esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Magma Sources and Petrogenesis
It is noteworthy that some of these volcanic rocks displayed geochemical characteristics similar to those of MORB, such as low K and Ti, and high Al concentrations and high Zr/Hf ratios with low La/Yb ratios. In the plot of P\({}_{2}\)O\({}_{5}\) versus TiO\({}_{2}\), several sample points entered the deep mantle source region. In addition, our samples did not contain picrite, but picrite was also reported in the lower part of the Beitashan Formation in the southwest of Qinghe County by [PERSON] et al. [82]. Therefore, the Early-Middle Devonian rock assemblages in the Fuyun-Qinghe area also contained picrite. Picrite is the product of high-level melting of garnet peridotite metasomatized by fluid under high-temperature conditions [82]. Therefore, all the above rocks such as picrite, basalt, HMA, and their characteristics suggested that the asthenosphere mantle was another magma source. Adakite and MA were also identified in the volcanic samples. Adakite primarily forms through two mechanisms: partial melting of young oceanic crust (less than 25 million years old) [83], due to its high heat flow units (HFUs, 2.8-8), or the partial melting of the thickened lower crust [84,85]. As previously noted, the study area was characterized by marine facies during the Early-Middle Devonian, eliminating the possibility of a thickened lower crust. Thus, the adakites in this region were likely generated by the subduction of young oceanic crust, which must have been younger than 25 Ma. Meanwhile, MA is thought to result from the interaction between magmas produced by the dehydration and melting of subducted oceanic crust and the overlying mantle wedge [86]. The discussions on fractional crystallization proved that the CA series rocks had different magma sources, and the \(\varepsilon\)Hf(t) and TDM\({}_{2}\) of zircon varied widely, ranging from +4.79 to +18.26 and 191 to 1060 Ma, respectively, which also suggests the existence of different magma sources. In summary, we believe that there were at least three magma sources for the volcanic rocks in the Fuyun-Qinghe area: young and hot subducted oceanic crust, overlying mantle wedge, and asthenosphere mantle.
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 3 Experiments and Results
### Real Data Illustration
#### 3.3.1 Real Datasets
The network architecture of our method, Dual Q-Net, relies on a Q-matrix \(\mathbf{Q}\) and a skill interactive Q-matrix \(\mathbf{Q^{\prime}}\). In the experiments with real datasets, we constructed corresponding skill interactive Q-matrices for the EPTT and FRAC datasets based on suggestions from mathematical experts, as shown in Equations (21) and (22). \[\mathbf{Q^{\prime}}_{e}^{\top}= \begin{pmatrix}1&0&0&0&0\\ 0&1&0&1&1\\ 1&1&1&0&1\\ 0&0&1&1&1\end{pmatrix}  \tag{21}\]  
\(\mathbf{Q^{\prime}}_{e}^{\top}\) denotes the skill interaction matrix of the dataset EPTT, which contains five skill interaction patterns. For example, the first column, \(\mathbf{q^{\prime}}_{e1}=(1,0,1,0)\), means there are mutual influence relationships between skill 1 (i.e., cp) and skill 3 (i.e., pb). \[\mathbf{Q^{\prime}}_{s}^{\top}= \begin{pmatrix}1&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\ 1&1&1&1&1&1&1&1&0&1&1&0&0&0&1\\ 0&0&1&1&0&0&0&1&1&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&1&1&0&0&0&0&0&0&1&1\\ 0&0&0&0&1&1&1&1&1&1&1&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&0&0&0&1&0&1&1&0&0\\ 0&1&0&1&0&1&0&1&0&1&1&0&1&1&1&1\\ 0&0&0&0&0&0&0&0&0&0&0&1&1&0&0&0&0\end{pmatrix}  \tag{22}\]  
The same as \(\mathbf{Q^{\prime}}_{e}^{\top}\), \(\mathbf{Q^{\prime}}_{s}^{\top}\) denotes the skill interaction matrix of the dataset FRAC, which contains 17 skill interaction patterns.For example, the first column, \(\mathbf{q^{\prime}}_{s1}=(1,1,0,0,0,0,0,0)\) indicates that there are mutual influence relationships between skill 1 (i.e., \(a_{1}\)) and skill 2 (i.e., \(a_{2}\)). In this case, the neural network parameter initialization was completed by generating simulated data from the benchmark model. The hyperparameter learning rates, batch size, and epochs for the trained models were 0.004, 64, and 100 on the EPTT datasets. However, the hyperparameters were adjusted to 0.001, 64, and 100 for the FRAC datasets. To mimic the simulation study design and emphasize the benefit of using our method, subsets were created from each real dataset using a stratified sampling strategy with sample sizes of \(N\) equaling 100, 200, and 300.
# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 6 Experiments and Results
### Datasets  
In this paper, two realistic datasets that are widely used in user identity linking tasks are selected for the experiments, and information for the datasets is shown in Table 1.  
* Douban Online-Offline: The data are published by COSNET [36]. First, a target network containing 1118 users is extracted from the offline network, and then a subnetwork containing these users is extracted from the online network as the source network, which has 3906 user nodes. There are 1118 anchor users between the two networks.
* Facebook-Twitter: The data are published by FINAL [7] and were collected from two completely different social networks, including 1792 Facebook users and 3493 Twitter users, including 1515 anchor users. Their subscription relationships in the two social networks were quite different.
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 4 Results and Discussion
### Discussion
#### 4.3.2 Human-Machine Collaboration Reduces the Burden While Respecting Driving Preferences  
In interviews, participants expressed concerns about the reliability of ADS technology applied to wheelchairs, while also hoping for machine assistance based on the understanding of the autonomous wheelchair's intentions. We established a human-machine collaboration relationship through the HITL approach, not relying entirely on the autonomous system to complete tasks, but rather using the system to assist humans in monitoring and ensuring driving safety, capitalizing on human strengths in precise environmental perception, adaptive decision-making, and complex pattern recognition. We also found that participants of different ages responded differently to the driving experience. Experienced young wheelchair participants prefer their driving skills over autonomous tech, while older participants lean towards trusting the technology. The collaboration allows the autonomous system to undertake basic tasks, significantly reducing the operational burden on users, especially beneficial for vulnerable groups like those with severe paralysis, limited hand mobility, or the elderly. Meanwhile, users can monitor the wheelchair's statuses and intentions projected on the ground, adjusting based on system alerts or personal experience and preferences.  
Human-machine collaboration not only enhances autonomy for users, but also meets their elevated driving needs and preferences. Users can perform other activities while driving, needing only to glance occasionally at ground projections. However, user needs and preferences vary. While older participants were concerned about the potential distraction of constant ground monitoring, younger users saw it as an opportunity for phone use. Most participants acknowledge the criticality of retaining control at any time, which enhances safety perception and reflects their desire for control [7]. Such flexibility allows users to adjust their driving according to personal state, environmental features, and social context. Additionally, diverse user preferences for interface features suggest the needs for an adaptive and intelligent system. According to the research [42], customizable interaction functions should meet individualized information needs, offering adaptive support based on the system context, learning from users' driving habits, and providing a comfortable experience tailored to their psychological state and abilities, thus fulfilling user needs while respecting personal preferences.
## 2 Related Work
### Transformer-Based Network  
Given the predominance of the Transformer in modeling long-range dependencies, it has also been applied to CD tasks in recent years. A bitemporal image Transformer network (BiT) was put forward in [20], in which the Transformer was firstly applied in the domain of CD. The BiT effectively modeled contextual information in the token-based spatial-temporal, and context-rich tokens were used to boost the original features. This method takes into account the influence of pseudo-change on the change detection results. Nevertheless, the BiT ignored the utilization of multiscale features. A Transformer-based siamese network was later presented in [21], which efficiently extracted multiscale long-range information by combining a hierarchical Transformer encoder well as a simple MLP decoder to locate the change location more precisely. Nevertheless, Changeformer did not have an advantage in terms of computation.  
Based on the above presentation and analysis of some of the previous works, we propose DETDNet, a model to compensate for the shortcomings of the above works, mainly including the effect of pseudo-change on the CD results, the detection of small target objects, the detection of change boundaries, etc. The model achieves a good tradeoff in performance and computation. The network presented in this article is described in detail in the next section.
