esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
## 4 Discussion
However, even with five iterations, the similarity does not fall below 0.5. Knowledge of the noise texture becomes important in determining the most suitable strategy in the denoising procedure because each filter has different characteristics and must be adapted to the type of noise and the clinical purpose [41]. In the case of the bilateral filter, the configuration is more efficient if it is set according to the noise level. Small values of sigma intensity (\(\sigma_{r}\)) will make the edges of an object look sharper, while a higher value will make it behave more like a Gaussian filter which reduces more noise but blurs the edges [32]. A study by [PERSON] (2010) [21] recommended \(\sigma_{r}=2.5\ \sigma\) for the best results on artificial images with Gaussian noise based on the peak signal-to-ratio (PSNR) value. In the case of medical images, further investigations need to be carried out to determine the optimal parameters for diagnostic purposes. This study observes the iterative effect of bilateral filters on homogeneous and anthropomorphic phantom images and was not implemented on patient images. This study is the first step in investigating the effect of the bilateral filter on noise texture. Our findings are relevant only within the scope of the QA procedure. Investigations of patient images will be performed in future studies by an expert radiologist. The paper only investigates the iterative bilateral filter in improving CT images; however, this approach can be implemented on other images such as images from mammography, magnetic resonance imaging (MRI), ultrasound (US), and other sources. In addition to bilateral filters, state-of-the-art filters such as the non-local mean filter (NLM) [42] are also interesting to explore in terms of changing the image structure with increasing iterations. Since iterative reconstruction (IR) [43] and deep learning reconstruction (DLR) [44] produce non-linear properties in terms of noise and spatial resolution, it would be interesting to examine the impact of the NLM filter on IR and DLR images and to compare it with the bilateral filter.
## 3 Results
### Influence of Temperature and ER on the Production of CO, H\({}_{2}\), and CH\({}_{4}\)  
Table 3 reports the CO, H\({}_{2}\), and CH\({}_{4}\) production evaluated in g of CO, H\({}_{2}\), or CH\({}_{4}\) per kg of olive cake. For the four selected temperatures, the production of CO, H\({}_{2}\), and CH\({}_{4}\) was estimated to be higher for tests conducted with an ER of 0.3, with averages of 511.2 g of CO per kg of olive cake, 11.9 g of H\({}_{2}\) per kg, and 68.7 g of CH\({}_{4}\) per kg. Furthermore, the results confirm that the production values of CO, H\({}_{2}\), and CH\({}_{4}\) obtained are like those of other researchers using comparable operating conditions [35; 36]. Thus, for example, those studies found that the production of CO and H\({}_{2}\) decreased significantly as the ER increased. Furthermore, the production of CH\({}_{4}\) varied considerably, from 50 to 80 g/kg of biomass in the range of the operating conditions analyzed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Temperature, \({}^{\circ}\)C**} & **ER** & \(\mathrm{LHV_{gas}}\)**, \(\mathrm{MJ/Nm^{3}}\) & \multicolumn{2}{c}{**Gas Flow**} & \multicolumn{2}{c}{**Gas Flow**} \\  & & **(0 \({}^{\circ}\)C, 1 atm), L/min** & **(0 \({}^{\circ}\)C, 1 atm), L/kg Feed** & **CGE, \%** \\ \hline \multirow{3}{*}{750} & 0.1 & 4.09 & 3.95 & 948.0 & 22.67 \\ \cline{2-6}  & 0.3 & 4.08 & 9.88 & 2371.2 & 56.59 \\ \cline{2-6}  & 0.5 & 2.21 & 14.89 & 3573.6 & 46.16 \\ \hline \multirow{3}{*}{800} & 0.1 & 5.11 & 3.44 & 825.6 & 24.70 \\ \cline{2-6}  & 0.3 & 4.24 & 9.72 & 2332.8 & 57.83 \\ \cline{2-6}  & 0.5 & 2.50 & 13.89 & 3333.6 & 48.73 \\ \hline \multirow{3}{*}{850} & 0.1 & 4.77 & 4.01 & 962.4 & 26.90 \\ \cline{2-6}  & 0.3 & 4.43 & 9.84 & 2361.6 & 61.19 \\ \cline{2-6}  & 0.5 & 2.79 & 13.39 & 3213.6 & 52.39 \\ \hline \multirow{3}{*}{900} & 0.1 & 5.01 & 3.88 & 931.2 & 27.29 \\ \cline{2-6}  & 0.3 & 4.28 & 9.92 & 2380.8 & 59.55 \\ \cline{1-1} \cline{2-6}  & 0.5 & 2.89 & 13.15 & 3156.0 & 53.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of temperature on \(\mathrm{LHV_{gas}}\) and gas flow for the three ER levels studied.
