esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Purposes of 3D-Printed Chest Phantoms  
Seven out of the twenty studies investigated and assessed the application of 3D-printed chest phantoms for specific purposes. This included optimising CT pulmonary angiography protocols [18; 31] and optimising CT angiography (CTA) post thoracic endovascular aortic repair (TEVAR) [47]. Four out of these seven studies utilised the 3D-printed thoracic replicas for quality assurance purposes, encompassing CT reproducibility assessments [32], X-ray image quality analysis [36], validating segmentation, and image registration algorithms [33], as well as comparing image reconstruction algorithms to enhance the detection sensitivity of paediatric lung nodules [45].  
In contrast, the majority of studies (60%) solely investigated the feasibility of 3D printing for creating radiation-attenuating equivalent thoracic phantoms, without analysing them for direct application [30; 34; 35; 37; 38; 39; 40; 41; 43; 44; 46; 48]. Despite not directly assessing these applications, studies suggested the utility of their 3D-printed thoracic phantoms for optimising CT protocols to reduce dose [30; 32; 35; 36; 41; 43; 44; 46; 48], evaluating protocols  
Figure 3: Three-dimensional-printing methods for creating chest phantoms. Note: PolyJet differs from Multijet by having more than one printhead, enabling multiple materials in a single print. SLA—stereolithography, FDM—Fused-Deposition Modelling, SLS—Selective Laser Sintering. Binder Jetting involves the jetting of a liquid adhesive onto a bed of ceramic or gypsum powder [49].  
for under-represented groups including infants and pregnant woman [40], quality assurance [32; 37; 38; 39; 41], validating CT software and procedures [34; 36; 37; 38; 39; 43; 46], and serving as ground truths for radiomics [43; 46] and CT research [44], as well as supporting anatomy education, surgical guidance, and patient comprehension [35; 38; 39].
## Materials and Methods  
### Materials  
HepG2 cells were purchased from the China Cell Bank of the Institute of Biochemistry and Cell Biology (Shanghai, China). Dulbecco's Modified Eagle's medium (DMEM) and Roswell Park Memorial Institute (RPMI)-1640 medium were purchased from Gino biotechnology Co., Ltd. (Hangzhou, China). Methylthiazolyldiphenyl-tetrazolium bromide (MTT), OA, simvastatin (SV), and \(\beta\)-actin were purchased from Sigma-Aldrich (Shanghai) Trading Co., Ltd. (China). Paraformaldehyde (4%) and lipoprotein 2000 were purchased from Thermo Fisher Scientific Co. Ltd. (Shanghai, China). PrimeScript RT reagent Kit with genomic DNA (gDNA) Eraser and 2 \(\times\) KAPA Master Mix were purchased from Takara Biomedical Technology (Beijing) Co. Ltd. (China). FA free bovine serum albumin (BSA) was purchased from Millipore Co. Ltd. (Shanghai, China). Bicinchoninic acid (BCA) protein assay kit and L-Glutamine were purchased from Beijing Solarbio Co. Ltd. (China). TransStart Top Green quantitative PCR (qPCR) superMix kit was purchased from TransGen Biotech Co. Ltd. (Beijing, China). SREBP-1c rabbit polyclonal antibody was purchased from Abcom Co. Ltd. (Maharashtra, India). Peptides (M1-M10) were chemically synthetized using a solid phase peptide synthesis method (synthetized by China Peptides Co. Ltd.) according to the amino acid sequences reported by [PERSON] et al. [23]. The purity of the synthesized peptides was higher than 98% as analyzed via reversed-phase high-performance liquid chromatography (RP-HPLC) method. The molecular mass of the synthesized peptides was confirmed by the manufacturer using electrospray ionization mass spectrometry (ESI-MS).
