esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Introduction of Data Processing Methods
#### 2.2.3 Error Analysis Methods  
It is necessary to carefully analyze detection accuracy during microwave detection (Figure 3). The main error sources of the MWR are as follows. (i) Signal enhancement caused by aiming the radiometer antenna towards the sun. The sun is a radiation blackbody, and its surface temperature is as high as 6000 K. Assuming that the angular range of the microwave radiometer antenna towards the sun is approximately 1%, the signal will be enhanced by approximately 60 K. To avoid the observation error caused by solar radiation, the antenna should be avoided in the solar range. (ii) Detection error introduced by the inversion algorithm. The accuracy of the inversion profile depends largely on the inversion algorithm itself. MWR uses an artificial neural network to invert the temperature and humidity profile. The representativeness of historical sounding data used for neural network training directly affects the inversion effect of NN. (iii) External environmental radiation interference. When there is a strong transmitter around the observation site, peak signals may be generated due to radio frequency interference. The peak signal will affect the accuracy of the inversion profile; thus, the observation site should be away from the radio transmitter. (iv) Detection errors caused by water, ice and snow on the instrument. If there is water on the antenna shield of the MWR, the measured radiation brightness temperature may be high. The accumulation of water, snow or ice over the radome should be cleared in time during snow observation [35; 36]. A hydrophobic radome and an air blower were adopted to dry raindrops and snowflakes on the radome [37]. Since the off-zenith method can mitigate the impact of snowfall on the accuracy of the MWR retrievals, both zenith and off-zenith observations were conducted [21].  
To further evaluate the accuracy of the radiosonde observations, the mean difference (\(MD\)), root mean square error (\(RMSE\)), and Pearson correlation coefficient (\(CC\)) were introduced as follows [11]:  
\[MD=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}(X_{i}-Y_{i}) \tag{9}\]  
\[RMSE=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}(X_{i}- Y_{i})^{2}} \tag{10}\]  
\[CC=\frac{\underset{N}{\overset{N}{\underset{n=1}{\sum}}}(X_{i}-\overline{X_{i }})(Y_{i}-\overline{Y_{i}})}{\sqrt{\underset{n=1}{\overset{N}{\underset{i=1}{ \sum}}}(X_{i}-\overline{X_{i}})^{2}}\sqrt{\underset{n=1}{\overset{N}{\underset{ n=1}{\sum}}}(Y_{i}-\overline{Y_{i}})^{2}}} \tag{11}\]  
where \(N\) denotes the total number of observation samples and \(X\) and \(Y\) denote the radiosonde observation and corresponding MWR products, respectively.
# Radiation-Induced Structural Changes of _Miscanthus_ Biomass
## 3 Results and Discussion
### Influence of Irradiation Treatment on Structural Properties of Miscanthus Biomass
#### 3.3.4 FTIR Analysis  
The FTIR of _Miscanthus_ biomass after irradiation treatment was investigated, and the spectra are shown in Figure 4. Three characteristic peaks of holocellulose are: 1730 cm\({}^{-1}\) is ascribed to C=O carbonyl group, 1372 cm\({}^{-1}\) is ascribed to -C-C-, and 1237 cm\({}^{-1}\) is ascribed to C-O- of acetyl group, as observed in Figure 4. With the increase in absorbed doses, the intensity of the three peaks increased, indicating that the structural damage degree of _Miscanthus_ fibers increases with the increase in the absorbed dose. The changes of the typical peaks of the guaiacyl and syringyl lignin vibration at 1230-1515 cm\({}^{-1}\) also showed absorbed dose-dependence. Simultaneously, the peak at 910 cm  
Figure 3: XRD patterns of the _Miscanthus_ biomass irradiated with different doses.  
Figure 2: Effect of absorbed doses on the thermal stability profiles of _Miscanthus_ biomass.  
ascribed to the C-O-C group in the epoxide guaiacyl lignin was so small that the change in its height was not significant, which is good agreement with the data of wheat straw treated by irradiation and diluted acid [27].
