esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Lidar Theory  
The vertical profiling wind lidars that were used in this study, measure wind speed up to 300 m. In profiling lidars, a laser beam is emitted at a fixed elevation angle \(\phi\) and at several azimuth angles \(\alpha\) (see Figure 3), with the number of azimuth angles varying based on the type of lidar.  
The WindCube V1 emits a sequence of pulsed lasers beams in four azimuth angles (\(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\)) in a Doppler-beam swinging (DBS) scanning pattern. This system simultaneously measures wind at several predetermined heights. The ZephIR 300 lidar, also referred to as ZX300, performs a velocity azimuth display (VAD) scanning pattern. Here, the lidar transmits a continuous wave (CW) laser beam at 50 different azimuth angles at a fixed height. The beam intercepts the wind at different angles as it moves, building up a series of measurements around a cone that can be used to retrieve the wind velocity vector. After completing one VAD scan, the laser is refocused on the next height. Both lidars have a cone half-opening angle or zenith angle \(\theta\) approximately \(30^{\circ}\).  
The LOS velocity, \(v_{r}\), observed by the lidar for a specific \(\theta\), and \(\alpha\), is linked to the wind velocity vector \(\vec{u}=(u,v,w)\) by  
\[\vec{v}_{r}=u\,\cos\alpha\,\sin\theta\,+\,v\,\sin\alpha\,\sin\theta\,+w\,\cos\theta, \tag{1}\]  
as explained in [32]. At least three independent LOS velocities are required to reconstruct one wind vector using Equation (1). The pulsed lidar WindCube V1 emits beams at four azimuth angles. In one cycle of the DBS scan, the following radial velocities are observed:  
\[\begin{pmatrix}v_{r}(\alpha=0^{\circ})\\ v_{r}(\alpha=90^{\circ})\\ v_{r}(\alpha=180^{\circ})\\ v_{r}(\alpha=270^{\circ})\end{pmatrix}=\begin{pmatrix}\sin\theta&0&\cos\theta \\ 0&\sin\theta&\cos\theta\\ -\sin\theta&0&\cos\theta\\ 0&-\sin\theta&\cos\theta\\ \end{pmatrix}\begin{pmatrix}u\\ v\\ w\end{pmatrix}, \tag{2}\]  
Figure 2: Grimstad experiment location. This experiment took place at the campus of University of Adger South-West of Norway.  
which can also be written as  
\[\vec{v}_{r}=\mathbf{N}\vec{u}. \tag{3}\]  
This system of equations can be solved analytically when only three radial velocity measurements are considered. In cases where the system is over-determined, the least-squares fitting method can be used.  
For ZephIR 300, at each VAD scan, horizontal wind speed \(u_{hor}\), vertical speed \(w\), and direction \(\Theta\) are extracted from  
\[v_{r}=|A\cos(\alpha-B)+C|, \tag{4}\]  
where \(A=u_{hor}\sin\theta\), \(B=\Theta\) or \(B=\Theta\pm 180^{\circ}\), and \(C=w\cos\theta\). The solution for B reflects the ambiguity issue in continuous-wave wind lidars, where the system cannot distinguish wind direction. We add or subtract \(180^{\circ}\) to ensure the solution remains within the range of 0-360\({}^{\circ}\). Equation (4) can be solved by finding the best fit using the least squares approach [18; 33].
## 3 Results and Discussion
### Error Comparison by Using Different Types of Water Vapor Data  
Though both relative humidity and specific humidity characterize the water vapor, previous analyses are based on specific humidity. The relative humidity is defined as the ratio of water vapor pressure to saturated water vapor pressure, which is also affected by temperature changes. In this session, both water vapor variables are used to calculate the AMVs' error. Figure 12a,b represent the wind-speed RE at different heights and time intervals tracked using relative humidity and specific humidity, respectively. It is found that the error of using specific humidity is significantly smaller than that of using relative humidity at nearly all heights. In particular, the error of upper and lower layers and the sensitivity of the error to the time resolution are reduced significantly by using specific humidity. This could be attributed to the continuous heat transfer among the upper and lower layers in severe convective areas, resulting in large temperature fluctuations. Therefore, the specific humidity can more accurately describe the water vapor change and movement, comparing with relative humidity, especially under convective areas.  
