esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion  
### Setting of Experiments  
#### 4.1.1 Dataset  
The experimental dataset used in this research is the Open University Learning Analytics (OULA) dataset [40], which is one of the most well-known open and high-quality datasets in the field of educational data mining. It includes demographic features of students and interactive records of students' learning activities in the virtual learning environment (VLE). The dataset contains the academic performance grades, 32,593 students' scores of assessments in 22 courses and 10,655,280 records of interaction between students and online learning activities.  
Figure 3: The structure of the proposed end-to-end prediction model of students’ performance.  
There are four reasons we used the OULA dataset as the experimental dataset in this study. Firstly, the dataset contains logs of students' online learning activities, which is consistent with the goals of this study. Secondly, the dataset contains the performance grade of each student in each course, which can be used to evaluate learning. Thirdly, the volume of the dataset is extremely large, which can effectively avoid overfitting during model training and ensure that the model has a better generalization ability. Fourthly, the dataset is publicly available, and many studies have been conducted on it, which allows for meaningful comparison.  
In this dataset, the performance of students in each course is described by four grades: Distinction, Fail, Pass, and Withdraw. As shown in Figure 4, in 22 courses, the number of students with different grades is imbalanced, and the students obtaining a Fail or Distinction are always fewer than the students obtaining a Pass and Withdraw.
## 3 Results and Discussion
### Impedance Spectroscopic Studies
The equivalent circuit designed in this way successfully described the impedance results in other materials, such as Pb(Fe\({}_{0.5}\)Nb\({}_{0.5}\))O\({}_{3}\), BiHoZnZrO\({}_{6}\), and BiHoCuTiO\({}_{6}\)[44; 45; 46]. Based on the proposed equivalent circuit model, and using Zwiew 4 software (Scribner Associates, Southern Pines, NC, USA), the impedance spectroscopy results obtained for all discussed samples in the temperature range of \(T=703\)-\(823\) K were fitted. The example of the fitting procedure results for unmodified and La-doped ceramics at selected temperature is presented in Table 2, together with the errors of the fits for each parameter. The obtained values of grain and grain boundary capacitances fall within the range characteristic for these microstructural components [47], while the resistance values of grains and grain boundaries are of the same order of magnitude as other ceramic materials with perovskite structure [48]. Figure 5: The frequency dependence of the real and imaginary components of the impedance of PBZT 25/70/30 ceramics modified with lanthanum ions. Figure 6: Nyquist plots for the lanthanum-modified PBZT 25/70/30 ceramics in the temperature range of 703–823 K.  
Figure 7: The equivalent circuit used for describing lanthanum-ion-modified PBZT 25/70/30 ceramics. The fitting values of grain and grain boundary resistance for all investigated temperatures are presented as natural logarithm dependencies versus reciprocal temperature in Figure 8 for PBZT 25/70/30 ceramics modified by the minimum and maximum amounts of lanthanum. The characteristics of the remaining discussed ceramic materials are included in Supplements S3 and S4. Table 3 presents the activation energy for all obtained materials. The obtained results clearly indicate a significant increase in the resistance of both the grains and the grain boundaries. For example, the grain resistance at a temperature of \(T\) = 773 K increases from approximately 217 \(\Omega\) for undoped PBZT 25/70/30 [37] ceramics to about 3.53 \(\times\) 10\({}^{5}\)\(\Omega\) for ceramics modified with 4 at.% lanthanum ions. At the same temperature, the grain boundary resistance increases from 3.6 \(\times\) 10\({}^{3}\)\(\Omega\) for pure ceramics [37] to 2.5 \(\times\) 10\({}^{6}\)\(\Omega\) for doped ceramics.
