esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
###### Abstract
The particles search for the best positioning locally and globally by looking for surrounding particles nearby and then forming the swarm in the whole system to find the best position globally. Particles float around in a multidimensional search space [11]. Using knowledge of the neighbors, the particles change their positions to find the best location. GWO is also a nature-inspired technique that has been used widely in a number of optimization problems. The GWO algorithm is based on the hunting of prey by grey wolf packs--which consist of different types of members in a hierarchy--and how the members are assigned different tasks and importance in the hunting process [2]. In this study, we have introduced a hybrid algorithm, BGWOPSO, by combining the Binary Grey Wolf Optimization algorithm (BGWO) with Particle Swarm Optimization (PSO) to find the best feature in the Instagram dataset. This hybrid method (BGWOPSO) is also considered a multi-objective function, the goals of this algorithm are a trade-off between minimizing the error and increasing the accuracy and recall in the fake account detection system. Classification techniques are applied to measure and compare the optimization techniques, which utilize many parameters to describe each object. Four classification methods that are tested include the Support Vector Machine (SVM), K-Nearest Neighbor, Neural Networks (NN), and Logistic Regression. The classifiers are evaluated by the measure of accuracy, precision, recall, and AUC (area under ROC) values. Considering the importance of the problem in detecting fake accounts on social networks and after analyzing the literature, the following solution is presented to answer this problem:  
* Identify fake accounts with high precision;
* Reduce false alarms while detecting fake accounts;* Propose a hybrid optimization technique to improve the accuracy of fake account detection.
# A Method for Supervisory Control of Manipulator of Underwater Vehicle
## 2 Specifics of the Method for Supervisory Control of MM
In this case, the optical axis of the camera, coinciding with the vector \(\overrightarrow{d}_{i}\) that extends from the point \(P_{i}\) towards the bottom plane (Equation (1)), is described by the following equation:  
\[A_{i}=P_{i}+t_{i}\overrightarrow{d}_{i}, \tag{2}\]  
where \(t_{i}\) is the parameter that varies within the limits \([0,\infty)\). The value of the parameter \(t_{i}\) at the point of intersection of the axis (Equation (2)) with the plane (Equation (1)) can be obtained by substituting Equation (2) in Equation (1) [34]:  
\[a(P_{ix}+t_{i}d_{ix})+b(P_{iy}+t_{i}d_{iy})+c(P_{tz}+t_{i}d_{iz})-d=0 \tag{3}\]  
\[t_{i}=\frac{-(aP_{ix}+bP_{iy}+cP_{iz}-d)}{ad_{ix}+bd_{iy}+cd_{iz}} \tag{4}\]  
The coordinates of the point \(A_{i}\) at the intersection of the camera optical axis with the bottom plane are determined by substituting the found value of the parameter \(t_{i}\) in Equation (2):  
\[A_{i}=P_{i}+\frac{-(aP_{ix}+bP_{iy}+cP_{iz}-d)}{ad_{ix}+bd_{iy}+cd_{iz}} \overrightarrow{d}_{i} \tag{5}\]  
After the operator points the camera's optical axis at the next target point \(A_{i+1}\), the value of the parameter \(t_{i+1}\) is calculated using Equation (3) for the new point \(P_{i+1}\) and vector \(\overrightarrow{d}_{i+1}\). The coordinates of the rest of the target points are determined in the same way. As a result, the motion trajectory of the MM working tool is represented in the form of its sequential movements through all target points. The vector \(\overrightarrow{n}_{i}\) of the tool is always parallel to the vector \(A_{i}\overrightarrow{A_{i+1}}\), connecting the passed, and the next, target points.
## 4 Discussion  
The aim of this experimental study is to determine if apigenin K and melatonin M5250 delivered into post-extraction sockets could stimulate the socket healing at different time frames (1, 2, and 3 months). Our findings suggest that apigenin and melatonin effectively increased the new bone formation during the first month compared to control sites without the bone stimulation substances.  
