esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 4 Simulation Test Results
### Effect of Particles Diameter on Contamination Non-Uniformity  
When the wind speed is 5 m/s, the diameter of the particles is 250, 400, 800 and 2000 meshes respectively, and relative humidity is 60% in DC energized condition. The results are shown in Table 5.  
As is shown in Table 5, decreased particle diameter contributes to contamination. When particle diameter decreased from 46.5 to 4.2 \(\mathrm{\SIUnitSymbolMicro m}\), NSDD increased by 66.6%. Thus, particle diameter has an effect on all three kinds of non-uniformity. With the decrease of particle diameter, \(\mathrm{K_{T/B}}\) value is closer to 1. For example, \(\mathrm{K_{T/B}}\) changed from 1/0.51 to 1/0.66 when particle diameter decreased from 46.5 to 4.2 \(\mathrm{\SIUnitSymbolMicro m}\). Which shows that the larger the particles diameter is, the more obvious the non-uniformity degree of contamination on top and bottom surfaces is. Large particles are not easily adhered to the lower surface due to gravity, which can explain the change of \(\mathrm{K_{T/B}}\).  
Similarly, with the decrease of particle diameter, \(\mathrm{K_{W/L}}\) value is also closer to 1, for example, \(\mathrm{K_{W/L}}\) changed from 1/1.45 to 1/1.36 when particle diameter decreased from 46.5 to 4.2 \(\mathrm{\SIUnitSymbolMicro m}\), which means that the contamination growth rate of the leeward side is lower than that of the windward side with the decrease of particle diameter. This is because small particles are more prone to cake in the position towards air flow, and a thicker pollution layer will get formed at round bar (windward side).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Speed (m/s)** & **1** & **3** & **5** & **7** \\ \hline NSDD (mg/cm\({}^{2}\)) & 0.12 & 0.16 & 0.21 & 0.32 \\ \(\sigma\) & 5.7\% & 6.8\% & 7.3\% & 6.5\% \\ \(\mathrm{K_{T/B}}\) & 1/0.48 & 1/0.52 & 1/0.51 & 1/0.55 \\ \(\mathrm{K_{W/L}}\) & 1/0.81 & 1/1.22 & 1/1.45 & 1/1.75 \\ \(\mathrm{K_{H/M}}\) & 1/0.49 & 1/0.56 & 1/0.65 & 1/0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average NSDD, \(\mathrm{K_{T/B}}\), \(\mathrm{K_{W/L}}\), \(\mathrm{K_{H/M}}\) under different wind speed.  
However, non-uniformity degree (K\({}_{\rm H/M}\)) of contamination on high voltage group and middle group along insulator string is more obvious with the decrease of particle diameter, which illustrates that the effect of electric field force on the contamination of smaller particles is more obvious.
## 2 Developed Methodology
### Computed Error Metrics  
Because we suggest the development of an IPIF whose intervals are computed using as baseline a PPF's predictions, both forecasters' error metrics must be examined. While PPF's error metrics are used to select the most suitable of above explained models, IPIF's error metrics analyze not only forecasters' reliability but also predicted intervals sharpness.
## 5 Conclusions  
In this study, we investigated the effects of dispersed fiber reinforcement (mixture of polypropylene and alkali-resistant glass fibers, in 1:2 ratio by mass) on the performance of hydraulically bound granular mixtures utilizing a small amount Portland cement and increasing amounts of fly ash as the binder.  
The most substantial findings of the presented work include the following:  
* The mixtures bound with Portland cement and fly ash with 0.05% (m/m) of fiber reinforcement yielded the most favorable compressive and indirect tensile strengths, surpassing those obtained with a higher amount of reinforcement (0.10%);
* The compressive strengths of the mixtures containing fly ash and 0.05% of fiber reinforcement exceeded those of the reference, non-reinforced CBCMs;
* In the case of indirect tensile strengths, only the mixture containing 1% of Portland cement and 14% of fly ash with 0.05% of reinforcement performed comparably to the weaker reference CBGM (bound with 5% Portland cement);
* Increasing the contents of fly ash from 3.5% to 14% in the investigated mixtures (also containing 1% of Portland cement) resulted in an improved mechanical performance; moreover, this improvement was magnified when dispersed reinforcement was used;
* The effects of fiber reinforcement on the frost resistance of the mixtures could not be clearly established because inconsistent results were obtained in mixtures containing different percentages of fly ash.  
