esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mathematical Model of the Algorithm
### Mathematical Model for Describing Positional Relation between Two Horizontal Parallel Surfaces
#### 4.1.2 Time-Space Conversion Algorithm  
Because the combined projection waveform is time series, it is necessary to converse the measurement waveform of time series into the measurement waveform of spatial sequence. It is known that the sampling frequency of the sensor is as high as 20 kHz, which means that only 0.00005 s is needed to obtain measurement data, indicating that the sampling density is extremely high. At such a high sampling density, the measurements at different times are the same, which means that duplicate measurements are obtained at some detection position, as shown in Figure 11.  
Further, due to the high resolution of the sensor, the two adjacent measurements may be at the same position even if the data are not identical. In fact, each detection position only corresponds to one real measurements. Duplicate measurements will interfere with the shape recognition of waveforms. Therefore, the purpose of the time-space conversion algorithm is to extract the measurements at  
Figure 11: Structure of measurements.  
At this time, the number of data in the sets \(F\) and \(K\) is \(N_{\text{c}}\). It is known that the sampling interval of the measured surface is two characteristic measurements with a certain value difference, so the measurement time between the two characteristic measurements can be expressed as  
\[t_{\text{c}}(u)=\frac{F(u)t}{N_{\text{L}p}} \tag{8}\]  
where \(F(u)\) represents the \(u\)-th data in set \(F\), \(u=1\), \(2\), \(3\), \(\ldots\), \(N_{\text{c}}\);  
The measurement waveform of spatial sequence corresponding to the coordinate position on the measured surface can be obtained according to Equation (9), so the time-space conversion of the data can be realized and Equation (9) can be expressed as follows  
\[\begin{array}{ccc}vt_{\text{c}}(1)&\to K(1)\\ vt_{\text{c}}(2)&\to K(2)\\ \cdot&\cdot\\ \cdot&\cdot\\ vt_{\text{c}}(N_{\text{c}})&\to K(N_{\text{c}})\end{array} \tag{9}\]  
where \(t_{\text{c}}(1,\ldots\), \(N_{\text{c}})\) is derived from Equation (8); \(K(1,\ldots\), \(N_{\text{c}})\) is derived from Equation (6).  
At this time, the spatial sequence waveform corresponding to the combined projection waveform \(C_{1}\) can be represented by \(R_{1}\), and the spatial sequence waveform corresponding to the combined projection waveform \(C_{2}\) can be represented by \(R_{2}\). After that, the trend curves \(\Delta_{1}\) and \(\Delta_{2}\) of \(R_{1}\) and \(R_{2}\) are extracted respectively, and \(\Delta_{1}\) is defined as the combined projection curve of \(R_{1}\), \(\Delta_{2}\) is defined as the combined projection curve of \(R_{2}\).
# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 2 Materials and Methods
### Datasets
#### 2.1.3 Dataset Analysis  
When developing neural network models that take advantage of very different data inputs, extra care has to be taken to make sure that such a model actually uses all the data inputs and does not over fit on some of them. This is why it is necessary to perform in-depth exploratory data analyses.  
Figure 1 is a visualization of the features used for the pollen classification task. The three types of features are obtained from a single aerosol particle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plant Genus**} & \# Samples & \# Samples & \# Samples & \# Samples \\  & SAU-SRB & SAU-LI & SAU-CH & MARS \\ \hline Alhus & 3944 & 47,816 & 6314 & 8148 \\ \hline Corylus & 16,299 & 34,065 & 11,100 & 9073 \\ \hline Betula & 12,784 & 64,461 & 14,858 & – \\ \hline Fraxinus & 5596 & – & 4747 & 4310 \\ \hline Quercus & 1889 & 34,019 & – & 5978 \\ \hline Populus & 6077 & 22,273 & – & 16,923 \\ \hline Taxus & 6006 & – & 4061 & 11,958 \\ \hline Pinus & – & 31,144 & 3593 & 4068 \\ \hline Acer & 985 & 16,114 & – & – \\ \hline Artemisia & 15,208 & 39,546 & – & – \\ \hline Dactylis & 1874 & – & – & 6902 \\ \hline Salix & 10,136 & 36,635 & – & – \\ \hline Brussonetta & – & – & – & 12,385 \\ \hline Carpinus & – & – & 2377 & – \\ \hline Cedrus & 1215 & – & – & – \\ \hline Cupressus & – & – & 1387 & – \\ \hline Cynodon & 1006 & – & – & – \\ \hline Fagus & – & – & 1075 & – \\ \hline Festuca & – & 27,556 & – & – \\ \hline Juglans & – & – & – & 7038 \\ \hline Juniperus & – & 45,888 & – & – \\ \hline Lolium Perene & – & – & – & 3020 \\ \hline Morus & – & – & – & 7674 \\ \hline Picea & 2743 & – & – & – \\ \hline Platanus & – & – & – & 8005 \\ \hline Poaceae & – & – & 1110 & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets overview.  
