esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 2 Related Work
### LSTM Networks  
The student learning process is a vital consideration for predicting student achievement. For sequential data such as a learning process, temporal algorithms, including traditional algorithms and deep learning-based algorithms, have been widely used. Among these algorithms, recurrent neural network (RNN) [24] or LSTM [25] networks have outperformed other traditional algorithms.  
A RNN is a class of artificial neural network, where connections between nodes form a directed graph along a temporal sequence. This allows the network to exhibit a temporally dynamic behavior. [PERSON] [24] tried to generalize back-propagation to recurrent neural networks, resembling the master/slave network of [PERSON] and [PERSON]. However, excessively long input sequences of RNNs will cause gradient explosion or gradient disappearance. LSTM [25; 26] networks are designed to fix the shortcomings of a RNN, where it is difficult to process a long sequence. In 2000, [PERSON] [26] proposed a novel, adaptive forget gate that enables a LSTM cell to learn and reset itself at an appropriate time.  
[PERSON] [18] extracted raw features from activity logs and used a RNN with LSTM cells to predict course dropout in MOOCs. [PERSON] [19] proposed a RNN-LSTM-based prediction model to predict learners' learning status and solve the problem of a high dropout rate. [PERSON] [19] trained a modified auto-encoder combined with a LSTM neural network to select features and predict student performance in MOOCs. The selected features are discriminative for prediction and reduce the overfitting for the low-performing student group. [PERSON] et al. [27] presented a multi-stream, bidirectional RNN for fine-grained action detection. [PERSON] [28] used features such as lecture video watching and forum activities during the study period to predict student dropout.  
These above algorithms can deal with the temporal problems well. However, some critical information is lost because of the long length of their time-series neural networks, and thus they affect the accuracy of the prediction.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Inpainting Performance Based on Frequency Analysis  
In order to provide an in-depth analysis of the inpainting performance, the frequency analysis method [33] was employed to analyse the spectrogram of the speech signals. The test speech signal was selected from the RyanSpeech dataset, with a duration of 4.7 s, and the gaps started from 1.62 s and lasted for 20 ms, 40 ms, 50 ms, 100 ms, 200 ms, 500 ms, and 1000 ms. A five-layer LSTM model was used to restore the missing speech samples.  
As can be seen in Figures 4 and 5, each figure consists of two zones: one on the left and the other on the right. The left side of each figure shows the time-domain waveform, spectrogram, and zoomed-in spectrogram (1.0-3.0 s on the x-axis) from top to bottom. The right side shows the time-domain waveforms and corresponding spectrograms of the zeroed and inpainted speech signals from top to bottom.  
It should be noted that except for the original signal and the original spectrogram in the left zone, all the subfigures show the same zoomed-in range of the x-axis (1.0-3.0 s) in order to facilitate a more intuitive comparison between the original and inpainted signals in the frequency domain.  
As shown in Figures 4 and 5, all the inpainted waveforms in the time domain follow the trend of the original signal throughout the entire gap length with sufficient accuracy. The predicted waveform envelope of the inpainted signal has extremely high accuracy compared to that of the original signal. In terms of the amplitude, there was only minimal loss, which can be considered negligible. In order to decrease the potential discrepancies caused by objective indicators in perceiving the quality of inpainted speech signals, we conducted subjective listening tests on the inpainted speech signals. During the tests, the inpainted signals revealed clear and highly recognisable characteristics, which led to a high value of the average MOS. The inpainted speech clips and the average MOSs are available at [[https://haohan-shi.github.io/](https://haohan-shi.github.io/)]([https://haohan-shi.github.io/](https://haohan-shi.github.io/)) (accessed on 9 February 2024).  
Figure 4: Frequency analysis for gap lengths less than 100 ms. The gaps start at 1.62 s and last for 20 ms, 40 ms, 50 ms, and 100 ms, respectively, corresponding to the first to fourth columns on the right side of the figure. The zoomed–in and gap areas are marked with red dashed lines.  
Figure 5: Frequency analysis for gap lengths greater than 100 ms. The gaps start at 1.62 s and last for 200 ms, 500 ms, and 1000 ms, respectively, corresponding to the first to third columns on the right side of the figure. The zoomed–in and gap areas are marked with red dashed lines.
