esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 5 Conclusions  
In this study, we present an Improved Detail-Enhancement CycleGAN methodology designed to address the complexities of style transfer in facial images. Our novel approach integrates the AdaLIN technique to mitigate detail attenuation resulting from facial feature highlights, while simultaneously incorporating Laplacian regularization to minimize post-transfer artifacts induced by image noise. To rigorously assess the efficacy of our proposed method, we conducted a comprehensive comparative analysis against state-of-the-art approaches published within the past triennium. Empirical findings conclusively demonstrate that the facial images generated by our method significantly outperform those produced by extant methods, as evidenced by superior scores in both subjective and objective evaluation metrics. These advancements represent a substantial contribution to the field of facial image style transfer. However, we acknowledge that our method exhibits limitations, which warrant further investigation and refinement in future studies. One of the research directions is to enhance style prominence while maintaining facial integrity, which may involve exploring adaptive style weight mechanisms or multi-scale style transfer approaches. Another direction is to increase personalization and user control, allowing the model to provide users with greater control over the style transfer process. This would enable users to adjust style intensity or select specific facial features for style emphasis, which may involve developing interfaces and addressing related issues. By addressing these future research directions, we aim to not only overcome the current limitations of ourmethod but also to push the boundaries of facial style transfer technology, opening up new possibilities in digital art, entertainment, and human-computer interaction.  
Conceptualization, J.L., Y.H. and H.L.; methodology, J.L. and H.L.; software, J.L. and Y.H.; validation, J.L.; formal analysis, J.L.; investigation, J.L. and Y.H.; resources, H.L.; data curation, Y.H. and S.T.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and H.L.; visualization, J.L., Y.H. and S.T.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants 92270117.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 9 Applications of Derived Analytic Expression of MSE of the MUSIC Algorithm
### Accuracy in the MUSIC-Based Scattering Center Estimation  
One of popular remote sensing sensors is radar. In radar target recognition, range profile of a radar target is a good feature vector in that the peaks of a range profile correspond to scattering centers of the radar target. For a specific aspect angle, the distance between each peak location of range profile and true location of scattering center can be defined as estimation accuracy. Traditionally, the FFT is used to get the range profile. In [33], the authors employ the MUSIC algorithm to get range profile.  
Although the authors proposed a new MUSIC-based scheme to get range profile, which is superior to the conventional FFT-based scheme, they did not present an expression of the MSE associated with the estimate obtained from the peaks of the MUSIC-based range profile. By modifying the derivation leading to (40), the MSE of the estimate from MUSIC-based range profile can be obtained. The modification should reflect how the MUSIC algorithm for generation of the range profile is different from the original MUSIC algorithm for generation of DOA spectrum.
## 5 Regional Division and Analysis
### Network Load  
In a traditional blockchain, when a node in the network receives a transaction, it broadcast the transaction immediately, and wait for miners to compete for the right of bookkeeping and package the transaction, at which point a large number of transactions are constantly transmitting around the network. In this paper, after regional division, each node calculates the transaction received separately; therefore, when the satellite node receives the transaction, there is no need to broadcast. After each satellite node in the region completes the verification transaction and calculates the hash, it sends the valid transaction and hash results to the master node, and then the master node broadcasts after completing the packaging of the transaction and the generation of new blocks. In this way, the transaction need to broadcast only once in the network, which can avoid repeated  
Figure 7: Regional division based on the Iridium satellite system.  
broadcast of the transaction and reduce the amount of data transmitted in the network and significantly reducing the network load.
## 5 Results
### LQC--RL Comparison
#### 5.2.2 Cut-In Scenario Results  
In this scenario, the truck platoon is travelling at 80 km/h on a highway road when a vehicle suddenly breaks the platoon lane. Figure 16 shows an example of the investigated scenario: the green vehicle performs a cut-in manoeuvre in the platoon lane, between the leader and the follower 1 truck. In the simulation, the cut-in vehicle behaviour is modelled through a linear variation in 2 s of the leader position while maintaining its initial velocity (80 km/h). The cut-in vehicle's final position is set to split equally the available space between the vehicles. The cut-in vehicle is assumed to be a truck with the same inertial and geometrical characteristics listed in Table 3. Figure 16 depicts the simulation results of the two controllers in terms of velocity profile, inter-vehicle distance, and longitudinal acceleration for the driving cycle. The solid green line represents the velocity of the cut-in vehicle, and the dashed black line represents the event that triggers the cut-in manoeuvre. Both the controllers properly reduce the vehicle speed when the disturbance occurs, but the LQC guarantees a minimum distance error that is half of the RL one, thus improving the platoon's safety under critical conditions. The LQC applies a braking torque higher than the RL one, thus resulting in a higher longitudinal acceleration. Such behaviour stems from the innate difference between the two algorithms. The RL controller has learned a smoother approach that allows for reaching lower distances during the cut-in, thus achieving greater comfort. On the contrary, LQC tries to maximise performance in terms of safety. However, both controllers proved to be able to reduce the distance error, and they reached the reference distance (33 m) at steady state. The LQC results in more aggressive behaviour, i.e., it brakes twice to obtain the reference distance.  