The findings suggest that it is possible to produce high-quality hydraulically bound granular mixtures, in which fly ash substitutes most of the Portland cement, by means of using relatively small amounts of fiber reinforcement composed of a mixture of polypropylene and alkali-resistant fibers. Future studies should attempt to explain these results by usingmore controlled mechanical tests (e.g., investigating the fracture mechanics of the material) together with imaging and analytical techniques in tests involving different compositions of fiber reinforcement. Additionally, the long-term performance of such mixtures should be evaluated.  
Conceptualization, A.C.-K. and K.M.; methodology, A.C.-K.; validation, A.C.-K.; investigation, K.M.; resources, A.C.-K.; data curation, K.M.; writing--original draft preparation, A.C.-K. and K.M.; writing--review and editing, A.C.-K. and K.M.; visualization, K.M.; supervision, A.C.-K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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.
## 3 Methodology and Materials
### Data Sample
Wind speed wv (m/s) and maximum wind speed max. wv (m/s), while showing low correlation with other variables, indicate the relative independence of wind characteristics from other meteorological factors. It is important to acknowledge data anomalies for precise interpretation. The dew point temperature Tdew (degC) closely aligns with air temperature, reinforcing the relationship between temperature and moisture content. Finally, the vapor pressure deficit VPdef (mbar) underscores its significance in understanding the air's drying capacity, showing a positive correlation with temperature and a negative correlation with relative humidity, which is crucial for studies on evaporation and plant transpiration. These relationships underscore the intricate and interdependent dynamics of climatic factors within the dataset. The experimental setup involves integrating the proposed cycling layer into a self-attention mechanism within an autoencoder architecture. We use the Adam optimizer and a learning rate of 0.001 for training, with early stopping based on validation performance to prevent overfitting. Meanwhile, we also need to undertake preprocessing steps to prepare the Jena Climate dataset for subsequent analysis using an autoencoder model. This section details the conversion of the date-time column from its original form into  
Figure 4: Correlation matrix of the Jena Climate dataset. a Unix timestamp, thereby standardizing it for easier model ingestion and alignment with other time series data processing methods. Also, we aim to address the problem of missing values, a common issue in real-world datasets. If this problem is addressed, it can significantly distort model training and predictions. The chosen method, mean imputation, replaces missing values with the mean of each column, maintaining the overall statistical characteristics of the dataset. Lastly, this section discusses the creation of sequences from the processed data. Autoencoders, especially those designed for time series data, benefit from learning sequential patterns and dependencies, necessitating the transformation of the dataset into a sequence of data points.
## 4 Proposed FGPQ Scheme
### Privacy-Preserving LBS Query
#### 4.2.3 Request Result Decryption  
After receiving the result, e.g., \(E_{T}(\widehat{W^{(i)}})=(\widehat{W^{(i)}_{a}}M_{1},\ \widehat{W^{(i)}_{b}}M_{2})\), the user recovers the vector \(\widehat{W^{(i)}}\) by means of the decryption function \(D\), and then extracts the first seven dimensions in it, i.e., \(W^{(i)}=(1,1,X_{i}^{2},Y_{i}^{2},-2X_{i},-2Y_{i},-1)\). According to \(W^{(i)}\), the user can obtain the matched location \((X_{i},Y_{i})\) about the searching content \(m_{*}\).
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 1 Introduction  
Helminthosporium leaf blotch (HLB) is a serious disease of wheat cultivation, which causes yield reduction globally [1]. Yield losses due to HLB were reported, and the reduction up to 20% were observed in [2, 3]. Usually, the HLB disease is controlled by uniform spraying of chemicals. However, increased use of chemicals has caused agronomic and environmental problems [4]. To solve this problem, accurate chemical spraying could be a key solution [5]. Accurate spraying emphasizesapplying adequate doses of chemicals based on the degree of disease infection [6]. Thus, it is possible to reduce the use of chemicals. On the other hand, accurate spraying applies enough chemicals according to the specific requirement of disease degrees, which may enhance the chemical effects. In this case, effective detection of the disease could provide detailed support information for the spraying machines. Traditional assessment of the HLB disease is made by manual investigation throughout the field, which is laborious and time-consuming [7].  
