esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Clinical Applications of Artificial Intelligence
### Quality Assurance (QA)  
According to the International Organization for Standardization, QA is a system that ensures quality for a given product, service, process. Quality is the degree to which the system fulfills requirements (need or expectation that is stated--generally implied or obligatory) [93], thus avoiding mistakes and defects. Quality controls (QC) are the tests performed to describe, measure, analyze, improve, and control a certain product or process. In radiological sciences, QCs are applied to verify and monitor devices and procedures for diagnosis and therapy, as well as the support systems used by clinicians. AI can be used to perform automatically QCs that, if carried out manually, would not be feasible routinely due to a large amount of time required. AI QC systems could be used to learn and improve their accuracy over time and develop new tests over time without human intervention.  
Quality assurance of radiotherapy (RT) is a significant part of the MP's work, and it is aimed at preventing radiological incidents and misadministration of radiation dose. A number of ML-based approaches have been explored to predict errors in treatment plans in order to automate chart check of plans. A K-means clustering algorithm was employed to learn from prior plans to perform the detection of errors in prostate plans [18].  
Automated quality control of LINACs is another promising application of ML, which can be used for predicting machine performance issues, such as deviation of dose output [94], multileaf collimator (MLC) positions [95], and beam symmetry [96]. A method for automated quality control of LINACs by ML applied to electronic portal imaging device (EPID) was proposed, which could identify sag and deviations in the vertical direction and field shift [97]. Other AI applications aim at predicting results of in-phantom patients' specific QA of intensity modulated RT (IMRT) or volumetric modulated arc therapy (VMAT) [98,99].
# Reliability Model Based Dynamic Multi-Level Trust Analysis
## 6 Experiment
### Accuracy  
As recommended in [37], our study uses a combination of metrics to assess the accuracy of models. First, we randomly select a link connecting a pair of users \(u\) and \(v\) in the dataset. We treat the trust information of the link as the actual trust from \(u\) to \(v\). Then we remove the edge (\(u,v\)) from the datasets and run two algorithms to infer the trust from \(u\) to \(v\), respectively. At last, we compare the inferred trust to the actual trust information.  
For a comprehensive comparison, we measured the accuracy of these algorithms using both root mean squared error (RMSE) and Pearson correlation coefficient (PCC). Figure 8 shows the RMSE and PCC. The x axis represents different hops from start node. To make a fair comparison, we took the average of RMSE and PCC of all levels as the computed result, respectively. Simultaneously, we repeat the experiment 20 times to get statistically significant results.  
In Figure 9, we find MDD-based trust achieves a higher accuracy compared to subjective logic based method. It can be observed that as the paths between users become longer (more hops can be chosen), the prediction accuracy decreases under both methods; but the proposed method is significantly less affected. This is because as the length of the optional path between users increases, the dependency between paths enhances. Meanwhile, the noise along the path will also expand due to the extension of the propagation distance. But our proposed method can alleviate the impact of these problems because of its unique MDD-based trust information combination mechanism (described in Section 3.3).
## 2 Materials and Methods
### Proposed Approach
#### 2.1.3 Base-Level Classifiers  
We trained seven of the most frequently used machine learning algorithms, namely K-Nearest Neighbor (KNN), LR, SVM, NB, RF, Extra trees (E-trees), and XGB, using 3-fold cross-validation.  
KNN is a nonparametric supervised machine learning method known as lazy learning as it has no training stage. The assumption in KNN is that similar data can be found in the same neighborhood. By the very nature of its decision rule, the performance of KNN classification depends crucially on the way that distances or similarities are computed between different examples [24].  
LR is a supervised machine learning algorithm that has been successfully utilized to solve various classification problems. It is a classification approach with two types: binary (two classes) and multi classes (more than two classes). A prediction is made by feeding a set of features to LR; it is referred to as a probabilistic classifier since it predicts the probability of an output.  
