esa-sceva/satcom-chunk-collection huggingface dataset:
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 4 Performance Analysis
### Results for Existing and New Data
An important note on Figure 16: the averaged curves and the error bands were computed from the image processing code output as outlined in Algorithm 7. The quantile spline envelopes (QSEs) were computed following [73] and using the code (_Wolfram Mathematica_ package) available on _GitHub_: Anton Antonov (_antononcube_): MathematicalForPrediction/QuantileRegression.m (accessed: 16 October 2021). For all the datasets represented in Figure 16, we used \(q=0.9\) with 3-rd order splines and \(\lfloor 2.5\%\cdot N\rceil\) spline knots for QSEs where \(N\) is the number of points in the dataset; we set \(N_{\text{QSE}}=\lfloor 14\%\cdot N\rceil\) and \(\delta_{\text{b}}=\lfloor 0.5\%\cdot N\rceil\) (point density-adaptive physical bin size); the TV regularization parameters [58] were 1 for QSEs and 0.25 for the binned data. Even though one cannot check how many bubbles the image processing code actually failed to detect without manual inspection (not feasible), one can evaluate the amount of detection events that are ruled out as false positives at the various stages of code execution for a sequence of images. The results for the five image sequences considered above (Figures 14 and 17) are presented in Table 1. Notice that in the most difficult case of the five (Figure 14d) most of the work is done by the luminance map-based filter and the object property filter. However, the intrinsic filtering capacity of MRIF is significant because it filters out the detection events that very likely would have passed both of the two following stages. Figure 17: The locations of bubbles detected in the FOV for a sequence of 3000 images (30 s) at a 120 sccm flow rate for the new model system (Section 2.1, vertical inlet): (**a**) an example of detected bubbles: white contours are shapes, orange dots are the current positions and white dots are the preceding detections; (**b–d**) all detected bubble positions with (**b**) no applied MF, (**c**) horizontal \(\sim\)125 mT MF and (**d**) vertical \(\sim\)125 mT MF. Bubble locations in (**b–d**) are marked as in Figure 14. All sub-figures are to scale.
# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 3 Feature Selection Based on Ensemble Learning
### Determination of Feature Selection Methods
Gradient Boosting Decision Trees (GBDTs) are ensemble learning algorithms that amalgamate the principles of decision trees and gradient boosting, consistently enhancing model performance through iterative training of multiple decision trees. They are well suited as feature selection methods owing to their ability to automatically estimate the importance of features, demonstrate adaptability to high-dimensional and large-scale datasets, and furnish insights and understanding of the data. The update rule for gradient boosting decision trees is as follows:  
\[F_{m}(x)=F_{m-1}(x)+\gamma\cdot h_{m}(x) \tag{8}\]  
where \(F_{m}(x)\) represents the model's prediction at the \(m\)-th round, \(\gamma\) is the learning rate, and \(h_{m}(x)\) is the decision tree fitted in the \(m\)-th iteration. XGBoost is an efficient, flexible, and scalable machine learning algorithm based on the gradient boosting framework. It consistently enhances predictive performance through the iterative training of multiple decision tree models. In comparison to traditional gradient boosting methods, XGBoost introduces additional regularization terms and tree depth limitations, thereby improving model stability and generalization. It is well suited as a feature selection method due to its notable advantages in both performance and robustness, while also aiding in mitigating the risk of overfitting. The objective function of XGBoost comprises the loss function, regularization term, and model complexity term. For regression problems, the objective function is as follows:  
\[\text{Obj}=\sum_{i=1}^{n}L(y_{i},\hat{y}_{i})+\sum_{k=1}^{K}\Omega(f_{k}) \tag{9}\]  
where \(L\) is the loss function, \(\Omega\) is the regularization term, and \(f_{k}\) represents the \(k\)-th decision tree model. The five methods mentioned above each exhibit distinct characteristics in feature selection and all adhere to the \"good but different\" principle. From the linear relationship measurement of the Pearson correlation coefficient method to the iterative feature elimination of the recursive feature elimination method, and from the robust performance of the random forest and gradient boosting decision tree in handling outliers and noise to the efficiency and versatility of XGBoost, each method is guided by the principle of being \"good but different\". The goal was to select features that contribute significantly while possessing unique advantages to adapt to the varied requirements of different problems and datasets. By integrating these five diverse feature selection methods, this paper aims to derive more powerful, robust, and comprehensive feature selection results, thereby contributing to the construction of enhanced time-series prediction models.