## 3 Numerical Method  
The density-based solver was utilized to solve the Reynolds-averaged Navier-Stokes equations. Considering the large-scale separated flows in the flow field under the un-starting state, the SST k-\(\omega\) model was selected as the turbulence model. By combining the advantages of the k-\(\varepsilon\) model in calculating the far field and k-\(\omega\) model in near-wall region flows, the SST k-\(\omega\) model satisfactorily simulates the boundary layer and separated flows. The ideal gas model was used as the airflow model and the Sutherland law was adopted to calculate the viscosity. Figure 4 shows the inlet computational domain and grid. Considering the axial symmetry of the model, the 2D flow field in a symmetry plane was considered the computational domain. The boundary layer grid was set up to accurately simulate the near-wall airflow. Moreover, to accurately simulate the complex shock system in the flow field, the grids in the cone tip, cowl lip, and internal contraction section are dense. A grid independence study was undertaken in a previous work, three grid scale, i.e., coarse (200 \(\times\) 120), medium (300 \(\times\) 180), and refined (400 \(\times\) 240) grids were set [31]. The results show that the wall pressures calculated by the medium and refined grids are similar, and the differences in the mass flow ratio and total pressure recovery coefficient (\(\sigma\)) are both below 0.05%. Considering the computing accuracy and cost, the medium grid was selected for the current study. The first cell height of the medium grid is 0.02 mm, which results in a y+ below 1 in the main portion of the wall. Moreover, the expansion ratio of the grid is 1.12. The far field pressure was considered as the inlet condition of the computational domain, the outlet was taken as the pressure outlet, and the compression wall and cowl were assumed to be non-slip adiabatic walls. The experiment data of the inlet flow field with a complex shock system obtained by [PERSON] were adopted to validate the accuracy of the current numerical method. The geometric configuration of the test model and the experimental conditions of the wind tunnel could refer to in Ref. [32]. In addition, the current grid setting and numerical method were employed to simulate the test process. Figure 5 shows the schlierens of the flow fields obtained by the experiment and the current numerical simulation. As can be seen, the locations and shapes of the shock waves obtained by the numerical method are almost the same as the experimental results,  
Figure 3: Comparison of the cowl configurations with different cow lip angles.  
Figure 2: Schematic of the parametric design of the cowl.  
demonstrating the reasonability of the proposed numerical method for calculating the inlet flow field.
# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
## 5 Cloud Resource Allocation Policy Based on Maximum Cloud Service Profit
### Maximum Cloud Service Profit Resource Allocation Method
Thus, Equation (24) becomes a strongly convex function, as shown in Figure 16.  
Figure 16: Cloud service costs with a medium task profit. VM, virtual machine. Figure 14: Cloud service costs with a high task profit. VM, virtual machine. Figure 15: Cloud service costs with a low task profit. VM, virtual machine. As mentioned above, the maximum cloud service profit resource allocation (MaxCSPR) algorithm is proposed to find a suitable number of VMs, according to the workload requirements. The details of the MaxCSPR algorithm are shown in Algorithm 1. ```
Input: The task arrival \(x(t+1)\) in the \((t+1)\)th time slot; The cloud service cost vector \(C=[C_{1},C_{2}]\); The average task execution time \((\overline{et})\); The allowable process time of the task \((dp^{max})\); The allowable system load of VM \((U^{max})\); The maximum number of VMs allowed \((N_{v}^{max})\). ```  
(1) Initialization: Set \(N_{v}(0)=\left\lceil\frac{N_{v}^{max}}{2}\right\rceil\) and obtain K from Equation (12). (2) Decision process: Obtain the task arrival workload prediction for the \((t+1)\)th time slot \((x(t+1))\), and calculate the task arrival rate of the VM using Equations (20) and (21). If \(x(t+1)>x(t)\), then \(N_{v}(t+1)\) should be increased to minimize the \(Cost(t+1)\), subject to \(\rho_{e}(\Delta_{\tau})\leq U^{max}\) and \(N_{v}(t+1)<N_{v}^{max}\). Else if \(x(t+1)<x(t)\), then \(N_{v}(t+1)\) should be decreased to minimize the \(Cost(t+1)\), subject to \(\rho_{e}(\Delta_{\tau})\leq U^{max}\) and \(N_{v}(t+1)>0\). Output: \(N_{v}(t+1)\). ```  
**Algorithm 1** MaxCSPR algorithm  
To fit the above three cases, MaxCSPR includes the increasing and decreasing search processes for the cloud resources. When the cost increases with an increasing number of VMs, the number of VMs should be decreased, and vice versa. Furthermore, the proposed algorithm sets the minimum resource size to 1, in order to avoid empty cloud resources. Similarly, the maximum resource size is set as \(N_{v}^{max}\). To achieve proactive elastic resource allocation in the cloud, the ATSMNN-LSTM workload prediction method is combined with the MaxCSPR algorithm. In the proactive elastic resource allocation method, the workload prediction result--namely, \(x^{\prime}(t+1)\)--is used as the \((t+1)\)th time slot input to the MaxCSPR algorithm, which allows the MaxCSPR algorithm to schedule VM resources for the \((t+1)\)th time slot. In this study, the algorithm combining the ATSMM-LSTM and MaxCSPR is named the maximum cloud service profit resource search algorithm based on network workload prediction (MaxCSPR-NWP).