These findings on melatonin can be explained through their effect on the osteoblast, in which it was shown that melatonin stimulates their differentiation [44; 45] and can regulate their function as well [18; 19; 20; 46; 47]. This is produced through a mechanism of acceleration in the mesenchymal stem cell differentiation to osteoblasts [44; 45]. Another potential explanation for the stimulation of bone formation observed in the melatonin group was presented by [PERSON] et al., who found that melatonin increased the angiogenesis during the first four weeks of bone healing, which explains the numerous endothelial buds and capillary blood vessels observed in our study at the first month in the melatonin group [48; 49; 50]. Furthermore, it has been shown that melatonin acts on the metabolism of calcium, which could explain the organized bone configuration observed during the second and third month in the melatonin group [51; 52]. In addition, the more lamellar appearance in the melatonin group compared to the control group can be explained by the stimulation of the synthesis of type I collagen, and other proteins of the bone matrix [24; 25], as well as increased osteoclast and osteopontin synthesis [48] induced by melatonin. The findings for the apigenin group also showed that apigenin K stimulated more bone formation than controls during the first month, which has been explained based on the effects of the pre-osteoblast differentiation [53; 54; 55; 56] and the reduction of bone loss in ovariectomized rats [57; 58; 59; 60; 61]. Besides, several studies have shown that apigenin increases the rate of osteoblastic differentiation [62] because it seems as though apigenin supports the binding of alpha/beta-type estrogen receptors (\(\alpha\), \(\beta\)) [63]. Apigenin increased the processes of differentiation and proliferation of osteoblasts, which can be explained by its effect on the \(\beta\) receptors present in osteoblasts [64; 61; 65; 66].  
The morphologic changes appreciated in the melatonin and apigenin K groups compared to the controls at two months (thicker cortical and more lamellar characteristics) are all the global effects of the bone stimulation expressed as an acceleration of the osteoblasts differentiation, reduced bone resorption, increased synthesis of collagen type I, and increased mineralization induced by these two substances [62; 67]. It should also be noted that the collagen sponge could have supported the bone formation but the lack of traces of the collagen sponge at one month indicated that the benefits observed in the bone healing were related to the stimulant substances (apigenin K and melatonin) and not to the collagen sponge.
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 4 Traffic Sign Recognition Experiment
### Performance Comparison of Recognition Algorithms
Compared with the aforementioned literature, the proposed algorithm has the best overall performance when using the same dataset. The accurate recognition rate reaches 99.75%, and the average processing time per frame is 5.4 ms. The generalization ability and recognition efficiency of the network model are also remarkably improved. In terms of performance improvement, evident advantages are observed. The fully improved traffic sign recognition accuracy is conducive to considerably enhancing the driving safety of intelligent vehicles in the actual driving environments. Meanwhile, the fully shortened average processing time is conducive to meeting the real-time target requirements of intelligent vehicles in the actual driving environments effectively. Thus, this study contributes to further improving the technical level of intelligent vehicle driving assistance.
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 Face Detection, Landmark Detection, and Frontalization  
First, face detection is required to locate and size human faces in images [34; 35]. Most studies use the Viola-Jones algorithm to perform face detection [36]; however, it cannot detect the top and bottom points of the eyelids and the top and bottom inner lips of the mouth. The proposed method uses Dlib, a robust frontal face detector capable of outputting 68 facial landmarks based on a Histogram of Oriented Gradients (HOG) alongside a linear SVM classifier. These landmarks are crucial to obtaining the key points in the human face that make each individual different [37], as facial components and contours differ from one person to another [38]. These landmarks were used to calculate the eye and mouth openings by measuring the distance between specific landmark points. The distances are then used to compute PERCLOS and POM. Furthermore, the facial landmarks detected using Dlib are also frontalized using a frontalizing method from [PERSON]. [39] to create normalized landmarks for each result of facial landmark detection. This was performed to create normalized landmarks to provide consistent and reliable data.
