esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging the Vocabulary Gap: Using Side Information for Deep Knowledge Tracing
## 4 Knowledge Tracing Model Based on Side Information
### Encoder Layer  
The encoder layer in our study is an encoder module of the standard Transformer model [35], which is mainly used to process student historical data. Firstly, the two sequences of data (\(SK_{t+1}\) and \(SQ_{t+1}\)) pass through a fully connected layer (see Figure 2 FL1 and FL2) to expand the data to the specific dimension (see Formulas (3) and (4)), and then the position information (see Figure 2 Position Encoding 1 and Position Encoding 2) learned through an embedding layer is spliced [24]. Finally, the sequences of data enter the two TF-Encoder layers, respectively (see Formulas (5) and (6)).  
\[SK_{fl}=SK_{t+1}W_{1}+b_{2} \tag{3}\]  
\[SQ_{fl}=QK_{t+1}W_{2}+b_{2} \tag{4}\]  
\[SK_{tf}=Encoder(SK_{fl}+Position(SK_{fl})) \tag{5}\]  
\[SQ_{lf}=Encoder(SQ_{fl}+Position(SQ_{fl})) \tag{6}\]  
These two TF-Encoder layers adopt the same structure, as shown in Figure 3. They are encoder modules within the Transformer architecture, utilizing their multi-head attention mechanism to process time-series data and extract features [35]. Each encoder consists of multiple layers, each comprising multi-head self-attention and a feed-forward network. The multi-head attention mechanism allows the model to attend to different positions of the input sequence simultaneously. The formula is as follows:  
\[\text{MultiHead}(Q,K,V)=\text{Concat}(\text{head}_{1},\dots,\text{head}_{h} )W^{O} \tag{7}\]  
\[\text{head}_{i}=\text{Attention}(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{8}\]  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{9}\]  
\[\text{output}=\text{LayerNorm}(\text{input}+\text{Sublayer}(\text{input})) \tag{10}\]  
\[\text{FFN}(x)=\max(0,xW_{1}+b_{1})W_{2}+b_{2} \tag{11}\]  
Here, \(Q,K,V\) represent the query, key, and value matrices, respectively; \(W^{Q},W^{K},W^{V},W^{O}\) are learnable parameter matrices; and \(d_{k}\) is the dimension of the key vectors used for scaling the dot product. The output of each submodule (self-attention and feed-forward network) is followed by a residual connection and then layer normalization (see Formula (10)), where \(\text{Sublayer}(\text{input})\) is the output of the sub-layer itself. Each encoder layer also includes a feed-forward network, which is a series of linear transformations applied independently but identically at each position (see Formula (11)), where \(W_{1},W_{2},b_{1},b_{2}\) are the parameters of the feed-forward layers.  
Figure 3: Framework of TF-Encoder layer.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 3 Experimental Results and Analysis  
The proposed architecture of the [PERSON] edge detection algorithm is tested on a grayscale image size of 512 \(\times\) 512 from Standard Test Image Database [29], which is implemented using Intel Quartus Prime 18.0 and is verified on Altera's Cyclone IV E: EP4 CE10F17C8 FPGA board.  
The proposed technique uses (1) an approximation technique for the computation of gradient magnitude and orientation, (2) [PERSON]'s algorithm adaptively calculates threshold (3) a logarithmic operation unit is introduced to reduce the complexity of [PERSON]'s algorithm.
## 4 Validation and Grid Independency Analysis  
Five different grids were used to confirm that the results were not dependent on the grid. The Nu\({}_{\text{avg}}\) and the stream function (shown in Table 2) are used as the indicators of the results. Due to the different results, the fourth grid was preferred as the final grid, ensuring the numerical solution method is one of the primary criteria for achieving results. Previous studies by [PERSON] et al. [41] and [PERSON] et al. [42] were used to validate our model, as shown in Figure 3 and Table 3. It should be noted that the streamlines in the upper part of the validation in Figure 3 do not coincide because of the grid size used in the simulated case. We believe that having a finer grid resolves these discrepancies and, also, that the discretization method used is different from the compared case in the literature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Mesh size & 2988 & 4740 & 2442 & **29912** & 41728 \\ \hline Nu\({}_{\text{avg}}\) & 7.2360 & 7.2876 & 7.4241 & **7.4959** & 7.4969 \\ \hline \(\psi_{\text{max}}\) & 1.3074 & 1.3181 & 1.3244 & **1.3279** & 1.3306 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of the mean Nusselt number (Nu\({}_{\text{avg}}\)) and \(\psi_{\text{max}}\) for diverse grid resolution.  
