esa-sceva/satcom-chunk-collection huggingface dataset:
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 3 Materials and Methods
* TMVP: Temporal motion vector prediction is a new tool introduced since H.265/HEVC and prevents the possibility to exchange predicted frames with keyframes. TMVP [29; 30] enables borrowing motion vectors from reference frames additional to pixel information. Because of a lack of motion information present in keyframes, the frames succeeding the introduced companion keyframe will lack such motion information resulting in explicit and annoying blocking artifacts. For this reason, TMVP had to be disabled in the presented results. In general, whenever there is packet loss present during transmission, it is advised to disable TMVP because of the artifacts it produces. On the dataset used in this paper, enabling TMVP would lead to a Bjontegaard-Delta bitrate (BD-Rate) decrease of \(-2.57\%\) (Std: 0.86%, Min: \(-3.92\%\), Max: \(-0.22\%\)).
# Machine Learning-Based Methods for Code Smell Detection: A Survey
## 4 Findings and Discussion
### Rationalize the Research Questions
#### 4.2.6 RQ4.1: What Are the Various Dataset Languages Used for Code Smell Detection?  
A wide variety of dataset languages have been applied in code smell detection. Notably, JAVA emerges as the primary dataset language for code smell detection. The dataset is further categorized into open-source, projects written in JAVA, and any language and varies in size: large, small, and medium. ARTN10 employed C# as the dataset language. ARTN3 used XML with the JAVA dataset language. This diversity in dataset languages underscores the multifaceted nature of code smell detection, as different programming languages may have different code smells and quality issues. Understanding the usage and characteristics of these dataset languages can provide valuable insights for developing language-specific code smell detection techniques. Figure 10 shows the applied dataset languages.  
The following are some brief descriptions of a variety of datasets:  
* Open-source dataset: It includes freely available software projects such as WEKA, Xerces v2.7.0, IYC, Gantt Project v1.10.2, Eclipse, Guava, Closure Compiler, and jUnit datasets.  
Figure 9: Research conducted on the various datasets used in code smell detection.  
* Student dataset: It includes datasets developed by students, such as [PERSON] et al., who designed datasets using four open-source JAVA projects with the help of three master's degree students [26].
* The language dataset includes a dataset selected from different language projects, such as JAVA, XML, C#, etc. JAVA is the most frequently used language in research articles.
This study has observed that the main datasets used are open-source datasets belonging to the JAVA language.
## 5 Control Policy Diagram  
Figure 7 illustrates the overall control system architecture, designed to guide the robot manipulator so that the camera reaches its desired pose, \(\overline{pose^{O}}\epsilon\)\(\mathbb{R}^{4\times 4}\), in the world space. This architecture follows a similar structure to that used in prior work on tool trajectory control for robotic manipulators [35]. To facilitate visual servoing, three fiducial markers are placed within the camera's field of view, with their coordinates in the inertial frame pre-determined and represented as \(P^{O}\epsilon\)\(\mathbb{R}^{9\times 1}\). Using the robot's inverse kinematics and the eye-in-hand kinematic model, the expected image coordinates of these fiducial markers, when viewed from the desired camera pose, are computed as \(\overline{p_{image}}\epsilon\)\(\mathbb{R}^{9\times 1}\). These computed image coordinates serve as reference targets in the feedback control loop.  
The IBVS framework is implemented within a cascaded feedback loop. In the outer control loop, the camera controller processes the visual feedback error, \(e_{p}\), which represents the difference between the image coordinates of the fiducial points at the current and desired camera poses. Based on this error, the outer loop generates reference joint angles, \(q_{feedback}\)\(\mathbb{R}^{6\times 1}\), to correct the robot's configuration.  
Figure 7: Feedforward-feedback control architecture. Note: In mathematics, the in-loop hardware is equivalent to the eye-in-hand kinematics model.  
The inner control loop, shown in Figure 8, incorporates the robot's dynamic model to regulate the joint angles, ensuring they align with the commanded reference angles, \(q_{ref}\in\mathbb{R}^{6\times 1}\). However, due to the limitations of low-fidelity and inexpensive joint encoders, as well as inherent dynamic errors such as joint compliance, high-frequency noises and low-frequency model disturbances are introduced into the system. All sources of errors from the joint control loop are collectively modeled as an input disturbance, \(d_{q_{T}}\in\mathbb{R}^{6\times 1}\), which affects the outer control loop.  
The feedforward control loop operates as an open-loop system, quickly bringing the camera as close as possible to its target pose, despite the presence of input disturbances. The feedforward controller outputs a reference joint angle command, \(q_{feedfarant}\in\mathbb{R}^{6\times 1}\), which is sent to the inner loop to facilitate rapid convergence to the desired configuration.
