esa-sceva/satcom-chunk-collection huggingface dataset:
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 4 Proposed Approach for Course Recommendation  
In this section, we propose a novel method for course recommendation based on HG meta-path embedding. For simplicity, in the following presentation we use the short name \"HGE-CRec\" for our proposed method. The framework of HGE-CRec is shown in Figure 2. As shown in the figure, HGE-CRec is a triple-layer architecture model that takes students' ratings of each course as the output. The first layer is named the _Meta-path Embedding Layer_ (MEL), the second layer is named the _Embedding Weight Aggregation Layer_ (EWAL), and the third layer is named the _Matrix Factorization based Prediction Layer_ (MFPL). The MEL generates meta-path embeddings for heterogeneous graphs and employs a GCN to simulate more meta-path embeddings. The EWAL employs GAT to aggregate neighbor node embeddings and concatenate them with the original embeddings. The MFPL uses a matrix factorization framework to predict students' ratings on courses. The details of MEL, EWAL, and MFPL are illustrated in Sections 4.1-4.3.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(G=(V,E)\) & The heterogeneous graph \\ \(A\) & The set of the types of node \\ \(P\) & A meta-path \\ \(\Gamma\) & The set of the meta-path \\ \(A_{t}\) & The type of node \(t\) \\ \(|N^{A_{t}}(v)|\) & The number of nodes of type \(A_{t}\) in the neighbor of node \(v\). \\ \(M^{t}\) & An adjacency matrix of HG. \\ \(e^{(l)}\) & A node embedding on the \(l\) meta-path. \\ \(e^{(l)}_{u_{i}}\),\(e^{(l)}_{i}\) & The finally embedding of user \(u\) and item \(i\). \\ \(\hat{r}_{u,i}\) & The rating predicted by user \(u\) on item \(i\). \\ \(u_{u},v_{i}\) & The potential factors for user \(u\) and item \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Important notations used in this paper.  
Figure 2: Framework of HGE-CRec.
## 2 Materials and Methods
### BMP_Net: A Point Cloud Deep Regression Network Incorporating Neighbor Attention
#### 2.3.2 Construction of the Point Cloud Transition Downsampling Module  
Point cloud networks, similar to convolutional neural networks, require downsampling operations to reduce the density of the data transmitted through the network. The Point Cloud Transition Downsampling module proposed in this paper is designed to decrease the density of point cloud data, thereby reducing computational complexity and memory consumption, while improving the model's efficiency. This module helps in extracting important features, reducing noise and redundant information, enhancing the model's generalization ability, and mitigating the risk of overfitting.  
The overall structure of the PTransDown (Point Transition Down Block) module is shown in Figure 4. It includes hierarchical set mixing, farthest point sampling, neighborhood query and grouping, channel contraction, Softmax normalization, and linear layers. Through farthest point sampling, the module selects representative points from the original point cloud to preserve key information. Next, feature information undergoes hierarchical set mixing and neighborhood query and grouping, enabling feature interaction and local feature acquisition. The channel contraction module reduces the dimensionality of neighborhood features, decreasing computational complexity. Softmax normalization determines feature weights, providing a reference for feature integration. The linear transformation module maps the input features to the output space and extracts feature information. Finally, the linear layer outputs the feature, which is fused with the hierarchical set mixing output features, offering decision support for the biomass prediction task. These steps work collaboratively, allowing the PTransDown module to effectively reduce the dimen  
Figure 3: Structure of the PFMixer module. Input point coordinates (p) and features (x) undergo kNN neighbor selection, followed by ‘intra-set’ mixing for local feature interaction and ‘inter-set’ mixing for feature propagation across regions, both with ReLU. Symmetric Operation Pooling (SOP) aggregates features to output updated features (y), enhancing biomass-related feature representation.  
sionality of point cloud data, extract key features, and provide a foundation for biomass prediction tasks.
## 3 Results and Discussion
### PXRD Analysis  
The crystallinity of the nanocomposites consisting of 1% CuO/TiO\({}_{2}\)@GCN (1:9) nanocomposite and 1% CuO/TiO\({}_{2}\)@GCN (1:9)-CA (CTG-500) membrane was investigated using PXRD. The patterns are shown in Figure 1. The CA membrane pattern was characterized by two prominent peaks 20 positions of 8\({}^{\circ}\) and 18\({}^{\circ}\). These peaks are attributable to the CA membrane as it is semicrystalline [39; 40].  