## 2 Problem Description and Modeling
### Computation Model  
According to Section 2.3, task can be executed only after receiving all predecessors' results, and each satellite can process only one task at a time. For task \(m_{p}\), let \(u_{\Pi(p)}\) be the assigned edge server; then the computation time \(T_{p}^{com}\) can be calculated as follows:  
\[T_{p}^{com}=\frac{\sum\limits_{m_{i}\in pre_{p}}d_{i,p}\times e_{p}}{f_{\prod(p )}} \tag{2}\]  
where the computing capacity of edge server \(u_{k}\in\mathbf{U}\) is fixed at \(f_{k}\) Gcycles/s. Specifically, for entry task \(m_{p}\), we have \(T_{p}^{com}=d_{p}\times e_{p}/f_{\Pi(p)}\).  
Then for any task \(m_{p}\), there are critical time parameters:  
* \(T_{p}^{avi}\): the time that the assigned edge server is available for executing \(m_{p}\);
* \(T_{p}^{ready}\): the time that the assigned edge server has received all predecessors' results of \(m_{p}\);
* \(T_{p}^{start}\): the time when the edge server starts processing \(m_{p}\); and
* \(T_{p}^{ finish}\): the finish time of \(m_{p}\).
* Since two situations exist when performing task \(m_{p}\): (i) having received the required results before the assigned server is available, or (ii) waiting for predecessors' results although the edge server is already available, the above parameters are detailed as follows: First, let \(m_{\chi(p)}\) be the preceding task of \(m_{p}\) in the pending queue of assigned server. The edge server is available only after completing task \(m_{\chi(p)}\), and we have \[T_{p}^{avi}=T_{\chi(p)}^{ finish}\] (3) For completeness, we set \(T_{p}^{avi}=0\) if \(m_{\chi(p)}\) is the first task of edge server. According to DAG, let \(m_{\gamma(p)}\) be a predecessor task of \(m_{p}\), i.e., \(m_{\gamma(p)}\in pre_{p}\). After completing task \(m_{\gamma(p)}\), the result of \(m_{\gamma(p)}\) can be transmitted. Then the time of transferring \(m_{\gamma(p)}\)'s result for processing task \(m_{p}\) is \[T_{\gamma(p),p}^{trans}=\partial_{\Pi(\gamma(p)),\Pi(p)}^{T_{\gamma(p)}^{ finish}}(d_{\gamma(p),p})\] (4) Given that task \(m_{p}\) can be executed only after receiving all results of \(pre_{p}\), then the ready time \(T_{p}^{ready}\) can be calculated by (5). \[T_{p}^{ready}=\max\Bigl{\{}T_{i}^{ finish}+T_{i,p}^{trans}\Bigl{|}\forall m_{i}\in pre_{p}\Bigr{\}}\] (5) When \(m_{p}\) is an entry task, let \(s_{l(p)}\) be \(m_{p}\)'s access satellite, and we have \(T_{p}^{ready}=\partial_{1(p),\Pi(p)}^{0}(d_{p})\). Afterward, Equation (6) is used to calculate the start time \(T_{p}^{start}\). \[T_{p}^{start}=\max\Bigl{\{}T_{p}^{avi},T_{p}^{ready}\Bigr{\}}\] (6) Finally, the finish time \(T_{p}^{finish}\) is: \[T_{p}^{finish}=T_{p}^{start}+T_{p}^{com}\] (7)
## 5 Performance Evaluation
### Comparison Algorithms
#### 5.3.3 Comparison Methods: SVM Model  
The core idea of SVM [33] is to map the sample vector to an N-dimensional feature space by selecting a kernel function and then construct an optimal decision function in this feature space. This idea can be expressed as follows:  
\[min\big{(}\frac{1}{2}\|\psi\|^{2}+\lambda\sum_{i=1}^{Z}\xi_{i}\big{)} \tag{25}\] \[s.t.\Bigg{\{}\begin{array}{c}u_{i}(\psi^{T}k_{i}+B)\geq 1- \xi_{i}\\ \xi_{i}\geq 0\\ i=1,2,\ldots,Z\end{array}\]  
where \(\omega\) is weight vector, \(\lambda\) is the penalty coefficient, and \(\xi_{i}\) is slack variables. RBF (Radial Basis Function) is selected as the kernel function in our evaluation, which is defined as follows:  
\[K(k,u)=e^{-\gamma\|k-u\|} \tag{26}\]  
We calculate the values of \(\lambda\) and \(\gamma\) by the iterative method and substitute the predicted values into the evaluation function to obtain the best evaluation results.