## 4 The Proposed Method
### Adaptive Multi-Channel Spatio-Temporal Hypergraph Neural Network
#### 4.3.1 Hypergraph Convolution Module Based on Spatio-Temporal Information
After \(L\) iterations of updating, the vertex feature matrix \(\mathbf{V}^{(L)}\) is obtained, and the final output feature matrix \(\tilde{\mathbf{X}}_{PCJ}\) for this channel is obtained after a global pooling layer. Similarly, the output feature matrices \(\tilde{\mathbf{X}}_{N-PCJ}\) and \(\tilde{\mathbf{X}}_{N-PCB}\) for the other two channels are obtained.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
###### Abstract  
The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates \((x,y,z)\), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. octree and kd-tree are the most used for Point Cloud data. However, octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than octree and 3D R*-tree.  
hybrid spatial indexing; octree; R-tree; 3D R*-tree; Point Cloud +
Footnote †: journal: Applied sciences
## 4 Discussion
FPP (MVA pathway) is further processed into sesquiterpenes while GPP and GGPP (MEP pathway) are the immediate precursors for mono- and dicterpene synthesis, respectively. It has been reported that terpene accumulation in grapevine and other plants is correlated with MEP pathway gene expression [3], such as _DXS_, _DXR_, _HDR_ and _GPPS_[26; 57; 26]. In this present study, we found that the expression levels of _CSLinNer_ in four treatments were completely consistent with _DXR_ in every period. We suggest that _DXR_ and _CSLinNer_ expressions are also somehow linked in grape terpene biosynthesis. Additionally, we found that only _VvDXS_ was significantly correlated withlinal and total terpene concentrations, while the expression of other terpene-related genes were apparently uncorrelated with terpene accumulation in general (Figure 5). DXS is the first enzyme involved in the MEP pathway of isopentenyl-5-pyrophosph (IPP) biosynthesis [59]. Our observation of the correlation between grapelinal content and _VvDXS_ expression is in agreement with [PERSON] et al. [57] and [PERSON] et al. [60] (Table 5), who demonstrated the colocalization of _VvDXS_ with the major QTL for monoterpene content positioned on chromosome 5, and indicating that _VvDXS_ is a candidate gene for the regulation of these important grape monoterenes. Regarding the sensory properties, the phenolic compounds are the major ingredients linked to food astringency, especially for tannins [61]. We found that girdling and foliar fertilization with K improved grapes' astringency, which was attributed to higher phenolic accumulation in G, F and GF treatments. It was assumed that the low overall acceptance score of G treatment could be attributed mainly to the higher score in astringency. The overall acceptance was affected by a combination of attributes. We concluded that the sensory property of GF is the best, and G is the worst, CK and F ranged between.
## 3 Proposed Work
### Model Development and Training
#### 3.3.1 Linear Regression  
Linear regression is a simple and widely used machine learning model for predicting a continuous outcome variable based on one or more predictor variables. It's a supervised learning algorithm that falls under the category of regression algorithms. The linear regression model assumes a linear relationship between the predictor variables and the target variable. Mathematically, it can be represented as:  
\[y=\beta_{0}+\sum_{\mathrm{i}=1}^{n}\beta_{\mathrm{i}}X_{\mathrm{i}}+\varepsilon\]  
where:  
* \(y\) is the target variable (dependent variable).
* \(x_{\mathrm{i}}\), \(x_{\mathrm{2}}\),...\(x_{n}\), are the predictor variables (independent variables).
* \(\beta_{0}\)... \(\beta_{1}\)... \(\beta_{n}\) are the coefficients (parameters) of the linear model.
* \(\varepsilon\) is the error term that shows the difference between the observed and predicted values.  
The goal of linear regression is to estimate the coefficients \(\beta_{0}\)...\(\beta_{1}\)... \(\beta_{n}\) that best fit the training data, this reduces the difference between the observed and predicted values, usually gotten by optimizing a loss function, such as mean squared error (MSE) or mean absolute error (MAE). Once the model is trained, it can make predictions on new data by plugging in the values of the predictor variables into the learned equation. As part of the hyperparameter tuning process, we examined the performance of the linear regression model without cycle count, as seen in Figure 7 below, the plot compares actual vs. predicted values, showing moderate accuracy with visible deviations. And with cycle count included in Figure 8, the model shows significantly improved alignment between actual and predicted values. Although the model appears to make near-perfect predictions when the cycle count is used, the pattern suggests potential overfitting. This limits its real-world applicability, especially considering that battery degradation is rarely perfectly linear. Since basic Linear Regression tends to overfit, especially when features like cycle count dominate the data, we explored a regularization technique by tuning the regularization strength (alpha). We found that Ridge regression with \(\alpha\) = 0.1 helped reduce overfitting while still giving strong predictive results.
