esa-sceva/satcom-chunk-collection huggingface dataset:
# A Short-Term Forecast Model of foF2 Based on Elman Neural Network
## 4 Discussion
Two-day data from 2014 and 2016 were selected and they were plotted together with the predicted values from the IPSO-ENN model, the ENN model and the BPNN model, as shown in Figure 6. We found that all three models fit the rising and falling segments of foF2 curve well and reflect the diurnal trend of foF2. However, the IPSO-ENN model fit significantly better in the peaks and troughs of the curve. We also found that the diurnal variation of foF2 in 2016 was less than that of foF2 in 2014, which may have caused the RMSE in 2016 to be less than the RMSE in 2014. In order to evaluate the forecasting ability of the model during the ionosphere disturbance period, the data of an ionosphere disturbance occurred on December 20, 2015 was selected for the test. This disturbance was caused by a geomagnetic storm, and the change of Kp index during the period is shown in Figure 7. Comparing Figures 7a and 7b, it can be found that ionosphere disturbances occur after geomagnetic storms. The change of foF2 during the ionosphere disturbance period is very intense, which leads to a large error in the prediction. The RMSE of the IPSO-ENN model during this period is 1.23 MHz, which is less than 1.36 MHz of the ENN model and 1.25 MHz of the BPNN model. Details of the ionosphere disturbance should be incorporated into the IPSO-ENN model to improve the prediction ability of the model, which requires further investigation. Figure 5: The monthly performance comparison. (**a**) RMSE of three models at each month in 2014; (**b**) RMSE of three models at each month in 2016. Figure 6: The diurnal performance comparison. (**a**) Observed and predicted values in 2014; (**b**) observed and predicted values in 2016
## 2 Methodology  
### Model Geometry  
The geometry of the model is two-dimensional (2D) (Figure 2) and consists of four NACA 0021 aerodynamic profiles with different geometric parameters, which is shown in Table 1. The NACA 0021 airfoil was selected for this study for several reasons: it is symmetric and has proven its effectiveness in vertical-axis wind turbines operating under varying angles of attack; it demonstrates resistance to dynamic stall; it is widely referenced in the literature, which allows for meaningful comparison with previous studies; and it  
Figure 1: Wind turbine design: 1–4—blades, 5–8—half-swings of the turbine with NACA profile, 9—support bearings, 10—central rotation shaft, 11—outer rotation shaft, 12—housing [27].  
shows good performance at low to medium wind speeds, aligning with the operating conditions of small-scale VAWTs considered in this research.  
Although 3D CFD modeling provides a more detailed understanding of the air flow around the rotor, 2D modeling remains more attractive for studying the aerodynamic characteristics of VAWTs due to its lower computational cost, simplified mesh, and sufficient accuracy of results.  
3D calculations require significant resources in terms of time and hardware, while 2D modeling allows for faster numerical experiments, which is especially important when analyzing a wide range of parameters. In addition, the complexity of the 3D mesh can lead to numerical errors and increased memory consumption, while a 2D model can use a denser mesh with lower computational costs, which increases the accuracy of local blade aerodynamic analysis. As shown in studies [28; 29], the 2D CFD method gives fairly accurate results for estimating lift and drag coefficients, angles of attack, and VAWT aerodynamic efficiency, while the influence of three-dimensional effects such as blade tip vortices and secondary flows, although important, is not critical in the main plane of rotor rotation. In addition, 2D calculations allow for rapid testing of various geometric parameters such as diameter, chord length, and installation angle, accelerating the design optimization process, especially in the early stages of design, making them more effective in this study.
