2\(\pi\)-phase lines. Experiment results indicate that the proposed method is accurate and reliable.  
camera calibration; feature detection; phase-shifting; wedge grating +
Footnote †: journal: Applied
## 3 Results
### Volume and Cytokine Profile of the GCF  
Mean GCF volume and cytokine profile were presented in Table 2. Mean GCF volume at week 4 (1.3 \(\pm\) 0.6 \(\upmu\)L) was significantly higher from the baseline (0.6 \(\pm\) 0.2 \(\upmu\)L) (\(p\) < 0.05). Mean volume at week 12 (0.8 \(\pm\) 0.3 \(\upmu\)L) and 24 (0.8 \(\pm\) 0.5 \(\upmu\)L) was found to be comparable to control (\(p\) > 0.05). GCF levels of IL-6 were increased at week 4 (15.6 \(\pm\) 8.2 pg/mL) from the baseline (5.4 \(\pm\) 3.6 pg/mL) (\(p\) < 0.05). However, there was no significant difference in GCF IL-6 level at week 12 (7.8 \(\pm\) 6.2 pg/mL) and 24 (7.4 \(\pm\) 5.2 pg/mL) from the baseline (\(p\) > 0.05). Moreover, level of TNF-\(\upalpha\) in GCF at week 4 (65.3 \(\pm\) 16.2 pg/mL), 12 (25 \(\pm\) 10.2 pg/mL), and 24 (21.3 \(\pm\) 7.6 pg/m) was found to be higher than the baseline (13.7 \(\pm\) 5.8 pg/mL) (\(p\) < 0.05) (Figure 4).  
Figure 3: Comparison of periodontal pocket depth (PPD) and clinical attachment loss (CAL) in millimeters (mm) as observed at baseline and different follow-up durations in patients treated with lumineers.  
Figure 2: Plaque index (PI) and bleeding on probing (BOP) percentage (%) observations from baseline to 24-week follow-up among patients treated with lumineers.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 5 Multi-Gateway-Based Protocol
### Data Collection and Transmission Phase  
After the CH setup phase, the CH receives data from member nodes, aggregates it, and forwards to the gateway node in its region. Now the gateway aggregates data received from different CHs and from the nodes in the gateway region and forwards it to the sink. If the distance between a cluster head and sink is less than the distance between the cluster head and the gateway, the CH transmits data directly to the sink. Figure 3 illustrates the complete process of the MGBEHA protocol. The complete area division has been divided into eight cluster regions, four gateway regions, and one sink region. There is direct data transmission from nodes to sink in sink region and from nodes to gateways in gateway regions. Nodes in cluster regions transmit data to the cluster heads which, in turn, transmits it to the gateway node which again transmits it to the sink. Thus, there is multi-hop transmission of data for nodes in the cluster region. Nodes which use direct communication (like nodes in SR and GR) deplete less energy because of the smaller number of control packets and reduced distance. To deplete the energy of all nodes in a uniform way, gateway nodes help in reducing the transmission distance between senders (cluster head) and receivers (sink).  
Figure 3: Schematic diagram showing the complete process of the MGBEHA protocol.