esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Establishment and Validation of Numerical Simulation Model for a Linear Heat Source
### Analysis and Validation of Numerical Simulation Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Medium**} & \multirow{2}{*}{**Time \(t\) (s)**} & \multicolumn{2}{c}{**Testing Results \(\delta t\)**} & \multicolumn{2}{c}{**Theoretical Analysis**} & \multicolumn{2}{c}{**Numerical Results**} \\ \cline{3-6}  & & **(\({}^{\circ}\)C)** & **Results (\({}^{\circ}\)C)** & **Error (\%)** & **Results (\({}^{\circ}\)C)** & **Error (\%)** \\ \hline \multirow{4}{*}{Water} & 600 & 34.5 & 40.0 & 13.8 & 33.4 & 3.2 \\  & 1200 & 36.0 & 40.0 & 9.9 & 33.8 & 6.1 \\  & 1800 & 38.3 & 40.0 & 4.3 & 34.1 & 11.0 \\  & 2400 & 38.6 & 40.0 & 3.6 & 34.2 & 11.4 \\ \hline \multirow{4}{*}{Soil} & 600 & 36.3 & 44.6 & 18.6 & 37.6 & 3.6 \\  & 1200 & 44.0 & 52.4 & 16.0 & 46.6 & 5.9 \\ \cline{1-1}  & 1800 & 49.8 & 58.1 & 14.3 & 53.5 & 7.4 \\ \cline{1-1}  & 2400 & 53.9 & 65.2 & 17.3 & 58.7 & 8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between numerical simulation model results and testing results. Figure 3: Temperature rising cloud map of iron linear heat source. the accuracy and reliability of the finite element numerical simulation model established in this section [27]. The established numerical simulation model can be used to guide the development and analysis of future monitoring devices for bank slopes' erosion and sedimentation. Of course, it is undeniable that there is a certain degree of error between the numerical model calculation results and the experimental results (less than 11.4% in water and less than 9% in soil). Figure 5 illustrates the schematic diagram of heat conduction in the transition section of the interface of water and soil, which directly reflects the conduction of heat at the interface of linear heat source and water and soil. As can be seen from Figure 5, heat can not only be conducted horizontally from the heat source to the water body and soil body but also within the heat source, the soil body, the water body, and the water-soil interface. Since the thermal conductivity of water is larger than that of soil, the heat dissipated by the linear heat source in water is more than that in soil, and the heat is transmitted diagonally at the interface of water and soil, thus forming the transition temperature change curve at the interface of water and soil, as shown in Figure 4.
## 4 Results
### Numerical Results
#### 4.1.4 Node Histogram Comparison  
The Histogram Comparison method demonstrated varying error rates and execution times depending on the precision level. With a precision of 0.1 (see Figure 8g), the method produced 274 errors and had an average execution time of 0.0585 s. While it exhibited a structure similar to the G-SSIM charts, Period 3 showed a more even distribution of similarity values for Low- and High-Quality images.  
Increasing the precision to 0.01 resulted in a higher error rate of 590, although the execution time remained comparable at 0.0594 s. Notably, this method consistently assigned higher similarity scores to Low-Quality images (see Figure 8h).  
Finally, at a precision of 0.001, the error rate increased to 736 and the average execution time rose to 0.0664 s. This further emphasized the tendency to overestimate the similarity of Low-Quality images (see Figure 8i).
## 4 Experiment Results
### Preliminary Experimental Tests  
In order to test the actual target imaging ability of the designed MIMO radar system, the experiments were preliminarily carried out in the air. In the first experiment, two small dihedral corner reflectors (DCR) were used as the targets in an open space. The positions of the two dihedral  
Figure 13: Flow chart of the improved TDBP algorithm. Part I: One-way travel time calculation based on the parallel algorithm; Part II: the whole travel time for the MIMO sparse array; Part III: TDBP imaging.  
corner reflectors were approximately about 3 m and 5 m away from the equivalent phase-center of the radar system. The experimental setup is shown in Figure 14 and the imaging results are shown in Figure 15. It can be observed that the two targets can be focused in 2D imaging. Compared to the results obtained by background removal (Figure 15c,d), it is obvious that the method of removing the presampled direct coupling (Figure 15a,b) is simple and can roughly distinguish the positions of the DCR targets. However, due to the influence of clutter signals, such as ground reflection, there are more noise points and artifacts in the direct coupling removed images. It is worth noting that after utilizing the CC-TDBP algorithm, the imaging results can further suppress the artifacts and clearly distinguish the exact locations of the DCR targets, no matter the methods of removing direct coupling or background, which are shown in Figure 15) and Figure 15d, respectively. In conclusion, the CC-TDBP algorithm can significantly improve image quality and suppress artifacts.  
