esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
## 3 Controller Design
### LQR Controller with Hybrid Algorithm  
In this paper, a hybrid particle swarm optimization genetic algorithm is used to find the optimal weight coefficient. The genetic algorithm has more excellent convergence compared with other traditional optimization algorithms. However, the ability of the local search convergence is poor, which causes the low efficiency. Meanwhile, the particle swarm optimization algorithm operates efficiently. Thus, in this paper, a hybrid optimization algorithm that combines the advantages of these two algorithms is used to find the optimal solution. The particle swarm optimization genetic hybrid algorithm flow chart is shown in Figure 4.  
We select sprung mass acceleration, suspension deflection, and tire dynamic load as performance indicators. However, for the inconsistent units and orders of magnitude, the performance indicators selected need to be normalized. Therefore, the fitness function is given by  
\[f(x)=\min\left(\frac{Sx_{s-a}}{5x_{s-p}}+\frac{Sx_{u-a}}{5x_{su-p}}+\frac{SK_{ u}x_{gu-a}}{5x_{u}x_{gu-p}}\right) \tag{13}\]  
where: \(Sx_{s-a}\), \(Sx_{su-a}\) and \(SK_{w}x_{gu-a}\) are the root mean square values of the sprung mass acceleration, sprung mass deflection, and tire load under control. \(Sx_{s-p}\), \(Sx_{su-p}\), and \(SK_{W}x_{gu-p}\) are the root mean square values of the sprung mass acceleration, sprung mass deflection, and tire load without control. The constraints are denoted as (14).  
\[s.t\left\{\begin{array}{rcl}S\ddot{x}_{s-a}<S\ddot{x}_{s-p}\\ Sx_{su-a}<Sx_{su-p}\\ SK_{w}x_{gu-a}<SK_{w}z_{gu-p}\\ \end{array}\right. \tag{14}\]  
The optimization process of the LQR control with the hybrid particle swarm optimization genetic algorithm is shown in Figure 5.  
Figure 4: Hybrid Particle Swarm Optimization Genetic Algorithm Flow Chart.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 3 Joint Optimization Algorithm
### Subproblem 1: UAV Horizontal Placement
```
0: Initial feasible UAV placement \(\mathbf{q}_{m}^{0}=\left(x^{0},y^{0}\right)\), iteration \(r_{1}=0\), maximum iterations \(r_{1}^{\max}\), step size \(\sigma\)
0: Optimal UAV location \(\mathbf{q}_{m}^{*}\)
1:while\(r_{1}\leq r_{1}^{\max}\)do
2: Use CVX to compute P4 optimal UAV location \(\mathbf{q}_{m}^{r_{1}^{\prime}}\)
3: Set \(\mathbf{q}_{m}^{r_{1}+1}=\mathbf{q}_{m}^{r_{1}}+\sigma\left(\mathbf{q}_{m}^{r _{1}^{\prime}}-\mathbf{q}_{m}^{r_{1}}\right)\)
4:if the access users has changed
5: Reinitialize \(\mathbf{f}\), \(\mathbf{t}\), \(\mathbf{p}\), \(\mathbf{b}\)
6:continue
7:endif
8:if\(\mathbf{q}_{m}^{r_{1}+1}\) is a convergent solution of P3
9:break
10:elseif
11: Set \(r_{1}=r_{1}+1\)
12:endif
13:endwhile
```  
**Algorithm 1**UAV Placement Optimization Algorithm
## 2 Power Kite Generator System Modeling
The PKGS is connected to the electrical grid through the power electronic conversion system illustrated in Figure 1:0 This system comprises several critical components that work to convert kinetic energy into electrical energy: a PMSM, which generates electrical power during generation phases and functions as a motor to reel in the cable during recovery phases; a machine-side converter (MSC) to regulate the PMSM current and torque, ensuring optimal shaft speed control to achieve MPPT; a grid-side converter (GSC) responsible for managing the power flow between the DC-link and the electrical grid, maintaining DC-link voltage stability, and controlling power exchange with the grid. Between the MSC and the GSC lies the DC-link capacitor, which maintains a stable voltage level and facilitates smooth power transfer. Finally, an inductive filter is employed to mitigate electrical noise and fluctuations of the power fed to the grid. This work focuses on the control design for the MSC in a PKGS, specifically addressing the challenges of machine speed regulation. In the PKGS operation, the optimal tether reel-out speed during power generation phases must maintain a ratio of one-third of the wind speed component along the tether direction, as derived in (9). This reference speed contains periodic variations, as illustrated in Figure 1:1 Furthermore, the system must handle abrupt transitions between generation and recovery phases, where the machine reverses rotation direction to enable rapid tether retraction. These operational requirements present two key control challenges:  
* Precise tracking of a periodically varying reference speed during power generation phases. * Ensuring stable transitions between generation and recovery phases. Conventional PI controllers, typically designed for first- or second-order closed-loop response characteristics, perform adequately for step references but struggle with periodic reference tracking as encountered in PKGS applications. This tracking limitation directly affects power generation efficiency. While increasing controller gains could potentially improve dynamic response, this approach compromises system stability, leads to excessive control effort, and can introduce undesirable overshoots during phase transition phases without achieving satisfactory tracking performance during generation phases. To address these limitations, this paper proposes an enhanced MFC approach incorporating a nonlinear hyperbolic tangent tracking differentiator. This novel approach aims to overcome both the periodic tracking and transition challenges inherent in PKGS operation.
