esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 3 Methodology
### Historical Data Set Preparation  
In the dataset preparation step, the variable data type is transformed, and the binary data are normalised. The goal of data preparation is to help the SentiChurn model deal with data easily [30]. The binary variable is normalised to '1' for 'yes' and '0' for 'no' as well as '0' for'male' and '1' for 'female'. Regarding the continuous variables, such as age and long period as a customer, we transform them into categories as an ordinal variable and then assign them by sequential numbering starting from 1. The final collected variables and their types that will be used as inputs for our prediction model are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Company** & **Predicted Customer Satisfaction** & **Actual Customer Satisfaction** \\ \hline STC & 31.06\% & 20.1\% \\ Mobily & 34.25\% & 22.89\% \\ Zain & 32.06\% & 22.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Customer satisfaction rate vs. actual customer satisfaction.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)  
The technique has been described in detail in the EFSUMB and WFUMB guidelines [1; 2; 4; 6]. A 2D-SWE techinique is exemplified in Figure 2.  
A recent meta-analysis analyzed 12 studies on 550 patients to assess the diagnostic performance of 2D-SWE for determining the severity of liver fibrosis in children and adolescents. The summary sensitivity was 81% (95% CI: 71-88) and the specificity was 91% (95% CI: 83-96) for the prediction of significant liver fibrosis. Subgroup analysis revealed that 2D-SWE had an excellent diagnostic  
Figure 2: Two-dimensional shear wave elastography (2D-SWE) in a 10 years old boy with cystic fibrosis associated liver disease. The median of liver stiffness measurements using the convex probe was 4.47 kPa, IQR = 1.13. Point SWE results were 1.47 m/s (convex probe).  
performance according to each degree of liver fibrosis. 2D-SWE had a higher sensitivity (\(p<0.01\)) and specificity (\(p<0.01\)) than VTQ(r) [71]. In this meta-analysis, the number of LSMs performed was a significant factor influencing study heterogeneity.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### Performance Measure  
Specific types of UAVs have many uses throughout the public, private enterprises, and the military. In the public sector, UAVs are used more for recreational use. These UAVs are typically smaller and lightweight (<2 kg) rotary or fixed-wing UAVs. Private companies mostly use slightly larger (2 kg to 150 kg) rotary-wing and hybrid UAVs. The uses of these UAVs range from delivery of packaging to aerial photography to crop dusting to powerline inspection. The military uses mostly fixed-wing UAVs for tasks such as armed reconnaissance or surveillance. These UAVs are much larger in size and weight than the public or private sectors (>25 kg) [17].  
When discussing a UAV's performance measure, it is integral to look at many things, such as maximum payload, the mass of the UAV, endurance, ceiling altitude, range of operation, and the flying mechanism. Table 1 summarizes performance measures of general rotary-wing, fixed-wing, and hybrid UAVs.  
The payload is typically referred to as the maximum weight that a UAV can carry. The payload of a UAV could be a camera on a more miniature public-use UAV or a missile on a military-use UAV. The endurance refers to the maximum flight time of the UAV. The endurance of a UAV directly relates to the energy source, typically fuel or battery. Thus, it is also essential to consider a UAV based on its power needs and source. The ceiling altitude  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flight Mechanism \({}^{\star}\)** & **Rotary-Wing** & **Fixed-Wing** & **Hybrid** \\ \hline Mass (kg) & 0.01 to 100 & 0.1 to 400,000 & 1.5 to 65 \\ Payload (kg) & 0 To 50 & 0 to 1000 & 0 to 10 \\ Ceiling altitude (km) & 4 & 0.1 to 30 & n/a \\ Endurance (min) & 6 to 180 & 60 to 3000 & 180 to 480 \\ Range (km) & 0.05 to 200 & 2 to 20 mil & n/a \\ Energy source & Battery & Fuel or Battery & Fuel or Battery \\ \hline \hline \end{tabular} \({}^{\star}\) These are estimated ranges from the references as they are specific for the specific UAV modes, but not flight group, or they did not cover all categories listed.  