Figure 11: The wind speed AE as a function of water vapor content (**a**), water vapor gradient (**b**), wind speed (**c**), and wind–moisture gradient angle (**d**) at 400 hPa.
###### Abstract  
We compared the efficiency droop of InGaN multiple-quantum-well (MQW) blue light-emitting diode (LED) structures grown on silicon(111) and \(c\)-plane sapphire substrates and analyzed the efficiency droop characteristics using the rate equation model with reduced effective active volume. The efficiency droop of the LED sample on silicon was observed to be reduced considerably compared with that of the identical LED sample on sapphire substrates. When the measured external quantum efficiency was fitted with the rate equation model, the effective active volume of the MQW on silicon was found to be \(\sim\)1.45 times larger than that of the MQW on sapphire. The lower efficiency droop in the LED on silicon could be attributed to its larger effective active volume compared with the LED on sapphire. The simulation results showed that the effective active volume decreased as the internal electric fields increased, as a result of the reduced overlap of the electron and hole distribution inside the quantum well and the inhomogeneous carrier distribution in the MQWs. The difference in the internal electric field of the MQW between the LED on silicon and sapphire could be a major reason for the difference in the effective active volume, and consequently, the efficiency droop.  
GaN; light-emitting diode; efficiency droop; quantum well; rate equation +
Footnote †: journal: Applied Physics Letters
## 2 The Continuous Damage Mechanics Model
### Load Block Method  
Typically, the lifespan of bridge cables made of high-strength steel wires is relatively long, and the cumulative damage of the wires under load cycles is relatively low. It is both time-consuming and unnecessary to calculate the damage for each load cycle. Therefore, it is common to establish a cyclic load region to accelerate the algorithmic process for simulating corrosion fatigue damage [15; 16]. Specifically, assuming the material's damage rate \(\delta D/\delta N\) remains constant over \(\Delta N\) cycles, these \(\Delta N\) cycles are treated as a cyclic load block, as illustrated in Figure 1. Consequently, the damage increment for each load block can be calculated using the following formula:  
\[\Delta D=\left(\frac{\delta D}{\delta N}\right)_{i}\times\Delta N \tag{7}\]  
Considering that the accuracy of the cyclic load block directly influences the precision of the results, seven sets of scenarios with cyclic load region accuracies ranging from 0.3 years to 0.9 years were calculated. It was observed that the average calculated result was approximately 68 years, with a difference of less than 3% compared to the result obtained with the minimum accuracy (as shown in Table 1). Therefore, taking into account therequirements for both computational efficiency and accuracy, a cyclic load block accuracy of 0.5 years was selected for this study.  
After calculating \(\Delta D_{e}\) and \(\Delta D_{p}\) for each cycle based on Equations (3) and (6), the damage variable \(D_{t}\) after that cycle can be determined using Equations (8) and (9). Using this data, the material parameters for the steel wire are updated to simulate the degradation of material performance, achieving the effect of material deterioration.  
\[\Delta D_{t}^{i+1}=Max\Big{\{}\Delta D_{e}^{i+1},\Delta D_{p}^{i+1}\Big{\}} \tag{8}\]  
\[D_{t}^{i+1}=D_{t}^{i}+\Delta D_{t}^{i+1} \tag{9}\]  
where \(\Delta D_{t}^{i}\) represents the damage increment obtained from the calculation of the \(i\)-th cyclic load block.
# Melting of Paraffin Waves Embedded in a Porous Matrix Made by Additive Manufacturing
## 5 Experimental Results
### Comparison against Metal Foams  
A comparison between the results of the modified BCC structure and metal foams is reported here. Two metal foams were chosen for the comparison: the first one is a Duocel\({}^{\circledR}\) aluminum foam with five pores per linear inch and a porosity of 92%, whose data will later be referred to as Al-5-8.0 (data from [PERSON] and [PERSON] [24]). The second Duocel\({}^{\circledR}\) foam is made of copper with ten pores per linear inch and a porosity of 93.3%, whose data will later be referred to as Cu-10-6.7 (data from [PERSON] and [PERSON] [28]). No data were available for a copper foam with five pores per linear inch, which correspond to a pore size of about 5 mm, equal to the edge of the base cell of the modified BCC structure here proposed. However, the number of pores per inch does not affect the thermal response of structures having the same porosity, as highlighted by [PERSON] et al. [10] and by [PERSON] and [PERSON] [24].  