## 3 System Model
### Problem Formulation  
This subsection will present the formulation of our optimization problem for which computation offloading and allocation of resources for a multi-user and multi-UAV-enabled mobile edge-cloud computing system are considered. The main issue of this problem is to decrease the consumption of energy for the system with a latency constraint. This formulation is as follows:  
\[\begin{array}{ll}\underset{\varphi}{\text{min}}&\Big{[}\sum_{i=1}^{M}\sum_{j =1}^{N}E_{i,j}\Big{]}\\ \text{s.t}&\Big{[}E_{i,j}-E_{i,j}^{l}\Big{]}\leq 0,&\forall_{i,j}&\text{C1}\\ &\tilde{T}_{i,j}\leq\tau_{i,j},&\forall_{i,j}&\text{C2}\\ &\sum_{j=1}^{N}\sum_{a=1}^{M+1}\varphi_{i,j,a}\tau_{i,j}\leq B_{i},&\forall_{i }\in[1..M]&\text{C3}\\ &\sum_{j=1}^{N}\sum_{a=1}^{M}\varphi_{i,j,a}f_{i,j}^{e}\leq F_{i},&\forall_{i }\in[1..M]&\text{C4}\\ &\sum_{a=0}^{M+1}\varphi_{i,j,a}=1,&\forall_{i}&\text{C5}\\ &\varphi_{i,j,a}\in\{0,1\},&\forall_{i,j},&\text{C6}\end{array} \tag{12}\]  
Constraint C1 is the upper bound of energy consumption. Constraint C2 controls the requirement deadline for each task. Constraint C3 manages the capacity of bandwidth. Constraint C4 describes the edge server's capacity upper limit. Constraint C5 ensures that each task must be processed only one time. Lastly, constraint C6 ensures that the variable of decision offloading is binary.  
The offloading decision can be derived by solving this problem. However, this problem is classified as NP-hard, in which the offloading decision variables are binary [45]. Therefore, to solve this problem in an efficient manner, a binary relaxation approach is utilized. Specifically, the binary variables are relaxed to new real variables, such as \(0\geq\varphi_{i,j,a}\leq 1\)[46]. Therefore, the formulation of the problem after relaxing the binary variables is shown in Equation (13).  
\[\begin{array}{ll}\underset{\varphi}{\text{min}}&\Big{[}\sum_{i=1}^{M}\sum_{j =1}^{N}E_{i,j}\Big{]}\\ \text{s.t}&\Big{[}E_{i,j}-E_{i,j}^{l}\Big{]}\leq 0,&\forall_{i,j}&\text{C1}\\ &\tilde{T}_{i,j}\leq\tau_{i,j},&\forall_{i,j}&\text{C2}\\ &\sum_{j=1}^{N}\sum_{a=1}^{M+1}\varphi_{i,j,a}\tau_{i,j}\leq B_{i},&\forall_{i }\in[1..M]&\text{C3}\\ &\sum_{j=1}^{N}\sum_{a=1}^{M}\varphi_{i,j,a}f_{i,j}^{e}\leq F_{i},&\forall_{i }\in[1..M]&\text{C4}\\ &\sum_{a=0}^{M+1}\varphi_{i,j,a}=1,&\forall_{i}&\text{C5}\\ &\varphi_{i,j,a}\in[0,1],&\forall_{i,j},&\text{C6}\end{array} \tag{13}\]  
Since the problem is considered as a linear problem, where the objective function and the constraints are linear, the near-optimal solution can be derived using well-studied linear approaches.
