esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### XAI Functions
#### 3.6.3 Example-Based  
In XAI with example-based functions, methodologies provide insights and understanding about model decisions by highlighting similarities to previously encountered cases, specific features, or patterns that the model has learned are indicative of a particular class or output. Example-based methods allow clinicians to compare the AI's findings in classification and segmentation tasks with decisive features or established cases, enhancing confidence in and acceptance of model predictions.  
Feature attribution approaches included BI-RADS-Net for classification. BI-RADS-Net employs specific descriptors that align with terms derived from medical standards to explain class decisions [52]. Each descriptor acts as a feature example, illustrating how the model classifies breast ultrasound images based on standardized criteria.  
Analogical reasoning approaches employ a case-based approach in AIBx and a counterfactual approach in XNML. In [PERSON] et al. (2020), AIBx actively involved operating clinicians in the decision process by, upon the input of an initial image for analysis, generating a collection of visually and clinically similar images based on previously confirmed cases, alongside an initial classification. Clinicians review these images to confirm or adjust the classification, where the image similarity mirrors clinicians' memory and expertise to deepen the trust in the AI system [51]. The XNML model leveraged causal and counterfactual explanations to enhance ML models' explainability. It linked statistical data with clinical outcomes, to provide rationales for each segmentation and to aid anomaly detection [45].
## 2 Proposed System  
Figure 1 shows the conceptual diagram of the proposed fever detection, medicine de-livery, and environmental disinfection system. It includes the modules of posture and action recognition, disinfection, safety control, infection risk assessment, navigation, and real-name system. The proposed system is activated when a person arrives at the fever station. With the information from the depth camera, it first utilizes the function of posture recognition described in Section 2.1 to make sure he/she is ready for temperature measurement, and then proceeds with the subsequent operations corresponding to the fever status. To serve for medicine delivery to the patients allocated in the isolation ward, it demands the function of navigation to move between the fever station and isolation ward, as well as posture and action recognition to judge whether the patient has actually taken the medicine. For the safety and comfort of the persons involved in the processes, the system monitors the distance between the human and the robot and then executes corresponding safety control, as described in Section 2.2. Disinfection of the fever station and isolation ward is conducted to ensure their hygiene under a regular basis or under request. The proposed strategy, which is capable of achieving an even disinfection effect for all the objects in the environment, is described in Section 2.3. To evaluate how the risk of infection can be alleviated due to the introduction of the proposed system, we came up with a method for quantitative assessment based on contact time and distance in a contagious environment, described in Section 2.4. Data collected by the thermometer and depth camera are linked to the real-name system for further analysis and utilization, as described in Section 2.5.  
Figure 1: Conceptual diagram of the proposed fever detection, medicine delivery, and environmental disinfection system.  
Navigation and control of the mobile robot manipulator follows that which was developed in our previous research [23]. Three kinds of sensors are adopted for position and orientation measurement, including Lidar, inertial measurement unit (IMU), and encoder. The A* algorithm is used for global path planning among static obstacles, and the dynamic window approach is used for avoiding moving staff and pedestrians [23]. Details of its implementation are referenced in [23]. Meanwhile, voice reminders are provided through a speaker to assist with temperature measurement and medicine delivery, a pump is used for alcohol disinfection, and UVC lamps are used for environmental disinfection.
## 5 Discussion
### Key Findings and Their Implications  
Our model identified that users' activity hotspots exhibit strong spatiotemporal clustering, aligning with previous studies that highlight the regularity and predictability of individual spatiotemporal behavior. By leveraging this pattern, we are able to select PLCs that are more likely to be on users' regular paths, thereby enhancing user satisfaction and operational efficiency. The selection of PLCs based on kernel density estimation values also considers the weight, volume, and urgency of parcels, providing a practical approach to accommodate the physical limitations of PLCs and the varying sizes of parcels.  
