esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Solution Approach
### Tuning of Algorithm Parameters  
The parameters of NSGA-II are the size of the population \(\left(\text{N}_{\text{plan}}\right)\), the number of generations (\(\text{N}_{\text{gen}}\)), the percentage of crossover (\(\text{P}_{\text{crossover}}\)), the percentage of mutation (\(\text{P}_{\text{mutation}}\)), and the mutation rate (\(\text{R}_{\text{mu}}\)). To tune these parameters, we used a Taguchi approach considering the set coverage function C, presented by [PERSON] et al. [32], as the response function. In order to compare two solution sets from Pareto optimization the set coverage was widely applied. For example, \(\text{X}^{\prime}\) and \(\text{X}^{\prime\prime}\) were the two target sets, and the function C maps the ordered pair \(\left(\text{X}^{\prime},\,\text{X}^{\prime\prime}\right)\) into the interval \([0,1]\):  
\[\text{C}\big{(}\,\text{X}^{\prime},\,\text{X}^{\prime\prime}\,\big{)}\,=\, \frac{\big{|}\big{\{}\text{a}^{\prime\prime}\in\,\text{X}^{\prime\prime};\exists \,\text{a}^{\prime}\in\,\text{X}^{\prime}:\,\text{a}^{\prime}\leq\,\text{a}^{ \prime\prime}\big{\}}\big{|}}{\mid\text{X}^{\prime\prime}\,\big{|}} \tag{28}\]  
The value of \(\text{C}\big{(}\,\text{X}^{\prime},\,\text{X}^{\prime\prime}\,\big{)}\) equal to 1 shows that all solutions of \(\text{X}^{\prime\prime}\) were dominated by or equal to solutions in \(\text{X}^{\prime}\). The value of \(\text{C}\big{(}\,\text{X}^{\prime},\,\text{X}^{\prime\prime}\,\big{)}\) equal to 0 means the opposite situation where none of the solutions in \(\text{X}^{\prime\prime}\) are dominated by or equal to solutions in \(\text{X}^{\prime}\).  
Using the set coverage, an L27 Taguchi design [33] was introduced to test the five factors with three levels (Low, Medium, High). Table 1 provides all the levels of tuning parameters, and Tables A1 and A2 in Appendix B show the detailed results of the experiment and the response table for means. Figure 5 provides the main effect plot for the means of the tuning results. Based on these results, the considered values of the parameters are shown in Table 2.  
Figure 5: NSGA-II main effects plot for means.
# Benchmarking Message Queues
## 2 Background
### RabbitMQ [9]  
RabbitMQ is a popular open-source message broker that is widely used in distributed systems. It is written in the Erlang programming language and is based on the Advanced Message Queuing Protocol (AMQP) standard.  
As a message broker, RabbitMQ acts as an intermediary between applications that need to send and receive messages. Producers send messages to RabbitMQ, which stores them until they are consumed by consumers. This decouples producers and consumers, allowing them to operate independently of each other and ensuring reliable message delivery even if one of the systems is down.  
RabbitMQ supports a variety of messaging patterns, including point-to-point, publish-subscribe, and request-response. It also provides advanced features such as message routing, message acknowledgments, and dead-letter queues.  
RabbitMQ uses exchanges and queues to route messages between producers and consumers. An exchange receives messages from producers and routes them to one or more queues based on a set of rules called bindings. Consumers then subscribe to specific queues to receive messages.
# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
###### Abstract
Moreover, supervised deep learning methods rely on labeled data in the training phase, which usually needs to cover suitable diversity and complexity to accommodate various defect situations in real application scenarios. However, different defect types may vary depending on factors such as shape, size, color, and lighting conditions, etc., which makes it difficult for supervised methods to effectively generalize to unseen defect samples. In addition, in real industrial environments, factors such as the captured background, image resolution, and light reflections may negatively affect the image quality, resulting in poor detection accuracy. In view of these problems, this paper proposes a feature-oriented reconstruction method for unsupervised surface-defect detection on aluminum profiles. In the preprocessing stage, the original image is processed through techniques such as boundary extraction, background removal, and data normalization to extract the main part of the image of the aluminum profile and reduce the influence of irrelevant data features. In the model-training stage, the image of the main part of the aluminum profile is cropped into an area image with uniform specifications as input, and the interference of complex texture is removed by the feature-optimization module on the premise of retaining its surface features, and the body features of the aluminum profile are extracted by mask reconstruction. In the detection stage, a specific mask is used to reconstruct the area-feature image, the average similarity of the reconstructed area-feature images before and after the reconstruction is determined, and a comprehensive evaluation of the detection results determines whether there are defects. The main contributions of this paper are the following two points:  
1. A new unsupervised surface-defect detection method for aluminum profiles is proposed. It solves the problems of the existing supervised learning surface-defect detection methods for aluminum profiles, which require a large number of manually labeled defect features in advance, and a small number of aluminum profile samples and incomplete defect types lead to the insufficient detection capability of undefined defect categories. 2. Incorporating the feature-optimization module into the Masked Auto-Encoders (MAE) model eliminates the complex texture randomly distributed on the surface of aluminum profiles and retains its surface feature information, which excludes the inter ference of irregular texture on the generated model and improves the performance of the model. This article is organized structurally as follows. Section 2 describes deep learning detection methods as well as transformer models. Section 3 presents a feature-oriented reconstruction method for unsupervised detection of surface defects on aluminum profiles. Section 4 describes the datasets, training details, and evaluation indicators used and discusses the results of each experiment. Section 5 summarizes the experimental results and looks at future research directions.
## 8 Open Issues, Challenges, and Future Research Directions
Furthermore, the persistent change in attack variations and the considerable increase in attack volume add to the challenge of identifying and responding to those incidents and threats. Moreover, several RPL-specific attacks are yet to be identified, thus requiring substantial defense mechanisms. Unfortunately, there are very few attempts to develop defense mechanisms against sophisticated attacks [14; 16]. Based on the outcomes of this SLR and our standpoint, we provide the following future research directions for ML- and DL-based approaches in detecting RPL attacks:  
* There is an urgent need for a standard comprehensive benchmark dataset that is publicly available to enable researchers to test the performance of their proposed works and compare them with others fairly. The proposed solution will address **Challenge 1**. * Since RPL networks comprise low-powered nodes, there is a need to evaluate the performance and impact of the existing solution using additional network parameters, such as PRC, computational cost, deployment strategy, and coverage area of the defense mechanism. Furthermore, there is a need to develop lightweight ML and DL approaches that operate in a constrained environment and are adaptable for deployment in tiny devices. The proposed solutions will tackle **Challenge 2**. * Researchers need to develop efficient mechanisms in dynamic network topology and support mobility options. In addition, the deployment of the detection mechanism in the network plays a crucial role in detecting the attacks successfully. Hence, there is a need to identify the optimum location in the network that contributes to a high detection rate with less energy and result in the lowest computational overhead for network nodes. The suggested solution will address **Challenge 3**. * Researchers need to develop highly scalable and fast response solutions that provide a minimal delay in information transmission, especially for crucial IoT applications. This suggested solution will address **Challenge 4**. * There is a lack of solutions that work with heterogeneous network devices. The expected future of IoT networks is towards technological convergence with different technologies, such as cloud computing, software-defined networking, blockchain, and 5G. The offered solution will solve **Challenge 5**. * There is a need for multiple defense approaches for guarding against newly discovered RPL attacks. Furthermore, there is also a need to incubate combined/hybrid ML and DL approaches to exploit their powerful features to identify known and zero-day threats. The proposed approaches must be highly robust, scalable, and support Quality of Service (QoS). The offered solutions will address **Challenge 6**.