###### Abstract  
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details' preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details' preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DrCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details' preservation.  
denoising CNN; image denoising; additive Gaussian blur noise; CT images; wavelet transform; anisotropic Gaussian filter +
Footnote †: journal: Applied  
Article  
An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Timely Message Dissemination  
To react adequately, the drivers (or electronic system, in the case of an autonomous vehicle) have to be informed about an approaching EV early enough. Surveys conducted amongst the EV crews suggest that 30 s represents a sufficient time for drivers to react properly under most conditions. However, it was suggested that this value should be examined more accurately by further studies [16]. To facilitate the assessment of the feasibility of reaching a 30 s reaction time, we illustrate in Figure 1 the relationship between the available reaction time, \(t_{att}\), and the relative speed, \(\Delta v\), of two vehicles (the EV and the vehicle that should be warned about the approaching EV), for six different values of the communication range, \(s\). The curves, shown in Figure 1, are obtained by considering the mathematical relationship \(t_{att}=s/\Delta v\). Hence, here we consider only the time the EV needs to reach the considered vehicle and the communication delay is neglected.  
Let us first consider the worst-case scenario in Figure 1, when the EV travels with maximum speed and the informed vehicle moves extremely slowly or even stops, e.g., because of construction work or an accident on the highway. The communication range must be approximately 1000 m to ensure a 30 s reaction time. Consequently, the vehicle's onboard unit must switch to the highest allowed transmission power. Typically, achieving the maximum communication range \(s=1000\) m in a highway scenario is feasible, as there are rarely any major obstacles to attenuate the signal along the road. The situation is, however, completely different in an urban environment with dense built-up areas, in which the receiving vehicle is often not in line-of-sight with the transmitter. Fortunately, in these areas, the speed limits are much lower. If we consider the maximum speed limit for urban areas, which is 50 km/h in most states of the European Union, the minimum distance needed to ensure  
Figure 1: Estimate of the reaction time available to the driver, for various values of the communication range, \(s\), as a function of the relative speed of the emergency vehicle (EV) and the vehicle.  
sufficient reaction time is approximately 400 m. Thus, a rough estimation indicates that a deployment of technology with a communication range reaching 1000 m could be sufficient.  
According to [PERSON] et al. [32], three performance classes for the VANET applications can be established. Considering the scale of the reaction time and the required communication range, the PEEV-WS falls under the high frequency and low latency performance class, implying a required message generation frequency of about 1 s.
## 4 Simulation Results
### Inverted Pendulum on a Cart
Tables 3, 7 and 8 compare the time response improvement of angle and position control, respectively, between \(PI\)-\(D^{AE}\) and \(PID\). Figure 23: \(PID\) position control derivatives affected by equilibrium controllers. Figure 22: Derivative behaviors of \(PID\) and \(PI\)-\(D^{AE}\) equilibrium control. \[\dot{x_{2}}=\frac{(2.4+0.23)9.81 Sinx_{1}-0.23\cdot 0.36\ x_{2}{}^{2}(Sinx_{1} Cosx_{1})-uCosx_{1}}{2.4\cdot 0.36+0.23\cdot 0.36\ \left(Sinx_{1}\right)^{2}} \tag{18}\]0 \(PID\) position control affected by equilibrium controllers. \begin{table}
\begin{tabular}{c c c c} \hline  & PID & \(\text{PI-D}^{\text{E}}\) & **Improvement** \\ \hline \hline \(t_{d}\) (s) & & & \\ \hline \(t_{r}\) (s) & 1.375 & 24.730 & \(-1698.54\%\) \\ \hline \(M_{p}\) & \(4.926\times 10^{-3}\) & \(3.052\times 10^{-5}\) & 99.38\% \\ \hline \(t_{p}\) (s) & 0.606 & 1.901 & \(-213.69\%\) \\ \hline \(t_{s}\) 2\% (s) [0.002, \(-0.002\)] & 2.586 & & 100.00\% \\ \hline \(t_{s}\) 5\% (s) [0.005, \(-0.005\)] & & & \\ \hline \(IAE\) & 0.008693 & 0.00217 & 75.03\% \\ \hline \(ISE\) & \(2.392\times 10^{-5}\) & \(6.572\times 10^{-7}\) & 97.25\% \\ \hline \(ITAE\) & 0.01739 & 0.0568 & \(-226.62\%\) \\ \hline \(ITSE\) & \(2.874\times 10^{-5}\) & \(1.802\times 10^{-5}\) & 37.30\% \\ \hline \end{tabular}
\end{table} 
Table 7: Angle control comparison. \begin{table}