Figure 16: Comparison between the LQC and RL during a cut-in scenario simulation: solid red line—RL; dotted blue trace—LQC; green trace (solid and dashed)—cut-in vehicle velocity and distance.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 5 Comprehensive Analysis of Genomic Comparison Tools: Human and Diverse Genomes
### Analyses of Human Genomes  
The initial phase of our study used MUMmer4, Sibeliaz, Dialign2, and Minimap2 for analyzing two human genomes. The contrasting results from these tools provided insights into their specific functionalities and limitations:  
* MUMmer4 demonstrated a remarkable ability to detect near-identical sequences in human genomes, reporting a similarity of almost 100%. This high similarity index indicates that MUMmer4 is particularly efficient at aligning genomes with minimal genetic variations, making it an ideal tool for studies where the genomes are closely related;
* Sibeliaz presented a different perspective, reporting a much lower similarity percentage. Sibeliaz's methodology, which focuses on k-mer pattern analysis, allows it to uncover subtle variations that direct sequence comparison methods might miss. This attribute is particularly beneficial for research that delves into genomic diversity, mutation analysis, and evolutionary biology;
* Dialign2 encountered limitations with the human genomes, primarily due to their size. This outcome underscores the necessity of considering genomic data scale when selecting analytical tools, particularly for large and complex genomes;
* Mirroring MUMmer4 in effectiveness, Minimap2 showed a 100% mapping rate, aligning the human genomes completely. It demonstrated additional capabilities in managing complex mapping situations, indicated by the presence of secondary and supplementary alignments, thus offering a broader scope in genomic analysis;
* The alignment process using GraphAligner failed, resulting in an empty output GAM file.
* Progressive Cactus encountered challenges in achieving the anticipated alignments across whole human genomes, demonstrating successful alignment only for chromosomes. This outcome underscores the necessity for refinement in accommodating the diversity and complexity of genomic datasets. The alignment between human Chromosome 1 and Chromosome 2 was effectively visualized using Circos, based on the output generated by Progressive Cactus. This visualization highlighted the comparative genomic architecture and facilitated the identification of conserved and divergent regions between these two chromosomes (Figure 12).
## 4 Proposed Algorithm: CMWN-DV-HOP
### CMWN-DV-Hop
#### 4.3.3 WRLS Is Applied to Calculate the Unknown Node Coordinates  
Based on the minimum number of hops and the hop distance \(\beta_{\lambda}\), the weighted recursive least-squares (WRLS) algorithm is used to determine the geographic and spatial coordinates of each node. The measurements of the currently unknown nodes in the network are corrected according to the recursive algorithm based on the results of the previous parameter estimation utilizing observed data. In this way, the amount of memory can be significantly reduced, especially for nodes where the unknown nodes are close to each other, and the amount of computation can also be reduced.  
Figure 7: Schematic diagram of the communication range when the splitting factor a=8.
## 2 Characterization of Random Fracture Networks in Broken Rock Masses
### Mathematical Description of Stochastic Model for Geometric Parameters of Structural Surfaces
#### 2.2.3 Density of Structural Planes  
The density of structural surfaces can generally be classified as line density, areal density, and volume density based on spatial dimensions, with interconvertibility among them. Line density of structural planes is the reciprocal of the spacing between structural planes, while areal density and volume density refer to the numbers of centerpoints of structural plane traces per unit area and unit volume of the rock mass, respectively. Among them, areal density is more widely used to evaluate the density of structural surfaces in geotechnical engineering, and the commonly used methods for estimating areal density include the Kulatilake estimation method [31] and the Mauldon estimation method [32].  
