esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
## 4 Overview of the Variables Used in the Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Week**} & \multicolumn{2}{c}{**Number of Learning Tests**} & \multicolumn{2}{c}{**Maximum of Learning Tests**} \\ \cline{2-5}  & **Mean (Std Dev)** & **Median (Min–Max)** & **Mean (Std Dev)** & **Median (Min–Max)** \\ \hline Week 1 & 0.14 (0.42) & 0 (0–2) & 0.08 (0.24) & 0.00 (0.00–1.00) \\ Week 2 & 0.32 (0.59) & 0 (0–2) & 0.20 (0.35) & 0.00 (0.00–1.00) \\ Week 3 & 0.50 (0.81) & 0 (0–3) & 0.26 (0.38) & 0.00 (0.00–1.00) \\ Week 4 & 1.18 (1.78) & 0 (0–9) & 0.39 (0.42) & 0.00 (0.00–1.00) \\ Week 5 & 0.13 (0.38) & 0 (0–2) & 0.08 (0.23) & 0.00 (0.00–0.94) \\ Week 6 & 0.21 (0.72) & 0 (0–4) & 0.08 (0.24) & 0.00 (0.00–0.92) \\ Week 7 & 0.20 (0.61) & 0 (0–3) & 0.05 (0.20) & 0.00 (0.00–0.92) \\ Week 8 & 0.13 (0.47) & 0 (0–3) & 0.05 (0.19) & 0.00 (0.00–0.94) \\ Week 9 & 0.13 (0.64) & 0 (0–5) & 0.04 (0.18) & 0.00 (0.00–1.00) \\ Week 10 & 1.66 (2.73) & 0 (0–17) & 0.28 (0.35) & 0.00 (0.00–0.95) \\ Week 11 & 0.30 (0.65) & 0 (0–3) & 0.07 (0.20) & 0.00 (0.00–0.84) \\ Week 12 & 0.33 (1.09) & 0 (0–8) & 0.10 (0.26) & 0.00 (0.00–1.00) \\ Week 13 & 0.11 (0.39) & 0 (0–2) & 0.05 (0.20) & 0.00 (0.00–0.93) \\ Week 14 & 0.11 (0.42) & 0 (0–3) & 0.02 (0.11) & 0.00 (0.00–0.71) \\ Week 15 & 3.54 (4.30) & 1.5 (0–16) & 0.38 (0.38) & 0.40 (0.00–1.00) \\ \hline \hline \end{tabular} Note: The weeks preceding midterm exams are highlighted in light blue.  
\end{table} 
Table 1: Descriptive statistical indicators for the variables _Number_ and _Maximum of learning tests_. In addition to the learning tests, course participants have access to additional practice tests within the Merlin e-course. These practice tests are also generated from a database of formulaic questions, allowing students to engage with a diverse set of problems and reinforce their understanding of the material. The variable _Number of practice tests_ represents the total number of times a student has completed a practice test in a given week, including repeated attempts for further reinforcement. The variable _Maximum of practice tests_ records the highest percentage of points a student achieved in that week, reflecting their best performance in all completed practice tests. The descriptive statistical indicators for these variables per week are presented in Table 2. The table indicates that students predominantly complete practice tests in the weeks leading up to midterm exams, mirroring the pattern observed with learning tests. The graphical representation of the arithmetic mean and the standard deviation for the variable _Number of practice tests_ is shown in Figure 3 to visually illustrate the increased activity of the students in the weeks before the midterm exams. If we look at the weeks before midterm exams, we can see in Figure 3 that interest in solving practice tests decreases throughout the semester, with interest being the highest in
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 4 Systems for Rail Defect Detection Based on Ultrasonic Guided Waves: Architecture
### On-Board Systems  
Over the years, _active_ and _passive_ detection strategies have been proposed for on-board systems. The main characteristics of the systems employing such strategies can be summarized as follows:  
* An _active_ rail inspection system is able to detect surface-breaking cracks and internal defects located in the railhead. It is based on UGWs and air-coupled contactless probing. Both the transmission of test signals and their reception are accomplished by means of air-coupled piezoelectric transducers arranged in a pitch-catch configuration. The use of a laser transmitter has been also investigated [40]; however, this option has been judged too costly and is potentially hard to maintain. In an active system, receivers are arranged in pairs and placed at the two sides of the transmitter: this allows for implementing a differential detection scheme. The system output, after data analysis, is represented by the so-called _damage index_ (DI), exhibiting peaks in correspondence of discontinuities (i.e., of potential defects) in rails. In recent years, the use of laser technology for both exciting UGWs in rails and sensing them has been investigated [68; 69]. In this case, non-ablative laser sources are used to generate a guided tensional wave system in the rail; the response to such a system can be sensed by means of a rotational laser vibrometer.
