esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 2 Literature Review
### Regression Imputation Method  
Regression imputation is a simple and common way of dealing with missing values. [PERSON] et al. [36] firstly investigated how pairs of neighboring detectors behave, and then established a linear regression model to estimate the missing values. [PERSON] et al. [37] compared the performances of eleven approaches based on three types of missing data. The eleven approaches included a simple linear regression model,multiple linear regression, local and global regression, and historical imputation. The three types of missing data consisted of random losses data, continuous losses data, and systematic losses data. The imputation results indicated that the regression model was highly sensitive to the input data, and the estimated results obtained by the historical method were less accurate. Although the regression methods are easy to apply, traffic conditions vary constantly, and the estimation performance might be unreliable.
# HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering
## 5 Experiments and Discussions
### Discussion and Result  
#### 5.3.1 Performance Comparison (Q1)  
Based on the collected dataset and experimental setup, we tested the performance of several state-of-the-art graph neural network methods to verify their effectiveness on the HBTBD dataset using random forest and SVM classifiers, and the corresponding precision, recall, and F1 scores are reported in Table 4, respectively. We also tested the classifier results without the graph embedding features. We have three findings, as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Illicit**} \\ \cline{2-4}  & **Precision** & **Recall** & **F1** \\ \hline Random Forest (LF + GCN) & 0.812 & 0.584 & 0.679 \\ Random Forest (AF + GCN) & 0.841 & 0.603 & 0.702 \\ Random Forest (LF + GAT) & 0.720 & 0.587 & 0.647 \\ Random Forest (AF + GAT) & 0.708 & 0.580 & 0.638 \\ Random Forest (LF + EvolveGCN) & 0.824 & 0.582 & 0.679 \\ Random Forest (AF + EvolveGCN) & 0.853 & 0.611 & 0.712 \\ Random Forest (LF + HAN) & 0.809 & 0.613 & 0.697 \\ Random Forest (AF + HAN) & 0.805 & 0.615 & 0.697 \\ Random Forest (LF + MAGNN) & 0.907 & 0.605 & 0.726 \\
**Random Forest (AF + MAGNN)** & **0.922** & 0.617 & 0.739 \\ SVM (LF + GCN) & 0.778 & 0.602 & 0.679 \\ SVM (AF + GCN) & 0.820 & 0.618 & 0.705 \\ SVM (LF + GAT) & 0.706 & 0.622 & 0.661 \\ SVM (AF + GAT) & 0.683 & 0.628 & 0.654 \\ SVM (LF + EvolveGCN) & 0.831 & 0.615 & 0.707 \\ SVM (AF + EvolveGCN) & 0.850 & 0.624 & 0.720 \\ SVM (LF + HAN) & 0.833 & 0.630 & 0.717 \\ SVM (AF + HAN) & 0.859 & 0.626 & 0.724 \\ SVM (LF + MAGNN) & 0.901 & 0.620 & 0.735 \\
**SVM (AF + MAGNN)** & 0.911 & **0.637** & **0.750** \\ \hline Random Forest(LF) & 0.865 & 0.586 & 0.699 \\ Random Forest(AF) & 0.789 & 0.609 & 0.687 \\ SVM(LF) & 0.831 & 0.597 & 0.695 \\ SVM(AF) & 0.849 & 0.602 & 0.704 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results with original attribute features.  
First, the feature embedding of GNN strengthens the model performance to some extent. We can see that the precision, recall, and F1 scores of most models increase after adding the graph embedding feature, so it can be shown that the graph structure information is effective in the Bitcoin anti-money laundering task.  
Second, We found that MAGNN is better than the other models under our designed metapath encoder; its precision reached the highest of 0.922 over EvolveGCN with about 7% better accuracy under random forest, and its recall also reached the highest of 0.637 by 1% more than GAT under SVM.  
Third, we found that random forest and SVM also have different metrics. Random forest is better on precision, but SVM is better on recall. The different classification strategies may cause this.
