esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research about Impact of AI on Education
### Limitations and Concerns about these AI Applications  
While AI has the potential to significantly enhance education for international students, it also presents several concerns and limitations. These include issues related to privacy, bias, and the potential for AI to widen educational inequalities. It is also crucial to understand that AI is not a substitute for human educators, but a tool that can complement and enhance their work.  
Of course, significant risks and limitations are associated with educational and technological innovations that specifically impact international students. For example, personalized learning experiences may raise privacy and security concerns for these students, as using data to tailor learning experiences may require collecting personal information that could be subject to regulations in their home countries [68]. Additionally, there may be cultural factors that impact how personal information is shared or used, which must be taken into consideration.  
Adaptive testing can also present risks for international students, particularly concerning language proficiency. For example, using algorithms to determine the complexity of questions may not accurately reflect a student's language skills or ability to comprehend nuanced phrasing. This could result in biased test results that do not fully capture the student's abilities or knowledge [69].  
Predictive analytics in education may also have ethical implications for international students. For example, identifying students at risk of dropping out or struggling may require the use of data that is not relevant or applicable to international students, leading to inaccurate or misleading predictions. There is also a risk that the data used to make predictions could be subject to different privacy laws or regulations in their home countries [70].  
Finally, using chatbots in learning and research can raise concerns about language barriers and cultural differences that may impact the quality and accuracy of the information provided [71]. For example, international students may have different needs or require more personalized assistance, and chatbots may not always be able to provide the level of support necessary. Additionally, there may be cultural differences in how students perceive the role of educators and their expectations for feedback and guidance.
## 4 Discussion
### Limitations  
Our initial objective was to additionally correlate the LEMOCOT with both the 2-Minute Walk Test (2 MWT) and the Timed Up and Go Test (TUG), which are reliable and valid tools for MS [32; 33], but this was not possible due to lack of space in the outpatient clinic where all the other tests were administered. Furthermore, not all patients were evaluated a second time, as they did not attend the outpatient clinic again during the period that the study was in progress. Also, due to fatigue, some patients refused to participate or to be re-evaluated for a second time. The use of convenience sampling in this study, although justified due to time constraints, might have potentially resulted in selection bias, thereby influencing the generalizability of our results. Also, due to time constraints, longitudinal validity was not examined in our study. Finally, as there is no gold standard test to assess lower limb coordination, external criterion validity could not be examined.
# Headspace Volatiles and Endogenous Extracts of _Prunus mume_ Cultivars with Different Aroma Types
## 5 Conclusions  
In this study, headspace solid-phase microextraction (HS-SPME) and organic solvent extraction (OSE), combined with gas chromatography-mass spectrometry (GC-MS), were used to comprehensively analyze the headspace volatiles and endogenous extracts of eight _P. mume_ cultivars with four aroma types. A total of 66 headspace volatiles and 77 endogenous extracts were tentatively identified. Among them, there were six main components in four aroma types, including benzyl acetate (1.55-61.26%), eugenol (0.87-6.03%), benzaldehyde (5.34-46.46%), benzyl alcohol (5.13-57.13%), chavicol (0-5.46%), and cinnamyl alcohol (0-6.49%). Among them, (Z)-2-hexenol, (E)-cinnamaldehyde, methyl salicate, cinnamyl alcohol, and benzoyl cyanide may be the reasons for the specific aroma of _P. mume_ based on the OPLS-DA and OAVs analyses. This research provides a reference for further enriching the aroma breeding of _P. mume_ cultivars.  
**Supplementary Materials:** The following are available online. Table S1: Relative amount of headspace volatiles of eight _P. mume_ cultivars with four aroma types; Table S2: Relative content of endogenous extracts of eight _P. mume_ cultivars with four aroma types; Table S3: Relative content of ten common headspace volatiles and endogenous extracts; Table S4: OAV of volatile biomarkers; Table S5: Comparative analysis of the content of volatile biomarkers with high OAV; Table S6: OAV of endogenous biomarkers; Table S7: Comparative analysis of the content of endogenous biomarkers with high OAV.  
**Author Contributions:** Conceptualization, J.Z.; sample collection, X.W. and H.Z. ([PERSON]); data analysis, H.Z. ([PERSON]), X.W., and J.X.; figures, X.W. and H.Z. ([PERSON]); literature search, X.W.; writing--original draft preparation, X.W. and Y.W.; writing--review and editing, X.W., Y.W., and J.Z., supervision, Q.F., M.B., and J.Z., funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Key R&D Program of China (grant 2018 YFD1000400, 2019 YFD1001500, Ministry of Science and Technology of the People's Republic of China) and the Fundamental Research Funds for the Central Universities (No: 2662020 YLPY006).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare there is no conflict of interest in this study.  