## 3 Numerical Discussion
### Transfer Path Analysis of Power Flow  
Because the transfer path of the truss structure is complex, to reduce the unwanted vibration of the truss, we first should know the most important transfer path in the truss structure. For example, connecting point P2, there are three beams (A1, A2, and A5, as shown in Figure 6) connected at the same point P2, and each beam has its own three in-plane directions; in fact, there are a total of nine transfer paths of power flow for point P2. According to transfer path analysis (TPA) theory [40], the vibration power flow transfer path analysis of point P2 is shown in Figure 13. The more highlighted in red, the larger the vibration power flow. On the contrary, with more blue highlights, the vibration power flow is smaller.  
From the contribution contour plot, we can easily find which path is the most important and transfer by which line frequency spectrum. In the low-frequency domain (1-100 Hz), this is a line spectrum of 46 Hz for all transfer paths, but A1Z, A5Z, and A5Y are the main transfer paths; these results can also be verified by Figures 10 and 11. In the  
Figure 12: Vibration power flow of P2 in MX direction.  
Figure 13: Vibration power flow transfer path analysis of P2.  
mid-frequency domain, the torsional vibration power flow is the main transfer path as A1 MX and A2 MX.  
There are four beams connected to point P8; each beam also has three kinds of power flow, so there is a total of 12 vibration power flow transfer paths for point P8. The vibration power flow transfer path analysis of point P8 is shown in Figure 14.  
There exist two obvious vibration line spectrums in low-frequency ranges, and A16Z is the highest contributing transfer path. The vibration power flows are basically distributed in the low- and mid-frequency domain. The results of this section can give significant advice for which frequency and which location should be considered for vibration control.
# Experimental Study on Anisotropic Mechanical Characteristics of Shale under Triaxial Loading
## 1 Introduction
[44] measured the anisotropic nature of mode I fracture toughness of Mancos shale by combining semi-circular bend test (SCB) and digital image correlation (DIC), and the experiments were carried out in different notch orientations with respect to bedding. [PERSON] et al. [45] characterized fracture toughness from Nash Point shale in multiple orientations, including angles oblique to the three principal fracture orientations, and found that the fracture toughness of Nash Point shale is mechanically highly anisotropic. [PERSON] et al. [46] employed shale samples with different bedding angles (the angle between loading axis and bedding planes) for cracked chevron notched Brazilian disc test (CCNBD) and Brazilian disc test, to characterize the bedding-plane-induced fracture toughness anisotropy in shale and its relationship with other physical and mechanical properties. [PERSON] et al. [47] carried out a series of fracture experiments on the Longmaxi shale specimens with five bedding orientations after constructing in situ temperatures, and the results revealed that increasing in situ temperature decreases the fracture toughness but increases the fracture toughness anisotropy. [PERSON] et al. [48] utilized real-time ultrasonic detection and post-test CT imaging to reveal anisotropic energy and ultrasonic characteristics of black shale under triaxial deformation, indicating that the strain energy evolution and fracture anisotropy were bedding orientation dependent. In summary, although some scholars have studied the intrinsic anisotropic properties of shale, there is still no clear and unified understanding of the shale's anisotropic mechanical behaviors under triaxial loading. Thus, it is necessary to systematically investigate the anisotropic mechanical properties of shale under triaxial loading, which is meant to provide reference basis for the design of tunnel excavation and support in shale formation. In this work, the composition and microstructure of shale in western Hubei were tested using XRD and SEM, and then a uniaxial AE compression test and triaxial compression test were carried out. Moreover, the anisotropy characteristics of shale's compressive strength, deformation parameter, and failure modes were studied, as well as the anisotropic index of shale's mechanical parameters. Finally, the reason for the mechanical anisotropy of shale under triaxial loading was discussed in detail.