\end{table}
Table 1: Characteristics of different types of UAVs [17, 20, 21, 22, 23].  
refers to the highest a UAV can fly. The distance from which a UAV can be controlled remotely is called its range [17].
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Drift Detection Algorithms
#### 6.2.1 Error Driven  
According to [36], most methods are based on the misclassification error rates of the actual classification algorithm. If the error rate increases, the learned algorithmic model no longer satisfyingly fits the data. One of the first error-based methods is the drift detection method (DDM) [47]. (Please note, that [85] categorizes DDM related methods as statistical-based techniques.) The error rates of the classification models are compared based on landmark windows as soon as new labeled data are available. If the error increases significantly, the method triggers the update of the classification model.  
Over the years, further developments have been made based on DDM. The extensions address different adjusting screws of the algorithm [36]. For instance, the _Early drift detection method_ (EDDM) monitors the distance between correctly classified instances in addition to the error rate in order to detect changes already at the beginning of a drift [158]. However, most of the algorithms listed in [36] and the methods discussed here can only detect the point at which time drift occurs. Please note that, e.g., in [85], the DDM approach is listed under statistical methods-based drift detection.  
To detect the critical region of the substantial change in the distribution, an extension of the algorithm based on the Hoeffdings Inequality (HDDM) can be used [159]. Manual specification of window sizes is naturally error-prone. The fuzzy window DDM (FWDDM) uses a fuzzy time window instead of the rigid one to address gradual drifts [160]. Also well-known is the Adaptive Windowing (ADWIN) method proposed by [110] (see Section 4.2). In the summarizing work of [85], the ADWIN algorithm is categorized as a window-based drift detection.
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 3 Control Methodology  
Two methods of finding the optimal control inputs for steering angles (i.e., \(\delta_{f}\) and \(\delta_{r}\)) and drive forces \(F_{Sli}\), \((i=1,...,4)\) are developed. The first method used model predictive control (MPC) with a sequential quadratic programming (SQP) solver. The second method is particle swarm optimization (PSO). The objective of the controller is to accurately follow a predetermined path through multiple terrain types.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Multimodal Feature Extraction
#### 3.3.1 Text Encoding Network  
For textual content, we leverage BERT (Bidirectional Encoder Representations from Transformers), a pre-trained language model developed by Google that understands the contextual meaning of words in sentences by analyzing text bidirectionally, enabling it to capture nuanced semantic relationships and linguistic patterns essential for fake news detection. BERT generates features containing word semantics and linguistic context. Given text content \(T\), it is modeled as a word sequence \(T=\{w_{1},w_{2},\dots,w_{m}\}\), where \(m\) represents the number of words in the article text.  
\[H_{T}=\text{BERT}(T)=\{h_{1},h_{2},\dots,h_{m}\} \tag{1}\]  
where \(h_{i}\in\mathbb{R}^{d_{i}}\) represents the hidden state output of the corresponding token in BERT, and \(d_{i}\) represents the dimension of text embeddings.
## 4 Results and Discussion
### Intelligent Detection of UAV Images
#### 4.2.1 Band Preference  
In examining the spectral characteristics of typical magnetite ores in the region, including the typical sample spectra collected and those from the USGS database (as shown in Figure 12), it is observed that the mineral exhibits a nearly linear decline from 400 nm to 1000 nm. In the typical magnetite spectral curve from the Yanshan Iron Ore Mine, there are small absorption peaks only at 650-720 nm and 900 nm. Therefore, this study introduces a new index--the band ratio index--based on the original spectral bands. This index reflects the relative spectral transformation of magnetite and can reveal its dynamic spectral properties. By calculating the Spearman correlation coefficient between the spectral bands and iron grade, the relationship between the spectral band information from remote sensing images and the actual iron grade measurements can be explored, allowing for the identification of bands and band ratio indices that have a high correlation with iron grade inversion (i.e., intelligent identification of magnetite).  
Figure 11: Spectrum TSG interpretation of mineral identification results: (**a**) shortwave infrared interpretation results, (**b**) thermal infrared interpretation results.  
