esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 2 Materials and Methods
### Methods
#### 2.2.2 Segmentation Algorithm
Because the original algorithm was designed for photonic color images, this algorithm was modified in our application to brain segmentation, as represented in Figure 2, in order to work optimally on MRI grey images. An n-dimensional real vector is used to represent each resolution, or \"harmony,\" in the fundamental HSA. A randomly generated starting population of harmony vectors is stored in harmony memory (HM). Thus, a new candidate harmony is made from the elements in the HM using a memory consideration method, either by a random re-initialization or a pitch adjustment operation. The HM is eventually updated by comparing the new candidate harmony to the HM's worst harmony vector. The poorest harmony vector is replaced when the new candidate vector offers a more advantageous solution in the HM. The aforementioned procedure is repeated until a particular termination condition is met. The initialization of the HM, the creation of new harmony vectors, and the updating of the HM are the three main phases of the basic HS algorithm. The optimization technique known as harmony search optimization (HSO) uses k various factors as decision variables. The recommended segmentation method and two different objective functions, the Otsu and Kapur functions, as discussed in the preceding section, were combined to generate two distinct segmentation algorithms. Ref. [30] provides instructions on how to use both strategies. The algorithm and its parameters have been modified in order to optimize the segmentation of MRI brain tumor pictures in this study. The following developed algorithm is presented as a flowchart in Figure 2.  
The performance of the HSO method is significantly influenced by the values given to parameters. As these parameters interact in a highly nonlinear manner and there are currently no mathematical models that capture this interaction, determining the most suitable parameter values for each specific circumstance is a challenging process. Finding the ideal set of parameter values frequently involves fixing each parameter value to a random number within the parameter limits, followed by running HSO. If the result is  
Figure 2: Developed an Algorithm describing the method used for brain segmentation. The red text in step 9 indicate the return to step 6 if the condition in step 9 is not accomplished. not what is desired, new parameter values are defined, and the evolutionary algorithm is rerun. This process takes a while because it could take multiple experiments to arrive at a set of acceptable parameter values. The user's chosen set of values may also not be the best possible but rather the best among an arbitrarily large number of trials. In order to reduce the number of experiments, the factorial design method first described in [38] was employed in this research to systematically identify the best HSO settings.
# Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
## 1 Introduction
### Motivation  
Certain drugs are especially susceptible to illicit practices, including the black market and drug trafficking. These drugs are frequently obtained illegally from pharmacies, either through the repeated use of the same prescription or the use of falsified sequences [10; 11]. Indeed, prescription tampering is a widespread drug fraud in the world, which is constantly developing. Recently, the results of a European Suspicious Orders Abuse Indicator Possible (OSIAP) [12] survey carried out in six European countries (France, Belgium, Spain, Italy, Sweden, and the Netherlands) have shown that prescription falsification is highly dependent on the organization of drug distribution, that is the medical prescription-management system. In fact, traditional prescription-management systems are unsatisfactory in fighting against fraud and falsification of prescriptions either for a weakness in the system itself or for its inability to be generalized such as for e-prescriptions or pharmaceutical record databases.  
Recently, thanks to its transparency and its unalterability, blockchain has been used as a perfect, traceability tool and for verifying authenticity, in the process of prescribing and dispensing medication. Indeed, one potential use case for blockchain in prescription management is to create a tamper-proof record of prescription orders and medication dispensation. With blockchain, every transaction is recorded in a decentralized, secure, and immutable ledger, making it extremely difficult to alter or delete data without detection. This can help highly prevent prescription fraud. Another potential benefit of using blockchain in prescription management is the ability to provide patients with more control over their medical data. With blockchain, patients can securely access their medication history and share it with healthcare providers as needed, without the need for a centralized database.  
Therefore, we propose in this work a blockchain-based prescription-management system, where all transactions made on medical prescriptions are recorded in chronological order and there will be no possibility of a transaction being modified or deleted. Hence, transparency and traceability, as well as the confidentiality of personal patient information are guaranteed.