## 3 Triaxial Photogrammetry and Numerical Simulation  
### Triaxial Photogrammetry  
Before the test, CT points (Coded Targets) were pasted on the triaxial apparatus and the outer surface of the rubber membrane, which was divided into three areas, as shown in Figure 11.  
Photogrammetry was carried out using the calibrated single-lens camera and conventional undrained consolidation triaxial tests (CU tests). The analysis was carried out after the images were collected and prepared for conversion of the coordinate system, ray tracing, image binarization techniques, and refraction correction (see Figure 4). The soft clay was saturated with back pressure, and the target confining pressure values were set to 100 kPa, 200 kPa, and 300 kPa for the tests. One image acquisition was carried out before the axial loading, serving as the initial reference image. After applying the confining pressure, another image was acquired, and thereafter, one wrap-around image acquisition was carried out for every 2.5% increase in axial strain, allowing us to observe the deformation process of the soft clay.  
Figure 12 presents the post-test state, with photogrammetry in line with the conventional triaxial test results in barrel deformation. The most significant deformation is in the middle zone II, with the end zones I and III showing smaller deformations after testing. This underscores the significant influence of the end constraints and the intriguing inhomogeneity of the deformation within the soft clay, prompting us to consider its implications.
## 3 Results and Discussion
### Size and Surface Chemical Composition
Errors are expressed as the larger value between the error associated with a single quantification and one standard deviation; the error on Au, Pt, Ag, and Si percentages is \(\pm\)0.2%; the error on the abundance of other elements is \(\pm\)0.5%. states, namely Pt(0), Pt(II), and Pt(IV). The corresponding positions of the main photoelectronic peak (Pt4f\({}_{7/2}\)) fall at BE = 70.3 \(\pm\) 0.2 eV, 71.8 \(\pm\) 0.2 eV, and 74.4 \(\pm\) 0.2 eV, respectively [23; 26]. As expected, the main component, with a relative abundance of about 78%, is associated with Pt(0), whereas Pt(II) (in the form of hydroxide) and Pt(IV) (like PtO\({}_{2}\)) account each for about 11%. Such results are quite similar to the ones obtained by laser-generated PtNPs, which were prepared in water using sodium citrate up to 0.1 mM, as reported in [26]. Figure 1: Representative TEM images of (**A**) AuNPs ablated in 100 \(\upmu\)M KCl, (**B**) AgNPs ablated in 100 \(\upmu\)M KCl and (**C**) PtNPs ablated in 100 \(\upmu\)M KCl, respectively. Figure 2: Gold (**A**), silver (**B**), and platinum (**C**) nanoparticles prepared by laser ablation in liquid in presence of 100 \(\upmu\)M KCl. Diameter distribution histograms obtained from the analysis of the TEM micrographs and the respective fit to a Lognormal distribution (blue curve). Also shown is the size distribution function retrieved from the DLS measurements (bold black curve).
# Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum
## 2 Dynamic System Modeling
### Linearized Model  
Even though a nonlinear model of the system is obtained and verified in the previous section, a linear model around an operating point is still required in order to synthesize the MPC. In common MPC design, a linear model in state-space form is used to predict the system's future behaviour. Compared with the nonlinear model, the linear one is simpler and more efficient in computation. By linearizing the nonlinear model around an operating point of the system, a continuous time linear model in state-space form can be obtained as:  
\[\begin{array}{c}\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}+\mathbf{B}\mathbf{\tau},\\ \mathbf{y}=\mathbf{C}\mathbf{x},\end{array} \tag{19}\]  
where \(\mathbf{x}\) is the state vector, \(\mathbf{y}\) is the outputs, \(\mathbf{\tau}\) is the control inputs, and \(\mathbf{A}\), \(\mathbf{B}\), \(\mathbf{C}\) are the coefficient matrices.  