The scattering image is a 120 \(\times\) 24 tensor that shows the amount of laser light scattered by the particle at 24 different angles. The scattering images are obtained in a manner similar to a photocopy; the only difference is that the scanned object moves and not the light source and detector.  
The fluorescence spectrum is a 32 \(\times\) 8 tensor representing the intensity of the fluorescence response at 32 wavelengths at 8 moments in time measured at half a microsecond apart from each other.  
Lifetime is a 64 \(\times\) 4 tensor which corresponds to the time from when a UV laser pulse is shot until the fluorescence response of the particle. The lifetime signal is measured at 4 wavelength bins, 350-400 nm, 420-460 nm, 511-572 nm and 672-800 nm, and at a temporal resolution of nanoseconds. The physical information carried by the fluorescence lifetime signal refers to the average time the organic molecules in the pollen particle stay in their excited states before emitting a photon.
## 4 Results and Discussion
### Detailed Analysis of Leak Position L2
#### 4.1.2 Improvement of the Leak Localization Technique Due to Least Squares Method  
Up to now, the difference in time-of-arrival of the NPW was defined as the difference in time between the points where the pressure starts increasing. That principle is very prone to the effects of noise as the selection of these points can be different on basis of the noise that is present on the signal. We therefore aim for a different method to estimate the difference in time-of-arrival of the NPW. The principle is based on a least squared method on the first rising part of the NPW. The signal of sensor S2 is shifted in time and compared with the measurement of sensor S1. The optimum shift of the signal is found when the least squares analysis reports the smallest error between the two signals. The plot in the top right corner of Figure 8 indicates the part of the signal of Figure 6 that will be used for the analysis. The bottom right corner shows that the optimum was found when the signal of sensor S2 was shifted 207 datapoints to the left. The resulting match is plotted on the left of the same figure.  
Based on this analysis, we can now estimate the location of the leak. We therefore again use Equation (3):  
\[x_{1}=\frac{0.462-343\times\frac{207}{204800}}{2}\text{ m}=57.7\pm 0.84\text{ mm}. \tag{8}\]  
This location should again be compared to the exact distance, 59 mm. This method clearly reduces the effect of noise on the localization of the leak. The error now falls within the theoretical uncertainty related to the finite sampling rate. Improving the localization accuracy (e.g., by using better processing techniques) enables the reduction of the sampling rate of the data acquisition while keeping the error on the localization constant, which is an important cost reduction when considering a later application in the eSHM system. Much research has been performed in the past to improve the leak localization accuracy for pipeline applications such as that presented in the papers of [PERSON] [16] and [PERSON] _et al._[17].  
Figure 8: Least square analysis on time-of-arrival of negative pressure waves originating from leak position L2.
# Analysis of Sequential Pretreatments to Enhance the Early-Stage Biorefinery Designs
## 3 Results
### Feedback Analysis  
A heuristic analysis for pretreatment in biorefinery designs can involve the use of operational indicators, as well as technical and economic approaches. Therefore, process simulation is relevant when it is desired to scale up sequential schemes. As a lignocellulosic raw material, it was proposed to work with two important residues in Colombia which are directly used in the food agroindustry. Table 3 shows the information collected for the indicators of raw material selection analysis. The agricultural crop data were considered since the processing scale of the sequential schemes depends on the agricultural capacity, determining the production and area of the crop. Additionally, harvesting times should be considered, as it would allow for establishing the continuity of the process or if it should be performed in batches based on crop productivity. Likewise, the generation of residues was considered, which can be a potential for valorization proposals in biorefinery schemes. High production and composition of residues will provide a greater flow to the proposed biorefinery. For NCSC, the residues were bagasse and sprout, while for rice, they were husk and straw. However, this work compares the NCSC-bagasse and rice husk, whose lignocellulosic composition is also described. The scoring of the indicators referring to NCSC and rice crops was performed based on the area cultivated in Colombia for traditional tropical crops and grains, with values of 1.63 \(\times\) 10\({}^{6}\) and 1.25 \(\times\) 10\({}^{6}\) Ha, respectively [29]. The total weighting results in Table 3 show that the rice husk has a slightly higher potential. Although both raw materials are agro-industrial residues, the market for NCSC-bagasse is limited because its main use is as fuel in heating systems for evaporation or juice concentration. In contrast, rice husks do not have a main market because they are usually incinerated on the field. Furthermore, few husk fractions are used in gardening due to water drainage; other studies have focused on producing functional materials and value-added products [30]. Both feedstocks showed potential for valorization in biorefinery schemes, differing considerably in production rates and harvesting time. Despite a higher quantity of NCSC-bagasse, rice husk may have a more continuous production due to its shorter annual harvesting time. On the other hand, it can be observed that NCSC bagasse may be more susceptible to bioconversion processes due to its higher sugar content, while rice husk for lignin valorization or as a precursor material due to its ash composition. After feedstock selection, different sequential pretreatment schemes were designed and evaluated considering the lignocellulosic composition of rice husk.