Figure 14: Experiment setup for dihedral corner reflector imaging.  
Figure 15: Imaging results of two corner reflectors by: (**a**) the TDBP algorithm with direct coupling removal; (**b**) the CC-TDBP algorithm with direct coupling removal; (**c**) the TDBP algorithm with background removal; (**d**) the CC-TDBP algorithm with background removal.  
In the second experiment, to validate the human imaging performance of the designed radar system, two humans acted as targets in the near range, as shown in Figure 16. In this case, only the CC-TDBP algorithm was used, and the imaging results are shown in Figure 17. We can see that the two human targets marked by the red circle are clearly focused. In this preliminary experiment, even if the direct wave is removed (Figure 17a), the designed MIMO radar system can clearly detect the human targets in the air.
## 4 Analysis of Sound Radiation Characteristics for Reinforced Laminated Cylindrical Shell Structures
### Effects of Reinforcement Thickness  
This section explores the influence of reinforcement thickness on the radiated noise of sandwich-reinforced cylindrical shells. Based on the cylindrical shell model with a reinforcement length of 32 mm, and keeping other parameters unchanged, the radiated noise of the cylindrical shell structure is calculated for reinforcement thicknesses of 1.4 mm, 2 mm, and 2.6 mm, and the results are shown in Figure 17.  
Figure 16: Structural sound power level curves under different reinforcement lengths.  
It can be observed from the graph that in the 1-200 Hz frequency range, the sound power level curves of cylindrical shells with different rib reinforcement thicknesses are almost the same. As the frequency increases, the sound power level peaks shift towards higher frequencies, and the shift is greater for thicker reinforcement. From the overall sound power level perspective, the total sound power levels of the cylindrical shells with reinforcement thicknesses of 1.4 mm, 2.0 mm, and 2.6 mm are 82.4 dB, 80.28 dB, and 78.32 dB, respectively, indicating that the best performance is achieved with a reinforcement thickness of 2.6 mm. The results indicate that as the reinforcement thickness increases, the radiated noise of the cylindrical shell structure decreases. This is because the larger reinforcement thickness increases the contact area between the reinforcement and the inner and outer shells, leading to a more uniform distribution of excitation forces on the shell. As a result, the occurrence of axial bending in the cylindrical shell is less likely, and its vibration is effectively reduced, thus lowering the structure's radiated noise.
# An Investigation of Applying Large Language Models to Spoken Language Learning
## 5 Conclusions and Future Work  
To evaluate the efficacy of LLMs in the realm of education, this study first introduced a dataset on spoken language intelligence, which can serve as a substantial benchmark for speech and language learning scenarios and provide valuable supplementation to existing benchmarks. Moreover, we explored zero-shot, few-shot, direct, and CoT prompts for answering phonology-related questions. These models all possessed strong conceptual knowledge and achieved high accuracy in simple zero- and few-shot learning scenarios. In practical question reasoning, the prompting methods attained notable performance improvements in comparison with the zero-shot baseline (GPT-3.5: 49.1% \(\rightarrow\) 64.4%; LLaMA2-70B-Chat: 42.2% \(\rightarrow\) 48.6%), and the strongest GPT-4 achieved 77.4% accuracy. The experimental results also demonstrated that in-domain example sampling techniques can consistently improve performance on domain-specific data. These performance results highlight the impressive SLI exhibited by LLMs. In addition, LLMs hold considerable promise for improving conversational spoken language learning. The findings of this study can shed light on the application of LLMs to spoken language learning and provide suggestions for other studies, e.g., in terms of model selection, prompting method, parameter setting, etc.  