## 3 Results
### Tribological Behavior
#### 3.5.3 Erosion Tests
In this context, it is also worth mentioning that another earlier reported work even on axial SPS employed a feed rate of only up to 1.8 kg:h\({}^{-1}\) for a suspension with solid loading similar to that used herein. The feed rates employed in this study also surpass by more than a factor of five, the 0.24 kg:h\({}^{-1}\) feed rate employed in APS deposition of a similar coating. Further comparison of the axial SPS deposited Al\({}_{2}\)O\({}_{3}\)-YSZ coatings with the conventional powder-derived APS coatings revealed significantly lower porosity in the present case compared to the latter [30]. The typical values of coating porosity observed in the present study and other reported SPS coatings is 1-4 vol.% [31], whereas APS spraying resulted in 20 vol.% porosity [30]. For tribological applications, for which these composite coatings are commonly considered, the processing and economic benefits suggested by the present study are significant and also supported by good wear properties. Figure 13: Surface morphologies of Al\({}_{2}\)O\({}_{3}\)-YSZ coatings after erosion studies at (**a**) 30\({}^{\circ}\) impact at 48 m:s\({}^{-1}\), (**b**) 30\({}^{\circ}\) impact at 108 m:s\({}^{-1}\), (**c**) 90\({}^{\circ}\) impact at 48 m:s\({}^{-1}\), and (**d**) 90\({}^{\circ}\) impact at 108 m:s\({}^{-1}\).
## 3 Results and Discussion
### PMF Profiles of Small Molecules across CNTs  
Eventually, we computed the free energy of CO\({}_{2}\) and urea across the two CNTs, depicted by PMF profiles in Figure 9. It was found that there was no energy barrier for CO\({}_{2}\) at the entrance of both PCNT and FCNT. On the other hand, CO\({}_{2}\) had lower free energy in the CNT (\(-20.4\) kJ/mol for PCNT and \(-29.7\) kJ/mol for FCNT) than in the lipid headgroups as well as in the water. As a comparison, the free energy of CO\({}_{2}\) in the lipid bilayer was only \(-6.5\) kJ/mol (Figure 9C), much higher than that in the CNTs, indicating that CO\({}_{2}\) in the CNTs was energetically favorable. As to urea molecules, the PMF kept rising and reached the highest peak (approximate 50 kJ/mol) at the bilayer center, which is why urea molecules cannot permeate into the membrane. However, there was no energy barrier at the PCNT entrance, though urea molecules were accompanied by water molecules in the PCNT. However, the energy barrier at the FCNT entrance reached approximately 9.3 kJ/mol, due to the presence of the hydroxyl groups at the entrance of FCNT. Their electrostatic interactions with urea molecules prevented them from entering the FCNT.  
Figure 8: The transport properties of (7,7) FCNT in the lipid bilayer. (**A**,**B**) Snapshots at \(t\) = 50 ns, the lipid membranes are not shown for clarity. (**C**,**D**) Number of water, CO\({}_{2}\), and urea within FCNT as a function of simulation time.
## 5 Fuzzy Logic-Based WSN Power Management and Optimization
Moreover, as in most of fuzzy-based power management algorithms for WSNs discussed in the literature, the metrics such as network lifetime and battery level were used to evaluate the proposed algorithms in [50; 51; 52; 53; 54; 55; 56]. The authors validated the performance of the proposed technique under different network densities. Table 4 depicts the network lifetime improvement percentages calculated as follows:  
Figure 13: The FLC model. Network lifetime improvement (%) = [(new lifetime \(-\) original lifetime)/original lifetime] \(\times\) 100 %. Finally, Table 5 summarizes the surveyed papers related to the most important power management and optimization mechanisms in WSNs that utilize fuzzy logic. \begin{table}
\begin{tabular}{c c} \hline
**Fuzzy-Based Power Management Algorithms for WSNs** & **Network Lifetime Improvement (\%)** \\ \hline DSA [50] & 37.5\% \\ DPMA [51] & 14.0625\% \\ Power consumption reduction mechanism using fuzzy logic [52] & 30.76\% \\ Distributed fuzzy logic-based sink selection algorithm [53] & 20\% \\ Fuzzy logic-based mechanism utilizing particle swarm & 54.545\% \\ optimization algorithm [54] & 6.666\% \\ FLC-PSO [55] & 41.666\% \\ A fuzzy logic-based algorithm for smart homes [56] & 41.666\% \\ \hline \end{tabular}
\end{table} 
Table 4: Lifetime improvement percentages.
