esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cast in situ concrete piles (CCPs) have been widely used in bridge projects to bear complex and large loads, especially the lateral load and overturning moment. During the cast in situ concrete pile construction process, steel casings are widely used. However, in some projects, these tubes cannot be removed before grouting. This will form a drilled shaft with partial casing (DSPC). Fortunately, it was found that these piles are more supportable than traditional CCPs. For example, in the Tai Zhou Bay Super Bridge of China, the lateral force from the upper bridge was so large that it caused a large bending moment in the pile foundation, of which the section strength could not bear; a similar phenomenon has been observed in the Hong Kong-Zhuhai-Macao Bridge. Some research has also been conducted on similar improvements for concrete-filled steel columns [1] and concrete plugs embedded in tubular steel piles [2, 3].  
DSPCs have some differences from traditional CCPs, concrete-filled steel columns, or concrete plugs embedded in tubular steel piles [2, 4, 5]. In a DSPC, the steel casing is partially used (Figure 1); then, a hole is drilled in the casing, and the soil in the tube is removed by the slurry method. This may result in a weak bond between the concrete body and steel tube in the composite structure. Many results have been reported about the bond stress capacity of concrete-filled steel columns. In these studies, the different shapes of column sections were tested [6, 7], and the interface length, interface condition [8], mechanical connectors, and diameter-to-thickness (\(D\)/\(t\)) ratio were also studied [9, 10, 11, 12]. However, few of them have investigated the assumed plane cross-section effect on the bending capacity of DSPCs, the mud effect on the bond strength, and the mechanical interlocking of concrete-steel interfaces under bending conditions.  
Therefore, in this study, a set of experimental programs regarding the Tai Zhou Bay Super Bridge was introduced. A DSPC with a diameter of 2 m, length of 87.9 m, a steel tube diameter of 2.0 m, and a length of 33 m was studied in clay beds. Strain gauges were distributed along the steel rebars in the concrete pile and at the surface of the steel tube at the same cross-sections along different embedment depths. Therefore, the longitudinal strains of the concrete pile and steel tube can be studied separately. Moreover, a set of laboratory experiments were implemented. They are reinforced concrete-filled steel tubular columns (RCFSTCs) under pure bending conditions. In these tests, strain gauges were distributed along the steel rebars in the concrete pile and at the wall of the steel tube at the pure bending section of these RCFSTCs. Different wall thicknesses and mud conditions were considered to study the influence of the bonding quality on the bending capacity.
# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 2 Methodology
### Datasets  
The proposed ensemble architecture is evaluated using public and locally acquired datasets that are two-dimensional (2D). The public dataset contains 7023 human brain MRI images, classified into four classes: glioma, meningioma, pituitary macroadenomas, and normal brain with no detected tumor. The 7023 images (of size \(512\times 512\)) are collected from Figshare, SARTAJ, and Br35H datasets [30] and the number of images are 1645, 1621, and 1757 images for meningioma, glioma, and pituitary, respectively. The objective of collecting from three sources is to obtain near-balanced classes to avoid bias in model training and prediction. In addition to the brain tumor types, an additional control (normal or no tumor) class was used, containing 2000 images. Class distributions are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dataset**} \\ \cline{2-5}  & **Figshare** & **SARTAJ** & **Br35H** & **Total** \\ \hline No Tumor & 0 & 500 & 1500 & 2000 \\ Glioma & 1426 & 195 & 0 & 1621 \\ Meningioma & 708 & 937 & 0 & 1645 \\ Pituitary Macroadenomas & 930 & 827 & 0 & 1757 \\ \hline Total & 3064 & 2459 & 1500 & 7023 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Per-class distribution for the public dataset.  
Figure 1: Schematic overview of the proposed methodology for brain tumor classification.  
A locally acquired dataset was also utilized for additional evaluation. This dataset is collected from an MR brain study of 64 patients at the diagnostic radiology department, Mansoura University Hospital, Mansoura, Egypt (IRB protocol # R.21.09.1437.R1). The images were acquired using a 1.5T Philips-Ingenia MR scanner using standard head coils, where non-contrast MRI sequences (T2-w, T1-w, and FLAIR) were first acquired. The locally acquired data included three classes: normal (no detected tumor), benign (including both meningioma and pituitary macroadenoma), and malignant (gliomas). The resolution of the images is 176 \(\times\) 176, representing MRI files originating in DICOM, with detailed parameters shown in Table 2.