## 7 Conclusions  
This study intends to develop a modularized simulation system to resolve hydrodynamic coefficients as well as motion responses of surge, heave, and pitch for the floating OMTs based on the concepts of spar buoy and TLP. The capability of the modularized simulation system has been examined by comparing the simulation results with those predicted using other codes as well as with experimental data. The following summaries by including the mooring effect would be described from the simulation results.  
1. For the spar buoy concept, the decrease in water depth results in larger added-mass coefficient in the pitch mode \(\mu_{55}\), but causes smaller radiation damping coefficient in the pitch mode \(\
u_{55}\).
2. For the design of the MIT/NREL TLP, the larger displaced water and the wetted surface in the surge and pitch modes would lead to higher added-mass coefficients \(\mu_{11}\) and \(\mu_{55}\) than the value in the heave mode \(\mu_{33}\).
3. Compared to the design of the MIT/NREL TLP, the large radius and diameter of the pontoon for the UMaine TLP would produce high value of the added-mass coefficient in the pitch mode \(\mu_{55}\). The increases in the radius and diameter of the pontoon are accompanied with low resonance frequency of the radiation damping coefficient in the surge mode, whereas the decrease in the diameter of the column is related to low resonance frequency of the heave coefficient.
4. Due to the influence of unstretched mooring lines parallel to the seabed on the OC3-Hywind spar, the catenary mooring system has considerable contribution to the surge RAO, but provides for less effect on the heave RAO.
5. From the computed results of UMaine-Hywind spar in irregular seas, the decrease in the water depth would lead to more prominent resonance peaks of pitch RAOs and also shift the peaks to the high-frequency region as a result of proximity to the seabed.
6. It is demonstrated in this paper that the surge RAO is considerably larger than heave and pitch RAOs for the TLP concept due to high stiffening in the vertical plane of the taut-leg mooring system. Specifically, the large cross-sectional areas of the pontoons for the UMaine TLP would produce larger radiation added-mass force in the surge mode than in the pitch mode.
7. Concerning the floater's stability in severe sea conditions, it is much suitable to install the spar buoy in deep water area when neglecting the cost of the mooring system. For the TLP option, it is better to consider UMaine TLP rather than MIT/NREL TLP because the surge RAO can be well suppressed.  
**Author Contributions:** Formal analysis, S.-H.K.; Investigation, C.-H.Y.; Methodology, Y.-H.L.; Supervision, Y.-H.L.; Writing--original draft, Y.-H.L.  
**Acknowledgments:** The authors would like to express their thanks to the Ministry of Science and Technology for a grant under Contract No. MOST 107-2221-E-006-229. The authors also acknowledge the support provided by the College of Engineering, National Cheng Kung University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Frame Design  
In order to avoid inter-block interference, we consider a zero-padded OTFS system design for UWA communications, as shown in Figure 1[17]. The blue dashed line represents the design of ZP-OTFS frames. Setting the last \(2l_{g}+1\) symbol vectors (rows) in the DD domain grid to zeros, we obtain an interleaved ZP guard band in the time domain. Pilot signals for channel estimation are inserted into the ZP section without additional data overhead. The ZP-OTFS frame in DD domain grid is defined as follows:  
\[x(q,l)=\left\{\begin{array}{ccc}x_{d}(q,l),&0\leq l<M-(2l_{g}+1),\,0\leq q \leq N-1\\ 0,&M-(2l_{g}+1)\leq l\leq M-1,\,0\leq q\leq N-1\\ x_{p}(q,l),&l=m_{p}=M-(l_{g}+1),\,q=n_{p}\in[0,N-1]\end{array}\right., \tag{9}\]  
Figure 1: Zero-padded OTFS system.  
where \(x_{d}(q,l)\) represents the OTFS data symbol at the \(l\)th row and \(q\)th column in the Doppler grid, \(x_{p}(q,l)\) is the pilot symbol at the \(m_{p}\)th row and \(n_{p}\)th column, and 0 signifies the guard symbol outside of \(x_{d}(q,l)\) and \(x_{p}(q,l)\) in the Doppler grid. Assuming that \(l_{g}\) is sufficiently long, channel effects on \(x_{d}(q,l)\) and \(x_{p}(q,l)\) will only extend into the ZP section, thereby effectively avoiding inter-block interference.