## 4 Results
### Debris Flow Susceptibility Mapping in Songpan  
The results of the landslide susceptibility assessment for the Songpan region were classified into five susceptibility levels (very high, high, medium, low, and very low) using the Jenks natural breaks classification method. As shown in Figure 7, the high-susceptibility areas of landslides exhibit a clear spatial clustering pattern, predominantly distributed in the central, southern, and northeastern-southeastern transitional zones of the study area, while the low-susceptibility areas are mainly located in the northwestern plateau region. The results show a high degree of concordance with the spatial distribution of landslide disaster points, thus validating the scientific and reliable nature of the assessment method employed in this study.
# Fuzzy Sliding Mode Control of Vehicle Magnetorheological Semi-Active Air Suspension
## 2 Damping Performance Test and Mechanical Modeling of MR Damper  
### Damping Performance Test of MR Damper  
In order to test the damping characteristics of MR damper, a vibration test system was established as shown in Figure 1, which is mainly composed of a damping suspension test bench, DC power supply, electro-hydraulic servo controller, computer and MR damper. The DC power supply was used to supply the required DC current to the coil in the MR damper. The hydraulic station provided hydraulic power for the vibration test bench. The computer was used to collect experimental data, and the vibration test bench was controlled by the electro-hydraulic servo controller.  
In this experiment, a sinusoidal excitation signal with a frequency of 1 Hz and amplitudes of 5 mm, 7.5 mm, 10 mm was adopted. The currents were 0 A, 0.25 A, 0.5 A, 0.75 A and 1 A, provided by a DC regulated power supply. As shown in Figure 2, the variation of the output damping force with displacement and velocity of the MR damper under different excitation currents is obtained. Figure 2a shows that the output damping force of the MR damper increases with the increase in the excitation current. This is mainly because the magnetic flux density in the damping channel increases with the increase in the excitation current. The output damping force varies little with the displacement, and it is distributed symmetrically with respect to the relative equilibrium position. As shown in Figure 2b, the damping force of the MR damper increases with the increase in the excitation current and vibration speed, but the influence of the speed is much smaller than that with the current. The proposed MR damper has a wide range of damping force adjustments, and thus, it can be used in a vehicle suspension system.  
Figure 1: Vibration test system of MR damper.
# Feasibility of Magneto-Encephulography Scan under Color-Tailored Illumination
## 4 Discussion  
Based on experimental results (Figure 5), we can conclude that the LED-lamp and the controller via USB connection can be used in conjunction of brain and color-related research. Further improvements of USB wire shielding can be implemented to further reduce noise amplitude at low \(f<10\) Hz frequencies, usually referred to as a building/structure noise.  
The color mixing by developed software and the LED-lamp can be calibrated by a colorimeter and measured in color temperature from tristimulus values (see Figure 1b). The desired color is generated by regulating the intensity of the LEDs with four colors. Concurrently switching clusters of LEDs may cause electromagnetic interference (EMI). Hence, it is compulsory to measure EMI of the lamp and controller software together [15], as it was carried out in this feasibility study.  
The created software and hardware control tools can serve future research in a wide range of applications, from brain functions and psychological behavior relevant to learning of hyperactive kids, mood control of prisoners, and in age care facilities, as well as a marketing tool and street lighting for the quality and safety of driving and walking [1; 2; 3; 16; 17]. In terms of neuro-scientific questions, it is interesting to use the LED lamp to investigate colour constancy, which is the tendency to perceive surface colour of objects as relatively constant under changing illumination conditions [17]. Previous studies have shown that warm or cool illumination can influence perception of colours. By use of brightening illusion, it is possible to shift the point at which observers switch between seeing the same dress as white/gold vs. blue/black. The challenge is in differentiating between signals associated with neuro-physiological responses to illumination vs. decision processes about illumination quality. Cognitive neuroscience researchers are using machine learning algorithms to decode patterns of EEG/MEG/fMRI data associated with different perceptions/tasks/decisions [16; 18]. Computer graphics and data visualisation strategies [19] could find more intuitive and informative presentations based on color rendering.  