## 4 Experimental Results and Discussion
### Output Power Fluctuation and Longitudinal Mode Shift of the SLM Laser
#### 4.2.2 Qualitative Analysis of Output Power Fluctuation and Longitudinal Mode Drift  
In measuring the Nd:YVO\({}_{4}\) laser's SLM output power, we observed the spontaneous drift of the oscillating longitudinal-mode frequency. We argued that the immediate cause of longitudinal mode drift was the change in the optical path length.  
That optical path length variation changed the frequency spacing between adjacent longitudinal modes. The frequency and consequent gain of these longitudinal modes changed accordingly. The mode gain was also subjected to the modulation from the FP etalons. All the longitudinal modes passing through the transmission peaks might oscillate if their gain exceeds the threshold value. A 2.5-mm thick FP etalon had a 38-GHz FSR, so the Nd:YVO\({}_{4}\)'s gain bandwidth covered at least seven transmission peaks of the etalon. In Figure 3c,e, the frequency differences between the longitudinal modes were lower than the laser's longitudinal mode spacing. This meant that the ever-oscillating longitudinal modes were not adjacent and inter-FSR. On the other hand, longitudinal mode drift and FP etalon modulation contributed to the output power fluctuation in Figure 3a,d.  
The optical path-length change mainly ensued from the heat deposited in the Nd:YVO\({}_{4}\) crystal [21; 22]. The heat came from quantum defect as well as various losses of pump light and oscillating laser, such as absorption loss and scattering loss. That heat failed to be conducted to the heat sink around the Nd:YVO\({}_{4}\) crystal, forming a refractive index gradient in the crystal. In this way, the optical path length changed. For the 1064 nm laser in the paper, the frequency of the oscillating longitudinal mode will change by 6 GHz when the optical path length changes by 1 \(\upmu\)m.
## 3 Methodology
### Long Short-Term Memory (LSTM)-Based Prediction  
LSTM is a special type of recurrent neural network (RNN) first proposed by [PERSON] and [PERSON] (1997) [42], which is mainly used to solve the problems of gradient disappearance and gradient explosion in the process of long sequence training [43]. To put it simply, compared with ordinary RNN, LSTM can learn long-term rules and has better learning ability in longer sequences.  
The chain structure of the LSTM contains three main gates, which are shown in Figure 2: (1) the forget gate \(f_{t}\), which determines how much of the cell state \(c_{t-1}\) from the previous moment is retained in the current cell state \(c_{t}\); (2) the input gate \(i_{t}\), which determines how much of the input \(x_{t}\) to the network is saved to the cell state \(c_{t}\) at the current moment; and (3) the output gate \(o_{t}\), which controls the unit state \(c_{t}\) and how many inputs it has to the current output value \(h_{t}\)[44].  
The forget gate determines what information is discarded from this node, and its main function is the Sigmoid, denoted by \(\sigma\); \(W_{f}\) is the weight matrix; \(b_{f}\) is the bias term. The formula is as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{4}\]  
The input gate selectively remembers the input information, and its primary function is similar to the forget gate. After the input, the candidate cell state can be combined with the forget gate and the input gate to \(\widetilde{c_{t}}\) to update the current moment cell state \(c_{t}\). The equation is as follows:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\]\[\widetilde{c}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{6}\]  
\[c_{t}=f_{t}\cdot c_{t-1}+i_{t}\cdot\widetilde{c_{t}} \tag{7}\]  
The output gate determines which will be retained as the current state output and combined with the current cell state to obtain the final output \(h_{t}\) of the LSTM, the formula is as follows:  
\[o_{t}=\sigma(W_{0}\cdot[h_{t-1},x_{t}]+b_{0}) \tag{8}\]  
\[h_{t}=o_{t}\cdot\tanh(c_{t}) \tag{9}\]
## 4 New Propagation Model
### Shadowing Path Loss  
As discussed earlier, the shadowing path loss for the environment was given by normal distribution with mean \(\mu\) and standard deviation \(\sigma\) (Equations (7) and (8)). The path loss is given by  
\[Ls=normand\left(\frac{p_{1}\theta+p_{2}}{\theta+p_{3}},\;s_{1}\theta+s_{2}\right) \tag{9}\]
## 2 Multi-Objective Optimization Model of Green FJSP  
### Problem Description  
A green FJSP that uses AGVs for material transportation can be described as follows: there are \(i\) independent jobs processed on \(m_{k}\) machines, \(n\) AGVs are responsible for moving jobs between machines, and each AGV can transport jobs between any machine and storage area. The AGV initially docks at the unprocessed job storage area \(m_{0}\). When the scheduling task occurs, the AGV will transport the job to the corresponding machine according to the processing sequence of the job and unload the job. If no new scheduling task is received at this time, the AGV will be on standby in situ. If a new scheduling task is received, the AGV goes to the task point for material transportation, and the above process is repeated until all the jobs are processed and transported to the finished product storage area \(m_{k+1}\) by the AGV.  