Based on the correlation between sample window traces and the window in the statistical window method, as shown in Figure 2, [PERSON] and [PERSON] [31] proposed a formula for calculating the areal density \(E(\rho_{a})\) of structural planes:  
\[E(\rho_{a})=\frac{\sum\limits_{i=1}^{N_{2}}\left[P_{0}(W)\right]_{i}+\sum \limits_{i=1}^{N_{1}}\left[P_{1}(W)\right]_{i}+\sum\limits_{i=1}^{N_{0}}\left[ P_{2}(W)\right]_{i}}{ab} \tag{7}\]  
Figure 4: Tendency and dip angle representation of structural plane.  
where \(a\) and \(b\) denote the length and width of the statistical window, respectively; \(N_{0}\), \(N_{1}\), and \(N_{2}\) represent the number of structural plane traces in the cutting, intersecting, and enclosing relationships with the statistical window, respectively; and \(P_{0}(W)\), \(P_{1}(W)\), and \(P_{2}(W)\) are the probabilities of the midpoint of surface trace corresponding to the above three relationships being inside the statistical window.  
Similarly, the expression of areal density of structural planes proposed by [PERSON] [32], \(\lambda\), can be described by the following equation based on the statistical window method:  
\[\lambda=(N-N_{2}+N_{0})/2 ab=(2N_{0}+N_{1})/2 ab \tag{8}\]  
By comparing Equations (7) and (8), it is evident that [PERSON]'s method for estimating the areal density of rock masses within a statistical window is more straightforward than [PERSON]'s method, as the [PERSON] method necessitates calculations involving the probability density functions of the trace length in the study area. Based on the statistical analysis results from the [PERSON] method, the study adopts the two-parameter negative exponential distribution model as the probabilistic statistical model for the surface density of structural planes in broken rock masses. The two-parameter negative exponential distribution can be expressed as:  
\[y=me^{-nx}\ \ \ \ \ (x\geq 0,\ \ \ m>0,\ \ \ n>0) \tag{9}\]  
where \(m\) and \(n\) are the model parameters.  
As the density of structural planes increases, the spatial distribution of fractures become more complex, which leads to more permutations and combinations of random geometry parameters that can be generated by the Monte Carlo method.
###### Abstract  
This work aimed to study the automation of measuring the speed of punches of boxers during shadow boxing using inertial measurement units (IMUs) based on an artificial neural network (ANN). In boxing, for the effective development of an athlete, constant control of the punch speed is required. However, even when using modern means of measuring kinematic parameters, it is necessary to record the circumstances under which the punch was performed: The type of punch (jab, cross, hook, or uppercut) and the type of activity (shadow boxing, single punch, or series of punches). Therefore, to eliminate errors and accelerate the process, that is, automate measurements, the use of an ANN in the form of a multilayer perceptron (MLP) is proposed. During the experiments, IMUs were installed on the boxers' wrists. The input parameters of the ANN were the absolute acceleration and angular velocity. The experiment was conducted for three groups of boxers with different levels of training. The developed model showed a high level of punch recognition for all groups, and it can be concluded that the use of the ANN significantly accelerates the collection of data on the kinetic characteristics of boxers' punches and allows this process to be automated.  
artificial neural network; inertial measurement units; boxing; punch recognition; multilayer perceptron; automation of measuring of boxers' kinematic parameters
## 2 Analysis of Forming Process  
### Forging Process Analysis  
The process flow of large-scale disc forging primarily consists of the following steps: ingot upsetting, drawing and cropping, the pre-upsetting of the blank, the rolling of the outer circumference, and spinning for forming. Upsetting and drawing are the main deformation processes in forging, aiming to eliminating casting defects, such as voids and porosity within the blank. Pre-upsetting, rolling of the outer circumference, and spinning for forming are shaping processes, mainly controlling the deformation amount per heating cycle, to transform the blank after slabbing into forgings which meet the target dimensions, as shown in Figure 1.  
Pre-upsetting primarily adjusts the shape and dimensions of the blank by modifying the upsetting deformation amount, thereby enhancing dimensional accuracy and the mechanical properties of forgings. Rolling of the outer circumference primarily facilitates material flow in the axial direction through radial deformation, which aids in eliminating the bulging shape generated post upsetting, corrects shape defects caused by uneven upsetting deformation, reduces machining allowance on the outer edge, and increases the height of the forging. Spinning for forming utilizes the rotating anvil method, employing a wide flat anvil to spin the outer edge of the workpiece, leaving a central boss, progressing towards the inner edge in appropriate anvil increments, and rotating to press the central boss. This method ensures the compaction of the central core of the disc forging, preventing the formation of lamellar defects within the forging, achieving precise control of blank deformation, and thereby obtaining higher-quality forgings.