The present study only investigated the SLI of LLMs from the textual perspective. However, in future studies, we expect to place greater emphasis on the acoustic perspective (i.e., speech modality). For example, when directly presented with a speech segment from a conversation, it is important for LLMs to accurately identify which vowel is being spoken at any given moment, along with associated parameters such as the sampling rate, information about the correct pronunciation of specific phonemes (e.g., duration, pitch, and formants), or other relevant downstream information. To differentiate from ASR, prompts like _\"Is this a high or low vowel?\"_ or _\"Which phoneme's pronunciation should be improved and how?\"_ could be utilized. Furthermore, in language learning settings, it is important to consider performance in multilingual scenarios. For example, Chinese English learners may prefer feedback presented in their mother tongue rather than English. Therefore, in future studies, it is worth paying attention to such factors and catering to the needs of learners to ensure effective language acquisition.  
Methodology, Y.G. and L.P.; writing--original draft preparation, Y.G., L.P. and B.N.; writing--review and editing, Y.G., L.P., B.N. and Y.L.; resources, B.N. and Y.L.; supervision, Y.G. and L.P.; project administration, Y.L.; funding acquisition, Y.G. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Project of the National Language Commission (Grant Number ZDI145-81), the Fundamental Research Funds for the Central Universities (Grant Number 2023 RC13), and the National Natural Science Foundation of China (NSFC) (Grant Number 62271083).
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
###### Abstract  
The gas-solid two-phase flow model is constructed based on the Euler-Lagrangian framework. The \(SST\)\(k-\omega\) two-equation turbulence model and the soft ball model are coupled by computational fluid dynamics (CFD) and a discrete element model (DEM). Brownout is then simulated by the above method with sliding mesh. As the calculation examples show, the simulations and experiments of the Lynx rotor and the Caradonna-Tung rotor are compared. The coupling method is verified through calculation of the rotor lift coefficient, blade section pressure coefficient and tip vortex shedding position. The results show that when the helicopter is hovering at a height of 0.52R from the ground, it will cause brownout and the pilot's vision will be obscured by sand. When the hovering height is 1R, the phenomenon of brownout is not serious. The movement speed of most sand dust is about 12 m/s, and the height of the sand dust from the ground will gradually increase over time. Large particles of sand are more difficult to be entrained into the air than the small particles, and the particles with a radius of 50 um are basically accumulated on the ground. Moreover, the slotted-Tip rotor has an effect on restraining brownout.  
helicopter; brownout; computational fluid dynamics; discrete element; gas-solid two-phase flow +
Footnote †: journal: Applied
## 3 System Optimization Model
### Redundancy Degree of Nodes
Then, for total \(t\) categories of node sets, the probability of point \(X\) being covered by a neighbor node with other categories in the range of \(N_{i}\) can be given as:  
\[\widetilde{\Pr}_{i}(\psi(X))=1-\prod_{1\leq k\leq l}\left(1-\Pr_{i,k}(x) \right)^{n_{k}(i)} \tag{23}\]  
Thus, when \(N_{i}\) is covered by at least one neighbor node, its redundancy degree can be calculated by:  
\[\iint\limits_{\rho(i,R_{i})}\widetilde{\Pr}_{i}(\psi(X)))d\rho \tag{24}\]  
According to the definition of node redundancy, the expected value of the redundancy of the node \(N_{i}\) is the ratio of the area covered by other nodes to its sensing area. Therefore, the expected value of the redundancy is:  
\[ \begin{array}{ll}E[\phi_{i}]&=\dfrac{1}{\rho(N_{i},R_{i})}\iint\limits_{\rho (i,R_{i})}\widetilde{\Pr}_{i}(\psi(X)))d\rho\\ &=\dfrac{2\pi}{\pi R_{j}^{2}}\int_{0}^{R_{i}}\widetilde{\Pr}_{i}(\psi(X)))xdx \end{array}  \tag{25}\]  
Therefore, the node's redundancy degree can be obtained as:  