* A _passive_ rail inspection system aims at estimating the acoustic transfer function of rails; the excitation is represented by the rolling wheels of the moving instrumented train [48]. When a train is in motion, its rotating wheels generate a continuous dynamic excitation of the rail. Such an excitation is uncontrolled, non-stationary, and difficult to characterize [48]. Therefore, in this case, the challenge to be faced is to generate a stable rail transfer function that is not affected by the excitation [49]. The presence of a discontinuity in the rail can be inferred from the estimated transfer function, using again the DI indicator.
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.5 Path Loss Prediction Method Based on Transformer  
Transformer has emerged as a novel and powerful architecture in recent years, and its application in path loss prediction shows great potential. We implemented this model using the PyTorch version 2.3.0 framework, leveraging relevant libraries for Transformer architectures. The hyperparameters of this network include the number of Transformer encoder layers, the model embedding dimension, the number of attention heads, the activation function, the learning rate, the weight decay coefficient, and the type of loss function. Table 9 shows the optimal combination of hyperparameters for the Transformer network determined through the hyperparameter optimization process.
## 3 System Overview
### OCC Channel Model and Data Retrieval Technique
The total number of strips projected inside the image sensor can be represented as follows [26]:  
\[\Gamma=\frac{\mathrm{A_{f}f_{0}^{2}(f_{on}+f_{off})t_{r}}}{\mathrm{\rho^{2}d_{ \alpha,\beta}^{2}}} \tag{4}\]  
where \(f_{0}\) is the focal length of the camera, \(t_{r}\) denotes the read-out time of a pixel of the camera, \(\rho\) is the edge length of a pixel, and \(f_{on}\) and \(f_{off}\) denote the ON and OFF frequencies of the LED, respectively. The appearance of the full LED to appear inside the image sensor is not necessary for successful communication. Particularly, the strips have a minimum number, denoted by \(\Gamma_{min}\), that should be formed to retrieve the transmitted bits. \(\Gamma_{min}\) is formulated as follows:  
\[\Gamma_{min}=\underset{d_{\alpha,\beta}}{\mathrm{argmin}}(\Gamma) \tag{5}\]
## 2 Study Region  
The tool is tested in the Ozttal Alps, Tyrol, Austria (Figure 1) covering 26 glaciers in three different basins (Niedertal, Rofental and Gepatsch), all draining northward. The southern part of the study region includes the main Alpine divide, which often acts as a cloud barrier for weather patterns driven by northerly and southerly flow. In summer, orographic convective clouds are frequent above mountain ridges, often casting shadows on glaciers. The glaciers span \(\sim\)2150 to \(\sim\)3750 m a.s.l., cover several aspect directions, have different slopes and shading conditions, and variable hypsometries, collectively resulting in a locally variable distribution of SC.  
The total area of all selected glaciers is 71 km\({}^{2}\) in 2011 (year of the glacier mask). The largest glacier is Gepatschferner (no. 21 in Figure 1) with 16.3 km\({}^{2}\), followed by Schalfferner (6.5 km\({}^{2}\), no. 5), Vernagfterner (6.1 km\({}^{2}\), no. 13), Taschachferner (5.5 km\({}^{2}\), no. 18), Hintereisferner (5.2 km\({}^{2}\), no. 11), Marzellferner (4 km\({}^{2}\), no. 6) and Kesselwandferner (no. 13) with 3.6 km\({}^{2}\). Of these, Hintereisferner  
Figure 1: Study region Ötztal showing the 2011 Landsat 7 ETM+ SLC-off satellite image (false colour composite: SWIR, NIR, red as RGB) used for deriving glacier extents. The outline colors indicate mean slopes (see legend for color), glacier numbers and mean aspect (cardinal sectors) are white. Stripes on the right hand side reveal the typically small impact of the scan line error on the glaciers. Landsat 7 ETM+ SLC-off scenes have thus also been used in the study region.  
