esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Technological Evolution of DES in Healthcare
#### 4.5.4 Impact on Healthcare Operations  
The integration of these technologies has demonstrated significant improvements in healthcare delivery efficiency and operational performance. [PERSON] et al. [60] conducted a comprehensive analysis of technology-enhanced DES implementations across multiple healthcare facilities, documenting substantial improvements in operational metrics. Their study demonstrated that integrated simulation approaches led to significant reductions in patient waiting times, enhanced resource utilization, and improved model accuracy. These findings underscore the transformative potential of technology-enhanced DES in healthcare settings, particularly when multiple technologies are integrated cohesively to support operational decision-making.
# Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy
## 3 Results
### Human Skin Tissue
#### 3.2.1 Reference Spectra
An additional difference observed in the layer of epidermis compared to dermis is Raman bands corresponding to traces of lipids in the stratum corneum (the uppermost epidermis layer). These are located at 1083 and 1303 cm\({}^{-1}\) and are very weak compared to those present at the transcutaneous fat layer which predominantly consists of lipids. Phospholipids found at 721 and 757 cm\({}^{-1}\) follow the same trend, reflecting a higher content in epidermis compared to the dermis layer. Contrary to epidermis and dermis, transcutaneous fat exhibits a distinct Raman signal with spectral ratios significantly different from the rest of the skin. The fat layer mainly consists of lipids in the form of triacylglycerlevel (TAG), a glycerol esterified with three fatty acids. We therefore expect to see an abundance of lipid components in the Raman spectrum of transcutaneous fat. Indeed, strong Raman bands can be found at 1063, 1083 and 1123 cm\({}^{-1}\) and are assigned to the C-C bending modes of the lipid skeleton. The Raman band at 1083 cm\({}^{-1}\) specifically indicates an organized conformation of the lipid components [43] in transcutaneous tissue, whereas the one at 1746 cm\({}^{-1}\) is characteristic to the fat tissue and does not overlap with any of the epidermis and dermis peaks. The latter band is assigned to the C=O stretching mode of the ester groups in the glycerol heads of TAG. Last, the vibration at 972 cm\({}^{-1}\) is attributed to the =C-H bending mode in unsaturated fatty chains whereas the amide I band (C=C stretching) is clearly shifted to the left (1655 cm\({}^{-1}\)), similarly to what has been observed in animal fat tissue before [47].
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 2 Methodology
### Determination of Grasp Position from Parallel Edges
With optimization, a grasping position that is better oriented can be generated, as shown in Figure 8f. Optimization is achieved by projecting the center of the shorter bounding box on the longer bounding box. Let \(E_{a}\) represent the shorter edge of length \(l_{E_{a}}\) with center \((x_{E_{a}},y_{E_{a}})\) and orientation \(\theta_{E_{a}}\), and \(E_{b}\) represent the longer edge of length \(l_{E_{b}}\) with center \((x_{E_{b}},y_{E_{b}})\) and orientation \(\theta_{E_{b}}\). With \((x_{E_{b}},y_{E_{b}})\) and \(\theta_{E_{b}}\), a line parallel to \(E_{b}\) can be formed. Similarly, with \((x_{E_{a}},y_{E_{a}})\) and \(\theta_{E_{a}}\), a line perpendicular to \(E_{a}\) can be formed. This line represents the projection of \((x_{E_{a}},y_{E_{a}})\) towards \(E_{b}\). The intersection of these two lines gives point \(K\), which is the resultant point from the projection of \((x_{E_{a}},y_{E_{a}})\) along the length of \(E_{b}\). Hence, the two points of contact for the gripper are \((x_{E_{a}},y_{E_{a}})\) and point \(K\) to achieve an optimized grasping position. This idea of optimization is illustrated in Figure 9.  