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
### Motivation  
In today's industry, the ANDON tower works as a sensor node in the WSN that sends an alarm signal without information about data reliability. After a comprehensive literature survey, we found that this alarm signal in conjunction with link quality classification has not been used in applications for wireless ANDON towers in the industry. In this paper, we propose a novel application for a deep neural network model based on a multilayer perceptron (MLP) to predict the link quality estimation (LQE) and consequently, the reliability of the information used for decision making in the workshop. In addition, the LQE can also be used to improve WSN performance.  
The packet reception rate (PRR) was used to define the LQE classes, and 10,000 data points were gathered from a realistic experimental scenario using actual industrial machinery. Using these data, we carried out comprehensive experimentation comparing different machine learning (ML) models, including random forest (RF), support vector machines (SVM), K-nearest neighbors (KNN), gradient boosting (GB), and MLP, which displayed the best performance from all the models, achieving an accuracy >99% on the test dataset after a thorough hyper-parameter tuning. In order to choose the most suitable learning rate,we carried out extensive random searches in the range of \(3\times 10^{-8}\leq\text{LR}\leq 3\times 10^{-2}\) until finding that an LR = 2.95 \(\times\) 10\({}^{-5}\) achieved the best performance in the training and validation sets. In spite of the model's simplicity, MLP achieved high precision in classification tasks, making it the ideal model for our industrial application due to its low computational requirements both for training and inference.  
The remainder of this paper is organized as follows. Section 2 presents a review of previous work related to our research. Then, Section 3 refers to materials and methods and describes the hardware, MLP model, and dataset, and Section 4 shows the results and comparison to other machine learning models. Finally, Sections 5 and 6 present the discussion and conclusions, respectively.
## 3 Results
### Correction of the Readout of a LIC: Experimental Data and Simulations
The reference dose per pulse for these calculations was 1 mGy, which results in a value \(u_{ref}=0.146\) (collection efficiency 0.933). The raw integrated current in this case amounts to 91.5% of the ionization current (i.e., 8.5% of the generated charge is lost to recombination). On the other hand, the corrected integrated current amounts to 99.5% of the ionization current. Therefore, even in this situation of very large recombination, the online recombination methods seem to work quite well, the residual 0.5% difference between corrected and generated signal arising from departures from Boag formula for collection efficiencies below \(\simeq\)0.9. Figure 1: Ionization current in a pixel of a liquid-filled ionization chamber during the verification of a clinical radiotherapy treatment delivered at 600 MU/min. The many segments composing the treatment are easily spotted in the (**left**) panel. The (**right**) panel shows a zoom-in of the region highlighted within the ellipse shown in the (**left**) panel, and illustrates the effect of the moving average procedure (Equation (16) with \(n=10\)): moving average (red line), raw data (blue line). Figure 2: Effect of recombination and recombination correction in the verification of a dynamic radiotherapy clinical treatment with a liquid-filled ionization chamber array of 43 \(\times\) 43 pixels of 2.5 mm \(\times\) 2.5 mm each. (**A**) Raw acquisition with a LINAC monitor unit rate of 600 MU/min. (**B**) Representative profiles (along the line highlighted in panel (**A**) with a red arrow) of the raw (solid line) and corrected (dashed line) acquisitions. (**C**) Profile of the raw readout current in a pixel of the detector (blue line) and the corrected readout (red line).
