esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 3 Human Pose Estimation Method by Progressive Feature Fusion  
Figure 1 presents a flowchart of the proposed HPE method. The core process involves progressively fusing features from color and depth images to enhance joint estimation. This approach enhances joint detection by incorporating edge, feature, and texture information from color images together with three-dimensional characteristics from depth images. The proposed method utilizes RGB and depth images as input data. Feature maps for each modality are extracted through separate feature extractors. Subsequently, the network progressively refines the representations of joints by combining features from both depth and color images. The enhanced features from both modalities are fused and passed as inputs to the next stage. This iterative process continues for several early stages, leading to a gradual strengthening of the joint representations.
## 4 FHB Management
### Chemical and Physical Control  
Chemical treatment including the widely used demethylation inhibitor (DMI) class fungicides including tebuoconazole (Folicur), prothioconazole plus tebuoconazole (Prosaro), and metonazole (Caramba) results in no greater than 60% control of FHB and 30% to 50% of DON in wheat, although the actual reductions are highly variable [29; 81]. In addition, multiple pesticide constraints have been reported including inadequate coverage and timely application, creating significant challenges for farmers [29]. Newer fungicides such as phenamacril, a cyanoacrylate compound that asserts its antifungal effect on susceptible _Fusarium_ by disrupting the activity of essential actin-motor protein have been shown to be more effective against _Fusarium_ spp. than DMI class fungicides [82]. However, field-resistant _Fusarium_ strains to phenamcril have been characterised [83]. There is also a newer fungicide polydilmetofen, a novel succinate dehydrogenase fungicide that was released in 2018 [83]. It appears effective against Fusarium and has been used on crops in America, Canada and China [84]. As a novel fungicide, its efficacy is still being tested. However, as history has shown, overuse of these chemicals eventually results in fungicide resistance [76; 85; 86; 87; 88]. There are also concerns about toxicity and related health and environmental issues [65; 78; 89].  
Over the years, alternatives to reduce the dependence on toxic synthetic chemicals have been explored. In terms of Fusarium toxins, mycotoxin adsorption agents are promising; they bind to mycotoxins in the gastrointestinal tracts of animals thereby decreasing their bioavailability and reducing any potential harm [90]. However, while this seems to be a promising alternative, a study by [PERSON] et al. [91] reported on various types of adsorption agents with varied efficacy. Furthermore, according to [PERSON] et al. [92] adsorption agents are effective in preventing aflat, but they are not very effective for other mycotoxins. Other alternatives include optimising host resistance which is favored by many scientists including [PERSON] et al. [49], who states that host genetics would be the most desirable approach to controlling FHB and preventing toxins accumulation and associated health risks.
# Eco-Efficient Mobility in Smart City Scenarios
## Personal Vehicles and C-ITS
### Communication Technologies
They do not require either great bandwidth or low latency to provide a good Quality of Service (QoS). Tracking or monitoring applications usually make use of few small packets per minute, which is perfectly assumable by these connectivity solutions. The same occurs for security systems that just need a limited number of messages per transaction for locking or unlocking a device. In addition, some applications that do not require real-time data visualization may adopt data-aggregation strategies. For example, sensing applications that take environmental measurements as a public service, e.g., pollution, temperature, noise-level, etc., may aggregate collected data until the end of the ride and then send all the information to the cloud server by using few long transmissions. As seen, LPWAN solutions are a firm alternative to provide personal vehicles with connectivity, hence enabling a plethora of novel services without harming other more urgent communication channels such as IEEE OCB or 5G, which should be devoted to critical applications.
## 3 Methods  
### Extraction of Canopy Height Model  
The direct results of the stereoscopic processing of images acquired in the leaf-off season are point clouds located on the vegetation and ground surfaces. The prerequisite of DTM extraction is to remove vegetation points. A simple automatic algorithm based on iterative median filtering (IMF) is used to identify ground points from the point cloud of the stereo imagery acquired on leaf-off season. The algorithm is described as follows:  
(1) Point cloud is rasterized to produce an elevation image with a given pixel size, such as 5.0 m;  
(2) The elevation image is filtered by median filter with a given window size, such as \(13\times 13\);  
(3) The filtered elevation image serves as a template to screen out point cloud; points above the template are discarded if their vertical distance to the template is larger than a given threshold, such as 1.5 m;  
(4) the remained point cloud is processed by repeating steps (1)-(3) until the number of discard points is smaller than a given threshold, such as 1%.  