# A Distributed Automatic Video Annotation Platform
## 3 Video Annotation Platform
### Distributed Video Data Processing Layer (DVDPL)  
The key part of the proposed architecture is the video data processing layer, wherein video data are processed in a distributed manner and intermediate feature data for the video data mining layer are returned. The main reason for distributed processing is to increase the performance and latency concerning the time that it takes for processing. Since the video data processing layer takes the more time than the other layer, we performed distributed processing in the DVDPL. Open-source distributed framework Apache Spark was utilized for distributed processing. Apache Spark provides a  
Figure 1: Architecture of the proposed distributed spatiotemporal video annotation platform.  
distributed in-memory based framework for data labels. However, they do not have any native support for video data. Furthermore, Spark does not provide any higher-level APIs for video processing. Existing literature for video annotation does not provide distributed services and only considers spatial information and ignores the most significant temporal information from a video. However, temporal information is required to find the superior knowledge.  
Therefore, to address the above-mentioned issues, we introduce a distributed video processing layer that integrates OpenCV with Apache Spark. Our work supports the video processing for both spatial and temporal information. Furthermore, we developed the state-of-the-art feature descriptors in a distributed manner.  
We divide the proposed DVDPL into three sub-parts: preprocessing, a spatial feature extractor, and a dynamic feature extractor. For preprocessing we employ RGBtoGray conversion, frame extraction, and so on. Several existing algorithms such as LBP, LDP, LTP, and LDTP were developed on top of Spark for spatial feature extraction. Moreover, we present three-channel-based color local directional ternary pattern (CLDTP). To extract the dynamic features, we developed the existing state-of-the-art algorithms such as VLBP, LBP-TOP, and MBP. Furthermore, we proposed a novel dynamic feature descriptor named volume local directional ternary pattern-three orthogonal planes (VLDTP-TOP). All the proposed feature descriptors show excellence in terms of accuracy, scalability, and throughput. The details of the proposed feature descriptors are discussed in the following section.
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
###### Abstract  
The impact of floods is the most severe among the natural calamities occurring in Malaysia. The knock of floods is consistent and annually forces thousands of Malaysians to relocate. The lack of information from the Ministry of Environment and Water, Malaysia is the foremost obstacle in upgrading the flood mapping. With the expeditious evolution of computer techniques, processing of satellite and unmanned aerial vehicle (UAV) images for river hydomorphological feature detection and flood management have gathered pace in the last two decades. Different image processing algorithms--structure from motion (SM), multi-view stereo (MVS), gradient vector flow (GVF) snake algorithm, etc.--and artificial neural networks are implemented for the monitoring and classification of river features. This paper presents the application of the k-means algorithm along with image thresholding to quantify variation in river surface flow areas and vegetation growth along Kerian River, Malaysia. The river characteristic recognition directly or indirectly assists in studying river behavior and flood monitoring. Dice similarity coefficient and Jaccard index are enumerated between thresholded images that are clustered using the k-means algorithm and manually segmented images. Based on quantitative evaluation, a dice similarity coefficient and Jaccard index of up to 97.86% and 94.36% were yielded for flow area and vegetation calculation. Thus, the present technique is functional in evaluating river characteristics with reduced errors. With minimum errors, the present technique can be utilized for quantifying agricultural areas and urban areas around the river basin.  
image processing; unmanned aerial vehicle; feature recognition; image segmentation; color space; floods; sediment +
Footnote †: journal: Sustainable
# Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
## 5 Discussion  
In this paper, a solution for automated tattoo detection and identification was implemented. The experimental investigation carried out in this research has focused on different types (n, s, n, l, x) of YOLOv5 models, YOLOv5 hyperparameters, data augmentation parameters, and similarity distances used in the identification stage. The newest versions of YOLO have not been analyzed because these algorithms have not been officially released and could be found in the public repository without any scientific investigation. Therefore, in this research, the most popular YOLOv5 version has been used today. Taking into account the research performed by other authors, some parameters have not been investigated due to the cost of time for each model training. A total of 135 models have been trained to find the influence of three main hyperparameters on the results of tattoo detection. It is necessary to admit that a complete investigation using more combinations has not been performed. Even with these research limitations, the results of this research have shown that for such a type of object detection task, the most suitable models are YOLOv5l.  
The chosen similarity distances used in the identification stage are the most used distances in various clustering algorithms, similarity detection tasks, recommendation systems, etc. Related works have shown that similarity distances, such as Jaccard, Spearman correlation, Manhattan, and others, can also be used for identification, but primary research has shown that in our case, it was not suitable.