Figure 9a reports the temperature of the heated side at the end of the melting process for the three structures as a function of the heat flux for the paraffin with a characteristic melting temperature of 42 \({}^{\circ}\)C. The worst thermal performance is shown by the aluminum foam. The modified BCC structure, even if it has the lowest parent thermal conductivity  
Figure 8: Temperature difference profiles at 10 kW\({}^{-2}\) for different paraffins.  
(175 W m\({}^{-1}\) K\({}^{-1}\) for the modified BCC structure vs. 390 W m\({}^{-1}\) K\({}^{-1}\) for the copper foam and 220 W m\({}^{-1}\) K\({}^{-1}\) for the aluminum foam), shows the best thermal performance, i.e., the lowest temperatures at the end of the melting process. This can be mainly attributed to its lower porosity compared to the other two structures, coupled with the particular structure of the BCC matrix, which augments the tortuosity compared to metal foams, globally enhancing the effective thermal conductivity of the PCM/porous matrix block. As a result, the modified BCC structure outperforms the proposed structures.  
The lower porosity means a lower amount of phase change material embedded inside. Figure 9b shows the time needed to completely melt the paraffin embedded within the three different structures. All the initial temperatures do not differ more than 3 K. As expected, the modified BCC structure needs a shorter time to completely melt the paraffin. However, it is interesting to highlight that, even if embeds a lower amount of PCM, the modified BCC structure outperforms the other two tested structures. This does not mean that the endless lowering of the porosity leads to better performance because lowering the porosity also means a lower effect of the latent heat of the PCM: there will be an optimum porosity that balances the effect of the thermal conductivity and of the latent heat of the PCM. Future work will investigate this aspect.
## 2 Experimental Study
### Time-Frequency Analysis
#### 2.4.3 Hilbert Huang Transform (HHT)  
The signal can also be presented in the time-frequency domain using the Hilbert Huang Transform (HHT). The HHT decomposes a signal into intrinsic mode functions (IMFs) along with a trend, and then obtains instantaneous frequency data. Compared to other transforms, HHT is more like an algorithm (an empirical approach) that can be applied to a data set, rather than a theoretical tool. The calculation of the HHT includes two steps: empirical mode decomposition (EMD) and Hilbert transform. By means of EMD, the acceleration data can be decomposed into different simple non-sinusoidal signals, called IMFs [19]. Each IMF is transformed into a time-frequency domain by Hilbert transform, and then the time-frequency spectrum can be obtained after the superposition.  
Figure 12 shows the time-frequency spectra of the signals analysed by HHT. The original signals were separated into several IMFs, and the first step of IMF was in the range of 600-1200 Hz. According to the previous analysis, the study would focus on the first-step IMF (600-1200 Hz). For condition A, the vibration energy peak is at 700 Hz and 5 ms, and the energy attenuates significantly after 5 ms, almost approaching zero at 10 ms. For condition B, there are two energy peaks, of which one appears at 1100 Hz and 3.5 ms, while the other one is at 700 Hz and 6 ms. The energy remains high until 10 ms.  
Figure 11: Time–frequency spectra by wavelet transform.  
Figure 12: The time–frequency spectrum.  
In summary, when the void exists, another vibration energy peak of 1100 Hz appears at 3.5 ms that is indicative of the reflection of the stress waves at the tunnel-air interface. This phenomenon also explains that the stress waves take longer time to propagate to the soil, which causes the energy peak of 700 Hz to appear 1 ms later in condition B. In addition, the vibration lasts longer when a void exists.  
By means of HHT, a signal was separated into several sub-signals dominant in different frequency ranges. As the key frequency band for the sub-signals could be studied individually, the stress wave propagation and the frequency component distribution could be seen clearly, however, it is less holistic because of the separation of the original signal.  
The STFT and wavelet transform analysis to focus on the overall vibration energy in a time-frequency plane that could be observed clearly. Similarly, the energy flow between different conditions could also be studied clearly. In principle, unlike the STFT, the wavelet transform can change the scale while analysing different frequencies of the signal. As a result, both high-time and high-frequency resolution can be achieved using wavelet a transform. Based on these aspects, the wavelet analysis method would be chosen for the following numerical study to analyse the signals in time-frequency domains.