In this paper, the FJSP was decomposed into three sub-problems: process sequencing, AGV selection and allocation, and machine selection. The optimization objective was to minimize the makespan and energy consumption in the workshop and maximize the processing quality. To simplify the problem, the following assumptions were made:  
(1) Both the machines and AGVs were assumed to be free of faults and fully powered for efficient dispatching.  
(2) All production equipment and AGVs were operational at zero hour and could only be completely shut down after all operations assigned to them had been completed.  
(3) The driving speed of all AGVs was constant, and the driving time only depended on the driving distance.  
(4) The machine needed to be set up before processing different operations.  
(5) AGV idle time incurred no energy consumption.  
(6) Machines had unlimited storage space for waiting jobs to be further processed.
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
###### Abstract
\begin{table}
\begin{tabular}{l l} \hline \hline
**Acronym** & **Explanation** \\ \hline AI & Artificial Intelligence \\ ALRT & Average Probability Ratio Test \\ AMAP & Approximated Maximum a Posteriori \\ ASB & Amplitude Spectrum of Bispectrum \\ AWGN & Additive White Gaussian Noise \\ BAT & Bit Allocation Table \\ BFSF & Bi-Fold Signal Fortification \\ BICM-ID & Bit-Interleaved Coded Modulation Iterative Decoding \\ CNN & Convolutional Neural Network \\ CSI & Channel State Information \\ DBN & Deep Belief network \\ DVB & Digital Video Broadcasting \\ FB & Feature Based \\ FCP & False Classification Probability \\ FFT & Fast Fourier Transform \\ FNSF & Frequency Non-Selective Fading Channel \\ FPGA & Field Programmable Gate Array \\ FSF & Frequency Selective Fading Channel \\ FSST & Fourier Synchrosqueezing Transformation \\ GLRT & Generalized Likelihood Ratio Test \\ HGWO & Hybrid Grey Wolf Optimization \\ HLRT & Hybrid Likelihood Ratio Test \\ HOC & Higher Order Cumulant \\ HOS & Higher Order Statistics \\ ICI & Inter-carrier Interference \\ IQ & In-phase and Quadrature \\ IQL & Improved Q-learning \\ KNN & K-Nearest Neighbors \\ KS & Kolmogorov-Smirnov \\ LLR & Log-likelihood ratio \\ MAP & Maximum a Posteriori \\ MC & Modulation Classification \\ MFCC & Mel Frequency Cepstral Coefficient \\ MDNCC & Multi-Distance-Based Nearest Centroid Classifier \\ NOMA & Non-Orthogonal Multiple Access \\ OFDM-IM & Orthogonal Frequency Division Multiplexing with Index Modulation \\ PCC & Percentage of Correct Classification \\ PDF & Probability Density Function \\ PER & Packet Error Ratio \\ PSO & Particle Swarm Optimization \\ SC & Single Carrier \\ SDR & Software-defined Radio \\ STFT & Short-Time Fourier Transform \\ TDD & Time Division Duplex \\ TF-HMS & Twin-Functional Human Mental Search \\ UMP & Uniformly Most Powerful \\ VLC & Visible Light Communication \\ WOA & Whale Optimization Algorithm \\ WPS & Wavelet Packet Signals \\ WT & Wavelet Transform \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations in alphabetical order.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 1 Introduction
There are shops and residential buildings with different heights in or around the chemical parks. Buildings result in turbulence and the distribution of the gas concentration will be distorted. [PERSON] et al. [16] utilized a CFD simulation dataset with obstacles to achieve results that are closer to the real diffusion mode. [PERSON] et al. [17] proposed a CNN method to find both the location of a leaking source and wind direction with obstacles including the tanks in the park. Six possible leaking sources and five possible wind directions are considered in their work. Here, the main contributions of this paper are summarized as follows:  
* A fully connected network is proposed, trained with the data generated by AERMOD, taking consideration of complex urban terrain, wind direction, wind speed, temperature, total cloud cover, and low cloud cover. * It is the first attempt to incorporate the self-attention mechanism into the leaking gas source tracking problem. The self-attention module shows its strong fitting ability when the input data are normalized. * To the best of our knowledge, it is the first time a hybrid training strategy is proposed with the combination of raw data and normalized data by manually adjusting the parameters of the deep network. * The effectiveness and generalizability of the proposed model is measured by utilizing random perturbation, which aims to simulate the differences between the measured values and the real ones.