## 4 Application: A Real Case  
### Geological and Engineering Background  
The Yanqianshan iron mine is located in Anshan City, Liaoning Province, China; see Figure 8. The geological section of the slope is shown in Figure 9. It is an old mine, approximately 20 km away from the center of Anshan City. The Yanqianshan iron mine was first constructed in 1960, and the open-pit mining was finished in September 2012. Since 2012, transitional ore body mining has been carried out, and the main ore bodies of the mine are in the west, east, and north sides. By the end of March 2015, the mining of iron ore body in the east and west side will be suspended. The ore body at the north side of the pit is underground-mined for the ore body below the bottom of the pit after the end of the transition period. The mining depth will not exceed -500 m for the Yanqianshan iron mine [26; 27; 41]. At present, the Yanqianshan iron mine is still active and the daily output is approximately 5500 tons.  
Figure 8: The location of the study area.  
Figure 9: Geological section of the slope.  
The mining area is located in the hilly area of Qianshan Branch, surrounded by mountains. The exposed strata in the mining area include Quaternary metamorphic rocks, the Liaohe Formation, and the Anshan Formation, while the main composition of the rock strata is Anshan metamorphic rocks. The basic structure of this area is a monoclinic structure, with a strike of 270 to 300 degrees and a large dip angle, mostly between 70 and 90 degrees. According to the geological section of the slope, and taking the left and right boundary of iron ore as the boundaries, the study area is divided into three sub-areas (i.e., the South, Middle, and North). Moreover, according to the north-south direction, the part in the south is called the south slope, the part in the north is called the north slope. The most common rocks in the southern sub-area are mixed granite rocks, forming massive and gneissic structures. The rocks in this sub-area are greatly affected by the structure and weathering. Banded magnetite quartzite is common in the middle sub-area. The formation is an Anshan-type iron ore bed (orebody), which is mainly composed of magnetite quartzite and hornblende magnetite quartzite. Carbonaceous phyllite is the most common rock formation in the northern sub-area. The rock mass in this stratum is broken and contains a strong weathering zone.
## 4 Fluid Structure Interaction Analysis
### Localized Fluid Column Model  
The local impact analysis was carried out by FSI using a mesh deformation scheme and an FSI coupling interface. In the coupling analysis step, in order to evaluate the structural response caused by the fluid impact load, the interactive analysis performed repeatedly at each timestep by two-way FSI analysis scheme. The FSI model consists of both the structure and fluid domains as shown in Figure 14. The localized CFD model of a fluid column was coupled with the transient structural FE model of an insulation panel. When the maximum velocities of real scale were determined, a fluid column can be assumed to generate sloshing impact for hydro-elastic analysis. Therefore, the FSI between a fluid column and an insulation panel was considered. For the fluid column model, the height (\(h\)) of fluid column is defined by fluid velocity (\(V\)) and the rise time (\(RT\)) that is defied by the maximum peak pressure signal as shown in Figure 9. The height (\(h\)) of the column is defined by Equation (13).  
\[h=V\times RT \tag{13}\]  
For fully coupling the FSI analysis, the boundary conditions on the FSI interfaces are that the displacements of the fluid and solid domain are compatible and tractions at these boundaries must be in equilibrium. To create the FSI analysis conditions, the fluid-structure interface was applied to set the displacement (\(u\)) at the nodes of the fluid region and solid region following Equation (14). Thus, the fluid and structure velocity at the interface behave identically. The stress term due to the hydro force is transferred to the solid region and structural analysis can proceed using Equation (15).  
\[u_{fluid}=u_{solid} \tag{14}\]  
Figure 13: Vector plot of fluid velocity at 5.75148 s.
## 2 Materials and Methods
### Data Analysis  
Descriptive statistics, including the number of samples or data points (\(N\)=), mean \(\pm\) standard deviation (95% confidence interval), and frequency distribution data, were calculated for each trial. These calculations were used to determine the overall mean \(\pm\) SD for various parameters, including the time taken to complete each trial (s), the distance covered (m), the number of satellites used as an indicator of availability and quality of satellite signals received by the receiver, the horizontal accuracy measured by the Hrms, and the integrity assessed by the geometric quality of satellite configurations represented by the HDOP and PDOP metrics.  