# Effect of Temperature, pH, and Reaction Duration on Microbially Induced Calcite Precipitation
###### Abstract
Although an optimal pH has been reported for various microbial species, the initial pH of the medium increased during precipitation, thus changing the environment for optimum precipitation. The increase in pH was due to the hydroxide ions (OH\({}^{-}\)) generated during ureolysis, which changed the pH of the soil to alkaline [36; 37]. When a source of ammonia was added, a temporal increase in pH was found in slightly acidic soil, but the difference was insignificant in slightly alkaline soil [38]. In this study, _Staphylococcus saprophyticus_, the microbe isolated from calcareous beach sand, and _S. pasteurii_, (ATCC 11859), the most commonly used microbe in MICP, were used to determine the optimal conditions for calcite precipitation. Each microbe was injected into the urea-CaCl\({}_{2}\) medium of different pH and cultured at various temperatures. The effects of the pH of the urea-CaCl\({}_{2}\) medium, the temperature during precipitation, and the reaction duration on the amount of calcite precipitated were investigated.
## 21 Practical Realization of Traveling Wave (TW) Based Commercial Relays
### Future Trends  
With the increased developments in multi-terminal VSC-HVdc systems, estimation of fault location is becoming more and more complex. It is a need to explore precise fault location algorithms so that performance in the complex dynamic environment could not be compromised. Therefore, with an ongoing, never ceasing motivation, research and development especially in the area of protection of dc grids, spread of dc network is inevitable.  
With this intention, intelligent estimation of fault location with the availability or unavailability of supervision is gaining attraction. This step opens the door of applications of machine learning and deep learning in the area of dc grid protection. These learning techniques have the ability to acquire information without being explicitly programmed. Machine learning can have direct impact on improvements of relaying strategies aiding HVdc circuit breakers. Convergence issues and correct estimation of fault location in a multi-terminal environment should be addressed because of extracting features in a multi-dimensional environment.  
Support vector machine is a supervised machine learning approach. It is found useful because of reduction in time of computations. Labeled training data of fault location is less required. Further, classification of fault with respect to location and type can also be made possible effectively with this approach.  
Neural networks can be considered as an alternate approach for estimation of fault location. In this technique, intelligent way of fault location estimation and classification is performed. Artificial neural networks (ANN) can effectively address the nonlinear nature of dc fault current. The learned features associated with the connections among hidden layers an input and output layers can depict the information about the location of fault in MT-HVdc systems.  
Radial basis function networks (RBFN) can build strong fault location classifiers by the activation of binomial regression and soft-max regression functions. These fault location classifiers can then be employed in Pearson Correlation for finding the similarity between these classifiers and untagged data.
# Wheeler Method for Evaluation of Antennas Submerged in Lossy Media
## 3 Analysis of the Wheeler Cap Method of Antenna Surrounded by the Lossy Media
### Simulation
#### 3.1.2 Radiation Efficiency in Media with Dielectric Constant  
The radiation efficiency is analyzed for a medium with a high dielectric constant in a lossless state. In this scenario, only the dielectric constant of the material is controlled. The results are sampled at a single frequency at which a resonance occurs at each medium. The cavity length \(r_{w}\) is expressed as a ratio of length-to-wavelength.  
The efficiency \(\eta_{WR}\) calculated according to the metallic cavity radius \(r_{w}\) is compared with the efficiency \(\eta_{MR}\) calculated based on the field computation (Figure 2). The radiation resistance can be extracted using a radiation shield with a radius equal to the radian length. The comparison shows that the same radiation efficiency can be obtained when the cavity radius \(r_{w}\) is equal to the radian length \(r_{l}\) for each medium (Figure 5). This ascertains that the Wheeler method is applicable even for media featuring a dielectric constant other than 1. The radiation efficiency is maintained over the radian length range.  
Figure 4: Geometry of simulation setup: (**a**) cross-sectional view of NMHA and (**b**) with metallic cavity, (**c**) perspective view. (\(d_{a}=20\), \(p_{a}=3\), \(h_{a}=6.4\), \(t_{a}=0.8\), \(r_{i}=15\), \(h_{i}=40\), \(t_{i}=1\), \(r_{g}=500\), \(t_{g}=1\), \(t_{w}=5\). Unit: mm.