# Incorporation of UV Filters into Oil-in-Water Emulsions--Release and Permeability Characteristics
## 2 Materials and Methods
### Physicochemical Characterization of Emulsions
#### 2.3. Release Studies of UV Filters from Emulsions  
The release studies of avobenzene and octyl methoxycinnamate from the emulsions were carried out through dialysis tubing cellulose membranes (Sigma Aldrich, St. Louis, MO, USA) in 200 mL of receptor medium mimicking the pH of the skin (mixture of phosphate buffer with pH 5.8 and ethanol in the ratio of 1:1) maintained at 32 \({}^{\circ}\)C \(\pm\) 0.50 \({}^{\circ}\)C. The samples were withdrawn at a certain time (0.5; 1; 2; 3; 6; 12; 24; 48 h) and variations in the concentration of the Avo and OMC were registered using a UV-Vis spectrophotometer (Cary 60, Agilent, Santa Clara, CA, USA) at wavelengths of 361 nm and 311 nm, respectively. The release results were fitted to different kinetic models, such as zero-order (F\({}_{\text{t}}\) = \(k_{0}\)t), first-order (F\({}_{\text{t}}\) = \(1-\mathrm{e}^{-\mathrm{k}\mathrm{t}}\)), [PERSON]'s model (F\({}_{\text{t}}\) = \(k_{\text{H}}\)\(\sqrt{\mathrm{t}}\)), [PERSON] model (F = \((M_{\text{t}}/M)\) = \(k_{\text{KP}}\)t\({}^{\text{n}}\)), and the Hixson-Crowell model (\(\sqrt[3]{F_{0}}\) = \(\mathrm{k_{HC}}\)t), where F\({}_{\text{t}}\) is the fraction of Avo/OMC released into the receptor medium over time, t (h); F\({}_{0}\) is the initial amount (mg) of the UV filter in emulsion; and k\({}_{0}\), k\({}_{\text{t}}\), k\({}_{\text{KP}}\), k\({}_{\text{HC}}\) are the release constants of the particular kinetic models [36]. The correlation coefficient (R\({}^{2}\)) established which kinetic model follows a certain release profile.
# Two-Phase Flow Simulations Using 1D Centerline-Based C- and U-Shaped Pipe Meshes
## 2 Numerical Methods
### Mesh Generation  
We employed a 1D centerline-based mesh generation algorithm [17] to construct a curve pipe, which is beneficial when performing a control volume analysis in a subdomain. The pipe geometry (Figure 2) was composed of a horizontal part and a curvature part with a specific cross-section diameter. The cross-sectional area was constant, and the inner surface was smooth; hence, the pipe mesh was generated by specifying the number of nodes via the radial and angular directions of the cross-section. In our code, the cross-sectional faces are defined at the pre-described nodal points of individually divided lines. These faces then become the intermediate boundary of each volume zone. The algorithm for the mesh generation in the pipe is elaborated below:  
* Sweep the centerline (Figure 1A) to create the geometry. The linear and curve lines are written at the output file as the start and end points of each line, where the curve line additionally has the center point of the arc.
* Divide the 1D centerline into several linear lines (Figure 1B) using the Python programming language. Each line contains information of the cross-section radius and the divided line length; thus, we use the radius-length ratio to set the number of divided parts.
* Generate the last output before applying the meshing algorithm (Figure 1C).
* Apply the existing meshing algorithm [17]. The bold red circles represent the edges of every cross-section zone (Figure 1D). Surface and volume meshes are then constructed.  
Figure 1: Schematic of the C-shape pipe mesh construction: (**A**) centerline from FreeCAD; (**B**) divided centerline and element number; (**C**) intermediate cross-sectional zones of the C-shape pipe before the mesh generation; and (**D**) mesh with cross-sectional zones.  
Figure 2: Schematic of the C-pipe geometry.  
Along with a library, called TetGen, the freeware mesh-generating software \"Gmsh\" [18] was used to construct the surface and volume meshes (Figure 3A,B). The inlet, outlet, walls, divided volumes, and cross-section zones at the pipe meshes are defined accordingly. This algorithm could also generate non-uniform meshes (Figure 3C) in both axial and radial directions by providing the surface geometry and non-uniform grid size distribution to Gmsh and the non-uniform surface mesh to TetGen for generating the non-uniform volume mesh. Further details could be found in the reference [17].
## 3 Integrated Driving HIL Simulator for Evaluation of Cooperative Eco-Driving System
### QualNet  
In the proposed study, a QualNet 4.5 was utilized as the network simulator. QualNet was used to model and simulate vehicular networks, including various protocols at different layers of the protocol stack, and the wireless communication channel. QualNet provides a comprehensive set of network models for the entire protocol stack from the application to the physical layer and includes a variety of ad hoc routing protocols. It also includes high-fidelity wireless models that incorporate physical environmental effects (e.g., fading, path loss, and shadowing). Extensive performance metrics for a complete understanding of network behavior can be collected, including the throughput, latency, and dropped packets. However, QualNet does not contain an IEEE 802.11p/wireless access in vehicular environment (WAVE) protocol, which is standard for vehicular communication systems, and thus we implemented IEEE 802.11p PHY/MAC by enhancing the IEEE 802.11a module included in the QualNet library, after which we implemented the WAVE/1609 communication stack to simulate the VANET environment. In addition, suitable channel models for V2X communication were implemented within QualNet, such as the Nakagami fading model, two-ray path loss model, and corner path loss  
Figure 2: TMED-type HEV simulation model.  
model, based on the results of the V2X communication performance test using the actual device on various road environments. With these protocol stacks and channel models, V2X communication can be accurately simulated.  