SVM is a very frequently used non-probabilistic binary classifier. It is used to classify linearly separable data into two classes, such as hate and no-hate, in two-dimensional space. One-vs-all or one-vs-one approaches may be used to adapt SVM to multiclass classification. The goal of SVM classification is to maximize the margin separating the target classes.  
NB is a simple but efficient probabilistic classifier based on the Bayes theorem with the assumption that all features are independent of each other. An NB classifier uses the Bayes theorem to compute each data item's probability of belonging to each class. The data item is tagged with the class that has the highest probability.  
RF is a large group of decision trees developed by [PERSON] in 2001, it is an ensemble model that employs several classification trees [25]. For each data item to be classified, each tree predicts a class; then, the class with the majority votes wins. The individual trees in the RF are trained on random subsets of the training data using different features for splitting the data. These two properties of the training phase ensure that the individual decision trees are uncorrelated, and thus, the ensemble classifier RF produces a better prediction.  
E-tree is a form of ensemble classification algorithm that outputs a classification result by combining the results of several less correlated decision trees gathered in a forest. E-tree is a variation of the RF model where the entire training set is used for training, whereas the RF trains the individual classifiers on subsets of the training data.  
XGB is an ensemble method based on Gradient Boosting, where the gradient descent algorithm is used to minimize loss. An iterative training process where a new tree trained using the previously incorrectly classified data items added to the ensemble is employed.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 3 Results
### Experiment Result
#### 3.2.3 Influence of QoS  
The degree of QoS violation of one on-board mission can be measured by the difference between the processing delay and its QoS constraint, which can be expressed by Equation (44).  
\[vio_{n,i}=\begin{cases}\frac{D_{n,j}-\tau_{n,j}}{\tau_{n,j}},&D_{n,i}>\tau_{n, j},\\ 0,&otherwise.\end{cases} \tag{44}\]  
Then, the overall degree of QoS violation for all on-board missions can be expressed as  
\[vio=\sum_{i=1}^{|L|}\sum_{n=1}^{|N_{i}|}vio_{n,i} \tag{45}\]  
A higher _vio_ indicates that the current offloading decision does not satisfy the QoS constraints of missions.  
From Figure 9, it can be seen that _vio_ of different computation offloading methods is decreasing as the QoS constraint gradually becomes loose. We notice that _vio_ of LOC is the highest under each QoS constraint. This is because of the limited processing capacity of LEO satellites, which leads to long processing delays, making it easy to violate QoS constraints. When the QoS constraint is set to 0.65 s, _vio_ of SSPCO is 0 and stays at 0. When the QoS constraint is relaxed to 0.68 s, _vio_ of GACO decreases to 0, and when the QoS constraint is relaxed to 0.69 s, _vio_ of PSOCO decreases to 0. In addition, _vio_ of GOC decreases to 0 when the QoS constraint is relaxed to 0.7 s. That is, SSPCO can satisfy more stringent QoS constraints for on-board missions than other methods.  
Figure 10 plots the total energy consumption with QoS constraints for different computation offloading methods. Since the offloading ratios of GOC and LOC are fixed, their total energy consumption is constant under different QoS constraints. From Figure 10, it can be seen that the total energy consumption of GACO, PSOCO, and SSPCO increases as QoS constraints become tighter. This is because these methods offload more missions to GEO satellites to ensure the QoS requirements are met, resulting in reduced processing delays but higher energy consumption. Obviously, the total energy consumption of SSPCO is always better than the other methods under different QoS constraints because SSPCO adjusts the offloading ratio to utilize the computational resources of LEO and GEO satellites fully, thus reducing the total energy consumption.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 5 Discussion  
This study analyzed various models, including pretrained transformer models and bidirectional deep learning (DL) models, using half-verse data comprising 14 m. Among the tested models, CAMeLBERT achieved the highest accuracy of 90.62%, marginally outperforming the BiLSTM model, which reached 90.53%.  