The DSM of the forest canopy top could be generated by rasterizing the point cloud of leaf-on with a resolution of 0.5 m in the same way as the rasterization of lidar point cloud. The DTM could be produced by the rasterization and interpolation of the filtered point cloud of leaf-off. Therefore, the MCHM could be extracted by subtracting DTM from DSM.
## 3 Wind Farm Controller
### Power Production Optimizer  
The power production optimizer uses the probability of occurrence of the wind direction by considering the wind direction range and standard deviation in the wind direction. The optimization algorithm considers wind speed, wind direction, turbulence intensity, velocity deficit, and dynamic atmospheric situations to improve the power output. When a wind farm is under greedy control strategy [6], i.e., under normal operating conditions, all the turbines operate to produce the maximum power, but this can result in a strong wake behind the upstream turbines. This wake eventually reduces the power production of the downstream turbine. An optimal selection of yaw angle or axial induction factor or both can boost the production of the downstream turbine. It is assumed that the overall wind farm power output is the sum of each turbine's power output for wind farm production maximization, through optimization of the yaw angle or the axial induction factor. The  
Figure 11: Schematic representation of wake redirection control strategy.  
Figure 10: Schematic representation of axial induction control strategy.  
objective function corresponding to the conventional optimization method that finds the optimal value of yaw misalignment or axial induction factor for maximizing wind farm power for a specified wind direction is given by the following equation [4]:  
\[X_{opt}(\theta)=\text{argmax}_{X}\sum_{n=1}^{N_{T}}P_{n}(x_{1},x_{2},......,x_{n },\theta) \tag{8}\]  
where \(P_{n}\) is the power developed by each turbines and \(\theta\) denotes the wind direction.  
Owing to the unreliability of the wind direction measurement, another robust yaw angle optimization technique is considered in which the wind direction's probability of occurrence is considered. So, here a probability density function \(\rho(\theta)\) is considered and the corresponding objective function is given by the following equation.  
\[X_{opt}(\rho(\theta))=\text{argmax}_{X}\int_{0}^{2\pi}\rho(\theta)\sum_{n=1} ^{N_{T}}P_{n}(x_{1},x_{2},......,x_{n},\theta)d\theta \tag{9}\]  
where \(\rho(\theta)\) denotes the probability density function.  
This is a gradient-based method designed for continuous objective and constraint functions with continuous first derivatives; it looks for the minimum of constrained multivariable function. The cost function is the whole wind farm power output in Equation (9) while the control variables, i.e., yaw angle and axial induction factor, are the ones altered to optimize the objective function. The lower and upper bounds of the optimization variables are the only constraints imposed. The lower and upper bounds of the yaw angle are \(0^{\circ}\) and \(30^{\circ}\), respectively, and those of the axial induction are 0 and \(1/3\), respectively.
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 5 Conclusions  
This paper describes the full evolution of a novel multi-objective particle swarm optimization algorithm with a variable number of dimensions. According to our knowledge, VNDMOPSO is the first PSO-based algorithm that is able to solve VND MO problems and that shares the information of individual variables among candidate solutions that have different dimensions without enlarging the size of the decision space vectors by additional variables. Algorithm VNDMOPSO was compared with two other state-of-the-art algorithms, namely VLMOPSO and VNDGDE3, on a suite of 21 benchmark problems. It was shown that the convergence properties of the VNDMOPSO algorithm are superior to the VLMOPSO algorithm and at least comparable to the convergence of VNDGDE3. Then, the influence of the only new controlling parameter (the probability of dimension transition) was assessed and the recommended values are given in the paper. Finally, VNDMOPSO was used to solve five space robot pathfinding problem instances and showed significantly better results compared with VLMOPSO, SCMOPSO, and VNDGDE3. Moreover, the SRPP has been formulated as a VND MO problem, which, on the one hand, brings better information about necessary compromises to the decision about the final path of the robot, but, on the other hand, it does not force the user to limit the route optimization process in any way--either in terms of the number of turning points or a priori setting of weights to individual objectives. Moreover, the proposed method allows adding any additional objective, e.g., to minimize the probability of collision and others, easily.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a16060307/s1](https://www.mdpi.com/article/10.3390/a16060307/s1)]([https://www.mdpi.com/article/10.3390/a16060307/s1](https://www.mdpi.com/article/10.3390/a16060307/s1)). Supplementary material has been entitled \"Definition of Multi-objective Benchmark Problems with Variable Number of Dimensions\" labeled as S1. It contains the definition of all the benchmark problems used in this study to assess the convergence properties of the VNDMOPSO algorithm. This material cites references [32; 38; 42; 43; 44; 45].  
