esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Processing Method  
The basic principle of data processing was to use the cross-correlation method to shoot objects in different motion states on two different images. The images were divided into square sub-regions of a certain size. The gray characteristic values of the corresponding sub-regions in the two images were compared to identify the similarity in the sub-regions and calculate the displacement of pixels, so as to obtain the local displacement field data of the images. After the analysis of the whole research area, the distribution information of the overall strain and displacement field was obtained, which was visually displayed in the form of clouds [30].  
It was assumed that the gray characteristic functions of the two digital images before and after deformation were \(f(x,y)\) and \(g\) (\(x^{\prime}\), \(y^{\prime}\)), where \(x\), \(y\) and \(x^{\prime}\), \(y^{\prime}\) are the positions of a pixel point before and after deformation, respectively, and \(u\) and \(v\) represent the displacement components of the feature point along the x and y directions, as shown in Formulas (1) and (2) [24; 25].  
\[x^{\prime}=x+u+u_{\mathrm{x}}\quad\Delta x+u_{y}\quad\Delta y, \tag{1}\]  
\[y^{\prime}=y+v+v_{\mathrm{x}}\quad\Delta x+v_{\mathrm{y}}\quad\Delta y. \tag{2}\]  
The correlation coefficient \(C\) in statistics was used to evaluate the degree of similarity between the two sub-regions, as shown in Formula (3).  
\[C=\frac{\sum f(x,y)\cdot g(x,y^{\prime})}{\left[\sum f^{2}(x,y)\cdot\sum g^{2} (x,y^{\prime})\right]^{1/2}} \tag{3}\]  
where \(f\) and \(g\) are gray eigenvalues of a sub-region before and after deformation, respectively. When \(C=1\), the two sub-regions are completely correlated. When \(C=0\), the two sub-regions are unrelated.  
Based on the above principles, image analysis and processing could be divided into three steps: image import and image quality pre-analysis, correlation analysis and calculation and data visualization.
# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
## 4 Ethiopic Script and Dataset Collection  
### Ethiopic Script  
Ethiopic script, which is derived from Geez, is one of the most ancient scripts in the world. It is used as a writing system for more than 43 languages, including Amharic, Geez, and Tigrigna. The script has largely been used by Geez and Amharic, which are the liturgical and official languages of Ethiopia, respectively. Amharic language is the second Semitic language after Arabic. The script is written down in a tabular format in which the first column denotes the base character and the other columns are vowels derived from the base characters, made by slightly deforming or modifying the base characters. The script has a total of 466 characters, out of which 20 are digits, 9 are punctuation marks, and the remaining 437 characters are parts of the alphabet. Developing a scene text recognition system for Ethiop script is challenging, due to the visually similar characters, especially between base characters and the derived vowels, and the number of characters in the script. Furthermore, the lack of training and testing datasets is another limitation in the development of a scene text reading system for Ethiop scripts. In this paper, we propose an end-to-end trainable bilingual scene text reading model using FPN, RPN, and time-restricted self-attention CTC.
An Improved Multiple-Target Tracking Scheme Based on IGGM-PMBM for Mobile Aquaculture Sensor Networks  
Chunfeng Lv  
1 SOU College of Engineering Science and Technology, Shanghai Ocean University, No. 999, Huchenghuan Road, Shanghai 201306, China 1, [PERSON]  
1 SOU College of Engineering Science and Technology, Shanghai Ocean University, No. 999, Huchenghuan Road, Shanghai 201306, China 1, [PERSON]  
2 Department of Computer, Mathematical and Physical Sciences, Sul Ross State University, Alpine, TX 79830, USA  
challenges [7]. Implementation of PMBM recursion mainly includes an analytic solution of a Gaussian mixture (GM) [8] and a particle solution of sequential Monte Carlo (SMC) [9; 10]. SMC-based implementation does not require any assumptions about target distributions, denoting the PMBM by a sequence of random weighted particles. Nevertheless, GM-based implementation can provide a closed-form solution, which is a preferable distributed implementation for a sensor network equipped with limited processing capabilities and communication capabilities. PMBM can adopt Bayesian recursion to largely eliminate the clutter or noise originated from measurements. Furthermore, the PMBM density is a conjugate prior to both the prediction and the update processing that can preserve the PMBM form of the density, which is handy for target state density and cardinality during iterations. As a result, the PMBM filter has been increasingly adopted in many target tracking applications.  