\begin{tabular}{c c c c} \hline  & PID & \(\text{PI-D}^{\text{E}}\) & **Improvement** \\ \hline \(t_{d}\) (s) & 1.938 & 18.807 & \(-870.43\%\) \\ \hline \(t_{r}\) (s) & 25.269 & 40.420 & \(-59.95\%\) \\ \hline \(M_{p}\) & & \(1.001\times 10^{-1}\) & \\ \hline \(t_{p}\) (s) & & 40.726 & \\ \hline \(t_{s}\) 2\% (s) & 10.168 & 31.699 & \(-211.75\%\) \\ \hline \(t_{s}\) 5\% (s) & 7.840 & 28.030 & \(-257.52\%\) \\ \hline \(IAE\) & 0.288 & 1.817 & \(-530.90\%\) \\ \hline \(ISE\) & 0.0169 & 0.142 & \(-740.23\%\) \\ \hline \(ITAE\) & 0.7144 & 19.08 & \(-2570.77\%\) \\ \hline \(ITSE\) & 0.02084 & 1.185 & \(-5586.18\%\) \\ \hline \end{tabular}
\end{table} 
Table 8: Position control comparison. Figure 25: System with disturbance under \(PID\) and \(PI\)-D\({}^{\text{E}}\) equilibrium control. Tables 9 and 10 present a comparison of the analysis of transient and steady-state responses, including an optimization percentage. Figure 28: System with disturbance and \(PID\) position control affected by equilibrium controllers. Figure 26: System with disturbance under \(PID\) and \(PI\)-\(D^{\text{AE}}\) equilibrium control of \(D\) and \(D^{\text{AE}}\) signals. Figure 27: System with disturbance under \(PID\) and \(PI\)-\(D^{\text{AE}}\) equilibrium control and derivative behaviors.
## 2 Simulation Model Development
### Fatigue Life Evaluation Method  
#### 2.5.1 Procedure  
Fatigue life testing is often performed under the relatively simple conditions of constant amplitude and frequency [28]. However, the load conditions of a tractor are not simple, as the stress level fluctuates according to various agricultural operations under irregular soil surface environments [3]. Therefore, the fatigue of a tractor can be defined as the sum of damages to its mechanical components due to repetitive loads in the field. Figure 4 shows the procedure for evaluating the fatigue life using torque data measured in the field. In general, agricultural operations of the tractor such as tillage are performed only during the forward movement, and since the reverse movement is only used for simple steering, not specific agriculture operation, almost no load is generated. Such low load is a level that can be neglected in terms of the overall fatigue life. Thus, in this study, only data during forward movement was considered. Fatigue life was evaluated using the torque data computed from data measured in the field using Equation (1). Torque data computed is converted into frequency using Rainflow cycle counting (RFC), which counts fatigue cycles throughout the transmission's history. The load spectrum (Number of cycles, Torque) was created using the data obtained through RFC, while the load spectrum (Number of cycles, Stress) was modified through torque-stress conversion. The damage of each load cycle was assessed using the S-N curve and the Miner cumulative damage rule, and the fatigue life was evaluated based on the service life.  
Figure 3: Simulation model of the tractor used in this study: (**a**) transmission and (**b**) spiral bevel gear.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 1 Introduction
Thus, the goals of this work were to assess the possibility of locating the epileptogenic focus in real-time, and study the simplification of the classification process to reduce its computational needs as much as possible while maintaining similar classification accuracy to previous works. We also studied the resource usage and performance of the real-time application on a recent Xilinx FPGA reconfigurable device. The main contributions of this work are:  
* The proposal of an automatic classification procedure optimized to classify in real-time the location of the epileptogenic focus from EEG inter-ictal signals. It is conceived to be used in a portable device as a decision-assisting tool by neurosurgeons during surgery. * The proposed feature set and the classifier model have been selected to minimize both the number of features and the computational cost, while preserving the classification accuracy at a level similar to that in previous works. * The classification procedure has been implemented using a reconfigurable logic FPGA device. This hardware implementation computes the whole procedure, accepts the EEG raw input signal and delivers the classification result. Two designs have been implemented, using single and double floating point precision following the IEEE 754 standard for floating-point arithmetic. * The analysis of the resource usage of this kind of implementation, its accuracy with respect a Matlab implementation and how fast the device can deliver results (maximum frequency of operation). The rest of the paper is organized as follows. Section 2 introduces the dataset, and the analyzed features and classifier models. The details of the hardware implementation and the proposed computational method are described in Section 3. Results of the analysis and discussion are presented in Sections 4 and 5, respectively. Finally, Section 6 concludes the paper.
