esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 3 Feasibility of the Quasi-Static Approach Under a Uniform Wind
### Enhanced Quasi-Static Approach Considering an Additional Rolling Moment  
A modification of the equilibrium equation was proposed for an enhanced quasi-static approach that could consider an additional rolling moment for the trailer. According to Figure 9, the additional rolling moment can be calculated as shown by Equation (24).  
\[M_{a}\ =\ (m_{s}g-F_{L})p\cos\alpha \tag{24}\]  
In Equation (24), \(M_{a}\) is the additional rolling moment induced by the lateral displacement, \(p\), of the sprung mass, \(m_{s}\). The trailer body rotates relative to the roll center of the sprung mass, as shown in Figure 9, and the lateral displacement, \(p\), is proportional to the roll angle, \(\phi\), which results in Equation (25).  
\[M_{a}\ =\ (m_{s}g-F_{L})\phi h_{roll}\cos\alpha \tag{25}\]  
In Equation (25), \(h_{roll}\) is the height of the roll center from the road surface. If the roll stiffness of the trailer, \(K_{\phi}\), is given, Equation (26) is the equilibrium.  
\[K_{\phi}\phi\ =\ M_{R}+\left(\left(F_{S}+\frac{m_{s}v^{2}}{R}\right)\cos \alpha+(F_{L}-m_{s}g)\sin\alpha\right)h_{roll}+M_{a} \tag{26}\]  
From Equations (25) and (26), the roll angle \(\phi\) can be derived as follows, which is similar to an equation proposed by [PERSON] [13].  
\[\phi\ =\ \frac{M_{R}+\left(\left(F_{S}+\frac{m_{s}v^{2}}{R}\right)\cos \alpha+(F_{L}-m_{s}g)\sin\alpha\right)h_{roll}}{K_{\phi}-(m_{s}g-F_{L})h_{roll} \cos\alpha} \tag{27}\]  
Figure 9: Additional rolling moment induced by lateral displacement of sprung mass.  
From Equations (25) and (27), the additional rolling moment, \(M_{a}\), could be calculated. The additional moment, \(M_{a}\), should be added to the right-hand side of Equation (12), as shown in Equation (28), which results in an enhanced quasi-static approach for a trailer.  
\[\frac{c}{2}(F_{x3}-F_{x2})+h_{T}\cdot F_{yf}\ =\ M_{R}^{\prime}+M_{a}+h_{c}\cdot F _{S}^{\prime} \tag{28}\]  
Figure 10 clearly shows the enhancement in the estimated vertical reaction forces when the proposed enhanced quasi-static approach is utilized. The data for \(h_{roll}\) (=1.035 m) and \(K_{\phi}\) (=8.5 kN-m/degree) were obtained from the TruckSim model. Accordingly, the accuracy of the vertical reaction force estimation for a trailer could be improved by adopting the proposed enhanced quasi-static approach when the roll stiffness information is available.