# A Cell Counting Framework Based on Random Forest and Density Map
## 3 Method
### Detection Framework
#### 3.2.2 Detection Map Generation  
Many works try to detect cells by finding the local maxima. However, we found it is not robust enough sometimes. Figure 7a shows the ground truth of a probability map we defined. In practice, the predicted density map could not recover the distribution perfectly. There may be some noisy points that are the local maxima but not the true centroids, which results in a false detection. To detect cells more robustly, we presented a curvature-based detection algorithm. Supporting by the ground truth, the centroids of cells are at peaks and values decrease along the radial, which can be well-captured by the eigenvalues of Hessian matrix. This is the basis we detect cells. For a two-dimensional image \(I\), the Hessian matrix is given as:  
\[H=\left[\begin{array}{cc}I_{xx}&I_{xy}\\ I_{yx}&I_{yy}\end{array}\right] \tag{2}\]  
where \(I_{xx}\), \(I_{xy}\), \(I_{yx}\), \(I_{yy}\) are the second partial derivatives of an image \(I\). The eigenvalues of Hessian matrix indicate the curvature and they are defined as  
\[\lambda_{1}=\frac{1}{2}\Bigg{(}I_{xx}+I_{yy}+\sqrt{\left(I_{xx}-I_{yy}\right)^ {2}+4I_{xy}^{2}}\Bigg{)} \tag{3}\]  
\[\lambda_{2}=\frac{1}{2}\Bigg{(}I_{xx}+I_{yy}-\sqrt{\left(I_{xx}-I_{yy}\right)^ {2}+4I_{xy}^{2}}\Bigg{)} \tag{4}\]  
From Equations (3) and (4), it can be inferred that the \(\lambda_{1}\) and \(\lambda_{2}\) reflect the changes of gradient. We use the signs of eigenvalues to ensure the cell regions and the values of eigenvalues to detect the centroids of cells. At different points of the image \(I\), the differences between \(\lambda_{1}\) and \(\lambda_{2}\) are shown in Figure 7b,c.  
Figure 6: The detection framework.  
Figure 8a explains the process of cell detection. First, we needed to set a threshold to segment the cell region proposals from the probability map. Next, we would detect the centroids of cells in the cell region proposals by curvature. The eigenvalues of the Hessian matrix reflect the curvature. Figure 7b,c are the two eigenvalues \(\lambda_{1}\) and \(\lambda_{2}\) of Figure 7a. It can be observed that both \(\lambda_{1}\), and \(\lambda_{2}\) of A are negative and \(\lambda_{1}\) and \(\lambda_{2}\) of B are opposite in sign which means it is not the centroid of cells. Hence we use \(\lambda_{1}\) to further select the cell region proposals where \(\lambda_{1}\) should be negative, and the saddle points like B are abandoned. Finally, the centroids of cells can be detected by local minima of \(\lambda_{1}\). The two cell region proposals in Figure 8a indicates that \(\lambda_{1}<0\) can help to narrow the cell detection region further, which makes the detection results more robust.
## 4 GDOP and WGDOP Calculation with Matrix Multiplication
### Proposed WGDOP Calculation with Matrix Multiplication  
In practice, the GDOP minimum criteria is not suitable to represent positioning accuracy when the measurements contain different error variances The WGDOP criterion can select more appropriate measurement units by combining GDOP with the weighted scheme. WGDOP has also been proven to have better performance in positioning schemes. The matrix inversion computation is a severe task for solving WGDOP. The training phase has to spend lots of costs, time and hardware and software resources to collect the training data. In some cases, the system is unable to collect sufficient historical information for training. The lack of sufficient training data could often affect the accuracy of prediction. It is very important that how to achieve higher accuracy when the training data is not enough. Therefore, faster convergence and fewer number of training are very important in the neural network field.  
Here, we propose an efficient solution of WGDOP calculation with less computation when more than four measurements are available for locating purposes. In order to obtain WGDOP efficiently, the proposed WGDOP scheme with matrix multiplication does not need a training phase. We apply [PERSON]'s identities to reduce the calculation complexity and obtain WGDOP value directly. The proposed novel computation architecture for WGDOP is beneficial for hardware design in more than four measurements. In the viewpoint of hardware, many computational rules would be a heavy burden to hardware equipment. Here, we derived the WGDOP value only from matrix multiplication. The proposed WGDOP calculation with matrix multiplication can provide very precise solution of WGDOP approximation and does not incur any approximation errors.  
