esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### DTH Dataset  
To build an efficient migratory bird counting dataset, we first strictly screened the original video data and eliminated the video content during the late-night hours to avoid the negative impact of low-light environments on image visibility. Then, we used a script to efficiently extract frames from the screened videos, saving an image as a sample every 50 frames to reduce redundancy between adjacent samples and improve the diversity of the dataset. Subsequently, all image frames were strictly manually screened to eliminate highly repetitive or blurred images to ensure the clarity and accuracy of the data. In the data labeling stage, we used mainstream semi-supervised learning algorithms for preliminary labeling and used LabelMe to refine the initial annotations, including deleting incorrect annotations, correcting deviations, and supplementing omissions to ensure the accuracy of the annotations. This provides high-quality supervision signals for deep learning models, thereby optimizing the training effect of the model.  
Considering that the surveillance video frame is in 4 K high resolution (2560 \(\times\) 1440 pixels), with more than two-thirds of the pixels being background, we reasonably cropped the screened image data to focus on the migratory birds to improve training efficiency and reduce the ineffective consumption of graphics card resources. At the same time, we collected migratory bird images in different periods and scenes and constructed a dataset called DTH (as shown in Figure 2). The DTH dataset contains 400 images with a total of 38,634 migratory bird points annotated, and the number of migratory birds in each image ranges from 17 to 234.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Sensor type & 1/27 inch CMOS \\ Pixels & 4 million \\ Maximum resolution & 4 MP@25 fps \\ Electronic shutter & 1/1 s-1/30,000 s \\ Minimum illumination color & 0.002 [EMAIL_ADDRESS] black and white: 0.0004 [EMAIL_ADDRESS] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main technical specifications of surveillance equipment.  
Figure 1: **(a)** The data collection locations, where the red marker with an arrow represents East Dongting Lake, and the blue marker indicates Yingtian Town; **(b)** the data collection equipment; **(c)** the visualization of a frame from a source video.  
We randomly split the images into training and test sets in a 7:3 ratio. This setup allows the model to fully utilize the training data while effectively assessing its performance and generalization ability on new data using the test set. Figure 3 shows the specific distribution of the number of migratory birds in the training and test sets. In addition, to enhance the model's generalization ability and mitigate overfitting, we employed various data augmentation techniques during training. These included geometric transformations (e.g., horizontal flipping and rotation) and pixel-level adjustments (e.g., color space transformation and brightness adjustment). These methods further enhance the model's adaptability to different scenarios.
## 3 Proposed Methodology  
This study proposes a hybrid ensemble method and a DL model to classify news articles as ham or spam. The purpose of the model is to assess and monitor the impact of this content. For this study Kaggle fake news dataset has been used. The extracted dataset was initially raw and contained ambiguities, noise, and optional information. Following the data collection stage, the datasets underwent a series of preparation stages, including removing links, stop words, punctuation, tokenizing, lemmatizing, and stemming the data. After removing any stop words, min-max normalization was performed on the dataset. The textual aspects were collected from the news data on top of the retrieved features. These ensemble methods consist of different ML-based algorithms, such as PA, LR, multinomial naive Bayes (MNB), Bernoulli NB (NN), Gaussian NB (GNN), and LSTM, BERT and RoBERTa. Next, we split the dataset into two sets: training and testing. The PA, LR, MNB, Bernoulli NN, Gaussian NN ML models, and DL-based LSTM models were used to estimate whether the news was ham or spam. The results were assessed using accuracy, precision, recall, and F1 scores as the performance metrics, and the ML and DL models were compared. The collected data were then evaluated considering previously established methods for classifying news as ham or spam. The proposed methodology is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Year** & **Model** & **Performance Evaluation Matrix** \\ \hline
[29] & 2017 & RNN + BioIE & Accuracy: 91.00 \\ \hline
[30] & 2022 & Attention + CNN & Accuracy: 89.00 \\ \hline
[32] & 2023 & MTTV + Transformers & Accuracy: 92.36 \\ \hline
[38] & 2020 & DC-CNN & Accuracy: 90.63 \\ \hline
[33] & 2021 & MLP & F1 Score: 81.01 \\ \hline
[34] & 2021 & MANN & Accuracy: 90.75 \\ \hline
[35] & 2021 & Self-Attention + Bi-LSTM & Accuracy: 92.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DL algorithms used for fake news classification.