## 2 Related Work
However, when focusing on detecting fake news in Spanish, especially in the political context, a significant challenge arises due to the lack of comprehensive Spanish training datasets. Our paper fills this gap by assembling an extensive and varied collection of high-quality Spanish political news articles, as we will describe in Section 3. This dataset has been curated specifically for the purpose of fine-tuning and evaluating Transformer \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Related Work** & **Adopted Technique** & **Dataset** & **Size** & **Language** & **Accuracy** \\ \hline [28] & BERT, XLNet, RoBERTa & WELFake, FakeNewsNet, and FakeNewsPrediction & 72,134 & English & 99\% \\ \hline [15] & BERT & EXIST [36] & 13,000 & English & 76.83\% \\ \hline \multirow{3}{*}{[16]} & \multirow{3}{*}{BERT} & IMDB movie reviews [37] & 50,000 & English & 93.87\% \\  & & RealOnNot tweets [38] & 10,000 & English & 83.64\% \\  & & Portuguese news [39] & 12,145 & Portuguese & 91.20\% \\  & & Chinese hotel reviews [40] & 6000 & Chinese & 93.81\% \\ \hline \multirow{3}{*}{[12]} & Random Forest & \multirow{3}{*}{Fakes1000 [34]} & \multirow{3}{*}{2000} & \multirow{3}{*}{Spanish} & 74.25\% \\  & XGBoost & & & & 64.25\% \\  & Logistic Regression & & & & 77\% \\ \hline \multirow{3}{*}{[14]} & SVM & \multirow{3}{*}{The Spanish Fake News corpus [35]} & \multirow{3}{*}{971} & \multirow{3}{*}{Mexican Spanish} & 81\% \\  & Naive Bayes & & & & 59\% \\  & KNN (K = 3) & & & 81\% \\  & Decision Tree & & & 62\% \\ \hline \multirow{3}{*}{[13]} & Random Forest & \multirow{3}{*}{Unpublished dataset} & \multirow{3}{*}{634} & \multirow{3}{*}{Spanish} & 89.80\% \\  & BERT & & & & 95.80\% \\ \hline \multirow{3}{*}{[33]} & Two parallel BERT & \multirow{3}{*}{Fakenewsnet [41]} & \multirow{3}{*}{23,000} & \multirow{3}{*}{English} & 85.4\% \\  & networks & & & & \\ \hline \multirow{3}{*}{[42]} & BETO & Corpus extracted from [34; 43] & 2500 & Spanish & 80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works about fake news detection and text classification by ML and Transformer models. -based models designed to detect fake news in Spanish within the domain of politics, a realm that has been relatively underexplored in the current state-of-the-art methodologies. As it will be detailed in Section 4, the objective is to illustrate the performance of these models, which serve as valuable instruments in the fight against misinformation in Spanish-speaking areas.
## 3 Results
### Model Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model (Hyperparameters)** & **AUROC** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline \hline Kernel SVM (kernel) & & & & \\ \hline (linear) \({}^{1}\) & 0.652 & 0.704 & 0.600 & 0.611 \\ (rbf) & 0.557 & 0.370 & 0.743 & 0.704 \\ \hline Logistic regression (penalty, C) & & & & \\ \hline (L1, 0.1) \({}^{1}\) & 0.698 & 0.778 & 0.617 & 0.624 \\ (L1, 1) & 0.663 & 0.704 & 0.622 & 0.630 \\ (L1, 100) & 0.658 & 0.704 & 0.613 & 0.623 \\ (L2, 0.1) & 0.654 & 0.704 & 0.604 & 0.615 \\ (L2, 0.5) & 0.648 & 0.667 & 0.630 & 0.634 \\ (L2, 0.01) & 0.637 & 0.704 & 0.570 & 0.584 \\ \hline KNN (neighbors) & & & & \\ \hline (3) & 0.527 & 0.260 & 0.800 & 0.739 \\ (5) \({}^{1}\) & 0.551 & 0.333 & 0.770 & 0.724 \\ (10) & 0.548 & 0.370 & 0.726 & 0.689 \\ \hline Random forest (number of trees) & & & & \\ \hline (10) & 0.538 & 0.259 & 0.817 & 0.759 \\ (50) \({}^{1}\) & 0.604 & 0.407 & 0.800 & 0.759 \\ (100) & 0.585 & 0.370 & 0.800 & 0.755 \\ \hline XGBoost (number of trees) & & & & \\ \hline (10) & 0.519 & 0.519 & 0.630 & 0.619 \\ (50) \({}^{1}\) & 0.585 & 0.444 & 0.726 & 0.696 \\ (100) & 0.512 & 0.259 & 0.765 & 0.712 \\ \hline AdaBoost (number of trees) & & & & \\ \hline (10) & 0.707 & 0.741 & 0.673 & 0.681 \\ (50) \({}^{1}\) & 0.716 & 0.741 & 0.691 & 0.696 \\ (100) & 0.684 & 0.704 & 0.665 & 0.669 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Indicates optimized hyperparameter values after parameter tuning.  