By combing GDOP with weighted scheme, WGDOP can be expressed as Equation (3). In order to guarantee the matrix \(H^{T}WH\) is a symmetric one in later operation, we refreshed the formulation above:  
\[WGDOP=\sqrt{tr(H^{T}WH)^{-1}}=\sqrt{tr(H^{T}W^{\frac{1}{2}}W^{\frac{1}{2}}H) ^{-1}}=\sqrt{tr(W^{\frac{1}{2}}HH^{T}W^{\frac{1}{2}})^{-1}} \tag{4}\]
# Topology Optimisation under Uncertainties with Neural Networks
## 2 Topology Optimisation under Uncertainties
### Deterministic Model Formulation
#### 2.1.1 Linear Elasticity Model  
The state equation corresponding to the above problem is described by the standard linear elasticity model [17]. To define the material tensor, we first define the _strain displacement_ (or strain tensor) by  
which specifies the displacement of the medium in the vicinity of position. Moreover, a so-called blend function is given by  
ensuring a smooth transition between the phases. According to [PERSON]'s law and by using the _Lame coefficients_ and, the _isotropic material tensor_ for the solid phase is given by  
This material tensor describes the acting forces between adjacent positions in the connected material, where and are two material parameters characterising the strain-stress relationship. For the void phase, to ensure solvability of the state equation in entire domain, we define the tensor as a fraction of the material phases. More precisely, we set with some small. Hence, the _material tensor_ (or stress tensor) is given by  
Using the material tensor, a force with load (a pressure field) and the phase field, the displacement vector field is described by the state equation of the standard linear elasticity model given by \[-\mathrm{div}[\sigma(\varphi(x),u(x))] =0 \text{for all }x\in D,\] \[u(x) =0 \text{for all }x\in\Gamma_{D},\] \[\sigma(\varphi(x),u(x)) =g \text{for all }x\in\Gamma_{g}, \tag{1}\] \[u(x)\cdot n(x) =0 \text{for all }x\in\Gamma_{s},\] \[\sigma(\varphi(x),u(x))\cdot n(x) =0 \text{for all }x\in\Gamma_{0}=\partial D\setminus(\Gamma_{D}\cup \Gamma_{g}\cup\Gamma_{s}).\]  
This implies that on boundary subspace \(\Gamma_{D}\subset D\) the material is fixed while on \(\Gamma_{g}\subset D\) the force \(g\) acts on the material. On the boundary \(\Gamma_{s}\subset D\) the material is barred from movement in normal direction \(n\). In the following, equality is to be generally understood in a pointwise manner.
## 2 Problem Statement  
The system configuration of PMSG based WECS is shown in Figure 1. Typically, a full-scale back-to-back converter is designed as the interface between generator and the electrical grid. The field-oriented control (FOC) is employed to transfer maximum power generated by wind turbine while tracking the rotor speed reference that requires measured values of rotor position \(\theta\), rotor speed \(\omega_{r}\) and three-phase currents \(i_{a}\), \(i_{b}\), \(i_{c}\).  
Machine-side control is designed to implement an MPPT scheme for variable-speed WECSs. Each wind turbine operates in a certain wind speed region according to its ideal power curve. This leads a varying rotor speed or torque to feed the generator. As shown in Figure 2, generator stops in Regions I and IV while it continues to generate electrical power in Regions II and III.  
The mechanical signals and current measurements are crucial to ensure a stable and optimal operating condition of the WECSs. Any sensor malfunction will be fed back into the control system, which could cause performance reduction or even system downtime. Sensor faults are investigated in [11] and exhibited as: (1) sensor gain drop **Type a**; (2) bias in sensor measurement **Type b** and (3) complete sensor outage **Type c**. **Type b** and **Type a** faults can be modeled as an addictive fault in sensor measurements  
\[y_{m}\left(k\right)=y_{r}\left(k\right)+f\left(k\right) \tag{1}\]  
Figure 1: Control and fault diagnosis scheme for the PMSG system (reproduced from [2; 16]).  
Figure 2: Operation regions of a PMSG based wind turbine (reproduced from [17]).  
in which \(y_{m}\left(k\right)\), \(y_{r}\left(k\right)\) and \(f\left(k\right)\) denote the faulty measurements, nominal values and fault signals respectively. **Type a** fault is the sensor gain degradation and modeled as a multiplicative fault in [2]  
\[y_{m}\left(k\right)=\beta\left(k\right)y_{r}\left(k\right). \tag{2}\]  
By defining \(f\left(k\right)=\left(\beta\left(k\right)-1\right)y_{r}\left(k\right)\), **Type a** fault in Equation (2) is rewritten as an additive fault with Formula (1). These three types of faults are uniformly modeled as additive faults.  