**Sample Availability:** Samples of the compounds are not available from the authors.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 3 Methods  
In light of the limitations of current methodologies in large-scale, long-time-series bare coal monitoring, which is primarily constrained by the accuracy of the recognition results as well as by the temporal constraints of image acquisition, this study introduces a novel bare coal detection method. This method achieves the accurate and rapid monitoring of extensive continuous open-pit bare coal mining tasks. It comprises the following three steps (Figure 2):  
Step 1: Based on Landsat images and the spectral characteristics of the bare coal fraction map (Figure 1), the sensitivity and separability of different bands to bare coal and other features are analyzed. This analysis helps identify the optimal bands and band combinations for bare coal identification, leading to the construction of a reasonable bare coal index.  
Step 2: Spectral simulation experiments are conducted to determine the theoretical threshold values for mixed image elements containing bare coal, bare soil, and man-made structures. The theoretical threshold is combined with practical application results to determine the optimal bare coal detection threshold.  
Step 3: The identification accuracy of the Bare Coal Index (BCI) was validated within sample areas, and the performance of existing bare coal identification methods across various environments was compared. Additionally, spectral variations of bare coal across different months were analyzed to construct a mathematical model linking the BCI to monthly changes. Finally, large-scale BCI-based bare coal identification was conducted to evaluate the feasibility of this approach for broader applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Types** & **Region** & **Data Information** & **Data Usage** \\ \hline \multirow{8}{*}{Landsat 8 OLI} & Chenqi Coalfield & Date: 2023.07.08 & \multirow{8}{*}{Spectral analysis of bare coal} \\  & & path/row: 124/26 & \\ \cline{1-1}  & \multirow{2}{*}{Shengli Coalfield} & Date: 2023.07.02 & \multirow{2}{*}{and index construction} \\  & & path/row: 124/29 & \\ \cline{1-1}  & \multirow{2}{*}{Dongsheng Coalfield} & Date: 2023.08.08 & \multirow{2}{*}{} \\  & & path/row: 127/33, 32 & \\ \hline \multirow{8}{*}{Landsat8 monthly image} & Chenqi Coalfield & \multirow{2}{*}{3-10} & \multirow{2}{*}{Constructing a monthly BCI} \\  & & Shengli Coalfield & & mathematical model \\ \cline{1-1}  & \multirow{2}{*}{Inner Mongolia} & \multirow{2}{*}{7-9} & \multirow{2}{*}{BCI application in other areas} \\  & & Chenqi Coalfield & 20230831\_024818\_54\_248c & \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{Shengli Coalfield} & 20230818\_021746\_07\_24 bc & \multirow{2}{*}{Extraction of ground-truth samples and accuracy evaluation} \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{Dongsheng Coalfield} & 20230822\_023635\_09\_24 cc & \\ \cline{1-1}  & & 20230822\_023633\_68\_24 cc & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Used in the Study.
## 3 Results and Discussion
### Kinetic Studies of Adsorption  
Kinetic studies provide information regarding the kinetic parameters and the mechanism of the dye adsorption process. The experimental data obtained at different periods of time for different temperatures were analyzed using the kinetic models pseudo first-order, proposed by [PERSON] (Equation (5)) [63], and pseudo second-order, proposed by [PERSON] (Equation (6)) [64].  
\[\ln(q_{e}-q_{t})=\ln q_{e}-k_{1}t \tag{5}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{6}\]  
where \(q_{e}\) and \(q_{t}\) are the amounts of dye adsorbed at equilibrium and at time t (mg/g), \(t\) is the contact time (min), \(k_{1}\) is the adsorption rate constant (min\({}^{-1}\)), and \(k_{2}\) is the adsorption rate constant (g/mg\(\cdot\)min).  
The kinetic models were fitted using the linear fitting method, and the obtained results are presented in Figures 12 and 13.  
The statistical criteria used to determine the best fitting kinetic model were the standard deviation (SD) and the squared multiple regression coefficient (R\({}^{2}\)). The comparison of the experimental adsorption capacity values (obtained at different temperatures) with the computed results estimated from Equations (5) and (6) is presented in Table 8.  
