esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, the combinatorial auction requires tremendous computational resources because the number of possible bundles increases exponentially with the number of tasks [13]. Sequential single-item (SSI) auctions [14] are an alternative to combinatorial auctions. In SSI auctions, robots bid on all unallocated tasks by the minimum cost increase in the sum of the minimum path to visit the assigned tasks. The robot with the smallest bid wins a task, and then, the bidding process begins again for the remaining unallocated tasks. Then robots compute the minimum path to their tasks and move accordingly [15]. This paper targets the applications in which robots are capable of performing the same task with different levels of accuracy/perfection (we call it quality) and tasks emerge with desired quality to be performed. Besides, the quality requirement system will try to balance the load distribution among robots and minimize the total travel distance. Examples of such systems are given in detail in Section 3.2. The contributions of this work are summarized as follows. First, we present an on-line distributed multi-objective task allocation approach (DYMO-Auction), which assigns the tasks emerging from a dynamic scenario. DYMO-Auction is distributed in the sense that there is no pre-defined auctioneer for all tasks. A robot that receives a task announcement is the auctioneer for this task, and the next task announcement may have different auctioneer. The task assignment process takes into consideration the task's quality requirement as well as load balancing and travel distance. The important novelty of this work is that we define a utility function to consider the four competing metrics, namely the cost, energy, distance, type of tasks. To the best of our knowledge, the considered utility metric was not addressed in other prior works. Second, we mathematically formulate the concept of quality in the allocation process (to the best of the authors' knowledge; this is the first work that considers quality in this context), see Section 3.3. Third, we provide a comprehensive simulation study of the proposed DYMO-Auction approach and benchmark with the offline task allocation approach (i.e., sequential single-item auction SSI). Finally, we validate the simulation results through real-world experimentation and deployment on Turtlebot robots. The remainder of the paper is structured as follows. Section 2 presents an overview of the literature for distributed multi-objective task allocation. In Section 3, we provide a detailed description of the proposed approach, including the target system model, real-world application examples, task's quality representation, the proposed auction process, and the cost function. We define the performance evaluation metrics in Section 4. Simulation setup and results are presented in Sections 5 and 6, respectively. Then, we validate the DYMO-Auction approach using real-world experiments in Section 8. Finally, we conclude this paper with the main findings and future research directions in Section 9.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Distributed Energy-Efficient Clustering (DEEC)
#### 1.3.3 Advantages and Limitations of the DEEC Protocol  
DEEC offers several advantages over traditional clustering protocols. Energy Adaptivity: by considering residual energy, DEEC dynamically adapts CH selection, preventing the early depletion of low-energy nodes. DEEC is well-suited for heterogeneous WSNs, where nodes may have different initial energies. Simulation studies consistently show that DEEC extends the stability period (time until the first node dies) and overall network lifetime compared with protocols like LEACH and SEP. The distributed nature of DEEC makes it scalable for large-scale WSN deployments.  
Despite its strengths, DEEC has some limitations. Cluster Head Overload: nodes with higher energy may be selected as CHs too frequently, leading to rapid energy depletion. Distance Ignorance: because the protocol does not explicitly consider the distance between CHs and the BS, this can affect energy consumption for long-range transmissions. To address these issues, several enhancements and variants of DEEC have been proposed, such as DDEEC (Developed DEEC), EDEEC (Enhanced DEEC), and hybrid approaches integrating fuzzy logic or ML for more intelligent CH selection [31].
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 3 Performance Evaluation
### Scenario 3--Ad hoc Network  
In this scenario, we consider an Ad hoc network scenario. All of the nodes are placed randomly in a square field. They move at a speed of 6 m/s and change their direction randomly every 3 s. They send a constant UDP traffic rate of 60 Mb/s to a sink node. Half of the nodes are traffic generators and the other half are sink nodes. The number of nodes is gradually increased from five to 50 nodes.  