## 4 Proposed AMR Algorithm
### Super Cumulant Features  
In this module, super cumulant features were computed using optimized weights \(\gamma\) from the genetic algorithm (GA). The objective was to maximize the distance between two classes. The linear combination of these constants with the HOCs resulted in super cumulant features. The super cumulant features were computed for each modulation scheme, i.e., BPSK, QPSK, QAM,16 QAM, and 64 QAM. The linear combination of the super cumulant features was calculated using Equation (14), as shown below:  
\[L_{i}=\sum_{k=1}^{M}\gamma_{k}.\ \zeta_{k} \tag{14}\]  
where \(L_{i}\) denotes the linear combination for the \(i\)th modulation type and \(M\) is the number of cumulants for each modulation format. In vector form, \(\gamma\) and \(\zeta\) are:  
\[\gamma=[\gamma_{1},\gamma_{2},\gamma_{3},\gamma_{4},\gamma_{5},\gamma_{6}, \gamma_{7},\gamma_{8},\gamma_{9},\gamma_{10}]\]  
\[\zeta=[\zeta_{1},\zeta_{2},\zeta_{3},\zeta_{4},\zeta_{5},\zeta_{6},\zeta_{7}, \zeta_{8},\zeta_{9},\zeta_{10}]\]  
The fitness function for the optimal weight finder using the GA is given in Equation (15):  
\[FF=arg_{max}\|L_{i}-L_{j}\|^{2}i\
eq j \tag{15}\]  
The super features for each modulation are presented in Table 2. The algorithm for finding the optimal weights and then the super cumulant features are in Algorithm 1. Algorithm 1 presents a stepwise summary of the computation of the optimized weights using the GA.  
```
1 Initialization; (Set GA parameters, number of chromosomes, number of genes, percentage of offspring and parents for making a new generation)
2 Generate \(\gamma_{k}\) randomly, whereas \(\gamma_{k}\)\(\epsilon\)[0, 1]. while\(\textit{Fitness}<\textit{Threshold}\)do
3 evaluate fitness\(\rightarrow\)\(arg_{max}\|L_{i}-L_{j}\|^{2}\), \(i\
eq j\)
4 apply\(\rightarrow\) Sorting (Descending Order);
5 generate\(\rightarrow\) New Population (Multi-Point Crossover);
6 for\(\textit{j}=\textit{1:K}\)do
7 for\(\textit{k}=\textit{1:K}\)do
8 Best-Fitted Population (Parents and OffSprings) ;
9 ifFF is achieved, or the number of iterations is reachedthen
10 terminate;
11 else
12 Repeat the whole process;
```  
**Algorithm 1**GA-Based Optimal Weight Finder
## 5 Results and Analysis of the Optimization Algorithm
### Comparison and Analysis of Diagnostic Method Results  
It can be seen from Table 4 that PNN and SVM are found to have high classification accuracy when dealing with engine fault data, indicating the correctness of the selection of these two intelligent algorithms and feature signal data samples. Through the optimization of the PSO algorithm, the classification accuracy of Sigmoid kernel function in SVM is greatly improved. The classification results of kernel function and PNN in SVM have improved. On the one hand, the importance of the range of parameter values is explained. On the other hand, it also shows that PSO has optimize the important parameters that affect the classification results. Overall, compared to PNN, after being optimized by PSO, SVM classification accuracy is higher, reflecting the advantages of SVM in small sample data classification. In the SVM classification, the polynomial kernel function of \(v\)-SVM and the Sigmoid kernel function in C-SVM have the highest classification accuracy. From the perspective of the  
Figure 14: PSO iterative optimization and final results. (**a**) Iterative optimization process, (**b**) Final classification results.  
whole optimization process, the SVM parameters are relatively numerous. It is difficult to select the penalty parameter \(c\) and the kernel function parameter \(g\). However, unlike SVM, PNN only needs to set the smoothing factor \(\sigma\), so the optimization process is relatively simple, and compared with SVM, PNN is more convenient to use.
## 7 Validation and Results
### Relating Cancer Diagnosis and Date Results  
The impact of negation and speculation to extract correctly the diagnosis date will be evaluated as it is shown in the planned experiments:  
1. The diagnosis date is chosen from all extracted named entities which belong to the patient.
2. The diagnosis date is chosen after filtering entities affected by negation.
3. The diagnosis date is chosen after filtering entities affected by negation and speculation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Negation**} & \multicolumn{3}{c}{**Speculation**} \\ \hline
**Rules** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Rule 1 & 0.72 & 0.70 & 0.71 & 0.72 & 0.69 & 0.70 \\ Rules 1,2 & 0.78 & 0.75 & 0.76 & 0.72 & 0.69 & 0.70 \\ Rules 1, 2, 3 & 0.81 & 0.79 & 0.80 & 0.73 & 0.71 & 0.72 \\ Rules 1, 2, 3, 4 & 0.86 & 0.82 & 0.84 & 0.82 & 0.79 & 0.80 \\ Rules 1, 2, 3, 4, 5 & **0.91** & **0.87** & **0.89** & **0.87** & **0.84** & **0.85** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for the scope recognition task.  