Tokenizers were also evaluated using the BiLSTM model. Specifically, SentencePiece (AraBERT) and WordPiece (CAMeLBERT) tokenizers were assessed. The model was tested with two layers, as depicted in Table 8. However, transformer tokenizers did not yield significant performance improvements with BiLSTM. Consequently, character encoding was preferred for the DL models.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tokenizer** & **Layers** & **F1-Score** & **Accuracy (\%)** & **Recall** & **Precision** \\ \hline \multirow{2}{*}{AraBERT} & \(1\) & 0.45 & 45.66 & 0.45 & 0.45 \\  & \(5\) & 0.52 & 52.28 & 0.52 & 0.53 \\ \hline \multirow{2}{*}{CAMeLBERT} & \(1\) & 0.40 & 40.98 & 0.40 & 0.41 \\  & \(5\) & 0.40 & 40.24 & 0.39 & 0.41 \\ \hline \end{tabular}
\end{table}
Table 8: Performance of the BiLSTM model using transformer tokenizers.  
Figure 11: Confusion matrix of the CAMeLBERT model with batch size 64.  
BiLSTM leverages the benefits of long-term memory and enhances traditional LSTM by processing sequences bidirectionally [67]. Compared to GRUs, LSTMs with memory cells are more effective at capturing long-range dependencies. [PERSON] [68] conducted further research using a balanced dataset of full-verse poems. By randomly selecting 1000 verses for each of the 14 m, their BiGRU model achieved 98.6% accuracy and scores of 0.90 for recall, precision, and F1-score.  
Transformer models have notable advanced NLP challenges, including Arabic meter categorization. These models use deep contextual embeddings to identify intricate linguistic patterns specific to Arabic [34; 69]. Although CAMeLBERT was trained on diverse datasets--including one related to poetry--it achieved only 80.9% accuracy on that set [43]. The exceptional performance of transformer models lies in their self-attention mechanism, which allows for more efficient modeling of long-range dependencies, outperforming recurrent networks such as BiLSTM and BiGRU. They are particularly adept at capturing complex linguistic characteristics [55].  
Arabic poetry is often governed by systematic metrical structures, which can be effectively represented using half-verses. Analyzing half-verses preserves essential rhythmic and structural features. Table 9 presents a comparative analysis of this study with previous research using the same half-verse dataset.  
Model explainability using LIME is visualized in Figures 12-14 for three representative sample texts. LIME highlights the words that most influence the model's prediction by underlining them in different colors. These visualizations demonstrate how particular syllables or phrases contribute to the identification of a specific meter, along with the associated probabilities for the top predicted meters.
## 1 Introduction  
Infrared (IR) Thermography is a widely used non-destructive method for quick testing and evaluation of materials and structures (Non-destructive Testing & Evaluation--NDT&E) [1; 2; 3; 4; 5; 6]. Depending on how the tested material is being heated, IR Thermography is either passive or active [7]. The most popular active IR thermography methods are Pulsed Thermography (PT), Lock-in Thermography (LT) and Pulsed Phase Thermography (PPT) [8; 9; 10; 11; 12; 13; 14]. PPT in fact integrates PT and LT, as it is quick and simple, like PT, and provides phase delay information, like LT. It involves Discrete Fourier Transformation (DFT). An optimized version is generally used in practice, known as Fast Fourier Transformation (FFT) [15]. FFT produces results in the form of amplifiers and phasegrams which are, in contrast to thermal images, less affected by ambient reflection [10]. It should be noted that phasegrams are virtually insensitive to non-uniform heating so that, compared to amplitude-based methods, they enable defect detection at greater depths [12].  