The research described in this paper was supported by the Internal Grant Agency of the Brno University of Technology under project no. FEKT-S-23-8191.  
The data that support the findings of this study are available upon request at: [EMAIL_ADDRESS].  
The author declares no conflict of interest.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 4 Dynamic Measuring Method of Incident Angle
### Principle of Dynamic Measurement  
Based on the measurement principle of LDV, this paper proposes a method of dynamic angle measurement, the basic principle of which is shown in Figure 6.  
In the figure, a rotating disk performs a high-precision rotating motion in the horizontal plane; the laser beam emitted by LDV is irradiated at point P of the rotating disk. Because the angle between the laser beam and the vertical direction is \(\beta\), the LDV can measure the component \(V_{\rm m}\) of the tangential velocity \(V_{\rm T}\) of the rotating disk at point P along the laser beam. Further, the angle \(\beta\) can be calculated according to the projection relationship between \(V_{\rm m}\) and \(V_{\rm T}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Group** & \(I_{i}\) **(mm)** & \(d_{i}\) **(mm)** & \(d_{i}^{\prime}\) **(mm)** & \(d_{i}^{\prime\prime}\) **(mm)** & \(\beta_{i}\) (\({}^{\circ}\)) & \(\beta_{i}^{\prime}\) (\({}^{\circ}\)) & \(\beta_{i}^{\prime\prime}\) (\({}^{\circ}\)) \\ \hline
1 & 1029.994 & 34.327 & 34.427 & 34.227 & 1.9088 & 1.9144 & 1.9033 \\
2 & 930.619 & 31.021 & 31.121 & 30.921 & 1.9092 & 1.9153 & 1.9030 \\
3 & 1015.023 & 33.882 & 33.982 & 33.782 & 1.9118 & 1.9175 & 1.9062 \\
4 & 976.006 & 32.552 & 32.652 & 32.452 & 1.9102 & 1.9161 & 1.9044 \\
5 & 960.845 & 32.059 & 32.159 & 31.959 & 1.911 & 1.9169 & 1.9050 \\
6 & 1011.97 & 33.835 & 33.935 & 33.735 & 1.915 & 1.9206 & 1.9093 \\
7 & 919.53 & 30.709 & 30.809 & 30.609 & 1.9127 & 1.9190 & 1.9065 \\
8 & 984.966 & 32.934 & 33.034 & 32.834 & 1.9151 & 1.9209 & 1.9093 \\
9 & 956.842 & 31.986 & 32.086 & 31.886 & 1.9146 & 1.9206 & 1.9086 \\
10 & 971.958 & 32.327 & 32.427 & 32.227 & 1.9088 & 1.9108 & 1.8990 \\ \hline \end{tabular}
\end{table}
Table 1: The results of 10 test data sets, \(i=1\), 2, 3, \(\ldots\), 10.  
