esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Evaluating the Design Scheme of the Air Outlets and Fresh-Air Intakes
#### 4.3.1 Cases with Different Wind Directions  
Provided that the wind speed at the height of 10 m was 1.9 m/s, cases with eight wind directions (i.e., Case-1.9-N, Case-1.9-NE, Case-1.9-E, Case-1.9-SE, Case-1.9-S, Case-1.9-SW, Case-1.9-W, and Case-1.9-NW) were simulated to investigate the most unfavorable wind direction. Figure 18 shows the typical visualizations of the harmful air dispersion under four wind directions, where the black particles represent the exhausted harmful air dispersing in the air, and the pink curved surfaces represent the isosurfaces with a concentration of \(20\times 10^{-6}\). This type of high-fidelity visualization can assist designers to more intuitively investigate the distribution characteristics of harmful air around the temporary hospital, thereby helping designers to compare and optimize design schemes in an improved manner.  
In this study, the infectivity data relating to SARS was adopted as a reference for the infectivity of SARS-COV-2, given that scientists over the world did not have a unified understanding of the infectivity of the virus during the design period of the hospital. According to [PERSON] et al. [1], if the air emitted from a SARS patient is diluted to more than 10,000 times using clean air, it would result in a low risk of infection. Hence, in this study, it was assumed that if the concentration of the exhausted harmful air is less than \(100\times 10^{-6}\), the harmful air would be accompanied by a low risk of infection. Figure 19 shows the typical time-history data of the harmful air concentration near the specific fresh-air intake with the highest instantaneous concentration under four wind directions. In the conditions with wind speeds of 1.9 m/s, the maximum concentrations of harmful air near fresh-air intakes under eight wind directions are all lower than \(100\times 10^{-6}\), indicating that the exhausted harmful air from air outlets results in a low risk of infection to the fresh-air intakes.  
Figure 18: Visualizations of the exhausted harmful air from air outlets under different wind directions.
# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Tools
#### 2.4.5 Decision Trees  
A decision tree is a type of classifier that consists of a series of binary decisions used to determine the class for each input pixel. The decisions are based on the characteristics of the datasets. Every decision partitions data into either one of two potential classes or groups of classes. Decisions are expressions applied to a single image or a set of images. Each of these images represents a property of the input pixels.  
One such property, for instance, could be the wavelength position of the deepest absorption feature of the spectrum of the input pixel. An expression could be built to check  
Figure 7: **(a)** Natural color composite of rock sample 15-91-FE (red 650 nm, green 550 nm, and blue 450 nm). **(b)** The wavelength map of the range of 500 nm to 950 nm shows the wavelength and depth of the deepest feature. Monazite is visible in orange (showing the 800 nm feature of Nd) or yellow–green when the 740 nm feature happens to be deeper than the 800 nm feature. **(c)** Legend to the wavelength map.  
whether the input pixel might contain a clay mineral with a deepest absorption feature of around 2.2 um. Subsequent decisions could check the water features of this pixel to see whether the material has a high crystallinity index. After a number of decisions, a class will be assigned to the pixel.  
Decision trees are useful for building classifiers that are based on physical properties rather than statistical inference. Of course, this can only be carried out if all the properties needed for the decision trees can be established with enough confidence.  
A package like ENVI supports the construction and visualization of new decision trees. However, ENVI has a number of disadvantages. The way decision trees are implemented in ENVI is rather rigid because once a node in the tree is constructed, its place in the decision tree is fixed. In a finalized ENVI decision tree, it is impossible to move elements around. In ENVI, if you want to change the decision tree, you have to rebuild it from scratch.  
HypPy can be used to execute ENVI decision trees. The only condition is that expressions used for decisions can be translated into expressions in Python.  
In addition to the ENVI decision tree format, HypPy has its own decision tree format. This format is a text-based format that can be edited using a text editor.  
For the visualization of decision trees, HypPy uses the Graphviz package [26]. To this end, the ENVI or HypPy decision tree must be converted to the 'dot' format first. The dot language is an extensive language for describing graphs. The binary decision trees used by ENVI and HypPy present a tiny subset of all possible graphs and types of graphs supported by the Graphviz package.