The application of the PT method to test defects in acrylic glass, which is extensively used at present, is coupled with problems during acquisition and analysis of captured thermal images, given that the surface of the material is highly reflective and there are issues associated with homogeneous heating and longer heat diffusion. Defect depths can be estimated by processing a sequence of thermal images captured at different sampling frequencies. Problems arise where the lowest sampling frequency that a specific camera supports is higher than required. The challenge is how to attain, through data processing, the effects achievable with a camera that supports lower frequencies as well. In the present research, two different procedures are followed to process sequences of thermal images, in order to compare their applicability to cases that involve the detection of simulated defects and determination of their sizes and depths, in acrylic test samples [16; 17]. In the first procedure, the effect of capturing at a lower sampling frequency was simulated by uniform frame extraction from the available sequence. This resulted in a smaller number of frames, so that FFT processing provided fewer spectral components. However, the visibility of the defects in the tested material decreased and for that reason, in the second procedure the number of available frames was not reduced, but a window function in the time domain enabled only the frames extracted in the previous procedure to take part in the calculations of spectral components, while disregarding the effect of all the remaining frames.
# Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
## 3 Material and Experimental Methods
### Support Vector Regression
The SVR method, guided by the principle of structural risk minimization, aims to optimize the regression model by striking a balance between fitting the data points within the threshold while minimizing the errors associated with them. This process facilitates the creation of a robust and generalizable regression model that accurately captures the underlying relationship between two variables. By effectively defining the threshold and mitigating the errors, SVR provides a powerful tool for generating regression models capable of making accurate predictions, even on unseen data. The strategic application of this method ensures the reliability and efficacy of the regression model in diverse real-world scenarios, solidifying its significance in the realm of advanced machine learning and predictive modeling.
## 3 Experiment and Results
### Analysis and Experimental Results on Network Stability
By comparing the data in columns 4-5 of Tables 1 and 2, it can be observed that after the same preprocessing and unsupervised feature learning training, the backend classifier using Bi-LSTM achieves a 4.31% higher AUC and a 4.83% higher sensitivity compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Patients** & **CNN** & **DCGAN** & **WGAN-GP** & **WGAN-GP-BI-LSTM** \\ \hline Patient 1 & 85.7 & 92.53 & 97.23 & 98.80 \\ Patient 2 & 33.3 & 32.25 & 40.86 & 83.46 \\ Patient 3 & 100 & 21.33 & 85.64 & 50.10 \\ Patient 5 & 80.0 & 45.67 & 50.38 & 82.81 \\ Patient  
to the one using fully connected layers. This result demonstrates that using a Bi-LSTM classifier can improve predictive performance for epileptic seizures.
## 2 Inverse Solution Algorithms
### Underdetermined Systems  
In this section, algorithms are presented for underdetermined problems, where the number of microphones (\(M\)) is lower than the number of potential sources (\(N\)). In this case, the solution is generally not unique unless we impose constraints. Although a pseudoinverse gives a unique minimum-norm least-squares solution, the resolution of the solution is generally not favorable because the solution error tends to be evenly distributed among all entries. Instead, we impose sparsity as the constraint to limit the cardinality (nonzero entries) of the solutions in the study, which suggests that pruning procedures of some sort must be incorporated into the iteration process.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Safety Applications
#### 3.1.3 Curve-Speed Warning (CSW)  
CSW applications are developed to inform drivers of any approaching curves, in case their current speed is considerably high and unsafe to pass through the curve. The application incorporates data provided by the infrastructure along with the vehicle sensor data to provide a recommended safe speed. The recommended speed depends on the geometrical shape of the curve, road and weather conditions in real-time, vehicles dynamics, and stability telematics information [85; 86].  