\end{table} 
Table 2: Performance of the machine learning (ML) algorithms. Figure 3: Receiver operating characteristic (ROC) curves of the ML models.
## 3 SDMob Architecture
### Basic SDMob Architecture
#### 3.1.4 Integration to the Objective Functions  
Once the controller runs the filter and announces the new best parent, it will start serving the MN in the data window. Selecting the closest parent to the MN can be a naive strategy since the characteristics of the multi-hop path will be ignored. An alternative approach is to estimate the one-hop link quality (reverse relation with distance) and use it by adding to the classic metrics such as ETX.
## 4 Implementation and Discussion of Results
### Experimental Setup  
All the tests are run on a 64-bit Windows operating system with an Intel Core i5-7200U (2.50 GHz) processor, 8.00 GB of RAM, a 128 GB solid-state drive, and no graphics card. ARIMA, LSTM, and CNN were implemented using well-known deep learning Python libraries including Keras, TensorFlow, and NumPy. The activation function for the input layer was set to ReLU with accurate return time series sequences. In the final layer, the Adam optimizer was employed. The validation length for the training process was set to 10 and the batch size to 1, together with the early-stopping epochs criteria. Furthermore, one model's experiment contains a total of 100 epochs, with 10 epochs every step.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 4 WSN Deployment
### Infrastructure and Work Safety Monitoring Application
The same applies to dangerously high levels of carbon monoxide or dioxide in the air or nitrogen compounds (monoxide, dioxide and other nitrogen oxides) to prevent poisoning of the maintenance staff. Considering the real case utility tunnel system and the proposed WSN deployment presented in the previous subsection, an infrastructure and work safety monitoring application prototype was developed in order to collect the data obtained by the sensors located in the gallery in real time and store them in a database allowing cloud computing. A schematic view of the monitoring tool architecture is depicted in Figure 18. The prototype was developed using open-source tools such as the Bootstrap framework [44] for the development of the web application Grafana [45] for the analytics and interactive visualization and the D3.js [46] JavaScript library for the production of dynamic and interactive data visualizations in web browsers. The system also includes a web service in charge of data collection, developed in Java, and a relational database (MySQL) [47]. The design premises are security and use simplicity. The monitoring tool is responsive, which allows its use on mobile devices (smartphones and tablets), work  
Figure 18: Architecture of the monitoring tool. It embeds Grafana's panels using iframes. On the one hand, there is a Grafana dashboard for the direct monitoring of the gallery's health parameters, which allows the visualization of the data provided by the tunnel's sensor network, which is depicted in Figure 19. On the other hand, the monitoring tool (see Figure 20), in addition to integrating the information provided by Grafana, includes the monitoring of the activities performed inside the gallery. These activities include dead man functionalities, monitoring of the task progress, access and exit points of the gallery, evacuation protocol and instructions in case of emergency, emergency acoustic and light signals activation, etc. The tool includes tree-based and sunburst visualization to describe the workflows. The data collected by the WSN is stored, processed and analyzed in a private cloud. For that purpose, an Apache CloudStack was implemented over hypervisors VMware ESXI managed by a VMware vSphere. The resulting infrastructure allows the agile and easy deployment and management of the services. Besides, it allows computing orchestration, user and account management and provides data storage and data analytics. The proposed computing cloud is easily scalable and allows hot migrations according to the availability of the servers. Figure 19: Time series monitoring by means of Grafana (iFrames).