## 3 Making of the Primary Plane Hologgram
### Calculation of the Primary Plane Hologram  
The primary plane hologram is calculated as the lensless Fourier hologram [15] as show in Figure 2. Since the detailed calculation method is described in the previous paper [9], the main points are described here. The 3D object model is assumed as a collection of self-illuminated points [16]. The sum of complex amplitudes from object points is interfered with the complex amplitude from the point reference source placed in the origin, or side of the object. Figure 3 shows a virtual window and a reference point source for CGH calculation. The virtual window has the same size and position of the secondary volume hologram to avoid unnecessary calculation. The calculation is done only for light waves which pass the virtual window. The reconstructed image will appear at the same position of the original object when the CGH is illuminated with the conjugate wave of the reference wave. The conjugate image will also appear at the point symmetric position to the origin as shown in Figures 2 and 3.  
Figure 3: Coordinates for virtual window in the final hologram plane. The reference point source for CGH is placed at the origin.  
Figure 2: Configuration of computer-generated hologram (CGH) calculation and transfer.
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
###### Abstract  
The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.  
fractional calculus; fractional order PID controllers; power electronics; DC-DC converters; meta-heuristics; cohort intelligence algorithm; Industry 4.0
###### Abstract  
After many years of exploitation in the petroleum field, most of the oil fields are in advanced stages of development, with a strong non-homogeneity of the reservoir, more residual oil, and low recovery efficiency. Therefore, research on various methods has been carried out by scholars to improve the rate of recovery and to understand the distribution pattern of residual oil in reservoirs. Among the whole elastic reservoirs, fluvial reservoirs occupy a large proportion, so fluvial reservoirs will be the priority for future reservoir research in China. The key to the fine characterization of fluvial-phase reservoirs is to able to reproduce the continuous curvature of the channel, and one important parameter is the width of the channel. The width of the channel sand body is one of the key factors in designing well programs, and accurately identifying the channel boundary is the key to identifying a single channel. Traditional research methods cannot accurately characterize the continuous bending and oscillating morphology of underwater diversion channels, and it is not easy to quantitatively characterize the spatial structure. Therefore, in this paper, a deep learning method is applied to quantitatively identify the width of a single channel within an underwater diversion channel at the delta front edge. Based on the sedimentary background of the block and modern depositional studies, we established candidate models for underwater diversion channels with channel widths of 100, 130, 160, 190, 220, and 250 m based on target simulation and human-computer interactions. The results show that when the width of the underwater diversion channel is 160 m, it has the highest matching rate with the conditional data and corresponds to the actual situation. Therefore, it can be determined that it is the common width of underwater diversion channel in the study area. And it is shown that the method can accurately identify the width of underwater diversion channels, and the results provide a basis for reservoir fine characterization studies.  
Keywords:neural network; underwater distributary channel; channel width; quantitative evaluation +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Combined Processing of Daytime and Nighttime LST Data  
To improve the accuracy of geothermal anomaly detection, this study integrated daytime and nighttime LST data to identify areas with consistently high temperatures. This approach targets regions that show elevated LST values during both day and night, effectively reducing false anomalies and minimizing the effects of solar radiation and surface property variations, thereby improving the reliability of geothermal anomaly detection.
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
## 2 Materials and Methods  
### Ship Model Design  
The precision of the hull model is essential for software simulation and forecasting the hydrodynamic performance of a ship. Parametric modeling of the hull typically involves three steps: selecting characteristic parameters, designing longitudinal and transverse characteristic curves, and generating the hull surface. The components of the hull profile encompass the cross-sectional curve, design waterline, hull profile curve, bow profile curve, and stern profile curve. Typically, these profiles are integrated into linear segments, quadratic curves, and arbitrary curves.  
Non-uniform rational B-spline (NURBS) curves are widely used in ship hull modeling, allowing shape manipulation through control points and weights for smooth surface transitions [18]. A NURBS curve \(C\) of order \(q\) can be characterized by a rational polynomial vector function, represented by the following expression:  
\[C(u)=\frac{\sum_{i=0}^{n}N_{i,q}(u)\omega_{i}p_{i}}{\sum_{i=0}^{n}N_{i,q}(u) \omega_{i}},0\leq u\leq 1 \tag{1}\]  
where \(\omega_{i}\) represents the weight factor for constructing the control polygon, \(p_{i}\) denotes the control point for the formation of the control polygon, and \(N_{i,q}(u)\) is a \(q\)-times B-spline function defined on a non-periodic node vector \(U\).  