Figure 11: Effect of temperature on dye removal capacity: AY 42 (**a**) and AR 213 (**b**); 1 g/L PM4, 25 \({}^{\circ}\)C, natural pH of solution.  
Figure 12: Pseudo first-order kinetic model fitting for the adsorption of AY 42 (**a**) and AR 213 (**b**) onto PM4.  
The best model that fitted the experimental data, as shown by the correlation coefficients (R\({}^{2}\)), was the pseudo second-order model. This was highlighted by the concordance between the values of the adsorption capacity determined experimentally (\(q_{e,exp}\)) and the adsorption capacity calculated (\(q_{e,calc}\)). As can be seen (Table 8), with an increasing temperature, the pseudo-second rate constant k\({}_{2}\) increased, pointing out that the time required to reach the equilibrium decreased with increasing temperature--results that are in agreement with the experimental data.
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 5 Discussion
In this last case, the SqueezeNet model reaches 98% accuracy, which is the highest compared to other techniques, corresponding to only one misclassfied Landsat 8 image. In summary, the SqueezeNet model has 100% accuracy with the Sentinel-2 images, whereas with Landsat 8 images, it achieves almost the maximum accuracy of 94%, similar to the fixed threshold method. However, the SqueezeNet model has the great advantage of being able to use Sentinel-2 and Landsat 8 data indifferently (both with high spatial resolution and with the similar spectral range of data for the infrared bands), achieving the highest accuracy compared to the other methods (98%). In this way, it is possible to  
Figure 7: Accuracy comparison between threshold-based algorithms (light green bar for fixed threshold and dark green bar for statistical threshold), machine learning techniques (orange bar for X-Means and yellow bar for RF) and the SqueezeNet deep learning model (blue bar), in volcanic thermal anomaly detection, using (**a**) only Sentinel-2 data, (**b**) only Landsat 8 data, and (**c**) both Sentinel-2 and Landsat 8 data. detect thermal anomalies in less time, as there is no need to wait for the temporal step between two acquisitions for a single satellite (Sentinel-2 or Landsat 8) because the first image available between the Sentinel-2 and Landsat 8 data can be referenced.
## 2 Materials and Methods  
### Derivation of NDVIis from In Situ Radiation Measurements  
In situ radiometric data were selected from FLUXNET eddy covariance databases (www.fluxdata.org), where particular use was made of a subset of open and fair-use data in the La Thuile database. From these, we selected 21 flux sites that represented a global spatial distribution and had high quality and continuous data for the calculation of NDVI\({}_{is}\) (Table S1). We defined and computed NDVI\({}_{is}\) based on half-hourly in situ measurements of incoming and outgoing photosynthetically active radiation (_PAR_\(\downarrow\) and _PAR_\(\uparrow\), respectively) and global incoming and outgoing shortwave radiation (_Rg_\(\downarrow\) and _Rg_\(\uparrow\), respectively), as proposed by [PERSON] et al. [37]. Reflectance (\(\rho\)) in the PAR region (\(\rho_{PAR}\), spectral range 400-700 nm) and reflectance in the NIR region (\(\rho_{NIR}\), spectral range 700-2800 nm) were calculated as:  
\[\rho_{PAR}=\frac{PAR\uparrow}{PAR\downarrow} \tag{1}\]  
\[\rho_{NIR}=\frac{Rg\uparrow-PAR\uparrow}{Rg\downarrow-PAR\downarrow} \tag{2}\]where \(PAR\downarrow\) and \(PAR\uparrow\) are expressed in J\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), using a conversion factor of 4.55 umol\(\cdot\)J\({}^{-1}\) from umol\(\cdot\)photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)[38]. \(Rg\downarrow\) and \(Rg\uparrow\) are expressed in J\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\).  