# Study on the Influence of a Soft Soil Interlayer on Spatially Varying Ground Motions
## 2 Theoretical Basis  
### One-Dimensional Wave Propagation Theory  
In the 1D wave propagation theory proposed by [PERSON] [53], it is assumed that the incoming seismic waves that propagate into the bedrock with an angle consist of out-of-plane SH wave or in-plane combined P-and SV-waves. For each soil layer, the out-of-plane and in-plane dynamic stiffness matrices can be established independently. Then, based on the relation between shear stresses and displacements on interfaces between soil layers and by assembling the dynamic stiffness matrices of soil layers and bedrock, the dynamic equilibrium equation of the site in frequency domain can be expressed as  
\[[S_{SH}]\{u_{SH}\}=\{P_{SH}\}\text{ for out-of-plane wave} \tag{1}\]  
and  
\[[S_{P-SV}]\{u_{P-SV}\}=\{P_{P-SV}\}\text{ for in-plane waves}, \tag{2}\]  
where \([S_{SH}]\) and \([S_{P-SV}]\) are the out-of-plane and in-plane dynamic stiffness matrices of site, respectively;\(\{u_{SH}\}\) and \(\{P_{SH}\}\) are the out-of-plane displacements and load vectors for the incident SH wave, respectively; \(\{u_{P-SV}\}\) and \(\{P_{P-SV}\}\) are the in-plane displacement and load vectors for the combined P-and SV-waves, respectively. The ratio between the displacement at certain soil layer and that at bedrock site defines the transfer function \(H(\omega)\), which can be derived by solving Equations (1) or (2).  
Taking a single soil layer resting on a half-space with incident SH wave as an example, the transfer function for SH wave can be expressed as:  
\[H(i\omega)=\frac{1}{\cos kt^{L}d+\frac{i}{p}\sin kt^{L}d}, \tag{3}\]  
\[p=\frac{t^{R}G^{*R}}{t^{L}G^{*L}}, \tag{4}\]  
\[G^{*L}=G^{L}(1+2\zeta^{L}i)\text{ and }G^{*R}=G^{R}(1+2\zeta^{R}i), \tag{5}\]  
in which \(k\) denotes the wave number; \(t^{R}\) or \(t^{L}\) is a function of incident angle; \(G\) is the shear modulus; \(d\) is the thickness of soil layer; superscript \(R\) and \(L\) denote bedrock and soil layer, respectively; \(\zeta\) is the damping ratio; \(i=\sqrt{-1}\).
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 3 Solution
### BSS Scoring for Image Sets  
Assume that similarity scores \(p_{i,j}\) are provided for every image pair \((i,j)\), \(I=1\), \(2\),..., \(n\) and \(j=1\), \(2\),..., \(m\). They are collected in a similarity matrix \(\mathbf{P}=[p_{ij}]\), where \(p_{ij}=\) similarity (\(li\), \(lj\)). The error index (or dissimilarity score) for two image sets is defined as [9]:  
\[EI(\mathbf{P})=\frac{1}{m}\left(\sum_{i}\sum_{j}\lvert\widetilde{p}_{ij}\rvert- n\right)+\frac{1}{n}\left(\sum_{j}\sum_{i}\lvert\overline{p}_{ij}\rvert-m\right) \tag{2}\]  
Two normalized versions of the matrix \(\mathbf{P}\) are computed. In the first case, in every row of matrix \(\mathbf{P}\) the maximum entry is found and all entries in a given row are divided by this maximum value:  
* every row \(i\) of P is scaled: \(\widetilde{P}=Norm(P)\), _such that_\(\forall_{i}(max_{j}\big{(}\widetilde{a}_{ij}\big{)}=1\big{)}\) In the second case, the maxima are found independently in every column of \(\mathbf{P}\) and the entries in every column are divided by their corresponding maximum value:
* every column \(j\) is scaled: \(\widetilde{P}=NormCol(P)\), _such that_\(\forall_{j}(max_{i}\big{(}\widetilde{a}_{ij}\big{)}=1)\).  
Therefore, in an ideal case of two set matching, the first normalized matrix will have in every row only one nonzero entry equal to 1, while the second matrix will have a single entry 1 and all other entries in every column a 0. By subtracting from the sum of all row- or column-normalized elements the number of columns and rows, appropriately, the nonzero result will represent an average distance score between two images, while the one-valued entries will verify whether the checked set contains a single car model and all the required different views.  