Table 6 shows obtained results for linking the proper date to the lung cancer diagnosis. According to this table, the best result was obtained in the third experiment with an 89% F-score. This fact suggests that detecting negation and speculation is a crucial step needed to extract a diagnosis date correctly. In fact, if the diagnosis date is extracted using only results from named entity recognition, a 64% F-score is obtained.  
In addition, according to Table 6, filtering entities affected by negation improves the F-score by 7%. Meanwhile, filtering entities affected by speculation improves the F-score by 18%. This indicates that the diagnosis date extraction is more sensitive to speculation than to negation. Moreover, when comparing the results of the proposed approach to those presented in [10], the F-score performance rate is improved by 5%, which is mainly due to the improving of cancer concept extraction and speculation detection.
## 5 Discussion  
In experiments, the frequencies of the underwater sound source are set to \(f_{0}=100\), \(f_{1}=200\), and the encoding method is 16 QAM, with one symbol carrying 4 bits of infor  
Figure 11: Weak WSAW, (**a**) signal detection based on periodogram, where the amplitude of the 130 Hz signal side lobe is greater than the amplitude of the 400 Hz signal (**b**) frequency estimation results of RELAX algorithm are 130 Hz and 144 Hz. (**c**) frequency estimation results of the improved RELAX algorithm 130 Hz and 400 Hz.  
Figure 12: (**a**) Sample generalized inner product value; (**b**) time–frequency analysis result.  
mation. Since the influence of the underwater channel is not considered, the cross-media communication will transmit 100 symbols within 1 s, and then the corresponding data rate is:  
\[R_{i}=100*4=400\;\text{bit/s} \tag{20}\]  
The communication rate is consistent with the highest rate achieved by TARF. Moreover, we can further increase the communication rate by changing the encoding method.  
Therefore, we propose an improved RELAX algorithm of the WSAS for cross-media communication. The results suggest that the improved algorithm can improve the frequency accuracy of the WSAS. The proposed algorithm offers a better estimation performance in case of a short signal frequency sampling time. However, the frequency estimation of the periodogram is related to the length of the sampling duration, and specifically, the shorter the duration of a single frequency point, the more inaccurate the estimation result.  
Compared with the existing algorithm, the proposed improved RELAX algorithm has three main characteristics. Specifically, the developed scheme first accurately estimates the frequency when there are fewer single-frequency signals, which is beneficial to use more frequency signals for encoding within a certain period, thereby increasing the rate of the communication system. Second, since nonlinear filtering is used to improve the kernel function, the sidelobes of the strong signal are suppressed, affording an accurate estimation of the weak wave signal. Finally, the underwater communication frequency band is a narrow band range. Thus, the developed algorithm can estimate multiple frequencies with a closer frequency interval to improve the utilization of underwater communication frequency points.  
Although these studies reveal essential discoveries, some limitations are also worth noting. Combining these schemes with GIP, and according to the inverse relationship between frequency and amplitude and the segmental stability characteristics, the WSAW signal is decomposed into multiple segmented stationary signals to achieve decoding. However, this strategy will increase further the calculation burden compared to the periodogram [33]. Further improvements may be possible using an optimizing parameter, which is a topic for future studies.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 2 Materials and Methods
### Impact Assessment
#### 2.3.1 Flood Model  
As seen in Figure 1, the image collection used for the flood extent image was taken from Sentinel-1 SAR GRD data, extracted, and processed through Google Earth Engine. This was clipped to the ROIs in Madagascar, namely the Melaky and Boeny regions, using the Human Data Exchange's Office for the Coordination of Humanitarian Affairs (OCHA) administrative boundaries at level 1. In the case of Mozambique, in the Sofala and Zambezia provinces, which have a quasi-similar extent and were also affected by the TC Ana, the Food and Agriculture Organization Global Administrative Unit Layers (FAO GAUL) were downloaded, and Sentinel-1 data were applied to it. The FAO GAUL administrative layer was used for the flood-extent study in Mozambique, as it was found that the OCHA boundaries significantly cropped the Mozambique coastal areas, including mangrove forests, which were the main topic of this study. Additional methodology on pre/post-processing, and explanations of the period of study used are further explained in the Supplementary Materials (Supplementary S1).
