esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] et al., who were the first to present the image database used in this paper, classified the tumor types using augmented tumor region of interest, image dilatation, and ring-form partition. They extracted features using intensity histogram, gray level co-occurrence matrix, and bag-of-words models, and achieved an accuracy of 91.28% [21]. More papers that used the same database are discussed in Section 3. There, we discuss different types of networks, pre-trained ones, capsule net networks, other architectures of convolutional networks, and combinations with neural networks for feature extraction and classifiers for the output result. The discussion also concerns approaches using different modifications of the database, as well as the original one. The papers that used the original or augmented database are listed in the tables for better comparison. The biggest problem with classifying and segmenting the MRI images with some neural networks lies in the number of images in the database. In addition, MRI images are acquired in different planes, so the option of using all the available planes could enlarge the database. As this could generally affect the classification output by overfitting, pre-processing is required before feeding the images into the neural network. However, one of the known advantages of convolutional neural networks (CNN) is that the pre-processing and the feature engineering do not have to be performed. The aim of this research is firstly to examine the classification of three tumor types from an imbalanced database with a CNN. Although considered large compared to other available MRI image databases, this database is still far smaller than databases generally used in the field of artificial intelligence. We wanted to show that the performance of the small architecture could compare favorably with the performance of the more complex ones. Using a simpler network requires fewer resources for training and implementation. This is a crucial problem to address because limited available resources make it difficult to use the system in clinical diagnostics and on mobile platforms. If the system is needed to be used in everyday clinical diagnostics, it should be generally applicable. We wanted to examine the network's generalization capability for clinical studies and to show how the subject-wise cross-validation approach gives more realistic results for further implementation. In this paper, we present a new CNN architecture for brain tumor classification of three tumor types: meningioma, glioma, and pituitary tumor from T1-weighted contrast-enhanced magnetic resonance images. The network performance was tested using four approaches: combinations of two _10_-fold cross-validation methods (record-wise and subject-wise) and two databases (original and augmented). The results are presented using the confusion matrices and accuracy metric. A comparison with the comparable state-of-the-art methods is also presented.
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## Proposed Method
### Mesh-Ring Formation Algorithm
Finally, the coordinator M1 distributes the corresponding piconet, ring connection, and mesh connection information to all the other masters including M2 to M8 in Figure 2.  
In the second phase, each master receives connection information including piconet information, inter-piconet connection information, and the mesh link information. The piconet information contains all the slave nodes in each individual piconet. The inter-piconet connection information includes bridges information to connect with its upward and downward masters in the ring backbone subnet. The mesh link information is used to connect with another corresponding master in the ring subnet. With the maximum path criterion, the path length in the ring subnet can be reduced to effectively mitigate the routing delay of packet transmission. At the same time, each master starts paging its downward master and connects them together as a ring backbone network. Then each master connects with one corresponding master in the ring to form the mesh-shaped subnet individually. In addition, each master pages its slaves in its piconet. As a result, the mesh-ring scatternet is generated to achieve both advantages of mesh and ring topologies. In Figure 2, each master in the ring subnet from M2 to M8 receives piconet, inter-piconet connection, and mesh link information from M1. Then M2 starts to connect with its downward master M3, M3 connects with M4, M4 connects with M5. In this way, the ring backbone subnet is generated until M8 connecting with M1. With the maximum path length \(pl=4\), M1 connects with M5,  
Figure 2: The formation topology of a Mesh-Ring backbone network. M2 connects with M6 until the M4 connects with M8 to form the mesh-ring backbone network. Finally each master from M2 to M8 connects with its corresponding slaves and the mesh-ring topology is generated as shown in Figure 1.
## 2 Relation between Tool Position Data and Machine Tool Axis Displacement
### Displacement of the Machine Tool Axis  
With the kinematic model of the machine tool established, we can use the tool position data to determine the moving component of each axis of the machine tool. It is important to note that the angle solution of the two rotary shafts is not necessarily unique, which means that a particular sequence of analysis must be followed to compute the values of each moving axis. Based on the information presented in Table 1, the feasible range of the B-axis is determined. By solving Equation (3), we can determine the moving component of the rotating axis B of the machine tool (as shown in Equation (5)). We can also calculate the angle of the B-axis using Equations (3) and (5), as expressed in Equation (6). Whenaddressing the angle solution of the A-axis, the analysis is broken down into the following scenarios (as presented in Equation (7)).  
