esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Works  
In this paper, _Tarantula's_ design, modeling, and its kinematic parameters identification are presented. The robot was designed for the specific geometry of the drains. The designed manipulator is modular and can adjust its height as per the environment. The simultaneous abduction or adduction of the two legs in a frontal plane was provided using the chain sprocket mechanisms connected using bevel gears arrangement with a single motor. A specially designed bi-directional suspension mechanism was fixed inside the trunk as a shock absorber. The limitation of the chain sprocket is that it imparts the rotational play because of slacking.  
To the best of the author's knowledge, this is the first attempt to estimate the kinematic parameters of the assembled robot with four leg using monocular camera mounted on the assembled _Tarantula_. This method also identified the effective dimension of the trunk where the proximal revolute joints were connected. The limitations of the monocular vision and fiducial markers to obtain the pose is that the camera must view the markers in the generated map. We also performed the experiments to evaluate the trajectory tracked by _Tarantula_ using a similar set-up. Overall, the contributions of this paper are listed below:  
* Design of the modular robot _Tarantula_ with the ability to reconfigure its height, mainly for inspecting the drains,
* A mechanism designed for the simultaneous abduction/adduction of legs,
* A methodology to identify the kinematic parameters using ArUco markers and monocular vision for the assembled mobile robot with legs. First, using the calibrated camera, poses of ArUco markers are reconstructed in 3D space. Second, by moving each joint and capturing images, a set of the tracked pose is determined. Then, the DH parameters were evaluated.  
The domains set for future research work will mainly focus on: design optimization of the legs to reduce the total weight. Since the design is modular, the newly designed legs can be easily attached to the existing trunk. The second focus will be to mount the Light Detection and Ranging (LiDAR) sensor near the trunk opening of _Tarantula_ to map the drains.  
[PERSON] and [PERSON] conceived and designed the experiments;the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON] and [PERSON], wrote the paper.  
This work is financially supported by the National Robotics R&D Program Office, Singapore, under the Grant No. RGAST1702 to Singapore University of Technology and Design (SUTD) which are greatly acknowledged to conduct this research.  
The authors declare no conflict of interest.
# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
###### Abstract  
The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers' internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers' fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.  
correlation coefficient; DBSCAN; fault diagnosis; transformer +
Footnote †: journal: Applied Sciences
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 3 Methodology
### Preprocessing and Visual-Inertial Alignment-Based IMU Calibration
* Visual-Inertial Alignment The process of visual-inertial alignment is shown in Figure 2. The basic idea is to match the visual SFM with the IMU preintegration values. For two consecutive frames \(b_{k}\) and \(b_{k+1}\), the system obtains rotation \(\mathbf{q}_{\mathcal{C}_{k}}^{c_{0}}\) and \(\mathbf{q}_{\mathcal{C}_{k+1}}^{c_{0}}\) from the visual SFM, as well as relative rotation \(\gamma_{b_{k+1}}^{b_{k}}\) from IMU preintegration. The system linearizes the IMU preintegration terms relative to the gyroscope bias and minimizes the following cost function: \[ \begin{split}&\min_{\delta\mathbf{b}_{\omega}}\sum_{k\in\mathcal{ B}}\left\|\mathbf{q}_{\mathcal{B}_{k+1}}^{c_{0}}-^{1}\otimes\mathbf{q}_{\mathcal{B}_{ k}}^{c_{0}}\otimes\gamma_{b_{k+1}}^{b_{k}}\right\|^{2},\\ &\gamma_{b_{k+1}}^{b_{k}}\approx\widehat{\gamma}_{b_{k+1}}^{b_{k} }\otimes\left[\begin{array}{c}1\\ \frac{1}{2}\mathbf{y}_{\mathcal{B}_{\omega}}^{\gamma}\delta\mathbf{b}_{\omega }\end{array}\right],\end{split} \] (4) where \(\mathcal{B}\) encompasses all the image frames in the sliding window, and the derivative of the preintegration terms \(\mathbf{J}_{\mathbf{b}_{\omega}}^{\gamma}\) with respect to the bias \(\mathbf{b}_{\omega}\) is used for the first-order approximation of the relative rotation \(\gamma\). In this way, the gyroscope bias can be estimated to correct the IMU measurements.