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 3 Spool Oscillation Experiment  
The test bench of DPV with HSVB as the pilot section was built as shown in Figure 3. Figure 3 is the schematic diagram, and Figure 3 is the physical diagram of the experimental system, which includes a measurement and control system, hydraulic pump station,  
Figure 3: Test bench of DPV: (**a**) Schematic diagram; (**b**) Physical diagram of the experimental system.  
hydraulic cylinder, and DPV under test. The measurement and control system includes a host computer, NI CompactRIO embedded controller, sensors, NI 9203 AI card, and NI 9401 DIO data acquisition card. These equipment were purchased in Lanzhou, China. The displacement sensor was used to acquire the displacement of the main spool, the pressure sensor was used to obtain the pressure of the control chamber on the right side of the main spool, and the temperature sensor was used to obtain the temperature of the control chamber. The temperature in the control chamber was 3 degrees Celsius. By adopting the existing position-based full bridge compensation control method, the experimental results of the displacement step response of the main valve spool, as shown in Figure 4 can be obtained.  
As can be observed from Figure 4, oscillations occur during the movement of the main spool, and the smaller the displacement is, the more prone it is to oscillate. In the existing control methods, when the opening of the main spool is small, the control input of the HSVB fluctuates significantly, which will cause the pressure to fluctuate in the two pilot control chambers. Consequently, the main spool will exhibit relatively large oscillations, thereby giving rise to rather prominent issues regarding response delay and stability. As depicted in Figure 4, the response delay time of the main spool under the existing control method is approximately 2.144 s. In view of this, this study proposes an oscillation suppression method based on fuzzy intelligent proportional-integral-derivative (PID) control.
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 5 Results
### Throughput  
Throughput is one of the major performance metrics in terms of routing protocols. Figure 5 shows that an average number of successful packets reached the sink. The optimized use of energy keeps the node alive for a longer period resulting in better throughput with respect to SIMPLE and ATTEMPT. Initially, until 6000 rounds, TAEO is not performing optimally in terms of throughput, as the proposed protocol tries to avoid heat generation due to rapid transmissions of nodes, however, after 6000 rounds, it performs better, as it was efficient in residual energy prolonging its network lifetime along with maximizing the number of packets reached at sink node, as can be seen in Figure 5.  
Figure 3: Evaluation of Network Lifetime & Stability Period.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 1 Introduction
### Motivation and Objectives  
The major aim of this research is to perform energy efficient health monitoring with low delay and high security. In WBAN, a patient's remote healthcare monitoring system design invoked various challenges [27]. Current WBAN approaches do not ensure end-to-end security, low energy consumption and can cause delays because it is achieved either with intra-WBAN or beyond WBAN. When WBANs communicate with the same channel simultaneously, then they interfere with each other. For critical data transmission, an idle channel is required and periodical data requires the best channel to transmit it without any interference. Due to single-hop communication for the critical data, high energy consumption and end-to-end delay occur. At the single hop, the contention window size (_CWS_) must be adjusted to transmit packets according to _QoS_ constraints. None of them considered environment-related data on a patient to monitor their health. We are motivated by the following problems that occur during health monitoring in which the major issues are listed as follows,  
* Higher energy consumption: Frequent sensor replacement is necessary when sensors drain their energy.  
* Security and privacy: Due to wireless channels and limited energy resources of sensors, data confidentiality and privacy are a failure.
* Mobility prediction: Communications must be energy-efficient and secure in patient dynamic movements. This research objectives designed in this paper are as follows,
* To propose an energy-efficient, secure and delay-aware health monitoring system that allows patients to sense and transmit data in an energy-efficient manner.
* To ensure \(QoS\) constraints while collecting and transmitting the data from all layers (intra-WBAN, inter-WBAN and beyond WBAN).
* To safely store the sensed data in a storage server with satisfying the security requirements (data confidentiality, and integrity).  
In this paper, we proposed a blockchain-assisted delay and energy-aware healthcare monitoring system for the early transmission of emergency packets to the respective authority to achieve these objectives.
## 5 Experimental Results and Analysis
### Effect of Deployment Deviation Ratio \(\lambda\) in a Grid Topology
With the increasing ratio of anchor nodes, there are more anchor nodes in the deployment area; accordingly the accuracy of the average distance per hop of anchor nodes is higher. DV-Hop is a calculation method based on the estimated distance, and the localization error decreases proportionally with the decrease of the estimated distance error. The increasing value of anchor nodes provides more favorable conditions for meta-heuristic localization algorithms and support to find the optimal solution. As a result, the average ALE is reduced because the location of nodes is accurately estimated.