The BSS-based similarity score for two sets is obtained in two different modes--the image-to-image comparison mode and the ROI-to-ROI comparison mode (Figure 5). In the first case, a feature vector (DeepRanking embeddings or SIFT descriptors) is calculated for every (masked) image in every set. The similarity matrix \(\mathbf{P}\) is of size \(4\times 4\), where every entry represents the similarity score for the features of a pair of images (Figure 5a). In the second mode, every image from a set of four images is represented by five ROIs and their feature vectors (DeepRanking embeddings or SIFT descriptors) are given. Thus, we calculate the BSS error index (\(EI(\mathbf{P})\)) for a \(20\times 20\) similarity matrix \(\mathbf{P}\), where the first set ROIs play the role of the \"BSS sources\", while the second set ROIs are the estimated \"separated signals\" (Figure 5b).
## 1 Introduction
### Problem Statement
This capability is particularly advantageous for detecting microscopic surface anomalies that may go unnoticed in 2D images. Additionally, a point cloud analysis is inherently less affected by variations in lighting conditions and surface reflectivity, ensuring greater robustness across different environments. By leveraging advanced point cloud processing techniques, such as curvature-based feature extraction, clustering, and deep learning models optimized for 3D data, a more comprehensive and precise assessment of magnetic tile defects can be achieved [12]. This shift from conventional image-based approaches to 3D point cloud analysis not only enhances defect detection accuracy but also enables a quantitative evaluation, providing critical insights into defect severity and its potential impact on motor performance.
## 3 Results and Discussion
### Fuel Consumption  
The above-mentioned CDA modes are evaluated by the brake-specific fuel consumption (BSFC) at engine speeds of 450 rpm and 556 rpm, and engine loads from 1000 to 4000 N-m (Figure 7).  
Figure 6: Injection profile under the normal operation and injection profiles in the active cylinder under two CDA modes at a speed of 450 rpm and a load of 1000 N-m.  
Figure 7: Comparison of BSFC for two CDA modes and the normal operation at engine torques from 1000 to 4000 N-m and engine speeds: (**a**) 450 rpm; (**b**) 556 rpm (the calculated results were obtained from 1D simulation of GT-SUITE).  
Figure 7 demonstrates that the BSFC reduction is only obtained at the CDAc mode by cutting off the fuel supply and closing the intake and exhaust valves in deactivated cylinders at the engine load from 1000 to 3000 N-m. At the engine torque of 1000 N-m, the BSFC at CDAc mode compared with the normal operation is decreased by 61.4 g/(kW\(\cdot\)h) (11%) at 450 rpm and by 82.02 g/(kW\(\cdot\)h) (14%) at 556 rpm, respectively. At a low torque range, the fuel-saving benefit from cutting off the fuel supply and closing valves to disable cylinders diminished with the load increase due to the limited amount of fresh air charged in the active cylinders, deteriorating the quality of the combustion process. The CDAo mode obviously leads to a BSFC increase, owing to extra energy losses and inadequate energy released. The effect of two CDA modes on the BSFC is a complex result of changes in the indicated efficiency and other energy losses. A detailed analysis of the impact of two CDA modes on these factors is given as follows:
## 3 Results and Discussion
### Proposed Mechanistic Model  
Based on the micro-mechanism analysis in Figure 18, the functional mechanism of CL-MgO carbonated solidified loess can be classified into two processes: the carbonated solidified mechanism, and the thermal conductivity mechanism. The carbonated solidified mechanism progresses through multiple stages. Initially, under uncarbonated solidified conditions, CL forms hydrogen bonds with soil particles via hydroxyl groups, enhancing inter-particle cohesion. Simultaneously, MgO undergoes hydration to form magnesium hydroxide (Mg(OH)\({}_{2}\)), and these combined processes improve structural integrity by filling  
Figure 17: MIP test results of solidified loess before and after carbonation: (**a**) cumulative pore volume; (**b**) differential pore volume.  