In situ \(\rho_{PAR}\) and \(\rho_{NIR}\) reflectances were then used to compute in situ NDVI [39], as defined by Equation (3):  
\[NDVI_{is}=\frac{\rho_{\text{NIR}}-\rho_{PAR}}{\rho_{\text{NIR}}+\rho_{PAR}} \tag{3}\]  
Typically, at FLUXNET sites, \(PAR\downarrow\) and \(PAR\uparrow\) are measured by coupling two quantum sensors--\(PAR\downarrow\) is measured by an upward-faced quantum sensor while \(PAR\uparrow\) by a downward-facing sensor. \(Rg\downarrow\) and \(Rg\uparrow\) are measured by two pyranometers--one faces upward to measure \(Rg\downarrow\) and the other faces downward to measure \(Rg\uparrow\)[40]. Both instruments are affected by very low uncertainty that is around 2% of \(PAR\) and spans from 2% to 3% for \(Rg\). Therefore, the uncertainty of radiation measurements is treasurable in NDVI\({}_{is}\) estimation. The quality of half-hourly radiometric measurements was checked by applying different physical tests (e.g., \(Rg\downarrow\) has to be less than the corresponding extraterrestrial radiation at the same point of time) and by the analysis of the statistical variability of the data (quantified by the standard deviation), as proposed in [35].  
Daily NDVI\({}_{is}\) time series were computed from the average of five half-hourly radiation data points recorded over the period 11:00 to 13:00 local solar time that corresponded with the overpassing of the MODIS/TERRA satellite.
## 3 Results and Discussion
### Station Performance at the Foret Montmoreny NEIGE Site near Quebec
One key result of their validation is a very good performance of the SWE determination capabilities of the in situ GNSS approach: the inter-comparison of the SnowSense GNSS station with a snow pillow and manual measurements show a very high agreement  
Figure 5: (**a**) daily GNSS-derived SWE and LWC values at the Foret Montmorey NEIGE site 2017/2018; (**b**) daily GNSS-derived SWE values and reference measurements from two GMON CS725 sensors and manual snow pit measurements of mean, minima and maxima SWE at the Foret Montmorey NEIGE site. indicated by the values of \(\mathbf{R}^{2}\) close to 1 [11]. The root mean square errors (RMSE) between the measurements from GNSS, snow pillow, and from snow pits fit very well; they lay in the range of 11-24 mm w.e. [11] but might be higher regarding wet snow conditions, e.g., due to an increase in GNSS signal attenuation (approx. 45 mm w.e.) [8]. Until mid-April 2018, the snowpack was predominantly dry at the study site Foret Montmorers. In January, February and March, only single wet-snow events of up to two to three days occurred. The occurrence of wet snow presented by the GNSS-derived LWC is shown in Figure 5a. As no further sensors for a comparison with the GNSS-derived LWC were available at this site, we compared the GNSS-derived LWC with meteorological parameters. In general, the measured LWC is in a good temporal correspondence with rainfall events and goes along with warm air temperatures (Figure 6), which was also demonstrated in [PERSON] et al. [10]. However, as shown in other previous studies, we were able to successfully validate the GNSS LWC measurements with other sensors like an upward-looking ground-penetrating radar and capacity probes at the study site Weissflujoch [45]. Figure 6: Daily GNSS-derived LWC values at the Foret Montmorers NEIGE site for the winter season 2017/2018 related to (**a**) rain and snowfall events and (**b**) air temperature development. Temperature and precipitation measurements were conducted at the close-by provincial and federal station sites.
## 3 Results
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean \(\pm\) 1 SD**} & \multicolumn{2}{c}{**Mean \(\pm\) 1 SD**} & \multicolumn{2}{c}{**S10–15 m**} & \multicolumn{2}{c}{**S15–20 m**} & \multicolumn{2}{c}{**Mean Difference**} & \multicolumn{2}{c}{**95 CI**} & \multicolumn{1}{c}{**d [Descriptor]**} \\ \hline v [m/s] & 1.21 \(\pm\) 0.16 & 1.22 \(\pm\) 0.17 & \(-\)1.391 (0.186) & \(-\)0.012 & \(-\)0.031 to 0.007 & 0.06 [trivial] \\ dv [\%] & 33.82 \(\pm\) 15.22 & 25.99 \(\pm\) 13.15 & 3.280 (0.005) & 8.241 & 2.853 to 13.629 & 0.58 [small] \\ SF [Hz] & 0.83 \(\pm\) 0.11 & 0.83 \(\pm\) 0.13 & \(-\)0.255 (0.803) & \(-\)0.003 & \(-\)0.025 to 0.020 & 0.00 [trivial] \\ SL [m] & 1.48 \(\pm\) 