Figure 6 shows the overall average physical throughput achieved by all sink nodes. Figures 7 and 8 show the FLR based on MPDU losses and A-MPDU losses respectively, combined with the results that are shown in Figure 9 for the selected MCS. It shows that MCS values selected by the stations using Ideal RAA are higher than the ones selected when using other algorithms. The average MCS values in Ad hoc mode are slightly lower than the MCS values in infrastructure mode. This is the reason why the throughput in Ad hoc mode is slightly lower than that of infrastructure mode.  
Combined with the FLR, we conclude that the overall throughput of the stations using Ideal RAA is the best among the studied algorithms. In the case of frame losses, the absence of feedback from the receiver prevents the transmitter from adapting the rate according to the changed channel conditions. If the channel conditions deteriorate and frame losses occur, the transmitter will keep sending frames at the same rate, leading to more frame losses until the channel conditions improve.  
The false-positive MCS rate decision may cause, in some cases, more A-MPDU frame losses because of channel conditions deterioration before transmitting the new frame, as seen in scenario 2.  
Figure 5: Mean value of the modulation and coding schemes (MCS) used by the nodes.  
Figure 6: Average throughput of sink nodes in an Ad hoc network.  
Figure 7: MPDU Frame Loss Ratio.  
Figure 8: A-MPDU Frame Loss Ratio.  
Figure 9: Mean value of the MCS selected by sender nodes.  
Note that, in scenarios 2 and 3, nodes are always in the communication range of each other. This makes the interference level go higher with every new sender node added to the network. This, in turn, explains the high values of FLR. Indeed, FLR based on MPDUs counted in BlockAck reach above 80% in both scenarios when we have more than 20 nodes in the network. FLR based on A-MPDUs increases gradually with the number of nodes in the network.
## 4 Discussion
### Statistical Significance Analysis and Model Comparison
#### 4.5.3 Model Validation Techniques  
The model validation techniques employed in this study were designed to rigorously assess the predictive performance and reliability of the proposed models. Initially, the dataset was randomly split into 80% training and 20% testing subsets to evaluate model generalization, with random seeds used to ensure reproducibility across multiple runs. To further reduce the risk of overfitting and obtain robust performance metrics, a 5-fold cross-validation approach was implemented, where the dataset was divided into five subsets, using four for training and one for validation in a rotating manner. To enhance the reliability of the results, the entire modelling process was repeated 30 times with different random splits, and the mean and standard deviation of key performance metrics (R\({}^{2}\), MSE, and MAE) were calculated to assess model stability. Finally, statistical significance tests, including ANOVA and Tukey's HSD, were conducted to verify that performance differences between models were statistically significant, adding an extra layer of validation to this study's findings.
# GPS Multipath Analysis Using Fresnel Zones
## 2 Multipath Theory
### Determination of Reflection Points via Ray-Tracing  
The determination of the Fresnel zones requires knowledge on the coordinates of the reflection point \(\mathbf{R}\).  
Therefore, point \(\mathbf{K}\) is determined by projecting the antenna position \(\mathbf{A}\) onto the reflector plane along the direction of the respective normal vector \(\vec{n}\) by  
\[\mathbf{K}=\mathbf{A}+\frac{\left(\mathbf{C}-\mathbf{A}\right)\cdot\vec{n}}{\vec{n}\cdot\vec{n }}\vec{n} \tag{16}\]  
where \(\mathbf{C}\) denotes an arbitrary point on the reflector plane (cf. Figure 10). Afterwards the mirrored antenna position \(\mathbf{A^{\prime}}\) is determined by  
\[\mathbf{A^{\prime}}=\mathbf{A}+2\left(\mathbf{K}-\mathbf{A}\right). \tag{17}\]  
Finally, the reflection point \(\mathbf{R}\) can be computed by intersecting the reflection plane and the line and joining the satellite position \(\mathbf{S}\) and the mirrored antenna position \(\mathbf{A^{\prime}}\) by  
\[\mathbf{R}=\mathbf{A^{\prime}}+\frac{\left(\mathbf{C}-\mathbf{A^{\prime}}\right)\cdot\vec{n}} {\left(\mathbf{S}-\mathbf{A^{\prime}}\right)\cdot\vec{n}}\left(\mathbf{S}-\mathbf{A^{\prime} }\right). \tag{18}\]  