# Research on Three-Phase Wireless Power Transfer System
## 2 Topology of WPT System
\begin{array}{ccc}jwM_{X1}&jwM_{X}&jwM_{X}\\ jwM_{X}&jwM_{X1}&jwM_{X1}\\ jwM_{X}&jwM_{X1}&jwM_{X1}\end{array} \right]\cdot\left[ \begin{array}{c}I_{a} \\ I_{b}\\ I_{c}\end{array} \right]+\left[ \begin{array}{ccc}-Z_{S}&-jwM&-jwM\\ -jwM&-Z_{S}&-jwM\\ -jwM&-jwM&-Z_{S}\end{array} \right]\cdot\left[ \begin{array}{c}I_{A}\\ I_{B}\\ I_{C}\end{array} \right] \tag{3}\]  
where \(Z_{p}=R_{p}+jwL_{p}+\frac{1}{jwC_{p}}\), \(Z_{s}=(R_{s}+R_{w})+jwL_{s}+\frac{1}{jwC_{s}}\). Because the receiving and transmitting coil structures are completely symmetrical, their self-inductance is also the same. In order to simplify the calculation formula, \(L_{p}\) and \(L_{s}\) represent the self-inductance of the transmitting coil and the receiving coil, respectively; \(M_{X}\) represents the mutual inductance of the receiving or transmitting coil; \(M_{X1}\) represents the mutual inductance between the transmitting coil and the receiving coil; \(R_{p}\) and \(R_{s}\) represent the corresponding coil resistance, respectively; and \(I_{x}\) (\(x=a,\,b,\,c,\,A,\,B,\,C\)) represents the loop current of the transmitting side and the receiving side, respectively. It can be seen from the above model that the coil structure has a great influence on the mutual inductance. \[\eta=\frac{P_{out}}{P_{in}}=\frac{U_{A}I_{A}+U_{B}I_{B}+U_{C}I_{C}}{U_{a}I_{a} +U_{b}I_{b}+U_{c}I_{c}} \tag{4}\]  
According to Formulas (2)-(4), the transmission efficiency of the coil is related to the mutual inductance. The mutual inductance coefficient of the coil is only related to the relative position and size of the mutual inductance winding. Therefore, we will focus on the relative position and structure size of the mutual inductance winding to design the three-phase coil.
## 2 Methodology  
The processing steps include the preprocessing of the vector maps, encryption of watermark information, construction of zero-watermarks based on both feature and non-feature vertices, and watermark extraction and assessment. Data preprocessing primarily includes map blocking and feature extraction. Watermark encryption is to scramble watermark images and the serialization of the encrypted information. The construction of two types of single zero-watermarking schemes is mainly based on the mining of the feature information of the feature vertices and non-feature vertices. Finally, watermark extraction and inspection methods mainly introduce extraction steps and related test indicators.
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 3 Experiments
### Case 2  
In this case, by using the dataset mentioned in Section 3.1, we compared the proposed model to that in [3], that is, the experiment of predicting the forward 10 steps of the PM2.5 average concentration data. Table 2 shows the statistical accuracy of the predictions by different models, as indicated by RMSE, NRMSE, MAE, SMAPE, and R.  
Table 2 indicates that the proposed model (decomposition with ARIMA-GRU-GRU) outperformed the SLI-ESN model with a smaller predictive error for the 10-step prediction. The RMSE were 9.50%,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **RMSE** & **NRMSE** & **MAE** & **SMAPE** & **R** \\ \hline GRU [37] & 83.0925 & 0.1848 & 56.3784 & 0.7133 & 0.6389 \\ Decomposition-GRU-GRU model & 81.2412 & 0.1625 & 56.1870 & 0.7398 & 0.6471 \\ The proposed Decomposition-ARIMA & 80.1620 & 0.1612 & 55.1060 & 0.6682 & 0.6508 \\ -GRU-GRU model & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the 24-step (24 h) prediction results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **RMSE** & **NRMSE** & **MAE** & **SMAPE** & **R** \\ \hline SLI-ESN [3] & 65.7108 & 0.1966 & 46.0633 & 0.5443 & 0.7314 \\ The proposed Decomposition-GRU-GRU-GRU model & 59.4658 & 0.1237 & 37.5384 & 0.4520 & 0.8136 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of 10-step (10 h) prediction results.  