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 3 Results and Discussion
### Path-Tracking Accuracy Test  
The test platform was automatically driven at a speed of 0.45 m/s, the test distance was 30 m and the measurement was carried out at intervals of 30 cm. The test was carried out in 5 groups; the test section of the unilateral fruit tree was missing at a driving distance of 10 m-15 m. At 20 m-25 m, the test section of the bilateral fruit trees was missing. The results after each test were shown in Table 5; the average tracking deviation of the test is shown in Figure 18.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multicolumn{4}{c}{\(\theta\) (\({}^{\circ}\))} & \multirow{2}{*}{\(\Delta d\) (cm)} \\ \cline{2-3} \cline{5-5}  & **Actual Value** & **Measurements** & **Error** & \\ \hline
1 & \(-30\) & \(-30.13\) & 0.13 & 2.75 \\
2 & \(-25\) & \(-25.40\) & \(-0.40\) & \(-2.18\) \\
3 & \(-20\) & \(-19.09\) & \(-0.91\) & 1.62 \\
4 & \(-15\) & \(-14.34\) & \(-  
As can be seen from Table 4, the maximum lateral deviation after the five tests was 0.13 m, the maximum average deviation was 0.08 m, the MAD of maximum deviation was 0.096 m, the MAD of average deviation was 0.048 m and the SD of average deviation was 0.034 m. It can be seen from Figure 18 that the lateral deviation gradually decreases after the vehicle was 2.5 m. At 5 m, the lateral deviation tends to zero, and then the lateral deviation gradually increases. At 10.6 m, the average maximum lateral deviation was 0.096 m., The deviation fluctuates continuously, and the lateral deviation was basically 0 m at 30 m. At the same time, we can observe that the variation of lateral deviation was not increased due to the loss of fruit trees. Therefore, the effect of missing fruit trees on the accuracy of the navigation control system proposed in this paper was not obvious, indicating that the system has good accuracy and robustness.
## 3 Results
### Translating S2-like RE1, RE2, and RE3 Bands from L8
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **ERGAS** & **SAM** & **SCC** & **PSNR** & **RMSE** & **UQI** \\ \hline \multicolumn{6}{l}{**(A) S2 RE1**} \\ \hline L8 G & 3597.13 & 0.2211 & 0.0631 & 20.98 & 24.71 & 0.8650 \\ Direct & 1666.10 & 0.1890 & 0.1169 & 23.16 & 19.27 & 0.9458 \\ Multistep & **1547.86** & **0.1718** & **0.1580** & **23.97** & **17.71** & **0.9499** \\ \hline \multicolumn{6}{l}{**(B) S2 RE2**} \\ \hline L8 NIR & 1868.29 & 0.2275 & 0.1926 & 19.30 & 30.46 & 0.9214 \\ Direct & **1804.38** & **0.2037** & **0.2938** & **20.74** & **25.45** & **0.9381** \\ Multistep & 1851.20 & 0.2126 & 0.2900 & 20.49 & 26.21 & 0.9353 \\ \hline \multicolumn{6}{l}{**(C) S2 RE3**} \\ \hline L8 NIR & **1439.74** & 0.1841 & 0.1887 & 21.21 & 23.79 & 0.9571 \\ Direct & 1527.85 & **0.1697** & 0.2650 & **22.08** & **21.43** & **0.9573** \\ Multistep & 1518.97 & 0.1762 & **0.2916** & 22.01 & 21.85 & 0.9552 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative evaluations for predicting S2-like red edges by direct and multistep approaches. Bold numbers are the highest scores per metric. Figure 7: S2-like RE1 (**A1,A2,A3**), S2-like RE2 (**B1,B2,B3**), and S2-like RE3 (**C1,C2,C3**) translations by Direct (**A2,B2,C2**) and multistep (**A3,B3,C3**) approaches from L8 green (RE1—(**A1**)) and NIR (RE2—(**A2**) and RE3—(**A3**)). Target S2 bands - RE1 (**A4**), RE2 (**B4**), and RE3 (**C4**).