## 3 Results and Discussion
### Adsorption Isotherms
Two different types of plots were obtained for the CPX adsorption, a straight line for the NC and concave curves for the three pillared clays. The first one suggests that there is one type of site for CPX adsorption on the adsorbent surface, whereas the curves acquired for the pillared clays are related to a negative cooperative phenomenon. This last is proposed when two independent sets of data that individually arrange in a linear combination are obtained. Each of them could be associated to two types of affinities of CPX to the surface, one being the consequence of a strong interaction like the formation of inner sphere complexes (high affinity sites) and the other resulting from low affinity interactions like the hydrophobic effect, van der Waals interactions, or hydrogen bridges [25; 34; 35]. On other hand, Scatchard plots obtained for TC adsorption showed three different shapes. The plot shown for NC resulted in a convex curve suggesting a positive cooperative phenomenon, similar to that proposed for its adsorption isotherms [34; 35]. Scatchard plots obtained for the pillared clays showed that Si-PILC 25 has a different behavior than other materials. The plot obtained for TCH\({}^{-}\) adsorption on Si-PILC 25 showed a similar behavior that the obtained for the CPX\({}^{-}\) on pillared clays, where a negative cooperative phenomenon was proposed as the result of the presence of two different adsorption sites for the adsorbate. However, for Si-PILC 50 and 75, straight lines were obtained suggesting that the adsorption of TCH\({}^{-}\) on these materials occurs in the same type of sites. This last could be due to the fact that TC species only has access to one type of adsorption site because the porous structure denies it access to another one. All the results obtained suggest that the adsorption capacity of Si-PILC depends on their porous structure and the adsorbate molecular sizes. Figure 10: Scatchard plots derived for adsorption data obtained for two antibiotics and the four materials.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 3 Results
### Impacts of Assimilation on Land Surface Variables Prediction
#### 3.1.4 Latent Heat Flux  
Statistical evaluation metrics (i.e., \(RMSD_{Analysis}\), \(RMSD_{Madd}\), \(NIC_{RMSD}\)) of LE predictions are summarized in Table 3. In general, data assimilation significantly improves the prediction skill of LE, and 10 out of 12 experiments obtain positive \(NIC_{RMSD}\). Similarly, the MLDAS prediction is slightly worse compared to OL in scenario I (assimilation for 15 days), with a negative \(NIC_{RMSD}\) for 2003 and 2005. The prediction performance in MLDAS is improved rapidly in scenarios II-IV, which is similar than that of H. As for the trend of \(NIC_{RMSD}\), \(NIC_{RMSD}\) increased from 25.69% to 58.08% with a longer assimilation period in 2003. However, the highest \(NIC_{RMSD}\) falls in scenario II in 2004 and 2005 (57.34% and 61.56%, respectively), which is similar than the trend of \(NIC_{RMSD}\) in H.  
Figure 5 shows the comparison of the LE prediction with a lead time of 30 days between MLDAS and OL. Prediction in MLDAS, especially in scenarios II and IV of each year, is closer to the benchmarks in most experiments. The most apparent improvement can be seen in scenario II, where OL tends to overestimate LE while MLDAS corrects it. Moreover, the differences between MLDAS prediction and OL prediction diminish with lead time in scenario II over 3 years, indicating that the prediction skill brought by data assimilation also decreases with forecast lead time.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Year & \multicolumn{2}{c}{**2003**} & \multicolumn{3}{c}{**2004**} & \multicolumn{3}{c}{**2005**} \\ \cline{2-9}  & \(RMSD_{Madd}\) & \(RMSD_{Analysis}\) & \(NIC_{RMSD}\) & \(RMSD_{Madd}\) & \(RMSD_{Analysis}\) & \(NIC_{RMSD}\) & \(RMSD_{Madd}\) & \(RMSD_{Analysis}\) & \(NIC_{RMSD}\) \\  & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** & **(\%)** & **(W/m\({}^{2}\))** & **(\%)** & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** & **(\%)** \\ \hline
**Scenario I** & 31.121 & 33.785 & \(-8.56\) & 56.808 & 54.251 & **4.50** & 44.714 & 59.053 & \(-32.07\) \\ \hline
**Scenario II** & 36.858 & 27.390 & **25.69** & 22.309 & 9.517 & **57.34** & 69.300 & 26.642 & **61.56** \\ \hline
**Scenario III** & 21.382 & 11.165 & **47.79** & 11.749 & 7.763 & **33.93** & 13.545 & 8.009 & **40.87** \\ \hline
**Scenario IV** & 32.459 & 13.608 & **58.08** & 17.408 & 9.235 & **46.95** & 39.647 & 21.527 & **45.70** \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(NIC_{RMSD}\) of latent heat fluxes in different assimilation scenarios (I–IV) in 2003, 2004, and 2005. Positive \(NIC_{RMSD}\) numbers are marked in bold.