# Using Educational Chatbots with Metacognitive Feedback to Improve Science Learning
## 5 Discussion
### RQ 2: Differences in Transfer  
Also in line with our assumptions (Hypothesis 2), it was found that the metacognitive feedback group exhibited significantly better transfer performance than the no feedback group. The metacognitive feedback group provided students with additional leading questions about metacognition. These additional questions contributed to instructing students to reflect on the previous learning process and paying more attention to the later learning process, which can also foster strategies relevant to deep understanding rather than strategies that lead to better retention of some essential concepts [46]. This could explain why the metacognitive feedback group achieved better transfer performance than the other groups. Moreover, according to [PERSON] et al. [46], the use of self-directed metacognitive prompts created by students in a hypermedia environment stimulated their selection of learning strategies and promoted deeper information processing, ultimately leading to improved transfer performance. Hence, providing metacognitive feedback may help students process information deeply on each learning module, which enhances transfer.  
Further, our findings were consistent with the meta-analysis review conducted by [PERSON] et al. [81] regarding the impact of feedback on learning performance. Specifically, when compared to a no feedback condition, metacognitive feedback demonstrated a medium effect size in enhancing learning in technology-rich learning environments (TREs). Building upon previous research on feedback effects, our study extended prior investigations by examining the effect of metacognitive feedback on intrinsic motivation.  
Finally, the reason why metacognition promoted knowledge transfer was that metacognition promoted intrinsic motivation, which in turn facilitated knowledge transfer. Additionally, metacognitive feedback might lead students to engage in deep learning and generate more flexible learning strategies. From a lens of neuroscience perspective, another reason might be that knowledge transfer requires memory and higher-order cognitive abilities, and its neurocognitive underpinnings lie in the increase of the dorsolateral prefrontal cortex (DLPFC) and frontopolar cortex (FPC) activities [82; 83]. Continual metacognitive feedback may activate the function of the FPC and DLPFC [84; 85]. The DLPFC is associated with working memory, learning, decision, attention, and motivation [86; 87]. The FPC plays a role in complex, higher-order behavior [88].  
Insights about each dimension of intrinsic motivation will be discussed below.
# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 3 Knowledge-Guided Evolutionary Algorithm  
In the evolutionary optimization problem for feature selection, high-dimensional features often lead to a large number of decision variables. With the increase in decision variables, the search space for the problem expands dramatically. To effectively enhance the efficiency of solving the problem, this paper designs and improves a knowledge-guided evolutionary method, the KGEA, based on the NSGA-II [16]. Firstly, a method of grouping features based on correlation is proposed. In this method, we fully utilize the correlation among different features to achieve balanced grouping results (considered as knowledge in subsequent evolution). Then, during the evolutionary process, the algorithm uses the acquired knowledge to guide crossover and mutation, reduce the search space, and enhance the efficiency of the search while maximizing the diversity of the population.
## 4 Conclusions  
This paper proposed a compressed sensing CT image blind restoration reconstruction method. A compound Gaussian distribution was assigned to the global sparse coefficients, and a multivariate Gaussian distribution was chosen for the PSF. In the Bayesian framework, the CT image blind restoration reconstruction is transformed into an optimization problem, and the hierarchical Bayesian model was used to solve the problem of parameter estimation and obtain the final reconstructed image. Experiments show that the proposed algorithm is superior to other algorithms in subjective visual effect. In the case that the projection data is far from satisfying the completeness condition, the algorithm proposed can reconstruct the high-quality image. In the aspect of objective evaluation, the algorithm proposed in this paper improves the objective image quality metrics such as PSNR, SSIM, and so on. Future works include real CT data test, better sparse representation and new prior distributions for sparse representation.  