## 3 Results and Discussion
For example, the pore pitch is in the range of 50-60 nm at a bias voltage of 19-25 V in sulfuric acid (H\({}_{2}\)SO\({}_{4}\)), 220 nm-300 nm at 120-250 V in oxal acid (H\({}_{2}\)C\({}_{2}\)O\({}_{2}\)),  
Figure 4: Various pattern transfer processes: Fabricated photonic crystal membrane: L1-PR, L2-Buffer, L3-Si (refer to Figure 3(a2)) on etching. Figure 3: Various pattern transfer processes: (**a**) Reactive ion etching, (**b**) Nanomolding, (**c**) Anodization; 1 Deposition materials on the different substrates, 2 Etching PR layer, 3 Pattern transference, 4 Final structures. and 405-500 nm at 160-195 V in phosphoric acid (H\({}_{3}\)PO\({}_{4}\)). If the bias voltage is lower than the optimal range, pores formed are not well ordered. If the bias voltage is higher than the optimal range, the barrier layer breaks down and substrate burning occurs. Fabricated anodized aluminum oxide layer samples are shown in Figure 8. A two-step AAO process has been used to form more uniform pore sizes. First, aluminum foil undergoes thermal pretreatment at 400 \({}^{\circ}\)C for one hour, which forms a thin layer of oxide layer, preventing initial breakdown upon applying bias voltage. The first anodization is performed in oxalic acid at 40 V for 6 min, which gives rise to relatively random pores, as shown in Figure 8a. The first anodized layer is etched away using a solution consisting of 1.8 wt% chromic acid and 6 wt% phosphoric acid for an hour. After etching, small bumps are formed along the barrier depicted in Figure 7. The bumps serve as guides for the second anodization. The second anodization is performed in oxalic acid at 40 V for 4 min. Optionally, pore widening step is performed using 6 wt% phosphoric acid for an hour. Figure 8b,c are the cross-section view and the top view, respectively. The average pore size and the average pitch are 60 nm and 100 nm, respectively [15]. Figure 3c shows a direct pattern transfer onto an aluminum layer using AAO immediately followed by MIL. The anodization is being performed using phosphoric acid at 160 V to have a pore pitch of 500 nm. As an alternative to the direct anodization process after MIL, template-assisted anodization can be used. However, there are some challenges, including reflection and pore size matching issues between one from MIL and one from AAO. In order to preserve the perfect pore ordering from MIL, further work is required. Figure 5: Transferred pattern on a glass substrate. Figure 6: Perfectly ordered large-area nanopore array: (**a**) Top view of nanopore array (inset shows a magnified oblique view), (**b**) Top view of nanomolded PDMS pillar array (inset shows a magnified view).
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Beamforming Technologies** & **Methodology** & **Contribution/Limitation** \\ \hline
[4] & Introduction of beamforming with multiple antennas &
\begin{tabular}{c} Constructive interference in desired \\ directions and destructive interference in \\ other directions \\ \end{tabular} & Enhanced SNR, energy-efficient and cost-efficient \\ \hline
[5] & On–Off analog beamforming & \
\begin{tabular}{c} A particular set of antennae is employed \\ for beam formation \\ \end{tabular} &
\begin{tabular}{c} Less complex and power-saving \\ than the analog beamforming \\ \end{tabular} \\ \hline
[6] & \
\begin{tabular}{c} Introduction of \\ digital beamforming \\ \end{tabular} & Every antenna element possesses an &
\begin{tabular}{c} More flexibility and enhanced \\ SNR/higher complexity and higher \\ power consumption \\ \end{tabular} \\ \hline
[7] & Hybrid beamforming & \
\begin{tabular}{c} A set of antennas are connected to an \\ RF chain \\ \end{tabular} &
\begin{tabular}{c} Good flexibility, comparable to \\ digital beamforming \\ \end{tabular} \\ \hline
[2,8] & \begin{tabular}{c} Introduction of \\ Analog beamforming \\ \end{tabular} & \
\begin{tabular}{c} Single radio frequency chain employed for \\ all antenna elements in MIMO \\ \end{tabular} &
\begin{tabular}{c} Enhanced SNR/single beam \\ formation at a time \\ \end{tabular} \\ \hline
[8, 9, 10] & Fully connected \\ hybrid beamforming & Every RF chain is connected to every \\ antenna element &
\begin{tabular}{c} Flexible, cost-efficient/high signal \\ processing complexity \\ \end{tabular} \\ \hline
[10, 11, 12] & \begin{tabular}{c} Partially connected \\ hybrid beamforming \\ \end{tabular} & \
\begin{tabular}{c} Set of antennas connected to every RF \\ chain not overlapping each other. \\ \end{tabular} &
\begin{tabular}{c} Less expensive and less signal \\ processing as compared to fully \\ connected hybrid beamforming \\ \end{tabular} \\ \hline
[8] & \begin{tabular}{c} Group-connected \\ Hybrid beamforming \\ \end{tabular} & \
\begin{tabular}{c} Antennas and RF chains are divided into \(\eta\) \\ groups; signals emanating from each RF \\ chain group are relayed via its matching \\ antenna group \\ \end{tabular} &
\begin{tabular}{c} A flexible grouping strategy, a \\ fully-connected mapping strategy is \\ followed within each group \\ \end{tabular} \\ \hline
[13] & Hybrid beamforming in \begin{tabular}{c} Based on geometric mean \\ MIMO setup \\ \end{tabular} & \
\begin{tabular}{c} Large antenna gain, improved BER \\ decomposition-based beamforming \\ \end{tabular} &
\begin{tabular}{c} BER \\ and SE \\ \end{tabular} \\ \hline
[4] & \begin{tabular}{c} 3D beamforming in \\ massive MIMO \\ \end{tabular} & \
\begin{tabular}{c} Based on user grouping and a group-based \\ feedback system \\ \end{tabular} &
\begin{tabular}{c} Allows flexibility in azimuth \\ and elevation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Investigation of beamforming in MIMO and its primary types.