[PERSON] et al. [87] have shed light on the lack of road geometry considerations in intelligent speed adaptation systems. Based only on fixed speed limits, drivers are only warned when they go over the suggested speed. The authors demonstrated a dynamic speed adaptation methodology that provides an ideal speed taking into consideration the curvature of the road. They also performed curve analysis extraction using an algorithm that utilizes the GPS data off-line identifiers, which indicate immense curvature segments and which provide speed estimation for each curvature.Their proposed model makes use of the GPS data based on real-time kinematics GPS to sense maximum speeds and to identify road curvature sharpness to guarantee a smooth shift between the existing speeds. The model has been put for testing through experimental simulations, which indicated a reduction in lateral errors on sharp curves [87]. These results were conducted using a simulation-based approach, and it would also be important if their findings were additionally validated using field test experiments to provide a more contextual analysis of the lateral errors. Moreover, the real-time kinematics GPS adopted in their study had high precision and centimeter-positional-accuracy levels, but it is still prone to residual atmospheric errors in long- and medium-distance ranges [88]. In [89], the authors discuss the potential of improving the effectiveness of CSW systems by adjusting to the driver's behavior instead of adapting only to road and traffic conditions. A personalized adaptive curve speed warning (ACSW) system adapts to the driver's response time and historical curve handling performance. On the other hand, the waiting time is modified according to the reward/punishment function that helps reinforce safer actions, while providing customized time alerts. The authors also conducted a comparison between ACSW and CSW and their findings, which suggest that the interactions of drivers with warning systems greatly depend on demographic factors, such as gender and age. Moreover, their results indicate that the driver's near-curve speed changes significantly according to road attributes such as advisory curve speed and curve direction.
# Power-Efficient Random Access Design for Machine Type Communication
## 3 LTE-nMTC Random Access Design  
In this section, we propose a solution to the issues present in the LTE-eMTC. The new design is specifically optimized for LTE MTC operating on narrow bands, henceforth referred to as \"LTE-_n_MTC\" or simply _n_MTC.  
Figure 1: Illustration of random access procedure in LTE/LTE-eMTC.
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 4 Discussion
### Registration and Tracking
However, line-of-sight problems have a great impact on these registration techniques' performances, thus limiting the surgical team in moving and adjusting the imaging device, such as the C-arm. Moreover, landmark acquisition requires a high degree of manual interaction to resolve the misregistration task, resulting in workflow disruption. Lately, depth cameras [91] were used for markerless registration. In particular, to overcome the limitation of camera stationarity, calibration methods were developed to be able to use these registration methods on HMD [81], providing a more robust approach. For example, [PERSON] et al. [92] implemented an \"inside-out\" tracking system to allow markerless registration for intraoperative 3D visualization directly on the anatomical lens using a HoloLens 1, while [PERSON] et al. [32] presented a HoloLens-to-world registration technique involving a customized registration cube and an external EM tracker, obtaining a more accurate method than [PERSON]. [PERSON] et al. [64] and [PERSON] et al. [72] used the OpenSight application on the HoloLens to automatically align the real surface area and the virtual surface area and so registered the target object to the real world. [PERSON] et al. [31] directly used the ToF depth camera of HoloLens 1 in short-throw mode, making markerless image-based registration possible, while [PERSON] et al. [43] introduced an efficient method of object detection and position estimation using Machine Learning; their CNN-based method allows detection of the implanted screw head positions in the world space at interactive speeds, applying a clustering technique to remove outliers after the 3D model reconstruction thanks to the stereoscopy. Although these registration methods are very appealing, the low accuracy of markerless registration and motion tracking compared to marker-based still remains an issue. Indeed, as these methods are mostly based on preoperative CT or fluoroscopic images, lesion location and size could be different at the time of surgery from the preoperative data, forcing the physician to perform a new CT image acquisition or recalibrate the instrument [45; 70]. In addition, some of the proposed methods are partly constrained by the current technical limitations of HoloLens [31; 64; 66]. Access to the HoloLens internal sensor data is only possible by enabling the HoloLens Research Mode [93], thus making navigation unstable due to the relatively limited computing power of HoloLens [94]. Furthermore, regarding the depth sensor, higher uncertainty along the depth axis was observed with respect to the horizontal and the vertical axes [64]. It can be assumed that the accuracy of the built-in tracking will be improved in future versions of HoloLens as the quality of its sensors increases.