To interact with external entities such as other simulators and programs, two external interfaces were implemented within QualNet. One of the external interfaces is the location synchronization interface (Local) which allows QualNet to update the movement information of nodes with the movement information provided by other simulators. In the proposed simulation environment, the Local server was implemented within QualNet and the Local client was implemented within ASM ControlDesk; in addition, the server-client communicated using user datagram protocol (UDP)/IP to synchronize traffic scenarios between two simulators. Another external interface is an IP network emulation (IPNE), which allows virtual communication packets generated within QualNet to be sent to external programs or hardware on a real network. Further, it allows QualNet to receive real communication packets generated within external programs or hardware on a real network. In the proposed simulation environment, QualNet sent virtual V2X messages collected by the HV in QualNet to the V2X communication device through the IPNE.
# Word Sense Disambiguation Using Clustered Sense Labels
###### Abstract  
Sequence labeling models for word sense disambiguation have proven highly effective when the sense vocabulary is compressed based on the thesaurus hierarchy. In this paper, we propose a method for compressing the sense vocabulary without using a thesaurus. For this, sense definitions in a dictionary are converted into sentence vectors and clustered into the compressed senses. First, the very large set of sense vectors is partitioned for less computational complexity, and then it is clustered hierarchically with awareness of homographs. The experiment was done on the English Senseval and Semeval datasets and the Korean Sejong sense annotated corpus. This process demonstrated that the performance greatly increased compared to that of the uncompressed sense model and is comparable to that of the thesaurus-based model.  
word sense disambiguation; clustering; deep learning; sense vocabulary +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, we discussed the technical route of swarm intelligence algorithm to solve the problem of trajectory segmentation and compression, and propose a PSOTSC algorithm based on particle swarm optimization, which transforms the trajectory segmentation problem into the global optimization problem of segment feature points. The PSOTSC algorithm makes the selection of feature points more accurate and improves the accuracy of trajectory segmentation. Meanwhile, the particle update strategy based on NARJ is established, which can effectively improve the search efficiency of trajectory segmentation solution. Compared with the standard particle swarm optimization method, the trajectory segmentation accuracy is higher under the same compression rate. The effectiveness of the proposed method is verified by real data set and simulation data set. The experimental results show that the PSOTSC algorithm can obtain a satisfactory segmentation and compression solution, and the compression ratio and segmentation accuracy are better than those in typical algorithms, such as DP, TRACLUS, and TCSS. Because the PSOTSC algorithm belongs to the global-oriented segmentation and compression method, the computational complexity will increase greatly when facing the long trajectory with more sampling points. In future, we will focus on reducing the time complexity of the algorithm and consider the long trajectory pre-segmentation, search by segment and synthesis optimization to expand the applicability of the algorithm. The PSOTSC algorithm takes advantage of SI to obtain the optimization solution through global search, but the problem inherent in the global search algorithm is the large computation. An intuitive and easy solution is that long trajectories can be divided into several short trajectories to search and optimize respectively and then complete the aggregation and overall optimization, but there may be some better way to deal with this issue. Therefore, the next research will focus on the long trajectory global segmentation and compression based on SI methods.  
Conceptualization, [PERSON]; Formal analysis, [PERSON]; Investigation, [PERSON]; Resources, [PERSON]; Supervision, [PERSON]; Visualization, [PERSON]; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by L.X.  
The authors declare no conflict of interest.  