## 2 Materials and Methods
### Division of the Study Area by Tie Station Location  
We employed a discrete model of the land territory of Poland comprising 1,250,008 points in a 0.5 \(\times\) 0.5 km grid to characterise the study area in terms of the number and lengths of baseline vectors. The spatial analysis involved two dimensions (x, y) only because of insignificant altitude differences (approx. 75% of Poland is lowlands with a mean elevation of 173 m AMSL) and a smooth north-south altitude gradient. We employed the EPSG 2180 system, which is a single-zone Gauss-Kruger projection of the GRS80 ellipsoid. According to the literature [34], the lengths of vectors between ASG-EUPOS stations in Poland are similar in 2D and 3D coordinate systems, and any differences are negligible. Thus, in this investigation, we omitted the height component for calculating the baseline lengths. The territory of Poland was divided in QGIS with the Distance Matrix (summary). Each point in the model was assigned its distance to the n nearest ASG-EUPOS stations. We further determined the mean length of a baseline vector to the n nearest stations, standard deviation, and maximum and minimum baseline vector length. The division of the Polish territory was performed for six baseline vectors, as currently used in the APPS of the ASG-EUPOS, and for five and three baseline vectors.
# Stationary Target Identification in a Traffic Monitoring Radar System
## 4 Proposed Stationary Target Identification Method
Figure 9: _Cont._  
As shown in the figure, although there are many detected points in the angle-velocity domain, the RANSAC-based estimation finds an appropriate trend line. Based on the estimated line, we can select the points with velocities close to zero. We also estimate the trend line using the LLS method. However, because the trend line is calculated using all detected points in the case of the LLS method, the accuracy decreases. For example, Figure 10 is an enlarged view of Figure 9a. Figure 10: Estimation result: velocity ranges from \(-1\) m/s to \(1\) m/s (same data as Figure 9a, but reduced velocity range). Figure 9: Estimation results for successive detection results: (**a**) \(t=0\) (ms); (**b**) \(t=33\) (ms); (**c**) \(t=66\) (ms); (**d**) \(t=99\) (ms). In the case of LLS-based estimation, the slope of the trend line deviates significantly from the sample points. To check the statistical performance, the slopes of the trend lines of the proposed method and the LLS method were evaluated for 100 frames, where one frame (e.g., 33 ms in Table 1) means one cycle of radar signal processing. Table 2 shows the average slopes of the estimated trend lines. Compared to the linear regression method, the proposed MSAC-based identification method estimates the slope of the trend line close to the ideal case within the algorithm parameters we set. Finally, Figure 11 shows the reclassification result of stationary structures and moving vehicles. In this figure, based on the classification result of the MSAC algorithm in Figure 9a, inliers and outliers are depicted in red circles and blue squares, respectively. In addition, the blue dotted line represents the boundary between the inside and outside of the road. As shown in the figure, the detection points from the moving vehicle are well classified through the proposed method. If a method of classifying stationary targets and moving targets by simply setting a threshold value for the velocity is used, it is ambiguous how to determine the threshold value. Our proposed method shows stable and reliable performance because it does not classify the points using the threshold value, but classifies them by estimating the trend in the measured data. Figure 12 shows the flowchart of our proposed signal processing technique. \begin{table}
\begin{tabular}{c c} \hline \hline
**Estimation Methods** & **Slope of the Trend Line** \\ \hline Ideal case (when the velocity is zero) & 0 \\ LLS method & 0.1226 \\ Proposed MSAC-based method & 0.0013 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Slope of the trend line. Figure 11: Reclassification result for detected targets.
## 3 Results  
As previously mentioned, traditional DSSs used in the design, monitoring, control, and execution of complex missions have limitations in their functionality, i.e., the type of mission they support, the volume of required input information, etc. In particular, CMS systems often struggle to expand their functionality capacities. This is because any further development of their conventional data cores necessitates updates, upgrades, and, ultimately, enhanced control mechanisms. Consequently, the sustainability of these data cores becomes a significant concern. Hence, the issue of the sustainability of these cores is apparent.  