**Remark 1**.: _In (1), \(f\left(k\right)\) is unknown when \(kT_{s}>t_{fault}\) ( \(\forall\)\(kT_{s}<t_{fault}\), \(f\left(k\right)=0\) ). A practical assumption of the sensor faults \(f\left(k\right)\) is introduced in this paper: \(f\left(k\right)\) is \(\mathcal{L}_{2}-\) bounded \(\left\|f\left(k\right)\right\|\leq\left\|\alpha\left(k\right)\right\|\) and \(\alpha\left(k\right)\) is a known function. The upper bound of fault is essential for fault estimator design in Theorem 2._
# Orifice versus Converging-Nozzle Grid Turbulence: A Wavelet Perspective
###### Abstract  
Grids such as perforated plates are of fundamental importance in flow turbulence study and are commonly utilised to promote mixing. An orificed perforated plate (OPP) and its reversed counterpart, the converging-nozzle perforated plate (CNPP), were applied to produce quasi-isotropic turbulence inside a wind tunnel. The three orthogonal velocity components were measured using a triple hotwire at 10D downstream of the perforated plate for Reynolds numbers, \(\mathrm{Rep}_{\mathrm{D}}\), 18,700 and 28,400, where D is the diameter of the perforated holes. The statistics of the grid-generated turbulence was analysed using the time-averaged local velocity profile and turbulence intensity, which revealed a more homogeneous distribution of the flow field with a higher level of turbulence for the OPP. Fourier and wavelet analyses were employed to investigate the energy of the eddies as a function of frequency and multiscale characteristics of the fluctuating velocity, respectively. At \(\mathrm{Rep}_{\mathrm{D}}=18\),700, the turbulent energy remains prominently with large-scale vortical structures which are non-intermittently present in the flow for both perforated plates. The thickness of the converging channels of the CNPP appears to provide the venue for spawning intermittent fluctuations. At higher \(\mathrm{Rep}\) 28,400, the effect of this intermittent behaviour becomes evident for the CNPP, leading to a multiscale distribution of turbulent energy.  
grid turbulence; wavelet transform; perforated plate; homogeneous turbulence; wind tunnel +
Footnote †: journal: Physics Letters B  
1 Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India; [EMAIL_ADDRESS]  
Turbulence de Energy Laboratory, University of Windsor, Windsor, ON N9B 3P4, Canada; [EMAIL_ADDRESS]
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 4 Analysis of the Impact of IIDG Access Location on Stage-Based Protection
### Fixed-Time Limit Overcurrent Protection Setting  
In order to ensure that overcurrent protective devices will not be activated incorrectly under normal operating conditions, it is essential that the settings adhere to a fundamental principle: that the operating current of the protective device is set at a level that is higher than the current of the grid under peak load conditions [23]. This criterion ensures that the overcurrent protective device does not falsely activate the action signal even under normal fluctuating conditions such as load transients.  
\[I_{act}=\frac{K_{rel}K_{mS}}{K_{re}}I_{L\text{-max}} \tag{12}\]  
where \(K_{rel}\) is the reliability factor; \(K_{mS}\) is the self-starting factor; and \(K_{rel}\) is the current relay return factor.  
The incorporation of inverter-based distributed generation (IIDG) systems into distribution networks has a discernible impact on the short circuit current characteristics of the grid. In particular, the introduction of IIDG may result in a reduction of short circuit currents in upstream lines during faults. This may consequently impact the sensitivity of time-limited current fast-break protection and time-limited overcurrent protection on these lines. This effect is particularly significant when the output power of the IIDG exceeds the downstream load, which may result in the protection devices being unable to act in a timely manner when required to do so.  
In order to address this challenge, it is proposed that an optimization strategy based on real-time monitoring and dynamic adjustment be implemented. In particular, the short circuit current at the protection point can be calculated in real time through the monitoring of the output parameters of the photovoltaic power system. The real-time calculation not only reflects the immediate state of the grid but also provides accurate set values for the protection devices, thus ensuring optimal performance. By adjusting these set values in real time, it is possible to significantly enhance the sensitivity of the current protection system for both upstream and downstream lines, thereby optimizing the protection performance of the grid [24].  
The implementation of this methodology ensures that the grid protection system can continue to respond promptly and accurately to faults, thereby guaranteeing the stable operation of the grid in the event of IIDG access to the distribution network. This is of considerable significance for enhancing the reliability and security of distribution networks.