Subsequently, the dilution of precision data was categorised based on a rating system [23; 26]. In this system, ratings were assigned as follows: values less than 1 were considered ideal, values between 1 and 2 were categorised as excellent, values between 2 and 5 were classified as good, values between 5 and 10 were deemed moderate, values between 10 and 20 were considered fair, and values greater than 20 were categorised as poor.  
For the dry-land slalom, the total distance (m) covered (test 1) was compared with the known distance for each trajectory and the known measure of that trajectory path using a two-tailed one sample \(t\)-test. The differences between all grouped dependent variables (Mean, SD, N) for the short and long trajectory were assessed using a non-paired \(t\)-test with [PERSON]'s correction if data did not pass [PERSON]'s normality test.  
Comparison between dry-land trajectories and the real-world on-water slalom for all mean trial dependent variable data was performed using a one-way ANOVA with [PERSON]'s multiple comparison's test with single pooled variance. If the data did not pass the normality test, then a Kruskal-Wallis non-parametric one-way ANOVA was employed. Post-hoc pairwise comparisons were conducted using [PERSON]'s multiple comparison test to assess the differences between paired tests. All statistical analysis was performed using Graphpad Prism (V6.0f) with the alpha value set at \(p=0.05\).
## 5 System Calibration  
To calibrate the system, it is necessary to compute the pixel sizes in both the \(x\) and \(y\) directions based on the actual size of the holes in the workpiece being measured. Camera calibration involves determining internal parameters like focal length and correcting lens distortions, as well as establishing the camera's position and orientation. This process, aided by specialized software and calibration targets, enhances measurement accuracy and computer vision applications, especially in robotics, augmented reality, and industrial quality control. The following steps are involved in this process:  
1. The user provides the 3 SMVI platform with the actual diameter (in millimeters) of the object being measured. If the object is comprised of multiple circular parts, the outer part's size (maximum diameter) should be utilized.
2. The software searches for the two edge pixels on the outer contour with the minimum and maximum \(x\) coordinates to determine the maximum diameter of the captured image in the \(x\) direction (\(D_{max}x\)) using the formula \[D_{max}x=Abs(X_{max}-X_{min})\] (5) where \(X_{max}\) and \(X_{min}\) are the maximum and minimum \(x\) coordinates of the edge pixels of the outer contour, respectively.
3. The calibration factor in the \(x\) direction \((CFx)\) is calculated as follows: \[CFx=Dactual/D_{max}x\] (6)
4. In the same manner, the calibration process also involves determining the calibration factor in the \(y\) direction \((CFy)\) by identifying the two pixels with the minimum and maximum \(y\) coordinates, which can be obtained by using the formula \[CFy=Dactual/D_{max}y\] (7)
5. After calculating \(CFx\) and \(CFy\), all \(x\) and \(y\) coordinates of the edge pixels are multiplied by these calibration factors.
6. The primary roundness error \((RE\ primary)\) is then calculated using the least squares circle technique on the edge pixels.
7. Furthermore, using a formula derived from Excel, the fixed error \((FE)\) in pixels is determined based on the user-provided value of the smoothing factor \((SF)\).