# Event-Based Path-Planning and Path-Following
## 2 Problem Formulation
### Relative Dynamics  
In order to solve the path-following problem, we use the virtual target-based method [23]. In this method, the virtual target (VT) as a point moves along a given path, and the AUV must pursue it, trying to match its position with the position of the VT.  
Figure 1: Reference frames.  
Denote the course angle of the VT by \(\psi_{B0}\), its forward and angular speeds by \(u_{0}\) and \(r_{0}\), respectively. Define the coordinates of the AUV in a Serret-Frenet reference frame \(\{F\}\) that is associated with the VT (see Figure 1), as  
\[\begin{bmatrix}s_{e}\\ y_{e}\end{bmatrix}=R\begin{bmatrix}x-x_{0}\\ y-y_{0}\end{bmatrix},\quad R=\begin{bmatrix}\cos\psi_{B0}&\sin\psi_{B0}\\ -\sin\psi_{B0}&\cos\psi_{B0}\end{bmatrix}, \tag{5}\]  
\((x_{0},y_{0})\) are the coordinates of the VT in the reference frame \(\{B\}\). As shown in [23], the error dynamics of the AUV in \(\{F\}\) can be described by  
\[\left\{\begin{array}{l}\delta_{e}=-u_{0}+r_{0}y_{e}+v_{t}\cos\psi,\\ \dot{y}_{e}=-r_{0}s_{e}+v_{t}\sin\psi,\\ \dot{\psi}=r+\dot{\beta}-r_{0},\end{array}\right. \tag{6}\]  
where \(\psi=\psi_{W}-\psi_{B0}\).
## 3 Mode Analysis of Beat Wave Pattern
### Period Length of Field Distribution  
In Sections 3.2 and 3.3, the theoretical transverse field distributions in the dielectric bar have been compared with the simulated results using CST Eigenmode Solver. In the longitudinal direction, a beat wave pattern consisting of a leaky wave and a guided wave should exist. Therefore, the period length observed in Figures 3 and 4 is determined as  
\[l_{p}=\frac{2\pi}{|\beta_{g}-\beta_{l}|} \tag{20}\]  
where \(l_{p}\) is the period length; \(\beta_{g}\) is the real part of the propagation constant of the guided wave; \(\beta_{l}\) is the real part of the propagation constant of the leaky wave.  
A comparison is made between the theoretical period length and the simulated period length as shown in Table 3, when the edge length a is set to be 14.4 mm. Field monitors are set at frequencies from 10 GHz to 11 GHz with a linear step of 0.2 GHz. It can be observed that the relative errors of the theoretical results are \(\sim\)10% at lower frequencies because [PERSON]'s method provides lower accuracy when the phase constant becomes close to \(k_{0}\)[18]. The period lengths obtained by Eigenmode Solver have good agreement with the period lengths obtained by CST Time-Domain Solver. The beat wave is therefore proved mainly to consist of a guided mode and a leaky mode.  
It can be observed from Table 3 that the simulation results by Eigenmode Solver and Time-Domain Solver are very close to each other. In the eigenmode simulation, the scattering effect of the incident wave occurring at the ends of the bar is neglected because of the periodic boundaries in the longitudinal direction. However, in the time-domain simulation, this scattering effect is considered. The very close results demonstrate that the influences of this scattering effect on the propagation of the leaky and guided modes are quite small. In addition, the time-domain method will be more accurate when the gap between adjacent bars is no larger than a wavelength and the mutual coupling effect should  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Theoretical Value** & **Simulation Result** & **Relative Error** \\
**(GHz)** & **(rad/m)** & **(rad/m)** & **(\%)** \\ \hline
10 & 198.2 & 209.4 & \(-\)5.38 \\
10.2 & 202.9 & 213.6 & \(-\)5.02 \\
10.4 & 207.9 & 217.8 & \(-\)4.55 \\
10.6 & 211.0 & 222.0 & \(-\)4.95 \\
10.8 & 215.6 & 226.2 & \(-\)4.71 \\
11 & 220.1 & 230.4 & \(-\)4.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Theoretical and simulated real parts of longitudinal phase constants for leaky mode.  