0.20 & 1.49 \(\pm\) 0.24 & \(-\)0.737 (0.473) & \(-\)0.015 & \(-\)0.059 to 0.029 & 0.05 [trivial] \\ \(\rm F_{mean,stable\)-cycle [N] & 1.80 \(\pm\) 0.38 & 1.83 \(\pm\) 0.43 & \(-\)1.031 (0.20) & \(-\)0.037 & \(-\)0.115 to 0.040 & 0.07 [trivial] \\ \(\rm F_{mean,stable\)-cycle [N] & 25.45 \(\pm\) 4.11 & 25.73 \(\pm\) 5.34 & \(-\)0.585 (0.568) & \(-\)0.274 & \(-\)1.279 to 0.731 & 0.06 [trivial] \\ \(\rm Imp_{mean,stable\)-cycle [N-s] & 21.03 \(\pm\) 4.68 & 21.32 \(\pm\) 5.76 & \(-\)0.570 (0.578) & \(-\)0.288 & \(-\)1.370 to 0.794 & 0.06 [trivial] \\ \(\rm F_{total}\) [N] & 50.91 \(\pm\) 8.24 & 51.46 \(\pm\) 10.68 & \(-\)0.583 (0.569) & \(-\)0.547 & \(-\)2.558 to 14.464 & 0.06 [trivial] \\ \(\rm C_{Da}\) [dimensionless] & 0.76 \(\pm\) 0.24 & 0.75 \(\pm\) 0.27 & 0.112 (0.912) & 0.002 & \(-\)0.035 to 0.039 & 0.04 [trivial] \\ \hline \multicolumn{6}{c}{—swimming velocity. dv—intra-cycle variation of swimming velocity; SF—stroke frequency; SL—stroke length; SI—stroke index; \(\rm F_{mean,stable\)-cycle—mean propulsive force (average of both hands) of the stroke cycle; \(\rm Imp_{mean,stable,cycle}\)—mean impulse (average of both hands) of the stroke cycle; \(\rm F_{total}\)—total propulsive force (sum of both hands) of the stroke cycle; \(\rm C_{Da}\)—active drag coefficient.} \\ \end{tabular}
\end{table} 
Table 2: Descriptive data (mean \(\pm\) one standard deviation—1 SD) of all variables measured and the \(t\)-test comparison between sections. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter Fixed Effect** & **Estimate (SE)** & **95 CI** & \(\mathbf{p}\)**-Value** \\ \hline SL [m] & 0.067 (0.025) & 0.018 to 0.116 & 0.014 \\ \(\rm C_{Da}\) dimensionless] & \(-\)0.593 (0.022) & \(-\)0.636 to \(-\)0.550 & \(<\)0.001 \\ \(\rm F_{mean,right}\) [N] & 0.007 (0.001) & 0.005 to 0.009 & \(<\)0.001 \\ \(\rm F_{mean,left}\) [N] & 0.008 (0.001) & 0.006 to 0.010 & \(<\)0.001 \\ \
###### Abstract  
Sentiment Analysis (SA) is an active research area. SA aims to classify the online unstructured user-generated contents (UUGC) into positive and negative classes. A reliable training data is vital to learn a sentiment classifier for textual sentiment classification, but due to domain heterogeneity, manually construction of reliable labeled sentiment corpora is a laborious and time-consuming task. In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the research community. However, state-of-the-art techniques for semi-supervised sentiment classification present research challenges expressed in questions like the following. How to effectively utilize the concealed significant information in the unstructured data? How to learn the model while considering the most effective sentiment features? How to remove the noise and redundant features? How to refine the initial training data for initial model learning as the random selection may lead to performance degradation? Besides, mainly existing lexicons have trouble with word coverage, which may ignore key domain-specific sentiment words. Further research is required to improve the sentiment lexicons for textual sentiment classification. In order to address such research issues, in this paper, we propose a novel unified sentiment analysis framework for textual sentiment classification called LeSSA. Our main contributions are threefold. (a) lexicon construction, generating quality and wide coverage sentiment lexicon. (b) training classification models based on a high-quality training dataset generated by using k-mean clustering, active learning, self-learning, and co-training algorithms. (c) classification fusion, whereby the predictions from numerous learners are confluences to determine final sentiment polarity based on majority voting, and (d) practicality, that is, we validate our claim while applying our model on benchmark datasets. The empirical evaluation of multiple domain benchmark datasets demonstrates that the proposed framework outperforms existing semi-supervised learning techniques in terms of classification accuracy.  
textual sentiment classification; sentiment lexicons; initial training set; semi-supervised learning; self-training; active learning; co-training; classification fusion +
Footnote †: journal: Accepted: 13 December 2019; Published: 17 December 2019