(HEF), Kesselwandferner (KWF), and Vernagfterner (VNF), have long and continuous time series of mass balance measurements, ELAs and AARs [18; 34; 35; 36; 37], providing a valuable dataset for comparison with the automatically generated SLA and SCR estimates. The highest glacier elevation is reached at Taschachferner (3745 m a.s.l.) and the lowest is at Gepatschferner (2148 m a.s.l.), which also has the largest elevation range (\(\sim\)1350 m). The glaciers often have steep slopes exposed to the north (ten glaciers) or northeast (six glaciers). The average value of the ELA\({}_{0}\) (balanced-budget ELA) is found between 3050 m and 3150 m a.s.l. (1953-2013) [34]. At this elevation range, the mean annual air temperature is around \(-\)5 \({}^{\circ}\)C and the annual precipitation (solid and liquid) about 1500 mm [34]. The ablation period usually lasts from May to September. Large area and mass losses have been observed in the Otztal Alps since the mid-1980s [38], indicating a continuous glacier shrinkage over the investigated period.
# Elastic Reverse Time Migration for Weakly Illuminated Structure
## 2 Methodology  
### Vector-Decoupled Wave Equation  
The velocity-stress formula in 2D isotropic medium is expressed as,  
\[\left\{\begin{array}{c}\rho\frac{\partial\Psi}{\partial t}=\mathcal{L} \boldsymbol{\sigma}\\ \frac{\partial\boldsymbol{\sigma}}{\partial t}=\rho\boldsymbol{A}\mathcal{L}^ {T}\boldsymbol{V}\end{array}\right. \tag{1}\]  
where \(\rho\) denotes the density of media, \(\boldsymbol{V}=\left[v_{x},v_{z}\right]^{T}\) is the particle-velocity vector, \(t\) signifies the time variable, \(\boldsymbol{\sigma}=\left[\tau_{xx},\tau_{zz},\tau_{xz}\right]^{T}\) denotes the stress vector and \(\mathcal{L}\) is a spatial partial derivative operator expressed as:  
\[\mathcal{L}=\left[\begin{array}{ccc}\partial_{x}&0&\partial_{z}\\ 0&\partial_{z}&\partial_{x}\end{array}\right] \tag{2}\]  
\(x\) and \(z\), respectively represent the horizontal and vertical directions, and \(\boldsymbol{A}\) is a parameter matrix as shown below:  
\[\boldsymbol{A}=\left[\begin{array}{ccc}c_{P}^{2}&c_{P}^{2}-2c_{S}^{2}&0\\ c_{P}^{2}-2c_{S}^{2}&c_{P}^{2}&0\\ 0&0&c_{S}^{2}\end{array}\right] \tag{3}\]  
where \(c_{P}\) and \(c_{S}\) are P-wave and S-wave velocity in the propagation medium. Equation (1) can be named as vector-coupled wave equation (VCWE).  
When the wavefield is simulated according to Equation (1), the P- and S-waves are coupled in the wavefield of particle velocity. If the coupled wavefield is directly applied to the ERTM, the imaging profile will suffer from crosstalk. To tackle this problem,Wang et al. [24] introduced a scalar P-waves stress \(\tau^{P}\) to decouple the wavefield, and the formulation is written as,  
\[\left\{\begin{array}{c}\frac{\partial\tau^{P}}{\partial t}=\rho c_{P}^{2}\Big{(} \frac{\partial\tau_{x}}{\partial x}+\frac{\partial\tau_{z}}{\partial z}\Big{)} \\ \rho\frac{\partial v_{z}^{P}}{\partial t}=\frac{\partial\tau^{P}}{\partial x}\\ \rho\frac{\partial v_{z}^{P}}{\partial t}=\frac{\partial\tau^{P}}{\partial z} \end{array}\right. \tag{4}\]  
where \(v_{x}^{P}\) and \(v_{z}^{P}\) are the decoupled P-waves in \(x\)- and \(z\)-directions. The residual S-waves are defined as \(v_{x}^{S}=v_{x}-v_{x}^{P}\) and \(v_{z}^{S}=v_{z}-v_{z}^{P}\). The decoupled wavefield can preserve the same physical properties as the coupled wavefield. The combination of Equations (1) and (4) is the vector-decoupled wave equation (VDWE).
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 5 Simulation and Result Analysis
### Time Cooperative Guidance Simulation Experiment of Multi-Hypersonic Vehicle for a Fixed Target  
To verify the effectiveness of the proposed algorithm, this section shows multi-aircraft reentry guidance experiments for fixed targets, reentry from different starting points, and time coordination. The experimental scene is set as in Table 2, and the expected cooperative flight time is 800 s.  
Figures 14-16 show the variation of altitude, latitude, longitude, and velocity obtained by the three scene-simulation experiments. It can be seen that under the solution of the proposed algorithm, the terminal state constraints and time constraints can be satisfied. The three aircraft can accurately reach the target point in the expected cooperative flight time.  