In addition, the results of this study demonstrate that integrating user spatiotemporal trajectories with KDE can significantly reduce detour distances for package pickup (e.g., a 68% reduction for User A). This finding aligns with the conclusion of [PERSON] et al. [25], that the regularity of user behavior is crucial for improving logistics efficiency. However, this research further reveals the dynamic impact of packages' physical characteristics (weight-volume-urgency) on route planning: high-urgency packages tend to require nearer PLCs to be selected, while large-volume packages require balancing distance with facility capacity (see Section 4.1). This differentiated decision-making mechanism addresses the limitation of traditional models that solely use distance as a single metric, providing theoretical support for personalized logistics services.
# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 3 Existing Fraud Detection Techniques
### Supervised Learning Models in CCFD
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Solutions for Imbalance Data** &
\begin{tabular}{c} **Feature Selection** \\ **Methods** \\ \end{tabular} & **Classification Methods** & **The Best Model** \\ \hline
[29] & RUS, ROS, SMOTE, and ADASYN & PCA &
\begin{tabular}{c} LR, SVM, NB, RF, DT, \\ and KNN \\ \end{tabular} & RF Model with ROS \\ \hline
[30] & ROS and CBM & PCA &
\begin{tabular}{c} MLP, DT, LR, RF, and \\ SVM \\ \end{tabular} & RF Model with RUS \\ \hline
[31] & ROS & PCA & GAN, NB, and MLP & MLP Model with ROS \\ \hline
[32] & SMOTE & PCA &
\begin{tabular}{c} LR, RF, DT, XGBoost, \\ and SXGBoost \\ \end{tabular} & SXGBoost \\ \hline
[33] & SMOTE & PCA & MLP and ELM & MLP \\ \hline
[34] & CCS & PCA &
\begin{tabular}{c} RF, LR, NB, and \\ GA-SVM \\ \end{tabular} & GA-SVM \\ \hline
[35] & SMOTE & PCA &
\begin{tabular}{c} RF, LR, MLP, SVM, NB, \\ and KNN \\ \end{tabular} & RF \\ \hline
[36] & SMOTE & GA &
\begin{tabular}{c} DT, RF, LR, MLP, and \\ NB \\ \end{tabular} & RF \\ \hline
[20] & SMOTEENN & PCA &
\begin{tabular}{c} SVM, MLP, DT, \\ AdaBoost, and LSTM \\ \end{tabular} & LSTM \\ \hline
[37] & ADASYN & PCA & CNN & CNN Model with ADASYN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different modelling approaches used in credit card fraud detection studies.
## 2 Numerical Methods
### Range-Doppler Algorithm  
For ISAL, the imaging algorithm is the significant procedure to achieve well-focused images. The conventional range-Doppler algorithm (RDA) is widely used in two-dimensional focused imaging, especially for chirp pulse signals [30]. The main algorithm steps of the RDA are given below, where the raw ISAL data are arranged in the range cells (range dimension) and the number of pulse (cross-range dimension) domains. First, we use range compression to obtain the ISAL range profiles. Then, we apply translational motion compensation (TMC) to remove the target's translational motion, which includes range alignment and phase adjustment. Secondly, we apply rational motion compensation (RMC), such as the polar reformatting algorithm (PFA) to correct rotation errors. Finally, after removing translational and rotational motion, an ISAI range-Doppler image is generated by using the Fourier transform in the pulse domain. To display ISAI images in the range and cross-range domains, cross-range scaling is also needed to convert the Doppler shift to the cross-range domain.