NURBS surfaces are characterized by an additional parameter in one direction compared to NURBS curves. A NURBS surface \(S\) defined \(q\) times in the \(u\) direction and \(t\) times in the \(v\) direction can be expressed as a two-parameter rational polynomial vector function, as follows:  
\[S(u,v)=\frac{\sum_{i=0}^{n}\sum_{j=0}^{m}N_{i,q}(u)N_{j,t}(v)\omega_{i,j}p_{i, j}}{\sum_{i=0}^{n}\sum_{j=0}^{m}N_{i,q}(u)N_{j,t}(v)\omega_{i,j}},0\leq u,v\leq 1 \tag{2}\]  
where \(\omega_{i,j}\) is the weight factor, \(p_{i,j}\) is the control point, and \(N_{i,q}(u)\) and \(N_{j,t}(v)\) are \(q\)-times and \(t\)-times B-spline functions defined on the non-periodic node vectors \(U\) and \(V\).
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 3 Propagation Attenuation Map
### PAM Algorithm
Find \(p\)th column (\(p\geq K\)-\(j+2\)) corresponding first non-zero cell in \(j\)th row (see purple cells in Figure 2). repeat
19. Find \(q\)th column corresponding next non-zero cell in \(j\)th row (see purple cells in Figure 2). 20. Calculate all zero cells in \(j\)th row between \(p\)th and \(g\)th columns, i.e., PAM(\(j\), \(k\)) for \(p<k\)\(<q\), based on Equation (3) (see purple cells in Figure 2). 21. Set \(p=q\). until\(q\leq j\)-\(1\). 22. Set \(j=j+1\). until\(j\leq K\). 23. Output PAM as full matrix (without zero cells) based on interpolation. ```  
**Algorithm 2** (_Creatting full PAM matrix based on interpolation_)
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 2 Communication Architectures
### Centralized Communication Architecture  
Centralized communication architecture evolved from single-UAV systems, and was implemented for UAV swarm technology. As shown in Figure 1, there is a central node (i.e., fixed networking infrastructure) to which all UAVs in the swarm are connected. Each UAV forms a one-to-one relationship with the infrastructure and directly receives control commands from the infrastructure. This type of centralized communication architecture is relatively stable, adopts simpler routing algorithms, and has a smaller scale. When the size of the UAV swarm and the coverage area of the mission are small and the mission relatively simple, this type of architecture is more suitable. One of the applications of centralized communication architecture is \"UAV-GCS Centralized Data-Oriented Communication Architecture\", which is designed for crowd surveillance [37].  
However, the connection between UAVs requires infrastructure for transmission. The U-T-I distance is much greater than the U-T-U distance, which causes longer delays. In addition, considering the high mobility of UAVs and the coverage requirements of swarm applications, this architecture is obviously not stable. The infrastructure as a central node also causes the entire communication network to be paralyzed once the ground station or satellite breaks down or is attacked. Therefore, centralized communication architecture has the disadvantage of Single Point of Failure (SPOF). As a result, centralized communication architecture can be regarded as unreliable.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Data-Driven FDD Methods for Railway S&C Systems
#### 3.2.3 FP Methods
Different possible faults could be also taken into consideration, especially those related to _'obstructed'_ failures, as they have a 40.1% of occurrence compared to only 17.9% for _'dry chairs'_ and 40.9% for all remaining failure types, as can be seen in Figure 19. It should be noted that it is hard to diagnose these _'obstructed'_ failures and it is almost impossible to predict their life time since they are an abrupt failure type; however, they can be detected to improve railway S&C system operating performance. Further, from this review of the existing FDD methods (both model-based and data-driven techniques) applied to railway S&C systems, it is observed that electrical fault modes (as shown in Figure 9) have not been considered at all, except for crushed cable fault mode [95], startup circuit disconnection (bed contact) [114], and electric relay switch failure [115]. This is another opportunity for researchers to study and analyze other possible fault modes in railway S&C systems. Analysis of the different signals adopted for analysis in all the reviewed articles, when applying either model-based methods or data-driven techniques, was performed and the results are shown in Figure 20.  
Analyzing the results shown in Figure 20, it can be seen that several types of signal are considered, including: voltage, force, current, speed, displacement, motor temperature, torque, tension, vibration, 2D/3D measurement, power, strain, audio (sound), and weather-related data (e.g., rain, showery rain,  
Figure 19: Failure mode distribution in 2009 [10]. Figure 20: Different signals analyzed in the field of FDD for railway S&C systems. snow, sleet, thunderstorm, etc.). The frequency of applying those signals is illustrated in Figure 21, which shows a clear tendency toward applying the first five signals: voltage, force, current, speed, and displacement; the current signal had the highest frequency. The results shown in Figures 20 and 21 further open doors for researchers to adopt the remaining signals such as vibration signals that are known to be sensitive to any fault that causes deterioration in the mechanical structure of the system, and which are used extensively in other fields such as rotating electrical machine FDD [54,84].