## 3 Evaluation and Results
### Evaluation Metrics
#### 3.1.2 Step 2: RF Power Penalty  
In the second step, we evaluate the performance of the RoF link with the inherent power fading mitigated by means of a single integrated add/drop MRR while considering the above dither. In particular, we will measure the PP function for several misalignments \(\Delta\lambda\) within the range governed by the dither \(\varepsilon_{\lambda}\). Finally, we will present the deterioration of the PP at a specific frequency \(f_{0}\) as a function of misalignment \(\Delta\lambda\):  
\[\Delta PP(f_{0},\Delta\lambda)[\text{dB}]=PP(f_{0},0)[\text{dB}]-PP(f_{0}, \Delta\lambda)[\text{dB}]\]  
The frequency \(f_{0}\) corresponds to the first extreme value (dip) in the PP function for the RoF link under consideration when uncompensated. This depends on the fiber-dispersion properties, the fiber length, and the carrier wavelength. Concrete values will be presented further on. The corresponding experimental setup variant is referred to as Variant 2.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Studies Related to Measuring Total Influence  
Pattern classification refers to the problem of partitioning a pattern space into classes and assigning a pattern to one of them [15]. As mentioned above, to improve the classification performance of the TRSC, this study addresses direct relationships measured in the grey TRSC [19]. The main issue addressed is that as a pattern is likely to influence another directly and/or indirectly, indirect relationships among patterns should be studied as well. To develop novel similarity measures for the TRS by means of relationships, this study focuses on ways of leveraging direct relationships among patterns measured by GRA to further generate the total influence, consisting of indirect and direct relationships, for a TRSC. It should be noted that many studies (e.g., [19; 27; 28; 29]) have shown the effectiveness of GRA in measuring relationships among attributes and patterns.  
The use of grey theory to measure direct influences among patterns for the DEMATEL has been addressed in recent studies, such as [30; 31; 32; 33; 34; 35; 36]. These studies derived the total influence matrix by representing a direct influence as a grey number [27]. As the proposed method derives the total influence matrix by using GRA to automatically generate direct influences from crisp-valued data, the focus is completely different from that of the aforementioned grey DEMATEL methods.
## 2 Related Work  
In ref. [37], evolutionary algorithms were reviewed in detail, including the neural network, fuzzy logic, natural inspired algorithms, and hybrid algorithms. The Q-learning method was improved to optimize three objectives in the path planning problem (traversal time, traversal state number, and 90-degree corner) in ref. [38]. Multi-objective memetic algorithms were presented to solve the path planning problem in ref. [39]. Multiple objectives were optimized simultaneously, such as the path length, smoothness, and safety. The multi-objective intelligent water drops (IWD) algorithm was designed for the path planning problem in ref. [40]. The path length and safety were taken into account. In ref. [41], [PERSON] adopted a step-by-step strategy to get the best path. First, the genetic algorithm was used to generate a collision-free path from the source to the target. The piecewise cubic Hermite interpolation polynomial was then used to smooth the generated path. [PERSON] used a similar strategy in ref. [31]. Initially, the surrounding point set algorithm was proposed to find the collision-free path, and then the particle swarm algorithm was used to improve the path. The results show that the generated path produced a great improvement in both length and smoothness indicators.  
[PERSON] optimized the path length and safety with multi-objective particle swarm optimization in ref. [29]. [PERSON] used multi-objective particle swarm optimization to solve path planning in an uncertain environment considering the path length and safety [30]. [PERSON] considered the path length and smoothness and adopted multi-objective particle swarm optimization [32]. [PERSON] employed NSGA-II to solve multi-objective path planning in a discrete space [33]. Moreover, the path length and safety were optimized at the same time. [PERSON] optimized three objectives--path length, safety and smoothness--but only uses traditional evolutionary operators. So, for complex environments, it requires more individuals and iterations to find optimal paths [34].  
We concentrated on planning collision-free paths for robots in the presence of obstacles. At the same time, we considered multiple objectives. The path length, smoothness, and safety were taken into account. In general, these objectives are contradictory, so it was necessary to propose a new multi-objective approach to solve this problem.