# Hybrid Fibres as Shear Reinforcement in High-Performance Concrete Beams with and without Openings
## 3 Experimental Results and Discussion
### Inelastic Parameters
#### 3.2.3 Ultimate Deflection and Ductility Factor  
The ultimate inelastic deflection of the beams used for calculating the ductility factor is related to the strength at which the ultimate strength is observed. Thus, the ductility factor is defined as the ultimate structural deflection to the yield strength deflection. The values of these parameters are plotted in Figure 10.  
As can be seen, beams B3 and BO3 with the highest hybrid fibre content, 1.05% and 1.6%, gained increases in the ultimate deflection, 66% and 51.4%, respectively. In the case of beam BO2 with openings and a 1.05% fibre content, the value of this parameter shows a decrease of 69.9%. In consequence, the evaluated values of ductility indicate the positive impact of hybrid fibres on increasing the ductility even more than that of shear reinforcement when an appropriate fibre volume content is applied. Similar to the ultimate deflection, the increases in the ductility factors are for beams B3 and BO3 with the 1.05% and 1.6% fibre content, 123.6% and 28%, respectively. In contrast, a decrease of 28% in the ductility factor is noted for beam BO2 with openings.
## 3 Example Analysis and Experiment
### Feature Set Construction  
The international standard ISO 10816-1:1995 [39] provides an industry benchmark based on the magnitude of mechanical vibrations. According to this standard, when the RMS value of vibration signals for small machinery exceeds 1.8, the equipment is considered to be in a dangerous state. Given the ambiguity and unclear boundaries between different condition levels, the Fuzzy C-Means (FCM) clustering algorithm was applied to cluster the RMS values of input shaft vibrations. According to a review of the domestic and international literature on equipment health assessment [40], the health status of rotating machinery is typically categorized into four levels. Therefore, the number of clusters was set to 4. Based on the clustering results and the industry standard, the final classification of the gearbox's health condition is shown in Figure 6.  
Based on the aforementioned classification, the label information for multimodal samples is constructed, culminating in a total sample size of 8382. This includes 5192 samples in the normal state, 895 samples in the mild state, 494 samples in the moderate state, and 1801 samples in the fault state. The sample construction integrates the GADF, MFCCs, and the Faster RCNN. The multimodal sample sets under different states are illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Health Level** & **Operating Condition** & **Level Label** \\ \hline Normal & Operating normally, no maintenance needed & 0 \\ Mild & Stable operating condition, scheduled & 1 \\  & maintenance & 1 \\ Moderate & Signs of deterioration in operating condition, & 2 \\  & timely maintenance required & 3 \\ Fault & Cannot operate normally, requires shutdown & \\  & for repair & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Health status grade of gearbox.  
Figure 6: The health status index of the gearbox is constructed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **Model** & **Sensitivity** & **Measurement Range** \\ \hline Vibration Sensor & CT1020 LC & 200 mV/g & \(\pm\)25 g \\ Voice Sensor & AWA14423 & 50 mV/Pa & 3.15–20 k/Hz \\ Infrared Image Sensor & K16E19 & 6500 V/W & \(-\)50\(\rightarrow\)125 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: The sensors used in the experiment.
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 1 Introduction
We later demonstrate that combining diffusion on the anchor graph with local anchor embedding is a good approximation of the diffusion on the complete graph. Despite the advancements in offline diffusion [5], a critical research gap remains: existing methods lack the capability to efficiently handle dynamically growing datasets, as recomputing the full graph and Laplacian inverse for new data incurs prohibitive computational costs. The goal of this work is to develop an incremental diffusion framework that enables continuous dataset updates while preserving retrieval efficiency and accuracy. Specifically, we address the following research questions: (1) How can diffusion be adapted to handle incremental data without recomputing the entire graph? (2) Can an anchor-based approximation retain the benefits of full-graph diffusion while reducing computational complexity? We hypothesize that leveraging anchor graphs and local anchor embedding (LAE) provides a viable solution, as anchors approximate the global data structure and LAE ensures local geometric consistency. This hypothesis will be validated through comparative experiments on benchmark datasets. The benefit of the proposed method is two-fold. On one hand, it empowers offline diffusion with the ability to handle new data; on the other hand, it inherits the efficiency of offline diffusion without adding much runtime computational cost.