## 2 Genetic Differential for VCP-MSP  
The majority of genetically diagnosed families with MSP are associated with _VCP_ mutations [15], although several other genes are associated with MSP phenotypes that may be indistinguishable from VCP-MSP. Mutations in the genes encoding heterogeneous nuclear riboproteins are, respectively, associated with MSP2 and MSP3 [16], although these are thought to be extremely rare [15], with very few reported cases of either disease. MSP2 is caused by mutations in _HNRNPA2B1_, a gene encoding two ribonucleoproteins by alternative splicing (HNRNPA2 and HNRNPB1), which are required for diverse RNA stabilization, splicing, and translation functions, as well as DNA integrity functions [17]. Most of the phenotypes associated with VCP-MSP have been identified in MSP2 patients (myopathy, FTD, ALS, and PDB) [16]. MSP3 is associated with mutations in _HNRNPA1_, a gene encoding a nuclear riboprotein with diverse RNA functions and telomere maintenance [18]. MSP3 presents with ALS, myopathy, and PDB phenotypes [16], including some atypical phenotypes such as fail arm presentation of ALS [19].  
MSP4 is caused by mutations in _SQSTM1_, a gene encoding the p62/SQSTM1 protein, a selective autophagy receptor with various other roles in the UPS system and apoptosis [20]. MSP4 has been associated with diverse phenotypes including myopathy [21], PDB [22], ALS [23] and FTD [22]. Interestingly, an uncommon polymorphic variant in _TIA1_ appears to modify the phenotype of _SQSTM1_ mutations, shifting the penetrance toward myopathy and manifesting as a late-onset, milder phenotype resembling Welander distal myopathy [24; 25]. TIA1 is an RNA-binding protein critical to the cellular stress response [26].  
Other genes have been associated with phenotypes resembling MSP and should be considered in the differential diagnosis. _MATR3_ encodes Martin 3, a DNA- and RNA-binding protein [27]. _MATR3_ mutations are linked to familial myopathy with ALS [28], as well as sporadic ALS disease [29; 30]. It was also suggested that some of the familial ALS patients may have had associated dementia, though the clinical presentation was not described [28]; the presence of FTD associated with _MATR3_ mutations is at best very rare [31] and there is only a single well-described case in the literature [32]. There are no cases reported of _MATR3_ mutations with PDB. _TIA1_ is associated with Welander distal myopathy [33], caused by a founder mutation present in Scandinavia [34]. More recently, a mutation in _TIA1_ was associated with ALS and FTD [35], but there has been uncertainty with this finding [36; 37]. _OPTN_ encodes Optineurin, an adaptor protein with numerous protein interactions involved in vesicular trafficking [38]. _OPTN_ mutations are another cause of monogenic ALS [39] that appears to have a relationship to other phenotypes resembling MSP, since variation on _OPTN_ is a risk factor for sporadic PDB [40] and has also been connected to cases of FTD [41].
# Surfactant-Modified Construction Waste Bricks for the Removal of Diclofenac from Aqueous Solutions
###### Abstract  
This study aimed to enhance the efficiency of construction waste bricks (PRBPs) in removing diclofenac (DFC) by preparing surfactant-modified waste bricks (CCBPs) as adsorbents. The properties of the adsorbents were analyzed, and the parameters related to the adsorption process were examined. The findings demonstrate that the addition of cetyltrimethyl ammonium bromide (CTAB) significantly improved the ability of the waste bricks to adsorb DCF. The pH values affected the adsorption behavior, with the adsorption decreasing as the pH increased. The adsorption process followed the pseudo-first-order kinetic equation, and the rate of adsorption was fast. The Langmuir model was used to fit the adsorption isotherms. According to the adsorption thermodynamics, the adsorption of DCF onto both adsorbents was exothermic, and it was more favorable at lower temperatures. The adsorption capacity of the CCBPs decreased sharply as the KCl concentration increased. The mechanism of adsorption might be explained by the interplay of the \(\pi\)-\(\pi\) interaction, surface complexation, and electrostatic interaction. This study offers a new method for removing micropollutants from aqueous solutions using waste bricks, thus extending the scope of their reuse applications.  
construction waste bricks; surfactant modified; diclofenac; adsorption; aqueous solutions
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Data-Driven Intent Recognition Methods  
Data-driven methods leverage machine learning and deep learning techniques to learn intent patterns directly from data, eliminating the need for extensive prior knowledge. These methods have gained popularity due to advancements in computational power and data availability. Key approaches are included below.