More specifically, under the motor's drive, the rotating disk performs a counterclockwise circular motion at an angular velocity \(\omega\). Assuming that the distance from the laser spot P to the rotation center \(O\) of the rotating disk is \(r\), then the tangential velocity \(V_{\rm T}\) at point P can be calculated as \(V_{\rm T}=\omega\cdot r\). According to the triangular relationship in the figure, LDV picks up the projected velocity \(V_{\rm m}\) of \(V_{\rm T}\), and \(V_{\rm m}=V_{\rm T}\cdot\sin(\beta)\). This expression shows that \(V_{\rm m}\) is a function of \(\omega\), \(r\), and \(\beta\). When these three parameters are fixed, \(V_{\rm m}\) is a fixed value. Based on this, the incident angle \(\beta\) of the LDV can be calculated by Equation (7).  
\[\beta=\arcsin(V_{\rm m}/(\omega r))\cdot 180/\pi \tag{7}\]  
The feasibility of the proposed angle dynamic measuring method is verified in the following experiment.
## 4 Discussion
### Advantages of Developed CET
#### 4.1.2 Elimination of Errors Caused by Shadows  
The integrated use of MS-derived spectral and DSM-derived elevation in CET successfully eliminates a majority of errors induced by noises in spectral data (e.g., shadows or sea reflections). While most existing methods for automated coastline extraction, e.g., [14], are characterized by the significant presence of errors in the coastline mapping within shaded areas of the multispectral image, CET managed to achieve high mapping accuracy even within these areas (Figures 12 and 13). Spectral characteristics of shaded areas are more similar to water than to land areas, which is why most existing extraction methods falsely classify those areas as water bodies. As seen in Figures 12 and 13, such shadow-induced errors are falsely extending the total coastline length, while at the same time total area of the affected island is falsely reduced (Table 5: ID 2,3,6,7,10, 12, 14, 17). At the same time, due to the combination of spectral and elevation information, the CET has managed to restrict the influence of such shadow-induced errors and extract the coastline accurately.  
Figure 11: Coastline deviation between evaluated CET-WW and reference CET-UAV coastlines calculated by CET.  
Therefore, CET should be applied in all cases where shadows are present in multispectral imagery, as, within these areas, other methods would not achieve satisfactory results.  
Figure 12: Successful automated extraction of coastline over significantly shaded areas.  
Figure 13: Successful automated extraction of coastline over significantly shaded areas.
# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
###### Abstract  
In this paper, we propose a novel model to extract highly precise depth maps from missing viewpoints, especially for generating holographic 3D content. These depth maps are essential elements for phase extraction, which is required for the synthesis of computer-generated holograms (CGHs). The proposed model, called the holographic dense depth, estimates depth maps through feature extraction, combining up-sampling. We designed and prepared a total of 9832 multi-view images with resolutions of 640 \(\times\) 360. We evaluated our model by comparing the estimated depth maps with their ground truths using various metrics. We further compared the CGH patterns created from estimated depth maps with those from ground truths and reconstructed the holographic 3D image scenes from their CGHs. Both quantitative and qualitative results demonstrate the effectiveness of the proposed method.  
depth map estimation; 360-degree holographic contents; deep learning Article
# Adapative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
## 4 Experimental Evaluation
### Datasets and Data Preprocessing  
Both CIC-IDS2017 and IoT-23 are public datasets that contain multiple labeled captures of benign and malicious network flows. The recorded data is extremely valuable for intrusion detection because it includes various types of common cyber-attacks and manifests real network traffic patterns.  
CIC-IDS2017 [37] consists of seven captures of cyber-attacks performed on a standard enterprise computer network with 25 interacting users. It includes denial-of-service and brute-force attacks, which were recorded in July 2017 and are available at the Canadian Institute for Cybersecurity. In contrast, IoT-23 [38] is directed at the emerging IoT networks, with wireless communications between interconnected devices. It contains network traffic created by malware attacks targeting IoT devices between 2018 and 2019, divided into 23 captures and available at the Stratosphere Research Laboratory.  
From each dataset, two captures were selected and merged, to be utilized for the corresponding scenario. Table 2 provides an overview of their characteristics, including the class proportions and the label of each class, either 'Benign' or a specific type of cyber-attack. The 'PartOfAHorizontalPortScan' label was shortened to 'POAHPS'.  
Before their data was usable, both datasets required a similar preprocessing stage. First, the features that did not provide any valuable information about a flow's benign or malicious purpose, such as timestamps and IP addresses, were discarded. Then, the categorical features were converted to numeric values by performing one-hot encoding. Due to the high cardinality of these features, the very low frequency categories were aggregated into a single category designated as 'Other', to avoid encoding qualitative values that were present in almost no samples and therefore had a small relevance.  