Moreover, the current code is validated with the case of [PERSON] et al. (2018), in which a porous media is included. The cavity considered is a square shape with porous media. Table 4 Shows that the current code gives results with an average error of less than 2% for all the cases compared.
## 3 Results and Discussions
### Comparison between Two- and Four-Probe Configurations and Corresponding Percolation Thresholds  
The comparison of the resistivity depending on the different probe configurations is shown in Figure 8. Regardless of the CF lengths, the resistivity of cement composites was lower for the four-probe configuration compared with the two-probe configuration, at the same electrode spacing. As mentioned previously, unlike the two-probe configuration, in the four-probe configuration, the contact resistance is not included in the measurement of resistivity [44; 45]. There was no significant decrease in the resistivity beyond the 1.0% CF content for the electrode spacing of 10 mm, indicating that the percolation threshold of the composites measured via the four-probe configuration was 1.0% CF. The resistivity of the cement composite measured via the two-probe configuration showed a continuous decrease as the CF content increased to 2% CF; therefore, the percolation threshold measured via the two-probe configuration was 2% or lower, corresponding to the electrode spacing of 10 mm.  
The differences in the variation in resistivity as a function of the CF content observed between the two- and four-probe configurations became less distinct with increasing CF content. The contact resistance depends on both the conductivities of the electrode and cement composite; however, the former can be neglected because of its very low resistance [44]. The influence of contact resistance is small at a high CF content [44]. Therefore, it may be concluded that the difference between the  
Figure 8: Comparison of resistivity of cement composites as a function of the CF content measured via two- and four-probe configurations corresponding to 10 mm electrode spacing (2E10 and 4E10) and CF lengths of (**a**) 4 mm (L04), (**b**) 6 mm (L06), and (**c**) 10 mm (L10).  
resistivities measured via the two different probe configurations would be insignificant at a high CF content of 2%.
## 4 Discussion  
The intent of this retrospective study was to uncover the prevalence of SOS, STM and their association with different phenotype factors related to EET using CBCT imaging in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Mean (mm)** & **SD (mm)** & **MD (mm)** & **95\% Confidence Interval** & \(p\) Value** \\  & & & & & **Lower** & **Upper** \\  & & & & & **Bound** & **Bound** & \\ \hline d. TS-SF & 13 & 7.014 & 1.355 & 13 vs. 23 & \(-\)0.784 & \(-\)1.470 & \(-\)0.097 & 0.016 \\  & 23 & 7.797 & 1.631 & 13 vs. 1323 & 0.345 & \(-\)0.302 & 0.991 & 0.945 \\  & 1323 & 6.669 & 1.382 & 13 vs. 4.00 & \(-\)0.310 & \(-\)1.009 & 0.390 & 1.000 \\  & 4.00 & 7.324 & 1.294 & 23 vs. 1323 & 1.128 & 0.473 & 1.783 & \(<\)0.001 \\  & & & & 23 vs. 4.00 & 0.474 & \(-\)0.234 & 1.181 & 0.458 \\  & & & & 1323 vs. 4.00 & \(-\)0.654 & \(-\)1.323 & 0.014 & 0.059 \\ e. PClin-SF & 13 & 7.126 & 1.348 & 13 vs. 23 & \(-\)0.321 & \(-\)0.987 & 0.344 & 1.000 \\  & 23 & 7.447 & 1.464 & 13 vs. 1323 & 0.346 & \(-\)0.281 & 0.972 & 0.861 \\  & 1323 & 6.780 & 1.334 & 13 vs. 4.00 & \(-\)0.262 & \(-\)0.940 & 0.416 & 1.000 \\  & 4.00 & 7.388 & 1.369 & 23 vs. 1323 & 0.667 & 0.032 & 1.302 & 0.034 \\  & & & & 23 vs. 4.00 & 0.059 & \(-\)0.627 & 0.745 & 1.000 \\  & & & & 1323 vs. 4.00 & \(-\)0.608 & \(-\)1.256 & 0.040 & 0.079 \\ f. SM-SF & 13 & 7.459 & 1.404 & 13 vs. 23 & \(-\)0.525 & \(-\)1.182 & 0.133 & 0.208 \\  & 23 & 7.983 & 1.360 & 13 vs. 1323 & 0.315 & \(-\)0.303 & 0.933 & 1.000 \\  & 1323 & 7.144 & 1.335 & 13 vs. 4.00 & \(-\)0.174 & \(-\)0.843 & 0.495 & 1.000 \\  & 4.00 & 7.633 & 1.335 & 23 vs. 1323 & 0.839 & 0.213 & 1.466 & 0.003 \\  & & & & 23 vs. 4.00 & 0.350 & \(-\)0.327 & 1.027 & 1.000 \\  & 1323 vs. 4.00 & \(-\)0.489 & \(-\)1.129 & 0.150 & 0.258 \\ g. Sellaarea & 13 & 60.329 & 15.731 & 13 vs. 23 & \(-\)3.756 & \(-\)12.166 & 4.655 & 1.000 \\  & 23 & 64.084 & 19.248 & 13 vs. 1323 & 5.309 & \(-\)2.604 & 13.223 & 0.454 \\  & 1323 & 55.019 & 17.112 & 13 vs. 4.00 & 1.941 & \(-\)6.625 & 10.507 & 1.000 \\  & 4.00 & 58.388 & 17.441 & 23 vs. 1323 & 0.965 & 1.041 & 17.088 & 0.018 \\  & & & & 23 vs. 4.00 & 5.697 & \(-\)2.971 & 14.364 & 0.490 \\  & & & & 1323 vs. 4.00 & \(-\)3.368 & \(-\)11.555 & 4.818 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 3 Results  
### Surface Water Area Validation Using SkySat Imagery  
The surface area obtained from PlanetScope, Basemap, and Planet Fusion showed great agreement (r\({}^{2}\geq\) 0.98) with the validation dataset. In addition, PlanetScope had the smallest MAPE (8.09%), followed by Basemap (8.21%) and Planet Fusion (9.17%) (Figure 3). When splitting the validation surface area observations into different size classes (Table 4), all three image sources presented similar agreement (r\({}^{2}\geq\) 0.87), and the highest r\({}^{2}\) values were found for surface area observations between 10 and 30 ha (r\({}
## 3 Results and Discussion
### Spatial Distributions of Categorical Skill Metrics  
In this section, the capabilities of IMERG products have been assessed for the detection of rainy days over India. During the southwest monsoon period, daily rainfall less than 2.5 mm is considered as a non-rainy day according to the IMD convention. Therefore, a threshold of 2.5 mm day\({}^{-1}\) is considered for the detection of rainy days from both multi-satellite and rain-gauge-based data. Figure 9 shows the spatial distributions of POD and FAR in IMERG products in the detection of rainy days across India, whereas FBI and PSS are shown in Figure 10. In general, higher POD and PSS and smaller FAR can be seen in IMERG products across the country except for few regions. IMERG products show smaller POD, FBI, and PSS and larger FAR over the northern India. Both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 shows marginal improvement over the IMERG-F V5 product. IMERG-F V6 shows notably better performance in rainy day detection during the GPM period than the pre-GPM period. Interestingly, POD is relatively smaller and FBI is rather larger in the IMERG suite over the orographic regions than the plain regions. It clearly indicates that satellite-based product performs better over the plain regions than orographic regions  
Figure 7: Spatial distributions of correlation coefficient and modified Kling–[PERSON] efficiency in IMERG estimates as compared to the rain-gauge-based data over India for the GPM (2014–2017) and pre-GPM (2010–2013) periods.  
in the detection of monsoon rainy days. FAR in the IMERG suite is smaller over the windward side of the Western Ghats than the leeward side, but the opposite is true in the case of PSS. Over the hilly northern part of the country (Jammu and Kashmir region), IMERG products show exceptionally small POD, FBI, PSS, and large FAR as compared to the rain-gauge-based dataset. However, rain gauge density is meager over this high altitude region, and the rain-gauge-based gridded data might have larger uncertainty over this region.  
Figure 8: Interannual variations in all-India monsoon rainfall from the IMD rain-gauge-based dataset and IMERG-F version 6 estimates. Bias and root mean square errors in IMERG-F product for the same period against the rain-gauge-based dataset are also shown in the figure.  
Figure 9: Spatial distributions of probability of detection and false alarm ratio in the detection of rainy days from the IMERG estimates as compared to the rain-gauge-based data over India for the GPM (2014–2017) and pre-GPM (2010–2013) periods.