\[j^{2}+k^{2}=\cos(\theta_{B})^{2} \tag{5}\]  
\[\theta_{B}=\arctan\left(\frac{i}{\sqrt{(j^{2}+k^{2})}}\right) \tag{6}\]  
\[\theta_{A}=\left\{\begin{array}{l}\arctan(\left|\frac{j}{k}\right|)\ (k>0,j>0)\\ \pi-\arctan(\left|\frac{j}{k}\right|)\ (k<0,j>0)\\ \frac{3}{2}\pi-\arctan(\left|\frac{j}{k}\right|)\ (k<0,j<0)\\ 2\pi-\arctan(\left|\frac{j}{k}\right|)(k>0,j<0)\\ \frac{1}{2}\pi\ (k=0,j>0)\\ \frac{3}{2}\pi\ (k=0,j<0)\\ 0\ (k>0,j=0)\\ \pi\ (k<0,j=0)\end{array}\right. \tag{7}\]  
It is important to note that the solutions for both the B-axis and A-axis are represented in radians and need to be converted to their corresponding angle values.  
\[\text{beta}=\frac{\theta_{B}\times 180}{\pi}\]  
\[\text{alpha}=\frac{\theta_{A}\times 180}{\pi}\]  
Once the angle of the rotary axis has been determined, the displacement of each linear axis can be obtained by Equations (8)-(10).  
\[X=x+\sin(\theta_{B})\times L \tag{8}\]  
\[Y=m_{y}+\cos(\theta_{A})\times\left(y-m_{y}\right)-\sin(\theta_{A})\times(z- m_{z}) \tag{9}\]  
\[Z=m_{z}-L\times(1-(\cos(\theta_{B}\ )))+\sin(\theta_{A})\times\left(y-m_{y} \right)+\cos(\theta_{A})\times(z-m_{z}) \tag{10}\]  
Through the process of creating and solving the kinematics model, it is possible to derive the kinematic displacement and rotation angle for each axis of the machine tool, with the help of tool data.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 2 Materials and Methods
### Unmanned Aerial Vehicle Model  
In this research, the UAVs used in the search task have the same performance and keep flying at a specific altitude. Under the inertial reference coordinates, the motion model can be described as follows (see References [4; 21] for details):  
\[\left\{\begin{array}{l}\dot{x}_{i}=v_{i}\cos\phi_{i}\\ \dot{y}_{i}=v_{i}\sin\phi_{i}\\ \dot{\phi}_{i}=u_{i}\eta_{\max i}\end{array}\right., \tag{7}\]  
where \(p_{i}=(x_{i},y_{i})\in R^{2}\) is the position state of the \(i\)th UAV in the search plane, and \(\phi_{i}\) and \(v_{i}\) are respectively yaw angle and speed, which needs to satisfy \(v_{i}\in[v_{\min},v_{\max}]\). \(u_{i}\in[-1,1]\) is a decision variable. \(\eta_{\max i}\) is the maximum turn angular velocity, and is constrained by the performance of the \(i\)th UAV.  
At each decision time \(k\), the UAV can take actions such as left deviation \(\eta_{\max i}\), direct flight, or right deviation \(\eta_{\max i}\). For simplicity, the control decision variable and state variable can be written as \(\mathbf{u}(k)=\left[u_{i}(k),i=1,2,\ldots,N_{v}\right]^{\mathrm{T}}\) and \(\mathbf{s}(k)=\left(\mathbf{s}_{i}(k),i=1,2,\ldots,N_{v}\right)\), with \(u_{i}(k)\in\{-1,0,1\}\) and \(\mathbf{s}_{i}(k)=\left[x_{i}(k),y_{i}(k),\varphi_{i}(k)\right]^{\mathrm{T}}\). In the process of multi-UAV cooperative search, in order to avoid collision between UAVs, \(D\) is defined as the minimum safe distance, and the requirement is as follows:  
\[d=\sqrt{\left(x_{i}(k)-x_{j}(k)\right)^{2}+\left(y_{i}(k)-y_{j}(k)\right)^{2} }\geq D, \tag{8}\]  
where \(d\) is the actual distance between UAVs.  