Figure 8: Field distribution of leaky mode in cross-plane: (**a**) cross-plane field distribution of leaky mode simulated by Eigenmode Solver, (**b**) simulated and calculated field strengths along x axis.  
be considered, which is neglected in the eigenmode simulation. As for the computing costs for these two numerical methods, the time-domain method is obviously more time-consuming and requires more physical memory.
## 3 Numerical Results
### Fluence Rate
The behaviors of the RD in the numerical calculations between the RTE and PDE are similar to those between the NS and AS of the RTE (Figure 7a). These results suggest that the diffusion approximation is valid inside the medium even at the lower volume fraction of 1%. As shown in Figure 8b, at the volume fraction of 20%, the numerical results agree well with each other, suggesting the diffusion approximation is valid at all the SC distances. At the \(z=2.5\) boundary, the RTE results slightly differ from the PDE results at the two-volume fractions, probably because of the difference in the boundary conditions between the RTE and PDE (RMBC and Robin BC). Figure 8c shows the MAPDs, \(D_{\Phi}(N,R;N,P)\), between the RTE and PDE are less than approximately 5%, meaning the diffusion approximation holds at all the SC distances with the volume fractions higher than 1%. Constant values of the MAPD (\(\sim\)2%) in the volume fractions from 7% to 20% are probably because the spatial discretization is the same for the RTE and PDE. From Figure 8a,b, the MAPD-values mostly come from the differences at the boundary regions (\(z=0.0\) cm and 2.5 cm). From the results in Figures 7c and 8c, we conclude the diffusion approximation holds at the internal regions with the whole volume fraction range 1-20% and all the SC distances with the volume fraction higher than 1%. Figure 6: (**a**,**b**) Fluence rates calculated from the PDE at different positions for the volume fractions of (**a**) 1% and (**b**) 20%. The analytical solutions (ASs) of the PDE (Equation (4)) for the infinite slab and semi–infinite medium are compared with the numerical solutions (NSs). The results are normalized by the values at the SC distance of 1.25 cm (\((z,I)=(1.25,\mathbf{0})\)). The relative differences (RDs) between the NS and AS for the infinite slab are plotted in the each figure’s bottom. (**c**) Mean absolute percentage difference (MAPD), \(D_{\Phi}(N,P;A,P)\) (Equation (8)), at different volume fractions. Figure 7: (**a**,**b**) Fluence rates calculated from the RTE at different positions for the volume fractions of (**a**) 1% (\(g=0.046\)) and (**b**) 20% (\(g=0.003\)). The AS of the RTE (Equation (6)) for the infinite slab and semi–infinite medium are compared. (**c**) MAPD, \(D_{\Phi}(N,R;A,R)\), between NS and AS for the RTE at different volume fractions. The other details are the same as Figure 6.
## 2 Materials and Methods
### Optimization Method  
Table 2 lists the hyperparameters used when training SBERT. We used the loss function of multiple negatives ranking (MNR) loss [22] to train the SBERT used in the search engine. MNR loss has the advantage of only requiring a pair of original text and similar text, whereas the loss function of triplet loss used in the first SBERT paper [14] required a set of original text (anchor), similar text (positive), and dissimilar text (negative). For optimizer, batch size, learning rate, pooling strategy, and warmup rate, we followed the parameters used in the first SBERT paper. We adopted 15 as the optimal number of epochs based on preliminary experiments with other parameters fixed. The maximum sequence length was given by the maximum number of tokens in the dataset plus 3, the number of special tokens ([CLS] and [SEP]), and a trailing blank. We split the dataset into training, evaluation, and testing sets and allocated 80% to training, 10% to evaluation, and 10% to testing. We used random numbers for the data splitting and for the initial value setting during training. We calculated the evaluation metrics based on the means of the results with 10 different random seeds.  