Figure 9: Radius of Fresnel zone for line-of-sight (LOS) transmission.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
###### Abstract  
Few-shot font generation seeks to create high-quality fonts using minimal reference style images, addressing traditional font design's labor-intensive and time-consuming nature, particularly for languages with large character sets like Chinese and Korean. Existing methods often require multi-stage training or predefined components, which can be time-consuming and limit generalizability. This paper introduces Patch-Font, a novel single-stage method that overcomes the limitations of prior approaches, such as multi-stage training or reliance on predefined components, by integrating a patch-based attention mechanism and a multitask encoder. Patch-Font jointly captures global style elements (e.g., overall font family characteristics) and local style details (e.g., serifs, stroke shapes), ensuring high fidelity to the target style while maintaining computational efficiency. Our approach incorporates triplet margin loss with hard positive/negative mining to disentangle style from content and a style fidelity loss to enhance local style consistency. Experiments on Korean (printed and handwritten) and Chinese fonts demonstrate that Patch-Font outperforms state-of-the-art methods in style accuracy, perceptual quality, and generation speed while generalizing robustly to unseen characters and font styles. By simplifying the font creation process and delivering high-quality results, Patch-Font represents a significant step forward in making font design more accessible and scalable for diverse languages.  
few-shot font generation; style transfer; patch-based attention; multitask encoding; image-to-image translation +
Footnote †: journal: Applied sciences
###### Abstract  
Background: Venous occlusion plethysmography (VOP) non-invasively measures forearm blood flow (FBF), whereas near-infrared spectroscopy (NIRS) assesses skeletal muscle oxygenation. Using these techniques has revealed sex differences in microvascular responses. However, it is not clear if NIRS and VOP results are interchangeable under various conditions like reactive hyperemia (RH). Our purpose was to evaluate sex-specific associations between FBF and NIRS-derived parameters: oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin, and hemoglobin difference (O\({}_{2}\)Hb, HrHb, tHb, and HbDiff). Methods: In total, 29 adults (15 men) participated, and a strain-gauge was placed on the forearm for VOP and a NIRS device was distally attached. Slopes for FBF and NIRS parameters were quantified during venous occlusion intervals at rest and during RH. Pearson's correlations were assessed between VOP and NIRS slopes. Intraclass correlation coefficients (ICC\({}_{2,1}\)) examined the sex-specific consistency of the slopes at rest. \(p\leq\) 0.05 was considered significant. Results: During RH, FBF was not correlated with O\({}_{2}\)Hb (\(r=-0.126\)), HHB (\(r=0.228\)), tHb (\(r=0.061\)), or HbDiff (\(r=0.046\)). Seemingly, there were no sex differences. Resting FBF and NIRS-derived variables, except for HbDiff, displayed suitable consistency as suggested by the reliability results (ICC\({}_{2,1}=0.115\)-0.577). Conclusions: The NIRS values collected did not match the strain-gauge slopes. Individuals should practice caution when generating blood flow inferences from NIRS-based data during VOP.  
microvascular; reactive hyperemia; vascular occlusion; muscle oxygenation; health technology; health promotion; sex differences +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences
# Investigating the Influence of Feature Sources for Malicious Website Detection
## 3 Preliminary Experiments
### Expanding [PERSON]'s Dataset
#### 3.3.1 Lexical Features  
Lexical features are based on the raw URL of the website and involve computing statistical properties of the characters plus certain additional variables specific to internet terminology. Examples of these features are: _ratio of digits to letters in URL/hostname_, _URL contains an IP_ and _number of subdomains_. This category of features has been used for malicious URL detection [32] and for classifying DNS Type-squatting attacks [12].