Figure 17 shows the change in the remaining range of the three aircraft with time. The terminal remaining ranges of the aircraft in the three scenarios are 11.05 km, 1.44 km, and 27.48 km, respectively, which can meet the allowable error of the terminal remaining range. Figure 18 shows that the path constraints of the three aircraft are all over the upper limit threshold, indicating the safety of the flight trajectory. Correspondingly, Figure 19 shows that the three aircraft can smoothly enter the resistance acceleration reentry corridor.  
Figure 12: Velocity histories of the AOA.  
Figure 13: Velocity histories of the bank angle.  
Figure 16: Time histories of the velocity.  
Figure 14: Time histories of the altitude.  
Figure 15: Latitude and longitude changes.  
Figure 19: Resistance acceleration reentry corridor.  
Figure 17: Time histories of the remaining voyage.  
switching point meets the requirements of Table 1. The bank angle profile has an obvious sign reversal phenomenon, which shows that the heading angle deviation corridor used in this paper can play an important role.  
In summary, the multi-aircraft analytical time reentry cooperative guidance method proposed in this paper can deal with the problem of cooperative target guidance at different launch points, and the time constraint can be accurately satisfied.
## 4 Experiments and Results
### Effects of LCCDNN and FCCDNN Parameters on Neuron Classification
#### 4.3.3 Accuracy Analysis of Different Numbers of Building Blocks  
In this experiment, the performance comparison of different numbers of building blocks in the LCCDNN and FCCDNN models was tested for solving the neuronal morphology classification problem. Figure 15 shows the classification results with different numbers of building blocks in the LCCDNNs. The number of building blocks in the deep neural network was increased, while the other settings remained the same. Figure 15a shows the classification accuracy in the C. elegans dataset and Figure 15b shows the classification accuracy in the zebrafish dataset. With the increase in the number of building blocks, the classification accuracy increased. However, when the building block number increased further, the classification accuracy decreased gradually.  
Figure 16a shows the variation of FCCDNN accuracy in the C. elegans dataset, when the number of building blocks was 3, 4, and 5, the accuracy of the training set was 97.45%, 96.86%, and 93.012%, and the accuracy of the test set was 83.33%, 82.82%, and 80.26%, respectively. Therefore, when the building block number is three, the FCCDNN model for the C. elegans dataset is reasonable. Figure 16b shows the classification accuracy in the zebrafish dataset; when the number of building blocks was 4, 5, and 6, the accuracy of the training set was 83.14%, 94.02%, and 78.34% and the accuracy of the test set was 66.28%, 72.33% and 65.12%, respectively. It can be seen that the zebrafish dataset had the highest classification accuracy when the number of building blocks was five.  
Figure 14: Comparison of accuracy with different numbers of intermediate layers by FCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
Figure 13: Comparison of accuracy with different numbers of intermediate layers by LCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.
## 2 Materials and Methods  
### Study Design and Study Population  
Our research is a type of correlational study. It was conducted in a retrospective manner on 260 patients from the Cardiology Department of the Emergency Clinical Hospital Bagdasar-Arseni. The target population was patients with chronic heart failure (HF), stable or decompensated, but the population of interest was patients with chronic decompensated HF. The parameter of interest in the research was MPV.  
The definition of decompensated chronic HF followed the indications of the 2021 ESC Guidelines for HF [2]: sudden or progressive deterioration in chronic HF, patients with chronic HF being those who have a diagnosis of HF in their personal history, characterized by typical symptoms with or without signs of congestion, systolic dysfunction, echocardiographic evidence of cardiac structural and functional abnormalities, objective evidence of increased filling pressures, and increased NT-pro BNP values.  
Regarding patients with stable chronic HF, the defining criteria are patients with a personal history of HF and who are included in functional classes II NYHA [2].  
We analyzed all the patients with HF admitted between January 2017 and January 2019 to the Cardiology Department of the Emergency Clinical Hospital Bagdasar-Arseni, of which we selected 260 patients who met the inclusion criteria. Of the 260 patients retained, 116 patients had class IV NYHA HF, 65 patients had APE, 40 patients had class III NYHA HF, and 39 patients had class II NYHA HF.  
The inclusion criteria were:  
* patients with decompensated chronic HF--defined by class III/IV NYHA HF or acute pulmonary edema (APE), regardless of left ventricular ejection fraction.
* patients with stable chronic HF--defined by class II NYHA HF, regardless of left ventricular ejection fraction.