voids and increasing compaction. Following the introduction of CO2 and 24 h carbonation, MgO reacts with CO2 to produce magnesium carbonate (MgCO3). These magnesium carbonate crystals effectively fill void spaces and establish a new solid framework. Additionally, their interaction with CL optimizes the microstructure, substantially enhancing soil stability and strength. The thermal conductivity mechanism explains how various factors influence thermal performance. Increased moisture content enhances thermal conductivity as water, with its superior thermal conductivity, replaces air in void spaces. A higher dry density promotes thermal conductivity by expanding particle contact areas and reducing porosity, thereby improving heat transfer pathways dominated by solid particles. The addition of CL modifies the material's microstructure and pore connectivity characteristics, though the inherently low thermal conductivity of CL and MgO complexes inhibits overall thermal conductivity. Furthermore, successive freeze-thaw cycles progressively increase pore formation and size during freezing, disrupting internal heat conduction pathways. These analyses offer comprehensive insights into the micro-carbonated structure and thermal conductivity performance of CL-MgO carbonated solidified loess, providing a theoretical foundation for material design and engineering applications.
## 3 Results
### Results for Dimensions and Tensile Strength for the Lower Level of the Working Chamber
#### 3.2.4 Results for Width at the Lower Level of the Working Chamber  
According to Table 18, it can be noted that a modified cubic model was chosen for the analysis of the width value in the lower level of the working chamber. The values that are deleted from the cubic model have no influence on the width, and with them, an inadequate model is achieved, and they must be removed from the analysis. In the case of the width for the lower level of the chamber, factors AB, A\({}^{2}\), B\({}^{2}\), and B\({}^{3}\) have an influence. Individually, factors A and B have no effect on the width.  
Table 19 shows the results of the statistics for width in the lower part of the working chamber. The predicted \(R^{2}\) of 0.7984 follows the date for the adjusted \(R^{2}\) of 0.8871. In this model, a ratio of 22.772 for adeq precision indicates an adequate signal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sum of** & **Degrees of** & **Mean** & **F Value** & **\(p\)-Value (Risk of** \\  & **Squares** & **Freedom DF** & **Square** & & **Rejection of H\({}_{0}\))** \\ \hline
**Model** & 0.0742 & 6 & 0.0124 & 25.89 & \textless{}0.0001 significant \\ A & 0.0003 & 1 & 0.0003 & 0.5650 & 0.4656 \\ B & 0.0005 & 1 & 0.0005 & 1.04 & 0.3267 \\ AB & 0.0156 & 1 & 0.0156 & 32.65 & \textless{}0.0001 \\ A\({}^{2}\) & 0.0053 & 1 & 0.0053 & 11.08 & 0.0054 \\ B\({}^{2}\) & 0.0028 & 1 & 0.0028 & 5.76 & 0.0321 \\ B\({}^{3}\) & 0.0083 & 1 & 0.0083 & 17.44 & 0.0011 \\
**Residual** & 0.0062 & 13 & 0.0005 & & \\ Lack of fit & 0.0052 & 10 & 0.0005 & 1.56 & 0.3933 not significant \\ Pure error & 0.0010 & 3 & 0.0003 & & \\
**Cor Total** & 0.0805 & 19 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 18: Analysis according to ANOVA for the width at the lower level of the working chamber.  
Figure 10: Dependence of hatch distance and orientation on the thickness in the lower part of the working chamber.  
\[b=10.23-1.32\times HD+0.004\times\alpha+0.0109\times HD\times\alpha+1.23\times HD^{2}-0.00034\times\alpha^{2}+4.12\times 10^{-6}\times\alpha^{3} \tag{10}\]  
where \(b\) [mm]--width, \(HD\) [mm]--hat distance, and \(\alpha\) [\({}^{\circ}\)]--orientation.  
Obtaining a value of 10 mm for the width in the lower part of the working chamber is possible for the orientation from 30\({}^{\circ}\) to 50\({}^{\circ}\) in the entire range of hatch distance limits, which can be observed in Figure 11.