Remote sensing (RS) has been frequently used as an effective, efficient, and safe tool for rapid detection of plant diseases [8; 9; 10; 11]. Compared with satellite and aircraft remote sensing, unmanned aerial vehicle (UAV) can fly at a low altitude and capture high resolution imagery [12], which would provide more detailed spatial information for plant disease detection. There are literatures applying UAV remote sensing for detection of wheat diseases. [PERSON] et al. [13] applied the UAV remote sensing for monitoring wheat stripe rust. Correlation analysis between the disease index and the image reflectance were conducted. The linear regression method was used to build the estimation model. A significant difference between the reflectance of the healthy plots and diseased plots was observed, which indicated that UAV-based monitoring of wheat stripe rust was feasible. [PERSON] et al. [14] employed the UAV imagery to detect the wheat powdery mildew. UAV imagery were captured at different altitudes over the wheat fields. Fitting the data with random coefficient regression models, the exact relationship between the image parameter lgR and the disease severity was observed. However, to our best knowledge, the research on HLB disease detection using UAV imagery cannot be accessed yet. The objective of this paper was to evaluate the potential of UAV-based imagery for HLB disease detection. In the framework of this research, it is unfeasible to distinguish other pathologies that may also cause similar symptoms as HLB disease. However, it is the first step to detect the disease infection, which may build a significant foundation for further analysis.  
The rest of this paper is organized as followed: Section 2 introduces the data collection and the processing methods; Section 3 demonstrates the experimental results of our methods and the comparison with other methods, and Section 4 describes the conclusion and further perspective.
# The Method of Multi-Camera Layout in Motion Capture System for Diverse Small Animals
## 4 Discussion  
Experimental results demonstrate that the average standard deviation rate of motion capture for multi-camera unit modules in a half-annular layout is significantly smaller than that for arched and annular layouts. The intersecting volume of the field of view and depth of the field have a certain impact on the capture accuracy. Moreover, in a half-annular layout under the motion capture experiments of geckos and spiders, the standard deviation rate and catching deviation rate of were obtained.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 5 Simulation Results and Analysis
### Impact of RIS Configuration  
#### 5.3.1 Performance Comparison of Four Types of RIS Solutions  
We preset the following four scenarios to demonstrate the impact of different RIS configurations on the performance of URLLC and eMBB services. Scheme 1: Using RIS configuration \(\Phi_{e}^{*}\) for eMBB and URLLC throughout the time slot. Scheme 2: In each mini-slot, if URLLC packets are present, use RIS configuration \(\Phi_{u}^{*}\). Otherwise, use configuration \(\Phi_{e}^{*}\). Scheme 3: In each mini-slot, if URLLC packets are present, use RIS configuration \(\Phi_{e,u}^{*}\). Otherwise, use configuration \(\Phi_{e}^{*}\). Scheme 4 (chosen in this paper): In each mini-slot, the gNB selects the phase shift matrix \(\Phi^{m}\in\{\Phi_{e}^{*},\Phi_{u}^{*},\Phi_{e,u}^{*}\}\) that maximizes the URLLC packet acceptance rate and minimizes the eMBB rate loss.  
Figures 5 and 6 show the URLLC packet acceptance rate curves and total eMBB loss rate with the number of RIS reflective elements for five different RIS phase shift matrix configurations. As seen from Figure 5, the URLLC packet admission rate increases with the number of RIS components for all four schemes except for the case without RIS. Among them, the result of Scheme 1 is relatively different from the other three schemes, with a  
Figure 4: The time complexity of different URLLC allocation policies.  