# Opportunistic Weather Sensing by Smart City Wireless Communication Networks
## 1 Introduction--Opportunistic Sensing of Weather
It states that sensing capabilities (such as dedicated sensors) can be implemented as part of wireless communication nodes, and thus, combined equipment capable of both communication and sensing can be developed and deployed (e.g., wireless communication terminals with on-board temperature sensors) [17; 18]. OISAC, on the other hand, is based on existing technologies, where the sensing function is added to communication (or vice versa). In our case, we utilize the wireless communication channel itself to sense the environment--especially the weather--opportunistically. As stated, tens of research groups around the world have reported that CMLs as well as satellite microwave links (SMLs) are capable of accurately monitoring rain and other weather phenomena such as fog, humidity, and dew in many parts of the world (see [7; 8; 9; 10; 12; 15; 19; 20; 21; 22; 23] among many others), all falling into the definition of OISAC since it uses existing communication for sensing. In the following sections, we begin by reviewing the current state of OISAC for weather, especially detailing the use of CMLs, and the CMLs deployed in a dedicated installment for smart cities' wireless networks (SCWNs). We then discuss and demonstrate the feasibility of using actual measurements for the potential of OISAC in smart city environments where SCWNs are integrated parts of every modern smart-city infrastructure. Lastly, we conclude this paper and discuss future directions and possibilities.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 2 Related Work
In the field of smart digital cities, academician [PERSON] proposed that digital twin cities are the construction goals of the future, conducted an in-depth analysis of the characteristics and advantages of digital twin cities, and provided construction directions and application guidelines for digital twin smart cities. In the satellite field, [PERSON] et al. conducted research on digital twin satellites based on satellite internet construction, focusing on the composition of satellites and the digital twinning of core functions. [PERSON] et al. proposed a scheme for the building of a satellite digital twin on the ground, using the constructed model to reflect and evaluate the real operational status of satellites and predict their service life. In both production manufacturing and aerospace, significant advances have been made in the development and application of digital twin technology. In the field of production manufacturing, Siemens has built a full-process digital twin production system model. The digital enterprise virtual model can cover the entire value chain and has been applied and verified in the production processes of Siemens industrial equipment, such as the Nanobox PC. In aerospace, digital twin technology generates extensive simulated data by replicating the continuous time history of a flight through simulation. These data serve as the foundation for the identification of various conditions encountered by an aircraft. Additionally, when coupled with diverse simulation techniques and computational fluid dynamics, digital twin technology can predict future maintenance requirements and potential flight safety challenges in the aerospace sector [12]. Currently, in the satellite field, digital twins [13] are primarily used for platform twinning. Based on rich sensing information [14], they monitor, reproduce, schedule, and optimize the status of satellite platforms. However, concerning network protocol twins, particularly for low-orbit constellations, the application of digital twin technology remains relatively unexplored. This study focuses on the popular low Earth orbit satellite constellation formation protocols and conducts key technological research on digital twin technology in this field. The difficulty lies in the fact that, since all of the above areas are in the unexplored stage, the existing digital twin technologies only draw on external concepts in this task, and there are no concrete and well-established approaches that we can refer to.
# The Prediction of the Performance of a Twisted Rudder
## 3 Numerical Approaches
### Cavitation Model  
A mixture approach was used to solve the cavitating flows around a hydrofoil, which assumes that the flow is a homogeneous vapor-liquid mixture, and the vapor is homogeneously distributed in a finite volume of liquid. Based on this assumption the mixture fluid can be treated as a single pseudo-fluid with variable fluid properties corresponding to the composition of two fluids. The density and viscosity of mixture fluid are averaged on a volume fraction basis as follows:  
\[\rho=\alpha_{v}\rho_{v}+(1-\alpha_{v})\rho_{l},\ \mu=\alpha_{v}\mu_{v}+(1- \alpha_{v})\mu_{l} \tag{10}\]  
where, \(\alpha\) is the fluid volume fraction and the subscripts \(v\) and \(l\) indicate the vapor and liquid phase, respectively. The continuity equation for the vapor volume fraction with the mass transfer rate \(\dot{m}\) from the vapor to the liquid can be written as,  
\[\frac{\partial}{\partial t}(\alpha_{v}\rho_{v})+\frac{\partial}{\partial x_{ j}}\big{(}\alpha_{v}\rho_{v}u_{j}\big{)}=-\dot{m} \tag{11}\]  
The modeling of a suitable mass transfer rate is the key for a cavitation model. The model proposed by [PERSON] and [PERSON] [41] which had been adopted in Star-CCM+ was used in the simulations of cavitating flows, which can be expressed as  
\[\dot{m}=3\frac{\rho_{v}\rho_{l}}{\rho_{m}}\frac{\alpha(1-\alpha)}{R}\sqrt{\frac {2}{3}\frac{|p-p_{v}|}{\rho_{l}}}sgn(p_{v}-p) \tag{12}\]  
In this model, the vapor fraction is described by \(N\) spherical bubbles of radius \(R\) and the nuclei concentration per unit volume of liquid, \(n_{o}\), as follows:  
\[\alpha=\frac{n_{0}(4/3)\pi R^{3}}{1+n_{0}(4/3)\pi R^{3}} \tag{13}\]  
The Schnerr-Sauer cavitation model uses a reduced Rayleigh-Plesset equation, which neglects the influence of surface tension, bubble growth acceleration, viscous effects, and the bubble-bubble interaction. Otherwise, this model includes scaling of the bubble growth and collapse rates for both single-component and multi-component materials. The cavitation bubble growth rate can be calculated through the simplified Rayleigh relation as follows:  
\[\frac{dR}{dt}=\sqrt{\frac{2}{3}\frac{|p-p_{v}|}{\rho_{l}}}sgn(p_{v}-p) \tag{14}\]
## 3 Design of The Experiment
### Accuracy Indicators  
Bias and the Root Mean Square Error (RMSE) are widely recognized as crucial metrics for assessing accuracy in the domains of positioning and navigation, including UWB [29; 30; 31]. Bias is a measure of the average error across all measurements, reflecting the systematic deviation from the true value. It reveals any consistent overestimation or underestimation by the system. On the other hand, the RMSE quantifies the average squared difference between the model's predictions and the true values. As a quadratic scoring rule, the RMSE offers an insightful measure of accuracy and precision. It accounts for the magnitude and direction of the errors, providing a robust assessment of the system's performance. Bias and the RMSE offer a holistic evaluation of a model's efficacy when used in tandem. They complement each other by highlighting systematic errors (bias) and the overall dispersion of errors (RMSE), thus giving a more complete picture of the model's performance and reliability. This dual approach enables researchers to refine their models and improve overall accuracy.  