The characteristic peaks of anatase-TiO\({}_{2}\) in 1% CuO/TiO\({}_{2}\)@GCN (1:9) were observed at 20 values of 25.3\({}^{\circ}\), 36.1\({}^{\circ}\), 37.9\({}^{\circ}\), 41.4\({}^{\circ}\), 48.0\({}^{\circ}\), 54.2\({}^{\circ}\), 56.6\({}^{\circ}\), 62.4\({}^{\circ}\), 67.0\({}^{\circ}\), 70.1\({}^{\circ}\), 75.2\({}^{\circ}\), and 82.7\({}^{\circ}\) were attributed to the diffraction planes (101), (103), (004), (112), (200), (105), (211), (204), (116), (220), (215) and (303), respectively [12; 41]. The only peak attributable to GCN is the (002) reflection at 20 = 27.6\({}^{\circ}\), attributable to an extended range of layer by layer aromatic repeating units [42]. Peaks characteristic of CuO were not observed as it was used in small quantities (1%), relative to the very crystalline TiO\({}_{2}\).  
The PXRD peaks attributable to 1% CuO/TiO\({}_{2}\)@GCN (1:9) nanocomposites upon its incorporation into the CA membrane were still observable with slight shifts in 20 positions. This indicates that the photocatalyst was loaded onto the membrane matrix, with a possibility of interactions between the materials. The evidence suggests that the interaction that occurred between CuO/TiO\({}_{2}\)@GCN and CA matrix were Van Der Waals  
Figure 1: XRD patterns of 1% CuO/TiO\({}_{2}\)@GCN (1:9) powder and 1% CuO/TiO\({}_{2}\)@GCN (1:9)-CA (CTG–500) membrane and cellulose acetate (CA) membrane.  
forces, i.e., hydrogen bonding formed between the hydroxyl group in the cellulose network and also the surface OH groups of the photocatalyst.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
###### Abstract  
As an emerging information technology, Wireless Body Area Networks (WBANs) provide a lot of convenience for the development of the medical field. A WBAN is composed of many miniature sensor nodes in the form of an ad hoc network, which can realize remote medical monitoring. However, the data transmission between sensor nodes in the WBAN not only consumes the energy of the node but also causes the temperature of the node to rise, thereby causing human tissue damage. Therefore, in response to the energy consumption problem in the Wireless Body Area Network and the hot node problem in the transmission path, this paper proposes a temperature state awareness-based energy-saving routing protocol (TSAER). The protocol senses the temperature state of nodes and then calculates the data receiving probability of nodes in different temperature state intervals. A benefit function based on several parameters such as the residual energy of the node, the distance to sink, and the probability of receiving data was constructed. The neighbor node with the maximum benefit function was selected as the best forwarding node, and the data was forwarded. The simulation results show that compared with the existing M-ATTEPMT and iM-SIMPLE protocols, TSAER effectively prolongs the network lifetime and controls the formation of hot nodes in the network.  
wireless body area networks; TSAER; energy consumption; route optimization Article
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 2 Data and Methods
### SF Position Accuracy  
This section describes the position accuracy computation algorithm used by the different institutions. The algorithm consists of three main steps, which are detailed below.  
1. The Galileo E1 SF solutions are calculated in post-processing mode based on RINEX files, whereby the settings are applied as shown in Table 1. In the first run, the NeQuick G model is applied for ionospheric corrections, while in the other one, the Klobuchar model is used.  
2. The reference site positions are based either on the official International Terrestrial Reference Frame 2014 (ITRF2014) SINEX file station position and velocity solution (epoch 1 January 2010) extrapolated to the selected epoch from the analysed quarter of  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **GRC** & **NMA** & **SRC** & **TUW** \\ \hline Software & TOMION [6] & Where [7] & GALAT 1  & raPPpid 2 [FOOTNOTE:2]Footnote 2: The reference site positions are based either on the official International Terrestrial Reference Frame 2014 (ITRF2014) SINEX file station position and velocity solution (epoch 1 January 2010) extrapolated to the selected epoch from the analysed quarter of  
\end{table}
Table 1: Settings applied by each institution to calculate SF position accuracy.  
observation (TUW) or yearly calculated ITRF14 coordinates, which refer to an epoch on 1 st January of each year (NMA, GRC).