The following are available online at [[http://www.mdpi.com/1999-4893/12/8/174/s1](http://www.mdpi.com/1999-4893/12/8/174/s1)]([http://www.mdpi.com/1999-4893/12/8/174/s1](http://www.mdpi.com/1999-4893/12/8/174/s1)).  
Conceptualization, Y.S. and L.Z.; methodology, Y.S.; software, Y.L. and J.M.; validation, L.Z.; writing--original draft preparation, Y.L. and Y.S.; writing--review and editing, Y.S.; visualization, Y.L. and J.M.; funding acquisition, Y.S. and L.Z.  
This research was funded by Tianjin Research Program of Application Foundation and Advanced Technology (No.16)CYBJC28800,13 JCYBJC15600).  
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions during the revision of this paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sampling Ratio** & **0.4** & **0.6** & **0.8** & **1** \\ \hline the image reconstruction method & — & 44.8787 & 48.1225 & 49.3421 \\ combined with image restoration [17] & — & 44.8787 & 48.1225 & 49.3421 \\ \hline the proposed approach & 45.2660 & 47.3095 & 48.3834 & 49.6093 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PSNR contrast among the image reconstruction method combined with image restoration [17] and the proposed approach.  
Figure 9: The 1 st row shows the image reconstruction method combined with image restoration [12] (left) and the proposed approach (right) for 80% of measurements, respectively. The 2 nd row shows the results of the image reconstruction method combined with image restoration [12] (left) and the proposed approach (right) for 60% of measurements, respectively.
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 2 Related Work  
### Visual SLAM Literature  
The core task of visual SLAM is to estimate camera poses and construct environmental maps through pixel matching in image sequences. Early MonoSLAM [15] utilized extended Kalman filtering as the backend, supporting single-threaded processing of a small number of sparse feature points. Subsequently, PTAM [16] introduced keyframes and a dual-thread architecture to achieve parallel processing of camera tracking and mapping, which significantly improved system performance. Building upon this foundation, ORB-SLAM2 [17] added loop closure detection and local bundle adjustment optimization, further enhancing robustness and accuracy. The latest ORB-SLAM3 [6] integrates visual-inertial information and introduces a multi-map system to improve adaptability in multi-sensor configurations. However, these methods generally assume a static environment; in dynamic scenes, the feature points on moving objects violate this assumption, easily introducing errors that reduce localization accuracy and map quality. Thus, optimizing SLAM for dynamic scenarios has become a current hot topic of research.
## 2 Materials and Methods  
### Data Acquisition and Preprocessing  
The hyperspectral data were collected at the potato demonstration base of Chahar Right Wing Banner in Hohhot, and the camera used was a new handheld hyperspectral camera Specim IQ. The resolution of the hyperspectral camera was 512 \(\times\) 512 pixels, and the total number of bands collected was 204, with a spectral range of 400-1000 nm and a spectral resolution of 7 nm. In this study, potato leaves were picked and photographed in the laboratory with the Specim IQ hyperspectral camera to address the problem that hyperspectral cameras are susceptible to environmental interference during photography. During the shooting, the white plate and the leaf were photographed simultaneously to eliminate the environmental mismatch, the integration time of the hyperspectral camera was adjusted to 5 ms, and the shooting height was 20 cm from the leaf height. A total of 126 hyperspectral potato disease data points, including 49 leaf blight, 28 anthracoses, 7 early blight, and 42 mixed hyperspectral images of three different diseases, were obtained. For data with mixed pixels, the pixels within a region are labeled as the same category based on the artificial region calibration of the RGB image at the time of acquisition, so the data with mixed pixels will have multiple disease category labels. Still, these disease category labels will not be pursued for disease species when they belong to the same disease category at the first classification. When performing the disease category identification, the second classification task needs to be completed based on the specific disease category labels calibrated and combined with the determination of the corresponding spectral information found on the disease pixels. The potato disease leaves taken with the Specim IQ hyperspectral camera are shown in Figure 1.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 4 Conclusions  