It is observed that the parallel edge logic also works on non-angular shapes, such as long ovals. The pointed sides of the oval are detected as corners and the elongated sides detected as edges, as illustrated in Figure 10a. Hence, the algorithm is able to generate a grasp position from these two edges, as shown in Figure 10b. Figure 8: An object with multiple straight edges. (**a**) All detected features of the object. (**b**) Edges that have a large angle difference are not graspable. (**c**) Similarly, edges that are poorly aligned are poor grasp choices. To check for this, a line is drawn between 2 edges and its angle difference with the edges should be larger than \(\theta_{diff\_min}\). (**d**) A pair can be parallel and be properly aligned, but if the distance between them is too far apart, it is not within gripper reach. (**e**) Another more suitable pair is chosen. If the differences in length of the bounding boxes are not accounted for and the grasp position is derived only by the middle of the bounding boxes, the grasp position is poorly oriented. (**f**) After optimization, a better grasp position is achieved. Figure 10: (**a**) For elongated ovals, the long sides are detected as straight edges and the pointed sides are detected as corners. (**b**) The algorithm can generate a grasp position based on the parallel edge logic. Figure 9: A line parallel to longer bounding box \(E_{b}\) and a line perpendicular to shorter bounding box \(E_{a}\) can be formed from bounding box information. The intersection of these 2 lines at point \(K\) gives the projection of the center of \(E_{a}\) along the length of \(E_{b}\). \((x_{E_{a}},y_{E_{a}})\) and point \(K\) provide an optimized grasp position.
###### Abstract  
Inertial loads induced by base motion excitation introduce significant complexities in equilibrium point determination and linearization of systems incorporating squeeze film dampers (SFDs). The coupled effects of base motion excitation and SFD characteristics on periodic stability have received limited attention in previous investigations. This study investigates the dynamic characteristics and periodic stability of a rotor system with SFD subjected to base motion excitation. A finite element model of the rotor-SFD-support system under non-inertial motion is established. The periodic responses are solved using the harmonic balance method with alternating frequency/time technique (HB-AFT) and the arc-length continuation algorithm incorporating the predictor-corrector method, while system stability is analyzed using Floquet theory and the Newmark method. A systematic parametric study is conducted to investigate the effects of base motion parameters, mass unbalance, and SFD parameters on the system's periodic response. Results demonstrate that base pitching motion enhances system stability, suppresses bistable responses and jump phenomena, and reduces unstable vibration regions. However, under specific parameter combinations, pitching motion can trigger secondary Hopf bifurcations, leading to quasi-periodic and chaotic motions, among other complex nonlinear behaviors. This research provides theoretical foundations for stability-oriented design optimization of rotor systems with SFDs under base motion excitation.  
rotor dynamics; squeeze film damper; base motion excitation; stability analysis Article
## 3 Analytical Solution of Different SHS Columns, Cross-Section Sizes, Lengths, and End Constraints  
### Results of the Critical Loads  
In this study, columns under pure compression were tested on pinned-pinned, fixed-free, fixed-fixed, and fixed-pinned ends.  
Table 1 shows the analytical calculations of the stability for different column lengths and cross-sections of SHS. By increasing the length of the column from 500 to 1500 mm, the critical load and the design buckling resistance of the SHS column always decrease. By increasing the cross-section size of the column (50, 60, and 80 mm), the critical loads of the SHS column always increase.  
Columns with fixed-fixed ends have higher resistance when compared to the other end constraints with a maximum value of the critical load equal to 16,047.5 kN and for the design buckling resistance equal to 113.5 kN, for cross-section B80 with length equal to 500 mm. The design buckling resistance always presents lower values concerning the critical load of the column.  
This study shows that column length, cross-sectional size, and applied boundary conditions affect the structural behavior of SHS columns.  