Finally, the holdout method was applied to randomly split the data into training and evaluation sets with 70% and 30% of the samples. To ensure that the original class proportions were preserved, the split was performed with stratification. The resulting CIC-IDS2017 sets were comprised of eight imbalanced classes and 83 features, 58 numerical and 25 categorical, whereas the IoT-23 sets contained four imbalanced classes and approximately half the structural size, with 42 features, 8 numerical and 34 categorical.
# Personalized Contextual Information Delivery Using Road Sign Recognition
###### Abstract  
Road sign recognition is essential for navigation and autonomous driving applications. While existing models focus primarily on text detection and extraction, they fail to incorporate user-specific contextual information, limiting their effectiveness in real-world scenarios. This study proposes a modular system that enhances road sign recognition by integrating user-adapted contextual reasoning. The system applies a step-by-step Chain of Thought (CoT) approach to link detected road signs with relevant contextual data, such as location, speed, and destination. Compared to traditional image captioning models, our approach significantly improves information relevance and usability. Experimental results show that the proposed system achieves a 23.4% increase in user-adapted information accuracy and reduces interpretation errors by 17.8% in real-world navigation scenarios. These findings demonstrate that semantic inference-based reasoning improves decision-making efficiency, making road sign recognition systems more practical for real-world applications. The study also discusses challenges such as real-time processing limitations and potential future improvements for broader infrastructure recognition.  
Keywords:road sign recognition; object detection; text recognition; chain of thought; contextual output +
Footnote †: journal: Applied sciences
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method  
### Dataset Collection  
#### 3.1.1 Financial Transaction Datasets  
In the subsection on financial transaction data, it is noted that financial transaction datasets typically consist of a vast array of transaction records, potentially including information such as transaction time, parties involved, amount, and type. These datasets are often employed for financial attack detection to identify anomalous transaction behaviors, such as fraud or money laundering. When selecting datasets, attention is paid to the diversity, authenticity, and completeness of the data. Prominent public financial transaction datasets include the Credit Card Fraud Detection dataset provided by Kaggle [29]. The reason for utilizing these datasets is that they offer a wealth of real-world cases, assisting in effectively testing and optimizing the performance of the Merkle-Transformer model in financial attack detection. By analyzing real transaction data, more accurate simulation and identification of financial fraud and other abnormal behaviors can be achieved.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Microarray Data  
The microarray data were accessed on 25 June 2022 from Gene Expression Omnibus (GEO) ([[http://www.ncbi.nlm.nih.gov/geo/](http://www.ncbi.nlm.nih.gov/geo/)]([http://www.ncbi.nlm.nih.gov/geo/](http://www.ncbi.nlm.nih.gov/geo/))) with the record number GSE41935. The experiment included 207 samples (arrays) and 59 unique experiments (treatments), including 10 genotypes of _A. thaliana_ subjected to salt, temperature, HL, FLG, and their combinations [7]. We extracted the expression set from the GEOquery R package (Version 2.62.2). The sample information (phenotype data) was obtained from the expression set of the series matrix file. To identify the differentially expressed genes (DEGs) out of 30,380 microarray genes (probes), we used the limma R package (Version 4.1.2) together with the false discovery rate (FDR) method (FDR was set to 0.01). Gene expression groups showing empirical Bayes moderated _p_-values < 0.01 were considered differentially expressed. The identified list comprised 439 DEGs used for further analysis.  
Differential expression (DE) analysis has been employed for gene expression profiles and uncovers the underlying mechanisms that govern tolerance to stress in Arabidopsis [17; 18]. Given that gene expression profiles are often high-dimension matrices encompassing  
Figure 1: Overview of the modeling process implemented to classify and interrogate gene expression relationships between control and stress conditions in Arabidopsis.  
thousands of strongly correlated genes (including hundreds of highly correlated DEGs), it is impractical to utilize all DEGs to pinpoint genes that transcriptionally respond to stress in plants. Consequently, data-driven approaches have been extensively employed to discern the gene signature using gene expression data in plants [19].