Three main contributions are presented in this paper:  
1. GAM distribution is introduced to present the augmented state of unknown and changing target detection probability. The intensity of newborn targets is adaptively derived presented by IG distribution on the basis of this augmented state.
2. The measurement likelihood is presented as a gamma distribution for the augmented state. Closed-form solutions are derived on these bases by means of approximating the intensity of target birth and potential targets to an IGGM form and the density of existing Bernoulli components to a single IGGM form. Furthermore, the target cardinality distribution is estimated in the proposed filter, which is a rare solution in most PMBM filters.
3. A distributed fusion strategy GCI is applied to a large-scale aquaculture tracking network.  
The remainder of this paper is structured as follows: Section 2 gives a summary of related works and the analysis premise of our model. The original PMBM filter, gamma distribution, and IG distribution, accompanied by the GCI fusion algorithm are briefly described after presenting the target dynamic model in Section 3. A novel algorithm IGGM-PMBM filter with a fusion strategy is proposed in Section 4, along with performance analysis models of IGGM-PMBM with an initialization strategy. In Section 5, accurate analyses and validations of tracking error, cardinality, and consumption are presented, and performance comparisons of the GCI-IGGM-PMBM filter with other multitarget tracking schemes are also proposed. Lastly, conclusions and discussions are presented in Section 6.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 2 Related Work
### MTL for Propaganda Detection  
MTL is a paradigm in which a single model is trained on multiple related tasks simultaneously, with the goal of improving generalization by sharing knowledge between tasks. In an MTL setup, it is common to have a shared encoder (shared layers) that learns representations used by all tasks and then task-specific decoders (output layers) that produce predictions for each task. This is often referred to as hard parameter sharing, and it has been a successful framework for many NLP problems.  
In the propaganda detection field, several recent studies have explored the use of MTL for detecting propaganda in both English and Arabic texts, often through shared task competition. These studies vary in the MTL architectures they adopt, the auxiliary tasks they incorporate, and the datasets they use for training. The earliest among the previous studies is [15], which investigated propaganda detection in English memes. It used three datasets, SemEval-2021 Task 6, SemEval-2020 Task 11, and a Fake News corpus, as auxiliary tasks in two MTL setups: sequential (pretraining on auxiliary, then fine-tuning on the main task) and parallel (joint training). This hierarchical approach aimed to enrich token-level, multi-label classification by first grounding the model in related propaganda and fake news tasks. For the 2023 SemEval shared task, the researchers in [16] proposed a hierarchical MTL architecture using BERT. The model first identified the start index of a persuasive span and then used the embedding of that token for multi-label classification. This structure was an example of a task hierarchy in which span detection feeds directly into persuasion classification. The model was trained using a global loss function that combines two subtasks' objectives.  
Regarding the Arabic language, the researchers in [17] introduced a multi-label classification setup using AraBERT. The MTL approach decomposed the problem into N binary classification tasks, one for each propaganda technique. This enabled the model to learn distinct representations per technique and fine-tune separate binary heads along with a shared encoder. Similarly, [18] incorporated MTL to jointly learn the genre of the text (as an auxiliary task) and a binary classification task for persuasion detection using a PLM called MARBERT. By injecting genre awareness, the model presumably obtained contextual cues that could influence the identification of persuasive strategies. Furthermore, the authors in [19] addressed Arabic propaganda detection but employed an LLM approach, specifically GPT-3. The architecture routed the [CLS] token embedding to separate the binary and multi-label classification heads for the subtask's binary and multi-label classification tasks, respectively. Although not explicitly referred to as MTL, the dual-headed structure functioned like one, sharing a base representation while optimizing multiple related objectives.