Numerical Solution of Elastic Deformation, Plastic and Design Buckling Resistance in Columns with Initial Imperfections and Nonlinearity Material
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 4 MVCG-SPS Method
### Meta-Path-Based Multi-View Generation
Below is the pseudocode (Algorithm 1) for generating multi-views based on interaction enhancement:  
Figure 2: Smart contract interaction enhancement example  
```
1:Input: Heterogeneous graph \(G=(\mathcal{N},\mathcal{E},\mathcal{T}_{n},\mathcal{T}_{e})\), predefined meta-path \(P_{m}\)
2:Output: Enhanced subgraph set \(\{G_{\text{enhanced}}\}\)
3: Initialize enhanced subgraph set \(\{G_{\text{enhanced}}\}\leftarrow\emptyset\)
4:for each target node \(P_{T}\) in graph \(G\)do
5: Start from target node \(P_{T}\), perform random walk based on meta-path \(P_{m}\)
6: Search for explicit neighborhood \(T_{P_{T}}\) and neighborhood of neighbors \(N_{P_{T}}\)
7: Expand neighbors \(T_{P_{T}}\) and \(N_{P_{T}}\) to first-order neighbors
8:for each node \(n\in T_{P_{T}}\cup N_{P_{T}}\)do
9: Perform random walk based on meta-path \(P_{m}\), searching for more interaction relationships
10: Update adjacency matrix, marking new interaction relationships as 1
11:endfor
12: Generate enhanced heterogeneous graph \(G_{\text{enhanced}}\)
13: Add enhanced subgraph \(G_{\text{enhanced}}\) to \(\{G_{\text{enhanced}}\}\)
14:endfor
15:return\(\{G_{\text{enhanced}}\}\)
```  
**Algorithm 1** Interaction-Enhancement-Based View Decomposition Algorithm  
2. Meta-Path Sampling  
The meta-path sampling method extracts subgraphs from the original heterogeneous graph based on predefined meta-paths, generating independent views. These views reveal behaviors such as fund flows, payments, and contract calls, further enhancing the detection of fraudulent activities like Ponzi schemes. We use the following two meta-paths to generate views related to Ponzi schemes:  
* \(P_{1}:Cat\xrightarrow{\text{call}}CA\xrightarrow{\text{trans}}EOA \xrightarrow{\text{call}}CA\): Represents the fund flow pattern where the Ponzi account interacts with external accounts via contract calls. * \(P_{2}:EOA\xrightarrow{\text{call}}CA\xrightarrow{\text{trans}}EOA \xrightarrow{\text{trans}}CA\): Describes the behavior pattern where the Ponzi account distributes funds after receiving them, revealing the fund transfer path. By applying meta-path sampling, we extract subgraphs and generate independent views for each subgraph. These views are then used for further contrastive learning and model training, enhancing the overall detection performance. Below is the pseudocode (Algorithm 2) for generating multi-views through meta-path sampling:  
```
1:Input: Heterogeneous graph \(G=(\mathcal{N},\mathcal{E},\mathcal{T}_{n},\mathcal{T}_{e})\), predefined meta-path \(P_{m}\)
2:Output: Set of subgraphs \(\{G_{P_{j}}\}\)
3: Initialize the set of subgraphs \(\{G_{P_{j}}\}\leftarrow\emptyset\)
4:for each meta-path \(P_{i}\) in the predefined set of meta-paths do
5:\(G_{P_{i}}\leftarrow\text{ExtractSubgraph}(G,P_{i})\)
6: Compute the node features of \(G_{P_{i}}\)
7: Add \(G_{P_{i}}\) to the set \(\{G_{P_{i}}\}\)
8:endforreturn\(\{G_{P_{i}}\}\)
```  
**Algorithm 2** Meta-Path Sampling View Decomposition Algorithm
## 3 Results and Discussion
### Optimization Studies of Different 'Green' Extraction Methods  
#### 3.5.1 Process Optimization of Ultrasound-Assisted Extraction (UAE)  
The effect of varying UAE times and temperatures on the antioxidant properties and phenolic composition of WOF extracts is presented in Table 4. Notably, the TPC appeared to increase significantly (\(p\leq\) 0.05) up to 20 min at a constant temperature of 30 \({}^{\circ}\)C, reaching the highest value of 63.98 mg GAE/g dw; however, an insignificant effect of extraction time within the range of 20-40 min was observed. Similarly, the highest level of antioxidant activity, as estimated by ABTS, DPPH, and FRAP values, is attained at 20 min, while the TPC values appeared to be independent of time extraction. Regarding oleuropein content, the predominant phenolic compound identified in WOF extracts, it was noticed that at higher extraction times, a higher value of oleuropein appeared, reaching the greatest value (69.27 mg/g dw) at 40 min; however, an insignificant effect was noticed at duration times of 20-40 min. Concerning the temperature effect in UAE, the increased temperature up to 45 \({}^{\circ}\)C favored the removal of the phenolic compounds present in the WOF, while longer increased temperatures until 60 \({}^{\circ}\)C showed a significant decrease. These results suggest that a sonication time of 20 min at 65 \({}^{\circ}\)C was sufficient to maximize TPC, TFC, and antioxidant activity as well as the oleuropein content, potentially  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{3}{c}{**Phenolic Components**} & \multicolumn{3}{c}{**Antioxidant Activity (mg TE/g dw)**} \\ \hline