This study aimed to prepare and to apply RSAC to remove copper ions from wastewater and to discuss the influence mechanism and the microstructure for adsorption by RSAC particles. One of the more significant findings to emerge from this study is that the adsorption of copper ions in a solution by RSAC particles is a spontaneous, heat absorption reaction. The mechanism of copper ion removal by RSAC particles includes an ion exchange reaction and chemical precipitation in addition to physical adsorption. The second major finding is that metal cations can replace the original cations in the RSAC particle ion exchange, change the environment of the adsorbent, and affect the adsorption of copper ions on RSAC particles. This study has also indicated that both Langmuir and Freundlich models can well describe the isothermal characteristics of the adsorption of copper ions by RSAC particles. Both the quasi-primary kinetic model and the quasi-secondary kinetic model can describe the adsorption process of copper ions on RSAC particles well. It can be further inferred that the adsorption process of copper ions on RSAC particles is a mixed control, and both surface diffusion and internal fine pore diffusion are important.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L. and Q.C.; validation, Y.L. and Q.C.; formal analysis, Q.C.; investigation, Q.C.; resources, Q.C.; data curation, Q.C.; writing--original draft preparation, Q.C. and R.P.S; writing--review and editing, R.P.S. and Q.C.; visualization, Y.L. and Q.C.; supervision, Y.L. and R.P.S; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Temp. (K)** & \begin{tabular}{c} **qmax** \\ **(mmol/g)** \\ \end{tabular} & \begin{tabular}{c} **K\({}_{\mathbf{L}}\times\mathbf{10^{-3}}\)** \\ **(L/mol)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\mathbf{G}^{0}\)** \\ **(kJ/mol)** \\ \end{tabular} & \
\begin{tabular}{c} **\(\Delta\mathbf{G}^{0}\)** \\ **(J/mol-K)** \\ \end{tabular} &
\begin{tabular}{c} **\(\mathbf{R^{2}}\)** \\ **(J/mol-K)** \\ \end{tabular} \\ \hline
288.15 & 0.0381 & 8.832 & \(-\)21.79 & & & \\
298.15 & 0.0411 & 12.544 & \(-\)23.33 & 22.55 & 153.89 & 0.9955 \\
308.15 & 0.0447 & 16.256 & \(-\)24.87 & & & \\ \hline \end{tabular}
\end{table}
Table 12: Thermodynamic parameters of copper ion adsorption on RSAC particles.  
Figure 10: Relationship between \(\mathrm{lnK}_{0}-1/\mathrm{T}\) for the process of copper ion adsorption by RSAC particles.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 6 Trade-Off Discussion  
In this section, we discuss comparative analysis of the performance parameters of the proposed schemes with respect to the AHH-VBF scheme tabulated in Table 4. The proposed scheme 2 hop-AHH-VBF performs better than the AHH-VBF scheme in PDR and energy tax, whereas end-to-end delay increases because of the increase in the propagation distance of the packet. 2 hop-AHH-VBF has a void hole avoidance mechanism that secures high PDR compared to the AHH-VBF scheme. Energy tax reduces due to less packet drops. The packet forwarding decision based on two-hop information has reduced void hole probability, which increases the PDR and energy tax of the 2 hop-AHH-VBF scheme. In the QF-2 hop-AHH-VBF scheme, the forwarder is selected on the basis of the composite function including multiple parameters. Efficient forwarder selection improves PDR, which is 5.6% more than the AHH-VBF scheme. Holding time calculations in QF-2 hop-AHH-VBF reduce end-to-end delay. Similarly, APD has also minimized end-to-end delay.  
Figure 11: Accumulative propagation distance comparison.  
Figure 10: End-to-end delay comparison.  
Due to the aforementioned reasons, end-to-end delay and APD in QF-2 hop-AHH-VBF are reduced by 28.5% and 28%, respectively. Energy tax increases due to a greater number of successful transmissions. Hence, end-to-end delay and PDR are improved at the cost of energy tax.