We note here that it is very labor intensive to manually create the text pairs needed to train SBERT with MNR loss; therefore, we developed the iterative method shown in Figure 4. First, as the zeroth iteration, a search engine using a pretrained SBERT and given the entire training dataset as a database corpus would find 10 similar texts for one query selected from the training dataset in order of best similarity score defined as Euclidean distance; here the same text as the query was excluded. If any of the 10 similar texts had labels that exactly matched those of the query, we created label-matched pairs with the query and the similar texts, and we repeated this with all texts in the training dataset as queries to build a dataset of label-matched pairs. Next, as the first iteration, we trained SBERT using the dataset of label-matched pairs with MNR loss. Then, we created a renewed dataset of label-matched pairs for the second iteration using a search engine with the trained SBERT (first optimized SBERT in Figure 4). We performed these iterations 10 times. We expected that this iterative process would efficiently expand the dataset of label-matched pairs and gradually improve the accuracy of the search. Finally, we chose the SBERT model that yielded the best retrieval accuracy among the 10 iterations.  
Figure 4: The SBERT iterative optimization method. Documents of the same color indicate that the labels are an exact match.
## 3 Methods and Techniques  
### Proposed Method for Lung Nodule Localization  
Computer-aided diagnostic (CAD) systems were developed to offer a second perspective. These technologies have the potential to detect and evaluate lung nodules shown on CT scans automatically. Because they are required to cope with various situations in a condensed amount of time daily, radiologists endure less stress than other medical professionals. By incorporating linear and non-linear adjustments, researchers improved the CAD system's ability to detect lung cancer. The researchers enhanced the system's capability to identify lung cancer due to these modifications, which provided more abstract and easily comprehendible representations of machine learning approaches and procedures. In this way, the system's accuracy was increased. These improvements have greatly improved the system's ability to identify the problem. However, it is of the utmost importance to develop an automated system that can locate lung nodules rapidly while requiring little human effort.  
This section breaks down and goes into great depth on a new CNN-based nodule localization technique for detecting lung nodules in CT images. Throughout the system's development, we discussed the pre-processing procedures needed to analyze the many meta-parameters that make up the system's configuration. The suggested approach generates a grid with the exact dimensions of the CT image being examined to identify lung nodules in those pictures. The positions and boundaries of lung nodules may be predicted using this grid. The technique also makes use of a convolutional neural network (CNN) [37; 38; 39]. Before being utilized in the pre-processing step, every DICOM image in RGB format is converted to grayscale. An adaptive median filter may be used to enhance image quality. The flow chart of our proposed system is presented in Figure 6.  
To better effectively manage the information, the Lung Image Database Consortium and the Image Database Resource Initiative (IDRI) divided the data gathering into training and testing data (LIDC). Several performance indicators, including accuracy, sensitivity, and specificity, were considered while assessing the proposed system's efficacy. The likelihood of discovering a nodule at each scan site is represented by a single grid that combines the results of two CT scan sub-volumes. This grid shows the possibility of finding a nodule.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 4 Experimental Evaluation
### Diagnosis of a Bearing with an Outer Race Fault
#### 4.2.3 Experimental Study #3  
In this experiment, to verify that the proposed method is still effective in the case of rapidly changing speed and wild fluctuations, the vibration signal and rotational speed are collected during the speed increase from 18 Hz to 28 Hz with a equal to 3 Hz/s. Figure 27a,b show the raw signal and its rotational speed, respectively. The optimal frequency band corresponding to different indexes is shown in Figure 28. The FK is paved in Figure 28a and the optimal frequency band is [5625, 6250] Hz. The optimal frequency band in the WPTK is [1875, 2500] Hz, as shown in Figure 28b. The Protrugram is shown in Figure 28c, where the BW equals 400 Hz and step is 100 Hz and the center frequency is 2105 Hz. Figure 28d shows the FOSCK, in which the maximum OSCK is calculated at the 3.5 th decomposition level, and its corresponding optimal frequency band is [1666.67, 2500] Hz. The envelope order spectrum analysis results are shown in Figure 29a-d. In Figure 29a,d, the FCO and its quadruple octaves are very clear, especially in Figure 29d, and more harmonic components of the FCO can be found. Although the FCO can be found in Figure 29b,c, only the first two octaves are obvious. Therefore, compared with the WPTK and the Protrugram, the FK and the FOSCK are more sensitive to the fault impulse resonance frequency under these conditions.  