Figure 3: Packet reception rates under different URLLC allocation policies.  
difference of nearly 3%. Schemes 2 and 3 perform comparably to Scheme 4 selected in this paper at larger network sizes (i.e., a larger number of RIS elements in the network), with an acceptance rate close to 99.8%. However, as seen in Figure 6, the differences among the schemes gradually increase with the expansion of the network scale. When \(N\) is greater than 40, the total loss rate of eMBB of the selected scheme remains below 6%, while other schemes are close to or even over 10%; especially, when RIS is not deployed, the total loss rate of eMBB is close to 16%. In summary, the RIS assistance effectively improves the associated performance. This is because the assistance of RIS enables the gNB to configure RIS by selecting the phase shift matrix from the set \(\boldsymbol{\Phi}^{m}\in\left\{\Phi_{e}^{*},\Phi_{u}^{*},\Phi_{e,u}^{*}\right\}\) according to the URLLC traffic and its channel conditions, thus making a good tradeoff between maximizing URLLC packet admission rate and minimizing eMBB loss.  
Figure 5: URLLC packet reception rate of different RIS phase shift matrices.  
Figure 6: Total eMBB loss rate of different RIS phase shift matrices.
## 2 Data and Methods  
### NRCS Data  
The NRCS data provided by the CMOD5.n GMF and the ASCAT measurements were used as a reference to improve and validate the modified TSM. The CMOD family was developed to provide the information of wind fields, in which the CMOD5.n was validated with many measured data and can be regarded as a reliable reference [16; 28; 29]. The CMOD5.n gives the relationship between C-band VV NRCS and the incidence angle, wind speed, and wind direction (relative to the radar look direction) [30]. It takes the form of:  
\[\sigma^{0}(\theta,U_{10},\phi)=B_{0}(\theta,U_{10})(1+B_{1}(\theta,U_{10}) \cos\phi+B_{2}(\theta,U_{10})\cos 2\phi)^{1.6} \tag{1}\]where \(\sigma^{0}\) is the natural NRCS, \(\theta\) is the incidence angle, \(U_{10}\) is the neutral wind speed at the height of 10 m above sea level, and \(\phi\) is the angle of wind direction. \(B_{0}\), \(B_{1}\), and \(B_{2}\) can be expressed as a function of \(\theta\) and \(U_{10}\), and they were obtained by fitting (1) to the European Remote Sensing Satellite ERS-2 scatterometer data. The average absolute difference between ASCAT and CMOD5.n is about 0.3 dB, and the root mean square difference is lower than 2 dB [31].  
The MetOp-A ASCAT level-1B 12.5-km \(\sigma^{0}\) products produced by EUMETSAT from January 2014 to December 2016 were used in this paper [32; 33]. The ASCAT-A \(\sigma^{0}\) measurements influenced by non-wave phenomena were rejected based on the following principles: (1) The default filling data and the abnormal data flagged as bad quality were excluded; (2) The measurements contaminated by rainfall, ice, and land were eliminated. Then, the ASCAT-A \(\sigma^{0}\) measurements were matched with the ocean surface winds provided by 57 moored buoys from the American National Data Buoy Center (NDBC) with a separation of time within 30 min and separation of space less than 25 km. The buoys are all located in waters more than 50 km away from land and 150 m deep, and correspondingly, the matched ASCAT-A measurements are also far away from land. Finally, 3,502,466 matching pairs were obtained. Additionally, the buoy wind speeds sampled at 2-5 m heights above the sea surface were converted to 10 m neutral wind speeds based on the logarithmic marine boundary layer assumption proposed by [PERSON] and [PERSON] [34] after eliminating low-quality observations:  
\[U_{10}=U_{Z}\sqrt{\frac{\kappa^{2}}{C_{d}}}\ln^{-1}\biggl{(}\frac{z}{z_{0}} \biggr{)} \tag{2}\]  
where \(U_{Z}\) is the wind speed measured at a height \(Z\) above sea level. \(z_{0}\) = 3.271 \(\times\) 10\({}^{-4}\) m is the surface roughness length, \(\kappa\) = 0.4 is the von [PERSON] constant, and \(C_{d}\) = 1.5 \(\times\) 10\({}^{-3}\) is the drag coefficient.
## 4 Results
### Evaluation of Interpretability and Usability for Decision-Making Results
#### 4.4.3 Accuracy  
Accuracy is paramount in decision-making processes, ensuring that the most critical features are prioritized correctly. In Case Study A for the logistics industry, the initial AHP-QFD matrix provided a comprehensive analysis of site selection criteria, but the complexity made it difficult to ensure precise prioritization. After integrating PCA, 75% of the experts deemed Method 2 more accurate. PCA allowed for a more precise evaluation by eliminating redundant data and highlighting the most important factors.  