8. The fixed error is then calibrated and expressed in millimeters (\(FE\ mm\)) using the following equation: \[FE\ mm=FE\ pixel\ CFx\times CFy\] (8)
9. The final roundness error \((RE\ final)\) is calculated as follows: \[RE\ final=RE\ primary-FE\ mm\] (9)
## 3 Data Acquisition and Processing
### EMG-to-Force Processing--Dynamic Electromyography (EMG)
The \"relaxation EMD\" is considerably longer than the delay observed at the onset of muscle contraction (250-275 ms) [65]. As a result, the use of a constant offset correction between EMG timing and predictions of force generation is contraindicated. Thus, a second-order critically damped Butterworth low-pass digital filter was applied to temporally match EMG activity and muscle force [39]. This method led to a relatively brief onset of EMD and more prolonged relaxation of EMD. The EMG input to the model (activation dynamics, (e(t)) was defined by  
\[e(t)=g[k_{1}E(t)+k_{2}E(t-1)+k_{3}E(t-2)+k_{4}e(t-1)+k_{5}e(t-2)]. \tag{1}\]  
where \(E\) represents the percent maximum voluntary contraction for the within-subject EMG profiles, \(e\) the percent maximum voluntary contraction for the within-subject EMG profiles following filtering, and the constant \(\gamma\) (=\(E_{MAX}\)/\(e_{MAX}\)) minimizes further attenuation of the EMG signal due to signal processing. The constants \(\kappa_{1}\)-\(\kappa_{5}\) are derived from the sampling frequency-to-cutoff frequency ratio (\(\mathrm{F_{s}}/\mathrm{F_{c}}\)), where a single \(\mathrm{F_{s}}/\mathrm{F_{c}}\) ratio was empirically determined [66] and was like previously reported values [39; 42; 67; 68; 69; 70]. Muscle-tendon unit length (\(L^{MTU}\)) and force (\(\mathbf{F}^{MTU}\)) were determined at percent gait cycle intervals. Muscle shortening or lengthening contraction velocity was established by mathematical differentiation of MTU length (\(v^{MTU}=dL^{MTU}/dt\)). Positive velocities were related to concentric (shortening) muscle contractions. Concentric (shortening) contractions were defined as positive, whereas negative power occurred when the muscle length increased during force production (suggesting a role in restraining movement). Power generated by each muscle was the dot product of that muscle's contraction force (above) and its contraction velocity along its line of pull (co-linear). The EFP net knee power for each study participant was obtained by summing the power produced by each muscle crossing the knee. Summed values for all fourteen muscles were determined at percent gait cycle intervals. The correlation between the across-subject mean EFP knee power and the mean kinetics plus kinematics knee power for these same study participants was performed, where  
\[\int_{i=1}^{N}P_{\textit{Hence}}^{\textit{EFP}}\approx\ \int_{i=1}^{N}P_{\textit{ knee}}^{\textit{KIN}} \tag{2}\]
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results
### HSI Data Sets  
In this section, three real public data sets, including Pavia University, Kennedy Space Center (KSC), and Botswana, were used to evaluate the performance of the proposed method. Figure 5 gives a brief overview of three HSI data sets.  
Figure 5: Details of Pavia University, KSC, and Botswana.  
* Pavia University: The first image is Pavia University, often used in HSI classification. The data were obtained by ROSIS sensors in Pavia, Italy. After processing, the size of the dataset is \(610\times 340\times 103\). The dataset contains 9 categories with 42,776 labelled pixels. The detailed information is tabulated in Figure 5.
* Kennedy Space Center: The second set of data comes from the Kennedy Space Center. It was collected on 23 March 1996, NASA AVIRIS (AVIRIS) at the Kennedy Space Center (KSC). A total of 176 bands were used in this experiment because of their absorptive capacity and low signal-to-noise ratio. There are 13 kinds of land covers, containing 5211 labelled pixels. The detailed information is tabulated in Figure 5.
* Botswana: The last dataset is Botswana. Between 2001 and 2004, NASA's EO-1 satellite collected a set of data in the Okavango Delta in Botswana. On the EO-1, the Hyperion sensor can get 30-m pixels over a 7.7-km strip, and within a 10-nm window, 242 wavelengths in the 400-2500 nm band. The bands without calibration and noise are removed, and the remaining 145 bands are used for HSI classification. The detailed information is tabulated in Figure 5.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Tracking  
UAV tracking is a type of object tracking task that involves estimating the state and trajectory of the UAV. Object tracking (OT) is a fundamental open problem that has been extensively studied in the past decade. Two prominent paradigms for OT are Filters-based methods and deep Siamese Networks (SNs) [102]. In this section, we review recent literature on UAV tracking and discuss the advancements made in this field.