## 4 Results and Discussion
### Effect of Cowl Lip Angle on Starting Performance of Inlets with Various ICRs  
The inlet is a contraction duct comprising a compression wall and a cowl wall. Cowl lip angles affect the starting performance by changing the cowl lip shock intensity and the contraction law of the duct. However, the effect laws of the cowl lip angles vary widely for the inlets with different ICR. In this section, the influences of cowl lip angle on starting performance are investigated for the inlets with ICR ranges from 1.8 to 2.2. Herein, the inlet ICR can be controlled by adjusting the external/internal compression walls, and the design method is described in detail in [7]. Figure 14 illustrates the effects of the cowl lip angle on starting performance of inlets with various ICRs. Each point in the figure represents the starting Mach number of the inlet at a specific ICR and cowl lip angle, with the inlets on the same line having the same ICR. As can be seen, ICR has no obvious effect on the starting ability for the inlets with small lip angles. However, for the inlets with large lip angles, the starting performance is greatly determined by ICR, exhibiting a trend of deteriorating starting performance with increasing ICR. For example, when the cowl lip angle is 0\({}^{\circ}\), the starting Mach numbers of inlets with ICR = 1.8 and 2.2, respectively, are 6.5 and 6.4. However, when the cowl lip angle reaches 12\({}^{\circ}\), the Mach numbers of inlets with ICR = 1.8 and 2.2 are 4.6 and 6.8, respectively, which is a large difference. In addition, for a given ICR, the inlets with excessively large or small lip angles exhibit poor starting performance. In other words, there is an optimal cowl lip angle to achieve the best starting ability among the researched ICR range. Figure 15 shows the distribution of the optimal cowl lip angle and the corresponding Ma\({}_{8}\) for the inlets with different ICRs. With an increase in ICR, the optimal cowl lip angle decreases gradually, and the starting performance of the inlet deteriorates. For example, for the inlet with ICR = 1.8, the cowl lip angle corresponding to the low start Mach number is 10\({}^{\circ}\), and the inlet enters the starting state at Ma = 4.26. However, for the inlet with ICR = 2.2, the optimal cowl lip angle is 6\({}^{\circ}\), and the corresponding start Mach number is 5.51.  
Figure 14: Effects of the cowl lip angle on starting performance of inlets with various ICRs.  
Figure 13: Wall pressure distributions during the starting process of the inlet with \(\delta\) = 12\({}^{\circ}\).
## 4 Influence of MICP on Properties of Unsaturated Soil
### Static Mechanical Behavior of MICP Unsaturated Soil  
In the existing research, many scholars have studied the change in soil properties after improvements from various aspects. [PERSON] and [PERSON] [111] investigated the mechanical property of soft soil treated by MICP through unconfined compression tests and found that the strength of MICP soil increased by 3-3.5 times more than that before MICP process. [PERSON] [112] found that the compressive strength of MICP sand was exponential with the amount of CaCO\({}_{3}\). [PERSON] and [PERSON] [113] finally found that the compressive strength increased by 300 kPa and the permeability decreased significantly after the sand was solidified by MICP. [PERSON] et al. [114] strengthened the sand column by biological grouting. Its unconfined compressive strength reached 1.91 MPa, and the permeability coefficient decreased by three orders of magnitude. [PERSON] et al. [115] added magnesium to optimize the precipitation rate and generation content of CaCO\({}_{3}\) precipitation, and the test results showed that the maximum uniaxial compressive strength obtained from the treated sample was 0.6 MPa. [PERSON] et al. [116] found that the improvement of hardness and expansibility of remolded biological treated sand was caused by the particle roughness of CaCO\({}_{3}\) coating. Compared with untreated sand, the shear strength was improved. [PERSON] et al. [117] concluded that the shear strength of the samples after MICP treatment increased by 44-86%, since the CaCO\({}_{3}\) coating improves the compactness and internal friction angle of the samples.  