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 3 Method
### Auto-Grounding Method  
In the auto-grounding method, it is assumed that the DG's line protection has been properly configured in order to detect all faults. In case of inverter based DG, overcurrent protection alone may not be sufficient and more advanced functions such as overcurrent with voltage restraint may be required. The auto-ground system consists of three main components: Sectionalizing Switch (SS), Auto-grounding Switch (AS), and the controller, which can be implemented using a variety of re-closer or breaker controls. The SS is required only to mirror the state of the substation breaker, and it is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **NDZ** & \begin{tabular}{c} **Detection** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Power** \\ **Quality** \\ \end{tabular} & **Cost** & \begin{tabular}{c} **Detection** \\ **Reliability** \\ \end{tabular} & \
\begin{tabular}{c} **Effect of** \\ **Multiple DGs** \\ \end{tabular} \\ \hline Passive & Broad & Short & No effect & Low & Low & None \\ \hline Active & Limited & Long & Degradation & Average & High & Synchronization issues \\ \hline Hybrid & Limited & Long & Degradation & High & High & Synchronization issues \\ \hline
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} & Limited & Variable & No effect & High & High & None \\ \hline Remote & None & Very short & No effect & High & Very high & None \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of islanding methods [3; 49; 50; 51; 73; 74].  
preferable to have the SS as a separate device for the simple reason that costs escalate when work within the substation is required. Any savings associated with integration of the SS function into the feeder breaker would be outweighed by the cost of linking it with the AS [65]. In this case, it is not required to interrupt fault currents over any device that can provide automated sectionalizer capabilities. For applications where the auto-ground is paired with an in-line re-closer, the SS is not required as its functionality can simply be integrated into the re-closer itself. Figure 2 shows the block of a system for the implementation of islanding detection via the auto-grounding method, and the following are the three logics/stages of the process:  
* Both of the circuit breakers (CB1 and CB2) are in closed conditions. That is the normal condition. Thus, the voltage and current waveforms found at the utilization level are purely sinusoidal.
* CB1 is initially closed and is opened with transition time 't', but CB2 is closed. After switching off the CB1, the whole part of the distribution system is isolated from the remaining part of the power system, which is called the islanding condition.
* CB1 is initially closed and is opened with transition time 't'. As another DG is there in the line where CB2 is connected, the opening of CB2 is the condition of anti-islanding.
## 2 Materials and Methods
surface roughness of both the substrates and copper coatings, quantified as the arithmetic mean roughness (Ra), was assessed using a profilometer (DGT-SRT200A, Baxit, Shanghai, China). Each sample underwent five independent profile measurements to ensure reliable and representative data. To evaluate the antibacterial effectiveness of the selective plated copper coatings, a series of microbiological tests were conducted using Gram-positive _Staphylococcus aureus_ ATCC 29213 (_S. aureus_) and Gram-negative _Escherichia coli_ ATCC 25922 (_E. coli_) utilizing the inhibition zone method in a specialized microbiological laboratory. Prior to the microbiological tests, the samples' surfaces underwent a rigorous cleaning process using 70% ethyl alcohol. This step was crucial to eliminate any microbial contaminants that could potentially interfere with the test results and compromise their accuracy. Furthermore, the cleaned samples were exposed to ultraviolet (UV) irradiation at a wavelength of 253.7 nm for 30 min. The UV exposure served as an additional measure to ensure the sterility of the sample surfaces. By implementing these rigorous cleaning and sterilization protocols, the integrity and reliability of the microbiological test results were upheld. To initiate bacterial cultures, both strains were inoculated into flasks containing nutrient broth (NB) and incubated at 37 \({}^{\circ}\)C under aerated conditions until the bacterial cultures reached an optical density of 0.5 McF, corresponding to 1.5 \(\times\) 10\({}^{8}\) colony-forming units per milliliter (CFU/mL). The inoculum was plated on Mueller-Hinton (MH) agar, and the resulting inhibition zones surrounding each coating were analyzed using the Kirby-Bauer method [24]--a standard practice for assessing antibacterial susceptibility in microbiology. The selective plated copper coatings were placed on the inoculated agar plates, and the entire setup was incubated at 37 \({}^{\circ}\)C for 24 h. The presence of clear zones around the selective plated samples indicated the presence of antibacterial activity, suggesting that the copper coatings exhibited inhibitory effects on the growth of the bacteria. Following the completion of the testing procedure, a sterilization cycle was carried out to assess the behavior of the coatings under standard sterilization conditions.