## 3 Results  
### Mass Concentration of PM\({}_{2.5}\) and PM\({}_{10}\) in Winter Season  
The results of the present investigations in Faisalabad city for which 12 different sites were selected and categorized as residential, commercial, industrial and health centers are presented (Figure 2). The concentration of PM\({}_{2.5}\) had the following decreasing order in the air samples collected near medical centers of Mian Trust Hospital (38.50 \(\pm\) 0.30 \(\upmu\)g/m\({}^{3}\)), Ithethal Welfare Dispensary (37.35 \(+\) 0.45 \(\upmu\)g/m\({}^{3}\)), Allied Hospital (36.65 \(\pm\) 0.27 \(\upmu\)g/m\({}^{3}\)). While, in residential areas, the highest concentration of fine particulate was found in Ghulam Muhammad Abad (39.1 \(\pm\) 0.50 \(\upmu\)g/m\({}^{3}\)) followed by Raza Abad (35.2 \(\pm\) 0.23 \(\upmu\)g/m\({}^{3}\)), Saleemi Chowk (33.83 \(\pm\) 0.74 \(\upmu\)g/m\({}^{3}\)), Kashmir Road (32.25 \(\pm\) 0.14 \(\upmu\)g/m\({}^{3}\)). In the selected commercial areas, Nazria Pakistan (43.63 \(\pm\) 0.59 \(\upmu\)g/m\({}^{3}\)) was the most polluted site of the city. The average concentration of PM\({}_{2.5}\) in small industrial estate was recorded as (37.93 \(\pm\) 0.19 \(\upmu\)g/m\({}^{3}\)) followed by Pepsi factory area (37.17 \(\pm\) 0.62 \(\upmu\)g/m\({}^{3}\)); while in and near transport station (35.08 \(\pm\) 0.61 \(\upmu\)g/m\({}^{3}\)) was analyzed. In contrast, the highest concentration level of PM\({}_{10}\) was found in the samples collected from Nazria Pakistan (800.85 \(\pm\) 0.93 \(\upmu\)g/m\({}^{3}\)) followed by Mian Trust Hospital (586.6 \(\pm\) 3.88 \(\upmu\)g/m\({}^{3}\)), Allied Hospital (584.62 \(\pm\) 3.41 \(\upmu\)g/m\({}^{3}\)), Small Industrial Estate (469.1 \(\pm\) 0.57 \(\upmu\)g/m\({}^{3}\)) and Ghulam Muhammad Abad (440.2 \(\pm\) 0.10 \(\upmu\)g/m\({}^{3}\)); while the lowest concentration was analyzed in the ambient air of Saleemi Chowk (280.18 \(\pm\) 0.12 \(\upmu\)g/m\({}^{3}\)). The average particulate concentration can be compared with guideline values proposed by WHO, US-EPA and NEQS. During the present study, the concentration of PM\({}_{2.5}\) was within the guideline value of the US-EPA and NEQS (35 \(\upmu\)g/m\({}^{3}\)) but still higher than the WHO guideline value of 25\(\upmu\)g/m\({}^{3}\)[25] for 24 h average, while coarse particles were drastically exceeding the safe limits of all the quality standards. The composition of particulate matter is strongly reliant with its sources, i.e., anthropogenic or natural (Figure 2).
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Experiment Design
#### 3.3.4 Segmentation Validation Metrics  
For the assessment of classification quality, we used a precision-recall curve, an Average Precision score (AP), which is defined as the Area Under the Precision-Recall Curve (PR-AUC), a Receiver Operating Curve (ROC), and, finally, the Area Under the Receiver Operating Curve (ROC-AUC). All evaluation methods were implemented in the Scikit-learn v. 0.24.2 package [41].  