* patients for whom the MPV value was available.
* patients for whom data regarding 3-month, 6-month rehospitalization and 1-year death were available.  
The exclusion criteria were:  
* patients without defining criteria for chronic HF.  
patients for whom the MPV value was not available.
* diseases with a survival time of less than one year.
* neoplasms.
* chronic kidney disease.
* acute coronary syndrome.
* acute stroke.
* acute pulmonary thromboembolism or deep vein thrombosis.
* autoimmune diseases.
* thrombocytopenia.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
## 4 Experimental Results and Discussion  
The performance of the TA designed in this study was evaluated in the chamber depicted in Figure 11. Figure 12 presents the measurement results of the E and H polarizations. The discrepancy between the actual measured gain and the simulated gain is likely due to the insufficient physical rigidity of the support structure holding the antenna within the chamber. During the measurement process, the antenna underwent 360-degree rotation supported by this structure, and it is assumed that measurement errors occurred due to physical deformation of the support structure under this weight.  
The actual measurement results revealed a maximum gain of 13.74 dBi for H polarization and 12.58 dBi for E polarization at 9.7 GHz. These maximum values were recorded when \(\Phi\) was 16 degrees and \(\theta\) was 26 degrees. While these results suggest that support structure deformation might have contributed to measurement inaccuracies, they cannot  
Figure 10: Simulated radiation pattern.  
Figure 9: Geometry of TA with 52 elements. (**a**) Phase distribution of TA. (**b**) Top view. (**c**) Sky view of TA with metallic side structure.  
solely be attributed to the structure's deformation due to its heavy weight. Future research will aim to achieve more accurate experimental verification results.  
This study proposes a method to enhance the gain of a single patch source by applying a side metal structure to a metasurface. The feasibility of this method was supported by  
Figure 11: Fabricated prototype. (**a**) Prototype under measurement in chamber. (**b**) Sky view of TA. (**c**) Top view. (**d**) Bottom view. (**e**) Side view.  
Figure 12: Linear polarization of patch source. (**a**) Phi-cut of theta polarization H. (**b**) Theta-cut of theta polarization H. (**c**) Phi-cut of phi polarization E. (**d**) Theta-cut of phi polarization E.  
theoretical analysis and simulation results. However, the actual measured gain was approximately 5 dB lower than the simulated gain, indicating the need for further investigation to reconcile these differences.
# Research on Real-Time Model of Turboshaft Engine with Surge Process
###### Abstract
All these models regard the engine's each component as an equivalent cavity unit, and the whole engine is calculated by stacking these units step by step, which simplifies the interaction with other components, and pays more attention to the dynamic process of stall and surge at a steady-state point of the engine, while the model itself lacks the steady-state and dynamic simulation capability of the whole envelope under different operating conditions. Additionally, only the simulation results of the compressor's parameters and its operating points are given in these researches while the changes of other engine's performance parameters are not shown. In the existing research, the component level model (CLM) is widely used to simulate the steady-state operating point and dynamic acceleration or deceleration process of aeroengine, but it cannot simulate the changes of various parameters during engine surge. Although there has been little progress in describing the whole engine's dynamic process of the surge, the establishment of a dynamic real-time model of aeroengine with the ability to simulate surge under full envelope and full working conditions is undoubted of great significance to further development of surge and engine active stability control. Thus, a novel real-time modeling method of turboshaft engine surge process based on the instability mechanism model is proposed in this paper. The CLM of turboshaft engine and classic MG model with instability mechanism are conducted firstly. Then the extended MG model which expands the compressor characteristics to full speed range is developed through stable analysis. Based on the extended MG and CLM, the coupling relationship is comprehensively considered to combine the MG model's ability to describe the dynamic process of compressor surge with the CLM's ability of full envelope steady and dynamic simulation of the engine, thus, the characteristic parameters of the whole engine surge process can be simulated. Finally, a surge detection method widely used is applied to verify the model's confidence. The surge model proposed provides a model basis for simulation tests of surge detection and anti-surge control to avoid high risk and cost surge tests, which unequivocally has important practical significance for the development and engineering application of surge active control technology in the future. Besides, it is worth mentioning that the modeling method proposed in this paper can be extended to other types of aero engines to simulate the surge process. This paper is organized as follows: Section 2 describes the calculation method of the CLM briefly, and Section 3 introduces the method for establishing a real-time surge model based on the surge mechanism model. The simulation and verification of the model is given in Sections 4 and 5 concludes this paper.