We calculated the bias and the Root Mean Square Error (RMSE) of the UWB positioning error according to the following formulas:  
\[\left\{\begin{aligned} \Delta_{i}=\text{L}_{i}(UWB)-\text{L}_{i}( RTS)\\ \text{Bias}=\sum\limits_{i=1}^{n}\Delta_{i}/n\\ \text{RMSE}=\sqrt{\sum\limits_{i=1}^{n}\Delta_{i}^{2}/n}\end{aligned}\right. \tag{7}\]  
where \(\Delta\) is the positioning error, L represents the coordinate observation in the \(x\), \(y\), or \(z\) component, and n denotes the number of matched observations.  
Two kinds of accuracy metrics are computed, including internal and external coincidence accuracy, which refers to the result from the training and verification sets, respectively. The internal coincidence accuracy mainly reflects the fitting effect, while the external coincidence accuracy can better measure the generalization ability of the neural network.
# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 4 Experimental Results  
Various experiments are performed to confirm the robustness and versatility of the proposed network. The first dataset is created using UCID [38], LIRMM [39], and Never-compressed (NC) [40] image databases, where UCID, LIRMM and NC contain 1338, 10,000 and 5150 uncompressed color images, respectively. The center block with 256 \(\times\) 256 of each image is taken from databases. Further, 16 non-overlapping blocks of size 64 \(\times\) 64 pixels are created. In the final, 263,808 image patches of size 64 \(\times\) 64 pixels are generated and 30,000 patches of size 64 \(\times\) 64 pixels are selected in each operator. Five operators--Gaussian blurring (GAU_P), median filtering of filter size 3 \(\times\) 3 and 5 \(\times\) 5 (MF3, MF5), un-sharp masking sharpening (SH_P), and up-sampling (UP_P) with different parameters _P_--are considered in experimental analysis. Symmetric padding is considered while applying the operator to the image. It is important to observe that 30,000 patches are selected randomly to get unbiased results for each operator. In the first dataset, 24,000 images are used for training and 6000 images for validation for each class. BOSbase [30] dataset is considered for testing as a cross-database. It contains 10,000 uncompressed images of size 512 \(\times\) 512. A similar approach is applied to create the image patches as in the first dataset and 160,000 image patches with 64 \(\times\) 64 size are created. For each operator, 15,000 patches are used for testing. Experiments are performed using NVIDIA GTX1070 GPU with 24 GB RAM.  
In the next part, the results of two operators with compressed and non-compressed images are discussed. Next, the results are shown for dissimilar parameters like compression, and the results are compared with some state-of-the-art methods.
## 3 Results and Discussions
### Modelling of Tensile Strength by ANN

Table 9: Tested feedforward ANN configurations. Figure 10: MAE train results of nine different ANN topologies. Figure 9: MAE test results of nine different ANN topologies. Figure 13 shows the comparison between the experimental and predicted values of tensile strength for all 25 data points. The predicted values were obtained with the ANN 6 configuration (trained for 400 epochs) and the mathematical model (generated according to the reduced cubic model) as described in the paragraph above. The average percent error (APE) between the predicted tensile strength values and the experimental values was 0.0012. Figure 11: MSE test results of nine different ANN topologies. Figure 12: MSE train results of nine different ANN topologies. calculated, where APE was 1.3016% for ANN 6 and 2.1948% for RSM predicted values. The RPD of the ANN6 configuration was 3.1985 for the MAE test and 11.9024 for the MSE test. A preliminary investigation was performed based on the results of ANOVA and RSM. Feature selection was adopted, wherein the B and D parameters were excluded. According to the eight tested configurations (similar to Table 9) of ANN 6, the best test MAE and MSE results were also achieved with the \"trainbr\" training algorithm and \"logsig\" transfer function of 0.2557 and 0.1039, respectively.