# Pt-Based Nanostructures for Observing Genuine SERS Spectra of _p_-Aminothiophenol (PATP) Molecules
## 4 Discussion  
Problems may result from the illumination time dependence in combination with variations to tip molecule concentration, therefore this was also taken into account in the SERS experiments. To further illustrate the stability of the substrate for ascertaining genuine PATP SERS spectrum, the concentration of PATP molecules was varied, while maintaining the increased the illumination time. Figure 4a-c display time dependence of SERS spectra (200 s) with different tip molecule concentrations at 10\({}^{-5}\), 10\({}^{-6}\), and 10\({}^{-7}\) mol/L. It shows that the spectra were stable and no new peaks (at 1140, 1390, and 1432 cm\({}^{-1}\)) attributed to DMAB emerged with PATP concentration changes. Figure 4d-f reveal the time dependence of SERS spectra with PATP molecule concentrations at 10\({}^{-5}\), 10\({}^{-6}\) and 10\({}^{-7}\) mol/L, for which the illumination time was increased from 200 s to 2000 s. By comparing with the results from Figure 4a-c, it can be seen that plasmon-driven chemical conversion of PATP to DMAB was not occurring, even though the peak intensity of SERS spectra continuously decreased. Thus, the results demonstrate that the plasmon-driven chemical conversion was suppressed and the substrate is stable under laser illumination.  
Figure 3: (**a**) Surface enhanced Raman scattering (SERS) spectrum of 10\({}^{-4}\) mol/L PATP molecules measured with 40 s of laser irradiating on the Pt-based hollow nanostructure substrate. (**b**) Time-dependent SERS spectra of 10\({}^{-4}\) mol/L PATP on the Pt-based hollow nanostructure substrate illuminated with the laser power of 0.3 mW.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.2 Post-Processing of GRACE-DA-DM GLV3.0 Data  
We processed GRACE-DA-DM GLV3.0 data in QGIS using NetCDF format on a 0.25\({}^{\circ}\) grid (-28 km) for national-scale analysis of GWS patterns.  
Bilinear interpolation, which estimates values between grid points by averaging the four nearest points assuming linear change, was applied to refine spatial patterns while acknowledging spatial correlation in the data.  
We selected this method because of its computational efficiency and ability to produce smooth transitions across heterogeneous landscapes, such as Peru's diverse coastal, Andean, and Amazon regions, while preserving the overall trends of the original data [45]. Bilinear interpolation calculates each interpolated value as a weighted average of the four nearest grid points, assuming a linear change in GWS between them, which is reasonable given the gradual hydrological gradients observed in the GRACE data. However, this approach introduces uncertainty by potentially over-smoothing local variations, particularly in areas with abrupt topographical or hydrological shifts (e.g., Andean slopes). The associated uncertainty was estimated at approximately 5%, as reflected in the 95% confidence intervalsreported in Appendix A (Table A7), and aligns with acceptable thresholds for regional-scale studies. The 5% uncertainty in GRACE-derived GWS estimates accounts for signal smoothing and orbital constraints, consistent with previous studies [37; 45]. For this national analysis, the original 0.25\({}^{\circ}\) resolution enhanced by bilinear interpolation was deemed appropriate, though finer-scale studies may require alternative methods, such as kriging, to capture localized anomalies. Figure 2 outlines the processing workflow, including data import, interpolation, and map generation.