In Case Study B for new earphone development, the initial matrix detailed numerous technical aspects of earphone design, making accurate prioritization challenging. With PCA integration, 62.5% of the experts believed that Method 2 allowed for a more accurate evaluation of feature requirements. The PCA process improved accuracy by concentrating on the most influential factors, such as material and wireless capabilities.  
Overall Effectiveness in Reducing Evaluation Time and Appropriateness of Methods  
The experts generally agreed that PCA integrated with AHP-QFD could significantly reduce the time required for evaluating new product and service designs. Specific estimates and opinion are included in Table 11.  
Participants were chosen based on their extensive experience in their respective fields related to product and service design. They were selected from automotive part companies, logistics organizations, research and development organizations, game and multimedia companies, and educational centers.  
The primary selection was conducted via a Google Form questionnaire, and those without experience in service and design objectives were excluded from the interview and evaluation questionnaire. The selected participants come from diverse backgrounds with substantial experience in their respective fields. This includes environmental technical artists in the Game Industry with architectural design knowledge, Senior IT specialist/robotic system design, IT/front-end developer, product and innovation design research center, interaction design and innovation master students, innovation designer, educational designers, and traffic and route designers. Their years of experience range from 2 to 41 years. This variety ensures a comprehensive perspective on the effectiveness of the methods.  
The interviews were meticulously constructed to capture detailed opinions on the effectiveness of the two methods. Each participant was provided with a comprehensive overview of both methods and their respective processes. Structured questions were designed to elicit specific feedback on the time efficiency, accuracy, and overall effectiveness of each method. Follow-up questions were used to delve deeper into their initial responses, ensuring a thorough understanding of their perspectives.  
These opinions collectively highlight the strengths of both methods, with Method 2 offering significant time savings and ease of use, while Method 1 provides the depth required for comprehensive analysis in early product development stages.
# Feasible Trajectories Generation for Autonomous Driving Vehicles
###### Abstract
Reference [6] presented an efficient solution to improve the vehicle control subject to emergency situations from the autonomous decision support. However, authors in [6] did not provide mathematic calculations for generating the vehicle optimal trajectory. Recent research references on vehicle trajectory generation are investigated, most of which presented new mathematic functions for generating online feasible trajectories. The authors in [7] updated the prediction and generation of vehicle trajectory in non-linear programming with consideration of the long short-term memory and generative adversarial networks. The authors in [8] presented trajectory generation for a vehicle changing lanes automatically with nonlinear mathematical functions while taking into account the public traffic laws. The authors in [9] introduced a new method for robust trajectory generation based on neuromorphic research and robotic control. The authors in [10] introduced a new model of predictive control for autonomous vehicle tracking of any given path. The authors in [11] presented a new approach for industrial robot trajectory generation based on motion modular technology. The authors in [12] reviewed recent advances in the generation of trajectory planning methodologies. The authors in [13] described the new method in motion planning for mobile robots with the use of jumping leap search with Bezier curves. The authors in [14] highlighted robust and the efficient tool path generation for curving along low triangular mesh surfaces. The authors in [15] presented research on trajectory generation optimization for robotics and autonomous systems. Finally, the authors References [16; 17; 18; 19; 20] presented updated mathematical algorithms and information technologies applied for mapping out the feasible paths of autonomous vehicles. Updatedreferences on vehicle trajectory generation and advanced autonomous vehicle control techniques are referred to in [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Online optimal trajectory generation subject to a vehicle's physical constraints and environmental limitations remain a challenge. This paper proposes new methods of trajectory generations subject to those constraints. The optimal trajectory is selected based on three criteria: The shortest distance, the smoothest curve, and the smallest tracking error. The outline of this paper is as follows: Section 2 outlines the conventional kinematic diagram and control model for the ground vehicle; Section 3 introduces a basic method for trajectory generation; Section 4 presents the selection of the optimal trajectory subject to the vehicle constraints and environmental limitations; Section 5 introduces a new method for quartic polynomial trajectory generation; Section 6 presents a new method for symmetric polynomial function; Section 7 reviews the performances comparison of all three methods; and Section 8 includes conclusions and recommendations.