In this study, the inverted pendulum is used to verify the controllers, which are designed to stabilize a single-track vehicle by the gyroscopic effect of two spinning flywheels. The operating point of the system is the upright position and the gimbal angles are zero, the flywheels are rotating at a constant speed of 4000 rpm in the opposite direction. Therefore, the linearization operating point for the system is  
\[\begin{array}{c}\mathbf{x}_{e}\\ =\left[\begin{array}{cccccccc}\dot{\theta}&\theta&\dot{\alpha}_{1}&\alpha_{1 }&\dot{\alpha}_{2}&\alpha_{2}&\dot{\phi}_{1}&\phi_{1}&\dot{\phi}_{2}&\phi_{2} \end{array}\right]^{T}\\ =\left[\begin{array}{cccccccc}0&0&0&0&0&0&4000 rpm&0&-4000 rpm&0\end{array} \right]^{T}\!.\end{array} \tag{20}\]  
By expanding the equations of motion (17) in the Taylor series for the operating point in the upright position, the numerical results for a linear model are:  
\[\mathbf{A}=\left[\begin{array}{cccccccc}0&19.43&-1.71&0&1.71&0\\ 1&0&0&0&0&0\\ 331.09&0&0&-2.35&0&0\\ 0&0&1&0&0&0\\ -331.09&0&0&0&0&-2.35\\ 0&0&0&0&1&0\end{array}\right]; \tag{21}\]  
\[\mathbf{B}=\left[\begin{array}{cccccccc}0&0\\ 0&0\\ 84.99&0\\ 0&0\\ 0&84.99\\ 0&0\\ 0&0\end{array}\right];\mathbf{C}=\left[\begin{array}{cccccccc}0&1&0&0&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&1\end{array}\right]\!. \tag{22}\]  
Since the speed of the flywheels is controlled independently, the four states of \(\dot{\phi}_{1}\), \(\phi_{1}\), \(\dot{\phi}_{2}\) and \(\phi_{2}\) are excluded, and the state variables of the linear model are:  
\[\mathbf{x}=\left[\begin{array}{cccccccc}\dot{\theta}&\theta&\dot{\alpha}_{1 }&\alpha_{1}&\dot{\alpha}_{2}&\alpha_{2}\end{array}\right]^{T}\!. \tag{23}\]
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Intrusion Detection System
#### 4.4.3 Detection Process  
In this section, we provide a detailed description of the intrusion detection process. The algorithm flow is presented in Algorithm 2. The algorithm takes the test data, base values, and thresholds as input.  
In Lines 3-6, the algorithm computes the Hamming distance for the test data. Lines 7-16 outline the detection steps of our algorithm. We compare the Hamming distance with the first threshold to identify DoS and fuzzy attacks. If the Hamming distance is below the threshold, it indicates a DoS attack; otherwise, it suggests a fuzzy attack. If the Hamming distance falls within the threshold range, we proceed to compare it with subsequent thresholds. If the Hamming distance falls within any of the thresholds, it implies the occurrence of the corresponding attack type. Otherwise, the data are considered normal.  
```
0:TestDataset, windows, Threshold\({}_{up}\), Threshold\({}_{down}\)
0:isAttack
1:\(windows\leftarrow[]\)
2:\(as\leftarrow[]\)
3:for packets in Dataset do
4: Combine \(K\) consecutive packets into a windows \(W\);
5:\(Hs\gets getHammingDistanceList(W,windows[0])\)
6:\(\alpha\gets getAvg(W,windows[0])\)
7:if\(\alpha\) not in range of \(Threshold_{up}[0]\) and \(Threshold_{down}[0]\)then
8:return Attack;
9:endif
10:for Thresholds except for the first do
11:\(Hs\gets getHammingDistanceList(W,windows[i])\)
12:\(\alpha\gets getAvg(W,windows[i])\)
13:if\(\alpha\) in range of \(Threshold_{up}[i]\) and \(Threshold_{down}[i]\)then
14:return Attack;
15:endif
16:endfor
```  
**Algorithm 2** Intrusion Detection System
# Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System
## 5 Flat-Top Pattern Synthesized Using a Genetic Algorithm
However, because the proposed flat-top radiation pattern and amplitude distribution of the fields due to the feeding element are radially symmetric around the \(z\)-axis, as shown in Figure 3, the required phase distribution should be also be radially symmetric around the \(z\)-axis. Thus, the number of the unknown variables can be reduced by taking into consideration this symmetry. For the case of a circular reflectarray as shown in Figure 1, the reflecting elements can be arranged into four image-symmetric quarters. Each quarter can also be divided into two symmetric halves, such that each column in the quarter would be the same as the corresponding perpendicular raw in the same quarter as shown in Figure 8.  