**Time** & **Tem. ** (\({}^{\circ}\)C) & **TPC (mg** & **TFC (mg** & **Oleuropein** & & \\
**(min)** & **Tem.
# eSCorpius-m: A Massive Multilingual Crawling Corpus with a Focus on Spanish
## 4 Data Download and Cleaning Process
### Output Storage Format  
The output format of this process and the format in which the corpus is distributed is JSONL. For each line of the corpus, there is a separate JSON document representing a separate paragraph of the corpus with the following fields:  
* id: unique document identifiers UUIDv4 (the complete specification of UUIDv4 can be found in [[https://www.ietf.org/rfc/rfc4122.txt](https://www.ietf.org/rfc/rfc4122.txt)]([https://www.ietf.org/rfc/rfc4122.txt](https://www.ietf.org/rfc/rfc4122.txt)), accessed on 15 September 2023) over the whole corpus.
* text: textual content of the paragraph.
* url_warc: this is the identifier of the WARC file from which the web page from which the text has been extracted following the Common Crawl segments nomenclature (\"s3://commoncrawl/crawl-data/CC-MAIN-YYYY-CMM>/segments/<id>/warc/CC-MAIN-<id>.warc.gz\", where YYYY is the 4 digits year and MM the WARC archive month).
* url: URL address from which the text has been extracted.  
A sample corpus JSON register is shown below. Note that it is a paragraph from the article that currently does not exist although it is archived on Common Crawl.  
{  \"id\":\"8280 bafd-5984-4a5e-8436-af56a474d9 cd\",  \"text\":\"<textual content>\",  \"url_warc\":\"s3://commoncrawl/crawl-data/CC-MAIN-2019-04/segments/  1547583730728.68/warc/ CC-MAIN-20190120184253-20190120210253-00091.warc.gz\",  \"url\":\"[[http://alertatierra.com/continente/61-noticias/volcanes/](http://alertatierra.com/continente/61-noticias/volcanes/)]([http://alertatierra.com/continente/61-noticias/volcanes/](http://alertatierra.com/continente/61-noticias/volcanes/))  2135-erupcion-de-turrialba-y-rincon-de-la-vieja-en-costa-rica\" } ```  
Consequently, escorpius-m is fully traceable as including _url_warc_ facilitates the retrieval of URLs from Common Crawl, ensuring the reproducibility of the study. Additionally, this traceability simplifies source-based dataset filtering.
## 5 Discussion  
### Base Models and Meta-Model  
This study introduces a novel ensemble structure for classifying crop types using multi-source and multi-temporal S1, S2, and L8/9 satellite data. The proposed structure consists of two parts: Pa and PCA-Ca. The Pa structure generates inputs for the second part, and accurate outputs from Pa enhance the inputs for PCA-Ca, leading to an improved classification performance. To ensure optimal outputs from the Pa structure, the best performing ML model was utilized in each branch for each specific FC (FC\({}_{1-5}\) in Table 3). The selection of the best performing ML model in each branch of the Pa structure mitigates any negative impact from low-performing models when they are combined in the PCA-Ca part. The findings in Figure 4 demonstrated that the RF model outperformed GBT, SVM, and CART in all branches, indicating its superior accuracy in classifying crop types across various optical and SAR-based FCs (FC\({}_{1-5}\) in Table 3). Previous studies have also recognized RF as the top-performing ML model for crop type classification [20; 40; 44; 48].  
Figure 11: CMs of different ML model architectures to illustrate the superior performance of the proposed methodology of this paper (refer to Table 5 for more details) using the validation dataset.  
Furthermore, the Meta-model within the Ca structure directly generates the final classification outcomes. Similar to the Pa part, the best performing model within the Ca structure was chosen from RF, GBT, SVM, and CART. The RF model also demonstrated superior performance among these ML models within the Ca structure as well, as indicated in Figure 5. As a result, RF was chosen as the base model in the Pa structure and as the Meta-model in the Ca structure. The same conclusion was mentioned by other articles in this field for selecting the Meta-model [44]. The superior performance of RF compared to other ML models in this study can be attributed to several reasons. Firstly, the ensemble nature of RF combines multiple decision trees to make predictions, reducing the impact of individual tree biases and variances [41; 43; 44]. Secondly, RF has the capability to capture non-linear relationships in the data by randomly selecting subsets of features and training decision trees on these subsets [34]. Lastly, the random feature selection and bootstrapping techniques employed in RF help to mitigate the impact of noisy or outlier observations and reduce the risk of overfitting to the training data [42].