# Seismic Response Effect on Base-Isolated Rigid Structures by Mass Eccentricity in Nuclear Plants
## 3 Sensitivity Analysis of Base Isolation Response Due to Mass Eccentricity  
### Design of Small Base Isolators for Nuclear Power Plant Equipment  
As a measure to enhance the seismic performance of internal equipment and facilities in operational nuclear power plants during beyond-design-basis earthquakes (BDBE) exceeding the design seismic level, small base-isolation bearings specifically designed for nuclear plant equipment were developed [13]. It should be noted that for export-standard nuclear power plants, the design-basis earthquake (DBE) is set at a safe shutdown earthquake (SSE) level of 0.3 g, and they are designed to have a seismic performance level of up to 0.5 g. However, in the event of a BDBE, some equipment vulnerable to earthquakes may be affected. To enhance the seismic performance of such earthquake-vulnerable equipment, research on the development of small lead-rubber bearings (LRB) for base-isolation devices was conducted.  
When making small-sized LRBs, there are limitations in the manufacture of the stacked rubber in the baseplate, resulting in a lower shape factor. This leads to decreased stability compared with larger base-isolation devices for building structures, and the energy dissipation effect is somewhat insufficient. To address these issues and improve damping performance, lead cores are inserted at the center of the base-isolation devices to enhance their base-isolation performance.  
In this study, the unit support weights of the small LRBs were initially manufactured with four different designs, 1-ton, 2-ton, 5-ton, and 10-ton LRBs, and static tests were conducted. Since much of the internal equipment in nuclear power plants is low-weight individual devices, the design was optimized primarily for 1-ton LRBs. The detailed design process is explained in reference [13]. Low-capacity base-isolation bearings are highly necessary for technical development and have high applicability at nuclear plant sites, especially for components like nuclear plant instrumentation. Furthermore, among design approaches with different sizes and dynamic characteristics, base isolation performance, including natural frequencies and shape factors, was compared and evaluated to determine the optimal design. The design details, including cross-sectional shape and the design specifications of the LRB finally chosen and used in this study, are provided in Figure 1 and Table 1, respectively.  
Table 2 indicates the mechanical properties of the rubber and lead materials used in the fabrication of small equipment LRBs. The properties of the rubber material are very similar to natural rubber, and pure lead-like materials were used for the lead core [13].
## 3 Research Purposes and Methodology  
The specific objectives of this research are as follows: (1) Collecting data on monetary losses caused by weather-induced railroad accidents, (2) Creating a loss estimate model utilizing a deep learning algorithm built upon data gathered from railroad accidents, and (3) Validating the model by comparing the outcomes estimated by the developed model with those estimated by a multiple regression analysis model. For model verification, a separate multiple regression analysis model was established using the same data used to develop the deep learning algorithm model.  
This research proceeded as follows: (1) Collection of input variables and output variables related to financial losses associated with weather-induced railroad accidents. (2) Development of a prediction model using a multiple regression analysis model and a deep learning algorithm model for the output and input variables, respectively. (3) Calculation of the root mean square error (RMSE) and mean absolute error (MAE) for the developed predictive models. (4) Verification of the predictive power of the developed deep learning algorithm model through comparison of the two values. To develop the multiple regression analysis model, IBM SPSS Statistics version 23 was applied, while the deep learning model was developed using Python 37.