###### Abstract  
A model for predicting shallow depth soil temperatures is important and effective to assess the changes in soil conditions related to global climate change and local disturbances. Shallow-depth soil temperature estimation model in cold region in Alaska is developed based on thermal response using air temperature and shallow-depth soil water content during active layer development period of 160 days from May to October. Among the seven soil temperature measurement sites, data from four sites were used for model development, and the remaining three sites were used for model validation. Near the middle of the seven measurement sites, air temperature is monitored at one location. The proposed model implemented concepts of thermal response and cumulative temperature. Temperatures and soil water contents were measured using automated remote sensing technology. Consequently, it was confirmed that the developed model enables fast and accurate assessment of shallow-depth soil temperature during active soil layer development period.  
air temperature; soil temperature; soil temperature modelling; permafrost dynamics; soil water content +
Footnote †: journal: Applied Sciences
## 5 Discussion
The comparative test results reveal that the parking space detection method relying on visual prior information exhibits poor generalization. Within the nuScenes dataset, certain parking lot scenes lack corner points on parking lines. The non-motorized lane lines of roadside parking are relatively clear, but parking space lines are often blurred, and some real-world scenes may entirely lack parking lines, as depicted in Figure 20. Moreover, visual images are significantly impacted by lighting conditions, resulting in unclear textures in binary images and unreliable detection of parking line corners. Consequently, reliance on visual parking line prior information, as seen in reference [3], leads to substantial gaps in algorithm reliability. In the original text [3], parking space recognition in PL3, which lacks parking lines, is conducted in conjunction with PL1, where parking lines are clear. This dependence implies that recognition in PL3 relies on data from other scenes with visible parking lines. Thus, the visual prior information method proposed in reference [3] is primarily suitable only for scenes featuring distinct parking line textures. An alternative approach to addressing the limited generalization of visual parking space recognition involves pre-building high-precision maps and manually marking parking spaces. However, this method incurs high investment costs and contradicts the goal of cost reduction and efficiency enhancement. Figure 19: Some parking spaces in the nuScenes [15] dataset have curbstones next to them. Figure 20: Some parking spaces in the nuScenes [15] dataset do not have line corners, or the parking space lines are unclear. In terms of storage usage, the evaluation of the method in literature [19] benefits from reduced model training steps due to loading pre-trained parameters for its 3D object detection process, giving it a certain advantage in parking slot prediction memory usage. However, considering the storage occupancy assessment based on output data, our algorithm demonstrates greater advantages. Specifically, compared to the comparison algorithm [3], our algorithm saves at least 48.38% of storage space, significantly reducing storage costs. In summary, our algorithm exhibits good performance in terms of effectiveness, accuracy, and robustness in annotating vacant parking spaces.
## 4 Brief Overview of EOICHD Protocol
Figure 2: Case1: Nodes \(\{n_{0},n_{2},n_{5}\}\) are candidate cluster head nodes. to operate. Figure 4 represents a simple network. Here, Figure 4b is a sample sensing region where sensor nodes are assumed to be deployed. Considered sensing region is segregated into several zones such as \(\{z_{1,1},z_{1,2},\cdots z_{1,u}\}\)\(\{z_{2,1},z_{2,1},\cdots z_{2,u}\}\)\(\{z_{v,1},z_{v,2},\cdots z_{v,u}\}\) depending on the requirement of WSN applications. The dimension of sensing region is \((x\times y)\) m\({}^{2}\) and dimension of each zone is \((\frac{x}{u}\times\frac{y}{v})\) m\({}^{2}\). An arbitrary sensing zone \(Z_{v_{0},u_{0}}\) is shown in Figure 4a. If length of the diagonal of each zone is considered as optimal inter cluster head distance, then the selected CH end covers sensing area of four neighbouring zones. Once a node residing at particular zone is selected as CH, then no node should be selected as CH in its neighbour zones. In this manner, CHs are separated by an optimal distance and are able to be separated from one another by a distance of \(d_{opt}\) meters. Optimal inter cluster head distance \((d_{opt})\) can be calculated as per Equation (4). \[d_{opt}=\sqrt{\left(\frac{x}{u}\right)^{2}+\left(\frac{y}{v}\right)^{2}} \tag{4}\]  
Thus, a candidate CH node \(n\) is selected as the final CH for the current round if it satisfies all the criteria mentioned so far. The CH selection process of EOICHD is described in the flowchart given in Figure 5. Once the CHs are selected for the current round, all the normal nodes join to appropriate CH nodes to form clusters. The last phase of the EOICHD protocol is the data transmission phase. The data transmission phase is similar to the procedure of the LEACH protocol. For more details of EOICHD, readers can refer to [27]. Figure 4: (**a**) An arbitrary sensing zone (**b**) Sensing region divided in \(v\times u\) zones. Figure 5: Cluster Head Selection Procedure of Energy and Optimal Inter Cluster Head Distance (EOICHID).