# Electric-Field-Coupled Resonator Antenna for 5G Applications
## 6 Conclusions  
In this paper, an electric-field-coupled wideband miniaturized antenna is presented that depends on loop inductance and gap capacitance for resonance. These types of resonators can achieve compactness with low dielectric constant substrates without changing the dimension of the overall structure. In this research, techniques are adopted to improve the performance parameters of the structure. The performance of the antenna, especially the bandwidth, is enhanced by employing a defected ground structure. In addition, the gain is improved by placing parasitic elements on both sides of the microstrip feed line. The bandwidth of Antenna 1 is only 9.12%, and the gain is very low (0.063 dBi at 2.89 GHz). After applying the techniques, the bandwidth is enhanced by 116.12% and the gain is increased by 1.1 dBi. The antenna covers almost the entire 3 GPP bands n77 and n78 of 5G. Because of its wideband property with compact structure and good radiation property, it is suitable for 5G embedded applications.
## 3 Results and Discussion
### Flavonols
#### 3.5.3 Phenolic Acids
Ferulic acid has been studied for its potential to improve cardiovascular health by reducing blood pressure, inhibiting the formation of blood clots, and improving lipid levels in the blood [84]. Additionally, some research suggests that ferulic acid may have anti-cancer, antioxidant, anti-inflammatory, and antimicrobial properties [85]. Ferulic acid has been shown to have photoprotective effects, helping to protect the skin from UV damage and reduce the risk of skin cancer [86]. Compounds **44**, **52** and **54** with [M\(-\)H]\({}^{-}\) at \(m/z\) 294.0631, 473.0736, and 359.0776 were putatively designated as caffeoyl glucose, chicoric acid and rosmarinic acid, respectively, which were detected in basil and mint leaves. Chicoric acid and rosmarinic acid have potential health benefits, including antioxidant, anti-inflammatory, antimicrobial, and anti-diabetic effects. It can reduce inflammation in the body, improve blood sugar levels, improve digestion, and protect against cellular damage caused by oxidative stress. Additionally, some research suggests that chicoric acid may help to improve cognitive function and protect against neurodegenerative diseases like Alzheimer's and Parkinson's [87]. Rosmarinic acid may have the potential as a natural allergy treatment by reducing inflammation in the airways and improving respiratory function [88]. While four more Compounds, **46**, **47**, **49** and **50**, were detected in fenugreek, basil and mint leaves and identified to be 3-feruloylquinic acid, caffeic acid, \(p\)-counamic acid 4-O-glucose, 3-\(p\)-counaroylquinic acid with the negative mode of ionization at \(m/z\) 367.1062, 179.0346, 325.0923 and 337.0939, respectively. Caffeic acid has several potential health benefits, including anti-cancer, antioxidant, anti-inflammatory, anti-diabetic and antimicrobial properties [89]. Compound **45** (\(m/z\) 294.0631--caffeoyl aspartic acid) was determined in mint leaves only in a negative mode of ionization. Compounds **42** (\(m/z\) 353.0867), **53** (\(m/z\) 515.1165), **48** (\(m/z\) 223.0626) and **55** (\(m/z\) 147.0460) were putatively recognized as 3-caffeoylquinic acid, 1,5-dicaffeoylquinic acid, sinapic acid and cinnamic acid, respectively, in fennel seeds and basil, fenugreek, and mint leaves extract in negative mode. Cinnamic acid has several potential health advantages, including anti-cancer, antioxidant, anti-diabetic, anti-inflammatory, and antimicrobial properties [90]. Some studies have suggested that cinnamic acid may have potential as a natural sunscreen ingredient, as it can help to absorb UV radiation and protect the skin from damage [91]. _P_-coumaric acid (compound **47**) was found in [M\(-\)H]\({}^{-}\) mode at \(m/z\) 163.0405.
## 4 Multi-Scale High-Dimensional Mapping Entropy (MHDME)  
### Refined Composite Multi-Scale Studies  
#### 4.1.1 Multi-Scale High-Dimensional Mapping Entropy  
Combined with the idea of multi-scaling to determine the original sequence coarse-graining law, matching forms the multi-scale high-dimensional mapping entropy (MHDME) algorithm as follows:  
(1)  
Pre-set the original sequence with \(r\), \(m\), and \(N\) to create a new coarse-grained sequence:  
\[y_{j}^{\tau}=\frac{1}{\tau}{\sum_{i=(j-1)\tau+1}^{\tau}x_{i}(1\leq j\leq\frac{ N}{\tau})} \tag{26}\]  
where \(\tau\) represents the scale factor. When \(\tau=1\), \(y_{i}(1)\) is the primary sequence. For the non-zero positive integer \(\tau\), the primary sequence is divided into \(\tau\) coarse grain sequences \(y_{j}^{\tau}\), each with length \(\frac{N}{\tau}\);  
(2)  
Calculate the high-dimensional mapping entropy for each coarse-grained sequence to obtain the variation of the multi-scale high-dimensional mapping entropy with increasing scale factor:  
\[MHDME(x,m,r,\tau,sigma,N)=HDME\big{(}y_{j}^{\tau},m,r,sigma\big{)} \tag{27}\]  
The MHDME reflects the regularity and complexity of time series at various scales. With the rising scale factor, the length of the coarse-grained sequence declines, indicating that the time series structure contains more information under the smaller scale factor.