Figure 6: Absolute error between the predicted results and ground truth (24 steps).  
37.08%, 18.51%, and 16.96% lower than the SLI-ESN model, and the correlation coefficient R was above 0.8, and higher than SLI-ESN, which represents a stronger relationship between the prediction and the original PM2.5 data.
## 2 Materials and Methods
### Ecosystem Greenness Assessment  
Since the ecosystem health assessment is conducted at the county level, which limits the identification of smaller areas with poor ecological quality, this study incorporates high-resolution (1 km \(\times\) 1 km) MODIS remote sensing data products to assess ecosystem greenness. Indicators such as the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Gross Primary Productivity (GPP) were utilized to evaluate the vegetation cover of the study area comprehensively. These indicators provide a basis for assessing ecological quality from the perspective of greenness characteristics.
# Flame Retardance and Smoke Suppression of CFA/APP/LDHs/EVA Composite
## 3 Results and Discussion  
### XRD and IR Analysis of the Prepared LDHs  
Figure 2A shows the XRD patterns of three LDHs. All patterns exhibit (003), (006), (012), (015), (018), (110) and (113) characteristic reflections of the LDHs structure, which can be indexed in a 3R polytype [23; 24]. The basal spacing (\(d_{\text{basal}}\)) of LDH-Ni, LDH-Co and LDH-Cu is 7.67, 7.56 and 7.67 A, respectively. The thickness of a brucite-like layer is 4.8 A [25], the interlayer spacing of stearate and nitrate are approximately 26.72 A [26] and 4.23 A [27], respectively. Therefore, the \(d_{\text{basal}}\) values of LDH-Ni, LDH-Co, and LDH-Cu indicate that the interlayer anions are CO\({}_{3}\)2\({}^{-}\), without the coexistence of NO\({}_{3}\)\({}^{-}\) or C\({}_{18}\)H\({}_{35}\)O\({}_{2}\)\({}^{-}\). Figure 2B gives the IR spectra of three LDHs. The bands at 3445 cm\({}^{-1}\) and 1630 cm\({}^{-1}\) are attributed to the O-H stretching vibrations of layer hydroxyl groups and interlayer water molecules, respectively [28]. The band at 451 cm\({}^{-1}\) is ascribed to deformation of M-OH in the brucite-like layers. The band at 1376 cm\({}^{-1}\) is assigned to the antisymmetric stretching mode \(\upgamma_{3}\) of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Code**} & \multirow{2}{*}{**EVA (g)**} & \multirow{2}{*}{**APP (g)**} & \multirow{2}{*}{**CFA (g)**} & \multirow{2}{*}{**LDH (g)**} & \multirow{2}{*}{**LOI (\%)**} & \multicolumn{2}{c}{**UL-94 Rating**} & \multirow{2}{*}{**Phenomenon**} \\  & & & & & & & **(at 1.6 mm)** & \\ \hline EVA & 180 & 0 & 0 & 0 & 18.0 & Fail & Dripping \\ ELDH0 & 144 & 27 & 9 & 0 & 28.5 & Fail & Dripping \\ ELDH-Ni & 144 & 24.6 & 8.2 & 3.2 & 30.3 & V-2 & Dripping \\ ELDH-Co & 144 & 24.6 & 8.2 & 3.2 & 29.7 & V-0 & No Dripping \\ ELDH-Cu & 144 & 24.6 & 8.2 & 3.2 & 30.8 & V-2 & Dripping \\ \hline \hline \end{tabular} LOI: limiting oxygen index; UL-94: vertical burning test; EVA: ethylene vinyl acetate: APP: ammonium polyphosphate; CFA: charing-foaming agent; LDH: layered double hydroxides; ELDH: composite containing different LDH.  