Due to no-fly zones in the mission sea area, the position of UAVs should satisfy  
\[(x_{i}(k),y_{i}(k))\
otin\mathit{Mence}_{j},(i=1,2,\ldots,N_{v},j=1,2,\ldots,N _{m}). \tag{9}\]  
In this research, the no-fly zones were set to be circular, and the position constraint of UAVs can be described as  
\[\sqrt{\left(x_{i}(k)-X_{j}\right)^{2}+\left(y_{i}(k)-Y_{j}\right)^{2}}>D^{*}, \tag{10}\]  
where \((X_{j},Y_{j})\) is the center coordinate of \(\mathit{Mence}_{j}\), and \(D^{*}\) is the radius of the no-fly zone.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 2 Background on Weighted Least-Squares Adjustment and Dilution of Precision (DOP)  
In this section, a brief background is presented for weighted Least-Squares (LS) adjustment for a pseudorange-based positioning and the concept of weighted dilution of precision (DOP) as a quality metric for geometry monitoring.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 4 Experiment
### Data Description  
The test cases are two PolSAR data sets that were collected by the RadarSat-2 system over the area of Flevoland in Netherlands and the AIRSAR system over the area of San Francisco Bay.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### Salinity
Oxidation of pigments present in chloroplasts leads to a decrease in chlorophyll content and impaired photosynthesis. In addition, gas exchange, growth of roots and shoots, and growth of fresh and dry weight of plants are inhibited [207,208]. According to [PERSON] et al. [137], [PERSON] et al. [204], [PERSON] et al. [205], and [PERSON] et al. [206], the _Trichoderma_ fungi analyzed by them induced resistance to salinity stress in plants, mainly by increasing the activities of enzymatic (POD, CAT, SOD) and non-enzymatic (proline) plant antioxidants, which eliminated reactive oxygen forms. In addition, these microorganisms enhanced the length of the roots, thus allowing plants to absorb many more nutrients and water. [PERSON] et al. [137] reported that the increase in the surface area of the root system may be a consequence of the production by _Trichoderma_ sp. of exogenous indole-3-acetic acid (IAA). In turn, [PERSON] et al. [205] found that analyzed microorganisms produced salicylic acid (SA), which induces antioxidant enzyme activity and reduces the accumulation of MDA, a byproduct of lipid peroxidation and a marker of plant stress tolerance. In contrast, [PERSON] et al. [206] documented that the enhancement in chlorophyll concentration could be associated with an increase in the leaf area of plants inoculated with _T. longibrachiatum_.
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node
### Choice of Function \(A(t)\) Based on a Reasonable Hypothesis
Among other distributions of the form \(A_{l}(t)\), the correctness of Formulas (7) and (8) was checked by analyzing the histograms obtained as a result of traffic measurements in the operated multiservice network. It turned out that both relationships are quite acceptable for the analysis of models with an arbitrary nature of the distribution included in the family \(A_{l}(t)\).
## 2 Materials and Methods
### Initialization Phase  
The message is sent by sensor nodes to the BS that incorporates the node's X and Y coordinates and ID addressing the energy level and location. The BS computes the distance to every node and stores data. The BS performs the K-Means [6] algorithm to create clusters. Each distinct color shows a separate cluster, as portrayed in Figure 1.  
_First cluster head selection in each cluster:_ The BS examines the stored energy level and location of the nodes in the individual cluster and selects one node as the CH that has a minimum distance from the participant nodes and maximum energy. This process repeats for all clusters, and the BS stores this information. As Figure 1 shows, the CH is symbolized in a dark color in each cluster.  
_First main cluster head selections:_ The MCH is the relay point between all CHs and the BS. After selecting the CH in each cluster, the BS selects the first MCH. This process includes distance calculations from the BS to each CH. After this, the BS selects one CH which is the MCH with the least distance from the BS, as Figure 1 portrays.  
_Feedback message from the base station to the sensor nodes:_ Once the CH and MCH are selected in each cluster, the BS sends a feedback message to each node. It includes the threshold energy and location of its corresponding CH and MCH. This information is useful in the transmission phase while searching for an optimum path.