3. Coordinates obtained from the two SF solutions are directly compared epoch-wise in a local topo-centric coordinate system (East, North, Up) to the reference values in ITRF 2014, whereby differences are computed for east, north, and upward coordinates. The horizontal positioning error (HPE) and vertical positioning error (VPE) can be derived based on the differences in east, north, and up (\(\Delta E\), \(\Delta N\), \(\Delta U\)) for each epoch \(i\), as follows: \[HPE_{i}=\sqrt{\Delta E_{i}^{2}+\Delta N_{i}^{2}}\] (1) \[VPE_{i}=|\Delta U_{i}|\] (2)  
Then, the single-frequency position errors were calculated as a mean of the 95 th percentile (HPE 95% and VPE 95%) for the reference sites on a daily basis. Hereby, epochs exceeding the position dilution of precision (PDOP) threshold are excluded. The results in Figure 2 show the monthly average of these daily HPE 95% and VPE 95% site values grouped into high-latitude, middle north, middle south, and low-latitude regions.  
Figure 2: _Cont._
# Diversity and Seasonal Dynamics of Airborne Fungi in Nerja Cave, Spain
## 2 Materials and Methods  
### Site Description  
Nerja Cave is one of the biggest and most important show caves in Spain, with a volume of about 300,000 m\({}^{3}\), almost 5 km of passages, and more than 400,000 visitors annually. It is also one of the most significant prehistoric sites in Europe, with more than 500 artistic representations. About a third of the cave, the Touristic Galleries, is open to the public since 1960. The other two sections, named High Galleries and New Galleries, only can be accessed by researchers.  
The climate of the area is coastal Mediterranean, with an average precipitation slightly lower than 500 mm yr\({}^{-1}\). The external air average temperature ranges from about 13 \({}^{\circ}\)C to 26 \({}^{\circ}\)C (January and August, respectively), with an average annual value of 18.8 \({}^{\circ}\)C (Janusy and August, respectively).  
The cave is developed within fissured and karstified Triassic dolomitic marbles more than 500 m thick at the unsaturated zone of the Sierra Almijara carbonate massif. A significant portion of the dolomitic marbles is devoid of vegetation. The soil cover is scarce, with a thickness lower than 0.15 m, and the natural vegetation consists mainly of shrubs and pine trees. However, in the areas nearest to the touristic entrance, there are several gardens with many ornamental plants ([PERSON], 2018), and thus, the vegetation cover is much more developed over the Touristic Galleries than over the nonvisited sector.  
The cavity develops between 123 and 191 m a.s.l. According to the outside orography, rock thickness above the cave varies from 4 to 50 m in the area open to tourism, and exceeds 80 m in the nonvisited area. The cavity has three natural entrances: an entrance with a semicircular form and about 12 m\({}^{2}\) of surface (E1 in Figure 1), which is used for the access and exit of visitors and two subcircular sinkholes (E2 and E3 in Figure 1).  
Nerja Cave is connected to at least two other nearby cavities, Pintada Cave (PC)--a small cavity with a natural entrance located to 250 m a.s.l.--and another great and nonaccessible cave from the surface (GC), found recently with indirect methods ([PERSON] and [PERSON], 2017). Although there is no speleological connection between these three caves, they are effectively linked for air circulation patterns from the surface.  
Four ventilation regimes (winter, summer, spring, and autumn) and two ventilation modes with opposite airflows direction (named DAF- and UAF-modes) were defined in Nerja Cave karstic system during the annual cycle ([PERSON] and [PERSON], 2017). In all the cases, the natural ventilation of the system is determined by variations in the differences of density between the external and internal air.
## 3 Results
### Microscopic and Macroscopic Droplet Measurements
#### 3.2.4 Effects of Liquid Temperature  
In a droplet chain and in a spray, evaporation effects have to be taken into account. In a hot ambience, the droplet will heat up first while, during evaporation, the droplet temperature may drop again due to the evaporation enthalpy of the fuel. The effect of the liquid temperature of a monodisperse droplet chain on the LIF/Mie ratio was investigated for E20. The corresponding microscopic fitting curves for the investigated temperatures are shown in Figure 9.  