Figure 12: (**a**) Spectral of typical magnetite sample; (**b**) magnetite spectral characteristics from the USGS spectral library.  
Given n samples, the original spectral data were first standardized and then transformed into rank data. The Spearman correlation coefficient (\(\rho\)) was subsequently calculated using the following equation to assess the relationships:  
\[\rho=\frac{\sum\limits_{i}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{ \sum\limits_{i}{(x_{i}-\overline{x})^{2}\sum\limits_{i}{(y_{i}-\overline{y})^{ 2}}}}} \tag{9}\]  
Utilizing the results of the Spearman correlation coefficient calculations (as shown in Figure 13), eight spectral bands and band indices with significant relationships with iron grades at a level above 0.2 were selected. These include six individual bands, as well as the ratios of 450 nm to 530 nm and 650 nm to 720 nm, which will provide features for the subsequent intelligent recognition of magnetite.
## 3 Results
#### 3.3.3 Transcription Analysis of Differentially Expressed Genes  
The fragments per kilobase of exon per million fragments mapped density distributions of NT and MHT tobacco leaf transcriptomes were compared. In Figure 3A, the log\({}_{10}\) (FPKM) value of a gene is plotted on the abscissa and the density corresponding to the log\({}_{10}\) (FPKM) value is plotted on the ordinate; the higher the value, the higher the gene expression level. As shown in Figure 3A, the log\({}_{10}\) (FPKM) density distributions were slightly different between NT and MHT samples. A volcano plot depicting the relationship between the -log\({}_{10}\) (padj) and log\({}_{2}\) (fold change) is shown in Figure 3B. The log2 (fold change) in gene expression of NT and MHT exposed tobacco leaf transcriptomes is plotted on the abscissa. The degree of statistical significance of the changes in gene expression is plotted on the ordinate. As shown in Figure 3B; 492 and 1440 genes were significantly up- and downregulated, respectively, by MHT treatment as compared to NT treatment. A heat map of the FPKM values of the 1932 DEGs after clustering is shown in Figure 3C. Moreover, the 1932 DEGs were divided into 18 clusters by hierarchical clustering.  
Figure 3: Expression analysis of differentially expressed genes (DEGs) between NT and MHT exposed tobacco leaves. (**A**) Fragments per kilobase of exon per million fragments mapped (FPKM) density  
distribution between NT and MHT tobacco leaf transcriptomes. (**B**) Volcano plot of DEGs between NT and MHT tobacco exposed leaves. The mean expression value of -log\({}_{10}\) (padj) is plotted on the ordinate, and the log\({}_{2}\) (fold change) value is plotted on the abscissa. Each dot represents an individual gene. Red dots represent significantly upregulated DEGs, and green dots represent significantly downregulated DEGs. Blue dots represent genes whose expression was not significantly different between the two treatment groups. (**C**) Hierarchical clustering analysis of DEGs between NT and MHT exposed tobacco leaves. The color scale indicates the log\({}_{10}\) (FPKM + 1) values. Red indicates high gene expression, and blue indicates low gene expression.; Padj, adjusted \(p\)-value.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Structural Analysis
#### 5.2.3 PEF Scenario Development  
To evaluate the structural response under combined earthquake-fire scenarios, numerical models are subjected to a series of sequential hazard loadings. These include the following:  
* Varying Peak Ground Accelerations (PGAs) to represent different seismic intensities;* Fire simulations incorporating diverse thermal profiles, including localized fires, compartmentalized fires, and fully developed post-flashover conditions;
* Control scenarios involving fire-only cases, which isolate the effects of fire without prior seismic damage for comparative assessment.