The techniques described in this work provide solutions with specific methodologies to address the issues of high volume, serious internal complexity, increased diversity, and interoperability concerning CMS systems. The combination of the aforementioned methodologies and techniques effectively reinforces the functionality of the proposed DSS. As shown in Figure 11, the proposed DSS architecture overview includes an IS that has two sub-systems:  
* The first sub-system (noted as \"a\" in Figure 11) concerns the preprocessing stage aiming at preparing to organize the input data before they enter the system that will  
Figure 10: “Themes over maps” architecture.  
Figure 9: “Maps over themes” architecture.  
support any mission and has a relationship with methodology \"1\" because it fixes the anarchic and disordered data input in the system.
* The second sub-system (noted as \"b\" in Figure 11) concerns the stage after the data pre-processing stage. In this phase, the preprocessed dataset undergoes more specialized organization based on a way of organizing data related to actionable actors' needs. This phase has a relationship with methodology \"5\" (\"themes over maps\").  
The other component running in parallel to the IS is the CMS system, which implements methodology \"2\" (noted as \"c\" in Figure 11) involving thematic mission decomposition through the divide and conquer technique and \"3\" (noted as \"d\" in Figure 11) encompassing the mission transport algorithm, actionable actors, segments, Points of Interest (POIs), and other elements. The data dissemination to actors is achieved according to the principle of \"need to know\". According to this principle, the actionable actor requests, handles, and analyzes only the absolutely necessary information according to one's judgement and according to the actor's operational needs (to avoid information overloading). Methodologies \"1\", \"2\", \"3\", and \"5\" optimally fit with methodology \"4\" (noted as \"e\" in Figure 11), teleological structure, for supporting complex missions.
## 3 Results
### Guided Wave Techniques Results
#### 3.2.2 Air-Coupled to Contact Transducer Guided Wave  
The air-coupled to contact guided wave B-scan is of length 170 mm with a step of 0.5 mm, which comprises of total 341 scans. The scan was made with 32 times averaging where each scan took 2 s. Since the contact transducer is used, there is not much signal pre-amplification necessary when compared to air-coupled through-transmission. In this case, the static gain and dynamic gain used were 50 dB and 10 dB, respectively.  
The A-scan and B-scan of this experiment is shown in Figures 18a and 18b, respectively. In the B-scan, the normalized absolute values are shown where the increase in amplitude means that there is a presence of debonding while the lower values represent the place where there is no debonding (perfect bond). This kind of increase in amplitude when GW is passing through the bond is also observed in the simulation. This can be visualized from the A-scans as shown in Figure 18a. Along the B-scan, a slice at the time instant 62.84 us is shown in Figure 18c, where it represents the damage profile along the scanning distance.  
When using the air-coupled transducer as the actuator, the incidence angle can be changed. Since the incidence angle is not normal to the plate, the amplitude of the guided waves can be directionally controlled. This effect can be also visualized in the spatial distributions in the simulation. Thus, this way of excitation can be highly beneficial to focusing the amplitude of the guided wave in one direction and, thereby, avoiding the boundary reflection from the edges of the plate.
## 2 Mathematical Modeling
### Proposed Drop Size Distribution  
The proposed drop size distribution model describes a drop population on a hybrid surface where one DWC region is bounded between FWC regions. It is a product of simulation work for drop growth by direct condensation, considering the coalescence of drops, drops sweeping area beneath them, and drops merging with neighboring FWC regions. The model takes into consideration the variation of effective radius \(r_{e}\), which is dependent on the total temperature drop, \(\Delta T\), or nucleation sites, \(N_{s}\), as given in Equations (1)-(3), and it defines the intensity of coalescence. Another important factor is the maximum drop radius, \(r_{max}\), provided by Equation (4), which indicates a drop departing radius. The variation in \(r_{max}\) includes the effect of surface inclination and surface hydrophobicity which is measured by the contact angle. Therefore, simulation work is conducted for vertical smooth plate with different hydrophobicity levels and total temperature drop values.  
Another factor to consider is the DWC region width, \(w_{DWC}\). In a special case, when the drop maximum diameter is equal to \(w_{DWC}\), drops merging with neighboring FWC regions become the only surface renewal mode. On the other hand, when \(w_{DWC}\) is infinite, it implies full DWC, and sweeping drops become the only surface renewal mode.