## 2 Materials and Methods  
### Experimental Set-Up  
One of the objectives of the present study is the application of the SVM algorithm for predicting the effect of horizontal screens on hydraulic characteristics. Experiments were performed in a laboratory flume that is 5 m in length, 0.3 m in width and 0.45 m in height with a zero degree slope. Note that this laboratory flume is similar to previous work done by [PERSON] et al. (2012, 2013) and [PERSON] et al. (2013). Transparent walls and floors were used to enable visualization of flow. A vertical drop with a height of 15 cm and a length of 1.20 m was made using glass boxes. The inlet flow to the flume was provided by two pumps, each with a capacity of 450 L per min. The flow rate was read using rotameters installed on the pumps with a relative error of \(\pm\)2%. A polyethylene sheet with a thickness of 0.01 m and planar dimensions of 0.7 \(\times\) 0.3 m with zigzag arrangements of holes with diameters of 0.01, 0.02 and 0.03 m was used to make the mesh plates (see, for more details, ([PERSON] et al., 2013)). Figure 1 shows a schematic of a laboratory flume and the equipment installed on it. In order to measure the depth of flow, a point gage with an error of \(\pm\)1 mm was used. In Figure 2, the side and top views of the vertical drop are shown, and the range of measured parameters is presented in Table 1.  
To evaluate the performance of the support vector machine (SVM) for predicting the relative energy dissipation (\(\Delta E/E_{0}\)) and the relative pool depth (\(y_{p}\)/H), a total of 164 datasets were obtained. The first 82 datasets are related to the relative energy consumption and the remaining datasets are related to the relative pool depth. Three different relative diameter sizes were used, namely 0.067, 0.133 and 0.2.
# Image Quality Improvement and Memory-Saving in a Permanent-Magnet-Array-Based MRI System
## 2 Theory
### Effects on Image Reconstruction Time
Similar to the group of systems in the previous comparison when the number of sampling points increases, the systems at 100 dB show rapid decreases in NRMSE before 15 iterations and show knee-shaped curves, and those at 20 dB have minima NRMSE and increases to saturation. The green curves show the case with \(N_{t}=128\) and \(N_{\theta}=90\) which are the same curves as the green ones in Figure 6a. At \(\text{SNR}=100\) dB, similar to the previous comparison, the three curves hit the saturation with the same amount of iterations (i.e., an approximately same amount of calculation time). It is noticed that the cases with a high \(N_{\theta}\), \(N_{\theta}=180\) and 360, shows a much smaller NRMSE compare to the case at \(N_{\theta}=90\), which indicates a better image quality and agree with the observation in Figure 4. On the other hand, when SNR decrease to 20 dB, similar to the previous comparison, the NRMSEs at the first iteration in each system is similar to those at 100 dB. At \(N_{\theta}=180\), and 360, a minimum NRMSE arrives at the third iteration at 0.0794 and 0.0696, respectively. Compared to the case at \(N_{\theta}=90\) where the NRMSE minimal is at the first iteration, an increase in \(N_{\theta}\) leads to an increase in the computational time. Figure 6: The relationship between image quality and iteration under conjugate gradient reconstruction (**a**) the number of sampling point changed in the system with 90 angles. (**b**) the number of angle changed in the system with 128 sampling points. For image reconstruction, conjugate gradient method was used and Equation (6) was solved where \(\vec{A}\) in Equation (6) is linked to the encoding matrix of the PMA-based MRI system as \(\vec{A}=\vec{E}^{\dagger}\vec{E}\). Matrix \(\vec{A}\) is not sparse for most of the systems, and the time consumed in each iteration is nearly same, as shown in Figure 5. At an SNR of 100 dB, based on the observations on both Figure 6a,b, all of systems need around 13-15 iterations to reach a minimal NRMSE. In the same comparison group, the number of iterations needed is the same, which is independent of the variable \(N_{t}\) or \(N_{\theta}\). This means the same amount of computational time in each group. When SNR drops and noise increases, noise corrupts the system through iterations, leading to a high NRMSE after more iterations. It is found that a noisy system with more sampling points/rotation angles requires more iterations and more time to reach the minimum NRMSE.