Figure 28: _Cont._  
Figure 27: The signal measured from an outer race fault bearing: (**a**) time-domain signal, (**b**) the shaft rotational frequency from 18 Hz to 28 Hz, (**c**) the frequency spectrum of (**a**,**d**) TFR by using STFT.
## 4 Simulation and Results
### Proposed Model with TMR Effect  
In BPMR systems, the TMR effect significantly degrades the BER performance. Usually, in practice, we do not know how much TMR affects the system. Therefore, in this simulation, we assume that the system suffered 10% TMR and 20% TMR in Figures 19 and 20, respectively. In this experiment, we just used the third model to consider the system's resistance to the TMR effect. In addition, we used the BPMR channel with the parameter \(-\Delta_{off}\) in Section 3.1 to create the TMR effect.  
Figure 19 illustrates the BER performance when the TMR was varied from 10% to 30% at SNR of 15 dB. Consequently, the system primarily suffers from the TMR effect rather than by additive noise. The neural network was trained at SNR of 11 dB and had two hidden layers.  
As shown in Figures 18 and 19, the proposed model performs better than the serial detection in [19]. In Figure 19, the proposed model is shown to exhibit the ability to overcome the TMR effect.
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 3 Results and Discussion
### Effect of GFs on an NACA0021 Isolated Aerofoil
#### 3.1.2 Trailing Edge Flow Structure  
Figure 11 presents the cloud chart of the streamlines colored by time-averaged velocity for the investigated airfoils. Comparing the streamlines of time-averaged flow fields near the trailing edge at \(\alpha=6^{\circ}\), similar flow structures appear around the flaps of different heights. Before the flap, the flow near the lower surface is impeded and recirculated. The static pressure in the flap corner is raised to flow stagnation pressure. Behind the flap, there is a pair of counter-rotating vortices as a flow over a blunt trailing edge. Given the asymmetry of the two surface flows over the airfoil, two vortices have different strengths. In the PGF case, the clockwise vortex due to the upper surface boundary layer is relatively large and strengthens the down-wash movement on the upper surface. The serrated flaps  
Figure 10: Skin friction coefficient, pressure coefficient, and LSB width distributions over the NACA0021 airfoils at \(\alpha=6^{\circ}\). (**a**) Skin friction coefficient, (**b**) pressure coefficient.  
exhibit a similar flow pattern to the plain flaps, but the clockwise vortex has a reduced strength and the anti-clockwise vortex is less visible due to the through-flow over the flap tooth gaps. This is the reason that the pressure difference between the upper and lower surfaces of the SGF airfoil is reduced and the lift enhancement is not as significant as for the PGF airfoil.  
Figure 12 displays instantaneous iso-surfaces of Q-criteria (Q = 2 \(\times\) 10\({}^{5}\) s\({}^{-2}\)) rendered by the velocity magnitude of the P-3 and S-3 airfoils at \(\alpha\) = 6\({}^{\circ}\). Behind the PGF airfoil, a distinct columnar vortex shedding of the blunt trailing edge is exhibited flowing downstream. But behind the SGF airfoil, a discrete vortex bundle appears which means some extra secondary flow smashed the column vortex. The time-averaged X-Vorticity contours at the x/c = 1.05 behind the flap reveal the existence of counter-rotating vortex pairs along the spanwise direction. These vortex pairs are generated behind each flap trough and regarded as the secondary flows to disrupt the main airfoil vortex flow. These induced streamwise vortices enhanced the momentum exchange and turbulent fluctuations in the downstream boundary layer. Figure 12 also indicates a much more complicated three-dimensional and multi-scaled flow behind a serrated flap which means a lot to the potential unsteady flow characteristics, for example, flow noise.  
Figure 11: Flow patterns near the trailing edge of airfoils at \(\alpha\) = 6\({}^{\circ}\) (**a**) with PGF, and (**b**) with SGF.