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 1 Introduction
[63] used ChOA to optimize the initial weights and thresholds of extreme learning machines and then applied the proposed model to COVID-19 detection to improve the prediction accuracy. [PERSON] et al. [64] combined the improved ChOA [60] and support vector machines (SVM) and proposed a novel SVM model that outperforms other methods in classification accuracy. In summary, there are numerous heuristic optimization algorithms and improvement mechanisms, each with its advantages; however, the No-free-Lunch (NFL) theorem [65] logically proves that no single population optimization algorithm can be used to solve all kinds of optimization problems and that it is suitable for solving some optimization problems, but reveals shortcomings for solving others. Therefore, to improve the performance and applicability of ChOA and explore a more suitable method for solving practical optimization problems, an improved ChOA (called RL-ChOA) is proposed in this paper. The main contributions of this paper are summarized as follows:  
1. A new ChOA framework is proposed that does not affect the configuration of the traditional ChOA. 2. Use Tent chaotic map to initialize the population to improve the diversity of the population. 3. A strategy of Refraction learning of light is proposed to prevent the population from falling into local optima. 4. In comparing RL-ChOA with five other state-of-the-art heuristic algorithms, the experimental results show that the RL-ChOA is more efficient and accurate than other algorithms in most cases. 5. The experimental comparison of two engineering design optimization problems shows that RL-ChOA can be applied more effectively to practical engineering problems. The rest of this paper is organized as follows: Section 2 introduces the preliminary knowledge, including the original ChOA and the principles of light refraction. Section 3 illustrates the proposed RL-ChOA in detail. Sections 4 and 5 detail the experimental simulations and experimental results, respectively. Section 6 discusses two engineering case application analyses of RL-ChOA. Finally, Section 7 concludes this paper.
## 2 Radar Techniques for Space Observation and Products
### Radar Imaging of Space Objects
#### 2.3.1 Imaging with the Ku-Band Wideband Radar
Imaging with the L-Band Narrowband Radar  
Recent work has demonstrated the possibility of imaging with the narrowband tracking radar of the TIRA system [17]. Again, the angular velocity of the object is a key parameter for imaging, as it determines the integration time and the sampling positions. Different techniques can be applied. The first method is a partially coherent processing. It exploits the micro-Doppler signature of the space object using a tomographic approach. This Doppler tomography technique was presented in [17] and applied to the monostatic and bistatic radar data of an Atlas-5 Centaur rocket body orbiting in the medium Earth orbit (MEO) region. The spatial resolution is a function of the PRF, the angular velocity, and the used radar wavelength. Figure 10(a) shows a Doppler tomography image [17] of the rocket body including a zoom of its upper part. The shape of the object is clearly recognizable. The radar waves were not backscattered by the center part of the rocket body towards the radar, explaining the dark area in the center of the radar image, except for a small region corresponding to the reflection off of a cable duct. The data can be also fully coherently processed similar to the ISAR reconstruction using a larger aperture angle [17]. The minimum achievable resolution is given by a quarter of the used wavelength. This processing technique can also be applied to the data of the imaging radar. Figure 10(b) presents the reconstructed radar image using a back-projection algorithm [17]. The resolution is finer compared to the Doppler tomographic reconstruction at the cost of high side lobes. Fine structures of the object are visible in the zoom area, in particular the structured pattern of the adapter ring (see Figure 12). To better analyze the object, a composite image merging the results of both reconstruction methods can be generated using different thresholds for imaging, as presented in Figure 10(c). Figure 10.: Tselina-D model [16].
## 4 Simulation Experiments and Results Analysis
### The Verification of Signals from a Real-World RF Device
Finally, constellations of the distorted weak 16 QAM by the intermodulation components are calculated in Figure 11. Randomly-distributed constellation points become regular after the nonlinearities are eliminated. This result is also the same as that obtained in the simulation signal. Therefore, when handling real-world signals, the proposed nonlinearity mitigation method achieves the desired result. Figure 10: The receiver operating characteristics (ROC) before and after nonlinearity mitigation. To sum up, the performance of the proposed method is demonstrated through the results of the simulation signals and signals from real-world measurements. The suppression of the nonlinear components is significantly better when best delay searching is performed. Thus, the proposed method provides a high-performance linearization solution for a broadband RF front-end in a sensor node.
## 3 Characterization
### Morphology and Surface Area  
SEM provides information regarding the structure and microscopic features of BC and BCPs. The sizes of the pores in the BC may be different extremely as the pyrolysis temperature increased. For example, the BC present honeycomb-like pores at a pyrolysis temperature of 700 \({}^{\circ}\)C while it disappeared at 200 \({}^{\circ}\)C [68]. However, the hollow structure of BC may be broken when the pyrolysis temperature increased to 700 \({}^{\circ}\)C [55]. TEM is exceptional for its quality to identify the morphology, crystallinity, and structure of BC and BCPs. [PERSON] et al. [69] found that graphene nanosheets were transparent and slightly aggregated within BC through the TEM images. [PERSON] et al. [70] observed the multi-layered structures and nanocrystalline structures of BC.  