## 5 Simulation Results
### Linear Sytsem  
Consider the F-16 aircraft continuous-time linear system with external perturbation as [12]  
\[\dot{x}=Ax(t)+Bu(t)+Hv(t) \tag{68}\]  
where  
\[A=\begin{bmatrix}-1.01887&0.90506&-0.00215\\ 0.82225&-1.07741&-0.17555\\ 0&0&-1\end{bmatrix},\,B=[0,0,1]^{\mathsf{T}},\,H=[1,0,1]^{\mathsf{T}}\]  
The three state vectors are denoted as \(x=[x_{1},x_{2},x_{3}]^{\mathsf{T}}\), and the initial state is \(x_{0}=[1,-1,2]^{\mathsf{T}}\). Choose a matrix of the appropriate dimension, where \(Q=I_{3}\) and \(R=I_{1}\). The constrained control can be satisfied with \(u\in\{u\in\mathbb{R}:\|u\|\leq 1\}\). The activation function of single NNs is \(\phi(x)=[x_{1}^{2},x_{2}^{2},x_{3}^{2},x_{1}x_{2},x_{1}x_{3},x_{2}x_{3}]^{ \mathsf{T}}\). Then, the approximated weight vectors are denoted as \(\hat{W}_{c}=[\hat{W}_{c},\hat{W}_{c},\hat{W}_{c_{3}},\hat{W}_{c_{4}},\hat{W}_{c _{5}},\hat{W}_{c_{6}}]\), which is the solution of the algebraic Riccati equation (ARE): \(A^{\mathsf{T}}P+PA-PBR^{-1}B^{\mathsf{T}}P+Q=0\). The NN learning and triggering parameters are needed in this paper: \(\gamma=0.5\), \(L\in(0,1)\), \(T=0.1\), \(\alpha_{c}=0.35\). The external disturbance is selected as \(v(t)=5\cos(t)e^{-t}\).  
Figure 2 shows the convergence of three state vectors. It is obvious that the states tend to be asymptotically stable. The iterative process of critic NN is illustrated in Figure 3. The critic weight vectors are converged to \(\hat{W}_{c}=[-0.0349,-0.0128,-0.1575,\)\(1.4738,\)\(0.6577,\)\(0.2075]^{\mathsf{T}}\) under the PE condition after 60s. Therefore, the PE condition  
Figure 3: The weight trajectories of a single NNs.  
Figure 5: The event error \(\|e_{k}\|^{2}\) and the triggering threshold \(e_{T}\).  
Figure 6: Sample intervals of two strategies. (**a**) The time-triggered control. (**b**) The event-triggered control.
## 4 Dynamic Splitting Test of Sandstone after Corrosion by Strong Alkali Solution
### Dynamic Tensile Strength and Dynamic Peak Strain Variation  
The changes of dynamic tensile strength and dynamic peak strain of sandstone after different times of corrosion by the strong alkali solution are shown in Figure 13a,b, respectively.  
Figure 11: High-speed camera and its supporting instruments and equipment.  
Figure 12: Dynamic tensile crack development process in sandstone under high-speed camera. (**a–g**) Crack extension pattern with time under high-speed camera.  
Figure 13: Changes in dynamic tensile strength and dynamic peak strain of the sandstone after different times of corrosion by the strong alkali solution. (**a**) Dynamic tensile strength. (**b**) Dynamic peak strain.  