The equation of the microscopic LIF/Mie ratio at 293 K is shown in Equation (7), the behavior of the microscopic LIF/Mie ratios at 263K and at 323K are shown in the subsequent equations:  
\[I_{LIF/Mie\_E20\_263K_{(micro)}} = f\Big{(}d_{ droplet}\Big{)}\ =\ 0.0037\cdot d_{ droplet}^{1.30} \tag{8}\] \[I_{LIF/Mie\_E20\_323K_{(micro)}} = f\Big{(}d_{ droplet}\Big{)}\ =\ 0.0105\cdot d_{ droplet}^{1.37} \tag{9}\]  
An increase in liquid temperature from 263 K to 293 K does not show distinct variations in the LIF/Mie ratio. For example, an increase of 9% was observed for a droplet size of 30 \(\upmu\)m. The temperature dependency of the LIF signal was in accordance with the spectral analysis of the LIF signals in the micro cell (see Figure 2) which shows an increase in integrated intensity of 13% for a temperature variation from 263 K to 293 K. Thus, it can be confirmed that the temperature dependence of the LIF signal had a low influence on the accuracy of the LIF/Mie ratio technique at moderate temperatures. However, the elevated droplet temperature (323 K) leads to a more distinct change in the LIF/Mie ratio in comparison to 263 K and 293 K. For a droplet size of 30 \(\upmu\)m, this temperature increase results in  
Figure 9: Effects of liquid temperature on the LIF/Mie ratio at a constant dye concentration (100 mg/L nile red) in E20, 293 K.  
an increase of the LIF/Mie ratio of 56%. In general, previous investigations of monodisperse ethanol droplets using eosin-Y as dye showed a similar temperature behavior [15]. The temperature study within the droplet chain shows that droplet heating and cooling have to be taken into account for planar droplet sizing. The LIF/Mie ratio technique is suitable for the investigated fuel-dye-mixtures at moderate temperature variations without temperature and concentration correction, i.e., under conditions with low fuel evaporation rates and reduced droplet heating or cooling.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### Multi-Drought Indices
#### 2.2.2 Agricultural Drought Indices  
Agricultural drought occurs when crops do not have enough water to start and maintain growth due to a lack of short-term rainfall, a lack of moisture in the ground surface and root areas, and an abnormality in the surface temperature. Drought indices representing the effects of the aforementioned variables were developed to quantify agricultural drought. The most commonly used vegetation data-based drought indices determined using satellite images include the vegetation health index (VHI, [22]), microwave integrated drought index (MIDI, [23]), and agricultural drought condition index (ADCI, [24]), as shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SPI Range** & **Moisture Condition** \\ \hline \(>\)2.00 & Extremely wet \\
1.50–1.99 & Very wet \\
1.00–1.49 & Moderately wet \\ \(-\)0.99–0.99 & Near normal \\ \(-\)1.00—1.49 & Moderately dry \\ \(-\)1.50—1.99 & Severely dry \\ \(<\)\(-\)2.00 & Extreme dry \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of drought conditions according to the range of each drought index.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**VHI, ADCI, MIDI Range** & **Classification of Drought Conditions** \\ \hline \(>\)40 & No drought \\
30–40 & Mild drought \\
20–30 & Moderate drought \\
10–20 & Severe drought \\
0–10 & Extreme drought \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of drought conditions according to VHI, ADCI, and MIDI ranges.
# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 3 Results
### Results of Sharpness and Resolution Measurements
#### 3.1.2 Cameras + DermLite 4 DL Handheld Dermoscope Sharpness Results  
The tested dermoscopy images were captured with the DermLite 4 DL handheld dermoscope attached to a smartphone or a mirrorless digital camera lens. For image sharpness analysis, we used a micro-test chart where a thin silver coating layer was applied onto a glass plate, which provided a slanted edge that was perfectly straight.  
To understand the image quality of the optical system Camera + DermLite 4 DL handheld dermoscope accurately, it was critical to combine the effects of each component to determine how the system will affect sharpness. Every component--the phone lens, the camera, and the handheld dermoscope--has an MTF value, and by multiplying each MTF, we were able to arrive at a value of a system MTF. In Figure 6, we present the graphs that analyse the sharpness of the iPhone 7 camera and DermLite 4 DL handheld dermoscope. The shape of the MTF shows a significant loss of sharpness. The MTF50 decreased to the value 1479 LW/PH, edge rise distance (10-90%) was 5.8 pixels, and we calculated under sharpening of 18.7.  