# The Pin Structure Design of LaVai-1.5-1400 Microwave Vertical Tubular Furnace Based on COMSOL
## 3 Modeling
### Electromagnetic Calculations  
COMSOL Multiphysics simulates the electromagnetic field distribution in a resonant cavity by computing the approximate solution of [PERSON]'s system of equations. In this model, the wall cavity is set up as a perfect conductor and can reflect the microwaves completely. The microwaves are fed by the waveguide in mode TE\({}_{10}\), and the distribution of the electromagnetic field in the resonant cavity is represented as:  
\[\
abla\times\mu_{r}^{-1}(\
abla\times E)-k_{0}^{2}(\varepsilon_{r}-\frac{j \sigma}{\omega\varepsilon_{0}})E=0 \tag{1}\]  
where \(\mu_{r}=\mu_{r}{}^{f}-\mathrm{j}\mu_{r}{}^{\prime\prime}\), \(\varepsilon_{r}=\varepsilon_{r}{}^{\prime}-\mathrm{j}\varepsilon_{r}{}^{\prime\prime}\), E = Electric field, \(\varepsilon_{0}\) = vacuum permittivity, k\({}_{0}\) = wavenumber of free space, \(\omega\) = angular frequency, \(\mu_{r}\) = relative permeability, \(\varepsilon_{r}\) = relative permittivity, \(\sigma\) = electrical conductivity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material Parameter** & **Mullite Insulation** & **Refactory Glass** & **Alumina [15]** & **Heated Samples (Al\({}_{2}\)O\({}_{3}\)/SiC) [14]** & **Units** \\ \hline Relative permeability (\(\mu\)) & 1 & 1 & 1 & 1 & 1 \\ \hline Thermal conductivity (k) & 2.2 & 1.03 & 27 & \(0.626\times 10^{-11}\mathrm{T}^{4}-4.337\times 10^{-6}\mathrm{T}^{3}+1.046\times \\ \multicolumn{4}{c}{\(10^{-4}\mathrm{T}^{2}-0.109\mathrm{T}+51.43\)} \\ \hline Density (\(\rho\)) & 875 & 2500 & 3900 & 2308 & kg/m\({}^{3}\) \\ \hline Heat capacity at constant pressure (\(\mathrm{C}_{p}\)) & 987 & 750 & 900 & \(-1.780\times 10^{-10}\mathrm{T}^{4}+0.906\times 10^{-6}\mathrm{T}^{3}-1.874\times \\ \multicolumn{4}{c}{\(10^{-3}\mathrm{T}^{2}+1.802\mathrm{T}+53.85\)} \\ \hline Relative permittivity (real part) & 1.97 & 3.75 & 8.9 & \(3.68\times 10^{-7}\mathrm{T}^{2}+3.879\times 10^{-4}\mathrm{T}+5.386\) & 1 \\ \hline Loss tangent (lan \(\delta\)) & 0 & 0 & \(5.917\times 10^{-14}\mathrm{T}^{4}-1.742\times 10^{-10}\mathrm{T}^{3}+1.746\times \\ \multicolumn{4}{c}{\(10^{-7}\mathrm{T}^{2}-4.247\times 10^{-5}\mathrm{T}+0.01542\)} \\ \hline Electrical conductivity & 0 & 0 & 0 & S/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electromagnetic and thermophysical parameters of the materials used in the simulation.  
Figure 2: 3D simulation model for Al\({}_{2}\)O\({}_{3}\)/SiC composite heating.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 4 Experiments and Results
### Comparison with Models from the Literature  
Further experiments were carried out to evaluate the effectiveness of the architecture of the proposed MARBERT-CNN model. Therefore, we used two public Arabic Twitter datasets covering the topic of COVID-19. The first dataset, named ArCOV19-Rumors [41], which contains 3584 Tweets, 1753 are false news and 1831 are true news. The second dataset, COVID-19 misinformation, was introduced by [3]. A total of 8783 Tweets were collected, consisting of 1311 false news and 7475 true news. We used Twitter API to retrieve Tweets from the two datasets. It is worth noting that Twitter API was unable to provide access to a  
Figure 5: The experimental results of the oversampling and undersampling techniques.  
total of 803 Tweets from ArCOV19-Rumors and 2820 Tweets of COVID-19 misinformation.  
Table 10 presents the comparison results of our model, MARBERT-CNN, against previous models on the ArCOV19-Rumors and COVID-19 misinformation datasets. Based on reported results, the proposed MARBERT-CNN model architecture achieved the best result among the other deep learning-based models for the two datasets.