## 4 Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order of Expt.** & **Measured (rad2/sec2)** & **Estimated (rad2/sec2)** & **Error (\%)** \\ \hline \(1^{\text{st}}\) & \(2.50677\times\text{e}^{6}\) & * & * \\ \(2^{\text{nd}}\) & \(2.50676\times\text{e}^{6}\) & * & * \\ \(3^{\text{rd}}\) & \(2.50672\times\text{e}^{6}\) & * & * \\ \(4^{\text{th}}\) & \(2.50674\times\text{e}^{6}\) & * & * \\ \(5^{\text{th}}\) & \(2.50670\times\text{e}^{6}\) & * & * \\ \(6^{\text{th}}\) & \(2.50671\times\text{e}^{6}\) & * & * \\ \(7^{\text{th}}\) & \(2.50656\times\text{e}^{6}\) & * & * \\ \(8^{\text{th}}\) & \(2.50645\times\text{e}^{6}\) & \(2.50646\times\text{e}^{6}\) & \(4\times\text{e}^{-4}\) \\ \(9^{\text{th}}\) & \(2.50624\times\text{e}^{6}\) & \(2.50641\text{E}\times\text{e}^{6}\) & \(6.5\times\text{e}^{-3}\) \\ \(10^{\text{th}}\) & \(2.50594\times\text{e}^{6}\) & \(2.50631\times\text{e}^{6}\) & \(1.5\times\text{e}^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of \(\omega_{n}^{2}\) in every experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order of Expt.** & **Measured (rad2/sec2)** & **Estimated (rad2/sec2)** & **Error (\%)** \\ \hline \(1^{\text{st}}\) to \(2^{\text{nd}}\) & \(8.18\) & * & * \\ \(2^{\text{nd}}\) to \(3^{\text{rd}}\) & \(40.45\) & * & * \\ \(3^{\text{rd}}\) to \(4^{\text{th}}\) & \(-23.11\) & * & * \\ \(4^{\text{th}}\) to \(5^{\text{th}}\) & \(39.92\) & * & * \\ \(5^{\text{th}}\) to \(6^{\text{th}}\) & \(59.58\) & * & * \\ \(6^{\text{th}}\) to \(7^{\text{th}}\) & \(148.04\) & * & * \\ \(7^{\text{th}}\) to \(8^{\text{th}}\) & \(114.08\) & \(110.71\) & \(-2.94\) \\ \(8^{\text{th}}\) to \(9^{\text{th}}\) & \(206.67\) & \(180.91\) & \(-12.47\) \\ \(9^{\text{th}}\) to \(10^{\text{th}}\) & \(304.64\) & \(283.82\) & \(-6.83\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of \(\Delta\omega_{n}^{2}\) between successive experiments.
## 3 Results
### Multi-Task Classification of PA Types, APS Types and EE Estimation  
Figure 5a shows the confusion matrix of PA types classification by using multi-task LSTM RNN model designed to simultaneously perform classification of APS types, classification of PA types and estimation of EE. Figure 5b depicts the confusion matrix of the corresponding APS classes estimated form multi-task LSTM RNN model. The results for mental stress are excluded because not enough EE data were collected during mental stress sessions. PLS-DA is used for feature selection for the classification task. A total of 244 features were selected by combining features from APS and the pair-wise mutual features from PA and EE. Weighted training is used to handle the imbalanced classes. The model achieved a RMSE of 0.728 \(\frac{\text{call}}{\text{hrkg}}\) for EE estimation. The architecture of multi-task LSTM RNN classification of APS types and PA types and estimation of EE is shown in Figure 4a.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1 Score (\%) for** & **F1 Score (\%) for** \\  & **PA Classification** & **APS Classification** \\ \hline Multi-task LSTM RNN model designed to simultaneously perform & & \\ classification of APS types (NS, EAS), classification of PA types (SR, SR, TR) and estimation of EE. & 98.00 & 98.97 \\ \hline Multi-task LSTM RNN model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with Weighted Training. & 99.8 & 99.3 \\ \hline Multi-task LSTM RNN model for classification of APS types (NS, EAS, MS) types and PA types (SS, SB, TR) with ADYSN. & 99.69 & 98.83 \\ \hline Independent LSTM RNN model with Weighted Training. & 99.58 & 98.83 \\ \hline Independent LSTM RNN model with ADYSN. & 99.64 & 98.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: F1-score for PA and APS Classification (LSTM RNN models).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1 Score (\%) for** & **F1 Score (\%) for** \\  & **PA Classification** & **APS Classification** \\ \hline Multi-task XGBoost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN. & 99.89 & 98.31 \\ \hline Independent XGBoost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN. & 99.68 & 96.77 \\ \hline \hline \end{tabular}
\end{table}
Table 7: F1 score for PA and APS Classification (XGBoost models).