A similar kind of a controller and LED lamps can serve other applications for greenhouse lighting system design. It has been scientifically proven that harmful nitrate can be significantly reduced by 20% to 40% in lettuce by red LED illumination two days before harvesting [20]. It was demonstrated that a \"light box\" with three RGB colors only were delivering viable conditions (similar production of biomass) to grow Christmas poinsettia _Euphoria pulcherrima_, a popular pot-plant, as well as to develop color change of leaves to red by only such artificial lighting [21].
# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Multicast-Based Routing Approaches
The authors [150] declare that PEERMR is more energy-efficient and reliable than the algorithm proposed in [151], but it is noteworthy that the root nodes in PEERMR are overloaded. [PERSON] et al. [152] introduced the Ant-Colony Optimization Protocol (ACOP), that provides optimized PDR, throughput with low power consumption, and reduced packet delay. Moreover, they performed a comparative study of ACOP with various existing routing protocols (DSDV, AODV, and AOMDV) using the Random Waypoint Mobility Model. The purpose of using this mobility model was to generate different scenarios for the same purpose. The analysis of these protocols was done by implementing irregularities in the scenario using Network Simulator (NS2). Various performance metrics, including packet delivery fraction, throughput, and end-to-end delay, were used for validating the comparative study. Experimental simulation results indicated that the performance of the ACOP protocol was better than the other routing protocols. The Energy-Efficient Lifetime Aware Multicast (EELAM) route selection strategy [153] is a multicast route discovery scheme for MANETs that was developed using an adaptive genetic algorithm. EELAM functions based on tree topology and adapts an evolutionary computation strategy (a genetic algorithm). This genetic algorithm plays a vital role in terms of selecting optimal middle nodes with maximal residual energy and minimal energy usage. The proposed adaptive genetic algorithm formulated the fitness function that aims to improve the energy consumption ratio, the residual battery life, and the multicasting range. Simulation results showed that EELAM is the best route discovery approach in its category because the process and the methods adopted are contemporary. Furthermore, the adaptive algorithm used in EELAM is different from conventional genetic algorithms. Finally, the Weight-based Energy-efficient Multicasting (WEEM) scheme [154] selects the path with the highest lifetime (weight) as optimal. If more than one path is expected to remain alive till the multicast session is over or none of the available path options have a chance to live till the end of the multicast session, weight is assigned to the paths by the destination. Three parts contribute to the calculation of Weight: (1) the residual energy; (2) the multicast packet transmission capability of nodes in a path; (3) the number of multicast destinations residing in that path. If more than one path has the same weight, then priority is given to the one path suffering less delay. Extensive simulation results demonstrated that WEEM produces more packet delivery ratio and alive node ratio at much less control message cost than other competing multicast protocols. In summary, existing power-aware multicast algorithms often produce additional control traffic. Moreover, they do not consider any packet loss, or the energy consumed by retransmissions because of the dynamic conditions (e.g., packet collision and interference) [20].
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Analytical Comparison of the Models
#### 5.2.1 Accessibility Features  
As shown in Table 3, HSALA includes accessibility considerations at each level, with a dedicated Accessibility Enhancement Layer at the top. In contrast, the traditional three-layered model typically treats accessibility as an add-on rather than a core requirement, risking incomplete or inconsistent accessibility support.  
HSALA provides a proactive and comprehensive approach to accessibility, embedding it within the architectural framework and ensuring compliance with standards. In contrast, the three-layered model's reactive and fragmented approach can lead to inconsistent accessibility features and potential security vulnerabilities related to accessibility implementations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Functionality** \\ \hline
1. Data Access Layer & Manages direct interactions with the database, performing CRUD operations. \\ \hline
2. Application Logic Layer & Contains the core functionality and application logic, processing data between the Data Access Layer and the Presentation Layer. \\ \hline
3. Presentation Layer & Presents the data and application functionalities to the user, often focusing on the visual aspects of the application. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of three-layered architectural model.
# Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes
## 3 Methodology for Fault Detection  
The methodology presented in this study consists of pre-processing of data, that is, cleaning and structuring data to obtain a matrix of \(m\) observations with \(n\) process variables. Once this is done, the different metaheuristics are implemented to optimize the training of a one-class classifier (SVDD), that is, the hyper-parameters are optimally found that improve the abilities of SVDD for faults detection in industrial processes where there is not much faulty information available. In particular, advantages of the exploration and exploitation of metaheuristic algorithms are taken in order to tune the hyper-parameters \(C\) in SVDD and \(s\) for the RBF kernel (16). Once the SVDD training is optimized,it will be ready to monitor new data and detect possible faults in the multivariate industrial process. Figure 1 shows the process of the described methodology:  
\[K(\mathbf{x}_{i},\mathbf{x}_{j})=exp\left(-\left\|\mathbf{x}_{i}-\mathbf{x}_{j} \right\|^{2}/2s^{2}\right) \tag{16}\]  
With the purpose of performance comparison during the optimization and training processes of SVDD, some metaheuristic algorithms such as SHO, KH, SSA, and PSO are implemented which show great efficiency in solving engineering problems. Thus, four approaches arise and need to be compared, that is: (1) SHO-SVDD, (2) KH-SVDD, (3) SSA-SVDD, and (4) PSO-SVDD. The metric under consideration is the well known F1 score (17) due to the fact that this metric takes into account precision and sensitivity. Such metric is computed based on the true and false positives (TP and FP) and negatives (TN and FN) (18) and (19). Moreover, this is highly relevant in the detection of faults in industrial processes. The F1 score value is found between 0 and 1, that is, a value of 1 means that the algorithm can detect the faults without errors. In this sense, the possible solutions generated by the different metaheuristics (particles, group of hyenas, etc.) for both hyper-parameters (\(C\) and \(s\)) are used in the training and testing phases of SVDD yielding several values of F1 score. Therefore, the different metaheuristics will adjust their solutions through the iterations in order to maximize the F1 score value:  
\[\text{F1 score}=\frac{2\cdot\text{Re}\cdot\text{Pr}}{\text{Re}+\text{Pr}} \tag{17}\]  
\[\text{Pr}=\frac{\text{TP}}{\text{TP+FP}} \tag{18}\]  
\[\text{Re}=\frac{\text{TP}}{\text{TP+FN}} \tag{19}\]
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 3 The Proposed Algorithm
### Data Preprocessing
```  
**Algorithm 1**Data Preprocessing  
The cutoff frequency of 20 Hz was chosen to focus on the dominant frequency components associated with road events, as vibrations and accelerations caused by such events typically occur below this threshold. The window size of 1 s balances capturing sufficient temporal context while maintaining computational efficiency for real-time processing.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 2 Models
### Kalman Filter
The innovation's covariance \(S(k)\) is used to calculate the Kalman gain \(K(k)\): a matrix that weighs the trust in the measurements against the trust in the model to find out which of these should have more influence in the filtered estimate. These two stages are then applied iteratively to estimate the state at every time step. After the prediction stage, the current time instant is updated (\(k\gets k+1\)), and the filtering stage can now be applied, since \(\hat{x}(k|k-1)\leftarrow\hat{x}(k+1|k)\) and \(P(k|k-1)\gets P(k+1|k)\). Then, the prediction stage is executed again, making this process cyclic. Note the need for an initial estimate (\(\hat{x}(0|-1)\)) and its covariance (\(P(0|-1)\)) in order to begin the algorithm. Recall that the Kalman filter assumes the system whose state is being estimated is linear. However, the robotic manipulator is not a linear system, some entries of the transformation matrices being trigonometric functions of the joints' angles. However, because the performance of the KF is such an appealing factor, many variants, capable of estimating the state of non-linear systems, have been developed. Some of these include the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), the Quadrature Kalman Filter (QKF), and the Cubature Kalman Filter (CKF), all of which have been proven reliable solutions to the state estimation problem in the context of robotic manipulators [1; 2; 3; 12; 13; 14].