SEM-EDS is widely applied to identify and quantify the elements in BC. For instance, it was observed that the predominant elements in the sludge-derived BC samples were carbon, oxygen, silica, aluminum, calcium, sulfur, phosphorus, and potassium, etc. [71]. [PERSON] et al. [68] used SEM-EDS to analyze the elements in the rapeseed stem biochar. They demonstrated the C content increased, and the O content decreased as the pyrolysis temperature rose.  
The specific surface areas (S\({}_{\text{BET}}\)) and porosity properties of the BC and BCPs could be investigated by N\({}_{2}\) adsorption-desorption isotherm. Generally, the large specific surface area facilitates the adsorption of pollutants. In most cases, after the combination, the S\({}_{\text{BET}}\) of the BCPs is higher than that of the original BC. However, it was found that the mesopores and micropores of BC may be clogged due to the aggregation of semiconductors, thus inducing the decrease of the S\({}_{\text{BET}}\)[62; 72].
## 2 Materials and Methods
### Description of DALEC-Crop
#### 2.2.1 Model Overview  
DALEC-Crop is a C mass-balance model consisting of C pools/stores that are linked by allocation fluxes (i.e., rate of C allocated to plant tissues) (Figure 2). The model includes a winter wheat C allocation look-up table that defines the fraction of C allocated to the plant organs (foliage, stem, storage, and root) based on empirical observations in [PERSON] et al. (2019). These allocation fractions, which are interpolated within DALEC-Crop to match the model's daily time-steps, are assigned according to the simulated developmental stage (DS)--ranging from \(-1\) (sowing) to \(2\) (maturity). The DS is calculated as a function of the key winter wheat developmental responses: temperature, photoperiod and vernalization (until emergence) ([PERSON], 2018; 2018).  
The Aggregated Canopy Model (ACM) ([PERSON], 2018) provides DALEC-Crop with estimates of gross primary productivity (GPP) using the atmospheric carbon dioxide (CO\({}_{2}\); \(\mu\) mol\({}^{-1}\)) concentration and daily inputs of short-wave radiation (MJ m\({}^{-2}\) d\({}^{-1}\)), minimum and maximum temperature (\({}^{\circ}\)C), leaf N and LAI (m\({}^{2}\) m\({}^{-2}\)). The meteorological station provided  
Figure 2: DALEC-Crop model structure relevant to canopy development and yield. Daily estimates of gross primary productivity (GPP) are determined by the Aggregated Canopy Model (ACM)— a function combining inputs of leaf area index (LAI), climate and leaf nitrogen (N). Where leaf N can be provided based on destructive samples or can be inferred based on LAI observations. After a fractional loss of carbon (C) through autotrophic respiration (Ra), remaining C allocation is applied as a function of crop developmental stage—calculated from daily accumulations of effective temperature, photoperiod and vernalization (until emergence). According to the calculated allocation (A) fraction, C is allocated to C pools (including leaf, stem, and storage). Allocated C is lost from the pools either through harvest exports or root litter. DALEC-Crop parameters are calibrated based on the comparison between modelled and observed leaf area index (LAI) values.  
the temperature and radiation inputs. Since no observations of atmospheric CO\({}_{2}\) existed at the study site, we used a fixed value of 407 ppm, which approximated that of the global mean CO\({}_{2}\) during the study period. To derive GPP from these drivers, ACM comprises of a series of simple equations calibrated to reproduce the daily photosynthesis estimates of the more detailed Soil-Plant-Atmosphere Crop model (SPA-Crop) [24], which simulates leaf-level processes at half-hourly time-steps. Further research in [PERSON] et al. [42] has demonstrated the successful calibration of ACM across multiple European wheat sites when compared to daily eddy-covariance C flux data.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Log-Logistic Distribution (LL3)  
The distribution was popularized in hydrology by [PERSON], et al. in 1988 and represents a generalized form of the two-parameter Log-Logistic distribution [11; 17].  
\[\mathrm{f(x)}=\frac{\alpha}{\beta}\cdot\left(\frac{\mathrm{x}-\gamma}{\beta} \right)^{\alpha-1}\cdot\left(\left(\frac{\mathrm{x}-\gamma}{\beta}\right)^{ \alpha}+1\right)^{-2} \tag{23}\]  
\[\mathrm{F(x)}=\left(1+\left(\frac{\mathrm{x}-\gamma}{\beta}\right)^{\alpha} \right)^{-1} \tag{24}\]  
\[\mathrm{x(p)}=\gamma+\beta\cdot\left(\frac{1}{\mathrm{p}}-1\right)^{\frac{1}{ \alpha}} \tag{25}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale and the position parameters; \(\alpha,\beta>0,\gamma<\mathrm{x_{min}}\).
# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 4 Experiments and Results
### Models Detail
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**\# of Sens**} & \multicolumn{2}{c}{**\# of Min**} & \multicolumn{2}{c}{**\# of Max**} & \multicolumn{2}{c}{**Avg Toks**} & \multicolumn{2}{c}{**\# of Min**} & \multicolumn{2}{c}{**\# of Max**} & \multicolumn{2}{c}{**Avg Chars**} \\  & & & & **Toks** & **Toks** & **Per 5** & **Chars** & **Chars** & **Per 5** \\ \hline \multirow{4}{*}{**Korean-English Parallel Corpus**} & \multirow{2}{*}{**Train**} & **KR** & 1,399,116 & 1 & 78 & 12.97 & 4 & 359 & 55.07 \\  & & **EN** & 1,399,116 & 2 & 180 & 22.57 & 10 & 999 & 135.24 \\ \cline{2-10}  & \multirow{2}{*}{**Valid**} & **KR** & 200,302 & 2 & 32 & 17.46 & 9 & 220 & 75.36 \\  & & **EN** & 200,302 & 3 & 110 & 30.73 & 14 & 706 & 187.89 \\ \cline{2-10}  & \multirow{2}{*}{**Test**} & **KR** & 3000 & 7 & 25 & 9.02 & 25 & 113 & 35.71 \\  & & **EN** & 3000 & 5 & 12 & 10.46 & 31 & 101 & 66.50 \\ \hline \multirow{4}{*}{**Korean-English Parallel Corpus (Social Science)**} & \multirow{2}{*}{**Train**} & **KR** & 474,907 & 2 & 138 & 12.57 & 11 & 629 & 52.94 \\  & & **EN** & 474,907 & 2 & 271 & 20.71 & 9 & 1617 & 128.78 \\ \cline{2-10}  & \multirow{2}{*}{**Valid**} & **KR** & 59,746 & 6 & 144 & 12.58 & 21 & 636 & 52.96 \\  & & **EN** & 59,746 & 6 & 280 & 20.73 & 29 & 1550 & 128.88 \\ \cline{2-10}  & \multirow{2}{*}{**Test**} & **KR** & 3000 & 6 & 45 & 12.60 & 22 & 236 & 530.00 \\  & & **EN** & 3000 & 7 & 69 & 20.86 & 35 & 451 & 129.33 \\ \hline \multirow{4}{*}{**Korean-English Parallel Corpus (Technology)**} & \multirow{2}{*}{**Train**} & **KR** & _697,665_ & 4 & 37 & 12.25 & 21 & 180 & 51.97 \\  & & **EN** & _697,665_ & 1 & 52 & 19.23 & 9 & 311 & 115.88 \\ \cline{2-10}  & \multirow{2}{*}{**Valid**} & **KR** & 57,583 & 4 & 35 & 12.25 & 23 & 155 & 51.95 \\  & & **EN** & 87,583 & 5 & 48 & 19.24 & 37 & 310 & 115.86 \\ \cline{2-10}  & \multirow{2}{*}{**Test**} & **KR** & 3000 & 6 & 34 & 12.23 & 26 & 161 & 51.98 \\  & & **EN** & 3000 & 6 & 42 & 19.17 & 45 & 294 & 115.75 \\ \hline \multirow{4}{*}{**Korean-English Domain-Specialized Parallel Corpus**} & \multirow{2}{*}{**Train**} & **KR** & 1,197,000 & 3 & 35 & 15.30 & 11 & 304 & 66.18 \\  & & **EN** & 1,197,000 & 1 & 145 & 27.53 & 1 & 1001 & 167.23 \\ \cline{1-1} \cline{2-10}  & \multirow{2}{*}{**Valid**} & **KR** & 150,000 & 5 & 32 & 15.30 & 14 & 192 & 66.21 \\  & & **EN** & 150,000 & 4 & 97 & 27.55 & 26 & 691 & 167.34 \\ \cline{1-1} \cline{2-10}  & \multirow{2}{*}{**Test**} & **KR** & 3000 & 7 & 30 & 15.80 & 27 & 147 & 67.79 \\ \cline{1-1}  & & **EN** & 3000 & 7 & 69 & 28.56 & 46 & 433 & 175.57 \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of overall AI Hub datasets. For statistics on tokens, we denote NA because there are no spaces in Japanese and Chinese.