## 3 Results and Considerations
### Results of Water Injection Test at Site A
The \(\Delta\rho\) at 1 h after the third water injection test (Run 40) was mostly distributed in the range of \(-10\) to 10% (Figure 5a), while at 4 h (Run 45), a positive \(\Delta\rho\) of 25% and a negative \(\Delta\rho\) of \(-30\%\) were observed in the surface layer at 1 m and below 1.5 m, respectively (Figure 5b). After 95 h (Run 65), the distribution of negative \(\Delta\rho\) became smaller and showed a tendency to return to the situation before water injection (Figure 5c). The increase in negative \(\Delta\rho\) indicates an increase in internal water content and a decrease in resistivity, indicating that the injected water moved from the surface to the subsurface, reaching a depth of 1.5 m or more after 4 h and then moving slowly with time. The numerical simulation results from [PERSON] et al. show that resistivity decreases with high sensitivity with water penetration into pore structures such as fractures [24]. And [PERSON] et al. found that injecting high-pressure air into the ground increased the rate of resistivity change and identified a decrease in water content as a factor in this increase [25]. These previous studies support the results of this study in that a sudden change in ground water content can affect resistivity change. The high-resistivity region near the gas extraction well mentioned above and the three regions circled by the white dotted lines in Figure 5 showed particularly large changes in \(\Delta\rho\), suggesting the possible existence of water channels in these areas. [PERSON] et al. also suggested that gas wells were affecting the vertical water flow in landfill using ERT monitoring [26]. These ERT monitoring results not only identified the extensive downward migration of water content from the water injection test but also  
Figure 4: Measured resistivity distributions before/after water injection test at Site A. The red arrow represents the ERT survey line. The white dotted circle at the left side represents that the resistivity decreases (the water content increases) due to the addition of water, and the other circle at right side represents that the area near the gas vent pipe has high permeability and gas permeability. strongly suggested that multiple water channels were unevenly distributed as prominent migration pathways in the waste layer where non-combustible residues were landfilled.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 5 Results and Analysis
### Evaluation Metrics  
Determining the effectiveness of the proposed risk-aware RBAC and ABAC model is significant to verify the applicability of the proposed model in real-world scenarios in the healthcare environment. Three evaluation metrics, accuracy, precision, and recall, which are mainly based on True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), are used to evaluate the effectiveness of the proposed model [42, 43].  
Accuracy measures the overall correctness of the proposed model decisions. It is calculated as the ratio of the number of correctly predicted access decisions (both positive and negative) to the total number of access decisions made. In other words, it represents the average number of successful access decisions that are matched with the access policies using the following formula:  
\[Accuracy=\left(TP+TN\right)/\left(Total\,AccessDecisions\right)\]  
Precision measures the accuracy of positive predictions made by the proposed model. It focuses on the proportion of correctly predicted positive access decisions out of all the access decisions that the model predicted as positive. Precision is particularly relevant when the cost of false positives (incorrectly granting access) is high. Precision is mathematically estimated using this formula:  
\[Precision=TP/\left(TP+FP\right)\]Recall measures the ability of the proposed model to correctly identify all positive instances (access decisions) out of all actual positive instances. It focuses on the proportion of correctly predicted positive access decisions out of all the actual positive access decisions. Recall is especially important when the cost of false negatives (incorrectly denying access) is high. Recall is mathematically estimated using this formula:  
\[Recall=TP/(TP+FN)\]