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
## 3 Materials and Methods
### Solution and Analysis of Results  
To validate the significant differences observed between the methods compared, a comprehensive statistical analysis will be performed, including hypothesis testing and other appropriate statistical methods. Specific details of the experimentation will be presented, along with graphical representations and tables that will facilitate the understanding and visualization of the results obtained. We will adopt an iterative approach of continuous review and improvement to evaluate possible adjustments in the implementations and experimental methodology, with the goal of refining the comparison between the tools used. The resources needed for the experiments include suitable hardware and specialized software such as AMPL (3.6.10), LKH-3 (3.0.9.) and Python (3.11.). The mathematical model is based on the adaptation of the vehicle routing problem (VRP) proposed by [PERSON] and [PERSON], modified to address the unique characteristics of the open vehicle routing problem (OVRP) [2]. For the evaluation and comparison of the results, instances of LKH-3 will be used due to its effectiveness in solving the OVRP. In AMPL, a time constraint of one hour will be imposed for obtaining solutions, using the Cplex solver (22.1.1.0).  
The results will be presented in a table that will include the solutions provided by LKH-3, the Best Known Solution (BKS), as the main reference, the results of the proposed model in AMPL and those obtained with the heuristic developed in Python. Since the problem is NP-Hard in nature, several heuristic algorithms will be employed, including Greedy, Nearest Neighbor, Local Search, and a combination of Nearest Neighbor and Local Search, as well as the Lin-Kernighan approximation algorithm (LKH-3). These algorithms will be evaluated in terms of solution quality and computational efficiency, providing a solid basis for comparison and analysis of the different methodologies implemented.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 3 Methodology
### Relief-F Algorithm and Frequency Analysis  
In this section, we detail the process of feature selection using the Relief-F algorithm and the subsequent frequency analysis conducted to identify the most relevant frequency components for each fault type.  
The Relief-F algorithm is a powerful feature selection method that helps identify and rank the most relevant features in a dataset, making it particularly useful for high-dimensional data [39]. Relief-F works by iteratively sampling instances from the dataset and comparing the sampled instance with its nearest neighbors of the same and different classes. For each feature, it increases the relevance score if the feature value of the instance is similar to that of its nearest neighbor from the same class and decreases the score if the feature value is similar to that of its nearest neighbor from a different class. This process effectively highlights features that are consistently good at distinguishing between classes while ignoring irrelevant or redundant features.  
To understand the frequency characteristics of the audio signals and identify the most diagnostically relevant frequencies for each fault type, we conducted a detailed frequency analysis using the following steps:  
1. Frequency Spectrum Calculation: The frequency spectrum of each audio signal was calculated using the Fast Fourier Transform (FFT). FFT transforms the time-domain signal into its frequency-domain representation, providing insight into the signal's frequency components.
2. Spectrum Binning: The resulting frequency spectrum was divided into bins, each 100 Hz wide. This binning process organizes the frequency data into manageable segments. As there are very limited frequency components beyond 15 kHz, a frequency range of 0-15 kHz was considered.
3. Power Calculation: For each 100 Hz bin, the total power was calculated. This step involves summing the squared magnitudes of the frequency components within each bin, giving a measure of the signal's energy within that frequency range.
4. Relevance Determination: For each fault type, the Relief-F feature selection algorithm was applied to the binned frequency spectrum. By evaluating the relevance scores of the bins, Relief-F identified the 5 most relevant bins that contributed most significantly to distinguishing between faulty and healthy conditions. These relevant bins highlight specific frequency components that are key indicators of each fault type.
## 5 Conclusions  
In a standalone microgrid, the intermittent nature of renewable energy and the load transition are the main factors of the system stability degradation. In this paper, a new control theme called VSG has been introduced to suppress the amplitude of the frequency deviation which results from the fluctuation power output of the PV. The VSG is implemented based on supercapacitor/battery hybrid energy storage system. Considering, the tunable of VSG parameters, an optimization method, BSA, is utilized for online tuning the inertia and damping factor of VSG. To investigate the performance of the proposed control, the standalone microgrid is implemented in MATLAB/Simulink. Two scenarios such as strong variation power and the load increase are conducted, and the comparison studies on  
Figure 21: Frequency response based on load transient: Adaptive VSG Vs Const. Vs Droop control.  
Figure 20: Total active power demand.  
the control performance of the dq-decoupling control versus proposed VSG control, the VSG control versus modified droop control, and the constant VSG versus adapting VSG control was presented. The simulation results prove the effectiveness of the proposed method to suppress the overshoot of frequency deviation which results from the intermittent power of PV and the load transient from 50.18 Hz to 50.03 Hz and 51.2 Hz to 50.4 Hz, respectively.  