Using this approach, it is possible to reduce the number of unknowns in the optimization process to one-eighth of the number of reflectarray elements. This significant reduction reduces the convergence computational time and also improves the resulting convergence. Figure 9 shows the phase distribution obtained by using GA to obtain the required flat-top radiation pattern. Moreover, the obtained flat-top radiation pattern is shown in Figure 10. It can be noted that the obtained flat-top radiation pattern almost satisfies the required conditions of the maximum coverage angle of \(29^{\circ}\), \(\Delta\theta\) of \(5^{\circ}\), and ARL of \(-3\) dB. Figure 8: Phase symmetry on half of the elements of the proposed reflectarray antenna. \[d_{max}=\sqrt{\left(R_{e}+h_{o}\right)^{2}-R_{e}{}^{2}} \tag{6}\]0 Flowchart of a general GA approach.
## 7 Discussion Section
### Significance of Proposed Control Logic  
The contribution of the proposed algorithm can be described from two perspectives: scientific and engineering. Scientific contribution means making another step forward toward new orthogonal endocrine neural networks that have shown significant control potential in the last few years. It was shown that Gegenbauer orthogonal functions could be as equally efficient as other already utilized functions, creating a suitable environment for further considerations of different functions' applicability. Further, artificial glands were networked within a single structure (AOG structure) and tuned in a manner to treat appropriate environmental stimuli with different levels of importance for a controlled system. Finally, a new type of artificial glands was proposed, with a specially tailored mechanism within it. It was shown that such a gland could be made as an adjustable unit, where each sub-mechanism could be tailored for a specific use case. Conversely, the engineering and practical contribution of the research is in the potential to implement a similar structure for control of real-world nonlinear dynamic systems. A significant value resides in the possibility of processing an unlimited number of environmental signals, noises and disturbances with the proposed architecture. The structure is upgradable, and every new noticed environmental factor can be easily added to the existing OENN + AOG control logic. That way, the control signal in each time step is modified based on the current system's factors and environmental factors, enabling robust control and stable system work.
## 4 The Proposed Model  
The proposed model hinges on feature extraction from project issues, estimating unknown SP values through comparison with issues that share semantic similarities. This involves transforming issue text into semantically meaningful vectors, facilitating semantic similarity comparisons using cosine similarity.  
The process incorporates advanced NLP techniques to examine word contexts and relationships, determining semantic similarities. The SBERT model, known for its sentence-level embeddings, was selected for its ability to capture sentence nuances more effectively than traditional BERT models that focus on token-level embeddings. This feature is crucial for accurately representing the complex nature of user story titles and descriptions.  
SBERT's design, optimized for similarity tasks, makes it suitable for this model, aiding in semantic comparisons crucial for SP estimation. By calculating cosine similarity between sentence embeddings, semantic proximity among textual data from different issues is quantified, grounding SP estimations in a deep understanding of textual semantics.  
For regression tasks, the model is prepared to work with gradient-boosted models. Therefore, three types of GBTs--extreme gradient boosting (XGBoost), LightGBM, and CatBoost--are selected for their proven effectiveness in similar tasks. These algorithms are trained on structured data derived from SBERT to predict SPs from Scrum software project text entries.  
During training, challenges like balancing the dataset to avoid bias and capturing the semantic essence of diverse terminologies are encountered. Overcoming these requires innovative strategies and advanced techniques to ensure model robustness.  
After training, the most suitable model is selected based on its accuracy using a validation subset from the TAWOS [35] dataset. This selection is guided by the need for a model that not only demonstrates high analytical accuracy but also aligns with practical project management needs.