Precision and recall are respectively defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{5}\]  
where \(TP\) stands for true positive and corresponds to the pixels that were labeled as a \"plume\" and were correctly identified by the classifier. \(FP\)--false positive; it corresponds to the pixels that were not labeled as a \"plume\", but were identified as a \"plume\" by the classifier. \(FN\) stands for false negative and corresponds to the pixels that were not classified as a \"plume\" by the classifier, but were labeled as such by the labeler. The ROC curve visualizes True Positive (\(TP\)) scores as a function of False Positive (\(FP\)) scores.
## 2 Modeling and Optimization Method  
### BCT Model and Pressure Drop Calculation  
#### 2.1.1 Conceptual Model of a BCT  
The bionic non-smooth surface drag reduction technology is based on the principle of bionics [23]. Changing the morphological characteristics of the object surface will change the kinematic and dynamic characteristics of the fluid flowing along the wall [24]. Research on bionic non-smooth surface-drag-reduction technology mainly focused on different rib surfaces [25; 26]. Currently, two main drag-reduction mechanisms of bionic non-smooth surfaces are recognized by many scholars, namely, the'second vortex group' theory and the 'bulgy height' theory [27; 28]. The'second vortex group' theory holds that a small secondary vortex is generated due to the interaction between the backward-rotating flow vortex and the tip of the groove. The existence of the groove restricts the movement of the flow direction vortex, which makes the wall suddenly weak, and ultimately reduces the wall friction [29; 30]. The 'bulgy height' theory argues that the existence of grooves increases the thickness of the viscous bottom in the boundary layer, resulting in a decrease in friction, which was confirmed by many researchers [31].  
The rib drag reduction method was used in many fields and was shown to positively reduce fluid resistance [32; 33]. In this study, according to the typical L-shaped rib, we proposed a new type of bionic circular tube (BCT), where the schematic diagram is shown in Figure 2.  
As shown in Figure 2, the typical non-smooth surface bionic prototype mainly includes shield scales of shark skin, the mastoid structure of the lotus leaf, the spherical nodular structure of a humpback fin, and radial stripes of scallops. A BCT model was proposed based on the bionic non-smooth surface drag reduction theory. A circular annular groove was uniformly added to the internal flow channel surface of the conventional circular pipe according to certain rules as the bionic unit so that the internal flow channel of the conventional circular pipe was changed from the original smooth flow channel to the  
Figure 2: Concept diagram of a BCT (note: d is the diameter (m), h is the groove depth (m), w is the groove width (m), and s is the slot pitch (m).  
bionic non-smooth surface structure. This could improve the kinematic and dynamic characteristics of the fluid flowing on the pipe wall to a certain extent.
# A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images
## 2 Materials  
### Research Process  
The steps were conducted to effectively classify vegetation in urban areas including long-run steel roofing, waterproof coated roofs, and urethane-coated areas as Figure 1.  
First, the study site was selected, and eight ground control points (GCPs) were determined through a Virtual Reference Station (VRS) survey as reference points for the UAV images. After installing a Micasense RedEdge MX sensor on the UAV, a UAV flight plan was established using DJI GS Pro S/W, and multispectral images were acquired through UAV photography.  
Next, by merging images using Pix4D Mapper S/W, orthomosaic images were constructed for the blue, green, red, and near-infrared bands. By combining the images for each band using ArcGIS S/W, various vegetation indices, such as the NDVI, GNDVI, BNDVI, RGBVI, GRVI, and SAVI, were calculated.  
In addition, by comparing and analyzing the vegetation index values for various land covers in the target area, points showing abnormal values were identified, and sections to be considered when analyzing vegetation in urban areas were reviewed. Finally, a novel vegetation index was derived to effectively analyze the vegetation distribution area reflecting the urban land cover.