# Assessment of Coating Properties in Car Body by Ultrasonic Method
## 3 Results  
### Ultrasonic Coating Width Testing  
The results of testing the width of the joint between the putty coating and the body sheet in the form of changes in the parameters of the ultrasonic surface wave pulse, propagating along the joint, are shown in Table 1. The results show that the height of the ultrasonic pulse decreases with increasing width of the putty coating. This is because the applied putty strongly attenuates the ultrasonic wave, which propagates along the interface between the coating and the substrate. The greatest difference in pulse height values occurs between no coating and a width of 1 mm. The value of this parameter drops from 99% to 48.6% of the measurement range. Furthermore, the relationship between pulse height and coating width is almost linear.
# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 3 Fall Detection System
### Datasets  
The analysis of the recent related literature showed that current studies tend to prefer the use of already existing public repositories containing falls and ADLs, although no particular dataset can be considered a globally accepted benchmarking tool. For neural network training, we used two different datasets.  
The first dataset chosen for recognition of falls and daily activities (ADL) [17] includes 11 activities and three trials for each of them. For the dataset collection, 17 different subjects performed six different activities of daily living (walking, standing, lifting an object, sitting and lying down) and five different types of falls (falling forward using hands, falling forward using knees, falling backwards, falling sitting, and falling sideways). Data were collected with a multi-modal approach using wearable sensors, ambient sensors and vision  
Figure 1: Arduino 33 Nano BLE Sense pinout.  
devices. For data consistency, we selected a single subject and chose to refer only to the data acquired by inertial sensors placed on the right wrist. For each type of activity (excluding walking), we selected five samples for a total of 50 samples for falls and daily activities, respectively.  
We used Power BI to eliminate redundant data and measurements unrelated to the right wrist or related to the three axes of acceleration and gyroscope. Therefore, the resulting dataset was trimmed to be used in the training of the neural network that we show in detail in the dedicated section.  
The _Run or Walk_ dataset [18] contains running and walking data collected from iOS devices. Initially, the dataset consisted of a single file representing 88,588 data samples collected by the device's accelerometer and gyroscope during an interval of 10 s and at a frequency of approximately 5.4/s. For each row, there is an activity type represented by \"activity\" column which acts as label and a \"wrist\" column which represents the wrist whereupon the device was placed to collect samples. Specifically, each row of the dataset contained:  
* acceleration_x;
* acceleration_y;
* acceleration_z;
* gyro_x;
* gyro_y;
* gyro_z;
* label \"0\" for walking;
* label \"1\" for running;
* label \"0\" for the left wrist;
* label \"1\" for the right wrist.  
The original dataset also contained the columns columns \"date\", \"time\" and \"username\", which for obvious reasons, have been eliminated by PowerBI. Moreover, we chose to consider the measurements made only on the right wrist (for consistency with data collected for the others activities). Then, we collected 50 samples for walking and 50 for running. Angular velocity values were transformed from rad/s to deg/s to align them with the fall/adl dataset values. Therefore, all values contained in the gyro axes columns were multiplied by 57.2958\({}^{\circ}\)/rad.
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 2 System Model
### Power Consumption Model  
Modeling the BS power consumption is an important problem to measure the EE. A typical BS is composed of many power consumption components, including a supply, cooling, baseband (BB), radio frequency (RF), power amplifier (PA), loss factors for DC-DC, feeding cable, and so on [5]. Some studies have roughly divided this into static power and dynamic power [21]. It is difficult to estimate how technology would evolve, and designing an exact model is beyond the scope of this paper. However, a realistic model could be helpful in analyzing the proposed methods. In this paper, we define the total power \(P_{total}\) for a tractable power consumption model that includes some of the important BS power consumption components. We define \(P_{total}\) as:  
\[P_{total}=P_{PA}+P_{Cir} \tag{1}\]  
where \(P_{PA}\) is the PA power consumption and \(P_{Cir}\) is the circuit power consumption except for the PA power consumption, including the BB power consumption, \(P_{BB}\), and RF front-end power consumption, \(P_{RF}\). In this model, since maximum radiation power is limited regardless of the number of TX antennas \(N_{t}\), we should keep in mind that \(P_{PA}\) can be reduced with the help of a beamforming effect, while \(P_{Cir}\) increases as \(N_{t}\) increases. We describe below each of the power consumption components in further detail.