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 5 Discussion
A distinctive feature of this approach is the incorporation of feature creation, a technique not deeply observed in other studies. Moreover, the selection methods resonate with the best practices in the field. It is also worth noting that this research was the only one, to the best of the authors' knowledge, that focused on wind gust and direction using feature engineering. Considering these findings, it becomes clear that the considerations of nowcast duration, feature selection, computational cost-benefit trade-offs, and spatial-temporal interrelationships are paramount in wind nowcasting. While advanced mathematical features and machine learning models show promise, their application should be carefully evaluated on a case-by-case basis due to the associated computational costs. Importantly, recognizing and leveraging spatial correlations present a promising pathway for enhancing the accuracy and reliability of wind nowcasting, an element vital for the operational efficiency of wind-dependent systems and industries.
## 6 Conclusions and Future Research  
In order to overcome the problems existing in current automatic parking schemes, ultrasonic radar and cameras are not efficient in identifying adequate parking spaces and cannot identify obstacles in parking paths or parking spaces. This paper presents a method to identify parking spaces and obstacles based on visual sensor and laser device recognition methods. This method only needs to install the laser transmitter on the body of the car. The changing images of the laser mesh are captured by the camera, and then, the changing area of the laser grids can be identified by image processing technology to realize the identification of parking spaces and obstacles. This method is expected to become a practical solution. The experimental results showed that this method could effectively identify obstacles and parking spaces.  
For future research, more obstacle samples will be taken into consideration. The methods to deal with the automatic classification of obstacles need to be found. In addition, experiments for identifying the parking space and acquiring the size of the parking space, as well as obstacles still need to be conducted. Overall, this research provided a new solution to recognize obstacles for automatic parking. The future research will also greatly help the development of this technology.  
[PERSON] and [PERSON] designed the scheme. [PERSON], [PERSON], and [PERSON] checked the feasibility of the scheme. [PERSON] and [PERSON] provided the resources Z. [PERSON] performed the software simulation and experiments [PERSON] wrote the paper with the help of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 12 KJD580002 and Grant 16 KJA580001, in part by the Innovation Plan for Postgraduate Research of Jiangsu Province in 2014 under Grant KYLX1057, and in part by the National Natural Science Foundation of China under Grant 51675235.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Types** & **Regular** & **Irregular** & **Similar to the Ground Color** \\ \hline Vehicle & \(\frac{63}{40}=98.4\%\) & - & - \\ Wall/Pillar & \(\frac{20}{40}=100\%\) & - & \(\frac{20}{20}=100\%\) \\ Parking lock & \(\frac{20}{40}=93.5\%\) & - & - \\ Stone & \(\frac{20}{40}=97.4\%\) & \(\frac{37}{40}=97.4\%\) & \(\frac{36}{40}=94.7\%\) \\ Pothole & \(\frac{20}{40}=95.0\%\) & \(\frac{32}{40}=97.5\%\) & \(\frac{38}{40}=95.0\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of recognizing the contours of different types of obstacles in parking spaces.  
Figure 26: The irregular pothole that was similar in color to the background. (**a**) The irregular pothole (**b**) the contour of the ground; (**c**) the contour of the irregular pothole.
# Reversible Image Processing for Color Images with Flexible Control
## 4 Experimental Results
### Reversibility  
We confirmed the quality of the restored images obtained with each method. Table 3 shows the mean values of PSNR, SSIM, and CIEDE2000. We evaluated the image quality under \(I_{VSH}=I_{VSM}=2\), \(I_{VI}=I_{VD}=I_{VC}=30\), and \(I_{S}=20\). As shown in this table, the proposed method and previous method [1] ensured perfect reversibility for all the test images. In comparison, the previous method [12] could never reconstruct the raw images completely, while the recovered images had a high image quality.