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 2 Literature Review
### Summary  
Previous experimental research, which aimed to elucidate the mechanisms underlying the emergence of cable fires, found that cable fires are caused by a complex combination of critical factors. To investigate the underlying processes, risk analysis should be conducted to quantitatively assess the risk level of cable fires. Compared with neural network-based models [30], bow-ties [31], and Petri nets [32], BNs have the advantages of flexible inference and are good at dealing with uncertain relations, not requiring large amounts of data. They are widely used in accident probability analysis [33], accident scenario deduction [34], fault diagnosis [35], and risk assessment [36]. BNs can be used for quantitative analysis using forward and reverse methods [37; 38]. Accordingly, to analyze the early risk of cable fires from the risk transmission perspective, the forward and reverse analyses of BNs are applied for risk assessment and risk cause reasoning, respectively. Combined with multi-factor coupling experiments of cable overload fires, the theory of risk transformation, and the BN-based risk assessment, this study proposes a composite early cable fire risk analysis method for investigating the fire risk in energized cables before cable fires, and for inferring the critical factors leading to cable fires.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 4 Experiment
### Experimental Setup
#### 4.3.3 Hyperparameter Setup  
To determine the hyperparameter \(\eta\), we make a validation set containing negative samples shown in Figure 5 and performed a grid search on this validation set until reaching the maximum AUROC. We randomly mask the anomaly-free images with black or gray rectangles that are 1% of the size of the prototype to generate negative samples. In the experiment, we set 0.0005 as the value of \(\eta\).
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 3 Taxonomy of Deep-Learning-Based Medical Video Analysis
### Deep-Learning-Based Analysis Tasks  
(1) Classification: Classification is a process that involves assigning a class label to the input data [13; 14]. Mostly, a classification task allocates a single label to the input. However, sometimes it involves predicting the probability across two or more class labels. The classification model assumes that the classes are mutually exclusive in these cases. The research articles that have explored deep-learning-based classification techniques for echocardiography, endoscopy, and ultrasound are reviewed in Sections 6.1, 7.1 and 8.1.  
(2) Segmentation:Image segmentation is a process in which the input is broken down into segments, which helps reduce the image's complexity to make further processing or analyzing the image easier and simpler [15]. Segmentation, in other words, is a method of assigning labels to pixels. All pixels belonging to the same class have a common label assigned to them. The research articles that have explored deep-learning-based segmentation techniques for echocardiography, endoscopy, and ultrasound are reviewed in Sections 6.2, 7.2 and 8.2.  
(3) Detection:Object detection is a technique that identifies a particular object's location in an image or video [16]. Object detection can be used to count objects in a scene and determine and track their precise locations while accurately labeling them. The research articles that have explored deep-learning-based detection techniques for echocardiography, endoscopy, and ultrasound are reviewed in Sections 6.3, 7.3 and 8.3.