Figure 11: Comparison results of segmentation accuracy and compression ratio: (**a**) Real trajectory data set; (**b**) Maneuvering target simulation data set.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 3 Results
### Land Cover Temporal Analysis
Analysing the percentage of 'Built-up Areas' in 2020 of these regions, we observed a rise of 110.37% (an additional 56.59 km\({}^{2}\)) in Castellon, a 70.47% increase (adding 143.30 km\({}^{2}\)) in Valencia, and a remarkable growth of 157.68% (an addition of 100.20 km\({}^{2}\)) in Alicante, compared to 1985 (Figure 11), revealing a significant increase over these 35 years. Figure 10: _Cont._  
Figure 11: _Cont._  
Figure 10: Maps illustrating the expansion of Built-up Areas class in Castellón (**top**), Valencia (**centre**), and Alicante (**bottom**) for the period 1985–2020.
## 2 Materials  
Figure 1 shows the process of data collection and pre-processing used to conduct topic modeling about geospatial data. For the analysis of geospatial data technologies and application trends, abstracts of papers related to geospatial data were collected from two paper databases, namely, ScienceDirect and Scopus. A total of 609 abstracts of papers were collected, which contained terms such as \"geospatial data \"from 2016 to 2020. In the ScienceDirect database, the query statement was \"TTILE-ABS-KEY (geospatial AND data)\", and in the Scopus database, the query statement was based on keywords (geospatial data). In the query result, only that were those connected with two words: \"geospatial\" and \"data\" were selected. In order to ensure that each abstract contains rich information, only those abstracts with more than 180 characters were selected, and 266 abstracts were finally analyzed. For those collected data, each abstract was used as an input to the Bert model, and obtained the corresponding word vector.  
Figure 2 represents the number of papers related to geospatial data published per year and by country. The number of published papers has been consistently increasing from 2016 to 2019, then with a significant decline in 2020 (Figure 2a). The number of papers in the top seven countries, namely, the USA, China, India, Germany, UK, RF (Russian Federation), and Italy, accounts for about 56% of the total number of papers, with the USA having the largest number of published papers (Figure 2b).
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 2 Methods
### Labeled Template Creation  
As shown in Figure 3, in a warehouse environment, pallets have the same spatial characteristics: goods are placed above the pallets, the pallet is set on the ground, and the pallets correspond to the category of the goods. Therefore, we have established a labeled template that preserves the dimensions of the pallet and the spatial characteristics mentioned above, as shown in Figure 5. The labeled template contains 4 categories: goods, pallet, pallet hole and the ground or shelf. The height of the goods and the ground label of the template are set as the height of the pallet.  
Element labels are represented with different colors. Green represents goods. Red represents pallets. Blue represents ground. Light blue represents pallet holes. The grids are compression units of label templet. \(s_{u}\) and \(T_{ij}\) represent the grid compressing unit size and the grid unit vector of Labeled templet, respectively (explained in Section 2.5 below). The pallet foot detection grids are explained in Section 2.7 below.  
Based on the projection principle of the camera and the spatial relationship, as shown in Figure 6, the pixel size of the pallet in the image coordinate system [I] is calculated with  
Figure 4: Category matrix example.  
Figure 5: Labeled template example.  
Equations (5) and (6), when the distance between the pallet and the camera is \({}^{C}z_{p}\). The whole template size is calculated with Equations (7) and (8).  
\[{}^{I}h_{p}=Round(\frac{f\cdot{}^{C}h_{p}}{\zeta_{2}{}_{p}\cdot{}dy}) \tag{5}\]  
\[{}^{I}l_{p}=Round({}^{(I}h_{p}\frac{{}^{C}l_{p}}{\zeta_{h}{}_{p}}) \tag{6}\]  
\[{}^{I}h_{t}=3\times{}^{I}h_{p} \tag{7}\]  
\[{}^{I}l_{t}={}^{I}l_{p} \tag{8}\]  
where \({}^{C}h_{p}\) and \({}^{C}l_{p}\) represent height and length of pallet in camera coordinate system {C}, respectively. \({}^{I}h_{p}\) and \({}^{I}l_{p}\) represent the number of pixels of the pallet height and length in image, respectively. \(f\) represents the focal length of the RGB camera in the RGB-D sensor. \(dy\) represents the pixel size of the CMOS in the RGB camera.