[PERSON] et al. [118] demonstrated that the shear strength and unconfined compressive strength of expansive soil had been significantly improved after improvement by the MICP method; the plastic limit water content of expansive soil had increased; the liquid limit water content, plastic index, and expansion rate had decreased; and the microstructure  
Figure 8: Comparison of SWCCs between plain soil and MICP improved soil with compactness of (a) 90% and (b) 95%.  
had changed significantly. Therefore, MICP technology is an effective technical means to improve the compressive strength, shear strength, and free expansion rate of unsaturated soil through laboratory tests. Figure 9a is the change in shear stress with the normal stress of soil samples before and after MICP treatment. It shows that the shear strength of expansive soil after MICP treatment has been significantly improved, and the increase in cohesive force of expansive soil samples before and after treatment is far greater than the increase in the internal friction angle. Figure 9b presents the change in shear strength with the curing days of MICP. It can be seen that MICP technology will significantly improve the shear strength of soft soil, and its shear strength will gradually increase with the increase in curing days (3d-21d).
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
## 5 Experiments
### Question-Concept Matrix  
We visualized the change in concepts for a particular student (as shown in Figure 3). The figure is divided into three parts.  
The top black-and-white matrix represents the coverage of concepts in the questions during interactions. The darker the color, the greater the weight of that concept. Most questions involve a single concept, while a few involve two or three. The middle section uses solid or hollow circles to indicate whether the response was correct during the interaction, with solid circles representing correct answers and hollow circles representing incorrect ones. The bottom color matrix displays the predicted knowledge state of the student for each concept, with each column representing the state after each interaction. Colors closer to yellow indicate better mastery of that concept as predicted by the model.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Assist09**} & \multicolumn{2}{c}{**Junyi**} \\ \cline{2-5}
**Model** & **ACC** & **AUC** & **ACC** & **AUC** \\ \hline DKT & 71.42 & 73.73 & 69.42 & 70.3 \\ DKVMN & 72.18 & 74.61 & 70.6 & 71.8 \\ \hline PEBG+DKT & 74.7 & 76.46 & 71.7 & 73.86 \\ PEBG+DKVMN & 75.83 & 77.87 & 72.23 & 74.75 \\ BiCo+DKT & 75.41 & 76.2 & 71.89 & 73.44 \\ BiCo+DKVMN & 77.19 & 78.31 & 73.15 & 75.17 \\ \hline PMQE+DKT & 75.95 & 77.18 & 71.67 & 73.73 \\ PMQE+DKVMN & 77.74 & 78.86 & 73.52 & 75.81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of PMQE.  
Figure 3: Knowledge state changes for a student.  
Our observations are as follows.  
For single concept interactions, as seen in the third and fourth columns, both questions pertain to concept \(c_{3}\). The third question was answered correctly, while the fourth was answered incorrectly, suggesting that the corresponding knowledge state should decrease and then increase. This matches the changes shown in the knowledge state matrix.  
Multiple concept interactions, as observed in the tenth column, involve concepts \(c_{5}\) and \(c_{6}\), and the question was answered correctly. Therefore, we predict an increase in knowledge state for \(c_{5}\) and \(c_{6}\), which is consistent with the changes shown in the figure.  
This indicates that our weight matrix can accurately describe the relationship between questions and concepts.
## 3 Results and Discussion
### Compressive Tests on Capsules  
Figure 7 shows the compressive testing results for different A/R ratios of capsules. In general, with the increase of curing temperature, especially from 100 \({}^{\circ}\)C to 160 \({}^{\circ}\)C, a decrease of compressive strength can be found. It can also be seen that higher rejuvenator content results in lower compressive strength on capsules, especially under higher curing temperatures. The compressive testing results indicate that the dehydration of the alginate gel results in degradation of some of the calcium alginate chains and a decrease in the compressive strength of calcium alginate capsules.  
The corresponding compression for the mastic mix is about 1.8 MPa, and the vehicle loading pressure is less than 1 MPa [22; 23]. After curing at 160 \({}^{\circ}\)C, capsules with A/R ratios of 20/80 and  
Figure 6: TGA results of capsules fabricated with varying A/R ratios.  
10/90 show a compressive strength less than 0.4 MPa, which is insufficient to survive the asphalt production process. Meanwhile, for the capsules with a ratio of 30/70, at the curing temperature of 160 \({}^{\circ}\)C, the lowest compressive strength in the curve remains 3.27 MPa. Hence, the capsules fabricated with A/R ratio of 30/70 are expected to show elastic behavior and survive the asphalt mixing process and dynamic vehicle loading during the asphalt pavement service life. Although capsules with other A/R ratios also have applicable compressive strength, they contain less rejuvenator. According to the compressive tests as well as the TGA test results, 30/70 is determined to be the optimum A/R ratio.