###### Abstract  
The Intra-Voxel Incoherent Motion (IVIM) model allows to estimate water diffusion and perfusion-related coefficients in biological tissues using diffusion weighted MR images. Among the available approaches to fit the IVIM bi-exponential decay, a segmented Bayesian algorithm with a Conditional Auto-Regressive (CAR) prior spatial regularization has been recently proposed to produce more reliable coefficient estimation. However, the CAR spatial regularization can generate inaccurate coefficient estimation, especially at the interfaces between different tissues. To overcome this problem, the segmented CAR model was coupled in this work with a \(k\)-means clustering approach, to separate different tissues and exclude voxels from other regions in the CAR prior specification. The proposed approach was compared with the original Bayesian CAR method without clustering and with a state-of-the-art Bayesian approach without CAR. The approaches were tested and compared on simulated images by calculating the estimation error and the coefficient of variation (\(CV\)). Furthermore, the proposed method was applied to some illustrative real images of oncologic patients. On simulated images, the proposed innovation reduced the average error of 47%, 21% and 58% for \(D\), \(f\) and \(D^{*}\), respectively, compared to the state-of-the-art Bayesian approach, and of 48% and 34% for \(D\) and \(f\), respectively, compared to the original CAR, while it achieved the same error for \(D^{*}\). The clustering approach was also able to consistently reduce the \(CV\) for each coefficient. On real images, the novel approach did not alter the IVIM maps obtained by the original CAR method, with the advantage of reducing their typical blotchy appearance at the boundaries. The proposed approach represents a valuable improvement over the state-of-the-art Bayesian CAR method and provides more reliable IVIM coefficient estimation, and is less sensitive to bias and inconsistency at tissue/tissue and tissue/background interfaces.  
DOW-MRI; IVIM; Bayesian conditional auto-regressive; \(k\)-means clustering
## 1 Introduction
Geocoding service separates address components by administrative district level (country, metropolitan city, city, county, etc.) for a given address string and provides latitude and longitude information for the address [6]. However, the address components received from geocoding may not match the actual administrative district database of the country when the address string contains typos or abbreviations (when translating addresses in foreign languages into English, incorrect or abbreviated place names are often used owing to differences in pronunciation, etc.). To overcome the limitations of geocoding, we constructed each country's administrative district database and developed a deep learning model for address verification and standardization. The generated deep learning model is useful in overcoming the limitations of existing geocoding, such as accurately classifying administrative district levels corresponding to address components, even when address strings contain typos or abbreviations. The proposed method can be commonly applied to all countries by using each country's administrative district database. Existing studies mostly verify addresses for specific countries. Address verification is generally divided into four levels, from the most detailed level: delivery point level, building/house number level, street level, and locality level. This technique is to utilize address verification in food safety management work and aims at verification above the locality level. This is because, in food safety management, it is more important to check the administrative district of the address and take administrative measures, such as export ban, rather than confirming the delivery point when a food risk occurs. The proposed method receives and verifies the English addresses of several countries and does not target the native language addresses of each country. The proposed method shows more than 90% accuracy in classifying each address component included in the Chinese address dataset by administrative district levels in China. Currently, the Ministry of Food and Drug Safety in Korea is unable to classify it from the address string to the administrative district levels, which is a very advanced result [5]. In addition, spelling errors or abbreviations included in the address are converted to standard terms and the location of the address can be checked on a map, which can be useful for food-safety management. As results from the verification on manufacturers' addresses of three different countries (China, Japan, and Cambodia), the proposed method is able to verify and evaluate the overseas address data quality significantly.
## 4 Data Obtained
### Data Format  
The files represented data from a product (antenna) produced on a manufacturing line, with each file referring to different products. The file information corresponded to the various tests a given product was subjected to during its production or testing. The following Figure 1 shows the basic format of the text files.  
The part number (EOL) represents a sequence of standard numbers and letters referring to the manufacturer. This sequence allows one to generate an identification number for the part produced. In the file, one can also identify the data on which the part was tested (line 2) and the name of the final assembly line (line 3) on which the testing process took place. Then, in the file, one can find the tests and their specific characteristics (line 4), submitted to the part on the final assembly line.  
The number of points for a given test indicates the frequency signal's size. The case studies address the tests with one frequency point since the most critical tests are the tests at a specific frequency, therefore testing one value/point in a given frequency range.  
Finally, there is the final validation that determines if the piece/product is ready to move on to the next phase of assembly or sale.  
Since it is desirable to subject these data to an exploratory analysis for a reflective view of the information, the next step involved using about 5000 files and creating a database (CSV) automatically, in which all the information formatted in rows and columns was appropriately stored. The creation of the database is a fundamental step since it guarantees access to the data in a structured way, facilitating data analysis and exploration.
## 4 Implementation and User Scenario
### Evaluation
#### 4.4.3 Question Collection  
As part of the study, questions related to these datasets are collected, classified, and aligned with CORES' objectives, enabling us to answer requests regarding kitchen appliances and provide detailed explanations, visualizations, and insights. A total of approximately 30 questions were gathered and categorized into 3 groups (as shown in Table 2).