The results from the other five cameras that we used in conjunction with a mounted handheld dermoscopy device are summarised in Table 3. The sharpness measurements obtained with the state-of-the-art digital dermoscopy system FotoFinder with a micro camera Medicam 1000s are presented in the last row of Table 3. Among all devices tested, the Medicam 1000s camera yielded the lowest values regarding image resolution. The numerical value indicates that this camera is not superior compared to the other cameras, at least if overall quality is evaluated only on sharpness and resolution. However, the Medicam 1000s is supported by an optical zoom lens with 20 to 400 times magnification, that compensates for the lower resolution of the camera sensor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera** & **Nyquist f (LW/PH)** & **10–90\% (Pixels)** & **MTF50 (LW/PH)** & **Over/Under Sharpness (\%)** \\ \hline lphone 5s & 2448 & 10 & 1116 & –36.6 \\ lphone 7 & 3024 & 5.8 & 1479 & –18.7 \\ lphone XR & 3024 & 15.7 & 1048 & –25.9 \\ Samsung S10 & 3024 & 16.3 & 1399 & 12.2 \\ Huawei P10 & 2976 & 8.6 & 796 & –38.1 \\ Canon EOS M10 & 3456 & 13.5 & 1399 & –34.5 \\ Medicam 1000s (FotoFinder) & 1080 & 1.9 & 519 & –9.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cameras with DermLite 4 DL dermoscope sharpness results—macro test chart.  
Figure 6: Sharpness results for iPhone 7 camera with DermLite 4 DL dermoscope.
These studies have shown that soil reinforcement has a significant effect on soil-engineering properties. The models commonly used to describe the SWCC include the models of [PERSON] (VG) [39], [PERSON] [40], [PERSON] [41], and [PERSON] [42]. The mixture of fibres to sand significantly influences the water-retention characteristics of the soil; however, existing models do not consider the effect of fibres on soil water-retention characteristics. In summary, previous studies on fibre-reinforced soil mostly focused on dry and saturated soil. The unsaturated mechanical properties and water-retention properties of fibre-reinforced soils require further investigation, particularly the lack of tools capable of predicting the hydraulic properties of fibre-reinforced soils. This poses numerous challenges to simulating fibre-reinforced soil structures, thereby limiting the practical application of fibres in soil reinforcement. In this study, the mechanical properties and water-retention characteristics of unsaturated fibre-reinforced sand are investigated. The mechanical properties are examined through compression and direct shear tests. Furthermore, the effects of fibre content, fibre length, and moisture content on the shear strength, internal friction angle, and cohesion of soil are analysed. The SWCC of fibre-reinforced sand was obtained using the axial translation method. Subsequently, based on the VG model, a modified model considering fibre reinforcement (VG-CFR) is derived for quantitatively analysing the influence of fibre content on the SWCC. The research results can provide a scientific basis for improving the engineering properties of soil and gaining a deeper understanding of the water-retention characteristics of unsaturated fibre-reinforced soil. The proposed model can be used as a valuable tool for predicting the hydraulic properties of unsaturated fibre-reinforced soils.
## 5 Conclusions  
In summary, we studied the performance of mixed RF-FSO system with hybrid PPM-BPSK-SIM and fixed-gain relay. The RF link and the FSO link undergo Rayleigh distribution and Gamma-Gamma distribution severally. The expression of the unconditional BER of the hybrid RF-FSO system have been derived. On basis of the mathematical representation above, influences of the FSO link including atmospheric turbulence and pointing error have been investigated.  
The results of our study show that the BER performance of the hybrid system could be ameliorated when it adopts hybrid PPM-BPSK-SIM, and the specific improvement effect has a lot to do with the length of symbol, L. The BER of the hybrid system degrades as the value of L increases, and when L is greater than eight, the hybrid system has a better BER performance than BPSK. The hybrid PPM-BPSK-SIM can improve the performance of the mixed RF-FSO system stably and obviously with strong turbulence and severe pointing error conditions. The outage probability and the average  
Figure 9: (**a**) Average capacity against average SNR per hop for different turbulence intensities and different pointing error parameters. (**b**) Average capacity difference between different pointing error parameters.  
channel capacity are sensitive to turbulence effect and pointing error of the FSO channel. Simulation results also indicate that the pointing error can aggravate the effect of atmospheric turbulence on channel capacity and outage probability. Thus, the performance of mixed RF-FSO system could be improved by adopting hybrid PPM-BPSK-SIM, especially under conditions with strong turbulence influence and severe pointing error effect.  
Conceptualization: H.L.; formal analysis and writing: T.J.; investigation: L.Z.; methodology: X.Y.; validation: D.D. and A.L.; software: Z.W.  
This study was funded by the National Natural Science Foundation of China; grant numbers are 61875057 and 61475049.  
The authors declare no conflict of interest.