## 3 Case Study
### Model Information
Table 2 and Figure 4 provide specific details about their distribution [58]. In this paper, two horizontal cracks near elevations of 105 m and 111.5 m in the downstream surface, some longitudinal vertical cracks on the dam crest, and representative vertical cracks are selected for analysis. The selected representative vertical cracks are all over 13 m in length, located in dam sections 6, 11, 16, 18, 20, 23, 26, 27, and 29, totaling nine vertical cracks. Figure 5a shows an on-site image of the arch dam. As illustrated in Figure 5b-d, the coordinate system of the dam model is defined with the dam axis direction as the \(x\)-axis, pointing toward the left bank as positive, along the river flow direction as the \(y\)-axis, pointing toward the upstream as positive, and the vertical direction as the \(z\)-axis, with positive direction pointing vertically upward. Figure 5b-d show the mesh of the dam body, foundation, and reservoir water, while Figure 5e displays the situation of contraction joints, and Figure 5f presents the mesh of crack elements. The calculations are conducted using ABAQUS. The entire dam body consists of 42,140 C3D8 elements, the foundation comprises 66,859 C3D8 elements, and the reservoir water consists of 49,875 AC3D8 elements. There are 692 COH3D8 elements for horizontal cracks and 469 COH3D8 elements for vertical cracks. The simulation methods in Section 2.2 are employed for cracks and contraction joints. The tensile strength of crack elements is set to 0.6 MPa [59]. The mesh size and element types have been calculated and calibrated for dynamic characteristics in the previous work, and the stress, displacement, and frequency calculated by the model are relatively stable. The specific process can be found in reference [60]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dam Sections** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Crack number & 1 & 3 & 3 & 2 & 3 & 2 & 1 & - & 1 & - \\ Crack length (m) & 8 & 9–10 & 2.5–6 & 3–13 & 2–3.5 & 2–6 & 10 & - & 20 & - \\ Crack width (mm) & - & - & 0.2 & 0.2 & - & 0.3 & - & - & - & - \\ \hline
**Dam sections** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** \\ \hline Crack number & 1 & 4 & 1 & 4 & 1 & 2 & 1 & 2 & 1 & 1 \\ Crack length (m) & 4 & 2.5–10 & 11 & 2–14 & 11 & 10–15 & 12 & 10–20 & 9 & 9 \\ Crack width (mm) & 0.5 & - & - & - & - & - & - & - & - & 0.5 \\ \hline
**Dam sections** & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** & & \\ \hline Crack number & 3 & - & - & 1 & 6 & 3 & 3 & 1 & & \\ Crack length (m) & 12–15 & - & - & 21 & 1.5–21 & 10–12 & 2–29 & 9 & & \\ Crack width (mm) & - & - & - & 0.3 & 0.3 & 0.3 & - & - & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical table of vertical cracks on the upstream surface of the arch dam. Figure 4: Schematic diagram of the location of vertical cracks and dam section number on the upstream surface of the arch dam.
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 3 Development of Height Indicators Using Global Appearance Descriptors
### Method 3: Multiscale Analysis of the Orthographic View  
In this method a multiscale analysis is carried out to estimate the relative height. This analysis consists in carrying out several artificial zoomings of the central area of the scenes and has been used previosly to estimate the topological distance between the capture points of two scenes when the robot moves in the ground plane [43]. To obtain consistent results, the projection plane of the images must be perpendicular to the direction of the movement. Since we consider vertical movements in this work, an orthographic projection of the omnidirectional image onto a horizontal plane must be used.  
The method consists in generating several orthographic projections of the reference image, considering different focal distances to the plane where the image is projected. This is equivalent to generating a set of orthographic projections with different zooms. Figure 8 shows a sample omnidirectional image and three of its orthographic views, assuming three different focal distances \(f_{c}\) for the projection plane.  
After that, the different projections are described using global appearance. This way, a set of descriptors is generated from \(im_{R}\), each one with a focal distance associated: \(\{(\vec{d}_{R,f_{c1}}\,,\,f_{c1})\,;\,(\vec{d}_{R,f_{c2}}\,,\,f_{c2})\,;\dots\,; \,  
We retain the focal distance of the reference image where the minimum is found, \(fc_{i_{0}}\). The difference between the focal distance of the test image projection and the focal distance of the matched reference image projection is a topological measurement of relative height: \(\Delta f_{c}=fc_{T}-fc_{i_{0}}\).  
In the experiments, the set of focal distances for the reference image are generated in the range \(fc_{i}=[4,11]\), while the focal distance of the test image is \(fc_{T}=7\).