## 2 Materials and Methods
### A Coordinate System, a Map Projection, and a Timeframe
Since the Lunar timescale is developed from measurements from all transceivers, special error correction algorithms are implemented to improve the accuracy of time measurements. When the Pathfinder constellation is completed, LRNTS transceivers would be able to collect measurements from the Pathfinder satellites and, thus, to calculate their coordinates in the Lunar reference frame. Here it is assumed that the Pathfinder satellites' coordinate frame will correspond to the Lunar reference frame. Then, transformation parameters from the local LRNTS system to the Lunar reference would be calculated using some Affine transformation models. Implementing the transformation parameters, all local coordinates could be transformed to the Lunar reference frame. LRNTS improves the concept [26] in a way that it will be used as a static transmitter constellation. After its establishment and self-calibration, when all LRNTS nodes obtain their coordinates, its role will be like any GNSS cosmic segment, providing navigation messages to the receivers performing regular surveying tasks within the crater.
## 2 EO Satellite and In Situ Observations for Earth Surface
### Terra/aqua--modis  
Terra and Aqua (Latin for land and water) are NASA Earth Science satellite missions equipped with multi-sensors aimed at studying the Earth's water cycle including evaporation from the oceans, water vapor in the atmosphere, clouds, precipitation, soil moisture, sea ice, land ice, and snow cover on the land and ice. Terra was launched in December 1999 and initiated data transmission in February 2000, with its five instruments, ASTER, CERES, MISR, MOPITT, and MODIS (see acronyms list for details, page 55). Aqua was launched on 4 May 2002, and had six Earth-observing instruments on board, AIRS, AMSU, AMSR-E, CERES, HSB, and MODIS, which are collecting a variety of global data sets. Aqua was originally developed for a six-year design life but has now far exceeded that original goal. About half of the sensors continue transmitting high-quality, in particular from AIRS, CERES, and MODIS, with reduced functionality from AMSU-A channels. AMSR-E became inactive since 2011, with HSB, collected approximately nine months of high quality data but failed in February 2003. For both satellite platforms MODIS (Moderate-resolution Imaging Spectroradiometer) is a key instrument with its 36 spectral bands ranging in wavelength from 0.4 \(\upmu\)m to 14.4 \(\upmu\)m and at varying spatial resolution (two bands at 250 m, five bands at 500 m and 29 bands at 1 km). Together, the instruments image the entire Earth every one to two days. A large amount of MODIS-based Earth's surface products are currently used in weather and climate modelling, as detailed in Section 2.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Challenges of IoMT
#### 3.2.3 Cost Effectiveness  
Health-monitoring systems have a significant number of sensors and medical equipment that are linked. These are expensive to upgrade and maintain, which affects both the manufacturer and the end user. Incorporating inexpensive, low-maintenance sensors can encourage the creation of more IoMT devices and help them become more widely used [52].
## 2 Materials and Methods
### Proposed GP-Based Feature Construction Approach
#### 2.3.2 Multi-Tree Representation  
The canonical GP algorithm encodes a potential solution in a single tree [26]; thus, only one feature can be constructed, risking a lack of separability between survivor and non-survivor class distributions. This issue can be overcome using a multi-tree representation to create a multi-dimensional feature space, in which a potential solution constructs a new feature set with \(m\) predictors from an original set with \(d\) features. We used the multi-tree representation proposed by [27], and Figure 3 illustrates this approach.  
In a multi-tree representation, every tree is denoted by \(t_{j}\) with \(j=1,\ldots,m\), having a maximum depth \(l_{\max}\), where a terminal node is an original radiomic feature, denoted by \(x_{i}\) with \(i=1,\ldots,d\). The inner nodes are arithmetic operators and mathematical functions that combine the inputs to construct a feature in the top node [27].  
In short, the terminal set comprises 105 radiomic features depicted in Section 2.2. No constants are considered. The function set includes four arithmetic operators \(\{+,-,\times,\div\}\) and two order statistics operators \(\{\min,\max\}\). Among the four arithmetic operators, the first three work with their usual meaning. However, the division operator is protected; it returns 1.0 if the denominator lies between \(-0.001\) and \(0.001\)[27].