\end{table}
Table 1: Composition, LOI and UL-94 test results for the pure EVA and composites.  
the interlayer carbonate. Two bands around 839 cm\({}^{-1}\) and 680 cm\({}^{-1}\) also are characteristic for the \(\
u_{2}\) (out-of-plane deformation) and the \(\
u_{2}\) (in-plane bending) of carbonate [29]. The IR analysis also confirms that the interlayer anions are only CO\({}_{3}\)\({}^{2-}\) without the coexistence of NO\({}_{3}\)\({}^{-}\) (at 1384 cm\({}^{-1}\)). In addition, two bands at 2927 cm\({}^{-1}\) and 2848 cm\({}^{-1}\) are associated with the C-H stretching vibrations of stearate anions. The weak bands at 1768 cm\({}^{-1}\) are the C=O vibrations of stearate anions [30; 31]. These weak bands confirmed that the H-bonding interaction occurred between the carboxyl and the hydroxyl [32]. The combination of IR and XRD characterization indicates that the stearate was grafted on the surface of LDHs.
## 3 Results and Discussion
### Catalyzed Oxidation of \(\beta\)-Myrcene
#### 3.4.6 Diels-Alder Investigation  
As explained previously, the Diels-Alder reaction was expected to be the most important side reaction for the oxidation of \(\beta\)-myrcene [13; 59]. Indeed, as observed during catalytic experiments, even under oxidative conditions, only a minor part of the substrate was converted into its corresponding oxide. The experiments clearly demonstrated that \(\beta\)-myrcene underwent a Diels-Alder dimerization at 80 \({}^{\circ}\)C in the absence of any catalyst or oxidant. This was confirmed by GC-MS analysis, which identified the dimerization product and showed no evidence of oxide formation, despite a 47% conversion of \(\beta\)-myrcene(Entry M1). Interestingly, when **(BmIm)\({}_{\bf 4}\)PMo\({}_{\bf 11}\)VO\({}_{\bf 40}\)** was used without an oxidant, the conversion dropped from 47% (Entry M19) to 37% after catalyst addition (Entry M20), suggesting that the catalyst interacted with \(\beta\)-myrcene and somehow slowed down the dimerization process.
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 4 Opportunities of ChatGPT in Education
### AI-Povered Learning and Student Engagement  
AI-powered educational tools, including ChatGPT, are shown to enhance student engagement by offering personalized learning experiences. According to [42], adaptive learning systems powered by AI can cater to individual learning needs by providing customized feedback and generating dynamic learning materials. Similarly, according to [43] highlights that ChatGPT fosters interactive learning by facilitating discussions and enabling real-time question answering. By leveraging these opportunities, ChatGPT can transform education by fostering innovation, improving learning accessibility, and enhancing engagement while ensuring ethical and responsible AI integration.  
Generally, ChatGPT offers transformative potential in education by enabling personalized learning, automated content creation, and interactive tutoring, while supporting teachers with lesson planning and administrative tasks. It enhances accessibility for underserved communities and learners with disabilities through features like speech-to-text and multilingual support. However, challenges such as academic integrity risks, algorithmic bias, data privacy concerns, and over-reliance on AI must be addressed through ethical governance, teacher training, and structured integration. Balancing innovation with responsible use is key to harnessing ChatGPT's benefits without compromising critical thinking or equity in education.
## 2 Methods
### Restrictions for Site Selection  
For a suitability analysis, the distance to highway networks (>200 m), higher slope regions (>15\({}^{\circ}\)), and non-installable areas (e.g., river and reservoir areas) were considered as constraint factors. First, the highway network data were confined to 200 m to limit the candidate sites for installing solar power plants (Figure 3b). When the distance between the transmission site and the solar panels exceeds 200 m, a high voltage drop causes a power generation loss. In addition, to prevent such losses, additional costs are often incurred for cable and solar inverter installations and impact the economic feasibility. Thus, this study limited the distance to highways to a maximum of 200 m, using a buffer function in GIS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Sub-Criterion** & **Source** & **Unit** \\ \hline Location & Highway networks & [22] & m \\  & Lake \& reservoir & [2] & m \\  & River & [2] & m \\  & PV output per capacity per hour & [11] & h \\  & Olography & Slope & [23] & \({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of data used for site selection.  
Figure 2: Case study area: Ucheon-myeon district in South Korea.  
Furthermore, this study used three conditions (e.g., river, reservoir, and slope) to limit areas that were not suitable for constructing power plants (Figure 3c). In South Korea, solar-power facilities are limited to 15\({}^{\circ}\) to ensure safety, such as landslide prevention. Therefore, this study constrains the search for candidate sites that satisfy gradients between 0\({}^{\circ}\) and 15\({}^{\circ}\). Overall, unsuitable sites are sorted out of the buffered regions representing highway network areas using a map-overlay technique. Multiple dataset layers are overlapped to identify the relationship of each layer, and then suitable areas are determined, as shown in Figure 3d.