## 3 Simulation Results
### Complexity Analysis
Figure 13 illustrates the relationship between multiplication complexity and the number of subbands  
Figure 12: CP-OFDM data processing with filtering. The number of modulated symbols, subbands, and subcarriers are set as \(K=8192\), \(N=8\) and \(M=1024\). for two waveforms, assuming \(N_{\text{iftr}}=16\),\(384\), \(L=4\), the total number of subcarriers is \(8192\), and that it is divided into \(N\) subbands. It can be seen that GFB-OFDM has lower complexity compared to CP-OFDM and that the difference becomes more pronounced as the number of subbands N increases. For example, using the calculations in Section 3.2 as an example, the number of multiplications for transmitting one OFDM symbol in CP-OFDM and GFB-OFDM are approximately \(9.0\times 10^{6}\) and \(1.1\times 10^{5}\), respectively. As previously mentioned, GFB-OFDM exhibits lower complexity compared to CP-OFDM due to the following reasons:  
1. In CP-OFDM, subbands with different subcarrier spacings require independent processing and are then combined in the time domain using IFFT, leading to increased computational complexity. As the number of subbands requiring separate processing grows, the overall complexity rises substantially. 2. Conversely, GFB-OFDM enables uniform processing of subbands with different subcarrier spacings, providing greater flexibility in their division. Modifying the size or increasing the number of subband divisions does not augment processing complexity while maintaining a constant bandwidth.
## 1 Introduction
In this sense, using serious games to teach sign language can facilitate that process by making the learning much more engaging and interactive [7]. In this work, the serious game SIGNIFY is introduced. SIGNIFY is an application specifically designed to teach Italian Sign Language (LIS) to primary school children. To deal with young learners, the necessary aim is to offer a tailored, engaging, and interactive learning environment. For this purpose, Machine Learning-based hand gesture recognition techniques were applied to assist in learning and easily mastering sign language gestures. By detecting and overlaying 21 hand landmarks and a bounding box on live camera feeds, the solution leverages an open-source framework to offer real-time visual feedback. Furthermore, different data sources were compared to evaluate the effectiveness of RGB versus RGB-D (depth) data for sign language recognition for the specific purpose of a children-friendly application. A new RGB-D database that records LIS gestures was needed to develop a tailored solution for the characteristic application, which serves as an essential resource for training and testing AI models. Finally, a usability analysis based on feedback collected from the children was performed, offering insights into their learning experience and interaction with the game. This analysis demonstrated to be remarkably beneficial for highlighting margins of improvement and ensuring the platform is engaging and accessible for young users. These contributions advance the technological and educational aspects of sign language learning through AI. The paper is structured as follows: Firstly, in the following section, related works are presented to better frame the project. Subsequently, in Section 3, all the technical aspects of the project are analyzed, ranging from the creation of the dataset to the machine learning method used to perform the classification. Furthermore, a brief overview of the developed serious game structure and functionalities is provided. The final two sections are dedicated, on the one hand, to the presentation of the results, in terms of the classifier's performances and ease of use of the game assessed in a case study with a group of children, and the future developments and perspectives on the project on the other hand.
# Methane Gas Density Monitoring and Predicting Based on RFID Sensor Tag and CNN Algorithm
###### Abstract  
According to the advantages of integrating wireless sensors networks (WSN) and radio frequency identification (RFID), this paper proposes a novel method for methane gas density monitoring and predicting based on a passive RFID sensor tag and a convolutional neural networks (CNN) algorithm. The proposed wireless sensor is based on electronic product code (EPC) generation2 (G2) protocol and the sensor data is embedded into the identification (ID) information of the RFID chip. The wireless sensor consists of a communication section, radio-frequency (RF) front-end section, and digital section. The communication section is used to perform the transmission and reception of wireless signals, modulation, and demodulation. The RF front-end section is adopted to provide the stable supply voltage for other parts. The digital section is employed to achieve sensor data and control the overall operation of the wireless sensor based on EPC protocol. Because the miscellaneous noises will decrease the accuracy during the process of data wireless transmission, the CNN algorithm is adopted to extract the robust feature from raw data. The measurement results show that the exploited RFID sensor can realize a maximum communication distance of 10.3 m and can accurately measure and predict the methane gas density in an underground mine. The RFID sensor technology is a beneficial supplement to the current underground WSN monitoring system.  