\[ \begin{array}{ll}E[\phi_{i}]&=\dfrac{2}{R_{i}^{2}}\int_{0}^{R_{i}}\left(1- \prod_{1\leq k\leq l}\left(1-Pr_{t_{i}k}(x)\right)^{n_{k}(i)}\right)xdx\\ &=1-\dfrac{2}{R_{i}^{2}}\int_{0}^{R_{i}}\left(\prod_{1\leq k\leq l}\left(1-Pr_ {t_{i}k}(x)\right)^{n_{k}(i)}\right)xdx\end{array}  \tag{26}\]
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)
### Results for DNI  
Table 7 gives the values of the performance metrics computed on the test data set (RMSE, MAE, and MBE are given in Wh.m\({}^{-2}\)) for DNI.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Metric** & **Model** & **h+1** & **h+2** & **h+3** & **h+4** & **h+5** & **h+6** \\ \hline \multirow{2}{*}{RMSE} & SP & **97.72** & 132.41 & 157.08 & 176.48 & 193.06 & 202.66 \\  & ANN & 101.79 & **126.65** & **141.95** & **150.28** & **154.82** & **157.27** \\ \hline \multirow{2}{*}{nRMSE} & SP & **21.67\%** & 29.36\% & 34.82\% & 39.12\% & 42.79\% & 44.91\% \\  & ANN & 22.57\% & **28.08\%** & **31.47\%** & **33.31\%** & **34.31\%** & **34.85\%** \\ \hline \multirow{2}{*}{MAE} & SP & **56.97** & **80.82** & **98.70** & 112.83 & 124.64 & 130.85 \\  & ANN & 72.87 & 90.97 & 106.83 & **112.60** & **117.56** & **118.59** \\ \hline \multirow{2}{*}{nMAE} & SP & **12.63\%** & **17.92\%** & **21.88\%** & 25.01\% & 27.62\% & 28.99\% \\  & ANN & 16.16\% & 20.17\% & 23.68\% & **24.96\%** & **26.05\%** & **26.28\%** \\ \hline \multirow{2}{*}{MBE} & SP & 4.62 & 3.79 & 0.33 & \(-\)4.63 & 9.42 & \(-\)12.9 \\  & ANN & \(-\)2.39 & \(-\)5.67 & 7.12 & 10.14 & 12.32 & 14.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics (in Wh/m\({}^{2}\) for RMSE, MAE, and MBE) for GHI (in bold the best predictor for each horizon and each error metric) (Odeillo site).  
Figure 10: Comparison of forecasting models for various horizons for hourly GHI (Odeillo site); (**a**) in term of nRMSE; (**b**) in term of RMSE.  
The results in terms of RMSE and nRMSE are presented in Figure 11 for BNI.  
The DNI forecasting is more difficult and the models' performances were less satisfying than with GHI particularly because DNI was more sensitive to meteorological conditions and because its variation was more rapid and of a greater magnitude than for GHI.  
Some differences are noted in terms of ranking according to the metric used (nRMSE or nMAE), the nRMSE gives more importance to large gaps between predicted and measured data and generally the forecasting models were better compared in term of nRMSE than nMAE.
## 3 Materials and Methods
### User Evaluation
#### 3.2.2 Questionnaires  
In our evaluation of the AR system, we aimed to assess the usability and immersive experience offered by two interfaces: touchscreen interaction and natural interaction. To achieve this, we utilized the System Usability Scale (SUS) [28] and the User Engagement Scale (UES) [29], administering these instruments to 28 participants to gather feedback on both AR interfaces.  
The SUS, a reliable tool for assessing the usability of various systems, consists of ten items that participants respond to on a five-point Likert scale ranging from'strongly disagree (1)' to'strongly agree (5)'. This scale enabled us to capture participants' perceptions of the usability of each interface, providing insights into how intuitive and user-friendly they found the touchscreen and natural interaction modalities.  
To complement the SUS and gain a deeper understanding of the engagement and immersion facilitated by each interface, we introduced UES. The UES was utilized to measure the participants' experience regarding the level of engagement on each AR interface. We used a version of the questionnaire with 30 questions, proposed by [PERSON] et al. [29], to be answered by the participants with five scales from'strongly disagree (1)' to'strongly agree (5)'. We modified the words in the questions to fit into our context. Through the questions, user engagement was measured in the following four dimensions: \"focused attention\" (FA), \"perceived usability\" (PU), and \"reward factor\" (RW). We randomized the order of questions and hid the dimension label to avoid potential biases that might influence participants' responses [29].  