[PERSON] and [PERSON] conceived and designed this study. [PERSON] carried out the experiments, performed the simulation and data analysis, and drafted the paper; while [PERSON] supervised this study. All authors revised and approved the manuscript.  
This research was funded by Meidensha Endowed Project, KMITL.  
The authors would like to acknowledge AUN/SEED-NET for financial support of one of the authors ([PERSON]) for his study at [PERSON]'s Institute of Technology Ladkrabang (KMITL), THAILAND.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BESS & Battery Energy Storage System \\ BSA & Backtracking Search Optimize Algorithm \\ DC & Direct Current \\ dq & Direct-quadrature \\ ESS & Energy Storage Systems \\ HESS & Hybrid Energy Storage Systems \\ LGBT & insulated-Gate Bipolar Transistor \\ LPF & Low-Pass Filter \\ PI & Proportional Integral \\ PLL & Phase Locked Loop \\ PV & PhotoVoltaic \\ PWM & Pulse Width Modulation \\ SOC & State of Charge \\ SG & Synchronous Generator \\ VSG & Virtual Synchronous Generator \\ \end{tabular}
## 3 Characterization of Anchor Cable in FDEM
### The Combined One-Dimensional and Entity Element Models in FDEM
The axial tension has an obvious yielding phenomenon, and after reaching the yield limit \(\epsilon_{\text{h}}\), the material enters the hardening stage, which is assumed to be linear here with a hardening coefficient \(K\) (Equation (7)) ([PERSON] et al., 2019), and the cyclic loading and unloading of the anchor cable is not considered. \[\sigma_{\text{b}}= \begin{cases}E\cdot\epsilon_{\text{b}},&\epsilon_{\text{b} }<\varepsilon_{\text{y}}\\ \sigma_{\text{y}},&\varepsilon_{\text{y}}<\epsilon_{\text{b}}<\epsilon_{ \text{h}}\\ \sigma_{\text{y}}+K\cdot(\epsilon_{\text{b}}-\epsilon_{\text{h}}),&\epsilon_{ \text{b}}>\epsilon_{\text{h}}\end{cases}  \tag{6}\]  
\[K=\frac{\sigma_{\text{u}}-\sigma_{\text{h}}}{\epsilon_{\text{u}}-\epsilon_{ \text{h}}} \tag{7}\]  
where \(\epsilon_{\text{b}}\) is the axial strain of the anchor cable in the joint element, \(\epsilon_{\text{y}}\) is the axial yield strain, \(\epsilon_{\text{h}}\) is the axial hardening strain, \(\epsilon_{\text{u}}\) is the ultimate strain in the axial hardening stage (failure strain), \(\sigma_{\text{y}}\) is the axial yield stress, and \(\sigma_{\text{u}}\) is the axial ultimate stress (failure stress). Figure 5: Schematic of combined one-dimensional and entity element model (ODEEM) of anchor cable in FDEM. FDEM Simulation of the Entire Dynamic Evolution Process of the Landslide-Anchorage Structure System Under Earthquake
## 3 Application of the Proposed Method
### Case Study Robotic Tasks
#### 3.1.2 The Path-Following Task  
A 2D Lissajous curve was considered as the path that the manipulator's end effector had to follow in an analogous way with a prescribed end effector velocity. The trajectory was considered to lie on a \(x\)-\(y\) plane at some given \(z\)-axis level. The curve equations are the following:  
\[x(t) =x_{o}+\cos(a)*A_{x}*\cos(\omega_{x}*t)-\sin(a)*A_{y}*\sin(\omega_{y}*t), \tag{12}\] \[y(t) =y_{o}+\sin(a)*A_{x}*\cos(\omega_{x}*t)+\cos(a)*A_{y}*\sin(\omega_{ y}*t),\] (13) \[z(t)=z_{o}, \tag{14}\]  
where \(x_{0}\), \(y_{0}\), and \(z_{0}\) depict the coordinates of the center of the curve, \(A_{x}\) and \(A_{y}\) are the major and minor axis length, \(\omega_{x}\) and \(\omega_{y}\) are the angular frequencies (in radians), and \(\alpha\) is the angle of the orientation of the curve.  
To follow the trajectory for the manipulator tool frame, an interpolation process was conducted by placing intermediate task frames along the end effectors path, determined by the use of straight line segments from the starting point of each intermediate path segment. All intermediate points generated were referenced to [L].