## 2 Materials and Methods
### Risk Management
#### 2.2.1 Risk Assessment  
The risk assessment process can be divided into three stages: risk identification, risk analysis, and risk evaluation [9; 10].  
**Risk Identification** deals with the determination of risks or threats that are interfering with an application's objectives. It explores potential risk factors, vulnerabilities, and threats [9].  
**Risk Analysis** is conducted once risks have been identified. It focuses on the exploitability of risk related events and their impacts on the system. However, the outcomes of such an analysis may differ depending on the application domain, viewpoints, and individual backgrounds [9].  
**Risk Evaluation** builds on the results from the risk analysis. It is necessary in identifying which risks must be addressed and how they must be addressed in terms of the techniques used [9]. However, there may be various solutions to a certain challenge. A strategy addressing even multiple weak spots may be suggested. The purpose of a risk assessment is to aid in decision making. While certain hazards require more investigation, others may not demand specific activity. Consequently, the assessment may disclose circumstances that do not satisfy the intended goals, and as a result, these goals must be re-evaluated.  
Once the risk assessment procedure has been completed, measures must be implemented for the evaluation results to take effect. This is accomplished through a procedure known as risk treatment.
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 2 Background  
### Free-Breathing and Ungated Cardiac MRI Recovery  
One of the main objectives in free-breathing and ungated cardiac MRI is to get high spatio-temporal resolution images from undersampled k-t space measurements. The main focus of this work is to reconstruct the images \(\mathbf{x}_{1},\cdots,\mathbf{x}_{M}\) in the time series from the undersampled multi-coil measurements. The images \(\mathbf{x}_{i},\ i=1,\cdots,M\) are usually represented by the Casorati matrix  
\[\mathbf{X}=[\mathbf{x}_{1},\cdots,\mathbf{x}_{M}].\]  
The MR images are acquired by the multi-coil measurements  
\[\mathbf{b}_{i}=\mathcal{A}_{i}(\mathbf{x}_{i})+\mathbf{n}_{i},\]  
where \(\mathbf{n}_{i}\) is a zero-mean Gaussian noise that is introduced into the measurements \(\mathbf{b}_{i}\) during the data acquisition process. The forward operator \(\mathcal{A}_{i}\) is the measurements operator, which is time-dependent. \(\mathcal{A}_{i}\) evaluates the multi-coil k-space measurements of the image frame \(\mathbf{x}_{i}\) corresponding to the time point \(i\), based on the k-space trajectory of data acquisition.
## 3 Data Reduction Methodologies
### Application of Response Surface Methodology (RSM) to Flight Data
Based on the linear relationships for the Mach number, unit Reynolds number, and the pressures shown in Figure 6, and on the definition of \(C_{p}\) described above, and by considering that the unit Reynolds number is a combination of other parameters, the influential parameters are evidently the flight Mach number and the pressures. Therefore, these two parameters were considered as governing parameters and the other parameters were screened out.
## 1 Introduction
To elucidate the mechanism of cellulose hydrolysis using the polymeric catalyst, theoretical calculations were performed in this work to investigate the direct interactions between PSSA, PIL, and cellulose chains using classical MD simulations coupled with metadynamics (MTD) [37; 38; 39; 40] simulations to determine its associated free energy surface (FES). Elucidating the catalytic mechanism will provide an insight and understanding to the biomass hydrolysis process, and it is valuable for developing better catalysts. MTD simulations have been used extensively in our previous investigations to map out the free energy surfaces [25; 27; 28; 29; 30; 31] for xylose and glucose condensation reactions, glucose ring opening, glucose-to-hydroxymethylfurfural (HMF) dehydration, and glucose-to-fructose isomerization reactions. It was found that the solvent plays a critical role in all of these reactions, and that the barriers are largely solvent-induced. Here MD-MTD simulations are used to investigate the FES for cellulose hydrolysis in the presence of explicit solvent ([EMIM]Cl and water) molecules. Figure 1: The proposed interaction between PSSA, PIL and cellulose chains in the hydrolysis. Intramolecular hydrogen bonds in the cellulose chain are the black dashed line. Hydrogen bonds formed between PIL/PSSA and cellulose chains are the green dashed line.