Figure 13 shows the variation pattern of the dynamic tensile strength \(\sigma_{t}\) and the dynamic peak strain \(\varepsilon_{t}\) of the sandstone specimens with corrosion time in the splitting test. It can be seen that the dynamic tensile strength of the sandstone specimen gradually decreases with increasing corrosion time, while the dynamic peak strain increases quadratically, polynomial with increasing corrosion time. The whole process can be divided into two stages: at 1 d-14 d, as the OH\({}^{*}\) ions in the strong alkali solution penetrate into the sandstone interior and chemically react with certain mineral components, which leads to the generation of new cracks or the growth of the original cracks, the dense structure of the sandstone gradually becomes relatively loose and fragile, causing a sharp deterioration of the kinetic properties of the specimen, so that the macroscopic dynamic tensile strength of the sandstone specimen decreases more rapidly, from 22.62 MPa to 19.01 MPa, decreasing by 15.95%, while the dynamic peak strain increases more rapidly, increasing by 59.07%. At 14 d-28 d, the strong alkali solution continues to produce a small amount of chemical reaction with some components in the sandstone, and the dynamic tensile strength decreases to 18.50 MPa compared with the previous reaction stage, with a smaller decrease; the dynamic peak strain increases to 2.86, with the same small increase. Throughout the corrosion process, the dynamic tensile strength of the sandstone specimens decreased by 18.23%, and the dynamic peak strain increased by 64.42%. Overall, the increase in the internal damage of the sandstone caused by chemical action resulted in a significant deterioration of its dynamic tensile strength. The fitted relationship for the dynamic peak strain \(\varepsilon_{t}\) of the split sandstone specimens after different times of strong alkali corrosion is shown in Equation (8).  
\[\varepsilon_{t}=-0.0024t^{2}+0.1085t+1.7043\ (R^{2}=0.9950) \tag{8}\]
# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 2 Pilot Descriptions, Data and Processing Methods
### Data Properties  
In this section the properties of the datasets and their pre-processing are described. Pre-processing was necessary to ensure a certain data quality and monitoring fraction.  
Table 1 summarizes the main features and properties of the data. This data consist of time series electrical power, PV production, and household characteristics. For PMC and Zonnedael, the electricity and gas consumption (time series as well as cumulative consumption and production values for PMC) were included. The collected data were registered by smart meters, which were continuously measuring the instantaneous power to average it to energy values over the time resolution (Table 1). The accuracy of all values are the worst case \(\pm\)2.5%, up to 0.5%, and even lower depending on which meter is chosen, as mentioned in the IEA standards on electrical meters [47]. The differences in the energy systems used in pilots are highlighted. The lowest time resolution was 15 min for JEM, so all other time resolutions are harmonized by using the mean values of 15 min. This leads to a significant effect on the load duration curves and peak analysis but has negligible impact on monthly and annual values.  
This analysis is effectuated as a part of CESEPS project [48], and non-disclosure agreements have been signed to access the data (except for Zonnedael). In the EU, the number of starting smart grid pilots and the total investments reached their peaks in 2012, and the number of running projects was the highest in 2013 [11; 13]. Therefore, 2013 has been chosen as the year for which data were available for JEM, R4I, and Zonnedael. Nonetheless, for PMC we could only access data for the year 2012. For R4I, data were collected from November 2013 to October 2014. Furthermore, data were not available for all households. From a data availability diagnosis, we took only the data for which the monitoring fraction was higher than 90%. The analysis presented in this paper is based on 21 households for PMC, 79 households for R4I, 117 households for JEM, 26 for Meulenspie, and 91 for Easy Street.
## 5 Discussion
### Error Setting in the Data Assimilation Algorithm  
In the data assimilation system, the observation-model error is critical for land surface albedo estimation. We calculated observation error from field observations and TM data and calculated model error from field observations and the albedo background. Field observation data are key in terms of error determination. For each site, we used the field observation data to generate errors resulting in high estimation accuracy. When extending the model to regional use where no field observation data was available, we attempted to generate a \"common\" error by averaging the error of the 18 ground stations and inputting it to the assimilation scheme. The test results on the AU-DaP site are shown in Figure 10. Errors caused by TM data are relatively small and uniform, and using the mean errors instead of the original observation error did not affect the estimation results significantly. In effect, the common error from different sites represents the averaged field condition and can be used to account for large areas.  
Figure 9: Directly estimated TM albedos and field albedos including all available TM images used in this study.
## 4 Experiments and Analysis
### Experimental Conclusion  
After comprehensive experimental validation, the emotion evaluation method proposed in this paper has demonstrated satisfactory results in evaluating the emotion performance in students' spoken English, thanks to the good performance of the LLM in emotional speech generation, transformer-based emotion encoding networks in emotion feature representation, and emotion evaluation networks in emotion score regression, respectively.  
This method is not only versatile but also practical, which provides advanced and effective means for English spoken language learning and emotion evaluation and is expected to provide strong tool support in the field of spoken language education, offering high practical value.