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## 5 Conclusions  
In this paper, an innovative configuration of RST-OIL was applied to MODIS NIR data at a spatial resolution of 250 m. In particular, this configuration was developed to detect the presence of oil under  
Figure 16: Oil area (km\({}^{2}\)) temporal trend for the period analyzed.  
extreme observation conditions occurring, for example, in the presence of a (high/moderate) sun glint effect. The obtained results were satisfactory with a reliability of up to 99% when compared with those achieved by a SAR-based methodology. A sensitivity accuracy of about 60% was also found, which, considering that a detailed mapping of the oil-affected areas was outside the scope of this paper, can be considered acceptable. Oil-affected areas, in positive contrast with surrounding seawater, were detected without any false alarm and with a good signal-to-noise ratio. Concerning oil in a negative contrast, although some limits related to the technique configuration exist, the oil area was detected even with a signal-to-noise ratio slightly lower than the previous case.  
It is worth explaining that, even though these results came out of a single case study (DeepWater Horizon accident), the numerous images available (acquired at different date-times and by different satellites) allow the analyses to be performed under boundary conditions (observation, environmental, and atmospheric), which are so different from each other that they could be considered independent.  
Considering the results already achieved by the same method using TIR data, RST-OIL can be seen as a suitable operational tool for the automatic and timely detection of oil spills, which is critical as a rapid response to oil spills using numerical models [52]. This is possible without any other ancillary/auxiliary data, which makes the techniques fully automatic and easily exportable everywhere. Furthermore, RST-OIL can be integrated with outputs coming from the SAR data analysis to improve the spatial resolution and provide an all-day/weather capability. Additionally, RST-OIL can be used jointly with other independent techniques that are better suited to mapping spilled areas in order to build a more effective satellite-based oil spill monitoring system.  
Conceptualization, V.S. and T.L.; Data curation, V.S. and E.C.; Formal analysis, V.S.; Investigation, V.S. and T.L.; Methodology, N.P. and V.T.; Software, V.S.; Supervision, T.L.; Validation, V.S.; Writing--original draft, V.S. and T.L.; Writing--review & editing, E.C., T.L., N.P. and V.T.  
SAPERE (Space Advanced Project Excellence in Research and Enterprise) project financed by MIUR, CTN01_00236, partly funded this research.  
The authors declare no conflict of interest.
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 3 Results and Analyses
### Compressive Behavior of Randomly Pitted Columns
#### 3.1.2 Axial Stiffness  
The axial stiffness of H-section columns with diverse corrosion patterns was examined at a higher level of pitting intensity, specifically \(DOV\) 13.8%. Figure 8 illustrates the load-displacement curves of H-section columns subjected to different corrosion patterns. It is evident that these patterns exerted a substantial influence on the load-displacement behavior during both the elastic and inelastic deformation stages of H-section columns. This is demonstrated by a noticeable deterioration in axial stiffness, which is represented by the slope of each column's load-displacement curve.  
The variation in axial stiffness in the damaged column was reflected by the secant stiffness of each column, as depicted in Figure 9, throughout the loading stage. In this study, the initial stiffness was denoted as \(K_{0}\) in the elastic deformation stage, which corresponded to the curve slope at 40% of the ultimate load [24].  
Figure 9a illustrates that the flange corrosion (H11.5 \(\times\) 7.5\(-\)SF) induced a more significant reduction in axial stiffness than web corrosion (H11.5 \(\times\) 7.5\(-\)W), with reductions of 7.7% and 4.6%, respectively. Consequently, when combined with flange corrosion, there was a notable decrease in axial stiffness, as depicted in Figure 9b. The reductions in axial stiffness for single-flange corrosion (H11.5 \(\times\) 7.5\(-\)SF + W) and double-flange corrosion (H11.5 \(\times\) 7.5\(-\)DF + W) were 12.3% and 19.9%, respectively. Similarly, the stiffness reduc  
Figure 8: Load–displacement curves of H-section columns under diverse corrosion patterns: (**a**) flange and web corrosion; (**b**) combined corrosion.  
tions for symmetric flange corrosion (H11.5 \(\times\) 7.5\(-\)DF) and asymmetric flange corrosion (H11.5 \(\times\) 7.5\(-\)AF) were nearly identical, with reductions of 15.4% and 15.1%, respectively, as shown in Figure 9a. Likewise, the combined corrosion of (H11.5 \(\times\) 7.5\(-\)DF + W) and (H11.5 \(\times\) 7.5\(-\)AF + W) led to reductions of 19.9% and 19.7%, respectively, as depicted in Figure 9b. Therefore, only the symmetric corrosion pattern was considered for subsequent investigations.  