Keywords:methane gas density; RFID sensor tag; CNN algorithm +
Footnote †: journal: Electronics
## 4 Time Stepping Schemes
### Implicit LU-SGS Scheme  
The implicit backward Euler time discretization of Equation (24) can be written as  
\[\left(\frac{\mathbf{M}}{\Delta t}-\frac{\partial\mathbf{R}^{n}}{\partial\widetilde{ \mathbf{q}}}\right)\Delta\widetilde{\mathbf{q}}^{n}=\mathbf{R}^{n}, \tag{27}\]  
where \(\Delta\widetilde{\mathbf{q}}^{n}=\widetilde{\mathbf{q}}^{n+1}-\widetilde{\mathbf{q}}^{n}\), in which the superscripts \(n\) and \(n+1\) denote the current and the next time steps, respectively. \(\frac{\partial\mathbf{R}^{n}}{\partial\widetilde{\mathbf{q}}}\) is the Jacobian matrix of the DG spatial discretization, which is computed analytically (except for the dissipative part of the numerical flux using divided differencing [34]) in this paper.  
Recall that \(\mathbf{M}=diag(\mathbf{M}_{1},\mathbf{M}_{2},\ldots,\mathbf{M}_{N_{c}})\), and let \(C_{i}\) be a set of the indexes of the neighboring connected elements of \(\Omega_{i}\), define \(\mathbf{A}=\frac{\mathbf{M}}{\Delta t}-\frac{\partial\mathbf{R}^{n}}{\partial\widetilde{ \mathbf{q}}}\) as the global system matrix, and the Equation (27) can then be written as  
\[\mathbf{A}\Delta\widetilde{\mathbf{q}}^{n}=\mathbf{R}^{n}, \tag{28}\]  
in which  
\[\mathbf{A}=\begin{bmatrix}\mathbf{A}_{ij}\end{bmatrix}\text{ with }\mathbf{A}_{ij}= \begin{cases}\frac{\mathbf{M}_{i}}{\Delta t}-\sum_{k\in C_{i}}\frac{\partial\mathbf{R} _{k}^{n}}{\partial\widetilde{\mathbf{q}}_{i}}&i=j\\ -\frac{\partial\mathbf{R}_{i}^{n}}{\partial\widetilde{\mathbf{q}}_{j}}&i\
eq j\end{cases}.\]  
The resultant system of Equation (28) encapsulates the implicit iteration schemes, and it can be solved iteratively to converge to a steady state. The traditional LU-SGS scheme consists of a forward and a backward sweeping through all the computational elements in a sequential order [16], which can be written as  
\[\text{Forward sweep}:\;\Delta\widetilde{\mathbf{q}}_{i}^{*}=\mathbf{A}_{ii}^{-1} \left[\mathbf{R}_{i}^{n}-\sum_{j\in C_{i}}^{j<i}\mathbf{A}_{ij}\Delta\widetilde{\mathbf{ q}}_{j}^{*}\right],i=1,2,\ldots,N_{c}-1,N_{c}. \tag{29}\]  
\[\text{Backward sweep}:\;\Delta\widetilde{\mathbf{q}}_{i}^{n}=\Delta\widetilde{ \mathbf{q}}_{i}^{*}-\mathbf{A}_{ii}^{-1}\sum_{j\in C_{i}}^{j>i}\mathbf{A}_{ij}\Delta \widetilde{\mathbf{q}}_{j}^{*},i=N_{c},N_{c}-1,\ldots 2,1. \tag{30}\]
## 5 Demonstrating the Methodology
### Intrusion Detection  
In order to experiment and test the methods for preventing such DoS attacks, traffic was recorded from the simulation and various mechanisms were investigated regarding how to recognize malicious traffic and the corresponding rogue sensors. Such mechanisms  
Figure 8: Residual energy of the gateway—an example from the OMNET++ experiments.  
Figure 7: IoT traffic visualization.  
can be either placed directly in the middlebox (see Figures 6 and 7) or can be executed in the according NOC (network operations center) or SOC (security operations center) once the traffic has been monitored and recorded in a real network.  