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
## 3 Results and Discussion
### Synthesis
#### 3.1.4 (E)-3-(4-nitrophenyl)-1-(4-((2-hydroxy-benzyl)amino)phenyl)prop-2-ene-1-one (A4)  
Red solid, very soluble in acetone, slightly soluble in ethanol and methanol, insoluble in water; mp: 202-203 \({}^{\circ}\)C; EIMS (\(m/z\)): 373.1234 [M \(-\) H]\({}^{-}\), (MW: 374.13); UV (\(\lambda_{\rm max}\) nm, EtOH): 262, 386; \({}^{1}\)H-NMR (500 MHz, DMSO-\(d_{6}\)) 8 ppm: 4.31 (d, 2H, \(J\) = 9 Hz, CH\({}_{2}\)), 6.68 (d, 2H, \(J\) = 8.5 Hz, H3\({}^{\prime}\), H5\({}^{\prime}\)), 6.74 (t, 1H, \(J\) = 7.5 Hz, NH), 6.84 (d, 1H, \(J\) = 8 Hz, H3\({}^{\prime\prime}\)), 7.07-7.18 (m, 3H, H4\({}^{\prime\prime}\), H5\({}^{\prime\prime}\), H6\({}^{\prime\prime}\)), 7.68 (d, 1H, \(J\) = 15.5 Hz, H\(\alpha\)), 7.97 (d, 2H, \(J\) = 8.5 Hz, H2\({}^{\prime}\), H6\({}^{\prime}\)), 8.05 (d, 1H, \(J\) = 15.5 Hz, H\({}_{\beta}\)), 8.11 (d, 2H, \(J\) = 8.5 Hz, H2, H6), 8.26 (d, 2H, \(J\) = 9 Hz, H3, H5), 9.62 (s, 1H, OH); \({}^{13}\)C-NMR (125 MHz, DMSO-\(d_{6}\)) 8 ppm: 56.0 (CH\({}_{2}\)), 114.9 (C3\({}^{\prime}\), C5\({}^{\prime}\)), 118.8 (C6\({}^{\prime\prime}\)), 123.8 (C5\({}^{\prime\prime}\)), 124.6 (C\({}_{\alpha}\)), 125.0 (C1\({}^{\prime\prime}\)), 126.7 (C3, C5), 127.8 (C1\({}^{\prime}\)), 128.3 (C4\({}^{\prime\prime}\)), 129.4 (C2\({}_{\rm C6}\)), 131.2 (C2\({}^{\prime}\), C6\({}^{\prime}\)), 138.6 (C1), 141.8 (C\({}_{\beta}\)), 147.7 (C4), 153.4 (C4\({}^{\prime}\)), 155.0 (C2\({}^{\prime\prime}\)), 185.4 (C=O).
# Electrospun Porous PDLLA Fiber Membrane Coated with nHA
## 2 Materials and Methods
### Measurements and Characterization  
The morphology of porous PDLLA fibrous membranes and the PDLLA/nHA composite fibrous membranes after gold-sputtering were observed under a scanning electron microscope (SEM, Shimadzu SSX-550) at an accelerating voltage of 3 kV. The phase of nHA was investigated by XRD using a diffractometer system (D/max-rB) equipped with monochromatic Cu Ka. XRD pattern was recorded over a diffraction angle (2\(\theta\)) ranging from 10\({}^{\circ}\) to 60\({}^{\circ}\) in 0.02\({}^{\circ}\) increments. Infrared spectra of the samples were recorded with a Fourier transform infrared spectrometer (FTIR, IRAffinity-1, Shimadzu, Kyoto, Japan) at a resolution of 2 cm\({}^{-1}\). Thermo-gravimetric Analysis (TGA) was recorded on a Pyris Diamond TG/DTA (Perkin Elmer, Waltham, Massachusetts, USA) under a 50 mL/min nitrogen flow ratio from 50 to 600 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min. The weight of samples was around 10 mg. The nHA content was calculated from the TGAs of the mixture ingredients' weight loss. Water contact angles were measured with a JC2000C2 contact angle goniometer (Shanghai Zhongchen Powereach Company, Shanghai, China) by the sessile drop method with a microsyringe at 25 \({}^{\circ}\)C. More than 10 contact angles were averaged to obtain a reliable value for each sample. Mechanical properties were performed in a Shimadzu Universal Testing Machine (AG-IS with a 10 N load cell) in tensile mode, at a strain rate of 0.5 mm/min. From the stress-strain data, the elasticity modulus was calculated. The values presented were calculated as the mean and standard deviation of the five individual measurements performed for each sample.