Similar to the strength reduction, the reduction in axial stiffness also exhibited a trend of linear superposition. The stiffness reduction caused by double-flange corrosion was approximately twice as large as that caused by single-flange corrosion, with reductions of 15.4% and 7.7%, respectively, as illustrated in Figure 9a. Additionally, the web corrosion (H11.5 \(\times\) 7.5\(-\)W) led to a stiffness reduction of 4.6%. As observed in Figure 9b, the stiffness reduction for the combined corrosion of single-flange (H11.5 \(\times\) 7.5\(-\)SF + W) was 12.3%, calculated as the sum of 7.7% and 4.6%. In the case of double-flange combined corrosion (H11.5 \(\times\) 7.5\(-\)DF + W), the stiffness reduction was 19.9%, derived from the sum of either 12.3% and 7.7% or 15.4% and 4.6%.
## 3 Model Construction
### Time-Series Prediction Models
#### 3.2.1 Lstm  
LSTM is a time series network that takes the previous output and the current input as the input for the next time, thus allowing the model to take into account historical or future information of the data when performing prediction tasks. Compared with the traditional time-series model RNN, LSTM incorporates the concept of \"gates\", which is used to solve the problem of retaining long sequences of historical information and long-range gradient transfer. To be specific, LSTM introduces cell states, input gates, forgetting gates, and output gates, which are used for storing information about historical data, inputting current and historical information, controlling the information that needs to be forgotten, and output predicted results, respectively. The schematic diagram of LSTM network architecture is shown in Figure 4.  
The input and output of the LSTM are controlled by gates at each moment. The parameter matrix is gradient-updated by the loss of the final loss function, which affects the weights of the gates. Through the feedback regulation of the three gates, important in  
Figure 4: LSTM architecture schematic.  
formation can be selectively retained, redundant information forgotten, and then beneficial information passed on. The data are processed as shown in the following equations:  
\[f_{t}=\sigma\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{9}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{10}\]  
\[\widetilde{C}_{t}=tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{11}\]  
\[h_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})*tanh(C_{t}) \tag{12}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t}, \tag{13}\]  
where \(x_{t}\) is the input, \(W_{S}\) is the weight parameter, \(b\) is bias parameter, \(\sigma(z)\) is an activation function, \(C_{t}\) refers to the previously stored information, \(h_{t-1}\) is the output at the previous moment, and \(h_{t}\) is the output.  
Figure 5 shows the training structure of the LSTM model in this paper, which mainly contains three LSTM layers with 32 neurons and one fully connected layer with the suitable number of neurons. The LSTM model extracts the anomaly information from input data for \(n\) consecutive days and uses it for time sequence analysis.
# Emergency Repair Scheduling Model for Road Network Integrating Rescheduling Feature
## 3 Methodology  
### Model Concept  
This study takes into account the resources required for repair activity at each disaster point and the limited equipment resources available to each maintenance unit. It assists decision makers to solve the problem of repair activity sequencing and resource allocation for all damaged road sections within the unit's area of responsibility. An RCPSP perspective is adopted to make an applicable repair plan of damaged road sections. In addition, the proposed model can also allocate resources to each disaster point in different combinations of types and quantities, according to the upper limit of the number of equipment resources that can be accommodated, and the changes in such combinations of resources corresponding to the different repair durations.  
Although the schedule for the initial stage of the disaster confirms that the maintenance units are responsible for repairing all damaged disaster points within the region, there is still a high probability that new disaster points will be created after the first-wave disaster. Therefore, this research model takes actual post disaster scenarios into consideration and uses a rescheduling feature to reintegrate the schedule, so that the repair activities can be updated immediately after the status quo of any new damages has been properly evaluated by decision makers. The conceptual diagram of the proposed model is shown in Figure 1.