The methods that can be applied for analyzing these data include different autoencoder and deep neural network architectures as well as the concept of a random forest that was mentioned before. In this line of thought, Figure 9 shows the current demonstrator that is based on the DoS recognition utilizing a random forest implemented in Python scikit-learn [32].  
The random forest classifier trained in the scope of this work is configured with the main hyperparameters listed in Table 2. The first parameter \"_n_estimators_\" defines the number of decision trees in the forest. By increasing the number of trees, we might be able to improve the model accuracy, but this could also lead to longer training time and increased risk of overfitting. Therefore, we used the default value of 100 decision trees which provides a good balance between accuracy and computational efficiency. In terms of the split/decision criterion, the random forest trained in this work utilizes the so-called _gini impurity_. _Gini impurity_[33]--a mathematical coefficient determining the degree to which a set consists of a particular type of data/labels--is a good split criterion for a decision tree because it is a fast and efficient way to measure the quality of a split and is effective at identifying the most frequent class in a dataset. The next parameter used for configuring the RF is _max_depth_. This parameter controls the maximum depth of each decision tree in the forest. For the purposes of our use case, we set the parameter to \"_None_\", which sets no depth limit. The reason is that while maximum depth can help with preventing overfitting, it can also decrease accuracy if the depth is set too low. The final specified parameter is _min_samples_split_, which controls the minimum number of samples required to split an internal node. We set the parameter to be equal to two. This means that a node must have at least two samples to be split. Increasing this value can help to prevent overfitting, but can also lead to a model that is too simple and has lower accuracy.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline n\_estimators & 100 \\ \hline
**criterion** & “gini” \\ max\_depth & None \\ \hline min\_samples\_split & 2 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of the used Random Forest Parameter.  
Figure 9: Visualization of the demonstrator based on random forest.
## 1 Introduction
Dimensional measurements were precisely acquired via 3D scanning and compared to those obtained from a CMM. Conclusively, 3D scanning was found to deliver a better inspection of the manufactured parts, resulting in a substantial reduction in lead time. [PERSON] et al. [30] explained that preliminary geometric errors are frequently obtained during 3D scanning. Point cloud scanning data usually result from the 3D scanning of physical objects. A self-balancing process can help 3D scanners reduce these geometric deviations; however, this self-balancing process is hard to understand and implement. Therefore, a reasonable gap exists in motivating us to find a convenient way of reducing scanning errors. [PERSON] et al. [31] highlighted another application of 3D scanning in dentistry. Dental implants can easily be made using 3D scanning techniques. Moreover, 3D scanning may likely be employed for the innovative creation of dentures, braces, veneers, and aligners. Dentistry teaching-learning methodology may also be improved using scanners. As can be seen from the previous studies, there is not even one single study on the systematic methodology of three dimensionally digitizing physical components that greatly emphasizes reducing 3D scanning errors. Error-free digital models can confidently be used to accurately and precisely implement the reverse-engineering theory in various engineering disciplines, e.g., additive manufacturing and non-engineering fields. After establishing a 3D scanning methodology, it must be optimized for the 3D scanning factors employed in the established methodology by using the appropriate statistical tools, resulting in a considerable reduction in 3D scanning errors. Past studies merely focused on the construction of the digital shapes of scanned objects without giving due attention to the right framework of 3D scanning. Therefore, a reasonable gap is present, leading us to investigate the acquisition of an optimal framework for 3D scanning, which will ultimately result in error-free digitally scanned objects. Therefore, this study is an effort to fill this gap, presenting the dimensional analyses of various physical objects of varying geometrical intricacies. The expected outcomes of this study include the substantial mitigation of 3D scanning errors. This method of reducing scanning errors may help experts to select an appropriate scanning methodology in diverse fields such as engineering, medicine, textiles, ergonomics, industry 4.0, cultural artifacts, additive manufacturing, and rapid prototyping. Both CMM and Solidworks were used for the first time to conduct a powerful dimensional comparison physically and virtually, respectively, for different geometries with an objective function of reducing 3D scanning errors.