Both questionnaires were made available in English and Chinese, ensuring accessibility and minimizing potential misunderstandings due to language barriers.  
In addition to these structured questionnaires, qualitative feedback was collected during the evaluation sessions. Participants' verbal comments were meticulously recorded and subsequently coded to distill valuable insights into their experiences and perceptions of the AR interfaces. These qualitative findings, alongside the quantitative data from the SUS and UES, equipped us with a holistic understanding of the user experience across both interface modalities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Number of** \\ **Participants (\(n=28\))** \\ \end{tabular} } & \multicolumn{1}{c}{**Percentage (\%)**} \\ \hline \multirow{3}{*}{Gender} & Male & 12 & 42.86\% \\  & Female & 16 & 57.14\% \\  & 18 & 4 & 14.29\% \\  & 19 & 8 & 28.57\% \\  & 20 & 6 & 21.43\% \\ Age & 21 & 2 & 7.14\% \\  & 22 & 4 & 14.29\% \\  & 23 & 3 & 10.71\% \\  & 24 & 1 & 3.57\% \\ Prior knowledge on AR & Yes & 26 & 92.86\% \\  & No & 2 & 7.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of 28 participants.
## 3 Physical Layer
### Reconfigurability  
As shown in Figure 1, spectrum-sensing data are passed upwards to establish environmental awareness [32]. The upper layer processes (to be detailed in later sections) analyze the data obtained at PHY, formulates spectrum decision and adapts PHY parameters on the fly (red arrow 1 in Figure 1). This reconfigurability serves as one of the fundamental infrastructures for adaptive communications because adaptations must have a means to be reflected through hardware behaviors in order to be meaningful [35, 36].  
Conventional radio systems were built of hardware with fixed functions, which was a necessity in an era of limited hardware capability. But advances in processor manufacturing makes reconfigurable radios possible [37], allowing for relatively easy customization as programming micro-controllers are widely understood with a large number of tools available [37]. For example, most modern wireless sensors have their communication routines implemented in program code which can be modified at users' discretion. Moreover, GNU radio, an open-source toolkit, allows users to customize the signal processing, including demodulation, encoding, and filtering. However, its targeting hardware is considered too complex and expensive for typical _IoT_ applications [38].  
The most pertinent PHY parameters in this paper are operating frequency and signal strength, which are important factors in adapting to spectrum utilization and radio interference circumstances in the context of _IoT_. Accordingly, our system model illustrated in Figure 1 reflects adaptive radios with tunable operating channel and transmission powers.
## 3 Results and Discussion
### The Seepage Anisotropy of Slurry-Bled Water  
Section 3.1 shows the seepage process of slurry-bled water along the strike direction and the vertical direction. To determine the difference between the vertical and horizontal saturation velocities of slurry-bled water seepage in the overburden, the saturation curves of three measurement points, #1, #2, and #10 (as shown in Figure 3), were selected for analysis. The grouting hole is located above measurement point #1, and the slurry-bled water seeped from measurement points #1 to #10 is located along the horizontal direction and from measurement points #1 to #2 along the vertical direction. The ratio of the distance between measurement points #1 and #10 to the time difference in which saturation between measurement points #1 and #10 begins to increase is used to represent the horizontal seepage velocity. The ratio of the distance between measurement points #1 and #2 to the time difference in which the saturation between measurement points #1 and #2 begins to increase is used to represent the vertical seepage velocity.  
Under the conditions of initial saturation \(S_{\mathrm{r}}=29\%\), \(51\%\), and \(73\%\), the relationship between the distance and time of seepage in horizontal and vertical directions is shown in Figure 6. The slope of the fitted curve represents the seepage velocity. During the grouting process, the seepage velocity of the slurry-bled water in the overburden is obviously different along the vertical direction and the horizontal direction. The seepage velocity in the horizontal direction is much higher than that in the vertical direction, and the experimental results of the three initial saturation conditions are similar. The horizontal seepage velocity is about 10 times the vertical seepage velocity, namely, \(K_{\mathrm{h}}\approx 10\)\(K_{\mathrm{v}}\).